Volume 14 Issue 12

One major contributor to the economic sector in many developing countries is Tourism. In Nigeria however, the tourism sector remains relatively underdeveloped. In this study the spatial distribution of tourism infrastructure in major cities like Awka, Onitsha and Nnewi  of Anambra state were examined with the aim of understanding existing patterns and their implication for tourism planning. Various categories of tourism infrastructures such as Hotels, Banks, Schools, Eateries etc. was mapped and analyze using Geographic Information System (GIS) techniques. In other to determine the spatial distribution of these facilities to know whether they are clustered, random or dispersed, Average Nearest Neighbour analysis was employed. The results of this analysis however revealed that in these major cities there exists predominantly clustered distribution of these tourism facilities with eateries, hotel and leisure being the most dominant. Clustered tourism facilities may however enhance the accessibility and convenience for tourists, but on the other hand it limits the activities to limited area. This study however encourages the need for strategic tourism planning in the state such that there will be a more balanced spatial distribution of tourism infrastructure across the state.

The paper examined the role of emerging technologies for sustainable blue economy and assessed the level of knowledge and skill development among stakeholders in Nigeria. Emerging technologies in the blue economy were highlighted as remote sensing & satellite monitoring, offshore renewable energy, marine biotechnology, artificial intelligence & big data and marine robotics. Educational needs include interdisciplinary marine studies, vocational training, online and modular courses, and collaborative programs and partnerships among universities, research institutes, and other organizations. Skills needed include marine robotics, data analytics, AI for ocean modelling, marine ecology, climate resilience, biodiversity conservation, and international maritime law. This paper utilised a field survey and literature on policy analysis. It revealed that the incorporation of advanced technologies is limited, and vocational training and education are inadequate. The implementation of inclusive programs emphasises the importance of collaboration amongst stakeholders, including communities, government, industry, and academic institutions. It concluded with recommendations for enhancing technology and models to promote a robust, inclusive, and sustainable blue economy in Nigeria.

This research examines students' perceptions of Artificial Intelligence (AI) in higher education in India. Data was collected from 100 students from different fields from engineering, management and science and used a quantitative research study design. A structured questionnaire used with a five-point Likert scale evaluated categories including Awareness, Perception, Perceived Benefits, Implementation, Adoption, and Challenges. Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to explore the data. The result of the measurement model confirmed construct reliability and validity. The structural model's findings showed that  high awareness  of students predicts perception ,perception strongly predicts implementation, and implementation significantly predicts adoption. Perception and challenges , on the other hand, had no visible impact on adoption, suggesting that barriers are less powerful than anticipated. The results shown  that the main factors influencing the adoption of AI in higher education are effective implementation and the way the implementation introduced . By highlighting implementation as a crucial mediator in adoption models, the work adds theoretical value and has applications for educators and policymakers.

Cybersecurity readiness has become a material organisational capability as digital transformation expands attack surfaces and regulatory scrutiny. Existing research and practice guidance largely frame readiness as a function of technical controls and compliance maturity, offering limited explanation of how executive leadership structures shape sustained resilience. This paper advances a theory building perspective that conceptualises cybersecurity readiness as an outcome of strategic leadership rather than a purely technical condition. Drawing on strategic leadership theory, enterprise risk management, and cyber risk quantification literature, the study develops a Strategic Cyber Leadership Model that integrates four core elements: distributed C suite accountability as a leadership input, Cyber Risk Quantification as a financial decision-making mechanism, enterprise-wide risk integration as a governance amplifier, and measurable readiness outcomes. The model explicitly incorporates contextual moderators, including regulatory intensity and digital transformation level, to explain variation in readiness across organisations and sectors. Five research propositions are advanced to articulate the causal relationships within the model, positioning the paper as a foundation for future empirical testing. By shifting the analytical focus from control inventories to leadership driven governance mechanisms, this study contributes to cybersecurity and management scholarship while offering a coherent conceptual framework for boards and senior executives seeking to institutionalise cyber resilience as a strategic capability.

Quantum dot–based solar cells have rapidly grown into one of the most promising candidates in next-generation photovoltaics, largely due to their quantum confinement–driven tunability, strong absorption coefficients, and compatibility with solution-processed fabrication. Their unique optical behaviour, particularly the ability to tailor bandgaps simply by adjusting nanocrystal size, offers a conceptual advantage over conventional bulk semiconductors. Over the past decade, improvements in surface passivation, ligand chemistry, nanocrystal synthesis, and device architecture have enabled efficiencies exceeding 18%, reflecting an impressive rise from early single-digit values (Ning et al., 2022). Yet, despite these encouraging advances, the field continues to face challenges related to long-term stability, environmental toxicity, and large-scale manufacturability. This paper traces the evolution of quantum dot solar cell research, highlighting key breakthroughs in material design, mechanistic understanding, and interfacial engineering, while also discussing the scientific and technological hurdles that must be confronted before widespread commercial adoption becomes feasible. The analysis suggests that continued collaboration between materials scientists, device engineers, chemists, and computational researchers will be crucial for realising the full potential of quantum dot photovoltaics in a sustainable global energy landscape.

Artificial Intelligence (AI) is rapidly transforming the nature of work across industries by automating routine tasks, improving productivity, and enabling data-driven decision-making. While AI contributes to efficiency and innovation, it also raises concerns related to job displacement, skill obsolescence, and workforce uncertainty. This study examines the impact of AI on employment trends and workforce dynamics across selected industries in India. Data collected from 150 employees working in IT, manufacturing, and service sectors reveal that AI simultaneously leads to job displacement and job creation, with a significant increase in demand for advanced skills. Correlation analysis shows a strong positive relationship between AI adoption and productivity, skill requirements, and employment restructuring. The study concludes that AI should be viewed not as a threat but as a catalyst for workforce transformation, provided organizations invest in reskilling and ethical AI adoption

Background: Calcaneal fractures are the most common tarsal bone injuries, accounting for 60–70% of tarsal fractures, and frequently occur due to high-energy trauma such as road traffic accidents and falls from height. These fractures significantly impair mobility and work capacity, particularly among individuals aged 21–45 years. Radiographic assessment using Bohler’s and Gissane’s angles is essential for determining fracture severity and guiding appropriate treatment planning. Methodology: A study was conducted in the SGT University Hospital Gurugram, Haryana. The study included 100 cases (60M, 40F), with mean age 41.66 ± 17.14 years (Range 15 - 89 years). Ankle lateral radiograph was taken by using CR X-ray machine (Fuji Modality). Lateral ankle radiographs in all the cases were evaluated for the measurement of Bohler’s and Gissane’s angle with the help of experienced Radiologist and Orthopedician. Result: The mean of Bohler’s angle and Gissane’s angle in the north Indian population were 28.70° (15 - 54°) and 112.18° (100 - 13°) respectively. The mean Bohler’s angle was found highest (30.57 ± 5.88°) in age group with age ranged from 15 - 29 years and lowest (27.44 ± 7.36°) in age group with age ranged from 30 - 44 years whereas Gissane’s angle was found highest (116.56 ± 10.03°) and lowest (109.77 ± 5.54°) in age group with age range of 75 - 89 years and 30 - 44 years respectively. There was no significant Difference in the Bohler’s and Gissane’s angle between males and females. Conclusion: In the north Indian population, the mean of Gissane’s angle and mean Bohler’s angle was found 28.70 ± 7.24° and 112.18 ± 7.09° respectively. There was no significant Difference in the Bohler’s and Gissane’s angle between genders. 

Generative Artificial Intelligence (AI) is revolutionizing healthcare with its transformative potential in medical imaging, drug discovery, and clinical decision-making. Generative models such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and large foundation models can synthesize realistic data, emulate biological systems, and accelerate innovation beyond traditional AI methods. In medical imaging, generative AI enhances diagnostic accuracy by enabling high-resolution image reconstruction, noise reduction, anatomical segmentation, and the creation of synthetic datasets to support algorithm training in data-scarce environments. These advancements assist radiologists in early disease detection, treatment planning, and longitudinal patient monitoring. In drug discovery, generative AI accelerates molecule design, lead optimization, and prediction of protein-ligand interactions, reducing time and cost while enabling precision therapeutics and drug repurposing. Clinically, it supports automated report generation, patient-specific treatment simulations, and digital twin development for disease modeling and trial optimization through synthetic patient cohorts. Despite these advances, challenges persist regarding data quality, interpretability, regulatory approval, ethical transparency, and bias mitigation, which are critical for ensuring patient trust and safety. This study explores generative AI’s applications across medical imaging, pharmacology, and clinical workflows, highlighting its opportunities, limitations, and future directions toward sustainable, ethical, and patient-centered healthcare integration.

Aggregates and binders are the two main components of concrete. Cement is an ecologically unsociable process since it releases CO2 gas into the air and causes ecological deterioration; it is also the most expensive and environmentally unfriendly component of concrete. To help promote environmentally friendly building methods, this research looks at the possibility of using coffee husk ash (CHA) in place of certain traditional Portland cement in concrete mixes. Because of its possible environmental advantages and resource saving, the use of agricultural waste, such coffee husk ash, as an additional cementitious ingredient has attracted interest. This study makes use of controlled laboratory trials to determine how different concentrations of CHA affect the mechanical, durability, and workability characteristics of concrete. The concrete's structural performance is evaluated by testing its compressive strength, flexural strength, and split tensile strength. Initial findings suggest that CHA, when used in part as a cement substitute, might enhance workability and maybe even certain mechanical qualities. The trade-offs between improving sustainability and preserving structural integrity must be carefully considered. By investigating the feasibility of using coffee husk ash as an additive in concrete, this study adds important new information to the continuing efforts towards sustainable building practices. In an effort to create infrastructure that is less harmful to the environment, the results should guide practices in the concrete sector and encourage greener options.

Dynamic contrast-enhanced magnetic-resonance imaging(DCE-MRI) has emerged as a transformative method in oncological imaging, contributing to findings on tumor vascularity, permeability, and treatment response. This review outlines an extensive assessment of DCE-MRI implementation across various malignancies, with a specific emphasis on liver tumors, spinal metastases, and breast cancer. Through examination key pharmacokinetic parameter such as Ktrans, Ve and Kep, the article highlights their diagnostic and prognostic value in discriminating lesion types and forecasting microvascular invasion. It also delves into the integration of artificial intelligence and radiomics in optimizing the interpretability and reproducibility of DCE-MRI data. The review confronts issues related to motion artifacts, regulation, and crossplatform variability, while recommending future directions or clinical uptake and research evolution. Through this synthesis, DCE-MRI is placed as a crucial tool in precision oncology, with the extension of implementation across anatomical systems.

The banking industry's quick adoption of artificial intelligence (AI) has drastically changed consumer interaction, service delivery, and operational effectiveness. Evaluating the uptake and acceptance of AI-enabled banking services requires an understanding of consumer demographics. This study looks at how certain bank customers in the Coimbatore district use AI banking based on personal criteria. A structured questionnaire was used to gather primary data from 385 respondents, and descriptive statistical methods were used for analysis. The results show that the majority of users of AI-based banking services are young, educated, and salaried consumers, especially those who work in the private sector. consumers with moderate income levels have considerable engagement with AI technologies, whereas female consumers exhibit slightly higher adoption levels than male customers. The study offers insights for banks to improve technology-driven financial inclusion and customer-centric initiatives while highlighting the increasing adoption of AI banking across various demographic categories.

There are many real-time situations which can be effectively solved by optimizing the basics of linear algebra by infusing it through latest AI models. This research paper is intended to bring into use the basic concepts of linear algebra along with the nuances of AI/ML to bring about optimization for solving supply chain scenario across industry. The challenge lies in Modeling disruptions (e.g., geopolitical events, pandemics) across global supply chains in real time. Linear Algebra’s Role lies in Matrix representations of supplier–buyer networks, eigenvalue analysis for systemic risk. The frontier lies in combining linear algebra with adaptive AI (reinforcement learning, quantum ML, and multi-agent systems).

The accelerating integration of Artificial Intelligence (AI) into internal auditing is reshaping assurance practices through continuous auditing, advanced analytics, and predictive risk assessment. While AI enhances audit efficiency, coverage, and timeliness, it also introduces significant ethical, accountability, and governance challenges, including algorithmic opacity, data bias, privacy risks, and the potential erosion of professional judgment. Addressing these issues is essential for preserving audit integrity and stakeholder trust in increasingly automated audit environments. This study adopts a conceptual and theory-driven approach, drawing on Stakeholder Theory, Ethical Decision-Making Theory, and Technology Governance Theory. Through a systematic synthesis of academic literature, professional auditing standards, and global ethical AI guidelines, the paper develops the Ethical AI Audit Framework (EAAF). The framework embeds core ethical principles—transparency, fairness, accountability, explainability, privacy, and integrity—across the internal audit lifecycle, from planning to follow-up. The EAAF emphasizes “human-in-command” oversight, highlights governance enablers such as ethical review mechanisms and bias audits, and identifies auditor competence as central to ethical AI outcomes. This study contributes a domain-specific ethical governance model to support responsible, transparent, and trustworthy AI-enabled internal auditing.

Global energy demand is projected to increase by nearly 1.5-fold by 2050, driven by fossil fuel depletion and the urgency of climate mitigation, thereby positioning thin-film photovoltaics as a critical component of sustainable energy transitions. This review traces the progression of solar-cell absorber materials-from crystalline silicon with efficiencies of ~27% and CdTe/CuInxGa1-xSe2 (CIGS) nanoparticle thin films with ~22%-toward earth-abundant chalcogenide ternaries such as SnSexTe1-x alloys. These materials exhibit tunable band gaps (0.9-1.5 eV), high absorption coefficients (>10⁵ cm⁻¹), and theoretical efficiencies approaching 36% in optimised heterostructures. However, experimental power conversion efficiencies remain limited to about 2.5%, primarily due to intrinsic defects, non-radiative recombination, and challenges in scalable fabrication. Drawing on 2024-2025 data from the Energy Institute and the International Renewable Energy Agency (IRENA), this analysis underscores the non-toxic and earth-abundant advantages of SnSexTe1-x, while contrasting them with the instability issues in perovskites and the phase complexity in kesterites. The review further highlights strategies such as bandgap engineering, atomic layer deposition (ALD)-based passivation, and multi-junction tandem architectures, along with scalable pulse laser deposition (PLD) routes, as promising approaches to achieving power conversion efficiencies exceeding 30%.

Social media platforms such as Facebook, Instagram, and WhatsApp have emerged as crucial tools for rural business promotion. These platforms provide low-cost, high-reach avenues for marketing, customer engagement, and brand building. This paper examines the adoption, usage patterns, benefits, and challenges of social media for rural entrepreneurs. Using a mixed-method approach combining surveys and secondary research, the study finds that WhatsApp is the most popular platform for direct communication and order management, Facebook is effective for community engagement and marketplace visibility, and Instagram is increasingly used for visual branding and targeting younger customers. The paper concludes with recommendations for digital literacy training, infrastructural support, and integrated marketing strategies to maximize rural business growth.

Home loans play a major role in India to improve the standard of living, particularly forth economically weaker sections and the middle economic class society. Housing, being one of   the basic needs for human, takes the major chunk of one’s income. Home loans came as a boon to people, who dreams of owing a house, even at the very early stage of one’s life. Home loan decisions are largely influenced by various factors including service quality, convenience, interest rates etc., With banks becoming more competitive and extending exclusive services for customers is the key to attract them, this paper analyses the determinants and the satisfaction level of home loan customers of SBI. Through a survey with a structured questionnaire, this project investigates the factors affecting their loan purchase decision based on various demographic factors. Ease in installments, monetary benefits, easy installments are the crucial factors in home loan decision making. At SBI, both have average rating due to more competition in the market.  The paper also explains the customer satisfaction level and the willingness of customers to recommend SBI home loans to others. The study not only evaluates the various factors affecting customer preferences, but also provides insights in to enhancing customer experience to increase satisfaction and loyalty in this competitive banking industry.

The needs of employees in the workplace, including those in healthcare organizations, have been universally regarded as important driver that steer many institutions toward organizational improvement and development. Training needs assessment in the workplace is generally important for any organization as it helps determine gaps, which can manifest in terms of employee knowledge, practice or skill, that are preventing the organization from reaching its desired goals. This study assessed the training needs of barangay service point officers in Baybay City, Leyte, using a descriptive correlational research design. A researcher-made questionnaire was used to obtain the profile and assess the training needs of the 110 research respondents. Majority of the respondents are between the ages of 50 to 59 years old, female, married, high school graduate, have 5 years and above length of service, and have attended training on responsible parenthood and family planning and mothers’ class. Overall, the respondents needed training in terms of knowledge and skills. In terms of the specific training needs, the respondents perceived indicators in knowledge improvement training on various family planning methods, mechanism, effectiveness, and misconceptions; teaching and client education methodologies, and creation and development of effective educational materials as highly needed. For skills improvement training, effective use of communication techniques, effective use of client teaching strategies, and culture competence and sensitivity training were found to be highly needed. In general, there    is    no significant   relationship   between   the     profile of the respondents and their training needs. The current study adds to the understanding of the different training needs of barangay service point officers in terms of their knowledge and skills. The findings offered insights on the importance of continuous capacity-building, which not only addresses current gaps but also prepares BSPOs to adapt to evolving healthcare needs.  This initiative advances the goal of providing high-quality, accessible healthcare to all community members, ensuring that services are both responsive and sustainable.

Satisfaction is the customers fulfillment response. It is a judgment that a product or a service feature, or the product or service itself, provides a pleasurable level of consumption related, fulfillment. In minimal technical terms, this definition can be translated to mean that satisfaction is the customers evaluation of a product or service in it . It is also important to recognize that, to measure the customer satisfaction at a particular point of time as if it were static, satisfaction is a dynamic, moving target that may evolve over the time, influenced by a variety of factors. Particularly when product usage or the service experience takes place over the time, satisfaction may be highly variable depending on which point the usage or experience cycle is focused on. This study focused on the customer satisfaction towards Fasttrack watches within Madurai district. The researcher followed convenience randam sampling method to collect data from 50 respondents. The researcher found that  Majority of the respondents are aware about the various range of products provided by Fastrack watches through advertisements. The researcher also suggested that In order to improve its sales, ad promotion should be taken care, excellent customer care should be provided and also it should reduce its service time. The researcher concluded that gaining and maintaining consumer preference is a battle that is never really won. Continued and consistent branding initiatives that reinforce the consumers purchase decision will, over time, land the product in consumer preference sets. Attaining and sustaining preference is an important step on the road to gaining brand loyalty.

The study investigates the influence of the Peclet number on the numerical solutions of modified convection-diffusion equations, specifically utilizing Berger's equation as the governing model. Finite difference methods has been employed to solve Convection diffusion equation under different Peclet numbers, analyzing the resulting numerical behavior, including solution profiles, error propagation, and computational efficiency. The findings reveal that as the Peclet number increases, the dominance of convective terms introduces numerical instabilities, such as oscillations and excessive diffusion, necessitating the implementation of specialized discretization techniques or stabilization methods. The study also explores the effectiveness of upwind schemes and adaptive mesh refinement in mitigating these challenges.

Sterility Mosaic Disease (SMD), commonly known as the "Green Plague" of pigeon pea, is one of the major threats to pulse production in South Asia, particularly India. The disease is spread by the eriophyid mite Aceria cajani and is brought on by the Pigeon pea Sterility Mosaic Virus (PPSMV).and causes partial or total sterility of plants, resulting in production losses that range from 30% to 100%. Although they produce accurate results, traditional diagnostic methods like field scouting, serological testing, and molecular procedures like PCR are labor-intensive, time-consuming, and not appropriate for widespread use. Deep learning (DL) and machine learning (ML) models have become more well-known in recent years as quick, scalable, and affordable approaches to early disease identification. These models, which include lightweight architectures, transfer learning, and Convolutional neural networks (CNNs) are increasingly being used in pigeon peas because they have demonstrated exceptional accuracy in recognizing plant diseases in a range of crops.  Existing information on SMD epidemiology, conventional and contemporary detection methods, databases, and difficulties is compiled in this review. It presents cutting-edge machine learning techniques and talks about how they may be incorporated into farmer-centric solutions. Model performance is summarized in tables, while publishing patterns, ML pipelines, and disease symptoms are depicted in figures. The analysis concludes by outlining future directions and research gaps, including as explainable AI, multimodal data integration, and policy-level adoption for sustainable SMD management.

Precise assessment of district-level crop yields is crucial for food security planning and targeted agricultural interventions in India; however, conventional statistical methods fail to account for spatial variability and nonlinear connections among agronomic variables. This study developed a Random Forest-based framework for predicting crop yield across Indian districts using multi-year data on crop type, season, production, and cultivated area, complemented by open-source agronomic datasets. Yield was log-transformed to stabilise variance, and the model was trained with an 80:20 train–test split and hyperparameter tuning via grid search and cross-validation, while permutation importance and SHAP analyses were applied to interpret feature contributions and district-level residual patterns. The Random Forest model achieved strong predictive performance on the test set, with , low RMSE and MAE, and close alignment between predicted and observed yields for most districts. Feature attribution indicated that production, cultivated area, and season were the most influential predictors, and spatial aggregation of residuals revealed clusters of systematic over- and under-prediction linked to data-poor or agro-ecologically complex regions. An explainable machine learning pipeline, resolved at the district level, can accurately forecast crop output variability in India, providing detailed insights that exceed those of conventional regression techniques and facilitate region-specific policy and management decisions.  The framework necessitates enhanced regional data quality and the incorporation of more comprehensive meteorological and soil information to better operational agriculture monitoring.

The study sought to examine the factors that hinder the teaching of computers in four selected secondary schools in Chiwundura Cluster Centre in Gweru in Midlands Province in Zimbabwe. Teaching and learning of computers in educational institutions cannot be over-emphasized the world over, as computers are now the lifeblood of any society. The study adopted a mixed research approach, thus the use of interviews, questionnaires and observations. 20 respondents who are teachers selected using probability sampling method from four (4) selected secondary schools from a pool of 5 secondary schools in the cluster. The findings established that some of the secondary schools in Chiwundura do not have computers hence this problem has contributed much to the non-teaching and learning of computers in Chiwundura secondary schools. The study revealed that lack of trained teachers and poor funding were considered as the major causes to the non-teaching and learning of computers in secondary schools, computer illiteracy, lack of computer usage infrastructure among others were negatively affecting the teaching of computers in selected secondary schools in Chiwundura. The study concluded that teacher training institutions should train more computer teachers for to curb the shortage of trained personnel that are meant for the teaching and learning of computers. In a bid to eliminate the non-teaching and learning of computers, schools’ administrators need to be trained or oriented on how to implement the teaching and learning of computers in secondary schools. The study recommended the stakeholders to resume the computerization and the rural electrification programmes as well as introduce the use of alternative source of power like solar energy. Further recommendations were that administrators in secondary schools in Chiwundura should priorities the implementation of the teaching and learning of computer studies when making their budgets, hence the need for the acquisition and development of computer infrastructure in their schools.

This research aims to assess customer perceptions of two leading Bangladeshi footwear brands, BATA and APEX. A structured questionnaire was designed to evaluate factors such as price, size, durability, design, and additional benefits influencing consumer beliefs. The study examines how sudden price changes, trust in information sources, aesthetic appeal, and willingness to pay affect customer perceptions. It also segments respondents by age, gender, occupation, monthly income, and preferred brand to gain deeper insights into consumer behavior. Additionally, the research analyzes media and promotional content of BATA and APEX, identifying implicit and explicit messages, characteristic language associated with positive or negative beliefs, and the impact of celebrity endorsements. Comparisons between the two brands are drawn across all examined dimensions, providing a comprehensive understanding of consumer perceptions and preferences in the Bangladeshi footwear market.

Vehicular emission is one of the major sources of pollution to the roadside vegetation and soil. Degree of pollution by vehicles depends on the traffic congestions on the road. Higher quantity of environmental pollution could pose several hazards to humans, animals and plants. This study investigated the impact of vehicular emission on lower plant (moss) along Akure-Ilesa and Ife-Ibadan roads. The study aims at: quantify the heavy metal (HM) load in the moss and compare the degree of pollution of Akure-Ilesa road with Ife-Ibadan road. Passive and active methods of biomonitoring was carried out using moss as indicator, digestion of the sample was done using aqua-regia method of digestion and the investigated metals: lead (Pb), chromium (Cr), cadmium (Cd), copper (Cu) and zinc (Zn) were analyzed with the use of Atomic Absorption Spectrophotometer (AAS). Results of these study reveal considerable quantity of the investigated metals present in the indicator material (moss) and it also shows that, Akure-Ilesa road have higher concentration of these metals than Ife-Ibadan road. It can be concluded from these study that vehicular emission contributed to the HM load of roadside lower plants. It is recommended that controlled vehicular emission systems be adopted to reduce emissions and improve fuel efficiency.

This study investigated the effects of microsilica incorporation on the fresh and hardened properties of Ordinary Portland Cement Concrete (OPCC). Microsilica, a highly reactive pozzolanic material, was used to partially replace cement at 5%, 10%, and 15% by weight. The concrete samples were cured under normal conditions for seven days. The fresh properties were evaluated using the slump test, and hardened properties were determined through density, porosity, and rebound hardness tests. Results indicated that the addition of microsilica significantly reduced workability due to its high surface area but improved density and surface hardness up to 10% replacement. Porosity decreased consistently with increasing microsilica content, demonstrating enhanced compactness and reduced permeability. The study concludes that an optimal replacement level of 10% microsilica enhances both the mechanical and durability-related characteristics of OPCC.

Integrating artificial intelligence (AI) into tourism becomes increasingly impactful for consumer behavior. We investigated the impact of AI on the tourism industry and the opportunities and challenges for travelers and service providers. Using a qualitative method, a structured questionnaire was created to explore the perceptions of travelers and industry stakeholders regarding AI integration. Data were collected through in-depth interviews with participants who had engaged with AI-enhanced services in tourism. The results revealed how AI technologies have reshaped travel experiences, service efficiency, and customer satisfaction. The results contribute to the understanding of AI's role in the tourism industry and provide a foundation for future research on effective strategies for AI implementation in tourism.

In the age of digital revolution, organizations increasingly use artificial intelligence (AI) and machine learning (ML) to improve their data-driven decision-making, especially in human resource management. This paper makes a comparative evaluation of AI-driven data warehousing systems and ML methods for forecasting employee turnover and maximizing employee performance. The study compares top data warehousing platforms like Redshift, BigQuery, Snowflake, and Databricks and their coupling with ML models with regard to prominent workforce features. Qualitative findings from HR managers were also examined, in order to evaluate the real-world effect of these technologies on the productivity of the workforce and employment strategies. Research shows that AI-based data warehousing integrated with competent machine learning models drastically enhances attrition prediction accuracy, performance tracking, and strategic workforce planning. This research identifies the strategic advantages of combining AI-driven data warehousing with HR analytics, offering organizations actionable findings to choose the best AI-enabled solutions. The findings contribute to extending knowledge on efficient data strategies in lessening attrition as well as improving employee performance, aiding organizations in their pursuit of strategic human capital objectives.

Mango is a vital tropical fruit crop, yet its productivity is often reduced by leaf diseases such as Powdery Mildew, Dieback, Anthracnose, Bacterial Canker, and Sooty Mold. These infections lower yield, degrade fruit quality, and cause major economic losses. Early detection is crucial but challenging for farmers with limited expert access. This study proposes an image-based classification system using Convolutional Neural Networks (CNN) for accurate disease recognition. A curated dataset of mango leaf images was pre-processed and augmented to address class imbalance. The CNN model outperformed traditional classifiers like Support Vector Machine (SVM) and Decision Tree in terms of accuracy, robustness, and efficiency. The system not only detects multiple diseases with high precision but also offers severity estimation, visual feedback, and farmer-friendly treatment recommendations. Designed for real-time use via smartphones or field cameras, it provides a scalable and accessible solution to support precision agriculture.

Background: In Uganda, climate change is amongst the most substantive challenges inflicting on the wellbeing of humans in many parts of the country. In the central and south western parts of Uganda where the majority of the population rely on subsistence agriculture, climate change has adversative effects. In rural areas, low resilient capacity to shocks exacerbates the impacts of climate change such as food production catastrophe, hence resulting into food insecurity.  It is upon the above experiences that this study assessed emphasis on mitigation strategies and practices of climate change among farmers Nakasongola district by using qualitative research. This research was guided by one research question, namely: what are the mitigation strategies practiced by local people to minimize the impact of climate change? Material and method: The study employed a case study design where several instrumental bound cases are examined. We utilized multiple data collection methods to explore perceptions of climate change. Qualitative data for this study was generated from 15 selected households through focus group discussions, key informant interviews, and in-depth interviews. Data were collected using semi-structured interviews from fifteen purposefully selected local farmers from selected sub counties. Results:  The findings of this study revealed that, farmers were found to practice mitigation strategies such as afforestation, agroforestry and agricultural intensification as ways to overcome climate change. Conclusion: Local farmers have intensively initiated agricultural policies that strive to protect the environment and such mitigation strategies are meant to enhance the capability of smallholder’s farmers to deal with the persistent effects of climate change

The integration of Augmented Reality (AR) and Virtual Reality (VR) has redefined the creative language of design and cinema by transforming how stories are visualized, experienced, and interpreted. This study examines how these immersive technologies extend beyond conventional screen-based narratives to establish new modes of spatial storytelling and participatory engagement. AR and VR enable designers and filmmakers to merge physical and digital realities, fostering interactive environments where audiences become active participants rather than passive viewers. The paper analyzes global and Indian examples to explore how immersive media influences production design, narrative construction, and sensory experience. It also investigates how AR and VR are reshaping creative education, visual aesthetics, and audience perception through real-time interaction, multisensory feedback, and user-centered storytelling. By situating these developments within post-digital design theory, the research highlights the emergence of a new design language that blurs boundaries between art, technology, and human experience. This evolving paradigm underscores the need to view AR and VR not merely as tools but as transformative mediums shaping the future discourse of visual communication and cinematic design.

The paradox of Africa's industrialization—enduring structural vulnerabilities alongside significant potential—calls for innovative institutional interventions. This research addresses the disjointed industrial policies in developing economies that consistently fail to leverage the transformative potential of public procurement. The "Ghana Flywheel" is a dynamic systems model that illustrates how strategically calibrated procurement reforms can initiate self-reinforcing cycles of local industry growth, attract high-value foreign direct investment (FDI), and promote sustainable development. The analysis, grounded in system dynamics modeling meticulously calibrated with Ghanaian procurement and FDI datasets from 2015 to 2023 and supplemented by stakeholder interviews, uncovers catalytic thresholds. Mandating 30% local procurement participation stimulates 15–25% productivity growth in SMEs by enhancing technological capabilities and strengthening supply chain connections, while tiered FDI incentives linked to sustainability metrics increase quality investment inflows by 40%. The model highlights that increased state capacity, driven by knowledge spillovers from advanced foreign direct investment, facilitates the development of more sophisticated procurement instruments, leading to upward developmental spirals. Simulations indicate that Ghana’s strategic implementation of digital procurement platforms, AI-driven supplier analytics, and integrity-focused contracting may reshape its industrial landscape by 2030, decreasing import reliance by 35% and enhancing domestic value creation. This research provides a replicable policy framework for utilizing public procurement as a fundamental industrial strategy, demonstrating its effectiveness as a catalyst for resilient, sovereign industrial ecosystems in emerging economies.

Ghana's annual public procurement expenditure of $3.5 billion, accounting for 11% of GDP, serves as a significant but underleveraged framework for addressing the country's interconnected issues of digital exclusion, climate vulnerability, and import dependency. This research goes beyond traditional administrative perspectives, introducing a transformative "Procurement 4.0" framework aimed at strategically harnessing fiscal power. The framework integrates artificial intelligence, blockchain, and multi-stakeholder governance to align procurement with three key imperatives: facilitating digital leapfrogging through AI-driven tender platforms that enhance SME access, catalyzing green industrialization with mandatory sustainability criteria and life-cycle assessments, and promoting SME-driven self-sufficiency via enforceable participation quotas and capability development. The study utilizes policy archaeology (2010-2024), multi-criteria scenario modeling, and stakeholder gap analysis to illustrate that recalibrating strategic procurement can enhance SME contributions to GDP from 17% to 25%, attain 40% green procurement compliance by 2035, and achieve notable AI-driven efficiency savings. The findings provide a clear and practical framework for Ghana and comparable Global South economies aiming to transform public spending into drivers of technological sovereignty, climate resilience, and inclusive structural change, fundamentally reshaping the role of procurement in fostering prosperity.

Urban sprawl and high-rise buildings are essential for meeting housing demands and optimising land use, thereby mitigating urban sprawl through vertical urbanisation in densely populated cities. This paper explores the evolution of forensic structural engineering through various case studies, emphasising the urgent need for forensic engineers and their recommendations following structural failures, including subsequent legal actions. Case studies of structural collapses in Bengaluru and Gurgaon are examined. In these instances, buildings were illegally constructed, resulting in fatalities. Forensic engineers, acting as consultants, discuss issues such as weak foundations, poor construction quality, and unauthorised vertical extensions, which violate building codes and bylaws. These violations have led to a disregard for quality and safety standards. The study employs Normalised Differential Built-up indices using Geographic Information Systems (GIS) to assess the increase in the built-up index. This article reviews the utility matrix of forensic engineers and highlights their growing importance in recent years. Furthermore, the discussion underscores the significance of geotechnical engineering, the need for stringent building codes that focus on people, materials, and machinery, regular inspections, and effective disaster management. The analysis draws lessons for policymakers, builders, and the community aimed at preventing illegal construction practices and enhancing disaster management strategies, thereby reducing the risk of structural failures.

The study aimed to integrate solid waste management (SWM) strategies into sustainable manufacturing systems using the Life Cycle Assessment (LCA) framework to evaluate environmental performance and identify waste reduction opportunities. Conducted among selected manufacturing facilities in Bulacan, Philippines, the research employed a descriptive–analytical design supported by quantitative and qualitative data. The study focused on determining the major sources and types of solid waste, assessing their environmental impacts through LCA, and developing an integrated framework for sustainable waste management. Results revealed that most waste originated from machining, packaging, and post-processing operations, with metal and plastic wastes comprising the largest share. The Life Cycle Impact Assessment (LCIA) indicated a 32% reduction in global warming potential, a 23% decrease in resource depletion, and a 40% reduction in landfill waste after integrating SWM strategies. These improvements were achieved through source segregation, recycling, and lean-green process optimization. The developed framework emphasized four core components: waste identification and classification, LCA-based monitoring, process optimization, and continuous improvement aligned with ISO 14001:2015 and Sustainable Development Goal 12. The findings affirm that the integration of SWM and LCA enhances resource efficiency, reduces environmental impacts, and supports the transition toward circular economy practices in the manufacturing sector.

The construction industry continues to face persistent challenges such as cost and time overruns, safety risks, low productivity, and labor shortages. Despite its economic significance, the sector remains one of the least digitized globally, limiting its ability to address these challenges effectively. Artificial Intelligence (AI), as an advanced digital technology, has demonstrated the potential to transform traditional construction practices, similar to its impact on manufacturing, retail, and telecommunications. This study presents a systematic literature review of AI applications in the construction industry, aiming to identify dominant application areas, commonly adopted AI techniques, and existing research gaps. The review was conducted in accordance with the PRISMA 2020 guidelines to ensure transparency and methodological rigor. Relevant peer-reviewed studies published between 2015 and 2025 were identified through structured searches of Scopus, Web of Science, Science Direct, and Google Scholar. Over 200 records were initially retrieved. After duplicate removal and multi-stage screening, 15 studies met the inclusion criteria and were selected for in-depth qualitative analysis. The findings indicate that AI has been applied across key construction domains, including structural health monitoring, safety and risk management, design and pre-construction planning, sustainability, waste management, and on-site robotics. Machine learning and neural network-based approaches were the most frequently used techniques. While the reviewed studies demonstrate AI’s strong potential to improve efficiency, safety, and sustainability in construction projects, significant challenges remain, particularly regarding data quality, lack of standardization, system integration, and user trust. This review provides a consolidated overview of AI applications in construction and outlines critical directions for future research and industry adoption.

India Post is one of the leading government organizations in India and has been serving the communication needs of the public for many years. Over time, it has developed a strong brand image and earned a high level of trust among customers. Its extensive reach across the country is a key reason behind the Government’s decision to establish the India Post Payments Bank, with the aim of delivering financial services to even the most remote areas. This study seeks to examine the relationship between the work environment and job satisfaction among employees of IPPB. The research follows a descriptive design, with data collected through online structured questionnaires from randomly selected employees working in various post office branches across Gujarat. The sample includes both permanent IPPB employees and those deputed from the Department of Posts across different job roles. Statistical tools such as ANOVA and t-tests were used for data analysis. The findings reveal that the workplace does not exhibit bias in employee treatment; however, there is a need for greater motivation to foster a more challenging and engaging work environment.

This investigation represents an experimental modal analysis of a non-uniform mild steel beam under clamped-free (C-F) boundary conditions. Non-uniform beams are commonly found in engineering applications, and their modal characteristics play a crucial role in design and performance optimization. The experimental modal analysis of a non-uniform clamped-free beam is performed with a laser vibrometer excited with impact hammer excitation. We describe the experimental setup, data acquisition, and signal processing techniques used to extract natural frequencies, mode shapes, and damping ratios. The excited wave signals are converted into the frequency domain and time domain by using FFT (Fast Fourier Transformation). The simulation is performed using COMSOL Multiphysics software 5.6 and ANSYS Workbench 18.1. Thus, the experimental natural frequency and mode shapes of the non-uniform beam are compared with simulation results. A comparison of results obtained experimentally shows a good validation with the results obtained from simulation. The obtained results be used as a reference for future investigation in this domain.

Cooking education in junior high school relies heavily on the quality of resources and environments that support hands-on learning. This study examined the effects of cooking facilities, laboratory environment, teacher supervision, and sustainability awareness on the cooking performance of Grade 9 students enrolled in the TLE–Cookery specialization. Using a descriptive-correlational design, data were gathered from 200 students in two national high schools in District 2, Bukidnon through a validated and reliable questionnaire and an actual performance rubric anchored on the TLE Cookery curriculum. Ethical approval, administrative permission, parental consent, and student assent were secured before administering the instruments. Findings showed that cooking facilities were rated low, while the laboratory environment, teacher supervision, and sustainability awareness were rated moderate. Students’ actual cooking performance was likewise low in preparation, production, and presentation. Multiple regression analysis revealed that cooking facilities significantly predicted cooking performance, while laboratory environment and sustainability awareness did not emerge as significant predictors. These results emphasize that functional, sufficient, and well-maintained kitchen equipment is foundational to effective experiential learning, consistent with Kolb’s Experiential Learning Theory and Moos’ Environmental Theory. Overall, the study underscores the need for improved facilities, stronger guided practice, and deeper integration of sustainability concepts in laboratory tasks to enhance students’ technical competence and engagement in cookery classes.

The Ministry of Health and Family Welfare, Government of India, appoints Accredited Social Health Activists (ASHAs) as health facilitators under the National Rural Health Mission. Local women are chosen and trained at the village level to provide health awareness and services. Their familiarity with the local culture and socioeconomic status of families or individuals allows them to effectively communicate health-related information and mobilize households to engage with formal healthcare services. The ASHAs perform a wide range of tasks, such as providing basic healthcare information, family welfare, immunisation services, maternal and child health promotion, health counselling, maintaining health records, and community-based health projects. By serving as a vital conduit between the rural community and the public health system, they are significant backers of India's National Rural Health Mission. In Kerala, ASHAs now play a much larger role, which puts more strain on their time and energy. Their pay is primarily determined by their performance. This study examines the social cost–benefit relationship and job satisfaction of ASHA workers in Kerala, focusing on the timeliness of incentives, perceived workload, and their influence on community contribution. A total of 300 samples were selected from fourteen districts of Kerala. Primary data were analysed using descriptive statistics, Pearson correlations, and Ordinary Least Squares (OLS) regression. The study finds that timely incentive distribution and manageable workload are key determinants of job satisfaction, which in turn enhances community participation. The results highlight the need for timely payments and workload rationalisation to strengthen ASHAs’ motivation and optimise their contribution to public health delivery.

Reliable and robust personal authentication technologies have become indispensable in modern digital and physical security infrastructures. Traditional unimodal biometric systems—using a single biometric trait—often suffer from noise, spoofing vulnerabilities, and intra-class variability. To overcome these limitations, multimodal biometric systems fuse evidence from multiple biometric sources. However, feature-level fusion, despite yielding richer discriminatory information, produces high-dimensional feature spaces that demand efficient dimensionality reduction or feature selection. This review presents a consolidated analysis of three optimization-driven multimodal biometric recognition systems: Particle Swarm Optimization (PSO) for fingerprint–palmprint fusion, Genetic Algorithm (GA) for iris–fingerprint fusion, and Artificial Bee Colony (ABC) optimization for iris–palmprint fusion. We critically examine preprocessing techniques, feature extraction schemes, fusion strategies, dimensionality-reduction approaches, classifier performance, and comparative advantages. The review highlights trends, challenges, and future research directions in optimization-enhanced multimodal biometrics.

The rapid growth of the demand for quick and easy services in schools and universities brought about the transferring of the traditional canteen operations to virtual means. The article presents orderly eats, a food ordering application, which has been developed in order to facilitate students in their canteen ordering. The app lets students check the menu, place orders at any spot, and enter a little info such as their name and faculty and pay via a secure UPI QR code-based payment system. Once a payment is completed, the app creates a specific order token. This allows students to avoid the long queues and collect their food when it is comfortable for them. The backend consists of Firebase Firestore for real-time database and order tracking, while the Android interface is built with Java using Android Studio. The canteen personnel is equipped with an admin interface that gets new orders, manually verifies payments, and changes order statuses such as Approved and Ready for Pickup. These changes are immediately shown in the student's app, which guarantees efficient communication. The system that has been presented increases the operational efficiency, lowers the waiting time, decreases the number of people at the canteen during peak hours, and improves the user's overall experience. The app was built as a mini-project that demonstrates the practical uses of mobile development, cloud database integration, and digital payment processes. It has been shown that Namma Canteen can not only modernize the management of the canteen to a great extent but also provide a solid ground for the future improvements such as automatic payment verification and push notifications.

In India, gold has long been regarded as a secure and dependable investment. New methods of purchasing gold have been made available by digital platforms in recent years, drawing in younger investors. The preferences of Gen-Z and older adults with regard to physical and digital gold are compared in this study. The study assesses their degree of awareness, the rationale behind selecting a specific form, and the influence of digitalization on investment choices. A questionnaire is used to gather primary data, which is then subjected to basic statistical analysis. The results show a distinct generational gap in gold investment decisions.

The word AI is derived by Artificial Intelligence has the potential to improve the quality of man’s intellectual power in modern day. This is the intertwined method to bring excitement for coming out information and its spread all over the world with in a small second. Virtually the man knowledgeable in AI make man perfect which trained in intelligence behavioral knowledge of algorithm. So, the main themes of this paper is to known about AI (Artificial intelligence) in Biological Science in NEP, 20-20.

Students' academic performance and emotional intelligence (EI) were examined. The study's goals were to: (1) describe the sociodemographic profile of the respondents; (2) measure their emotional intelligence (EI); (3) calculate their academic performance using the General Weighted Average (GWA); (4) investigate the relationship between academic performance and emotional intelligence; (5) identify variations in academic performance and EI across demographic factors; and (6) suggest an intervention program to improve academic outcomes and emotional intelligence. A correlational, quantitative study design was used. A validated questionnaire was used to gather the data, which were then examined using one-way ANOVA, Pearson's correlation coefficient, and descriptive analysis. 203 of the respondents were female, between the ages of 18 and 20, and from low-income families. The results showed that pupils had excellent emotional intelligence, especially when it came to motivation and self-awareness. In general, academic achievement was satisfactory. A statistically significant but weak inverse relationship (r = -0.10975, p = 0.0133) between emotional intelligence and academic achievement was found using Pearson's correlation analysis, indicating that higher EI is linked to higher grades. When respondents were categorized based on sociodemographic traits, one-way ANOVA findings revealed no significant differences in EI or academic achievement. These results suggested that emotional intelligence had a moderate and consistent impact on academic achievement across all demographic groups. Based on the findings, the study suggested that CBA students develop their emotional competencies by implementing an Emotional Intelligence Enhancement Program (EIEP). Marinduque State University could generate graduates who are socially responsible, emotionally mature, and intellectually capable of handling the challenges of modern society by incorporating EI development into academic and student activities.

Barangay Health Workers (BHWs) play a vital role in delivering primary healthcare services at the community level; however, many continue to experience persistent financial challenges that affect their well-being and capacity to perform their functions effectively. This study examined the financial constraints and coping mechanisms of BHWs in Poblacion, Sta. Cruz, Marinduque, with particular emphasis on savings behavior and credit utilization. Employing a quantitative descriptive–correlational research design, data were collected from 38 BHWs selected through stratified random sampling across five barangays. A researcher-developed questionnaire was used, and data were analyzed using descriptive statistics, Pearson correlation, and the Kruskal–Wallis H test, guided by Financial Strain Theory and Goal Setting Theory. Results revealed that low and irregular honoraria, high out-of-pocket expenses, and limited access to formal financial resources were the primary sources of financial strain among BHWs. A moderate positive relationship was found between financial constraints and coping mechanisms, indicating that increased financial difficulty was associated with greater reliance on adaptive strategies such as borrowing and informal savings. No significant differences were observed in financial constraints when respondents were grouped according to demographic profile, suggesting that financial challenges are structurally driven rather than demographic-specific. The findings support the implementation of inclusive Financial Wellness and Resilience programs, improved access to formal financial services, and strengthened institutional and policy support to enhance the financial stability and quality of life of BHWs. These recommendations contribute to the advancement of Sustainable Development Goals, particularly SDG 1 (No Poverty), SDG 3 (Good Health and Well-being), and SDG 8 (Decent Work and Economic Growth).

Transformation of lives of rural women towards their betterment is a critical issue in the development process of the countries around the world. Poverty, lack of financial awareness, minimal or no education and women disempowerment are reasons of poor condition of rural women. Globally, microfinance has become an integral part of poverty reduction, economic regeneration, and development. As part of India’s microfinance programs, Self Help Groups (SHGs) are utilised. The group consists of 10-20 voluntary members. These groups were formed from people who lived nearby and had mutual trust. Each member contributed a small amount to the corpus fund. SHG members could use those funds for livelihood activities or household needs. Numerous studies on the impact of SHGs reveal that SHG membership has led to economic, social, and political empowerment of rural women. SHGs have been actively used to promote livelihoods among rural women. The objective of this research paper is to analyse the role of Self-help groups in achieving the Sustainable Development Goals (SDGs). It presents case studies developed using a mixed methods approach, including literature review and qualitative data collected through focus group discussions and in-depth interviews with key informants and women SHG members in four districts of Rajasthan. Case study method has been chosen for this paper because it is regarded as a useful research method for exploring social phenomenon through multi-dimensional lenses. It helps in detailed analysis of a phenomenon in its real-life context. These women were from four districts across the 2 major geographical and agro-climatic regions of Rajasthan– Bikaner and Churu districts from northern region (irrigated through canals), Dungarpur and Sirohi from southern region (tribal dominated, rain-fed). The respondents belonged to different age groups of SHGs, namely, 0 to less than 1 year old, 1 to 3 years old, 3 to 5 years old, 5 to 8 years old, and more than 8 years old. The study concludes that SHGs have demonstrated some success in attaining various Sustainable Development Goals, such as SDG 1, SDG 2, SDG 5, SDG 8 and lastly SDG 10. Therefore, it’s important for the SHG program to be re-energized so that better access to financial service like availability of larger loans, trainings, and awareness programmes can be made available to women. It is utmost pertinent to ensure women’s nobility and security in all the policies and programmes on rural livelihoods.

Efficient customer service delivery in banking operations is critical for maintaining customer satisfaction and resource utilization. Queuing theory is an approach to analyzing waiting lines, it provides a robust framework for modeling and improving service delivery in banks. This research explores the application of queuing theory to model customer service delivery processes in banks, focusing on service efficiency, customer wait times, and resource allocation. The data for this research was taken for a period of four weeks, covering all work days from Mondays to Fridays and hours from 8am to 4pm. The data analysis was done with the aid of EXCEL package for descriptive statistics and TORA for optimization system. From the performance measures researched, it shows that increasing the teller points to 3 would reduce the waiting time in the queue and system to 0.01481hours (53.32 seconds) and 0.05829hours (3.50 minutes) respectively as against the present situation where each customer has to wait in the queue and system for 0.30095hours (18.06 minutes) and 0,34443hours (20.67 minutes) respectively. This will result to each teller being busy for 62.3% of the time while remaining idle for 37.7% of the time.

Cyber-Physical Systems (CPS) represent a foundational paradigm shift in modern engineered systems by integrating computation, control, communication, and physical processes into a unified architecture. As CPS rapidly expand across critical domains such as smart grids, industrial automation, and smart agriculture, the growing complexity, dynamicity, and scale of these environments necessitate the adoption of advanced Machine Learning (ML) techniques capable of enabling autonomous decision-making, predictive intelligence, and resilience under uncertainty. This review presents a comprehensive synthesis of ML methodologies applied to CPS, covering supervised, unsupervised, reinforcement, and deep learning paradigms. The paper further examines their domain-specific applications, architectural integration challenges, security implications, deployment issues across edge–fog–cloud infrastructures, and emerging research opportunities. The analysis highlights the indispensable role of ML in shaping next-generation CPS with improved efficiency, robustness, and adaptability.

UPI has become a widely accepted and preferred payment mode since its inception in 2016 in India. UPI is an advanced payment system which offers greater ease of use for consumers along with high security and it has shown greater adoption by the people. In 2008 demonetization and then covid-19 led to the growth in online and digital mode of payment. Availability of highspeed uninterrupted internet further facilitates the acceptance of UPI mode in urban as well as rural area. According to the studies India's digital payments volume has increased at an average annual rate of about 50 percent over the last five years. This study intends to understand the factors influencing customer preferences towards UPI apps. This study was conducted in Kalol and Gandhinagar districts of Gujarat. Simple Random sampling method was used to select 203 respondents for the study. The influence of various factors like convenience, popularity, safety, ease of access was analysed to know customer preferences for UPIs. Test like ANOVA and t-Test were used for analysis. The results indicate that customers have a favourable opinion for unified payment interface method. The usage of UPI services is positively correlated with the respondents' level of education. Customers prefer UPI app that offer more safety and trustworthiness over convenience, popularity and other services like cash back and rewards.

Cyber Threat Intelligence (CTI) has become increasingly important as organizations face persistent and sophisticated cyberattacks. Modern digital environments—driven by cloud adoption, hyperconnectivity, and automation—require security strategies that anticipate adversarial behavior rather than respond only after incidents occur. This paper presents a concise review of CTI research and industry practices, focusing on intelligence types, analytical models, and operational applications. A streamlined conceptual framework is proposed to help undergraduate- level readers understand how CTI can be integrated into security operations. The framework emphasizes continuous intelligence requirements, structured analysis, and feedback-driven improvement. The review also highlights current limitations in CTI adoption, including data volume challenges, limited analyst expertise, and organizational barriers to information sharing.

The emergence of digital platforms has greatly increased the scope of free expression by enabling instantaneous cross-border opinion sharing. Article 19[1](a) of the Indian Constitution, which states that "everyone has right to express their own thoughts freely through various means such as printing, media, publishing, writings, signs etc., but they come with reasonable restrictions under Article 19[2] to maintain decency, social order and peace, security of state, friendly relations with international states, etc." In any case, the distinction between free expression and disparaging speech has become more hazy due to the proliferation of hate speech on digital platforms that incites violence, discrimination, or disrupts public order. Legal obscurity has resulted from selective enforcement and the lack of a precise legal definition of disparaging speech on the internet in India. Even with laws like the Information Technology Act of 2000, the Indian Penal Code (now known as the Bharatiya Nyaya Sanhita, 2023), and others, controlling hate speech online continues to be a difficult task. This study examines the tension between limiting online hate speech and defending free expression, highlighting the necessity of clear legal regulations, technological responsibility, and striking a balance between constitutional rights and social harmony on digital platforms.

This study investigates a low-cost post-production workflow and equipment chain that meets the financial constraints of the Sri Lankan film industry while maintaining industry standard visual quality. Post-production represents a significant amount of the production costs, often challenging independent and student filmmakers and other low-budget productions. The research identifies current post-production systems, evaluates their costs and standards, and explores human audio-visual perception to identify an affordable alternative. The proposed system incorporates a cost-effective combination of hardware and software optimized for efficient editing, color grading and creating DCI-recommended outputs. The workflow emphasizes streamlined processes including proxy-based offline editing, high-resolution confirmation, and final delivery to expected quality. The system and workflow were validated through the audio-visual content and a comparative analysis with two alternative versions. Focus group feedback assessed technical parameters such as brightness, contrast, color accuracy and emotional impact. The results show that the proposed workflow outperformed its counterparts by providing high-quality outputs within tangible constraints and budget constraints. The study concludes that the proposed workflow offers a practical solution for the film industry in Sri Lanka, balancing affordability with visual satisfaction. However, given the rapid evolution of post-production technology and artificial intelligence, continuous adaptability is essential to ensure long-term relevance and sustainability.

Athlete scouting and development are critical pillars in the construction of a sustainable sports ecosystem. In Africa, where a vast youth population presents significant potential, the lack of structured and equitable scouting systems remains a major barrier to global competitiveness. This paper explores strategic frameworks for athlete identification, nurturing, and progression from grassroots to elite levels. It proposes a comprehensive, inclusive, and technology-driven scouting and development model tailored to the continent’s socio-economic realities and global sporting aspirations. Drawing from case studies and global best practices, the study advocates for integrating athlete development into educational curricula, strengthening community-based programs, and building talent pathways that connect schools, clubs, federations, and national teams. It emphasizes the role of sports science, digital platforms, and multidisciplinary support systems in enhancing long-term athlete performance and welfare. The study concludes by calling for policy innovation and intersectoral partnerships to maximize Africa’s athletic potential in an increasingly competitive global sports landscape.Mobile-based and cloud-enabled platforms represent feasible technological entry points for African contexts. Phased implementation aligned with funding capacity, coach education, and digital literacy is recommended.

Diphtheria, a potentially fatal vaccine-preventable disease caused by Corynebacterium diphtheriae, has seen a resurgence in several regions, including sub-Saharan Africa. This study presents a comprehensive mathematical model for diphtheria transmission dynamics that incorporates environmental bacterial persistence, public awareness campaigns, and vaccine hesitancy. The model subdivides the human population into epidemiologically relevant classes, including susceptible, exposed, symptomatic infectious, asymptomatic infectious, vaccinated, and hesitant individuals, with an additional compartment representing environmental bacterial load. Analytical results establish the model’s positivity, boundedness, and stability properties, with the basic reproduction number ( ) derived to determine disease threshold conditions. A global sensitivity analysis using the Partial Rank Correlation Coefficient (PRCC) identified the transmission rate , exposure progression rate , and vaccination rate  as the most influential parameters affecting . The optimal control analysis, formulated via Pontryagin’s Maximum Principle, evaluated time-dependent interventions representing vaccination, treatment, and public enlightenment efforts. Numerical simulations using parameterized incidence data from Bauchi State, Northeast Nigeria, revealed that combined control strategies significantly reduce infection prevalence and environmental contamination compared to single interventions. The cost-effectiveness analysis (CEA) demonstrated that the combined vaccination and public enlightenment campaign strategy produced the lowest Incremental Cost-Effectiveness Ratio (ICER) and was classified as “very cost-effective” according to WHO-CHOICE thresholds. The findings underscore that effective control requires a multi-pronged, dynamically-adapted strategy targeting all transmission pathways. High vaccination coverage remains the cornerstone, but its impact is significantly amplified when synergistically combined with public health campaigns and environmental decontamination. The results offer evidence-based, cost-effective guidance for public health policymakers to design efficient diphtheria outbreak response and elimination programs. Moreover, numerical solutions obtained for different fractional orders highlight the Caputo-Fabrizio fractional derivative's capability to simulate memory effects, thereby offering a more nuanced representation of the real-life problem under study.

Metal Matrix Composites (MMCs) are extensively used in advanced engineering applications due to their superior mechanical and thermal properties; however, the presence of hard reinforcements makes their machining challenging using conventional methods. Non-conventional machining (NCM) processes such as Electrical Discharge Machining (EDM), Wire EDM, Abrasive Water Jet Machining (AWJM), Ultrasonic Machining (USM), and Laser Beam Machining (LBM) have therefore gained significant attention for effective machining of MMCs. This paper presents a comprehensive study on the effects of key process parameters—including discharge current, pulse duration, voltage, abrasive flow rate, jet pressure, stand-off distance, and tool vibration—on machining performance characteristics such as material removal rate, surface roughness, tool wear rate, kerf width, and heat-affected zone. In addition, various single- and multi-objective optimization strategies, including Taguchi-based approaches, Response Surface Methodology, Grey Relational Analysis, evolutionary algorithms, and hybrid techniques, are critically reviewed and compared. The study identifies optimal parametric combinations, highlights trade-offs between productivity and surface integrity, and discusses existing challenges and future research directions, thereby providing valuable insights for enhancing the machining efficiency and quality of MMCs using non-conventional processes.

The increasing demand for sustainable construction materials has encouraged the use of industrial and agricultural wastes as partial replacements for conventional concrete constituents. This paper presents an experimental analysis of green concrete incorporating selected industrial and agricultural waste materials with the objective of enhancing strength performance while reducing environmental impact. Various concrete mixes were prepared by partially replacing cement and fine aggregates with waste-derived materials in different proportions. Standard experimental tests were conducted to evaluate the mechanical properties of the developed green concrete, including compressive strength, split tensile strength, and flexural strength at different curing ages. The experimental results indicate that the incorporation of waste materials can significantly improve or maintain the strength characteristics of concrete when used in optimal proportions. Furthermore, the study demonstrates the potential of waste-based green concrete as a viable and eco-friendly alternative for conventional concrete, contributing to sustainable construction practices and effective waste management.

Glass Fiber Reinforced Polymer (GFRP) composites are widely used in aerospace, automotive, and structural applications due to their high strength-to-weight ratio and corrosion resistance. However, machining these heterogeneous and anisotropic materials often leads to delamination, adversely affecting structural integrity and service performance. This paper presents a hybrid fuzzy-based optimization approach designed to minimize delamination during GFRP machining by integrating fuzzy logic inference with a multi-objective optimization framework. The proposed method captures the nonlinear relationships between machining parameters—such as cutting speed, feed rate, drill diameter, and tool geometry—and delamination factors. Experimental data were used to develop fuzzy rule sets, while the optimization module systematically identified optimal parameter combinations that balance machining quality and productivity. Results demonstrate that the hybrid fuzzy system effectively reduces delamination compared to conventional optimization techniques, providing a robust and intelligent decision-support tool for machining GFRP composites. The study highlights the potential of combining fuzzy systems with optimization algorithms to address challenges inherent in the machining of advanced composite materials.

This paper presents a comparative study of printed antennas and dielectric resonator antennas (DRAs) for ultra-wideband (UWB) applications. The work focuses on the design, simulation, and performance evaluation of both antenna types in terms of impedance bandwidth, radiation patterns, gain, and efficiency. The printed antenna offers advantages in terms of compactness and ease of fabrication, while the dielectric resonator antenna demonstrates superior radiation characteristics and higher efficiency. Through detailed parametric analysis and numerical simulations, the study highlights the trade-offs between size, bandwidth, and radiation performance, providing valuable insights for selecting suitable antenna types for various UWB communication systems. The results indicate that the choice between printed and dielectric resonator antennas depends on specific application requirements, such as compactness versus performance efficiency.

The rapid growth of wireless communication devices has intensified the need for efficient spectrum utilization and sustainable energy consumption. Cognitive Radio (CR) systems offer a promising solution by dynamically accessing underutilized frequency bands, but energy consumption remains a critical concern. This paper proposes an AI-enabled framework for cognitive radio networks that intelligently balances energy efficiency with communication performance. Leveraging machine learning algorithms, the system optimizes spectrum sensing, power allocation, and transmission scheduling to minimize power usage while maximizing throughput. Simulation results demonstrate that the proposed approach significantly reduces energy consumption without compromising data rates, highlighting its potential for green wireless communications. The integration of AI in CR networks paves the way for more adaptive, energy-aware, and high-performance communication systems.

Vehicle-to-Everything (V2X) communication has emerged as a cornerstone technology for intelligent transportation systems, enabling real-time data exchange between vehicles, infrastructure, and pedestrians. The FR1 frequency band, as defined in 5G New Radio (NR), offers significant potential for low-latency and high-reliability V2X applications. This paper presents the design, optimization, and performance evaluation of a compact FR1 band antenna tailored for low-latency V2X communication. The proposed antenna utilizes a novel geometrical configuration to achieve wide impedance bandwidth, high radiation efficiency, and stable gain across the FR1 spectrum. Detailed simulations are conducted to optimize key antenna parameters, followed by prototype fabrication and experimental validation. The results demonstrate that the antenna provides excellent return loss characteristics, omnidirectional radiation patterns suitable for vehicular deployment, and minimal latency impact, making it highly suitable for real-time V2X applications. Comparative analysis with existing FR1 antennas indicates significant improvements in bandwidth efficiency, signal integrity, and overall V2X system performance. This work contributes a practical antenna solution for next-generation connected vehicles, supporting safer and more efficient transportation networks.

Background: Modern medical administration has pivoted toward the Patient Experience (PX) as the definitive metric for institutional success. This shift moves beyond traditional clinical outcomes to evaluate holistic service quality, recognising that administrative efficiency and environmental safety are now critical pillars of healthcare delivery. Objective: This study analyses operational bottlenecks and infrastructure deficits in Gujarat’s healthcare facilities to bridge the gap between perceived service quality and National Building Code safety standards while evaluating their impact on patient loyalty and accreditation. Methodology: This research analysed a dataset of 340 respondents across Gujarat using a cross-sectional survey, the SERVQUAL framework, and regression analysis to determine the impact of environmental factors on OPD and IPD patient satisfaction. Findings: The data identifies a significant "Service-Safety Divergence." While clinical nursing satisfaction reached a high of 88%, critical deficits were identified in emergency wayfinding (55%) and waittime management (46%). Regression results indicate that environmental factors—specifically signage clarity and potable water access—carry a disproportionate weight in patient loyalty scores, revealing a systemic reliance on human capital to mask infrastructural weaknesses. Conclusion: To resolve these gaps, the study advocates for digital queue management and trilingual safety wayfinding. Actionable strategies are provided to align Gujarat’s healthcare infrastructure with international quality benchmarks, ensuring long-term operational excellence.

Ageing is a multifactorial degeneration in body organs and physiological functions. Adansonia digitata linn (AD), Bryophyllum pinnatum (BP), Vernonia amygdalina (VA), and Canna indica (CI) are commonly used in folk medicine as anti-ageing remedies in Southwest Nigeria with little or no scientific bases. This study investigated in-vitro antioxidant, anti-inflammatory, and anti-ageing potentials of these medicinal plants. Ethylacetate Extracts of the powdered leaves of the plants were prepared by cold extraction and were subjected to in-vitro assays: Total Flavonoids content, Hydroxyl radical (OH‑) and 2,2- Diphenyl-1-picrylhydrazyl(DPPH) radical scavenging potentials, Inhibition of lipid peroxidation, Inhibition of Proteinase activity and Protein denaturation, while anti-tyrosinase and Anti-glycation potentials were also evaluated, using standard methods. Results showed that, ethylacetate extract of BP, at 400µg/ml, showed highest Total Flavonoids content Quercetin Equivalent (QE)(1.90mg/g/QE) compared with VA(1.52mg/g/QE), CI(0.80mg/g/QE) and AD(0.4mg/g/QE) respectively and highest OH- radical scavenging potential (66.57%) compared with CI(48.25%), VA(26.53%) and AD(26.43%) respectively. Furthermore, at 400µg/ml, VA exhibited the highest DPPH radical scavenging activity (62.02%) compare with BP(61.98%), AD(53.80%) and CI(45.20%), while AD was more potent at lower concentrations. Moreover, at 400µg/ml, Inhibition of lipid peroxidation was highest in CI(33.07%) compared with AD(26.60%), VA(24.98%), and BP(21.49%), although VA was more potent at lower concentrations. Furthermore, CI exhibited highest proteinase activity inhibition and Inhibition of protein denaturation at concentrations (100 µg/ml - 400µg/ml) while at 400 µg/ml, it elicited highest proteinase inhibition of 94.88% compared with AD(90.50%,), BP(85.89%) and VA(76.1%) respectively and also, highest protein denaturation inhibition of 71.82% compared with BP(59.60%), AD(36.40%) and VA(32.92%) respectively. At the highest concentration of 1mg/ml, VA exhibited the highest anti-tyrosinase activity (IC50 0.28±0.004, 87.28%), while BP, AD, and CI were (IC50 0.32±0.008mg/ml, 77.56%; IC50 0.57±0.009mg/ml, 51.24%; IC50 1.05±0.015mg/ml, 44.35%), respectively. Moreover, across all concentrations (0.3125mg/ml – 5mg/ml) from week 1- 4, VA also, exhibited highest anti-glycation potentials (71.23%, 87.54%, 86.20% and 90.06%) compare with AD(63.30%,75.98%,83.68% and 82.84%), CI (58.75%, 52.62%, 59.94%, and 78.29%) and BP(47.08%, 51.43%, 53.02% and 63.89%) respectively. Vernonia Amygdalina exhibited the highest DPPH radical scavenging, anti-tyrosinase, and anti-glycation properties, while CI showed the highest inhibition of lipid peroxidation, protein denaturation, and proteinase activity. BP exhibited the highest OH- radical scavenging potential and total flavonoids content. The plants may be employed as anti-ageing remedies and also in the management and treatment of other oxidative stress related diseases.

This study aims to record and analyze the ongoing construction process of Toba Batak traditional houses in Samosir Regency in 2025 as part of the Indonesian architectural heritage. The approach used is descriptive qualitative with a case study method through field observations, visual documentation, in-depth interviews, and literature review. The results show that the construction process of Toba Batak traditional houses still maintains traditional construction stages, wooden structural systems, and nailless connection techniques passed down through generations, although there are adjustments to current conditions. The construction process functions not only as a construction activity but also as a cultural practice that embodies customary values, local knowledge, and social togetherness. This study confirms that Toba Batak traditional houses constitute a dynamic architectural heritage, where building traditions remain alive and relevant amidst changing times. Documentation of this construction process is expected to support efforts to preserve and develop knowledge of Indonesian architectural knowledge sustainably.

Mine disposal is a critical facility that requires a stable slope design to ensure operational safety and the sustainability of mining activities. This study aims to analyze the stability of the planned mine disposal design at PT. XYZ, South Sumatra, and to evaluate the necessity of design improvement through a resloping approach. The initial disposal design applied a slope angle of 35°. The slope stability analysis results indicate that, at several locations, this slope angle produced factor of safety (FoS) values below the required stability criteria, classifying the disposal slopes as unstable. As a mitigation measure against slope failure risks, the slope geometry was modified by reducing the slope angle to 20°. The evaluation results demonstrate that the implementation of resloping significantly increased the FoS values to meet the disposal slope stability criteria. Therefore, a disposal slope design with a 20° inclination is recommended as a safer alternative for implementation at PT. XYZ to enhance operational safety and reduce the potential for slope failure.

Motor vehicle accident (MVA) compensation systems in North America adopts distinct policy approaches, with the United States emphasizing tort-based, fault-driven frameworks, and Canada employing primarily no-fault or hybrid models. This review draws evidence from academic literature, government reports, and insurance industry sources to compare system structures, benefits, and practical outcomes as of 2023. Major differences between these provinces include the speed of access to medical care, availability of income replacement, legal costs, and eligibility for non-economic damages such as pain and suffering. Generally, no-fault and hybrid systems provide faster, more predictable support, whereas tort-based systems allow higher potential awards but often involve delayed compensation and high legal fees. However, the differences that exists across states and provinces demonstrates jurisdiction-specific effects on recovery, equity, and financial burden. The review identifies knowledge gaps, including comparative recovery trajectories and long-term outcomes, and offers recommendations for policymakers, researchers, and clinicians to enhance evidence-based informed decisions. These findings support efforts to design compensation models that balance timeliness, fairness, and sustainability for individuals affected by motor vehicle accidents.

The properties of clay that influence the soil geotechnical behaviour are fundamentally determined by the physio-chemical characteristics of the composite minerals and the relative proportion in which the minerals are present. It requires a number of sophisticated laboratory equipment and time to determine these characteristics. However, an alternative and all-encompassing method which combines simple and normal laboratory tests that gives a quantitative measure of the composite effect of all the basic properties of clay, termed Clay Colloidal Activity was used in this study. It is an index property, the ratio of plasticity index to clay fractions that combines the Atterberg Limits and the particle size distribution, computed by using the normal routine laboratory tests. In this study, the Clay Colloidal Activity of soil samples collected from Dunde, Belel and Sorau in Maiha Local Government Area of Adamawa State, Northeastern Nigeria were tested. The moisture content of the soils ranged from 10.3% to 12.6%. Plasticity Index for the soils studied ranges from 16 to 25, while the ratio of Moisture Content to Liquid Limit was from 0.21 and 0.35. These values suggested low risk of liquefaction thereby mitigating the possibilities of danger and associated unfavorable engineering issues.  Dunde has Colloidal Activity value of 0.80, Sorau has 1.25 and Bele has 1.09. All of them were found to have clay of moderate values of Clay Colloidal Activity, and are considered normal clays that may not pose danger to engineering projects.

The COVID-19 pandemic really highlighted some serious weaknesses in how businesses operate around the globe, particularly for Small and Medium Enterprises (SMEs) in emerging markets like Nigeria. A lot of these SMEs faced major challenges, including supply chain disruptions, financial setbacks, workforce issues, and a drop in consumer demand. As we move into the post-COVID world, it’s become clear that building organizational resilience is crucial for both sustainability and growth. This research dived into how strategic leadership and innovation can be harnessed to boost the resilience of SMEs in Nigeria's changing economic environment. It looks at various leadership practices, the adoption of innovative solutions, risk management tactics, and flexible business models that SMEs are using to tackle the challenges that have arisen after the pandemic. The study employed a mixed-methods approach, combining quantitative surveys with qualitative interviews to gather rich insights from SME owners, managers, and employees. This research took place in Kano and Plateau States, two vital economic hubs in Northern Nigeria that boast a variety of thriving small and medium-sized enterprises (SMEs). This study selected 25 SMEs, interviewing one or two key informants from each, such as CEOs, heads of innovation, or HR managers. This study used purposive sampling technique to pinpoint SMEs that fit our inclusion criteria and have shown some level of strategic adaptation during or after the COVID-19 pandemic. This study analysed the interview transcripts using thematic analysis. This study used simple linear regression analysis facilitated by SPSS version 28. The findings demonstrate that strategic leadership, innovation, and organizational challenges each play an important role in strengthening organizational resilience. Strategic leadership showed a significant positive effect on organizational resilience, indicating that organizations with visionary, proactive, and adaptive leaders are better equipped to withstand disruptions and maintain stable performance. Innovation also had a strong and positive impact on resilience, suggesting that organizations that consistently invest in new ideas. Organizations should implement continuous leadership development programs focusing on decision-making, crisis management, and strategic foresight. Organizations should allocate a fixed annual budget for research, technology upgrades, and new product or service development.  Leaders should conduct regular environmental scanning and risk assessments to better understand emerging challenges and respond proactively.

A thermosiphonic solar water heating system that uses a simple flat plate collector to heat water and in which circulation takes place with natural convention. The system was designed, constructed and tested at Yelwa campus, ATBU Bauchi with latitude 10.330N and longitude 9.830E. The system was designed to heat a total of 15litres of water between 500 and 800 at the worst and best month of the year respectively. The system was designed using weather data of Bauchi state and the resulting parameters of the design was used to construct the system. It was tested between December and January. Two tests were carried out on the system; for the system with ordinary collector, and that with diffusers incorporated within the collector risers. The first system (without diffusers) has an efficiency of 52.6% and was able to generate collector outlet temperature of 610C and water mean temperature of 600C at an average daily solar radiation of 912W/m2. In contrast, the second system (with diffusers) has an efficiency of 58.6% and was able to generate collector temperature of 700C and water mean temperature of 680C at an average daily solar radiation of 913.3W/m2. It was concluded from the results that incorporating diffusers within risers improved the performance of the system by 6.6%.

The increasing complexity and dynamic nature of modern power systems demand advanced optimization techniques to ensure reliable, efficient, and cost-effective operation. This paper explores the development and application of soft computing techniques, including fuzzy logic, genetic algorithms, and artificial neural networks, for enhancing power system performance and optimization. By integrating these intelligent computational approaches, the study addresses challenges such as load balancing, economic dispatch, voltage stability, and fault management. Simulation results demonstrate that soft computing-based methods outperform conventional optimization techniques in terms of accuracy, convergence speed, and adaptability to changing system conditions. The proposed framework provides a robust and flexible solution for real-time power system optimization, contributing to improved operational efficiency and sustainability.

Microplastic pollution has become a critical environmental concern due to its persistence, widespread distribution, and adverse impacts on aquatic ecosystems and human health. Large waterbodies and waterways serve as major accumulation and transport channels for microplastics originating from industrial effluents, urban runoff, and degradation of plastic waste. Existing remediation techniques often lack scalability, sustainability, or real-time adaptability. This paper presents a smart and sustainable technological framework for microplastic pollution mitigation, focusing on environmentally friendly and energy-efficient solutions. The proposed framework integrates intelligent monitoring, data-driven analytics, and eco-friendly remediation technologies to enable effective microplastic management. Low-power sensing and Internet of Things (IoT)–based monitoring systems are utilized for continuous detection and spatial assessment of microplastic contamination. Machine learning–based data analysis enhances detection accuracy, trend analysis, and hotspot identification. For mitigation, sustainable filtration mechanisms, biodegradable adsorbent materials, and nature-inspired separation techniques are incorporated to minimize ecological disruption and secondary pollution. The framework also emphasizes modular design, renewable energy integration, and scalability to support long-term deployment across diverse aquatic environments.

Vehicular Ad Hoc Networks (VANETs) play a crucial role in enabling intelligent transportation systems by supporting real-time vehicle-to-vehicle and vehicle-to-infrastructure communications. However, high node mobility, frequent topology changes, and intermittent connectivity significantly affect communication reliability and network performance. To address these challenges, this paper proposes an intelligent relay vehicle optimization approach aimed at enhancing mobility management in VANET environments. The proposed scheme dynamically selects optimal relay vehicles based on key parameters such as vehicle mobility patterns, relative speed, link stability, and network connectivity conditions. By intelligently adapting to rapidly changing vehicular scenarios, the approach improves data forwarding efficiency, reduces packet loss, and enhances overall communication reliability. Simulation-based performance evaluation demonstrates that the proposed method outperforms conventional relay selection techniques in terms of packet delivery ratio, end-to-end delay, and network throughput. The results indicate that intelligent relay vehicle optimization is an effective solution for robust and efficient mobility management in VANETs, particularly in high-speed and dense traffic conditions.

Wireless Sensor Networks (WSNs) are widely deployed in applications such as environmental monitoring, healthcare, smart cities, and industrial automation, where network performance and energy efficiency are critical constraints. Due to limited battery power, computational capability, and communication bandwidth of sensor nodes, the design of efficient optimization algorithms plays a vital role in enhancing overall network performance. This paper presents the design and performance analysis of optimization algorithms aimed at improving key WSN performance metrics, including energy consumption, network lifetime, throughput, packet delivery ratio, and end-to-end delay. The proposed approach focuses on algorithmic optimization at the routing and resource management levels to achieve balanced energy utilization and reduced communication overhead. Analytical evaluation and simulation-based results demonstrate that the optimized algorithms significantly outperform conventional schemes in terms of energy efficiency and network stability under varying network conditions. The findings highlight the effectiveness of optimization-driven algorithm design in addressing the inherent challenges of Wireless Sensor Networks and provide insights for future algorithm development in resource-constrained wireless environments.

Cooperatives in the Philippines to this present day already existed for more than a century since its legislated amalgamation to the socio-economic governance of the country in 1916. Throughout those more than 100 years, it has its successes and failures of which management is a crucial factor. This study investigated further the managerial competency of cooperative officers and financial performance of selected agricultural micro cooperatives in the province of Isabela. Data were gathered from all the selected 37 only compliant cooperative represented by 185 officers as respondents. The study mainly sought to answer if there is relationship between cooperative officers and managerial performance, cooperatives profile and financial performance and core managerial competency and cooperative financial performance of cooperatives. The study revealed that majority of the selected agricultural micro cooperatives served as respondents of this study had a fair financial performance rating, that only educational attainment variable had significant correlation with managerial competency. The cooperative cooperatives profile and financial performance was found of having no significant relationship and there is no significant correlation of the managerial competencies and cooperative financial performance. Based on the findings of the study a proposed action plan was formulated for the agricultural micro cooperatives.  Notably recommended that the Cooperative Development Authority and other government and private entities and stakeholders in the administration, supervision and supporting the cooperatives may conduct necessary, relevant and timely trainings or continuing education and other development initiatives and actions to assist more the agricultural micro cooperative to have a reliable and accurate Management Information System and data analytics useful in decision-making specially to achieve a sound financial performance.

This paper is developed to shed light on the old perspective that language is never enough to grasp the feeling, and no matter how much we express, there is always something ineffable about it. A concept is an umbrella term that can be described in a number of ways.  In section 1, I discussed different notions of concepts. Section 2 will address the ineffability of the phenomenal mental states, specifically focusing on Wittgenstein’s (1953/2009) notion of the use of words and private language. In section 3, I will shed light on pure phenomenal concepts (Chalmers 2010). Finally, I will show why the language categories cannot adequately capture the phenomenal concepts. I will conclude that language is never enough to grasp what one feels.

Air pollution poses a serious environmental and public health challenge, particularly due to fine particulate matter (PM₂. ₅), which can penetrate deep into the human respiratory system. Accurate forecasting of PM₂. ₅ concentrations is therefore essential for early warning systems and mitigation planning. This study presents a comparative evaluation of five predictive models—Linear Regression, Random Forest, XGBoost, CatBoost, and Long Short-Term Memory (LSTM) using a multi-year hourly (PM₂. ₅) dataset from India. Model performance is assessed using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the coefficient of determination (R²). The results show that all models achieve strong predictive performance, with LSTM yielding the lowest MAE and RMSE, while CatBoost attains the highest R². Visual analyses, including time-series comparisons and observed-versus-predicted plots, further validate model robustness. The findings demonstrate that machine learning and deep learning approaches can provide accurate and interpretable PM₂. ₅ forecasts, supporting effective air quality management and decision-making air quality forecasts to facilitate prompt decision-making.

Quality education is very important for a nation’s prosperity and development. NEP 2020 has emphasized the holistic development of students. For quality education and holistic development innovative way of teaching as well as innovative evaluative systems are needed. Authentic assessment approach helps students to demonstrate their knowledge and skills in different ways. Authentic tasks may include making projects, creating artwork or videos or other products. In the present study, researchers try to find out the academic performance on the basis of an authentic assessment approach (doing project on infectious diseases) of secondary students of Uttar Dinajpur District of West Bengal in life science concerning location (rural and urban) and gender (female and male). Descriptive Survey Method was applied for this study. The data was collected from 202 students of 10th standard students of secondary schools in Uttar Dinajpur district of West Bengal in life science through a probability sampling technique. There are some socio-economic and school level variables that can influence performance, to control this researcher took some classes in rural and urban schools to guide them on authentic assessment approach. From the analysis and interpretations of the result it has been found that, the boys’ students had better academic performance than the girls’ students when an authentic assessment approach was applied. The urban students had better academic performance than the rural students in case of the authentic assessment approach. In this perspective, proper guidance, counseling and orientation programs for an authentic assessment approach should be organized for the teachers, and the students to know about the importance of an authentic assessment approach in the secondary school for holistic development, because when student apply their knowledge and skill at the practical situation then only the proper utilization of knowledge and skill can happen (Mueller, 2008). Thus, the government should take the initiative to apply the authentic assessment approach for better performance of the students in life science along with other academic subjects.

The fire safety evaluation is a necessary process in industrial, commercial, and institutional settings to avoid fire-related incidents, protect lives, and reduce property losses. To assess fire hazards   and ignition sources and to prevent fire and control systems. This paper gives the methodology of fire safety precaution and prevention in different places like industries, workplaces, and domestic places. This paper analyzes risk assessment, engineering control systems, emergency preparation, and precaution and prevention methods in the chemical industry. By systematic approach in the fire evaluation system in the chemical industry to give training in all zones in the industry, maintenance of equipment, and periodic audits, it is recommended to give a compliance report correctly regarding fire safety and sustainable fire safety permanence.

This qualitative study underscores the importance of Learning Activity Sheets (LAS) as reinforcement tools in Grade 11 Earth and Life Science, particularly in enhancing student comprehension, engagement, and independent learning within senior high school classrooms. Despite the widespread implementation of LAS, a research gap remains in the limited qualitative literature that examines teachers’ perspectives on their instructional effectiveness and the practical challenges encountered during classroom implementation. Addressing this gap, the study aimed to explore how LAS support student learning, identify the challenges teachers face in using them, and describe strategies employed to enhance their effectiveness. A qualitative research design was utilized, employing purposive sampling to select Grade 11 science teachers as participants. Data were collected through semi-structured interviews that allowed teachers to share their experiences and insights, and the responses were analyzed using thematic analysis to identify recurring patterns and meaningful themes. Findings revealed that teachers perceive LAS as effective instructional tools that improve student understanding, engagement, and independent learning, particularly when activities are well-structured, promote collaboration, and are linked to real-life contexts. However, teachers also reported challenges such as limited instructional time, varying levels of student motivation, and difficulties in aligning LAS with lesson objectives. Anchored on B.F. Skinner’s Reinforcement Theory and Piaget’s Constructivist Theory, the study implies that thoughtfully designed and contextually relevant LAS can strengthen instructional practices, promote learner-centered engagement, and improve learning outcomes in Earth and Life Science. The findings provide valuable insights for teachers, curriculum developers, and school administrators in improving the design and implementation of LAS in senior high school science education.

Despite tourism destinations being recognized as ideal contexts for coopetition due to their structural interdependence and network characteristics, coopetition research in tourism remains severely underexplored, representing only 5.6% of total coopetition scholarship. This study provides a comprehensive scientometric analysis to map the intellectual structure, identify key contributors, and reveal emerging trends in this nascent field. Using Web of Science and Scopus data spanning 1996-2025, we analyzed 157 publications employing CiteSpace software to generate co-citation networks, keyword co-occurrence patterns, and cluster analysis. The analysis reveals significant geographic concentration, with 43.7% of research originating from European institutions and 28.5% from the Americas. Four distinct intellectual communities emerge: conceptualization and operationalization studies, trust and network governance mechanisms, strategic applications and performance outcomes, and entrepreneurial cognition. The field demonstrates evolution from basic definitions toward multidimensional constructs encompassing managerial, strategic, behavioral, and contextual dimensions, yet exhibits structural fragmentation with minimal international collaboration. Tourism research predominantly treats coopetition as an explanatory variable for strategic advantages rather than investigating its formation mechanisms, contrasting with broader coopetition literature. Critical gaps include absence of intra-organizational coopetition studies, limited cross-destination comparative research, and narrow focus on specific tourism types. This research advances tourism coopetition scholarship by documenting disciplinary knowledge flows through dual-map overlay analysis, quantifying structural fragmentation that impedes knowledge accumulation, and establishing temporal evolution patterns across consolidation and acceleration phases, thereby providing actionable priorities for destination managers and researchers seeking to bridge theoretical development with practical implementation.

The Indian higher education landscape is undergoing a profound transformation following the National Education Policy (NEP) 2020, which mandates a shift from rigid disciplinary silos toward multidisciplinary learning ecosystems. Psychology, traditionally confined to social science departments, is now being reimagined as a foundational “hub science” essential across diverse undergraduate programs. This comprehensive review examines the integration of psychology in engineering, business, healthcare, law, and teacher education between 2020 and 2025, analysing over 700 research inputs including government reports, university syllabi, and empirical studies. The analysis reveals that while interdisciplinary psychology education significantly enhances critical thinking, emotional intelligence, and professional competencies, implementation faces substantial challenges from regulatory conflicts - particularly the National Commission for Allied and Healthcare Professions (NCAHP) Act 2021 and the 2024 University Grants Commission (UGC) ban on distance education psychology degrees. This paper argues for distinguishing “clinical psychology” (healthcare profession) from “applied psychology” (academic discipline) to resolve regulatory tensions while advocating for systematic integration of psychological literacy across curricula, including indigenous Indian Knowledge Systems (IKS). The findings demonstrate that psychology integration is not merely academically relevant but structurally necessary for preparing graduates to navigate the complex interface of human behaviour and technical systems in contemporary society.

Technological advancements, particularly the widespread penetration of smartphones, have substantially accelerated the adoption of digital payment systems across both formal and informal segments of the economy. This shift towards electronic transactions has emerged as a critical driver of financial inclusion by enabling broader access to formal financial services. The present study investigates the impact of digital payment adoption on participants within the informal economy—specifically vegetable vendors, small retail shop owners, street vendors, and pan parlour operators—in the Sabarkantha district of Gujarat. Research Purpose: This study evaluates the socio-economic and systemic determinants facilitating the transition from traditional paper-based transactions to Fintech-driven paperless systems within the informal economic sector of the Sabarkantha district of Gujarat. It specifically examines how demographic variables influence the perception of legal, social, and technological risks. Research Methodology: A quantitative approach was employed, utilizing a structured survey administered to 211 participants in the Sabarkantha region. Data were analyzed using Descriptive Statistics, Independent Samples T-tests, One-Way ANOVA, and Spearman’s Rho Correlation to test the relationships between demographic factors and digital payment adoption. Findings: The analysis reveals that while the transition is accelerated by younger demographics (64% aged 18–45), it is significantly moderated by education (p=0.003) and income level (p=0.003). Furthermore, a strong correlation exists between economic benefits and E-payment preference (rho value = 0.406); however, "Unsafe Technology" remains a significant psychological barrier to full-scale digital integration. The findings underscore that while technological innovation plays a pivotal role in transforming economic transactions, ensuring robust data protection mechanisms and enhancing digital literacy are essential for sustaining trust and promoting inclusive growth across informal economic sectors. Originality/Value: This research contributes to the "Digital Financial Inclusion" discourse by identifying the specific socio-economic friction points that impede the "Paper to Paperless" journey in emerging informal markets.

The chassis of an electric vehicle represents a critical nexus between safety, efficiency, and performance. Its role extends beyond conventional vehicular frameworks, assuming responsibility for housing and protecting vital components, managing thermal dynamics, and providing a robust safety envelope. With the rapid evolution of EV technology, continued advancements in chassis design and engineering will undoubtedly play a pivotal role in shaping the future of sustainable mobility. Structural analysis of the Chassis yields deformation, stress and strain. Similarly, vibration analysis provides the natural frequencies of vibration. Under dynamic conditions, the investigations of these characteristics are done in the present work. Further, failure modes of vibrations are also found using Finite Element Analysis.  From the present study, it is concluded that, with an increase of pressure by 19%, the average percentage increase in the stress, strain and total deformation is observed to lie in the range of 18 to 20%. Further, it is observed that, the amplitude in the Grey Cast Iron is higher as compared to the other materials considered in the present investigation while the amplitude in the Titanium Alloy is lowest. Additionally, vibration frequency in the Structural Steel is higher as compared to the other materials considered in the present investigation while vibration frequency in the Grey Cast Iron is lowest.  

This analysis offers a thorough overview of the RSA algorithm, a key element of modern digital security that addresses the historic challenge of secure communication over insecure public channels. Its innovation is based on number theory, using a "trapdoor" one-way function reliant on the significant computational difference between multiplying large prime numbers and factoring their product. Paired with modular arithmetic and Euler's Totient Theorem, this enables the practical use of public-key cryptography, where a public key encrypts data that only the matching private key can decrypt. The text highlights RSA's vital role in internet security, especially in securing web connections via SSL/TLS and ensuring authenticity and integrity with digital signatures. It discusses the cybersecurity arms race, stressing the importance of sufficient key lengths and secure practices to fend off classical attacks. The analysis also examines RSA's main vulnerability: the threat from quantum computing. Shor's algorithm can efficiently solve the integer factorisation problem, making RSA obsolete and prompting a global shift to Post-Quantum Cryptography (PQC). Despite its eventual replacement, RSA's legacy as the first framework that enabled trust in the digital world remains a monumental achievement in computer science.

This study examined the adoption, performance, challenges, and enhancement opportunities of the LANDBANK Electronic Modified Disbursement System (eMDS) at the Boac Branch in Marinduque, Philippines. Using a mixed-method design, survey data were collected from 26 eMDS users (clients and employees) and complemented by qualitative feedback. Results indicate a very high level of system adoption and satisfaction (composite mean = 3.87, Strongly Agree), with users rating eMDS as efficient, secure, and preferable to manual disbursement processes. Key challenges identified include transaction delays during peak usage (WM = 2.69) and system lag under limited internet connectivity (WM = 2.73), despite generally strong perceptions of system stability. Users also reported high confidence in security features (composite mean = 3.85) and workflow efficiency (composite mean = 3.69). The findings underscore the importance of optimizing transaction speed, improving low-bandwidth performance, and strengthening embedded user support tools. Anchored on the Sustainable Development Goals, the study highlights how eMDS contributes to SDGs 8, 9, 10, and 16 by enhancing efficiency, inclusivity, and transparency in public financial management.

Institutional Innovation Councils (IICs), initiated under the aegis of the Ministry of Education, Government of India, have emerged as a significant mechanism for fostering a culture of innovation and entrepreneurship in higher education institutions. The primary objective of IICs is to systematically nurture innovative thinking, problem-solving abilities, and entrepreneurial skills among students and faculty members. This study examines the role of Institutional Innovation Councils in enhancing innovation and entrepreneurship within higher education institutions by promoting start-up ecosystems, industry–academia collaboration, capacity-building programs, and experiential learning opportunities. Using a structured research framework, the study analyzes the effectiveness of IIC initiatives such as innovation challenges, entrepreneurship development programs, incubation support, and intellectual property awareness. The findings highlight that IICs play a pivotal role in strengthening innovation culture, improving entrepreneurial intent among students, and aligning academic institutions with national innovation goals. The study concludes that effective implementation of IIC activities significantly contributes to institutional transformation, skill development, and sustainable entrepreneurial outcomes in higher education.

Efficient ambulance response is vital for saving lives during emergencies. Cellular Vehicle-to-Everything (C-V2X) technology offers a transformative solution by enabling real-time communication between vehicles (V2V), infrastructure (V2I), and pedestrians (V2P). This facilitates traffic signal preemption, dynamic routing, collision avoidance, and improved public safety through timely alerts. Integrated with 5G networks, C-V2X also allows ambulances to transmit patient data to hospitals in advance, ensuring better preparedness. Despite challenges like infrastructure requirements, regulatory compliance, and data security, C-V2X significantly enhances response times, safety, and patient outcomes, revolutionizing emergency medical services and paving the way for intelligent transportation systems.

This study examined the effects of tax planning strategies on the firm value of listed manufacturing firms in Nigeria: an empirical analysis. Tax planning is a significant aspect of business strategy and a crucial exercise for effective and efficient financial planning activity which requires attention from managers of all functional areas in the firm. This study is guided by two specific objectives which include: to examine the effect of tax planning strategies on firm value of listed manufacturing firms in Nigeria and to evaluate the effect of effective tax rate on the firm value of listed manufacturing firms in Nigeria. Relevant literatures were reviewed including conceptual, theoretical and empirical literatures. Ex post facto research design was adopted. Secondary data from the audited financial statement of the listed manufacturing firms on Nigerian Exchange Group were collected. Descriptive and inferential statistics were used to analyse the data. In conclusion the result of the regression shows that tax planning strategies have demonstrated to have an insignificant relationship with farm value of listed manufacturing firms in Nigeria. Income tax rate and effective marginal tax rate have negative relationship with firm value while effective tax rate has positive relationship with firm value. The study recommends that both people and corporations be encouraged to proactively adopt a thorough tax planning policy. Taxpayers might find chances to maximize credits, deductions, and tax-exempt investments by knowing the newest tax rules and consulting a knowledgeable tax professional.

Precise solar power forecasting is critical for the stability and efficiency of modern intelligent power networks. However, the reliability of these forecasts is often compromised by communication network impairments, such as latency and packet loss, occurring between solar plants and control centers. This paper proposes a Communication-Aware Long Short-Term Memory (Comm-Aware LSTM) framework designed to integrate network-state information directly into the forecasting process. We model the system using a distributed communication topology consisting of solar plants, edge nodes, and cloud-based control centers. Our experimental results demonstrate that the proposed model significantly outperforms traditional Baseline LSTM architectures under varying network conditions. Specifically, the Comm-Aware LSTM exhibits superior training convergence, achieving lower Mean Squared Error (MSE) while maintaining a negligible computational overhead—adding only 1.6 more trainable parameters and approximately 0.3 of inference latency. Correlation analysis further reveals that by explicitly accounting for latency-induced errors, the model provides robust predictions even in high-latency scenarios 0.5s. This research confirms that communication-aware deep learning architectures are essential for the next generation of resilient, edge-integrated smart grids.

Dynamic networks require routing mechanisms that are simple, reliable, and capable of adapting to frequent topology changes. Distance vector routing remains widely used due to its low computational overhead and ease of implementation. This paper presents a hop count–based distance vector routing model designed for dynamic network environments. The proposed model uses hop count as the primary routing metric and periodically exchanges routing information among neighboring nodes to maintain updated path selections. Techniques such as route aging, periodic updates, and loop prevention are incorporated to improve convergence and stability. The model is evaluated under varying network conditions, including link failures and topology changes, to assess convergence time, routing overhead, and path efficiency. Experimental results demonstrate that the proposed approach provides consistent routing performance with minimal control overhead, making it suitable for small to medium-scale dynamic networks where simplicity and robustness are critical.

This study develops and evaluates a diesel rotary injection pump mock-up as a pedagogical innovation to bridge the gap between theoretical instruction and hands-on experience in automotive technology programs. The mock-up simulates key diesel fuel injection functions through manipulable components, addressing resource constraints in laboratories. Employing a descriptive quantitative pre-test–post-test design, 30 BS Autotronics students, 5 teachers from University of Science and Technology of Southern Philippines (USTP), and 3 automotive experts from Toyota/Isuzu Motors rated its content, technical, and pedagogic quality via a validated 15-item, 5-point Likert scale instrument. Post-test means ranged 4.6–4.8, reflecting strong agreement on accuracy, reliability, safety, and instructional alignment, with minor concerns on usability and sustained engagement. Wilcoxon Signed-Rank tests across all parameters showed W = 0, Z = -5.37 (p < .001), and large effect sizes (r = 0.87), confirming highly significant, unanimous positive shifts in perceptions with substantial practical impact. These findings validate the mock-up as a cost-effective tool enhancing autotronics education, better preparing students for industry diesel maintenance while highlighting areas for usability refinement.

This study examined the association between budgeting practices and health wellness among senior citizen social pensioners in Mogpog, Marinduque, Philippines. Anchored on the Theory of Planned Behavior and Dunn’s Theory of Wellness, a quantitative descriptive–correlational design was employed involving 263 senior citizen social pensioners from 11 barangays. Data were collected using a validated survey instrument measuring budgeting practices attitude toward budgeting, subjective norms, perceived behavioral control, behavioral intention, and actual budgeting behavior and health wellness across physical, mental, and social dimensions. Descriptive statistics, reliability testing, non parametric tests, and multivariate analysis were used to ensure analytical rigor. Results revealed that respondents generally demonstrated strong budgeting practices (grand mean = 3.89) and favorable health wellness outcomes (grand mean = 4.18). However, correlation and multivariate analyses indicated no statistically significant association between budgeting practices and health wellness (rs = 0.05, p = 0.42). Significant differences in budgeting practices were observed when respondents were grouped by gender and living arrangement, while no significant differences were found across marital status and educational attainment. The findings suggest that among low-income senior citizens reliant on social pensions, wellness is shaped more by structural and social determinants such as healthcare access, family support, and pension adequacy than by individual financial behavior alone. The study underscores the need for integrated financial, health, and social support interventions to enhance elderly well-being in rural communities.

The makeup color industry for women in India is rapidly expanding, fueled by a youthful population, rising disposable income, and the impact of social media. Over the past ten years, there has been a significant shift in how Indian women perceive beauty and personal care. The Indian cosmetics and makeup market is witnessing substantial growth, with forecasts for 2025 and beyond indicating strong compound annual growth rates, although estimates differ by source, with some suggesting around 3.8% (according to Statista for cosmetics) and others indicating potentially over 8-10% (as per Fortune Business Insights and Mordor Intelligence) for the broader market. This growth is propelled by increased disposable income, urbanization, the effects of social media, and a growing preference for natural or herbal products, with major segments such as skincare and color cosmetics leading the way, while premiumization and male grooming also play roles in the industry's expansion.

Self-compacting concrete (SCC) enhances constructability through high flowability and self-consolidation; however, its elevated cement demand increases cost and environmental impact. This study evaluates Corn Sheath Ash (CSA), Cassava Pulp Ash (CPA), and Ground Granulated Blast Furnace Slag (GBFS) as partial cement replacements in SCC. Cement was replaced at levels of 0–20% by mass at a constant water-to-binder ratio of 0.50, following EFNARC guidelines. Workability was assessed using slump flow tests, while compressive strength was measured at 7, 14, and 28 days. Increasing replacement levels resulted in reduced flowability for all materials. At 20% replacement, slump flow decreased from 755 mm for the control mix to 662 mm and 645 mm for CSA- and CPA-based SCC, respectively, whereas GBFS mixes maintained higher flowability (≈684 mm). Compressive strength declined with increasing CSA and CPA content, with 28-day strengths reducing to 25.5 MPa and 21.3 MPa, respectively, due to cement dilution and limited reactivity. In contrast, GBFS-containing SCC achieved a 28-day compressive strength of approximately 29.1 MPa at 20% replacement, attributed to its latent hydraulic behavior. CSA and CPA are suitable up to 10–15% replacement, while GBFS can be used up to 20% to produce sustainable SCC.

This research aims to develop web learning to improve student learning outcomes in the Science subject matter on Plant Parts for Class IV at SDN 4 Bungbulang, Garut Regency, for the 2024-2025 academic year. The method used is the ADDIE development model, which includes the stages of analysis, design, development, implementation, and evaluation. Web learning is designed to be interactive, easily accessible, and integrated with various Google applications. The research results show that the use of Google Sites-based web learning can significantly improve student learning outcomes. Regression analysis yielded a linear equation: Ŷ = 35.505 + 0.729X. Furthermore, hypothesis testing to assess significance yielded a calculated t-value of 4.428, which is greater than the t-table value of 1.708, meaning H0 is rejected and H1 is accepted. This indicates that there is an influence of Google Sites-based web learning media on student learning outcomes. To see the extent of the influence of variable X, the R-squared value of 0.440 is observed, meaning that Google Sites-based web learning media has a 44.0% effect on student learning outcomes. Therefore, the Google Sites-based web learning product is suitable for use and development in learning.

This study investigates the casting of aluminum alloy 6061 using green sand molds to evaluate the heat transfer coefficient (HTC) and solidification behaviour for different cast shapes. The shapes employed for the study includes rectangular plate, square bar and cylindrical bar. Aluminum alloy 6061 ingots were melted and poured at 700°C into the moulds of rectangular plate, square bar, and cylindrical bar configurations. Experimental results revealed that the HTC varies across different shapes, with the rectangular plate exhibiting the highest average HTC of 131.6 W/m²K, followed by the cylindrical bar (98 W/m²K) and square bar (73.7 W/m²K). The study demonstrated that shapes with lower casting modulus exhibit faster solidification due to enhanced heat dissipation. The casting of shapes that involves using aluminium alloy 6061 solidified within 105 seconds after pouring of the molten metal into mould cavity and also plate was the first to solidify. The thermal conductivity of the alloy was consistent with ASTM standards, confirming the material's suitability for casting applications. The experimental result shows that thermal conductivity of the alloy was 102.3 WmK and the value was within range of stated value (85 - 173 W/mK) by ASTM. Heat transfer coefficient (HTC) in the cast objects was 101.1 W/m2K.  These findings highlight the influence of casting geometry on heat transfer and solidification characteristics, providing valuable insights for optimizing casting processes.

This study examines the impact of celebrity endorsements on brand equity in Bulawayo supermarkets, framed within a positivist philosophy and a deductive approach. A quantitative survey design was employed, using a structured questionnaire administered to a convenience sample of 222 consumers. Reliability was confirmed with Cronbach’s alpha values above 0.70, ensuring internal consistency. Data analysis involved descriptive statistics, correlation tests, and multiple regression modelling. Empirical results from the study revealed significant positive associations, with credibility as the strongest predictor, followed by familiarity, while message effectiveness played a modest role. The regression model explained 62 percent of the variance in brand equity, highlighting the centrality of endorsement cues in competitive retail contexts. It is concluded that Zimbabwean consumers prefer credibility and cultural familiarity over message strength, consistent with heuristic processing. The study recommends marketers to prioritise credible, locally resonant celebrities to strengthen consumer trust and loyalty.

Aspect-based sentiment analysis (ABSA) has become an important analytical technique in e-commerce research for understanding how customers perceive specific product features. Conventional sentiment analysis approaches typically treat a review as a single unit and assign an overall sentiment label, even though customers frequently express differing opinions about multiple product attributes within the same review. Such aggregation often obscures feature-level preferences and limits the usefulness of sentiment outputs for personalization and decision support. To address this limitation, this study proposes a transformer-based ABSA pipeline that integrates KeyBERT for unsupervised aspect extraction with Bidirectional Encoder Representations from Transformers (BERT) for contextual sentiment classification at the aspect level. The proposed approach is evaluated using a dataset of 10,000 Amazon product reviews obtained from publicly available open-source review data and is benchmarked against a widely used lexicon-based sentiment analysis method, Valence Aware Dictionary and Sentiment Reasoner (VADER). Model performance is assessed using precision, recall, and F1-score to capture both classification accuracy and balance across sentiment classes. Experimental results demonstrate that the transformer-based pipeline consistently outperforms the lexicon-based baseline, particularly in reviews containing mixed or contrasting sentiments across different product attributes. The findings show that contextual embeddings enable more accurate identification of sentiment polarity shifts and nuanced opinion expressions that are frequently missed by rule-based methods. Overall, the results indicate that transformer-based ABSA provides more reliable and interpretable sentiment representations, making it well suited for supporting personalized recommendations, feature-level analysis, and improved customer insight generation in e-commerce systems.

Maintaining the natural environment and striking a balance when using its resources is one of the main goals of Islamic law. The Kingdom of Saudi Arabia has been a leader in the search for alternative energy sources in order to meet life's demands and attain economic progress. It is working hard to generate renewable energy, the majority of which is derived from solar radiation. The Almighty God spoke the truth when He declared: "And He has subjected to you the night and the day and the sun and the moon, and the stars are subjected by His command. Surely , in this are proofs for a people who understand." (An-Nahl, ( 12)) . Allah has created the sun obedient to humans, and people have realized this and sought to take use of it. It is an amazing energy source whose significance stems from its enormous potential, which may be applied wherever. When utilized in numerous fields, it is regarded as clean energy since it is a free and limitless fuel supply. As a result, it need to be researched and used since it is a valid goal, particularly given the limited supply of conventional energy sources. To participate The Kingdom has prioritized investing in the field of solar energy for electricity production, working to reduce gas emissions, and paying attention to developing renewable energy sources, the most important of which is solar energy, which is something that Sharia calls for and urges, in order to achieve the goals of the Kingdom's Vision 2030 in diversifying the economy and transforming the Kingdom from an advanced oil-exporting country to an exporter of sustainable energy. The Kingdom of Saudi Arabia is blessed by God with an abundance of solar radiation in the Arab world. The intensity of solar radiation in the Arab region reaches 1000 watts/square meter at midday and an average of 250 to 300 watts/square meter per day, which is equivalent to 6 kilowatt-hours/square meter per day. Additionally, the Kingdom receives approximately 2200 kilowatts of photovoltaic energy annually. The King Abdullah University of Science and Technology's solar power generation project, the solar village project, the National Initiative for the Production of Water and Electricity Using Solar Energy under the auspices of King Abdulaziz City for Science and Technology, and other initiatives have been proposed by the government to capitalize on this. We must overcome some obstacles in order for the Kingdom to profit from solar energy. In addition to encouraging research in solar energy and scientific exchange with other nations, the government has offered material and spiritual support. As we shall discuss the function of solar energy in sustainable development, its significance and components, and how to take advantage of it as a clean energy source, the significance of this research is clear. The benefits of employing solar cells as a source of energy will also be discussed, along with how photovoltaic cells can be used to generate power. All of this will be connected to the specific scientific miracles stated in the Holy Quran and the Prophetic Sunnah, which demand advancement and modernization. In order to promote and realize Vision 2030, which the beloved Kingdom has called for, this research closes by showcasing advancements in solar energy technologies and applications.

The automotive industry has a strong demand for electric vehicles because of their exceptional ability to compete with internal combustion engines. In any electric car, batteries are an essential part. They basically act as the heart of the car. Controlling the battery system can significantly speed up the creation of an electric vehicle. Our study and research provide insight into the present battery scenario, namely Lithium-ion batteries, which are currently driving the era. Nonetheless, there are superior alternatives to lithium-ion batteries due to their variable downsides. Flow batteries are a worthy competitor to Li-ion batteries, as they give various advantages over current conventional batteries. The study also covers the benefits and advancements of batteries for high-voltage applications. Our research examines the replacement of Li-ion batteries with Vanadium redox flow batteries and analyses the results to support the decision to employ flow batteries. The flow batteries will significantly enhance electric vehicles.

The Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), causing COVID-19, lacks specific antiviral treatments, escalating the global health crisis. This study employs Quantitative Structure-Property Relationship (QSPR) modelling to explore eight physicochemical properties of antiviral compounds, including Arbidol, Chloroquine, Hydroxychloroquine, Lopinavir, Remdesivir, Ritonavir, Thalidomide, and Theaflavin. Centrality measures, used as molecular descriptors, quantify the relationship between molecular structure and physicochemical attributes in QSPR studies. To address missing data for Remdesivir’s Boiling Point (BP), Enthalpy of Vaporisation (E), and Flash Point (FP), correlation-based linear regression imputation was applied using descriptors like Normalised Harmonic Centrality Weight ( 0.976 for BP, 0.973 for E) and Eccentricity Weight ( 0.957 for FP  ensuring dataset integrity. Nine graph-based centrality measures were evaluated for their correlation with the physicochemical properties of these drugs. Pearson correlation analysis revealed strong positive correlations, notably Normalised Harmonic Centrality Weight with BP (0.978), E (0.974), and Polar Surface Area (PSA) (0.894), and Eccentricity Weight with Flash Point (0.959), Molar Refractivity (MR) (0.975), and Molar Volume (MV) (0.923). Conversely, Total Closeness Centrality Weight and Leverage Centrality Weight showed significant negative correlations (below 0.5). Single-predictor linear regression models were developed, with robustness assessed via predictive  using leave-one-out cross-validation and the PRESS statistic. These models offer interpretable predictions of structural influences on physicochemical behaviour, aiding pharmaceutical researchers in predicting antiviral drug properties for COVID-19 before experimental validation. The Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), causing COVID-19, lacks specific antiviral treatments, escalating the global health crisis. This study employs Quantitative Structure-Property Relationship (QSPR) modelling to explore eight physicochemical properties of antiviral compounds, including Arbidol, Chloroquine, Hydroxychloroquine, Lopinavir, Remdesivir, Ritonavir, Thalidomide, and Theaflavin. Centrality measures, used as molecular descriptors, quantify the relationship between molecular structure and physicochemical attributes in QSPR studies. To address missing data for Remdesivir’s Boiling Point (BP), Enthalpy of Vaporisation (E), and Flash Point (FP), correlation-based linear regression imputation was applied using descriptors like Normalised Harmonic Centrality Weight ( 0.976 for BP, 0.973 for E) and Eccentricity Weight ( 0.957 for FP  ensuring dataset integrity. Nine graph-based centrality measures were evaluated for their correlation with the physicochemical properties of these drugs. Pearson correlation analysis revealed strong positive correlations, notably Normalised Harmonic Centrality Weight with BP (0.978), E (0.974), and Polar Surface Area (PSA) (0.894), and Eccentricity Weight with Flash Point (0.959), Molar Refractivity (MR) (0.975), and Molar Volume (MV) (0.923). Conversely, Total Closeness Centrality Weight and Leverage Centrality Weight showed significant negative correlations (below 0.5). Single-predictor linear regression models were developed, with robustness assessed via predictive  using leave-one-out cross-validation and the PRESS statistic. These models offer interpretable predictions of structural influences on physicochemical behaviour, aiding pharmaceutical researchers in predicting antiviral drug properties for COVID-19 before experimental validation.

The main issues facing modern metropolitan transportation systems are traffic bottlenecks and variations in travel times. They impede mobility, damage the environment, and compromise commute safety. Intelligent transportation systems and smart city applications need precise estimations of traffic congestion and journey durations in the short term to function effectively. This study introduces a hybrid deep learning framework that employs a Convolutional Neural Network–Bidirectional Long Short-Term Memory (CNN–BiLSTM) architecture for simultaneous forecasting of traffic congestion and travel time. The model employs CNN layers to acquire bidirectional temporal relationships and BiLSTM units to capture spatial correlations across adjacent road segments. The framework just employs fundamental traffic information, such as volume, speed, and flow, enhancing its practicality for real-world applications since it does not need additional data sources. We used traffic data from the Delhi–Meerut Expressway to evaluate the proposed technique. An extensive exploratory data analysis was conducted to understand traffic patterns and confirm the spatial-temporal modeling methodology. Experimental findings demonstrate persistent model convergence and enhanced prediction accuracy, achieving a low Mean Squared Error (0.0158), Root Mean Squared Error (0.1257), Mean Absolute Error (0.0912), and a Mean Absolute Percentage Error of around 4.6%. Visual comparisons indicate that forecast traffic numbers closely align with actual patterns, and an analysis of error distribution demonstrates that the model is very effective in the presence of noisy data. The model provides comprehensible outputs, including congestion level, anticipated speed, and travel duration, enhancing its practical use. The proposed CNN–BiLSTM architecture is an effective, robust, and scalable method for real-time prediction of traffic congestion and travel durations in urban areas.

This study explores the operational status and reliability of rural broadband infrastructure in 113 Gram Panchayats (GPs) in the Puducherry district, focusing on critical assets such as Optical Network Terminals (ONT), Central Control Units (CCU), battery systems, and solar panels, using a condition-based performance evaluation. Results from data analysis in SPSS show that 87.6% of GPs are operational (\"UP\"), but technical issues, including high fault rates in earthing and solar systems, are still prevalent, highlighting the need for predictive maintenance and real-time monitoring to ensure uninterrupted rural connectivity in support of India's digital empowerment objectives.

The environmental impact of steel production has prompted the construction industry to seek sustainable alternatives for concrete reinforcement. This study evaluates bamboo as a viable substitute, emphasizing its structural performance in two beam geometries: T-beams and rectangular beams. Mature bamboo culms, aged 3–4 years and sourced from Gbokoto village in Ogun State, Nigeria, were carefully selected and subjected to rigorous pre-treatment and durability assessments—including Accelerated Aging and Graveyard tests in accordance with ASTM D1037-99 and BS 350:2016 standards—to ensure optimal mechanical properties and resistance to biological degradation. Bamboo-reinforced concrete beams were cast with uniform reinforcement ratios and mix designs, and flexural tests were conducted following ASTM and ISO standards. Treated bamboo splints demonstrated significantly enhanced tensile strength and durability compared to untreated samples. T-beams consistently outperformed rectangular beams in stiffness, load-bearing capacity, and ductility, attributed to their flanged geometry. Statistical analysis using ANOVA confirmed a significant difference in structural performance between the two geometries, underscoring bamboo’s potential as an eco-friendly and structurally reliable reinforcement material for modern concrete construction.

Fused Deposition Modeling (FDM) has emerged as one of the most widely adopted additive manufacturing technologies due to its cost-effectiveness, design flexibility, and ease of operation. However, the quality of FDM-fabricated components is often limited by issues related to surface finish and dimensional accuracy, which are critical parameters for functional and end-use applications. This paper presents a comparative study on the influence of key FDM process parameters on surface roughness and dimensional deviation of 3D printed parts. Standard test specimens were fabricated using commonly used thermoplastic materials under varying printing conditions, including layer thickness, print speed, build orientation, and infill density. Surface finish was evaluated using surface roughness measurements, while dimensional accuracy was assessed through precise dimensional inspection and deviation analysis. The experimental results reveal that layer thickness and build orientation have a significant impact on surface quality, whereas print speed and infill density play a crucial role in dimensional stability. A comparative analysis is carried out to identify optimal parameter combinations that achieve improved surface finish without compromising dimensional accuracy. 

Metal Matrix Composites (MMCs) are widely utilized in aerospace, automotive, and defence sectors due to their high strength-to-weight ratio, thermal stability, and superior wear resistance. However, these properties also present significant challenges during machining, making non-conventional machining (NCM) techniques the preferred choice. This study proposes a hybrid optimization framework that integrates Response Surface Methodology (RSM) with a meta-heuristic algorithm to jointly enhance surface roughness (Ra) and material removal rate (MRR) during the NCM of MMCs. A structured experimental design was implemented to examine the influence of critical parameters such as discharge current, pulse-on time, pulse-off time, and electrode type. The hybrid RSM–GA/PSO model delivered improved prediction accuracy and outperformed individual optimization methods by generating a superior Pareto-based multi-objective solution. Experimental validation revealed that the optimized parameter combination achieved an average 22–30% reduction in surface roughness and a 15–25% enhancement in MRR, demonstrating the effectiveness of the proposed hybrid approach. The findings contribute a robust, industry-ready decision-support mechanism for optimizing machinability in advanced MMC materials and pave the way for high-performance non-conventional machining strategies.

The rapid adoption of electric vehicles (EVs) has created a pressing need for efficient, reliable, and scalable charging infrastructure integrated with existing power distribution networks. This paper presents the design and simulation of electric vehicle charging substations with a focus on load flow and performance analysis. A detailed substation model is developed considering typical EV charging loads, transformer ratings, feeder configurations, and protection constraints. Load flow analysis is carried out to evaluate key performance parameters such as voltage profile, power losses, loading conditions, and system stability under different charging scenarios. Simulation results demonstrate the impact of high EV penetration on distribution networks and highlight critical operational challenges, including voltage deviations and increased losses. The proposed design framework aids in optimizing substation capacity planning and operational efficiency, ensuring reliable power delivery to EV charging stations. The findings provide valuable insights for utilities, planners, and researchers involved in the development of sustainable EV charging infrastructure.

Efficient stock management and accurate market price prediction are essential in dynamic and volatile business environments, where traditional forecasting and inventory control methods often lack adaptability and real-time responsiveness. This paper proposes a hybrid artificial intelligence (AI) framework for real-time stock management and market price prediction, integrating machine learning and deep learning models to capture both linear trends and nonlinear market patterns. The system processes real-time transactional data, historical stock prices, and relevant market indicators to continuously update inventory levels and forecast future price movements. Feature engineering, data normalization, and model fusion techniques are employed to enhance prediction accuracy and robustness. A decision-support module utilizes predicted demand and price trends to optimize inventory replenishment, reduce stockouts, and minimize overstocking costs. Experimental evaluation using real-world market datasets demonstrates that the proposed hybrid model outperforms individual predictive approaches in terms of forecasting accuracy, adaptability, and inventory efficiency, as reflected by improved MAE and RMSE values. The results confirm the effectiveness of the proposed approach as a scalable and intelligent solution for real-time stock management and market price prediction applications. Experimental results show that the proposed RT-HAF model reduces RMSE by approximately 23% compared to LSTM and improves inventory service level by over 10%.

Activation functions are at the heart of how deep neural networks perform non-linear transformations. The use of an activation function allows a neural network to approximate highly complex functions, train using a gradient-based optimization technique and generalize to new data. However, existing activation functions, such as ReLU, GELU, and Swish, have limitations that restrict their use in practice. Specifically, they can saturate gradients during training due to their inherent structure, cause vanishing gradients on deeply stacked architectures, and are inefficient at learning periodic dependency relationships while performing poorly at modeling highly heterogeneous non-linear interactions. These limitations are of particular importance for scientific, financial, and engineering use cases where data represent polynomial, periodic, saturating, and exponential shapes on the same data manifold. This paper introduces HyperNova++, a smooth, adaptive, parameterized activation function that unifies bounded saturation, periodic oscillation, and unbounded growth into a single learnable formula. HyperNova++ is architectured and designed to overcome the expressive constraints of existing activations which enables dynamic, data-driven modulation of curvature, frequency, and growth behavior using three trainable parameters (α,β,γ). These above mentioned parameters respectively govern contributions from the hyperbolic tangent (tanh) for bounded saturation, sine (sin) for periodic oscillations, and Softplus (log(1+ex)) for getting a smooth monotonic growth all thorughout. The resulting function obtained ensures non-vanishing gradients, smooth transitions, and controlled Lipschitz continuity, along with maintaining computational efficiency comparable to contemporary activations and other counterparts. After doing a rigorous, large-scale evaluation on a meticulously crafted synthetic dataset with a known ground-truth decision boundary that stimulates reall life linear, polynomial, and periodic interactions. This controlled environment enables precise, unbiased comparisons against various functions including ReLU, GELU, and Swish under identical architectural, optimization, and hyperparameter settings. HyperNova++ achieves statistically significant superior performance compared to all , exceeding 99% accuracy (0.9903) compared to 98.34% for ReLU, 98.08% for GELU, and 97.60% for Swish,  while also attaining the highest F1-score (0.9906) and ROC-AUC (0.9997). Gradient analyses obtained confirm stable, non-vanishing gradients and accelerated convergence. We supplement empirical results obtained during testing with comprehensive theoretical analysis, thus establishing HyperNova++’s universal approximation guarantee, Lipschitz properties, gradient bounds, and optimization landscape characteristics. Practical implementation guidelines, computational complexity dissections, and prospective applications in scientific machine learning, time-series analysis, and multimodal inference are being discussed. Collectively, this work positions HyperNova++ as a potent, versatile activation function for advanced deep learning architectures confronting intricate nonlinear manifolds in upcoming future.

The popularity of social media has increased curiosity in psychology, mental health, and human circumstances. Twitter and other social media platforms have been utilised for data collection, personality type prediction, and sentiment analysis during emergencies. Deep learning techniques examine both positive and negative feelings. The retrieval of Covid 19 tweets, both positive and negative, is not perfect, and low accuracy may lead to the detection of unidentified tweets from social sites. The objective is to improve the retrieval and comparison of pertinent Covid 19 positive and negative tweets in terms of precision, recall, and train/validation accuracy. A LSTM is defined by a sequence of layers, with the first layer defining inputs. The ADAM optimization algorithm updates weights, and the model is evaluated. The proposed model for sentiment content categorization from tweets uses LSTM-ADAM approaches, with a 78% accuracy rate compared to the 76% accuracy of SVM methodology. It supports manual participation and accepts text information in any format.

The leadership abilities of a nurse is a key part of effective nursing care. Nurses in any field of practice, including community health nursing, must demonstrate leadership skills by providing, facilitating and promoting the best possible care for their patient. Thus, it is imperative that nurses must develop leadership abilities in order to effectively perform their roles and functions as primary patient care providers.  This study determined the leadership challenges and coping strategies of community health nurses in selected municipalities. Descriptive correlational research was employed. A researcher modified questionnaire was used to obtain the profile of the respondents as well as their leadership challenges and coping strategies. Forty (40) community health nurses were included as research respondents. Majority of the respondents were ages of 18 to 39 years old, female, registered or licensed nurses with more than 3-years length of service. Overall, the respondents are less challenged in all four leadership domains as to challenges to self, challenges to communication, challenges with supervisor and organizational challenges; but presented as moderately challenged in some of the specific indicators for each domain.  They also moderately utilized the different coping strategies, with high utilization on planful problem-solving as a coping strategy in the performance of their leadership functions while confrontative coping is the least coping strategy used. In general, the profile of the respondents has significant relationship to their leadership challenges and coping strategies, however, gender and educational attainment showed no significant relationship to both. Leadership challenges have significant relationship to coping strategies. In conclusion, community health nurses are facing different leadership challenges in their leadership roles and responsibilities. The ability of the nurse leader to effectively cope with the challenges imposed by her leadership functions will greatly aid her in positively performing her tasks and influencing better performance of her team. It is recommended that further studies on leadership profile, leadership styles and leadership training needs be implement based on the study’s findings.

acceptance sampling plan was adopted to study mainly for valid conclusions with regard to consideration accept or reject of the finished products.  In this way numbers of optimal techniques were developed to increase and control the quality of the products.  Basing on the assumption the variable with regard to quality characteristic is distributed accordingly to certain probability law.  In our study we optimized CASP-CUSUM Schemes based on the assumption that the continuous variable which is under the consideration follows a Truncated Expoentiated Gompertz distribution utilized in Statistical Quality Control and Reliability analysis.  In particular the distribution is meant for estimating the optimal truncated point and probability of acceptance of lot. The operating characteristic and Average run length values are presented.  The results are illustrated by figures.

This paper examines Pune’s evolving municipal wastewater management framework as a model for sustainable urban development policy in rapidly urbanising Indian cities. Through a comprehensive analysis of infrastructure expansion, technological innovation, and regulatory frameworks, we assess the effectiveness of decentralised treatment strategies in addressing urban environmental challenges. Our study reveals that despite significant infrastructure investments totalling ₹1,173 crore under the JICA-funded Project for Pollution Abatement of River Mula-Mutha (PARMM), Pune continues to face a 503 MLD daily treatment gap, with only 49% of generated sewage receiving treatment. Using a multi-parametric policy assessment, integrating performance data from 11 sewage treatment plants, policy documents, and environmental monitoring reports, the research finds that while technological diversification demonstrates promise, persistent funding delays, land acquisition disputes, and the "subsidy barrier" in water pricing threaten environmental sustainability outcomes. The research contributes to sustainable urban development literature by demonstrating how policy integration, technology choice, institutional coordination, and financial sustainability mechanisms determine wastewater management effectiveness.

Citizenship is more than just a legal status; it is a gateway to political participation, social inclusion, and long-term security. However, globally, states regulate access to citizenship through rules that determine how long applicants must reside, how challenging the process is, and how decisions are made to conform with standards. Therefore, this review paper examines citizenship acquisition rules while also comparing liberal (free) democracies, including Canada, the United States, the United Kingdom, and selected European Union states. Most importantly, it asks three major questions: how long does citizenship take, how hard is it to obtain, and how fair are the processes in practice? Hence, drawing on legal theory, policy reports, and justice, the paper argues that while states have legal rights in regulating membership, prolong, complex, or unnecessary processes risk undermine fairness, equality, and the rule of law.

Embedded AI devices operate under tight resource constraints (limited RAM, CPU, and power), yet they need to run inference efficiently. Dynamic memory allocation and dynamic variables play a crucial role here. Embedded systems often use C for AI deployment, so dynamic memory management (malloc, calloc, realloc, free) is central for ‘Model Parameter Storage’, ‘Input Buffers for Sensor Data’, ‘Batch Processing and Streaming’, ‘Memory Pooling’, ‘Dynamic variables for adaptability’. 

This paper reviews and synthesizes current trends shaping marketing practice in 2024–2025, with a focus on generative AI (GenAI), hyper-personalization, social commerce, creator/influencer strategies, privacy-driven data governance, and sustainability-driven branding. Drawing on industry reports and recent scholarship, the paper: (1) maps the major forces reshaping marketer decision-making; (2) proposes a conceptual framework that links technological adoption (GenAI + analytics) to customer-centric outcomes (personalization, engagement, conversion); and (3) outlines a mixed-methods research design to empirically test that framework across B2C retail and D2C settings. Practical implications, managerial guidelines, limitations, and directions for future research are provided. The work is written and formatted for submission to marketing journals that accept applied-conceptual manuscripts.

This study examines how socioeconomic status (SES) and race/ethnicity shape U.S. parents’ strategies for managing children’s screen time, highlighting digital inequality in family socialization. Using secondary analysis of Pew Research Center surveys (2020, 2024), the research employs descriptive statistics and qualitative coding to explore sources of screen time advice and perceived regulatory ability across demographic groups. Findings reveal that higher-SES parents leverage greater access to digital tools and institutional advice, reflecting structured socialization, while lower-SES parents face resource constraints, limiting their control over digital engagement. Racial trends suggest higher platform use among Black teens, pointing to subcultural influences on parenting challenges. These disparities underscore a digital divide extending beyond access to agency, amplifying socialization inequalities. The study enriches family sociology and stratification theory by linking technology management to social hierarchies, proposing interventions like digital literacy programs, and advocating for longitudinal research to assess long-term impacts on equitable digital parenting.

Public healthcare facilities in India frequently face operational inefficiencies due to high patient inflow and limited medical resources, resulting in prolonged waiting times and congestion. This study presents a queueing theory–based analytical framework to examine patient flow in government hospitals in India, with specific emphasis on outpatient and emergency services. Patient arrivals are modeled as stochastic processes, while service mechanisms are characterized by the availability of medical personnel and service counters. Single-server and multi-server queueing models are employed to evaluate key system performance indicators such as average waiting time, expected queue length, and server utilization. The findings indicate that peak-hour congestion significantly affects service efficiency, whereas off-peak periods exhibit resource underutilization. Numerical results show that strategic reallocation of existing staff and minor modifications in service configuration can lead to substantial reductions in patient waiting time without increasing infrastructure costs. The study demonstrates the effectiveness of operations research techniques in addressing real-world healthcare challenges and offers quantitative decision-support insights for improving operational efficiency in Indian government hospitals.

Financial crimes, including money laundering, fraud, and terrorism financing, remain persistent threats to financial systems due to the increasing sophistication of perpetrators and their extensive use of layering (tumbling) techniques to obscure transaction trails. Conventional machine learning–based anomaly detection systems often exhibit high false negative rates, particularly in streaming financial environments where transaction behaviors evolve dynamically. This study proposes a Systematic Detection Learning framework for real-time identification of layering activities in financial transaction data. The framework employs a user-centric, step-wise analytical process that systematically structures transaction attributes to extract recurring behavioral patterns associated with layering. Using SFinDSet for Systematic Detection of Financial Crimes, a publicly available financial crime dataset hosted on Kaggle, the proposed model is evaluated against established anomaly detection, classification and clustering techniques, including Isolation Forest, One-Class Support Vector Machine (O-C SVM), and Online k-Means. Performance evaluation focuses on the detection of layering instances, identification of unique layerers, and consistency across models. Experimental results show that the Systematic Detection approach identifies 7,694 confirmed layering instances and 441 unique layerers, thus outperforming Isolation Forest (with 99.54% consistency), Online k-Means (with 78.91%), and O-C SVM (27.43%). The results demonstrate that the proposed framework significantly reduces false negatives while maintaining high detection accuracy. By leveraging structured domain knowledge alongside adaptive learning, the Systematic Detection model provides a robust and interpretable benchmark for layering detection in streaming financial data. This research contributes an effective and scalable framework that can be integrated with machine learning techniques to enhance real-time financial crime detection and mitigation.

An Agrivoltaic gardening system offers a promising pathway to simultaneously addressing food security and renewable energy production through the integration of photovoltaic panels and crop cultivation. However, balancing crop productivity and energy generation remains a complex challenge due to variable microclimatic conditions and crop-specific responses to partial shading. This study presents a machine learning–based framework for optimizing both crop yield and photovoltaic energy output in small-scale agrivoltaics gardening systems. Using multi-source data including solar irradiance, soil moisture, temperature, panel configuration parameters, and crop growth indicators, supervised learning models were developed to predict crop yield and energy performance. Random Forest and Artificial Neural Network models demonstrated strong predictive capability, achieving coefficients of determination (R²) above 0.85 for crop yield estimation under shaded conditions. Multi-objective optimization revealed design configurations that improved combined land-use efficiency by up to 10% compared to conventional layouts. The results highlight the potential of machine learning to support adaptive design and real-time decision-making in agrivoltaics gardening, particularly in resource-constrained environments. This work contributes to emerging research on AI-enabled agrivoltaics and provides a scalable framework for sustainable food–energy co-production.

Climate change in the 21st century threatens global food security through reduced agricultural productivity, disrupted food supply chains, increased food-borne illness, land degradation, and rising food prices. Extreme weather events like droughts and floods, altered temperature and rainfall patterns, and rising CO2 levels directly impact crop yields and the nutritional quality of food. Vulnerable populations suffer disproportionately as they have less capacity to adapt to climate shocks and other stressors, highlighting the need for climate-smart and nutrition-sensitive food systems and integrated policies to achieve both climate action and "zero hunger".  This review envisages pointing out the factors which all stakeholders and policymakers must consider and act judiciously to combat climate change issues collectively to ensure global food security.

This research assesses the role of climate variability in aquaculture development and its growth among stakeholders in Ghana's Upper East Region, a dry zone marked by unpredictable precipitation and growing climate instability. Conducted within the Kasena Nankana Municipal Area, the study focused on the Tono irrigation project's catchment area, which spans approximately 1,674 square kilometres, with 80 percent of the area being terrestrial. The investigation utilized descriptive and statistical methods to analyse both secondary and primary datasets. Data collection involved questionnaires, interviews, and direct observation to evaluate how climate fluctuations impact those engaged in fishing activities, with analysis performed using MS Excel and SPSS software. The sample consisted of 150 participants from the Kasena Nankana municipality, comprising 80 fishing practitioners and 70 food vendors (including fish traders, kenkey vendors, rice sellers, and fried yam vendors). Findings indicate that fishing practitioners encounter multiple interrelated challenges, with climatic conditions (identified by 42.5% of participants) and economic limitations (noted by 41.3% of participants) representing the primary concerns. These challenges are mutually reinforcing - variable climate patterns decrease fish yields, subsequently diminishing earnings and restricting capacity for equipment upgrades. The interconnected character of these challenges indicates that successful interventions require comprehensive approaches addressing multiple dimensions concurrently. Combining climate adaptation measures, economic assistance programs, and equipment modernization initiatives would prove more beneficial than tackling individual challenges separately.

The escalating climate crisis presents a fundamental threat to child rights globally, yet current measurement frameworks remain critically insufficient. This research addresses the urgent gap in child-specific climate risk assessment by evaluating the limitations of existing indices and proposing a comprehensive framework for a Global Child-Specific Climate Risk Index. Through a systematic desk review of data from UNICEF, WHO, and peer-reviewed literature published between 2020 and 2025, this study analyses the physiological, developmental, and social vulnerabilities unique to children. Key findings reveal that while over 1 billion children live in extremely high-risk countries, mainstream indices such as ND-GAIN and INFORM primarily focus on economic assets or general population data, failing to capture child-specific nuances. The research highlights multidimensional impacts, documenting how extreme weather disrupts education for millions, exacerbates malnutrition, and drives displacement. The analysis demonstrates that without age-disaggregated data, adaptation policies inadvertently marginalize children. The paper proposes a new multidimensional indicator framework integrating health, education, nutrition, and displacement metrics. It concludes that safeguarding children’s future requires an immediate paradigm shift: the integration of standardized, child-centric climate metrics into National Adaptation Plans (NAPs) and international disaster risk reduction strategies. This study provides a roadmap for governments and humanitarian organizations to move beyond generic risk assessments toward targeted interventions that protect the most vulnerable demographic in a warming world.

Blockchain technology has emerged as a foundational digital infrastructure capable of redefining global trade and financial ecosystems through its decentralized, immutable, and trust‐enhancing architecture. By eliminating conventional intermediaries and reducing informational asymmetries, blockchain strengthens transactional transparency, accelerates cross-border settlements, and enhances the authenticity of trade documentation. Its applications including distributed ledgers for supply chain traceability, smart contracts for automated trade finance, and digital identities for customs and compliance are enabling unprecedented operational efficiencies across international logistics and regulatory environments. In the financial domain, blockchain facilitates secure and near-instantaneous value transfers, supports innovative instruments such as asset tokenization, and expands financial accessibility through decentralized finance (DeFi). Central bank digital currencies (CBDCs) further signal a structural transformation in global monetary governance by promoting interoperability and reducing systemic frictions. Despite such transformative potential, significant challenges remain: fragmented regulatory frameworks, scalability constraints, cybersecurity concerns, and the need for harmonized global standards. This study critically evaluates blockchain’s multi-dimensional impact on international trade and financial systems, examining its strategic advantages, evolving use cases, and institutional implications. The analysis underscores that long-term global adoption will require coordinated policy reforms, cross-border regulatory convergence, and robust technological infrastructure. The findings aim to contribute to international scholarly discourse by mapping blockchain’s trajectory as a catalyst for a more transparent, resilient, and integrated global economic order.

The story of online payments really begins with the boom of the internet and smartphones. Back in the late 1990s and early 2000s, “digital payments” mostly meant swiping a card or using a basic net-banking page. Things started to change once smartphones became common and mobile data turned cheap. Banks and young fintech companies jumped in, rolling out quicker and easier ways to pay without ever stepping inside a branch. For India, the big game-changer arrived in 2016 with the launch of the Unified Payments Interface (UPI). Its open design let any bank connect with any other in real time, so money could move instantly something people adopted at lightning speed. Government moves helped push things along too. The Digital India drive, the 2016 demonetization shock, and cashless incentives gave both shoppers and shopkeepers reasons to go digital. At the same time, wallet apps and fintech firms polished the experience with secure log-ins, QR codes, and slick app designs. In just a few years, a task that once meant standing in a bank queue could be done in seconds on a phone. This paper explores how people use and understand online payment apps, as well as what happens when payments fail or when complaints need to be raised. With the growing shift from cash to digital options like UPI, mobile wallets, and net banking, the convenience is clear. Still, many users face gaps in awareness especially around security, fraud risks, and the official channels available for resolving issues. This study does not collect fresh survey data but instead uses already available sources such as research papers, reports, and official guidelines to build a clear picture. The findings show that although the use of digital payments is rising quickly, many people are still not fully aware of grievance redressal options such as the RBI Ombudsman or online dispute resolution platforms. The paper also highlights the need for more consumer education, transparent complaint processes, and stronger digital literacy efforts to make online payments both safe and trustworthy.

This study investigates the investment preferences of salaried employees in Ettumanoor Municipality, Kerala, India. It aims to identify factors influencing their investment decisions, assess their knowledge and risk-taking capacity, and evaluate their levels of satisfaction. The study employs a questionnaire-based survey with 100 respondents. Key findings reveal a preference for bank deposits among respondents, driven primarily by savings motives. Most respondents exhibit moderate awareness of investment avenues and are comfortable with moderate risks. Factors influencing investment decisions include age, income, and family responsibilities. While satisfaction levels are generally high, respondents express concerns about potential risks and seek professional advice. The study suggests that promoting investment awareness, providing financial literacy training, and simplifying investment procedures can enhance employee investment behaviour and contribute to economic growth.

Anxiety and depression affect people, around the world. The need, for effective, accessible non‑drug treatments is clear. Sahaja Yoga meditation is a practice that can lower stress and boost emotional health. The study examines how Sahaja Yoga meditation changes anxiety and depression using machine learning models. The researchers collected a dataset from participants who practiced Sahaja Yoga meditation over a period. The data were gathered over weeks. It observed that the participants reported anxiety and less depression after practicing Sahaja Yoga meditation. We did health assessments before the intervention and, after the intervention using the scales GAD-7 and PHQ-9. It applied machine learning algorithms. The machine learning algorithms included Random Forest, Support Vector Machines and Gradient Boosting. It used the machine learning algorithms to analyze and predict improvements in health. It observed drops, in anxiety scores and depression scores after practice of Sahaja Yoga. The machine learning models identified factors that helped health improvements. The machine learning models found the factors that mattered. These findings suggest that combining meditation practices with data-driven methods can enhance mental health monitoring and lead to personalized well-being interventions. This work also demonstrates how machine learning can objectively confirm the therapeutic benefits of Sahaja Yoga meditation.

Nutraceuticals are biologically active compounds derived from natural food sources that provide health benefits beyond basic nutrition, including the prevention and management of chronic diseases. Among them, resveratrol, a non-flavonoid polyphenolic phytoalexin found mainly in grapes, red wine, peanuts, and berries, has gained significant attention for its potential role in cardiovascular disease (CVD) prevention. Cardiovascular diseases remain the leading cause of morbidity and mortality worldwide, largely driven by oxidative stress, chronic inflammation, endothelial dysfunction, dyslipidemia, hypertension, and abnormal platelet aggregation. Resveratrol exerts its effects by modulating key signaling pathways, including SIRT1, AMPK, Nrf2, NF-κB, and eNOS, thereby enhancing nitric oxide bioavailability, reducing oxidative stress and inflammation, improving endothelial function, inhibiting platelet aggregation, and regulating lipid metabolism. Preclinical and clinical studies suggest beneficial roles of resveratrol in atherosclerosis, hypertension, myocardial infarction or stroke, endothelial dysfunction, and heart failure. Overall, resveratrol shows strong potential as a safe, natural, and multifunctional nutraceutical for cardiovascular disease prevention and as an adjunct to conventional therapies, although further large-scale clinical trials are required to establish optimal dosage, long-term safety, and clinical effectiveness. However, translation of these promising findings into clinical practice remains constrained by limited human clinical evidence, heterogeneity in study design, short intervention durations, small sample sizes, and substantial variability in clinical outcomes. Furthermore, the clinical utility of resveratrol is hindered by poor oral bioavailability, rapid metabolism, uncertain dose–response relationships, and lack of standardized formulations. Emerging strategies, including micronized, encapsulated, and nano-based delivery systems, may enhance systemic availability and therapeutic efficacy. In conclusion, resveratrol represents a compelling nutraceutical candidate for cardiovascular protection, yet definitive validation through large-scale, well-designed human clinical trials with standardized formulations, optimized dosing, and clinically relevant cardiovascular endpoints is essential before its routine incorporation into CVD prevention and treatment strategies.

Self-compacting concrete (SCC) enhances constructability through high flowability and self-consolidation; however, its elevated cement demand increases cost and environmental impact. This study evaluates Corn Sheath Ash (CSA), Cassava Pulp Ash (CPA), and Ground Granulated Blast Furnace Slag (GBFS) as partial cement replacements in SCC. Cement was replaced at levels of 0–20% by mass at a constant water-to-binder ratio of 0.50, following EFNARC guidelines. Workability was assessed using slump flow tests, while compressive strength was measured at 7, 14, and 28 days. Increasing replacement levels resulted in reduced flowability for all materials. At 20% replacement, slump flow decreased from 755 mm for the control mix to 662 mm and 645 mm for CSA- and CPA-based SCC, respectively, whereas GBFS mixes maintained higher flowability (≈684 mm). Compressive strength declined with increasing CSA and CPA content, with 28-day strengths reducing to 25.5 MPa and 21.3 MPa, respectively, due to cement dilution and limited reactivity. In contrast, GBFS-containing SCC achieved a 28-day compressive strength of approximately 29.1 MPa at 20% replacement, attributed to its latent hydraulic behavior. CSA and CPA are suitable up to 10–15% replacement, while GBFS can be used up to 20% to produce sustainable SCC.

Optimistic investors invest their capital into businesses and projects which result into expansion of businesses, create new job possibility and support to the growth and development of the nation economy. Information disclosed by the financial statements enables stakeholders to better understand the performance or otherwise of the business, as well as the assessment of the financial strength and growth potentials of the firm. For any business organization to grow it needs to publish adequate, reliable, clear and fair financial information. Evidence from research showed that most businesses released window dressing financial statement for public consumption without realizing how it would affect them, which result into lack of trust from the investors. This study therefore examined the effect of corporate financial reporting on investor’s confidence of listed consumer goods companies in Nigeria for the period between 2015 – 2024. The study adopted ex post facto research design and the population consisted of 28 listed consumer goods companies upon which 10 companies were selected using purposive sampling techniques. The results from the GMM estimator showed that accounting conservatism and cash flow value have positive and significant effect with the co-efficient (0.0068 and 0.0025) and p-value (0.009 and 0.001) at 5% level of significant respectively. While equity value has negative coefficient -0.6150 but significant with p-value 0.000 at 5% level of significant. The study concluded that corporate financial reporting enhanced investors’ confidence of listed consumer goods companies in Nigeria. The study recommended that companies should prepare their annual financial reports with transparency, truthfulness and without compromising the standard in such a way that investors can place the reliance on the contents of the reports, which will subsequently propel a positive market response and increase their performance.

The continuous advancement of educational technologies has led to the development of innovative academic tools aimed at enhancing assessment methods and student performance analysis. This study introduces the Decision-Based Grading Model System and Student Performance Analysis Using Rule-Based Algorithm, a system designed to modernize the grading process and provide tailored academic support. The system features a flexible grading simulator that allows educators to set minimum passing scores based on predefined parameters such as course requirements, learning outcomes, and institutional policies. It also integrates a rule-based recommendation system that suggests appropriate learning materials and assessments for students who require remediation. The study utilized both qualitative and quantitative approaches, involving expert validation, user feedback, and system evaluation through the ISO/IEC 25010 Software Quality Model. Results show high levels of effectiveness in functionality, performance efficiency, usability, reliability, security, maintainability, and portability. Additionally, accuracy metrics revealed 80% precision, 89% recall, and an F1-score of 84% for the recommendation system, confirming its capacity to deliver relevant interventions. The system promotes academic transparency, reduces manual workload, and aligns grading and assessment strategies with actual student needs. Overall, the study contributes to the evolving landscape of educational technology by offering a dynamic, data-driven approach to academic management.

This research explored the impact of a STEM-based approach on the academic achievement of the grade 8 students in genetics. By incorporating Science, Technology, Engineering, and Mathematics (STEM) into genetics teaching, the study aimed at investigating the potential of a STEM-based approach as a teaching strategy in establishing fundamental concepts and deepen understanding of genetics, and improve their academic achievement in a specific period by using the researcher-developed and validated training packet in genetics. A quasi-experimental design was employed, featuring pre-test and post-test evaluations of two groups: one taught through conventional methods and the other with STEM-based techniques. The results revealed a notable enhancement in the academic achievement of students who were taught using the STEM method, demonstrating its effectiveness in promoting critical thinking, problem-solving, and collaboration. These findings highlighted the transformative potential of STEM approaches in reshaping science lessons and equipping students for the challenges of the 21st century.

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.

The rapid expansion of urban areas has intensified the Urban Heat Island (UHI) effect, particularly in high-density cities characterized by extensive impervious surfaces and limited green spaces. Rising surface temperatures increase energy consumption, reduce outdoor thermal comfort, and pose serious public health risks during extreme heat events. Urban greening is widely recognized as an effective heat mitigation strategy; however, in densely built environments, indiscriminate or uniform distribution of green spaces often fails to achieve optimal cooling benefits. This study proposes an artificial intelligence–based framework for strategically optimizing urban green space placement to maximize heat reduction while accounting for land-use constraints. The proposed approach integrates multisource remote sensing data, vegetation indices, land surface temperature measurements, and urban morphological indicators with machine learning–based thermal modeling. A Random Forest Regression model is employed to capture the nonlinear relationships between vegetation cover, built-up density, and surface temperature, followed by a spatial optimization process to identify priority locations for greening interventions. Experimental results demonstrate a strong negative relationship between vegetation density and land surface temperature, with optimized greening scenarios achieving temperature reductions of up to 2.6°C, significantly outperforming uniform greening strategies with equivalent green area allocation. The findings highlight that the spatial configuration and targeted placement of green spaces are more influential than total green cover alone. By incorporating explainable AI techniques, the framework also provides interpretable insights into the dominant drivers of urban heat, enhancing transparency for planning applications. Overall, this study offers a data-driven and decision-oriented methodology that can support urban planners and policymakers in designing effective, climate-resilient strategies for mitigating heat stress in high-density urban environments.

Steganography refers to various means of concealing a secret message from view in a digital environment by concealing its visibility so that no one sees it. There are many types of steganography; however, many prefer the use of images and the Least Significant Bit method as it is very simple to use and can hide a great deal of information within an image. In this paper, an efficient LSB-based image steganography technique is designed and implemented to securely hide sensitive information while preserving the visual quality of the original image. The secret information is placed in the least significant bit of each pixel, making the stego image indistinguishable from the original image presented as a cover image. Experimental evaluation using P. S. N. R and M. S.E ensures minimal distortion and high opacity.

Degradation of freshwater resources within Nigeria’s industrial zones threatens ecosystems and human health. This study examined the Ona River in Ibadan’s Oluyole Industrial Area through physicochemical, nutrient, heavy metal, and bacteriological analyses. Water samples from upstream, industrial discharge points, and a downstream residential site were collected in June 2024 and analyzed using standard protocols by the American Public Health Association (apha). Results showed elevated levels of Total Dissolved Solids (TDS) (0.73- 3.48 mg/L), Electrical Conductivity (EC) (119- 328 µS/cm), Biological Oxygen Demand (BOD₅) (9.2- 37.4 mg/L), and Chemical Oxygen Demand (COD) (2.9- 57.1 mg/L), with Dissolved Oxygen (DO) below 2.6 mg/L. Manganese levels (0.71- 0.93 mg/L) exceeded WHO (0.08 mg/L) and Nigerian standards (0.2 mg/l), while magnesium (17.7- 31.2 mg/L) surpassed FAO irrigation guidelines (0.2 mg/l). Water Quality Index (WQI) rated industrial sites as poor (43.2- 48.9) and the residential site as moderate (54.4). Trace coliforms downstream (0.001 cfu/mL) indicated occasional contamination. These findings highlight effluent discharge's role in water degradation, emphasizing the need for industrial pretreatment, standards aligned with FAO and WHO, and continuous multi-parameter monitoring to protect ecosystems.

This study investigates the impact of culture shock on employee morale in the Indian IT sector, with particular emphasis on the mediating role of emotional labour. While previous research has examined culture shock in relation to job satisfaction and performance, this paper highlights morale as a broader indicator of employee well-being.A descriptive–analytical design was adopted. Data were collected from 430 early-career IT employees across 57 NASSCOM-listed firms in South India. Validated scales measured organisational climate, self-efficacy, role ambiguity, emotional labour, and employee morale. Confirmatory Factor Analysis (CFA), Structural Equation Modelling (SEM), and bootstrapping techniques were used to test the hypothesised direct and mediating effects.The results confirmed the reliability and validity of the constructs, with the CFA showing a good model fit. SEM revealed that organisational climate, self-efficacy, and role ambiguity significantly impacted morale, with role ambiguity being the strongest predictor. Mediation analysis showed emotional labour partially mediated these effects. All hypotheses were supported, confirming that culture shock influences morale directly and indirectly.

Emotional intelligence has become an essential psychological asset impacting job results in emotionally taxing fields, including special education. This study investigates the structural links between emotional intelligence and employee outcomes among special school teachers in Kerala, employing a CFA–SEM integrated analytical approach. Data were gathered from 606 special school teachers using a structured questionnaire and evaluated using confirmatory factor analysis to validate the measurement model, subsequently employing covariance-based structural equation modeling to assess the proposed correlations. The findings demonstrate that emotional intelligence exerts a substantial and favorable influence on job satisfaction, job commitment, job performance, and organizational citizenship behavior. Mediation research indicates that emotional intelligence has a substantial direct impact on job commitment, although job satisfaction does not significantly mediate this link. These results indicate that emotional intelligence serves as an inherent attribute that directly improves professional efficacy, rather than functioning through intermediary processes. The study emphasizes the significance of enhancing emotional intelligence to elevate employee outcomes and maintain efficacy in special education settings.

Lead leakage is defined as the loss of potential patient inquiries due to delayed or absent response mechanisms and represents a significant revenue challenge for independent medical practices. This paper presents a hybrid AI-automation framework that integrates WhatsApp Business API with conversational voice AI agents to minimize response latency and improve inquiry-to-appointment conversion rates. The proposed system employs natural language understanding (NLU) for intent classification, automated acknowledgment protocols and intelligent call routing to address the temporal gaps in traditional receptionist-based workflows. Deployment across three dental practices in India demonstrated a 47% reduction in inquiry abandonment, 89% decrease in mean response time (from 2.1 hours to 7 seconds for asynchronous channels), and projected revenue recovery of ₹8.4 lakhs per practice annually. The framework's modular architecture enables adaptation across medical specialties while maintaining data privacy standards compliant with Indian regulations.

The primary goal of the study was to determine the level of competence among selected Sangguniang Kabataan officials in Mogpog, Marinduque, in utilizing the accounting system for transparent and good financial management. More specifically, it aimed at evaluating their skills in preparing budgets, recording and bookkeeping, reporting, and digital accounting software. The study further gave an account of the main difficulties as well as the financial training practices that the respondents thought would elevate their financial skills. A descriptive research design and survey methods were employed in the study to gather data from 60 SK officials representing the different neighborhoods of Mogpog. The outcomes showed that the corresponding groups of people had an overall competency level from moderate to high, and the area of their highest proficiency was budget preparation and record keeping. Whereas the SK officials' inability to timely report and use digital tools for accounting was pointed out as their weakness areas, mainly caused by a lack of experience and complicated government regulations. The areas identified for training needs in the future were Basic Bookkeeping and Financial Recording. The conclusion reached by the researcher was that training, mentoring, and the application of technology should be the key to the development of SK officials' financial management skills. It was also pointed out that the local government, along with the Commission on Audit (COA) and the Department of the Interior and Local Government (DILG) could facilitate regular workshops, hands-on simulations, and institutionalized training that would be professionally supported by these organizations. The investment in the accounting skills of the SK officials will help them in being the custodians of transparency, accountability, and efficiency in public fund management, thus paving the way for a more trusted and effective youth governance.     

This study assessed the status of cooperative learning strategies at Haramaya University, focusing on the College of Education and Behavioral Sciences. A qualitative research design was employed, utilizing interviews, focus group discussions (FGDs), and observations for data collection. The study involved 80 participants, including students from four departments, department heads, the college dean, and the Academic Assessment and Quality Assurance director. Third-year students were specifically chosen for their relevant experience with cooperative learning. Findings indicated that the implementation of cooperative learning strategies has significantly declined and become ineffective since the COVID-19 outbreak. Key factors contributing to this decline included distancing policies, lack of commitment, instructor workload, additional administrative duties, student interest, time constraints, inadequate supervision, lack of awareness, poor classroom facilities, and insufficient support. The study emphasizes the importance of cooperative learning for enhancing student performance, suggesting that students benefit from peer learning at their own pace rather than solely from instructors. It calls for all stakeholders, including students and administrative bodies, to prioritize the effective practice and implementation of cooperative learning strategies.

Employee Engagement is one of the vital approaches to improve the quality and productivity in any enterprise. Employee engagement is not a replacement for management nor is it the final word in quality improvement. It is a means to bettermeet the organizations goals for quality and productivity at all levels of an organisation. Employee engagement, therefore, pushes up the level of commitment, involvement and dedication that an employee has towards the organisation. The concept of employee engagement overlaps with the concept of commitment and organisational citizenship behaviour to a large extent but is different conceptually. The latter two concepts are uni-directional but engagement is a two-way concept- organisation work hard to engage their employees and the employees in turn decide on the level of engagement they would offer their employers. The present research paper focusses on the Employee engagement practices incorporated in Banking sector, since the banking sector plays a crucial role in the revenue generation under the service industry it is important to study the factors influencing to contribute more revenue as part of which from the literatures we understood that in recent years the employee engagement is the major contributing factor for the improvement of overall performance of the organization, hence, the study is conducted to explore and through light on the comparison between the public and private sector banks in incorporating the employee engagement practices.

Human Resource Management (HRM) has moved far beyond its traditional administrative role and now functions as a strategic driver of organizational effectiveness. In recent years, rapid advances in information and digital technologies have fundamentally reshaped HR practices through the adoption of Human Resource Information Systems (HRIS), e-HRM platforms, automation tools, and analytics-driven decision-making. These technological interventions have transformed core HR functions such as recruitment, selection, training, performance appraisal, compensation, and employee engagement. The present study empirically examines the influence of technological interventions in HR practices on HR efficiency and organizational effectiveness in selected IT firms operating in Bengaluru. Primary data were collected from 300 respondents using a structured questionnaire. The data were analyzed using descriptive statistics, factor analysis, ANOVA, and multiple regression techniques. The findings reveal a statistically significant and positive relationship between technology-enabled HR practices and HR efficiency, indicating that digital HR systems contribute meaningfully to improved employee satisfaction, operational efficiency, and strategic alignment. The study highlights the growing importance of integrating technology with HR strategy to achieve sustainable organizational performance.

The publication of IS 1893 (Part 1): 2025 represents a significant update to Indian seismic design practices, formally recognizing Seismic Zone VI as the highest hazard category. With a zone factor of Z = 0.75, this new zone substantially increases the reference design seismic demand beyond the previous maximum of Zone V. This change requires designers to consider not just higher force levels, but also more fundamental aspects of safety, such as system integrity, redundancy, ductile response, and reliable load transfer mechanisms. This paper critically reviews the code evolution leading to Zone VI and examines its design rationale and implications. It demonstrates the practical impact through a numerical comparison of a typical mid-rise reinforced-concrete building designed for both Zone V and Zone VI conditions. The study synthesizes the main structural consequences for configuration control, torsional behaviour, soft-storey vulnerability, diaphragm and collector design, and foundation-soil interaction. It consolidates these findings into a practical checklist for senior designers. The paper concludes by highlighting a shift from implicit life-safety goals to explicit collapse-prevention objectives and outlines directions for future research and practice.

The present advancement of health and medical sciences has been greatly aided by the fundamental pillar of physics. It has supplied the scientific and practical underpinnings for comprehending biological events and precisely identifying and curing illnesses. Medical imaging technologies including computed tomography (CT), magnetic resonance imaging (MRI), nuclear medicine, and X-rays have been made possible by the concepts of radiation and medical physics, allowing doctors to identify illnesses more quickly and accurately. By precisely controlling radiation doses and protecting healthy tissues, physics has also improved treatment techniques, especially in radiotherapy and cancer treatment. Additionally, biophysics has been essential in comprehending how organs and biological systems work, including respiratory mechanics, blood flow, and nerve signal transmission. In the current era, physics has been connected to digital technology and artificial intelligence, which has made it easier to create intelligent medical devices, physical simulation models for disease behavior prediction, and higher-quality healthcare. Therefore, physics plays a crucial role in attaining medical breakthroughs, improving the effectiveness of diagnosis and therapy, and fostering innovation in the modern health sciences.