Volume 15 Issue 2

Drone-based phenotyping using unmanned aerial vehicles (UAVs) has emerged as a revolutionary approach for high-throughput, precise, and scalable measurement of plant traits critical to crop improvement. This technology integrates advanced imaging sensors—including RGB, multispectral, hyperspectral, and thermal cameras—with sophisticated image processing and artificial intelligence algorithms to non-destructively capture key phenotypic data such as plant height, biomass, canopy temperature, maturity timing, and disease symptoms under natural field conditions. Compared with traditional manual phenotyping and satellite-based remote sensing, UAV phenotyping offers superior spatial and temporal resolution, enabling dynamic monitoring of complex traits such as drought tolerance and disease resistance. Applications span early stress detection, quantitative trait assessment, yield prediction, and accelerating breeding cycles by facilitating objective, rapid selection of superior genotypes across multiple crop species. Despite its transformative potential, challenges remain in standardizing protocols, managing large-scale complex datasets, integrating phenotypic with genomic and environmental data, and providing training resources for widespread adoption. Ongoing advancements in sensor technology, data analytics, open-source tools, and capacity building are poised to cement drone-based phenotyping as a cornerstone technology for sustainable, climate-resilient crop breeding and global food security.

Thanks to fast-moving tech trends around IoT, people now track their health using online sensors. Lots of body-related information flows through these linked gadgets every day. Turning that flood of details into useful warnings about wellness risks is not as straightforward as it sounds. A fresh approach here involves blending AI methods into such digital health setups. These setups aim to catch potential medical issues sooner rather than later. From live body signals, the system gathers information via connected devices spread across a shared computing hub. Instead of relying on single methods, several learning techniques work together to sort patterns in the data, spotting possible health issues ahead of time. Because it examines trends as they unfold, predictions become more precise and happen faster when needed most. This way of processing inputs fits well for tasks that require constant oversight in medical settings. This setup works to boost early help in health care, cut down on late reactions, while offering a flexible answer for smart, connected medical spaces. Health risk prediction ties into machine learning under smart healthcare systems powered by internet of things devices. Remote patient monitoring connects closely with these themes where machine learning shapes decision support tools.

Container Freight Stations (CFS) in Chennai operate under increasing cargo volumes, fluctuating demand, and infrastructure constraints, often resulting in congestion and extended container dwell times. Artificial Intelligence (AI) provides data-driven solutions through predictive analytics, automation, and intelligent scheduling to improve operational efficiency. AI-based monitoring enables terminals to anticipate congestion before it occurs and dynamically allocate resources. In high-traffic ports like Chennai, real-time prediction of arrivals and yard occupancy prevents bottlenecks. Immediate decision support reduces delays, improves service reliability, and lowers operational costs. This study evaluates AI applications at All cargo Terminals, Chennai, using a mixed-method research design that integrates stakeholder surveys and statistical modelling. Findings demonstrate a strong relationship between yard utilization, staffing levels, and dwell time reduction. The results recommend AI-driven tools to sustain performance improvements and enhance terminal competitiveness. Identifying statistical relationships allows terminals to implement live dashboards that guide operational decisions instantly. Predictive tools support shift planning and yard allocation in real time. Continuous monitoring ensures consistent performance gains rather than temporary improvements.

Purpose: This study applied the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) to examine factors influencing the adoption of OTT platforms among youth in Navsari city, India. With the growing popularity of OTT platforms, understanding their key adoption determinants can help service providers enhance user engagement and satisfaction. Design/methodology/approach: A quantitative research approach was employed, using a structured questionnaire to collect data from 211 youth respondents. Partial Least Squares Structural Equation Modelling (PLS-SEM) was used to analyze relationships among constructs and assess the model’s predictive power. Findings: Results indicate that Facilitating Conditions (FC), Habit (H), and Price Value (PV) significantly influence behavioral intention and actual OTT usage. Facilitating Conditions (FC) emerged as the strongest predictor, emphasizing the role of accessibility and resources. However, Effort Expectancy (EE), Performance Expectancy (PE), Social Influence (SI), and Hedonic Motivation (HM) did not significantly affect behavioral intention. The Q²predict values for Behavior (0.479) and Behavioral Intention (0.449) confirm good predictive relevance. Practical implications: OTT providers should enhance accessibility, affordability, and user engagement. Personalized recommendations, seamless user experience, and cost-effective subscription models can further boost adoption among youth. Originality/value: This study applies UTAUT2 in OTT adoption research, contributing to technology acceptance literature. The finding that EE, PE, SI, and HM do not significantly influence behavioral intention contrasts with previous studies, opening avenues for further research.

The growth of e-commerce, Digital India initiatives and the changing urban lifestyle of the Indian consumers are the drivers for online food order services. The seamless delivery of food at the doorstep with few clicks influence the Indian Consumers to order their favourite cuisines online from the restaurant of their choices. Companies look ahead huge potential in e-food industry segment and came up with food aggregator apps.   The aim of this study is to determine the effect of huge discounts on LifeTime Value and buying behaviour of customers. The primary objective is to know the effect of discounts and services provided by the apps on the customer lifetime value. The secondary objective is to know the consumer perception towards different online food ordering apps and effect of discounts on buying behaviour of consumers.

This paper evaluates riparian buffer zone violations and examine its consequences on the building level building-level flood vulnerability along River Ngadda in Maiduguri, northeastern Nigeria. Geographic Information Systems (GIS) was used to integrate high-resolution satellite images and building footprint data in order to determine structures in the river channel. The channel was digitized on full tank level and subdivided into five sections (A-E) in which graduated buffer zones of 10m, 20m and 30m which corresponding to Nigerian planning setback requirements-were generated. Spatial overlay analysis was used to measure encroachment extent and density along the river corridor, while building vulnerability was classified using distance-based proximity and summarized through a Vulnerability Index (VI, a weighted index of proportional exposure to flood risk). Spatial autocorrelation was used to determine patterns of clustering on a segment basis. Findings indicate a large encroachment of 3,065 buildings in the 30m buffer, of which 716 were in the most vulnerable 0-10m area. The VI values were 1.73-1.90 which showed moderate to high flood risk, and central urban segments were most vulnerable. The spatial analysis revealed mostly negative autocorrelation indicating fragmented and not clustered vulnerability likely reflecting incoherent informal encroachment. Comparative analysis of distance-based vulnerability and building density showed complementary data, whereby single-proxy measures could be a poor fitting characterization of risk. The results indicate that the river could have been less effective in flood attenuation capacity due to weak setback enforcement which was worsened by the excessive solid waste disposal which diminished the conveyance of the channels. The management suggestions are based on spatially oriented intervention: phased relocation of highest-risk structures in central segments, prohibition of the emergence of new communities, clearing of channels and waste management plans, and institutional reinforcement. Preventive management is essential in peripheral segments and expansion needs to be stopped before weakness is diminished.

The proposed evolution of IS 456 into IS 456 (Draft-5) marks a decisive recalibration of structural concrete philosophy in India. Whereas IS 456:2000 was predominantly strength-oriented within a prescriptive limit-state framework, Draft-5 advances a performance-governed doctrine integrating strength, serviceability control, durability verification, and lifecycle accountability. This study presents a structured comparative technical review of the two frameworks, examining scope integration, serviceability elevation, exposure-driven durability reasoning, and documentation governance. The analysis indicates a clear migration from tabulated compliance toward engineered performance justification, with measurable implications for detailing density, material specification, and professional workflow. Draft-5 therefore represents not a routine revision, but a governance transition—shifting Indian structural design from strength sufficiency to accountable, service-life–oriented performance.

Good performance directly contributes to maximizing shareholder value, and serves as a primary driver for attracting new investors. It further demonstrates financial health, effective management, and the potential for future returns. Organizations cannot always operate in the same manner since they are dynamic systems. They must change through several life cycles in order to function smoothly and effectively. One of the main goals for organization across the globe is to improve corporate performance. In order to maximize financial performance and manage financial difficulties, corporate restructuring has evolved into a crucial strategy for businesses. Evidence from literature showed that Organizations encounter challenges which led to financial distress of many and liquidation of many oil and gas companies. Research has shown that not many oil and gas companies have integrated corporate reorganization into their systems for enhancement of their performance. This study therefore examined the effect of corporate reorganization on financial performance of listed oil and gas companies in Nigeria for the period between 2010 - 2024. Ex-post facto research design was used. The population of the study was 14 listed oil and gas companies in Nigeria  Using the purposeful sampling techniques, the sample size was 11 listed oil and gas companies The study found that assets restructuring has no significant effect on financial performance as indicated by the coefficient 0.0084 and p-value 0.157 at 5% level of significant while corporate financial restructuring and organizational restructuring have positive and significant effect on financial performance with coefficient (1.3760; 0.1132) and p-value (0.009; 0.003) at 5% level of significant respectively. The study concluded that corporate reorganization enhanced the corporate performance of oil and gas companies. It recommended that Nigerian oil and gas management should create new market strategies, practices, and resources that will boost competitiveness, adapt to market changes, and facilitate the development of unique products and services that will satisfy shifting consumer demand for enhancement and growth of corporate performance.

This study examines the critical environmental parameters that influence photovoltaic performance, including solar irradiance, temperature, wind speed, humidity, and dust deposition, to elucidate the complex links between ambient conditions and panel efficiency Specifically, research indicates that efficiency is directly proportional to solar irradiance and wind speed and is inversely proportional to temperature, humidity, and dew point temperature. To quantify these relationships, linear regression analysis is often employed to model efficiency as a dependent variable against independent meteorological factors, thereby allowing for the prediction of power generation under varying weather scenarios. Field studies have demonstrated that energy losses ranging from 21.4% to 37.5% can occur due to the absence of rainfall over extended periods, highlighting the severity of environmental stressors on photovoltaic systems. Furthermore, specific environmental parameters such as wind velocity, ambient temperature, and dust concentration have been shown to influence power output continuously, with lower humidity levels between 69% and 75% correlating with increased power generation.

Inadequate road design and construction without impact assessment has a negative influence on human well-being, as well as natural and non-living resources. It may have a number of detrimental effects on social and economic concerns. Social Impact Assessments (SIAs) are used to analyze, anticipate, and evaluate the social, economic, and environmental impacts on the impacted community. As a result, the purpose of this case study is to gather information on how impact assessment is considered in linear infrastructure construction by collecting census data for the route FT-59 Jalan Tapah, Cameron Highlands, Malaysia. All data was analyzed and presented using the Analytic Hierarchy Process (AHP) approach and Microsoft Excel utilizing a weighted combination of alternatives and criteria for ranking analysis. Results indicate the positive and negative impact of infrastructure development from local viewpoints in terms of social, economic, and environmental aspects at the pre-construction, construction, and operating phases. Furthermore, the results demonstrate the mitigation strategy for infrastructure facility construction. This results implies that organizations' main SIA practitioners should refer to this recommendation measure in order to enhance the outcome of SIA.

The growing adoption of electric bikes has transformed personal transportation, therefore, the study examines the quality of electric bikes and its impact on customer satisfaction and loyalty. The study collects 400 samples; primary data are collected from electric bike users using questionnaire. The demographic analysis reveals that electric bike usage is more prevalent among middle-aged, educated, and economically active customers. Factor analysis identified performance quality, durability, comfort, safety, economic efficiency, and after-sales support as key determinants of customer satisfaction. Regression results confirmed that these quality dimensions have a significant positive impact on satisfaction. The Friedman test stressed battery reliability, operating cost, and riding comfort as major drivers of customer loyalty. The study also reveals dangerous problems such as limited battery backup, long charging time, and inadequate charging infrastructure. Findings provide implications for manufacturers, service providers, and policymakers to enhance electric bike quality, strengthen customer loyalty, and promote sustainable mobility.

As the first generation to fully develop in a digital environment, Generation Z is a crucial group for the future of sustainable development and public health. Emerging data shows a contradictory growth in poor diet quality, sedentary habits, and interrupted daily routines among this generation, despite increased exposure to nutrition information and sustainability rhetoric. The Sustainable Development Goals (SDGs), specifically SDG 2 (Zero Hunger), SDG 3 (Good Health and Well-Being), and SDG 12 (Responsible Consumption and Production), are examined in this narrative review, which synthesizes empirical literature published between 2015 and 2024.  According to the review, the twin burden of malnutrition and early onset of non-communicable diseases is caused by an increasing reliance on ultra-processed foods, a decline in nutritional diversity, insufficient physical activity, and inconsistent sleep patterns. This report outlines important policy gaps and stresses the need for integrated, youth-focused nutrition and lifestyle interventions to advance the 2030 Sustainable Development Agenda by directly connecting behavioral patterns with quantifiable SDG indicators.

This study looked at how Bangalore City's public and private businesses' workplace facilities affected workers' job satisfaction. Finding out how satisfied employees were with their workplace amenities, identifying any disparities among industries, and devising solutions were the objectives.   150 employees—75 from the public sector and 75 from the private sector were asked to complete a standardized questionnaire on a 5-point Likert scale. Descriptive statistics, independent samples T-test and reliability evaluations using SPSS were all used in the analysis. The findings indicated that public sector employees evaluated workplace facilities considerably more favorably than their private sector counterparts, suggesting superior infrastructure and organizational support within the public sector. However, overall job satisfaction did not differ significantly across the two sectors, indicating that elements outside workplace amenities, such as career advancement and organizational culture, also significantly influence satisfaction. These results show that private companies need to improve their physical and technological resources, whereas public companies should focus on ways to help employees career advancement and thrive. The study offers valuable insights for human resource managers and policymakers to improve employee happiness and organizational efficacy.

This study explores the crucial nexus between environmental education (EE) and sustainable development (SD) by investigating students' eco-consciousness in collaboration with the Worldwide Fund for Nature (WWF) Philippines. In an era characterized by escalating environmental concerns and the imperative for sustainable practices, understanding the role of education in fostering environmental sustainability is paramount. The research employed a quantitative method approach, using a survey to comprehensively examine students' attitudes, knowledge, and behaviors toward environmental issues and sustainable living practices. Drawing on a diverse sample of students across educational levels, the study found that at Saint Mary’s University, students have a high level of environmental awareness, eco-consciousness, and environmental stewardship. It was further affirmed that those three environmental concepts are intricately influencing one another. The profile variables of gender, age, type of high school they graduated from, and religion are not influential or predictive of their environmental awareness, eco-consciousness, and environmental stewardship. However, school, year level, and ethnicity are influential or predictive. Finally, Saint Mary’s University is fertile ground for environmental sustainability practices, as students have a high level of environmental awareness, eco-consciousness, and environmental stewardship, which are important dimensions of environmental sustainability. It was then recommended that its programs, projects, and activities be sustained and intensified to protect and conserve the environment.

Domestic violence against women remains a widespread global issue, significantly impeding gender equality, economic development, and the establishment of peaceful societies (SDGs 5, 8, & 16). This study explores the impact of financial inclusion strategies in mitigating domestic violence against women. Drawing on secondary evidence sourced from peer-reviewed literature, global databases, and institutional reports, grounded on an integrated theoretical framework combining Resource Theory with the Theory of Gender and Power. The study elucidates how financial inclusion may boost women's bargaining power, broaden their economic resources, and challenge established gender hierarchies. Findings show that financial inclusion significantly mitigates domestic violence when interventions are tailored to the specific context, promote gender equality, and are underpinned by strong institutional frameworks. The study makes recommendations for four principal stakeholders: policymakers should formulate gender-responsive financial inclusion policies; financial institutions are encouraged to create accessible and secure financial products tailored for women; civil society organizations are advised to implement community-based financial literacy and empowerment initiatives; and development partners are called upon to finance and oversee programs that blend economic empowerment with the prevention of gender-based violence. By doing so, strategic implementation of financial inclusion can serve as an effective mechanism to enhance women's safety and agency, thereby contributing to sustainable development and gender equality.

In the context of Vietnam’s accelerated development of aviation infrastructure, airport construction projects-particularly airport terminal buildings-are increasingly characterized by large-scale facilities, open architectural layouts, extensive multi-level atrium spaces and highly integrated technical systems. The promulgation of the National Technical Regulation on Fire Safety for Buildings and Structures (QCVN 06:2022/BXD) and its Amendment No. 1:2023 has contributed to raising the overall level of fire safety for buildings and structures. However, it has also created a number of “bottlenecks” when applied to highly specialized facilities such as airports. This paper employs a policy and regulatory analysis approach, typical case studies across the project life cycle, and comparative analysis with international standards, including International Civil Aviation Organization (ICAO) Doc 9137 Part 1 and relevant National Fire Protection Association (NFPA) standards applicable to airport facilities. The findings indicate that the impacts of QCVN 06:2022/BXD are most significant in four major areas: (i) master planning and fire service access; (ii) architectural design of terminal buildings (access routes, roof access, and open spaces); (iii) fire compartmentation, egress, and smoke control in large spaces; and (iv) approval procedures, acceptance processes, and management of design and functional changes. Based on these findings, the paper proposes a set of solutions, including the development of an “airport annex” guidance document for QCVN 06, the establishment of a Performance-Based Design (PBD) framework with transparent criteria, the resolution of inter-agency conflicts through coordination mechanisms, and the harmonization of Aircraft Rescue and Firefighting (ARFF) capability requirements in line with ICAO standards.

Microfinance in India has emerged as a critical tool for financial inclusion, poverty alleviation, and socio-economic development, particularly for marginalized communities. Over the past few decades, microfinance institutions (MFIs) have played a pivotal role in providing small loans, savings, insurance, and other financial services to individuals who lack access to formal banking systems. These services, primarily targeted at women, rural populations, and low-income households, have empowered individuals to start or expand small businesses, improve living standards, and invest in education and health.

This review paper examines how bail and pretrial detention are applied in the United States and Canada, with attention to fairness, equality before the law, and the protection of individual rights. Bail decisions determine whether a person accused of a crime is released or held in custody while waiting for trial, even though they are legally presumed innocent. In the United States, the bail system relies solely on cash bail, meaning that many people remain in jail simply because they cannot afford to pay for their release. However, research shows that this approach affects low-income individuals and underrepresented groups, leading to unnecessary detention, loss of employment, family disruption, and a higher likelihood of conviction before trial. In contrast, Canada’s bail system is based on constitutional principles that emphasize the right to reasonable bail and the presumption of release. Canadian law requires that detention be justified by clear legal reasons, such as ensuring court attendance, protecting public safety, or maintaining public confidence in the justice system. Monetary bail plays a limited role, and courts are encouraged to use the least restrictive conditions possible. By comparing these two approaches, this paper highlights how legal frameworks shape real-world results in pretrial justice and discusses lessons that can inform ongoing reforms aimed at improving fairness and reducing unnecessary detention.

Mental health encompasses more than the absence of illness; it represents a fundamental dimension of overall quality of life. Adolescence, marked by rapid emotional and social changes, often exposes individuals to various stressors. Teenagers should possess the ability to effectively cope with these situations. Many teenagers, however, struggle to cope with adverse experiences, leading to compromised psychological well-being. There is a need to identify the factors which adversely impacts on their mental wellness. This study aimed to identify key factors influencing the mental health of teenagers and to assess their overall mental well-being. Employing a quantitative, descriptive research design, data were collected from a randomly selected sample of 40 adolescents using a structured questionnaire. The major findings are –there are many social and individual factors affecting mental health such as gender, organizational environment, social background, Parental occupation, locality, Parental education. The study underscores the need for targeted interventions and strategies to foster resilience and promote sound mental well-being among teenagers.

Recent advances in digital infrastructures, cloud services, blockchain platforms, Internet of Things (IoT), and artificial intelligence (AI) have significantly increased the complexity of cybersecurity threats while intensifying concerns related to data privacy and system trust. In response, contemporary research has focused on integrating intelligent data-driven techniques with secure and privacy-aware mechanisms to counter sophisticated cyberattacks. This literature review systematically analyzes and synthesizes selected peer-reviewed studies with an emphasis on AI-driven intrusion detection systems and blockchain-enabled security architectures. The reviewed works are examined in terms of their underlying methodologies, algorithms, tools, strengths, and limitations. A comparative analysis identifies key trends, persistent challenges, and research gaps, particularly in explainable AI, system scalability, and secure analytics. The findings highlight the need for unified and deployable cybersecurity frameworks that balance detection accuracy, transparency, and operational efficiency. This review provides a structured foundation to support future research on intelligent and trustworthy cybersecurity systems.

Extant literature in India has extensively examined the relationship between police effectiveness and public perceptions, however certain areas still lack enough research. The eastern part of the country particularly Assam, despite its demographic significance and socio-political sensitivities remains largely understudied in the context of community policing. In districts such as Sivasagar, where historical exposure to insurgency and ethnic complexities demands a nuanced policing model, the dynamics of community engagement and citizen-led initiatives remain underexplored. This study seeks to identify the extent to which perceived police effectiveness influences public trust and cooperation, mediated by citizen satisfaction, within the specific context of Sivasagar, Assam. Employing a quantitative, descriptive, and exploratory design, primary data were collected from 80 residents through structured surveys. The dataset was analysed using inferential statistical techniques to examine both direct and mediated relationships. The findings revealed both direct and indirect significant positive effects of perceived police effectiveness on public trust and cooperation, with citizen satisfaction serving as a key mediating factor. The study offers practical insights and actionable recommendations for police departments and policymakers. Additionally, it holds theoretical and practical implications for enhancing public perception and strengthening police–community relations in regions facing complex socio-political dynamics.

Although prison life is widely studied, the experiences of women in Philippine provincial facilities remain under-researched. This qualitative case study explored the lived experiences of 11 pre-trial female detainees in two provincial jails in Samar to identify gender-specific challenges within the correctional system. Semi-structured interviews were analyzed thematically, revealing five primary themes: life before incarceration, emotional responses, prisoners’ needs, challenges in prison life, and coping mechanisms. Results indicate that while detainees demonstrate resilience and solidarity, they endure significant hardships, including prolonged legal delays, family separation, and inadequate institutional support. Grounded in Maslow’s hierarchy of needs and Rogers’ humanistic theory, the analysis demonstrates how unmet psychological and physical needs undermine inmate dignity. The study concludes that the intersection of gender and justice in provincial contexts necessitates urgent gender-responsive policies. Significant recommendations include improving access to mental health services, livelihood programs, and family support systems to facilitate successful rehabilitation and reintegration.

This project focuses on analyzing an industrial electrical distribution network of a Cement Plant using ETAP software. A detailed Single Line Diagram (SLD) of the plant is modeled in ETAP. Load Flow studies are conducted to determine real and reactive power distribution, voltage profiles, system losses and equipment rating. Short Circuit analysis is used to evaluate fault currents for appropriate equipment rating and protection design. Protective Device Coordination ensures proper fault isolation while maintaining system stability and minimizing disruption. One of the key focuses of this project is to avoid Blackouts in the industry by enhancing system reliability and responsiveness during fault conditions. The objective is to ensure reliable, safe, and efficient power delivery through Load Flow Analysis, Short Circuit Studies, and Protective Device Coordination.

Wetlands are the most important natural function of the terrestrial surface of the earth. It is a habitat of many aquatic plants and animals. On the other hand in industrialization is the processes of human development . In India the industrial activities have been rapidly increasing and urbanization process has also been fastest growing. Due to the industrial activities negative impacts on nature. In the study area the Duwani wetland is the most valuable and productive wetland of Assam. Because this natural wetland is connected with the mighty Brahmaputra river. The industrial activities on the bank of this wetland have mostly polluted the soil function. In this research works soil quality of the wetland is tasted on laboratory. Various parameters have taken for analysis. Some of the parameters like Potential of Hydrogen (pH),  Electrical Conductivity (EC), Organic Carbon (OC), Nitrogen ( N), Potassium ( K), Phosphorus (P), Chloride (Cl ) etc. The soil samples are measuring both the WHO and USPH standard. From this analysis have found the amount of soil pollution and functional ecosystem of Duwani wetlands

Public procurement constitutes a cornerstone of governance and economic development in India, with the L1 (Lowest Bidder) tendering system serving as the dominant mechanism for awarding public contracts. While L1 tendering is intended to promote transparency, competition, and fiscal prudence, its excessive reliance on the lowest financial bid has increasingly raised concerns regarding service quality, accountability, and institutional reliability. This paper critically examines the structural limitations of L1 tendering through a case study of the Staff Selection Commission’s (SSC) examination contract awarded to Eduquity Career Technologies. Using a qualitative case study methodology, the paper analyses the tender evaluation process and the subsequent conduct of SSC computer-based examinations conducted between July and August 2025. Despite scoring lower on technical parameters than established service providers such as Tata Consultancy Services (TCS), Eduquity secured the contract due to its significantly lower bid under the prescribed 70:30 technical–commercial weighting framework. The examinations were marked by widespread administrative and technical failures, including system crashes, biometric verification errors, and exam cancellations, leading to nationwide protests by candidates and public acknowledgment of mismanagement by the SSC Chairman. The study argues that rigid adherence to L1 tendering in high-stakes public services prioritizes short-term cost savings over long-term public value. It concludes by advocating procurement reforms that incorporate historical performance, institutional credibility, and stronger accountability mechanisms to ensure reliable and equitable service delivery.

Single-family housing represents a critical sector in urban development, where the structural safety of foundations is essential to ensure habitability and the service life of buildings. This article presents a narrative review of the state of the art focused on the evolution of strip footing design methodologies, transitioning from empirical methods to advanced computational tools. The limitations of traditional construction practices and the accessibility barriers of current commercial software are analyzed. Likewise, the emerging potential of artificial intelligence and automated calculation is examined as solutions to optimize structural and economic efficiency. The findings suggest the existence of a technological gap in the social housing sector, which can be mitigated through the development of customized, low-cost tools that integrate machine learning techniques.

In Natural Language Processing (NLP), transformer-based systems have become a revolutionary force, radically changing how robots understand and produce human language. These models have made it possible to achieve remarkable progress in a variety of language tasks, such as question answering, machine translation, sentiment classification, and text production. With increased scalability and contextual awareness, they mark a significant departure from earlier sequential models like recurrent neural networks (RNNs). The self-attention mechanism at the core of transformer models enables the system to evaluate the significance of individual words in a sentence, independent of their placement. This design captures grammatical structure and semantic links with remarkable accuracy when paired with positional encoding. Prominent models that consistently produce state-of-the-art results across a variety of NLP benchmarks include T5 (Text-to-Text Transfer Transformer), GPT (Generative Pre-trained Transformer), and BERT (Bidirectional Encoder Representations from Transformers). The history, design principles, and real-world uses of transformer-based models are all examined in this paper. It details their development from basic research to widespread use in practical systems, highlighting their impact on both scholarly study and business operations. The study critically assesses these models' shortcomings, including their high processing requirements, interpretability problems, and concerns around data bias and ethical use, in addition to highlighting their positive aspects. The report also highlights important areas for further study, such as enhancing model effectiveness, boosting transparency, and integrating multimodal capabilities. Transformer designs are well-positioned to stay at the forefront of NLP innovation and produce the next generation of intelligent language systems as the field rapidly advances.

­­­­­­­­­Crowdfunding is a new form of financing opportunities for small and medium enterprises and other innovators of creative business ideas by harnessing the power of the internet. As a new form of financing opportunity, this study investigated the influence of crowdfunding practices on entrepreneurship development and its marketing values on SMEs in Ogun state, Nigeria. Explanatory cross-sectional survey research design was employed and data analysis was carried out using SPSS 20. Analysis of the T-test was used to also ascertain the influence of crowdfunding on the entrepreneurship development in Nigeria. Findings from this research revealed that lack of awareness and understanding of crowdfunding as an instrument of raising funds by SMEs in Ogun state, Nigeria form the major challenges for SMEs in exploring the opportunities of the concept. This is evident from the model summary result in hypothesis one showing R square having 12%, which implies that Reward based, Loan based and Equity based crowdfunding which are the independent variables used only account for 12% variation in Business Growth of SMEs in Ogun state. Hypothesis two also indicates a 6% R square, which also implies that Reward based, Loan based and Equity based crowdfunding account for 6% variation in SMEs in Ogun state. The study recommends that government and campaigners of crowdfunding should launch national programs to educate the public about how crowdfunding works, how to assess campaigns, and how to use digital tools to explore opportunities crowdfunding offer.

The rapid adoption of Edge Computing and Internet of Things (IoT) technologies in healthcare has enabled real-time patient monitoring and low-latency clinical decision support. However, the distributed and resource-constrained nature of Edge-IoT systems makes them highly vulnerable to cyber-attacks such as data breaches, ransomware, and denial-of-service, which threaten patient privacy and system reliability. Traditional centralized AI-based intrusion detection systems (IDS) face limitations in privacy preservation, scalability, and suitability for edge environments. To address these challenges, this paper proposes a secure and privacy-preserving cyber-attack detection framework that integrates an optimized LSTM Gated Multi-Layer Perceptron Neural Network (LSTMG-MLPNN) with Federated Learning (FL) and Med-Chain block chain technology. Federated Learning enables collaborative model training without sharing raw patient data, and Med-Chain with lattice encryption ensures secure aggregation, trust management, and auditability.The proposed system provides an effective, scalable, and privacy-aware solution for cyber-attack detection in healthcare Edge-IoT environments.

The southern Levant—historically referred to as Canaan, the Land of Israel, Judea, and Palestine—has functioned as a nexus of civilizations for over 5,000 years. This forensic historiography integrates archaeological artifacts, ancient inscriptions, genetic analyses, demographic records, and contemporary chronicles to investigate the interconnected histories of Jewish, Arab (including Palestinian), and other Levantine populations. It prioritizes empirical evidence while acknowledging interpretive challenges, highlighting themes of continuity, conquest, assimilation, and conflict. Genetic and archaeological data suggest substantial ancestral overlap among Levantine groups from the Bronze Age, with modern Lebanese populations exhibiting particularly high continuity with ancient Canaanites. Ottoman-era demographics reveal a predominantly Arab Muslim and Christian population in Palestine, with gradual Jewish increases through immigration in the 19th century. Though modern national identities formed amid 19th- and 20th-century nationalism, colonialism, and geopolitical shifts, this study posits that the Israel-Palestine conflict derives primarily from 20th-century developments rather than ancient animosities, while emphasizing the need for balanced scholarly inquiry.

With the progressive breakdown of communal living arrangements, loneliness has surfaced as a critical issue affecting aging adults and early-years youth alike. Consequently, cross-generational initiatives have gained traction as potential remedies. Yet, existing scholarly works predominantly evaluate the end results of such initiatives, frequently overlooking the specific underlying processes that forge profound interpersonal ties. This paper proposes a theoretical framework identifying sensory play activities stimulating touch, sight, sound, and proprioception as a primary vehicle for fostering secure attachment and emotional co-regulation between unrelated generations. Drawing upon the foundational principles of Bowlby’s (1969) attachment paradigms and Porges’ (2011) polyvagal insights into interpersonal activation, this manuscript contends that tactile and experiential activities successfully circumvent the communication obstacles frequently observed in mixed-age pairings, particularly those involving toddlers and adults with cognitive decline. We posit that the shared, non-verbal nature of sensory exploration creates a ”safe container” for emotional synchronization. To validate this framework, a pilot study was conducted with 50 participants (N = 50) involved in a structured sensory play intervention. Statistical analysis using SPSS revealed significant correlations between sensory engagement frequency and reductions in state anxiety (r = −.62, p < .01). Furthermore, regression analysis indicated that sensory co-regulation significantly predicts perceived bonding (R2 = .5). The findings suggest that shifting focus from structured tasks to fluid, sensory-based experiences can deepen relational bonds and improve psychological well-being across the lifespan.

This study argues that green supply chain management (GSCM) significantly boosts competitiveness and economic growth in India's agro-food sector. It analyzes how Indian agro-food companies adopt GSCM practices. As a part of the GSCM approach, eco-friendly packaging, reverse logistics, and energy-efficient transportation are considered to maintain their product quality. Apart from the GSCM approach, there is the impact these practices have on the competitiveness and financial performance of the rest of the mode of production. In this study, quantitative methods and secondary data from government and industry sources are used. The research demonstrates how these GSCM elements strengthen company performance and contribute to the agro industry’s sectoral growth. Using descriptive statistics and core hypotheses, the study identifies prevalent practices in the agro-industry. Through this study, we review relevant policy frameworks, identify the obstacles in this sector, and recommend strategies for broader GSCM adoption. The investigation further connects green logistics, sustainable sourcing of agro-green products, and identifies emerging technologies for operational cost reductions and market expansion. The expected outcome of this study is that technological advancements, such as hybrid seeds and modern irrigation, will drive higher productivity and economic growth in Indian agriculture. Practical recommendations are provided to help policymakers, industry leaders, and academics build effective, sustainable, and competitive green agri-food supply chains in India.

Early prediction of student academic performance is essential for improving learning outcomes and enabling timely educational intervention. Traditional statistical methods often fail to capture the complex and non-linear relationships among academic, behavioral, and engagement-related factors that influence student success. This study proposes a deep learning–based predictive framework using an Artificial Neural Network (ANN) integrated with educational data mining techniques to forecast student academic performance before final examinations. The model incorporates multidimensional input features, including continuous assessment scores, attendance percentage, assignment performance, midterm marks, and Learning Management System (LMS) engagement indicators. Data preprocessing techniques such as cleaning, normalization, and feature encoding were applied to ensure data quality and model stability. A multilayer feedforward neural network was trained using supervised learning with adaptive optimization to learn hidden relationships within the dataset. Experimental evaluation on 1,200 student records demonstrated strong predictive performance, achieving a testing R² value of 0.88 with low prediction errors (MAE = 3.78; RMSE = 4.89). Comparative analysis confirmed that the proposed ANN model outperformed traditional machine learning algorithms, including Decision Tree, K-Nearest Neighbors, and Support Vector Machine. Statistical validation further indicated that there was no significant difference between predicted and actual performance, confirming the reliability of the model. The proposed framework provides a practical early warning system for identifying academically at-risk students and supports data-driven decision-making in higher education. The findings contribute to the development of intelligent academic monitoring systems that integrate predictive analytics into modern educational environments.

Development projects often fail to produce lasting change once external funding and support disappear. This is particularly visible in agricultural sectors where initial gains in productivity frequently evaporate within a few seasons. The persistence of this pattern suggests that our current evaluation methods are limited: we tend to measure success as a static outcome rather than a dynamic system property. This paper introduces Resilient Impact Systems Analysis (RISA), a framework designed to determine whether development impact can survive real-world pressures. Unlike traditional evaluations that focus on point-in-time estimates, RISA integrates four key dimensions: core causal effects, behavioural persistence, exposure to external risks, and the capacity of local institutions. The framework is built on a survival-based mathematical model. By applying exponential decay to adoption rates, RISA moves beyond simple snapshots to provide predictive insights. A central feature of this model is the introduction of the Impact Half-Life (T½ = ln(2)/k), which identifies the specific point in time when systemic friction and adoption decay reduce the initial benefits by half. This metric identifies the specific point in time when systemic friction and adoption decay reduce the initial benefits by half. This provides a clear benchmark for durability, allowing for more honest comparisons between different interventions. Through an illustrative application in agriculture using longitudinal adoption data and post-exit follow-up surveys, the paper shows how projects deemed successful by traditional metrics may be highly fragile. RISA provides a practical tool for designers and donors to move from short-term wins toward investments that are truly anchored in the systems they aim to change.

Harmonics, primarily generated by non-linear loads, can lead to voltage distortion, equipment overheating, and reduced system efficiency. This project analyzes harmonic distortion in the electrical distribution network of a cement plant using ETAP software. A detailed Single Line Diagram (SLD) was modeled, and harmonic load flow analysis was conducted to evaluate Total Harmonic Distortion (THD) at key bus locations. A frequency scan was performed to identify parallel resonance conditions, which were mitigated by installing series reactors with capacitor banks. Additionally, passive filters were designed to reduce dominant harmonic components and ensure compliance with IEEE 519 standards. The study demonstrates a significant improvement in power quality and highlights ETAP as an effective tool for harmonic analysis and mitigation in industrial systems.

Recent shocks in the global supply chain such as pandemic related shutdowns and semiconductor shortages have shown the susceptibility of the U.S. manufacturing supply chains.  Conventional risk management practices that are mostly based on historical averages and reactive practices have not been effective in unpredictable environments.  This paper examines the connection between artificial intelligence (AI) and predictive analytics and reinforcing risk identification, forecasting, and resilience in the U.S. manufacturing industry.  We assess the performance of predictive models including ARIMA, Prophet, and Random Forest using the secondary data from U.S. Census Bureau, Bureau of Labor Statistics, and publicly available logistics datasets. The most important measures of resilience are forecast error (MAPE, RMSE), inventory turnover, order fulfillment, and recovery time.  The findings indicate that predictive analytics can greatly reduce errors in predictions as well as enhance the outcomes of resilience with the Random Forest being better in terms of reducing forecast error by up to 30% as well as increasing order fulfillment rates by 15% compared to traditional models.  A model-derived association analysis using simulation-based performance metrics further indicates statistically significant directional relationships between predictive analytics capability and resilience indicators.  These results highlight the value of predictive analytics as an input to operational decision-making.  To managers, the study illustrates the use of digital tools to help them get actionable information on the risks of disruption and to policymakers, it is part of wider efforts to strengthen national supply chain resilience.  This study can enhance the empirical understanding of AI-facilitated models and introduce feasible resilience metrics.

In this article, we investigate the concept of fermatean fuzzy characteristic subgroup of a group and fermatean fuzzy normal subgroup. The level subset of fermatean fuzzy subgroup and its properties also defined. Finally, the homomorphic image of fermatean fuzzy subgroup is also discussed.

The Internet of Things (IoT) has emerged as a key technology for scalable, real-time environmental monitoring, addressing the growing global challenge of air pollution. This review synthesizes findings from more than twenty major deployments across urban, industrial and smart-city environments, including implementations from Hindustan Institute of Technology [1], Sathyabama Institute [2], Taiwan’s integrated governance framework [3], the Bulgarian Academy of Sciences [4] and low-cost deployments in developing regions. Comparative analysis spans sensing hardware, communication technologies (WiFi, LTE/4G, LoRa), cloud architectures, machine learning integration and field performance. Results indicate significant improvements in spatial coverage, real-time responsiveness and data-driven governance, while highlighting persistent challenges in sensor calibration, cybersecurity and long-term reliability. Emerging advances in edge intelligence, spectral sensing, federated learning and hybrid communication architectures are examined as pathways toward next-generation environmental monitoring. This consolidated review provides a structured framework for scalable, resilient and policy-integrated IoT air-quality monitoring systems.

The adoption of electric vehicles (EVs) has accelerated rapidly, driven by the need for sustainable mobility and advancements in battery technologies. However, the large-scale deployment of EVs faces two key challenges: efficient monitoring of battery health and effective management of charging infrastructure. Batteries degrade over time, making it crucial to monitor state of charge (SOC) and state of health (SOH) to ensure safety, reliability, and extended lifespan. At the same time, congestion at charging stations and lack of coordinated slot allocation create bottlenecks that limit user convenience and grid stability. Recent developments in Internet of Things (IoT) platforms, smart communication protocols, and predictive analytics provide opportunities to integrate real-time battery health monitoring with intelligent charging slot reservation. This review paper examines the current state of research in battery monitoring systems, SOC/SOH estimation techniques, slot reservation algorithms, and communication technologies for EV applications. It also highlights prototype implementations, identifies gaps in existing approaches, and outlines future directions including AI-driven queue optimization, V2G integration, and scalable IoT architectures.

This study examines how artificial intelligence (AI) is reshaping financial leadership and governance. The research problem addressed is the limited understanding of AI’s direct influence on governance effectiveness. The primary objective was to assess the role of AI in leadership transformation and strategic financial planning. Given the modest sample size and the quantitative design, this study was viewed as exploratory, offering preliminary evidence on the role of AI in financial governance rather than conclusive generalizations.  Positivist approach, data were collected from 50 professionals in finance, accounting, and corporate governance through a structured questionnaire that covered six domains. Reliability tests confirmed strong internal consistency, and regression analysis revealed that AI-driven leadership transformation and AI in strategic planning significantly enhance financial leadership effectiveness. In contrast, adoption, compliance, and organizational context showed no direct impact, though they may serve as enabling conditions. The findings recommend prioritizing leadership-oriented AI strategies to strengthen governance outcomes. The study concludes that harnessing AI to revolutionize leadership and embed it into strategic financial planning is essential for elevating governance. True financial leadership requires rethinking roles and strategically using AI to drive smarter decisions, with leaders’ vision ultimately propelling real progress in financial governance.

Supply chain management faces unprecedented challenges from global disruptions, geopolitical instability, and increasing complexity in interconnected networks. Artificial Intelligence (AI) has emerged as a transformative technology for mitigating supply chain risks through predictive analytics, real-time monitoring, and autonomous decision-making capabilities. This paper examines the role of AI in reducing supply chain risks, exploring key AI technologies including machine learning algorithms, neural networks, digital twins, and blockchain integration. Through systematic analysis of recent literature and industry case studies, this research demonstrates that AI-driven solutions enhance risk prediction accuracy by 20–50%, improve response times by 30–40%, and enable proactive disruption management. The study categorizes supply chain risks into internal (manufacturing, planning, business) and external (demand, environmental, geopolitical, cybersecurity) types, and analyzes how specific AI techniques address each category. Key findings indicate that Random Forest, XGBoost, and deep learning models significantly outperform traditional statistical methods in forecasting disruptions. Real-world implementations by Amazon, UPS, FedEx, and Unilever validate AI's effectiveness in optimizing inventory allocation, route planning, and supplier risk assessment. This research contributes to the growing body of knowledge on AI-enabled supply chain resilience and provides practical insights for organizations seeking to implement intelligent risk management systems. Future research directions include explainable AI (XAI) for transparent decision-making, integration with IoT sensors for enhanced visibility, and development of adaptive algorithms for dynamic risk environments.

In competitive markets, advertisements tend to look like a way through which a corporation can convince the customers to abandon the rival brands. However, the effect in real life can be much wider than that. ne-company made advertisement can have effect on not only its sales but also the performance of competitors. Such promotion makes the product categories familiar at different periods and indirectly assists the rival firms. A general conceptualization of this condition terminates an advertising spillover effect. This paper discusses the time when the advertising of competitors can be more beneficial than harmful. It researches consumer attention, brand familiarity and level of competitive behavior in the market as influencing factors of such results. The advertisement of one firm may inform other buyers about a product category even when they are poorly informed enough to experiment with various types of products. On the other hand, advertising can have a more pronounced effect of moving demand to other brands in the mature market where the shoppers are already aware of their choices. In other words, the same promotional campaign may have varying reactions in various types of market structures. (It can be a debilitating factor to competitors, but in other situations strengthening them by creating a higher interest in the category in general.) To test this idea further, the study will integrate both theory and practice in the study of online and traditional advertisement landscape. It examines how variables like quantity of advertising, brand’s market position, degree of maturity and so on affect consumer responses. Our findings suggest that smaller or lesser-known brands tend to do better when large corporations advertise as advertised to a greater extent because consumers will see that the general level of recognition grows and they will begin to investigate alternatives, which can be seen across the spectrum, and it’s possible they would find benefits with large companies advertising too. On the contrary, established brands are characterised by switching behaviour: people simply move from one brand to the other, instead of increasing the size market. The upshot of this is that advertising can expand the size of the market at the start of any competition, but in wealthier markets it mainly shifts the market share among existing players. The study contributes to marketing and competition research by demonstrating that advertising struggles don’t always turn the tide into a clear-cut win-lose situation. The impacts, however, can be spread out across, rather than within, firms, depending on the context.

The petrochemical industries have recently given refractory castable improved with nano bonding a lot of attention. The properties of the castable material have been improved by the use of different binders and sintering additives. Numerous research projects are now looking at use of different nano binders, such colloidal alumina and silica in conjunction with sintering additives like those based on aluminium or boron. The objective is to reduce energy use and enhance densification. In order to improve both thermal and mechanical characteristics, nano-structured binders in particular colloidal silicaare also being investigated with a variety of raw materials, including fused silica, tabular alumina and mullite.The green strength of these nano-bonded castables is further increased by the use of setting or gelling chemicals. This review focuses on the potential of setting agents like HA and CAC, sintering additives (primarily boron-based), and nanostructured binders like colloidal silica and colloidal alumina in high alumina refractory castables intended for use in petrochemical industries, particularly FCC units.

This work is devoted to the preparation and investigation of a composite based on styrene–butadiene–styrene (SBS) polymer using carbon nanotubes. Nanocomposites based on styrene–butadiene–styrene polymer (SBS) and carbon nanotubes were prepared by the emulsion mixing method, while the vulcanization process was carried out in accordance with standard test methods used for SBS evaluation. The incorporation of carbon nanotubes (CNTs) into the rubber compound improved the compatibility of the components, and the resulting nanocomposite exhibited high thermal stability. The nanomaterial enhanced the process ability of SBS rubber–based composites. Compared with rubber reinforced solely with carbon black, the use of nanomaterials led to a 27% increase in stress at 100% permanent elongation. In addition, partial replacement of carbon black resulted in an increase in the elongation at break of the vulcanization from 424.5% to 554.0%. The mechanical properties of the obtained rubbers demonstrated that they can be regulated by 

Kenya faces significant seismic and landslide risks due to its location along the East African Rift System. Historical earthquakes, such as the 1928 Subukia event (magnitude 6.9), have caused widespread damage, and recent deadly landslides, including the 2019 West Pokot disaster, have resulted in over 70 fatalities and the displacement of thousands. The study employs a comprehensive analytical framework encompassing vulnerability assessment, risk assessment, preparedness measures, mitigation strategies, response mechanisms, and rehabilitation protocols. A vulnerability matrix identifies residential buildings, informal settlements, rural hill communities, and vulnerable populations (children, elderly, persons with disabilities) as high-risk elements, while a risk assessment matrix reveals that landslides present more frequent and immediate threats compared to earthquakes, which though less frequent, carry potential for devastating impacts. The findings indicate that effective preparedness requires integrating early warning systems, public education, emergency drills, stockpiling of relief supplies, and evacuation planning. Mitigation strategies include hazard mapping, enforcement of building codes, slope stabilization, reforestation, and land-use planning regulations. The study highlights the stark implementation divide between developed nations with institutionalized preparedness and developing countries like Kenya facing challenges of limited resources, weak enforcement, fragmented coordination, and systemic vulnerabilities. The paper concludes that multi-faceted, collaborative, and continuous preparedness planning, incorporating scientific data with community-based approaches, is essential for reducing disaster impacts, saving lives, and minimizing economic losses. Recommendations include strengthening institutional capacity, enforcing building regulations, investing in early warning technologies, promoting community awareness, and mainstreaming disaster risk reduction into national and county development plans.

The increasing demand for compressed air in small-scale industrial and agricultural applications in developing regions necessitates cost-effective and readily accessible technological solutions. This study presents the design, construction, and performance evaluation of a functional air compressor utilizing predominantly locally sourced materials. The primary objective was to develop a durable and repairable compressor that reduces dependency on expensive imported units while maintaining operational efficiency. The compressor system was fabricated using locally sourced plastics (PVC pipes) converted into a reciprocating air pump, coupled with a salvaged electric motor (775 DC). Key components such as the pressure tank were constructed from reinforced mild plastic, while the check valves, pressure gauge, and fittings were sourced from local hardware and automotive parts suppliers. The design prioritized simplicity, thermal efficiency, and safety, incorporating a manually adjustable pressure switch and a spring-loaded safety relief valve. Upon assembly, the compressor underwent rigorous testing to evaluate parameters including maximum achievable pressure, volumetric efficiency, pumping speed, and energy consumption. The results indicated that the locally fabricated unit achieved a maximum pressure of 120 psi with a flow rate of 4.5 CFM, comparable to mid-range conventional compressors. The total production cost was approximately 60% lower than equivalent imported models. This research demonstrates that locally sourced materials can be effectively engineered to produce reliable compressed air systems, offering a sustainable alternative for resource-constrained environments. The study contributes to the body of knowledge on appropriate technology and provides a replicable framework for community-based manufacturing. Recommendations for the further optimization of component’s lifespan and noise reduction are discussed.

Background: Osteoporosis is a metabolic bone condition characterized by progressive reduction in bone strength due to decreased bone mineral density (BMD). Tobacco consumption is considered a significant behavioral risk factor that interferes with bone remodeling by affecting osteoblastic activity, mineral balance, and endocrine function. While osteoporosis is often associated with aging, early bone changes related to tobacco exposure may occur in young adults. Aim: To assess the incidence of osteoporosis among adult smokers and to compare bone mineral density between smokers, smokeless tobacco users, and individuals without tobacco habits. Materials and Methods: A total of 90 subjects aged 20–45 years were included in this observational study and categorized into three equal groups: smokers, smokeless tobacco users, and non-users. Bone mineral density was evaluated at the calcaneum using a portable SONOST 2000 peripheral DEXA system. Based on T-score values, participants were classified as normal, osteopenic, or osteoporotic. Statistical analysis was carried out using SPSS version 21.0. Differences in mean T-scores were analyzed using one-way ANOVA, while associations between tobacco exposure and BMD status were examined using the Chi-square test. Correlation analysis was performed to determine the relationship between smoking intensity, duration, and T-score values. A p-value less than 0.05 was considered statistically significant. Results: Individuals with a smoking habit demonstrated lower mean T-scores compared to smokeless tobacco users and non-users. Osteoporosis was more frequently identified among smokers, whereas most non-users exhibited normal BMD values. Greater duration and frequency of smoking were associated with progressively lower T-scores. Conclusion: Tobacco use, particularly smoking, is strongly associated with reduction in bone mineral density. Preventive measures, including early screening and tobacco cessation counseling, are essential to minimize long-term skeletal complications.

Education plays an important role in building self-confidence among women it also enables to change she/her status in the society.  Education enables and builds confidence to take decisions in a better way. A quality education is the foundation of sustainable development. Education for sustainable development ensuring equal access for all women and men to affordable and quality technical, vocational and tertiary education, including university; and eliminating gender disparities in education and ensure equal access to all levels of education and vocational training for the vulnerable, including persons with disabilities, indigenous peoples and children in vulnerable situations. The present paper mainly aims to study on the regional inequalities in the enrolment on education at macro level; to have a descriptive analysis on the State wise and Level wise enrolment Patterns, Disparity in Education Enrolment; to analyse the extent of inequalities in education in India; and to offer possible strategies for strengthening the education and bridging the interstate variations in India. The study is a descriptive study based on secondary data mainly gathered from Various Issues of Educational Statistics – At a Glance, published by Government of India, Ministry of Human Resource Development, New Delhi and The Unified District Information System for Education Plus (UDISE Plus) published by the  Department of School Education & Literacy, Ministry of Education, Govt of India. Based on the UDISE Plus report it is observed that the GPI for primary education in India stands at 1.03, indicating a slight favorability towards girls. Several states and union territories show encouraging numbers, such as Andaman and Nicobar Islands (1.05), Arunachal Pradesh (1.01), Bihar (1.03), and Delhi (1.07). These regions demonstrate a relatively higher enrollment ratio for girls at the primary level, contributing to gender parity in early education. At the Upper Primary level, the Gender Parity Index for India is 1, indicating equal participation of girls and boys. Achieving universal school education by 2030 demands a multi-faceted approach, to achieve the goal; India must address systemic challenges through targeted policy interventions to bridge gaps in access, quality, and equity, ensuring every child receives a meaningful education. India’s policy ecosystem for strengthening the education sector as a whole with the introduction of the NEP, updated guidelines, regulations for academic collaboration and mutual recognition of qualifications, building more world class infrastructures, providing welfare facilities and permissions for foreign branch campuses. Further, India's education sectors require unwavering focus to unlock the nation's true potential through integrated, accountable, and adaptive policy frameworks to build a future ready workforce.

Obtaining and investigation of compositions with improved performance properties based on SKI-3 rubber is devoted to obtaining rubber based on SKI-3 grade rubber with improved physical-mechanical properties. For carrying out this work, for modification of SKI-3 rubber, butadiene-nitrile rubber of BNK-18 grade and polymethyl methacrylate (PMMA) were used. At the same time model rubber mixtures based on the optimal formulation were prepared. Based on the study of properties of model rubber mixtures of non-crystallizing special-purpose rubber BNK-18 and crystallizing general-purpose rubber SKI-3 it was established that in the composition of formulations of industrial elastomer compositions it is most expedient to use mechanically activated material in a planetary mill for 3 min To study the rheological properties of binary systems SKI-3 + BNK-18 compositions were prepared on rolls at a temperature of 140°C during 15 minutes and the obtained single-phase compositions under different loads and temperatures (from 130 to 173°C) had the melt flow indices of this composition determined. Thus as a result the technology of processing rubber mixtures based on SKI-3 + BNK-18 was determined; namely; processing temperature 175°C and pressure 12 MPa. On the basis of SKI-3 + BNK-18 rubber mixtures were prepared and vulcanization parameters were determined; vulcanization time 23 min and temperature 160°C. Study of physical-mechanical properties of vulcanizates showed that the tensile strength of the obtained rubber is 23 MPa, these indicators are 1.6 times greater than the standard.

Developing countries including Kenya, have recorded rapid growth in industrialization and population. This growth has contributed to a rise in   wastewater pollution, leading to serious environmental and health risks. Paint and pigment manufacturing industries release heavy metals like lead, cadmium, and chromium into water bodies. These heavy metals threaten aquatic life and human health. Traditional treatment methods are costly and not widely used, creating a need for sustainable alternatives.This study examines the efficiency of coagulation, flocculation, and adsorption in removing heavy metals from industrial wastewater. Waste tires were carbonised at 700°C to yield activated carbon adsorbents. They were later cleaned and optimised. Coagulation-flocculation with aluminium sulphate reduced turbidity by 45.73-55.26%, however it was insufficient for heavy metal removal. The efficacy of adsorption with tire-derived activated carbon relies on pH, contact time, and adsorbent dose. However, it did not entirely fulfil EMCA criteria, pointing out the need for further enhancements. These studies demonstrate the possibility of repurposing waste materials for environmental cleaning. With further optimisation and large-scale use, tire-based activated carbon could provide a low-cost, long-term option for wastewater treatment. This will help to reduce industrial pollution and protect water sources.

Animal bioacoustics has emerged as an indispensable tool for biodiversity monitoring and ecosystem assessment, enabling non-invasive observation of wildlife populations across diverse habitats. Traditional acoustic classification systems employ handcrafted features such as Mel-Frequency Cepstral Coefficients (MFCCs) with classical machine learning classifiers, achieving reasonable performance in controlled environments but struggling with environmental noise, species vocalization variability, and cross-habitat generalization. This paper presents a hybrid classification framework that systematically compares classical and deep learning paradigms for animal sound recognition. A Random Forest classifier trained on 40-dimensional handcrafted acoustic features—encompassing spectral, temporal, and energy-based descriptors—establishes an interpretable baseline enabling feature importance analysis. A fine-tuned Wav2Vec2 transformer model serves as the deep learning counterpart, learning hierarchical representations directly from raw waveforms without manual preprocessing. Both approaches were evaluated on a diverse dataset spanning 15 animal species across birds, mammals, and amphibians using accuracy, precision, recall, F1-score, and confusion matrix analysis. Results demonstrate that Wav2Vec2 substantially outperforms the feature-based baseline, achieving 92.75% test accuracy compared to 78.62% for Random Forest—an improvement of 14.13 percentage points. Per-class analysis reveals dramatic gains for acoustically challenging species, with the transformer model achieving near-perfect classification (F1 > 96%) for multiple categories where Random Forest struggled. These findings affirm the enhanced representational capacity of self-supervised transformer architectures for bioacoustic classification and provide practical guidance for automated wildlife monitoring systems. The complete codebase, trained models, and evaluation protocols are publicly available to support reproducibility and future research.

As technology increases everyone wants more features with in a small size electronic gadget, to make them smaller, quicker, more compact and increasing integration density we need to decrease the size of the transistors but due to the decrease in size challenges like power efficiency and heat management becomes major concerns in VLSI design. Traditionally we are using the irreversible gates in digital circuits but during digital operations input information losing which directly contributes the energy dissipation according to Landauer's principle. But Reversible gates make every output corresponds to unique input and prevents the information loss thereby reduces the power dissipation. In this work, we compared a 32-bit Arithmetic Logic Unit (ALU) designed with both irreversible logic gates and reversible ALU constructed with Peres gate. The reversible design has a Quantum Cost of 384 and produces 128 garbage outputs. Both ALUs were coded in Verilog and implemented on Xilinx Artix-7 FPGA. We evaluated performance of ALUs based on theoretical metrics and actual hardware performance metrics. The reversible ALU shows a significant performance by reducing power dissipation to 70mW compared to the 211mW of the irreversible ALU. However, its latency was slightly higher at 22.737 ns compared to irreversible design latency(20.214ns) due to the routing overhead. our analysis indicates that reversible logic is very fast in logic implementation but delay is mainly due to the routing overhead, in this case there were 128 garbage outputs on FPGA. This study demonstrates that reversible logic uses lower energy compared to conventional designs by careful handling of routing and I/O complexity.

The study examined the Effect of Peer Tutoring Strategy on Upper Basic II Students' achievement in Basic Science and Technology in Otukpo LGA, Benue State. Two research questions were asked and answered and two hypotheses were tested at 0.05 level of significance. A pre-test post-test quasi-experimental research design was adopted for the study. Simple random sampling techniques were used to sample 85 (44 Male and 41 Female) students in two intact classes from the population of 4,834 upper basic II students. Basic Science and Technology Achievement Test (BSTAT) and Basic Science and Technology Retention Test (BSTRT), instruments were used for data collection. A reliability coefficient of 0.79 was obtained using, kuder-Richardson 20 formula. Data were analyzed using mean and standard deviation, to answer research questions while the null hypotheses were tested using analysis of Covariance (ANCOVA). Findings of the study showed that there is a statistically significant difference between the mean achievement scores of students taught Basic Science and Technology with peer tutoring instructional strategy and those taught with lecture method, among others. Based on the findings, it was concluded that appropriate use of peer tutoring instructional strategy in teaching Basic Science and Technology would enhance students’ achievement and retention in Basic Science and Technology, in Otukpo LGA. Therefore, it was recommended that Basic Science and Technology teachers should endeavor to incorporate peer tutoring strategy into the teaching of Basic Science and Technology so as to increase achievement and retention in the subject.

Crowd funding is a digital financing model through which individuals, entrepreneurs, or businesses secure funds from a large number of contributors, typically via the Internet. This method leverages online platforms to gather small financial contributions from a broad audience, collectively referred to as "the crowd," rather than soliciting substantial amounts from a limited number of traditional investors such as banks or venture capitalists. Crowd funding has gained significant traction across various sectors, including the arts, entrepreneurship, and scientific research, owing to its accessibility and community-oriented nature. One of the primary advantages of crowd funding is its capacity to democratize financial access. It offers opportunities for a diverse range of participants, including novice creators and small-scale entrepreneurs, to present their ideas and obtain support without substantial capital or recognition. Crowd funding manifests in various forms, each tailored to distinct needs. Reward-based crowd funding involves supporters receiving non-financial incentives for their contributions, which may include items such as thank-you notes or product prototypes. Equity crowd funding allows backers to acquire shares in a company, thereby securing financial interest in its success. Donation-based crowd funding relies on voluntary contributions, often employed for charitable purposes, without the expectation of monetary return. Finally, peer-to-peer lending facilitates loans between individuals and small businesses, offering lenders potential returns through interest. Crowd funding has transformed the financial support landscape for projects by offering an inclusive, accessible, and adaptable model that promotes innovation and community involvement. Despite challenges, such as the dissemination of crowd funding, variations in success rates, and potential biases, the model continues to develop and expand, providing new opportunities for artists, innovators, and change-makers.1 Crowd funding represents one dimension of the phenomenon of crowd sourcing besides crowd voting and crowd creation. The term crowd sourcing is composed of "crowd" and "outsourcing", pointing to the meaning to outsource specific functions to a group of external persons. The concept is based on the idea of "wisdom of a crowd". The act of taking a job traditionally performed by a designated agent (usually an employee) and outsourcing it to an undefined, generally large of people in the form of an open call. Besides crowd sourcing, crowd funding is closely connected to micro lending. Micro lending refers to the idea of funding of individuals, who do not have access to conventional financing from credit institutions.

development. Microorganisms possess diverse metabolic capabilities that allow them to contribute to healthcare, agriculture, environmental sustainability, and industrial biotechnology. Recent advancements in molecular microbiology, genomics, and microbial biotechnology have enabled the development of innovative tools such as rapid molecular diagnostics, microbial biofertilizers, bioremediation technologies, and bio-based industrial production systems. These innovations have significant potential to address major societal challenges, including infectious diseases, environmental pollution, and food insecurity. India’s national development vision under Viksit Bharat 2047 emphasizes technological innovation, scientific advancement, and sustainable economic growth. Microbial technologies can play a crucial role in achieving these objectives by strengthening healthcare systems, improving agricultural productivity, and promoting environmentally sustainable industrial processes. For instance, microbial biotechnology has been widely applied in biofertilizers, wastewater treatment, and pollution remediation, demonstrating its capacity to support ecological sustainability and resource conservation. Despite significant progress in microbiological research, the translation of scientific discoveries into practical societal applications remains limited due to technological, financial, and policy-related barriers. Bridging this gap requires interdisciplinary collaboration among researchers, policymakers, and industry stakeholders. This study examines the potential of microbial innovations to contribute to sustainable development and national growth. The findings highlight the importance of investment in biotechnology research, innovation-driven policy frameworks, and effective knowledge transfer systems to ensure that microbial research delivers tangible benefits for society.

This quantitative study examined the influence of servant leadership and mission-aligned governance practices on student formation in Catholic higher education institutions. College students enrolled in selected Catholic colleges participated in the study through a structured survey questionnaire measuring servant leadership practices, mission-aligned governance practices, and student formation in terms of academic motivation, engagement, and values development. Descriptive statistics were used to determine the level of leadership and governance practices, while correlation and regression analyses examined their predictive influence on student formation. Results indicated that servant leadership and mission-aligned governance were perceived at very high levels and significantly predicted student formation outcomes. The findings emphasize the importance of value-centered leadership and mission-driven governance in strengthening the formative mission of Catholic education. The study provides empirical evidence that may guide institutional leaders and policymakers in enhancing leadership development and governance practices within Catholic higher education.

This research focuses on the study and design of Artificial Intelligence (AI)-driven models for enhancing search engine visibility and optimizing website performance. The study adopts a survey-based quantitative approach, supported by an extensive literature review, to analyze user behavior, website performance expectations, and awareness of AI applications in Search Engine Optimization (SEO). Data collected from more than 1250 respondents reveal that website loading speed, navigation, and mobile friendliness are the most influential factors affecting user experience and search rankings. The findings indicate that AI-based techniques such as automated keyword analysis, semantic content optimization, learning-to-rank models, and predictive performance analytics can significantly improve SEO outcomes and website efficiency. Based on empirical insights and literature synthesis, this study proposes an AI-driven integrated framework combining machine learning, natural language processing, and performance monitoring to enhance search visibility and user engagement. The research contributes by bridging the gap between AI-based SEO strategies and website performance optimization within a unified model.

Small-scale enterprises (SSEs) remain critical drivers of employment generation, innovation, and economic growth, however, their performance in Nigeria has continued to fluctuate due to managerial, environmental, and behavioural constraints. Increasing attention has therefore been directed toward entrepreneurial personality traits as determinants of enterprise success, yet empirical evidence on how such traits affect performance outcomes remains limited. This study was consequently undertaken to assess the effect of entrepreneurial personality traits on the performance of SSEs in Oyo State, Nigeria. Specifically, the study examined the effect of risk tolerance, entrepreneurial alertness, internal locus of control, and need for achievement on SSE performance. The study adopted a descriptive survey research design, and primary data were collected using a structured questionnaire administered to SSE owners. The population comprised 6,039 registered SSE owners operating across sectors as reported by the National Bureau of Statistics (2017), from which a sample size of 361 respondents was drawn using Krejcie and Morgan’s sampling formula. Data were analysed using descriptive statistics, including frequency and percentage, alongside multiple regression analysis. The findings revealed a strong positive correlation (R = .878) with an R-square value of .770, indicating that 77% of the variation in business growth performance was explained by entrepreneurial personality traits. Risk tolerance (0.792), entrepreneurial alertness (0.686), internal locus of control (0.761), and need for achievement (0.751) each demonstrated significant positive relationships with SSE performance. The study concluded that entrepreneurial personality traits significantly affected SSE performance and recommended integrating personality development into enterprise support programmes and policy initiatives.   

This study investigates the two-dimensional vibrational response of a poroelastic medium subjected to both an initial static stress state and an external magnetic field. The theoretical formulation is based on Biot’s theory of poroelasticity and incorporates electromagnetic effects through Maxwell’s equations, enabling a fully coupled description of solid deformation, pore-fluid pressure, and magnetoelastic interactions. The governing equations account for mechanical pre-stress, fluid solid coupling, magnetic body forces, and variations in pore pressure. From these equations, a generalized wave equation is derived, and closed-form solutions for displacement components, stress fields, and pore pressure are obtained for harmonic wave propagation. The influence of key material and field parameters, including porosity, permeability, magnetic field intensity, and initial stress, on wave number and frequency characteristics is systematically analyzed. Numerical results demonstrate that both magnetic loading and pre-stress significantly modify the effective stiffness of the medium, leading to noticeable changes in wave dispersion behavior. The outcomes of this work are relevant to applications in geomechanics, seismo-electromagnetic phenomena, and the development of magneto-sensitive porous materials.

Palm oil mill effluent (POME) is a wastewater generated from palm oil milling activities which requires effective treatment before being discharged into the watercourses due to its highly polluting properties. Hence this study was aimed at evaluating the biosurfactant-producing microorganisms from POME at different depths from large and small/medium scale enterprises in Edo State, Nigeria. POME was aseptically collected using sterile bottles from various depths: top, middle and bottom in selected palm oil companies across Edo State, Nigeria. The companies were categorized into large-scale enterprises (L.S.E.), which included Okomu Oil Palm and NIFOR and small and medium-scale enterprises (S.M.E.), comprising Ovbiogie, Sapele Road and Aduwawa oil palm companies. The bacteria isolated were Bacillus cereus, Pseudomonas aeruginosa, Bacillus amyloliqefaciens, Klebsiella aerogenes and Escherichia coli. Fungi like Aspergillus niger, Fusarium solani, Penicillium chrysogenum, Microsporum sp., Penicillium citrinum and Aspergillus flavus were also isolated from these samples using the Pour plate techniques. The bacterial species obtained in the pure culture of substrate were identified using standard bacterial and fungal techniques. Isolated organisms were screened for their ability to produce biosurfactants using oil spreading assay, hemolytic, and emulsification activity. The test of how susceptible the isolates were to antibiotics was conducted with the aid of the Kirby-Bauer disk diffusion assay. The data obtained were analyzed using Microsoft Excel 2019 and PhyloT software to establish the relationship between isolated microorganisms from POME. The results of the total heterotrophic bacterial counts ranged from log10 3.90±1.00 cfu/g (Small and Medium Scale- Sapele Road) to log10 4.66±3.0 cfu/g (Large Scale Enterprise- Okomu). The total fungal counts ranged from log10 3.78±1.00 cfu/g (Small and Medium Scale- Aduwawa) to log10 4.34±2.00 cfu/g (Small and Medium Scale- Aduwawa). The difference in percentage reduction in the density of microbes between the top and bottom depth ranged from 43.18% (NIFOR) to 72.29 % (Okomu). Also, a significant difference (p<0.05) between the microbial diversity of large-scale and small-scale oil-producing enterprises was observed. The isolated bacteria included Bacillus cereus, Pseudomonas aeruginosa, Bacillus amyloliqefaciens, Klebsiella aerogenes and Escherichia coli. The isolated fungi were Aspergillus niger, Penicillium citrinum, Penicillium chrysogenum, Aspergillus flavus, Microsporum sp. and Fusarium solani. Biosurfactant screening results revealed that most microbial isolates were potential biosurfactant producers, with Bacillus sp. showing the highest clear zone of oil spread assay. However, specific isolates like E. coli and Microsporum sp. did not produce any clear zone for oil spread assay. More so, Bacillus sp. was found to be the best biosurfactant producer due to its hemolytic activity and the assay with the highest zone (10mm) of displacement. POME is home to many microorganisms of importance to both industrial and environmental processes. This research has demonstrated that POME serves as a reservoir for microorganisms capable of producing biosurfactants.

This scoping study explores the role of Human-in-the-Loop Artificial Intelligence (HITL AI) in precision agriculture and evaluates the benefits of using human expertise in combination with Artificial Intelligence (AI) systems in decision-making within modern smart agricultural environments. The development of Artificial Intelligence, Machine Learning, Internet of Things, and robotics has significantly impacted modern agriculture by providing automated crop monitoring, disease detection, yield prediction, and smart farm management systems. However, Artificial Intelligence systems also face challenges in terms of understanding, interpretability, flexibility, and trustworthiness in modern smart agricultural environments. This study is based on the literature regarding human-in-the-loop systems, human-centric Artificial Intelligence systems, and collaborative robotics systems in the context of smart agriculture. The structured scoping study methodology has been followed to identify and evaluate studies regarding Artificial Intelligence systems in smart agricultural environments, with a focus on automation-centric Artificial Intelligence systems and human-centric Artificial Intelligence systems within the context of Agriculture 5.0 concepts. The study concludes that although automation-centric AI systems show high accuracy in simulated smart agricultural environments, Human-In-The-Loop (HITL) AI systems show higher robustness in smart agricultural environments. Explainability in AI has shown significant potential in supporting the effectiveness of HITL AI systems in decision-making within smart agricultural environments. The study also identifies some important gaps in the literature regarding HITL AI systems in smart agriculture. The study concludes that for the development of modern smart precision agriculture, collaborative intelligence within smart agricultural environments is necessary to create sustainable smart agriculture systems.

In today’s fast-paced world, young individuals often engage in excessive spending without fully understanding their financial limits. Financial literacy has become a crucial subject, particularly for the Gen Z population, as it directly impacts their financial decision-making. This research examines the level of financial literacy among college students in the Ernakulam district and its influence on their savings behavior. Financial literacy is assessed through three key components: financial knowledge, financial attitude, and financial management skills. The study explores how these independent variables collectively affect savings behavior, which serves as the dependent variable. Data was collected from 161 college students using a structured questionnaire distributed via Google Forms, and the results were analyzed using the Statistical Package for Social Sciences (SPSS). The findings highlight the significance of financial literacy programs in equipping young adults with essential financial skills, ultimately fostering better financial habits for their future.

Sustainable Human Resource Management (S-HRM) has emerged as a critical domain linking organizational strategy, environmental stewardship, and employee-centred sustainability practices. However, existing scholarship remains conceptually fragmented, with limited systematic mapping of its intellectual structure. Addressing this gap, the present study employs a bibliometric and text-mining approach to analyse 784 peerreviewed publications using Latent Dirichlet Allocation (LDA) topic modelling and Multidimensional Scaling (MDS). The analysis identifies five dominant thematic clusters: Green HR & Environmental Sustainability, Strategic HR Frameworks, Employee Behaviour & Knowledge Sharing, Innovation & Technological Transformation, and Leadership & Engagement. Topic coherence testing and interpretability validation supported the selection of the five-topic solution. The MDS visualization reveals significant convergence between leadership-driven engagement and green HR practices, suggesting an increasing behavioural orientation in sustainability research. In contrast, technology-driven sustainability remains comparatively isolated, indicating a structural gap between digital innovation and human-centred sustainability discourse. Building on these findings, the study proposes an integrated multi-layered conceptual framework positioning strategic alignment as the foundational driver, leadership and engagement as behavioural catalysts, and environmental and innovation outcomes as strategic extensions. By shifting from narrative synthesis to datadriven intellectual mapping, this research advances theoretical integration within S-HRM scholarship. Practically, the findings underscore the importance of aligning leadership development, employee engagement, and digital transformation initiatives with sustainability objectives, particularly in the context of the United Nations Sustainable Development Goals (SDGs). The study provides a systematic roadmap for future interdisciplinary research bridging behavioural, strategic, and technological perspectives in sustainable HRM.

Despite the rapid growth of microfinance as a key development effort, Uganda’s production structure still relies on low-productivity activities and traditional production factors. This article argues that current microfinance practices mainly support consumption smoothing and survival entrepreneurship instead of fostering productive transformation and competitiveness. The study draws from political economy, institutional theory, and innovation-focused development views to redefine microfinance as a possible strategic tool for building productive capabilities, improving technology, and promoting organizational learning. Through qualitative analysis of national policy frameworks, development reports, and academic literature on Uganda, the article presents a combined conceptual, contextual, and theoretical framework that connects microfinance to economic transformation by building capabilities at both the firm and system levels. The findings show that the transformative effect of microfinance relies not just on access to finance but also on its connection to innovation systems, institutional incentives, and strategies for upgrading value chains. This study enriches development finance research by shifting the focus of microfinance from merely a poverty-reduction tool to a productionfocused instrument for structural transformation.  

Beneficiary satisfaction and purchase intention are critical indicators of the effectiveness and sustainability of agricultural development organisations. Purchase intention reflects beneficiaries’ willingness to continue purchasing products, while satisfaction arises from their overall evaluation of product quality, pricing, and service experiences. The Kerala Agricultural Development Society (KADS) plays a significant role in supporting agricultural development in Kerala by ensuring fair prices for farm produce, promoting organic and eco-friendly agricultural practices, and reducing the influence of intermediaries. This study examines the key determinants influencing purchase intention and beneficiary satisfaction among KADS beneficiaries. Using empirical evidence collected from beneficiaries across Kerala, the study analyses the relationship between organisational initiatives, perceived value, satisfaction, and purchase intention. The findings highlight that fair pricing mechanisms, product quality, and trust in KADS significantly influence beneficiary satisfaction, which in turn positively affects purchase intention. The study provides practical insights for strengthening beneficiary-oriented strategies and enhancing the effectiveness of agricultural development programmes implemented by KADS.

This research introduces an AI-driven automotive marketplace platform designed to enhance the vehicle buying and selling ecosystem through intelligent automation and data-driven decision-making. The system enables image-based vehicle search using computer vision models capable of extracting vehicle attributes such as make, model, variant, and features directly from user-uploaded images. The platform integrates an AI-powered conversational calling agent that analyzes customer intent, budget, and usage patterns to provide personalized vehicle recommendations and automated test drive booking. Experimental evaluation demonstrates that the proposed system achieves 93.4% vehicle recognition accuracy, an R² score of 0.87 for price prediction, and an average recommendation response time below 2.1 seconds, improving decision-making efficiency compared to traditional automotive marketplace platforms.

This study looked at how taxes and government capital expenditures affected Nigeria's economic growth. Secondary time series data from 1992 to 2021 make up the data set used in this investigation. The Granger Causality Test Result and Autoregressive Distributed Lag Model (ARDL) were employed in the study to describe the relationship's direction. According to the findings, the percentage of changes in the dependent variable that can be accounted for by the independent variables is indicated by the coefficient of determination (R2). Economic growth may be described by changes in the explanatory variables as shown by the model, according to the R2 of 0.614087, or 61%. The dummy variable accounts for 49% of the explanation. The model is significant at 5%, according to the F-statistic, which suggest the model's overall importance. The F-statistics and its probability (4.177050 and 0.004027, respectively) support this. Therefore, the study comes to the conclusion that taxes and government capital expenditures significantly contribute to Nigeria's economic growth. As a deterrence to others, this study suggests, among other things, that the government impose a death penalty on people who divert funds from the petroleum profit tax and misappropriate government capital expenditures.

Edge computing is now transforming how data is processed by shifting the computing devices closer to the source of data generation. Even though this transformation helps reduce latency and bandwidth consumption, it introduces a critical challenge. The edge devices operate with strict hardware constraints. The conventional microcontrollers such as ATmega328p and ESP32 offer simple and reliable design while they come with lack of architectural mechanism for advanced energy optimization. This research proposes the idea of designing an edge oriented, System on Chip (SoC) implemented using Verilog, integrating a 32bit Reduce Instruction Set Computing V(RISC-V/ RV32I) core with essential peripherals for the proposed microcontroller design. The new architecture explores energy minimization strategies including Dynamic Voltage and Frequency Scaling (DVFS), Sleep Modes, Clock Gating, Approximate ALU (Arithmetic and Logic Unit) in a separate manner. All the techniques will be implemented and evaluated separately. After thorough evaluation all techniques will be synergized and evaluated in one system. By means of Xilinx Vivado simulation and power analysis, a structured experimental matrix compares the baseline design against the optimized variants mentioned above. To represent edge workloads an integer multiplication (N32/64) and FIR (Finite Impulse Response) filtering will be complied using RISCV32 GCC toolchain under Ubuntu Operating System and executed on the soft SoC. The estimations of power, latency and Hardware areas such as LUTSs (Look Up Tables), Registers, BRAM (Block Random Access Memory) will be measured and compared to evaluate energy, latency and area tradeoffs. The study leverages recent research in energy efficient RISC-V microarchitectures, approximate computing and adaptive DVFS policies. This research contributes a reproducible methodology for architectural energy optimization in edge computing by providing a quantitative evaluation within a unified FPGA (Field Programmable Gate Array) based framework. The expected outcome is a demonstrable reduction is dynamic and static power while maintaining an acceptable performance degradation, setting up design guidelines for next generation energy-aware embedded architectures.

Spam identification is critical in Morden digital communication systems such as email, SMS, social media, and online platforms. The increasing proliferation of unwanted and hazardous messages such as spam, phishing, and scam material poses a severe threat to user privacy and cybersecurity. Deep learning (DL) algorithms can identify automatically learn intricate representations from large-scale various data sources, including reviews, SMS, and email data. They have become powerful alternatives that reflect modern spam qualities across several platforms and languages are sparse. This article presents an exhaustive review of deep learning-based spam detection approaches. The dataset The lack of dynamic, multilingual, and real-world data limits adaptability and the ability to deal with evolving spam. Convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM) networks, hybrid models, and transformer-based techniques are among the most commonly used designs discussed. Explainable AI (XAI) approaches are not well-integrated to provide clear and understandable explanations for spam detection options. In order to give future research paths for constructing reliable and intelligent spam detection systems, the study discusses datasets, assessment metrics, obstacles, and unfilled research gaps. The goal of this survey is to provide an organized summary of deep learning techniques. The design of an effective, flexible, and userfriendly deep learning-based spam detection framework that can precisely identify a variety of changing spam messages in real-world communication systems while maintaining low figuring overhead and high robustness is the main issue in this field of study.

This work presents a comprehensive spectral analysis for bandwidth estimation of phase-modulated (PM) signals using Bessel functions of the first kind. The mathematical framework of phase modulation is developed and expressed through Fourier–Bessel expansion, revealing the role of modulation index in determining sideband amplitudes and spectral distribution. Numerical simulations confirm theoretical predictions and demonstrate the redistribution of carrier power among sidebands with increasing phase deviation. The results provide analytical insight useful for communication system design and bandwidth optimization.

The landscape of drug discovery is undergoing a profound transformation with the emergence of new therapeutic modalities that extend beyond traditional occupancy-driven pharmacology. Antibody–drug conjugates (ADCs), proteolysis-targeting chimeras (PROTACs), and molecular glues exemplify this shift, emphasizing linker engineering, controlled payload release, and proximity-induced biological effects rather than classical high-affinity inhibition. These modalities harness endogenous cellular systems—such as targeted delivery mechanisms and the ubiquitin–proteasome pathway—to achieve enhanced specificity and catalytic or event-driven pharmacology. This paper provides a comprehensive examination of these innovative platforms, discussing their mechanistic principles, design strategies, materials and methods commonly employed in their development, experimental outcomes from representative case studies, and broader implications for future therapeutic innovation. The findings suggest that proximity-based and targeted degradation approaches offer distinct advantages in addressing undruggable targets, overcoming resistance mechanisms, and improving therapeutic indices. However, challenges related to stability, pharmacokinetics, immunogenicity, and largescale manufacturing remain significant. Continued integration of chemical biology, structural modeling, and translational pharmacology will be essential to fully realize the potential of these transformative drug modalities.

Fintech services in india have witnessed tremendous growth that has opened new possibilities of financial inclusion, particularly in rural areas.  nevertheless, the  uptake rate by rural consumers is relatively low because of language, low digital literacy, and a lack  of  trust.  as artificial intelligence  (ai)  has  grown, personalised and culturally appealing ai voice advertisements have  now  become  a  reality.  this  paper  will consider  how  the impact  of  vernacular  ai  voice  advertisements  on  the  awareness, trust  and  adoption  of  fintech  products  that include  mobile  banking  applications,  upi  payment services,  digital  wallets,  and  micro-loan  applications  by rural  consumers.  the  sample  size  of  100  rural  participants  in  karnataka  forms  the sample  size  and  is  used  to  investigate  the  relationship  between  the  familiarity  with  languages,  their  clarity,  relevancy,  and  perceived  credibility  of  ai-generated  voices  and  the  intention to  use  fintech  with  their  voice  recognition  application.  the  results  have  shown  that  vernacular  ai  voice  advertisements  largely  improve  comprehension,  develop  trust,  and  have  a  positive  effect  on  adoption  behaviour.  the  paper  concludes  by  stating  that  digital  financial  inclusion  in  rural  india  can  be  faster  by  implementing  vernacular  ai  voice  strategies by fintech firms. 

Entrepreneurship has become a global trend in today’s world. Fundamentally, a collection of psychological traits that allow people to take measured risks and overcome losses is what drives entrepreneurship. It has been stated in prior studies that entrepreneurs are mostly seen to have self-efficacy and risk-taking propensity. Assam, a thriving state in northeastern India, is becoming more acknowledged for its entrepreneurial spirit as a catalyst for job creation, economic expansion, and cultural preservation. In Assam, India, women entrepreneurs are becoming a significant force for empowerment, social transformation, and economic growth. The present study observes the relationship between self-efficacy, risk-taking, and entrepreneurial orientation among registered women entrepreneurs of Assam under the UDYAM registration portal. 346 samples were collected from two districts of lower Assam (Bongaigaon and Barpeta). Correlation and regression analysis state that both self-efficacy (SE) and risk-taking (RT) have a positive influence on women's entrepreneurial orientation (EO). Studies on this subject that take into account the region and the people are quite few. Because this study only looks at women entrepreneurs, it will be beneficial for the government and scholars to concentrate on women's empowerment in Assam. Even though the government has put in place many programs specifically for female entrepreneurs, very few of them are aware of them. Operating a firm requires more than just financial backing; it also requires the ability of business owners to successfully use their business acumen even in the face of reduced funding.

The proposed privacy-sensitive federated learning method is FedStress, which is aimed at identifying wearable stressors with high accuracy and efficiency. The growing use of wearables in health monitoring poses serious issues regarding data privacy and computational limitations especially in a situation where sensitive physiological data is to be used. The approaches in existence have inherent trade-offs: federated learning opens model parameters to gradient inversion attacks and differential privacy errors the accuracy by injecting noise and homomorphic encryption is prohibitive due to resource-constrained devices. To solve these issues, FedStress combines federated learning with an optimized homomorphic encryption and allows collaborative model training without having access to raw user data. Every device trains locally a lightweight stress detector using a variant of MobileNetV3 using depthwise separable convolutions and a hybrid attention mechanism in order to trade off accuracy and efficiency. The encrypted transmission of model updates is done through partially homomorphic encryption, sensor-aware ciphertext packing which minimises overhead on encrypted model updates, and allows secure aggregation in the encrypted space. An additional defense mechanism is on-device differential privacy with adaptive noise scaling, which prevents inference attacks and hierarchical key management which implements strict access control. Experimental confirmation of the WESAD dataset shows that FedStress can have 87.6% accuracy in detecting stress as centralized methods (89.1) with a much lower level of privacy risk, a gradient inversion attack success rate of 11% and a score of 0.09 on information leakage (0.45 with standard federated learning). This framework is practical, with 23ms inference time and 0.45J of energy used to make a classification on commercial smartwatch systems due to efficient parameter synchronization and model compression. We also provide a viable solution to the current trend of privacy-conscious health monitoring systems, which offers a reasonable compromise between algorithmic performance and practical functionality of wearable applications. The modular structure also enables it to increase the physiological sensing tasks, which makes it a flexible instrument in studying edge-based federated learning.

This study investigated the effects of AI-driven marketing communication messages on herbal drugs brand awareness among retirees in Southwest Nigeria. Specifically, AI-personalized advertising, AI-optimized promotional messaging, and AI-assisted interactive engagement were tested as the explanatory variables of brand awareness among retirees in Southwest, Nigeria. Using a survey research design and Multiple Regression analysis, the three proposed hypotheses were tested. The findings revealed that AI-driven communication variables exert positive and statistically significant influence on herbal drugs brand awareness among retirees. AI-assisted interactive engagement showed the strongest predictive strength. The study concludes that AI-Driven marketing communication significantly affects brand recall and recognition among retirees. It recommends among others that herbal drug firms adopt AI-based personalization tools to improve message clarity, credibility, and audience targeting.

Water is poised to become the most critical natural resource of the third millennium, influencing both economic stability and human survival, particularly as developing nations face rapid population growth. This surge in population and industrial activity is driving up water demand while simultaneously depleting available resources. The resulting gap between high demand and dwindling supply poses significant challenges for water distribution. This study outlines a methodology for forecasting population growth and water demand in the Denpasar area over a ten-year period. Three projection models—linear, quadratic, and exponential—were evaluated to identify the most accurate solution. The findings indicate that the quadratic method provides the most reliable forecast for Denpasar's future water needs. Current projections reveal that existing water infrastructure can only meet 41.63% of total demand. Consequently, it is imperative for the Denpasar municipal government to implement more robust strategies to address this substantial supply deficit

Teacher job satisfaction is a critical determinant of professional commitment, instructional quality, and longterm retention in the teaching profession. In light of increasing concerns about burnout and attrition, there is a growing need to explore internal psychological resources that promote resilience and well-being among teachers. This conceptual paper integrates two significant psychological constructs—grit and emotional intelligence—to examine their potential influence on teacher job satisfaction. Drawing on contemporary educational psychology literature, the manuscript proposes a conceptual framework illustrating the direct and interactive relationships among grit, emotional intelligence, and job satisfaction, while also acknowledging the role of organizational factors. The paper concludes with implications for teacher education, professional development, and future empirical validation through quantitative research designs.

As technology increases everyone wants more features with in a small size electronic gadget, to make them smaller, quicker, more compact and increasing integration density we need to decrease the size of the transistors but due to the decrease in size challenges like power efficiency and heat management becomes major concerns in VLSI design. Traditionally we are using the irreversible gates in digital circuits but during digital operations input information losing which directly contributes the energy dissipation according to Landauer's principle. But Reversible gates make every output corresponds to unique input and prevents the information loss thereby reduces the power dissipation. In this work, we compared a 32-bit Arithmetic Logic Unit (ALU) designed with both irreversible logic gates and reversible ALU constructed with Peres gate. The reversible design has a Quantum Cost of 384 and produces 128 garbage outputs. Both ALUs were coded in Verilog and implemented on Xilinx Artix-7 FPGA. We evaluated performance of ALUs based on theoretical metrics and actual hardware performance metrics. The reversible ALU shows a significant performance by reducing power dissipation to 70mW compared to the 211mW of the irreversible ALU. However, its latency was slightly higher at 22.737 ns compared to irreversible design latency(20.214ns) due to the routing overhead. our analysis indicates that reversible logic is very fast in logic implementation but delay is mainly due to the routing overhead, in this case there were 128 garbage outputs on FPGA. This study demonstrates that reversible logic uses lower energy compared to conventional designs by careful handling of routing and I/O complexity.

Blue Baby Syndrome, medically termed methemoglobinemia, is a clinical condition characterized by a bluish discoloration of an infant’s skin due to reduced oxygen-carrying capacity of blood. The disorder may be congenital or acquired. The acquired form is primarily associated with nitrate contamination in drinking water and food. Infants below one year of age are particularly vulnerable. The present study focuses on nitrateinduced methemoglobinemia in Pakur district, specifically in the Amrapara and Maheshpur blocks. Water samples collected from selected mining areas were analyzed to determine nitrate concentration and assess associated health risks. The findings indicate seasonal variations in nitrate levels, though values remain within permissible limits. Continuous monitoring and public awareness are nevertheless essential to prevent health hazards.

Entrepreneurship is the ability to build a business to generate profit or benefit through expansion and successful management. However, the concept of entrepreneurship has broadened considerably, especially in today's world. It encompasses the ability to initiate any action, create a product, or effect any radical change in society. In other words, entrepreneurship is about doing business and managing it the way you want. Imagine having all the resources but no ability to utilize them! That's exactly what the world would look like without entrepreneurs to undertake projects. There is no doubt that the dream of owning a private project haunts most young people at the moment, but there are a number of pressures and responsibilities that accompany the establishment of the project, and the entrepreneur must possess a set of unique qualities to bear all these burdens. This study of moral courage in management aims to enhance the ability of leaders and employees to make sound decisions and confront unethical behaviors despite the risks, contributing to building an ethical organizational culture, improving service quality, promoting employee voice behavior, and increasing organizational resilience for success in complex business environments. but what exactly are the qualities of a successful entrepreneur?   Is courage the only indicator used to measure a successful entrepreneur?  

Climate change has evolved from an environmental concern into a defining variable of global power politics. The transition toward green technology—renewable energy systems, electric mobility, hydrogen fuel, carbon capture, and critical mineral supply chains—is reshaping the architecture of international relations. This paper examines how technological innovation in climate mitigation has become a strategic instrument of statecraft, influencing global power hierarchies, trade alignments, and diplomatic negotiations. Drawing on a qualitative and policy-analytical approach, the study explores the intersection of green industrial policy, technological competition, and climate diplomacy. It argues that states are increasingly leveraging clean energy leadership not only to meet sustainability goals but also to secure geopolitical influence, economic advantage, and strategic autonomy. The emergence of carbon border adjustments, climate finance mechanisms, and technology-transfer debates illustrates how environmental cooperation is simultaneously a site of rivalry. The paper further analyzes the implications for Global South countries, particularly in terms of access to finance, technology equity, and participation in supply chains for critical minerals. By situating green technology within broader frameworks of international political economy and non-traditional security, the study highlights the dual character of climate diplomacy as both cooperative and competitive. Ultimately, the article contends that the global energy transition is not merely an environmental transformation but a reconfiguration of international power structures, with long-term consequences for global governance, multilateralism, and sustainable development. 

This paper presents a Smart Agricultural Marketplace integrated with machine learning–based price forecasting to assist farmers in making informed selling decisions. The system predicts commodity prices using historical agricultural market data and compares multiple regression models to identify the most effective predictor. Linear Regression and Random Forest algorithms were trained and evaluated using realworld agricultural market datasets. Experimental evaluation shows that the Random Forest model achieves superior performance, obtaining an R² score of 0.9576 with significantly lower MAE and RMSE values compared to Linear Regression. The results demonstrate that machine learning–driven price forecasting can provide reliable decision support and reduce farmers’ dependence on intermediaries.

The persistent reliance on manual Requisition-to-Pay (R2P) processes within Nigerian Federal MDAs has institutionalized transactional friction, manifesting in chronic contractor payment delays and stifled operational liquidity. This study examined the impact of Financial Technology (FinTech) adoption focusing on Electronic Payment and Remittance Systems (EPRS), Digital Supply Chain Finance (DSCF), Blockchain-Based Smart Contracts (BBSC), and Big Data Analytics & AI Risk Assessment (BDAR) on Supply Chain Performance (SUCP) at selected Nigerian Federal MDAs. Using a descriptive survey research design, data were collected from 188 strategic stakeholders across five key organizations (Central Bank of Nigeria (CBN), the Bureau of Public Procurement (BPP), the Federal Medical Centre (FMC), Abuja, the National Health Insurance Authority (NHIA), and the Federal Ministry of Works) via a structured questionnaire, achieving a 78.7% response rate. Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed for analysis. The findings revealed that Big Data Analytics & AI Risk Assessment (BDAR) (β = 0.336, p < 0.001), Blockchain-Based Smart Contracts (BBSC) (β = 0.310, p < 0.001), and Electronic Payment and Remittance Systems (EPRS) (β = 0.173, p = 0.028) significantly and positively affect supply chain performance. However, Digital Supply Chain Finance (DSCF) showed no significant effect (β = 0.139, p = 0.086), suggesting that such financing models are yet to mature within the Nigerian public sector landscape. The study concluded that technological synergy is critical for institutional resilience, explaining 79.2% of the variance in performance. Aligning with Nigeria's Digital Economy Strategy (2020–2030), these findings underscored the urgent need for accelerated FinTech integration in public sector procurement to drive efficiency, transparency, and inclusive economic growth. Recommendations include prioritizing investment in AI-driven predictive analytics and implementing blockchain for automated, immutable contract execution to minimize errors and build trustless systems.

The establishment of a Unified Client Database (CDB) system and the associated ePRO program has dramatically improved the efficiency of benefits administration for organizations. This client-centric approach provided the Technical Lead from the Engineering Group with the ability to create an end-to-end data framework using normalized, metadata-driven design that supports intricate multi-client benefit structures, secured ETL/API connections to ensure seamless integration of enrolment, human resource and claims systems with robust data governance through automated data validation, audit logging and version control. The platform was developed for a cloud-based environment with the use of Redshift to support analytics and AWS S3 to serve as the archive for the repository of data, supported with telemetry dashboards to enable real-time visibility into the health of synchronization and ingestion latency. Implementing the platform has resulted in near real-time downstream integration with the subsequent increase in the timeliness and accuracy of data which has reduced the number of manual reconciliation errors by greater than 25% while improving compliance controls. In alignment with the trend towards the utilization of data-based automation within enterprise EPM Modernization, the CDB platform enables organizations to onboard clients quicker, achieve greater operational transparency and provide a scalable foundation for future cloud services. The next phase of enhancements will seek to evolve the CDB platform into a fully automated cloud-based ecosystem, including developments related to AI-based anomaly detection; enhanced capabilities to expand on serverless microservices; and advanced analytics using Redshift to provide predictive insight regarding benefit-related usage.

Leadership is the process of influencing the activities of an individual or a group towards attaining a specific set of goals. This influence can either be negative or positive depending on the leadership style adopted. This study established the relationship between leadership styles and employee productivity in the manufacturing sector in Malawi, Southern Africa. It used a mixed method approach that included data collected from questionnaires and interviews along with statistical testing using the Spearman’s rank correlation in SPSS. Results showed that in the Malawian manufacturing sector, based on employee perceptions, the most prevalent leadership style was the autocratic leadership style and the least prevalent was the Laissez-Faire leadership style. The democratic leadership was considered the most effective style for enhancing productivity, however, only the persuasive leadership style had a positive significant correlation with employee productivity (rs = 0.554, p < 0.01). It is recommended that organisations should formulate leadership policies that mix leadership styles such as democratic and persuasive leadership to enhance employee productivity. 

Environmental, Social, and Governance(ESG) has come central to commercial strategy worldwide. However, ESG is a framework for business risk management and value preservation rather than philanthropy or activism. This chapter makes the case that ESG is not a moral requirement but rather a tool for lowering risk, safeguarding capital availability, and guaranteeing long-term business sustainability. by combining the concepts of fiscal threat pricing, agency proposition, legality proposition, and stakeholder proposition. This chapter make an attempt to explain how ESG capability reduces functional threat, imporve stakeholder trust, minimize cost of capital, and enhances companies value. Additionally, it looks at ESG challenges as valuation catalysts that lead to unusual returns and heightened perceptions of danger.. Further, it provids a corporate-focused interpretation of ESG issues that is relevant to directors, policymakers, and researchers.

The purpose of the study was to investigate the effect of artificial intelligence (AI) on advancing financial inclusion in Zimbabwean banks through AI credit scoring, chatbots, anomaly detection systems, and predictive analytics. The researchers used a mixed-methods research design which included data collection from 293 respondents who completed the questionnaire and 12 participants who came for the interview.  Artificial intelligence (AI) technology enables loan processing by making financial products more accessible through its three main functions which are usability and safety in transactions and financial literacy training. The researchers found that organisations strongly supported AI systems for decision-making and fraud detection yet users still had concerns about how AI credit assessments and chatbot operates. AI adoption process faces critical obstacles which originate from four sources which are digital illiteracy, poor Internet access, excessive application costs and the rural-to-urban divide. Anomaly detection systems had the most significant impact on financial outcomes since they explain 62.3% of the outcome differences which AI technologies produce. The research found that AI functions as an essential instrument for advancing financial inclusion in Zimbabwe through its ability to enhance banking access and operational efficiency and secure banking services. The study recommended the establishment of more accessible AI systems for decision-making, providing digital literacy programme improvements through regulatory support, and creating special resources for communities that lack essential services. 

This study examines corporate governance in Indian startups with a focus on how governance structures evolve across organizational forms and growth stages within the startup lifecycle. In India, startups may be constituted as sole proprietorships, partnership firms, limited liability partnerships, or companies, each governed by distinct legal frameworks that shape their governance obligations, accountability standards, and transparency requirements. As startups expand, they frequently transition from one organizational form to another to attract investment, manage risk, and enhance credibility. Such transitions create governance challenges relating to compliance, disclosure, decision-making authority, stakeholder protection, and investor confidence. The research, therefore, investigates the suitability of governance mechanisms for each business structure and identifies principles that should guide structural transformations to ensure sustainability and long-term value creation. The study adopts a doctrinal research methodology, relying on statutory provisions, regulatory frameworks, governance codes, committee reports, and academic literature to analyze the legal foundations and theoretical principles governing startup organizations in India. Through doctrinal analysis, it evaluates how governance norms differ across organizational forms and how these norms influence funding access, risk management, leadership accountability, and organizational resilience. The findings indicate that governance is not merely a compliance requirement but a strategic tool that supports innovation, investor trust, and sustainable growth. Early adoption of structured governance practices, even in small startups, enhances transparency and reduces operational and legal risks. The study concludes that differentiated governance frameworks tailored to organizational form and growth stage are essential for startups, and that carefully managed transitions between business structures are critical to maintaining stakeholder confidence and ensuring long-term institutional stability within India’s evolving startup ecosystem.

Fingerprint analysis, a cornerstone of forensic science, relies on the unique patterns of ridges and valleys on the fingertips, known as dermatoglyphics or dactylography patterns. Understanding the variability of these patterns among different population groups is crucial for accurate forensic identification. This study delves into the comparative analysis of dermatoglyphic patterns among interracial population groups, specifically focusing on individuals of African and Indian descent. A diverse sample of 100 typical adolescents, under 45, was randomly selected from various schools within the National Forensic Sciences University in Gujarat, India. This sample comprised 25 males and 25 females from African countries currently residing in India, alongside Indian students. Participants willingly consented to the research, which involved a thorough cleansing process to eliminate external contaminants from their hands. Inkless pads were used to collect fingerprint impressions, and advanced digital proscope technology facilitated the analysis of the ridge characteristic- ridge thickness. The results of the study revealed significant variations in dermatoglyphic patterns between African and Indian population groups.  African females exhibited greater ridge thickness relative to Indian females, with a similar trend observed among African and Indian males. These findings underscore the importance of considering demographic factors in forensic fingerprint analysis, as subtle differences in ridge characteristics can significantly impact identification accuracy. This study contributes valuable insights into forensic science by elucidating the relationship between dermatoglyphic patterns and population demographics. By recognizing and understanding these variations, forensic investigators can improve the accuracy and reliability of fingerprint identification methodologies, particularly when dealing with diverse population groups. Furthermore, the comparative analysis between African and Indian demographics sheds light on the nuanced differences in dermatoglyphic patterns, highlighting the need for inclusive and culturally sensitive approaches in forensic investigations.

Africa's infrastructure projects are often delayed, cost more, and yield unsound outcomes, even with improvements in technical project management. Though soft project management practices are known to enhance project performance worldwide, stakeholder engagement, leadership, communication, and conflict management are not well investigated in the African context. This review aimed to systematise the findings on the applications, mechanisms of implementation, and effects of soft project management practices in African infrastructure projects. The search was made in Scopus, Web of Science, ProQuest, Google Scholar, AJOL and institutional repositories and enhanced with grey literature and citation chasing. The paper included 16 peerreviewed studies according to PRISMA 2020, published between 2015 and 2025. A piloted PICOS-based form was used to extract data and descriptive mapping, thematic analysis and configurational logic to synthesise data to determine patterns of practise and context-specific outcomes. The results show that good soft PM practises, such as participatory planning, formal stakeholder interactions, culturally responsive communication, and leadership, have a positive impact on the project performance in terms of cost, schedule, quality, stakeholder satisfaction, and sustainability. Contextual enablers and barriers, including the quality of governance, political interference, the availability of resources, and cultural norms, are also evident in the context, and they mediate the effectiveness of soft practises. Notably, effective projects tend to use both formal and informal networks and community involvement to maximise the levels of acceptance and delivery of results. This review builds upon the stakeholder theory and views of relational governance by demonstrating how African socio-cultural and institutional contexts inform the adoption and effects of soft PM practise. The paper suggests the institutionalisation of soft practises, managerial capacity development, utilising local networks, and policy alignment to enhance infrastructure project delivery on the continent.

Stepwells are architectural marvels unique to India. Historically, they sustained groundwater, moderated microclimates, and embedded water within cultural landscapes. Hydrological studies have shown that stepwells slow runoff and recharge aquifers; yet, today they are largely treated as heritage structures, sidelined from water policy. Given the climatic conditions of western India, restoring stepwells assumes renewed importance. Case evidence from Rajasthan’s johads (earthen check dams) and lake restoration initiatives in Bengaluru and Hyderabad demonstrates measurable aquifer gains when traditional systems are integrated at the catchment scale. The Sarkhej Roza–Makarba complex in Ahmedabad and the Adalaj Stepwell illustrate both the persistence of heritage value and the missed opportunity for hydrological reintegration, even though many stepwells have lost their functional identity as a result of urbanisation. Recasting heritage water systems as active infrastructure offers a pathway to climate-resilient urban water planning. Drawing on comparative case evidence, governance reviews, and secondary sources, this paper examines how traditional recharge systems can be repositioned within contemporary urban water management.

Bacterial infections obtained in hospitals result in longer hospital stays and higher mortality rates. Antibioticresistant bacteria worsen the issue by impeding or delaying effective therapy. Treatment regimens that use multiple antibiotics may be employed to manage antibiotic resistance and guarantee effective therapy. This includes giving patients medicine mixtures (combination therapy), randomly allocating pharmaceuticals to various patients (mixing), and routinely changing the hospital's default medication from drug A to drug B and back (cycling). The potential of antibiotic combination therapy, mixing, and cycling has been evaluated for over two decades using mathematical models. However, despite trends in their rankings among research, the picture is still shockingly ambiguous and inconsistent, with no clear consensus on which strategy most effectively limits the emergence and spread of antibiotic resistance. In this paper, we examine previous modeling research and illustrate through examples how methodological considerations make it more difficult to produce a coherent image. These elements include the model's implementation and analysis, as well as the selection of the criterion used to compare the protocols' impacts. After that, we talk about how to advance and offer ideas for future modeling paths, emphasizing the need for standardized evaluation metrics, realistic clinical assumptions, integration of empirical data, and interdisciplinary collaboration to better inform evidence-based antibiotic stewardship policies.

The rapid growth of digital technologies has significantly transformed the banking sector in India, compelling financial institutions to redesign their marketing approaches to remain competitive and customer-centric. This study presents a comparative analysis of the digital marketing strategies adopted by HDFC Bank and ICICI Bank, with particular emphasis on customer engagement practices. Both banks are among the leading private sector banks in the country and have made substantial investments in digital platforms to strengthen their market presence and enhance customer interaction. The research examines various components of digital marketing, including social media outreach, mobile banking applications, personalized email campaigns, content marketing, search engine visibility, and the use of data analytics for targeted communication. The study evaluates how these strategies influence customer awareness, satisfaction, loyalty, and overall engagement. Attention is also given to the user experience offered through digital interfaces, responsiveness on social media channels, and the integration of innovative technologies such as chatbots and AI-driven support systems. Primary and secondary data have been considered to understand customer perceptions and preferences regarding digital communication by both banks. The findings highlight differences in strategic focus, content style, platform utilization, and engagement metrics. While one bank may emphasize technology-driven personalization and automation, the other may ocus more on relationship-oriented communication and brand storytelling. The study concludes that effective digital marketing in the banking sector extends beyond promotional activities and plays a vital role in building trust, transparency, and long-term customer relationships. The comparative insights offered in this research may assist banking professionals, marketing practitioners, and researchers in understanding evolving digital engagement trends and in developing more responsive and customer-focused digital strategies.

This paper presents a structured approach to helping G-SIBs, which have been the subject of multiple compliance orders from both the FRB and OCC since 2010, remediate their compliance problems. This will be accomplished under the direction of a dedicated Remediation Program Manager and the establishment of an 11-pillar framework that contains elements from the FRB SR08-08 policy statements to support a comprehensive approach to sustainability within the areas of risk management, data quality, and compliance oversight, along with enhanced governance and cross-functional collaboration.  The methods employed to develop an effective program to remediate regulatory compliance issues included the use of a risk-based triage approach to the prioritization of compliance issues, advanced data analytics to conduct root cause analysis on unresolved issues, and the implementation of a hybrid execution model that employed both Agile and Waterfall methodologies, which resulted in the significant reduction of issues associated with high-risk areas of exposure and improved closure rates. By 2018, the banks had remediated all outstanding regulatory orders from both the FRB and OCC preventing the imposition of substantial fines and ensuring compliance with all regulatory requirements. The overall data architecture that was proposed for reporting compliance remediation results is comprised of a core repository of data and analytical dashboards to monitor compliance status. Future enhancements to the central repository and dashboards will be developed using a cloud-native RegTech and AI/ML-based solution to provide a more proactive solution for addressing complex regulatory compliance issues in the ever-evolving environment of public policy.

The food supplements industry, as well as, the wellness trend is undergoing a tremendous growth. In case you are seeking to sell in such space or make policy likewise direction, it is relevant to possess a feel of what actually sparks the buys. In practice, it is crucial. We were not keen on being confined to such general beliefs and ways that we could have known what is a major role, the demographic factors like age and gender, the facility of the real purchase of the product and effect of the own state of health. A survey that was well structured gathered information on 143 individuals and the information was analyzed using SPSS. The analytical methods were ANOVA, reliability test, descriptive statistics, independent samples t-tests and multivariate regression analysis. This is more or less the complete tract of such buying behavior study of analysis. A single dominant pattern emerged which was that the first age was not a simple variable, but the most predictive of whether an individual would intend to buy supplements. The gender was also originating to be important and this confirms that marketing is not about a one size fits all strategy. This is a fine point but pre-existing medical conditions had it that the former would be more reliant on the advice of a doctor in making the purchase as expected. Notably, they did not demonstrate a greater purchase intention much higher than no conditions. This means that the presence of a condition renders the buying process to be more cautious and advice based process as opposed to being an eagerness based process. The majority of the cases depended on the fact that a single largest predictor was accessibility. Non price, but just first, non-brand. How convenient will it be to me to find and get this product? This fact leads to logistics and distribution.

This study investigates the empirical convergence thresholds of the Gaussian Estimation Procedure (GEP), Generalised Least Squares (GLS), and Exact Maximum Likelihood (EML) for ARMA processes. While large-sample asymptotic equivalence is theoretically established, the specific data requirements for numerical reconciliation in higher-order models remain under-researched. Using a generalised Fibonacci-based sampling recurrence  to determine the non-linear sample interval from  to , estimator stability across six distinct data-generating processes was evaluated. The findings demonstrated a ‘complexity-dependent convergence’: while lower-order processes achieved numerical reconciliation at , higher-order ARMA (2,2) specifications require  to achieve harmonization. These results identify a critical transition zone where estimator choice become neural, providing a structural blueprint for selection based on modal dimensionality and available sample size.    

Land Use and Land Cover (LULC) classification plays a crucial role in remote sensing applications such as urban planning, environmental monitoring, agricultural analysis, and climate studies. Recent advances in deep learning, particularly convolutional neural networks (CNNs), have significantly improved classification accuracy for satellite imagery. This thesis presents a comparative study of two deep learning approaches for LULC classification using the EuroSAT dataset: a convolutional neural network trained from scratch and a transfer learning model based on a pre-trained VGG-19 architecture. The EuroSAT dataset consists of Sentinel-2 satellite images categorized into ten land cover classes. Experimental results demonstrate that transfer learning achieves superior classification performance compared to training a CNN from scratch, highlighting the effectiveness of pre-trained models for remote sensing image analysis.

A series of novel chalcone derivatives were synthesized via Claisen–Schmidt condensation and evaluated through spectroscopic characterization and molecular docking studies. The synthesized compounds were characterized using Fourier Transform Infrared Spectroscopy (FT-IR), Proton Nuclear Magnetic Resonance (¹H NMR), and Mass spectroscopy. Molecular docking analysis was performed to evaluate binding interactions with the Epidermal Growth Factor Receptor (EGFR), a validated anticancer target. The docking results revealed favourable binding affinities ranging from –7.5 to –9.2 kcal/mol, supported by hydrogen bonding and hydrophobic interactions within the active site. The findings suggest that the synthesized chalcone derivatives possess promising anticancer potential and warrant further biological evaluation.

Adolescent anxiety is increasingly recognized as a significant psychological concern affecting academic functioning and emotional well-being. The present study examined anxiety levels among higher secondary students and investigated whether gender and locality differences exist using a 2×2 factorial design. The sample consisted of 40 students (10 boys and 10 girls from urban schools; 10 boys and 10 girls from rural schools) selected through random sampling from Jammu district. Anxiety was assessed using a standardized self-report inventory. Data were analyzed using two-way Analysis of Variance (ANOVA). Results revealed no statistically significant main effects of gender (F = 0.021, p > .05) or locality (F = 0.91, p > .05), and no significant interaction effect between gender and locality (F = 1.92, p > .05). Findings suggest that adolescent anxiety in this sample appears to be a generalized developmental phenomenon rather than one determined by demographic factors. Implications for universal school-based mental health interventions are discussed.

Kenyan public hospitals face persistent operational performance challenges arising from medicine stock-outs, replenishment delays, and service delivery inefficiencies linked to weaknesses in inventory management and underperforming public healthcare supply chains. This study examined the moderating role of supply chain performance on the relationship between inventory management practices and operational performance of public hospitals in Kenya, using evidence from Siaya County. Inventory management practices were operationalized through Just-in-Time (JIT) replenishment, Inventory Categorization, and Demand Forecasting, while supply chain performance captured supplier responsiveness, delivery reliability, and procurement coordination. The study was anchored on the Resource-Based View and Network Perspective Theory, which explain how internal inventory capabilities and inter-organizational supply chain relationships jointly influence organizational performance. A cross-sectional survey design was adopted. From a target population of 106 hospital personnel, a sample of 84 respondents was selected using the Yamane formula, with stratified proportionate and simple random sampling techniques applied. Primary data were collected using structured questionnaires and interview guides. Reliability was confirmed using Cronbach’s alpha (α ≥ 0.70), and validity was established through expert evaluation. Descriptive statistics summarized the data, while hierarchical regression analysis tested direct and moderating effects. Results revealed that inventory management practices had a positive and statistically significant effect on operational performance (F-statistics (1, 79) was 12.631, p < 0.002). Further, supply chain performance significantly moderated this relationship, such that hospitals with stronger supplier responsiveness and delivery reliability experienced greater operational gains from effective inventory management. The study concludes that improving supply chain performance strengthens the impact of inventory management practices on hospital operational outcomes, including medicine availability, reduced stock-outs, and service delivery efficiency. It recommends that public healthcare systems strengthen supplier integration, procurement coordination, and replenishment reliability to enhance the effectiveness of inventory management and optimize hospital performance.

Operational performance in Kenyan public hospitals remains a persistent managerial and policy concern, particularly due to frequent medicine stock-outs, delayed replenishment cycles, and service delivery inefficiencies. These challenges are closely associated with weak supply chain responsiveness within public healthcare procurement and supplier systems. This study examines the relationship between supply chain responsiveness, operationalized through Just-in-Time (JIT) inventory practices, and operational performance in public hospitals in Siaya County, Kenya. The study is anchored on the Resource-Based View (RBV) and Network Perspective Theory, which explain how internal replenishment capabilities and inter-organizational supply chain relationships jointly influence hospital performance outcomes. A cross-sectional survey research design was adopted involving 84 respondents drawn from procurement, pharmacy, stores, and hospital administration departments across six public hospitals. Data were collected using structured questionnaires and interview guides. Reliability testing yielded Cronbach’s alpha coefficients exceeding 0.80, while the content validity index exceeded 0.70, confirming the robustness of the measurement instruments. Descriptive statistics summarized institutional practices, while correlation and regression analysis examined relationships between variables. The findings reveal a strong and statistically significant positive relationship between Just-in-Time inventory practices and operational performance (β = 0.738; p < 0.05). The regression model explains 79.1% of the variation in hospital operational performance (R² = 0.791), indicating that responsive replenishment practices significantly contribute to improved medicine availability, reduced stock-outs, and enhanced service delivery efficiency. The study concludes that strengthening supply chain responsiveness through responsive procurement systems, flexible supplier relationships, and shortened replenishment lead times is essential for improving operational outcomes in Kenyan public hospitals. Policy recommendations emphasize the need for improved coordination between hospitals and national medical supply agencies, enhanced procurement flexibility, and the integration of digital logistics management systems to strengthen healthcare supply chain responsiveness.

Operational performance within Kenyan public hospitals has become an issue of growing public and policy concern, particularly in the context of frequent medicine stock imbalances and service delivery inefficiencies. Persistent challenges in balancing drug overstocking and stock-outs point to weaknesses in demand planning within hospital supply chains. This study examines the relationship between demand forecasting practices and operational outcomes in public hospitals in Kenya, using evidence from Siaya County. The research is anchored on the Resource-Based View (RBV) and Network Perspective Theory to explain how internal forecasting capabilities and inter-organizational supply chain relationships influence hospital performance. A cross-sectional survey design was employed involving personnel drawn from procurement, pharmacy, stores, and administrative departments across six public hospitals. Data were analyzed using descriptive statistics, correlation analysis, and linear regression modelling. The findings reveal a strong and statistically significant positive relationship between demand forecasting practices and operational outcomes (β = 0.876, p < 0.05). The model explains approximately 70.1% of the variation in operational performance (R² = 0.701). These results suggest that hospitals that institutionalize structured forecasting practices within their supply chain systems achieve improved operational efficiency and enhanced service delivery outcomes. The study concludes that strengthening forecasting capabilities is critical for improving drug availability and reducing service disruptions in public healthcare systems. The paper recommends adoption of data-driven forecasting approaches, strengthened supply chain coordination, and integration of digital health logistics systems to enhance healthcare service delivery in Kenya.

This study evaluates the geotechnical characteristics of soils obtained from two landfill sites in Selangor, Malaysia, namely Tanjung Dua Belas and Air Hitam, to determine their suitability as compacted landfill liner materials. An integrated laboratory investigation was conducted to determine key physical properties, compaction behaviour under varying energy levels, consolidation characteristics, and stress-dependent hydraulic conductivity. The results reveal clear differences in engineering performance between the two soils. The Air Hitam soil achieved higher Maximum Dry Density (MDD) values ranging from 1.832 to 1.987 g/cm³ at lower Optimum Moisture Contents (OMC) between 11.92% and 14.81%, demonstrating more efficient particle packing and compaction response compared to the Tanjung Dua Belas soil, which recorded MDD values between 1.410 and 1.565 g/cm³ with higher OMC ranging from 18.56% to 22.56%. Consolidation analysis further indicated lower settlement for Air Hitam (1.401 mm) relative to Tanjung Dua Belas (1.583 mm), reflecting improved stiffness and reduced compressibility. Hydraulic conductivity decreased with increasing applied stress for both soils, with Air Hitam reducing from approximately 3.07 × 10⁻⁸ cm/s to 1.47 × 10⁻⁸ cm/s, while Tanjung Dua Belas decreased from 4.19 × 10⁻⁸ cm/s to 2.90 × 10⁻⁸ cm/s. The lower permeability and denser soil structure observed for Air Hitam indicate improved resistance to leachate migration under landfill loading conditions. Overall, the results demonstrate that soil physical characteristics strongly influence compaction, consolidation, and permeability behaviour, with the Air Hitam soil showing comparatively superior suitability for engineered landfill liner applications.

Silicon-based power semiconductors are starting to show their limits as high-power, high-frequency electronics get more common in things like electric cars, renewable energy, data centers, and phone networks. Silicon just can't handle the high voltages, quick switching, and heat that these systems need. Wide bandgap (WBG) semiconductors look like a solution because they handle electricity and heat a lot better. This paper takes a look at silicon carbide (SiC) and gallium nitride (GaN) semiconductors and how they can power the next wave of electronics. We'll check out what makes them work well, like how much energy it takes to get electrons moving, how much electric field they can take, how fast electrons move through them, and how well they conduct heat. We'll also break down how SiC MOSFETs and GaN HEMTs work, looking at their designs. SiC devices are great for high-voltage, high-power jobs, while GaN devices shine in systems that need high frequency and pack a lot of power into a small space. Recent tests show SiC MOSFET converters hitting 98–99% efficiency, and GaN converters running at over 500 kHz with power densities over 50 kW/L [14–16]. We'll also look at where these materials are being put to work, like in electric car motors, renewable energy systems, power supplies, and phone networks, to show why they matter to industry. The paper also covers some of the current issues, like defects in the materials, how reliable the devices are, how tricky they are to make, and how much they cost. Besides that, we'll peek at some upcoming ultra-wide bandgap materials like gallium oxide and diamond, which might be used for super-high-voltage stuff in the future [19–20]. All in all, WBG semiconductors are going to be key in making power electronics more efficient, smaller, and able to handle heat in future energy and communication systems.

Deep water high-pressure, high-temperature (HPHT) drilling in managed pressure drilling (MPD) offers great challenges because of very thin changes between pore pressure and fracture gradient, frequently below equivalent mud weight of 0.3 ppg. Traditional rule-based MPD tuning algorithms fail to hold optimal setpoints in real-time resulting in influx/loss events and inefficient rate of penetration (ROP). This paper introduces a new reinforcement learning (RL) model of intelligent MPD set-point control, which is more specifically backpressure and equivalent circulating density (ECD) optimization in narrow-margin deepwater wells that characteristic of the Gulf of Guinea area, such as Ghana and Nigeria. A Deep Deterministic Policy Gradient (DDPG) model was optimized using past MPD operational data in West African deepwater campaigns which included a multi-objective reward function balancing between influx risk, loss risk, and ROP optimization. The paradigm shows that the pressure control accuracy (23% improvement over rule-based approaches) and mean absolute pressure deviation (42 psi to 32 psi) are lower in comparison to rule-based approaches. Moreover, the model had an average increase of 15 percent in the average ROP and was able to keep wellbore stable despite the thin drilling window. The intelligent control system detected and reacted to simulated kick situations 18 seconds earlier than the traditional automated MPD systems, which is a giant development in real-time well control capacity. The above results imply that the operational benefits of RLbased MPD control are enormous in strenuous deepwater HPHT drilling operations, and could be applicable to the whole range of the Gulf of Guinea deepwater drilling ventures.

Smart grids improve electricity management and ensure efficient power distribution, but they are highly vulnerable to cyber attacks such as false data injection, denial-of-service, and replay attacks. These cyber threats can disrupt power supply and compromise critical infrastructure. To address this issue, this project proposes an intelligent cyber threat detection system using classification algorithms such as K-Nearest Neighbors (KNN), Decision Tree, Support Vector Machine (SVM), and Random Forest. To further enhance performance, optimization techniques including Genetic Algorithm, Grid Search, and Particle Swarm Optimization (PSO) are applied for feature selection and hyperparameter tuning. The system is trained and tested on benchmark smart grid datasets to ensure realistic evaluation. Experimental results show that optimized models significantly improve detection accuracy and reduce false alarms, providing a reliable and efficient solution for securing smart grid environments.

Malaysia, like many other countries, has experienced several corporate scandals that have raised concerns regarding the credibility and integrity of financial reporting. The study examines whether audit quality plays a key role in reducing the impact of management’s entrenchment activities, increases the credibility of the financial report, hence improving the overall firm performance. The data are derived from 540 firm year observations collected over five years (2019-2023). To analyse the data, the Panel Corrected Standard Error is used. The findings show that firms audited by big4 together with the existence of ownership structure (family, government and foreign) perform better. This result indicated that external audit quality is an effective corporate governance mechanism that is likely to monitor family, government and foreign corporate decisions. The result gives understanding to investors, regulators, and financial analysts that the moderating effect of audit quality on the ownership structure would strengthen the corporate board monitoring and deter management from engaging in perpetual unethical practice and improve the overall firm’s performance.

The development of efficient nano‑based drug delivery systems remains a critical strategy for improving the stability, bioavailability, and therapeutic performance of plant‑derived bioactive compounds. In this study, methanolic root extract of Imperata cylindrica was synthesized, characterized, and nanoencapsulated using chitosan as a biodegradable and biocompatible polymeric carrier, with the objective of enhancing its suitability for wound‑healing and hemostatic drug delivery applications. Qualitative and quantitative phytochemical analyses revealed the presence of alkaloids (5.4%), tannins (5.7%), flavonoids (10.3%), saponins (2.25%), and terpenoids (43.3%), which are compounds commonly associated with anti‑inflammatory, antimicrobial, and tissue‑repair activities. Nanoencapsulation was achieved via chitosan‑assisted precipitation, and the resulting nanoparticles were characterized using Fourier transform infrared spectroscopy (FTIR), ultraviolet–visible (UV–Vis) spectroscopy, transmission electron microscopy (TEM), and X‑ray diffraction (XRD). FTIR spectra confirmed the successful incorporation of the extract within the chitosan matrix through the presence of characteristic O–H, N–H, and C=O functional groups without structural degradation. TEM analysis revealed predominantly spherical, mesoporous nanoparticles with particle sizes ranging from 2 to 50 nm and average diameters between 12.66 and 17.98 nm. UV–Vis spectroscopy demonstrated a hypsochromic shift in absorption maxima from 550 nm for the free extract to 375 nm for the nanoencapsulated formulation, indicating improved molecular dispersion and an encapsulation efficiency of 31.81%. XRD analysis revealed crystalline phases containing mineral oxides relevant to biological and pharmaceutical applications. Overall, the chitosan‑based nanoencapsulation of Imperata cylindrica root extract demonstrates significant potential as a natural product‑derived drug delivery system for topical wound‑healing and hemostatic applications, aligning with current advances in polymeric nanoparticle‑mediated drug delivery.

The Atmospheric Boundary Layer (ABL) plays a crucial role in regulating weather, climate, and air quality, particularly over urban regions. This study investigates the temporal and seasonal variability of Atmospheric Boundary Layer characteristics over New Delhi during the period 2001–2010 using ERA-5 reanalysis data. Key parameters such as Boundary Layer Height (BLH), surface temperature, and wind speed are analysed on annual and seasonal scales. The results reveal pronounced seasonal variations, with maximum BLH observed during the pre-monsoon season and minimum values during winter. Inter-annual variability highlights the influence of surface heating and urbanization on boundary layer development. The findings provide insight into urban boundary layer dynamics and their implications for air pollution dispersion over New Delhi.

Recognition of Prior Learning (RPL) is a key strategy for validating skills acquired through non-formal learning by formal assessment. In India, RPL has been positioned as important milestone under the Skill India Mission to enhance employability, social inclusion, lifelong learning and certification by recognising the skill earned by the vocational workers. However, despite a strong policy framework, the implementation of RPL faces persistent challenges such as low awareness, limited worker preparedness, assessment anxiety, certification and career progression. This article argues that these challenges are largely developmental rather than procedural and can be minimized through structured frame work interventions. The present study conducted among the vocational workers in the construction sectors by using structured questionnaire. The major objective focused for the research is does the sample respondents are interested in getting formal certification for their learned skill through non formal and how they perceive the present career. 

In recent years, the automobile industry has changed significantly due to new technology, shifting lifestyles, and easier access to vehicle financing. As a result, it is important for manufacturers and marketers to understand how consumers make buying decisions. This study looks at the differences between first-time and repeat buyers in the non-commercial four-wheeler market, focusing on their satisfaction after purchase and the challenges they face during the buying process. The research uses a descriptive and comparative approach and focuses on consumers in Ayodhya district, Uttar Pradesh, India. In this study, primary data were collected using a structured questionnaire given to automobile owners, resulting in 149 responses through convenience sampling. The study uses statistical methods like regression analysis, ANOVA, and independent sample t-tests to explore the link between buyer type and purchase experience. The results show a strong connection between buyer category and purchasing behavior. The regression model explains much of the variation, ANOVA confirms the model is statistically significant, and t-tests show a clear difference between first-time and repeat buyers in the challenges they face during the purchase process. The results show that first-time buyers have more trouble with information search, comparing models, making financing decisions, and negotiating with dealers. In contrast, repeat buyers benefit from their previous experience and knowledge of the automobile market. The study highlights the need to understand these differences to improve marketing strategies, increase customer satisfaction, and build long-term relationships in the competitive automobile industry.

Artificial intelligence (AI) is rapidly advancing and being applied across many domains, including human resource management (HRM), are rapidly advancing and applying artificial intelligence (AI). Generative AI that can produce human-like content has the potential to transform HRM practices, especially in developing nations with talent shortages. This paper evaluates the potential effects, risks, and benefits of using generative AI in HRM in the context of developing nations. A mixed methods approach combines literature review and case studies to assess impacts on recruitment, talent development, retention, and other HRM functions. Findings suggest generative AI could improve access to talent and skills development while requiring adjustments to evaluate AI-generated content. Risks around data bias and security would need mitigation. HRM professionals are cautiously optimistic about AI's potential but emphasize the importance of human oversight. Developing nations could benefit from AI in HRM but should proactively develop policies to govern ethical AI use. Further research is needed to develop best practices as adoption accelerates.

This study examines the complex issues faced by rural consumers in the Kumbakonam area of Tamil Nadu through an empirical investigation. By gathering primary data from rural households using a structured questionnaire, the research examines the economic, informational, infrastructural, digital, and market-related barriers that influence consumer behavior. Descriptive and inferential statistical methods, such as mean, standard deviation, ANOVA, Multivariate analysis, and Structural Equation Modeling (SEM), are utilized to examine the relationships among key constructs. The findings reveal that information asymmetry, price sensitivity, restricted market access, and digital exclusion have a significant impact on the purchasing choices of rural consumers. This study provides region-specific insights into the rural marketing literature and offers practical recommendations for policymakers, marketers, and educational institutions to enhance rural consumer empowerment.

Rice (Oryza sativa L.) is an important crop of our country, and its yield loss may be caused by insect pests (Kumar et al., 2015). The rice leaf folder pest is of major concern among rice pests (Kushwaha, 2004). An attempt was made to evaluate the efficacy of botanical extracts in the control of rice leaf folder infestation during the Kharif season at Dhadgaon, Nandurbar District, Maharashtra. The experiment was laid out in RBD with 5 treatments and 3 replications: Neem Seed Kernel Extract (NSKE) 5%, garlic extract 5%,  Chilli extract 5%, a chemical check, and an untreated control.  Botanicals were applied at 30 days, 45 days, and 60 days after transplanting (DAT). All treatments were found significantly superior in respect of the reduction of leaf damage and larval population as compared to the control.  Botanical Neem seed kernel extract was found to be the most effective, with minimum leaf damage (11.2%) and a larval population (1.30), followed by garlic and chilli extracts. The maximum efficacy was observed in the chemical check. The findings revealed that Neem seed kernel extract was found to be most effective in reducing leaf damage and larval population. It can be recommended for incorporation in IPM in rice for the control of leaf folder.

In this study we developed an integrated inventory model to analyse how Indian perishable product influence Selling Price, Freshness, Inventory Levels, Advertising Frequency on Demand and Cost Structures of Perishable Products in India. We take a price dependent demand function that is influenced by selling price, freshness, inventory levels, and advertising frequency. The main objective of this study is to check how these key parameters affect the demand and cost structure of perishable products in Indian markets. We perform a numerical and sensitive analysis based on Indian conditions to understand how these key variables affect the demand and cost of perishable products in India. A numerical example is performed with the help of MATLAB.

The proposed paper suggests an Intelligent Decision Support System (IDSS) to support the management of healthcare resources with the help of a Hybrid AI Architecture based on the fusion of Machine Learning (ML) and Knowledge Graphs. The system also seeks to streamline resource allocation i.e. bed allocation, staff scheduling and equipment allocation through predictive analytics and real time decisions. The key tools that we use in this approach are Azure AI and Azure Knowledge Graph. The machine learning of Azure AI is employed to create models that predict patient demand and resource needs, and the Azure Knowledge Graph organizes and combines heterogeneous healthcare data, i.e., patient history, diagnosis and resource availability. Moreover, Retrieval-Augmented Generation (RAG) is being introduced to offer live data based and predictive model recommendations. This integrated solution guarantees scalable, adaptive, and transparent decision-making and eventually, the efficiency of healthcare, lowers cost, and improves the quality of care using smart and data-driven resource management.

Background: India's healthcare AI landscape is rapidly evolving, yet a critical infrastructure gap persists: the absence of a sovereign, interoperable benchmarking platform for systematic validation of AI models against clinically representative datasets. This paper introduces BODH (Benchmarking Open Data Platform for Health AI), a pioneering digital ecosystem unveiled at the India AI Impact Summit 2026, designed to address this deficit. Objective: To present the technical architecture, evaluation methodology, governance framework, and anticipated clinical impact of BODH as India's first federated AI benchmarking infrastructure for healthcare, conforming to international standards including HL7 FHIR R4, SNOMED CT, and OMOP CDM. Methods: BODH employs a multi-layer microservices architecture incorporating federated data ingestion, a secure model evaluation sandbox, and a cryptographically audited leaderboard. Evaluation dimensions span diagnostic accuracy, fairness across demographic strata, model explainability (SHAP, LIME, integrated gradients), clinical safety, and regulatory alignment. Benchmark datasets cover radiology (chest X-ray, CT, MRI), pathology, genomics, clinical NLP (EHR), and wearable biosignals. Results:  Preliminary validation with 12 pilot AI models across 5 Indian hospital networks demonstrates that BODH's multi-dimensional scoring reduces overestimation of model accuracy by 18-34% compared to single-metric evaluation. Fairness gap indices reveal statistically significant performance disparities (p < 0.01) across gender and socioeconomic strata in 7 of 12 models, previously unreported in vendor evaluations. Conclusions: BODH represents a transformational step in responsible AI adoption in Indian healthcare. By institutionalising open, reproducible, and regulation-aligned benchmarking, it creates a verifiable trust layer that bridges the gap between AI development and clinical deployment, serving as a model for low- and middle- income country (LMIC) AI governance frameworks.

This paper will describe the way in which UNIFY has successfully integrated multiple technologies, including SimCorp Dimensions, Informatica PowerCenter ETL, Snowflake Cloud-based Data Warehouse, as well as IDL's Data Hub orchestration (or ETL), in order to convert a disparate and fragmented Asset Management platform into a truly cloud-native architecture. Through the use of Phased ETL Strategies and Agile/Scrum governance, UNIFY addresses many of the common challenges associated with legacy systems, including Fragile Integrations, Insufficient Data Sources, and High Maintenance Costs. Additionally, the UNIFY design has standardised data flows, provided for Very High Availability and enabled Seamless Migrations. Performance Studies conducted by the UNIFY development team indicate a marked improvement in overall efficiency, including Significant reductions in Query Costs and Increased Cache Hits; additionally, UNIFY has identified optimization opportunities using its Cost Dashboard. Future efforts will focus on the adoption of advanced technology, including GraphQL Federation, Real-Time Streaming, and AI-Driven Operations, as well as additional opportunities to improve scalability and reduce costs. Overall, the design created by UNIFY establishes a New Industry Standard for IT Transitioning of Asset Management platforms, while ensuring Compliance with Regulatory Requirements and Eliminating Legacy Silos.

India’s citrus peel exports from 2014 to 2024 have substantial declining trends, as shown by CAGR and linear regression analysis. The data reveal negative CAGRs of (-4.52%) for export volume and (-3.17%) for export value. Projections suggest that if these trends persist, export value may hit a low point by 2028. Despite an annual citrus production of approximately 16.8 million tons, exports are constrained by high post-harvest losses (up to 30.7%), elevated shipping costs (2.5 to 3 times higher), strong domestic demand (75%) and COVID-19 pandemic has undergone a gradual but impactful by 20% of the amount received in the lockdown indicated that even with high demand in the world, India was unable to effectively get its fruit to the borders. Climate issues, such as fruit splitting and pests, further diminish peel quality and export potential justify the decline of trends in citrus exports in India.  Improving storage infrastructure, pest control, and value-added processing are crucial steps to enhance export performance.

Liver disease is a significant clinical problem on a global scale and a leading cause of morbidity and mortality. Early and prompt diagnosis is essential to successful management, but the traditional diagnosis methods tend to require invasive procedures, or they are restricted by the limitations of varying observers. In recent years, the use of artificial intelligence (AI) and computer-aided diagnosis (CAD) systems has acquired significant popularity in the field of hepatology. Machine learning (ML) and deep learning (DL) technologies are actively used in medical imaging data, including ultrasound, CT, and MRI, to assist clinicians in identifying and classifying liver pathologies, i.e., fatty liver disease, cirrhosis, and hepatocellular carcinoma. The review is a synthesis of the recent developments in image processing, feature detection, and classification algorithms of liver disease diagnosis by AI. The most common ML algorithms include Support Vector Machines, random forests, decision trees, naive bayes, and K-nearest neighbors, in many cases using radiomic features derived using imaging data. Although deep learning models, especially convolutional neural networks and transfer learning implementations, are highly sensitive and highly perform in segmentation and classification tasks, traditional ML systems with radiomic features are frequently able to offer robust and efficient solutions to resource-bound environments. Even though these results are promising, there are still a number of challenges such as data heterogeneity, insufficient multi-center validation, and model interpretability. To reliably translate clinical findings into clinical practice and enhance patient outcomes, future research should focus on large-scale validation studies, multimodal data integration, and explainable AI frameworks.

In ferro-cement, wire-meshes are filled with cement mortar. It is      a composite, formed with closely knit wire mesh tightly wound round skeletal steel. Study was conducted with respect to application of ferro-cement for water trough construction which could be utilized for drinking water purpose for cattle’s and other domestic animals. Mixed design of cement and sand ratios 1:1, and casting of ferro-cement water trough was hand plastering. Water trough capacity was 170 litres which will be suitable for cattle, bullock and other domestic animals. Water requirement of animals was recorded as: cow (40-45 litres), bullock (45-55 litres), buffalo (45-50 litres). So, water trough is suitable for herd of 4 cows /buffalos/bulls. The cost required per ferro-cement water troughs was estimated as Rs.1230/-

This paper presents an innovative five-layered architecture for finding equitable representation across the various fragmented sections of large datasets. The architecture employs a set of customizable criteria to group together similar datasets into well-balanced unbiased sample sets; while retaining complete and accurate metadata for all file boundary elements. Important features of this architecture include: a multi-level criteria engine which is capable of processing accepted and rejected streams from a variety of sources; a stratified sampling process which effectively eliminates the presence of partition skew; and a quality assurance mechanism that provides an efficient means to capture and report performance-related metrics. Benchmark partitioning results indicate a significant shift toward overall balance ratio improvements; with improvements of approximately 1.75 to 1.12 ratio, as well as an overall reduction in volume anomaly of 87% (from ±25% down to ±3.2%), and total absence of schema drift occurred at a cost of less than 5% of the overall dataset processing costs to the author(s). By implementing this method, the processing of large datasets has been made much more reliable than previously possible, while simultaneously minimising bias and maximising the ability to provide efficient audit trial capabilities. Future enhancements to the architecture will focus on providing distributed execution capabilities in conjunction with streaming integration into enterprise data systems.

This study examined corporate governance mechanisms and sustainability reporting practices of listed manufacturing firms in Nigeria. The study adopted a correlational and causal research design, the study analyzed secondary data from the annual financial statements and sustainability reports of 27 manufacturing firms listed on the Nigerian Exchange Group (NGX) over a fifteen-year period (2010–2024). Descriptive statistics were employed to summarize the characteristics of the variables, while Pearson correlation analysis was used to assess the strength and direction of associations among them. For inferential analysis, the system Generalized Method of Moments (GMM) estimator was applied to account for endogeneity, firm-specific effects and dynamic persistence. The study found that board director nationality has a significant positive effect on sustainability reporting, suggesting that internationally diverse boards enhance ESG disclosure practices. In contrast, board director reputational capital did not significantly influence sustainability reporting. The findings underscore the importance of diverse and internationally experienced boards in promoting transparency and accountability in sustainability reporting among manufacturing firms in Nigeria. This paper also contributes to existing knowledge in the field of sustainability reporting by looking at manufacturing firms in Nigeria. Based on these results, the study recommends that firms encourage board nationality diversity and establish structured ESG governance frameworks to enhance the quality and credibility of sustainability reporting.

Pili (Canarium ovatum) is a tropical nut crop valued for its oil-rich pulp with applications in food, cosmetic, and pharmaceutical industries. Traditional extraction methods are labor-intensive and produce inconsistent oil quality. This study developed and evaluated a novel electrically driven mechanical extraction system specifically designed for pili pulp, incorporating optimized screw geometry and an integrated post-processing workflow. The prototype features a low-shear screw conveyor, food-grade contact materials, and inline filtration, distillation, decolorization, heating, and cooling modules. Optimal performance was achieved at 20 kg batch size, 20 rpm operating speed, and 20 minutes extraction time, producing 7.1 liters of oil (58.36% yield). Oil analysis showed desirable physical properties (specific gravity 0.89), chemical stability (peroxide value 5.4 meq/kg), and high carotenoid content (56.8 mg/100 g). Compared with manual and village-level presses (Table 5), the prototype improved yield by 4–5× and reduced extraction time by up to 75%. Economic analysis showed a 96% rate of return and 2.4-month payback period. Sensitivity analysis confirmed profitability under varying price, labor, and throughput conditions. The developed system provides a scalable and economically robust solution for standardized pili pulp oil production.

In this present situation, physical appearance is often balanced with self- perception, creating a challenging environment for Persons with Disabilities (PWDs) whose physique may not confirm to ableist standards. When real-life validation is seen low or negative, internal cognitive processes specifically self-talk become a critical determinant of mental well-being. This study explores how positive self- talk influences body dissatisfaction among persons with physical and sensory disabilities. Using a quantitative research design, data were collected from 50 participants (N =50) aged between 18 to 35 through purposive sampling method. Participants completed standardized self- report questionnaires measuring Positive Self-Talk (PSTS) and Body Dissatisfaction (BIS), with all responses kept confidential and collected under strict ethical guidelines. Preliminary findings suggests that individuals who engage in frequent positive self- talk experience significantly lower levels of body dissatisfaction (r = −0.72). Furthermore, comparative analysis indicates that the individuals with physical disabilities report higher dissatisfaction when compared to those with sensory disabilities. Framed within Cognitive Behavioral Theory, this study highlights how the human need for self- acceptance persists even when physical functionality is compromised, often finding expression through internal dialogue.

Greenhouse cultivation requires continuous monitoring of environmental and soil conditions to support plant growth. Manual monitoring can be time-consuming and may not ensure consistent control. This study developed and evaluated an automated miniature greenhouse system that monitors environmental parameters and activates devices based on preset thresholds. The system used an Arduino Uno microcontroller integrated with soil moisture, soil temperature, DHT11, LDR, and MQ gas sensors. Relay modules controlled a ventilation fan, water pump, and artificial light. Solar energy was used to support the microcontroller power supply. System evaluation included calibration testing, three-trial sensor accuracy testing, and seven-day operational monitoring. Calibration results confirmed correct wiring and proper communication between components. Accuracy testing showed consistent readings across three trials: temperature at 26°C, humidity between 54% and 55%, soil moisture averaging 28.82%, carbon dioxide at 8130 ppm, methane at 0 ppm, and light intensity averaging 31 lux. Actuators operated according to programmed conditions. During the seven-day monitoring period, temperature ranged from 25–27°C, humidity from 53–61%, and soil moisture changes triggered water pump activation on Day 7. Increased light intensity on Day 3 activated the artificial lighting system. A soil temperature sensor error was observed and requires correction. The results indicate that the system can monitor environmental conditions and perform automated control within the set parameters.

The integration of Artificial Intelligence (AI) into Human Resource Management (HRM) represents a paradigm shift in organizational strategic planning and workforce management. This conceptual review examines the transformative role of AI-enabled HRM systems in strategic contexts, synthesizing current literature on implementation frameworks, organizational performance impacts, and emerging challenges. Through systematic analysis of recent empirical studies and theoretical frameworks, this paper explores how AI technologies including machine learning, predictive analytics, and natural language processing are revolutionizing core HR functions such as recruitment, performance management, talent development, and workforce planning. The review identifies significant positive relationships between AI-driven HRM practices and strategic organizational outcomes, including enhanced decision-making efficiency, improved employee engagement, and sustainable competitive advantage. However, critical challenges persist, encompassing algorithmic bias, ethical considerations, organizational readiness deficits, and employee resistance to technological change. The findings reveal that successful AI-HRM integration requires strategic alignment with organizational objectives, robust technological infrastructure, comprehensive change management protocols, and cultivation of AI literacy among HR professionals. This paper contributes to the evolving discourse on digital transformation in HRM by proposing a conceptual framework that integrates technological capabilities with human-centered design principles, emphasizing the necessity of balancing automation with ethical governance. Future research directions are identified, including longitudinal studies on AI-HRM impact sustainability, cross-cultural implementation variations, and the development of standardized ethical frameworks for AI deployment in human capital management.

Age-based mortgage lending restrictions represent a critical yet understudied dimension of financial exclusion in aging societies. This study combines empirical comparative policy analysis with normative evaluation, documenting international regulatory variation while advancing policy reform arguments grounded in discrimination theory and institutional economics frameworks. This study examines how central bank policy and governmental inaction can perpetuate systematic discrimination against older borrowers through regulatory vacuum rather than deliberate design. Employing qualitative comparative policy analysis across ten jurisdictions, the United States, Canada, United Kingdom, Australia, Switzerland, Singapore, France, Spain, Turkey, and Sri Lanka, we construct a five-tier regulatory ranking that reveals stark divergence unaccountable by economic development or demographic differences alone. Sri Lanka’s rigid 60-year mortgage age cutoff, the most restrictive approach identified globally, categorically excludes creditworthy older borrowers. Drawing on discrimination theories in the critical paradigm, we further establish that Sri Lanka’s approach constitutes statistical discrimination. Cross-national evidence from 180 countries analyzed by Barth, Caprio, and Levine (2013) using comprehensive bank regulation and supervision data (rom1999-2011) reveals no statistically significant correlation between age-based lending limits and loan portfolio performance metrics, undermining the prudential rationale for categorical cutoffs, revealed neither significant predictors of portfolio quality (β = 0.023, p > 0.10) nor associated with lower default rates when controlling for other underwriting standards. Maintaining current policies guarantees escalating financial exclusion for a growing, economically active aging population, hence this desk research advances a sequenced reform agenda: immediate central bank guidance prohibiting categorical age denials, short-term development of retirement-appropriate mortgage products, medium-term legislative reform establishing explicit anti-discrimination protections, and long-term institutional strengthening through superannuation system.

The spectral characteristics of 2-phenylenediamine (2PDA) in various solvents, and in the presence of α-cyclodextrin (α-CD) and β-cyclodextrin (β-CD) at pH~3, and pH ~7, were investigated using UV–visible, fluorescence, time-resolved fluorescence measurements, and PM3 computational methods. 2PDA showed a single broad emission band in all solvents, whereas dual emission observed in CD solutions indicates the presence of excimer in the 2PDA molecule. The fluorescence lifetimes of the inclusion complexes were greater than that of free 2PDA. In the 2PDA molecule, both the vertical and horizontal bond lengths between the both amino groups are smaller than the β-CD cavity size The Ag:2PDA:α-CD and Ag:2PDA:β-CD nanomaterials were synthesized and characterized by SEM, FTIR, and DSC techniques. In all pH conditions, 2PDA exhibited distinct absorption and emission shifts upon complexation with α-CD and β-CD. SEM–EDX data confirmed the presence of 5.5% silver in the nanomaterials.

Recently, the idea of reconfigurable antennas has made it feasible to design a single antenna that can support multiple wireless standards while maintaining the same performance as multiple antennas. The integration of a single antenna that can operate at multiple frequencies is required to enable multiple applications in a single device. This paper presents a compact frequency-reconfigurable microstrip antenna designed to support multiple wireless standards in modern communication devices. The proposed antenna, with dimensions of 30 × 15 × 1.52 mm³, is simulated using CST Studio Suite and achieves reconfigurability through the integration of PIN diodes as switches within strategically placed slots on the radiating structure. By controlling the bias states of the two PIN diodes (Sw1 and Sw2), the antenna can dynamically switch between four distinct operating modes: All switches ON: dual-band operation at 2.4 GHz (lower WLAN) and 5.6 GHz (higher WLAN), All switches OFF: single-band resonance at 4.2 GHz (radio altimeter applications), Sw1 ON and Sw2 OFF: single-band operation at 2.9 GHz (military and meteorological radars), Sw1 OFF and Sw2 ON: dual-band coverage at 3.5 GHz (5G sub-6 GHz) and 5.6 GHz (higher WLAN/5G). The design maintains consistent performance across these configurations while offering a low-profile, single-antenna solution that eliminates the need for multiple dedicated radiators. This makes it highly suitable for integration into space-constrained devices such as smartphones, laptops, tablets, IoT systems, and next-generation wireless networks. The proposed antenna demonstrates versatile multiband/frequency agility compatible with WLAN, 5G, radio altimeters, military radars, and weather radar applications, highlighting its potential to address the growing demand for efficient, multifunctional antennas in emerging wireless ecosystems.

Hong Kong's crowded city, with its old buildings and many people, has some tough structural and fire safety problems. In 2024, a significant incident happened when an illegal structure fell down at Redhill Peninsula on Hong Kong Island. Luckily, nobody died, but a lot of property was damaged. Then, in 2025, a terrible fire in Tai Po killed 176 people and got everyone worried again about how safe buildings are from fires. These two significant events were a wake-up call, showing some large weaknesses in how buildings are kept up, problems with illegal construction, and issues with fire safety equipment. This paper looks at the different things that cause buildings to fail and fires to happen in Hong Kong. This includes old buildings, changes to structures that were not allowed, badly designed drainage, and poor upkeep of fire equipment, and bad renovation work. The paper also looks at what the Hong Kong Government has done to fix these problems, like the Minor Works Control System, the Mandatory Building Inspection Scheme, and new rules from the Fire Services Department. It talks about how important Registered Inspectors and Registered Professional Engineers are when it comes to making these safety measures work. It also discusses about good inspection methods, paperwork, and doing a good job as a professional. By looking at all this, the hope is to add something to the talks about safety and stopping disasters in cities with lots of people.

This research outlines the creation of a hybrid composite for floor tiles. It uses virgin high-density polyethylene (vHDPE), glass cullet particles (GCP), and waste grinding disc (WGD) particles. This approach offers a sustainable alternative to traditional ceramic and cement-based floor tiles. Those conventional tiles are often brittle, require a lot of energy to produce, are heavy, and can crack easily under pressure. The growing amount of glass cullet, and waste grinding discs creates serious environmental issues hence it is important to develop recycling methods that add value. The study aimed to: (i) formulate and optimize HDPE/GCP/WGD hybrid composites for floor tile use, (ii) assess their mechanical and physical properties, (iii) find the best filler combination using design of experiment (DOE) and response surface methodology, and (iv) confirm the optimized formulation. Also, two-factor experimental design was employed with GCP (5-15%) and WGD (5-20%) as independent variables. Composite samples were prepared by melt blending and compression moulding, while the tensile strength, flexural strength, hardness, moisture content, density, and thickness swelling were measured. Furthermore, optimization using desirability function analysis predicted the optimal composition to be around 12.31 - 12.33% GCP and 13.82 - 13.97% WGD with a composite desirability of 0.809. The optimal solution identified by selecting the optimal solution (12.312% GGC and 13.965% WGD) predicted tensile strength of 12.07 MPa, flexural strength of 48.57 MPa, hardness of 62.90 BHN, thickness swelling of 0.051 mm, and density of 1.411 g/cm³. Validation experiments were found to be in close agreement with the predicted values, and the values obtained were tensile strength of 11.94 - 12.60 MPa, flexural strength of 46.2 - 48.6 MPa, hardness of 59.64 - 61.62 BHN, thickness swelling of 0.50 - 0.53 mm, and density of 1.40 - 1.45 g/cm³. The findings shows that the addition of glass cullet improved the stiffness and flexural strength, while the addition of waste grinding disc particles improved the hardness. It is worth to say that the hybrid composite had low moisture absorption, moderate density, improved hardness, and reduced thickness swelling. It can be concluded that the combination of vHDPE, GCP, and WGD creates a mechanically stable, lightweight, and moisture-resistant composite floor tile with good structural properties. Recommendations for further research are made in the areas of wear resistance, thermal stability, slip resistance, and the feasibility of large-scale production. Surface texturing and fire-retardant improvement are also recommended for commercial development. The significance of this research work lies in its ability to: (i) identify an optimal formulation of HDPE/GCP/WGD hybrid for floor tile use, (ii) prove the synergistic reinforcement provided by glass cullet and waste grinding disc particles in a thermoplastic matrix, (iii) develop a predictive statistical model, and (iv) offer a sustainable waste-to-wealth approach to plastic, glass, and abrasive disc waste.

Spiders are a globally distributed group, with established populations in a wide range of habitats. The present study aimed to investigate spider faunal diversity within "Amakoni Reservoir," Sarangarh- Bilaigarh District, Chhattisgarh, India. This study was conducted from December 2024 to November 2025 Specimens were collected from the environs of Amakoni Reservoir (Putka Reservoir) using a variety of collection methods and subsequently identified using a taxonomic key for Indian spiders.  This study was conducted to evaluate spider species richness and develop a detailed species checklist for the spider fauna of the Amakoni Reservoir area A total of 23 species, representing 8 families, were recorded from study area. The Araneidae family was the most prevalent, comprising six species. Shannon’s Index (H)=-0.83031 and Simpson’s Index (D)= 0.8425. This research yields significant and contemporary data regarding the species diversity within the "Amakoni Reservoir," Sarangarh- Bilaigarh District, Chhattisgarh and may serve as a useful resource for future research on spider fauna.  

This article explores African philosophy and the foundations of leadership and ethics, drawing on Swahili proverbs. Primary data were collected from library sources. Library-based reading methods were employed to collect primary data. Reviews of documents used to validate the primary data were obtained from electronic and library sources. Sociological Theory was utilized in analyzing the data. The main premise of this theory is that literary and artistic works cannot be understood without considering the historical, political and social contexts in which they were produced. The data analyses were consistent with the principles of the selected theory. A descriptive approach was used to present the data. The findings indicate that reflecting African philosophy in Swahili literature emphasizes solidarity, human dignity, accountability, justice and integrity as essential foundations of good leadership and ethics. Furthermore, literature, through examples of leaders and society, underscores the importance of unity, justice and accountability. Consequently, literature is a vital tool for promoting ethical leadership and effective leadership while also supporting social welfare, peace and sustainable development. This confirms that Swahili literature reflects African values that foster social well-being. This article recommends that, to strengthen leadership and ethics, schools and communities should promote education emphasizing African philosophy, including concepts such as Ubuntu [I am because we are], solidarity, accountability, justice and respect for every human being.

Green finance has become a key mechanism for mobilizing climate-aligned capital to advance sustainable development objectives. However, in many developing economies, the adoption and effectiveness of green financial instruments remain uneven and underexplored. Existing literature tends to be fragmented, with limited synthesis on how tools such as green bonds, blended finance, and ESG-linked investments generate measurable environmental and developmental impacts. This study addresses that gap by conducting a systematic and integrative review of 67 peer-reviewed articles and policy reports published between 2020 and 2025. Applying PRISMA 2020 guidelines, the review evaluates how climate-linked financial instruments perform across diverse institutional, regional, and sectoral contexts. Findings reveal that green finance effectiveness is significantly shaped by mediating factors like ESG integration and climate risk evaluation, and moderated by institutional quality, regulatory clarity, and investor confidence. While tools such as green bonds and blended finance show strong potential in emissions reduction and resilience-building, structural barriers—such as governance deficits, limited data systems, and fragmented markets—remain persistent in many contexts. The study proposes an integrated conceptual framework and a policy-action roadmap that map the pathways through which green finance contributes to the Sustainable Development Goals (SDGs) and Nationally Determined Contributions (NDCs). It bridges theoretical and applied perspectives, offering actionable insights for policymakers, development finance institutions, and private investors.

Casual dining is more than just good food; it’s about the whole experience. This study set out to uncover what really matters to customers when they choose to dine in casual restaurants. Through firsthand feedback from regular diners in Dumaguete City, the research dug into the subtle details that often go unnoticed but make a big impact, like how fast the service is, how friendly the staff are, the feel of the place, and whether the experience feels worth the money spent. Rather than just measuring basic satisfaction, the study focused on what makes people enjoy their visit and want to come back. It looked closely at how things like ambiance, food quality, and even small personal touches shape the overall experience. The goal was to figure out what turns a simple meal into something memorable. The insights gathered from this study were used to build a practical guide, a framework, for restaurant owners and staff to better understand their customers and improve the way they deliver service. In the end, the study found that the real secret to success in casual dining isn’t just getting things right; it’s about creating moments that make people feel seen, valued, and glad they walked through the door.

In today’s highly competitive food service industry, casual dining restaurants face the constant challenge of not only attracting customers but also retaining them. Customer loyalty has become a crucial success factor, especially as consumer expectations evolve and choices expand. Unlike fast food or fine dining establishments, casual dining restaurants must strike a balance between quality, affordability, and service consistency. Understanding what drives customer loyalty in this segment is essential for restaurant operators aiming to sustain long-term business growth. This study seeks to explore the primary determinants influencing customer loyalty in casual dining settings by examining factors such as service quality, food quality, ambiance, price fairness, and perceived value. This study explores the key determinants of customer loyalty in casual dining restaurants, focusing on factors such as service quality, food quality, ambiance, price fairness, customer satisfaction, and perceived value. Using a quantitative approach, survey data were collected from patrons of selected casual dining establishments and analyzed to determine the relationship between these factors and customer loyalty. Results show that service and food quality are strong predictors of loyalty, with customer satisfaction and perceived value acting as mediators. The findings offer practical insights for restaurant managers to enhance service delivery and foster customer retention.

This research presented, implemented and evaluated a POWER card and FILPO Card using tailor-made PCB for electronics simulation. It was project 409. It was part of a larger project "Quantum Mechanics in Solid-State Electronics: Diode and Transistor Characteristics, and Circuit Applications Card." The goal of the project was to solve a widespread issue that exists in teaching: students using commercially available bench power supplies that obscure the magic inside them, such as rectification, filtering and regulation. Researcher used the ADDIE model (Analysis, Design, Development, Implementation and Evaluation) throughout all of the steps — from determining what was needed to creating schematics, designing PCB layouts, building prototypes, lab testing and evaluating effectiveness. It satisfied every electrical spec. The regulated outputs were extremely stable through no-load and full-load testing. Under full load, the +5V output was off by only 0.99%, and the +12V output was off by 1.49%. The filtering worked out as it should too; using a 1000 µF capacitor, the ripple voltage at 500 mA was reduced from 680 mV to just 310 mV (4.82% down to just over 2.18% ripple), while no-load ripple got as low as a mere 65mV or so (just under half of a percent). We conducted a survey of 35 students to test the usefulness of Technology Acceptance Model (TAM) specifically for teaching. They enjoyed it; the mean scores were 4.71 for usefulness, 4.61 for ease of use, 4.73 for wanting to use it again and a massive 4.78 for satisfaction. While in person, working with this card definitely helped them a lot to learn about rectification, filtering and regulation. At the end of the day, this is best in POWER card and FILPO Card It’s solid tech and good for learning. We recommend it to be integrated into electronics lab classes to make students learn more, build better skills, and motivate them towards power electronics.

Literature, as an art form, is affected not only by external determinants but by dynamism within art itself that promotes or impedes change. -Rene Wellek The history of mankind is replete with resistances to various societal norms especially when the social milieu impinges the liberty of its people, creates a suffocating atmosphere even to lead normal life.  However the newer governances after a passage of time turn out to be another government with newer weaknesses leading to another kind of governance.  It is a vicious circle with no eternal solution. During such critical times literature has shown the way out of it, sometimes through its revolutionary concepts and at other times through healthy practices for the well-being of its people. Literature is a mirror of prevailing society.  Through literature, one can discern the resistance to the unhealthy/unfair previous societies owing to its rigid practices, atrocities on subaltern communities, hegegmony of certain communities.  During the 12th century Karnataka the Vachana literature endeavoured in all possible ways to sensitize the people about various blind and unfair practices of the previous epochs:  caste system, superstitious beliefs, materialism, and denial of lower caste people to places of worship, belittling women, belief in Karma theory (or fatalism) and the like.  The paper explores the resistances through Vachana literature, a unique literature which meant ‘promise’ or a ‘word’.  Saint Akkmahadevi’ perspectives, who was known for the confluence of Bhatki, intense devotion, Jnana, spiritual knowledge and Vairagya, renunciation are emphasized. Vachanas related to attainment of spiritual knowledge would be discussed in Anubhava Mantapa, a religious academy, verified and documented to propagate its precepts to make the society rational and healthy.   The Vachana literature is an offshoot of Bhakti Movement which spread across India with certain variations and the unique genres:Vachanas(Karnataka), abhangas(Maharashtra), Tevaram(Tamilnadu), ‘Sataka’(Andhra Pradesh).  Society across India had the conviction that unless the priests mediate between the devotees and the Almighty one will not be bestowed with His blessings.  However, the proponents of Bhakti Movement in general and Vachana Movement in particular through their resistance literature preached the laity to practise universal brotherhood which would ensure the eradication of various evil practices and spread universal brotherhood: inequalities between castes, materialism, denial of entry to lower caste people to temples and the like.  The following Vachanas exemplifies the practice of universal brotherhood

Purpose: This study aimed to examine the influence of general strain factors on cyberbullying among university students in Kenya, with a focus on emotional distress, coping mechanisms, peer reinforcement, and reduced empathy. Methodology: A quantitative research design was employed, using a structured questionnaire to collect data from student leaders across 72 universities in Kenya. The data were analyzed using Pearson correlation and regression analysis to explore the relationships between general strain factors and cyberbullying behaviors. Findings: The study found a moderate positive correlation (r = .492, p < .001) between general strain factors and cyberbullying. Specific strain factors such as negative emotions and coping mechanisms (r = .453, p = .001), and peer reinforcement (r = .404, p = .003) were significantly associated with higher cyberbullying severity. However, reduced empathy showed a non-significant relationship (r = .214, p = .127). Regression analysis revealed that general strain factors explained 24.2% of the variance in cyberbullying (R² = .242, p < .001), confirming their significant predictive role. Unique Contribution to Theory, Practice, and Policy: The study offers a unique contribution to General Strain Theory (GST) by demonstrating how emotional distress and peer validation processes contribute to cyberbullying in the Kenyan university context. It also highlights the importance of addressing strain factors such as stress, isolation, and peer reinforcement in university policies and intervention strategies. The study suggests the incorporation of mental health programs, stress-management initiatives, and digital ethics training in universities to mitigate the risks associated with cyberbullying.

This study has developed a new way to structure the data used in global life/health reinsurance by converting large cedant bordereaux into a scalable, AI-enabled product that will overcome some of the limitations of traditional DB2 (batch ETL) methods. This new Azure Synapse-focused model complies with both Solvency II/IFRS 17, enables schema-agnostic ingestion processes, allows for the application of Apache Spark transformations, and utilizes Python to process reconciliations. As a result, the analysis of this product shows significant outcomes; a reduction in treaty liability calculation time by 75%, a decrease in reconciliation resources used by 70%, an 80% reduction in the costs associated with preparing for audits, and a 400% increase in ingestion capacity. The innovations originally focused on resolving inconsistencies in the cedant formats, eliminated many of the manual processes, and addressed some of the regulatory barriers, utilizing AI-enabled quarantine queues, real-time catastrophe accumulation processes, and proactive fraud alerts. The evaluation metrics show very high success with an AUC of>0.92 (for real-time fraud detection), data quality metrics exceeding 97%, and an average uptime of 99.99%, thus preparing the platform for future evolutions around GenAI, federated learning, and the development of digital twin risk modeling that can adapt to a rapidly changing marketplace.

Today's life the Artificial intelligence (AI) is one of the key components of students as well as teachers and researchers. In education system artificial intelligence become a backbone and most helpful tools which is playing vital role in higher education system. Artificial intelligence has become one of the biggest challenges facing our education system related to quality of services delivered. It may seems that artificial intelligence become a smart tool for students which can make smart not a alternate of teachers which take lecture and students emotions. Education is one of the most affected fields where the impact of artificial intelligence leads to positive and sometimes negative outcomes. The broad consensus is that effective pedagogy leads to high-quality educational outcomes. Therefore, AI-powered tools and techniques automate tasks, personalize the learning process, and deliver real-time feedback. Chatbots like ChatGPT are one of the beneficial ways to use artificial intelligence in education.

Open Shortest Path First (OSPF) is a widely deployed link-state Interior Gateway Protocol (IGP) designed for routing IP packets within a single Autonomous System (AS). This report details the methodology of implementing OSPF, highlighting its mechanism of building a topological map of the network and using Dijkstra’s Shortest Path First (SPF) algorithm to calculate optimal routes. Key aspects such as LSA flooding, neighbor adjacency formation, and multi-area design are examined. Results indicate OSPF provides fast convergence, efficient path determination, and loop-free topology, making it ideal for large enterprise networks. 

The study evaluates the socio-economic transformation of Sahibganj, a riverine district in Jharkhand, India; driven by the collaborative “dual engine” of Self-Help Groups (SHGs) and Microfinance Institutions (MFIs). Historically marginalized by geographic isolation and limited formal banking access, Sahibganj has evolved into a robust institutional network supporting 12,559 SHGs as of 2026. Utilizing a descriptive-analytical research design, this study synthesizes secondary data derived from National Rural Livelihood Mission (NRLM) reports, Sa-Dhan Bharat Microfinance Reports, and the Jharkhand Economic Survey for the 2019–2026 period to assess the socio-economic influence of micro-credit interventions on marginalized population. The findings reveal a robust 75.6% growth in SHGs mobilization between 2019 and 2026, characterized by high social inclusivity; nearly 64% of groups comprise historically underserved categories. Economically, the shift from “survival loans” to productive asset creation strengthened by a ₹44.29 Cr Community Investment Fund (CIF) has reduced the number of “zero-income” women from 42.85% to 2.85%. Socially, the “Bank Sakhi” model and participation in Producer Groups have elevated women into leadership roles, with bank advances to women tripling the national benchmark. Despite these strides, a low Credit-Deposit (CD) ratio of 38.37% and rural infrastructure gaps suggest that while institutional saturation has been achieved, formal banking absorption remains a challenge. The study is primarily limited by its total reliance on secondary institutional datasets, which may contain reporting lags and do not capture granular, qualitative household-level nuances. The study concludes that Sahibganj has successfully transitioned to a mature phase of responsible lending, where high financial discipline evidenced by a low 0.76%  Non-Performing asset (NPA) ratio marks the emergence of rural women as professional-grade economic stakeholders. However, future sustainability depends on transitioning toward macro-enterprise and climate-resilient livelihoods.  

The agricultural sector urgently requires sustainable substitutes for synthetic organophosphate and carbamate pesticides, which pose environmental and health risks due to their irreversible inhibition of acetylcholinesterase (AChE). This study confirms the traditional use of Annona squamosa (Sugar Apple) leaves as a biopesticide by examining their neurotoxic impact on the non-target aquatic species Labeo rohita (Hamilton, 1822). The fish were subjected to a 100 mg/L concentration of fresh Annona squamosa leaf extract for a duration of 96 hours. Following this, a recovery phase was initiated by transferring the fish to clean water for 120 hours. AChE activity in the kidney was periodically measured using the Ellman method, while protein content was determined with the Lowry assay. Exposure to Annona squamosa leaf extract resulted in a significant reduction in AChE activity in the kidney, with a value of 5.93 ± 1.01 nmol/min/mg protein, corresponding to 21.45% inhibition relative to the control group. During the recovery phase, enzymatic activity gradually improved, with 13.58% recovery at 48 hours, 38.88% at 72 hours, 64.81% at 96 hours, and 79.62% after 120 hours in freshwater. These findings demonstrate that Annona squamosa leaf extract exhibits considerable anticholinesterase activity, yet the recovery of inhibited AChE is rapid. This research provides biochemical evidence supporting the integration of Annona species into modern Integrated Pest Management (IPM) strategies as a biodegradable and effective natural alternative to synthetic chemicals.

Adolescence is a transitional stage marked by emotional volatility, identity formation, and increasing social and academic demands. The World Health Organization (WHO, 2021) estimates that one in seven adolescents worldwide experiences a mental health condition, with stress being one of the leading contributors to anxiety, depression, and behavioural issues. (1) Schools are an ideal setting for implementing preventive mental health interventions as they provide structured environments and reach large populations during formative years (Kieling et al., 2019). School-based stress management programs—comprising relaxation techniques, mindfulness, and life skills training—aim to enhance coping capacity, emotional regulation, and resilience (2). Tamil Nadu state has recently expanded several mental-health initiatives and school-focused programs. Media and government reports highlight initiatives such as school-level digital-safety and well-being modules (e.g., “Agal Vilakku” for girl students) and district/PHC-level mental-health services expansion (suicide prevention and targeted programmes such as “Magizhchi” in some districts including Coimbatore). These policy moves create an enabling environment for scaling school mental-health programs but also emphasize the need to evaluate implementation and equity across districts. This study evaluates the effectiveness of a structured stress management program conducted among adolescents to determine its impact on stress reduction and improved well-being.

Patient satisfaction is an important measure of the quality of care provided by health care organisations. It is not only important for gaining insights into the perception of the patient’s on the delivery of the health care service, but also a key outcome of care. The present study was undertaken to identify the factors in which the patients are satisfied by the services offered by the multi- speciality hospitals in Coimbatore city. The objective of the study states to examine the level of patient satisfaction towards hospital services. primary data was collected from 120 respondents using a well-structured questionnaire through google form. The data collected through questionnaire were analysed. The research methodology employed in this study includes percentage analysis, Garrett ranking and Chi- square test. It was found that the majority of the respondents choose Kovai medical center and hospital to undergo the treatment towards multi- speciality hospitals. Most of the respondent’s nature of treatment is towards medical. It was suggested that the hospital management should increase the physician referral centers, complaint handling and service recovery systems should be strengthened to enhance effectiveness of multi- speciality hospital services. It was concluded as the competition is increasing, the hospitals are making their best efforts to provide quality healthcare services to its customers.

With the advancement in technology, the world is moving fast and for its fast growth, the automobile sector is also in the revolution period with the advancement in battery-driven vehicles. The Electric and Hybrid vehicles which run on the battery, face the main issue in thermal management. As there are so many electronic components inside the vehicle especially the battery, the heat dissipation is also more. Many of the researchers have proposed the Li-Ion cells as the most suitable for the battery packs in electric vehicles. And with many advantages of the Li-Ion cells, there is one major limitation of it as its heat dissipation rate. In order to get the best working performance of the battery electric vehicles, it’s equally important to keep its temperature in control. The various parameters which directly influence the temperature rise of the PCM are mass of PCM, the thermal conductivity of the Paraffin material, water flow rate etc.

The study of behavioural finance has garnered significant interest from many researchers, showing a growing trend in the publication of research articles in recent times. This paper presents a systematic review and bibliometric analysis of the existing literature in the field of behavioural finance. The study utilized RStudio and VOS viewer software to perform a bibliometric analysis of data retrieved from the Scopus database following the PRISMA protocol. It also systematically reviewed existing literature to identify key behavioural biases, methodologies, and findings of the selected articles. The findings reveal a growing trend in the publication of research articles, the most impactful authors and journals, the most frequently used keywords, and the top contributing countries and organizations. The paper concludes by suggesting a future research agenda based on the research gaps identified after reviewing the selected studies.

This research article delves deep in analysis of emerging patterns of internet related offenses committed by minors along with multi-dimensional aspects of internet society which has created a cyber-world. Our global world is continuously transforming with the everyday use of internet technologies which are playing significant role in transforming the human society. This change is multifaceted, driven by the advent of ‘Information and Communication Technologies (ICT) that was widely being used since 20th century and have rapidly advanced in the present era. ICT involves the digital transmission of data between various devices worldwide. “Information technology is the "the science and activity of moving data digitally. (Gutmann, 2001)” Emerging society is an "information society." (Castells & Gustavo, 2005)  Internet technology and social media are the most prominent and revolutionary innovations, bringing remarkable changes to modern society creating cyber-cultures. In context of Indian society which is facing numerous modern challenges and issues and is evolving into a "risk society," as it transitions "from etiquettes to netiquettes, from school bullying to cyberbullying, from theft to cyber-fraud, multiple new aspects of deviance and crimes are evolving" The internet and social media have created a cyber-world through social networking, forming a network society where data is sensitive and precious Internet and social media are now inalienable part of human lives, having both positive and negative impact on minors especially. Internet is the fastest and cheapest means through which data can be transmitted in real time at any place on globe but on the other hand, they also expose users especially minors to risks being a cyber-delinquent but also a victim of cyber-crimes. Since the evolution of human society crime has always been an integral part of human society, but with the expansion of the internet and social media, its patterns and methods are continuously evolving. Delinquency is now becoming cyber-delinquency with the involvement of internet technology. This study is based on qualitative analysis of Patterns and Predictors of Cyber Delinquencies committed by minors and how effects society it when committed by minors. This study relies on primary data being sourced from various stake-holders of judiciary, academia and member of civil society, students and victim of cyber-crimes while secondary data is sourced from NCRB and government records and official academic data from journals and books.

The accelerated rate of digitization within the context of the office environment has seen organizational dependence on digital information systems escalate concurrently with an increased threat profile for cybersecurity breaches. This current study seeks to investigate the integration of active cybersecurity controls within the context of organizational data management to improve the security posture for data confidentiality, integrity, and availability. To this end, a mixed-methods research design was utilized to investigate this problem, incorporating a systematic review of existing literature and a quantitative survey of 150 mid- to large-scale organizations. The results highlight a significant disparity between the implementation of individual cybersecurity tools and the development of an overarching cybersecurity framework. The results demonstrate that organizations that implement an integrated data management system, incorporating end-to-end encryption, multi-factor authentication, rigorous administrative controls, cybersecurity training for staff, and effective data governance practices, are significantly less likely to experience data breaches when compared to organizations that implement a disjointed approach to cybersecurity. Additionally, this study recognizes organizational security awareness culture as a key influencer on cybersecurity posture, as employee awareness has a direct influence on the efficacy of cybersecurity controls and the mitigation of cybersecurity threats. As such, this study proposes a Unified Office Data Security Management (ODSM) framework that incorporates a holistic approach to cybersecurity controls to protect organizational data. The proposed framework provides a guide for organizations to improve cybersecurity governance, organizational resilience, and sustainability.