Volume 15 Issue 3

This study investigated the effect of colour oxide pigments on the production of environmentally sustainable oil paints utilizing a novel bio-based binder. Solid waste polystyrene was efficiently converted into liquid polystyrene (PS) and subsequently blended with cashew nut shell oil (CNSO) extracted from waste cashew nuts to produce a composite binder, designated as PS/CNSO. The physicochemical analysis of the PS/CNSO binder was carried out which including pH, melting point, refractive index, density and moisture uptake. Also the instrumental analysis was carried out such as: differential thermal analysis (DTA), X-ray diffraction (XRD), and Fourier-transform infrared spectroscopy (FTIR) were thoroughly characterized to assess its suitability for paint formulation. Oil paints were then formulated using the PS/CNSO binder, incorporating varying concentrations of natural colour oxide pigments (1g, 1.5g, 2g, 2.5g, 3g, 3.5g, and 4g). The influence of pigment concentration on the physical, chemical, and aesthetic properties of the resulting paints was systematically evaluated. The findings demonstrate that the bio-based binder, combined with natural pigments, offers a sustainable alternative for oil paint production, with pigment concentration significantly affecting the performance and quality of the final product. This work highlights the potential of waste-derived materials in developing eco-friendly coating solutions.

Fabric defect detection is a critical task in textile manufacturing, where manual inspection remains inconsistent, labour-intensive, and unsuitable for high-speed production environments. Although deep learning–based detectors have shown strong potential, many existing models are too computationally demanding for practical deployment in real-time industrial inspection systems. This study proposes a lightweight deformable YOLO-based framework for accurate and efficient fabric defect detection. The model is built on YOLOv5s and enhanced through three efficiency-oriented architectural improvements: Bidirectional Feature Pyramid Network (BiFPN) for improved multi-scale feature fusion, Deformable Convolutional Networks (DCNv2) for stronger geometric adaptability, and Efficient Pyramid Split Attention (EPSA) for enhanced feature discrimination. The proposed model was trained and evaluated on the Alibaba Tianchi fabric defect dataset, comprising 5,913 images across 20 defect categories. Experimental evaluation was conducted using mean Average Precision (mAP), model size, and real-time suitability, supported by ablation and comparative analyses. Results show that the proposed method improved mAP from 41.9% for the baseline YOLOv5s to 48.2%, representing a gain of 6.3 percentage points. The findings indicate that targeted architectural optimisation can improve detection accuracy while preserving the lightweight characteristics required for industrial implementation. The proposed framework offers a practical solution for automated fabric inspection and provides a useful reference for efficiency-oriented defect detection in smart manufacturing environments.

The stock market performs a crucial function in modern economic systems by mobilizing savings and allocating financial resources to productive sectors. During the last decade capital markets have experienced remarkable transformation due to financial technology innovation, digital trading infrastructure and expanding participation of retail investors. This research examines recent stock market trends with emphasis on investor behaviour, macroeconomic variables and technological development. Statistical methods including correlation analysis, multiple regression modelling and analysis of variance are employed to evaluate relationships among selected variables. The results indicate that retail investor participation and technological development contribute positively to market performance while inflation and interest rate changes may create negative pressure on equity returns.

This study evaluates the equilibrium state and dynamic response of large cantilever projections (1 m, 2 m, 4 m, 6 m) in a ten-storey reinforced concrete building located in Lagos State, Nigeria. The research was motivated by the increasing architectural trend of wide cantilever balconies and façades in high-rise buildings, which often lead to excessive deflection, loss of equilibrium, and cracking when conventional reinforcement systems are adopted. The structural assessment was carried out in accordance with Eurocode 2 (EN 1992-1-1: 2004), applying finite-element analysis for the reinforced concrete (RC) models and manual analytical procedures for the post-tensioned (PT) systems. Both RC and PT slabs were evaluated under identical loading and boundary conditions. The investigation covered moment–shear–reaction equilibrium, deflection behaviour, and crack-width control, based on C30/37 concrete and Grade 460 steel reinforcement. Results show that RC slabs developed higher bending moments and deflections with increasing projection length, while PT slabs sustained equilibrium more effectively through internal compressive actions generated by prestressing. Overall, PT systems achieved over 50 % reduction in deflection, minimised cracking, and improved serviceability. The study concludes that manually designed PT slabs provide a stable equilibrium state and a structurally efficient solution for cantilever projections exceeding 2 m, ensuring safer and more economical multi-storey construction.

The effectiveness of adaptive learning platforms in higher education is shaped by multiple interacting factors that involve subjective judgment and uncertainty. This study employs the Fuzzy Analytic Hierarchy Process (Fuzzy AHP) to examine the relative importance of key evaluation criteria and sub-criteria based on the perceptions of higher education users with prior experience in technology-enabled learning systems. Data were collected through a structured survey of 150 respondents using fuzzy pairwise comparisons. The evaluation framework was structured around four main criteria—Technological Factors, Content Quality, User Factors, and Institutional Support—supported by twelve sub-criteria. The findings show a clear and consistent emphasis on learner-oriented and practical considerations. Within the technological dimension, system usability is ranked as the most influential factor, surpassing platform reliability and internet accessibility. For content quality, content relevance is prioritized over interactivity and multimedia features, highlighting the importance of meaningful and well-structured instructional material. Among user-related factors, learner motivation emerges as the dominant determinant, followed by digital literacy, while engagement level carries comparatively less weight. In the institutional support category, technical support is identified as the most critical element, reflecting the need for timely assistance to ensure uninterrupted platform use. Overall, the results indicate that effective adaptive learning platforms are driven primarily by ease of use, high-quality instructional content, and motivated learners, supported by responsive technical services. The study provides a structured evaluation framework that can assist educators, institutions, and system designers in making informed decisions regarding the development and adoption of learning platforms in higher education contexts.

Stage organization including its physical structure, spatial arrangement and even the audiences organization has had a profound effect on the career of playwriting throughout history. The development of the techniques and forms of the dramatic literature is the obvious result of the stage details evolving. The successive civilizations of humanity throughout history give the stage development a great deal of importance to reflect their vision and lifestyle of their time. we can find different kinds of the theaters which differentiate  certain civilization from others by its unique style. Greek. Roman and English theaters are the greatest throughout history. This study will follow up the theater stage organization development which affect the playwrights viewpoints and thoughts in these civilizations.   

Physalis angulata L. is traditionally used for various medicinal purposes; however, its antioxidant efficacy and safety profile remain insufficiently validated. This study aimed to optimize aqueous extraction conditions to obtain an extract with high antioxidant activity and to evaluate its preliminary toxicity. Dried plant material was extracted using water at different temperatures (40–100 °C) and extraction times (15 min–3 h). Antioxidant activity was assessed using DPPH radical scavenging, ferric reducing antioxidant power (FRAP), total phenolic content (TPC), and malondialdehyde (MDA) inhibition assays, while phytochemical profiling was performed using HPLC. Toxicity was evaluated using the brine shrimp lethality test (BSLT) and acute oral toxicity in rats. Extraction at 80 °C for 30 min exhibited the highest DPPH inhibition and TPC, while optimal FRAP values were observed at 60–80 °C for 1 h. The lowest MDA levels were recorded at 100 °C for 2 h. HPLC analysis identified chlorogenic acid and rutin as the major phenolic constituents. The extract demonstrated low toxicity in the BSLT, with significant lethality only at high concentrations, and no mortality or adverse effects were observed in rats following acute oral administration. In conclusion, optimized aqueous extracts of P. angulata exhibit strong antioxidant activity with a favorable preliminary safety profile, supporting their potential application as a natural antioxidant source for further development.

Corrosion resistance is a critical factor influencing the long-term performance and biocompatibility of dental and biomedical alloys. This study explores the corrosion behaviour of 21K gold alloy and other metallic materials in artificial saliva both in the presence and absence of cinnamon powder. Cinnamon, a common natural flavouring agent used in food and oral care products contains bioactive compounds that may alter the electrochemical environment of the oral cavity. Corrosion testing was performed using potentiodynamic polarisation and electrochemical impedance spectroscopy (EIS) to evaluate the electrochemical stability of the materials. The results indicated that the presence of cinnamon powder enhances the corrosion resistance of 21K gold alloy. These findings suggest that the dietary or therapeutic use of cinnamon powder could influence the durability and performance of metallic dental and orthodontic devices.

The Indian Knowledge System (IKS) represents one of the oldest, most diverse, and most profound intellectual traditions in the world. From the oral transmission of the Vedas to the establishment of ancient manuscript libraries and modern digital repositories, libraries have played a vital role in preserving, organizing, and disseminating knowledge.   This research article examines the historical evolution of libraries in India, their philosophical foundation within the Indian Knowledge System, and their critical role in safeguarding religion, history, culture, and scientific traditions. The study highlights how the destruction of libraries leads to irreversible cultural loss and discusses the transformation of libraries from manuscript repositories to modern information dissemination centers. The article concludes with suggestions for strengthening libraries as cultural and knowledge preservation institutions in contemporary India.

Road safety has become a major concern with the increasing number of accidents caused by common driver errors, such as not wearing seat belts or failing to adjust headlights appropriately at night. To address these safety issues, this project introduces a Smart Safety System for Drivers that focuses on encouraging responsible driving behavior through automated safety features.The system includes two essential functions: Seat Belt Speed Control, which limits the vehicle speed to 20 km/h unless the seat belt is properly fastened, and an Automatic Headlight Dim-Dip System, which utilizes an LDR sensor to detect oncoming vehicles within a range of 100 meters and automatically switches to low beam to prevent glare. The hardware model required for the project is developed using ATmega328 micro controller and is programmed in Embedded C via Arduino IDE, offering a practical, low-cost solution aimed at reducing the risk of accidents and improving road safety.

Financial Inclusion refers to provide affordable financial services – such as banking, credit, insurance and digital payments-to all individuals and businesses, especially, those traditionally underserved. Micro, Small and Medium Enterprises (MSME)from the backbone of developing economy, contributing significantly to employment generation, Innovation and GDP growth. Financial Inclusion enhances the development of MSMEs by improving access to essential financial services like credit, savings and digital payments, which boosts their performance, facilitates expansion, promote employment and contributes to their overall sustainability. This study aims to explore the impact of Financial Inclusion on the Micro, Small and Medium enterprises’ (MSMEs) in its development and growth in India. This study takes into account the schemes launched by the Indian Government which are majorly contributing to the growth of these MSMEs. This study also evaluate the impact of digital financial services on MSME. It identifies challenges faced by MSMEs in accessing inclusive finance and also suggests policy measure for strengthening financial inclusion to promote industrial growth. The data-analysis is exclusively done based on the secondary sources of data collected from articles, textbooks, journals, websites, annual reports of MSMEs &published press releases made by the Government officially via the Press Information Bureau.

Entrepreneurs play a vital role in an economy as agents of economic growth. They create mechanism that connects different sections of society by enhancing the transmission of goods and services from one end to another. Studies on motivational factors confirms the importance of the role of motivational factors in determining the nature and types of enterprises  entrepreneurs would choose to take up. The study reveals that internal motivation (p<0.5) has far greater impact on the entrepreneurs than the external factor. Desire to be self employed and attain financial independence are the most important personal motives driving individuals towards entrepreneurship. Encouragement from family and relatives is found to be an important external factor influencing entrepreneurial success.

The study investigated the proximate and elemental composition of plantain peel ash to assess its potential as an organic-based fertilizer. Plantain peels collected from local markets were dried, burnt under controlled conditions to obtain ash, and analyzed using standard procedures. Proximate analysis revealed a high ash content (89.15%), low moisture (3.62%), and minimal organic fractions (protein, fat, and fiber), indicating a stable, mineral-rich material suitable for soil enrichment. Elemental analysis showed that potassium (35,420 mg/kg) was the dominant nutrient, followed by calcium (9,830 mg/kg), magnesium (2,740 mg/kg), and phosphorus (1,960 mg/kg). Trace amounts of iron (Fe) and zinc (Zn) were also detected. These results demonstrate that plantain peel ash contains essential macro- and micronutrients capable of improving soil structure, enhancing nutrient availability, and promoting crop growth. The high mineral and alkaline nature of the ash supports its use as an eco-friendly, low-cost soil amendment for sustainable agriculture. However, field trials are recommended to determine its agronomic efficiency and long-term effects on soil fertility.

The widespread adoption of facial masks during the COVID-19 pandemic significantly challenged existing facial recognition systems by occluding critical biometric features. This paper proposes a Vision Transformer (ViT) based approach for robust Masked Face Recognition (MFR). Unlike traditional Convolutional Neural Networks (CNNs) that rely on local receptive fields, the ViT architecture utilizes global self-attention to capture long-range dependencies, making it more resilient to the information loss caused by masks. We evaluate our approach on the MFR2 dataset, by implementing a standardized training methodology, and our model achieves a peak accuracy of 98.22%. This study demonstrates that transformer-based architectures, combined with specialized attention mechanisms and contrastive learning, offer a state-of-the-art solution for secure authentication in masked environments.

Underfunded schools face challenges such as inadequate infrastructure and limited resources, which affect education quality. This study explored the leadership practices of principals in these environments through in-depth interviews with seven principals. Findings indicated that they used transformational leadership, resource mobilization, and community engagement to mitigate financial constraints. The study recommended increasing professional development for principals and enhancing policy support to improve leadership effectiveness in underfunded schools.

Fabric defect detection is a fundamental quality control process in textile manufacturing, yet achieving accurate and reliable automated inspection remains difficult because of complex background textures, subtle defect patterns, and substantial variation in defect scale and shape. Although deep learning–based detectors have improved inspection performance, many lightweight models still suffer from limited feature discrimination, particularly in real-time industrial environments where computational efficiency is critical. To address this limitation, this study proposes an enhanced fabric defect detection framework by integrating an Efficient Pyramid Split Attention (EPSA) mechanism into a YOLOv5-based convolutional network. The EPSA module is designed to adaptively recalibrate multi-scale feature responses, enabling the network to emphasize defect-relevant information more effectively while preserving inference efficiency. A quantitative experimental design was employed using a labeled fabric defect image dataset, and the proposed model was evaluated through comparative and ablation analyses against baseline and alternative attention-based configurations. Experimental results indicate that the EPSA-enhanced model achieves superior detection performance in terms of mean Average Precision while maintaining real-time processing capability. The improvement is especially evident for small, low-contrast, and irregular defects embedded in repetitive fabric textures. These findings confirm that pyramid-based attention can substantially improve feature representation without imposing significant computational overhead. The proposed approach offers a practical and efficient solution for automated textile inspection and provides a useful foundation for future research on lightweight attention modeling for industrial vision systems.

Purpose: This systematic review examines current risk assessment practices in construction projects, analyzing methodological approaches, technological integration, and thematic priorities in literature published between 2020 and 2026. The study aims to identify prevailing risk assessment practices and research gaps while providing a comparative analysis of methodological evolution. Methodology: Following PRISMA guidelines, a systematic literature review was conducted across Scopus, Web of Science, and Dimensions databases. The search targeted peer-reviewed articles on construction project risk assessment published from January 2020 to February 2026. From 847 initial records, 187 articles met inclusion criteria and were analyzed using bibliometric and content analysis methods with inter-reviewer agreement validation. Findings: Analysis reveals quantitative methods dominate (47%), followed by qualitative (32%) and mixed-methods (21%). Current practices comprise traditional techniques (31%), advanced methods including AI and machine learning (37%), hybrid approaches (18%), and real-time assessment (14%). Advanced methods demonstrate 15–25% higher prediction accuracy but face implementation barriers including data requirements and interpretability challenges. Real-time assessment, despite 35–50% accident reduction potential in trials, remains limited due to cost and infrastructure constraints. Significantly, 65% of studies exclude post-construction variables, and developing economies remain underrepresented (24%). Originality: This review provides the first comprehensive synthesis of risk assessment practices spanning 2020-2026, quantifying methodological trends and identifying that 65% of studies lack integration of post-construction variables. The findings establish a baseline for understanding the evolution toward technology-enabled, proactive risk assessment frameworks in construction projects.

Behavioral finance shows that bias influences personal finance decisions. This study focuses on Sri Lankan customers during the turbulent economic situation in the country and the biases that come with it. It samples all present biases, loss aversion, anchoring, mental accounting, overconfidence, herding, status quo bias, and a further, valence-biased, psychologically motivated bias. A structured questionnaire with demographic, financial literacy, and behavioral-based banking questions, and behavioral bias on a Likert scale, was distributed to 400 personal banking customers selected through stratified random sampling, with 323 valid responses processed. Descriptive statistics, chi-square, independent samples t, and logit regression were the analytical methods used. Status quo bias and mental accounting were the leading ones with the highest mean scores. Present bias, overconfidence, and psychology bias significantly predicted the respondents reporting that their savings were always increasing. Bivariate tests also revealed age, education, and income association with gains in savings perception. Age and market condition attention were positive predictors of perceived success in savings, while higher present bias and emotion-driven tendencies were negative predictors. The study also shows strong dependence on fixed deposits, considerable use of online banking, and ongoing reliance on personal judgement and family advice when making financial decisions. The research offers the results of an under researched South Asian environment and recommends the creation of banking products with a perception of bias, the establishment of more comprehensible communication with the consumers, the creation of specific financial literacy programs, and the increased inclusion of digitally reluctant and low-income clients in the banking strategies.

Good quality of financial reports enhances stakeholders’ confidence and value of listed business entities. The quality of financial reports has attracted the concern of researchers in accounting literature and that there has been an ongoing issue of corporate report quality concern.  The capital market globalisation has heightened the pressure to have a standardised financial reporting language, resulting in the expansion in the use of International Financial Reporting Standards (IFRS). Literature has shown that in Nigeria, listed companies were required to use IFRS since 2012 in order to improve the quality of financial reporting and to become a part of the global economy. Nonetheless, ten years after the adoption, there are still concerns on whether there was a real change in the quality of financial reports. This paper explores how the adoption of IFRS affects the quality of corporate financial reporting in particular, in the qualitative attributes of relevance and faithful representation of fundamental nature among listed consumer goods firms in Nigeria. The survey research design was used with the data to be gathered through questionnaire of 100 persons in the top management of 10 purposely selected companies with a response rate of 88%. The analysis of data was performed based on descriptive and correlation. The results indicate strong, positive, and statistically significant effect of IFRS adoption on relevance (Adj R2 = 0.111, F-stat (2, 97) = 68.343, p < 0.05) and also indicate strong, positive, and statistically significant effect of IFRS adoption on faithful representation (Adj R 2 = 0.166, F-stat (2, 97) = 107.840, p < 0.05). The research also finds out that the adoption of IFRS has a tremendous positive impact on the quality of financial reporting in the consumer goods industry of Nigeria but it depends on the strong management skills, the high level of compliance, and effective institutional and regulatory frameworks. Regulators and corporate management are given recommendations on how to enhance these enablers to ensure a good quality of reporting is maintained.

This study investigates the hydrological characteristics of prospective watersheds in Niger State, Nigeria, using a space-based approach to support enhanced and sustainable irrigation agriculture. The methodology integrates climatic and hydrological data to develop a comprehensive, data-rich framework for water resource management within geo-hydrological units, supporting agricultural planning and environmental protection. The study employs statistical computation of land resources using Sentinel-2 imagery in Google Earth Engine, along with spatial analysis of hydrological systems using Digital Elevation Model (DEM) data for stream network analysis. A Multi-Criteria Decision Analysis (MCDA) was applied to identify potential reservoir sites, incorporating topography, stream order, catchment area, and slope as key criteria. Criteria weights were assigned using expert judgment and a pairwise comparison approach, with topography and stream order receiving the highest weights given their primary influence on water accumulation and flow routing. The study delineated twenty-two (22) prospective watersheds as geo-hydrological units, ranging in size from 675.77 km² to 12,358.8 km². The circularity ratios indicate that more than 55% of the watersheds have values between 0.4 and 0.5, suggesting irregular shape, moderate surface runoff, and high permeability, characteristics consistent with structural controls from remnant tectonic features in the underlying crystalline rocks. Multiple factors, including topography, land use, soil type, geology, and climate, influence the hydrological characteristics of these watersheds. Stream networks display a dendritic pattern flowing predominantly NE–SW, parallel to the Nigerian regional lineament, indicating structural control and confirming their role as conduits for groundwater recharge. The failed triple-arm rifting system responsible for the Niger and Benue river valleys and sedimentary basins has exerted significant structural influence on the region. Niger State has an agricultural land area of approximately 25,361.27 km², which accounts for more than 80% of the state's total land area, while water bodies cover 460.51 km², indicating substantial irrigation potential. The prospective water reservoirs identified in this study have the combined capacity to irrigate more than 22,000 km² of agricultural land. Specifically, the Shiroro and Zungeru Dams can irrigate 2,642.41 km² of agricultural land within their basins, while the Kainji and Jebba Dams have the capacity to irrigate approximately 7,000 km² and 4,000 km², respectively. The study recommends effective multi-sectoral collaboration, participatory planning, integrated management, and adaptive strategies to sustainably manage these prospective watersheds, with the overarching goal of balancing ecological, economic, and social needs through integrated land and water resource management in support of SDG 2 (Zero Hunger) and SDG 6 (Clean Water and Sanitation).

This study investigated the effectiveness of Computer-Assisted Instructional Strategies (CAIS) in teaching General Studies (GS) courses in public tertiary institutions in Anambra State, Nigeria. Specifically, it examined lecturers’ perceptions of CAIS in fostering students’ critical thinking, self-confidence, technical proficiency, and learning outcomes, while also exploring challenges and institutional support structures. A mixed-method design was adopted, combining quantitative and qualitative approaches. The quantitative strand employed survey questionnaires administered to male and female GS lecturers, with hypotheses tested using the t-test at 0.05 significance level. The qualitative strand involved semi-structured interviews with lecturers across universities, polytechnics, and colleges of education, thematically analysed to capture experiences and perceptions. Findings revealed that lecturers generally perceived CAIS as effective in enhancing students’ critical thinking, confidence, and digital competence, with mean ratings above the 2.50 benchmark. T-test results showed no significant gender differences in perceptions, suggesting shared experiences across male and female lecturers. Qualitative insights highlighted CAIS as valuable in managing large GS classes, fostering debate and analytical reasoning, and promoting self-confidence, especially through interactive tools, online forums, and multimedia. However, persistent challenges—including epileptic electricity, poor internet connectivity, inadequate ICT facilities, and limited institutional support—significantly constrained CAIS adoption. Lecturers maintained generally positive attitudes toward CAIS but emphasized the need for sustained training, infrastructural development, and supportive policies. The study concludes that CAIS holds strong potential for transforming GS instruction in Nigeria, but institutional investment, equity of access, and capacity-building remain critical for maximising its impact.

Yoga has gained considerable attention in recent decades as a complementary mind–body intervention with potential benefits for cognitive functioning, emotional regulation, and brain health. The present study provides a broad systematic review and meta-analytic synthesis of approximately 1000 research publications examining the relationship between yoga practice and neuropsychological outcomes. Major academic databases including PubMed, Web of Science, Scopus, and PsycINFO were systematically searched for studies published between 2000 and 2025. After screening more than 1000 articles, 140 studies were identified as relevant empirical investigations, and 42 studies were included in quantitative meta-analysis. Results indicate that yoga practice produces moderate improvements in cognitive domains such as attention, executive functioning, and working memory. Previous meta-analytic research indicates that yoga interventions significantly improve cognitive functioning among healthy adults and older populations. Neuroimaging studies further demonstrate structural and functional changes in brain regions associated with cognitive processing, including the hippocampus, insula, prefrontal cortex, and cingulate cortex. The findings suggest that yoga promotes neuroplasticity, enhances neural efficiency, and reduces stress-related neuroendocrine responses. Overall, yoga represents a promising non-pharmacological strategy for improving neuropsychological functioning across diverse populations.

Disaster preparedness is essential in developing community resilience among future educators; however, existing studies indicate a gap between students’ disaster awareness, participation in Disaster Risk Reduction and Management (DRRM) activities, disaster preparedness, and their engagement in emergency management practices. This study examined the influence of disaster awareness, participation in DRRM activities, and disaster preparedness on student engagement in emergency management practices among education students. Guided by the Protection Motivation Theory (Rogers, 1975), the study employed a descriptive-correlational research design involving 141 students selected through stratified random sampling. Data were collected using a validated survey questionnaire measuring the variables of disaster awareness, participation in DRRM activities, disaster preparedness, and student engagement in emergency management practices. Descriptive analysis revealed generally high levels across the measured variables. Multiple regression analysis indicated that disaster awareness, participation in DRRM activities, and disaster preparedness significantly influenced student engagement in emergency management practices, with participation emerging as the strongest predictor. The findings highlight that while awareness provides foundational knowledge, active participation in DRRM initiatives and practical preparedness activities play a stronger role in enhancing students’ readiness and engagement in emergency management practices. These results underscore the importance of strengthening experiential and participatory DRRM initiatives within teacher education programs to further enhance students’ preparedness and engagement in emergency management practices.

Housekeeping services are a critical component of the hospitality industry, especially in deluxe-class hotels, where guest expectations are exceptionally high. These services directly influence the quality of a guest’s stay, affecting both their overall experience and satisfaction. In a luxury hotel setting, cleanliness, comfort, and attention to detail are paramount, and housekeeping plays a pivotal role in maintaining these standards. The efficiency, professionalism, and consistency of housekeeping services can significantly impact a guest's perception of the hotel and contribute to their overall satisfaction. This is particularly important in deluxe-class hotels, where the competition for high-end clientele is fierce, and guest satisfaction is a key factor in ensuring repeat visits and positive reviews. This study utilized the descriptive-correlational method of research using a researcher-made survey checklist questionnaire, which includes the demographic profile of the respondents in terms of age, gender, civil status, nationality, and purpose of stay; and the impact of the housekeeping services to guest in a hotel accommodation in terms of cleanliness, room presentation, timeliness of the service, and staff professionalism. This study utilized a random sampling of ten (10) respondents per selected six (6) hotels in Cebu, and a total of fifty (60) respondents who will take part in this study. A researcher-made survey questionnaire will be used to administer the conduct and distribution as a means to gather the facts and information of this study the instrument tool is composed of three (3) parts; the first part is the profile of the respondents where the respondents utilized the checklist questionnaires and the second part is the impact of the housekeeping services on the guest satisfaction as rated by the respondents where utilized also the checklist questionnaire in accordance to the rating scales and categorical responses of 4-Highl Impact, 3-Moderate Impact, 2-Less Impact, and 1-No Impact, and the third part is the problems encountered by the respondents were also rated by them. The statistical tools that will be used in this study are frequency count and percent, weighted mean and ranking, along with inferential statistics including Chi-Square, Analysis of Variance, or t-test to comprehensively test the hypotheses and significant differences of the variables of the study. The findings of the study will serve as the basis for a proposed action plan.

School-Based Management (SBM) is a governance reform that decentralizes decision-making and enables schools to take a more active role in improving educational quality and learner outcomes. In the Philippines, School-Based Management (SBM) has been institutionalized through the Department of Education's policies and supported by Republic Act No. 9155, which emphasizes shared governance and accountability in schools. Despite these efforts, several challenges continue to affect the successful implementation of SBM practices. This study examined the challenges encountered in implementing School-Based Management in selected public elementary schools in the District of Bohol Province during the school year 2025. A descriptive–quantitative research design was used to determine the extent of challenges experienced by school personnel. Data were gathered through a st ructured questionnaire administered to school heads, assistant principals, department heads, master teachers, and classroom teachers. The findings revealed that limited financial resources, heavy administrative workload, and insufficient training are among the most significant challenges affecting the implementation of SBM. These results highlight the need for stronger institutional support, including adequate funding, continuous professional development, and improved administrative systems. The study provides insights that may assist school leaders and policymakers in strengthening SBM implementation and addressing barriers that affect school governance practices.

Climate change poses a significant challenge to semi-arid regions such as Telangana, India, where increasing temperatures, erratic rainfall, and rapid urban expansion intensify environmental stress. This study presents a comprehensive and analytically enriched assessment of climate change mitigation strategies in Telangana, focusing on renewable energy, afforestation, sustainable agriculture, and urban climate interventions. The research integrates secondary datasets with quantitative trend analysis, including percentage change, variability indices, and comparative sectoral evaluation. Results reveal a 2.8°C rise in average temperature (2000–2025) and high rainfall variability with fluctuations between 760 mm and 950 mm, indicating climatic instability. Mitigation initiatives such as the Cool Roof Policy, solar energy expansion, and afforestation programs demonstrate measurable impacts, including reduced energy consumption (up to 15–20%) and increased carbon sequestration potential. However, effectiveness varies across sectors due to implementation gaps, limited monitoring, and socio-economic constraints. This study introduces a performance-based evaluation framework using measurable indicators such as emission reduction potential, energy savings, and resilience capacity. The findings highlight the need for integrated policy enforcement, technological innovation, and stakeholder participation. The study contributes to regional climate policy discourse and provides a replicable framework for semi-arid regions.

The National Maritime Heritage Complex can powerfully convey the story of Lothal Dock as a brilliant example of multimodal transport integration, showcasing the advanced capabilities of the Indus Valley Civilization to a global audience Lothal functioned as a thriving port city and a key economic hub of the Indus Valley Civilization, showcasing remarkable urban planning, engineering prowess, and extensive trade networks that connected it to both inland regions and far-flung ancient civilizations with multimodal maritime connectivity Lothal's dock was not merely a place for ships; it was the central node where maritime voyages converged with inland river networks and overland routes, forming a seamless, efficient system for trade and commerce. This integration allowed Lothal to serve as the gateway for goods flowing into and out of the vast Indus Valley Civilization.

Lothal, a prominent city of the Indus Valley Civilization (IVC), provides valuable insights into the social, cultural, and religious aspects of this ancient civilization. While direct evidence like written texts is still undeciphered, archaeological findings at Lothal and other Harappan sites allow us to infer a great deal. Understanding the religious, philosophical, and mythological aspects of Lothal, like the broader Indus Valley Civilization (IVC)

This paper presents the design and implementation of an Internet of Things (IoT)-based Health and Alcohol Monitoring and Safety Control System aimed at enhancing safety in industrial, transportation, and other safety-critical environments. Accidents caused by alcohol consumption and abnormal health conditions pose significant risks, while traditional monitoring methods are often manual, inefficient, and lack real-time capabilities. The proposed system integrates multiple sensors, including an MQ-3 alcohol sensor, heart rate sensor, blood pressure sensor, and DS18B20 temperature sensor, to continuously monitor physiological parameters of individuals. The collected data is processed using an ESP32 microcontroller, which analyzes sensor values and compares them with predefined safety thresholds to determine the user’s fitness for operation. When abnormal conditions are detected, the system activates alert mechanisms such as a buzzer and LCD display notifications, and can restrict access to machinery or workplace environments. Furthermore, the system utilizes Wi-Fi connectivity to transmit real-time data to an IoT cloud platform, enabling remote monitoring, data logging, and safety analysis through web or mobile interfaces. Experimental results demonstrate that the system accurately detects unsafe conditions and responds in real time, thereby reducing human error and improving overall safety. The proposed solution offers a reliable, scalable, and cost-effective approach for preventing accidents and enhancing safety monitoring in modern industrial applications.

Xylanases are important hydrolytic enzymes responsible for the degradation of xylan, the major hemicellulosic component of lignocellulosic biomass. These enzymes play an essential role in various industrial processes, including pulp and paper bleaching, food processing, textile manufacturing, animal feed formulation, and biofuel production. However, the high cost of enzyme production has limited their widespread industrial application. One promising approach to reducing production costs is the utilization of inexpensive lignocellulosic biomass as a fermentation substrate. The present investigation focuses on the production of extracellular xylanase by the white-rot fungus Ganoderma lucidum using hydrolysates derived from widely growing non-food plant biomass. Six terrestrial and aquatic weeds—Commelina benghalensis, Cynodon dactylon, Eichhornia crassipes, Parthenium hysterophorus, Pistia stratiotes, and Setaria viridis—were evaluated as potential substrates for xylanase production under submerged fermentation conditions. Plant biomass samples were subjected to autohydrolysis to release hemicellulose-rich soluble compounds that could serve as carbon sources for fungal growth and enzyme production. Fermentation experiments were conducted at 25 ± 2 °C and pH 5.5 under static conditions for 7–14 days. Among the substrates tested, hydrolysate derived from Setaria viridis resulted in the highest xylanase production, followed by Cynodon dactylon and Parthenium hysterophorus. Moderate enzyme activity was observed with aquatic plant substrates such as Eichhornia crassipes and Pistia stratiotes, whereas Commelina benghalensis supported minimal enzyme production. The results demonstrate that non-food plant biomass can serve as an economical and sustainable substrate for xylanase production. Utilization of such biomass not only reduces production costs but also contributes to effective management of invasive plant species and lignocellulosic waste. This study highlights the potential of Ganoderma lucidum as a promising organism for cost-effective industrial enzyme production.

AI systems are operating with increasing autonomy and capability in complex domains in the real world becoming more and more ubiquitous in ways that we do not understand yet. These systems will continue to become more complex and pervasive as they become more intelligent and newer applications are enabled. Increased autonomy in such systems means that they have the potential take actions that can be detrimental to humans within their increased sphere of interaction unless they are designed to be otherwise. Thus systems with AI capabilities urgently require better alignment with human values. This is called the AI Value alignment problem (AI VAP). In this paper, a framework for creating such an AI system is proposed based on the traditional Indian conceptualization of intelligence and values. Human values have been the foundation of Indian scriptures, including the Bhagwad Gita, Vedas, and the Upanishads. Values act as guidelines for our behavior. They have a significant influence on our personalities, attitudes, and perceptions. The traditional Indian philosophy offers a holistic perspective on intelligence and values by considering various aspects of human cognition and consciousness. The proposed framework for creating Value-aligned AI system based on these perspectives provides conceptualization of the different modules required to actually implement Value aligned AI systems. This makes the proposed framework different from those that provide high level ideas without delineating implementation aspects. A rudimentary case of AI value aligned system is also provided to illustrate ideas presented in the paper.

This study aims to evaluate Bangalore investors' perceptions of AI-powered stock market platforms. The study's primary objectives are to ascertain respondents' familiarity with AI-based stock trading platforms, identify the factors influencing their adoption, and assess perceived risks associated with these technologies. It examines how investors' opinions of AI platforms connect to their past experiences with AI-based financial instruments in addition to contrasting trust, efficiency, and decision-making accuracy between AI systems and traditional human financial advisors. The study also examines security and privacy issues as well as consumer preferences when utilizing AI-powered trading platforms. The study is based on primary data collected from 100 respondents in Bangalore, 73 of whom were men and 27 of whom were women. The findings indicate an increasing awareness of technological advancements in the stock market, since a significant portion of respondents are either somewhat or very familiar with AI-based trading platforms. Accuracy and transparency were shown to be the most significant factors influencing trust in AI-driven platforms. AI-based systems may be helpful in reducing human prejudice, as most respondents acknowledged that emotional bias affects stock market decisions. Despite the fact that many investors consider AI trading to be moderately to extremely risky, the majority of respondents preferred a hybrid approach that combines both AI systems and human experience, and half of the respondents said they would be willing to rely on AI over traditional financial advisors.

Corrosion–fatigue of high-strength aluminium alloys is governed not only by reductions in mean fatigue life but, more critically, by systematic changes in fatigue-life dispersion that directly affect structural reliability. In this study, the corrosion–fatigue behaviour of AA 7075-T651 under multiaxial loading is analysed from a dispersion-centred statistical perspective using a Weibull-based framework to quantify how variability evolves with stress level, corrosion exposure, and loading mode. Fatigue data obtained under uncorroded conditions and after 7-day and 14-day NaCl exposure were analysed for bending and torsional loading. Stress-level binning was employed to ensure statistically robust parameter estimation without extrapolation beyond the experimentally tested domain. Rather than treating scatter as experimental noise, the Weibull shape parameter is explicitly interpreted as a stress- and environment-dependent descriptor of fatigue-life variability. The results show that, under uncorroded conditions, fatigue-life dispersion increases significantly at lower stress levels for both bending and torsional loading, consistent with a transition toward initiation-controlled failure. Corrosion exposure produces a pronounced and persistent reduction in the Weibull shape parameter, indicating a fundamental broadening of the fatigue-life distribution that intensifies with increasing exposure duration. Loading mode is further shown to modulate this behaviour, with torsional loading exhibiting distinct dispersion characteristics relative to bending, particularly in corrosive environments. These findings demonstrate that fatigue-life dispersion in AA 7075-T651 cannot be characterised using stress-independent statistical parameters or inferred reliably from mean-life trends alone. Instead, dispersion evolves systematically with stress level, environmental degradation, and loading mode, necessitating stress- and environment-dependent probabilistic descriptions. By explicitly quantifying these effects, the present study provides a statistical basis for advancing corrosion–fatigue assessment beyond conventional deterministic and mean-life-based approaches toward reliability-oriented evaluation under multiaxial loading.

Power transformers are vital parts of electric energy transmission and generation systems. This article evaluates the causes of solid insulating dielectric degradation in power transformers based on operational stresses.  Dissipation Factor (DF) or tan delta (Tan  and power factor (PF) tests was conducted on the transformer windings based on the guard-to-ground-to-shield (GST) method using the Delta-3000 Megger instrument to measure winding capacitance (PF), leakage current, and Tan values. Findings revealed that there were large, sudden increases in variations in the winding capacitance values ranges from 2954 to 9638 and tan  values from 0.9976 % to 1.241% measured after failure at 40 when compared to base value capacitance from 2569 to 8381 and tan  values from 0.344 % to 0.428% measured at 30 before transformer failure. These higher changes in tan  overtime, capacitance (PF), and leakage current indicated deteriorating insulating material conditions, contaminations, dielectric losses and degradation. Also, thermal stress caused mechanical deterioration with time at an elevated temperature and degradation; Mechanical stress caused transient currents during switching on power transformer, damaged insulation due to mechanical vibration; Electrical stresses create significant high-voltage exposure to voltage transients; and Environmental stresses results due to presence of moisture, dirt, chemicals, radiation, or other contaminants. The proposed Condition monitoring can be used to monitor transformers parameters such as dissolving gas analysis, partial discharge, oil quality, vibration, moisture, windings temperatures, detect faults at incipient stage; avoid catastrophic events; Optimized maintenance and prevent failures.

This research introduces a new exponential ratio-type estimator for estimating the population mean when survey data are affected by nonresponse. The proposed estimator utilizes two auxiliary variables within a double sampling framework in order to enhance estimation accuracy. Expressions for the mean squared error (MSE), coefficient of variation (CV), and relative efficiency (RE) of the estimator were derived. The performance of the estimator was assessed using numerical illustrations and compared with some existing estimators. The empirical results reveal that the proposed estimator yields smaller MSE and CV values while achieving higher relative efficiency across different subsampling rates at a 5% nonresponse level. The findings indicate that the integration of auxiliary variables through exponential adjustments leads to substantial improvement in estimator performance. Consequently, the proposed estimator provides a more reliable and efficient alternative for estimating population means in the presence of nonresponse.

Micro, Small, and Medium Enterprises (MSMEs) play a critical role in economic development, employment creation, and innovation in developing economies. Public procurement has increasingly been recognized as an important policy instrument for promoting inclusive economic growth by expanding business opportunities for MSMEs. In Kenya, procurement reforms such as the Access to Government Procurement Opportunities (AGPO) initiative and provisions under the Public Procurement and Asset Disposal Act were introduced to enhance MSME participation in government contracting. Despite these reforms, many MSMEs continue to face structural barriers that limit their effective participation in public procurement markets. This study examines the determinants of MSME participation in public procurement by focusing on access to procurement information, financial capacity, and digital procurement adoption. Guided by the Resource-Based View (RBV) and Institutional Theory, the study analyzes how institutional factors and firm-level capabilities influence MSME engagement in procurement markets. Data were collected from 305 MSMEs registered under the AGPO program in Kenya and analyzed using Structural Equation Modeling (SEM). The results indicate that access to procurement information has the strongest positive influence on MSME participation (β = 0.41, p < 0.001), followed by financial capacity (β = 0.33, p < 0.001) and digital procurement adoption (β = 0.29, p < 0.001). The structural model explains 58% of the variance in MSME participation (R² = 0.58), indicating strong explanatory power of the proposed determinants. The measurement model demonstrates satisfactory reliability and validity, while the overall model fit indices confirm an acceptable model fit (CFI = 0.95, TLI = 0.94, RMSEA = 0.046, SRMR = 0.041). The study contributes to procurement literature by applying SEM to evaluate MSME participation in public procurement, integrating institutional and firm-level determinants within a unified analytical framework, and providing updated empirical evidence from Kenya more than a decade after the introduction of procurement reforms. The findings offer important policy insights for strengthening procurement transparency, expanding MSME access to procurement financing, and enhancing the effectiveness of digital procurement systems to support inclusive participation in public procurement markets.

Excessive screen time in early childhood has become a major public health concern, especially after the COVID-19 pandemic. This review of the literature critically analyzes current studies examining the effects of screen time on cognitive, language, social-emotional skills, physical growth, and preparedness for school in children aged 0–6 years. Five significant peer-reviewed studies released from 2022 to 2023 were examined, comprising systematic evaluations and forward-looking cohort studies. Results consistently show correlations between heightened screen time and setbacks in language development, diminished academic preparedness, reduced quality of parent-child engagement, and possible cognitive and social-emotional weaknesses. Despite methodological constraints, causality cannot be definitively determined, Accumulating evidence backs suggestions for controlled, age-appropriate, and monitored screen usage. This analysis compiles existing evidence, highlights research shortcomings, and explores effects on clinical practice, parental support, and policies for early education.

Purpose Post Office investment schemes play a significant role in promoting financial inclusion in India, particularly among women investors who often prefer safe and accessible investment avenues. The purpose of this study is to examine women investors’ perceptions, attitudes, and responses toward Post Office investment schemes and to assess their role as instruments of financial inclusion through the lens of investment behaviour. Design/methodology/approach The study adopts a quantitative descriptive research design based on secondary data. Unlike traditional survey-based studies, this research utilizes "Revealed Preference Theory," inferring investor perception from actual behavioural data. Data was collated from the Ministry of Finance (Lok Sabha/Rajya Sabha Starred Questions), Department of Posts Annual Reports, and RBI statistics covering the period 2019–2025. Findings The analysis reveals a strong behavioural preference for government-backed schemes among women. The rapid uptake of the Mahila Samman Savings Certificate (43.30 lakh accounts in 18 months) and the sustained growth of Sukanya Samriddhi Yojana (4.10 crore accounts) indicate that "Safety" and "Institutional Trust" are the primary drivers of women's financial inclusion. The study confirms that despite the rise of digital banking, the Post Office remains a critical "safe haven" for women's capital. Originality/value This study contributes to the literature by utilizing macro-level government data to validate micro-level assumptions about women's risk aversion. It provides empirical evidence of the "Trust Factor" in public sector financial institutions.

In this paper, we establish a generalized fixed-point theorem for fuzzy contractions in fuzzy metric spaces. The result extends the well-known Banach contraction principle into the setting of fuzzy metric spaces by employing a generalized fuzzy contraction. Examples are provided to demonstrate the applicability and generalization of classical fixed-point results. Fuzzy fixed-point techniques are used in mathematical modelling to solve problems where traditional methods fail due to imprecise or uncertain data. To obtain fuzzy fixed points, different contraction conditions are implemented in a fuzzy context.

This study examined the effect of international migration, on unemployment, and poverty in Nigeria from 1985-2020). The data used in this study were obtained from Central Bank of Nigeria (CBN) statistical bulletin (2020), World Development Indicators and the KOF Swiss Economic Institute. These comprises of annual data of the following variables unemployment rate and poverty index serves as the dependent variables in the two models respectively while international migration remittances, Globalization Index, and Adult literacy rate serves as the independent variables. The test statistics used in the analysis of data was; Auto Regressive Distributed Lag (ARDL). The results showed that; International migration proxy by international migration remittances has a positive and significant relationship with unemployment and a negative and significant relationship with poverty in Nigeria. it was recommended that; Enlightenment campaigns directed at recipients of remittances regarding the benefit of investing remittance money in small and medium scale enterprises be encouraged rather than using the money as a substitute for labor income; Nigerian governments and their agencies should manage the economy properly and put up policies that can help to eliminate poverty which has been identified as the major force that pushes its citizens into migration. In other words, the formulation of National Migration Policy in 2015 is not enough. There is need to develop an accompanying policy on poverty reduction to propel the migration policy to success; the need for Government to encourage globalization, by embarking on trade liberalization policies in order to accelerate and sustain industrial growth and in turn a reduce poverty. They should also monitor the movement of factor inputs as well as imported and exported goods both in and out the country by way of creating a well secured boarders across the country and a strong and efficient Custom Officials. 

In October 2016, Tata Sons found itself at the centre of an unexpected leadership crisis when Cyrus P. Mistry was removed as Chairman. What followed was not just a corporate dispute, but one of the most widely debated governance episodes in modern Indian business. The situation offers a compelling lens to understand how power, leadership, ownership, and institutional structures interact within large business groups (Chakrabarti et al., 2008; Tricker, 2019). Tata Sons represents a distinctive organizational form. Unlike many global corporations, it operates as a professionally managed conglomerate with a majority ownership held by philanthropic trusts. This arrangement has historically supported long-term thinking and ethical governance, but it also creates complex and sometimes ambiguous lines of authority between trustees, board members, executives, and minority shareholders (Khanna & Palepu, 2010; OECD, 2015). Recent work suggests that such hybrid ownership models can be both a strength and a source of governance tension, particularly in emerging market contexts (Singh & Delios, 2023). What makes the Tata–Mistry episode particularly significant is how several forces converged at once—leadership succession, concentrated ownership, and boardroom dynamics—each reinforcing the other. Formal governance mechanisms existed, but informal influence, legacy considerations, and relational power played an equally important role in shaping outcomes (Pfeffer, 2010; Mintzberg, 1983). The episode raised uncomfortable but important questions about board independence, transparency, and the actual autonomy available to professional leaders in legacy-driven organizations (Aguilera et al., 2021).

The role of Lactic Acid Bacteria (LAB) is vital during bio-processing (fermentation) which could result to desirable nutritional, sensory and safety quality product. This study investigated on growth kinetics variables, metabolites and safety effect of Lactobacillus acidophilus, Streptococcus salivarious subsp. thermophilus and Lactobacillus delbrueckii subsp. bulgaricus (as starter culture) on Cocos typical based extract during time-monitoring bio-processing. This was done anaerobically in the bioreactor with optimized timing of 0-72 h at 37 °C, but samples were drawn every 12 h intervals. Specific LAB growth rate was calculated using Monod equation while specific consumption rate of substrates and generation rate of metabolite in the fermenting samples were modeled using Luedeking-Piret equation. The total LAB count (6.36 to 10.54 log10 cfu/ml) and lactic acid concentration (0.07 to 1.74%) increased but pH (6.48 to 4.03), total sugar (20.96 to 10.88%), total soluble sugar (3.01 to 0.17%), total solid (5.98 to 2.81%) and fructo-oligosaccharide (100.05 to 34.31 mg/kg) decreased with increasing fermentation period. At 12 h, higher specific growth rate with the lowest doubling time of 0.22 h-1 and 3.15 h respectively. Both specific fructose and sucrose rate consumed by the lactic acid bacteria were higher at 72 h. Similarly, at 72 h, more lactic acid was produced and the least concentration was observed at 36 h of fermetation period. Zone of inhibition as antibacterial effect of each samples against Escherichia coli, Salmonella typhi and Staphylococcus aureus ranged from 10.11-19.33 mm, 9.13-19.83 mm and 9.89-21.17 mm respectively. It was deduced accordingly that the Cocos typical extracts (at 24 h and 36 h) served as good carbon sources for Lactobacillus acidophilus, Streptococcus salivarious subsp. thermophilus and Lactobacillus delbrueckii subsp. bulgaricus) and the production of useful metabolites that could guarantee the prevention of pathogens was enhanced.

Entrepreneurship is the dominant culture these days by the government, public, and private institutions. Small institutions are looking to collaborate with others to establish an Entrepreneurial Ecosystem. Thus, providing the facilities and resources for the students to become entrepreneurs. An Entrepreneurial Ecosystem can be defined as any collaboration established between a university, industry, or institution to enhance and strengthen the entrepreneurial culture. An Entrepreneurial Ecosystem allows startups to create, take risks, and interact successfully. Robust Ecosystems make startups to access the information and resources they require to be competitive. After identifying the importance of inculcating entrepreneurial culture into students, which cannot happen individually, many institutions have started collaborating with ecosystem elements that share knowledge and facilities to enhance entrepreneurship. This entrepreneurial ecosystem plays a vital role in creating and sustaining innovations and creativity in the Country. The concept of the ecosystem is growing rigorously in educational institutions in the country. All the educational institutions are giving a lot of importance to entrepreneurship. This study aims to understand the components of the ecosystem, trends in the evolution of the entrepreneurial ecosystem, and the consequences of the startup era in India.

This study develops and evaluates a framework for subsea power generation tailored to the Niger Delta, addressing persistent deficiencies in conventional onshore electricity systems. Using a multidisciplinary approach, the research integrates technical feasibility, economic analysis, environmental impact assessment, and comparative performance evaluation. Key parameters, including tidal velocity, seabed conditions, and system reliability, were analyzed alongside life cycle and cost metrics. Findings indicate that subsea systems offer improved energy efficiency, enhanced reliability, and reduced environmental footprint compared to conventional onshore methods. Economic modeling suggests long-term cost competitiveness despite high initial capital expenditure. The study further demonstrates that localized environmental and geotechnical considerations are critical to system optimization. Overall, the proposed framework presents a viable pathway for sustainable energy development, with significant implications for energy security, industrial growth, and environmental stewardship in the Niger Delta.

Tourism is a major driver of the Philippine economy, contributing significantly to employment, income generation, and regional development. Despite its importance, empirical modeling of international tourism demand in the Philippines remains limited. This study addresses this gap by estimating a tourism demand function using two econometric approaches—Ordinary Least Squares (OLS) and Seemingly Unrelated Regression (SUR)—to examine the macroeconomic determinants of international tourist arrivals. The analysis focuses on the top five source markets—South Korea, the United States, Japan, China, and Australia—using annual time-series data from 2007 to 2023. Data were sourced from the Department of Tourism, World Bank, Philippine Statistics Authority, EM-DAT, and the Bangko Sentral ng Pilipinas. Descriptive results reveal a steady increase in tourist arrivals from 2007 to 2019, followed by a sharp decline during the COVID-19 pandemic (2020–2021), and a gradual recovery beginning in 2022 with the easing of global travel restrictions. Econometric findings indicate that real per capita income and exchange rates positively and statistically significantly affect tourism demand, while relative prices and crime rates negatively affect arrivals. Notably, the frequency of natural disasters exhibits a moderate positive association with tourism demand, possibly reflecting post-disaster reconstruction efforts and intensified tourism promotion. Comparative results show that the SUR model yields more efficient and consistent estimates than OLS, due to the presence of contemporaneous correlation across country-specific equations. Overall, the findings underscore the interdependence of tourism demand across major source markets and provide policy-relevant insights for enhancing the competitiveness and resilience of the Philippine tourism sector.

Digital technology has transformed the way manufacturing firms approach marketing. Industrial buyers no longer search for suppliers as they once did. Now, they explore digital catalogs, examine company websites, and depend on analytics-driven communication to obtain the information they require. This study examines how digital marketing alters the behavior of industrial buyers where they seek information, how rapidly they make decisions, what they value in a supplier, and how they assess value. By using both surveys with procurement professionals and case studies of digitally advanced manufacturers, we aimed for a clear understanding. The results are compelling. When manufacturing firms embrace digital marketing such as SEO, robust web content, and digital catalogs buyers receive better technical information quickly, and information gaps are reduced (Jarvinen & Taiminen, 2016). Digital channels allow buyers to conduct their own research in advance which shortens decision times and makes the evaluation process more efficient (Chaffey & Ellis-Chadwick, 2019). However, it is not just a matter of speed. High-quality digital content builds trust. Buyers begin to view suppliers as more credible, and that trust encourages them to make purchases and maintain long-term relationships (Tiago & Verissimo, 2014). Nonetheless, not all buyers benefit equally. Those with greater digital literacy, or organizations equipped for digital procurement, experience the greatest advantages. By directly linking digital marketing adoption to changes in buyer behavior, this study adds a new dimension to our understanding of B2B digitalization. For manufacturers, the message is clear: update your digital marketing strategies to keep pace with the expectations of today’s industrial buyers.

The problem of analyzing crime has become crucial to law enforcement agencies because of growing urbanization, advanced criminal activities, and the weaknesses of the conventional manualized approaches. Traditional methods are likely to fail when dealing with large volumes of data, slow reaction time, and offensive patterns or risky spots. This paper intends to test and build intelligent crime pattern and hotspots analysis system to facilitate proactive policing and decision-making based on data. The proposed system is an approach based on simulation that involves the integration of machine-learning algorithms and spatial analysis as ways of identifying, classifying, and visualizing crime patterns. The artificial data sets were created to imitate a real-life crime situation and controlled experimentation was possible. The main methods used are clustering which detects patterns (K-Means and DBSCAN), classification models (Random Forest, which predicts types of crime) and Kernel Density Estimation (KDE) (identifies hotspots). The results of the study proved that the system was effectively used to group crime incidences into significant clusters, detect high-density crime localities and offer predictive data that are highly accurate, precise, and recall. Spatial visualizations and heat maps were used to identify the common hotspots of crime, allowing resources to be focused. Conclusively, the smart system is enhanced when compared to the general crime analysis systems since it improves efficiency, accuracy, and actionable data to law enforcement agencies. Combining machine learning and spatial analytics gives it a scalable, data-driven platform that can be used to support proactive policing, optimized resource allocation, and more effective crime prevention strategies, which lead to better community safety.

The purpose of this study was to compare the effectiveness of animated fables and audio fables in improving the synopsis writing skills of ninth-grade students at SMPN 4 Bungbulang. In the Indonesian language curriculum, synopsis writing is an important literacy skill that requires students to summarize, organize, and convey the content of a story effectively. However, many students find it difficult to identify story elements, maintain a consistent plot, and use appropriate language. This study conducted a quasi-experiment with a pretest-posttest control group design. Two groups were tested: the experimental group, which used animated story films, and the control group, which used audio stories. Data were collected through writing tests and response questionnaires given to students. Normality tests, homogeneity tests, t-tests, improvement score calculations, and effect sizes were used to analyze the data. The results showed that there was variation in.

Urban agriculture has become increasingly significant in African cities as a strategy for food security, income generation, and sustainable urban development. This study employed geospatial techniques, anchored in the Sustainable Livelihoods Framework (SLF), to map and analyze market gardening activities and land use changes in Jos South Metropolis, Nigeria, over a ten-year period (2014–2024). The research addresses critical knowledge gaps regarding the spatio-temporal dynamics and sustainability challenges of urban agriculture in Jos Metropolis, where comprehensive data on arable farming activities had been lacking despite extensive crop cultivation. High-resolution satellite imagery with 15 cm spatial resolution was acquired from the National Space Research and Development Agency (NASRDA) for both time periods and processed using ArcGIS 10.8 software. Through systematic on-screen digitization, spatial analysis, and change-detection algorithms, the study quantified changes in agricultural land use, settlements, and water bodies. Extensive fieldwork involved systematic reconnaissance of Jos South Local Government Area (LGA) to identify active market gardening sites, establish ground control points, and conduct interviews with local farmers and agricultural extension agents. Results revealed that market gardens covered 9.59 km² in 2014, declining to 9.03 km² in 2024, representing a 5.84% reduction. Conversely, settlements expanded dramatically from 43.82 km² to 79.32 km², a 34.76% increase, while water bodies decreased marginally from 4.57 km² to 4.46 km², highlighting increasing pressure on water resources driven by urbanization. The study identified key constraints to sustainable urban agriculture including land tenure insecurity, with 63% of dry-season farmers being landless; limited access to irrigation water; pest and disease pressure; inadequate fertilizer supply; and increasing competition for land from urban development. Field surveys documented diverse cropping systems, with farmers cultivating temperate vegetables such as tomatoes, lettuce, cabbage, carrots, and Irish potatoes throughout the year using fadama lands along river channels and mine ponds. The intensive cropping systems documented included plots undergoing three cropping cycles per year, demonstrating both the productivity potential and sustainability challenges. The findings align with global evidence that urban agriculture persists in the face of urbanization, serving multiple livelihood, ecological, and social functions. The research demonstrates the capability of remote sensing and GIS in monitoring urban agricultural dynamics and provides baseline data for urban planning and agricultural policy formulation. The study recommends establishing agricultural zones in fadama areas, strengthening extension services, enacting secure land tenure policies, and fostering rural–urban production synergies to sustain this vital economic sector in the face of rapid urbanization.

This paper discusses the most important bottlenecks in triage (the process of determining who gets medical help first) in public health emergency situations and how delays in providing data through batch processing can lead to higher death rates in resource-constrained areas. It proposes a scalable artificial intelligence architecture consisting of event-driven microservices, long short-term memory (LSTM) predictive layers, and Kubernetes auto-scaling, while ensuring adherence to ethical governance standards. The key contributions of this paper include an Operational Data Store (ODS) that consolidates multiple data streams from various sources a phased implementation framework that has been validated; and Federated Learning (which minimizes bias and adds privacy protection). The results show that there are significant improvements to the overall system performance (i.e. improvements to throughput and reductions to latency), as well as effective forecasting during times of crisis, all verified in real-world settings. Ultimately, the objective of the framework is to provide improved operational efficiency and allocation of resources, thereby increasing national health resilience with respect to the large population of India.

The explosion of enterprises adopting many cloud functionalities has created problems associated with the management of these disparate systems. To meet this challenge, enterprises will begin to move from old ways of executing reactive management to newer, more forward-looking methodologies. This paper will provide an overview of how incorporating predictive analytics and AI can enhance automation in managing multi-cloud environments through an innovative conceptual model that uses machine learning (ML) to improve real-time visibility, anomaly detection and automated remediation to improve operational efficiency and resiliency. Further, this paper will identify measurable performance indicators such as decreased mean-time-to-resolution (MTTR) and fewer service-level agreement (SLA) violations after implementing this model. Challenges in managing multi-cloud environments, including normalizing data between providers, model drift, and issues with integration will also be addressed, as well as potential solutions such as federated learning and autonomous IT operations, to facilitate better governance of multi-cloud environments.

The rapid expansion of the digital economy has intensified demand for data science talent in China, yet higher education curricula often lag behind evolving industry requirements. While the skills gap is widely acknowledged, few studies offer large-scale empirical evidence linking labor-market signals to curriculum design in the Chinese context. This study analyzes 12,436 data science–related job postings from major Chinese recruitment platforms between 2022 and 2024 to map employer demands and their educational implications. Using text preprocessing, natural language processing, skill extraction, clustering, and regression techniques, we identify key patterns in geographic distribution, required competencies, and salary drivers. Results show that job demand is heavily concentrated in Tier-1 and Tier-2 cities. The most frequently required skills include Python, SQL, machine learning, big data tools (e.g., Spark and Hadoop), statistical analysis, and communication abilities. Salaries are most strongly influenced by city tier, company size, educational qualifications, and proficiency in specialized technical areas such as cloud platforms and deep learning. A notable mismatch persists between university training and market expectations—particularly in applied technical skills and interdisciplinary problem-solving. These findings provide an evidence-based foundation for curriculum redesign, stronger industry–academia collaboration, and more responsive educational planning. Future research should extend to longitudinal forecasting and cross-country comparisons.

Career development planning is a critical element of succession planning, focusing on systematically preparing employees for future organizational roles. Career Development planning face significant challenges such as Inconsistent and inadequate implementation of career development practices particularly unequal access to training, mentorship and promotion opportunities which continue to undermine employee performance in public health facilities in Bungoma County. Thus the primary aim of the research was to establish the effect of Career Development Planning on Health Sector performance in the County Government of Bungoma, Kenya. The research was guided by the Human Capital Theory and was conducted in the among the Health Sector in the County Government of Bungoma. This research employed a descriptive survey research design, analysing data through descriptive statistics, including mean, standard deviation, frequency, and percentage, as well as inferential statistics, comprising Pearson correlation and regression analysis, given in tabular form the target population of 240 respondents was drawn from the County Referral Hospital and 9 Sub-County Hospitals. Simple random sampling was used to select 15 Heads of Ward Sections while census method was adopted in selecting 94 respondents within the County Ministry of Health to give a total of 109 respondents as the sample size. The research incorporated primary data sources collected using closed and open-ended questionnaires and interviews, which were pretested in Turkana County to evaluate validity and reliability. Quantitative data collected through questionnaires were analysed using SPSS, while qualitative data from open-ended questions and interviews were subjected to thematic analysis. The findings indicated that all aspects of Career Development Planning exhibited a positive and significant correlation with Health Sector Performance in Bungoma County, Kenya. The  results indicated that Health Sector Performance increases by 0.321 units for each unit rise in Career Development Planning (β2=0.321, p<0.05). This outcome suggests that the County Government of Bungoma should develop and implement a Career Development plan as to improve on the performance of the health sector.

Urban waste management presents still major difficulties, especially in fast-growing cities like Kampala. This paper looks at how using digital technologies might help rural-urban communities better manage their waste. Data came from secondary sources, field observations, and questionnaires. With 60% of issues still unresolved, Kampala City struggles greatly with waste management. Just 28,000 tons of municipal garbage find their way to landfills every month, or only 40% of all created waste. The waste situation in Kampala has gotten rather bad. Previously managing 1,400 to 1,700 tons of waste daily, Kiteezi landfill, the main disposal site for the city, exceeded its capacity since 2009 and collapsed in 2024, with 64% efficiency, the waste collecting system lets most of the trash go unharmed. For locals, this poses serious health hazards as well as environmental ones. Given Africa's urban population is predicted to rise from 470 million in 2015 to 1.2 billion by 2025, the scenario could get more difficult. This study will explore how digital technologies might transform waste management in Kampala City. To address mounting urban waste issues, smart solutions including loT-enabled recycling bins and mobile apps now link homeowners with collectors. We will discuss how artificial intelligence can be used in recycling centers, how block chains provide waste handling transparency, and how data analytics might forecast waste trends to help to allocate resources more effectively. Results point to low waste collecting frequency, high organic waste composition, and inadequate recycling programs. Modern ideas come from digital technologies including smart bins, GIS tracking, and mobile waste collecting apps. Proposed to maximize operations is an integrated solid waste management model. The findings of this study end with suggestions for community involvement techniques and digital infrastructure acceptance.

The increasing number of road accidents involving two-wheelers has become a major concern due to factors such as overspeeding, drunk driving, poor road conditions, and lack of real-time hazard awareness. Existing safety systems mainly focus on post-accident response rather than prevention. This project proposes an intelligent Accident Prevention System for Two- Wheelers using embedded and sensor-based technologies to enhance rider safety through proactive monitoring and control. The system integrates multiple sensors, including LiDAR for front obstacle detection, ultrasonic sensors for rear vehicle monitoring and pothole detection, an alcohol sensor for detecting intoxicated driving, and an MPU6050 sensor for monitoring tilt and sudden movements. An ESP32 microcontroller processes real-time sensor data and determines unsafe conditions based on predefined thresholds. When a potential risk is detected, the system provides immediate alerts through an LCD display and buzzer, and automatically limits or cuts off motor operation using a relay module. By combining real-time sensing, intelligent decision-making, and automated control mechanisms, the system ensures early detection of hazards and reduces dependency on human reaction time. The proposed solution is cost-effective, reliable, and scalable, making it suitable for practical implementation in two- wheelers. Overall, the system significantly improves road safety by preventing accidents and promoting responsible riding behavior.

Ship longitudinal strength analysis is critical for ensuring structural integrity and safety throughout the vessel's operational life. This study presents a comprehensive finite element analysis (FEA) of the MST-3 vessel's longitudinal strength using ANSYS software, focusing on hull girder behaviour under extreme loading conditions. The research employed advanced computational methods to evaluate structural response under sagging and hogging conditions, incorporating material nonlinearity, initial imperfections, and residual stresses from welding processes. The MST-3 vessel, with principal dimensions of 185.0m LOA, 28.5m beam, and 15.2m depth, was modelled using 68,530 finite elements (SHELL181 and BEAM188) with 72,840 nodes. The analysis incorporated AH36 steel material properties with yield strength of 355 MPa and considered initial deflections following elastic buckling modes. Boundary conditions were applied using multi-point constraints (MPC) at the model extremities to simulate simply supported conditions. Results demonstrate that the vessel meets all classification rule requirements with significant safety margins. The ultimate bending moment capacity reached 1,245,680 kN⋅m under sagging conditions and 1,187,420 kN⋅m under hogging conditions, exceeding design requirements by 39.1%. Maximum von Mises stress of 284.7 MPa occurred at hatch corner connections, representing 80.2% of yield strength. Critical stress concentrations were identified at deck-side shell junctions (267.3 MPa), engine room bulkheads (245.8 MPa), and cargo hold corners (231.5 MPa). The progressive collapse analysis revealed ductile failure behaviour with adequate post-ultimate strength reserves. Buckling analysis showed minimum safety factors of 1.85 for all structural components, with longitudinal girders exhibiting the lowest buckling margins. The finite element methodology demonstrated excellent correlation with analytical beam theory solutions, validating the computational approach with maximum differences below 1%. Key findings indicate that while the vessel structure is adequate, hatch corner reinforcement is recommended to address stress concentrations. The study concludes that modern finite element techniques provide reliable tools for ship structural assessment when properly validated. The developed methodology offers practical engineering solutions for longitudinal strength evaluation and optimization of marine structures.

In recent years, digital financial services have been a major driver of financial inclusion. While there is evidence that traditional financial inclusion has a positive impact on economic growth. Any country's economic development is built on the foundation of digital financial services. Mobile banking and mobile money are two new concepts that are transforming bulk services into personalised services. This has resulted in the creation of digital financial inclusion, which fosters efficient interconnection among participants in economic activity. When a poor, previously unbanked consumer begins to transact digitally with his or her family and friends, formal banking and financial institutions, and utility companies, it is called a success. This paper attempts to identify the preliminary concepts of digital financial inclusion, status in India and Tumakuru District and reasons for digital financial exclusions.

The impact of environmental degradation and health inequity has raised the need for sustainable healthcare sytems. Though policies and technology address the healthcare challenges, ethical and value based foundatons are neglected. Linking the healthcare with spiritaulity will help in understanding the interconnectedness, compassion, orientation for life and moral frame work for practicing sustainable healthcare. This article examines the conceptual and practical relationship between spirituality and sustainability in healthcare, taking examples from Indian context. The study draws on philosophical traditions, environmental ethics, and case studies, including the Chipko Movement, practices of the Bishnoi community, sacred groves, Gandhian philosophy, and traditional medicine systems like Yoga and Ayurveda.  The study highlights on the points that achieving sustainable healthcare necessitates both systemic change and moral growth among healthcare professionals and communities. By integrating spiritual values into healthcare sustainability initiatives, we can provide a holistic pathway for achieving ecological reponsibility, social equity and patient well-being.

Industrial machinery is highly susceptible to faults such as oil leakage, overheating, excessive vibration, and abnormal current consumption, which may lead to equipment damage, production loss, or safety hazards. This paper presents an IoT-based industrial monitoring and protection system that continuously observes machine health parameters including vibration direction, temperature, oil leakage, and motor current consumption. The system uses a MEMS accelerometer to detect vibration intensity and direction, along with temperature sensors, oil leakage sensors, and current sensors to monitor critical operational conditions. When abnormal conditions are detected, the system automatically stops the motor using a motor driver and activates a buzzer for immediate alert. Additionally, all sensor data and total current usage are transmitted to an IoT platform for real-time monitoring and notifications. This system enhances equipment safety, reduces downtime, improves operational efficiency, and supports predictive maintenance in modern industrial environments

The conventional asset-backed lending business is often reliant on outdated data management platforms and opaque storage platforms, presenting the industry with very daunting problems, including security breaches, the lack of verifiable ownership histories, and obscure asset disposal practices. To address this, this study came up with a safe web-based system that digitalizes the activities of loans by applying the blockchain technology. The platform is integrated with a modern online architecture, which uses a Virtual Vault Guard to implement the dual-key access protocol, as well as AES encryption and SHA-256 hashing that would guarantee that data about the assets are safely secured on-chain. Smart contracts were automated to create digital receipts that were immutable and to conduct digital bidding of defaulted loans in an atomic swap. The empirical research indicates that the ecosystem improves the general performance of management in terms of availing very structured, secure digital data storage and reliable auditing facilities. The paper concludes that the implementation of a blockchain-based system, which is associated with immutable receipts and transparent bidding, is necessary to ensure complete security, fairness, and efficiency. Based on this, the study suggests that the lending institutions should incorporate this secure ledger of assets and automated system of bidding to enhance customer confidence by providing verifiable digital receipts as well as to provide fair and transparent recovery of capital.

The integration of wearable technology and machine learning has significantly advanced continuous health monitoring systems. However, most existing wearable solutions focus primarily on physiological parameters while neglecting emotional and mental health factors that strongly influence overall well-being. This paper proposes an Emotion-Aware Wearable Health Monitoring System that integrates physiological sensor data with machine learning–based sentiment inference to provide comprehensive health insights. The system collects real-time data from heart rate, skin temperature, galvanic skin response (GSR), and accelerometer sensors, along with contextual inputs such as speech or text. A multi-modal machine learning framework analyzes physiological and sentiment features to detect emotional states including stress, anxiety, fatigue, and calmness. The inferred emotional states are correlated with physical health parameters to generate personalized recommendations and real-time alerts via a cloud-based dashboard. Experimental evaluation using collected sensor data demonstrates the feasibility of emotion-aware health monitoring for preventive and personalized healthcare applications.

The removal of fuel subsidies in Nigeria in 2023 resulted in a 150–200% increase in transportation costs, severely disrupting food distribution systems in a country where logistics costs account for approximately 22% of GDP. This study develops and evaluates a dual-optimization framework for minimizing food distribution costs under volatile economic conditions. First, classical transportation methods—Northwest Corner Method (NWCM), Least Cost Method (LCM), Vogel’s Approximation Method (VAM), and Modified Distribution (MODI)—are evaluated using a 4×5 supply-demand case study (50,000 metric tons). Second, a novel Gokir-Nannim (GN) Model incorporating adaptive penalty functions is developed and integrated with Multi-Objective Mesh Routing (MOMR). Results indicate that VAM achieves a 37.4% cost reduction compared to NWCM, while the GN Model achieves 39.6%. Integration with MOMR produces a 41% reduction in Total Transportation Overhead (TTO), while simultaneously reducing delivery time by 28% and increasing reliability by 17%. Sensitivity analysis under ±30% fuel volatility confirms superior resilience of GN+MOMR compared to classical methods. The findings demonstrate that adaptive, multi-objective optimization can substantially mitigate the inflationary effects of subsidy removal and reduce national logistics costs from 22% to approximately 13% of GDP.

Although wireless sensor networks (WSNs) are crucial parts of modern communication systems, their energy consumption and network longevity are severely constrained. Enhancements to the geographic and energy-efficient routing protocol (GF-EERP) are suggested in this study. This we do by use of precise node location info which in turn improves routing decisions. The protocol will also incorporate reinforcement learning algorithms. To make adaptive routing techniques possible, which improve network efficiency and accomplish balanced traffic distribution. On a number of performance metrics, including latency, packet delivery ratio, network lifetime, and energy consumption, the suggested approach will be carefully contrasted and examined with alternative routing methods. The simulation results will demonstrate that, in a variety of network circumstances, the enhanced GF-EERP will offer superior scalability and reliability compared to the current protocols. In order to enhance energy-efficient communications in WSNs and, consequently, guarantee dependable and sustainable network deployment, this study will highlight the significance of combining geographic routing with machine learning techniques.

Emotional intelligence (EI) is increasingly recognised as a key determinant of leadership, teaching effectiveness, job satisfaction, and stress management in higher education. While prior research largely views EI as a personal trait, research on socio-demographic influences has received limited attention, particularly in the Indian context. This study examines differences in EI across age, gender, marital status, experience, employment status, income, educational qualification, and administrative roles among 106 faculty members from HEI in Telangana. A descriptive, cross-sectional design was employed, and data were collected using a structured questionnaire. Findings reveal significant variations in EI by age, experience, employment status, and income, with older, more experienced, regular employees, and those with higher incomes reporting greater EI. No significant differences emerged for gender, marital status, education, or administrative responsibility. The study highlights EI as a dynamic skill shaped by socio-demographic factors, emphasising its importance for implementing emotional interventions within institutional policies to foster emotional well-being and performance in HEIs.

Large vision-language models have been used to create cross-modal retrieval systems, including CLIP, that have achieved large performance improvements but often act as black boxes, which makes it more difficult to use these models and apply them in more critical areas. This non-disclosure is a great impediment to the responsible implementation of such systems in high stakes applications. As a measure to counter this shortcoming, we suggest an explainability-based design with an embedded post-hoc interpretation modules as part of the CLIP retrieval pipeline. The framework provides sensible, dual- mode accounts of bidirectional retrieval tasks; to begin with, it produces visual heatmaps that both outline the regions in the image that have the strongest impact on a retrieval decision and to a second end, it deactivates word-level attribution to quantify the relative significance of textual tokens in the query or caption. As we will discuss later, with our implementation and subsequent evaluation of our system on Flickr8k one can see that we provide these interpretable insights whilst maintaining, and in fact slightly increasing the baseline retrieval accuracy of the vanilla CLIP model. The empirical evidence confirms that the incorporation of interpretability layers is not accompanied by the trade-off in terms of performance. Together, this work confirms that principled explainability mechanisms should be augmented to multimodal retrieval systems in order to foster trustful, responsible AI solutions. Based on the increased transparency, the approach prepares the foundation of more solid and trustworthy human-AI cooperation.

Low carbon steel is widely used in structural and engineering applications due to its excellent formability and weldability; however, its mechanical properties are highly dependent on microstructure. In the present study, the influence of different heat treatment processes on the microstructure and hardness of low carbon steel was investigated. Different heat treatment conditions, namely annealing, normalizing, conventional hardening (water quenching), Neem Bath ,Ice Bath,Salt Bath,brine Bath quenching were applied. Metallographic analysis was carried out using optical microscopy at magnifications of 100× and 500×. The results reveal significant variations in phase distribution, grain size, and morphology due to different cooling rates. Fine pearlitic structures were observed in normalized samples, whereas martensitic structures were dominant in quenched specimens.  Hardness values increased with increasing cooling rate, with brine-quenched samples showing maximum hardness. The study establishes a clear correlation between heat treatment, microstructure, and hardness of low carbon steel.

Electrical power distribution systems often face challenges due to environmental disturbances and delayed fault detection. This paper presents an Overhead AC Line Fault Detection and Monitoring System for improving safety and reliability. The system uses an ESP32 microcontroller integrated with IoT technology for real-time monitoring. It continuously observes voltage levels across multiple AC poles to detect faults and abnormalities. Temperature and flame sensors are used to monitor battery health and detect fire hazards. Sensor data is processed using embedded programming with predefined threshold values. In case of faults, the system generates instant alerts and updates the IoT cloud platform. An automatic cooling mechanism using a DC fan prevents overheating conditions. The ESP32 enables wireless data transmission for remote monitoring and quick response. The system reduces downtime, manual inspection, and improves operational efficiency. Overall, the solution is cost-effective, scalable, and suitable for modern power distribution networks.

This paper proposes the design and implementa- tion of an integrated, cloud-based Smart Attendance System, specifically designed to meet the growing demands of modern educational institutions. The proposed Smart Attendance System is designed to incorporate two-factor authentication with facial recognition and RFID card identification to improve authenti- cation accuracy. The proposed Smart Attendance System em- ploys microcontroller technology, which may include NodeMCU ESP8266 or Raspberry Pi, in conjunction with MFRC522 RFID card identification module and camera configurations. The pro- posed facial recognition and detection are achieved by employing OpenCV or cloud-based APIs, while attendance is synchronized using cloud platforms like Firebase or AWS. The experimental results show a 99.4% authentication accuracy, reduced ad- ministrative burden, and improved accessibility compared to traditional manual attendance systems.

The increasing demand for sustainable construction materials has driven the exploration of agricultural and industrial wastes as alternative fillers in asphalt concrete. This study investigates the synergistic effect of Plantain Leaf Ash (PLA), an agricultural waste, and Quarry Dust (QD), an industrial by-product, as composite fillers on the mechanical properties of asphalt concrete. The research aimed to determine the optimal blend that balances sustainability with structural performance. The sieve analysis, specific gravity, penetration, softening point and viscosity test were conducted to characterize the materials used. The coarse aggregate (gravel) was found to be gap-graded and deficient in intermediate particles. The bitumen penetration grade is 60/70 which has good temperature susceptibility and is workable, suited for paving applications. Experimental analyses were conducted using varying percentages of PLA (0%, 2%,4%, 6%, 8%, and 10%) and QD (0%, 2%,4%, 6%, 8%, and 10%) %) to assess their impact on the marshal stability, compressive strength and elastic modulus of  asphalt mixture. The results revealed that the incorporation of Quarry Dust significantly enhanced both the Marshall stability, compressive strength and the elastic modulus, indicating its effectiveness as a mineral filler. Conversely, increasing PLA content consistently led to reductions in both compressive strength and elastic modulus, suggesting limited benefits in its role as a partial replacement. The optimal mix identified for superior mechanical performance is 10% QD with 0% PLA, achieving the highest strength and stiffness measures. These findings underscore the potential of Quarry Dust in asphalt concrete applications, while highlighting the need for further investigation into the long-term performance implications of using Plantain Leaf Ash in construction materials. The insights gained contribute to advancements in sustainable construction practices by optimizing material use in asphalt pavements.

Microwave irradiation has emerged as a transformative technique in organic synthesis, enabling rapid and energy-efficient chemical transformations. This study investigates the oxidative potential of di-tert-butyl chromate (TBC) in the oxidation of cyclohexanecarboxamide, aiming to develop a faster, sustainable alternative to traditional thermal reflux methods. The oxidation was explored across three distinct organic solvent systems: tetrahydrofuran (THF), 1,4-dioxane, and dichloromethane (DCM). Reaction mixtures were prepared by combining substrate solutions with TBC at standardized stoichiometric ratios. Synthesis was conducted using microwave irradiation for precisely calibrated periods (44–110 s). The resulting coordination complexes were characterized through elemental analysis, Fourier-transform infrared (FTIR) spectroscopy, and thermal analyses, including differential thermal analysis (DTA) and thermogravimetric analysis (TGA). The microwave-assisted approach demonstrated significant kinetic enhancement, reducing reaction times from several hours to under two minutes. Solvent-dielectric synergy was observed, with THF providing the highest efficiency and yield. Characterization confirmed the formation of stable binuclear Cr2O3 cores stabilized by a homologous series of dicarboxylate ligands. Mass loss patterns from TGA/DTA further validated the empirical formulations and structural stability of the synthesized complexes. Our findings demonstrate that this microwave-assisted methodology aligns with the principles of green chemistry by minimizing reaction time and energy expenditure. This study provides an efficient and sustainable synthetic route for the oxidation of cyclohexanecarboxamide, offering a versatile template for the development of higher-order chromium (III) coordination frameworks.

Due to hectic working hours and rapidly expanding digital infrastructure and usage of information and technology tools, there is bombarding of information. Due flood of information the human attention has become scarce. The average time spend by an Indian on mobile is more than 4.5 hours and total screen time is as about 7 to 8 hrs. Especially after covid the screen time habit shows increasing trend. With heavy population of 140 crore and high screen time the marketers see huge opportunity to promote the product. But attentions of the viewers is the biggest problem for them. The current paper aims to study the attention economy in India and the various marketing strategies used by the marketer to capture and convert the captured attention into revenue. Different strategies like visual hooks, flash sales, AI tools and many more are used to increase the sales turnover by the marketers.

India’s aspiration to emerge as a developed nation by 2047 under the vision of Viksit Bharat necessitates the identification and scaling of localized innovations. Odisha, characterized by its socio-cultural diversity and rapid technological transition, presents a compelling case as a living laboratory for future-ready developmental models. This study positions Odisha at the intersection of three transformative domains: algorithmic welfare systems, governance challenges arising from silent urbanization, and the digitization of cultural heritage through Web3 technologies such as NFTs. By integrating quantitative modeling, qualitative fieldwork, and policy analysis, the study proposes a framework for culturally rooted, inclusive, and decentralized development. The findings suggest that Odisha’s experiments in adaptive welfare delivery, the recognition of hybrid urban spaces, and digital cultural economies not only address local challenges but also offer scalable insights for national policy. The state thus emerges as a microcosm of a technologically empowered, socially equitable, and culturally vibrant Viksit Bharat.

This research was carried out to uncover the relationship between change management practices and the sustainability of ICT Innovation Hubs in Uyo Metropolis, Akwa Ibom State, Nigeria. Executive support and change communication were the dimensions of change management practices used in this study. The survey research design was adopted for this study. The population was made up of personnel drawn from 10 identified ICT Innovation Hubs operating within Uyo Metropolis, Akwa Ibom State making a total of 203 personnel. Census sampling technique was adopted. 203 copies of the questionnaire were distributed to the respondents while 200 copies were returned and used as the basis of analysis. The data collected for this study was analyzed using the Pearson Product-Moment Correlation Coefficient (PPMC) at 0.05 level of significance using the Statistical Package for Social Sciences (SPSS) version 23. The findings of the study revealed that executive support and change communication had a positive and significant relationship with organizational sustainability at ICT Innovation Hubs in Uyo metropolis, Akwa Ibom State. It was recommended that top management should actively participates in change initiatives by providing clear direction, necessary resources, and continuous motivation and organizations should develop structured and transparent communication plans that engage employees at all levels.

Fishing communities operating in remote maritime environments face significant safety challenges due to unreliable communication systems, unclear international sea borders, harsh weather conditions, and delayed emergency response. These factors often lead to accidental border crossings and hazardous situations, threatening the lives of fishermen. To address these issues, this paper proposes a LoRaWAN-Enabled Intelligent Maritime Border Security and Crew Safety System that provides a reliable and energy- efficient solution for enhancing maritime safety. The proposed system is built using an ESP32 LoRa module integrated with a GPS module, DHT11 temperature and humidity sensor, air quality sensor, DC motor with motor driver, buzzer, LCD display, and an emergency switch. The GPS module continuously monitors the real-time location of the fishing boat, while predefined maritime boundary coordinates are used to detect proximity to restricted zones. Environmental conditions inside the boat are monitored using the DHT11 and air quality sensors to ensure crew safety. The system operates on a three-level safety mechanism. Level 1 provides early warnings through visual and audible alerts, Level 2 issues danger alerts with motor speed reduction, and Level 3 initiates critical actions such as automatic motor reversal to prevent border crossing. Environmental threshold violations also trigger alerts to notify the crew. In emergency situations, fishermen can activate the emergency switch to transmit distress signals along with GPS coordinates via LoRaWAN. The system also supports boat-to-boat communication, enabling nearby vessels to share alerts and assist each other without relying on cellular networks. The proposed system is cost-effective, reliable, and suitable for small-scale fishing operations. It enhances safety by integrating border monitoring, environmental sensing, and long-range communication in a single platform.

Sustainable development discourse has operated for over three decades under an unresolved foundational tension: the Brundtland Commission's canonical 1987 definition accommodated continued economic growth as a political condition of international consensus, while the ecological systems that definition was designed to protect have continued to deteriorate. By 2023, six of nine quantified planetary boundaries had been transgressed. This paper conducts a thematic review of seventeen papers spanning the bibliometric, normative-ecological, and political-economic traditions within sustainability research, identifying four structural problems that the field has not resolved: the causal gap between knowledge production and measurable outcomes; the paradigm-replacement problem concerning neoclassical economics; the foundational dependency of the entire literature on Planetary Boundaries evidence it does not independently validate; and the Global South absence problem. The review finds that alternative economic frameworks — Doughnut Economics, post-growth theory, ecological economics, and capabilities-based approaches — have reached theoretical maturity and demonstrated governance operationalizability, but have been developed exclusively in and for high-income, post-industrial economies. The paper argues that the Global South is not a representational gap to be corrected by inclusive citation practice, but the site where existing frameworks' deepest assumptions about the relationship between economic growth, human development, and ecological constraint are most consequentially tested. It proposes a theoretical reconstruction of post-growth and doughnut economics frameworks that engages the specific political economy of Southern developmental contexts — marked by unmet social foundations, constrained fiscal and institutional capacity, colonial resource extraction histories, and asymmetric international financial architecture — as the condition for producing a genuinely global alternative economics of sustainability.

Universal Design (UD) is increasingly recognized as an essential framework for promoting accessibility and inclusivity within the built environment; however, its integration into construction practice remains inconsistent, particularly in developing countries. This study examines the level of awareness of Universal Design among construction professionals and identifies key barriers affecting its adoption within construction projects in Nigeria. A quantitative cross-sectional survey design was employed using a structured questionnaire administered to construction professionals, including architects, engineers, quantity surveyors, contractors, project managers, builders, and procurement officers. A total of 102 valid responses were analyzed using descriptive statistical techniques, including frequencies, percentages, mean scores, and ranking analysis. The findings indicate a high level of professional awareness of Universal Design principles, with over 98% of respondents reporting familiarity with the concept and 85.3% indicating prior involvement in projects incorporating UD features. Despite this high awareness, consistent implementation remains limited. The most significant barriers identified include client priorities related to cost and aesthetics, perceived cost implications of UD features, limited client demand, insufficient technical training opportunities, and project time constraints. The results suggest that constraints affecting Universal Design adoption are influenced more by institutional and market driven factors than by lack of professional knowledge. In order to facilitate the broader incorporation of Universal Design principles in construction practice, especially in developing nation contexts, the study provides empirical evidence emphasizing the necessity of addressing structural hurdles within project delivery environments.

Individuals with paralysis or severe physical disabilities often face significant barriers in communicating their needs, which can limit their independence and quality of care. This study presents a novel system that translates hand movements into corresponding audio messages and displays them on an LCD, enabling effective communication between patients and caregivers. The device incorporates a 3-axis accelerometer and a microcontroller that processes directional hand gestures to generate preprogrammed messages. Testing demonstrates that the system reliably interprets four primary gestures corresponding to the messages: “I am hungry,” “I want to go to the bathroom,” “I want to watch television,” and “I want to go out.” The device operates effectively for users with partial mobility and accommodates those with speech impairments. Maximum speaker volume is 60 dB, with an operational range of approximately 2 meters. The system provides a practical and low-cost assistive technology solution that may improve patient autonomy, caregiver responsiveness, and quality of life.

Looking closer at how consumer buy things on Myntra, an Indian online hub for clothes and lifestyle goods, reveals what really influences their choices. Price often tops the list, though quality holds strong weight, too. Discounts grab attention fast, yet brand reputation significantly shapes long-term loyalty. Customer feedback and reviews can build trust or spark doubt. A smooth application interface keeps users around longer than clumsy ones tend to. Ads seen on social platforms encourage some toward checkout without them even planning to. Young buyers act differently from older groups; young consumers are influenced by what is trending in the market whereas older consumers look for quality and durability. Seasonal sale events bring waves of activity that few other triggers match. Suggestions tailored to past clicks sometimes feel oddly spot-on. Returning items with little hassle removes one big worry mid-purchase. A fresh look at data came from surveys, online tools, printed material, web pages, and academic papers. Results are meant to help store owners, promotion teams, and companies improve shoppers' experience and build trust. Work adds depth to online selling strategies while clarifying how buyers act using sites such as Myntra.

This study presents HARVEST, a mobile-based platform designed to facilitate direct farmer-to-buyer transactions and address inefficiencies in agricultural trading in Mabini, Pangasinan. Farmers in the area face challenges such as dependence on intermediaries, limited market access, and unstable pricing, which reduce income and increase costs for consumers. To address these issues, the system provides key features including product listing, direct messaging, order processing, and transaction monitoring, enabling more transparent and efficient trading. The platform was developed using a three-tier architecture and the Agile development methodology, incorporating continuous feedback from farmers, cooperatives, and the Municipal Agricultural Office to ensure usability and contextual relevance. System evaluation was conducted using the ISO/IEC 25010 Software Quality Model, with fourteen (14) respondents participating in the assessment. Results indicated an overall weighted mean of 4.27, interpreted as Excellent, reflecting high acceptability in terms of usability, performance efficiency, and reliability. The findings demonstrate that HARVEST improves market accessibility, enhances transaction efficiency, and reduces dependence on intermediaries. While the sample size is limited, the results provide initial evidence of the platform’s potential to support more transparent and sustainable agricultural trade. The study contributes to digital agriculture by presenting a localized and user-centered mobile solution tailored to rural farming communities.

This research is motivated by the need to improve operational efficiency and service quality at Ella Digital Printing, which still faces obstacles in the form of unstructured workflows, overlapping employee tasks, and the absence of standard service procedures. This research aims to develop a Service Blueprint as an effort to improve operational efficiency and customer satisfaction at Ella Digital Printing. This research uses the Research and Development (R&D) method with stages of analysis, design, development, implementation, and evaluation. The analysis stage is carried out through observation, interviews, and questionnaires to identify problems and service needs. The design stage produces a Service Blueprint and service SOP which are then validated by management experts, work system design experts, and practitioners with a very feasible category. The implementation results show that the Service Blueprint can be implemented well even though it requires initial adaptation from employees. Evaluation through customer questionnaires shows an increase in operational efficiency and customer satisfaction, especially in aspects of service speed, clarity of process flow, and print quality. This study concludes that the development of the Service Blueprint is effective as a business development approach to improve operational efficiency and customer satisfaction at Ella Digital Printing. This research is recommended to be further developed on a wider business object and scale to obtain more comprehensive results.

Agriculture plays a vital role in the Indian economy, providing livelihood to a significant portion of the population and contributing substantially to national income and export earnings. Among agricultural commodities, turmeric holds a prominent position due to its wide applications in food, medicine, cosmetics, and industry. India is the largest producer and exporter of turmeric, contributing nearly 80 percent of global production. However, despite its strong production base, the sector faces challenges such as limited value addition, inadequate processing infrastructure, and fluctuating market prices, which affect farmers’ income and export competitiveness. In this context, the establishment of the National Turmeric Board (NTB) marks a significant institutional initiative aimed at strengthening the turmeric value chain. The present study examines the role of the National Turmeric Board in promoting value addition and enhancing export competitiveness in India. The study is based on secondary data collected from government reports, publications of the Spices Board of India, and relevant research articles for the period 2023–2025. Analytical tools such as percentage analysis and comparative analysis are used to interpret the data. The study identifies low value addition, inadequate infrastructure, and market inefficiencies as major challenges affecting the turmeric sector. The findings highlight that the NTB plays a crucial role in improving quality standards, supporting research and development, promoting value-added products, and facilitating better market linkages for farmers and exporters. The study concludes that with effective implementation of policies and infrastructure support, the National Turmeric Board can significantly enhance farmer income, strengthen India's position in the global spice market, and promote sustainable agricultural development.

Introduction: Adolescence and young adulthood are critical periods of neurocognitive, psychological, and emotional development. In Assam, the rising prevalence of substance use among youth has become a pressing public health concern, shaped by rapid socio-cultural transitions and limited mental health awareness. Early initiation of substance use is strongly linked to impairments in attention, working memory, executive functioning, and emotion regulation. However, empirical evidence on these deficits among Assamese youth remains scarce. This study examined the cognitive and emotional regulation difficulties associated with Substance Use Disorder (SUD). Methodology: A cross-sectional, comparative study was conducted with 320 participants (160 youths with clinically identified SUD and 160 matched healthy controls). Substance use severity was assessed using WHO-ASSIST, emotion regulation with the Difficulties in Emotion Regulation Scale (DERS), and cognitive functioning with the Trail Making Test (TMT-A and TMT-B) and a Cognitive Assessment Questionnaire. Independent t-tests evaluated group differences, and MANOVA assessed multivariate effects of substance use on cognition. Results:Youths with Substance Use Disorder demonstrated significantly poorer cognitive performance across all measures compared to healthy controls (p<.001), with large effect sizes observed for attention, processing speed, and executive functioning. Deficits were particularly evident on TMT-A and TMT-B, indicating impairments in processing speed, cognitive flexibility, working memory, inhibitory control, and sustained attention. Additionally, youths with SUD reported significantly greater difficulties in emotion regulation, particularly in impulse control, goal-directed behavior, and emotional clarity (p<.001). Conclusion:Substance use among youths in Assam is associated with marked cognitive impairments and significant emotion regulation difficulties, highlighting the need for early screening, preventive strategies, and integrated interventions tailored to young populations.

Companies achieving sustainable business practices through Environmental Social and Governance (ESG) metrics integration now drive transparency and stakeholder involvement effectively. Electing qualitative research methods allows this investigation to study ESG metrics' effects on corporate sustainability strategy development through text analysis performed using both Latent Dirichlet Allocation (LDA) and Correspondence Analysis (CA) on ESG reports, regulatory texts, and academic publications. ESG metrics demonstrate essential importance in both corporate governance structures and regulatory conformity, as well as stakeholder commitment and financial planning applications. Future sustainability trends, together with corporate accountability, social impacts, and risk management, formed ten main themes that emerged from this analysis. The remaining themes were environmental practices with credibility, ESG reporting standardization, strategic ESG initiatives, and ESG disclosure communication and investor influence, followed by regulatory compliance and climate risk management and financial reporting with ESG risk analysis. The research objectives align well with the identified themes according to thematic mapping and CA results, which together create an extensive framework for ESG metric assessment in corporate sustainability strategies. A thorough analysis demonstrates that organizations must prioritize disclosure of ESG metrics together with sound governance systems while allowing investors to play a role and follow regulations in order to advance sustainability. Corporate decision-makers need to understand the essential implications that emerge from these findings because they demonstrate why structured ESG implementation methods are vital. Research must continue to analyze the developing ESG reporting environment and emerging regulatory changes and technological advancements that improve corporate accountability and governance transparency.

In an increasingly complex and fast-paced construction industry, the gap between theoretical instruction and practical application remains a persistent challenge in engineering education. Many students struggle to fully grasp development planning processes ranging from initial site surveys to multidisciplinary technical drawings due to limited exposure to real-world practices. Addressing this issue, the present study evaluates the effectiveness of an academic–industry collaboration initiative, specifically an industrial talk, in enhancing students’ understanding of development planning within the construction context. Adopting a quantitative pre–post research design, the study collected 77 valid responses from participants comprising predominantly undergraduate engineering students, alongside lecturers and an industry speaker. A structured questionnaire measured self-reported understanding before and after the session, complemented by nine Likert-scale items assessing awareness, integration, and perceived benefits. Data were analysed using SPSS through descriptive statistics, reliability testing, paired samples t-tests, and group comparison analyses. The findings reveal a substantial and statistically significant improvement in participants’ understanding following the intervention. The mean score increased from 2.75 (pre-session) to 8.35 (post-session), with a highly significant t-test result (p < 0.001), indicating strong learning gains. The instrument demonstrated excellent reliability (Cronbach’s α = 0.965), confirming internal consistency. Importantly, no significant differences were observed across gender or participant groups, suggesting that the learning experience was inclusive and broadly effective. High satisfaction levels (mean = 4.68/5) further underscore the perceived value of the initiative. This study concludes that structured academic–industry engagements serve as powerful pedagogical tools in bridging the theory–practice divide in construction education. Beyond academic outcomes, such initiatives contribute to societal and environmental advancement by fostering industry-ready graduates equipped with practical competencies, collaborative skills, and a deeper appreciation of sustainable construction practices. By strengthening the alignment between education and industry needs, these efforts support the development of a more competent workforce capable of delivering efficient, well-coordinated, and environmentally responsible construction projects.

Migrant workers face significant challenges in maintaining continuous and accessible healthcare records due to frequent relocation, limited infrastructure, and reliance on paper-based systems. These issues often lead to loss of medical history, repeated diagnostic procedures, increased healthcare costs, and delays in treatment. Existing digital healthcare solutions, including cloud-based and blockchain-based systems, are often unsuitable for low-resource environments due to their dependence on continuous internet connectivity, high implementation cost, and complex infrastructure requirements. This paper proposes a Digital Health Record Management System based on a client-first architecture that enables offline data storage and retrieval directly on the user’s device. The system integrates QR-based identification for instant access to patient records, multilingual support for improved usability, and lightweight data management techniques suitable for low-resource settings. Unlike traditional systems, the proposed approach eliminates dependency on centralized servers and provides a portable and cost-effective solution tailored for migrant populations. Experimental evaluation using a simulated dataset demonstrates that the system reduces data retrieval time from approximately 15–20 seconds to 2–3 seconds, achieving an improvement of nearly 70–80%. Additionally, the system ensures 100% offline accessibility and minimizes the risk of data loss associated with physical records. Usability testing indicates improved efficiency, faster navigation, and ease of use for non-technical users. The proposed system highlights the potential of client-side digital healthcare solutions in improving accessibility, efficiency, and continuity of care in underserved environments. It also provides a scalable foundation for future enhancements, including cloud integration, advanced security mechanisms, and healthcare interoperability standards.

This study aimed to understand the factors that contributed to the abandonment of the health technician career at the Nampula Central Hospital. The research adopted a quantitative, descriptive approach, using bibliographic and case study procedures. The universe consisted of 188 employees who abandoned their health technician career, from which a sample of 30 participants was extracted, selected through non-probabilistic convenience sampling. The results show that abandonment occurred mainly in areas considered strategic by the Ministry of Health, namely Pharmacy Technician, General Medicine Technician, Maternal and Child Health Nursing Technician, General Nursing Technician, and Anesthesia Technician, representing 96.70% of the cases analyzed. Occupational stress was found to be one of the main factors associated with abandonment, and 26.6% of participants reported being under pressure due to excessive tasks and shift work. It was also observed that 50% of respondents stated that the decision to abandon their career was not related to financial factors, but to a loss of professional interest and a desire to face new challenges. On the other hand, 46.67% indicated the search for better salary conditions as the main motivation, while 3.33% stated that they no longer identified with the technical training they had obtained. Abandoning a career as a health technician stems from a combination of organizational, motivational, and economic factors, requiring institutional strategies aimed at professional development, improving working conditions, and strengthening motivation in the hospital context.

Groundwater contamination by open dumpsites is one of the most widespread but least quantified public health concerns in Sub-Saharan Africa's peri-urban areas. Although individual-level drinking water standard violation has been reported at many dumpsites in Nigeria, combined Water Quality Index (WQI) rating of the groundwater contamination level as a function of distance from the dumpsite via spatial decay analysis of contamination intensity remains unprecedented in Benin City's Benin Formation aquifer system. This study utilizes the WQI method and Human Health Risk Assessment (HHRA) protocol as per the USEPA guidelines to analyze the groundwater quality data from eight borehole wells located at varying distances ranging from 100 meters up to about 800 meters from the Upper Ekehuan (Asoro) open dumpsite in Ovia North-East LGA, Edo State, Nigeria. WQI scores ranged from 198.4 (100 m borehole, 'Very Poor') to 89.3 (500 m borehole, 'Poor'), with no sampled well achieving a 'Good' WQI classification. Spatial regression analysis reveals a statistically significant exponential decay in composite contamination intensity with increasing distance (R² = 0.84). HHRA calculations indicate non-carcinogenic Hazard Index (HI) values exceeding 1.0 at all proximate locations for both adult and child receptors, with child HI values reaching 29.4. Carcinogenic risk from Chromium(VI) and Cadmium exceeds the USEPA acceptable risk level of 1×10⁻⁴ at all wells within 300 meters. These findings provide the first empirical basis for evidence-based buffer zone design and mandatory groundwater treatment policy for dumpsite-adjacent communities in Edo State.

This study explores the effects of Entrepreneurial Orientation (EO) on venture capitalist investment in Technology Medium Scale Enterprises (Tech MSEs) in North-Central,, Nigeria, while considering the moderating role of organizational culture. The research highlights the critical importance of Tech MSEs as engines of economic growth and innovation, particularly in emerging markets. Utilizing a quantitative survey design, data were collected from 358 tech MSEs owners across various sectors for analysis. The findings indicate a significant positive relationship between EO and venture capital investment, underscoring that firms with a strong entrepreneurial orientation are more likely to attract funding. Organizational culture was found to moderate this relationship, enhancing the effectiveness of EO in securing investments. The results suggest that fostering a supportive organizational culture can mitigate perceived risks for investors, thereby improving the attractiveness of Tech MSEs. This study contributes valuable insights for entrepreneurs and policymakers, emphasizing the need for strategic alignment between entrepreneurial practices and organizational culture to enhance venture capital attraction and promote sustainable growth in the Tech sector.

The inhibition of mild steel corrosion in dilute hydrochloric acid (1 M HCl) by the xylene extract of Lagenaria breviflora (XEL-B) was studied using a Central Composite Design (CCD) structured Response Surface Methodology (RSM). A statistically optimised 20-run experimental matrix was employed to evaluate the simultaneous effects of inhibitor concentration, immersion time and temperature on mass loss, corrosion rate (Rc), inhibitor efficiency (IE), and surface coverage (θ). The fitted quadratic response surface model was highly significant (F = 55.81, p < 0.0001) with a non-significant lack of fit (p = 0.2730), confirming adequate model predictability across the experimental domain. Inhibitor efficiency ranged from 28.68% at 31 ppm of inhibitor concentration to 77.01% at 368 ppm, with inhibitor concentration identified as the dominant process variable statistically validated (F = 423.40, p < 0.0001) cosnsistent with 4D response surface analysis, and a 500-tree Random Forest ensemble machine learning model (factor importance: IE = 77.54%, Rc = 74.20%). Adsorption of XEL-B on mild steel conformed to the Langmuir monolayer isotherm (R² = 0.9950), equilibrium adsorption constant Kads of 11.3649Lg⁻¹ and standard Gibbs free energy of adsorption ΔG°ads of −16.24 kJ/mol, confirming spontaneous, thermodynamically favourable adsorption with a mixed physisorptive–chemisorptive mechanism. These integrated experimental-computational results established XEL-B as a potential green corrosion inhibitor for mild steel in dilute acidic environments relevant to oilfield and industrial acid-treatment operations.

In this study, a hybrid architecture that combines the feature-extraction capability of CNN and the classification power of RF is proposed to focus on the correct detection of plant diseases, which is Convolutional Neural Network-Random Forest (CNN-RF). Data acquisition and preprocessing, which consisted of image normalization, augmentation, and resizing to make sure that the models could fit the data and enhance generalization, started with the methodology. The CNN element was trained to automatically learn discriminative features on the plant leaf images, which were then inputted into an RF classifier which was optimized by hyperparameter optimization. The performance measurement utilized conventional measures, such as accuracy, precision, recall, and F1-score and the Receiver Operating Characteristic (ROC) curve analysis. It has been proven by experimental results that the hybrid CNN-RF model is better than the standalone CNN model and RF model. The proposed model attained an accuracy of 96.3, precision of 95.8, recall of 96.7 and F1-score of 96.2, which was better than CNN (93.5% accuracy) and RF (88.4% accuracy) baselines. The tuning of hyperparameters was demonstrated to be of great benefit to the outcomes of classification as illustrated in the tuning heat map. The hybrid model had a close Area Under the Curve (AUC) of 1.0 on the ROC curve, which is ideal sensitivity and specificity.

The rapid growth of electric vehicles (EVs) has increased so the need for efficient and reliable Battery Management Systems (BMS) to ensure safe and optimal battery operation. This paper presents the design and implementation of a smart BMS for a load-carrying electric vehicle powered by a 60 V, 60 Ah Lithium Iron Phosphate (LiFePO₄) battery. The system utilizes a 32-bit microcontroller integrated with a smart BMS to monitor key battery parameters such as voltage, current, and temperature. The proposed system incorporates features such as fault detection and alert mechanisms, adaptive on-board charging, and an LCD-based display for real-time monitoring. In addition, a multi-mode access system using NFC/Wi-Fi card, remote control, and key-based operation is implemented along with an anti-theft alarm to enhance vehicle security. The EV also includes a three-level gear system and reverse operation for improved usability. The system ensures reliable performance, enhanced safety, and efficient energy utilization. The proposed smart BMS provides a practical and effective solution for modern load-carrying electric vehicle applications.

This study evaluates the feasibility of Dracaena trifasciata (snake plant) fibers as an alternative raw material for eco-friendly paper bag production. A descriptive–experimental design was employed to compare fabricated paper bags with commercial counterparts in terms of biodegradability, water absorption, tensile strength, and dimensional properties. Results indicate that snake plant-based paper bags exhibit faster biodegradation (5 days) than commercial paper bags (7 days), while maintaining comparable water absorption. Notably, tensile strength was significantly higher for the experimental group (M = 2.55) than the commercial group (M = 1.25), with statistical significance (p < 0.05) and a very large effect size. These findings suggest that D. trifasciata fibers are a viable, sustainable, and high-performance alternative for packaging applications. The study contributes to the advancement of non-wood fiber utilization in sustainable materials science.

In this study, we developed an inventory model for Indian perishable products by the incorporation of time-dependent holding costs into the model. Demand is influenced by pricing and advertising decisions for Indian perishable products. We take a linear time-dependent holding cost, which shows the insurance and storage cost of perishable products increases over time. Many traditional studies take holding cost as a constant, which cannot match the real scenario. The optimal replenishment cycle is obtained in this inventory model. Numerical and sensitive analysis is done to validate the inventory model for Indian perishable products.

The design and optimization of mobile communication networks depend heavily on accurate path loss prediction, especially in settings with complicated topography and changing meteorological conditions. The predicted accuracy of traditional empirical propagation models, such Hata and COST-231, is limited in heterogeneous situations since they mainly take distance and antenna parameters into consideration, frequently ignoring the combined impact of topographical variability and weather conditions. This study presents the empirical development of a terrain–meteorological path loss model for mobile networks in Bwari Area Council, Abuja, Nigeria. By combining important climatic factors like temperature, relative humidity, and rainfall with topography descriptors like elevation, building density, and vegetation, the suggested model expands upon the traditional log-distance formulation. Multiple linear regression techniques were employed to estimate model parameters after field measurements of received signal strength were gathered in various propagation settings. Improved statistical performance indicators, such as lower root mean square error (RMSE) and higher coefficient of determination (R2), show that the addition of environmental factors considerably improves prediction accuracy when compared to traditional models. The developed model is especially well-suited for deployment in tropical and heterogeneous environments because it successfully captures both spatial and temporal fluctuations in signal transmission. By offering an experimentally verified, environment-aware approach that enhances path loss prediction and facilitates effective mobile network planning and optimization, the study advances propagation modeling. For improving coverage estimation and network performance in comparable geographic areas, the suggested model provides a scalable method.

Escalating deployment of low-speed autonomous vehicles across manufacturing and logistics environments has created urgent demand for safety systems capable of distinguishing human workers from inanimate obstacles in real time. This paper presents a revised and empirically strengthened embedded multi-layer safety framework integrating three-axis ultrasonic obstacle avoidance, transfer-learned SSD MobileNetV2 human detection, YOLOv8n centroid-based activity classification, cooldown-gated text-to-speech alerting, and a Flask supervisory dashboard within a single platform costing under INR 5,000. Extended evaluation introduces mAP@0.5, precision-recall metrics, and memory profiling for both models; an ablation study quantifies the contribution of each layer; and a quantization benchmark demonstrates inference acceleration via TensorFlow Lite INT8 and ONNX INT8 conversion. The SSD MobileNetV2 model achieves 72.4% classification accuracy, mAP@0.5 of 0.58, and a mean IoU of 0.61; YOLOv8n attains 94.0% activity classification accuracy at 24 FPS with mAP@0.5 of 0.89. Full three-layer operation achieves a 96.7% collision avoidance rate and reduces false alert frequency to 1.9 per hour with the cooldown gate active. A 60-minute continuous deployment confirmed sensor precision within 3% error and stable concurrent operation. Limitations regarding industrial dataset coverage, regulatory compliance, and edge-only deployment are discussed alongside a roadmap for future work.

Human–machine interction plays an important role in modern industrial automation systems. In many industrial environments, operators are required to interact directly with machines through switches, buttons, and control panels. In hazardous environments such as chemical plants, radioactive facilities, and high-voltage power stations, this direct interaction may expose operators to serious safety risks. This paper presents a computer vision–based hand gesture control system that enables contactless interaction with machines. The proposed system uses a camera to capture hand gestures, which are processed using Python with OpenCV and MediaPipe libraries. The system detects hand landmarks and interprets gestures in real time. The recognized gesture is converted into control signals and transmitted to an Arduino microcontroller through serial communication. The Arduino generates PWM signals to control servo motors connected to a robotic hand mechanism that replicates the user’s gesture. The developed prototype demonstrates the feasibility of integrating artificial intelligence and embedded systems for safer human–machine interaction in industrial environments.

Artificial Intelligence (AI) has emerged as a powerful tool in modern chemical research, significantly accelerating the discovery and development of new drugs and advanced materials. Traditional experimental approaches in chemistry are often time-consuming, expensive, and require extensive trial-and-error processes. AI techniques, particularly machine learning and deep learning, enable researchers to analyse large chemical datasets, predict molecular properties, and identify potential compounds with greater efficiency and accuracy. In drug discovery, AI helps in predicting drug–target interactions, optimizing molecular structures, and reducing the time required for lead identification. Similarly, in materials science, AI assists in designing novel materials with desired properties for applications in energy storage, catalysis, and electronics. This paper highlights the role of AI-driven computational models in transforming chemical research, discusses key methodologies, and examines current challenges and future prospects. The integration of artificial intelligence with chemical sciences is expected to revolutionize drug development and materials innovation, making the discovery process faster, more cost-effective, and highly efficient.

The problem of groundwater pollution by heavy metals creates acute and chronic health hazards to the rural populace who rely on this untreated sources to meet their daily water needs. This paper presents a comprehensive seasonal heavy metal pollution risk assessment and human health risk analysis of twenty hand-dug wells in three municipal districts: Uffa, Itua, and Ugbogbo in Igarra town, Akoko-Edo Local Government Area, Northern Edo State, Nigeria. Water samples collected in the wet season (July 2011) and dry season (December 2011) were analyzed for eight trace metals: copper (Cu), chromium (Cr), nickel (Ni), lead (Pb), cadmium (Cd), zinc (Zn), iron (Fe), and manganese (Mn), using atomic absorption spectrophotometry. The results were compared with the permissible limits set by the World Health Organization (WHO), Standards Organization of Nigeria (SON), and Federal Ministry of Environment (FME) regulations. The results revealed that iron and zinc were the dominant contaminants in the hand-dug wells in the study area. Both heavy metals exceeded the safe limits at more than 85% and 65%, respectively. Manganese also exceeded the safe limits at 75% of the sampling points. Geochemical source apportionment indicated that high levels of iron and manganese in the hand-dug wells in the study area were geogenic in origin. To evaluate the human health risk in the hand-dug wells in the study area, the Pollution Index (PI), Enrichment Factor (EF), Non-carcinogenic Hazard Quotient (HQ), Hazard Index (HI), and Carcinogenic Risk (CR) were calculated. The results revealed that the Hazard Index values for children were greater than 1.0 at 14 out of 20 sampling points. This indicated that non-carcinogenic risk existed in the hand-dug wells in the study area. The results also revealed that the Carcinogenic Risk values for lead ingestion by children exceeded the USEPA threshold value of 10⁻⁴ at four sampling points.

Ganoderma is a notable Indian conventional mushroom utilized in different ethnomedicinal practices. New advancements connected with this mushroom and their exercises are consistently reported. A few researchers working with spores of this therapeutic mushroom however data of this species are still limited. This review describes about the main advances for the extraction of biocomponents and their pharmacological and cell protective effects on health system because of their importance in drug discovery. Recognizing the bioavailability of the naturally dynamic parts of G. lucidum after oral administration, and very little has been published in this area. Although single compounds can mediate a biological effect, nutraceuticals often act through the synergistic activity of multiple metabolites. It is significant to gain a better understanding of the bioavailability of the active components in this mushroom under various conditions to ensure robust study designs and to maximise consumer advantage. Nanoparticles derived from macro fungi, including various mushroom species like Ganoderma spp. are well known to possess immune-modulatory, high nutritional, antimicrobial, antioxidant, and anticancerous effect. Fungi have intracellular metal uptake capacity and supreme wall binding ability. In this review also discussed the nanoparticles synthesis from micro fungi of Ganoderma species and mechanism of nanoparticles derived from the micro fungus.

This study describes the representation of Indonesian Generation Z's national identity on IDN Times' Instagram. The results show that Indonesian Generation Z prefers content that combines traditional cultural elements with modernity, in creative and easily accessible forms, such as through social media, short videos, and digital platforms. The research design used mixed methods, namely by conducting quantitative content analysis on Indonesian Generation Z's national identity content on IDN Times' Instagram from January to September 2024. Qualitative data were obtained through in-depth interviews with 20 Generation Z individuals aged 18 to 25, representing all provinces in Indonesia. The qualitative approach was carried out using virtual anthropology methods, focusing on how information and communication technology influences social life, culture, and individual identity in virtual environments. Three elements studied include the representation of traditional symbols, local-global hybridization, and digital activities. Content featuring traditional music with a modern touch, or memes that reflect Indonesia's rich culture in a lighthearted and humorous manner, is more engaging. The presence of social media has become a primary space for expressing, criticizing, and capturing global issues. Generation Z also tends to support environmentally friendly products and engage in activities that can reduce negative impacts on the Earth. They use social media for positive change to maintain their Indonesian identity, but still adapt to global trends.

The present study investigates the effect of heat treatment on the microstructure and mechanical properties of AISI 4130 steel. The material was subjected to a sequence of normalizing, hardening, and tempering treatments to evaluate the influence of tempering temperature on performance characteristics. Normalizing was carried out at 890°C followed by air cooling, resulting in a refined ferrite–pearlite microstructure. Subsequently, hardening was performed at 860°C and followed by quenching to obtain a martensitic structure. Tempering was conducted at two different temperatures, 565°C (Sample A) and 655°C (Sample B), to study the variation in mechanical behavior. The results highlight that tempering temperature plays a critical role in tailoring the balance between strength and toughness in AISI 4130 steel. This study provides useful insights for optimizing heat treatment parameters for engineering applications requiring a combination of mechanical performance and structural reliability.

The present study investigates the effect of heat treatment on the microstructure and mechanical properties of AISI 4130 steel. The material was subjected to a sequence of normalizing, hardening, and tempering treatments to evaluate the influence of tempering temperature on performance characteristics. Normalizing was carried out at 890°C followed by air cooling, resulting in a refined ferrite–pearlite microstructure. Subsequently, hardening was performed at 860°C and followed by quenching to obtain a martensitic structure. Tempering was conducted at two different temperatures, 472°C (Sample A) and 518°C (Sample B), to study the variation in mechanical behavior. The results highlight that tempering temperature plays a critical role in tailoring the balance between strength and toughness in AISI 4130 steel. This study provides useful insights for optimizing heat treatment parameters for engineering applications requiring a combination of mechanical performance and structural reliability.

Heavy metal contamination of water systems remains a critical environmental challenge due to its persistence, toxicity and bio-accumulative nature. This study investigates the potential of activated carbon derived from mango (Mangifera indica) seed coats as a low cost and sustainable adsorbent for the removal of cadmium (Cd2+) ions from aqueous solutions. The adsorbent was prepared via carbonization followed by chemical activation and characterized using Fourier Transform Infrared Spectroscopy (FTIR), which revealed the presence of functional groups such as hydroxyl and carbonyl responsible for metal binding. Batch adsorption experiments were conducted to evaluate the effects of operational parameters, including initial metal ion concentration (10 to 50 mg/L) and adsorbent dosage (0.1 to 0.5 g) at an optimized pH of 5. The results indicated that adsorption efficiency decreased with increasing Cd2+ concentration, reaching a maximum removal efficiency of about 81.00 % at 10 mg/L. Furthermore, increasing adsorbent dosage beyond 0.1 g resulted in only marginal improvements due to possible site overlap and particle aggregation, identifying 0.1 g as the optimal dosage. Equilibrium data were best described by the Freundlich isotherm model, indicating heterogeneous surface adsorption, while kinetic studies showed that the adsorption process followed pseudo second order kinetics, suggesting chemisorption as the dominant mechanism. The adsorption process was governed by electrostatic interactions, ion exchange, and surface complexation. The study demonstrates that mango seed coat derived activated carbon is an effective, eco-friendly and economically viable alternative for Cd2+ removal. This work highlights the dual benefits of agricultural waste valorization and sustainable water treatment, making it particularly relevant for resource limited environments.

The fast growth of cities in developing nations has put pressure on formal waste management institutions, which leave large service gaps that can be occupied by informal waste collectors. The paper investigates the socio-economic attributes, business environment, and significant factors that can affect the willingness of informal waste collectors to include themselves in the formal waste management systems. The study adopted a quantitative research design. 65 respondents took part in the study and descriptive statistics, Chi-square tests, and binary logistic regression were used to analyze the results. The results indicate that the knowledge of the formal waste management systems, perceived significance of integration, low income, and poor working conditions have a high impact on the willingness of collectors to integrate. Conversely, demographic variables such as gender and education level were not statistically significant predictors. The output of the logistic regression also shows that awareness is the most powerful predictor, which raises the chances of being integrated into formal systems significantly. The regression model has a moderate level of explanatory power and satisfactory predictive accuracy. These findings are important in highlighting the role of policy intervention strategies intended to strengthen awareness, working environments, and income support programs to informal waste collectors. These areas would be fortified, which may create more involvement in formal systems. Incorporation of informal waste collectors can enhance efficiency of operations, environmental sustainability as well as inclusive urban development. Therefore, the policymakers and other interested parties should identify and embrace the contribution of informal actors to the wider waste management process in order to bring in sustainable and fair city waste management.

Pseudorandom bit generators (PRBGs) are indispensable in modern cryptography, forming the backbone of secure communication protocols, authentication mechanisms, and privacy-preserving systems. A PRBG must produce sequences that appear statistically random while being computationally unpredictable. Traditional designs such as linear feedback shift registers (LFSR) and linear congruential generators (LCG) are attractive due to their simplicity and low hardware cost, but they fail several National Institute of Standards and Technology (NIST) randomness tests because of inherent linearity. Coupled LCG (CLCG) and dual-CLCG methods improve resilience by combining multiple generators, but they suffer from irregular timing, high latency, and excessive hardware usage. This paper proposes a modified dual-CLCG algorithm and its VLSI architecture designed to produce pseudorandom bits at a consistent clock rate with minimal hardware overhead. The novelty lies in the use of a simplified XOR stage at the output, which ensures uniform bit generation at every clock cycle. Unlike the dual-CLCG, which requires multiple flip-flops and suffers from asynchronous bit release, the modified design achieves a maximum sequence length of 2^n, requires only one initial delay cycle, and passes all fifteen NIST benchmark tests. The architecture was implemented using Verilog HDL and prototyped on FPGA hardware. Experimental results demonstrate significant improvements in area efficiency, latency reduction, and power consumption compared to existing designs. The proposed generator not only meets the randomness requirements but also achieves polynomial-time unpredictability, making it suitable for resource-constrained IoT devices where lightweight cryptographic primitives are essential.

Beach ridges and barrier islands along the Nigerian coastline are a critical geomorphic unit which serve as foundation for several significant engineering structures, yet their geotechnical behavior remains unevenly characterized. This study synthesizes existing sedimentologic, geotechnical, and geophysical data to characterize the engineering properties of Nigerian beach‐ridge and barrier–island systems, with emphasis on the barrier–lagoon complex of Lagos, Brass, Bonny and Eket. The study identified hidden, shallow, incompetent, and highly compressible plastic clay/peat layers beneath the predominant seemingly stable sand, which can lead to significant consolidation settlement or building collapse. The near-surface sediments reveal poorly graded sands or silty sands, with high natural moisture content, low plasticity, moderate to high hydraulic conductivity, and decreasing relative density and relative compaction within the depth of significance to shallow foundation construction. Grain-size analyses from multiple barrier beaches indicate predominantly fine sands that are moderately to well sorted, reflecting moderately high wave-energy swash and backwash processes and indicating high susceptibility to coastal erosion. Integrated geophysical–geotechnical approaches have proven effective in mapping these contrasting strata and in estimating relative density and compactness, compressibility and settlement distribution and suitable foundation depths. This study further shows that a sandy top of thickness 3.5m can present an average safe bearing pressure for shallow foundation of 100kN/m2 and that satisfactory foundation performance may be expected, provided the bearing resistance of the soil at 3.5m depth is not less than 10% of the structural load placed on the surface. The combined evidence indicates that while Nigerian beach ridges and barrier islands often provide adequate near-surface materials for light to moderate structures, careful site-specific characterization is essential to account for underlying soft sediments, shoreline erosion, and associated geohazard risks.

Permanent Magnet Synchronous Motors (PMSMs) are widely used in electric vehicle applications due to their high efficiency, compact size, and superior performance. However, their reliability is affected by electrical and thermal stresses, which can lead to both external and internal faults. Conventional protection methods primarily address external faults such as overcurrent, overvoltage, and overheating, but they are not effective in detecting internal issues such as gradual demagnetization of rotor magnets. This work proposes a PMSM drive system with integrated safety and monitoring features to enhance operational reliability. The system employs Field Oriented Control (FOC) for precise control of torque and speed while continuously monitoring key parameters such as stator current, voltage, and temperature. In addition to standard protection mechanisms, a sensorless demagnetization detection method is incorporated using variations in electrical signals, eliminating the need for additional hardware sensors. Based on the detected level of demagnetization, appropriate control actions are implemented using a microcontroller. In the case of slight demagnetization, the system compensates for torque reduction by increasing the current through the control strategy. In severe demagnetization conditions, the system initiates a protective shutdown to prevent further damage to the motor and drive components. In conclusion, the proposed system enhances the safety, reliability, and fault-handling capability of PMSM drives while maintaining a simple and cost-effective design, making it suitable for practical electric vehicle applications.

Kidney stone disease continues to be a major global health challenge, largely due to its high recurrence rate even after successful surgical treatment. This study introduces LIFE Lens-AI, an intelligent framework designed to support both prediction and long-term prevention of kidney stone recurrence through the use of artificial intelligence. The proposed system combines predictive analytics, medical image analysis, and personalized recommendation techniques within a single integrated platform. It leverages diverse data sources, including patient demographics, clinical history, lifestyle patterns, and medical imaging, to generate accurate and clinically meaningful insights. The predictive module operates in two stages: recurrence risk prediction using XGBoost and surgical intervention prediction using a Random Forest classifier. The latter is modeled as a binary classification task, where labels are derived from established clinical treatment guidelines and historical decision patterns. Experimental results show that the system achieves an accuracy of 87.9% for recurrence prediction and 90.5% for surgical decision classification, supported by strong performance across precision, recall, F1-score, and AUC-ROC metrics. Beyond prediction, the framework emphasizes patientcentric care by offering personalized dietary suggestions, specialist recommendations, and continuous monitoring support. The system is implemented as a secure webbased application with modern encryption and authentication mechanisms. Overall, LIFE Lens-AI provides a practical step toward proactive, AI-enabled healthcare and improved long-term patient outcomes.

Climate change has emerged as one of the most pressing global challenges, and India’s western state of Gujarat faces distinctive risks due to its semi-arid geography, long coastline, and industrial profile. Non-Governmental Organizations (NGOs) have become crucial actors in bridging local realities with national and global climate policies. This study explores the empirical role of NGOs in mitigating climate change across Gujarat. Drawing upon secondary data from the Gujarat State Action Plan on Climate Change (SAPCC), the Indian Meteorological Department (IMD), and reports from organizations such as SEWA, Vasudha Foundation, and TERI, the paper identifies how NGOs implement renewable energy solutions, afforestation, water conservation, and community-based resilience programs. The findings demonstrate that NGOs not only complement state interventions but also pioneer bottom-up innovations that strengthen climate governance. The study concludes with policy recommendations to institutionalize NGO participation within Gujarat’s climate framework.

This paper explores the transformation of Dalit identity through the literary contributions of Bama, a pioneering Dalit feminist writer in Tamil literature. Her works, including Karukku, Vanmam, Ponnuthayi, and Those Days, chronicle the social, emotional, and political journey of Dalits from subjugation to assertion. Drawing from lived experiences and oral histories, Bama’s narratives provide a platform for voices long silenced by caste and gender hierarchies. This paper examines how Bama uses storytelling as resistance, portraying education, collective unity, and the awakening of self-worth as keys to liberation. It also evaluates how her female characters challenge patriarchal norms and contribute to the broader Dalit movement, reflecting a powerful shift from caste-based slavery to human dignity.

India’s religious traditions have evolved through sustained interaction across cultures, languages, and spiritual systems. Syncretism defined as the blending or cross-fertilisation of ideas, practices, and symbols has been a hallmark of the subcontinent’s intellectual history. This paper examines syncretism within major Indian religions and situates Sikhism within this broader landscape. While Sikhism appeared amid rich inter-religious dialogue, it simultaneously articulated a distinct theological vision. Through an overview of Vedic-Shramanic exchanges, Bhakti-Sufi interactions, and the pluralistic milieu of medieval Punjab, the paper evaluates scholarly debates on whether Sikhism is a syncretic tradition or an original revelation that engaged with its context in transformative ways.

This project presents the development of a low-cost, non-invasive Brain-Computer Interface (BCI) designed to support individuals with paralysis or Amyotrophic Lateral Sclerosis (ALS) in communicating their basic needs. Built using the NPG Lite platform, the system integrates both electroencephalography (EEG) and electrooculography (EOG) signals to create an intuitive and accessible human-computer interaction method. The proposed system adopts a hybrid signal-processing approach to improve usability and accuracy. Navigation through the user interface is achieved by detecting eye blinks using EOG signals, allowing users to scroll through predefined menu options without physical movement. Selection of a desired option is performed through EEG signals by measuring the user’s level of mental focus, particularly from the frontal lobe region. This dual-input mechanism ensures a balance between ease of use and reliability, minimizing false activations while maintaining responsiveness. Captured neural and ocular data are processed in real time and transmitted wirelessly via Bluetooth Low Energy (BLE) to a laptop-based interface. The interface interprets these signals and converts them into actionable commands, enabling users to communicate essential needs such as requesting assistance, expressing discomfort, or interacting with caregivers. By combining affordability, portability, and open-source accessibility, this system aims to provide a practical assistive technology solution for non-verbal individuals. Ultimately, the project seeks to restore a degree of independence and autonomy, improving quality of life while offering a scalable platform for further research and development in assistive BCI technologies.

The accelerating volume and structural diversity of data generated by healthcare systems, smart medical devices, and enterprise platforms has rendered conventional data analysis pipelines increasingly impractical for nonexpert users. This paper presents an extensible, machine learning-integrated analytics platform designed to enable interactive, code-free analysis of heterogeneous medical datasets through a web-based interface. The system accepts structured and semi-structured data in CSV and Excel formats and executes an automated pipeline encompassing data preprocessing, descriptive statistical analysis, interactive visualization, and machine learning — including regression, clustering, and anomaly detection — without requiring users to possess programming skills. The platform is implemented on a scalable three-tier architecture comprising a React-based frontend, a FastAPI backend for request routing and model orchestration, and a Python-based data processing layer utilizing Pandas, NumPy, Scikit-learn, and Matplotlib. Experimental evaluation across multiple medical datasets demonstrates strong predictive performance — achieving an R² of 0.87 on a clinical regression task and an F1-score of 0.84 on a binary classification task — with end-to-end pipeline latencies consistently below one second. The system advances data-driven decision-making in healthcare, business intelligence, and research environments while maintaining an architecture designed for modular extension.

The growing demand for efficient and sustainable lawn maintenance systems has led to the development of automated solutions that minimize human effort and environmental impact. Conventional grass-cutting methods rely heavily on manual operation or fuel-powered machines, which are not only labor-Intensive but also contribute to noise and air pollution. To address these limitations, this study presents a GPS-guided solar-powered robotic system integrated with Internet of Things (IoT) technology and live video monitoring. The proposed system is designed using an ESP32 microcontroller, which enables wireless communication and real-time control. A solar panel combined with a Maximum Power Point Tracking (MPPT) charge controller is used to optimize energy harvesting and ensure efficient battery charging. The system is equipped with ultrasonic sensors for obstacle detection, allowing the robot to navigate safely in dynamic environments. Additionally, a GPS module is incorporated to provide accurate location tracking, enabling efficient area coverage and navigation. Furthermore, an ESP32-CAM module is used to provide live video streaming, allowing users to monitor the robot remotely. The integration of IoT facilitates real-time data transmission and remote control through a web interface. Experimental analysis shows that the system operates with stable voltage levels, efficient energy utilization, and reliable performance in real-time conditions. The results confirm that the proposed system offers a cost-effective, eco-friendly, and intelligent solution for automated lawn maintenance applications.

The rise of multiple drug resistant (MDR) bacteria requires a shift from conventional diagnostics to integrated methods that can help explain unpredictable strain-specific antimicrobial responses. This study employed a multi-method characterization strategy of clinical isolates to investigate divergent susceptibility to novel agents. Clinical isolates of Enterococcus faecalis, Bacillus cereus, and two Proteus mirabilis strains were obtained from Sacred Heart Hospital, Abeokuta. Comprehensive profiling included biochemical identification, 16S rRNA gene sequencing, phylogenetic reconstruction, and restriction enzyme analysis. Susceptibility to Nigella sativa seed oil (NSO), biosynthesized silver nanoparticles (AgNPs), and silver nitrate was evaluated using broth micro-dilution and agar well diffusion assays. Phylogenetic analysis confirmed that the two P. mirabilis strains were closely related though restriction enzyme mapping revealed distinct, strain-specific molecular fingerprints. Antimicrobial susceptibility testing showed significant variation. E. faecalis was most susceptible to AgNPs, while P. mirabilis FELIX004 was completely resistant to AgNPs even though susceptible to both NSO and silver nitrate, a paradoxical profile not seen in the closely related susceptible strain FELIX003. Comparative analysis is suggestive of an association between unique restriction patterns in FELIX004 and its AgNP-resistant phenotype. Integrative phenotypic and molecular profiling uncovered substantial intraspecies variation in novel antimicrobial susceptibility testing. The specific paradoxical resistance of P. mirabilis FELIX004 to AgNPs underscores the emergence of agent-specific resistance mechanisms. The study demonstrates that standard phylogenetic relatedness is insufficient to predict strain-specific behavior and highlights the value of multiple approaches for identifying phenotypic outliers that warrant deeper genomic investigation.

The green economy has emerged as a transformative paradigm that reconciles environmental sustainability with economic growth and social equity. Despite India's ambitious commitments to net-zero emissions by 2070 and its rapidly expanding renewable energy sector, existing literature remains fragmented across individual industries, offering little integrative conceptual guidance. This article addresses that gap by developing a multi-sectoral conceptual framework that maps the architecture of India's green economy across twelve interrelated industry clusters. Drawing on conceptual synthesis and a systematic review of secondary literature, the study integrates Green Economy Theory, the Sustainable Development Framework, Circular Economy principles, Innovation Systems Theory, and Environmental, Social, and Governance (ESG) criteria into a unified analytical model. The framework identifies four functional layers—core productive sectors, enabling technology and finance sectors, circular and resource management systems, and social and inclusive drivers—and theorises the relational logic connecting them. The paper further discusses India-specific drivers, structural challenges, and policy implications. The findings contribute a replicable conceptual architecture applicable to large emerging economies navigating the sustainability transition, while providing a foundation for empirical validation and sector-specific research.

Absorption, emission and molecular docking characteristics of the naproxen drug with (DNA, RNA, BSA, Dendrimer) biomolecules and silver nanoparticles were analysed. With the addition of NP, the absorption and emission maxima of the biomolecules completely disappeared, and no significant spectral shift was noticed in the NP drug. When biomolecule concentrations increased, the absorption and emission intensities of the drug were gradually changed. The negative free energy values indicate the spontaneity of the binding between the drugs and biomolecules. van der Waals force and hydrogen bonding play major roles in the sensing of the drugs and biomolecules. Due to Ag nanoparticles interaction with NP/biomolecules, a blue or red shift was noticed in the absorption and emission spectra. Molecular docking results indicated that the biomolecules interacted with the O and H groups of the NP drug. The sensing behaviour of NP with DNA is higher than other biomolecules. NP drug demonstrates promising anticancer activity through interactions with both the 1r51 and 2oh4 EGFR protein targets.

This study examines the influence of Artificial Intelligence (AI) on time optimization within Human Resource (HR) functions, focusing on how AI-driven tools enhance efficiency and organizational outcomes. With HR departments increasingly expected to act as strategic partners, the integration of AI helps automate repetitive tasks such as recruitment, payroll, and performance management, thereby reducing time consumption and improving decision-making. Using a descriptive research design, data was collected from 70\ HR professionals in IT companies in Chennai through a structured questionnaire. The study highlights that AI significantly improves time efficiency, enabling HR professionals to focus more on strategic and employee-centric roles. It also addresses concerns related to adoption, ethical considerations, and human acceptance, emphasizing the need for a balanced, human-cantered approach. Overall, the research contributes to understanding how AI-powered time optimization can transform HR functions and support organizational effectiveness.

This paper presents an energy-efficient smart automation system based on a hybrid sensing approach using Passive Infrared (PIR) and ultrasonic sensors integrated with Internet of Things (IOT) technology. The main objective of the system is to reduce unnecessary energy consumption by automatically controlling electrical appliances based on human presence. The PIR sensor is used to detect motion, while the ultrasonic sensor measures distance to identify both moving and stationary occupants. A predefined threshold distance of 150 cm and a delay time of 45 seconds are implemented to ensure reliable operation and to avoid unnecessary switching. The sensed data is processed using the ESP 32 microcontroller, which controls the appliances through a relay module. The system supports both local and remote control. A web-based interface developed using XAMPP enables users to monitor and control appliances within a local network, while Blynk Cloud allows remote access through a mobile application. In addition, voice control functionality is provided through the application interface for user convenience. Experimental results confirm that the proposed system effectively reduces energy consumption and achieves significant energy and cost savings under practical operating conditions. The system is simple, cost-effective, and suitable for applications such as smart classrooms, homes, and offices

Across the globe, cardiovascular diseases remain a leading contributor to death rates and accurate prediction are essential to modern health systems. Unhealthy lifestyles are one of the elements leading to the increasing occurrence of heart disease, stress and aging, and it has become essential to create a system capable of delivering precise and reliable results diagnosis. With the growing accessibility of vast healthcare data, machine learning technology is emerging as an important tool for helping clinical decision-making by identifying hidden patterns and relationships in complex data sets. In this study, we developed a machine learning-based system for predicting heart disease. The proposed system uses a structured set of data obtained from a publicly available UCI source, contain important medical parameters. To guarantee high data quality and raise the level of performance of models, multiple preprocessing techniques were implemented, including data cleaning, feature normalization, and handling of missing values, classification variable encoding and outlier detection. Different approaches were tested to identify the most effective model. The models were evaluated based on performance indicators such as recall, accuracy, and precision and ROC-AUC points. The study focuses on the performance of ensemble learning using Random Forest, while comparative analysis shows that KNN achieved slightly higher accuracy on the given dataset. K-Nearest Neighbors performed the best, achieving an accuracy of around 91.8% and superior classification capabilities indicated by ROC curves and overall evaluation metrics. Our proposed approach can be used as an effective decision-making tool for medical professionals to identify high-risk patients in time. Finally, this approach helps reduce mortality rates and can assist doctors in early detection and better decision-making.

The procurement suite and external service management systems face a variety of integration problems, which means that they often create operational inefficiencies for organizations. In the article, Modulus Ticketing is explained as a brand new modular ticketing solution with a seamless integration into procurement suites and also solves problems associated with the current systems, such as the complex user setup requirements and ticketing history. There are numerous benefits attached to Modulus, including eliminating repetitive logins, the capability of linking workflows directly into Modulus, automated resolution of tickets during processing, and a significant increase in the amount of time saved per ticket resolved. The benefits of the Modulus Ticketing solution include the elimination of many of the shortcomings of previous (legacy) systems and a scalable solution that will continue to drive improvement in procurement support through a truly unified data approach and application of intelligent/automated data utilization. Other future ambitions in development, such as intake management modules, will support the modular ticketing concept. The article further states that there have been some updated words and phrases that have been revised to have a broader interpretation and can be applied to a larger audience while still remaining consistent with systems such as Ivalua and ServiceNow. The findings of this study suggest that all companies should modernize their procurement support systems and enhance their tools in order to realize the most efficiency and usability from these systems.

The industry collaboration with institution is a long back talk, but it goes every year as emerging talk in the field of academic and the education world. Because its parallel travel not yet completed. So, the present article tries to address why industry and academia collaboration is a matter and how various researcher have addressed the matter and what was their findings. This will help the present and future researcher in the field to frame conceptual model, attributes and available gap. It is purely an analytical study. The present article discusses about why it’s a matter, importance and benefits and various national and international researcher findings. The researcher of this article is taking research in addressing the connecting local industries for local employment.

Modern recommender systems operate in highly dynamic environments where user preferences evolve over time. This phenomenon, commonly referred to as preference drift, leads to performance degradation when models assume stationary behavior. In this work, we propose a drift-aware recommendation framework that models and predicts user preference evolution using graph neural networks combined with temporal sequence modeling. We represent user–item interactions as a sequence of time-dependent bipartite graphs and learn user embeddings via GraphSAGE. Temporal dynamics are captured using a recurrent neural network that predicts future user embeddings. We further incorporate Monte Carlo dropout to estimate predictive uncertainty and quantify drift magnitude as geometric displacement in latent space. Experiments on MovieLens and Yambda datasets demonstrate that the proposed method improves embedding prediction accuracy, enhances recommendation robustness under high-drift conditions, and enables reliable detection of anomalous behavioral changes. The results show that drift-aware modeling significantly mitigates performance degradation in non-stationary environments while providing actionable uncertainty signals.

In the modern world social media literacy plays a significant role in enhancing cybersecurity measures specifically in India. Background information regarding the research topic has been discussed effectively along with highlighting the aim, objectives along with research questions that reflect as a foundation of the research. Besides this, lack of digital literacy in rural and urban areas of India has been discussed as an issue. In-depth literature review along with thematic implications such as “Protection Motivation Theory (PMT)” and “Theory of Planned Behaviour (TPB)” has also been illustrated in this research. Furthermore, the selection of “secondary data collection method” along with expected outcome has also been discussed in this report.

Sampling is a fundamental tool in statistical research, providing a practical alternative to complete enumeration where  of time, cost, personal and accessibility are constraints Choosing the best estimator for population total estimation is one of the main issues in survey sampling.  Even though many different sampling strategies and estimators are available, it is still difficult to assess how effective they are in diverse situations. This study presents a comparative analysis of the efficiency of sampling scheme estimators (Hansen Hurwitz, Horvitz-Thompson, Rao-Hartley-Cochran’s and Sen Yates-Grundy) in estimating population total using child birth data) from Ekiti State . Population totals and variances of each estimator were obtained and the most efficient estimator determined in terms of variance. The results revealed that the Rao–Hartley–Cochran estimator consistently produced the lowest population total estimates with the least variance for 2 years, while in the other year, the Sen–Yates–Grundy estimator demonstrated superior efficiency with minimal variance. (The study offers empirical evidence regarding the relative efficiency of the probability proportional to size estimators with replacement (Hansen–Hurwitz) and those without (Horvitz–Thompson, Rao–Hartley–Cochran, and Sen–Yates–Grundy in estimating population totals).  The study further showed that' efficiency  of estimators  is not constant but rather fluctuates over time and across data distributions. The study also closes the gap between theoretical sampling principles and real-world application in demographic and health statistics by using these sample strategies on actual child birth registration data from Ekiti State.

The rapid advancement of Artificial Intelligence (AI) and Machine Learning (ML) across healthcare domains has significantly improved diagnostic accuracy, operational efficiency, and patient outcomes (Rajkomar et al., 2019; Jiang et al., 2017). Despite these benefits, concerns regarding the lack of transparency and interpretability in AI systems remain critical, particularly in high-stakes environments such as healthcare (Doshi-Velez & Kim, 2017; Tjoa & Guan, 2020). Many AI models operate as “black boxes,” making it difficult for stakeholders to understand the rationale behind their decisions, thereby raising issues of trust, accountability, and ethical compliance (Arrieta et al., 2020; Floridi et al., 2018). Healthcare Business Process Outsourcing (BPO) organizations increasingly utilize AI-driven tools to automate medical document processing for insurance claims. These documents, including prescriptions, lab reports, and radiology records, must be classified and chronologically organized to reflect patient history. Traditional ML approaches such as Count Vectorization and TF-IDF combined with classification algorithms achieve high accuracy; however, they rely heavily on word frequency, which can introduce bias and lead to misclassification (Wang et al., 2018; Obermeyer et al., 2019). To address these limitations, Explainable Artificial Intelligence (XAI) has emerged as a promising solution that enhances transparency and interpretability in AI systems (Ribeiro et al., 2016; Lundberg & Lee, 2017). This study presents a systematic review of XAI techniques in healthcare, focusing on their application in medical document classification. It further identifies key challenges, including the lack of standardized evaluation metrics, and proposes future research directions to enhance transparency, fairness, and reliability in AI-driven healthcare systems (Arrieta et al., 2020; Tjoa & Guan, 2020).

CampusBites a Food Ordering System Mobile Application for Faculty and Staff of Pangasinan State University, Alaminos City Campus; addresses the challenges faced by vendors, faculty, and staff in managing food orders by providing a centralized platform for them to operate on, enhancing communication and overall ordering experience within the campus. The existing manual ordering process through the Facebook Messenger often leads to miscommunication, delayed transactions, and difficulty in tracking orders, highlighting the need for a more organized and efficient digital solution. Using the Agile Methodology, the study aims to design and develop a mobile application that provides a centralized platform for food ordering and order management. Data were collected through interviews, observations, and online research providing the researchers with the existing ordering process and challenges the customers face. These methods allowed the team to gather firsthand information from vendors and faculty members, which ensures that the system addresses real pain points such as order accuracy, response time, and ease of use. Purposive Sampling was used to select the participants, with a total of 91 responses from a combination of vendors, faculty, and staff. System performance and user acceptance were evaluated using a Likert-scale survey, which measured  key indicators such as functionality, reliability, and user satisfaction. By digitizing and streamlining the food ordering process and improving communication between vendors and faculty, this study contributes to enhanced service at Pangasinan State University Alaminos City Campus. The findings are expected to support the adoption of digital solutions in academic settings, promoting efficiency and convenience within the university community while also serving as a reference model for similar campus-based food ordering systems.

Hospital Information Management System (HIMS) has become an essential digital tool in modern healthcare institutions for improving patient care documentation and clinical communication. Nursing documentation is a critical component of patient safety, legal accountability, and continuity of care. This descriptive cross-sectional quantitative study was conducted to assess the impact of HIMS on nursing documentation among 150 staff nurses working in selected hospital wards including intensive care unit, medical ward, surgical ward, and emergency unit. Convenient sampling technique was used to select participants. A structured questionnaire consisting of demographic variables, ease of HIMS use, documentation accuracy, time management, and challenges faced by nurses was administered. The findings revealed that 84% of nurses reported improved documentation accuracy, 61.3% experienced reduced documentation time after adaptation, and 92% stated that patient records became easier to retrieve after HIMS implementation. Medication documentation errors were reduced according to 87.3% of respondents. Major challenges identified included slow server response (38.7%), frequent login/logout issues (28%), duplicate entries (20.7%), and limited computer terminals (12.6%). Positive perception score regarding HIMS was 78%. Significant association was observed between years of professional experience and ease of HIMS use. The study concludes that HIMS has a positive impact on nursing documentation, though technical barriers remain a challenge.

Fuzzy Bd-ideals provide a useful framework for studying Bd-algebras. This paper focuses on the Cartesian and direct products of finite prime fuzzy Bd-ideals. We extend the idea of primeness to these constructions and analyze how it behaves under product operations. Key properties of the Cartesian product are obtained, including conditions that keep primeness intact. We also study the direct product case and identify when the prime structure is preserved. These findings give a clearer view of how fuzzy ideals interact within product structures in Bd-algebras.

This study used the Auto Regression Distributed Lag (ARDL) technique to analyze the relationship between public debt and economic development in Nigeria between 1999 and 2023. The study specifically looked into how Nigeria's inflation rate, external debt, and domestic debt affected the country's economic growth. GDP served as a stand-in for economic growth, and the explanatory variables were the inflation rate (IFN), external debt (ED), and domestic debt (DM). The findings showed that all three factors (DM), ED, and INF had little long-term effects on economic growth. All variables, however, were found to have a negligible long-term relationship with economic growth. Long-term economic growth was found to be positively and marginally correlated with both the inflation rate and domestic debt, suggesting that raising both will eventually boost Nigeria's economy. However, there was a negative and negligible impact of external debt on economic development. In other words, their rise will eventually slow Nigeria's economic progress. The study came to the conclusion that public debt indices have little long-term effect on Nigeria's economic growth. Therefore, it is advised that policymakers incorporate the necessary steps to guarantee appropriate management of domestic debts. The government should make sure that accrued national debts are used to promote investment in the nation and, through the appropriate monitoring committees, ensure that national debts are used to provide the essential facilities and services needed for the advancement of the nation's communities and societies.

This paper presents the implementation of Incremental Conductance (IC) Maximum Power Point Tracking (MPPT) for photovoltaic (PV) system integrated with a DC-DC boost converter to enhance energy harvesting efficiency. Output power varies with environmental conditions such as solar irradiance and temperature due to the nonlinear characteristics of PV modules. To address this challenge, The IC MPPT technique is employed to accurately track the maximum power point (MPP) under both steady-state and rapidly changing environmental conditions. Unlike conventional methods, the incremental conductance approach determines the MPP by comparing IC ( with the instantaneous conductance (), reduced oscillations and improved tracking speed. The proposed system consists of a PV array, an IC based MPPT controller and a boost converter that steps up the output voltage to a required level for efficient load utilization. The boost converter is controller through pulse width modulation (PWM) signals generated by the MPPT algorithm, enabling optimal power extraction from the PV panel. MATLAB/Simulink is used to model and simulate the system, validating its performance under varying irradiance conditions. Simulation outcome indicates that the IC MPPT technique achieves improved efficiency, quicker tracking of MPP and minimizes steady-state fluctuations when compared to conventional approaches like Perturb and Observe (P&O) method. The system ensures reliable and stable operation, making it suitable for renewable energy applications. Overall, the integration of the IC MPPT with a boost converter significantly improves the performance and efficiency of photovoltaic energy systems.

Post-pinching regrowth and plant spacing are key determinants of vegetative performance, biomass allocation, and canopy development in Moringa species under semi-arid conditions. A factorial experiment was conducted in a Randomized Complete Block Design with three replicates to evaluate the effects of four spacing treatments (15×15, 15×20, 20×20, 20×30 cm) on the growth performance, root–shoot ratio, and leaf area index (LAI) of Moringa peregrina, M. oleifera, M. stenopetala, and M. oleifera ‘PKM 1’. Seedlings were pinched at 4 weeks after emergence to stimulate branching, and growth parameters were measured over 8 weeks. Post-pinching regrowth significantly influenced the number of leaves (NL) and branches (NB), with M. oleifera ‘PKM 1’ showing superior performance (NL = 261 ± 10.5; NB = 18 ± 1.5). Closer spacing enhanced NL, NB, and plant height, whereas wider spacing promoted root allocation and structural development. Root–shoot ratio decreased with increasing spacing, indicating adaptive biomass partitioning to optimize resource acquisition, while LAI varied among species, reflecting differences in canopy architecture and light interception efficiency. Regression analyses revealed strong positive relationships between root and shoot biomass (R² = 0.85–0.94) and negative correlations between R/S ratio and spacing (R² = 0.80–0.89), demonstrating species-specific plasticity in growth strategies. These findings highlight the importance of optimizing spacing and post-pinching management to enhance leaf yield, biomass allocation, and productivity in Moringa agroforestry systems, providing actionable insights for sustainable and resilient cultivation in semi-arid environments.

A quarry is an area from which rocks such as marble, limestone, and granite are extracted for industrial use. Once depleted of their desired resources, quarries are frequently abandoned. The resulting gaping holes can fill with water and form dangerous quarry lakes while others are turned into unsightly landfills. When quarries are in close proximity to urban environments, inhabitants are subjected to pollution and noise, and the undeniable eyesore of an abandoned quarry remains long after excavation is completed. Sustainable redevelopment has become a shining solution for these abandoned, resource depleted quarries. Dozens of cities have undertaken adaptive re-use projects to transform quarries into a variety of public and private spaces. The potential new uses for these expanses of land include sites for research and education, aquaculture, recreational activities, storage, industry and housing. Not only do quarries often negatively impact those who live nearby, but they often leave residual negative impacts on the environment. Runoff of chemical pollutants into bodies of water, loss of natural habitats, farmland, and vegetation, and natural resource exhaustion are among the most harmful environmental impacts. While quarrying can be a negative industry for society and for the environment, the necessity of quarrying is undeniable. In order for human civilization to continue as it has since the industrial revolution, we need the retrieval of resources from quarries in order to create our homes foundations, transportation structures with cement, concrete, asphalt, and crushed stone, and other industrial uses such as abrasives, binders, additives, and roofing. Millions of people worldwide are employed by quarrying practices, and therefore a removal of the quarrying industry would result in the loss of jobs for countless families. Therefore, in order to remedy the negative effects of quarrying, we must use the resource depleted spaces for other practices once the quarries cease being operational. The potential transformation of quarry sites into a variety of sustainable uses would not only remedy the negative effects of quarrying, but could create sites of greater social, environmental value. The goal of this research is to encourage the rehabilitation of land disturbed by quarrying by making the areas suitable for new sustainable land uses.

Until now, most defence-oriented immersive systems relied primarily on VR and AR interfaces to visualize mission data, maps, and environmental cues. These solutions offered useful overlays but lacked the capability to perform real-time threat identification directly from the soldier’s visual feed. Building on these earlier technologies, the proposed system introduces a next-generation Extended Reality (XR)–based tactical platform that not only displays information but also performs intelligent on-ground analysis using integrated image processing.The XR headset is equipped with a compact camera module that captures images of individuals encountered during mission entry. These images are processed through an AI-driven facial recognition model, enabling the system to instantly determine whether the person matches a known terrorist or high-risk suspect stored in an encrypted database. When a match is found, the soldier receives an immediate XR alert, while the base station simultaneously receives the captured image and identity confirmation for coordinated decision-making.

In the digital age, it is essential for the Bachelor of Science in Information Technology (BSIT) department to adapt to advancements in the educational system. Despite this digital transformation, students and faculty still encounter challenges because learning resources are dispersed across platforms such as Google Drives, USB drives, and chat groups. Faculty members also handle various responsibilities and designations, which often prevent them from immediately assisting students and cause delays in accessing important academic materials. To address these issues, KnowHub was proposed to centralize and organize educational content, allowing users to access resources more efficiently. With proposed features such as version control, and secure role-based permissions to protect sensitive academic assets. By consolidating essential information into one platform, the system reduces confusion, minimizes redundancy, and ensures that academic materials are consistently available when needed. This study presents a developmental and descriptive research approach and follows the Agile Model. Interviews were conducted, and purposive sampling was applied to gather evaluations through survey questionnaires completed by students and alumni. The study demonstrate that KnowHub will effectively address existing problems and provides a solution for improving resource management within the BSIT department. The system will not only enhance access to information but also supports more efficient academic workflow, contributing to a more organized and technology-responsive learning environment.

Conflict management in healthcare is a persistent organisational challenge, with unmanaged conflict undermining staff well‑being, team effectiveness and patient safety. International evidence links workplace conflict, violence and incivility among healthcare workers to burnout, organisational silence and reduced patient safety competence (Kim et al., 2022; Aunger et al., 2025; Han et al., 2025). In Nigeria, chronic resource constraints, heavy workloads and role ambiguity further intensify conflict in public hospitals (Ayodele & Akinmoladun, 2023; Olabode et al., 2022; Valentine & Lavizzo‑Mourey, 2025). This study examined the relationship between strategic leadership and conflict management at State Hospital Ijaye, Abeokuta, and State Hospital Ifo, Ogun State, using a convergent mixed-methods design. From a population of 418 staff, Taro Yamane’s formula yielded a sample of 204, with 165 usable questionnaires and 20 semi‑structured interviews. Conflict management (constructive styles, dysfunctional conflict, team effectiveness) and strategic leadership (vision, participation, staff support) were measured with validated Likert scales; Cronbach’s alpha exceeded 0.80, and factor analysis supported construct validity. Descriptive statistics showed moderately high levels of constructive conflict management (M = 3.72), team effectiveness (M = 3.79), and strategic leadership dimensions (Ms = 3.76–3.88), alongside nontrivial dysfunctional conflict (M = 3.45). Pearson correlation revealed that strategic leadership correlated positively with constructive conflict styles (r = 0.68, p < 0.01) and team effectiveness (r = 0.72, p < 0.01) and negatively with dysfunctional conflict (r = −0.56, p < 0.01). Qualitative findings indicated that clear communication, participative decision‑making and supportive leadership foster collaborative conflict cultures, whereas distant or biased leadership, compounded by resource shortages and unclear policies, sustains destructive conflict. Although limited by self-report, two-hospital scope, and cross-sectional design, the evidence suggests that strategic leadership, embedded in a supportive culture and adequate resources, is a key lever for improving conflict management in Nigerian state hospitals.

The smart portable avenue divider is an innovative IoT-based solution designed to improve traffic management, especially during peak hours and emergency situations. This system helps in dynamically controlling road space by adjusting the divider position based on real-time traffic conditions. It is particularly useful when there is a higher vehicle density on one side of the road compared to the other, allowing better utilization of available lanes and reducing congestion. The device operates by monitoring traffic flow on both sides of the road. When the incoming traffic on one side is significantly higher than the outgoing traffic on the opposite side, the divider gradually shifts to provide additional space for the congested lane. This movement is controlled carefully to ensure safety and avoid sudden changes that could lead to accidents. Additionally, the system prioritizes emergency vehicles such as ambulances by clearing the path quickly, ensuring faster response times and improved public safety. With the rapid increase in the number of vehicles due to population growth and urbanization, existing road infrastructure often becomes insufficient. Expanding road capacity is not always feasible due to limited space and resources. Therefore, efficient management of existing lanes becomes essential. The smart divider addresses this challenge by optimizing road usage without requiring major infrastructure changes. This system is especially beneficial in urban areas where traffic patterns vary throughout the day, such as business districts and commercial zones. By adapting to changing traffic conditions in real time, the smart portable divider enhances traffic flow, reduces waiting time, and contributes to a more efficient and safer transportation system. The proposed system, Smart Movable Road Divider and Clearance Ambulance Path using IoT, is designed to provide a fast and efficient route for emergency vehicles, especially ambulances, in heavy traffic conditions. In urban areas, traffic congestion often delays ambulances, which can lead to critical loss of time and risk to human life. This system uses IoT-based sensors such a RFID to detect the presence and approach of an ambulance in real time. Once an ambulance is identified, the system automatically sends signals to a central control unit, which processes the data and activates actuators to adjust the position of movable road dividers. This creates a clear and dedicated path for the ambulance, ensuring smooth and uninterrupted movement through traffic. Additionally, the system continuously monitors traffic density and optimizes divider movement to minimize disruption to other vehicles. By prioritizing emergency vehicles and reducing response time, this system enhances road safety, improves traffic management, and plays a crucial role in saving lives

The increasing demand for cooling in Sub-Saharan Africa, driven by rapid urbanisation and rising temperatures, is placing significant pressure on already constrained energy systems. Conventional air conditioning technologies are energy-intensive and contribute to greenhouse gas emissions. This study evaluates the potential of ground source heat pump (GSHP) technology as a sustainable alternative for building cooling in the region. A pilot case study based on Cameroon is used to assess the technical feasibility, energy performance, and economic viability of GSHP systems under local climatic and geological conditions. The analysis incorporates empirical data and simulation-based modelling to estimate energy savings, carbon emission reductions, with a payback period of approximately 6 years. Results indicate that GSHP systems can reduce national cooling energy demand by approximately 3691 GWh/year in Cameroon, representing a substantial decrease compared to conventional air conditioning systems. A lifecycle cost perspective suggests long-term economic benefits, particularly when supported by appropriate financing mechanisms. The study also highlights the influence of geological variability and climatic diversity across Sub-Saharan Africa on system performance. Key barriers to adoption, including high upfront costs, limited technical expertise, and weak policy support, are critically examined. The findings demonstrate that GSHP systems can significantly reduce energy demand and emissions while offering long-term economic benefits, although widespread adoption requires policy support and capacity development. Ground source heat pump (GSHP) technology can become a key component of the region's cooling infrastructure. Their adoption not only supports the transition to low-carbon energy systems but also enhances resilience to the impacts of climate change, contributing to improved living standards and sustainable development in sub-Saharan African countries.

The rapid proliferation of artificial intelligence tools among institutional investors is reshaping corporate governance and accountability, yet its consequences for ESG disclosure quality in frontier markets remain poorly understood. This study examined one precisely bounded question: does investor AI monitoring capability cause East African commercial banks to disclose ESG information more granularly? The analysis was grounded in agency, signaling, and institutional theories, which together position AI capability as a governance mechanism that conditions the depth — not merely the breadth — of ESG reporting by altering the strategic cost of disclosure imprecision. Using hand-coded ESG disclosure data from 31 commercial banks — 23 domestic private and 8 globally affiliated — across Kenya, Tanzania, Uganda, Rwanda, and Ethiopia (2018–2024), and surveys of 418 institutional investors, the study constructs an ownership-weighted AI capability measure and a comprehensive ESG disclosure granularity index comprising 47 items across environmental, social, and governance dimensions. The empirical analysis employed OLS, instrumental variable estimation exploiting EU SFDR mandates, staggered difference-in-differences, and event studies, controlling for firm size, profitability, leverage, ownership structure, and board characteristics. Investor AI capability is a strong, robust predictor of disclosure granularity (β = 0.52, p < 0.001, ΔR² = 0.22), with a one-standard-deviation increase associating with a 14.9-point granularity rise. Results were consistent across all five identification strategies (β range: 0.49–0.68). Crucially, actual ESG performance does not predict granularity, and AI effects are strongest among poor ESG performers — consistent with strategic impression management rather than genuine accountability. The study introduced the concept of 'algorithmic greenwashing': the production of granular, machine-readable disclosures optimised for AI detection without substantive improvement to underlying ESG practices. Regulators should mandate granularity standards with independent verification mechanisms, and must not treat algorithmically optimised disclosure as a proxy for genuine sustainability progress. Investors and bank boards must ensure detailed reporting reflects substantive rather than reputational compliance.