The rapid development of Artificial Intelligence (AI) and machine-learning technologies has resulted in the emergence of deepfakes, which are digitally manipulated audio, video, images or other forms of media that can realistically imitate or replace the identity, appearance or voice of an individual. Although such technology may have legitimate applications in entertainment, education and creative industries, its malicious use has created serious legal and social concerns. Deepfakes may be used for identity theft, financial fraud, impersonation, defamation, misinformation, sexual exploitation, harassment and manipulation of electronic evidence.

In India, there is presently no single comprehensive legislation specifically dedicated to deepfakes. Instead, different aspects of deepfake-related conduct may be addressed through existing provisions of the Information Technology Act, 2000, the Bharatiya Nyaya Sanhita, 2023, the Bharatiya Sakshya Adhiniyam, 2023 and rules governing intermediaries. The legal framework therefore faces difficulties relating to identification of offenders, attribution, jurisdiction, preservation of digital evidence, intermediary responsibility and protection of victims.

This paper examines the nature of deepfakes, their relationship with cyber crimes, the existing Indian legal framework and the major challenges faced by law-enforcement agencies and courts. It also proposes legal and technological remedies including stronger intermediary accountability, rapid takedown mechanisms, digital watermarking, public awareness, specialised investigation mechanisms and clearer legislative provisions.

Cyber addiction refers to a behavioral disorder characterized by excessive and compulsive use of digital technologies such as the internet, smartphones, social media platforms, online gaming, and digital content. With rapid technological advancement and increased accessibility to the internet, cyber addiction has emerged as a significant psychological, social, and educational concern, especially among adolescents and young adults. This condition negatively affects mental health, leading to anxiety, depression, sleep disturbances, reduced academic or work performance, and impaired social relationships. Cyber addiction also influences emotional regulation, self-control, and real-life interpersonal interactions. The present abstract highlights the concept, causes, symptoms, and consequences of cyber addiction, emphasizing the need for awareness, early identification, and preventive strategies. Effective interventions such as digital literacy, time management, counseling, and balanced technology use are essential to minimize its harmful effects and promote healthy digital behaviour in modern society.

Co-operative banking is a type of bank set up on a co-operative model. Like other banks, co-operative banks raise funds through shares, accept deposits, and provide loans. This system is part of the Indian banking structure and aims to reach rural areas in India. The co-operative banking sector has a vast network that offers credit services to rural agricultural credit seekers. It plays a significant role in socio-economic development and has contributed to the country’s GDP growth. Primary Agricultural Credit Societies (PACS)which accounts for 30 percent of microcredit in India and are vital for community development in all states. Consequently, a study has been conducted to examine the structure of agricultural credit in Karnataka, as well as to assess the current status and performance of co-operative banking related to agricultural credit in rural and urban areas of the state.

Artificial Intelligence (AI) is increasingly becoming part of professional and public life. The Indian legal sector is also increasingly using AI for legal research, document review, contract analysis, case management, translation, transcription, and access to legal information. AI-assisted tools may reduce repetitive work and help legal professionals process large volumes of information more efficiently. At the same time, their use raises concerns relating to accuracy, fabricated authorities, privacy, confidentiality, bias, transparency, accountability, and professional ethics. This paper examines the growing role of AI in the Indian legal system and discusses its opportunities and challenges. It argues that AI should be treated as an assistive tool rather than a substitute for judges, advocates, or other legal professionals. Responsible use requires meaningful human oversight, verification against primary legal sources, protection of confidential information, and respect for constitutional values.

Indian Knowledge Systems represent the diverse intellectual, cultural, ecological and experiential traditions developed in India through generations of observation, practice and community learning. Indigenous knowledge maintained by tribal communities constitutes an important dimension of this knowledge heritage. The present research paper examines the educational relevance, cultural preservation potential and sustainable-development significance of indigenous knowledge among tribal communities in Surat District, Gujarat. The study particularly focuses on traditional ecological knowledge, medicinal plant knowledge, food practices, agriculture, sacred groves, cultural traditions and intergenerational knowledge transmission. Surat District provides an important setting for such research because the tribal regions of Mandvi, Mangrol and Umarpada contain documented examples of sacred groves and traditional plant knowledge. A recent study of these areas documented 13 sacred groves and 382 plant species belonging to 83 families, including 50 ethno-medicinal plants. These findings indicate the substantial ecological and cultural value of local knowledge systems.
The paper adopts a descriptive and qualitative research perspective based primarily on documented literature and Surat-specific studies. It examines the relationship between indigenous knowledge, formal education, cultural identity and sustainable development. The paper proposes an educational framework through which appropriate local knowledge can be documented, critically understood and incorporated into teacher education and school-based experiential learning. The study argues that educational institutions can contribute to the preservation of indigenous knowledge by involving community knowledge holders, developing local learning resources, encouraging field-based projects and creating responsible digital documentation initiatives. However, such integration must respect community ownership, informed consent and ethical principles. The paper concludes that the responsible integration of indigenous knowledge into Indian Knowledge Systems can strengthen culturally responsive education while contributing to biodiversity conservation, cultural continuity and sustainable community development in Surat District.

Imagine a farmer receiving timely advice on irrigation through an AI-powered application, a patient in a remote village getting an early diagnosis with the help of AI, or a judge using AI tools to quickly locate relevant precedents from thousands of case files. These examples show how Artificial Intelligence (AI) is quietly becoming a part of everyday governance in India. While it has the potential to improve efficiency, accuracy, and access to public services, it also raises important legal and ethical concerns that cannot be ignored.
India has made significant progress in adopting AI across sectors such as healthcare, agriculture, education, public administration, and the judiciary. However, the increasing use of AI has also brought challenges including algorithmic bias, privacy violations, deepfakes, copyright disputes, lack of transparency, and uncertainty regarding liability for AI-generated decisions. At present, India relies on a combination of the Digital Personal Data Protection Act, 2023, the Information Technology Act, 2000, and sector-specific policy guidelines to govern AI. Although these measures address certain aspects of AI regulation, they do not provide a comprehensive legal framwork to ensure accountability, explainability, safety, and effective remedies for individuals affected by AI systems.
This paper examines both the opportunities and the legal challenges associated with AI-driven governance in India. It also compares India's existing regulatory approach with the models adopted by the European Union and the United States to identify gaps in the present framework. The paper argues that policy guidelines and voluntary compliance alone are not sufficient to regulate high-risk AI applications. It therefore recommends the enactment of a dedicated Artificial Intelligence (Regulation and Governance) Act and the establishment of an independent Artificial Intelligence Governance Board of India (AIGBI) to encourage innovation while protecting the constitutional guarantees of equality, freedom, and privacy under Articles 14, 19, and 21 of the Constitution of India.

This study investigated the influence of large class size on the effective teaching and learning of Chemistry in secondary schools in Onitsha North Local Government Area of Anambra State, Nigeria. The study was guided by four purposes and four corresponding research questions, examining class size's influence on teaching, learning, classroom interaction, and teacher support during Chemistry lessons. A descriptive survey design was adopted. The population comprised 2,605 Senior Secondary Two (SS2) Chemistry students and 67 Chemistry teachers across sixteen public secondary schools in the study area. Using simple random sampling, ten schools were selected, yielding a sample of 180 students and 20 teachers (200 respondents in total). Data were collected using a structured, researcher-developed questionnaire (Cronbach's alpha = 0.85) and analysed using mean and standard deviation, with a criterion mean of 2.50 for acceptance. Findings indicated that while large class size showed a mixed, largely non-significant relationship with teaching practice itself (cluster mean = 2.33), it was found to negatively affect student learning (cluster mean = 3.32), classroom interaction (cluster mean = 2.48), and the level of individualised teacher support students received (cluster mean = 2.92). The study concludes that large class size is a substantive barrier to effective Chemistry instruction, particularly in relation to student learning outcomes and the quality of teacher-student engagement, and recommends policy attention to classroom capacity, teacher recruitment, and differentiated instructional strategies as viable responses.

The human microbiome represents a complex, dynamic ecosystem that co-evolved with the host to maintain physiological homeostasis. In classical homeopathy, the "Constitution" and "Susceptibility" define an individual's unique response to noxious influences. This paper presents a comprehensive framework mapping the human microbiota to homeopathic philosophy, pathology, and clinical therapeutics. We explore the microbiome as the biological substrate of susceptibility and analyze its specific alterations in neoplastic disorders alongside relevant homeopathic indications. Furthermore, this study delineates a protocol for case taking, remedy selection, potency determination, and dose repetition based on microbial history. Finally, we integrate modern bacteriology, mycology, and virology with the Miasmatic theory, while evaluating the roles of mother tinctures, nosodes, and conventional vaccines in the context of Antimicrobial Resistance (AMR).

Kenya has experienced stable economic growth over the past two decades, with Gross Domestic Product (GDP) growing at an average annual rate of approximately 5% prior to the COVID-19 pandemic and improving strongly thereafter. Despite this growth, a large proportion of Kenyan households keep on to experience poverty, food insecurity, unemployment, inadequate housing, and inadequate access to quality healthcare and education. This evident contradiction, commonly referred to as the paradox of growth, raises significant questions concerning the extensiveness and sustainability of economic growth in Kenya. The paper examines the socio-economic drivers that contribute to deteriorating household living conditions in spite of positive macroeconomic performance and identifies the policy gaps that perpetually hinder general development.
This paper draws upon existing literature, development theories, and empirical evidence from Kenya and comparable developing economies to study the relationship between economic growth and household welfare. Key socio-economic drivers include income inequality, unemployment and underemployment, inflation, regional disparities, rapid urbanization, corruption, weak institutional governance, climate-related shocks, public debt, and uneven access to useful resources. Even though Kenya has implemented various poverty reduction and social protection programmes, there are several challenges: inadequate policy coordination, weak accountability, and resource constraints have limited their effectiveness. Consequently, the benefits of economic growth remain concentrated among a relatively small segment of the population, while many households continue to experience declining living standards. The study concludes that economic growth alone cannot assure improvements in household welfare without planned policies aimed at promoting reasonable income distribution, employment creation, institutional effectiveness, and inclusive social protection.

This study aims to improve the competence of employees at the Center for Education and Training on Social Welfare (BBPPKS) Makassar in using Microsoft Excel through training designed based on a training needs analysis. This study employed a descriptive approach, with data collected through observation, interviews, questionnaires, and documentation. The participants consisted of 30 employees of BBPPKS Makassar. The research instruments were tested for validity and reliability using IBM SPSS Statistics, while the pre-test and post-test results were analyzed using descriptive statistics.
The results showed that Microsoft Excel training improved participants' competence in understanding the basic functions of Microsoft Excel, applying basic formulas, using IF and VLOOKUP functions, and completing administrative tasks with Microsoft Excel. This improvement was reflected in the higher post-test results compared to the pre-test results after the participants completed the training.
In conclusion, Microsoft Excel training was effective in improving the knowledge and skills of BBPPKS Makassar employees, thereby supporting the completion of administrative tasks more effectively and efficiently.

टिकाऊ विकास वह विकास हैं जो भविष्य की पीढियो की अपनी जरूरतों को पूरा करने की क्षमता से समझौता किये बिना वर्तमान पीढ़ी की जरूरतों को पूरा करे | सामान्य को प्राप्त करने या प्रदान करने कि प्रक्रिया, तर्क, और निर्णय की शक्तियों का विकास करना तथा परिपक्व जीवन के लिए खुद को या दुसरे को बौद्धिक रूप से तैयार करना ही शिक्षा हैं | टिकाऊ विकास के लिए शिक्षा, प्रत्येक व्यक्ति को एक टिकाऊ भविष्य का आकार देने के लिए आवश्यक ज्ञान, कौशल, अभिवृत्ति और मूल्यों को प्राप्त करने की अनुमति प्रदान करती हैं | शिक्षा के द्वारा ही व्यक्ति के अभिवृत्ति को परिवर्तित किया जा सकता हैं, जिसके कारण किसी भी उत्पादकता में सुधार लाया जा सकता हैं | शिक्षा के द्वारा संस्थागत सुधार, पाठ्यक्रम सुधार, और स्थानीय विशिष्ट संशाधन के विकास को प्राथमिकता दिया जाये तभी टिकाऊ विकास किया जा सकता हैं | टिकाऊ विकास के लिए विभिन्न संसाधनों का उपयोग कैसे किया जाये जैसे मुद्दों को शिक्षा के माध्यम से व्यक्ति को एक सही दिशा दिया जा सकता हैं | टिकाऊ विकास के लिए सहभागी शिक्षण और सीखने की विधियों की भी आवश्यकता होती हैं, जो विद्यार्थियों के व्यवहार को बदलने के लिए प्रेरित और सशक्त बनाती हैं | यदि टिकाऊ विकास के लक्ष्य को प्राप्त करना हैं तो हमारी वर्त्तमान जीवन शैली से सम्बंधित सभी स्तरों पर शिक्षा के सभी हितधारको के दृष्टिकोण की आवश्यकता होगी | प्रस्तुत लेख में अध्येता ने टिकाऊ विकास के अर्थ, आवश्यकता, टिकाऊ विकास के ल्लिये शिक्षण, अभिवृत्ति एवं इसकी विशेषता, अभिवृति के घटक, एवं इसके द्वारा किन उद्देश्यों की पूर्ति होती हैं और अभिवृत्ति के कार्य एवं प्रभावित करने वाले कारक, मापनी सरंचना तथा मापनी सरंचना तैयार करते समय किन –किन बातो का ध्यान रखना चाहिए के बारे में चर्चा किया हैं |

This study determined the effectiveness of professional development programs and the pedagogical practices of public secondary school teachers in Buenavista, Guimaras. It also examined differences in the two variables when respondents were classified according to age, educational attainment, position, grade level taught, and school location, as well as their significant relationship. A quantitative descriptive-correlational design was employed. The respondents were 151 teachers from Buenavista 1 and Buenavista 2. Data were gathered using a validated and reliability-tested questionnaire and analyzed through frequency, percentage, mean, Mann–Whitney U test, Kruskal–Wallis H test, and Pearson’s product–moment correlation at the .05 significance level. Results showed that professional development programs were rated Very High, with an overall mean of 4.32, indicating that they consistently provided relevant and effective opportunities for improving teachers’ knowledge, skills, and instructional competence. Teachers’ pedagogical practices were Mostly Practiced, with an overall mean of 2.52, showing frequent application of instructional delivery, classroom management, learner engagement, assessment, differentiation, and learner-centered approaches. No significant differences were found in professional development effectiveness or pedagogical practices across all profile variables. A negligible negative and nonsignificant relationship existed between professional development effectiveness and pedagogical practices (r = −.033, p = .685). The findings implied that favorable perceptions of professional development did not automatically correspond to more frequent classroom application. Schools should therefore strengthen follow-up coaching, mentoring, collaborative learning, reflective practice, technology integration, differentiated instruction, and monitoring of training transfer through classroom observations, instructional artifacts, and learner evidence to ensure sustained, contextualized, and measurable improvements in everyday teaching.

This study determined the school climate, school heads’ instructional leadership, and teachers’ performance in the Schools Division of Antique, Philippines, during School Year 2025–2026. The study involved 326 randomly selected public secondary school teachers. A descriptive-correlational research design was employed. The dependent variables examined were school climate, school heads’ instructional leadership, and teachers’ performance, while age, position, highest educational attainment, length of teaching experience, and school location as independent variables. Data on school climate and instructional leadership were gathered through validated questionnaires while teachers’ performance was obtained from the Classroom Observation Tool (COT) ratings for the first and second quarters of School Year 2025–2026. Data were analyzed using frequency, percentage, mean, t-test, Analysis of Variance (ANOVA), and Pearson’s correlation at .05 level of significance through SPSS. Findings revealed that school climate was “Very Highly Favorable,” the level of school heads’ instructional leadership was “Very High,” and teachers’ performance was “Outstanding.” No significant differences in school climate were found when respondents were classified as to age, but significant differences emerged when respondents were classified as to position, highest educational attainment, teaching experience, and school location. Similarly, the level of school heads’ instructional leadership showed no significant differences when the respondents were classified as to age and teaching experience but differed significantly when respondents were classified as to position, highest educational attainment, and school location. Teachers’ performance did not significantly differ by age and highest educational attainment but varied significantly by position, teaching experience, and school location. Moreover, significant relationships existed among school climate, school heads’ instructional leadership, and teachers’ performance.

This study examined school heads’ transformational leadership and supervisory engagement and their influence on school climate in the Schools Division of Antique, Philippines, during School Year 2025–2026. The study involved 365 randomly selected public elementary school teachers. The independent variables were age, highest educational attainment, school size, school location, and type of school, while the dependent variables were school heads’ transformational leadership, supervisory engagement, and school climate. Data were gathered using validated questionnaires. Frequency, percentage, mean, t-test, Analysis of Variance (ANOVA), and Linear Regression Analysis were used for data analysis at the .05 level of significance through SPSS. Results revealed that respondents assessed the level of school heads’ transformational leadership as Very High, the extent of supervisory engagement as Very Great, and school climate as Very Highly Favorable when taken as a whole and when classified as to identified variables. Significant differences in the level of school heads’ transformational leadership were found when respondents were classified according to highest educational attainment and school size but not age, school location, or type of school. The extent of school heads supervisory engagement differed only when respondents were classified as to their highest educational attainment. School climate significantly differed according to respondents’ highest educational attainment, school size, and type of school but not age or school location. Finally, school heads’ transformational leadership and supervisory engagement did not significantly influence school climate.

This study determined the governance, accountability mechanisms, and policy implementation in learning institutions in the Schools Division of Antique, Philippines, during the school year 2025–2026. The study involved 218 randomly selected school heads of public elementary schools. The study examined governance, accountability mechanisms, and policy implementation as dependent variables, with age, highest educational attainment, length of administrative experience, school location, and type of school as independent variables. Data were gathered through validated questionnaires based on previous studies on governance, accountability mechanisms, and policy implementation. Descriptive statistics such as frequency, percentage, and mean were used, while t-test, Analysis of Variance, and Pearson’s r were employed as inferential statistical tools at .05 level of significance. Data analysis was conducted using the Statistical Package for the Social Sciences (SPSS). Results revealed that governance in learning institutions was described as “Advancing,” while extent of policy implementation was “Very Great.” Accountability mechanisms were manifested through the presence of consequences for policy non-compliance, systematic monitoring of school projects and programs, and regular review of school performance reports for decision-making. No significant differences were found in the status of governance, accountability mechanisms, and extent of policy implementation in learning institutions when respondents were grouped according to age, highest educational attainment, length of administrative experience, and school location. However, significant differences existed when classified according to type of school. Furthermore, significant relationships were found among governance, accountability mechanisms, and policy implementation.

This study examined the teachers’ professional commitment, teaching competence and readiness in curriculum reforms in the Schools Division of Antique, Philippines, during the 2026-2027 school year. The study involved 326 teachers from public secondary schools in the division and employed a descriptive-correlational research design. The study included sex, highest educational attainment, length of teaching experience, position, school size, and school location as independent variables and teachers’ professional commitment, teaching competence, and readiness in curriculum reforms as dependent variables. Data were collected through a validated questionnaire and statistical analysis was conducted using SPSS. Statistical tools were frequency, percentage, mean, t-test, Analysis of Variance (F-test) and Pearson’s r) with .05 significance level. Findings revealed that, overall, the level of teachers’ professional commitment, teaching competence, and readiness in curriculum reforms was “Very High” when classified according to sex, highest educational attainment, length of teaching experience, position, and school location. Significant differences were noted in the level of teachers’ professional commitment when classified according to length of teaching experience and school location while no significant differences were noted in the level of teachers’ professional commitment when classified according to sex, position, and highest educational attainment. Similarly, significant differences were noted in the level of teachers’ teaching competence when classified according to length of teaching experience, highest educational attainment, and school location while no significant differences were noted when classified according to sex and position. On the other hand, no significant differences were noted in the level of teachers’ readiness in curriculum reforms when classified according to sex, length of teaching experience, position, highest educational attainment and school location. Finally, significant relationships were found between teachers’ professional commitment and teaching competence and teachers' professional commitment and readiness in curriculum reforms while no significant relationship between teachers’ teaching competence and readiness in curriculum reforms.

This paper examines the history, adoption, and symbolic significance of the protest music slogan “Solidarity Forever, For the union makes us strong” by the University Academic Staff Union (UASU) and Kenya Universities Staff Union (KUSU) in Kenya today, with the recent one being September,2025, involving 42 public universities and their constituent Colleges. The industrial action lasted for nearly seven weeks disrupting teaching and learning in higher education institutions. Drawing on historical accounts, media reports, and union archives, the paper analyzes how this iconic labour anthem has been appropriated in Kenyan higher education industrial disputes. It highlights the dual role of protest music in unifying striking staff and projecting their demands related to salaries, allowances, and the implementation of Collective Bargaining Agreements (CBAs). to the public and authorities. The paper also reflects on the limited use of protest music in ensuring structural change. Findings suggest that the slogan strengthens collective identity and morale, amplifies public visibility, and situates Kenyan staff struggles within a transnational labour heritage. However, over-reliance on symbolic performance risks diminishing material effectiveness.

The rapid development of Artificial Intelligence, especially Generative AI, has brought about major changes in the way literary, artistic and other creative works are created. This has raised important legal questions under Copyright Law regarding authorship, ownership and originality. This research examines whether the current Indian Copyright Law is sufficient to provide clear answers to these questions regarding works created by or with the help of AI. This study uses Doctrinal Legal Research Methodology and analyses relevant legal provisions, judicial decisions and secondary legal literature. The research focuses on the legal framework related to computer-generated works, first ownership and originality. The research reveals that Indian Copyright Law provides a legal basis for computer-generated works, but there are still some uncertainties in the context of highly autonomous generative AI systems. This paper recommends giving importance to meaningful human creative contribution and creating clearer legal and policy guidelines on copyright protection.

The rapid development and use of AI raises important questions about the protection of personal data and individual privacy in India. AI systems often rely on large amounts of personal data for training, analysis and decision-making. This can lead to problems such as excessive data collection, lack of informed consent, data breaches, profiling, surveillance and lack of transparency. The main objective of this research article is to study the main challenges of data privacy arising from the use of AI in India, as well as to analyze the current legal framework for the protection of personal data and privacy. This study mainly uses doctrinal, legal research methodology, in which laws, judicial decisions, government materials, as well as other relevant secondary sources have been studied. The focus is particularly on the constitutional rights to privacy, as well as the Digital Personal Data Protection Act 2023. This study shows that despite the significant legal framework for data protection in India, some new challenges are emerging with the continued development of AI. Hence, effective consent, data security, transparency, and accountability are needed to maintain a balance between privacy and technological innovation.

Artificial Intelligence, AI, plays an important role and is rapidly working in the creation of literature, art, speech, music and other creative works, which in the present day, with the development of AI, with little or no significant human intervention, are capable of creating various types of creations, thus raising many questions about the traditional, which is copyright law, based on authorship and human creation.
Under the Indian Copyright Act, 1957, the concept of authorship is closely linked to things created by AI. Section 2(D) specifically addresses computer-generated works, which identify the person responsible for the creation of the work and suggest its authorship, but in the present era, in the context of rapidly evolving AI systems, the Indian Copyright Act, 1957, is still unable to make a clear provision, because the AI that creates it may be based on various levels of human intervention, algorithms, training data, etc.
In the context of AI-generated works, the very essence of determining authorship under Indian Copyright Law has also become a major challenge, as it becomes difficult to attribute authorship to a work where human input is minimal. On the other hand, it is also unfair to grant copyright to a person who creates the work with his own skills and on the basis of this, other persons may, at such times, take advantage.
This research article will examine how the current copyright legal framework in India is able to determine authorship, and analyze the implications for AI-generated creations, focusing primarily on the relationship between human creativity and AI-generated creations, the legal understanding of computer-generated creations, and the new challenges and issues arising related to authorship and ownership based on them.
Based on this article, it will be seen that the current legal framework provides a primary basis for AI-generated creations, but does not provide a basis for fully addressing all the issues that arise in the context of rapidly evolving AI, thus requiring a clear and robust legal framework that protects human creativity, while also promoting the responsible development and use of AI.

The Protection of Children from Sexual Offences Act, 2012 (POCSO) is India's principal special legislation for protecting children from sexual assault, sexual harassment, and sexual exploitation. Its defining statutory feature is the use of an age-based threshold: a "child" is a person below eighteen years. This bright-line rule creates a strong protective framework, but it also produces a difficult legal and policy question in cases involving older adolescents, particularly those between sixteen and eighteen years, where the factual background may include a close-in-age relationship, an adolescent romance, or an allegation arising from family or social opposition.
This paper examines the statutory framework of POCSO, the 2019 amendment, the POCSO Rules, 2020, and recent judicial developments. It gives special attention to the Supreme Court's 2024 judgment in In Re: Right to Privacy of Adolescents, which rejected the idea that a court can treat consensual sexual activity with a minor as legally outside POCSO merely because the relationship is described as romantic. The paper also considers the Supreme Court's January 2026 decision in State of Uttar Pradesh v. Anurudh, in which the Court directed that its judgment be circulated for consideration of legislative steps including a possible "Romeo-Juliet clause" for genuine adolescent relationships.
The paper argues for a carefully designed graduated approach rather than an unrestricted lowering of the age of protection. The author's proposed model is that children below sixteen should continue to receive uncompromising statutory protection, while the sixteen-to-eighteen age group could be subject to a narrowly defined close-in-age exception or judicial assessment in genuine, non-exploitative cases. Coercion, force, grooming, trafficking, abuse of authority, significant age disparity, and exploitation should remain fully punishable. The aim is to preserve the protective purpose of POCSO while reducing the risk of treating every adolescent relationship as equivalent to predatory sexual abuse.

This study examined the influence of logistics management on the performance of Sterling Oil Exploration and Energy Production Company Ltd. (SEEPCO), Akwa Ibom State, Nigeria. The study was motivated by the need to improve organizational performance through effective management of logistics activities, particularly transportation and inventory control. The study adopted a cross-sectional survey research design. The population comprised 46 management staff of SEEPCO, and a census sampling technique was employed, resulting in a sample size of 46 respondents. Primary data were collected through a structured questionnaire designed using a five-point Likert scale. Forty questionnaires were correctly completed and retrieved, representing an 87.0% response rate. The reliability of the research instrument was established using Cronbach’s alpha, with coefficients of 0.771 for transportation, 0.824 for inventory control, and 0.814 for organizational performance, while the overall reliability coefficient was 0.841, indicating satisfactory internal consistency. Data were analysed using descriptive statistics and simple linear regression at a 0.05 level of significance. The findings revealed that transport management has a significant positive influence on the performance of SEEPCO, with a correlation coefficient of R = 0.907 and coefficient of determination of R² = 0.823. The regression model was statistically significant, F(1, 39) = 1126.259, p < .001, while transport management had a significant positive coefficient (β = 0.907, p < .001). Similarly, inventory control had a significant positive influence on organizational performance, with R = 0.893 and R² = 0.797. The regression model was statistically significant, F(1, 39) = 953.111, p < .001, and inventory control demonstrated a significant positive effect on performance (β = 0.893, p < .001). Based on these findings, the study concludes that effective logistics management, particularly transport management and inventory control, significantly enhances organizational performance at SEEPCO. The study recommends that SEEPCO optimize fleet utilization through regular vehicle assessments, route planning, fleet management systems, and preventive maintenance, while also implementing automated inventory tracking technologies such as barcode or RFID systems to improve inventory accuracy, reduce stockouts, and enhance operational efficiency.

This study examined the influence of business intelligence on the competitive advantage of food and beverage firms in South-West Nigeria. The study specifically investigated the influence of knowledge sharing and data management systems on the competitive advantage of selected food and beverage firms. The study adopted a survey research design. The population comprised 14,230 managers, senior staff, and junior staff of selected food and beverage firms, namely Nestlé Nigeria Plc, Cadbury Nigeria Plc, Guinness Nigeria Plc, Nigeria Breweries Plc, and Flour Mills of Nigeria Plc. Using the Krejcie and Morgan sample size determination approach, a sample size of 370 respondents was obtained. Proportionate allocation was used to distribute the sample across the selected firms, while simple random sampling was adopted for the selection of respondents. Data were collected through a structured questionnaire using a five-point Likert scale, and 362 completed questionnaires were retrieved and used for analysis. Descriptive statistics were employed to answer the research questions, while simple linear regression was used to test the hypotheses at a 0.05 level of significance. The findings revealed that knowledge sharing has a significant positive influence on competitive advantage of food and beverage firms in South-West Nigeria (R = .871, R² = .759, F = 1138.559, p < .05). The findings further showed that data management system has a significant positive influence on competitive advantage (R = .872, R² = .760, F = 1144.388, p < .05). The study concluded that effective knowledge sharing and data management systems are important business intelligence capabilities for strengthening the competitive advantage of food and beverage firms in South-West Nigeria. The study recommended that firms should promote a culture of knowledge sharing through effective internal collaboration platforms and communication mechanisms, while also investing in advanced data management systems to facilitate efficient data collection, storage, retrieval, and utilisation for data-driven strategic decision-making.

Food price volatility in conflict-affected regions of Nigeria poses a critical threat to food security, yet existing forecasting models struggle to capture the complex, non-linear dynamics and sudden shocks characteristic of these environments. This study proposes a novel hybrid forecasting framework—VMD-IWOA-LSTM—integrating Variational Mode Decomposition (VMD) with an Improved Whale Optimization Algorithm (IWOA) to optimize a Long Short-Term Memory (LSTM) network. The model is applied to staple food price data (maize and rice) from Nigeria's conflict-affected regions, incorporating external drivers including conflict events, inflation rates, and fuel prices. Experimental results on monthly price data demonstrate that the proposed VMD-IWOA-LSTM model achieves superior predictive performance: for maize, RMSE of 0.5926 and MAPE of 1.95%; for rice, RMSE of 0.5518 and MAPE of 7.55%. The model reduces RMSE by 21.83% to 56.93% compared to the next best decomposition-based LSTM models and significantly outperforms standard LSTM and WOA-LSTM benchmarks. These results validate the framework's robustness in capturing conflict-driven volatility spikes. The paper's primary contribution is the development of a Volatility-Driven Insecurity Index (VDII)—an operational early warning tool for humanitarian agencies and policymakers to anticipate and mitigate food insecurity in Nigeria.

Low learning quality and weak initial Academic Ability Test (Tes Kemampuan Akademik/TKA) results at SMP Negeri 1 Kendal (State Junior Secondary School 1, Kendal), Indonesia, were traced to slow, manual academic supervision with little concrete modeling for teachers. The 2024 Education Report Card recorded a Learning Quality score of 60.00 (Moderate), and an initial TKA Mathematics simulation of only 32.10. This paper describes the implementation and documented impact of the DITA-AI Strategy (Data-Driven, Insight-Generating with AI, Targeted Coaching, Action & Assessment), applied as part of the KAPAS GULA Module 125 teacher-capacity program in Kendal Regency. The study is a single-school, descriptive best-practice case study led by the school principal, comparing baseline (2024) and post-intervention (2025-2026) documentation, without a comparison group or inferential statistical testing. The strategy combined data-based diagnosis, structured prompt-engineering on generative-AI platforms to draft teaching modules and higher-order-thinking-skill (HOTS) item banks, and three-stage clinical supervision under a teacher-as-final-validator principle. Documented changes include a reduction in pre-supervision analysis time from 3-4 days to a principal-estimated 15-30 minutes per teacher; a rise in teachers delivering HOTS-based instruction from 35.00% to 87.50%; an increase in the Learning Quality indicator from 60.00 to 70.83; and higher mean TKA scores in Indonesian Language (65.60) and Mathematics (41.84), both above the national average. These findings are consistent with the strategy's effectiveness, but because the design is a single-site descriptive account without a control group, causal claims cannot be statistically confirmed and require further quasi-experimental or multi-school verification.

Due to their varied phytochemical makeup and possible cosmetic advantages, herbal compounds have drawn more attention in lip-care formulations. The development of various lip-care products can benefit from the moisturizing, antioxidant, calming, anti-inflammatory, antibacterial, and natural colouring qualities of various herbs and plant-derived compounds. The significance of herbal substances in lip care, their key characteristics, and their uses in different formulations are all outlined in this paper. It also covers the benefits and drawbacks of using herbal compounds, such as stability issues, standardization issues, and variations in phytochemical makeup. Additionally highlighted are recent advancements and potential future improvements in the application of sophisticated delivery technologies and enhanced scientific assessment of herbal compounds. All things considered, herbal substances are promising components for the creation of multipurpose, organically produced lip care solutions.

This study was conducted to find teachers' challenges in teaching reading. It followed the descriptive-correlational research design and was conducted in the elementary schools of Dangcagan District, Division of Bukidnon, SY 2023-2024. The respondents were all public school teachers. Complete Enumeration was used as a sampling procedure. A researcher-made survey questionnaire was used to gather data. The data were analyzed using descriptive statistics such as frequency count, percentage, mean, standard deviation, t-test of significant difference, and Pearson r Product Moment Correlation Coefficient.
The research yielded the following findings: There was a Large Extent of challenge sexperienced by teachers in teaching reading. The interventions implemented to address the challenges experienced by teachers in teaching reading after the COVID-19 lockdown were rated as Effective. Most of the learners' level of oral reading skills in Dangcagan District, Division of Bukidnon, SY 2023-2024 were Instructional Readers.
Four factors, including student assistance, technology utilization, diverse reading resources, and parental partnerships, exhibit a highly significant positive correlation with students' reading proficiency at a high level of statistical significance. These findings indicate that these interventions have a beneficial effect on pupils' reading proficiency.
In light of the findings and conclusions, the following recommendations are offered: Teachers need specific treatments and instructional techniques to assist these learners in enhancing their oral reading abilities. Teachers may prioritize assisting children, conducting lessons in small groups or individually, utilizing technology, providing reading materials, and enhancing collaborations with parents or guardians.
Teachers should concentrate on implementing specific tactics to assist these students. School heads may commit resources towards student support programs, facilitate the efficient utilization of technology in reading instruction, provide access to a wide range of reading materials, and foster active participation of parents in promoting their children's reading development.

This study examined the impact of computerized media on students’ English language learning in public secondary schools in Mbeya City, Tanzania. The study adopted a quantitative approach and descriptive research design to examine the perceived impact of computerized media on students’ English language learning. The study employed purposive sampling to select Form Three students and four public secondary schools that utilized computerized media. A sample of 242 out of 612 Form Three students was selected from four public secondary schools using the Yamane formula, whereby simple random sampling was used to select the number of students from each selected school. Grounded in Mayer’s Cognitive Theory of Multimedia Learning, data were collected through a semi-structured questionnaire and analyzed using descriptive statistics, particularly frequencies and percentages. Findings revealed that computerized media had a significant positive impact on students’ English language learning by enhancing their language skills and lesson engagement. However, significant challenges constrained effective utilization of computerized media, including poor internet connectivity, unreliable electricity, limited digital skills, authority restrictions and limited access to devices. The study concludes that computerized media significantly improves English language learning; however, their educational potential depends on reliable digital infrastructure, adequate devices and students’ digital competencies.

The present research focuses on the formulation and evaluation of nanocrystals of Losartan Potassium to enhance its solubility and bioavailability, utilizing the antisolvent precipitation technique. Losartan Potassium, an antihypertensive agent, belongs to Biopharmaceutical Classification System (BCS) Class II drugs, characterized by low aqueous solubility and high permeability. To overcome its solubility limitations, nanocrystals were prepared using a bottom-up antisolvent precipitation method, employing stabilizers to prevent agglomeration and promote uniform particle size. The prepared nanocrystals were characterized by particle size analysis, zeta potential, scanning electron microscopy (SEM), differential scanning calorimetry (DSC), and X-ray diffraction (XRD) to assess their physical and chemical properties. In vitro solubility and dissolution studies demonstrated a significant improvement in the solubility and dissolution rate of the nanocrystals compared to the pure drug. The results indicate that the antisolvent precipitation technique is an effective and scalable approach for enhancing the solubility of poorly water-soluble drugs like Losartan Potassium.

This study examined Monetary Incentive and the performance of selected small and medium scale enterprise, Abuja Nigeria. Specifically, the study examined the effect of Wages and Bonus on performance of selected small and medium scale enterprise in Abuja Nigeria. A survey research design was adopted for the study. The research focused on employees of selected small and medium scale enterprise, Abuja Nigeria, with a total population of 653 employees. The sample size of 251 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. Regression was employed to test the formulated hypotheses, using the Statistical Package for the Social Sciences (SPSS, version 25). The study found that Wages and Bonus, all statistically had significant positive effects on the performance of selected small and medium scale enterprise, Abuja, Nigeria. The study concluded that, competitive and well-structured compensation serves as a key motivator, employee commitment, productivity, and overall performance of selected small and medium scale enterprise, Abuja. Based on these findings, it was recommended that the management of selected small and medium scale enterprise Abuja, Nigeria should ensure that wages and bonus are aligned with organisational standards to improved overall performance.

Large language models (LLMs) are increasingly used in educational applications for tutoring, question generation, formative feedback, summarization, programming assistance, and learning support. However, model performance is strongly influenced by how tasks are specified through prompts. This paper presents a comparative framework for evaluating prompt engineering techniques in educational scenarios. Five prompting strategies—zero-shot, few-shot, structured role-and-constraint prompting, reasoning-oriented prompting, and retrieval-augmented/context-grounded prompting—are compared using a common evaluation protocol. The framework evaluates response correctness, relevance, pedagogical alignment, groundedness, hallucination tendency, consistency, and prompt efficiency. Rather than claiming universal superiority of one technique, the study analyzes task-dependent trade-offs and proposes a reproducible evaluation matrix for educational LLM systems. Recent literature indicates that simple prompting remains useful for low-complexity tasks, demonstrations and structured prompts can improve task alignment, reasoning-oriented prompting can support complex problem solving, and retrieval-grounded approaches can improve grounding when authoritative course material is available. The paper concludes with recommendations for selecting prompting strategies according to educational task characteristics and identifies directions for automated prompt optimization and human-centered evaluation.

The investigation was based on using rice husk ash as filler(reinforcement) with 0%, 5%, 10%, 15%, 20% and 25% by weight of the LDPE binder with average melting point of 1100C (sachet water nylon). RHA was obtained by burning the Rice husk at 6000C for 2 hours and later grinded after cooling and sieved using 300micron sieve to obtained a very finer material. A mix ratio of 1:4 was adopted in producing the pavement block. The laboratory tests conducted for both the materials and pavement blocks include specific gravity, particle size distribution (sieve analysis), chemical composition of RHA, temperature resistivity test, water absorption and chemical resistivity test. The specific gravity of the fine aggregate and RHA was 2.64 and 2.17 respectively, with the fine aggregate classified under zone II. The sieve analysis confirmed the fine aggregate as well graded and suitable for manufacturing. Key findings include a significant improvement in compressive strength with optimal RHA dossage at 15%, leading to an enhancement of up to 33% compared to conventional blocks. Also, the Temperature resistance analysis indicated that blocks with 15% RHA retained higher compressive strength than controls after 1 and 2 hours at 50°C, though extended exposure led to strength reductions or even collapse. The highest notable water absorption was 4.18% at control, decreases as the content of RHA was increases. The study further revealed that RHA enhances chemical Resistance, with blocks showing increased in compressive strength when exposed to hydrochloric acid and sodium hydroxide, improving by (18N/mm2 and 19N/mm2) 20% and 31.6% respectively compared to controls. These findings underscore the potential of RHA as a sustainable additive in pavement block manufacturing, enhancing durability under various environmental conditions.

Market failures occur when goods and services alocated through market mechanisms results in inefficient outcomes that fail to maximize social welfare.These failures may arise from various sources,which includes externalities publicgoods,information asymmetry,monopoly power,and factor immobility.Market failure have disadvantages for economic development because they distort how resources are allocated,reduces production,deject investment,and enhance inequality.While competitive markets are generally expected to allocate resources efficiently,numerous studies point out that market imperfections often prevent economies from achieving best possible outcomes(Stiglitz,2000; Mankiw,2021).This article examines the impact of market failures on economic development by reviewing theoretical and empirical literature from different scholars. The article is based on welfare, economics, Public Goods Theory, Externality Theory information Asymmetry Theory,and Institutional Theory to explain how market imperfections affect growthand development. The article reveals that negative externalities, monopolistic practices,insufficient provision of public goods, and information asymmetry contribute extensively to inefficiencies in how goods are produced and sfects the level of the production and consumption. These, distortions avertinnovation level which leads to low level of ,productivity,andaggravate poverty and inequality, particularly in global south economies(Acemoglu& Robinson2012,Rodrik2018).This article concludes that there must be effective government intervention necessary to correct market failures and promote sustainable growth.Policiesintend at regulating monopolies, improving information transparency,strengthening institutions, and investing in public goods which should be an essential goal for achieving long-term economic development and social welfare.The article contributes to existing literature by integrating theoretical perspectives and pragmaticproof to provide a complete understanding of the relationship between market failures and economic development.

Fungal infections affecting the skin, hair, and nails are among the most prevalent infectious diseases worldwide and continue to increase due to immunosuppression, antimicrobial resistance, and environmental factors. Conventional antifungal therapies often suffer from toxicity, resistance, and limited efficacy. Therefore, the development of novel antimicrobial systems is essential. Silver nanoparticles (AgNPs) have emerged as potent antimicrobial agents because of their broad-spectrum activity, multi-target mechanisms, and low incidence of microbial resistance. The present study aimed to synthesize silver nanoparticles using Aconitum ferox root extract through a green synthesis approach and formulate them into a topical gel for antifungal application. Root extracts were prepared using hydroalcoholic extraction after defatting with petroleum ether. Phytochemical screening confirmed the presence of alkaloids, flavonoids, tannins, phenols, saponins, proteins, carbohydrates, glycosides, and diterpenes. Total flavonoid and phenolic contents were estimated using aluminium chloride and Folin-Ciocalteu methods, respectively. AgNPs were biosynthesized using varying concentrations of silver nitrate and characterized by UV-Visible spectroscopy, FTIR, particle size analysis, zeta potential, and entrapment efficiency studies. The nanoparticle-loaded gel was prepared using Carbopol 940 and evaluated for physicochemical properties including pH, spreadability, viscosity, extrudability, drug content, and in vitro drug release. Antifungal activity was assessed by the well diffusion method using potato dextrose agar medium. The formulated AgNP gel demonstrated promising antifungal activity, suggesting that Aconitum ferox-mediated silver nanoparticles may serve as an effective topical antifungal delivery system.

Background: Immunomodulation maintains immune homeostasis by regulating innate and adaptive responses; dysregulation of immunomodulation contributes to autoimmune and inflammatory diseases. Homoeopathic medicines may influence cytokines, immune cells, inflammation and gene expression.
Objective: To review the evidence on immunomodulatory effects of homoeopathic medicines from experimental, preclinical and clinical studies.
Methods: We conducted a PRISMA 2020-based review of peer-reviewed in vitro, animal and clinical studies that assessed outcomes including cytokines, immune cells, inflammatory markers, antibodies and gene expression.
Results: Evidence suggests possible regulation of cytokines, restoration of Th1/Th2 balance, activation of immune cells and reduction of inflammation and oxidative stress. Animal and in vitro studies showed increased immunity and alterations in inflammatory mediators. Clinical data demonstrated symptomatic and biomarker improvement.
Conclusion: Experimental findings suggest immunomodulatory potential, but robust clinical trials are needed.

Maize production plays a critical role in ensuring food security, economic growth, and agricultural sustainability in developing countries, where maize is a major staple crop. Yet maize yield prediction remains difficult because of the complex interactions among soil characteristics, rainfall, temperature, and other climatic and management variables. This study develops and compares five supervised machine learning models, Linear Regression, Support Vector Regression (SVR), Random Forest Regression, Artificial Neural Network (ANN), and Extreme Gradient Boosting (XGBoost), for predicting maize yield from soil nutrient composition and weather data collected from farms in Owo, Ondo State, Nigeria, between 2020 and 2025. Model inputs included soil nitrogen, phosphorus, potassium, pH and organic carbon, alongside rainfall, temperature and relative humidity; data were cleaned, Min-Max normalised, and split 80:20 into training and test sets. Model performance was evaluated using Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and the coefficient of determination (R²). Linear Regression achieved an R² of 0.72; SVR, 0.83; Random Forest, 0.88; ANN, 0.93; and XGBoost, the best-performing model, an R² of 0.95 with the lowest error values (MAE = 0.19, RMSE = 0.29). Feature-importance analysis identified rainfall (28%) and nitrogen (25%) as the strongest predictors, followed by temperature, phosphorus, potassium and humidity. These results confirm that ensemble and deep-learning approaches outperform conventional statistical methods for maize yield forecasting and that soil fertility and rainfall jointly determine productivity. The study recommends integrating the model into a farmer-facing decision-support tool and expanding data collection to strengthen future predictions.

English communication competence is an essential component of graduate employability, particularly in engineering education, where technical knowledge must be communicated effectively in academic and professional contexts. Rural engineering students may face challenges due to limited exposure to English-speaking environments, fewer opportunities for practice, and lack of confidence. This study examines the communication competence of rural engineering students and explores its relationship with employability. It focuses on speaking, listening, reading, writing, pronunciation, vocabulary, presentations, group discussions, interviews, and professional writing. The study adopts a mixed-methods approach using a structured questionnaire, classroom observations, and interviews with English faculty. It also investigates barriers such as communication anxiety, vocabulary limitations, mother-tongue influence, and inadequate opportunities for authentic interaction. The study proposes an employability-oriented framework integrating needs analysis, language practice, workplace tasks, feedback, and reflective assessment to strengthen students’ communication competence and improve their readiness for professional employment.

This study investigated how combining educational technology (EdTech) with traditional laboratory equipment affects STEM competency among secondary school students in Nigeria. Conducted over 12 weeks, it involved 227 senior secondary students (112 control; 115 experimental) from six government schools in Maiduguri, Borno State, using a quasi-experimental design with Structural Equation Modelling (SEM). The experimental group used virtual laboratory simulations, digital microscopes, augmented reality modules, smart sensors, and data-logging devices alongside conventional science equipment. Structural relationships among EdTech integration, laboratory usage, student engagement, science learning outcomes, and STEM competency were examined across six hypotheses. Results showed excellent model fit (CFI = 0.963; RMSEA = 0.048). EdTech integration strongly predicted student engagement (β = 0.71) and science learning outcomes (β = 0.43), while laboratory equipment usage predicted learning outcomes (β = 0.64) and engagement (β = 0.38). Both mediators significantly predicted STEM competency. Experimental group post-test scores Biology (78.6%), Chemistry (76.2%), and Physics (80.1%), notably exceeded control group scores.

Secondary education is a developmental stage of education that shapes the perception and knowledge of children all over the world including Nigeria, and the management of safety and health in schools is essential for creating safe and healthy learning environments for learners and staff. The aim of this research was to assess the health and safety practices in selected secondary schools in Akwa Ibom State, Nigeria. The study used a cross-sectional survey design. Purposive sampling was used in the selection of schools based on their field of study (science and technology), location (urban), size (large), and type (public and private). The respondents used were science and technology teachers and students, and they were selected through simple random sampling. Copies of safety awareness questionnaires were used to collect data which were analyzed using descriptive statistical techniques which included frequencies and percentages presented in tables. Overall, the study indicated a reasonable positive level of awareness among students and teachers regarding health and safety hazards in school. Revealed significant gaps in safety practices within schools, found an overwhelming consensus among students and teachers regarding the importance of health and safety in schools, and identified the numerous benefits of implementing effective safety practices in schools. The data interpretation showed that there is a significant positive correlation between the level of awareness among the teachers and students about the health and safety hazards and the implementation of effective safety practices within schools. It was recommended that there is a need to prioritize safety as a core value, providing comprehensive training to all staff and students. Encouraging a safety culture, involving students in decision-making, and conducting regular safety audits are essential to identifying and addressing potential hazards. Developing and implementing comprehensive emergency response plans, particularly for students with special needs, is also crucial. Policymakers to allocate enough funding to support safety programs, create clear guidelines and regulations, and encourage collaboration between stakeholders.

This paper presents the development of a robust H∞ Power System Stabilizer (PSS) using a Linear Matrix Inequality (LMI) approach to enhance the dynamic stability of the Nigerian 330 kV transmission network. A detailed 54-bus, 13-generator multi-machine model was developed using real network data and linearised around its operating point obtained through Newton–Raphson load flow analysis. The H∞ control problem was formulated using a mixed-sensitivity framework and solved as an LMI optimisation problem to obtain a centralised dynamic output-feedback controller. The controller was evaluated under small-signal disturbances using time-domain and frequency-domain analyses. Simulation results demonstrated significant improvements in system damping, voltage regulation, and oscillation suppression. The minimum damping ratio was substantially increased, rotor speed deviations were reduced from ±0.05 p.u. to below ±0.02 p.u., and settling times were improved to approximately 3 to 5 seconds. Furthermore, voltage violations observed in the uncompensated network were effectively mitigated, with most bus voltages maintained within acceptable operating limits. The results confirm that the LMI-based H∞ PSS provides superior robustness and stability enhancement compared with conventional stabiliser approaches, making it a viable solution for improving the reliability and operational security of the Nigerian interconnected power system.

Ethnomedicinal plants are medicinal plants used by the indigenous communities, tribes or local inhabitants for therapeutic purposes. They’re part of traditional medicine passed down through generations, relaying on traditional knowledge and cultural practices rather than scientific methods.Jaundice is a prevalent symptom of liver dysfunction. It is one of the medical conditions related to liver dysfunction, hemolytic disorders, and biliary obstruction, where the skin and eyes turn yellow due to elevated levels of bilirubin in the blood.Ethnomedicinal plants have been being used for centuries to manage jaundice, offering a promising alternative or adjunct therapy even as conventional treatments are available. Traditional formulations typically combine multiple plant species or specific plant parts, enhancing efficacy and reducing bilirubin levels. From the bitter root of “Picrorhizakurroa” to the anti-oxidant rich leaves of “Phyllanthus amarus”, these plants have been being used for centuries to soothe the liver.Notable species with reported therapeutic efficacy are Tinospora Cordifolia, Phyllanthus niruri, Emblica officinalis, Terminalia chebula, Ricinus communis, Boerhaviadiffusa, and Justicaadhatoda. Traditional preparations involved decoctions, leaf juices, powders, and combinations with ghee, honey, or milk, administered orally or topically for periods ranging from 5 to 14 days. A comprehensive analysis of existing literature reveals a diverse array of plants with hepatoprotective, antioxidant and anti-inflammatory properties, masking them significant in traditional system like Ayurveda, which may contribute their therapeutic efficacy. Ethnomedicinal plants can facilitate the development of cost-effective, accessible, and culturally acceptable therapeutic agents for treating jaundice. This review highlights the enduring relevance and therapeutic promise of ethnomedicinal plants in managing jaundice and liver disorders. It explores plants traditionally used to treat jaundice, covering the plant organs used, methods of preparation, ethnomedicinal uses, and their bioactive compounds.

Successful adoption of electronic health records (EHRs) depends on more than technical installation; it depends on the beliefs, attitudes, and organizational conditions of the clinical workforce expected to use the system. This study assessed the perception and attitude of health workers toward EHR adoption at State Specialist Hospital, Lisaluwa, Ondo, Ondo State, Nigeria, examining perception, attitude, facilitating conditions, perceived barriers, and behavioral intention together, and their inter-relationships, among the hospital's clinical workforce.
An institution-based descriptive cross-sectional design with an analytical component was employed. Of 420 questionnaires distributed to clinical health workers across seven cadres, using Cochran's formula for sample-size determination, 357 were returned complete and analysed, a response rate of 85.0%. The structured, validated instrument (Cronbach's alpha 0.869–0.950 across scales) measured perception (15 items), attitude (10 items), facilitating conditions (8 items), perceived barriers (10 items), and behavioral intention (5 items). Data were analysed descriptively and inferentially in SPSS, including Pearson correlation, multiple linear regression with heteroscedasticity-consistent (HC3) standard errors, a Yates-corrected chi-square test, and Welch's one-way ANOVA.
Positive perception was recorded among 236 respondents (66.1%), 231 (64.7%) had a favourable attitude, and 221 (61.9%) had high behavioral intention. Mean scores were 3.77 (SD = 0.74) for perception, 3.72 (SD = 0.78) for attitude, 3.43 (SD = 0.79) for facilitating conditions, 3.36 (SD = 0.81) for perceived barriers, and 3.68 (SD = 0.81) for behavioral intention. Behavioral intention correlated positively with perception (r = .513), attitude (r = .583), and facilitating conditions (r = .329), and negatively with perceived barriers (r = −.425; all p < .001). The regression model explained 54.0% of the variance in intention (F(7,349) = 58.62, p < .001; adjusted R² = .531). Attitude (β = .369), computer proficiency (β = .295), perceived barriers (β = −.201), and facilitating conditions (β = .150) remained independently significant predictors, whereas perception, formal computer training, and prior EHR use did not remain significant after adjustment. Formal computer training was associated with high intention at the bivariate level (OR = 2.12, 95% CI [1.37, 3.28]).
The results indicate broad, conditional support for EHR adoption at the study hospital, but willingness is closely tied to digital competence and credible organizational support rather than to general approval alone. The hospital should prioritize competency-based training, workflow preparation, reliable connectivity, clear operating policies, and phased implementation with sustained technical support to translate this favourable disposition into effective, routine use.

This paper examines the manifestation and dynamics of case conflict in Ngũgĩ wa Thiong’o’s Devil on the Cross (1980) and Festus Iyayi’s Violence (1979), with a focus on economic inequality, gender oppression, ideological manipulation, and the development of resistance and consciousness. Employing a qualitative, comparative literary analysis, the paper integrates textual evidence from both novels under thematic categories: economic and class conflict, gender conflict, ideological conflict, and resistance and consciousness, and leans on Marxist and postcolonial theoretical frameworks to interpret the social, political, and psychological dimensions of conflict. The analysis reveals that economic disparity and structural inequality serve as primary sources of tension, while patriarchal and ideological systems reinforce oppression and obscure the mechanisms of exploitation. Again, both writers’ narrative strategies - Ngũgĩ utilizes satire and allegory, and Iyayi employs social realism – vividly expose systemic injustice, and the lived experiences of the oppressed. The paper argues that Ngugi and Iyayi, through their major characters, Wariinga in Devil on the Cross and Idemudia and Adisa in Violence, demonstrate that conflict catalyzes awareness, ethical reflection, and forms of resistance, whether overt or subtle. This paper concludes that literature not only reflects societal inequities but also serves as a medium for critical reflection and socio-political engagement, and that class, gender and ideology are the wheel upon which case conflict spins in African literature .The paper recommends among others, that the case conflict in African Literature should be approached from a comparative and interdisciplinary perspective to uncover how historical, political, cultural, economic and religious underpinnings engender Class, gender and ideological conflicts.

Event-streaming platforms such as Apache Kafka guarantee ordering only within a single partition, yet production pipelines routinely fan events out across many partitions, where causal relationships between events are silently lost. Logical clocks restore causal reasoning, but their relative cost and correctness under realistic streaming conditions have not been quantified. This paper presents causality-bench, a benchmark harness that empirically compares Lamport clocks, vector clocks, and hybrid logical clocks (HLC), together with a Kafka-offset baseline, across five research questions: header overhead, CPU cost, skew-induced mis-ordering, stream-join correctness, and causal-graph replay fidelity. Every experiment runs on two transports, an in-process simulator and a real single-node Kafka (KRaft) broker, allowing transport effects to be isolated. Vector clocks are the only mechanism that maintains 0% mis-ordering at every skew level and achieves recall = precision = 1.000 in causal replay at every partition count, at a header cost that grows linearly with node count. The central empirical finding is that HLC's safe operating window relative to Lamport clocks is transport-dependent: HLC mis-orders fewer event pairs than Lamport only below approximately 375 ms of injected clock skew in-process, but only below approximately 60 ms over a real broker, a roughly six-fold narrowing caused by real delivery jitter. These results provide practitioners with measured decision thresholds for selecting a causality mechanism in partitioned streaming systems.

The increasing use of digital transformation has raised expectations for better organizational competitiveness and performance among companies listed on the Nairobi Securities Exchange (NSE).However, many companies listed on the exchange continue to face issues such as falling profitability, low returns on assets and equity, inefficient operations, and limited growth. These problems were linked to increasing operating costs, fluctuating exchange rates, high interest rates, and stronger competition. In response, many firms invested substantially in digital transformation initiatives to enhance competitiveness. Nevertheless, the varying performance outcomes among firms undertaking similar digital transformation initiatives still remains a major concern for the firms. This study sought to investigate the effect of digital transformation on firm the operational performance of firms listed in the NSE. The study was anchored on Dynamic Capabilities Theory and used a positivist research approach with a correlational design.The focus was on NSE-listed firms, with top management executives serving as key sources of information.Data was gathered using structured electronic questionnaires sent to a systematically selected sample of executives from listed firms. The results showed that digital transformation had a positive and statistically significant effect on firm performance (β = 0.572, p < 0.001), explaining 32.7% of the variation in firm performance (R² = 0.327).Among its components, Digital Technology Adoption (β = 0.297, p < 0.001) and Digital Culture (β = 0.254, p = 0.002) significantly improved firm performance, while Digital Process Integration (β = 0.103, p = 0.209) had a positive but statistically insignificant effect, indicating that technology adoption and a supportive digital culture contribute more directly to better organizational performance than process integration alone. The study recommends that management of companies listed on the Nairobi Securities Exchange should strengthen investments in comprehensive digital transformation initiatives by prioritizing the adoption of modern digital technologies while simultaneously fostering a digital organizational culture that encourages innovation, continuous learning, employee empowerment, and effective digital leadership.

Genetic algorithm-finite element streamflow-based optimization model application was developed and applied for the Upper Ebonyi Watershed. To assess the performance capability of the model, hydrograph of the simulated and the optimized for the downstream phase of the watershed were compared with measured hydrograph, and statistically analyzed using three efficiency criteria: coefficient of determination , Nash Sutcliffe efficiency coefficient , and index of agreement The comparison of the simulated hydrograph with the measured gave efficiency criteria values of and respectively for coefficient of determination , Nash-Sutcliffe efficiency and Index of agreement , while the optimized hydrograph with measured hydrograph gave efficiency values of and respectively, showing an improvement of and respectively with optimization techniques. The simulated and optimized hydrographs were similar, with efficiency values of and respectively for , and . From the efficiency criteria results obtained, the incorporation of an optimization capability improved the model prediction, and the model is relatively satisfactory considering the fact that no model can be said to be absolutely perfect as there often uncertainties (errors) still inherent in such a model.

Introduction: Problematic social media scrolling is an emerging public health concern among adolescents and young adults. Although it is not yet recognized as a formal diagnostic category by the DSM-5-TR or the ICD-11, this behavior is increasingly studied through the lens of behavioral addiction models. This study aimed to describe scrolling habits in adolescents and young adults and to examine their psychological correlates and their association with sleep and life satisfaction.
Methods: This descriptive and analytical, quantitative study included 110 participants aged 15 to 22 years, recruited online through social media platforms. Data were collected using the Bergen Social Media Addiction Scale (BSMAS), the Fear of Missing Out Scale (FoMO), the Insomnia Severity Index (ISI), the Satisfaction With Life Scale (SWLS), and study-specific self-control and post-scrolling satisfaction scales. Pearson correlation coefficients were used to examine associations among variables, with significance set at P < 0.05.
Results: The mean BSMAS score was 19.7 (SD 4.8), close to the commonly used cut-off of 19 for problematic use. Ninety percent of participants reported at least mild insomnia, and a majority reported low to average life satisfaction. FoMO, BSMAS, and self-control scores all decreased with age. Significant correlations were found between BSMAS scores, FoMO, self-control, sleep difficulties, and life satisfaction (all P < 0.05). An at-risk profile emerged, characterized by younger age (15-17 years), high FoMO, low self-control, use of more than three platforms, and scrolling distributed throughout the day.
Conclusions: Problematic scrolling among adolescents and young adults is closely associated with fear of missing out, poor self-control, sleep disturbance, and reduced life satisfaction, consistent with mechanisms described in behavioral addiction frameworks, particularly the developmental mismatch between reward sensitivity and prefrontal control. These findings support targeted screening of younger adolescents and routine clinical assessment of scrolling habits when sleep complaints or life dissatisfaction are reported.

This study examines the effect of firms' perception of government tax officials' corruption on their tax compliance perception in Nigeria, using trust in the tax authority, perceived fairness of the tax system, and tax morale as sequential mediating mechanisms. Anchored on fiscal exchange theory and the slippery slope framework, a structural model comprising five reflective constructs — Perceived Corruption of Tax Officials (PCTO), Trust in Tax Authority (TTA), Perceived Fairness of the Tax System (PFTS), Tax Morale (TM), and Tax Compliance Perception (TCP) — was estimated using Partial Least Squares Structural Equation Modelling (PLS-SEM) on data from 385 registered firms operating across Nigeria's six geopolitical zones. The measurement model demonstrated satisfactory convergent validity (average variance extracted ranging from 0.576 to 0.677) and internal consistency reliability (composite reliability ranging from 0.870 to 0.893), while the Fornell-Larcker criterion and heterotrait-monotrait ratio confirmed discriminant validity across constructs. The structural model results indicate that perceived corruption of tax officials significantly and negatively predicts both trust in the tax authority (β = -0.308, p < .001) and perceived fairness of the tax system (β = -0.264, p < .001), and exerts a direct negative effect on tax compliance perception (β = -0.175, p < .001). Trust and fairness both positively predict tax morale, which in turn is the strongest positive predictor of tax compliance perception (β = 0.366, p < .001). The model explains 34.9% of the variance in tax compliance perception. The findings extend the fiscal-exchange and slippery-slope literatures to a firm-level, corruption-salient developing-economy context and suggest that anti-corruption reform within revenue agencies is a precondition, rather than a substitute, for morale-based voluntary compliance strategies. Practical and policy implications for the Federal Inland Revenue Service, state internal revenue services, and firm-level tax administration reforms in North-East Nigeria are discussed.

Background: Tuberous sclerosis complex (TSC) is a multisystem genetic disorder characterized by hamartomatous lesions affecting multiple organs. Beyond its neurological manifestations, TSC is frequently associated with a broad spectrum of neuropsychiatric disorders, collectively referred to as TSC-associated neuropsychiatric disorders (TAND), including autism spectrum disorder (ASD), intellectual disability (ID), behavioural disturbances, and psychiatric comorbidities. These manifestations represent a major source of disability but remain under-recognized and often undertreated.
Case presentation: We report the case of a 13-year-old boy, the youngest of three siblings, born to non-consanguineous parents with no family history of TSC or neurodevelopmental disorders. Early psychomotor development was initially reassuring, with emergence of expressive language and reciprocal social interaction before two years of age. He subsequently developed recurrent seizures that never achieved sustained control despite several successive antiseizure-medication combinations, evolving into drug-resistant epilepsy (currently generalized tonic-clonic seizures). Developmental regression followed, with loss of language, impaired social communication, stereotyped behaviours, and marked intolerance to change. Brain magnetic resonance imaging (MRI) revealed multiple cortical tubers and subependymal nodules, and dermatological examination identified hypomelanotic macules, confetti skin lesions, and facial angiofibromas, fulfilling the 2021 International TSC clinical diagnostic criteria for definite TSC; molecular genetic testing was not performed. At three years of age, ASD (DSM-5, supported by ADI-R and ADOS) and severe ID (uniformly very low adaptive functioning on the Vineland Adaptive Behavior Scales) were diagnosed. Despite intensive multidisciplinary rehabilitation, clinical improvement remained minimal. During adolescence, the clinical picture became dominated by severe TAND: non-verbal ASD, persistent intellectual disability, repetitive head-banging requiring protective measures, and frequent heteroaggression. Management was particularly challenging because drug-resistant epilepsy limited pharmacological options, while serial follow-up MRI demonstrated progression of cerebral lesions, leading to evaluation for epilepsy surgery.
Conclusion: This case highlights the complex interplay between progressive TSC, early drug-resistant epilepsy, developmental regression, and severe neuropsychiatric comorbidities. It emphasizes that TAND may become the predominant source of long-term disability and underscores the need for early multidisciplinary assessment, systematic psychiatric evaluation, and individualized therapeutic strategies in children with TSC.

The expansion of digital government has changed the relationship between citizens and public authorities. Automated systems can now assist with application processing, verification, risk identification, classification and other administrative functions. Such systems can improve speed and consistency, but their use also raises a fundamental administrative-law question: what happens when an automated system produces an adverse governmental decision and the citizen cannot understand or effectively challenge the result?
This article examines automated government decision-making through the principles of natural justice, constitutional equality, procedural fairness and judicial review in India. It argues that technology cannot become a substitute for constitutional accountability. Where automation materially contributes to a significant adverse decision, the affected person should have access to understandable reasons, a practical opportunity to correct relevant factual errors, and meaningful review by an authorised human decision-maker. The article develops a proportionate model in which the intensity of safeguards increases with the seriousness of the decision's impact.
The article does not argue against automation. Instead, it proposes accountable automation: a system in which technology assists public administration while legal authority, fairness and responsibility remain traceable to the State. Such an approach can preserve the efficiency of digital governance without allowing the complexity of algorithms to weaken the rule of law.

The rapid development of Artificial Intelligence, especially Generative AI, has brought about major changes in the way literary, artistic and other creative works are created. This has raised important legal questions under Copyright Law regarding authorship, ownership and originality. This research examines whether the current Indian Copyright Law is sufficient to provide clear answers to these questions regarding works created by or with the help of AI. This study uses Doctrinal Legal Research Methodology and analyses relevant legal provisions, judicial decisions and secondary legal literature. The research focuses on the legal framework related to computer-generated works, first ownership and originality. The research reveals that Indian Copyright Law provides a legal basis for computer-generated works, but there are still some uncertainties in the context of highly autonomous generative AI systems. This paper recommends giving importance to meaningful human creative contribution and creating clearer legal and policy guidelines on copyright protection.
This study explores how artificial intelligence systems learn and solve complex problems.

The digitalisation of governance, commerce and everyday life has transformed personal data into one of the most valuable resources in the modern economy. At the same time, the collection and processing of personal data creates significant risks to individual autonomy, dignity and liberty. In India, the constitutional recognition of privacy in Justice K.S. Puttaswamy (Retd.) v. Union of India marked a decisive shift from viewing privacy as merely a common-law interest to recognising it as a fundamental right. The enactment of the Digital Personal Data Protection Act, 2023 (“DPDP Act”) and the subsequent notification of the Digital Personal Data Protection Rules, 2025 (“DPDP Rules”) represent the legislative attempt to translate this constitutional principle into an operational regulatory framework.
This article argues that the success of India's data protection regime cannot be measured merely by the existence of consent notices, compliance programmes or monetary penalties. The deeper question is whether the framework meaningfully protects informational self-determination against both private and State power. The article examines the consent architecture, duties of Data Fiduciaries, rights of Data Principals, treatment of children's data, governmental exemptions and the institutional design of the Data Protection Board of India. It contends that the DPDP framework should be interpreted through the constitutional values of dignity, autonomy, proportionality, transparency and accountability. The article concludes by proposing a constitutional model of data protection in which consent is treated as one component of privacy protection rather than its complete substitute.

Team cohesion reflects the strength of interpersonal relationships among team members and their shared commitment to achieving common goals. This study examined the effect of team cohesion on organizational performance in selected manufacturing firms in South–South Nigeria. In line with this objective, relevant research questions and hypotheses were formulated to guide the study. The study adopted a cross-sectional survey research design. A sample size of 105 respondents was drawn from the study population using the census sampling technique. The major instrument for data collection was a structured questionnaire administered to respondents using proportionate and purposive sampling techniques. Data collected were analyzed using descriptive statistics and simple linear regression analysis. The results revealed a significant positive relationship between the two dimensions of team cohesion—task cohesion and social cohesion—and organizational performance in the selected manufacturing firms in South–South Nigeria. The findings indicate that team cohesion plays a critical role in enhancing organizational performance. Specifically, both task cohesion and social cohesion were found to have significant positive effects on performance outcomes. Based on these findings, the study recommends that manufacturing firms should promote task cohesion by encouraging joint problem-solving, shared goals, and collective accountability systems in order to enhance overall organizational performance.

This study examined the influence of workplace wellness programs on employee productivity. Specifically, the study investigated the effects of fitness class participation, and stress management on employee productivity. A quantitative research design was adopted, and data were collected from a sample of 165 respondents using a structured questionnaire based on a five-point Likert scale. Descriptive statistics, simple linear regression, and multiple regression analyses were employed to analyze the data. The descriptive findings revealed high levels of participation in wellness programs and high perceived employee productivity. The simple regression results showed that fitness class participation (R² = 0.869), and stress management (R² = 0.916), each had a strong positive and statistically significant effect on employee productivity. Among the variables, stress management demonstrated the strongest individual influence. The study concludes that participation in workplace wellness initiatives such as fitness classes, and stress management programs, significantly enhances employee productivity. Employees who engage in these programs tend to be healthier, more focused, and more efficient in their work. It was recommended that organizations should adopt comprehensive and integrated wellness strategies to improve employee performance, reduce absenteeism, and achieve overall organizational effectiveness.

This paper delivers a critical, document-based review of the Central Bank of Kenya's (CBK) monetary policy response to the COVID-19 shock between March 2020 and early 2021. This analysis takes into account the various interventions undertaken at the directive of and on information from the Monetary Policy Committee (MPC) reports, Central Bank of Kenya (CBK) press releases and World Bank and International Monetary Fund assessments: from its series of policy interest rate reductions and central bank rate cut, credit-weighted, and an exceptionally large loan restructured, right through its extensive digital finance initiatives and loan restructuring facility. Each intervention is then viewed in relation to Keynesian analysis as well as models from credit-rationing and financial fragility theories. The results show that the response was rapid and broadly stabilizing, anchoring inflation expectations and preventing an acute banking crisis. However, transmission through the credit channel had been erratic, the reduction in the CRR was shallow since sector liquidity was already abundant, and the high level of loan moratorium (which touched over 57% of the total loans in February 2021) imposed an overhang of debt restructuring necessitating the additional year of regulatory forbearance. The review concludes by extracting a year of lessons for future shocks, along with reforms at the institution level, with the intention of increasing Kenya’s monetary policy instruments toolbox.

This paper examines the manifestation and dynamics of case conflict in Ngũgĩ wa Thiong’o’s Devil on the Cross (1980) and Festus Iyayi’s Violence (1979), with a focus on economic inequality, gender oppression, ideological manipulation, and the development of resistance and consciousness. Employing a qualitative, comparative literary analysis, the paper integrates textual evidence from both novels under thematic categories: economic and class conflict, gender conflict, ideological conflict, and resistance and consciousness, and leans on Marxist and postcolonial theoretical frameworks to interpret the social, political, and psychological dimensions of conflict. The analysis reveals that economic disparity and structural inequality serve as primary sources of tension, while patriarchal and ideological systems reinforce oppression and obscure the mechanisms of exploitation. Again, both writers’ narrative strategies - Ngũgĩ utilizes satire and allegory, and Iyayi employs social realism – vividly expose systemic injustice, and the lived experiences of the oppressed. The paper argues that Ngugi and Iyayi, through their major characters, Wariinga in Devil on the Cross and Idemudia and Adisa in Violence, demonstrate that conflict catalyzes awareness, ethical reflection, and forms of resistance, whether overt or subtle. This paper concludes that literature not only reflects societal inequities but also serves as a medium for critical reflection and socio-political engagement, and that class, gender and ideology are the wheel upon which case conflict spins in African literature .The paper recommends among others, that the case conflict in African Literature should be approached from a comparative and interdisciplinary perspective to uncover how historical, political, cultural, economic and religious underpinnings engender Class, gender and ideological conflicts.

The rapid expansion of digital technology has transformed the way individuals communicate, work, study, shop, access public services, and participate in social life. Alongside these developments, however, personal information has become one of the most valuable forms of digital currency. Every online search, financial transaction, social-media interaction, location update, and application may generate information about an individual. This development raises an important constitutional and legal question: how can the right to privacy be protected when personal information is continuously collected, processed, shared, and analysed?
In India, privacy has evolved from being viewed primarily as a matter of personal liberty into a constitutionally protected fundamental right. The recognition of privacy as an intrinsic part of life and personal liberty has created a constitutional foundation for protecting individuals against unjustified interference by both the State and private actors. At the same time, technological development has created new challenges that cannot always be addressed through traditional legal principles.
This article examines the changing nature of the right to privacy in India, its constitutional foundations, the challenges created by digital technology, the importance of data protection, and the need to balance individual privacy with legitimate State and commercial interests. It argues that privacy in the digital age should not be understood merely as the right to keep information secret. Rather, it should be understood as a right to dignity, autonomy, informed choice, and meaningful control over personal information.

The Goods and Services Tax (GST), introduced on 1 July 2017, transformed India's indirect-tax framework by replacing several Central and State levies and creating a technology-enabled compliance system. Although GST seeks to reduce cascading taxes, improve market integration, and strengthen transparency, its compliance architecture can impose disproportionate administrative and financial burdens on small businesses with limited accounting capacity, digital infrastructure, and working capital. This literature review examines statutory provisions, official guidance, and selected academic literature concerning registration, return filing, input tax credit (ITC), digital compliance, professional costs, regulatory change, and liquidity pressures. It also considers relief measures such as the Quarterly Return Monthly Payment (QRMP) scheme and GST Council recommendations. The review concludes that simplification must be assessed not only by filing frequency but also by the practical ability of small taxpayers to comply accurately and at reasonable cost.

Tinospora cordifolia, commonly known as guduchi or giloy, is an important medicinal plant belonging to the Menispermaceae family and is widely used in traditional Indian medicine, particularly in Ayurveda. The plant has attracted considerable scientific interest due to its diverse phytochemical constituents and extensive pharmacological potential. T. Various parts of Cordifolia, especially stems and roots, contain several bioactive compounds, including alkaloids, diterpenoid lactones, glycosides, steroids, sesquiterpenoids, phenolic compounds, polysaccharides and other secondary metabolites. These components are associated with various biological activities.
In the present review, T. The herbal characteristics, traditional uses, pharmacognostic properties, phytochemical constituents and pharmacological activities of cordifolia are summarized. Reported pharmacological properties include antidiabetic, antioxidant, anti-inflammatory, antimicrobial, immunomodulatory, hepatoprotective, anticancer, ulcer, antipruritic and neuroprotective activities.
Review t. It also highlights the importance of standardization, quality control, toxicity evaluation and well-designed clinical studies to establish the therapeutic efficacy and safety of cordifolia. Overall, T. Cordifolia represents a promising medicinal plant with significant potential for the development of standard herbal preparations and novel phytopharmaceutical products.

Artificial Intelligence (AI) is rapidly transforming modern society. It is increasingly used in healthcare, education, finance, employment, policing, public administration, communication and commercial activities. While Artificial Intelligence provides significant opportunities for economic and social development, its increasing use also raises serious questions concerning constitutional rights. Automated decision-making, algorithmic profiling, facial recognition, predictive policing and large-scale data processing may directly affect an individual’s equality, liberty, privacy and dignity.
The Constitution of India was enacted long before the emergence of modern Artificial Intelligence. However, its fundamental rights framework is sufficiently broad to address technological developments that affect constitutional values. Articles 14, 19 and 21 are particularly relevant to AI governance. Article 14 protects individuals against arbitrary and discriminatory State action; Article 19 protects important freedoms including freedom of speech and expression; and Article 21 has been judicially interpreted to protect dignity, autonomy and privacy.
This paper examines the constitutional implications of Artificial Intelligence in India with particular emphasis on privacy, equality and procedural fairness. It analyses the landmark decision in Justice K.S. Puttaswamy (Retd.) v. Union of India, in which the Supreme Court recognised privacy as a constitutionally protected right. The paper also considers India’s Digital Personal Data Protection Act, 2023 and the Digital Personal Data Protection Rules, 2025. It argues that data protection alone cannot address all AI-related constitutional concerns. Algorithmic transparency, accountability, human oversight, non-discrimination and effective remedies are equally important.
The paper concludes that India should adopt a rights-based approach to Artificial Intelligence in which innovation is encouraged but technological power remains subject to constitutional principles of dignity, equality, liberty, proportionality and rule of law.

This study assessed the level of employees’ awareness of labour dispute resolution procedures at Iringa Municipal Council. The study adopted a mixed-methods approach and a descriptive research design. The study targeted approximately 1,270 employees, from whom 100 respondents were selected using stratified random sampling, while key informants were selected purposively. Primary data were collected through structured questionnaires and semi-structured interviews, while secondary information was obtained from relevant Council records, labour-related documents, government publications and academic literature. The assessment of awareness focused on employees’ understanding of grievance procedures, knowledge of the steps involved in resolving labour disputes, awareness of appropriate reporting channels and understanding of employee rights under labour laws. Quantitative data were analysed using frequencies and percentages, while qualitative information was analysed thematically. The reliability of the research instrument was satisfactory, with a Cronbach’s Alpha coefficient of 0.941. The findings revealed that employees had a generally high level of awareness of labour dispute resolution procedures. Specifically, 93% of respondents agreed or strongly agreed that they understood grievance procedures, 93% indicated that they knew the steps involved in resolving labour disputes and 93% reported understanding their rights under labour laws. Awareness of the channels used to report labour disputes was comparatively lower, with 78% agreeing or strongly agreeing, while 15% disagreed and 7% remained neutral. The study concludes that employees at Iringa Municipal Council possess substantial awareness of labour dispute resolution procedures, although some gaps remain regarding formal reporting channels. The study recommends that the Council strengthen regular sensitisation and communication programmes, particularly for newly recruited employees, through staff meetings, orientation programmes, circulars and accessible labour-policy documents to improve employees’ understanding of where and how labour disputes should be reported.

Entrepreneurship is widely regarded as a central engine of job creation, innovation, and structural transformation in developing economies, and Nigeria is no exception. Yet the same economy that offers Africa's largest consumer market, a fast-digitising population, and an expanding services sector also confronts entrepreneurs with a demanding risk environment: currency instability, double-digit inflation, high borrowing costs, insecurity, multiple and overlapping taxation, unreliable power supply, and weak institutional support. This paper examines the relationship between entrepreneurial risk and business opportunity in Nigeria, drawing on recent empirical and policy literature (2023–2026) alongside macroeconomic and survey data from the Central Bank of Nigeria, the National Bureau of Statistics, and the Small and Medium Enterprises Development Agency of Nigeria. The paper classifies the principal categories of entrepreneurial risk confronting Nigerian businesses financial, operational, regulatory/policy, security, and market risk and matches these against emerging opportunity spaces in digital financial services, agribusiness, the creative and digital economy, renewable energy, and youth-driven micro enterprise. It argues, consistent with classical Knightian theory, that Nigeria's high-risk operating environment is also a source of above-average returns for entrepreneurs who can manage uncertainty through diversification, digital adoption, cooperative financing, and policy engagement. The paper concludes with recommendations for entrepreneurs, financial institutions, and policymakers aimed at converting risk exposure into sustainable opportunity capture.

Andrographis paniculata (Burm. f.) Wall. Ex Nees, commonly known as Kalmegha or the “King of Bitters”, is an important medicinal plant widely used in traditional systems of medicine such as Ayurveda, Siddha, and Traditional Chinese Medicine. The plant has been traditionally employed for the treatment of fever, liver disorders, infections, inflammation, and immune-related diseases. Modern pharmacological studies have validated many of these traditional claims and revealed a wide range of biological activities attributed mainly to diterpenoid lactones such as andrographolide. This review summarizes the ethnobotany, morphology, phytochemistry, and pharmacological properties of A. paniculata based on experimental and clinical evidence.

A school's safety, like a country's justice system, is only as strong as the weakest point in the chain between a rule being written and a rule being known. This paper compares the institutional safeguards that protect K-12 students from sexual harassment by staff and by peers across three very different legal traditions: the United States, where protection is enforced mainly through the threat of a lawsuit; the United Kingdom, where protection is enforced mainly through a standing duty and a named inspector's clipboard; and India, where protection is enforced mainly through the threat of criminal prosecution. Drawing on peer-reviewed secondary research, I argue that each system's safeguards are strongest exactly where its underlying legal logic applies the most pressure, and weakest everywhere else, and that in all three countries, the single most common point of failure is not the absence of a rule, but a child's inability to say what the rule is, who enforces it, or how to invoke it.

Artificial Intelligence (AI) is increasingly being used in criminal investigations through tools such as facial recognition, predictive systems, and digital forensic technologies. These tools can help investigators work faster and find evidence more efficiently. However, the use of AI-generated evidence in Indian criminal trials raises important questions about its reliability, authenticity, and admissibility. The Bharatiya Sakshya Adhiniyam, 2023 deals with electronic and digital evidence, but it does not clearly provide specific rules for AI-generated evidence. This paper examines major issues such as AI bias, lack of transparency, difficulty in checking the accuracy of AI results, and the risk of manipulation and deepfakes. It also discusses the possible impact of AI evidence on the right to a fair trial and the presumption of innocence. The paper suggests that India needs clearer legal rules and proper standards to ensure that AI-generated evidence is reliable, transparent, and fairly used in criminal trials.