Volume 15 Issue 1

Electric vehicles (EVs) are becoming increasingly important in the shift toward sustainable mobility. While their adoption is accelerating, ensuring the health and reliability of EV powertrains remains a critical challenge. Failures in subsystems such as batteries, motors, and controllers may cause unexpected breakdowns, reduced efficiency, and safety issues. Predictive Health Monitoring (PHM) systems aim to prevent such failures by identifying anomalies before they escalate. Traditional PHM solutions often depend on cloud platforms, but these face drawbacks such as latency, high bandwidth requirements, and privacy risks. To address these challenges, edge-based PHM employs embedded devices to process Controller Area Network (CAN) bus data locally, enabling real-time diagnostics, enhanced privacy, and cost efficiency. This paper presents a structured survey of PHM techniques for EV powertrains using Edge Artificial Intelligence (Edge AI) and CAN bus data. The contributions include: (i) classification of PHM approaches into model-based, data-driven, security- focused, and edge-deployed methods, (ii) comparative analysis of recent works, (iii) a summary table of surveyed papers, (iv) discussion of hardware implementations reported in literature, and (v) identification of future research directions.

This review's main contribution is the identification of research gaps and new trends, such as virtual internship models, secure verification frameworks, and AI-driven recommendation systems.by giving students, employers, and educational institutions a common digital platform, internship portals are crucial in bridging the gap between academic learning and real world industry experience. Online internship platforms have received a lot of attention lately due to the growing need for remote internship opportunities and skill-based employability. The functionality, efficacy, and limitations of online internship portals are the main topics of this paper's systematic review of the literature. The review examines research papers from academic databases like IEEE Xplore, Scopus, and Google Scholar that were published between 2020 and 2024. This review offers a comprehensive analysis of platform features, security and verification issues, and the function of intelligent matching mechanisms, in contrast to previous surveys that primarily concentrate on usability or employability outcomes. Common drawbacks identified by the study include limited integration with academic institutions, inadequate verification of internship postings, a lack of personalized recommendations, and data privacy issues.

Background Pedestrian infrastructure quality significantly influences urban mobility, particularly in developing regions undergoing rapid urbanization. This study assesses pedestrian infrastructure in Naval, Biliran Province, Philippines—a municipality transitioning towards cityhood—to address mobility challenges and inform sustainable development planning. Methods A mixed-methods approach was employed using a validated assessment framework with 17 criteria aligned with Philippine accessibility standards. Data collection involved systematic field observations covering 12.20 kilometers across 15 streets in downtown Naval and structured interviews with 211 pedestrians. Assessment combined physical measurements, photographic documentation, and statistical analysis of user perceptions. Results Field assessments revealed significant deficiencies, with 87% of sidewalks failing to meet minimum width standards (1.20 meters) and 80% exhibiting inadequate headroom clearance. Surface discontinuities were documented in 73% of segments, while 67% showed moderate to severe obstructions. Despite high utilization rates (82.9% of respondents regularly walking), only 34.0% reported positive experiences with sidewalk conditions. Statistical analysis identified significant correlations between infrastructure quality and pedestrian behavior, with safety concerns forcing pedestrians into vehicular pathways. Principal concerns included inadequate width (32.70% dissatisfaction), poor lighting (50.98%), and stray animals (66.67%). Conclusion This study establishes an evidence base highlighting the need for strategic interventions in Naval's pedestrian infrastructure. Findings underscore significant gaps between current provision and user requirements, particularly regarding dimensions, surface quality, and safety features. By identifying specific deficiencies, this research provides municipal authorities with actionable data to prioritize improvements that enhance walkability, safety, and overall urban mobility in Naval and similar developing urban contexts.

In the era of rapid industrialization, automation plays a vital role in enhancing efficiency and accuracy in manufacturing processes. This project presents the design and implementation of an Arduino-based color sorting system that automates the sorting of objects based on their colors. The system uses an infrared (IR) sensor to detect the presence of objects on a conveyor belt and a TCS3200 color sensor to identify their color. The Arduino microcontroller processes the sensor data and controls a servo motor that directs objects to designated bins according to their detected color red, green, or blue. This automated solution offers an affordable and effective alternative to expensive industrial sorting machines, making it suitable for small-scale industries and educational purposes. The system also addresses challenges such as sensor calibration and ambient light variations to ensure accurate color detection and sorting. Testing under various conditions demonstrates the system’s reliability, accuracy, and efficiency, highlighting its potential for wider adoption in automation and prototyping.

This study examined the financial stress and mobility patterns of jeepney passengers in Buenavista, Marinduque, to help guide transportation policy. A total of 156 respondents from five barangays—Uno, Dos, Tres, Quatro, and Libas—represented different demographic backgrounds. The research focused on income stability, unexpected expenses,  lack of savings, and coping strategies  in managing  daily travel. Using  a quantitative design,  data were gathered through surveys and analyzed to determine the relationship between financial stress and travel behavior, including frequency, route selection, and timing. Results showed that unstable income, unexpected expenses, and lack of savings cause financial stress. Despite this, passengers continue to travel regularly  by  adjusting  schedules  or  reducing  other  expenses.  Demographic factors  such  as occupation and income level also influenced financial stress and mobility. The study highlights the need for transportation policies that consider commuters’ financial conditions to improve both mobility and the overall well-being of jeepney passengers.

This study examines the comparative effectiveness of social media marketing and traditional marketing in influencing customer visit intention to local restaurants in Boac, Marinduque, Philippines, a provincial and community-based market context that remains underrepresented in marketing research. Guided by the Theory of Planned Behavior, Integrated Marketing Communication (IMC) framework, and the DINESERV model, the study adopts a quantitative, descriptive–comparative research design involving 200 restaurant customers selected through convenience sampling.Findings reveal that social media marketing particularly social networking platforms (Facebook, Instagram, TikTok) and electronic media campaigns is significantly more effective than traditional marketing in shaping customer visit intention across four key dimensions: food quality, price perception, service quality, and restaurant atmosphere. Visual content, responsiveness, and real-time engagement emerged as dominant drivers of customer perception and behavioral intention. In contrast, traditional marketing strategies such as print advertisements, promotional handouts, and public relations were found to be only moderately effective, with word-of-mouth remaining the most influential traditional channel.Inferential analysis indicates that gender and employment status significantly moderate perceptions of marketing effectiveness, while age and income level do not exert a statistically significant influence. These results suggest that digital marketing strategies in provincial settings demonstrate broad demographic reach, reinforcing their strategic value for small and medium enterprises (SMEs).The study contributes empirically by extending marketing effectiveness research to a localized, non-metropolitan context, offering actionable insights for restaurant owners and policymakers. Practically, the findings support the adoption of hybrid marketing strategies that integrate digital visibility with community-based engagement. The study aligns with Sustainable Development Goal (SDG) 8 (Decent Work and Economic Growth) by strengthening local enterprise competitiveness, SDG 9 (Industry, Innovation, and Infrastructure) through digital adoption, and SDG 11 (Sustainable Cities and Communities) by supporting resilient local food systems.Overall, the study underscores that strategic social media marketing is a critical lever for sustainable business growth, customer engagement, and inclusive economic development in provincial economies.

Soap noodles are the primary intermediate raw material used in the manufacture of toilet soaps and bathing bars worldwide. Their uniform chemical composition, physical consistency, and controlled quality enable manufacturers to formulate soaps with desired cleansing efficiency, mildness, and aesthetic appeal. The toilet soap industry plays a crucial role in maintaining personal hygiene and public health while contributing significantly to the chemical and consumer goods sector. This journal paper presents a comprehensive review of soap noodles and toilet soaps, focusing on their raw materials, chemical composition, manufacturing processes, quality control parameters, finished product specifications, industrial applications, and environmental considerations. Emphasis is given to Total Fatty Matter (TFM), process standardization, and sustainability aspects relevant to modern soap manufacturing.

The important issues of code reuse in contemporary software development are covered in this study. Reusing code is an essential technique that raises software quality, lowers development costs, and increases productivity. However, because of problems like code repetition, a lack of knowledge of reusable components, maintenance difficulties, and scalability constraints, traditional approaches like libraries, APIs, and design patterns frequently fail in large-scale software development. Especially in big, distributed, and dynamic systems, these difficulties result in inefficiencies, longer development times, and lower software quality. In order to overcome these obstacles, this study suggests using transfer learning, a machine learning method that makes use of pre-trained models to enhance the recognition, modification, and incorporation of reusable code elements. The use of transfer learning in code reuse is a promising way to overcome the drawbacks of conventional approaches, and it has demonstrated notable success in domains such as computer vision and natural language processing. Through the use of models that have already been trained on sizable code corpora (such as open-source repositories like GitHub), transfer learning can help developers find and reuse high-quality code fragments across projects and programming languages more effectively, cutting down on duplication of efforts and enhancing code maintainability. The main aim of this study is to model how to use transfer learning for efficient code reuse in large-scale software development. Using extensive code datasets from private codebases or open-source repositories (like GitHub and GitLab), the study employed a quantitative methodology with a population size of 225. The findings show that CodeBERT is both robust and adaptable, offering high value for software engineering automation and developer assistance tools. The results demonstrate that the model whether trained from scratch or through transfer learning achieved perfect performance metrics in classifying and evaluating code snippets. This high accuracy indicates a strong capacity to enhance software development efficiency by enabling faster and more reliable code assessment. The equal performance of the transfer learning approach further shows that pretrained knowledge from large-scale open-source code can be effectively adapted to new tasks without compromising code quality or consistency. Overall, the findings confirm that the transfer learning model is not only stable and effective but also capable of delivering performance comparable to a fully trained model while requiring significantly less training data and computational resources. Use CodeBERT for automated code assessment, early bug detection, and identifying risky code patterns. Embed the model into Continuous Integration/Continuous Deployment (CI/CD) systems to enable automatic code review and error detection. Fine-tune with domain-specific datasets to maintain consistency with organizational coding standards. Conduct workshops to demonstrate efficiency gains from automated code evaluation.

The question of whether human language plays a constitutive role in shaping human thought, or merely dresses up pre-formed thoughts—thus occupying a secondary role in the constitution of thought—has occupied scientists and philosophers of language and mind for centuries. The present paper undertakes a philosophical investigation of the Sapir–Whorf hypothesis and seeks to dispel certain confusions surrounding it. It is demonstrated that human language is both an inseparable component of human psychology (a cognitive mechanism) and an indispensable part of the social practices in which humans participate (a social tool, a technology that each culture uses creatively). The distinction between language as a cognitive–semiotic system and as a culture specific-national constituent is crucial when we investigate linguistic relativity. Ultimately, language coordinates, influences, and directs thought and psychological processes; it also constitutes and reproduces culture, helping it to evolve.

This analysis explores the likelihood that a reference's original citation number, based on its initial position in the text, matches its final number after sorting the bibliography alphabetically. It models this as counting the fixed points in a random permutation—a classic problem in combinatorial probability known as the Rencontres or Hat-Check problem—assuming a uniform random order. Using the Principle of Inclusion-Exclusion, an exact probability mass function for the number of references (n) with exactly k matches is derived, based on derangements. The study reveals a key asymptotic result: as n grows, the distribution of matches quickly and strongly approaches a Poisson distribution with a mean of λ=1. This means that for typical bibliography sizes (n ≥ 10), the chance of any given number of matches becomes nearly independent of total references. Thus, the expected number of matches remains 1, regardless of the bibliography's length. The study also provides specific probabilities for at least k matches, computed via the Poisson approximation and involving the Incomplete Gamma Function. Notably, there is a 63.2% chance of at least one match, 26.4% for two or more, and 8.0% for three or more, indicating that multiple coincidences are rare. By distinguishing this from phenomena like the Birthday Paradox and highlighting its use in assessing shuffling algorithms and cryptographic security, the report presents a clear framework for understanding fixed-point matches in permutations. It concludes that despite the apparent randomness of reordering references, the number of matches follows a strict, predictable probabilistic law governed by the constant e.

Aluminium corrosion can deleteriously reduce the structural strength, integrity, safety, and serviceability of the metal. In addition, it is an inevitable issue of health concern in food environments because the substances resulting from the corrosion can have toxicological, mutagenic, and carcinogenic effects in the human body, potentially leading to many chronic ailments. This paper presents an inquiry into the inhibitory effects of calcium sulphate as a cheap, worldwide-available, and environmentally friendly food substance on corrosion of cookware aluminium in highly acidic and alkaline media. Coupons were produced from the aluminium 6061 alloy as a common cookware aluminium type and immersed for 1-48 hours in sequentially treated acidic media of 2.5, 2.7, and 3 pH and alkaline media of 10.5, 10.7, and 11 pH with anhydrite calcium sulphate concentrations of 0, 20, 25, 30, and 35% of the media at 30, 50, and 70°C temperature conditions. Corrosion penetration rates (CPRs), inhibition efficiencies, and micro-topographic changes of the coupons were evaluated relative to those of the coupons immersed in the untreated media. Results show a CPR range of 0.121-1.579 in the acidic and 0.101-1.105 mm/yr in the alkaline media. The CPR increased with increasing exposure temperature and media acidity and alkalinity levels but decreased with the immersion durations and the treatment concentrations to more or less constant values.  The highest corrosion inhibition efficiency of 74.34% occurred in the acidic media after a 3-hour immersion in the 2.7-pH medium at 30°C, with 35% treatment of the medium, while the highest value in the alkaline media was 72.95% after a 3-hour immersion in the 10.5-pH medium at 30°C, with 30% treatment of the medium. SEM micro-topographic analyses indicate that calcium sulphate facilitates corrosion inhibition of the cookware coupons in the media by forming coatings on them. Thermodynamic and kinetic analysis at the temperature conditions of the media shows that the inhibitory process of the sulphate is essentially by physisorption and is well fitted to the Langmuir adsorption isotherm model, as indicated by the Gibbs free energy, enthalpy, and entropy of the sulphate adsorption on the cookware coupons with average values of -23.345 kJ, -30.13 kJ, and 0.195 kJ/K, respectively. The study indicates that anhydrite calcium concentrations of greater than 35% can satisfactorily inhibit corrosion of cookware aluminium under the media conditions.

This study delved into how both intrinsic and extrinsic factors can predict lecturers' anxiety and their willingness to use instructional technologies in teaching electrical courses at polytechnics in North-Central Nigeria. With the increasing push for digital integration in higher education, it’s crucial to understand the psychological and systemic elements that affect educators' adoption of technology in technical fields. The primary goal of this study is to explore how both intrinsic and extrinsic factors influence lecturers' anxiety and their willingness to use instructional technologies while teaching electrical courses in polytechnics located in North-Central Nigeria. This study adopted population size of 250 lecturers. A mixed-methods approach was utilized, blending quantitative surveys with qualitative interviews, targeting lecturers from selected public and private polytechnics in the region. This study employed multiple linear regression analysis. The results show that motivational elements, intrinsic motivation, level of anxiety, and suggestion all significantly increase lecturers’ anxiety. This indicates that certain motivational pressures such as personal drive, expectations, or performance-related suggestions can unintentionally heighten anxiety levels. While institutional support had a positive but insignificant effect, it shows that existing support structures are not strong enough to reduce anxiety. Extrinsic motivation had a negative but insignificant effect, suggesting that external rewards alone do not meaningfully reduce anxiety. Overall, the findings highlight the need for stronger institutional interventions and healthier motivational environments to reduce lecturers’ anxiety. Establish counseling and wellness units dedicated to managing academic stress and mental health. Encourage self-paced professional development rather than excessive self-imposed pressure. Avoid using high-pressure performance metrics without providing adequate support. Train administrators and supervisors to give encouraging, clear, and supportive suggestions. Provide meaningful rewards such as promotion opportunities, research grants, or reduced administrative workload. Introduce mindfulness, relaxation, and mental health awareness programs within the institution.

Due to the realm of the blooming generative models, like GANs, VAE and diffusion-based environment, the trustworthiness of authentication in visual media has been vastly challenged. AI-generated images appearing quite photorealistic go beyond human perceptual boundaries often overlying concerns for disinformation, digital tampering, forensic evidence authentication, and or the security of biometric purposes. This review organizes throughout the chronology of deep learning advancement to segregate real from synthetic images by focusing not much on its generalization snags, extraction of fake image or artifacts from sustainability in the aspect of datasets. Also this review provides systematic analysis on publicly available datasets and those essential research directions in the future being necessary to become a robust detection mechanism for fake images.

This paper investigates the optimization of thermal conductivity in sustainable aluminum nitride (AlN)–fly ash based composite materials aimed at eco-friendly thermal management applications. Fly ash, an industrial waste by-product, is incorporated to enhance sustainability and reduce material cost, while AlN is used to improve heat conduction due to its high intrinsic thermal conductivity. Composite samples were fabricated with varying AlN and fly ash compositions, and their thermal conductivity was experimentally evaluated using standard measurement techniques. The influence of material composition and microstructural characteristics, including particle dispersion, interfacial bonding, and porosity, on thermal performance was systematically analyzed. An optimization strategy was employed to determine the optimal reinforcement ratio that achieves a balance between enhanced thermal conductivity and environmental sustainability. The results indicate that increasing AlN content significantly improves thermal conductivity, while the presence of fly ash maintains eco-efficiency with acceptable performance trade-offs. The optimized composite demonstrates superior thermal behavior compared to conventional sustainable composites, highlighting its potential for applications in electronics cooling, energy systems, and environmentally conscious construction materials.

This phenomenological study explores the lived experiences of teachers affected by direct and structural violence in the divided community of Lukulu district of the Western Province of Zambia. The objectives of this study were to; explore teachers' experiences of direct and structural violence in some selected schools of Lukulu district, investigate the causes of  direct and structural violence against teachers as well as provide recommendations for resolving direct and structural violence against teachers in Lukulu district. The study was guided by the structural violence theory (Galtung, 1969), Peace education theory (Harris, 2003) and the Critical Peace education theory (Wulf,1974). The study employed interpretive phenomenological analysis, thereby interviewing fifteen participants in the categories of five school managers and ten teachers from five purposively sampled schools. Thematic analysis was used to interpret the findings and investigate the ways in which division, direct and structural violence against teachers affected the delivery of education, development and welfare of teachers. The study revealed that structural violence against teachers manifested in verbal abuse of teachers by senior managers, direct political violence on the teachers perceived to belong to other political parties by the ruling party cadres, violence in form of insults from pupils, parents and community members and chronic gossiping and rumour mongering which proved to be detrimental to the wellbeing of the teachers. Additionally, the study highlighted some of the causes of violence against teachers such as political interference in the running of educational institutions, corruption, bribery and favouritism; lack of transparency and accountability in the appointment of teachers to management positions and poor representation and support from respective teacher unions.  Besides that, the study revealed several effects of violence against teachers such as physical injuries, loss of human life, suicide, loss of property, closure of schools, loss of teaching and learning time as teachers tend to pursue court cases against perpetrators of the violence. In view of the foregoing, this study recommended implementation of  policy reforms that addresses the structural inequalities and  peace education  such as establishment of  the directorate of peace education, development and international education at national, provincial, district and school levels that should have in its structure a national peace education, development and international education director, provincial peace education and development mentors, district peace education and development mentors and school peace education and development mentors. These officers would be critical to advancing the peace education agenda through comparative and international education advocacy.  Finally, this study contributes to Galtung (1969)'s concept of positive peace by proposing three forms of positive peace education. These are direct positive peace education, structural positive peace education and cultural positive peace education. These are critical to the resolution of direct, cultural and structural violence against teachers.

This study explores the role of digital transformation in advancing financial inclusion among rural residents of Ranchi, Jharkhand. With the rapid expansion of digital financial services (DFS) such as UPI, mobile banking, and Aadhaar-enabled Payment Systems, rural populations are increasingly gaining access to formal financial systems. The research employs a quantitative approach, based on primary data collected from 123 respondents using a structured questionnaire. Descriptive statistics were used to analyse demographic factors, benefits, and barriers, while simple linear regression tested the causal relationship between usage of digital financial services and financial inclusion. The results show a significant and positive impact of digital financial services on financial inclusion, with regression analysis confirming that greater use of digital tools enhances access, reduces barriers, and improves overall participation in financial systems. Benefits such as convenience, time-saving, and easier access to government schemes were strongly acknowledged by respondents, while barriers such as poor internet connectivity and lack of digital literacy remain key challenges. The findings validate the hypothesis and indicate that digital financial tools can be powerful drivers of inclusion, provided infrastructural and awareness gaps are addressed. The study contributes to both academic literature and policy discourse by offering localized evidence on digital adoption in rural areas, thereby guiding targeted strategies to bridge the digital divide and promote inclusive economic growth.

Heat pipes are highly efficient passive thermal management devices widely used in electronics cooling, energy systems, and aerospace applications due to their excellent heat transfer capability. This paper presents a comprehensive numerical and experimental investigation of the thermal–hydraulic behavior of a heat pipe system under varying heat input and operating conditions. A detailed numerical model is developed to simulate heat transfer, fluid flow, phase change, and pressure distribution within the evaporator, adiabatic, and condenser sections. The numerical results are validated through controlled experimental testing, focusing on temperature distribution, thermal resistance, heat transfer coefficient, and working fluid dynamics. The comparative analysis demonstrates good agreement between numerical predictions and experimental observations, confirming the accuracy of the proposed model. The results reveal the influence of heat input, working fluid behavior, and wick structure on overall thermal performance and hydraulic stability. The findings provide valuable insights for optimizing heat pipe design and enhancing their reliability and efficiency in advanced thermal management applications.

The rapid increase of Artificial Intelligence (AI) within Supply Chain Management (SCM), has transitioned SCM from using primarily Predictive Analytics & Decision Support, toward increased Autonomy of Decision Execution. However, although there are many examples of AI-driven SCM systems currently being used, they generally suffer from low levels of Trust, poor Governance structures, inadequate Auditability, and unresolved Data Sovereignty issues; all of which limit their potential deployment in High Consequence & Regulated Operational Environments. In an effort to address this important gap, this research introduces a comprehensive Governance First Framework for Trustworthy Agentic Supply Chains; where Autonomous AI Agents use Digital Twin Orchestrated Decision Intelligence to make decisions in accordance with explicit Compliance, Auditability, and Sovereignty Constraints. Agentic Supply Chains are defined as Socio Technical Systems, where Decision Authority is delegated to AI Agents that Continuously Sense, Simulate, Decide, and Act Across Dynamic Supply Networks. Digital Twins are redefined from Passive Visualization Tools to Active Orchestration Substrates that facilitate Real Time State Synchronization, Policy Execution, and Controlled Interaction Between Autonomous Agents and Enterprise Systems. On top of this base, the paper provides a Layered Reference Architecture for integrating Agentic Decision Intelligence, Bounded Autonomy, Governance by Design, and Human Oversight into a Unified Operational Model. The Framework addresses Key Adoption Barriers via Explicit Mechanisms for Regulatory Alignment, Decision Traceability, Data Sovereignty Preservation, and Risk Containment. The Architectural Constructs provided include Agent Drift Detection, Rollback and Safe Recovery, Simulation Based Stress Testing, and Resilience under Adversarial and Extreme Disruption Scenarios. Through the embedding of Governance within the Decision Architecture, the proposed model allows Autonomous Supply Chain Systems to be Auditable, Compliant, and Strategically Controllable while Retaining Adaptive Intelligence. Additionally, beyond technical design, the paper outlines Evaluation Metrics, Organizational Integration Principles, Ethical Considerations, and Strategic Implications related to Delegating Decision Authority to Agentic AI Systems. Finally, the Study identifies a Forward-Looking Research Agenda addressing Multi-Agent Coordination, Cross-Enterprise Autonomy, and Next Generation Optimization Paradigms. Overall, this Work establishes Trustworthy Agentic Supply Chains as a Distinct and Necessary Evolution of Supply Chain Intelligence, providing a Reusable Reference Framework for Researchers, Practitioners, and Policymakers looking to operationalize Autonomous Decision Systems Responsibly and at Scale.

The Kalman filter code used in the active control system is described in detail in this thesis. In order to react to variations in the primary noise and 5G environment, traditional active noise management techniques typically use an adaptive filter, such as the filtered reference least mean square (FxLMS) algorithm. However, the weak convergence features of the FxLMS algorithm typically affect how well dynamic noise is reduced. This research suggests utilizing the Kalman filter in the active noise control (ANC) system to enhance the efficacy of noise reduction for dynamic noise. The Kalman filter is used effectively by the ANC application using a new dynamic ANC model. The numerical simulation shows that the proposed Kalman filter works better than the FxLMS approach in terms of convergence performance for handling dynamic noise. This suggests that the transition of the control filter has a higher degree of confidence than the observation function. When the effects of various mu and K values were examined, it was discovered that the LMS algorithms had a slow rate of convergence for mu = 0.01 and high starting error signals for both echo and noise cancellation that subsequently decreased. Although there was some improvement in the noise and echo-canceled signals, the residual noise and echo persisted. Error signals dropped more quickly and the convergence rate was better with mu = 0.05 than with mu = 0.01; this suggests more efficient cancellation. There was less lingering echo and noise in the resulting clearer signals, with echo and noise cancelled. Hybrid LMS algorithms exhibited rapid convergence at μ = 0.1, with error signals declining sharply, indicating effective cancellation. With little lingering interference, the noise-cancelled and echo-cancelled signals were noticeably clearer. Although the convergence was quite quick with mu = 0.5, there was a higher chance of instability, particularly for the LMS method. It is often advised to use a hybrid algorithm with a mu value of between 0.05 and 0.1. Achieving the ideal balance between convergence speed and stability requires proper mu tuning, which guarantees efficient cancellation without creating instability.

The explosion of electronic communication has made email a foundation of contemporary professional and educational existence. Nevertheless, the large amount of unstructured correspondence places an enormous cognitive burden on the users, resulting in ineffective task processing, lost deadlines, and lowered productivity. This paper presents an intelligent personal assistant aimed at addressing these issues. The system combines a cutting-edge large language model (LLM) with general productivity APIs, such as Gmail and Google Calendar, to develop a seamless, automated process. The system feeds on and parses email content automatically to carry out three fundamental functions: creating brief summaries for rapid understanding, extracting action items and deadlines to schedule matching events in an electronic calendar, and offering a question-answering interface where users can pose particular questions about an email's content. A strict experimental analysis carried out on a manually annotated subset of the Enron email corpus proves the effectiveness of the system. The system attained an F1-score of 0.87 for extracting tasks and deadlines and ROUGE-L scores of 0.42 for summarization, reflecting high-quality performance. Qualitative analysis also supports the question-answering module's capability to correctly retrieve information. The main contribution of this project is the conception, implementation, and evaluation of a strong, end-to-end system that adequately closes the semantic gap between passive email information and actionable, queryable intelligence, offering an effective solution for individual digital productivity enhancement.

Over 40 years after the identification of human immunodeficiency virus (HIV), Nigeria remain one of the highest burdens of HIV infections in the world, accounting for almost 10% of new infections in sub-Saharan Africa. Despite significant investments and technical supports from different foreign donors including the United States President’s Emergency Plan for AIDS Relief (PEPFAR), the Global Fund, and bilateral partners. The persistent structural, financial, and programmatic gaps continue to hamper the country’s HIV response. This assessment of HIV-related interventions in Nigeria examines what has been achieved, what still need to be done, and how to establish a sustainable and domestically owned HIV care. The review summarizes evidence from peer-reviewed literature (2018–2025) and major institutional reports (UNAIDS, NACA, WHO, PEPFAR) to assess five key domains: coverage and access, funding and sustainability, health system strengthening, monitoring and evaluation, and sociocultural barriers. Evidence shows that while substantial progress has been achieved in testing, antiretroviral therapy (ART) coverage, and community-based care, the HIV response remains heavily donor-dependent, urban-centered, and fragmented across vertical program streams. The review concludes that to achieve long-term epidemic control (EC) and universal health coverage (UHC) in Nigeria’s HIV care and programming with there is a need for domestic financing, health system integration, decentralized service delivery, and data-driven accountability frameworks.

This study presents an experimental investigation on the performance of nano-silica–modified concrete under varying cement replacement ratios. Nano-silica, owing to its high surface area and pozzolanic reactivity, was incorporated as a partial replacement of ordinary Portland cement at different percentages to evaluate its influence on concrete properties. A comprehensive experimental program was conducted to assess fresh and hardened characteristics, including workability, compressive strength, tensile strength, and durability-related parameters. The results demonstrate that the inclusion of nano-silica significantly enhances the mechanical performance and microstructural densification of concrete up to an optimum replacement level, beyond which marginal reductions in performance were observed due to particle agglomeration and reduced workability. The findings highlight the potential of nano-silica as an effective supplementary cementitious material for producing high-performance and sustainable concrete, offering improved strength and durability while reducing cement consumption.

Hydrodynamic journal bearings are critical components in high-speed rotating machinery, where their performance is strongly influenced by thermal effects generated due to viscous shearing of the lubricant. This paper investigates the impact of thermal variations on the load-carrying capacity and stability characteristics of hydrodynamic journal bearings. Temperature rise within the lubricant film alters viscosity distribution, pressure development, and film thickness, thereby affecting bearing stiffness, damping coefficients, and dynamic stability limits. A thermo-hydrodynamic framework is employed to analyze the coupled effects of heat generation, heat dissipation, and fluid–structure interaction on bearing behavior under varying operating conditions. The results demonstrate that increased thermal gradients lead to a reduction in load capacity and can significantly influence the onset of instability phenomena such as oil whirl and oil whip. The study highlights the necessity of incorporating thermal considerations in bearing design and performance prediction to ensure reliable and stable operation of rotating systems.

Artificial intelligence (AI) has demonstrated considerable potential in enhancing educational practices, while the integration of socio-scientific issues (SSI) in science instruction provides meaningful learning experiences that promote higher-order thinking and societal responsibility. This study examined the effects of an AI-mediated instructional approach integrating Socio-Scientific Issues within Green Chemistry Education (AI-SSI in GCE) on learners’ chemical literacy (CL) and scientific argumentation skills (SAS). A one-group pretest–posttest quasi-experimental design was employed involving 31 Grade 11 learners. Data were collected using a Hydrocarbons Chemical Literacy Test (H-CLT), a 30-item multiple-choice instrument aligned with established chemical literacy domains, and a Claim–Evidence–Reasoning–based Argumentative Writing Assessment (CER-AWA). The results revealed statistically significant improvements with large effect sizes in both chemical literacy and scientific argumentation skills following the intervention. Learners demonstrated enhanced abilities to apply chemical knowledge across content, contextual, higher-order, and affective domains, particularly in addressing real-world environmental issues such as plastic pollution. Analysis of scientific argumentation indicated that learners were able to construct complete claims, select relevant evidence, and partially articulate coherent scientific reasoning linking evidence to claims. Additionally, learners exhibited increased awareness of green chemistry principles, environmental sustainability, and the societal implications of chemical decision-making. In conclusion, the AI-SSI in GCE instructional approach effectively fostered cognitive, argumentative, green chemistry awareness, and socio-affective learning outcomes. The findings support the integration of socio-scientific issues and green chemistry concepts into chemistry instruction and highlight the potential of AI as a pedagogical scaffold. However, ethical considerations related to AI use in education may be addressed, and continued optimization and professional dialogue among educators are essential to ensure responsible and learner-centered implementation.

The utilisation of electric vehicles (EVs) plays an integral role in the transition toward a more sustainable future in the automotive industry. The purpose of this review is to provide a structured and systematically organised analysis of EVs, focusing on the factors that encourage their usage and how these factors relate to the performance of the vehicles. With this structured overview, this review will improve the understanding of EV fundamentals and shed light on dubious or debated areas. By comparing and examining a range of research papers, this review singles out key correlations and insights to present the most reliable information. Ultimately, it not only enhances the overall understanding of electric vehicles but also offers a promising foundation for future research in this field.

This study focuses on the design and fabrication of wear-resistant coatings using advanced thermal spray techniques to enhance the surface durability of engineering components. Various coating materials, including ceramics, metallic alloys, and composite powders, were deposited onto substrate surfaces through controlled thermal spraying processes such as plasma spraying and high-velocity oxy-fuel (HVOF) spraying. The microstructural characteristics, adhesion strength, and wear resistance of the fabricated coatings were systematically evaluated under different operational conditions. Experimental results demonstrate that optimized thermal spray parameters significantly improve coating density, hardness, and resistance to abrasive and erosive wear, highlighting the potential of these coatings for extending the service life of critical industrial components. The results demonstrate that coating performance strongly depends on deposition technique and process parameters, with HVOF-sprayed composite coatings exhibiting superior density, adhesion, and wear resistance. This study presents a comparative parametric analysis of thermal spray techniques rather than a universal coating framework.

The digital transformation of commerce has reshaped e-commerce from a transaction-oriented channel into a complex, platform-mediated ecosystem involving multiple interdependent actors. This study positions itself as a conceptual and narrative review that critically examines the evolution of the e-commerce ecosystem and its implications for contemporary business models. Drawing on recent peer-reviewed literature, policy reports, and conceptual frameworks, the paper analyses how platform governance, ecosystem power asymmetries, data control, and network effects influence value creation and strategic decision-making. The review highlights a structural transition from linear value chains to ecosystem-based business models while critically addressing risks related to platform dominance, strategic dependency, and long-term sustainability. The study contributes theoretically by integrating ecosystem and platform perspectives into business model analysis and offers informed implications for firms and policymakers.

The contemporary climate crisis is often framed as a scientific, technological or economic challenge, yet at its core it reflects a deeper philosophical and civilizational rupture: humanity’s loss of ecological consciousness. Ancient Indian ecological thought—expressed through the Vedas, Upanishads, epics, Buddhist and Jain traditions, and classical environmental ethics—offered a view of life grounded in interdependence, restraint, and moral responsibility toward nature. This article re-examines that wisdom not as nostalgia but as a conceptual resource for navigating global warming. It argues that ancient Indian thought recognized ecological limits long before modern sustainability discourse emerged, viewing nature as a living, sacred continuum rather than an inert resource. By exploring principles such as ṛta (cosmic order), ahimsa (non-harm), aparigraha (non-possession), lokasaṃgraha (welfare of all), and prithvi dharma (Earth ethics), this study demonstrates how Indian ecological philosophy provides a moral grammar for climate resilience. The article also analyses how industrial modernity’s extractive worldview diverges from these insights, resulting in accelerated ecological collapse. By placing ancient Indian ideas in dialogue with contemporary climate science, environmental justice debates, and global policy failures, the paper shows how this wisdom can enrich climate ethics, inspire alternative development models, and offer a deeply human orientation to planetary survival. The conclusion suggests that the climate crisis is not merely an environmental failure but a civilizational amnesia—and recovering ancient Indian ecological sensibilities may illuminate pathways toward a more sustainable future.

Start-up firms these days are always seeking a means of acquiring funding to achieve their objectives and foster the growth of their enterprises. Over the last decade, new approaches to acquiring capital have emerged in the business sector. One option is crowdsourcing. Crowdfunding has rapidly become a popular method of financing in a short time.  Crowdfunding is a concept that has significant importance. It contributes to the rapid growth of a new and emerging business, while also providing new investment opportunities and diversifying the portfolios of investors. Startups with unique and inventive ideas may be able to secure funding by advertising their concepts on social media platforms. These social media platforms facilitate the connection between entrepreneurs and those who support the cause or have a potential interest in creative concepts. Crowdfunding is still in its nascent stage of development in India. This study examines the use of crowdfunding in India as an alternative means of obtaining company capital compared to stock shares and other financial sources. The objective of this research has been to the challenges and complexities it entails, and its emphasis on Indian circumstances. The study focused on analysing the various forms of crowdfunding and the regulatory framework governing crowdfunding in India. The study asserts that crowdfunding initiatives have significant potential inside the Indian venture capitalist sector, provided that the uncertainty surrounding its laws and regulations is resolved. This concept was first used in the year 2000 and had been rapidly gaining popularity worldwide

This study examines critical success factors for implementing Agricultural Technology (AgriTech) solutions in rural African Small and Medium Enterprises (SMEs), focusing on comparative case studies from Zimbabwe, Kenya, Nigeria, Ghana, and Rwanda. The research adopts a comprehensive desk review methodology, analyzing recent literature, policy documents, and implementation reports from 2020-2025. Key findings reveal that successful AgriTech implementation depends on five primary factors: robust stakeholder involvement encompassing government, private sector, academia, and farmer organisations; comprehensive ICT infrastructure development including broadband connectivity and digital literacy programs; sustained capacity building initiatives targeting technical skills and agricultural knowledge; deliberate gender inclusion strategies addressing women farmers' specific needs; and innovative public-private partnership models facilitating resource mobilisation and risk sharing. The study identifies significant barriers including the digital divide affecting 61% of rural African populations, funding constraints limiting technology adoption, and skills gaps impeding effective utilisation. Comparative analysis demonstrates that countries with integrated national digital agriculture strategies, such as Rwanda's Smart Agriculture program and Kenya's DigiFarm platform, achieve higher implementation success rates. The research contributes to understanding how contextual factors influence AgriTech adoption patterns across different African economies. Results indicate that successful implementations require minimum 3:1 return on investment demonstration, localised content development, and phygital service delivery combining digital and physical channels. The study concludes that sustainable AgriTech implementation necessitates holistic approaches addressing technological, social, economic, and institutional dimensions simultaneously. Key policy takeaways include prioritising gender-inclusive public-private partnership models with demonstrated 3:1 ROI benchmarks and establishing national broadband rollouts achieving minimum 85% rural coverage to ensure equitable technology access across diverse agricultural contexts.

Body fat plays a crucial role in human physiology, and its distribution throughout the body influences the health outcomes (1). The types of fats differ based on location, function, and metabolic impact. Anatomically, there are several different depots of fat, including subcutaneous, visceral, intramuscular, intermuscular, and ectopic (2). Skinfold thickness measurement at common sites of the body, like triceps, biceps, subscapular, suprailiac, and thigh regions, provides an estimate of the body fat for health professionals to assess the health of a person (3). This research examines the skinfold thickness measurements of 106 female students between the ages of 16 and 25 years to evaluate their nutritional status. The measurement results of skinfold vary across participant girls, ranging from 47 mm to 140 mm, indicating substantial differences in subcutaneous fat. The Sum of skinfold (SSF) of samples Around 37.5% of the sample fell into the high-adiposity category (SSF >100 mm), while 33% had moderate levels of adiposity (SSF 70–100 mm). Subscapular and suprailiac skinfold measurements were used to measure central adiposity, which was highest in girls with higher body weights.

The Fast-Moving Consumer Goods (FMCG) industry in Bangladesh has become highly competitive, making effective marketing strategies essential for influencing consumer purchasing behavior. This study examines the role of marketing strategies in shaping consumer buying decisions with special reference to Bashundhara Food and Beverages Limited. The research focuses on key marketing dimensions such as product quality, pricing strategy, promotional activities, and distribution effectiveness. A quantitative research approach was adopted, and primary data were collected from 150 consumers using a structured questionnaire based on a five-point Likert scale. The findings indicate that marketing strategies have a significant and positive impact on consumer purchasing behavior, with product quality and promotional activities emerging as the most influential factors. The study provides practical insights for FMCG companies to develop effective marketing strategies that enhance consumer satisfaction, brand preference, and long-term loyalty in the Bangladeshi market.

Aloe vera is a perennial, drought-tolerant succulent that belongs to the family Liliaceae, widely valued for its therapeutic, nutritional, and cosmetic properties. The objective of this review is to critically summarize the chemical composition, medicinal properties, and industrial uses of Aloe vera, with emphasis on its major bioactive constituents. A systematic review of peer-reviewed literature, books, and authoritative databases was conducted to compile and synthesize information on phytochemistry, pharmacological activities, and commercial utilization of Aloe vera. The analysis shows that Aloe vera contains numerous biologically active compounds, including saponins, minerals, enzymes, sugars, vitamins, lignin, amino acids, anthraquinones, fatty acids, and polysaccharides. The leaf pulp comprises a complex mixture of enzymes, proteins, lipids, carbohydrates, and inorganic and organic compounds, which collectively contribute to its broad pharmacological profile. Reported studies demonstrate anti-inflammatory, antimicrobial, antioxidant, anti-diabetic, anti-aging, laxative, antitumor, wound-healing, and immunomodulatory activities. Owing to these properties, Aloe vera is extensively incorporated into pharmaceutical formulations, functional foods, nutraceuticals, and cosmetic products for both internal and topical applications. In conclusion, Aloe vera is a multifunctional medicinal plant of considerable therapeutic and commercial significance. Its biological effects are primarily attributed to the synergistic interaction of multiple bioactive compounds rather than a single active principle. However, variability in chemical composition arising from environmental conditions, cultivation practices, and processing methods poses challenges for standardization and quality control. Further well-designed experimental and clinical studies are required to clarify structure–function relationships and to ensure the safe, effective, and standardized utilization of Aloe vera in health-related industries worldwide and future applications.

This study assessed the effectiveness of e-governance on public service delivery in Zanzibar. Specifically, the study examined the contribution of e-governance in improving the accessibility of public service, the impact of e-governance in facilitating resource utilization on the delivery of public service, and the effect of e-governance in enhancing accountability on the public service delivery in public Institutions with a focus on three key variables: accessibility, resource utilization, and accountability. Accessibility is critical in ensuring that more citizens can engage with digital services, while effective resource utilization enhances the system’s capacity to meet public needs and accountability, though slightly less impactful, is vital for maintaining public trust and transparency in service delivery. Using a quantitative approach data was collected from 70 respondents. The findings indicate that accessibility has the strongest positive effect on public service delivery, followed by resource utilization and accountability, all of which are statistically significant. Accessibility has the strongest positive impact on Public Service Delivery with a notable unstandardized coefficient (B = 0.675) and standardized coefficient (β = 0.456). This suggests that for every one-unit increase in ACB, there is a corresponding increase of 0.675 units in PSD. While Resource Utilization and Accountability have smaller coefficients than ACB, their effects are nonetheless statistically significant. Resource Utilization (B = 0.320, β = 0.287) has a stronger effect than Accountability (B = 0.245, β = 0.198), indicating that effective resource allocation is more closely tied to perceived service delivery than accountability mechanisms. The study concludes that improving digital access, optimizing resources, and enhancing accountability mechanisms are essential for the success of e-governance initiatives in public institutions. Recommendations for policy improvements and further research are provided to guide the continued development of e-governance systems for better public service delivery.

The exponential growth of digital healthcare data and the increasing need for secure, interoperable medical information systems have positioned blockchain technology as a promising solution for medical data storage and sharing. This survey provides a comprehensive analysis of blockchain-based secure storage schemes for medical information, examining 140+ research papers published between 2018-2025. We systematically categorize existing approaches into five primary themes: privacy-preserving storage mechanisms, access control frameworks, interoperability solutions, consensus and trust models, and smart contract implementations. Our analysis reveals that hybrid architectures combining on-chain metadata with off-chain encrypted storage (particularly using IPFS and cloud services) have emerged as the dominant paradigm. Key cryptographic techniques include attribute-based encryption (ABE), homomorphic encryption, and differential privacy for protecting sensitive medical data. We identify permissioned blockchain platforms, especially Hyperledger Fabric and Ethereum-based private networks, as preferred choices for healthcare consortiums. Major challenges include scalability limitations, regulatory compliance (HIPAA, GDPR), interoperability with legacy systems, and governance frameworks. Recent advancements focus on post-quantum cryptography integration, AI-enabled healthcare blockchains, and patient-centric digital twin implementations. This survey concludes with a discussion of future research directions, including quantum-resistant security schemes, cross-chain interoperability, and standardization efforts for blockchain-based healthcare systems.

This article aims to understand the influence of Artificial Intelligence (AI) on Hospital Administration and Management in Mozambique (HAM). To this end, the methodology employed was a literature review of articles on AI-related topics, conducted through searches in Science Direct, PubMed, Scopus, the website of the Mozambican Ministry of Health, and the libraries of the Faculty of Health Sciences at Zambeze University and the Faculty of Medicine at Eduardo Mondlane University. The main results concluded that Mozambican HAM does not apply AI tools, particularly those related to increasing productivity in sectors such as Predictive Maintenance, Bed Management, and Staffing Planning, due to high equipment installation and maintenance costs, precarious transportation, connectivity problems, and staff shortages. Another important conclusion was that, in the literature review process on the subject, little material was found, as little is published, especially in the Mozambican context. Therefore, aware that this article does not cover the full scope of AGH, other aspects such as the application of AI in transport (ambulances), medication management, hospital logistics, competency-based accounting, and others, may constitute topics of interest

This study examined the potential and challenges for sustainable plastic waste management in Karisimbi Commune, Goma City, North Kivu Province in DRC. It aimed to identify infrastructural and institutional gaps, assessed community attitudes and practices, explored local recycling initiatives, and proposed improvement strategies. Using a mixed-method approach involving surveys, interviews and observations, the study founded major infrastructural shortcomings, notably poor waste collection systems, limited sorting and recycling facilities, and difficult access in densely populated areas. Findings showed that most respondents agreed there were a lack of designated plastic waste collection points (Mean = 3.33; SD = 1.40), indicating a strong perception of inadequate infrastructure, further exacerbated by weak policy enforcement and limited institutional capacity. Although awareness of plastic waste hazards is high, unsustainable behaviors persist. Nonetheless, local initiatives such as NGOs partnerships, youth-led cleanup campaigns, and informal recycling efforts demonstrated growing community engagement. The study recommended decentralized waste governance, strengthened public-private partnerships and environmental education to enhance sustainable plastic waste management and urban environmental resilience in Karisimbi commune and beyond.

Climate change happened to be one of the most serious global challenges of modern times, with legal, political, and social consequences. However, international law has been used as a tool to guide how states respond to climate risks, reduce greenhouse gas emissions, and protect vulnerable populations. This review paper examines how Canada and the United States respond to climate change within the framework of international law as of 2025. Although both countries are economically advanced and historically high emitters of greenhouse gases, their legal approaches to climate governance vary considerably. Canada remains actively engaged in international climate agreements and incorporates these commitments into domestic legislation. On the contrary, the United States has adopted a reserved approach, including withdrawal from major international climate instruments and reduced reliance on international legal commitment. By reviewing legal instruments, policies, and scholarly literature, this paper highlights how differences in policy objectives and legal interpretation shape national climate responses. The paper concludes that these contrasting approaches have important implications for the effectiveness of international climate law and global climate cooperation.

Potato (Solanum tuberosum L.) is one of the most important food crops worldwide, playing a dual role as a staple food for economically weaker populations and as a culinary delicacy for affluent societies. It is valued for its high productivity, adaptability to diverse agro-climatic conditions, and rich nutritional profile comprising carbohydrates, dietary fibre, vitamins, minerals, and bioactive compounds. Potatoes contribute significantly to food security, poverty alleviation, rural employment, and agro-industrial development. Simultaneously, processed and gourmet potato-based products dominate global food markets and modern gastronomy. This review highlights the nutritional composition, socio-economic importance, culinary diversity, health implications, and future prospects of potato cultivation, emphasizing its role in sustainable food systems and socio-economic development.

This study examines the growing influence of financial technology (FinTech) on sustainable economic growth in Oman, with a focus on how digital financial solutions support the country’s transition toward a more resilient and innovation driven economy. The research explores key FinTech developments, including mobile payment systems, digital banking platforms, blockchain applications, and automated financial services and evaluates their contribution to financial inclusion, operational efficiency, and environmentally responsible economic activities. Using a mixed-method approach, the study analyzes policy initiatives, market trends, and stakeholder perspectives to assess the effectiveness of FinTech in advancing national sustainability objectives outlined in Oman Vision 2040. The findings indicate that FinTech plays a critical role in strengthening SME financing, promoting green investment, and enhancing transparency in financial transactions. The study concludes that continued expansion of digital infrastructure, regulatory support, and public–private collaboration is essential for maximizing the long-term economic and environmental benefits of FinTech in Oman.

Digital transformation in spatial planning management is an important requirement for local governments in providing information Which accurate, latest, And easy accessible. City Binjai as region urban The ever-evolving spatial information system requires a spatial information system capable of effectively integrating spatial and non-spatial data. This study aims to realize the transformation of spatial information in Binjai City through the development and maintenance of web-based WebGIS. The research methods used include inventory and identification of geospatial data availability, processing and updating spatial data, and designing and implementing a WebGIS system. The data used consist of the Indonesian Topographic Map (RBI) at a scale of 1:10,000–1:25,000, high-resolution satellite imagery of Spot 7, base maps, thematic maps, and spatial planning maps. WebGIS development is carried out using open source technology, namely PostgreSQL/PostGIS as a spatial database, GeoServer as a geospatial data server, and LeafletJS as a web-based mapping interface. The research results show that the developed WebGIS is capable of presenting spatial information about Binjai City interactively and in an integrated manner, including base maps, thematic maps, and spatial planning maps. Furthermore, the system maintenance strategy involves regular data updates and database management. support sustainability and reliability WebGIS. Existence WebGIS This contribute to improvement accessibility, transparency, and effectiveness provision information order room, as well as support spatial data-based decision making.

This study examines the lived experiences of Jan Aushadhi Kendra (JAK) owners to understand how the Pradhan Mantri Bhartiya Jan Aushadhi Pariyojana (PMBJP) is enacted at the grassroots level and what factors shape its long-term sustainability. Positioned at the intersection of entrepreneurship and public health delivery, JAK owners play a critical role in translating policy intent into everyday access to affordable medicines. Using a qualitative research design, in-depth semi-structured interviews were conducted with 74 JAK owners across Kerala and Karnataka. Data collection continued until thematic saturation was achieved, and the interviews were analysed using reflexive thematic analysis following Braun and Clarke's framework. The findings reveal four interrelated themes. First, owners articulated a social mission and hybrid entrepreneurial identity, framing their participation as public service-oriented rather than profit-driven. Second, the medical community emerged as institutional gatekeepers of trust, with limited endorsement from doctors and allied health professionals constraining consumer confidence in generic medicines. Third, business sustainability was found to be fragile, shaped by low margins, expiry-related losses, supply chain disruptions, and stock-outs that undermined both financial viability and customer trust. Finally, trust-building, consumer perceptions, and awareness gaps highlighted how persistent quality–price misconceptions and weak institutional communication compelled owners to act as informal educators, relying heavily on interpersonal relationships and word of mouth to sustain demand. Overall, the study demonstrates that while PMBJP has been effective in improving affordability, its sustainability depends on factors extending beyond price, including institutional trust, operational reliability, and coordinated communication. Strengthening medical endorsement, improving supply chain stability, and embedding systematic awareness initiatives are essential for positioning generic medicines as a trusted and mainstream component of India's healthcare system.

The research reports the inclusion of admixture to Self-Consolidating Concrete (SCC) to use locally sourced organic waste material as an alternative building material in reducing the burden of housing provision.  Self-Consolidating Concrete (SCC) is a flowable type of concrete that doesn't require mechanical compaction. It uses high paste and fine aggregate content, small coarse aggregate size, and Supplementary Cementitious Materials (SCMs) to achieve this. SCC's adoption in construction projects has increased cement demand, which contributes to CO2 emissions and environmental issues. SCMs like Locust Bean Pod Ash (LBPA) and Groundnut Shell Ash (GSA) are being studied to improve SCC's properties. In this study, LBPA and GSA were used to replace 20% of cement in SCC mixes. Results indicate that LBPA and GSA are effective pozzolans, but at 20% replacement, they didn't enhance mechanical performance significantly. However, a mix with 75% GSA and 25% LBPA showed comparable performance at 28 days, with compressive strength of 33.64N/mm2 and split tensile strength of 3.91N/mm2. The fresh properties of the SCC mixes met EFNARC standards.

Campus placement is a critical milestone in a student’s academic journey, as it significantly influences their professional future. To address the challenges of fragmented and unstructured placement preparation, this paper presents Preplyte, an AI-powered placement preparation and simulation platform. The platform provides structured training for aptitude assessments, coding evaluations, interview preparation, and soft skill development within a single integrated system. Preplyte offers company-specific preparation processes, personalized practice modules, and continuous feedback to improve student confidence and performance. AI-driven mock interviews are conducted based on student resumes, while an integrated resume builder with ATS scoring helps optimize resumes for real recruitment systems. In addition, the platform enables educational institutions to track student progress, analyze performance, and provide targeted guidance. By combining intelligent automation with realistic placement simulations, Preplyte aims to enhance placement readiness and reduce failure rates in campus recruitment processes.

Africa faces increasing thermal stress from rising temperatures, rapid urbanisation, and energy poverty, making it hard to provide indoor environments that are both comfortable and sustainable. Among the range of cooling solutions, passive cooling strategies using natural ventilation, shading devices, and materials with high thermal inertia are considered urban-scale interventions, all low-energy approaches that align with bioclimatic design principles and allow for the incorporation of local knowledge. In this systematic review, the researcher seeks to bring together previous work on the thermal comfort outcomes of passive cooling strategies in contemporary architecture and to identify performance patterns that depend on context. Following the PRISMA 2020 guidelines, we selected ten post-2018 studies from databases including Scopus, Web of Science, ScienceDirect, and African Journals Online, along with grey literature and regional climate reports. The data was extracted according to strategy type, building typology, climate zone, thermal metrics (PMV, PPD, adaptive hours), and energy impact. The findings show that the application of multiple strategies together always produced better results than that of the single-strategy approaches, with a reduction in the indoor temperatures of 3-5°C and the cooling energy demand of 20-60%. The effectiveness highly depended on the climate, with cross-ventilation and shading being the best in hot-humid areas and high thermal mass being the most important in arid regions. The application of historical and vernacular designs increased the adaptability, especially when combined with modern innovations such as reflective coatings and PV shading. The analysis reveals a persistent gap between the designer's expectations and the users' comfort, thus highlighting an urgent requirement for standard evaluation and climate-based guidelines. Generally, the application of climate-responsive PCS can cause thermal comfort to be better, energy consumption to be less, and African urban resilience to be more durable.

The rapid evolution of financial technology has revolutionized traditional banking operation, particularly in loan sanctioning and recovery. This paper examines the transformative impact of FINTECH innovation, including AI Based Credit scoring, peer to peer lending, block chain technology, and Machine learning and digital document verification. Additionally, data for this study were gathered through interviews with bank Managers and Officers. Finding of the study highlight that digital lending platform have significant accelerated loan approval process, and enhanced the accuracy of credit risk assessments. Furthermore, for loan recovery automated reminders, communication channels and predictive analytics have improved collection rates and minimized defaults. However, challenges remain including cybersecurity threats, regulatory uncertainties and borrower data privacy concern. 

The boundary layer flow due to a surface stretching with a power law distribution in the presence of a transverse magnetic field is studied. A approximate Numerical solution for the flow problem has been obtained by solving the governing equations using Numerical Technique. A magnetic field is applied transversely to the direction of the flow. Adopting the similarity transformation, governing non linear partial differential equation of the problem are transformed to non linear ordinary differential equations. Then the numerical solution of the problem is derived using Quasilinearization method, for different values of the dimensionless parameter. The results obtained show that the flow field is influenced appreciably by the presence of chemical reaction and magnetic field.

This study examines the effect of workplace flexibility on millennial job engagement in state hospitals within Ogun West Senatorial District, Nigeria, drawing on the Job Demands–Resources (JD‑R) Model. A descriptive cross-sectional survey was conducted among healthcare professionals in two state hospitals in Ogun West, with a total population of 246 and a proportionate sample of 151 respondents selected through probability-based methods. The study employed a structured questionnaire comprising 16 items on workplace flexibility, 15 on autonomy, 14 on professional growth, and 13 on engagement, all measured on a 5-point Likert scale. A pilot study with 30 healthcare professionals was used to validate the instrument. Data were analysed using IBM SPSS Statistics (version 28.0), including descriptive statistics, Pearson correlation analysis, and multiple linear regression. The study found that workplace flexibility, autonomy, and professional growth are positively linked to engagement, with autonomy and professional growth identified as significant predictors, and the regression model explaining 42% of the variance in engagement (R² = 0.42; F(2, 148) = 24.87; p < 0.01). A small minority reported that excessive autonomy created ambiguity and stress, suggesting that autonomy should be structured by clear expectations and feedback. The findings are situated within emerging evidence on generational differences in Nigerian healthcare. The study recommends that hospital administrators and policymakers implement structured autonomy, strengthen professional development systems, and institutionalise flexible work policies to enhance millennial engagement and retention.

This research produces and analyzes an innovative nonlinear compartmental model to explore the infiltration of firearms into Nigeria and its resulting insecurity dynamics in Northern Nigeria. The model incorporates four interacting components: susceptible individuals, violent actors, arms and ammunition, and recovered individuals, capturing the bidirectional feedback between arms infiltration and violence recruitment. An effective reproduction number, Re​, was derived to characterize the threshold conditions governing the persistence or elimination of violence. Stability analyses, including Lyapunov-based proofs (), create the conditions for global stability of both the violence-free and endemic equilibria. Normalized sensitivity analysis using Python revealed that arms-related parameters, particularly arms inflow (σ =+0.312), arms to violence activation (α= -0.020), and arms-induced susceptibility (γ=+0.254), are the most influential drivers of violence propagation. Numerical simulations were used to validate the investigative results, which showed how reducing arms availability can substantially lower the long-term levels of violent actors. These findings showed that illicit arms proliferation is the primary catalyst sustaining endemic violence. The necessary interventions, such as addressing arms inflow, distribution networks, and de-escalation processes, are most effective for violence reduction. The simulation results establish that the long-term dynamics of violence in Northern Nigeria are strongly driven by the circulation and availability of illicit firearms. The trajectories for S(t), V(t), and A(t) showed that the reductions in arms-related parameters produce meaningful declines in violence, confirming the sensitivity analysis results. This study provides a quantitative framework and actionable insights to support evidence-based security policy, arms-control strategies, and conflict-mitigation efforts in Northern Nigeria.

In recent years, digital technologies and artificial intelligence (AI) have begun to transform nutrition research and dietetics practice. This review examines how AI and digital tools are applied in dietary assessment, personalized planning and behaviour monitoring; how mobile apps and digital platforms support nutrition counselling; how machine-learning models predict nutrient intake and health outcomes; the ethical, privacy and equity concerns that arise; and future trends including AI-driven virtual dietitians and fully integrated nutrition-care ecosystems. Although promising, the deployment of these innovations must navigate issues of data quality, algorithmic bias, professional roles, user engagement and regulatory frameworks. Effective integration into dietetics will require interdisciplinary collaboration, transparent methods, and equitable access.

Survival time analysis focuses on the time until an event occurs and is used to identify risks in survival data. This study employs Non-Parametric (Kaplan-Meier) methods to assess median survival time, Log-rank tests to compare hazard and survivor functions, Semi-Parametric (Cox Proportional Hazards), and Parametric approaches to determine the best-fitting distribution. Prostate Cancer (PC) is the second most common malignancy in men worldwide, with 1,276,106 new cases and 358,989 deaths in 2018 (Rawla, 2019). The incidence and mortality of prostate cancer increase with age, with the average diagnosis age being 66 years. African-American men have higher incidence rates (158.3 new cases per 100,000 men) and nearly double the mortality rate compared to White men (Capece et al., 2020). This study found that both the age of patients and the year of admission were consistently significant factors. The Log-logistic model was identified as the bestfitting model with an AIC value of 302.7047, compared to the Cox Regression model's AIC value of 434.0985.

This study discusses the usage of machine learning algorithms in the detection of emotions. It helps to fill the gap between the human understanding of emotions and the artificial intelligence being developed, both in relation to technological progress and human-machine interactions. Applying the KDD process to NLP systematically involves identifying, preprocessing, mining patterns, and interpreting textual data. Techniques of Data Mining, like Logistic Regression, Linear SVC, and Multinomial Naïve Bayes, have been applied for the purpose, accompanied by metrics like Classification Reports for Precision, Recall, F1-score, and Accuracy and k-fold cross-validation for a very robust and accurate analysis of the unstructured text domain. Logistic Regression remained the most scoring model of 90%. Its score now was a few percentage points smaller than its training score. As expected, this time Linear SVC found itself in second place with an 88%, and Multinomial Naïve Bayes also stayed in third place on 86%. With cross-validation applied, Logistic Regression proves to be the most reliable model for this NLP study. It scores high and is quite generalizable and stable, thus suitable for deployment in scenarios where robustness and consistency are essential. Potential further improvements to such models could involve hyperparameter and fine-tuning. By trying them out on much larger and varied datasets, they can be put through several checks for testing capabilities and limits within applications. One other extension involves ensemble techniques to build even stronger, more reliable solutions by merging different model strengths together.

Predicting students’ academic outcomes has become a central focus within Educational Data Mining (EDM) to support early academic interventions, minimize dropout risks, and promote personalized learning strategies. The availability of diverse educational data—ranging from demographic details and previous grades to behavioral engagement and digital learning activity—has encouraged the adoption of machine learning (ML) approaches for this purpose. This analysis synthesizes major ML techniques applied to student performance prediction, including regression models, decision trees, random forests, support vector machines, k-nearest neighbors, naïve Bayes classifiers, artificial neural networks, gradient boosting methods such as XGBoost and LightGBM, and deep learning models like CNNs and RNNs. Common evaluation measures—such as accuracy, precision, recall, F1-score, ROC-AUC, MAE, MSE, RMSE, and R²—are also examined. Despite their potential, challenges persist, including inconsistent data quality, complex feature selection, privacy and ethical concerns, limited interpretability, poor generalization across institutions, and the need for frequent model updates. Future research should emphasize explainable AI (XAI), temporal modeling techniques, integration of behavioral and psychological indicators, and transfer learning to enhance scalability and adaptability. Overall, this study highlights the promise of ML-driven systems in improving educational outcomes when combined with ethical, interpretable, and context-aware design principles.

The increasing incidence of urban fire emergencies in Manila City emphasizes the need for efficient, technology-driven response systems capable of improving situational awareness and communication. Conventional fire reporting methods, which rely primarily on manual calls and delayed verification, often result in slower response times and limited coordination among emergency responders. This study aimed to develop and evaluate the Smart Application for Emergency Response (S.A.F.E.R.), an Android-based mobile application designed to enhance fire emergency preparedness and response through real-time location tracking, rapid incident reporting, safety guidance alerts, direct communication channels, and community resilience tools. A descriptive research design was employed, supported by an agile iterative development methodology. The system was evaluated using the Mobile App Rating Scale (MARS) for user-based assessment and the ISO/IEC 25010 software quality model for technical evaluation. A total of 56 respondents participated in the study, comprising 50 Manila City residents and 6 IT/subject matter experts. Descriptive statistical techniques, including mean and standard deviation, were used to analyze the collected data. Evaluation results showed that

ABSTRACT Maternal mortality remains a significant public health issue, particularly in rural regions of developing countries. Early pregnancy (pregnancy among adolescents and young women) is a major contributor to maternal deaths, resulting in profound impacts on community health and well-being. This study focuses on Reckjoani Rural Hospital in Rajarhat Block, West Bengal, to explore the prevalence, causes, and effects of maternal mortality due to early pregnancy. The objective of the study focused on the below  To determine the incidence of maternal mortality among early pregnancies in Reckjoani Rural Hospital. To identify the primary causes contributing to maternal deaths in young mothers. To analyse the impact of these deaths on community healthcare and overall community health. A mixed-method approach was employed, combining quantitative data from hospital records (2023-2024) with qualitative information and data through personal interviews. Quantitative data were analysed to identify trends and common causes of maternal deaths, while qualitative data provided insights into the socio-economic and cultural factors that are influencing early pregnancies and maternal health results and outcomes. The study found that maternal mortality among early pregnancies in Reckjoani Rural Hospital is alarmingly high. Key factors contributing to these deaths include lack of access to prenatal care, inadequate healthcare infrastructure, socio-economic barriers, and cultural practices. The qualitative analysis revealed that early marriage, limited education, and poverty are significant underlying contributors to early pregnancies. The impact on community healthcare is profound, with increased healthcare costs, emotional and psychological stress on families, and loss of productive workforce being notable consequences. Maternal mortality due to early pregnancy in Reckjoani Rural Hospital presents a critical public health challenge. To conduct the study on this issues it requires a multi-focused approach, including implementation of minimum marriage age laws at panchayat ,an elective village council in India  level, improving healthcare infrastructure, enhancing access to education, and implementing community-based awareness to delay the age of first pregnancy. Strengthening healthcare policies like Janani Suraksha Yojana (JSY), Weekly Iron and Folic Acid Supplement (WIFS) programmes under National Health Mission (NHM) by continuous effort by health workers, teachers and block official, increasing community awareness are essential steps towards reducing maternal mortality and improving community health outcomes in Rajarhat Block, West Bengal.

Strut-free retaining wall systems offer important advantages for deep excavations, including improved construction efficiency, safety, and workspace availability. However, their application in excavations deeper than 20 m in soft clay remains limited. This study investigates the deformation behavior and optimal design parameters of a strut-free retaining wall system for a 30 m deep excavation in soft clay using three-dimensional finite element analysis. The retaining system consists of diaphragm walls, buttress walls, cross walls, and rib walls. A total of sixty-one numerical simulations were conducted to evaluate the influence of the number, thickness, and height of buttress and cross walls on wall deflection and ground settlement. The Hardening Soil model was adopted to represent the nonlinear behavior of soft clay, while structural components were modeled as elastic plate elements. The results indicate that increasing the number of buttress walls in the long excavation direction significantly reduces maximum wall deflection and surface settlement, whereas the effect in the short direction is relatively small. Among the investigated layouts, the configuration with two rib walls in the long direction and one in the short direction provided the most effective deformation control.

Medical textiles, also known as healthcare textiles, are a rapidly advancing segment of technical textiles with applications in wound care, infection control, tissue engineering, and medical devices. Current research focuses on developing multifunctional textiles that enhance safety, therapeutic performance, and environmental sustainability while minimizing cross-infection risks in healthcare settings. In this context, the incorporation of herbal bioactives has gained attention due to their inherent antimicrobial, anti-inflammatory, antioxidant, and wound-healing properties. Extracts derived from medicinal plants such as Azadirachta indica (neem), Aloe vera, Curcuma longa (turmeric), and Ocimum sanctum (tulsi) offer natural, biocompatible, and biodegradable alternatives to conventional synthetic finishes used in medical textiles. The intention of this review article is to critically examine recent developments in medical textiles, with a specific emphasis on the medical classification, requirements, and performance of healthcare textiles. It covers various techniques involved in the finishing of fabrics with herbal extracts, such as coating, grafting, microencapsulation, sol-gel processing, and nanotechnology-assisted delivery systems. This review presents future opportunities for herbal-functionalized medical textiles as sustainable and patient-centric solutions for next-generation healthcare applications.

Traffic congestion on major transportation corridors poses significant economic and social challenges, particularly in developing urban regions. This study applies queuing theory to analyze traffic congestion on the Asaba–Onitsha Bridge, a critical transportation link between Delta State and Anambra State, Nigeria. Using real-life traffic data, the study evaluates the performance of the bridge in terms of traffic intensity, queue length, and waiting time under varying service capacity conditions. A quantitative and observational research design was adopted. Primary traffic data were collected through direct field observation during morning and afternoon periods, with vehicles classified into small and big categories to reflect traffic composition. An M/M/2 queuing model was employed. Traffic performance was analyzed under three assumed service rates of 600 veh/hr, 800 veh/hr, and 1000 veh/hr to assess the impact of service capacity on congestion levels. The results reveal clear temporal variations in traffic flow, with the morning period experiencing significantly higher traffic volumes, largely due to the dominance of small vehicles, while the flow of big vehicles remains relatively stable throughout the day. At a service rate of 600 veh/hr, the system operates close to capacity during the morning peak, resulting in higher traffic intensity, noticeable queues, and increased waiting times, indicating a marginally adequate service level. Increasing the service rate to 800 veh/hr leads to substantial reductions in queue length and waiting time, producing efficient traffic flow during both peak and off-peak periods. The best performance is observed at a service rate of 1000 veh/hr, where traffic intensity remains low and near free-flow conditions are achieved even during peak demand. The study concludes that congestion on the Asaba–Onitsha Bridge is strongly influenced by service capacity and temporal traffic demand variations. Enhancing service capacity through improved traffic management or infrastructure expansion can significantly reduce congestion, minimize delays, and improve overall traffic flow on the bridge. The findings provide quantitative evidence to support capacity enhancement through improved traffic management, infrastructure development, and the integration of Intelligent Transportation Systems that provides a modern, cost-effective, and adaptive approach for optimizing existing infrastructure and minimizing traffic delays.

The dietary amino acid requirement pattern established by the World Health Organization is the standard criterion for measurement of protein quality in food and treatment of malnutrition. Indigenous Leafy Vegetables (ILVs) in the region of Kenya are a source of proteins that are culturally acceptable but incomplete; their critical deficiency is in essential amino acids, mainly lysine and Methionine while others are available but limited to the WHO standards. Fermentation has been reported to improve the nutritional bioavailability but the possibility of producing a WHO-compliant complete protein from ILVs through this process has not been investigated extensively. Aiming to do so, the present study sets off to formulate and evaluate a soy-fortified fermented ILV blend through direct quantitative comparison against the WHO standard. In a randomized simulated design, a 1:1:1 mixture of Nightshade, African Spider Plant, and Slender leaf was simulated to fermentation using Lactobacillus plantarum, both without soy (S0) and with soy fortification (S1). The amino acid profiles were generated through a simulated HPLC and evaluated based on the Amino Acid Score (AAS). The formulation comprising of ILVs only remained substandard (AAS 84%), with lysine being the limiting amino acid. On the other hand, the soyfortified product attained an AAS of 118%, thus meeting or surpassing WHO requirement for all essential amino acids, thereby being classified as a high-quality complete protein. The study states that it is not fermentation alone but rather strategic soy fortification that is essential for the achievement of the WHOcompliant nutritional completeness. The optimized 60:40 vegetable-soy blend is a to validated by conducting actual experiment, locally sourced food model which can be used for the sustainable addressing of protein malnutrition in Kenya and other similar regions

This study analyses the role of Supplier Relationship Management (SRM) in enhancing procurement performance at FQM Trident Zambia, a major mining company. Drawing on survey data from 96 procurement professionals, the research identifies the most prevalent SRM practices, assesses their impact on core procurement performance indicators, and explores implementation challenges. The findings show that long-term contracts (100%), strategic partnerships (87.5%), and regular feedback and communication (81.3%) are the most widely adopted SRM practices. Statistical analysis using chi-square tests revealed a significant positive association between SRM integration and cost efficiency (χ² = 13.12, p = 0.041), as well as delivery timeliness (χ² = 17.09, p = 0.009). Specifically, 62.5% of respondents rated procurement cost efficiency as "Good," and 56.2% rated delivery timeliness as "Good." Product quality was rated "Good" by 56.2% of respondents, though the association with SRM integration was not statistically significant. The majority (62.5%) believed SRM had "significantly improved" overall procurement performance. Key challenges cited were limited supplier cooperation (37.5%), lack of internal capacity or training (25%), and resistance to change (25%). The study concludes that while SRM has delivered measurable benefits in cost and delivery, further advances in quality and reliability will require greater investment in training, process standardization, and supplier engagement. These findings offer data-driven guidance for mining organizations aiming to achieve procurement excellence through SRM.

This study used a Structural Equation Modelling approach to examine the socioeconomic and cultural factors influencing the adoption of clean cooking energy in rural Ghana, specifically within the Bunkpurugu-Nakpanduri district. The study bridges a gap in the literature by combining the Diffusion of Innovations Theory and the Technology Acceptance Model, which tends to disregard cultural aspects in SEM analyses. The data was gathered using a mixed-methods approach that included 7 focus groups, interviews with 30 opinion leaders, and surveys with 500 women. SPSS, NVivo, and AMOS were used for Data analysis. Consistent with previous research, the results validate that adoption of clean cooking technology is significantly positively influenced by household income and education. Most importantly, the study finds that cultural norms play a key mediating role, demonstrating that, even when economic and educational limitations are taken into account, deeply rooted customs and social expectations pose a distinct challenge in the Ghanaian context. According to local hospital data, biomass dependence is linked to thousands of respiratory and other illnesses among women and children each year, underscoring the serious health effects of this practice. The study concludes that effective energy transition policies in Ghana's rural areas need to include culturally sensitive interventions in addition to subsidies and awareness campaigns. To achieve sustainable adoption, it is essential that community leaders be involved, that technology be tailored to local culinary customs, that targeted financial support be put in place, and that an integrated strategy be promoted that respects local culture while enhancing affordability and education.

This study explores the relationship between financial literacy and employee personal income management at MYTH Myanmar Legal Services Co., Ltd. Financial literacy encompassing budgeting, saving, investing, and managing debt is vital for promoting individual financial stability and workplace productivity. Using a descriptive mixed-method approach, the research examines how employees’ financial knowledge shapes their ability to manage income effectively. Findings reveal that most employees possess strong budgeting and saving habits but show limited engagement in investment and long-term financial planning. Respondents also expressed positive attitudes toward financial education, recognizing its role in improving decision-making and reducing financial stress. Moreover, enhanced financial literacy was found to contribute to higher job satisfaction and better performance. The study recommends implementing structured financial training and digital literacy programs to strengthen employees’ financial skills, build financial confidence, and support both personal and organizational well-being in Myanmar’s evolving financial environment

Morphometric and meristic analyses are crucial tools for distinguishing fish populations and assessing ecological adaptations within aquatic environments. This study investigated the morphometric and meristic characteristics of Oreochromis niloticus and Hepsetus odoe inhabiting the Owalla Reservoir, Osun State, Nigeria, to determine their population structure, growth patterns, and environmental responses. Ninety-eight specimens (53 O. niloticus and 45 H. odoe) were collected between July and October 2024 using gill and cast nets. Standard morphometric measurements and meristic counts were obtained and analyzed using SPSS version 22.0. Length–weight relationships (LWR) were established through regression models, while the physicochemical properties of the reservoir water were assessed to evaluate environmental suitability. Results revealed positive allometric growth in both species (b > 3), indicating improved weight gain relative to body length, with O. niloticus showing a stronger correlation (r² = 0.96) than H. odoe (r² = 0.91). The water’s physicochemical parameters, including dissolved oxygen (5.10–7.32 mg/L), temperature (24–27.6 °C), and pH (6.57–7.10), were within optimal limits for tropical fish sustainability. Statistical visualizations showed distinct linear growth trends between total length and body weight across species and age groups. The findings underscore the ecological adaptability of both species and their potential for aquaculture development.

Artificial intelligence and full-stack web technology are combined in the solution to the most distressing issues in online retail. Comfy is the name of this solution. By employing one of the most innovative hybrid recommender systems utilizing conversational AI chatbots and combining it with intent analytics, purchase history, and content-based filtering, it assists customers in making purchasing decisions. Since the Comfy platform is built on the MERN (MongoDB, Express.js, React, Node.js) technology stack, it has two components: An AI-based product recommendation system, and an shopping portal for customers, and a admin analytics console for real-time system analytics. This demonstrates how even small and medium-sized enterprises (SME) can harness the power of advanced AI for hyper-personalization at a highly reasonable cost using open-source technology and a thoughtfully architected API. Other noteworthy attributes include the AI-based progressive payment solution with backend security from Razorpay, unique approaches to the safety and security of e-commerce AI systems, and an adaptive recommendation system with multi-layered filtering and responsiveness. With the test system to reality computing, Comfy shows the possibilities of AI and helps e-commerce systems to incorporate AI, making it an essential tool for companies wanting to create an e-commerce system with AI.

This study investigated the supervised work-based learning (SWBL) performance of Technical-Vocational Education and Training (TVET) tourism students deployed in partner tourism establishments in Digos City, Philippines, during Academic Year 2022–2023. Employing a descriptive-comparative quantitative research design, the study assessed students’ performance across basic, common, and core competencies as defined by the Technical Education and Skills Development Authority (TESDA). A structured questionnaire using a five-point Likert scale was administered to industry trainers who directly supervised the trainees. Descriptive statistics and analysis of variance (ANOVA) were used to analyze the data.

Social media has emerged as a critical driver of entrepreneurial activity in contemporary economies, particularly in developing countries such as Nigeria where traditional business infrastructures are often limited. Digital platforms including Facebook, Instagram, WhatsApp, X (formerly Twitter), TikTok, and LinkedIn have transformed how entrepreneurs identify opportunities, promote products and services, interact with customers, and scale their ventures. By lowering entry barriers and reducing marketing and transaction costs, social media enables Nigerian entrepreneurs—especially small and medium-sized enterprise (SME) owners and informal sector operators—to compete beyond local markets and reach diverse consumer segments. This article examines the impact of social media on entrepreneurship in Nigeria, with particular attention to its role in innovation, brand visibility, market access, customer relationship management, and overall entrepreneurial performance. Drawing on existing academic literature, policy reports, and contextual examples from Nigerian SMEs, the study highlights how social media supports product innovation through customer feedback, strengthens brand development through visual and interactive content, and facilitates market expansion through targeted advertising and online networks. The findings suggest that entrepreneurs who strategically integrate social media into their business models and possess adequate digital and entrepreneurial skills tend to experience improved sales performance, customer loyalty, and business sustainability. Despite these benefits, the article also identifies key challenges associated with social media–driven entrepreneurship in Nigeria. These include intense market competition and saturation, reputational and cyber risks, misinformation, unreliable digital infrastructure, and overdependence on platform algorithms that can unpredictably affect business visibility. The study argues that without appropriate skills, regulatory awareness, and supportive digital policies, these challenges may undermine the long-term benefits of social media adoption. Overall, the article contributes to IT and entrepreneurship scholarship by offering a balanced, context-specific analysis of social media’s role in Nigerian entrepreneurship, providing insights relevant for academics, policymakers, and practitioners seeking to leverage digital platforms for inclusive and sustainable economic development.

This paper is composed of a literature review discussing the methods of improving Intrusion Detection Systems (IDS) using the UNSW-NB15 dataset to predict intrusion. The traditional IDS has the disadvantage of having too many false positives in detecting new threats. Supervised algorithms, including the Random Forest, performed well of 95.2 to eradicate all 0-day attacks and 85% of unsupervised autoencoders, as compared to the composite of the supervised and unsupervised encoders with a score of 94.8. False positives decreased to 4.2, and it supported high-rate operations at the network. Therefore, the datasets cannot be effortlessly represented, and even some tasks can be computed, although the situation has been improved. This study provides a sound ML-based IDS model that is more precise and versatile and has the potential for direct effects with regard to the implementation of cybersecurity in the real world.

Due to low temperatures, decreased immunity, and increased indoor activity, the winter months have been related to a major increase in viral respiratory diseases. The most common viral infections are influenza (flu), respiratory syncytial virus (RSV), and adenovirus, which frequently cause common clinical symptoms such as fever, cough, cold, and difficulty breathing. For effective treatment and the prevention of problems, early and clear division is necessary due to differences in appearance. Ayurveda, Unani, and homoeopathy are examples of traditional medical systems that promote a holistic and individualized treatment of disease by applying their own theoretical frameworks to describe these diseases. Influenza, RSV, and adenovirus as they are defined in these traditional systems are theoretically and scientifically described in this article. The methods covered are not meant to be used as evidence-based therapeutic treatment recommendations; rather, they are founded on traditional interpretations and classical ideas. The goal of the study is to highlight conventional viewpoints while recognizing the significance of conventional medical care in the treatment of viral diseases.

This study examines the impact of the COSO Internal Control Framework on mitigating cybersecurity risks in banks operating in the Republic of Yemen. The study adopts a descriptive–analytical approach due to its suitability for the research objectives. Primary data were collected through a questionnaire administered to financial managers, internal audit managers, and employees working in financial management, internal auditing, and information technology departments. A total of 154 valid responses were analysed. The findings reveal a statistically significant impact of implementing the COSO framework on reducing cybersecurity risks in Yemeni banks. Specifically, a strong control environment, effective risk assessment, and well-designed control activities enhance banks’ ability to address cyber threats and minimize system vulnerabilities. The results also emphasize the critical role of information and communication, as well as continuous monitoring, in strengthening responses to cyberattacks and ensuring compliance with relevant standards and regulations.

In this paper, a Microgrid has been developed consist of renewable energy resources in MATLAB Simulink environment. First, a DVR has been modeled and used in the developed microgrid model. Power quality analysis is carried out with the help of simulation results considering the effect of Dynamic Voltage Restorer (DVR). Also, a MATLAB Simulink model of the DSTATCOM has been developed to compare its performance with DVR. The same DSTATCOM has been used with the earlier developed microgrid model to test its effectiveness. The performance of both DVR and DSTATCOM has been compared in common scenarios related to power quality issues and results are analyzed.

This study aims to analyze the effect of web-based interactive multimedia on students’ learning outcomes in Pancasila Education at SMP Negeri 4 Bungbulang. The research was motivated by students’ low conceptual understanding due to the dominant use of conventional lecture methods. A quantitative approach with a One-Group Pretest–Posttest design was employed. Data were collected through learning outcome tests, questionnaires, and observations, and analyzed using t-tests and effect size calculations. The results show a significant improvement in learning outcomes, with gain scores of 36.12 and 40.24 and a very large effect size (d > 5.4). The use of multimedia also produced more homogeneous scores and increased the average achievement above the minimum mastery criteria (KKM), rising from 40–46 to 76–87. These findings align with multimedia learning theory, emphasizing the contribution of text, audio, and visual elements to conceptual understanding. The study concludes that web-based interactive multimedia is effective in improving learning outcomes and student engagement, and is recommended for integration into Pancasila Education and other value-based subjects.

The accelerated digital transformation of economic, governmental, and social systems has fundamentally reshaped the global crime landscape, resulting in a marked escalation in cybercrime and digital fraud. Threat actors increasingly exploit emerging technologies such as artificial intelligence (AI), cloud computing, cryptocurrencies, and automation to scale attacks, evade detection, and monetize illicit activities. This study critically appraises emerging trends in cybercrime and digital fraud through an empirical investigation grounded in socio-technical and governance perspectives. Using survey data from 312 cybersecurity professionals across three regions and triangulating the findings with authoritative global cybercrime reports, the study examines the relationships among technological enablers, organizational vulnerabilities, regulatory governance, and cybercrime impact outcomes. Reliability testing, correlation analysis, and multivariate regression modeling provide evidence that AI-enabled fraud, ransomware-as-a-service, identity-centric attacks, and cryptocurrency-related crimes significantly increase financial losses, operational disruption, and reputational damage. Regulatory governance is found to moderate, though not eliminate, these impacts.  The study contributes empirical validation to cybercrime scholarship, advances an integrated conceptual framework, and offers evidence-based recommendations for policymakers, regulators, and organizational risk managers.

Kerala’s growth and development circuit has been uniquely shaped by the large amount of international migration and remittance influxes. This study analyses household consumption patterns in Kerala over two decades (2003–2023) using data from the Kerala Migration Surveys of 2003, 2008, 2013, 2018, and 2023. The research paper concentrates only on two objectives: examining how remittances amount that influence household consumption patterns and comparing the changes in expenditure preferences across varieties such as food, housing, education, health, and consumer durables. The findings show that remittance inflows increased more than elevenfold in absolute terms, while their share in Net State Domestic Product (NSDP) fluctuated, peaking in 2008 and partially recovering by 2023. Household spending patterns shifted from basic consumption toward housing, savings, investments, debt repayment, and consumer durables, reflecting a structural changeover in expenditure preferences. Pearson correlation analysis indicates a negative association; however, results should be interpreted cautiously due to the limited number of observations. Chi-Square and Symmetric Measures prove significant differences in expenditure patterns over time. The Kerala Migration directions also show a gradual diversification from GCC to Non-GCC Countries and a slowing increase of emigrants. The research underscores the evolving role of remittance amounts in boosting asset accumulation, financial stability, and human capital investment, the study provides useful insights into the relationship between remittances and household consumption. The findings are consistent with migration–development theories that highlight a transition from consumption smoothing to asset accumulation as remittance dependence matures.

The African Continental Free Trade Area (AfCFTA) represents the most ambitious integration project in Africa’s contemporary economic history, carrying the potential to reshape regional supply chains and reposition the continent within global trade dynamics. Yet the extent to which the agreement can deliver meaningful competitiveness hinges on how effectively African economies leverage its provisions to deepen productive linkages, harmonise regulatory frameworks, and stimulate cross-border industrial cooperation. This study examines the strategic pathways through which AfCFTA can serve as a catalyst for supply chain integration across the continent, with particular attention to the institutional, infrastructural, and policy foundations required for its success. Drawing on comparative experiences from established regional blocs including the European Union, ASEAN, and the USMCA, the research highlights global lessons that can inform Africa’s own integration trajectory. The paper also interrogates the current structure of intra-African trade, identifying persistent fragmentation, limited manufacturing depth, and infrastructural deficits as major impediments to cohesive regional value chains. Within this context, the AfCFTA’s core protocols on trade in goods, services, investment, and digital commerce are analysed as critical tools capable of reducing transaction costs, creating market predictability, and fostering cross-border production networks. The study further evaluates the enabling role of trade facilitation, logistics optimisation, digital transformation, and sustainable financing in building resilient supply chains that can withstand global shocks. The paper argues that realising the competitiveness promise of AfCFTA requires coordinated policy alignment, targeted investments in regional infrastructure, and a deliberate shift towards technology-driven industrialisation. By outlining concrete strategic priorities, the study contributes to ongoing debates on Africa’s integration agenda and offers actionable insights for governments, private sector actors, and development institutions committed to strengthening the continent’s position in global value chains.

The Pradhan Mantri Mudra Yojana (PMMY), launched in April 2015, is a transformative financial inclusion initiative designed to empower India's vast informal enterprise sector by providing collateral-free institutional credit. This study conducts a comprehensive economic evaluation of PMMY’s impact on Micro, Small, and Medium Enterprises (MSMEs) in Karnataka, with an in-depth empirical focus on Mandya District. Utilizing a mixed-method research design that synthesizes primary field data from 60 MSME beneficiaries with secondary sources, the analysis assesses changes in key performance indicators: capital formation, turnover, employment, income levels, and financial integration. The findings demonstrate that PMMY has been instrumental in driving enterprise growth, facilitating job creation, enhancing incomes, and reducing reliance on informal credit. However, the study also identifies persistent structural challenges, including procedural inefficiencies, limited financial literacy, and insufficient post-loan support mechanisms, which constrain the scheme's full developmental potential. The paper concludes that while PMMY stands as a critical pillar for inclusive and regional economic growth, its long-term success necessitates institutional strengthening and integrated entrepreneurial ecosystem support

This study is motivated by the low motivation to learn and skills to write speech scripts in grade XI students of SMK Negeri 15 Garut. There is a gap between conventional learning methods that are less attractive and the need for an interactive approach that can increase student participation. The purpose of this study is to determine the effectiveness of the application of ClassPoint interactive learning media in improving students' motivation and writing skills. This study uses a quantitative approach with a quasi-experimental non-equivalent control group design. The sample consisted of two classes with a total of 28 students each. The experimental class uses ClassPoint, while the control class uses conventional methods. The instruments used included a learning motivation questionnaire and a writing skill test. The results of the analysis showed that the implementation of ClassPoint significantly improved students' motivation and writing skills. The average normalized gain for writing skills in the experimental class was 0.48 (medium category), while the control class only reached 0.12 (low category). Thus, it can be concluded that the use of ClassPoint interactive learning media is effective in increasing learning motivation and speech writing skills among grade XI students.

Digital transformation and technological innovation have emerged as critical forces shaping India’s contemporary economic development. The diffusion of digital technologies—including artificial intelligence, cloud computing, blockchain, and the Internet of Things—has reconfigured production processes, governance mechanisms, and market interactions, thereby enhancing productivity and competitiveness across sectors. India’s transition toward a digital economy is driven by the interaction of public policy initiatives, entrepreneurial dynamism, and expanding digital infrastructure, creating opportunities for inclusive and sustainable growth. Government-led programmes such as Digital India, Startup India, and Make in India have accelerated the adoption of digital tools, strengthened innovation ecosystems, and improved public service delivery. The rapid expansion of digital financial platforms, particularly the Unified Payments Interface (UPI), has significantly advanced financial inclusion by integrating previously excluded populations into formal financial systems. Simultaneously, digital startups in sectors such as healthcare, agriculture, education, and manufacturing have contributed to employment generation and economic diversification. Despite these gains, India’s digital transformation faces persistent challenges, including unequal access to digital infrastructure, cybersecurity and data privacy concerns, and skill mismatches in the workforce. The rural–urban digital divide remains a major constraint on inclusive development. This study argues that the developmental impact of digital transformation depends on complementary investments in infrastructure, human capital, and institutional capacity. The paper concludes that sustained and inclusive economic growth in India requires a balanced policy approach that integrates technological innovation with social and regional equity considerations.

Dental conditions, particularly calculus and caries, are among the primary causes of poor oral health. They frequently result in pain, discomfort, infections, and occasionally even tooth loss. To prevent these problems from getting worse, early detection is vital. In many places, though, access to dental specialists is still restricted. In order to automatically identify and categorize four crucial aspects of oral health—caries, calculus, discoloration, and healthy teeth—this study proposes a deep learning approach. By using readily available intraoral images, the model removes the need for radiographic methods up to an extent, allowing for affordable and non-invasive screening. The system uses Transfer Learning with a ResNet-based Convolutional Neural Network (CNN), which provides excellent feature extraction and quick learning even with smaller datasets. We trained the model extensively with pre-processed intraoral images and fine-tuned the higher convolutional layers. In tests on the validation dataset, the model reached a classification accuracy of 97.35 percent. This shows it can accurately tell the difference between healthy and diseased dental states with great precision and low variance 0.34 percent across the cross-validation tests. We can expand the community's access to early detection through integrating them into applications. This framework marks a major breakthrough in the use of deep learning models in preventive dentistry with its rapid, scalable, and accurate screening that improves patient care and public health outcomes.

The impact of project management practices in achieving quality delivery in public sector projects is the target focus of research scholars. This trend of study is subjected to an investigation of the role of project management practices in achieving quality delivery in public sector projects, particularly within Bayelsa State, Nigeria. Thus, the presentation of this paper is one of such using SPSS software for the data analysis. This study involves the collection of empirical data, primarily through a structured Likert-scale questionnaire. The methodological approach adopted was a descriptive survey design, within a quantitative research framework. To examine the influence of the independent variables on the dependent variable, Multiple Regression Analysis (MRA) was employed. The findings from the data analysis reveal a substantial 75% correlation between the independent and dependent variables, accompanied by a coefficient of determination (R-squared) of 0.563, or 56.3%. In addition, the analysis yielded a highly statistically significant p-value (p < 0.001). These results collectively depicts that the predictive model, involving six independent variables such as project planning, resource management, risk management, monitoring/evaluation, stakeholder engagement, and the utilization of project management tools/methodologies, is highly significant in predicting the role of project management practices in achieving quality delivery in public sector projects.

This study examined the prevalence of academic burnout among higher education students and explored its relationship with study habits. A sample of 300 students completed the Maslach Burnout Inventory–Student Survey and the Study Habits Inventory. Results showed that 26% of students reported high burnout levels, while 47% experienced moderate burnout. A significant negative correlation was found between study habits and burnout (r = -0.61, p = 0.001), indicating that students with stronger study habits experienced lower burnout. The study highlights the importance of effective study behaviours as protective factors against burnout. Findings may inform educational policies, counselling interventions, and academic‑support programmes.

Forests play a vital role in sustaining livelihoods, biodiversity, and climate stability, yet in Rivers State, Nigeria, rapid deforestation threatens these benefits. This paper examines the critical role of environmental education and awareness creation in strengthening afforestation efforts and promote sustainable forest management in Rivers State. It emphasises the growing threats of deforestation, environmental degradation and climate change and argues that limited public awareness remains a major barrier to sustainable forest management. Based on existing literature and circumstances in Rivers State, the paper highlights the need for a people-centered approach that goes beyond tree planting to foster environmental stewardship among communities,  Five key suggestions were proposed as a call to action amongst which were: that there should be an implementation of comprehensive environmental education programmes at all levels of society, including schools, communities, and workplaces by the Rivers State Ministries of Education, Agriculture and Forestry; that there should be an advocacy for the development and implementation of policies that  incentivise afforestation and sustainable forest management practices; that there should be a facilitation of collaboration and partnerships between governments, non-governmental organisations, businesses, and international stakeholders to support afforestation initiatives and promote forest conservation globally. By implementing these suggestions, we can harness the transformative power of environmental awareness to address the urgent challenges facing forests and pave the way towards a more sustainable and resilient future for all.

The five striped palm squirrel, Funambulus pennantii pennantii is native to Northern India, Iran, Pakistan and Bangladesh. The vocal communication of F. p. pennantii was studied at four different sites at Agra district of Uttar Pradesh, India. These sites were selected on the basis of maximum cluster locations and movements of squirrels tracked by the GPS tracking device GARMIN eTrex 10. The locations data was uploaded on QGIS open access software to map the sites. To record the vocalizations of squirrels, the AE mini digital recording device was used. Since this species enjoys the Least Concern (LC) status, Godden’s formula for infinite population was employed to determine the sample size, which was approximately 400 squirrels. To generate the audio spectrograms of squirrel vocalizations, MATLAB software version R2019b was used. Photography and videography were done through ad libitum sampling technique with NIKON COOLPIX 500 camera. Four distinct patterns emerged in the audio spectrograms, and they were identified as four distinct call types. The Buzz is a mating and play call. It is also used by mothers to communicate with their juveniles. The Growl is a threat call and is followed by chase or attack. The Bark is an alarm call which is of two types- a predator class specific alarm call for aerial predators and another predator class specific alarm call for terrestrial predators. The Rattle is a territorial declaration by squirrels as their routine at a specific time of a day. This is the first report of acoustic identification and analysis of calls of F. p. pennantii in their behavioral contexts.

The Global Financial Crisis has revived the concept of macro-prudential regulation and has given way to the introduction of many new instruments. Today, the framework stands as an overarching public policy that aims towards achievement of financial stability across the globe. It has become an effective tool to combat the imbalances that arise due to interconnected balance sheets of financial institutions and has emerged as a complement to the traditional micro-prudential regulatory apparatus. The framework has exclusive measures to counter any unprecedent growth in credit, liquidity and capital components of the financial intermediaries. The present study discusses these measures rigorously. Many Emerging Market Economies have been using this toolkit since 1997 and have hence stayed insulated against the repercussions of the global recession. India, in particular, has a long-standing experience with the operation of the policy instruments particularly to contain the credit cycle and mitigate the systemic tendency of any financial risk. It has witnessed the exercise of the policy framework without any conflict with its macroeconomic goals like price stability and GDP growth. However, macroprudential policy is an infant regulatory framework. So, policy makers should take into account the limitations of the policy and make it work in conjunction with other major policies for effective functioning of an economy.

Tourism has emerged as an important driver of economic activity in Asia, yet its growth implications remain closely intertwined with energy use and environmental sustainability. This study examines the long-run and short-run relationships among tourism receipts, economic growth, energy consumption and carbon emissions across 19 Asian economies over the period 2000-2023. Using panel data framework, the analysis employs Fully Modified Ordinary Least Squares (FMOLS), Dynamic Ordinary Least Squares (DOLS) and Granger Causality techniques to capture both equilibrium dynamics and short-term interactions. The results provide robust evidence in support of Tourism-Led Growth Hypothesis indicating that tourism development and energy consumption exert significant positive effects on income growth in the long run. In contrast, short-run causal relationships between tourism and growth are weak, suggesting that tourism’s macroeconomic benefits materialize gradually. Structural break analysis further reveals that the COVID-19 pandemic did not fundamentally alter the long-run tourism-growth relationship, highlighting the sector’s resilience despite severe sort-term disruptions. The findings emphasize the importance of sustainable tourism and energy policies in promoting long-term, inclusive economic growth.

Since ancient times, the art of painting in Assam has evolved around the culture of adorning manuscripts with lines and colors. In other words, painting as an art form is a unique and significant resource of the Assamese cultural legacy. Since the turn of the 21st century, artists from Assam have absorbed and become acclimated to modern art practices such as installation art, performance art, and modern graffiti. My goals are to research art and artists in Assam who use a variety of methods and materials, particularly in relation to a regional context and new art genres in the midst of globalization, a complex socio-political landscape, and a zero-market for works of art and art connoisseurs.

Erectile Dysfunction (ED) is quite common in India, affecting a significant portion of the male population, with studies suggesting rates around 30-40% in various groups, increasing significantly with age and linked heavily to lifestyle diseases like diabetes, stress, and obesity, though stigma often prevents people from seeking help. Erectile dysfunction (ED) is affecting men's ability to achieve or maintain an erection sufficient for satisfactory sexual performance. The condition has multifactorial oetiology encompassing psychological, physiological, and relational factors. ED profoundly impacts quality of life, often leading to psychological stress, anxiety, and relationship difficulties. Whilst conventional treatments exist, they may not address underlying causes and can present unwanted side effects.

CoreWatch is a lightweight, cross-platform monitoring system designed to provide real-time insights into CPU, GPU, memory, disk, and network performance. It uses Flask with Socket IO for low-latency metric streaming and integrates psutil and NVIDIA-SMI for accurate data collection. The dashboard employs Chart.js for smooth, interactive visualizations. An LSTM-based prediction module enhances monitoring by forecasting short-term CPU and GPU trends. CoreWatch maintains under 3% system overhead, ensuring efficient performance without additional load. Testing confirms stable responsiveness across Windows, Linux, and macOS. The system focuses on accessibility, minimal setup, and clear visual analysis. Future extensions include historical logging, improved AI models, and remote monitoring capabilities.

The study examined the effect of board characteristics on the corporate reserve of listed service firms in Nigeria. The specific objective was to examine the effect of board gender diversity, board diligence, board independence and board size on the earnings retention rate of listed service firms in Nigeria. Ex-post facto research design was adopted in the study. The population comprised all the 21 listed service firms in Nigeria. Purposive sampling was deployed in selecting the sample size of 16 firms. Secondary data were collected from the firms’ annual reports over an eleven year period from 2014-2024. Descriptive analysis was carried out on the data after which tests of cross-sectional and heteroskedasticity were conducted. Panel estimated generalised least square regression was used to test the hypotheses. The findings revealed that: board gender diversity has a positive and significant effect on earnings retention rate among listed service firms in Nigeria (β = 35.5009; p = 0.0014); board diligence has a positive and significant effect on earnings retention rate among listed service firms in Nigeria (β = 1.4354; p = 0.0162); board independence has a negative but non-significant effect on earnings retention rate among listed service firms in Nigeria (β = –0.0264; p = 0.9966); board size has a negative and insignificant effect on earnings retention rate among listed service firms in Nigeria (β = –0.1175; p = 0.8411). In conclusion, while traits such as gender inclusiveness and diligence appear to strengthen internal capital accumulation, the non-significant outcomes associated with board size and independence suggest that formal structures alone are not sufficient. The study recommends that listed service firms in Nigeria need to institutionalize gender-inclusive board policies by setting internal targets or quotas for female representation. In addition, companies should focus on recruiting competent and experienced female professionals, not just for compliance, but to strategically improve long-term financial sustainability through prudent profit retention.

Leadership relies on empathy and open-mindedness to understand employee’s expectation. As a leader, your role is to improve the self-confidence and decision-making ability of your direct reports. To get the employee to take responsibility for the objective to be achieved, the leader must give in-depth explanations on the reasons which motivated this or that decision. Influence. This Article try to clarify the Role of Leadership in Creating values ​​and Ideals Within the Organization, and who to avoid organizational failure, leaders must adopt a clear strategic vision, foster resilience to challenges, engage employees and empower management, and learn from mistakes as opportunities for growth. Avoiding failure also hinges on effective communication, intelligent change management, and building a culture of trust and active listening. Organization helps unify and direct employee efforts toward common goals and objectives. It prevents duplication of work while carrying out activities necessary to achieve specific goals. Good organization ensures the optimal utilization of human and material resources. But How leaders can influence the formation of the values of their team Leadership and achieve the ideal organizational status and objectives?

The coal fly ash (CFA) is produced on large scale in India by thermal power plant. CFA is the waste material having aluminosilicate as the major portion. Zeolite is the aluminosilicates microporous crystalline material.  So CFA can be used as a source material for the synthesis of ZSM5 zeolite. In the present study ZSM5 zeolite is synthesized from CFA by reflux method. The synthesized zeolite is characterized by XRD, IR, SEM, EDAX and TG-DTA.

Traditional hypothesis testing may cause delay to take important decisions, as it depends on collecting a lot of evidence before conclusions are drawn. An alternative method for assessing software reliability is the sequential analysis, the Sequential Probability Ratio Test (SPRT). SPRT provides the mechanism of continuous monitoring which made it possible to attribute reliable or unreliable software rapidly. A framework of SPRT is proposed by Wald for different probability distributions. This paper proposed to use SPRT on ungrouped software failure data of six datasets collected from literature categorized as ungrouped with two popular S-shaped models. Real Valued Genetic Algorithm is proposed for parameter estimation to assess the performance evaluation.

Zinc oxide nanoparticles (ZnO NPs) are among the most frequently utilized nanomaterials because they inhibit microbial growth; however, their precise mode of action (MOA) remains incompletely understood. This study details the synthesis of zinc oxide (ZnO) nanoparticles via the sol-gel method, selected for its high purity, homogeneity, and precise controllability. The research evaluates the impact of calcination temperature on the structural, morphological, and antimicrobial properties of the resulting particles. Characterization was conducted using X-ray diffraction (XRD), thermogravimetric analysis (TGA), and Fourier-transform infrared spectroscopy (FTIR) to determine crystallite size and surface composition. Additionally, the antifungal efficacy of the synthesized ZnO nanoparticles was rigorously tested against Saccharomyces cerevisiae to assess their potential as bioactive agents. The nanoparticles' ability to fight fungus was then assessed using the yeast strain Saccharomyces cerevisiae. The findings indicated that the ZnO nanoparticles did not demonstrate any antifungal effect against Saccharomyces cerevisiae. The minimal toxicity observed in both nano and bulk forms of ZnO towards this yeast is likely attributable to S. cerevisiae's notable tolerance for high concentrations of zinc ions.

All organisations are hoping to have employees that can adapt well to the workplace cultures and remain long in the organisation. One of the organisation's objectives is to have an employee who stays with the company throughout its journey to achieve exceptional performance. However, there are certain barriers that make it difficult for employees to fit in with the workplace culture, which leads to employee turnover, particularly in global corporations.  As a result of this scenario, the issue of employees' adaptability in workplace cultures has emerged, with this study focusing on local employees who work in Japanese-based organisations. Therefore, this study has identified four objectives which are designed to examine (1) the level of employee adaptability among local employees that work in Japanese-based organisations; (2) the relationship between communication, innovation, teamwork and decision-making cultures and the employee's adaptability by the local employees; (3) what the best predictor is that contributes towards employee adaptability by the local employees that work in Japanese-based organisations; and (4) the years of experience as a moderator in the relationship between workplace cultures (communication, innovation, teamwork and decision-making) and employee adaptability.  This study was conducted among Malaysian employees who work for Japanese-based businesses. Structured questionnaires and stratified random sampling were used. The data was collected from 360 local employees. The study revealed a high level of employee adaptation among local employees working in Japanese-based businesses. The three factors, namely communication, innovation, and teamwork cultures, have a substantial association with employees' adaptability. This study also reveals there is no significant relationship between decision-making culture and employee adaptability. 

Part-of-Speech (POS) tagging for code-mixed text is notably challenging due to frequent language switching, non-standard orthography, transliteration issues, and the prevalence of informal syntactic structures in user-generated content. This study presents a Hidden Markov Model (HMM)-based approach tailored to English–Punjabi code-mixed text, specifically addressing bilingual interactions in which Punjabi is written in Romanized script. A code-mixed corpus was strategically compiled from platforms such as YouTube, Facebook, and WhatsApp, and meticulously annotated at the token level. The resulting dataset comprises 900 sentences totalling 10,117 words, showcasing diverse mixing patterns and typical social media artefacts, including abbreviations, emojis, and irregular punctuation. The proposed framework conceptualizes POS tagging as a sequence labelling problem. It estimates emission and transition probabilities through the annotated corpus and employs the Viterbi algorithm to decode the most probable tag sequences. Experimental evaluations yield an overall tagging accuracy of 71.52%, establishing a probabilistic baseline for English–Punjabi code-mixed POS tagging. This work lays the groundwork for future research to integrate richer feature sets and leverage neural architectures to enhance performance.

This study uses the Water-Sensitive Urban Planning and Design (WSUPD) paradigm to investigate the relationship between stormwater flow performance and rainfall variability in Mysore City. Stormwater stagnation and urban flooding are becoming more common in Mysore, despite the city's historical reputation for having a planned urban shape and moderate rainfall. Understanding how rainfall intensity and solid waste mismanagement interact to impact drainage system performance is a crucial research gap that the current Mysore Master Plan does not adequately address. All municipal wards, including J.P. Nagar, Jayanagar, Kuvempunagara, Vijayanagar, Gokulam, Udayagiri, and important arterial corridors, are included in the study, which combines a 35-year rainfall trend analysis with hydrological assessment and in-depth field studies. According to field observations, silt, organic matter, plastic garbage, and construction debris have all accumulated widely in stormwater drains, severely reducing their flow capacity. The results show a clear connection between drainage obstructions and solid waste buildup, which worsen flooding during periods of heavy precipitation. The study comes to the conclusion that poor drainage and waste management have a significant impact on flooding in Mysore and are not only caused by variations in rainfall. In order to improve flood resilience and ecological sustainability, it suggests integrated urban planning reforms that harmonize solid waste management, drainage infrastructure upkeep, and water-sensitive design principles with Mysore's urban growth policies.

Anaerobic digestion (AD) systems exhibit complex, nonlinear interactions between operational parameters, making methane yield prediction challenging using traditional analytical methods. This study evaluates multiple machine learning (ML) techniques to model methane production from an industrial reactor dataset containing operational and environmental variables. A structured workflow incorporating correlation analysis, dimensionality reduction, clustering, and supervised learning was implemented to identify key predictors and assess model performance. Reactor temperature emerged as the dominant factor influencing methane yield, while most other variables showed weak direct correlations. Principal component analysis and K-means clustering revealed distinct operational regimes associated with different performance levels. Baseline linear regression models achieved moderate predictive accuracy (R² ≈ 0.5), while nonlinear models—including ensemble methods and artificial neural networks—provided only marginal improvements, suggesting dataset limitations rather than algorithmic constraints. The results highlight the importance of richer biochemical and temporal data for improving predictive modelling and supporting data-driven optimization of industrial AD processes.

Tyre degradation caused by tread wear and abnormal temperature rise directly affects vehicle safety, braking efficiency, and handling stability. Conventional tyre monitoring techniques primarily rely on manual inspection or pressure-based systems, which fail to provide continuous health assessment. This paper presents a real-time, non-contact Smart Tyre Health Monitoring System that simultaneously measures tyre tread depth and surface temperature. A VL53L0X Time-of-Flight laser distance sensor is used for accurate tread wear measurement, while an MLX90614 infrared sensor monitors tyre surface temperature without physical contact. Sensor data are processed using an Arduino microcontroller and continuously compared with predefined safety thresholds. Experimental evaluation demonstrates tread depth measurement accuracy within ±1 mm and reliable temperature monitoring up to 75 °C under dynamic conditions. Visual and audible alerts are generated when unsafe conditions are detected. The proposed system offers a low-cost, non-invasive, and scalable solution suitable for preventive maintenance and enhanced vehicle safety.

Within the context of the digital age, the development of a professional network plays a vital role in the career progression and advancement of students who seek to transition from academic environments to a professional setting. While the global platform offers a sense of widespread opportunity in the context of a networking forum, there remains a lack of academic nuance and personalized attention with regard to the student community. The goal of this project is to design and develop CAMPUS CONNECT, a professional networking site exclusively for the student community of the college. Through this platform, various features are to be provided, such that students can showcase their skills, share projects, internships, jobs, and form relationships with others within an academic environment that they trust and are comfortable with. To achieve this goal, the system was designed using user-centered design, which follows an agile methodology in its creation. The system also includes core components of a social networking site, such as user profiles, connections, sharing of projects, mentoring, and job sharing. The database for the system was designed with a structured form to handle user data, connections, and content efficiently while promoting a smooth flow of operations. Through the CAMPUS CONNECT implementation, the accessibility of networked opportunities, better collaboration between students, and the highlighting of student skills and accomplishments within the school community have been achieved. CAMPUS CONNECT has established a personalized and specific network system unlike other general network systems. In conclusion, CAMPUS CONNECT creates a nurturing digital environment that enhances community participation, sharing of knowledge, and increases the employability of students through closing the gap between learning and networking in a professional domain.

With the increased awareness of Industry 4.0 and big data, this study provides an opportunity to examine how AI can address the limitations in traditional knowledge management. This study aims to determine the relationship between artificial intelligence (AI), knowledge management (KM), and organizational competitiveness (OC), in the banking sector using a case study of Access Bank Nigeria PLC. A quantitative method has been employed to analytically explore the research aims. Seven hypotheses were created; three hypotheses were analyzed based on correlational analysis between AI and KM infrastructure capabilities (KMI), KM process capabilities (KMP) and KM relational capabilities (KMR); four was analyzed using the regression analysis (using SPSS) at a significance level of 0.05 to test the relationship between AI improved KMI, KMP, and KMR on organizational competitiveness (OC). Data was collected through a survey of 133 employees of Access Bank Nigeria PLC. This study shows that there is a positive relationship between AI and KMI (β = 0.397, p < 0.001), KMP (β = 0.392, p < 0.001), and KMR (β = 0.312, p < 0.001) from the correlation analysis; and that AI-improved knowledge management capabilities have more impact on Access Bank Nigeria’s competitiveness than the traditional KM strategies. This indicates that AI helps improve the KM capabilities which further strengthens that various KM strategies. Lastly it was found that AI has a positive impact on Access Bank competitiveness (R² = 0.461, F = 35.445, p < 0.001) and that organizations should further strengthen their integration of AI into their knowledge management strategies.

The growing popularity, the exponentially expanding market size, and the volatility of Cryptocurrency are gaining the attention of all, whether it is investors, policymakers, miners, or academicians. So, this paper has used Bibliometric analysis to explore the existing works of literature in the area of Business, Finance, and Economics. We have reviewed and analysed 1344 articles extracted from the Web of Science core collection, Clarivate Analytics of the period from 2011 to mid-2022 using VOSviewer and Biblioshiny (Biblimetrix: R package) analytical tools. This paper has presented citations, publications, and the impact of sources, documents, authors, organizations, countries, etc., along with their relationships with the help of tables, charts, and network diagrams. The analysis shows exponential growth in the last 4-5 years. Bitcoin and Cryptocurrency (or Cryptocurrencies) are the most frequent keywords. With many ups and downs, cryptocurrency is maintaining its pace with a gradual increase in its acceptability worldwide.

Modern web browsers have evolved into sophisticated platforms where extensions play a crucial role in enhancing user productivity and customization. However, this extensibility significantly increases the attack surface, as malicious extensions often abuse excessive permissions to harvest cookies, manipulate the Document Object Model (DOM), and exfiltration sensitive user data. Existing browser sandboxing mechanisms provide limited visibility into such threats, while cloud-based detection approaches raise privacy concerns. This paper proposes a hybrid static analysis framework for detecting malicious Google Chrome extensions that preserves user privacy while enabling deep security inspection. The detection logic is decoupled from the browser environment and implemented as a Python-based local analysis service, which securely communicates with a lightweight Chrome extension frontend via Restful APIs. The framework employs a dual-layer detection strategy: a weighted permission risk scoring model to assess privilege abuse potential, and Signature-based correlation against a local threat intelligence repository containing known malicious patterns and indicators of compromise. Experimental results demonstrate that the proposed approach improves detection accuracy and significantly reduces false positives compared to standalone permission-based techniques, offering an effective and privacy-preserving defense for end-users.

The favourable water aquifers serve as critical water resources for agricultural, industrial and domestic purposes. Understanding the distribution, movement, and potential of saturation ability of these aquifers is essential for efficient water management and planning. Horana Divisional Secretariat Division (DSD) has different thickness water aquifer layers perpendicular to the soil structures and topographical variations. The present research aims to analyse the groundwater aquifer and its movements by analysing geographic information systems (GIS). The mapping of groundwater aquifer characteristics and movement patterns, interactions were analysed using geophysical data, the dug well depths and geological information. The electrical sound resistivity (VES) method was used to identify each layer using resistivity (ohm - m) (Ω) values, which were collected from the local provincial council (Western Province) applied survey project reports. The 27 dug well points were measured using measuring tape to verify the water aquifer layers' depth values, as well as soil percolation test, and their rates were calculated using the “(cm/hour)” method (Joleha et al., 2023). The USGS satellite image was downloaded to prepare the digital elevation model (DEM). The 100m contours and the groundwater flow direction map were prepared by the dug well location depth with contours using ArcGIS 10.4.1 software. The result indicates a clear relationship between dug well depths and resistivity valves, which shows topsoil, not saturated, water saturated, weathered and fractured bedrocks and bedrock layers. The dug well depths showed the water aquifer depths in the research area. Contours and dug well depths were used to prepare the groundwater flow map. This flow pattern linked the topography of this area. The above finding mentioned that groundwater storage, capacities, layers and flow patterns can be mapped using the ArcGIS application. According to the area aquifer and flow patterns, the analysis will help with the groundwater exploration and its planning in the Horana (DSD) area.

In the contemporary era, climate change has emerged as a major global concern, with agriculture being a significant contributor due to its heavy reliance on mechanization and fossil fuel consumption. In India, this dependence has led to increased greenhouse gas emissions, particularly carbon dioxide and methane, along with accelerated depletion of natural resources such as water, thereby raising serious concerns about environmental sustainability. In this context, renewable energy-especially solar power-offers a viable pathway toward promoting sustainable agricultural practices. Recognizing its importance, the Government of India has introduced several subsidized programmes to facilitate the adoption of solar energy in agriculture. This study examines the adoption of solar energy in agricultural production systems, its socio-economic impacts on farming communities, and its overall efficiency and sustainability. Using primary data collected from 502 tribal farm households across five blocks each in Koraput and Nabarangapur districts of Odisha, the study reveals that solar energy plays a significant role in enhancing the socio-economic well-being of farming households. Solar energy is primarily utilized for crop production, irrigation, processing, value addition, and storage. Nearly 99% of the respondents reported a highly positive socio-economic impact, resulting in an average additional annual income of approximately ₹50,000 per household. Despite these benefits, several operational challenges were identified, including low voltage supply, pump set inefficiencies, damage caused by wild animals, and the lack of locally available maintenance and repair services. Addressing these constraints, the study proposes key policy recommendations, including the development of localized maintenance ecosystems, enhanced subsidy support, promotion of group-based solar models, and provision of training and exposure programmes for farmers to ensure effective adoption and long-term sustainability of solar energy in agriculture.

Artificial Intelligence (AI) is increasingly recognized as a transformative tool in governance, peace building, and social development, particularly in conflict-prone regions such as West Pokot County, Kenya, where intercommunal clashes, cattle rustling, and cross-border disputes persist. This study investigates the integration of AI into conflict resolution while ensuring the protection of human rights under Kenya’s regulatory frameworks. The objectives were to examine the opportunities and risks associated with AI in peace building, evaluate the adequacy of existing legal and ethical safeguards, and propose models that balance technological innovation with community-driven conflict resolution strategies. A mixed-methods design was employed, engaging 60 purposively selected respondents, including government officials, community leaders, human rights activists, members of peace committees, and youth representatives. Data were collected through semi-structured interviews, focus group discussions, and document reviews of policy frameworks and human rights reports. To strengthen the robustness of findings, both quantitative statistical analyses (descriptive statistics, cross-tabulations, Chi-square tests, and factor analysis) and qualitative thematic coding were employed. Results indicate that AI applications particularly predictive analytics, mobile-based early warning systems, digital mediation platforms, and resource-mapping tools hold significant potential to enhance conflict anticipation, coordination, and resolution. However, gaps in regulatory enforcement, algorithmic bias, digital exclusion of women and elderly populations, and infrastructural limitations constrain effective implementation. The study highlights a critical knowledge gap between global AI innovations and localized conflict management practices in marginalized regions. Sustainable peace in West Pokot requires a hybrid governance model that integrates AI-driven tools with culturally grounded conflict resolution mechanisms, reinforced by robust human rights protections and inclusive leadership. Recommendations include strengthening Kenya’s AI-specific legal and ethical frameworks, investing in community-focused digital literacy, piloting scalable AI interventions, and establishing safeguards to prevent misuse in fragile contexts. By aligning technological innovation with community needs and ethical oversight, AI can become a complementary mechanism for building sustainable peace and resilience in conflict-affected areas.

Rectangular labyrinth side weirs are widely used as flow-diversion structures in irrigation and drainage systems due to their ability to enhance discharge capacity by increasing the effective crest length within limited channel widths. In the present study, laboratory experiments were conducted under subcritical flow conditions to investigate the discharge characteristics of a rectangular labyrinth side weir installed in a straight channel. The influence of key hydraulic and geometric parameters on the discharge coefficient (CD) was systematically examined. A statistical F-test was applied to identify the relative significance of the governing dimensionless parameters affecting CD. Based on the experimental dataset, a nonlinear regression model was developed to establish a predictive relationship between CD and the dominant flow variables. The developed regression model showed good agreement with experimental data, with an overall correlation coefficient (R = 0.8572) and a low RMSE value of 0.1282 respectively, along with reasonable prediction accuracy reflected by low RMSE values. The outcomes of this study improve the understanding of discharge behaviour of rectangular labyrinth side weirs and provide a practical regression-based formulation that can assist in hydraulic design and performance evaluation of flow-diversion structures.

Good performance enhances shareholders interest as they provide essential capital, governance and long-term stability in anticipation of financial returns. Shareholders interest protection ensures the business capacity to fund operations, manage risks and maintain accountability and quality financial reports. Shareholders are concerned with their investment and the profitability of companies to yield maximum returns on their investment through dividends, capital appreciation and sustainable firm value. Evidence from research showed that poor profitability and poor quality of financial reports affected shareholders’ confidence and interests in listed companies especially listed oil and gas companies. Literature has revealed that not many listed oil and gas companies integrated aggressive earnings management in their operations in order to attract shareholder’s interest.  This study examined the effect of aggressive earnings management on shareholders’ interest on profitability of listed oil and gas companies for the period between 2015 -2024. Using the purposeful sampling techniques, the population of the study comprise of 14 listed oil and gas companies, using the purposeful sampling techniques, a sample eight companies were sampled for the study. The study found that discretionary accrual had positive and significant effect on shareholders’ interest in profitability of the sampled firms with the coefficient 0.1824 and p-value 0.009 while auditors’ independent had insignificant effect with coefficient -0.9286 and p-value 0.380 at 5% level of significant. The study concluded that discretionary accrual affected shareholders interest in oil and gas companies. In this, company with high discretionary accruals may attract regulatory attention, potentially leading to increased oversight and enforcement actions. The study recommended that management should ensure that the interest of shareholders is secured while ensuring transparency in financial reporting of quoted oil and gas companies. 

Objective: To present the design, development, and preliminary validation of IndiOss™, a novel 100% indigenous dental intraosseous implant system integrating biomechanical optimization and digital CAD/CAM compatibility. Methods: The implant design process followed biomechanical modeling principles and digital engineering standards. The geometry was optimized for osseointegration and primary stability using CAD/CAM and finite element–based validation. Material characterization ensured strength, corrosion resistance, and biocompatibility. Results: The Indi-Ossdesign achieved precision-engineered geometry, modular adaptability, and strong mechanical reliability. Prototype analysis indicated superior load distribution and potential for enhanced bone-implant contact. Conclusion: The Indi-OssDental Implant System demonstrates technological readiness for preclinical validation (TRL-2) and commercialization. It represents an affordable, indigenous alternative to imported implant systems, aligning with Make in India and Atmanirbhar Bharat initiatives.

Job satisfaction plays an important role in shaping the effectiveness and productivity of professionals in the library and information field. This research investigates the level of job satisfaction and the factors that influence it among library professionals working in engineering college libraries connected with universities and private institutions across Nepal. From a total population of 120 professionals, 77 completed the Job Satisfaction Survey (JSS). The collected data were examined using both descriptive and inferential statistical methods such as mean, standard deviation, correlation, regression analysis, and analysis of variance with the support of Microsoft Excel. The results show that professional commitment to the library field is a significant predictor of job satisfaction, contributing to about 31% of the observed variation. No meaningful differences were found based on demographic or institutional characteristics. Key elements influencing satisfaction included workplace environment, salary and benefits, transparent promotion systems, perceived academic value of libraries in engineering education, and professional respect and recognition. The study highlights the need for supportive organizational environments and stronger professional identity to improve job satisfaction among library professionals in Nepal.

The expansion of artificial intelligence technologies in the advertising technology (AdTech) sector has spawned paradigmatic changes in the digital marketing practices, especially in the new markets like Egypt. This extensive research paper discusses the complex aspects of AI-driven AdTech integration and social media marketing optimisation in the context of the Egyptian digital environment, with particular emphasis on the process of budget allocation, platform-specific optimisation models, and advanced performance evaluation models. The Egyptian digital advertising market has been growing exponentially with the highest rates recorded before 2020-2025 at excess of 18.7% and driven mostly by increased internet penetration rates of above 72% and increasing smartphone usage rates of almost 65 million active users. This study uses an analytical paradigm to explain the effect of machine learning algorithms, predictive analytics, and automated bidding systems to revolutionise the efficacy of the campaign management process and, at the same time, increases the return on advertising expenditure (ROAS). Moreover, the paper outlines the strategic requirements that inform digital budget redistribution out of the conventional channel to programmatic advertising medium scrutinizing socio-economic factors that drive this shift within the Egyptian market. The study summarizes the existing literature that proves the fact that AI-boosted targeting functionalities have increased the conversion rates by an average of 43 per cent and dropped the acquisition expenses of the customers by a mean of 31 per cent in various industry segments. Further, this paper delves into the incorporation of more sophisticated attribution modelling, multi-touch analytics, and real-time performance dashboards that enable making decisions regarding data. The results highlight the dire need of the Egyptian marketing practitioners to develop a technological competence in AI-based applications without losing to the cultural sensitivity and localisation requirements that appeal convincingly to the Egyptian consumer base profiles.

Smart agriculture has witnessed significant advancements through the integration of digital technologies; however, traditional agricultural trade systems still suffer from issues such as lack of transparency, unfair pricing, and dependence on intermediaries. These challenges often limit farmer profitability and reduce youth participation in agriculture. To address these issues, this article presents a blockchain-based tokenization approach designed to enable secure, transparent, and decentralized agricultural trade. In the proposed system, agricultural produce is represented as digital tokens on a blockchain network, allowing farmers to tokenize crop batches and directly engage with buyers through smart contracts. The use of blockchain ensures immutability, traceability, and trustless transactions, while smart contracts automate ownership transfer and payment settlement without the need for third-party intermediaries. The framework promotes fair trade practices, enhances trust among stakeholders, and encourages youth involvement by introducing digital ownership and decentralized transaction mechanisms. The proposed approach demonstrates how blockchain-enabled tokenization can support sustainable smart agriculture and create an inclusive digital agricultural ecosystem.

“Sustainable mobility is not achieved by policy alone, but by aligning technological innovation with consumer trust and everyday usability.” The transition toward electric vehicles (EVs) is critical for addressing environmental degradation, fuel dependency, and sustainable mobility challenges. In India, EV adoption is increasing, yet regional disparities remain significant, particularly in semi-urban industrial regions such as Durg–Bhilai in Chhattisgarh. Existing studies largely focus on metropolitan areas and often overlook the influence of emerging technologies such as nanotechnology-enhanced electric motors on consumer behaviour. This study aims to examine the factors influencing consumer purchase intention toward EVs while evaluating the potential role of nanotechnology-based motor enhancements in improving consumer confidence and adoption. A quantitative descriptive research design was adopted using an extended Theory of Planned Behavior framework. Primary data were collected from 51 EV users through structured questionnaires using convenience sampling, and data were analysed using descriptive statistical techniques. The findings reveal that economic benefits, especially long-term cost savings, significantly influence purchase intention, with 76.51% of respondents identifying affordability as a major motivating factor. Environmental awareness also strongly influenced decision-making, with over 80% acknowledging EVs as beneficial for reducing pollution and global warming. However, limited charging infrastructure and high initial costs remain key barriers. The study further highlights that advancements in nanotechnology, particularly carbon nanotube-based motor components, have the potential to enhance vehicle efficiency, range, and reliability, thereby strengthening consumer trust. The research contributes to sustainable mobility literature by integrating behavioural and technological perspectives in a localized industrial context. The findings provide valuable insights for policymakers, automobile manufacturers, and infrastructure planners to promote EV adoption through technology-driven innovation, awareness programs, and strategic infrastructure development in emerging industrial regions.

Employees in Bangalore City's public and private sectors were surveyed to determine their degree of contentment with their jobs.   Culture, pay, job security, and advancement opportunities were the main points.   One hundred employees from public sector agencies and one hundred from private sector organizations made up the 200 total survey takers.   A standardized questionnaire on a 5-point Likert scale was used to collect data.   The results were then interpreted using regression analysis, correlation, independent samples t-tests, and descriptive statistics.   Based on these findings, it seems that private and public sector companies' work cultures are very similar.   This provides more evidence that workers in the two sectors shared views on the nature of the workplace.   Pay and job stability, though, were very different.  Workers in the private sector were more optimistic about their career advancement opportunities, although government employees reported higher levels of happiness.   According to the results, public organizations guarantee safety and security, whereas private organizations offer more chances for advancement in one's career.   This study adds to what is already known about the impact of different industries on workers' happiness on the job and gives politicians and business owners practical recommendations for improving workplace diversity.  The needs of each sector can be satisfied in a well-thought-out plan, leading to happier personnel.  There will be less employee turnover and better performance as a result of this.

Background: Upper Cross Syndrome (UCS) is a common postural imbalance that often affects individuals, including college students who may spend extended periods sitting and studying. This syndrome is characterized by a specific pattern of muscle imbalances and joint dysfunction in the upper body. Understanding the causes, symptoms, and preventive measures for UCS is crucial for college students to maintain good posture and overall musculoskeletal health.. Objective: To approximate the prevalence of Upper Cross Syndrome among college students and measure associated neck-related disability and pain. Methods: A cross-sectional observational study was conducted on 300 college students aged 18–25 years from institutions in Dehradun, India, selected using a convenient sampling technique. Participants were evaluated using the Neck Disability Index (NDI), Visual Analog Scale (VAS), and standardized clinical tests for pectoralis muscle tightness and trapezius muscle weakness. Descriptive statistical analysis was performed using SPSS version 23. Results: The mean NDI score was 12.2 ± 10.5, reflecting mild to moderate neck disability, while the mean VAS score was 2.1 ± 1.9. Sixty participants (20%) demonstrated concurrent pectoralis tightness and trapezius weakness, confirming the presence of UCS. Trapezius muscle involvement was more frequently observed (57.6%) compared to pectoralis muscle tightness (20%). Conclusion: A notable proportion of college students exhibited features of Upper Cross Syndrome. Our findings emphasize the need for early detection, ergonomic correction, and targeted exercise interventions to control the burden of postural musculoskeletal disorders in young adults.

This paper consists of the examination of the important concepts and policy frameworks relating to inclusion of financial services in the digital economy like India. Financial inclusion refers to the measures taken in order to make financial services such as savings, facility of credit etc. and products available at a reasonable rate to all individuals and business houses in India. It particularly targets marginalized and low-income groups with an objective of financial sel sufficiency and reduction of poverty. The expansion of Digital Financial Services (DFS) along with Digital Public Infrastructure (DPI) such as Aadhaar, Unified Payments Interface (UPI), and Direct Benefit Transfer (DBT) within the digital economy like India helps to overcome potential traditional barriers in providing access of these services, especially in low-income groups and rural populations with the help of government regulatory frameworks and digital literacy, which emerge as an important tool for converting access of financial services into meaningful financial inclusion in the society. However, the challenges, including the digital divide, non-universal access to bank accounts, consumer protection against digital frauds and financial illiteracy, still exist. The study concludes that sustained policy focus on scope and technology, regulatory safeguards, and digital up skilling are essential for deepening inclusion of financial services in the digital economy like India.

Electrochemical capacitors have emerged as promising complementary energy storage systems to conventional batteries due to their high-power density, rapid charge–discharge capability, and long cycle life. Extensive research has focused on carbon-based electrode materials, which offer high surface area and good electrical conductivity, enabling enhanced charge storage through electric double-layer mechanisms. However, despite these advantages, the widespread replacement of batteries by electrochemical capacitors remains constrained by limitations associated with electrode materials and fabrication methodologies. In particular, complex synthesis routes, material costs, and scalability challenges continue to impede large-scale industrial deployment. Consequently, the development of cost-effective materials and simplified fabrication strategies remains a critical objective in advancing next-generation supercapacitor technologies. In this paper Nickel–cobalt layered double hydroxide (NiCo-LDH) self-supported electrodes were fabricated via a cost-effective electrodeposition route on hydrophilically treated indium tin oxide substrates for high-performance supercapacitor applications. Surface roughening using potassium permanganate treatment enhanced wettability and effective surface area, while subsequent nickel electroplating reduced substrate resistance from 34.4 Ω to 1.8 Ω, significantly improving charge transport. Field-emission scanning electron microscopy revealed a hierarchical nanosheet morphology with pronounced peaks and valleys, providing abundant electroactive sites for Faradaic reactions. Electrochemical performance was evaluated in 2 M KOH using cyclic voltammetry, galvanostatic charge–discharge, and electrochemical impedance spectroscopy. The electrode exhibited well-defined redox peaks within a potential window of 0–0.55 V and delivered a high areal capacitance of 475 mF cm⁻² at 1 mA cm⁻², retaining 275 mF cm⁻² at 30 mA cm⁻² (42% retention). The small semicircle in Nyquist plots indicates low charge-transfer resistance and efficient ion diffusion. Compared with previously reported carbon-based electrodes and conventional NiCo-LDH systems, the present electrode exhibits competitive areal capacitance and improved conductivity through direct current collector integration. The combination of simple fabrication, reduced internal resistance, and stable electrochemical behavior highlights its potential for scalable industrial energy storage applications such as backup power systems and hybrid capacitive devices.

The high rate of the electric vehicle (EV) adoption increased the pressure of finding the efficient and convenient to use and reliable types of charging technologies. The traditional plug-in charging solutions have the drawbacks of connector wear, environmental interaction and exposure, inconvenience to the user and limited accessibility. To eliminate these drawbacks, this project suggests and designs a 4-wheel electric vehicle wireless charging system based on the concept of resonant inductive coupling with several transmission pads. The proposed system incorporates two or three copper-based transmission pads beneath the charging surface and they produce a high-frequency alternating magnetic field. This magnetic flux is then picked up by corresponding receiver coils installed under the electric vehicle and converted into electrical energy that is used to charge up the battery. At a resonant frequency, the system greatly increases the power transfer efficiency and allows efficient transfer of energy over a specified air gap without the need to touch.This design provided several transmission pads, which are the main characteristic of a design, so that it is more tolerant to misalignment of the vehicle and changes in the parking position. The multi-pad design guarantees a closer distribution of the magnetic field to create a balanced power distribution to the battery system and less power loss when compared to single-pad wireless charging systems. Moreover, the modular pad system can scale the power level and can be used in a flexible deployment in both residential and public charging applications. The system architecture comprises of the high frequency inverter, resonant compensation networks, rectification and regulation phases and battery management interface to provide safe and efficient charging. The parameters that performance analysis is concerned with include the efficiency of coupling, power transfer ability, alignment tolerability and thermal characteristics of the coils. The experiment using a scaled version has shown credible results of wireless power transfer, increased flexibility of alignment and constant stationary charging..