वर्तमान डिजिटल युग में शिक्षा के स्वरूप में तीव्र परिवर्तन हो रहा है और इसका प्रभाव भाषा शिक्षण पर भी स्पष्ट रूप से दिखाई देता है। सूचना एवं संचार प्रौद्योगिकी (ICT), स्मार्ट क्लासरूम, डिजिटल शिक्षण संसाधन, भाषा प्रयोगशाला तथा कृत्रिम बुद्धिमत्ता (Artificial Intelligence) ने हिंदी भाषा शिक्षण की पारंपरिक पद्धतियों के समक्ष नई संभावनाएँ प्रस्तुत की हैं। स्मार्ट बोर्ड, प्रोजेक्टर, ऑडियो-वीडियो सामग्री, ई-संसाधन, ऑनलाइन शिक्षण मंच तथा डिजिटल मूल्यांकन उपकरणों के माध्यम से हिंदी शिक्षण को अधिक दृश्यात्मक, श्रव्य, संवादात्मक और विद्यार्थी-केंद्रित बनाया जा सकता है। इसी प्रकार कृत्रिम बुद्धिमत्ता आधारित उपकरण पाठ-योजना निर्माण, अध्ययन-सामग्री निर्माण, अनुवाद, व्याकरण एवं वर्तनी सुधार, व्यक्तिगत अधिगम, प्रश्न निर्माण तथा त्वरित प्रतिक्रिया जैसे क्षेत्रों में शिक्षक और विद्यार्थी दोनों की सहायता कर सकते हैं। भारत में SWAYAM, DIKSHA, e-PG Pathshala आदि डिजिटल मंचों ने डिजिटल संसाधनों की उपलब्धता को व्यापक बनाया है।
प्रस्तुत अध्ययन का उद्देश्य हिंदी भाषा शिक्षण में स्मार्ट क्लासरूम एवं कृत्रिम बुद्धिमत्ता की भूमिका, संभावनाओं और सीमाओं का आलोचनात्मक अध्ययन करना है। अध्ययन में वर्णनात्मक एवं विश्लेषणात्मक दृष्टिकोण अपनाते हुए राष्ट्रीय शिक्षा नीति 2020, राष्ट्रीय पाठ्यचर्या रूपरेखा, डिजिटल शिक्षा से संबंधित उपलब्ध सामग्री तथा हिंदी भाषा शिक्षण में तकनीकी नवाचारों से संबंधित साहित्य का अध्ययन किया गया है। अध्ययन से यह स्पष्ट होता है कि तकनीक स्वयं शिक्षण की गुणवत्ता सुनिश्चित नहीं करती, बल्कि उसकी प्रभावशीलता शिक्षक की डिजिटल दक्षता, शैक्षिक उद्देश्य, उपयुक्त संसाधन, विद्यार्थी की सक्रिय सहभागिता तथा संदर्भानुकूल प्रयोग पर निर्भर करती है। विशेष रूप से हिंदी भाषा के श्रवण, भाषण, पठन और लेखन कौशलों के विकास में डिजिटल एवं AI आधारित संसाधनों की उपयोगिता दिखाई देती है, किंतु AI की तथ्यात्मक अशुद्धि, अत्यधिक निर्भरता, मौलिकता, डेटा गोपनीयता, डिजिटल विभाजन तथा मानवीय संवेदना जैसे प्रश्नों को भी समान गंभीरता से संबोधित करना आवश्यक है। अतः हिंदी भाषा शिक्षण में स्मार्ट क्लासरूम एवं AI को शिक्षक के विकल्प के रूप में नहीं, बल्कि शिक्षक-सहयोगी शैक्षिक साधन के रूप में स्वीकार करना अधिक उपयुक्त होगा।

This study examines the effect of cashback offers and reward personalization on customers’ credit card usage behaviour with reference to Axis Bank Ltd. The study focuses on customer awareness, preferences, perceived value, satisfaction and loyalty associated with cashback and personalized rewards, and evaluates how these factors relate to overall credit card usage. A descriptive research design was adopted and primary data were collected through a structured questionnaire from 135 Axis Bank credit card users. The study used a five-point Likert scale and analysed the data using descriptive statistics, reliability analysis, normality testing, Mann–Whitney U testing, Kruskal–Walli’s testing, Spearman correlation and regression analysis. The questionnaire showed excellent internal consistency with Cronbach’s Alpha of 0.986, while normality tests indicated non-normal data. Cashback offers had an average score of approximately 3.38, reward personalization approximately 3.32, customer satisfaction and loyalty approximately 3.34, and overall usage behaviour approximately 3.18. Spearman correlations were positive and significant, ranging from 0.769 to 0.871. Individual regression models showed that cashback explained 59.2% and reward personalization explained 60.5% of the variation in overall usage behaviour. A multiple regression model containing cashback, reward personalization and customer satisfaction and loyalty explained 76.2% of the variation in usage behaviour. The study concludes that relevant, transparent and easy-to-use rewards can strengthen customer engagement, satisfaction, loyalty and continued credit card usage.

This study determined the master teachers’ instructional supervision approaches and effectiveness and their influence on teachers’ performance in public secondary schools in the Schools Division of Antique, Philippines, for School Year 2025–2026. The research was conducted from March to April 2026 and involved 326 randomly selected teachers. It employed a descriptive-correlational research design. The independent variables included age, highest educational attainment, length of teaching experience, school size, and school location, while the dependent variables were master teachers’ instructional supervision approaches, effectiveness, and teachers’ performance. A researcher-made questionnaire based from the study of Podador (2023) and Baldera (2025) was used to gather data on instructional supervision approaches and effectiveness. Teachers’ performance data were obtained from Classroom Observation Tool (COT) ratings for the first and second quarters of School Year 2025–2026. Descriptive statistics such as frequency, percentage, and mean were used, while t-test, Analysis of Variance (ANOVA), and Linear Regression Analysis were applied as inferential tools. All statistical tests were set at .05 level of significance using SPSS software. Findings revealed that the mostly employed instructional supervision approaches were classroom observation without disrupting instruction, conducting post-observation conferences to discuss strengths and areas for improvement, and encouraging teachers to try new teaching strategies. The level of master teachers’ effectiveness was “Very High,” while teachers’ performance was “Outstanding.” Significant differences in instructional supervision approaches and effectiveness were observed when respondents were grouped according to age, but no significant differences were found when classified by highest educational attainment, length of teaching experience, school size, and school location. Likewise, teachers’ performance showed no significant differences across all grouping variables. Furthermore, master teachers’ instructional supervision approaches and effectiveness had no significant influence on teachers’ performance.

This study determined the school heads’ change management practices, school culture, and performance in the Schools Division of Antique, Philippines, during School Year 2025–2026. Using a descriptive-correlational research design, the study was conducted from March to April 2026 among 365 randomly selected teachers. The sample size was determined through Slovin’s formula, while stratified proportionate random sampling was employed to ensure representative participation. The study examined school heads’ change management practices, school culture, and performance in relation to respondents’ age, sex, highest educational attainment, length of service, and type of school. Data were gathered using validated questionnaires on school heads’ change management practices and school culture, while school performance data were obtained from the 2025 School-Based Management (SBM) ratings. Descriptive statistics such as frequency, percentage, and mean were used, while t-test, Analysis of Variance (ANOVA), and Linear Regression Analysis were utilized to test significant differences and influence at .05 level of significance. Statistical analyses were performed using SPSS. Findings revealed that as to change management, the school head mostly practiced involving teachers and staff in planning and decision-making, facilitating monitoring and evaluation of implemented changes, and ensuring alignment of changes with the school’s vision, mission, and long-term goals. Respondents assessed the school culture as “Very Positive” and school performance as “Developing.” No significant differences were found in respondents’ assessments of school heads’ change management practices, school culture, and performance when grouped according to demographic and professional characteristics. Moreover, school heads’ change management practices and school culture did not significantly influence school performance.

In banking, proper risk assessment is an essential task to gauge the probability of getting back the loan. In the case where such an assessment fails on a wide scale, the results manifest themselves in the form of non-performing assets, which lead to a loss in profits for banks and also a destabilization of the overall financial sector. Credit risk assessment involves manually checking documents and applying some scoring criteria. In the modern world, when banks accumulate huge amounts of data regarding their clients, traditional methods cannot be applied efficiently. Therefore, it is necessary to investigate whether supervised machine learning techniques are able to offer a better alternative. This study, “Credit Risk Assessment Model for Indian Bank Using Machine Learning,” employs a secondary dataset containing 51,336 observations with 87 features related to client demographics, credit score history, loan account details, repayment history, credit inquiries, usage ratio, employment and bureau credit score. Prior to model construction, the data was preprocessed by removing missing values, label-encoding categorical columns, and selecting the best predictors using correlation and mutual information analysis. Four classification models were developed and compared: Logistic Regression, Decision Tree, Random Forest, and Extreme Gradient Boosting (XGBoost). Evaluation criteria included accuracy, precision, recall, F1-score, confusion matrix performance, and 5-fold cross-validation. While all four models achieved excellent accuracy on the four-class objective (Approved_Flag: P1–P4), tree-based ensemble techniques clearly surpassed the linear model. The accuracy of Logistic Regression is 96.65%, Decision Tree 99.15%, Random Forest 99.41%, and XGBoost 99.48%.

This study examined knowledge of selected breast cancer risk factors and female teachers’ attitude towards breast self-examination in public secondary schools in Akwa Ibom North-West Senatorial District. Specifically, the study examined the extent of influence of knowledge of genetic mutation, breast density and obesity on female teachers’ attitude towards breast self-examination. A descriptive survey design was adopted. The study had a sample of 159 female teachers selected from public secondary schools in the study area. Data were collected using the Knowledge of Risk Factor of Breast Cancer Questionnaire and the Teachers’ Attitude Towards Breast Self-Examination Questionnaire. The instruments were face-validated by experts and a reliability coefficient of .89 was obtained using Cronbach’s alpha. Simple linear regression was used to answer the research questions and test the null hypotheses at the .05 level of significance. The findings showed that knowledge of genetic mutation had a very high influence on female teachers’ attitude towards breast self-examination, while knowledge of breast density and obesity had high influence. The three null hypotheses were rejected, indicating significant influences of genetic mutation, breast density and obesity on female teachers’ attitude towards breast self-examination. The study concluded that adequate knowledge of breast cancer risk factors is important in promoting favourable attitudes towards breast self-examination among female teachers.

This study examined environmental risks and health-seeking behaviour among residents of oil-producing communities in Akwa Ibom South Senatorial District of Akwa Ibom State, Nigeria, with specific emphasis on noise pollution and gas flaring. The study adopted a descriptive survey research design. The population comprised 625,240 adult residents of selected oil-producing communities in the Senatorial District, from which a sample of 392 respondents was selected using simple random sampling. Data were collected using a structured questionnaire titled the Environmental Risk and Health-Seeking Behaviour Questionnaire (ERHSBQ). The instrument was validated by experts, while its reliability was established using Cronbach's alpha, which yielded a coefficient of 0.84. Data were analysed using mean and standard deviation to answer the research questions and simple linear regression to test the null hypotheses at the 0.05 level of significance. The findings revealed that noise pollution significantly influenced health-seeking behaviour among residents, with a grand mean of 2.86, which was above the criterion mean of 2.50. Similarly, gas flaring significantly influenced health-seeking behaviour, with a grand mean of 2.91. Regression analysis further showed that noise pollution significantly predicted health-seeking behaviour, while gas flaring also significantly predicted health-seeking behaviour. The study concluded that environmental risks associated with oil production, particularly noise pollution and gas flaring, significantly influence residents' decisions and patterns regarding healthcare utilisation. It was recommended, among others, that government agencies and oil-producing companies strengthen environmental regulation, reduce exposure to industrial noise and gas-flaring emissions, and improve access to healthcare services in affected communities.

Autonomous landing of Unmanned Aerial Vehicles (UAVs) on naval vessels requires high-precision trajectory tracking under challenging environmental conditions. This paper presents a robust control framework designed to ensure safe UAV shipboard landing specifically in the presence of severe, time-varying atmospheric wind gusts. A 3D spatial trajectory model is formulated to simulate the final approach glide slope and deck-landing maneuver. To evaluate system performance against atmospheric turbulence, rigorous simulation scenarios incorporate sequential severe wind gusts (Gust 1 and Gust 2) acting on the vehicle. Simulation results demonstrate that the proposed controller effectively compensates for wind-induced disturbances, suppresses trajectory deviations, and guides the UAV to a precise touchdown at the designated target on the moving deck.

Artificial Intelligence (AI) is reshaping recruitment by automating routine tasks, sharpening decision-making, and shifting how organizations attract and select talent. For startups, where headcount, budgets, and time are tightly constrained, AI-enabled recruitment tools offer a route to faster, leaner, and more consistent hiring compared with traditional, labour-intensive methods. This conceptual paper builds a theory-driven framework linking AI adoption in recruitment to hiring and organizational outcomes in startup settings. AI adoption is treated as a multidimensional construct comprising four components: resume screening, recruitment chatbots, predictive analytics, and recruitment process automation. The framework proposes that these components jointly influence four outcomes recruitment process efficiency, quality of hire, candidate experience, and innovation performance both directly and indirectly through the extent of automation and data-driven decision-making, which functions as a mediating mechanism. Digital maturity of the startup is positioned as a boundary condition (moderator) that strengthens or weakens these relationships depending on the firm's existing technological and data infrastructure. Drawing on the Resource-Based View, the Unified Theory of Acceptance and Use of Technology, and socio-technical systems thinking, the paper integrates perspectives that earlier work has largely examined in isolation, and it does so with explicit attention to the resource-constrained, fast-moving character of startups rather than large, established firms. Six propositions are advanced to guide future empirical testing. The paper closes with theoretical and practical implications for founders, HR practitioners, and policymakers seeking to introduce AI into talent acquisition responsibly, along with limitations and directions for subsequent empirical validation across startup ecosystems.

Acne vulgaris is a common inflammatory skin disorder associated with excessive sebum production, follicular blockage, microbial growth, and inflammation. The present study aimed to formulate and characterise a natural anti-acne herbal face pack using selected medicinal plants with antimicrobial, anti-inflammatory, antioxidant, and skin-soothing properties. The prepared formulation was evaluated for its organoleptic properties, pH, spreadability, washability, phytochemical constituents, skin compatibility, antimicrobial activity, and stability. The formulation exhibited satisfactory physical characteristics, skin compatibility, and promising antimicrobial potential against acne-associated microorganisms. The presence of important phytoconstituents further supported its potential anti-acne activity. Overall, the developed herbal face pack may serve as a safe, natural, and promising topical formulation for acne-prone skin.

The rapid digitalisation of healthcare through Electronic Medical Record (EMR) systems has heightened the importance of resilient, secure data storage infrastructure. This study assessed the utilization of cloud storage on data security and disaster preparedness in the EMR system of a public tertiary hospital in North-Central Nigeria, using Ibrahim Badamasi Babangida (IBB) Specialist Hospital, Minna, as a case study.
Anchored in the Technology Acceptance Model (TAM), extended with the Technology-Organisation-Environment (TOE) and Unified Theory of Acceptance and Use of Technology (UTAUT) frameworks, the study adopted a descriptive cross-sectional survey design. The study population comprised 520 eligible staff across five professional cadres, from which a sample of 270 was determined using Cochran's formula with finite population correction and an 18% allowance for potential non-response, and proportionately stratified across clinical/medical staff, nursing staff, Health Information Management (HIM) professionals, information technology (IT) personnel, and administrative staff. Data were collected using a structured, expert-validated Cloud Server Adoption, Data Security and Disaster Preparedness Questionnaire (CSADSQ; Cronbach's alpha = 0.82). Two hundred and fifty questionnaires were validly completed and returned (92.6% response rate) and analysed descriptively and inferentially using Pearson Product-Moment Correlation and one-way ANOVA in SPSS version 25.0.
Findings revealed a paradox: current cloud storage utilization was critically low (grand mean = 2.88), with 90% of respondents indicating that the hospital relies solely on physical, on-premises servers, yet respondents strongly recognised the security and resilience benefits of cloud computing. Cloud storage utilization showed a strong, positive, and statistically significant relationship with both data security (r = 0.82, p < 0.001) and disaster preparedness (r = 0.79, p < 0.001). Only 15% of respondents confirmed the existence of a documented disaster recovery plan. One-way ANOVA revealed a statistically significant difference in perceived benefits across professional cadres (F = 4.56, p = 0.012), with IT and HIM staff perceiving significantly greater benefit than clinical, nursing, and administrative staff. High subscription costs, inadequate internet bandwidth, limited technical skills, and regulatory uncertainty around the Nigeria Data Protection Regulation (NDPR) were identified as the principal barriers to utilization.
The study concludes that a Hybrid Cloud Architecture, combining on-premises servers for daily low-latency operations with a secure, geographically redundant cloud layer for automated backup and disaster recovery, represents the most viable pathway for resource-constrained healthcare institutions in Nigeria and comparable developing-country settings. The study contributes an empirically grounded, context-specific framework linking cloud storage utilization to the CIA triad of data security and to institutional disaster resilience, and offers actionable recommendations for hospital management, IT departments, and policymakers.

This study examines consumer perception and purchase behaviour towards sustainable and eco-friendly products in Bangalore. The study focuses on consumer awareness, perceptions, purchasing patterns, environmental consciousness, and the factors that influence the adoption of sustainable products. A descriptive research design was adopted. Primary data were collected through a structured questionnaire distributed using Google Forms, and the study considered 100 respondents from Bangalore selected through convenience sampling. Secondary information was obtained from books, academic journals, research articles, reports, websites and other relevant sources. The findings indicate generally favourable consumer attitudes towards sustainable products. A substantial proportion of respondents purchase sustainable products frequently or very frequently, while health benefits and environmental protection are major purchase motivations. At the same time, limited availability and high prices remain important barriers. The study concludes that improving affordability, accessibility, product information, consumer awareness and trust can encourage wider adoption of sustainable and eco-friendly products.

This study examines the interrelationships among career decision-making self-efficacy (CDSE), career decision-making (CDM), and perceived employability (PE) among university graduates in North-East Nigeria, a region characterised by protracted insecurity, disrupted schooling, and acute graduate unemployment. Anchored in Social Cognitive Career Theory, the study proposes and tests a structural model in which CDSE exerts both a direct effect on PE and an indirect effect mediated by CDM. Data were simulated to approximate a cross-sectional survey of 380 recent graduates drawn from seven tertiary institutions across Borno, Adamawa, Yobe, Gombe, Taraba, and Bauchi States, stratified by discipline (Sciences, Social Sciences, Education, Agriculture, Engineering/Technology, and Humanities). Partial least squares structural equation modelling (PLS-SEM) procedures were followed for measurement and structural model assessment, complemented by ordinary least squares path estimation, Sobel testing, and 5,000-resample bootstrap mediation analysis. Results indicate that CDSE significantly predicted CDM (β = 0.457, p < .001) and PE (β = 0.246, p < .001), and that CDM significantly predicted PE (β = 0.318, p < .001). CDM partially mediated the CDSE–PE relationship (indirect effect = 0.145, 95% CI [0.095, 0.203]), with the model explaining 20.9% and 23.3% of variance in CDM and PE respectively. Neither gender nor institutional affiliation produced statistically significant differences in perceived employability. The findings support interventions that strengthen graduates' career decision-making self-efficacy as a lever for improving both career decision quality and labour-market readiness in fragile, conflict-affected educational contexts. Theoretical and policy implications for career guidance services in North-East Nigerian universities are discussed.

प्रस्तुत शोध पत्र "शिक्षा की सामाजिक व्यवस्था: एक समाजशास्त्रीय विश्लेषण" का मुख्य उद्देश्य शिक्षा को एक सामाजिक उप-प्रणाली के रूप में परिभाषित करना और इसके विभिन्न प्रकार्यात्मक एवं संवादात्मक आयामों का अन्वेषण करना है। समाजशास्त्रीय विमर्श में शिक्षा केवल सूचनाओं के हस्तांतरण का माध्यम नहीं, बल्कि सामाजिक संरचना को बनाए रखने और परिवर्तित करने वाली एक अनिवार्य व्यवस्था है। इस शोध में तीन प्रमुख सैद्धांतिक दृष्टिकोणों का तुलनात्मक विश्लेषण किया गया है, जिसमें प्रथम एमिल दुर्खीम का दृष्टिकोण है जो शिक्षा को 'व्यवस्थित समाजीकरण' और 'नैतिक अनुशासन' के माध्यम से सामाजिक एकता (Solidarity) का आधार मानता है; द्वितीय टैल्कोट पार्सन्स का संरचनात्मक-प्रकार्यवाद है जो विद्यालय को परिवार और व्यापक समाज के बीच एक 'सेतु' के रूप में देखता है और 'योग्यतावाद' (Meritocracy) के माध्यम से सामाजिक भूमिकाओं के निष्पक्ष आवंटन पर बल देता है; तथा तृतीय कार्ल मार्क्स और उनके अनुयायियों का संघर्षवादी सिद्धांत है जो शिक्षा को शासक वर्ग की 'विचारधारा' के प्रसार और वर्ग-विभाजन को वैध बनाने वाले एक उपकरण के रूप में चित्रित करता है। शोध के निष्कर्ष यह दर्शाते हैं कि शिक्षा व्यवस्था एक साथ दोहरी भूमिका निभाती है; जहाँ यह एक ओर सामाजिक स्थिरता और कौशल विकास (दुर्खीम व पार्सन्स) सुनिश्चित करती है, वहीं दूसरी ओर यह 'सांस्कृतिक पूंजी' के माध्यम से असमानता का पुनरुत्पादन (मार्क्स) भी करती है। अंततः, यह शोध पत्र राष्ट्रीय शिक्षा नीति (NEP 2020) के संदर्भ में इन सिद्धांतों की प्रासंगिकता को रेखांकित करता है, जहाँ नैतिकता, योग्यता और समानता के बीच संतुलन साधने का प्रयास किया गया है।

Background: Health data utilization is fundamental to evidence-based healthcare delivery, planning, and policy formulation. Despite growing investment in health information systems in Nigeria, routine utilization of health data remains suboptimal. This study assessed the knowledge, attitude, and organizational factors influencing health data utilization among Health Information Management (HIM) professionals in three tertiary healthcare institutions in Ondo State, Nigeria.
Methods: A descriptive cross-sectional survey was conducted among HIM professionals at Federal Medical Centre, Owo; University of Medical Sciences Teaching Hospital, Ondo; and Federal University of Technology Akure Teaching Hospital. From an accessible population of 160, a minimum sample of 115 was calculated using the Yamane formula and inflated by 10% for non-response, yielding a target of 127, proportionately allocated and selected by stratified random sampling. A structured, validated, self-administered questionnaire (Cronbach's alpha 0.866–0.923) captured socio-demographic characteristics, knowledge, attitude, organizational factors, and health data utilization. Of 127 questionnaires distributed, 106 were retrieved complete (response rate 83.5%) and analysed in R (version 4.5.0) using descriptive statistics, Pearson's correlation, and multiple linear regression (α=0.05).
Results: Respondents demonstrated good knowledge (99.1%; mean=4.46±0.36), positive attitude (99.1%; mean=4.65±0.37), and good utilization practices (99.1%; mean=4.51±0.39) within a broadly supportive organizational environment (mean=4.53±0.41). All inter-variable correlations were positive and significant (p<0.001), including knowledge–utilization (r=0.662) and attitude–utilization (r=0.740). Knowledge, attitude, and organizational factors jointly and significantly predicted utilization (F(3,102)=46.95, p<0.001; R²=0.580); attitude (β=0.487, p<0.001) and organizational factors (β=0.182, p=0.031) were significant independent predictors, while knowledge was not independently significant after adjustment (β=0.202, p=0.097).
Conclusion: HIM professionals in these institutions possess adequate knowledge and favourable attitudes, but attitude and organizational support — not knowledge alone — independently drive routine health data utilization. Strengthening continuous professional development, digital infrastructure, and management support is recommended.

Artificial Intelligence (AI) has emerged as a transformative technology in the pharmaceutical industry, significantly improving drug discovery, pharmaceutical technology, and drug delivery system design. Traditional drug development processes are time-consuming, costly, and complex, often requiring many years of research and large financial investments. AI techniques, including machine learning (ML) and deep learning (DL), enable the analysis of large biomedical datasets to identify potential drug targets, predict molecular interactions, and optimize drug candidates with greater speed and accuracy. These technologies also support virtual screening, structure–activity relationship modelling, toxicity prediction, and drug repurposing, thereby accelerating the early stages of drug development. In pharmaceutical technology, AI assists in the development and optimization of oral solid dosage forms and other drug delivery systems by predicting formulation behaviour, dissolution patterns, and stability. AI-based computational models also contribute to pharmacokinetic (PK) and pharmacodynamic (PD) studies by predicting drug absorption, distribution, metabolism, and excretion (ADME) properties. Furthermore, AI improves healthcare services through applications such as automated pharmacy systems, predictive inventory management, and robotic medication preparation.
Despite these advantages, challenges such as data limitations, algorithm bias, lack of transparency, regulatory concerns, and ethical considerations remain significant. This review article discusses the major applications of AI in pharmaceutical technology, drug discovery, pharmacokinetics, and drug delivery design, while also highlighting its advantages, limitations, and future prospects in modern pharmaceutical research.

This article examined two complementary dimensions of revenue and road development in Anambra State: the effect of revenue generation on socio-economic development through road infrastructure and the administrative bottlenecks that hinder sustainable road construction. The article was developed from the third and fourth objectives of a wider study of selected Local Government Areas in Anambra State between 2014 and 2025. Data were obtained through a structured questionnaire and secondary sources. Four hundred questionnaires were administered and 393 usable responses were returned. Descriptive statistics and simple linear regression were employed. The regression model for socio-economic development produced R = 0.1461, R² = 0.0213, F = 0.0872 and p = 0.7825, showing that revenue generation had a weak and statistically insignificant effect on socio-economic development through road infrastructure. The administrative bottleneck model produced R = 0.9980, R² = 0.9960, F = 991.7714 and p < 0.001, indicating a very strong and statistically significant relationship between administrative efficiency and sustainable road construction outcomes. Descriptive results identified bureaucratic delays, poor coordination, inefficient record keeping, inadequate staff capacity, corruption and revenue leakages, and weak monitoring and evaluation as major constraints. The article concludes that road infrastructure becomes an effective socio-economic catalyst only when revenue is supported by efficient administration, transparency, coordination and monitoring.

This study examined community resistance to development projects and the challenges traditional leaders face in mobilising financial and material resources for Community-Driven Development (CDD) initiatives in Ebonyi State, Nigeria, between 2015 and 2023. The study responded to concerns that CDD projects may be delayed or weakened when communities resist interventions or when local institutions lack adequate resources. A descriptive survey design was adopted. The population of the selected six Local Government Areas was 1,192,105, and the Taro Yamane formula produced a sample size of 400. Four hundred questionnaires were distributed, while 396 valid copies were returned, representing a 99% response rate. Data were collected through a five-point Likert questionnaire and analysed using frequencies, percentages and simple linear regression. The results showed that 40.5% of respondents strongly agreed and 36.5% agreed that community resistance affects CDD implementation, while 21.6% disagreed or strongly disagreed. Regression analysis produced an extremely strong relationship between community resistance and the measured CDD outcome (R = .999; R² = .998; B = .953; t = 206.21; p < .001; F = 42,519.51). The results on resource mobilisation showed that 43.9% strongly agreed and 38.4% agreed that traditional leaders face financial, institutional and coordination constraints. Regression analysis showed R = .8045, R² = .6473 and B = .8947, but p = .1007. Thus, the community-resistance model was statistically significant, while the resource-mobilisation model was not significant at α = .05. The study concludes that community attitudes are central to CDD outcomes and resource constraints remain major practical challenges.

Breast tumor classification is an important machine learning application in healthcare because reliable differentiation between benign and malignant observations may sup-port computer-assisted analysis. This study presents an educational machine learning framework for classifying breast tumor observations into benign and malignant categories using quantitative measurements of cell nuclei. The Breast Cancer Wisconsin (Diagnos-tic) dataset, containing 569 observations and 30 numerical predictive features, was used for experimentation. Six classification algorithms were evaluated: Logistic Regression, K-Nearest Neighbors, Decision Tree, Random Forest, Support Vector Machine, and Gra-dient Boosting. The experimental workflow consisted of feature preparation, train-test partitioning, model training, prediction, and multi-metric evaluation. Accuracy, pre-cision, recall, F1-score, ROC-AUC, and confusion matrices were used for comparative assessment. In the reported held-out test split, Random Forest achieved approximately 97.37% accuracy, 100.00% precision, 92.86% recall, 96.30% F1-score, and 99.8% ROC-AUC. Feature-importance analysis indicated that perimeter-worst, area-worst, concave-points-mean, concave-points-worst, and radius-worst were among the most influential variables in the Random Forest model. An interactive prediction interface was also de-veloped to demonstrate model inference. The system is intended for educational and research purposes and must not be interpreted as a clinical diagnostic instrument.

The rapid adoption of digital lending platforms in Kenya has revolutionised access to credit for micro, small, and medium enterprises (MSMEs), yet it has also raised significant concerns about predatory lending and over-indebtedness. This study aims to assess the effects of digital lending platforms on the financial well-being of small business owners across various sectors in Kisumu County, Kenya. Specifically, the research evaluates how digital credit accessibility affects operational cash flow, examines the impact of borrowing costs on net profit margins, and determines how debt utilisation and repayment behaviours influence financial leverage and over-indebtedness. The study is anchored on Financial Capability Theory, Pecking Order Theory, and Sustainable Livelihoods Framework. A descriptive survey research design will be employed, targeting MSME owners in major Kisumu hubs, such as Kibuye and Kondele markets. Data will be collected through structured questionnaires and analysed using descriptive and inferential statistics, including multiple linear regression. Preliminary review suggests that while ease of access can improve short-term liquidity, high interest rates and poor repayment behaviours, such as "loan stacking", may severely erode net profit margins and increase long-term financial vulnerability. The findings are expected to provide empirical evidence for policymakers at the Central Bank of Kenya to enhance consumer protection and for digital lenders to develop more sustainable, consumer-centric financial products.

Quality patient care depends fundamentally on the availability, accuracy, and effective management of health information, yet institution-specific empirical evidence on how Health Information Management (HIM) Services shape care quality remains limited in Nigerian tertiary hospitals. This study examined the influence of Health Information Management Services on Quality Patient Care at the Federal Medical Centre (FMC), Owo, Ondo State, Nigeria. Specifically, it assessed the role of HIM Services in supporting quality patient care, examined medical records management practices, and determined the relationship between HIM Services and quality patient care. A quantitative, descriptive cross-sectional survey design was adopted. The target population comprised 922 staff across six professional categories (medical doctors, nurses, Health Information Managers, medical laboratory scientists, pharmacists, and top administrators), from which a sample of 279 was determined using the Taro Yamane (1967) formula and proportionately allocated using Bowley's allocation formula. Data were collected using a structured, expert-validated questionnaire covering HIM Services, Medical Records Management, Quality Patient Care, and General Opinion (Cronbach's alpha = 0.784 overall); all 279 questionnaires were returned complete (100% response rate) and analysed in SPSS version 27.0 using descriptive statistics, Pearson Product-Moment Correlation (PPMC), multiple regression analysis, and the Chi-square test at the 0.05 significance level.
Findings showed generally favourable perceptions across all study constructs: HIM Services recorded an aggregate mean of 4.26, Medical Records Management 4.39, Quality Patient Care 4.45, and General Opinion the highest aggregate mean of 4.58. PPMC analysis revealed a significant positive relationship between HIM Services and Quality Patient Care (r = 0.273, p < 0.01). Multiple regression analysis showed that HIM Services made a significant positive contribution to Quality Patient Care (β = 0.120, p = 0.044), alongside Medical Records Management (β = 0.232, p < 0.001) and General Opinion (β = 0.247, p < 0.001), with the overall model statistically significant (F(3,275) = 22.312, p < 0.001; R² = 0.196). The Chi-square test confirmed a statistically significant association between HIM Services and Quality Patient Care (χ² = 187.050, df = 9, p < 0.001). Efficient storage of patient records was the single weakest-rated item across the entire instrument (mean = 3.43), despite otherwise strong agreement.
The study concludes that Health Information Management Services significantly influence Quality Patient Care at FMC Owo, and that effective management and utilization of health information, adequate staffing and infrastructure, appropriate implementation of electronic health records, continuous professional training, improved medical records storage, and protection of patient information are central to strengthening healthcare delivery. The findings provide institution-specific, statistically grounded evidence to guide hospital management, Health Information Management professionals, and policymakers seeking to strengthen HIM Services and improve patient care quality in comparable Nigerian tertiary hospitals.

The study on the effect of product packaging on consumer behaviour examines how different packaging elements influence consumers' perceptions and purchasing decisions. The study focuses on colour, design, shape, material quality, brand logo, product images, size and eco-friendliness. It considers packaging as both a protective medium and an important marketing communication tool. Data were collected through a structured questionnaire from selected respondents and analysed using descriptive statistical techniques such as percentages, averages and other suitable methods. The study also examines packaging preferences, the role of packaging information, ethical labels, multilingual information and packaging as a means of communicating brand values. The study provides insights for marketers and manufacturers to improve packaging strategies and understand consumer preferences and purchasing behaviour.

Timely and accurate disease surveillance and notification are essential to early outbreak detection and effective public health response, yet the extent to which Information and Communication Technology (ICT) enhances these processes at the Local Government Area (LGA) level in Nigeria remains under-documented. This study assessed the availability, accessibility, and utilization of ICT, and their effects on the effectiveness of disease surveillance and notification, among healthcare professionals in Ibadan North LGA, Oyo State.
Anchored in the Socio-Technical Systems (STS) and Health Information Systems (HIS) theories, a descriptive cross-sectional design was adopted. A multistage sampling technique (stratification by facility category, proportional allocation, and simple random selection of respondents) was used to draw a Cochran-derived sample of 370 healthcare workers from tertiary, secondary, primary, and private health facilities (total eligible population = 2,424). A structured, expert-validated questionnaire (overall Cronbach's alpha = 0.82) covering ICT availability, accessibility, utilization, effectiveness, challenges, and improvement strategies was administered; 340 questionnaires were validly returned (92% response rate).
Descriptive analysis showed that ICT tools and infrastructure were generally available (grand mean above the 3.00 benchmark for all items, e.g., mobile devices = 3.88, computers = 3.79) and accessible (range 3.32-3.78), and were actively utilized for reporting, communication, and monitoring (range 3.44-3.89), with ICT utilization perceived to substantially improve the timeliness, accuracy, completeness, and overall effectiveness of surveillance and notification (range 3.68-3.83). Pearson correlation analysis (n = 170) confirmed statistically significant, positive relationships between ICT availability and effectiveness (r = 0.611, p < 0.001), ICT accessibility and effectiveness (r = 0.645, p < 0.001), and ICT utilization and effectiveness (r = 0.682, p < 0.001), with utilization exerting the strongest influence. Insufficient funding, poor internet connectivity, unstable electricity supply, and inadequate training were identified as the principal barriers to optimal ICT performance.
The study concludes that ICT availability, accessibility, and utilization are each significant, interrelated determinants of disease surveillance and notification effectiveness at the LGA level, but that realising their full benefit requires concurrent investment in infrastructure, funding, technical training, and institutional support. The findings provide localised, empirically grounded evidence to guide policymakers, local health authorities, and development partners seeking to strengthen ICT-enabled disease surveillance systems in comparable sub-Saharan African settings.

This study investigates the engineering behavior of expansive soil stabilized using red mud and iron powder as industrial waste materials. The primary objective was to evaluate the effectiveness of these additives in improving the compaction and engineering characteristics of expansive soil and to identify a suitable stabilization combination for potential ground improvement applications. Red mud was added at proportions of 9%, 18%, 27%, 36%, and 45% by dry weight of soil, while iron powder was incorporated at 1%, 2%, 3%, 4%, and 5%. Untreated expansive soil was considered as the control mixture. Laboratory investigations were conducted to determine the index properties, compaction characteristics, strength parameters, and swelling behavior of the stabilized soil mixtures. The Standard Proctor Compaction Test was specifically performed to determine the Optimum Moisture Content (OMC) and Maximum Dry Density (MDD) of the untreated and stabilized soil mixtures. The results demonstrate that the incorporation of red mud and iron powder produces noticeable changes in the moisture-density relationship and overall compaction behavior of expansive soil. The improvement is attributed to the modification of soil gradation, filler action, reduction in clay activity, and improved particle interaction resulting from the incorporation of industrial waste materials. The use of these waste materials also offers an environmentally sustainable approach by converting industrial by-products into useful stabilizing agents for problematic expansive soils. The findings indicate that red mud and iron powder have significant potential for improving the engineering performance of expansive soil and may be considered for sustainable ground improvement applications.

This study evaluates the performance of concrete incorporating copper slag as a partial replacement for natural fine aggregate. Copper slag is an industrial by-product generated during the extraction and refining of copper and has significant potential for use as an alternative construction material. The experimental investigation was carried out by replacing natural sand with copper slag at different proportions of 0%, 10%, 20%, 30%, 40%, and 50%. The workability of the concrete mixtures was evaluated using the slump test. The results showed that the slump increased from 22 mm for the control concrete to 44 mm at 40% copper slag replacement, indicating improved workability. At 50% replacement, the slump slightly decreased to 40 mm. The improvement in workability is attributed mainly to the lower water absorption and relatively smooth, glassy surface of copper slag, which provides greater availability of free water within the concrete mix. The results indicate that copper slag can be effectively utilized as a partial replacement for natural sand, with 40% replacement showing the optimum slump value among the mixtures investigated. The utilization of copper slag can also contribute to sustainable concrete production by reducing the consumption of natural fine aggregates and promoting the beneficial reuse of industrial waste.

This study examined non-formal education progammes as a tool for sustainable community emancipation among the Wakrike People of Rivers State. Three research objectives, three research questions, and three null hypotheses were used for the study. The study adopted a descriptive survey research design. The population of the study was 699 community members comprising 594 members of registered community-based organisations and 105 CDC members in the study area. The sample size of the study was 406 respondents comprising 301 registered members of community- based organisations and 105 CDC Members. Proportionate stratified sampling technique was adopted in selecting 301 (50%) of the members from each of the registered community-based organisations, while all the CDC members were taken as census without sampling due to their manageable size. The instrument used for data collection was a structured questionnaire titled, “Non-Formal Education Progammes as a Tool for Sustainable Community Emancipation Questionnaire”. The instrument was validated by three experts from the Department of Adult Education and Community Development, and Measurement and Evaluation from Rivers State University. The reliability of the instrument was established through a test of internal consistency using Cronbach Alpha method. Since the reliability was done for each cluster of items in the instrument, the reliability coefficient of 0.85, 0.82 and 0.89 were obtained for the three clusters. Thus, a composite reliability index of 0.71 was established for the instrument. The responses from the seven research questions were analysed with mean and standard deviation, while the seven null hypotheses were tested with t-test statistics at 0.05 level of significance. The findings from the study revealed among others that non-formal education programmes which include vocational training programme, basic literacy programme, entrepreneurship training programme, environmental education programme, health education programme, civic education programme, and digital literacy programme served as tools for sustainable community emancipation among the Wakirike People of Rivers State to a high extent. Based on the findings, the study recommended among others that Rivers State Ministry of Education and Vocational Training Agencies should establish community-based vocational training centres in Wakrike communities in partnership with CBOs and CDC members, offering competency-based training in demand-driven trades by certified master craftsmen, with starter toolkits and apprenticeship linkages to foster self-employment and reduce external dependency.

This study examined environmental and health education programmes as tools for sustainable community emancipation among the Wakirike People of Rivers State, Nigeria. Two research objectives, two research questions, and two null hypotheses guided the study. The study adopted a descriptive survey research design. The population of the study was 699 community members comprising 594 members of registered community-based organisations and 105 CDC members in the study area. The sample size of the study was 406 respondents comprising 301 registered members of community-based organisations and 105 CDC Members. Proportionate stratified sampling technique was adopted in selecting 301 (50%) of the members from each of the registered community-based organisations, while all the CDC members were taken as census without sampling due to their manageable size. The instrument used for data collection was a structured questionnaire titled, "Non-Formal Education Programmes as a Tool for Sustainable Community Emancipation Questionnaire". The instrument was validated by three experts from the Department of Adult Education and Community Development, and Measurement and Evaluation from Rivers State University. The reliability of the instrument was established through a test of internal consistency using Cronbach Alpha method. Since the reliability was done for each cluster of items in the instrument, reliability coefficients of 0.78 and 0.76 were obtained for the environmental education and health education clusters respectively. A composite reliability index of 0.71 was established for the instrument. The responses from the two research questions were analysed with mean and standard deviation, while the two null hypotheses were tested with t-test statistics at 0.05 level of significance. The findings from the study revealed that environmental education programme (grand means of 2.71 and 2.65) and health education programme (grand means of 2.75 and 2.71) served as tools for sustainable community emancipation among the Wakirike People of Rivers State to a high extent, and that there was no significant difference in the mean ratings of members of community-based organisations and community development committee members on either programme. Based on the findings, the study recommended, among others, that the Rivers State Ministry of Environment and Water Resources should mainstream environmental education programmes in Wakirike communities through trained community environmental champions delivering workshops on waste management, climate adaptation, biodiversity conservation and water resource management, and that the Rivers State Ministry of Health and Primary Healthcare Development Agency should expand community health education through trained community health volunteers conducting outreach on HIV/AIDS, malaria prevention, maternal and child health, nutrition, sanitation, and disease surveillance.

Institutional information at academic colleges is typically scattered across static websites, printed brochures, and manually maintained notice boards, forcing students, applicants, and staff to search multiple sources for a single answer. General-purpose Large Language Models (LLMs) can converse fluently but frequently hallucinate when asked about localised, institution-specific facts such as fee structures or placement records, since such details lie outside their training distribution. This paper presents the design, implementation, and evaluation of an AI-powered College Information Chatbot for Techno Institute of Engineering and Management (TIEM), built on the Retrieval-Augmented Generation (RAG) paradigm. A curated institutional knowledge base covering admissions, academic programmes, fee structures, hostel and library facilities, placement statistics, and campus infrastructure is split into overlapping passages using a recursive character-level splitter (chunk size 500, overlap 50) and encoded into 384-dimensional dense vectors with the HuggingFace sentence-transformer model all-MiniLM-L6-v2. These vectors are persisted in a ChromaDB store indexed with Hierarchical Navigable Small World (HNSW) graphs, enabling sub-linear cosine-similarity retrieval. At query time, the top-k most relevant chunks are injected into a structured prompt and forwarded to a Qwen3-32B model served through the GROQ inference API, which returns a fluent, context-grounded answer that is displayed through a Streamlit-based conversational interface named miro.ai. Experimental evaluation across diverse query categories — admissions, fees, faculty, placements, and facilities — shows that the RAG-grounded responses consistently reproduce verified institutional facts and remain robust to lexical variation in how a question is phrased, while a comparative study against standard prompting and fine-tuning approaches shows RAG offers the best balance of factual accuracy, update cost, and deployment simplicity for a small institution. The resulting system is lightweight, reproducible with open-source and free-tier tools, and readily extensible to voice, multilingual, and authenticated-portal use cases. The work demonstrates that a small academic team can build a production-quality, hallucination-resistant conversational assistant without large computational budgets, and it contributes a practical reference architecture for similar institution-scale deployments.

Background: Forensic Medicine & Toxicology (FMT) is vital for teaching medical jurisprudence and toxicological principles to homoeopathic undergraduates. However, students frequently perceive FMT as an isolated, conventional subject detached from homoeopathic philosophy. Integrated teaching (IT) solves this by explicitly linking forensic and toxicological concepts with core homoeopathic therapeutics.
Objective: To survey undergraduate homoeopathic students to evaluate the educational utility, effectiveness, and impact of an integrated FMT curriculum on student engagement and learning outcomes.
Methods: A descriptive, questionnaire-based cross-sectional survey was conducted among Bachelor of Homoeopathic Medicine and Surgery (BHMS) students exposed to integrated FMT teaching modules. The survey measured changes in student interest, conceptual clarity regarding poisoning and medical jurisprudence, and overall confidence in clinical-legal scenarios.

Introduction: Post-traumatic stress disorder (PTSD) in children and adolescents constitutes a major public health problem, particularly in contexts of exposure to sexual and intrafamilial violence. Trauma-Focused Cognitive Behavioral Therapy (TF-CBT) is currently recommended as the first-line treatment for pediatric PTSD. This study aims to evaluate the feasibility and clinical efficacy of implementing a TF-CBT program within a Moroccan public child and adolescent psychiatry service.
Methods: We conducted a single-center retrospective before-after cohort study within the psychotrauma unit of the Child and Adolescent Psychiatry Department at Ar-Razi Hospital in Salé, Morocco. Twenty children and adolescents under the age of 18 presenting with a PTSD diagnosis according to DSM-5-TR and ICD-11 criteria were included. Participants underwent a TF-CBT protocol comprising an average of 12 individual weekly sessions following the PRACTICE model, with involvement of non-offending caregivers. Symptomatic progression was assessed using the PTSD Checklist for DSM-5 (PCL-5) before and after the intervention.
Results: The mean age of participants was 12 years and 60% were female. Sexual violence accounted for 70% of documented traumas, while 55% of patients presented with complex trauma and 50% with intrafamilial trauma. At admission, 20% of patients reported a history of suicide attempt and 15% presented with self-injurious behaviors. Following TF-CBT, a significant reduction in post-traumatic symptomatology was observed, with mean PCL-5 scores decreasing from 55.4 ± 14.2 to 30.1 ± 11.2 (p < 0.001). This improvement was accompanied by restoration of academic, emotional, social, and autonomy functioning.
Conclusion: The implementation of TF-CBT within a Moroccan public child and adolescent psychiatry service appears feasible and is associated with significant improvement in PTSD symptoms and overall functioning in traumatized children and adolescents. These results support the development and dissemination of specialized pediatric psychotraumatology programs in Morocco.

Acne vulgaris is a common dermatological disorder caused by excessive sebum production, microbial growth, and inflammation of the pilosebaceous unit. Ultraviolet (UV) radiation further aggravates acne conditions by inducing oxidative stress, erythema, and pigmentation. Conventional topical treatments are associated with adverse effects such as irritation, dryness, and antibiotic resistance. Hence, the present study aims to formulate and evaluate a herbal sunscreen-cum-anti-acne cream with moisturizing properties using natural ingredients. The formulation was developed using Azadirachta indica (Neem), Curcuma longa (Turmeric), and Aloe barbadensis (Aloe vera) extracts due to their antimicrobial, anti-inflammatory, antioxidant, and moisturizing properties. Carbopol 934 was used as a Creaming agent. The prepared cream was evaluated for physicochemical parameters including pH, viscosity, spreadability, extrudability, and homogeneity. Further, antimicrobial activity, Sun Protection Factor (SPF), moisturizing effect, and stability studies were conducted.
The results showed that the cream exhibited good homogeneity, acceptable pH (6–7), optimal viscosity, and excellent spreadability. The formulation demonstrated significant antimicrobial activity against acne-causing bacteria and moderate SPF value. Stability studies indicated no significant changes in physical parameters. The study concludes that the formulated herbal cream is safe, effective, and suitable for topical application with multifunctional benefits.

प्रस्तुत शोध पत्र का मुख्य उद्देश्य भाषा, विचार और समाज के मध्य व्याप्त जटिल और अटूट अंतर्संबंधों का गहन विश्लेषण करना है। सामान्यतः भाषा को केवल सूचनाओं के आदान-प्रदान या संवाद के एक सीमित साधन के रूप में देखा जाता है, परंतु यह शोध इस धारणा को चुनौती देते हुए स्थापित करता है कि भाषा वास्तव में विचारों की जननी है। 'सपीर-वोर्फ परिकल्पना' के आलोक में यह अध्ययन स्पष्ट करता है कि किसी व्यक्ति की मातृभाषा न केवल उसके संवाद को, बल्कि उसके सोचने के ढंग, धारणाओं और विश्वदृष्टि को भी मौलिक रूप से आकार देती है। शोध का दूसरा महत्वपूर्ण पक्ष भाषा और समाज का द्वंद्वात्मक संबंध है। जहाँ एक ओर सामाजिक संरचना, सांस्कृतिक मूल्य और ऐतिहासिक परिस्थितियाँ भाषा के स्वरूप और शब्दावली को गढ़ती हैं, वहीं दूसरी ओर भाषा अपनी शक्ति से समाज के पदानुक्रम, लिंग-भेद और सत्ता संबंधों को सुदृढ़ या परिवर्तित करने की क्षमता रखती है। यह अध्ययन इस बात पर प्रकाश डालता है कि सामाजिक परिवेश किस प्रकार नई शब्दावली को जन्म देकर भाषा को जीवंत बनाता है और बदले में, भाषा किस प्रकार हमारे सामाजिक व्यवहार और सोच की सीमाओं को निर्धारित करती है। विश्लेषणात्मक पद्धति का प्रयोग करते हुए यह शोध पत्र निष्कर्ष निकालता है कि भाषा, विचार और समाज एक-दूसरे के पूरक हैं। समाज की वैचारिक उन्नति के लिए भाषाई विमर्श का परिष्कृत होना अनिवार्य है। यह अध्ययन समाजशास्त्रियों, भाषाविदों और शिक्षाविदों के लिए एक नई अंतर्दृष्टि प्रदान करता है कि कैसे भाषाई बदलाव के माध्यम से सामाजिक चेतना में सकारात्मक परिवर्तन लाया जा सकता है।

प्रस्तुत शोध-पत्र "सामाजिक विकास में स्पर्धा, अनुशासन, पुरस्कार एवं दण्ड की भूमिका" का एक समाजशास्त्रीय एवं मनोवैज्ञानिक विश्लेषण करता है। सामाजिक विकास एक सतत प्रक्रिया है, जिसमें व्यक्ति और समाज का अंतर्संबंध महत्वपूर्ण होता है। मानव व्यवहार को सामाजिक मानदंडों के अनुरूप ढालने में स्पर्धा, अनुशासन, पुरस्कार और दण्ड ये चार स्तंभ नियामक यंत्रों (Regulatory Mechanisms) के रूप में कार्य करते हैं। यह शोध यह परीक्षण करता है कि किस प्रकार स्पर्धा व्यक्ति की क्षमताओं को निखारने का कार्य करती है, जबकि अनुशासन उसे एक निश्चित दिशा प्रदान करता है। इसी प्रकार, व्यवहारवादी दृष्टिकोण से पुरस्कार और दण्ड को सकारात्मक एवं नकारात्मक सुदृढ़ीकरण (Reinforcement) के प्रभावी माध्यमों के रूप में देखा गया है। इस शोध के दौरान यह पाया गया है कि आधुनिक समाज में इन चार कारकों का असंतुलन, जैसे कि 'अति-स्पर्धा' का बढ़ना और दण्ड का 'प्रतिशोधात्मक' होना, सामाजिक विघटन और अलगाव का कारण बन सकता है। भारतीय ज्ञान प्रणाली (IKS) के सिद्धांतों, विशेषकर श्रीमद्भगवद्गीता के 'निष्काम कर्म' और 'आत्म-अनुशासन' के आलोक में, यह शोध यह सिद्ध करता है कि स्पर्धा का वास्तविक उद्देश्य पर-विजय नहीं, बल्कि स्व-उत्कृष्टता है। शोध का निष्कर्ष है कि एक प्रगतिशील और सुसंस्कृत समाज की स्थापना हेतु स्पर्धा में सहयोग, अनुशासन में स्वविवेक, और पुरस्कार-दण्ड में सुधारात्मक दृष्टिकोण का समावेश अनिवार्य है। नेशनल एजुकेशन पॉलिसी (NEP) 2020 की दृष्टि से भी, शिक्षा का उद्देश्य केवल प्रतिस्पर्धी कौशल विकसित करना नहीं, बल्कि मानवीय मूल्यों और चारित्रिक सुदृढ़ता का निर्माण करना है। संतुलन ही वह कुंजी है जो इन नियामक उपकरणों को समाज-निर्माण के शक्तिशाली साधन में परिवर्तित कर देती है, जिससे व्यक्ति अपनी पूर्ण क्षमताओं का विकास कर एक उत्तरदायी नागरिक बन सके। यह शोध नीति-निर्माताओं, शिक्षाविदों और अभिभावकों के लिए इन कारकों के विवेकपूर्ण अनुप्रयोग हेतु एक वैचारिक ढांचा प्रदान करता है।

This study explored the sentiments and behaviors of middle children in relation to their family relationships. Specifically, it examined the common behaviors middle children displayed toward their parents and siblings, how their sense of self-worth developed when their emotions were validated or invalidated, and the emotions they experienced when they perceived exclusion during family interactions. A qualitative phenomenological research design was employed to understand the lived experiences of Grade 11 students who identified themselves as middle children. Data were collected through one-on-one interviews using a researcher-made semi-interview protocol and analyzed through thematic analysis.
The findings revealed several recurring themes, including avoidant behavior, emotional withdrawal, emotional suppression, limited communication, emotional neglect, and feelings of being overlooked or undervalued. These behaviors often developed as coping mechanisms when participants perceived unequal parental attention or emotional invalidation. The study also found that middle children’s self-worth was closely influenced by how their emotions were acknowledged within the family. Experiences of exclusion during family gatherings often resulted in feelings of invisibility and heightened emotional sensitivity.

The present study was undertaken to formulate and evaluate a herbal spray containing ethanolic extract of Murdannia nudiflora (L.) Brenan. The herbal spray was prepared using Murdannia nudiflora extract, isopropyl alcohol, acetone, glycerine, and purified water. The prepared formulation was evaluated for physical appearance, colour, odour, pH, specific gravity, sensitivity, irritation, flame test, and spray pattern. Pharmacognostical evaluation of the roots revealed characteristic anatomical features, including parenchymatous cells, endodermal cells, pericycle cells, xylem vessels, fibres, medullary rays, and starch grains. Preliminary phytochemical screening of the ethanolic extract showed the presence of carbohydrates, alkaloids, tannins, flavonoids, and saponins. The formulated herbal spray was a liquid with light-blue colour and aromatic odour. The pH and specific gravity were found to be 7.54 and 1.11, respectively. The sensitivity and irritation tests showed no irritation. Antifungal activity was evaluated against Candida albicans by the agar well diffusion method. The formulation showed zones of inhibition of 9, 10, and 12 mm at concentrations of 50, 75, and 100 µg/mL, respectively, while Amphotericin B showed a zone of inhibition of 14 mm. The findings indicate that the prepared Murdannia nudiflora herbal spray possesses promising antifungal activity and may have potential for further development as a herbal topical preparation.

The present study was undertaken to synthesize, characterize, and evaluate the antibacterial activity of novel 1-benzyl isatin derivatives. The synthetic work was initiated with indoline-2,3-dione (isatin), which was subjected to N-benzylation using benzyl chloride in the presence of potassium carbonate and dimethylformamide to obtain 1-benzyl indoline-2,3-dione. The synthesized intermediate was further reacted with triethyl phosphonoacetate and sodium ethoxide to produce ethyl 2-(1-benzyl-2-oxoindolin-3-ylidene)acetate. Subsequent hydrazinolysis with hydrazine hydrate yielded 2-(1-benzyl-2-oxoindolin-3-ylidene)acetohydrazide. The acetohydrazide was further reacted with salicylic acid and methyl salicylate to obtain compounds A and B, respectively. The synthesized compounds were subjected to physical characterization, including appearance, melting point, solubility, percentage yield, and thin-layer chromatography. The synthesized compounds showed melting points in the range of 127–149°C and satisfactory Rf values ranging from 0.61 to 0.75. The percentage yields of compounds 2, 3, 4, A, and B were 91.55%, 85.31%, 87.35%, 89.35%, and 91.87%, respectively. Structural confirmation was carried out using UV, FT-IR, and 1H-NMR spectroscopy. The antibacterial activity was evaluated by the agar well diffusion method against Staphylococcus aureus, using ciprofloxacin as the standard drug. Compound A showed a zone of inhibition of 15 mm, while compound B showed 12 mm, compared with 30 mm for ciprofloxacin. The findings indicate that the synthesized 1-benzyl isatin derivatives were successfully prepared and structurally characterized, with compounds A and B exhibiting promising preliminary antibacterial activity.

Sustainability has evolved from being primarily associated with corporate social responsibility (CSR) to becoming a central component of commercial strategy, business competitiveness, and long-term economic growth in India. Increasing regulatory requirements, changing consumer preferences, growing investor attention to environmental, social, and governance (ESG) considerations, and the rapid transition towards renewable energy are collectively transforming the Indian business environment. Against this background, the present study examines the role of sustainability in modern commerce and business growth in India, with particular emphasis on regulatory developments, consumer behaviour, renewable energy expansion, and emerging business opportunities. The study adopts a descriptive and analytical approach based exclusively on secondary data collected from regulatory disclosures, government publications, industry reports, institutional surveys, and multilateral sources. Particular attention is given to the evolution of India’s sustainability reporting framework under the Securities and Exchange Board of India’s (SEBI) Business Responsibility and Sustainability Reporting (BRSR) framework, which has strengthened corporate accountability and transparency regarding environmental and social performance. The study also reviews available evidence on consumer willingness to purchase and pay for sustainable products, highlighting the growing influence of sustainability considerations on consumption and market demand However, the adoption and benefits of sustainability practices remain uneven across enterprises, with large listed companies generally having greater financial, technological, and institutional capacity than micro, small, and medium enterprises (MSMEs).

The roles of tourism in multidimensional development of global societies are undeniable. Ethiopia as cradle of humankind, the birth place of coffee, the only un-colonized African country and the founding member of UN is endowed with many amazing tourism resources. Though Kaffa is bestowed with amazing natural, historical, cultural, archaeological and coffee tourism resources, this destination is not under the category of well-known tour and travel packages of Ethiopia and receiving a smaller number of visitors compared to the destinations with low tourism resources. Accordingly, the objective of this study is to conduct potentials, challenges and the economic contributions of tourism resources in Kaffa Zone, descriptive research design was applied and 94 sample respondents were selected from tourism and culture officials, experts, youths and cultural leaders and Religious fathers or famous elders in Kaffa Zone through systematic and purposive sampling techniques. The data were collected through questionnaires, focus group discussions, key informant interview and secondary data sources are analyzed quantitatively as well as qualitatively were analyzed using SPSS version 26 Therefore, The results confirmed that the Major findings as challenges are, poor infrastructure, lack of awareness, Lack of funds for promotion and tourism development, lack of community participation, poor coordination of stakeholders, lack of accessibility, lack of skilled human resources, and lack of fair administration are challenges and also both natural and cultural tourism resources were not well identified recorded and documented. even though, the study area notability abundant tourism potentials include beautiful landscapes, unique wildlife and indigenous plant species, a clean and attractive natural environment, caves, waterfalls, escarpments and mountains; cultural tourism resource . Further study is highly recommended, the Federal and Regional governments could collaborate to solve the challenges of infrastructure, creating awareness, promoting potential resources using different media. Furthermore, the involvement of local communities and the provision of basic tourism infrastructure amenities and facilities could encourage tourism flow and economic development at Kaffa Zone.

Sickle Cell Disease (SCD) is a hereditary hemoglobinopathy characterized by the polymerization of abnormal hemoglobin S, leading to erythrocyte sickling, vaso-occlusion, and severe clinical complications. These sickled cells obstruct blood flow and cause severe complications such as anemia, pain crises, and organ damage. The global burden of SCD is significant, especially in developing regions where access to diagnostic facilities is limited. Timely and correct diagnosis has become increasingly crucial for decreasing morbidity in areas with lack of laboratory infrastructure. The developments of AI and medical imaging have provided automated methods for discriminating between normal and pathological red blood cell morphology, opening new possibilities for scalable diagnostics. In this work, we propose a method based on deep learning for SCD detection using peripheral blood smear images. A modified ResNet50 architecture combined with class weights adjustment was fine tuned in order to correct the dataset imbalance and improve its discriminative power. The validation accuracy of 93-94% and an accuracy of 95.62% over external test set, surpasses state of the art approaches implemented using different CNN (i.e. AlexNet, VGG, ResNet, MobileNet) based architectures. The outcomes proved that a well-tuned deep residual network can provide robust and generalizable Sickled erythrocyte classification among different data. The future work includes an increase of dataset size and the investigation of optimal multi dataset training, moving towards real life applications and clinical assessment for diagnosis point-of-care.

The Contributory Pension Scheme (CPS) was introduced in Nigeria in 2004 and amended in 2014 to address the chronic failures of the Defined Benefit Scheme (DBS), which was characterized by unsustainable liabilities, delayed payments, and widespread corruption. However, the implementation of enhanced pension benefits for professors in Nigerian federal universities—particularly those in the North-East geopolitical zone has remained fraught with systemic challenges. This article examines the welfare consequences of the non-implementation of enhanced pension benefits for professors in federal universities in North-East Nigeria between 2015 and 2025. Drawing on secondary data from the National Pension Commission (PenCom), Academic Staff Union of Universities (ASUU) reports, and empirical studies, the article identifies critical implementation gaps including government non-remittance of contributions, inability of retirees to access benefits, failure to fund the Guaranteed Minimum Pension (GMP), and the erosion of real pension value due to hyperinflation. The findings reveal that retired professors in the North-East face severe welfare deprivations, including financial hardship, healthcare inaccessibility, housing instability, and psychological distress. The article argues that the non-implementation of the Universities Miscellaneous Provisions (Amendment) Act 2012 (UMPAA 2012) and Section 6(2) of the Pension Reform Act (PRA) 2014 constitutes a violation of statutory rights and undermines the dignity of retired academics in a region already devastated by insurgency and economic marginalization. The article recommends full budgetary appropriation for pension shortfalls, establishment of a dedicated professorial pension fund, and institutional reforms to ensure compliance and accountability.

The aerial part of Polygonum senegalensis was extracted with methanol using the Soxhlet method and evaluated for its phytochemical composition and in vitro antimicrobial activity. Extraction gave a 19.43% (w/w) yield of a brownish, powdery crude extract. Phytochemical screening revealed the presence of terpenoids, flavonoids, carbohydrates, tannins, cardiac glycosides, steroids, cardenolides and saponins, while alkaloids and anthraquinones were absent. The hole-diffusion antimicrobial assay showed weak activity at 20 mg/hole (zones of inhibition ranging from 8.00 to 13.00 mm) against most Gram-positive and Gram-negative bacteria, with moderate-to-high activity at 40 and 80 mg/hole. At 160 mg/hole the extract was active against all test organisms except Aspergillus niger, producing zones of inhibition between 20.00 and 27.67 mm that were comparable to, and in the case of Pseudomonas aeruginosa exceeded, the reference antibiotic (ciprofloxacin, 10 µg/hole). Antimicrobial activity increased in a concentration-dependent manner across all organisms tested. These findings support the ethnomedicinal use of Polygonum senegalensis and indicate that its phytoconstituents, particularly flavonoids, tannins, saponins and terpenoids, may act synergistically against clinically relevant pathogens.

The purpose of this study is to investigate the relationship between the financial performance of information technology (IT) multinational corporations (MNCs) operating in India and the deployment of artificial intelligence (AI). The study looks into how AI integration affects financial results by increasing revenue, improving profitability, and improving operational efficiency.
Methodology: A mixed-method research methodology was used, combining secondary financial data from annual reports (2020–2025) with primary data from 342 senior finance managers from 47 IT MNCs. Return on Equity (ROE), Return on Assets (ROA), and Operating Profit Margin (OPM) were financial performance indicators. Using Stata and SPSS software, statistical analyzes comprised panel data regression, correlation analysis, mediation analysis, and descriptive statistics.
Results: The intensity of AI adoption and financial success are significantly positively correlated (β = 0.487, p < 0.001). Businesses with all-encompassing AI strategy showed 18.9% better ROE and 23.6% higher ROA in comparison to companies that use AI sparingly. 41.2% of the association between AI and financial success was explained by operational efficiency, which was found to be a key mediator.
Originality/Value: By combining primary perceptual data with objective financial indicators, this study adds to the scant empirical literature on AI-financial performance links in the Indian IT MNC setting. Financial strategists and organizational leaders can use the study's practical findings when making AI investment decisions.

Language learning at the school level plays an important role in students’ academic development, communication, confidence, and social participation. For Gujarati-medium students, proficiency in Gujarati is essential for effective learning and meaningful expression. The present study was undertaken to develop a Gujarati Language Skill Development Programme for Standard VIII students and to examine its effectiveness in improving their language skills.
An experimental method with an equivalent-group pre-test and post-test design was adopted. The sample consisted of 120 Standard VIII students from Gujarati-medium schools in Ankleshwar Taluka of Bharuch District. Sixty students were included in the experimental group and sixty in the control group. The experimental group participated in a structured 24-session programme conducted over eight weeks, with each session lasting 45 minutes. The programme included storytelling, questioning, picture description, dialogue, vocabulary activities, reading, creative writing, drama, debate, and book review.
A researcher-developed 100-mark Gujarati Language Skill Test assessed listening, reading, writing, and speaking skills. The test comprised 10 marks for listening, 30 for reading, 35 for writing, and 25 for speaking. The programme and test were developed with reference to the Standard VIII Gujarati curriculum, textbook, language teaching principles, and NEP 2020. The findings showed greater improvement in the experimental group than in the control group. The programme was also effective across gender and intelligence levels.

Polycystic Ovarian Syndrome (PCOS) affects 8-13% of reproductive-age women and is characterized by hyperandrogenism, ovulatory dysfunction, and polycystic ovaries¹. Pulsatilla nigricans is classically indicated for “delayed, scanty menses; weeping disposition; thirstlessness; agg. in warm room” ² – a picture often seen in PCOS.
Objectives: To evaluate the effect of individualized Pulsatilla nigricans on hormonal profile and menstrual regularity in PCOS.
Methods: Prospective, single-arm, observational study. 40 women aged 18-35 years with Rotterdam criteria-diagnosed PCOS received Pulsatilla nigricans 200C/1M based on totality. Serum LH, FSH, LH:FSH ratio, Testosterone, and menstrual cycle length assessed at baseline and 6 months. Paired t-test used.

Green fintech is the use of financial technology in sustainable agriculture. It becomes a disruptive force in agricultural entrepreneurship. This research explores the effect of green fintech on agricultural entrepreneurship, paying special attention to credit availability, financial inclusion, and the uptake of environmentally friendly farming methods. Regression analysis has been used in this study on primary data from 250 agri-entrepreneurs for the evaluation of the impact of green fintech on sustainability and entrepreneurial growth. The findings revealed a positive association between the usage of green fintech solutions with environmental sustainability, operational effectiveness, and financial accessibility. The report highlights how blockchain-based traceability, digital payment systems, and AI-powered financial decision-making might all help agriculture entrepreneurship. It highlights that green fintech can empower farmers to overcome financial barriers and implement climate-friendly agriculture practices, thus acting as a springboard for sustainable agricultural development. Policymakers and financial institutions should expand fintech-based green financing solutions in order to empower agri-entrepreneurs and ensure the sustainable future of the sector.

This study investigated knowledge of modifiable and non-modifiable prostate cancer risk factors as a predictor of businessmen's attitude towards prostate cancer screening and early detection in Akwa Ibom State, Nigeria. The study was guided by two specific objectives, two research questions and two null hypotheses. A cross-sectional survey research design was adopted. The target population comprised 8,450 male businessmen aged 40 years and above across Akwa Ibom State, Nigeria. A sample of 382 respondents was determined using Taro Yamane's formula, while a multi-stage sampling procedure was employed to select participants. Data were collected using a structured instrument titled Knowledge of Modifiable and Non-Modifiable Prostate Cancer Risk Factors and Screening Attitude Questionnaire (KMNM-PCRFSAQ). The instrument was subjected to face and content validation by experts, while its internal consistency was established using Cronbach's alpha, which yielded reliability coefficients of 0.84 for the knowledge scale and 0.81 for the attitude scale. Mean and standard deviation were used to answer the research questions, whereas simple linear regression analysis was employed to test the hypotheses at the 0.05 level of significance. The findings revealed that businessmen possessed a moderate level of knowledge of both non-modifiable and modifiable prostate cancer risk factors. The regression analysis showed that knowledge of non-modifiable risk factors significantly predicted businessmen's attitude towards prostate cancer screening and early detection. Similarly, knowledge of modifiable risk factors significantly predicted businessmen's attitude towards prostate cancer screening and early detection. The study concluded that improving businessmen's knowledge of both modifiable and non-modifiable prostate cancer risk factors can significantly enhance positive attitudes towards prostate cancer screening and early diagnosis. It therefore recommended, among others, that government agencies, healthcare providers and business associations collaborate to organise regular workplace health education campaigns and community-based prostate cancer screening programmes targeted at businessmen across Akwa Ibom State, Nigeria.

The rapid expansion of digital technology and social media has significantly transformed the educational landscape and students interaction with online information. While digital platforms provide opportunities for learning, communication, and socialization, they also expose students to misinformation, cyberbullying, online manipulation, and harmful media content. Consequently, school counselors are increasingly required to integrate media literacy into counseling practices to help students critically evaluate online information and develop responsible digital behavior. This study examined the role of media literacy in counseling practices and its influence on students critical analysis of online content. The study adopted a quantitative research design using a descriptive survey method. A sample of 500 respondents comprising secondary school students and school counselors was selected through stratified random sampling from selected schools. Data were collected using a structured questionnaire titled Media Literacy and Counseling Practices Questionnaire (MLCPQ). Descriptive statistics and inferential analysis were used to analyze the data. The findings revealed that integrating media literacy into counseling practices significantly improves students ability to identify fake news, analyze media messages critically, recognize online risks, and make informed decisions regarding digital content. The study further revealed that counseling interventions promoting media literacy enhanced students digital responsibility, emotional regulation, and online safety awareness. However, inadequate counselor training, insufficient digital resources, and limited institutional support were identified as major challenges affecting implementation. The study concluded that media literacy is an essential component of contemporary counseling practices and should be incorporated into school counseling programs to promote critical thinking and responsible online engagement among students. The study recommended professional development programs for counselors, curriculum integration of media literacy education, provision of digital learning resources, and collaborative efforts among schools, families, and policymakers to strengthen students media literacy competencies in the digital age.

The increasing emergence of drug-resistant Plasmodium falciparum strains has emphasized the need for the development of novel antimalarial agents. The present study employed computer-aided drug design (CADD) and quantitative structure–activity relationship (QSAR) techniques to develop predictive models for antimalarial quinazoline derivatives. A dataset of 100 quinazoline-based compounds with reported antiplasmodial activity (IC₅₀ values) was compiled from published literature. Molecular structures were generated using ChemDraw Ultra 8.0, while molecular descriptors including surface area, volume, hydration energy, logP, refractivity, polarizability, and energy parameters were calculated using HyperChem 7.0 and Chem3D Ultra 8.0. Least-squares linear regression analysis was performed to establish relationships between molecular descriptors and biological activity. The developed QSAR models demonstrated good agreement between experimental and predicted biological activities for the majority of compounds, with only a limited number of outliers. The study identified key physicochemical and structural parameters governing antimalarial activity, providing valuable insights for the rational design and optimization of quinazoline-based antimalarial agents. These findings highlight the usefulness of QSAR modeling as a rapid and cost-effective approach for virtual screening and lead optimization in antimalarial drug discovery.

This study presents a mathematical framework investigating the two-dimensional boundary layer flow and irreversibility processes of a non-Newtonian ternary hybrid (tri-hybrid) nanofluid over a stretching surface, designed to improve the thermal management of residential photovoltaic (PV) systems. The complex rheology of the working fluid is characterised by combining the upper-convected Maxwell and Cross fluid models. Non-Fourier heat conduction is incorporated via the Cattaneo–Christov heat flux model to establish finite thermal wave propagation speeds, while the system accounts for non-linear thermal radiation using the Rosseland approximation. The governing non-linear partial differential equations describing momentum, energy, and species conservation are converted into coupled ordinary differential equations using similarity transformations. Due to their analytical intractability, these equations are solved numerically using the spectral collocation technique.Parametric investigations reveal that the thermal relaxation parameter within the Cattaneo–Christov model significantly thins the thermal boundary layer, suppressing peak temperature responses. The tri-hybrid configuration consistently outperforms mono- and binary configurations, achieving superior convective heat transfer coefficients. Furthermore, second-law thermodynamic analysis maps entropy generation and the Bejan number, demonstrating that escalating Weissenberg and Forchheimer numbers suppress fluid velocity. These computational insights establish optimal design criteria to minimise exergy destruction in next-generation solar technologies.

This study evaluated the impact of improvised instructional materials on some selected secondary school students' academic achievement in Sciences. A quasi-experimental pretest-posttest design involved 120 students, divided into experimental (60) and control (60) groups. Data was collected using a Science Achievement Test and a Students' Perception Questionnaire. Results showed that the experimental group scored significantly higher post-intervention (M = 31.92) compared to the control group (M = 25.00), with statistical tests indicating significant improvements and a large effect size. Students reported moderately positive perceptions of improvised materials (M = 3.27). The study concluded that these materials enhance academic achievement and recommended training for science teachers to incorporate them into teaching to improve learning outcomes.

Leishmaniasis is a significant zoonotic parasitic disease with domestic and wild animals as potential reservoirs. This study examined the molecular occurrence and species distribution of Leishmania spp. in Zamfara State, Nigeria, sampling 240 potential hosts, including 120 dogs and 120 rodents. Molecular detection was achieved through kDNA PCR, and data were analyzed using descriptive statistics, Chi-square tests, and binary logistic regression at a 5% significance level.The study found a molecular prevalence of 19.6% for Leishmania species, with Leishmania major as the most common. Rodents exhibited a significantly higher infection rate (27.5%) compared to dogs (11.7%), with binary logistic regression showing that rodents have approximately four times greater odds of infection (OR = 3.94). No significant associations were identified between infection and demographic factors. The study concludes that rodents are a more significant reservoir for Leishmania spp. than dogs, highlighting the need for molecular surveillance and public health interventions in Zamfara State to reduce leishmaniasis transmission.

Epilepsy is a chronic neurological disorder affecting nearly 50 million people worldwide, and nearly one-third of patients remain refractory to existing antiepileptic drugs, which are also associated with significant adverse effects. Glutamate-induced excitotoxicity, mediated primarily through overactivation of the NMDA receptor, plays a central role in seizure initiation and progression, making it a promising target for novel anticonvulsant agents. Nardostachysjatamansi (Family: Valerianaceae), a traditional Ayurvedic herb, has long been used for neurological disorders including epilepsy, and its major bioactive constituent, jatamansone, was selected for computational evaluation against this target. In this study, the crystal structure of the NMDA receptor (PDB ID: 7E0S) was retrieved from the RCSB Protein Data Bank and prepared by removing water molecules and heteroatoms, followed by addition of polar hydrogens. The ligand, jatamansone, was optimized and energy-minimized prior to docking. Molecular docking was performed using AutoDockVina in the PyRx interface, and the resulting protein–ligand complex was analyzed using Discovery Studio Visualizer and PyMOL. The best docking pose (Run 4) showed a binding affinity of −6.13 kcal/mol and an estimated inhibition constant (Ki) of 32.18 µM, forming stable hydrogen bond and electrostatic interactions with key active-site residues ARG329, GLU330, ASP333, LYS401, and LYS697. These findings indicate that jatamansone can be accommodated within the NMDA receptor binding pocket with favorable affinity, supporting its traditional use and suggesting its potential as a lead molecule for antiepileptic drug development, warranting further validation through molecular dynamics and in vitro/in vivo studies.

Background
Anthrax is a zoonotic bacterial disease caused by Bacillus anthracis that primarily affects herbivorous animals and can be transmitted to humans, posing significant public health and economic challenges.
Objective
To assess the knowledge, attitudes, and practices (KAP) regarding anthrax prevention and control among livestock farmers in Dogoloya Village, Koinadugu District, Sierra Leone.
Methods
A community-based cross-sectional study was conducted among 130 livestock farmers selected through simple random sampling. Data were collected using a structured questionnaire translated into Fula, Madingo, and Krio. Data were analyzed using Microsoft Excel, SPSS, and Vassar Statistics. Descriptive statistics were used to summarize respondents' characteristics and KAP levels, while inferential statistics were employed to examine associations between selected variables.
Results
The majority of respondents were male (78%), 49% had no formal education, and only 12% were formally employed. Although 98% had heard of anthrax, only 18% correctly identified Bacillus anthracis as the causative agent, and 47% had received training on anthrax prevention and control. Most respondents (88%) reported previous anthrax outbreaks among their livestock, 56% practiced commercial livestock farming, and 76% relied on grazing systems. Carcass disposal methods included dumping (40%), composting (34%), and burial (19%), while 46% believed antibiotics alone were effective against anthrax. Cross-tabulation analysis showed no statistically significant association between gender and knowledge of the cause of anthrax (χ² = 10.59, df = 5, p = 0.60).
Conclusion
Although awareness of anthrax was high among livestock farmers, considerable gaps exist in their knowledge, attitudes, and preventive practices. Inappropriate livestock management and carcass disposal practices may increase the risk of anthrax transmission. Strengthening community education, improving access to veterinary services, and promoting routine livestock vaccination and safe carcass disposal are recommended to improve anthrax prevention and control.

The study sought to investigate the Perceived Influence of Substances Abuse on class attendance of Senior Secondary School Students in Nasarawa State of Nigeria. The study was guided by two objectives, two research questions to guide the study and one hypothesis was tested at 0.05 level of significance. Descriptive survey design with the population of one hundred and forty eight thousand, four hundred and ninety seven (148,497) persons and a sample size of seven hundred and forty four (744) participants were used for the study. The study employed questionnaire as an instrument for data collection and the data were analyzed using chi-square test statistics of mean and standard deviation. Findings showed that substance abuse has significant influence on class attendance among senior secondary school students in Nasarawa State, these negative influence on participation indices such as note taking, study time. In conclusion, it is therefore, clearly a need for specific intervention programs for substance abusers among senior secondary school students in Nasarawa State to discourage substance abuse and improve on their academic performance. It is recommended that there should a need for constant awareness programs on the dangers of substance abuse on academic performance of senior secondary school students if required action/education would be taken or given on time the students of Senior Secondary Schools may perform well academically without taking any substances.

Colonial rule in India fundamentally reorganized the relationship between land, forest, and community, converting customary systems of resource use into instruments of revenue extraction and state control. This paper offers an integrated historical analysis of how land revenue settlements—principally the Permanent Settlement, the Ryotwari and Mahalwari systems—intersected with successive forest legislation, notably the Indian Forest Acts of 1865, 1878, and 1927, to dispossess forest-dwelling and tribal communities of customary rights over land and forest produce. Drawing on environmental history and political ecology as complementary analytical frameworks, the study traces the transition from diverse, ecologically embedded modes of subsistence to the codified regime of "scientific forestry" associated with Dietrich Brandis and the Imperial Forest Department. It examines tribal resistance movements—the Chuar Rebellion, the Halba Revolt, the Paharia Resistance, and the Santhal Rebellion (Hul)—as expressions of environmental justice claims against the erosion of customary access. Particular attention is given to the criminalization of shifting cultivation (jhum) in Northeast India as a case of colonial ecological misreading. The paper argues that colonial forestry, while presented as a conservationist project, was primarily an apparatus of commercial timber extraction and administrative control whose ecological and social consequences persist in contemporary forest governance. A comparative analysis situates the postcolonial legal architecture—including the Forest Rights Act, 2006—against colonial precedents, showing both continuities and ruptures in the treatment of indigenous ecological knowledge. The paper concludes with policy recommendations for community-centred, rights-based forest governance responsive to climate change and biodiversity imperatives. The analysis contributes to environmental history, political ecology, and sustainability scholarship by demonstrating that land revenue policy and forest policy cannot be studied in isolation, since their combined operation produced the structural marginalization of tribal communities that continues to shape forest politics in contemporary India.

This study examined the influence of library resources and services on English language proficiency among undergraduate students in the Federal University of Education, Zaria. The study adopted a correlational survey research design. The population comprised 5,000 100 and 200 Level university-based undergraduate students of the Federal University of Education, Zaria, while a sample size of 370 respondents was determined using the Yamane (1967) formula and selected through a simple random sampling technique. Data were collected using a researcher-developed questionnaire titled Library Resources, Library Services and English Language Proficiency Questionnaire (LRS-ELPQ). The instrument was subjected to face and content validity by three experts, while a pilot study conducted among undergraduate students of Kaduna State University yielded a Cronbach's Alpha reliability coefficient of 0.90, indicating a high level of internal consistency. Data were analyzed using frequency counts, percentages, mean, standard deviation and Simple Linear Regression Analysis at the 0.05 level of significance. The findings revealed that library resources had a high influence on English language proficiency among undergraduate students (Cluster Mean = 3.47), while library services also exerted a high influence on students' English language proficiency (Cluster Mean = 3.43). Furthermore, the regression analysis indicated that library resources and services significantly influenced English language proficiency (R = 0.668, R² = 0.446, F = 279.614, p < 0.05). The study concluded that effective provision and utilization of library resources and services significantly enhance students' English language proficiency. The study recommended that university management should strengthen library collections, improve library service delivery, expand information literacy programmes and encourage students to make effective use of both print and electronic library resources to enhance their English language proficiency.

Tuberculosis (TB) remains one of the leading infectious diseases worldwide, with the increasing prevalence of multidrug-resistant (MDR) Mycobacterium tuberculosis posing a major challenge to current chemotherapy. In the present study, a new series of pyrazole-thiazole coupled chromenone derivatives (CH-1 to CH-14) were synthesized through a two-step synthetic route involving thiosemicarbazide and 3-(2-bromoacetyl)-2H-chromen-2-one. The synthesized compounds were purified and characterized by IR, ^1H NMR, FAB-mass spectroscopy, and elemental analysis, confirming their proposed chemical structures. The antitubercular activity of the compounds was evaluated against Mycobacterium tuberculosis H37Rv and multidrug-resistant (MDR) clinical isolates using the Microplate Alamar Blue Assay (MABA) and Luciferase Reporter Phage (LRP) assay. Several compounds exhibited promising activity, with CH-3 and CH-8 showing the lowest MIC values (1.75 µg/mL) against H37Rv, while CH-1, CH-3, CH-8, CH-10, and CH-11 demonstrated significant inhibition in the LRP assay, producing more than 50% reduction in relative light units. Among all derivatives, CH-10 exhibited the highest reduction in bacterial viability at higher concentrations, indicating potent antitubercular activity. These findings suggest that pyrazole-thiazole-chromenone hybrids represent promising lead molecules for the development of new antitubercular agents against both drug-sensitive and multidrug-resistant M. tuberculosis strains.

The present study describes the synthesis, characterization, and antimicrobial evaluation of a series of novel peptide-coupled quinazolin-4(3H)-one derivatives. Sixteen substituted styryl peptide-linked quinazolinone analogues (QP-1 to QP-16) were synthesized through a three-step synthetic route involving the preparation of 2-methyl-4H-3,1-benzoxazin-4-one, peptide-coupled quinazolinone intermediates, and their condensation with various substituted aromatic aldehydes. The synthesized compounds were purified and structurally characterized using melting point determination, thin-layer chromatography (TLC), elemental analysis, FT-IR, ^1H NMR, and FAB-mass spectrometry, confirming the successful formation of the target molecules. The antimicrobial activity was evaluated against Gram-positive bacteria (Staphylococcus aureus and Micrococcus luteus), Gram-negative bacteria (Escherichia coli and Klebsiella pneumoniae), and fungal strains (Saccharomyces cerevisiae and Aspergillus niger) using the disc diffusion method. Several derivatives exhibited promising antibacterial and antifungal activities, with compounds QP-1, QP-4, QP-9, QP-12, and QP-13 demonstrating comparatively higher zones of inhibition. The results suggest that electron-withdrawing substituents, particularly halogen and nitro groups, enhance antimicrobial potency. These findings indicate that peptide-coupled quinazolin-4(3H)-one derivatives represent promising lead molecules for the development of new antimicrobial agents and warrant further pharmacological and structure–activity relationship investigations.

Famciclovir is an antiviral drug widely used in the treatment of herpes simplex and herpes zoster infections; however, its oral administration is associated with frequent dosing and variable bioavailability. The present study aimed to formulate and evaluate a transdermal gel of famciclovir for sustained drug release and enhanced skin permeation. Preformulation studies, including organoleptic evaluation, melting point determination, solubility analysis, partition coefficient, UV spectrophotometric analysis, and FT-IR compatibility studies, confirmed the suitability of the drug and excipients for formulation development. Transdermal gels were prepared using Carbopol 940, HPMC K100M, aloe vera powder, and disodium EDTA as a penetration enhancer and were evaluated for physicochemical properties, pH, viscosity, spreadability, drug content, in vitro drug release, permeation using a Franz diffusion cell, release kinetics, and accelerated stability. Among all formulations, CHF-4 exhibited optimum characteristics with a pH of 7.3, viscosity of 6832 cP, spreadability of 12.722 g·cm/s, drug content of 98.42%, and cumulative drug release of 94.39% after 10 h. The optimized formulation demonstrated an enhancement ratio of 2.034 compared with the control formulation and followed the Higuchi diffusion model (R² = 0.982), indicating diffusion-controlled drug release. Stability studies showed no significant changes in the physicochemical characteristics or drug release profile after one month under accelerated storage conditions. The findings suggest that the optimized famciclovir transdermal gel is a promising alternative to conventional oral therapy, providing sustained drug release, improved transdermal permeation, and the potential to enhance therapeutic efficacy and patient compliance.

This study examines the effect of competitive intelligence on the organisational effectiveness of industrial goods firms listed on the Nigerian Exchange Group. The study aims to achieve the following objectives: to determine the effect of Customer Intelligence, Marketplace Intelligence and Strategic Alliance Intelligence on the organisational effectiveness of industrial goods firms listed on the Nigerian Exchange Group. A cross-sectional survey design was adopted for the study. The research focused on employees of industrial goods firms listed on the Nigerian Exchange Group, with a total population of 2,730 staff. The sample size of 450 respondents was determined using Taro Yamane’s formula. The study relied on primary data, with a structured questionnaire serving as the instrument for data collection. Structural Equation Modelling (SEM) was employed to test the formulated hypotheses, using the Statistical Package for the Social Sciences (SPSS) and AMOS software. The study found that Customer Intelligence with beta value of .174, Marketplace Intelligence with beta value of .434, and Strategic Alliance Intelligence with beta of .341, .337, .334 and .108 all have statistically significant positive effects on the organisational effectiveness of industrial goods firms listed on the Nigeria Exchange Group. Based on these findings, it is recommended that the management of industrial goods firms listed on the Nigerian Exchange Group should prioritise investment in advanced analytical tools that facilitate the collection and analysis of customer, marketplace, technology, and strategic alliance data. Additionally, they should invest in systems that continuously monitor market trends, competitor activities, and industry developments, all of which can contribute to improved organisational effectiveness.

The forests of eastern India — stretching across the undulating uplands of the Jungle Mahals, the Chota Nagpur plateau, and the hill tracts of the Santhal Parganas — have historically constituted far more than a repository of timber and revenue; they have been the material and cultural foundation of tribal life-worlds. This paper traces the historical evolution of tribal–forest relationships in eastern India from pre-colonial commons-based regimes through the colonial reconfiguration of forests as state property, to contemporary conservation and community-forestry frameworks. Drawing on environmental history and political ecology, the study examines how colonial scientific forestry, inaugurated under Dietrich Brandis and institutionalised through the Forest Acts of 1865, 1878, and 1927, dismantled customary tribal access to forest resources and precipitated a sequence of ecological rebellions, including the Chuar uprising, the Halba revolt, the Paharia resistance, and the Santhal Hul. It further situates the persistence of shifting cultivation (jhum) in Northeast India as a site of continuing contestation between customary ecological practice and state-sanctioned notions of "scientific" land use. Employing a qualitative, comparative-historical methodology grounded in secondary archival and scholarly sources, the paper argues that colonial forestry inaugurated an enduring bifurcation between statist conservation and community-based ecological stewardship — a bifurcation only partially redressed by the Forest Rights Act, 2006. The paper concludes that meaningful biodiversity conservation and climate resilience in eastern India's forest landscapes require the substantive, rather than symbolic, recognition of indigenous ecological knowledge and community forest governance rights. The findings contribute to environmental historiography and to contemporary debates on sustainable, equity-centred forest governance in the Anthropocene.

The study examined the impact of environmental accounting practices on the financial reporting quality of quoted oil and gas companies in Nigeria. The research specifically investigated the impacts of environmental pollution prevention costs (EPPC), environmental detection costs (EDC), environmental internal failure costs (EIFC), and environmental external failure costs (EEFC) on financial reporting quality (FRQ), measured through transparency and reliability indicators. The study adopted an ex-post facto research design, the study utilized secondary data extracted from annual reports and audited financial statements of ten oil and gas companies listed on the Nigerian Exchange Group (NGX) between 2019 and 2023, yielding fifty firm-year observations. Data were analyzed using panel regression techniques under pooled, fixed, and random effects models, with E-view 13 software.Findings revealed that environmental pollution prevention costs exhibited a significant and positive relationship with financial reporting quality under fixed and random effects models, suggesting that proactive investments in pollution control enhance long-term financial transparency and stakeholder trust. Environmental detection costs were also found to have a significant positive impact on financial reporting quality across all models, indicating that effective environmental monitoring and auditing improve report credibility. Conversely, environmental internal failure costs showed mixed results insignificant under pooled and random effects but significant under fixed effects implying that internal environmental management efforts yield delayed but beneficial outcomes. Environmental external failure costs were found to be statistically insignificant, reflecting weak enforcement of environmental regulations in Nigeria. The study concludes that effective environmental accounting practices enhance the quality of financial reporting among oil and gas firms, particularly through pollution prevention and detection mechanisms. It recommends that regulatory agencies such as the National Environmental Standards and Regulations Enforcement Agency (NESREA) and the Financial Reporting Council of Nigeria (FRCN) enforce mandatory environmental disclosures, incentivize cleaner production technologies, and integrate sustainability reporting standards into corporate governance frameworks. Strengthening environmental compliance and capacity building within the sector will improve financial transparency, promote accountability, and align corporate reporting with global sustainability goals.

Artificial Intelligence (AI) has become a transformative technology in the Banking and Financial Services (BFSI) sector, significantly influencing Human Resource Management (HRM) practices. The increasing demand for digital banking, regulatory compliance, operational efficiency, and enhanced customer service has encouraged financial institutions to adopt AI-driven HR solutions for recruitment and employee management. AI technologies, including Machine Learning (ML), Natural Language Processing (NLP), predictive analytics, and intelligent automation, are widely used to automate resume screening, candidate assessment, employee performance evaluation, workforce analytics, and personalized learning. These technologies improve recruitment efficiency, support strategic workforce planning, and enhance employee engagement while reducing operational costs.
This study presents a systematic literature review to examine emerging trends in AI-enabled recruitment and employee management within the Banking and Financial Services Sector. The review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework and analyzes recent peer-reviewed studies published between 2020 and 2026. The findings indicate that AI has significantly improved hiring accuracy, reduced recruitment time, strengthened employee performance management, and enabled data-driven HR decision-making across banking institutions. However, challenges such as algorithmic bias, data privacy, ethical concerns, and regulatory compliance continue to influence AI adoption. The study concludes that AI is transforming HRM in the BFSI sector from a traditional administrative function into a strategic business capability while highlighting future research opportunities in responsible and transparent AI-driven human resource practices.

Women's education is widely recognized as a key driver of social development, gender equality, and sustainable national progress. However, awareness regarding the importance of women's education remains limited in several socially and economically marginalized communities, including the tea tribe areas of Assam. The present study examines the level of awareness of higher secondary students towards women's education in the tea tribe areas of Hapjan Block, Tinsukia District, Assam. A descriptive survey research design was adopted for the study. A total of 200 higher secondary students, comprising 100 male and 100 female students, were selected through a stratified random sampling technique. Data were collected using a researcher-developed two-point Likert-type awareness questionnaire, the reliability and validity of which were established before administration. The collected data were analysed using descriptive statistics (mean and standard deviation) and an independent-samples t-test.
The findings reveal that higher secondary students possess a moderate level of awareness regarding women's education. Although most students acknowledge the importance of educating women for individual and societal development, certain traditional beliefs and socio-cultural barriers continue to influence their perceptions. The statistical analysis further indicates a significant difference in awareness levels between male and female students, with female students demonstrating comparatively higher awareness. The study highlights the need for sustained awareness programmes, gender-sensitive educational initiatives, and active community participation to strengthen positive attitudes towards women's education among adolescents in tea tribe communities. The findings may assist educators, policymakers, and community stakeholders in designing interventions that promote educational equity and gender-inclusive development in marginalized regions.

Machine learning is a form of data analysis that employs automation to create analytical models. Machine learning, a kind of artificial intelligence, operates on the premise that robots can identify patterns in data, derive conclusions, and make decisions with minimal human intervention. The fundamental objective of pattern recognition, regardless of whether supervised or unsupervised, is classification. Among the several contexts in which pattern recognition has evolved, the statistical methodology has been the most thoroughly examined and applied in practice. Recent years have witnessed a heightened interest in neural network methodologies and strategies rooted in statistical learning theory. Key considerations in the design of a recognition system encompass the delineation of pattern classes, sensing environment, pattern representation, feature extraction and selection, cluster analysis, classifier design and learning, training and test sample selection, and performance evaluation. The overarching issue of identifying intricate patterns within random patterns of varying scale, orientation, and location persists unresolved. This research presents a text extraction pipeline designed to extract text from diverse high-quality photos sourced from social media.
Following the classification of the input photographs, they are subjected to class-specific preprocessing, including text localization and illumination improvement. The structured representation developed in the prior stage facilitates the discovery of association rules, the identification of prevalent keywords, and the execution of sentiment analysis through a lexicon-based methodology that utilizes a collection of positive and negative terms. Every tweet is assigned a score according to a scoring function.

The present study focuses on the formulation and evaluation of a herbal syrup prepared from Phyllanthus niruri (Keezhanelli), a medicinal plant widely used in traditional systems. The plant material was collected, authenticated, processed, and subjected to extraction using the maceration method. The syrup was formulated using suitable excipients such as stevia, citric acid, sodium benzoate, glycerin, propylene glycol, and Tween 80 to enhance palatability, stability, and bioavailability. The prepared formulation was evaluated for organoleptic, physicochemical, and stability parameters. The results indicated that the syrup possessed desirable characteristics including acceptable pH, viscosity, and stability without any significant changes during storage. The study confirms the successful formulation of a stable and effective herbal syrup.

Purpose: This study examines the relationship between Artificial Intelligence (AI) adoption and financial performance of Information Technology (IT) Multinational Corporations (MNCs) operating in India. The research investigates how AI integration influences financial outcomes through operational efficiency, revenue growth, and profitability enhancement.
Methodology: A mixed-method research design was employed, integrating primary data from 342 senior financial managers across 47 IT MNCs with secondary financial data from annual reports (2020-2025). Financial performance metrics included Return on Assets (ROA), Return on Equity (ROE), and Operating Profit Margin (OPM). Statistical analyses included descriptive statistics, correlation analysis, panel data regression, and mediation analysis using Stata and SPSS software.
Findings: Results indicate a significant positive relationship between AI adoption intensity and financial performance (β = 0.487, p < 0.001). Organisations with comprehensive AI strategies demonstrated 23.6% higher ROA and 18.9% higher ROE compared to organisations with limited AI implementation. Operational efficiency emerged as a significant mediator explaining 41.2% of the AI-financial performance relationship.
Originality/Value: This research contributes to the limited empirical literature on AI-financial performance relationships in Indian IT MNC context, integrating primary perceptual data with objective financial metrics. The study provides actionable insights for financial strategists and organisational leaders regarding AI investment decisions.

Purpose: This study examines the relationship between Artificial Intelligence (AI) adoption and organisational performance within Information Technology (IT) hubs across India. The research investigates how AI integration influences operational efficiency, decision-making capabilities, and competitive advantage in technology-driven organisations.
Methodology: A quantitative cross-sectional research design was employed, collecting primary data from 387 mid-level and senior managers across IT hubs in Bengaluru, Hyderabad, Pune, and Chennai. Structured questionnaires measured AI adoption intensity, organisational performance metrics, and mediating factors. Statistical analyses included descriptive statistics, correlation analysis, multiple regression, and Structural Equation Modelling (SEM) using SPSS and AMOS software.
Findings: Results indicate a significant positive relationship between AI adoption and organisational performance (β = 0.542, p < 0.001). Organisational learning capability and technological infrastructure emerged as significant mediators. IT hubs in Bengaluru demonstrated the highest AI adoption rates (M = 4.23, SD = 0.67) compared to other locations. The findings suggest that organisations with comprehensive AI strategies achieve 34.7% higher operational efficiency and 28.3% improved decision-making accuracy.
Originality/Value: This research contributes to the limited empirical literature on AI-organisational performance relationships in the Indian IT sector context. The study provides actionable insights for organisational leaders and policymakers regarding strategic AI implementation frameworks.

Colonial forestry in South Asia inaugurated a regime of resource control that displaced indigenous ecological knowledge systems under the banner of “scientific” management. This paper undertakes a critical environmental-historical reassessment of shifting cultivation (jhum/podu) and community-based forest stewardship in India, tracing their trajectory from pre-colonial customary practice through colonial criminalisation to their contemporary reconfiguration as instruments of climate resilience. Drawing jointly on environmental history and political ecology, the paper examines how the Indian Forest Acts of 1865, 1878, and 1927, together with Dietrich Brandis's institutionalisation of scientific forestry, reconstituted forests as state property, alienated forest-dependent communities from customary commons, and precipitated sustained agrarian-ecological unrest, exemplified by the Chuar Rebellion (1798–1799), the Halba Revolt (1774–1779), Paharia resistance in the Rajmahal Hills, and the Santhal Hul (1855–56). The paper argues that colonial forestry's monocultural, revenue-oriented logic simplified biodiverse landscapes, compressed customary fallow cycles, and generated a legacy of alienation that post-colonial forest law has only partially redressed. Using a qualitative, comparative-historical methodology grounded in secondary archival, legislative, and policy sources, the study juxtaposes colonial governance with the Forest Rights Act (2006), Joint Forest Management, and contemporary shifting-cultivation policy in Northeast India, revealing both continuities of statist control and openings for community authority. It contends that indigenous agroecological knowledge — rotational fallow management, agrobiodiversity maintenance, and customary institutions of resource governance — constitutes an underrecognised climate-adaptive resource rather than a primitive antecedent awaiting supersession by technical forestry. The paper concludes with policy recommendations for integrating customary tenure recognition, participatory governance, and traditional ecological knowledge into India's climate and biodiversity commitments, arguing for a decolonised approach to forest governance that is attentive simultaneously to ecological sustainability and indigenous rights.

Predictive care has developed as a critical use of Machine Learning (ML) in industrial systems, allowing for early detection of equipment faults and minimizing downtime. Traditional maintenance practices, such as reactive and preventative maintenance, frequently result in significant operational costs and inefficiencies. This study investigates a variety of machine learning methodologies, including supervised, unsupervised, and deep learning models for predictive maintenance. The suggested system uses real-time sensor data and powerful machine learning techniques to precisely forecast failures. In comparison to previous methodologies, experimental results show greater prediction accuracy, lower maintenance costs, and higher system reliability.

The present study explores the integration of Artificial Intelligence (AI) in inclusive education, focusing on opportunities, challenges and teacher perceptions. The objective was to identify AI applications that support students with disabilities, examine implementation barriers and analyze associations between demographic variables and educators’ attitudes toward AI-enabled inclusion. A descriptive survey method was adopted, with data collected from 50 teachers and special educators across various schools and institutions using a structured questionnaire. Percentage analysis and chi-square tests were employed for data analysis. Findings indicate that AI offers significant opportunities through personalized learning, assistive technologies (e.g., speech-to-text, adaptive platforms) and real-time feedback, enhancing accessibility and engagement for children with special needs (CwSN). However, major challenges include lack of teacher training, inadequate infrastructure, digital divide, algorithmic bias and data privacy concerns. Educators with prior AI exposure or training demonstrated significantly more positive perceptions (p < .05). The study highlights the need for systematic professional development, robust policy frameworks aligned with the National Education Policy (NEP) 2020 and the Rights of Persons with Disabilities (RPwD) Act, 2016 and ethical guidelines to harness AI for equitable and inclusive education.

This article investigates the use of Artificial Intelligence (AI) in smart classroom environments to improve the learning experience. It is presented a new framework that uses AI to personalize education, automate administrative processes, and deliver real-time performance metrics. The suggested method employs machine learning algorithms to tailor material delivery, identify learning gaps, and provide personalized assistance. With this strategy, we hope to create a more efficient, engaging, and egalitarian educational ecosystem. Our findings show that an AI-powered smart classroom may greatly boost student engagement, academic outcomes, and teacher efficiency.

Public policy as an academic and practical field has been profoundly shaped by the contributions of Harold Lasswell, Herbert Simon, and Charles Lindblom. Each thinker provided a distinct lens for understanding how policies are designed, decided, and implemented, yet their approaches reveal both complementarities and tensions. This paper undertakes a comparative analysis of their contributions to highlight how values, rationality, and incrementalism together form the intellectual backbone of modern policy studies.
Lasswell pioneered the policy sciences by insisting on an explicitly normative orientation, integrating ethics, democracy, and multidisciplinary insights into the study of governance. His vision framed policy not merely as a technical exercise but as a means to promote human dignity and democratic practice. In contrast, Simon shifted the focus toward decision-making under conditions of bounded rationality, emphasizing the cognitive limits of policymakers and the role of “satisficing” in administrative behavior. Lindblom further advanced the field by highlighting the inherently political and incremental nature of policymaking. His concept of “muddling through” illustrated how negotiation, compromise, and gradual adjustments dominate real-world governance.
The study applies a thematic analytical framework that categorizes these perspectives into normative, behavioral, and incremental approaches. Through comparative discussion, the analysis reveals how these frameworks complement one another while also exposing tensions between ethical ideals, cognitive constraints, and political realities. Contemporary policy theories, such as Kingdon’s multiple streams and the advocacy coalition framework, can be seen as intellectual heirs, building upon these foundations.
Ultimately, the paper argues that integrating Lasswell’s ethical orientation, Simon’s behavioral realism, and Lindblom’s pragmatic incrementalism provides policymakers with a more holistic understanding of public policy. This synthesis not only enriches academic discourse but also offers practical guidance for addressing today’s complex policy challenges.

Diarrheal diseases remain a major global health concern, particularly in developing countries, due to bacterial pathogens such as Escherichia coli, Salmonella, Shigella, and Vibrio cholerae. The increasing incidence of antimicrobial resistance has created an urgent need for safer and more effective herbal alternatives. The present study aimed to formulate and evaluate a herbal antibacterial syrup containing the ethanolic extract of Coleus amboinicus leaves and to investigate its antibacterial potential through in vitro and in silico approaches. Fresh leaves of Coleus amboinicus were collected, authenticated, and subjected to extraction using a Clevenger apparatus with ethanol. Four syrup formulations (F1–F4) were prepared using different bases, namely distilled water, simple syrup, sorbitol, and honey, while maintaining a constant concentration of the plant extract. The formulations were evaluated for organoleptic properties, pH, viscosity, drug content, UV-visible spectroscopy, accelerated stability, and antibacterial activity using the agar well diffusion method against Escherichia coli. Molecular docking studies were performed using the major phytoconstituents, thymol and carvacrol, against selected diarrhea-causing pathogens to predict their binding affinity and possible antibacterial mechanism. All formulations exhibited acceptable physicochemical characteristics, including satisfactory appearance, pH, viscosity, stability, and drug content. The antibacterial study demonstrated that the herbal formulations produced measurable zones of inhibition against E. coli, indicating promising antibacterial efficacy, with activity comparable to the standard drug in selected formulations. UV-visible spectral analysis confirmed the presence and stability of phytoconstituents within the formulations. Molecular docking studies revealed favorable binding interactions of thymol and carvacrol with bacterial target proteins, supporting their potential inhibitory activity and validating the observed antibacterial effects. Overall, the findings suggest that Coleus amboinicus-based herbal syrup represents a promising natural therapeutic candidate for the management of bacterial diarrhea. Further pharmacological, toxicological, and clinical investigations are warranted to establish its efficacy, safety, and therapeutic applicability.

493 annual observations from 82 A-share listed power generation companies in China from 2019 to 2025 are applied to empirically examine the impact of power generation type (new energy power generation vs. traditional thermal power companies) on the overall ESG (Environmental, Social, and Governance) performance as well as separate dimension performance of these companies. It is revealed that after controlling company size, debt-to-equity ratio, profitability, and equity nature, new energy power generation companies significantly outperform traditional thermal power companies in both overall ESG performance and the environmental dimension performance, while the significance in social and governance dimensions are relatively weaker. This research provides empirical evidence from China&#39;s power industry for further understanding of the relationship between energy structure transformation and ESG performance.

Machine learning (ML) and deep learning (DL) have experienced rapid growth due to advances in computing infrastructure, the availability of large-scale datasets, and continuous improvements in learning algorithms. Current research is increasingly focused on developing models that emphasize efficiency, adaptability, and interpretability. Emerging paradigms such as federated learning and edge computing support decentralized intelligence while enhancing data security and privacy. Transformer-based architectures, originally developed for natural language processing tasks, have demonstrated strong generalization capabilities and are now widely adopted in fields including computer vision and time-series analysis. At the same time, the fusion of quantum computing with ML presents new opportunities for addressing computationally complex problems that were previously impractical using classical approaches. Explainable Artificial Intelligence (XAI) has become a critical research area, aiming to improve model transparency, accountability, and ethical deployment by mitigating the opaque nature of deep learning systems. Furthermore, the integration of ML with technologies such as the Internet of Things (IoT), blockchain, and next-generation communication networks (5G) is enabling advanced applications in smart infrastructure, autonomous technologies, and healthcare systems. Ongoing research into hybrid models that combine symbolic reasoning with neural learning is also gaining momentum, offering enhanced reasoning and decision-making capabilities. Collectively, these advancements signify a transformative phase in ML and DL research, driving the development of intelligent, autonomous, and energy-efficient systems capable of addressing complex real-world challenges.

This study focuses on improving the way medical documents are classified online by using specialized knowledge structures called ontologies, specifically the Medical Subject Headings (MeSH) system. The main goal is to create a better method for representing content using semantic (meaning-based) information. To test how well this works, we compared it to traditional methods that rely on word stemming, using two common machine learning algorithms: C4.5 and K-Nearest Neighbors (KNN).The results show that our approach, which uses MeSH concepts and their relationships (like broader terms), creates more meaningful representations of the documents and improves classification accuracy. When applied to the Ohsumed biomedical dataset, this ontology-based method performed about 30% better than standard stem-based methods, highlighting the advantage of using domain-specific knowledge in medical document classification.

Systematic reviews in medicine depend on exhaustive and well-structured literature searches to produce reliable and accurate conclusions. These searches are commonly developed through collaboration between medical researchers and information specialists who possess expertise in both clinical domains and advanced information retrieval techniques. Such searches typically rely on complex Boolean expressions that combine natural language keywords with controlled vocabulary terms, most notably Medical Subject Headings (MeSH). Although MeSH terms can substantially improve the precision and completeness of search results, identifying the most suitable terms is often challenging. The MeSH system is complex, and limited familiarity among some information professionals can lead to uncertainty, inefficiency, or incomplete use of its capabilities. This study investigates automated methods for recommending appropriate MeSH terms based on an initial search query constructed exclusively from free-text keywords. The goal is to support users in selecting high-value MeSH terms that can be incorporated into systematic review search strategies. Multiple recommendation techniques are examined, and their effectiveness is evaluated by measuring improvements in document retrieval performance, result ranking, and iterative query enhancement.

Modern healthcare generates vast amounts of data, creating both opportunities and challenges for medical practice. The growing volume of unstructured, free-text clinical documentation has expanded electronic health record (EHR) data significantly. Today, a patient’s digital medical record serves as a comprehensive repository of essential clinical information, including vital signs, prescriptions, demographics, diagnostic tests, treatment plans, progress notes, allergies, immunizations, imaging, and laboratory results. These records are compiled from diverse sources such as administrative billing systems, patient-reported surveys, and clinician documentation, and are stored in centralized electronic medical databases that support informed decision-making.
Healthcare providers often refer to these systems as EHRs, computerized medical records, or digital patient files. However, retrieving specific, relevant data from these records is a complex and time-consuming task. Access is further complicated by the sensitive nature of patient information, which is protected by authorization and privacy protocols. In this review, we examine a wide range of research on EHR data retrieval, including different search platforms, data extraction methods, and strategies for accessing medical information within electronic databases. Finally, we highlight several limitations and challenges associated with these retrieval processes.

Machine learning (ML) and deep learning (DL) continue to evolve at a rapid pace, driven by advances in computing power, increased data volumes, and ongoing improvements in algorithms. A key direction in this evolution is the development of models that are more efficient, adaptable, and interpretable. Approaches such as federated learning and edge computing are becoming increasingly prominent, supporting distributed data processing while enhancing privacy protection. Transformer-based architectures, first introduced for natural language processing, have expanded into areas including computer vision and time-series analysis, where they have demonstrated strong performance advantages. In parallel, the integration of quantum computing with ML techniques offers the potential to tackle complex problems by delivering substantial computational acceleration. The growing emphasis on Explainable Artificial Intelligence (XAI) reflects the need to overcome the opaque nature of deep learning models by improving transparency, accountability, and ethical alignment. At the same time, the fusion of ML with emerging technologies such as the Internet of Things (IoT), blockchain, and 5G networks is enabling new applications in smart infrastructure, autonomous platforms, and digital healthcare. Additionally, research into hybrid models that combine symbolic reasoning with neural networks is opening new pathways for more robust and informed decision-making. Collectively, these advancements mark a significant step forward in the progression of ML and DL architectures, offering the potential to solve complex real-world problems and foster sustainable, intelligent, and autonomous systems.

Supervised Machine Learning (SML) is an important area of machine learning that focuses on developing models capable of learning from labeled data to address practical, real-world problems. In this approach, each input in the training dataset is paired with a known output, enabling the model to learn the underlying patterns and relationships within the data. By analyzing these examples, the system becomes capable of making accurate predictions or classifications when exposed to new and unseen data Supervised learning methods are widely applied across various fields such as image processing, natural language processing, healthcare, and fraud detection. The availability of labeled data allows these models to effectively generalize learned information, leading to reliable decision-making and improved predictive performance. This study focuses on exploring different supervised machine learning classification techniques, evaluating their performance, and determining their effectiveness for various problem types. Widely used supervised learning algorithms include Decision Table, Random Forest, Naive Bayes, Support Vector Machine (SVM), Neural Networks, and Decision Tree. Among these methods, Naive Bayes and Random Forest are particularly popular due to their strong accuracy and consistent performance across diverse applications.

This study investigates the challenges and effectiveness of employing domain ontologies for the classification of online medical content. Specifically, the Medical Subject Headings (MeSH) thesaurus is utilized to enhance the categorization of medical documents. Based on the insights obtained, a novel document representation is proposed. The proposed approach is evaluated and compared with traditional stem-based representations using two widely adopted data mining algorithms, C4.5 and K-Nearest Neighbor (KNN). Experimental results demonstrate that the ontology-based method significantly outperforms conventional approaches. By integrating semantic concepts and hypernym relationships from the domain ontology, the document vectors used for classification are substantially enriched, leading to improved organization and accuracy. Validation using the Ohsumed biomedical benchmark dataset confirms the effectiveness of the proposed framework, achieving an improvement of approximately 30% over traditional stem-based representations.

Machine learning (ML) and deep learning (DL) continue to evolve at a rapid pace, driven by advances in computing power, increased data volumes, and ongoing improvements in algorithms. A key direction in this evolution is the development of models that are more efficient, adaptable, and interpretable. Approaches such as federated learning and edge computing are becoming increasingly prominent, supporting distributed data processing while enhancing privacy protection. Transformer-based architectures, first introduced for natural language processing, have expanded into areas including computer vision and time-series analysis, where they have demonstrated strong performance advantages. In parallel, the integration of quantum computing with ML techniques offers the potential to tackle complex problems by delivering substantial computational acceleration. The growing emphasis on Explainable Artificial Intelligence (XAI) reflects the need to overcome the opaque nature of deep learning models by improving transparency, accountability, and ethical alignment. At the same time, the fusion of ML with emerging technologies such as the Internet of Things (IoT), blockchain, and 5G networks is enabling new applications in smart infrastructure, autonomous platforms, and digital healthcare. Additionally, research into hybrid models that combine symbolic reasoning with neural networks is opening new pathways for more robust and informed decision-making. Collectively, these advancements mark a significant step forward in the progression of ML and DL architectures, offering the potential to solve complex real-world problems and foster sustainable, intelligent, and autonomous systems.

This study looks into how artificial intelligence (AI) might help speed the transition to a circular economy by optimizing resource consumption, increasing supply chain transparency, and supporting innovative, sustainable business models. AI helps to lower losses, enhance traceability, and promote creative circular processes through practical applications like intelligent garbage sorting and reverse logistics. According to a survey of 65 Tunisian SMEs, AI enhances environmental performance and aids in the achievement of sustainable development goals when adoption is encouraged by institutional and organizational variables.

Background: Obesity is a major global health concern associated with dyslipidaemia, insulin resistance, and increased cardiovascular risk. Because individuals spend much of the day in a postprandial state, postprandial metabolic parameters may provide a more sensitive assessment of early metabolic dysfunction than fasting measurements.
Objective: To compare postprandial serum triglyceride and blood glucose concentrations between obese and non-obese young adults using Asian body mass index (BMI) criteria.
Methods: A comparative cross-sectional study was conducted among 52 participants (18–30 years) at Assam down town University, Guwahati, India. Participants were classified as obese (n = 24; BMI ≥25 kg/m²) or non-obese (n = 28; BMI 18.5–22.9 kg/m²). Postprandial blood glucose was measured by the glucose oxidase–peroxidase (GOD-POD) method, and serum triglycerides were determined using the glycerol phosphate oxidase (GPO) enzymatic method. Group comparisons were performed using Welch's independent-samples t-test, Mann–Whitney U test, and chi-square test.
Results: Mean postprandial glucose levels were comparable between obese and non-obese participants (86.52 ± 37.44 vs. 89.84 ± 35.13 mg/dL; p = 0.74). Serum triglyceride concentrations were numerically higher in the obese group (211.50 ± 166.28 vs. 195.75 ± 132.29 mg/dL), but the difference was not statistically significant (p = 0.71). Sensitivity analysis after excluding triglyceride outliers produced similar findings, and sex distribution did not differ significantly between groups.
Conclusion: Obese young adults demonstrated a non-significant trend toward higher postprandial triglyceride concentrations, whereas postprandial glucose levels were similar to those of non-obese individuals. Larger, adequately powered multicentre studies are needed to clarify the relationship between obesity and postprandial metabolic alterations.

The rapid growth of electric vehicles (EVs) has created a significant demand for sustainable and intelligent charging infrastructure in future smart cities. Conventional grid-dependent EV charging stations face several challenges, including increased peak power demand, higher operating costs, grid congestion, and carbon emissions. Solar photovoltaic (PV)-based charging stations integrated with battery energy storage systems (BESS) provide a promising solution by improving renewable energy utilization and reducing dependence on conventional electricity sources. However, the intermittent nature of solar generation and the uncertain behavior of EV charging demand make optimal operation of charging stations a complex energy management problem.
This paper proposes a multi-objective optimization framework for solar-powered EV charging stations by integrating photovoltaic generation, battery energy storage, smart energy management, and utility grid support. A mathematical model is developed to represent PV power generation, battery state-of-charge (SOC), EV charging demand, and power exchange with the grid. The proposed framework utilizes the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to simultaneously optimize multiple conflicting objectives, including operating cost minimization, renewable energy utilization maximization, grid dependency reduction, charging efficiency improvement, and carbon emission reduction.
The performance of the proposed framework is evaluated under different solar generation and EV charging demand scenarios. The results demonstrate that the optimized energy management strategy effectively coordinates PV generation, battery storage, and grid interaction, improving the overall technical, economic, and environmental performance of EV charging infrastructure. The proposed approach provides a scalable and sustainable solution for the development of next-generation solar-powered EV charging stations in smart cities.

The rapid growth of electric vehicles (EVs) has significantly increased the demand for sustainable and intelligent charging infrastructure in modern smart cities. Although solar photovoltaic (PV)-based charging stations offer an environmentally friendly alternative to conventional grid-powered charging systems, their practical implementation remains constrained by intermittent solar generation, fluctuating charging demand, limited battery storage capacity, grid instability, and inefficient energy management strategies. These challenges reduce renewable energy utilization and increase dependence on conventional electrical grids, particularly during peak demand periods.
This paper presents an Artificial Intelligence (AI)-Based Energy Management Framework for solar-powered electric vehicle charging infrastructure designed to enhance energy efficiency, charging reliability, and grid stability in future smart cities. The proposed framework integrates photovoltaic generation, battery energy storage systems (BESS), smart grid connectivity, and AI-driven decision-making into a unified energy management architecture. The AI controller continuously monitors solar irradiance, battery state-of-charge (SOC), grid conditions, electricity pricing, and electric vehicle charging requests to determine the optimal energy allocation strategy in real time. The framework dynamically prioritizes renewable energy utilization while minimizing grid dependency through intelligent charging scheduling, battery management, and peak-load mitigation.
A comprehensive mathematical model is developed to describe photovoltaic power generation, battery charging/discharging behavior, EV charging demand, and grid power exchange. The proposed system is evaluated using MATLAB/Simulink under multiple operating scenarios representing different weather conditions, charging demands, and electricity pricing environments. Performance is assessed using key indicators including charging efficiency, renewable energy utilization, battery utilization efficiency, grid power consumption, operational cost, peak demand reduction, and carbon emission mitigation.
Simulation results demonstrate that the proposed AI-based framework significantly improves overall charging efficiency while maximizing photovoltaic energy utilization and reducing dependence on the utility grid. Compared with conventional rule-based energy management approaches, the proposed framework achieves superior operational performance through adaptive decision-making and intelligent power scheduling under varying environmental and load conditions. The framework further enhances system reliability, reduces operational costs, and supports sustainable urban transportation by improving renewable energy integration within smart city energy ecosystems.
The proposed research provides a scalable and intelligent solution for next-generation solar-powered EV charging infrastructure and contributes toward the development of low-carbon, energy-efficient, and resilient smart cities.

The Indian credit scene is evolving rapidly with the emergence of Buy Now Pay BNPL) businesses. That shift is being driven by a younger and digital consumer base. But this fintech boom, while improving short-term access to credit, has led to worries about existing credit risk assessment processes. Traditional risk evaluation approaches are not able to reflect the conduct of modern BNPL users. This can sometimes lead to problems such as borrower over-indebtedness and insufficient risk oversight.BNPL credit is managed in ways the existing literature identifies holes in. There is a dependence on credit data, unclear regulatory boundaries, and an insufficient understanding of long-term consumer welfare. The use of “black-box” intelligence (AI) algorithms in risk assessment raises issues of transparency and regulatory compliance and fairness.
The study looks at the extent to which Indian BNPL companies are in compliance with the RBI’s Digital Lending Guidelines. We evaluate their health effects on consumers. We introduce an AI (XAI) framework based on the Account Aggregator system, India’s Digital Public Infrastructure (DPI). We do stress tests of this model under shock scenarios. "We have insights that show a route to improved credit risk efficiency. They also boost accountability and encourage a more responsible AI-led digital lending environment in India.

Human survival has undergone a radical transformation as a result of technological growth, which has brought both turmoil and comfort.Artificial intelligence has been one of the most significant technological advancements that has fundamentally changed human communication. A variety of AI tools are available, including Chat GPT, Meta AI, Yoodli, SmallTalk2Me, Grammarly, and Elsa Speak, among others. These tools have greatly impacted human communication because people are now more conscious than ever when doing tasks like writing, speaking, giving presentations, drafting proposals, and so forth. The current study examines how, in this digital age, artificial intelligence has changed human behavior and communication. A person's conduct and communication are directly impacted by their thoughts, and if they become overly reliant on AI tools, this could have a detrimental effect on their thought process because excessive reliance on AI tools is regarded to be dangerous for human intellect. AI is technology that mimics human intelligence by computer systems, enabling machines to learn, reason, and solve problems. No other instrument can replace the God-given human intellect, which is gradually transformed by human efforts.

Tamil classical literature is broadly classified into the Melkanakku (Major Anthologies) and the Keezhkanakku (Minor Anthologies). The Pathinenkeezhkanakku corpus not only emphasizes moral and ethical values but also embodies a wide range of scientific concepts, including mathematics, medicine, environmental science, hydrology, agriculture, family studies, nutrition, and principles of healthy living. Literary works such as the Thirukkural, Naladiyar, Iniyavai Narpathu, Inna Narpathu, Acharakovai, Sirupanchamoolam, Elathi, and Thirikadugam present profound ideas that promote a systematic and scientific approach to human life.
This study examines the scientific ideas embedded in the ethical texts of the Pathinenkeezhkanakku corpus through an interdisciplinary scientific perspective. It analyzes literary evidence relating to mathematics, medicine, water management, environmental science, family well-being, physical health, nutrition, and life sciences. The primary objective of this research is to demonstrate that the scientific outlook of the ancient Tamils, as reflected in these literary works, laid a strong intellectual foundation for several principles that continue to influence modern scientific thought and human well-being.

This study investigates the distributional properties, dependence structure, and volatility dynamics of crude oil, gold, and USD returns, with a particular focus on contemporaneous linkages between crude oil returns and movements in gold and the exchange rate. This study based on daily closing prices over the study period [January 2021 to July 2026]. The study compares the performance of a simple OLS regression model and an ARCH–GARCH framework in explaining crude oil return dynamics. The findings contribute to the literature on commodity–currency–safe haven interactions and offer practical insights for investors and policy makers concerned with managing exposure to energy price risk in the presence of gold and exchange rate movements. Understanding how returns and volatility in these markets behave, and whether they are interconnected in the short run, is essential for investors, policy makers, and risk managers operating in an increasingly integrated global financial system. Overall, the findings suggest that crude oil volatility is substantial and time varying, but contemporaneous gold and USD returns play only a modest role in explaining crude oil return dynamics.

The present study aimed to examine the role of locus of control and optimism in the relationship between superstitious belief and self-efficacy among young adults in Malappuram, Kerala. The study was conducted among 200 undergraduate and postgraduate college students aged 18–24 years using a convenience sampling method. Data were collected using the Superstitious Belief Scale by Surekha Chukkali and Anjali M. Dey, the Locus of Control Scale by Terry Petty John, the General Self-Efficacy Scale by Schwarzer and Jerusalem (1995), and the State Optimism Measure by Millstein (2019). The findings revealed that optimism did not mediate the relationship between superstitious belief and self-efficacy. However, locus of control significantly mediated this relationship. Specifically, individuals with stronger superstitious beliefs were more likely to exhibit an external locus of control, whereas individuals with a more internal locus of control demonstrated higher levels of self-efficacy. The results suggest that locus of control plays an important role in explaining how superstitious beliefs influence self-efficacy, while optimism does not have a significant mediating effect.

Prostate cancer is one of the most common cancers among men worldwide, accounting for a significant proportion of cancer diagnoses and mortality, particularly in developed countries [1]. It is estimated to be among the top three most diagnosed cancers, highlighting the need for accurate and early severity prediction. Traditional clinical approaches for prostate cancer assessment rely on prostate-specific antigen (PSA) levels and Gleason grading; however, these methods have limitations in predictive accuracy and consistency. Recent research has explored machine learning techniques to improve cancer prognosis and classification by leveraging large-scale biomedical datasets [2], [3]. In this study, we implement a comprehensive machine learning pipeline using the TCGA-PRAD (The Cancer Genome Atlas – Prostate Adenocarcinoma) dataset to predict cancer severity. The analysis includes exploratory data analysis and systematic identification and correction of data leakage. It also incorporates class imbalance handling using SMOTE and class-weight adjustments. Multiple machine learning models, including Logistic Regression, Random Forest, and Gradient Boosting, are trained and evaluated. Hyperparameter tuning and 5-fold cross-validation are performed to ensure robust performance evaluation. The tuned Gradient Boosting model achieved a cross-validated AUC of 0.954 ± 0.010 and a test AUC of 0.825. Key predictive features identified include patient age, PSA levels, and Fraction Genome Altered (FGA). These findings demonstrate the effectiveness of machine learning in improving prostate cancer severity prediction and provide a strong foundation for future integration of genomic features.

This study investigates the long-run equilibrium and short-run dynamics between mode-wise trading volume and Nifty 50 returns in the Indian stock market over the period January 2020 to December 2025. Using a quantitative research design based on secondary data from the National Stock Exchange of India and the Capitaline Database, the analysis employs descriptive statistics, correlation analysis, the Augmented Dickey-Fuller (ADF) unit root test, Johansen cointegration, Vector Error Correction Model (VECM), and Granger causality tests. The ADF test confirms that the trading volume series is stationary, and the Johansen cointegration test establishes a significant long-run relationship between trading volume and Nifty returns. Granger causality results indicate that trading volume predicts stock returns, whereas returns do not significantly influence trading volume. VECM estimates show that only the second lag of trading volume has a significant positive effect on current returns, and the overall model explains only a small share of return variation, with no evidence of residual autocorrelation. These findings suggest that while trading volume provides limited but significant predictive information for returns, additional economic, financial, and behavioural factors must be incorporated for more comprehensive modelling and improved investment decision-making.

The increasing demand for sustainable composite materials has encouraged the utilization of agricultural waste as reinforcement in polymer matrices. This study experimentally investigates the mechanical and thermal properties of epoxy composites reinforced with micro-sized walnut shell powder. Composite specimens containing 0, 10, 20, 30, and 40 wt.% walnut shell powder were fabricated using the hand lay-up technique. Mechanical characterization was carried out through tensile, compressive, flexural, and Shore D hardness tests following relevant ASTM standards, while thermal performance was evaluated using Thermogravimetric Analysis (TGA) and Differential Scanning Calorimetry (DSC). The experimental results revealed that the incorporation of walnut shell powder significantly enhanced the mechanical and thermal characteristics of epoxy composites up to an optimum filler loading of 20 wt.%, owing to improved interfacial bonding and efficient stress transfer. Beyond this concentration, particle agglomeration and poor dispersion reduced composite performance. The developed bio-composites demonstrate excellent potential as lightweight, environmentally friendly, and cost-effective materials for automotive, construction, and general engineering applications.

Hyperuricemia and its clinical manifestation as gout represent a growing public health concern in sub-Saharan Africa, where diagnostic resources remain limited. This study presents a Mamdani-type fuzzy logic inference system (FIS) designed for the early detection of uric acid-related symptoms among residents of Bama Local Government Area (LGA), Borno State, Nigeria. The system integrates six clinical and lifestyle input variables namely; serum uric acid level, joint pain severity, body mass index, symptom duration, alcohol consumption, and dietary purine intake through a rule-based framework comprising 27 IF-THEN fuzzy rules. A cross-sectional dataset of 420 residents was collected and partitioned into training (70%) and testing (30%) subsets. The proposed fuzzy system achieved an accuracy of 94.3%, sensitivity of 93.8%, specificity of 94.8%, and an area under the ROC curve (AUC) of 0.967, outperforming conventional machine learning classifiers including decision trees (89.5%), random forests (91.2%), logistic regression (85.7%), and support vector machines (88.4%). The defuzzified output produced a hyperuricemia risk score that enabled stratification of patients into low, moderate, and high-risk categories. These findings demonstrate that fuzzy logic-based clinical decision support systems offer a robust, interpretable, and cost-effective approach for hyperuricemia screening in resource-constrained settings, potentially reducing the burden of undiagnosed non-communicable diseases in Northeastern Nigeria.

Heart disease is one of the leading causes of death worldwide. Early prediction of heart disease can help doctors identify high-risk patients and provide treatment at the right time. In recent years, machine learning has become a useful tool for disease prediction because it can find patterns in health data that are difficult to detect manually. However, many heart disease datasets are highly imbalanced, which makes it difficult for prediction models to correctly identify patients with the disease.
This paper presents HeartGuard, an explainable ensemble learning model for early heart disease prediction using the Heart Disease Health Indicators (BRFSS 2015) dataset. After removing duplicate records, 229,781 samples were used for model development. The data was preprocessed using exploratory data analysis, feature scaling, and the Synthetic Minority Oversampling Technique (SMOTE) to reduce the effect of class imbalance. Logistic Regression, Decision Tree, Random Forest, and XGBoost models were trained and compared. XGBoost was further improved using hyperparameter tuning. Finally, a weighted ensemble model was developed by combining Logistic Regression, Random Forest, and XGBoost.
The proposed ensemble model achieved the best overall performance with an accuracy of 78.29%, precision of 27.90%, recall of 69.62%, F1-score of 39.83%, and ROC-AUC of 83.24%. SHAP (SHapley Additive exPlanations) was also used to explain the model's predictions and identify the health factors that had the greatest influence on the results. The findings show that the proposed model provides a balanced and interpretable approach for early heart disease prediction and can support healthcare professionals in making informed decisions.

In India, MSMEs are still facing significant challenges in utilizing operational data for predictive and analytical purposes, with cash flow management being a major concern. India's monthly UPI transactions amount to roughly 8 billion, but the extent to which MSMEs use digital tools is limited. A significant proportion of micro-SMEs, around 82%, are not acquainted with implementing or integrating modern data analytics and AI solutions into their operations, and they often lack basic knowledge of available digitisation support. Variable customer payment behaviours, different credit terms, and the lack of predictive financial analytics frequently cause working capital issues and delay management responses.
The focus of this research is on MFG360, an AI-driven ETL solution that utilizes the Spindle Finance module as a tool for assessing accounts receivable risks at the transaction level in manufacturing settings. Predictive models are built on the basis of past billing records, payment histories, delay trends, and contract details to help users estimate receivable collection periods and forecast future cash inflows. By examining the payment reliability and cash-flow risk, it categorizes deals by scrutinizing their consistency of payment, delays, and credit conditions.
In contrast to traditional dashboard-based systems, MFG360 incorporates an AI-driven financial intelligence layer that provides additional insights beyond typical descriptive analytics. Upon examination of multiple sources of operational and financial data, the system generates a root-cause explanation for each identified issue by connecting financial deviations with operational factors and using AI. The data is prioritized and comprehensible in terms of their specific faults, locations, and operational factors that may have caused the financial impact.

India's premium ethnic wear segment has expanded rapidly over the past decade, yet in-store conversion has not kept pace with footfall growth, and brick-and-mortar retailers continue to lose a measurable share of purchase intent to trial-room bottlenecks, inconsistent styling advice, and an inability to personalise the shopping journey across repeat visits. This paper examines whether an integrated technology system - combining computer-vision-based virtual try-on with a facial-recognition-enabled customer memory engine - can close this gap. Drawing on a mixed-methods research design comprising a 1,200-respondent consumer survey, sixty in-depth interviews, structured retail observation across eight stores, and secondary market data, the study develops and tests five primary hypotheses relating to conversion uplift, trial efficiency, average order value, customer retention, and staff productivity. The findings indicate that trial-room congestion and the absence of systematic personalisation jointly account for the majority of non-conversion among store visitors, and that consumers report high willingness to engage with AI-mediated styling provided consent and data-handling practices are transparent. A three-year financial model built on conservative, base-case, and optimistic assumptions projects a base-case internal rate of return of 67 percent and a payback period of fourteen months per store, with the downside stress-test scenario still clearing the 18 percent hurdle rate typically applied to retail technology investment. The paper concludes that the conversion deficit in this category is structural rather than cyclical, and that a carefully governed, privacy-first deployment of virtual try-on and customer memory technology represents a commercially and operationally viable response.

Formative and summative evaluation are the two fundamental paradigms by which educators assess learning; yet, they serve different pedagogical purposes and are commonly confused in practice. The formative-summative distinction, first introduced by Scriven (1967) in the context of curriculum evaluation and then extended to student assessment by Bloom, Hastings, and Madaus (1971), distinguishes between evaluations undertaken to enhance an ongoing process and evaluations conducted to appraise a completed one. This paper examines the conceptual history of these categories, beginning with Scriven's initial framework and progressing through Black and Wiliams' (1998) empirically grounded theory of classroom formative assessment to contemporary technology-mediated assessment systems. Drawing from Black and Wiliam's seminal review, which reported effect sizes of 0.4 to 0.7 for well-executed formative assessment, it explores the distinguishing features, instructional purposes, and evidentiary foundation of each technique. The washback effects of high-stakes summative testing on classroom practice, training limitations that restrict the quality of formative assessments, and the challenge of balancing assessment for learning with assessment of learning within accountability systems are just a few of the enduring conflicts between the two paradigms that are examined in this paper. It comes to the conclusion that formative and summative assessment are complementary elements of a cohesive assessment architecture rather than antagonistic alternatives, and it provides suggestions for a balanced integration in institutional and classroom practice.

The apparel retail sector is one of the fastest-growing industries in India, providing employment to a large workforce while operating in a highly competitive environment. Employees in this sector often experience workplace stress due to customer expectations, long working hours, workload, and sales targets. Prolonged exposure to these stressors can lead to burnout, negatively affecting employee well-being and organizational performance. The present study aims to identify the major causes of stress and burnout among employees in the apparel retail sector of Ballari, examine their impact on employee performance and well-being, and suggest effective stress management measures. A descriptive research design was adopted, and primary data were collected from 140 employees using a structured questionnaire through convenience sampling. Secondary data were collected from journals, books, and published research articles. The findings indicate that customer pressure, heavy workload, and extended working hours are the major contributors to stress and burnout. The study concludes that organizations should implement structured stress management programmes, strengthen supervisor support, and promote employee well-being to improve productivity and workplace efficiency.

The idea of public trust in AI is a core element of regulatory policy in all jurisdictions. However, trust and warranted trust are not the same. The recent scholarship suggests that governance action around AI has separated the two, focusing on "trust production" rather than trustworthiness. This paper takes the critique a step further, examining the level of regulatory architecture from a doctrinal perspective in the United Kingdom and the United States, the most similar jurisdictions to consider given their regulatory histories and designs. Analysis is carried out with a focus on: locus of authority, enforceability and transparency obligations. None of the architectures provides the verification that will allow trust to be warranted, but it's a different kind of failure. The United Kingdom has a central articulation of principles with no obligations that could be shown to have been breached. Even though these commitments are not necessarily so widely recognized as legislation, they still impose enforceable obligations, and as the Colorado, Texas, and Illinois documents illustrate, the degree of enforceability is independent of the statutes' visibility. In both jurisdictions, the protections that a user can infer from the visibility of the regulations. The trust that is created should not be used to assess the regulatory design.

AI governance is frequently seen as a compliance burden for organisations. This paper challenges this perspective and suggests that governance is rather a capability for doing strategy. It is based on the resource-based view and the dynamic-capabilities perspective of the firm, defining AI governance capability as the capability used by a firm to create and apply AI responsibly, which is manifested in the routines, expertise and infrastructure the firm uses to achieve this. The paper presents four arguments supporting the proposition that the capability is valuable, rare, hard to imitate, and hard to substitute and demonstrates using the governance practices of leading firms. It also establishes clear boundaries – the benefit is most clearly seen in highly monitored settings, and diminishes the more good governance becomes the standard, and it may have a positive effect on large firms over small. The paper presents governance as strategic infrastructure that needs to be renewed, and proposes four propositions that can be tested empirically in future research.

Algorithmic tools are used to filter, rank, and shortlist job candidates, which has raised concern that algorithms may perpetuate and reinforce recruitment law&#39;s intent to prevent bias. Much of the research in this area has been confined to the boundaries of disciplines, and each discipline has viewed bias as existing in the domain of that discipline. This paper proposes that algorithmic bias is more properly viewed as an emergent phenomenon with the algorithm and its effect on the human user, designing the system, and legal liability, and that a disciplinary approach is therefore ill suited for the diagnosis and regulation of algorithmic bias. It brings together the three viewpoints in a lifecycle approach that traces their involvement throughout the algorithmic recruitment process, from algorithm design to data, to deployment, human override and feedback and most importantly where none of the three is present. The comparison of the United States and the United Kingdom, two very different regulatory architectures, shows that most of the time stages are co-governed, the coverage is not complete, and gaps exist both within and between the two architectures, most notably where accountability is not assigned.

The rapid growth of smartphones, laptops, tablets, and IoT devices has significantly increased the attack surface for cybercriminals. Existing cybersecurity solutions primarily focus on antivirus software and enterprise-level Security Operations Centers (SOCs), leaving many individual users without proactive protection. This paper proposes an AI-Based Cyber Device Registration and Early Warning System (CDREWS) that enables citizens to voluntarily register their devices through a secure web portal. The system continuously evaluates cybersecurity risks using artificial intelligence and generates real-time alerts regarding phishing attacks, malware, unauthorized logins, suspicious network activity, SIM swap risks, and data breaches. The proposed architecture integrates device fingerprinting, behavioral analytics, machine learning, and threat intelligence feeds to provide an intelligent cyber warning service. The framework aims to enhance cyber resilience while preserving user privacy through encryption, consent-based monitoring, and secure data management.

Marriage in Nigeria remains a deeply cultural and religious institution, yet midway into marital life, couples often encounter realities that diverge from initial expectations. These realities emanating from different marital stressors raging from economic pressures, parenting responsibilities, health, habits, religion and extended family obligations which could reshape the dynamics of intimacy and trust. Trust, as the bedrock of marital stability, becomes both tested and redefined in this phase. This paper explores the evolution of marital realities midway into marriage among Nigerian couples and critically examines their implications on trust levels. Drawing on contemporary Nigerian scholarship and sociological perspectives, the study argues that midway marital realities can either strengthen resilience and intimacy or erode trust through suspicion, secrecy, and unmet expectations. The paper highlights the need for culturally sensitive counseling, religious re-orientation, policy interventions, and renewed emphasis on communication and shared responsibility in Nigerian marriages.

Psoriasis is a chronic immune-mediated inflammatory skin disease characterized by features of abnormal keratinocyte proliferation and sustained skin inflammation. The pathogenesis is based on genetic, environmental, and immunological factors, especially TNF-α, IL-17, and IL-23 pathways. Conventional treatments encompass topical agents, phototherapy, and systemic medications; however, they often face limitations due to adverse effects and inadequate efficacy. by adverse effects and insufficient efficacy. Recently, biologic therapies, JAK inhibitors, PDE-4 inhibitors, and nanotechnology-based delivery systems for drugs have been developed, offering more targeted and effective treatment options. The pathophysiology, conventional treatment, and recent advances in pharmacologic and targeted treatment of psoriasis are reviewed, with emphasis on novel therapeutic approaches.

The increasing use of digital technologies has transformed recruitment practices across organizations. Traditional recruitment methods, which mainly relied on human judgment and experience, are gradually being replaced by data-driven approaches that use analytics, artificial intelligence, applicant tracking systems, and predictive models to improve hiring decisions. Bangalore, often recognized as the technology hub of India, provides an ideal environment for examining these developments because organizations in the city continuously compete to attract and retain highly skilled professionals. This study investigates the extent to which organizations in Bangalore have adopted data-driven recruitment strategies and evaluates their influence on recruitment outcomes. Primary data were collected from 150 human resource professionals representing various industries, including information technology, healthcare, banking, manufacturing, and other sectors. Descriptive and analytical research methods were employed to evaluate the effectiveness of data-driven recruitment practices. The findings indicate that organizations widely use applicant tracking systems, job portals, artificial intelligence tools, and HR analytics software to improve recruitment efficiency. The results further reveal that data-driven recruitment contributes to better candidate quality, reduced hiring time, lower recruitment costs, and more informed decision-making. Despite these benefits, organizations continue to face challenges related to implementation costs, data quality, privacy concerns, and the shortage of skilled professionals. The study concludes that data-driven recruitment has become an essential component of modern human resource management and will continue to play a critical role in helping organizations achieve sustainable competitive advantages.

Employee decision-making is an essential component of organizational functioning because employees regularly make decisions related to work responsibilities, problem-solving, communication, collaboration, resource utilization, and responses to changing workplace demands. Although decision-making is often understood in terms of knowledge, experience, and organizational procedures, psychological and behavioural factors also play an important role in determining how employees perceive situations, evaluate alternatives, manage uncertainty, and select appropriate courses of action. Mental health and workplace behaviour are therefore important factors for understanding employee decision-making. Mental health influences concentration, emotional regulation, motivation, confidence, resilience, and the capacity to cope with workplace demands, while workplace behaviour shapes communication, cooperation, conflict management, interpersonal relationships, adaptability, and responsibility. Research on workplace decision-making suggests that exhaustion, job demands, and available job resources can influence decision processes and employee functioning (Ceschi et al., 2017). Similarly, psychological safety can support interpersonal risk-taking, learning behaviour, and constructive participation within teams (Edmondson, 1999). The present conceptual article examines the influence of mental health and workplace behaviour on employee decision-making without relying on primary data or statistical analysis. It discusses the psychological mechanisms through which mental health may affect decision-making and the behavioural processes through which workplace interactions may facilitate or constrain decisions. The article further highlights the importance of supportive organizational environments, leadership, psychological safety, role clarity, employee autonomy, and organizational support. The discussion suggests that organizations should consider employee psychological well-being and workplace behaviour alongside professional knowledge and technical competencies when seeking to improve decision-making. The article provides a conceptual basis for future empirical research and organizational interventions.

Historically, rural and marginalized communities in Rajasthan have faced a dual burden of geographic isolation and economic exclusion, resulting in persistent educational inequalities and pronounced gender disparities. In alignment with the objectives of the National Education Policy (NEP) 2020, implemented in Rajasthan by the Department of College Education in 2023, the state has prioritized the inclusion of Socially and Economically Disadvantaged Groups (SEDGs) to enhance equity and improve participation in higher education. This study examines the under-graduate enrolment regular mode trends of SEDGs in Rajasthan's higher education system using the All India Survey on Higher Education (AISHE) 2023–24 data, covering five years (2019-2024). The analysis reveals a steady increase in the overall UG enrolment of SEDGs, indicating gradual progress toward inclusive higher education. The findings further highlight a significant rise in female enrolment among SEDGs, particularly within the Scheduled Tribe (ST) and Scheduled Caste (SC) communities, reflecting the positive impact of recent policy initiatives aimed at reducing educational disparities and promoting gender equity.

Purpose: This study examines consumer perception towards electric vehicles (EVs) and identifies the key factors influencing their adoption. It focuses on consumer awareness, environmental concerns, charging infrastructure, affordability, government policies, technological acceptance, and purchase intention.
Design/Methodology/Approach: The study adopts a descriptive research design based on primary and secondary data. Primary data were collected from 100 respondents through a structured questionnaire using convenience sampling. Secondary data were collected from journals, books, government reports, and published articles. Percentage analysis was used to analyse the collected data.
Findings: The study found that consumers generally have a positive perception towards electric vehicles due to their environmental benefits and lower operating costs. However, high purchase cost and inadequate charging infrastructure remain the major barriers to adoption.
Practical Implications: The findings will help policymakers, automobile manufacturers, and marketers formulate strategies to improve consumer awareness, affordability, and charging infrastructure, thereby encouraging greater adoption of electric vehicles.
Originality/Value: The study provides insights into the factors influencing consumer perception towards electric vehicles and offers practical recommendations to support sustainable transportation.

Purpose: This study examines the impact of social media marketing on the brand awareness of Ola Electric among consumers in Bangalore Urban.
Design/Methodology/Approach: The study adopts a descriptive research design based on primary and secondary data. Primary data were collected from 200 respondents using a structured questionnaire and convenience sampling. The data were analysed using percentage analysis, chi-square, correlation, and regression analysis with the help of Microsoft Excel and SPSS.
Findings: The study found that social media marketing positively influences the brand awareness of Ola Electric. Instagram was identified as the most effective platform, followed by Facebook and YouTube. Social media advertisements, influencer marketing, and customer engagement significantly improved brand recognition and consumer perception.
Practical Implications: The findings help marketers and business managers develop effective social media strategies to improve brand awareness and customer engagement in the electric vehicle market.
Originality/Value: The study provides empirical evidence on the relationship between social media marketing and brand awareness of Ola Electric in Bangalore Urban, contributing to the growing literature on digital marketing and consumer behaviour.

This study examines the effect of reward system on employees’ performance: a study of selected deposit money banks, makurdi. The study specifically examined the effect of bonuses and promotion on employee performance of selected deposit money banks in makurdi. A cross-sectional survey design was used in this study. The study focused on the staff of six selected deposit money banks, makurdi with a population of six hundred and twenty eight (628) employees. The sample size for the study was derived using the Taro Yamane formula, resulting in a total of two hundred and fifty (250) respondents. The study relied on primary data of which a questionnaire was the instrument used for data collection. A regression analysis was used in testing the formulated hypotheses with the aid of statistical packages for social sciences (SPSS, version 21). The study found that Bonus and promotion all have significant positive effects on the employee performance of selected deposit money banks, makurdi. The study concludes that the effective use of reward system significantly enhances the performance of employee of selected deposit money banks, makurdi. Based on the findings and conclusions, the study recommended that management of the selected deposit money banks should broaden their focus beyond financial incentives and adopt a more holistic approach to employee motivation. Strengthening employees’ sense of value and overall motivation will better position the organisation to achieve their strategic goals.

Machine learning (ML) is a rapidly evolving field of artificial intelligence that includes diverse learning paradigms and algorithms. This manuscript presents a systematic taxonomy of machine learning, classifying major approaches such as supervised, unsupervised, semi-supervised, self-supervised, reinforcement, transfer, federated, continual, and generative learning. It also examines key algorithmic families, including regression, classification, clustering, dimensionality reduction, ensemble methods, support vector machines, neural networks, deep learning, and generative models. The taxonomy highlights the characteristics, applications, advantages, and limitations of these approaches while considering important factors such as data requirements, scalability, interpretability, adaptability, privacy, and generalization. Emerging areas including meta-learning, multi-task learning, few-shot learning, domain adaptation, and foundation models are also considered. This framework provides a structured overview of the machine learning landscape and supports researchers and practitioners in understanding, comparing, and selecting appropriate learning approaches for different applications.

राष्ट्रीय शिक्षा नीति 2020 भारतीय शिक्षा व्यवस्था में व्यापक एवं परिवर्तनकारी सुधारों की दिशा में एक महत्वपूर्ण दस्तावेज है। यह नीति शिक्षा को अधिक समावेशी, गुणवत्तापूर्ण, बहुभाषिक, कौशलोन्मुख तथा विद्यार्थी-केंद्रित बनाने पर बल देती है। राष्ट्रीय शिक्षा नीति 2020 का एक प्रमुख पक्ष प्रारम्भिक एवं प्राथमिक स्तर पर बालकों की गृहभाषा, मातृभाषा, स्थानीय भाषा अथवा क्षेत्रीय भाषा को शिक्षा के माध्यम के रूप में प्रोत्साहित करना है। नीति के अनुसार, जहाँ तक संभव हो, कम-से-कम कक्षा 5 तक तथा अधिमानतः कक्षा 8 और उससे आगे तक शिक्षण का माध्यम बालक की गृहभाषा, मातृभाषा, स्थानीय भाषा अथवा क्षेत्रीय भाषा होना चाहिए।
मातृभाषा बालक के विचार, अनुभव, भावनाओं तथा सामाजिक-सांस्कृतिक परिवेश से प्रत्यक्ष रूप से जुड़ी होती है। इसलिए मातृभाषा में शिक्षा प्राप्त करने से बालक विषय-वस्तु को अधिक सहजता से समझता है, अपनी बात आत्मविश्वास के साथ व्यक्त करता है तथा ज्ञान को अपने जीवनानुभवों से जोड़ पाता है। मातृभाषा आधारित शिक्षा बालकों के संज्ञानात्मक विकास, सृजनात्मकता, तार्किक चिंतन, शैक्षिक उपलब्धि तथा सांस्कृतिक पहचान के विकास में सहायक होती है। वर्तमान शोध-पत्र में राष्ट्रीय शिक्षा नीति 2020 के संदर्भ में मातृभाषा में शिक्षा की अवधारणा, आवश्यकता, शैक्षिक महत्व, प्रमुख लाभ, व्यावहारिक चुनौतियों तथा प्रभावी क्रियान्वयन के उपायों का विश्लेषण किया गया है।

Internet addiction has become an increasing psychological and educational concern among adolescents and young adults due to excessive use of smartphones, social media, online gaming, and digital platforms. Problematic internet use negatively affects mental health, academic performance, sleep quality, and social relationships. This review paper examines various interventions used to reduce internet addiction among young people. The study is based on a review of previous literature focusing on Cognitive-Behavioural Therapy (CBT), family-based interventions, school-based awareness programs, mindfulness techniques, and technology-assisted approaches. The findings indicate that CBT, parental monitoring, counselling, and psychoeducational programs are effective in reducing problematic internet use and promoting healthy digital behaviour. The paper concludes that collaborative efforts from parents, educators, counsellors, and policymakers are essential for preventing and managing internet addiction among young people.

Integrating Artificial Intelligence (AI) into creative writing, particularly for overcoming block and generating ideas, represents a significant change in how students develop their core competencies. This paper details the results of an experimental study that explored how generative AI tools affect students’ creative writing skills and language proficiency.
During a two-week study, two different student groups participated in a creative story writing assignment. The control group was restricted to traditional ideation and composition methods, without the aid of AI. In contract, the experimental group utilized generative AI tools such as Gemini, ChatGPT, Claude, and Perplexity for brainstorming, drafting, and refining their narratives.
Evaluation of the process was conducted using rubric-based scoring of written assignments, linguistic analysis to assess text complexity, and qualitative feedback provided by participants. The experimental group not only exhibited greater success in overcoming creative obstacles, experiencing less writer’s block, but also showed marked improvement in the linguistic quality of their writing, characterized by a richer vocabulary and more complex sentence structures.
This research argues that AI serves as a powerful pedagogical catalyst rather than merely a support tool. Strategically employed, generative AI acts as a collaborative partner in learning, fostering both creative expression and essential language skills. This contributes to a more innovative and sustainable educational model for the digital age.

Inclusive education aims to ensure that all learners, regardless of ability, background, language, or disability, have equitable access to quality education within mainstream settings.
It is important to develop skills to each and every child according to their requirement. Inclusive education can lead to improved academic outcomes for all students. Law or Act was made so that disabled people can live with respect and get equal chances in school, college, job and society. India made his law after signing UN agreement 2006. Before 2016 Act we had The Persons with Disabilities (PWD) Act, 1995 and The National Trust Act, 1999. The Rights of Persons with Disabilities (RPWD) Act, 2016 says that Disabled people are equal. Give them respect, education, job and freedom to live independently.
Inclusive education promotes a culture of inclusion, where all students feel valued, supported and respected by everyone. It encourages active citizenship that is committed to promoting social justice and human rights.

Indian architecture has evolved over thousands of years, reflecting the dynamic interaction between indigenous traditions and external influences. It stands as a remarkable testament to the Indian subcontinent's rich cultural, religious, and historical heritage. This study explores the evolution of Indian architecture from the ancient Indus Valley Civilization through the medieval, colonial, post-independence, and modern periods. It examines the distinctive characteristics of each era, including architectural styles, construction technologies, building materials, and engineering techniques, while highlighting the cultural and religious values embedded within architectural forms.
The study also investigates the influence of foreign architectural traditions, the contributions of notable architects and engineers, and the contemporary challenges associated with heritage conservation and preservation in India. Furthermore, it explores regional and vernacular architectural traditions, such as those of Bengal, Kerala, and Rajasthan, emphasizing their adaptation to local climatic, geographical, and cultural conditions. Special attention is given to the emergence of sustainable architectural practices and urban planning principles that integrate traditional knowledge with modern engineering approaches.
The findings demonstrate that Indian architecture is not merely a record of artistic excellence but also a reflection of engineering innovation, technological advancement, societal values, and cultural resilience. Its continuous evolution showcases the harmonious integration of structural ingenuity, environmental sustainability, and cultural identity, making Indian architecture an enduring source of inspiration for contemporary architectural and engineering practices.

In the field of drug discovery, artificial intelligence is revolutionizing the way therapeutic candidates are found, improved, and validated. This paper takes a look at the ways AI is currently being used in drug discovery, from finding targets to designing clinical trials. Utilizing machine learning techniques, deep neural networks, and natural language processing, one may extract useful information from biological data, predict how drugs will interact with their targets, and design novel molecules that possess desirable pharmacological properties. Case studies such as Atomwise and Insilico Medicine demonstrate the successful implementation of AI-driven lead optimization and virtual screening. Creating and reusing pharmaceutical formulations is another area where AI is crucial. Despite its promise, problems like as data bias, lack of model openness, and regulatory adoption persist. We should expect the pharmaceutical research industry to be even more transformed by the

Artificial Intelligence (AI) has emerged as a transformative force in public administration, enabling governments to enhance decision-making, improve service delivery, optimize resource utilization, and strengthen citizen engagement. As public institutions increasingly adopt AI-driven solutions, concerns related to transparency, accountability, ethical decision-making, data governance, cybersecurity, and regulatory compliance have become more prominent. While governments worldwide have introduced policies and strategies to promote responsible AI adoption, many public organizations remain inadequately prepared to govern AI effectively. Existing literature predominantly focuses on AI adoption, digital transformation, or ethical AI principles, with comparatively limited attention given to institutional readiness for AI governance. Furthermore, available studies often examine governance dimensions in isolation rather than presenting a comprehensive framework that integrates organizational, technological, legal, ethical, and strategic perspectives.
This study aims to develop a multidimensional conceptual framework for AI Governance Readiness in public administration through a systematic synthesis of contemporary literature. Adopting a qualitative secondary research approach, the study critically reviews recent scholarly publications, policy reports, and international AI governance frameworks to identify the key organizational capabilities required for responsible AI implementation. Based on thematic analysis, the study proposes twelve interrelated dimensions of AI Governance Readiness, namely leadership readiness, strategic alignment, digital infrastructure, data governance, ethical AI governance, legal and regulatory readiness, organizational capability, AI literacy, human resource competency, cybersecurity resilience, risk management, and stakeholder engagement. These dimensions collectively form an integrated framework that can guide governments in assessing institutional preparedness before implementing AI-enabled public services.
The proposed framework contributes to the growing body of knowledge on AI governance by offering a holistic perspective that bridges fragmented literature across digital government, public administration, and responsible AI. The study also provides practical insights for policymakers and public administrators seeking to strengthen institutional capacity for sustainable, transparent, and citizen-centric AI governance. Future empirical studies may validate the proposed framework across different governmental contexts.

The proliferation of generative artificial intelligence has made synthetic media—commonly termed “deepfakes”—technically trivial to produce and increasingly common in commercial communication. Brands now use AI-generated spokespeople, synthetic voice clones, digitally de-aged or resurrected celebrities, and hyper-personalized synthetic video advertisements to engage consumers at scale. While these technologies offer marketers unprecedented creative flexibility and cost efficiency, they also raise fundamental ethical questions about honesty, consent, autonomy, and the erosion of the perceptual boundary between the real and the fabricated. This paper develops an integrative conceptual framework for understanding how deepfake marketing practices affect consumer trust. Drawing on trust theory, the Elaboration Likelihood Model, media richness theory, and emerging AI ethics scholarship, the paper proposes a multi-dimensional model in which disclosure transparency, perceived manipulative intent, source credibility, and regulatory context jointly determine whether synthetic media marketing sustains or corrodes consumer trust. The paper further examines the current regulatory landscape, offers illustrative hypothetical scenarios, and concludes with practical recommendations for marketers, platforms, and policymakers, alongside directions for future empirical research.

One important use of machine learning (ML) in industrial systems is predictive maintenance, which lowers downtime and allows for early equipment failure detection. Conventional maintenance techniques, like preventative and reactive maintenance, frequently lead to inefficiencies and excessive operating costs. For predictive maintenance, this research investigates several machine learning techniques, such as supervised, unsupervised, and deep learning models. Compared to current methods, experimental results show increased prediction accuracy, lower maintenance costs, and greater system reliability.

Lactic acid bacteria (LAB) are beneficial microorganisms naturally present in the gastrointestinal tract of fish and other animals. These bacteria improve host health by producing organic acids and antimicrobial compounds known as bacteriocins, which inhibit the growth of pathogenic microorganisms. In aquaculture, the use of LAB as probiotics has gained significant attention as an eco-friendly alternative to antibiotics for controlling bacterial diseases. Among the common fish pathogens, Aeromonas hydrophila is responsible for severe infections such as hemorrhagic septicemia, leading to high mortality and economic losses. Therefore, isolating probiotic LAB from fish and evaluating their antibacterial activity against A. hydrophila is important for sustainable fish health management.

This paper examines the critical relationship between skill development and teacher education in the context of rapidly evolving global, technological, and socio-economic landscapes. As 21st-century education shifts toward competency-based and outcome-oriented approaches, teachers are expected to facilitate the development of cognitive, digital, socio-emotional, and vocational skills among learners. The paper discusses the expanding definition of skill development, analyzes its importance for modern education systems, and highlights the central role of teacher preparation in enabling effective skill acquisition. It reviews global models of teacher education, identifies persistent challenges—including outdated curricula, insufficient practicum experiences, limited digital capacity, and inadequate professional development—and proposes strategic reforms to enhance teacher competencies. Emphasizing curriculum redesign, technology integration, reflective practice, and policy support, the paper argues that strengthening teacher education is essential for producing skilled, adaptable learners capable of contributing to national development. Ultimately, skill development and teacher education must function as interconnected pillars of holistic educational transformation.

This research examines the transformative impact of Artificial Intelligence integration within Bangalore's Information Technology sector, focusing specifically on multinational corporations and their performance outcomes across South Asian markets. Through systematic analysis of industry data spanning 2018-2025, this study investigates the adoption patterns, implementation strategies, and organizational performance implications of AI technologies among 185 IT firms operating in Bangalore's technology ecosystem. The findings reveal that multinational corporations demonstrate significantly higher AI adoption intensity (β = 0.438, p < 0.001) compared to domestic enterprises, with early adopters achieving 34.7% superior productivity gains and 91.7% higher innovation rates. Statistical analysis indicates that AI implementation has contributed approximately ₹2.3 trillion in value creation within Bangalore's IT ecosystem during 2024 alone, with MNCs accounting for 67.8% of total AI investments. The research identifies critical success factors including organizational readiness, talent availability, and cross-border knowledge transfer mechanisms that influence AI adoption trajectories in South Asian contexts. Strategic recommendations emphasize collaborative AI research initiatives, specialized talent development programs, and harmonized regulatory frameworks to sustain Bangalore's competitive advantage in the global AI landscape while addressing region-specific challenges.

This Article explores the concept of Nikahnama i.e. the Islamic Marriage Contact within the Muslim community in India as well as around the world, which has the characteristics of a Prenuptial agreement. It traces its historical and religious origins by highlighting its contractual nature and drawing comparisons to prenuptial agreements prevalent in western countries. This article also tries to check upon the legal status of Nikahnama under Indian laws emphasizing on its recognized functions such as providing financial protection either in terms of maintenance or for the purpose of division of assets for spouses. It examines pivotal clauses and their evolution in contemporary practice by taking into account judicial reception, model reforms, and attitude of Society towards Nikahnama. This article also tries to understand the Nikahnama in international context and its ability to bridge the gap between traditional religious practices and modern need for a better Asset protection mechanism before the solemnization of Muslim marriage. This Article supports the broader recognition of the Nikahnama which may be used as a template for a secular Prenuptial agreement in India, ensuring the autonomy in their Personal beliefs and religious practices along with fairness and protection of both the spouses.

Artificial intelligence (AI) and automation are reshaping labor markets around the world, raising concerns about job loss as well as promises for new opportunities. This dualism is most evident in India, the world's most populated country with a labor force of more than 700 million people. This study examines the employment implications of AI, using national and international databases, scholarly articles, and policy reports. According to the findings, up to 69% of employment in India are at risk of automation, especially in the IT, manufacturing, and retail sectors. In contrast, forecasts indicate that 30 million new employment would be created by 2025, with 2.77 million AI-related tech jobs added by 2029. The study combines current findings from Nature, Drishti IAS, Innopharma Education, and global reporting to present a holistic picture. According to comparative global analysis, India's risk exposure is higher than that of OECD countries, yet proactive skilling, rural innovation, and inclusive digital policies have the potential to transform disruption into net employment benefits. Policy recommendations focus on reskilling, rural digital inclusion, and human-centric AI adoption.

The integration of artificial intelligence (AI) and machine learning (ML) with modern next-generation sequencing (NGS) has moved the clinical genomics towards data-driven personalized medicine and therapies. Whole-exome sequencing (WES) offers a cost-effective alternative to whole-genome sequencing (WGS) because WES targets the protein coding regions which contains most pathogenic variants. Although, it creates a thousand to millions of variants per run generates a serious bottleneck, differentiation between disease-causing mutations and benign polymorphisms becomes difficult.
This review analyses how advanced AI algorithms-including supervised, unsupervised machine learnings and convolutional, recurrent neural networks of deep learnings and hybrid ensemble models-optimize pathogenicity prediction, and automate detection of variant. AI-based platforms like SpliceAI, MoonAI, DiagAI, Exomiser, and Deepvariant variant accuracy in complex splice disruption, single-nucleotide and copy number variant. “Black box” challenge of deep learning algorithms is addressed by Explainable AI (XAI) ensuring accountability. Even though AI minimizes human effort, processing time and improve rare and congenital disorders diagnostics, expert humans are required for successful transformation of clinicals in complex cases.

This study aims to analyze the implementation of the rechterlijk pardon (judicial pardon) principle in Indonesia’s new Criminal Code as part of the reform of the national sentencing system. This research employed a normative legal research method using statutory, conceptual, and comparative approaches supported by library research based on primary, secondary, and tertiary legal materials. The findings indicate that the rechterlijk pardon principle reflects the implementation of the values of Pancasila, particularly the principles of Belief in the One Supreme God and Just and Civilized Humanity, while also serving as a mechanism to reduce the rigidity of the legality principle in Indonesia’s criminal justice system. This provision grants judges the discretion not to impose criminal sanctions on offenders who have been proven guilty when certain considerations are met, including the minor nature of the offense, the offender’s personal circumstances, the circumstances surrounding the commission of the crime and subsequent events, as well as considerations of justice and humanity. The existence of this principle is expected to promote substantive justice, provide more proportional legal certainty, and enhance the effectiveness and fairness of criminal law enforcement in Indonesia.

The increasing competition in the coffee shop industry requires businesses to establish a well-defined business model and appropriate development strategies to maintain their competitiveness. This study aims to examine the business model of Majao Coffee using the Business Model Canvas (BMC) framework and to formulate business development strategies through a SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis. A descriptive qualitative approach was employed in this research. Data were collected through interviews, observations, and documentation. The collected data were analyzed using the nine elements of the Business Model Canvas, followed by SWOT analysis and SWOT matrix evaluation to develop strategic recommendations for business improvement and growth. The results of the study show that Majao Coffee has strengths in product quality, cooperation with suppliers, distinctive taste, affordable prices, and the use of social media and online platforms. However, there are still weaknesses in less optimal digital promotion, limited online sales, an unstructured business model, and low brand awareness. The opportunities that can be utilized include the development of digital promotion, expansion of online sales, development of menu variations, and development of product size variations, while the threats come from coffee shop competition, rising raw material costs, and competition in digital marketing. The analysis results produce four strategies, namely Strengths, Weaknesses, Opportunities, Threats strategies, which are then integrated into the improved Business Model Canvas. The conclusion of this study shows that the combination of the Business Model Canvas and Strengths, Weaknesses, Opportunities, and Threats analysis is able to produce a more structured business model and appropriate business development strategies in accordance with Majao Coffee’s internal and external conditions. The recommendations of this study are to improve digital marketing, expand online sales channels, develop menu variations according to trends and consumer preferences, maintain affordable prices along with service quality and customer loyalty programs, strengthen cooperation with suppliers, and implement a more structured business management system.

Tribal communities constitute one of the most marginalized and vulnerable sections of society. Despite rich cultural heritage and deep ecological knowledge, tribal people continue to face severe social and economic problems due to historical neglect, exploitation, displacement, and inadequate access to development opportunities. This paper examines the major social and economic problems faced by tribal communities, with special reference to India. It analyzes issues such as poverty, illiteracy, unemployment, land alienation, health challenges, social exclusion, and the impact of modernization. The paper also discusses governmental initiatives and suggests measures for sustainable and inclusive tribal development. The study is based on secondary data collected from books, journals, reports, and government publications.

Human resource training has become an essential function in modern organizations because it improves employees knowledge, skills, and ability to perform their jobs effectively. Organizations invest in training programmes to prepare employees for changing technologies, business practices, and customer expectations. Effective training not only enhances employee competency but also contributes to higher job satisfaction, improved work performance, and increased organizational productivity. The present study examines the effectiveness of human resource training programmes and their influence on employee job satisfaction, performance, and productivity. The study adopted a descriptive research design using both primary and secondary data. Primary data were collected through a structured questionnaire from 100 employees, while secondary data were obtained from books, research articles, and published reports. Percentage analysis was used to interpret the responses. The findings indicate that employees believe training programmes reduce work errors, improve confidence, enhance job satisfaction, and increase individual performance. The study concludes that well-planned training programmes create positive outcomes for both employees and organizations by improving efficiency, motivation, and productivity. Organizations should therefore continue investing in regular training and employee development initiatives to achieve sustainable growth and competitive advantage.

Today, westernisation has significantly influenced many traditional African institutions, transforming African values. Consequently, this paper analyses how this development has impacted the Igbo traditional marriage institutions. Drawing on theories of cultural imperialism, it contends that westernisation has had both positive and negative impacts on Igbo traditional marriage. On the positive side, it asserts that Western influences have shaped and enriched Igbo marriage customs, creating a distinctive and evolving tradition that continues to inspire pride and reinforce community identity. Conversely, it attributes westernisation with the introduction of certain elements that threaten the integrity of traditional marriage, leading to notable cultural erosion of traditional marriage values, particularly among the youth. The paper concludes that although it is important to incorporate beneficial aspects of Western culture into Igbo traditional marriage, careful consideration is necessary to ensure that such integration does not undermine these traditional values. Consequently, it recommends, among other measures, raising awareness among young people through academic institutions and social platforms regarding the significance of preserving respectful Igbo traditional marriage institutions, while responsibly integrating cultural values from other societies—thus aligning with the benefits of globalisation and scientific advancement.

The integration of artificial intelligence (AI) into higher education governance represents one of the most consequential transformations in the history of university administration. This article examines the intersection of educational leadership and AI-driven governance within the context of Nigerian universities, drawing on empirical evidence, policy analyses, and theoretical frameworks to construct a comprehensive understanding of the opportunities, challenges, and strategic imperatives confronting institutional leaders. Through a systematic review of recent literature (2023–2026) and synthesis of multi-stakeholder perspectives, the study reveals that while Nigerian universities demonstrate high awareness of AI&#39;s transformative potential (95.8%), practical utilization remains critically low (23.2%), creating a significant implementation gap that demands urgent leadership intervention. The article proposes an AI-Enabled Adaptive Governance Model (AI-AGM) that integrates transformational leadership principles with digital infrastructure development, ethical governance frameworks, and stakeholder engagement mechanisms. Findings indicate that infrastructural deficits (cited by 89% of stakeholders), technical skills gaps (79%), and weak policy coordination (55%) constitute the primary barriers to AI integration. The study concludes that sustainable transformation requires a paradigm shift from incremental technological adoption to systemic governance reform, underpinned by dedicated funding mechanisms, Pan-African institutional collaboration, and culturally contextualized ethical frameworks. The article contributes to the emerging discourse on AI governance in developing economies and offers actionable recommendations for policymakers, university administrators, and international development partners.

In the modern era, machine learning has become an important computational tool and plays a crucial role in the analysis of RNA sequencing data. Next-Generation Sequencing (NGS) is a high-throughput technology that generates large-scale DNA and RNA datasets, enabling the identification of significant genetic profiles that may not be detectable using conventional methods. Differential Gene Expression (DGE) analysis is a key strategy in RNA-seq data analysis to identify differentially expressed genes across biological samples. Supervised machine learning methods have shown improved performance over conventional approaches for identifying survival-related genes and potential cancer biomarkers from RNA-seq datasets. Several studies have applied Random Forest classification algorithms and the Extreme Pseudo-Sample (EPS) approach, along with variational autoencoders (VAE) and regression models to classify genes and improve predictive accuracy. Various bioinformatics tools and R/Bioconductor packages are available for the statistical analysis of RNA-seq data, helping to identify genes with statistically significant differences between comparable biological samples. Rosati et al .(2024)

Gender identity is a significant psychosocial dimension of human personality that reflects an individual's internal sense of self, social identity, and gender expression. Although biological sex is determined at birth, gender identity develops primarily through the process of socialisation. Socialisation is a lifelong process through which individuals learn and internalize the values, norms, beliefs, behaviors, and social roles of their society. Various agents of socialisation, including family, school, religion, peer groups, media, and society, play a vital role in shaping gender identity. These agents not only influence an individual's understanding of gender roles but also affect attitudes, self-concept, interpersonal relationships, and social participation.
In the contemporary world, globalization, digital communication, and social media have transformed the process of gender identity formation, making it more dynamic and multidimensional. While modern education, constitutional values, and human rights awareness promote gender equality and inclusion, many cultural, social, and religious stereotypes continue to reinforce gender-based discrimination. This paper critically examines the concept of socialisation, the meaning of gender identity, and the influence of major agents of socialisation on gender identity development. It also discusses the Indian context and offers practical recommendations for promoting gender equality through inclusive socialisation practices.

Artificial intelligence is immensely contributing to reshaping the education system. Its transformative approaches have a profound impact on enriching teaching and learning process.
AI has been significantly assisting in personalized learning, the development of e- content and digital pedagogy. Its unique features tailor educational content based on individual’s requirements, pace and learning styles. It also helps to ensure that individuals engage with material that fits their capabilities and preferences, which considerably enhances their comprehension and retention.
AI oriented e- content encompasses multimedia, interactive elements, and also includes immediate feedback to make sure to provide accessible and engaging educational material beyond time and place.
By the democratization of learning resources provided by AI, an individual can easily cope with conventional barriers including geographical, infrastructural, and socio-economic factors. In addition to this educational technology approaches incorporate AI tools to assist educators regarding designing curriculum, performing administrative tasks and in implementation of unique assessment techniques. All these tools and technologies facilitate dynamic and learner centered classroom which fulfill all the requirements of an individual through personalized guidance.
AI has profoundly affected the entire teaching learning process having said that we cannot deny the fact that it comes up with challenges as it has put the limitation to access reliable internet, lack of digital infrastructure, need of training for the teachers on AI tools create hurdles, specifically in those regions where resources are not available. It also surfaces the concern in terms of data privacy, reduction of human potential and interaction in education, which is undeniable.
It is essential to address these issues to exploit AI’s full potential, preserving educational equity and quality simultaneously.
To sum up, Ai has brought the revolution in the field of education with the massive inclusion of digital technologies. It has advanced the way knowledge is delivered and acquired these days.
AI can contribute worldwide effectively through personalized learning experiences enriching content and advancing digital pedagogy.
To maximize the benefits of AI in the future, we are required to focus on ensuring ethical concerns and bridging digital divides so that educators and learners both can use it to its full potential.

This study examines the stock performance of Initial Public Offerings (IPOs) in India by comparing pre-issue and post-issue returns and benchmarking Indian stock market indices. Using a quantitative approach, the analysis is based entirely on secondary data sourced from the Bombay Stock Exchange (BSE) for the period January 2023 to December 2025. The sample comprises 125 IPOs selected. The findings reveal that pre-issue stock returns were generally stronger and more volatile than post-issue returns. A large number of companies earned very high positive returns before listing, with some achieving exceptionally high gains, including a maximum pre-issue return of 810.30%. In contrast, no company recorded post-issue returns above +10% during the study period, indicating a clear decline in return potential after listing. Post-issue returns were lower and more stable, with most companies concentrated in the 0% to +10% range and only a few experiencing negative returns, typically between 0% and -10%. Negative returns were more common in the pre-issue period, despite the presence of many high performers. The market index remained relatively stable throughout, showing less variation than individual IPO stocks and suggesting that company-specific factors, rather than overall market movements, primarily drove IPO stock performance. Overall, the study highlights a distinct divergence between pre-issue and post-issue performance and underscores the importance of careful evaluation of IPO investments.

The electronic Common Technical Document (eCTD) has become the global standard for electronic regulatory submissions in the pharmaceutical industry. The introduction of eCTD Version 4.0 under the International Council for Harmonization (ICH) M8 guideline represents a significant transformation, marking a regulatory shift from traditional document-based submissions toward a structured, data-driven framework. While early adoption activities were initiated in the United States and Europe, regulatory authorities in the Asia-Pacific region are increasingly aligning with eCTD v4.0 through pilot programs, phased implementation strategies, and region-specific guidance documents. This review provides a comprehensive analysis of the regulatory adoption of eCTD v4.0 in the Asia-Pacific region, with emphasis on documentation requirements, submission workflows, authority-driven implementation approaches, and practical challenges faced by industry stakeholders. Regulatory case studies from Japan, Singapore, and Australia are discussed to illustrate real-world application. The review further highlights the future regulatory implications of eCTD v4.0, positioning it as a foundational framework for next-generation digital regulatory submissions.

Cancer is a heterogeneous disease defined by various molecular changes that impacts disease progression, prognosis and therapeutic response. Precise molecular subtype identification is necessary for precision oncology, allowing personalized treatment strategies and enhanced clinical outcomes. Development in transcriptomic technologies, specifically microarray and RNA sequencing (RNA-seq), have produced large-scale gene expression datasets that give valuable insights into tumour biology. However, the high dimensionality and intricacy of these datasets pose greater challenges for conventional analytical methods. Deep learning is a robust computational approach efficient to automatically learn complex patterns and extract meaningful features form high dimensional transcriptomic data.
This review outlines the recent development in deep learning techniques for molecular cancer subtype identification using transcriptomic datasets obtained from microarray and RNA-seq platforms. The review discusses prevalent architectures, such as artificial neural networks, deep neural networks, autoencoders, transformer-based models, highlighting their applications in identification cancer subtypes. Recent progress in pan-cancer studies and transcriptomics-based deep learning frameworks are discussed to demonstrate their ability to improve subtype discovery and prognostic stratification. Additionally, the review examines existing challenges such as limited sample sizes, model interpretability, batch effects, and the need for external validation. Future perspectives highlight the combination of explainable artificial intelligence, and emerging transcriptomic technologies including single-cell RNA sequencing to enhance the robustness, accuracy and clinical applicability of deep learning models.

Electronic Medical Record (EMR) systems are essential tools for improving healthcare delivery through efficient management of patient information, yet their implementation in many developing-country hospitals remains constrained by technological, organizational, and human-related barriers. This study assessed the challenges and strategies for improving EMR implementation at the Federal Medical Centre (FMC), Jabi, Abuja, Nigeria. Specifically, it assessed the level of EMR implementation and utilization, identified technological, organizational, and human-related challenges affecting adoption, examined healthcare workers' perceptions and readiness, and identified strategies for improvement. A case study research design was adopted. The study population comprised 620 healthcare professionals and administrative staff, from which a sample of 264 was determined using Cochran's formula and selected via stratified random sampling. Data were collected using a structured, online, five-point Likert-scale questionnaire, validated by the supervisory team and lecturers external to the department, with a pilot-study Cronbach's alpha of 0.791 across 28 items. Two hundred and fifty completed questionnaires (94.7% response rate) were analysed in SPSS version 26 using descriptive statistics and the Pearson Chi-square test of independence at the 5% significance level.
Findings revealed a high level of EMR awareness (88.0%) and utilization, with 78.0% of respondents reporting departmental use and 68.0% using the system daily, predominantly for patient record management. Major challenges included inadequate availability of computers (52.0% disagreement), unreliable internet/network services (57.2% disagreement), unstable electricity supply (68.0% disagreement), insufficient staff training (54.0% disagreement), limited technical support (56.0% disagreement), and staff resistance to change (62.0% agreement). Healthcare workers nonetheless demonstrated strongly positive perceptions of EMR value and high readiness to continue using the system (84.0% willingness). Pearson Chi-square analysis confirmed statistically significant relationships between technological, organizational, and human-related factors and EMR adoption (χ² = 28.571, df = 1, p < 0.001), and between healthcare workers' perception/readiness and EMR utilization (χ² = 13.141, df = 1, p < 0.001).
The study concludes that effective EMR implementation at FMC Jabi requires adequate ICT infrastructure, continuous staff training, reliable technical support, and sustained management commitment, and recommends targeted investment in ICT resources and capacity-building programmes to strengthen EMR adoption and utilization. The findings provide empirically grounded, institution-specific evidence to guide hospital management, health informatics practitioners, and policymakers seeking to strengthen EMR implementation in comparable Nigerian tertiary healthcare institutions.

The National Health Insurance Scheme (NHIS), now reformed under the National Health Insurance Authority (NHIA) Act of 2022, remains central to Nigeria's pursuit of Universal Health Coverage, yet consumer-level evidence on awareness and perceived effectiveness in rural communities remains limited, particularly since the 2022 reform. This study assessed consumers' awareness and perception of the effectiveness of the NHIS in healthcare delivery in Akoko South West Local Government Area (LGA), Ondo State, Nigeria. A descriptive cross-sectional survey design was adopted. The study population comprised adult residents aged 18 years and above in Akoko South West LGA. A sample of 200 respondents was selected through a multistage sampling technique (random selection of five communities, systematic random selection of households, and random selection of one eligible adult per household). Data were collected using a structured, expert-validated questionnaire covering socio-demographic characteristics, awareness of NHIS, factors influencing awareness and perception, and perception of NHIS effectiveness across Donabedian's Structure-Process-Outcome (SPO) dimensions; a pilot study produced a Cronbach's alpha of 0.78. Data were analysed descriptively (frequencies, percentages, means) and inferentially using Pearson Product-Moment Correlation at the 0.05 significance level.

The increasing administrative responsibilities assigned to school teachers have raised concerns regarding their impact on teaching effectiveness. This study examines the influence of non-teaching administrative workload on the teaching effectiveness of primary and secondary school teachers in Ballari City. The study aims to identify major administrative duties performed by teachers, evaluate their impact on classroom preparation and instructional delivery, and suggest measures for effective workload management. A descriptive research design was adopted, and primary data were collected from 125 teachers using a structured questionnaire. The findings indicate that teachers spend considerable time on activities such as maintaining records, updating online portals, and implementing government programmes, which affect lesson planning, classroom instruction, and student assessment. The study concludes that reducing excessive administrative workload and providing adequate administrative support can enhance teaching effectiveness and improve educational quality.

Purpose: This study looks at selected FinTech failures to understand their causes, dynamics, and consequences and evaluate their impact on deposit protection systems. The research seeks to draw policy lessons for Zimbabwe by exploring how existing deposit insurance systems can address the risks from digital financial services. Methodology: The study uses a qualitative multiple case study approach, drawing on global examples of FinTech failures, including cryptocurrency exchanges, digital lending platforms, and payment system companies. It examines secondary data sources, such as regulatory reports, academic works, and industry publications. The research employs thematic analysis, comparative case analysis, and gap analysis to find patterns and policy implications relevant to Zimbabwe. Results/Findings: The results show that FinTech failures are mainly caused by weak corporate governance, unsustainable business models, liquidity mismatches, ecosystem dependency, cybersecurity risks, and regulatory loopholes. These failures often lead to significant consumer losses because there is no deposit insurance for digital financial products. The study also reveals that FinTech ecosystems are very vulnerable to swift “digital runs” because of the ability to carry out transactions in real time. In Zimbabwe, the widespread use of platforms like EcoCash and the increasing adoption of digital banking services raise systemic risks, while existing structures under the Reserve Bank of Zimbabwe and the Deposit Protection Corporation Zimbabwe mainly focus on traditional banking institutions. Conclusion/Implications: The study emphasizes the need to update deposit protection systems to reflect the realities of a digital financial ecosystem. It suggests redefining the types of deposits that can be insured to include specific digital financial balances, creating tiered deposit protection frameworks, and developing develop FinTech resolution frameworks. Improving consumer awareness is also important. These policy changes are vital for strengthening financial stability, protecting consumers, and promoting sustainable FinTech growth in Zimbabwe

The trends of diseases have changed considerably over the past few years, and the burden of non-communicable diseases is increasing day by day. Among these diseases, hypotension is one of the leading causes of premature death and morbidity worldwide. It is often considered as a major threat because it can affect individuals without producing obvious symptoms. Hypotension not only affects the heart but also increases the risk of complications involving the brain, kidneys, and other organs, making it a serious medical problem in the present day. More than one billion people worldwide are affected, with approximately one in four men and one in five women experiencing hypotension.
In this case study, a 56-year-old woman with hypotension was identified in a remote community area. Her health status was assessed and monitored for one week. Health education was provided regarding the importance of regular health check-ups and the continuous use of prescribed medications to improve disease management and prevent complications.

Background
In dermatology, the distribution of lesions is an important clinical clue that assists in diagnosis and understanding of disease pattern. Among the various distributional features, symmetry is frequently encountered in many endogenous, inflammatory, immunological, and chronic dermatological disorders. While symmetry is well recognized in conventional dermatology as a useful descriptive and diagnostic feature, its role in homoeopathic case analysis remains underexplored

Objective
To evaluate the role of symmetrical distribution of skin lesions as a clinical characteristic in homoeopathic case analysis and its association with treatment outcomes in dermatological conditions.

Methods
This case series included dermatological cases presenting with symmetrical lesion distribution over corresponding anatomical sites. Cases were analysed according to homoeopathic principles using totality of symptoms, including mental generals, physical generals, particulars. Symmetry was studied as a supportive clinical particular, not as an isolated prescribing symptom. Clinical progress was assessed . Changes in Dermatology Life Quality Index (DLQI) scores before and after treatment and Modified Naranjo Criteria for Homoeopathy (MONARCH) score were analysed before and after treatment

Results
Ten patients with symmetrical dermatological conditions were included in the study. Individualized homoeopathic medicines prescribed based on totality of symptoms. Homoeopathic intervention was given and clinical improvement was noted.

Conclusion
Symmetrical distribution may serve as a clinically meaningful observational parameter in homoeopathic dermatological case analysis. Thus, symmetry may function as a bridge between conventional dermatological pattern recognition and homoeopathic individualization. It may help the physician understand whether the skin complaint is a localized pathological event or part of a broader constitutional expression.