Volume 14 Issue 11

Abstract: In the coming years, India’s developmental narrative is poised for a fundamental shift— from a traditional Gross Domestic Product (GDP)-driven model toward a more inclusive and empowerment-based framework of progress. This emerging paradigm is increasingly reflected in national policy discourse and statements by the Prime Minister, who emphasizes the need for a development approach centered on the Gross Empowerment of People (GEP) rather than solely on economic output. The concept of GEP underscores a multidimensional understanding of development that includes economic, social, political, digital, and cultural empowerment, moving beyond conventional growth metrics to focus on the real capabilities, freedoms, and dignity of individuals. This research paper investigates the theoretical foundations and practical significance of the GEP model as a guiding principle in India’s evolving development strategy. Recognizing the complexity of measuring empowerment, the study proposes a comprehensive and context-sensitive GEP Index, designed to capture the multidimensional nature of empowerment within India’s unique socio-political landscape. The research applies this proposed GEP Index to a detailed case study of the state of Haryana. Haryana presents a compelling case for analysis due to its paradoxical developmental profile: the state boasts one of the highest per capita incomes and robust industrial growth rates nationally, yet it continues to grapple with persistent social challenges, including gender disparities, high dropout rates in education, and significant rural-urban inequalities. These contradictions make Haryana an ideal laboratory to test the efficacy of the GEP Index in revealing the nuanced dimensions of empowerment that traditional economic indicators may obscure. Using a composite methodology that integrates quantitative data from government reports, surveys, and indices, alongside qualitative assessments of policy impacts, the study evaluates Haryana’s performance across five key dimensions of the GEP Index—economic empowerment, social empowerment, political participation, digital inclusion, and psychological and cultural empowerment. The findings highlight areas of strength, such as economic and digital empowerment, alongside critical gaps in social and cultural dimensions, particularly related to gender equality and educational access. The study concludes by discussing the implications of adopting a GEP-centric development framework for policymakers, emphasizing how such an approach can help bridge the gap between economic growth and genuine human development. It argues that the GEP Index offers a more holistic and equitable alternative to GDP, enabling governments to design targeted interventions that foster inclusive empowerment. Ultimately, this research contributes to the growing global and Indian discourse on redefining progress by offering a practical, measurable, and policy-relevant empowerment framework. It demonstrates that while Haryana exemplifies some of India’s economic successes, true development must be measured by the extent to which its people are empowered to participate fully in society and shape their own futures.

Abstract: The physicochemical properties of the premium motor Spirit (PMS) and Automotive Gas Oil (AGO) were determined to ascertain with each other and their compliance with American Society for Testing Materials (ASTM) standards and the Nigerian Midstream and Downstream petroleum Regulatory Authority (NMDPRA). The properties studied for NNPC and Zone 4 oil depots results obtained were respectively as follows: density 0.7445, 0.7425, Research octane number 91.8, 93.1, ethanol content was absent for both petroleum depots samples, distillation test at the initial boiling point is 34, 30, at the final boiling point were 181, 181 for both NNPC and zone 4 depots samples, benzene content were 1.0, 0.9, Sulfur content test 0.0275, 0.0215 and Reid Vapour pressure 49.5, 49.0, the study revealed that the PMS quality was within the ASTM and NMDPRA requirements for both NNPC and zone 4 depots and has positive effect on motor engines. AGO samples from two different depots (NNPC and Hudson) was analyzed. The parameters analyzed and the results obtained as follows; density test 0.8530 and 0.8404, flash point 69, 70, distillation at the initial boiling point 127, 80 for NNPC and Hudson oil depots, diesel Index 38.2, 42.8, cetane number 37.9, 46.2, kinematic viscosity 3.324, 3.144, sulfur content 0.0740, 0.0475, Acidity were all within the ASTM and NMDPRA specifications for PMS for both NNPC and Hudson depots. Hudson depot cetane number analyzed was in compliance with the regulatory authority standards while NNPC depot cetane number, and carbon residue were off specifications. There was deviation in the diesel index number and carbon residue, distillation test for both NNPC and Hudson oil depots for AGO samples will have adverse effect on motor engine. Based on the research studied, the AGO might have been poorly refined or adulterated and could constitute problems to automobile engines if utilized. It is imperative that regular quality tests be conducted randomly to check depots petroleum products compliance with the required standards.

Abstract: The physicochemical properties of the premium motor Spirit (PMS) and Automotive Gas Oil (AGO) were determined to ascertain with each other and their compliance with American Society for Testing Materials (ASTM) standards and the Nigerian Midstream and Downstream petroleum Regulatory Authority (NMDPRA). The properties studied for NNPC and Zone 4 oil depots results obtained were respectively as follows: density 0.7445, 0.7425, Research octane number 91.8, 93.1, ethanol content was absent for both petroleum depots samples, distillation test at the initial boiling point is 34, 30, at the final boiling point were 181, 181 for both NNPC and zone 4 depots samples, benzene content were 1.0, 0.9, Sulfur content test 0.0275, 0.0215 and Reid Vapour pressure 49.5, 49.0, the study revealed that the PMS quality was within the ASTM and NMDPRA requirements for both NNPC and zone 4 depots and has positive effect on motor engines. AGO samples from two different depots (NNPC and Hudson) was analyzed. The parameters analyzed and the results obtained as follows; density test 0.8530 and 0.8404, flash point 69, 70, distillation at the initial boiling point 127, 80 for NNPC and Hudson oil depots, diesel Index 38.2, 42.8, cetane number 37.9, 46.2, kinematic viscosity 3.324, 3.144, sulfur content 0.0740, 0.0475, Acidity were all within the ASTM and NMDPRA specifications for PMS for both NNPC and Hudson depots. Hudson depot cetane number analyzed was in compliance with the regulatory authority standards while NNPC depot cetane number, and carbon residue were off specifications. There was deviation in the diesel index number and carbon residue, distillation test for both NNPC and Hudson oil depots for AGO samples will have adverse effect on motor engine. Based on the research studied, the AGO might have been poorly refined or adulterated and could constitute problems to automobile engines if utilized. It is imperative that regular quality tests be conducted randomly to check depots petroleum products compliance with the required standards.

Abstract: India is an agriculture-oriented country. The country of agriculture of India towards employment is often not right recognized and well appreciated, although it provides employment to a great majority of people, that is, about 60 per cent of the country’s population. It is feeding its huge population with food grains and other food items. The food grain production is around 230 million tonnes. Agriculture is contributing to industrial development through supply of raw materials. Agriculture commodities constitute 25 per cent of India’s exports, while its share in the national income works out to be 18 per cent. The agrarian economy is the base on which the entire distorted structure of the economy and the society is constructed. Nowadays, these farming techniques are necessary as day-to-day farming lands are becoming apartments and factories. Actually, everything is happening in a contrast as for the increasing population. There should be an increase in agriculture land, but instead it is in a decreasing stage. In such a case, the awareness among the farmers is lacking. The government has to take some initiatives about such issues and appoint some persons to spread awareness about the profitable techniques of farming to uneducated farmers. Some are non-eco-friendly but we still continue the same. We must have some awareness about those things which may give profit in instant, but not an everlasting one better future.

This study examines the asymmetric effects of globalization dimensions (economic, social, political) and foreign direct investment on Tunisia's ecological footprint from 1979 to 2021 using the Quantile Autoregressive Distributed Lag (QARDL) approach. Moving beyond conventional mean-based estimates, our results reveal heterogeneous impacts across environmental stress regimes. Economic globalization and FDI increase environmental degradation at low pressure quantiles (τ = 0.25), but this relationship reverses at high pressure levels (τ = 0.75), suggesting a conditional Environmental Kuznets Curve trajectory. The findings demonstrate that the relationship between globalization and the environment is not uniform, but rather varies substantially with ecological conditions. We advocate for tailored policy strategies, including stringent FDI regulation in polluting sectors and incentives for green technology transfer. Enhancing social and political globalization dimensions emerges as crucial for aligning economic integration with ecological sustainability.

Accurate and timely fault detection in power systems is essential to ensure system reliability, minimize equipment damage, and maintain continuous power delivery. This paper proposes an integrated signal processing and machine learning (ML) model designed to enhance the precision and speed of power system fault diagnosis. The approach leverages advanced signal processing techniques—such as Wavelet Transform and Fast Fourier Transform (FFT)—to extract critical time–frequency features from voltage and current signals under various fault conditions. These extracted features are then fed into optimized machine learning classifiers, including Support Vector Machines (SVM), Random Forest (RF), and Deep Neural Networks (DNN), to accurately identify fault types, locations, and severities. The integration of signal processing with ML significantly improves fault detection accuracy compared to conventional methods, particularly under noisy and dynamic operating conditions. Simulation results on standard IEEE test systems demonstrate the model’s robustness and scalability, achieving high accuracy and reduced computational latency. The proposed hybrid framework provides a reliable diagnostic tool for real-time monitoring and intelligent decision-making in modern power grids. Future work will focus on extending the model for predictive maintenance and integration with smart grid communication infrastructures.

ABSTRACT: Background: Local sports in Kenema City, Eastern Sierra Leone, often suffer from low visibility and limited institutional support. FM radio is the most accessible medium in the region, yet its specific role and effectiveness in promoting local sports remain under-examined. Objectives: This study assessed the strategies used by FM radio stations in Kenema to promote local sports, examined listener perceptions and attitudes, and identified the challenges faced by sports presenters. Methods: A mixed-methods approach was employed. Quantitative data were collected via structured questionnaires from 120 listeners and stakeholders. Qualitative data were gathered through 10 interviews with sports presenters and 3 focus group discussions with community sports fans. Data were analyzed using descriptive statistics and thematic content analysis. Findings: Findings indicate that while most FM stations offer sports coverage, programming is often irregular and lacks depth. Key promotional strategies include discussions (33.3%), call-in shows (16.7%), and interviews (16.7%). Coverage is heavily dominated by international football (79.2%), with local sports receiving minimal attention. Listeners value sports programming for community cohesion but report dissatisfaction due to presentation quality and perceived bias. Presenters face significant constraints, including inadequate funding (33.3%), lack of training (33.3%), and poor equipment. Conclusion:  FM radio holds significant potential to enhance local sports promotion in Kenema. However, structural and capacity limitations hinder its effectiveness. Stations should establish regular, dedicated sports slots, improve presenter training, and actively engage with local sports associations. Diversifying coverage beyond football is critical for fostering a more inclusive local sports culture.

Emergency power supply is essential in large commercial infrastructures such as shopping malls, where critical loads including lighting, pumps, and safety systems must remain operational during grid outages. Conventional diesel generators are widely used but suffer from delayed startup, noise, emissions, and high maintenance costs. To address these issues, this work proposes the design and simulation of an Emergency Battery Deployment System for terrace-level installation in malls. The proposed system employs a Battery Energy Storage System (BESS) integrated with a Battery Management System (BMS) to ensure reliable, efficient, and safe operation. A preliminary sizing of 260 kWh has been carried out to supply approximately 130 kW of critical loads for up to two hours of autonomy. The system is modeled and analyzed using ETAP software to perform load flow, outage, and fault simulations. The results demonstrate that the battery-based system offers instantaneous response, reduced operational costs, and zero local emissions, making it a sustainable alternative to diesel generators. The project also contributes to the United Nations Sustainable Development Goals (SDGs), namely Affordable and Clean Energy (SDG 7), Industry, Innovation, and Infrastructure (SDG 9), and Sustainable Cities and Communities (SDG 11)

Oil spills pose severe threats to marine ecosystems, human health, and global economies. The paper examines oil spill causes, environmental impacts, detection technologies, and mitigation strategies. It also evaluates modern approaches such as remote sensing, AI-enabled spill detection, nanotechnology-based cleanup, and emerging eco-friendly solutions. The findings highlight global challenges and propose sustainable solutions for improved preparedness and response.

A lot of people struggle with sleep disorders, and when these problems go undiagnosed, they can lead to serious health issues. Right now, doctors mostly rely on tests like polysomnography (PSG) and expert analysis to spot these disorders, but that process eats up time and resources. Plus, it’s not always consistent—different experts might interpret results in their own way. In this paper, we introduce a new method for detecting sleep disorders that uses multi-layered ensemble learning and smart data balancing. One big hurdle is that sleep disorder datasets are usually imbalanced—some conditions show up way more often than others. To tackle this, we use data balancing tools like SMOTE, ADASYN, and both random over-sampling and under-sampling. When you combine these with ensemble methods like stacking and boosting, you get much better results in terms of accuracy, sensitivity, and specificity. Our framework is all about making sleep disorder detection more reliable, automated, and accurate. The goal is to catch these disorders earlier and more effectively, so patients get the care they need sooner, and healthcare systems don’t get overwhelmed.

This paper investigates the connection between the decreasing density of trade unions and the mechanisms for resolving industrial disputes within India's manufacturing sector. By utilizing data from Labour Bureau reports (2015-2024), statistics from the Industrial Disputes Act, and recent labour reforms, this study evaluates how the 06resolution. The research uncovers a paradoxical trend: despite a decline in union membership from roughly 30% density in 1985 to 15% in 2024, industrial disputes saw a reduction of 73% between 2010 and 2021. This seemingly positive development conceals deeper issues such as heightened power imbalances, the diminishing of collective voice mechanisms, and an increasing dependence on individual grievance redressal. The study employs a quantitative analysis of official statistics alongside a qualitative review of legislative changes, particularly focusing on the Industrial Relations Code 2020. The findings suggest that the decline of unions has transitioned dispute resolution from collective negotiations to employer-dominated alternative dispute resolution (ADR) mechanisms, which raises concerns regarding worker agency and the sustainability of industrial harmony in the long run. This paper contributes to applied science by presenting empirical evidence of institutional changes in labour relations and offers policy recommendations aimed at establishing balanced dispute resolution frameworks in emerging economies.

Cyclooxygenase-2 (COX-2) is a key inducible enzyme in the inflammatory pathway, catalyzing the conversion of arachidonic acid into prostaglandins that mediate pain, fever, and inflammation. Selective COX-2 inhibitors such as celecoxib and rofecoxib have demonstrated strong therapeutic effects but are associated with adverse cardiovascular and gastrointestinal complications, underscoring the need for safer alternatives. Medicinal plants represent a valuable source of novel bioactive compounds with promising anti-inflammatory properties. Hyptis verticillata, a member of the Lamiaceae family, has been widely used in ethnomedicine for treating fever, colds, and inflammatory conditions, and is known to contain diverse phytochemicals including terpenoids, flavonoids, sterols, and essential oils. This study aimed to evaluate the molecular docking interactions of phytocompounds from H. verticillata with human COX-2 (PDB ID: 6COX) as potential natural anti-inflammatory agents. Seven phytochemicals reported in previous phytochemical profiling of the plant were docked against the COX-2 active site using AutoDock Vina implemented in PyRx, and their interactions compared with reference inhibitors celecoxib and rofecoxib. Binding affinities ranged from −3.7 to −8.2 kcal/mol. Squalene demonstrated the strongest affinity (−8.2 kcal/mol), comparable to rofecoxib (−8.2 kcal/mol), while aliphatic hydrocarbons such as 1-octadecyne (−6.9 kcal/mol) and 1-fluorodecane (−6.1 kcal/mol) showed moderate activity. Celecoxib, unexpectedly scoring −3.7 kcal/mol, highlighted potential docking protocol limitations that warrant revalidation. Interaction analysis revealed that hydrophobic contacts dominated ligand binding, consistent with the structural hydrophobicity of the COX-2 catalytic tunnel. Although squalene showed high docking affinity, ADMET predictions indicated poor solubility and oral bioavailability, limiting its drug-likeness. In contrast, smaller hydrocarbons displayed more favorable pharmacokinetic profiles but weaker binding energies. These findings suggest that H. verticillata harbors compounds with structural potential for COX-2 inhibition, though optimization and experimental validation are required. The study provides a computational foundation for developing safer plant-derived anti-inflammatory agents.

This study examines the adoption of electric scooters, emphasizing the factors influencing consumers’ purchase decisions and post-purchase satisfaction. It explores key determinants such as cost, range, charging infrastructure, design, and environmental benefits, while also addressing challenges like high initial investment and inadequate charging facilities. Data were collected from 50 electric scooter users in Alappuzha using a structured questionnaire. The findings offer valuable insights for manufacturers and policymakers to enhance electric scooter adoption and user satisfaction, thereby supporting the transition toward sustainable urban mobility.

For shallow geothermal applications, the U-tube ground source heat pump (GSHP) system, the standing column well(SCW) geothermal heat pump system, and the forced external circulation single well(FECSW) geothermal heat pump system are recognized as the most advanced methods of geothermal cooling and heating systems, either by using no groundwater or by recycling groundwater. In order to improve the heat extraction efficiency of these single-well modes, it is vital to experimentally analyze the heat extraction efficiency in different experimental environments that can be encountered under realistic conditions. Based on the 1:20scaled model we design 10 experimental scenarios considering different experimental conditions, such as soil moisture, groundwater convection, heat conduction, abstraction and injection interval selection, and compared the heat extraction efficiency according to the scheme. The experimental results shows that the order of increasing heat extraction capacity is U-tube, SCW, FECSW. Also, in three methods, the heat extraction capability is changed by the water flow into the ground, ground moisture, operating approaches, etc. The results of the scaled model experiments are used as a comparative validation data for numerical simulation of a single well geothermal heat pump system and contribute to the development of modeling method of geothermal single well in real conditions.

Despite the transformative potential of advanced technologies such as Artificial Intelligence (AI) and soft robotics, the Indian apparel manufacturing sector exhibits a critical lag in achieving comprehensive, integrated digitalization. Implementation is hampered by systemic, multi-dimensional barriers. Analysis reveals these constraints include acute data fragmentation coupled with poor IT infrastructure; severe financial stress resulting from high implementation costs with an uncertain Return on Investment (ROI); and the persistent technical difficulty of automating the handling of limp, deformable fabrics. This report addresses this pervasive adoption gap by analyzing constraints across Technological, Organizational, and Environmental contexts. This research introduces the Apparel Technology Adoption and Supply Chain Resilience (ATASCR) framework to model the assimilation process necessary for industry modernization and strategic resilience. The empirical findings, based on correlation analysis, confirm that Market Demand (V3) is the dominant positive driver for adoption, while anticipated barriers like Limp Material Difficulty (V1) and Financial Strain (V2) show surprisingly weak or contrary correlation with the level of technology adoption (V4). Crucially, the analysis confirms that digital adoption (V4) is strongly associated with achieving Supply Chain Resilience (V5).

This study aims to strengthen decision-making in medical relief supply management through computational modeling and machine learning. Specifically, it seeks to develop a predictive model that supports effective medical supply allocation during disaster response operations. By experimenting with multiple algorithms and optimization techniques, the study endeavors to identify the most suitable machine learning approach for accurate and real-time prediction of supply reorder needs. The study adopts the SEMMA methodology as the framework for developing decision-making models that focus on optimizing medical supply distribution during natural calamities. Both ensemble and classical algorithms were implemented to compare predictive performance. Ensemble methods included Random Forest, Gradient Boosting, HistGradientBoosting, AdaBoost, XGBoost, LightGBM, and CatBoost, while classical algorithms comprised of Decision Tree, Logistic Regression, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). Model evaluation utilized the F1-score and ROC-AUC metrics, with hyperparameter tuning conducted via Randomized Search and 5-fold cross-validation to ensure robust generalization. Results revealed that CatBoost achieved the highest F1-score (0.9316), outperforming other models such as XGBoost (0.9282), Gradient Boosting (0.9279), and LightGBM (0.9277). These findings underscore the superiority of gradient boosting ensemble methods in capturing complex relationships and improving prediction accuracy. Traditional models, including AdaBoost, Decision Tree, Logistic Regression, KNN, and SVM are yielded with lower F1-scores ranging from 0.8051 to 0.8738 only and indicating its reduced predictive capability after optimization. The CatBoost algorithm demonstrated the most reliable and accurate predictive performance by highlighting its potential as a robust decision- support tool for optimizing medical supply allocation during disaster response operations.

This research was conducted in Tiga Runggu, Simalungun Regency, Indonesia. This research was conducted using a factorial randomized block design consisting of two factors, i.e. application of manure types consisting of 3 treatments, namely J1: chicken manure, J2: cow manure and J3: buffalo manure. The second factor consisted of three treatments, namely: D1: 75g/plant, D2: 100g/plant, and D3: 125g/plant. The parameters observed in this study were: (a) rate of crop formation, (b) gross crop weight per sample plant, (c) net crop weight per sample plant, (d) gross crop weight per plot, and (e) net crop weight per plot. The results of the study showed that the type of manure treatment had a significant effect on the rate of crop formation, gross crop weight per sample plant, net crop weight per sample plant, gross crop weight per plot and net crop weight per plot.

In many countries,Corporate Governance is adopted as a means for ensuring shareholders interest are protected. Corporate Governance Code is a blue print to achieve the objective of corporate governance.The Nigeria Corporate governance code emphasized the implementation of gender board diversity among firms. However, the World Economic Forum report on Global Gender Gap in 2024 showed that Nigeria is 125th out of 140 countries on the Global Gender Gap Index. This index showed that Nigeria is still way behind other countries in terms of gender equality and inclusion. Hence, it is essential to evaluate the implementation of the Nigerian corporate governance code in order to determine the extent of its implementation around board gender diversity. The study adopted comparative survey design. The population involved twenty-six listed firms in Nigeria. Five listed banks and five list food and beverage firms were selected using Purposive sampling technique. Data were collected from their annual financial reports (2021-2024) and data were analysed using frequency, percentage and graphs. Results showed that the Nigerian Corporate Governance Code promoted gender board diversity more in the banking industry when compared to the food and beverage industries due to the mandatory CBN 30% threshold. It means that 2022 Corporate Governance policy by the Central Bank of Nigeria (CBN) promoted gender board diversity better in the finance industry than Nigerian Corporate Governance code 2018 by security and exchange commission (SEC) in the food and beverage industry. Comparing firms in both sectors, the companies in banking sector were better than their counterpart in food and beverage industries in term of the trend of compliance among the firms. Drawing from the conclusion, regulatory agencies like Security and exchange commission (SEC) should introduce a regulatory threshold in the Nigerian Corporate Governance code 2018 in order to promote board gender diversity in the food and beverage industry.

Questionnaire-based research remains one of the most commonly used methods in social sciences, management, psychology, education, and health studies. However, the reliability and validity of the conclusions depend heavily on the quality of the questionnaire. Factor Analysis (FA), a multivariate statistical technique, provides a systematic approach to evaluate and refine questionnaires by identifying underlying latent constructs, reducing redundant items, and improving overall measurement accuracy. This article discusses the conceptual foundation of factor analysis, its methodological applications in questionnaire design, and practical guidelines for researchers to enhance instrument reliability and validity.

Tribal women serve as the backbone of the socio-economic and cultural fabric of indigenous communities in India. Their contributions to agriculture, forest-based livelihoods, domestic maintenance, and community well-being are unparalleled. Yet, despite their extensive involvement in subsistence activities, their voices remain less documented, and their socio-economic struggles often remain unaddressed. This study explores the multidimensional roles, challenges, and lived experiences of tribal women in Valagiri, Shenbaganur, Perumalmalai, Poomparai, and Mannavanur of the Kodaikanal region. Using an interview schedule and convenience sampling method, primary data were collected from 349 respondents. Percentage analysis was adopted to interpret the findings. Results show that tribal women face a disproportionate workload, combining wage labour, agricultural tasks, and complete responsibility for household and childcare duties. Limited access to education, healthcare, financial independence, and decision-making opportunities restricts their socio-economic mobility. However, their influence in sustaining households and community systems remains substantial. The study emphasizes the need for policy interventions that strengthen their livelihood skills, improve health and education outcomes, and promote equitable participation in community development. Empowering tribal women is crucial not only for gender equality but also for the sustainable development of tribal communities.

This study investigates the role of government schemes and commercial banks’ credit facilities in promoting women entrepreneurship in Kollam district, Kerala. It aims to identify the challenges women entrepreneurs face in accessing credit and financial services, and to evaluate the effectiveness of government-sponsored training and capacity-building initiatives. To achieve this, a survey was conducted among 50 women entrepreneurs in the district. The findings indicate that government schemes and commercial bank credit facilities have significantly contributed to the growth of women entrepreneurship in Kollam. However, women entrepreneurs continue to encounter major obstacles in securing financial support, particularly due to inadequate collateral, limited financial literacy, and discriminatory lending practices. The study recommends that both government agencies and commercial banks simplify loan procedures, provide collateral-free credit options, and expand training programs focused on financial literacy and business management. Overall, the study offers valuable insights for policymakers, financial institutions, entrepreneurs, and researchers committed to advancing women’s entrepreneurship and economic empowerment in India.

Expanding rural infrastructure remains a key focus of Malaysia’s national agenda, though the merits of conventional, top-down approaches have come under criticism. This paper examines the pros and cons of using community-driven approaches to rural infrastructure development in Malaysia especially in Sarawak and Sabah. While using Empowerment Theory and Multi-Level Governance as theoretical perspectives, the literature and case study synthesis highlights the rhetoric vs. reality of participation of this situation. It argues that community-driven approaches to rural infrastructure development economically and socially empower the community, enhance the sustainability of the initiative through ownership, and promote effective problem solving by leveraging local knowledge. However, these advantages are largely absent in the community-driven approaches due to Malaysia’s centrally coordinated governance system, which comprises structural silos, a chronic lack of resources and capabilities, and token participation that remains community consultation. The Comparative Governance Framework (CGF) focuses on the deficits of the centralized governance system, particularly the Rural Transformation Programme (RTP), and the potential of hybrid governance as a governance model. It provides for centralised control with local governance autonomy, thereby offering a hybrid of central and local governance as a model. A genuine example happen in Sabah and Sarawak, showcases the mixed outcomes of top-down megaprojects such as the Pan Borneo Highway in contrast with bottom-up community initiatives in renewable energy, community water management, and ecotourism. The argue of participation and the reality of its implementation suggest the necessity of a shift from a protective to an empowering state. This paper understands bridging the gap between the rhetoric of participation and the reality of underground that requires a transition from a federalism to an enabling state. It proposes a set of policy and practice recommendations to support a hybrid model of governance that formally recognizes rural communities as active stakeholders and partners in their development, in order to achieve greater equity and sustainability.

Negotiation and implementation of business transaction agreements are crucial for enhancing financial performance and sustainability of firms in Uganda. Business transactions, whether public procurement contracts, private-sector collaborations, or investment agreements, require careful negotiation to optimize resource allocation, reduce risk, and maximize returns. This review examines the processes, challenges, and outcomes of business negotiations in Uganda using a financial management lens. It emphasizes how budgeting, cash flow management, cost-benefit analysis, risk assessment, and performance monitoring influence both negotiation strategies and contract implementation. Drawing from financial management theories, transaction cost economics, and negotiation scholarship, the review highlights gaps in financial literacy, institutional enforcement, and professional negotiation capacity among Ugandan firms and SMEs. Case studies from agriculture, oil and gas, and public procurement illustrate common negotiation pitfalls, including misaligned financial expectations, weak due diligence, and lack of monitoring mechanisms. The review concludes that integrating financial management principles into negotiation and contract execution strengthens compliance, reduces disputes, enhances profitability, and fosters sustainable business relationships.

This paper would present a case study on solving a parallel machine scheduling problem in the printing department of a global leader in labeling solutions. Motivated by low-season conditions with below-capacity order volumes, the study prioritizes accelerating job completions to reduce resource idle time in downstream processes. It would focus on minimizing the total weighted completion time while addressing real-world constraints including machine-job compatibility, shift boundaries, and resource limitations in color matching processes. The problem was formulated as a three parallel machines with different speeds model and used Weighted Shortest Processing Time dispatching rule to find out solutions. The scheduling objective is to minimize total weighted completion time. Our approach progressively incorporates operational constraints and achieves around 11.11% improvement in objective value compared to the current manual scheduling method. Some discussions on implementation of research results, limitations, and future optimization opportunities and real-time production data integration would be mentioned.

To increase the antitubercular potency, we synthesized a series of novel pyrazolylpyrazoline derivatives (9a−n) using the one-pot multi component reaction of the substituted hetero aryl aldehyde (3a,b), 2-acetyl pyrrole/thiazole (4a,b),and substituted hydrazine hydrates(5−8) in the presence of base KOH as a catalyst in ethanol as the solvent at room temperature. Substituted hetero aryl aldehyde (3a,b) was synthesized from 5-chloro-3-methyl-1-phenyl-1H-pyrazole-4 methyl-carbaldehyde  by treatment with 4-amino triazole/5-amino tetrazole. 5-Chloro-4-(1,3-dioxolan-2-yl)-3-methyl-1-phenyl-1H-pyrazole (1) was reacted with 4H-1,2,4-triazol-4-amine (a) and 1H-tetrazol-5-amine (b) in the presence of K2CO3 in 1,4-dioxane  at 80 °C for 13 h. Similarly, 5-((4H-1,2,4-triazol-4-yl)amino)-3-methyl-1-phenyl-1H-pyrazole-4-carbaldehyde (3a) and 5-((1H-tetrazol-5-yl)amino)-3-methyl-1-phenyl-1H-pyrazole-4-carbaldehyde (3b) were achieved further reacted with 2-acetyle thiophene (4a) and 2-acetyle furan (4b) and substituted hydrazide derivatives (5–8) in the presence of 50% ethanolic KOH solution at room temperature All of the compounds were tested against Mycobacteriumtuberculosis H37Rv, Theoretical results were in good accord with the observed experimental values. The docking score of the most active compound 9n was found good Glide energy .

Anthropogenic climate change, as seen in the form of increasing global temperatures, increasing climate driven extreme weather events, and increasing rate of biodiversity loss, is one of the greater challenges that face architecture, urban planning, and infrastructure today. In this context climate responsive design and adaptation, are an important paradigm through which to achieve reconciliation between environmental concerns, human development and development wedges. This project outlines a multidisciplinary framework that combines resilient building and infrastructure planning, basics of nature and equity-based solutions, climate adaptive architecture and total risk assessment into a distinct approach for enhancing the adaptive capacity of urban and rural spaces. Resilient planning sits at the strategic core of the recommendation, specifically through was of climate risk maps and scenarios based forecasting into preliminary design stages; to enhance climate sensitive site selection, redundancy of vital systems and adopting materials and technology for durability based on previously modelling and known stressors into climate adaptive architecture. Nature-based solution- not just as an aesthetic measure, but that are part of this "continuum" of urban reforestation and multi-use green infrastructure, coastal ecosystem restoration and permeable surfaces - may help regulate micro-climates, manage hydrological risk, and sequester carbon on the landscape. Climate adaptive architecture operates on a building scale, using passive design methods to enhance naturally occurring factors like solar orientation, natural ventilation, thermal massing, and day lighting and to limit energy intensive mechanical systems. This can be accompanied by adaptive envelopes, renewable energy, and responsive building systems that can adapt to changes in the environment. Risk assessments serve a critical analytical function, utilizing Geographic Information Systems (GIS), climate simulation models, and socio-economic vulnerability mapping to establish exposure pathways and to prioritize adaptive measures. The proposed framework promotes the climate-resilience of disaster, while maintaining synergies with socio-ecological resilience. With this framework, communities are developed to be potentially disaster resilient, with functional integrity and actively regenerative environmental integrity. This research has highlighted that successful climate-responsive design requires the employment of technical solutions, environmental stewardship, and participatory governance. Resilient planning, nature-based solutions, and climate-adaptive design provide synergies that reduce the immediate impacts of climate, while co-benefitting public health and economic well-being, and while pursuit of cultural continuity and equity. The research recommends embracing adaptability, redundancy, and ecology as fundamental to build infrastructure in the pursuit of sustainable, climate-positive futures during times of uncertainty in the Anthropocene.

The modern era is marked by a noticeable acceleration of technological development andinnovation nvolving the ongoing development of new technologies as well as the improvement and evelopment of existing ones.Success in the modern era is primarily determined by innovation and the application of cutting edge technologies. The industry's digital trends are related to the necessity of keeping up with the most recent developments in technology or encouraging creative thinking in IT companies .This technological innovation will maintain the business's operations and keep it in line with the outside world by giving it a competitive advantage or out performing the competition. Since innovation is a driving force behind development and expansion, its importance in the business world cannot be emphasized.Businesses are urged to embrace innovation and give it top priority,especially by implementing cutting edge technology that improve  the   customer experience in general. There are many opportunities for    corporations to investigate and take advantage of in the modern day. These prospects cover a number of approaches, including the inclusion of innovative digital business solutions, the use of open networking, the option of distributed computing, the integration of intelligent technologies, and the quickening of the growth of internet based services. The study aims to investigate the elements that determine technological innovation in the information technology (IT) industry and analyse how it affects employee performance in the workplace. It also aims to determine whether worker performance varies significantly depending on demographic factors.

In this paper, we propose an optimal method for resource allocation in OFDMA (Orthogonal Frequency Division Multiplexing Access) system. The utility function is used to balance the efficiency and fairness of wireless resource allocation. We analyze the mathematical property of resource allocation by modified utility function. We develop optimal algorithm for dynamic subcarrier assignment and adaptive power allocation based on modified utility function, and demonstrate the convergence properties of optimization. Simulation results show that the proposed method provides the effective tradeoff between fairness and efficiency in radio resource allocation.

Porous asphalt pavement is a particular asphalt mixture made up of both fine and coarse aggregates bonded by a bituminous binder. Although it has a rougher texture, porous asphalt looks like conventional asphalt on the outside. In the past decade, porous asphalt pavement has gained popularity as a storm water best management practice and utilize similar mixtures as those used for open-graded friction courses. Additive materials such as fly ash, coal ash, and many more had been widely used successfully in the original specification of porous asphalt mixtures to improve their durability and rutting resistance performance. In this study, fly ash has been used to replace aggregate dust and the aggregate gradation limit in the porous asphalt mixture to determine the Optimum Bitumen Content (OBC) as well as to improve rutting resistance and durability. The OBC was then determined for all the mixes by Marshall Mix design. Laboratory specimens were prepared using 75 blows of the Marshall hammer per side. The OBC for the conventional porous asphalt mix with aggregate dust was found to be 6 percent, while the OBC for the modified porous asphalt mix with fly ash was found to be 5 percent. Rut depth for porous asphalt mixes with aggregate dust was lower (1.6 mm) compared with porous asphalt mixes with fly ash (1.93 mm). This indicates that the additive material could efficiently retain the binder in the mix. As a result, fly ash could be used as an alternative to aggregate dust in the porous asphalt mixture.

This systematic review investigates the effectiveness of reading programs aligned with the Philippine Department of Education's Academic Recovery and Accessible Learning (ARAL) program in improving Filipino students' numeracy and problem-solving skills. The review synthesizes empirical evidence published between 2020 and 2024, focusing on studies evaluating interventions that incorporate learning recovery and accessible learning principles. A comprehensive search of multiple databases will identify relevant studies, which will then undergo rigorous methodological quality assessment. Meta-analysis will be used to determine the overall effect size of ARAL-aligned reading programs, while subgroup analyses and meta-regression will explore potential moderators of intervention effectiveness, such as student characteristics, intervention features, and contextual factors. Qualitative synthesis will examine implementation, acceptability, and feasibility aspects. The findings will provide evidence-based recommendations for educators, policymakers, and researchers seeking to enhance students' numeracy and problem-solving abilities through targeted reading programs within the ARAL framework. This review will contribute to optimizing the design and implementation of the ARAL program, maximizing its impact on foundational skills and promoting equitable access to quality education for Filipino students.

The exponential expansion of IP-based networked services across finance, healthcare, education, and government has intensified the need for effective, real-time anomaly detection mechanisms. Traditional threshold-based and machine-learning-driven systems struggle with dynamic traffic variability, high false-positive rates, and computational inefficiency. This paper proposes a Probability Distribution–Based Anomaly Detection Framework (PD-ADF) that models normal network behavior through univariate and multivariate statistical fitting using Maximum Likelihood Estimation and validated by Kolmogorov–Smirnov and Anderson–Darling tests. Anomalies are identified through adaptive confidence-interval thresholding and probabilistic scoring, enabling fast, resource-efficient detection in large-scale IP environments. Evaluations on NSL-KDD, KDD-Cup ’99, and proprietary enterprise datasets yield an accuracy of 0.96, F1-score 0.91, and false-positive rate 0.02 — surpassing Support Vector Machine and threshold baselines while requiring only 0.1 seconds per observation. The proposed model demonstrates that statistical inference can deliver ML-level precision with significantly lower complexity, offering a scalable, interpretable, and energy-efficient alternative for next-generation cyber-defense infrastructures.

This article presents the main challenges of the Mozambican Hospital Administrator. To this end, the research was of a qualitative, exploratory and descriptive nature, which, through bibliographical research, sought to identify and understand what may constitute challenges for the Mozambican hospital administrator in a context, not only due to technological developments that are constantly being updated, but also due to the multi-professional understanding of the health sector, which tends to be increasingly professional. Challenges for the Mozambican Hospital administrator were identified as those related to the inabilities of the Mozambican Ministry of Health and its levels of administrative and technical attention in affecting HR graduates in AGH in subsidy of top structures; the non-application of the 2025 agenda and its subsequent review that could encompass better strategies for the placement and insertion of AGH. In the form of strategic recommendations, the study emphasizes the need for government institutions and other interested agencies to prioritize the allocation of Hospital Administrators within hierarchical structures, and other professional care classes within care structures.

Cost–benefit analysis (CBA) is a fundamental decision-making tool used to evaluate the financial and strategic viability of projects by comparing expected costs with anticipated benefits. Originating from the work of Jules Dupuit and later refined by scholars such as Alfred Marshall and the U.S. Corps of Engineers, CBA incorporates both tangible and intangible factors to determine whether a project is worthwhile. In today’s dynamic and competitive business environment, firms increasingly rely on CBA to guide strategic choices, enhance performance, and allocate resources effectively. This conceptual paper reviews key scholarly literature to explore how the principles of CBA can contribute to improved firm performance, particularly within the context of twenty-first-century organizational challenges. The study also highlights the relevance of applying strategic management accounting perspectives to CBA and proposes hypotheses for future research. The paper is especially significant for Iraq and other socioculturally connected Middle Eastern economies, offering theoretical and methodological insights into the relationship between strategic CBA applications and firm performance.

The proliferation of Shared Contact Centre Services (SCCs) across public and private sectors in the Eastern Cape reflects a broader global trend toward centralized service delivery. However, the conceptual underpinnings of SCCs particularly in relation to synergy, competitive advantage, and definitional precision remain underexplored. This study examines the synergistic interface between SCCs and organisational business units, with a focus on their collective impact on organisational performance, strategic competitiveness, and customer service within the Eastern Cape Provincial Administration. Anchored in the Attention-Based View (ABV) and informed by synergy theory, the research adopts a sequential Mixed Methods Research (MMR) design. A qualitative constructionist methodology enabled the co-creation of contextual insights, while quantitative analysis facilitated triangulation, enhancing the reliability and generalisability of findings across South African provincial contexts. The study culminated in the development of a multidimensional conceptual framework for evaluating synergy, structured around six core dimensions: strategic alignment, process integration, technological enablement, performance metrics, customer centricity, and continuous improvement.  The empirical findings demonstrated that the proposed framework significantly enhances interdepartmental collaboration, communication, and knowledge exchange, thereby improving productivity, employee engagement, and organisational health. Furthermore, the framework contributes to elevated customer satisfaction, loyalty, and retention factors that collectively drive revenue growth and market expansion. By mitigating operational silos and fostering strategic coherence, the framework enables organisations to systematically assess SCC performance, identify areas for optimisation, and align service delivery with overarching corporate strategy to maximise customer value.

This study delves at the intents and ability of women business owners from Madhya Pradesh to launch their own firms while juggling job and family responsibilities. Indore, Bhopal, and Jabalpur, the three largest entrepreneurial centres in the state, were evaluated using a standardised 5-point Likert scale questionnaire. Three hundred and fifty female business owners took part. Both descriptive and inferential statistics were conducted using SPSS 21 in this study. We used reliability, factor, correlation, and one-way ANOVA as our descriptive statistics. The statistical robustness of the instrument is shown by its outstanding sampling (KMO = 0.929; Bartlett's Test p < 0.001) and great internal consistency (Cronbach's Alpha = 0.942). Improving women-led enterprises requires empowering women financially, since work-life balance and entrepreneurial drive are greatly influenced by economic factors (F = 61.23, p < 0.001; r = 0.456). Individual and vocational characteristics, as opposed to family variables, have a greater impact on self-regulation and adaptation. Women report higher levels of happiness and success when they are financially independent and have the support of their families. The results of this research provide credence to a model that attempts to explain the effects of familial and socioeconomic factors on the entrepreneurial success of women in regional markets. For the benefit of women entrepreneurs in Madhya Pradesh, policymakers should encourage the formation of family networks, simplify the process of taking out loans, and highlight the importance of personal finance.

Smart farming is an emerging approach that integrates advanced technologies to revolutionize traditional agricultural practices. It leverages tools like the Internet of Things (IoT), Artificial Intelligence (AI), drones to improve productivity, sustainability, and decision-making in agriculture.  In India, where over 70% of the rural population depends on farming, precision agriculture offers a powerful solution to challenges faced by farmers and also to adopt the climate changes. IoT sensors enable real-time monitoring of soil moisture, weather, and crop health, optimizing irrigation and input use. AI-driven analytics support early detection of pests and diseases and guide farmers on crop management strategies.  Smart farming significantly boosts crop yield, reduces resource wastage, and improves market access for farmers. Customized models for small and large landholders ensure affordability and scalability. Real-world implementations like Harita-Priya, AgSpeak, Cropin, and AgriRain have shown measurable success in improving yields and farmer incomes, which was included as uses case in this paper.  This paper attempts to address the needs of farmers of below 5 acres and above 5 acres and suggests the smart farming technology required and cost of implementation suits to both categories of farmers groups.  As the country advances toward a digital agricultural ecosystem, smart farming will be pivotal for achieving food security and climate resilience and marks the beginning of a transformative era in Indian agriculture, promoting environmental sustainability.

This study looks at the dynamics of volatility clustering in Bitcoin’s price data by comparing the performance of three econometric models namely, the Generalized Autoregressive Conditional Heteroskedasticity (GARCH), Exponential GARCH (EGARCH), and Glosten Jagannathan Runkle GARCH (GJR-GARCH) models. Using daily closing price data, the analysis explores the sensitivity of Bitcoin’s conditional variance to market shocks and the persistence of these effects over time. To achieve this, the data is cleaned and the series is differenced to achieve stationarity, after which the conditional mean and variance equations are estimated to model time-varying volatility. Model adequacy and comparative performance are assessed using the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and log-likelihood values, while out-of-sample forecast accuracy is evaluated through Root Mean Squared Error (RMSE) and Mean Squared Error (MSE) metrics. The study examines the real-world relevance of volatility modeling by integrating the best-performing model into volatility-adjusted trading strategies and comparing their risk-adjusted returns measured by the Sharpe ratio with those of the conventional buy-and-hold strategy. The empirical results obtained confirm the presence of significant volatility clustering in Bitcoin’s price and indicate that the EGARCH (3,2) model most effectively captures volatility responses to shocks in the market. The EGARCH (3,2) model also demonstrates higher forecasting performance relative to the others. When implemented within a volatility-based position sizing framework, it yields higher risk-adjusted returns than the buy and hold strategy. These findings show the value of advanced conditional heteroskedasticity models in enhancing predictive accuracy and informing more efficient cryptocurrency trading and risk management strategies.

The emergence of enzyme-mimicking nanomaterials (nanozymes) represents a transformative development in biomedical research, offering catalytic versatility, enhanced stability, and cost-effective scalability compared to natural enzymes. This review consolidates current advances in the design, synthesis, and biomedical deployment of nanozymes, particularly those based on noble metals, metal oxides, and functional nanocomposites. A systematic comparative analysis was conducted across diverse studies to elucidate the catalytic mechanisms, structural-functional relationships, and environmental adaptability of these nanomaterials. The literature was critically evaluated with emphasis on the physicochemical factors governing nanozyme activity such as particle size, surface ligands, and crystal facets and their modulation strategies. Trends indicate that noble metal nanoparticles (e.g., Au, Pt, Pd) and metal oxides (e.g., Fe₃O₄, CeO₂, V₂O₅) exhibit peroxidase, oxidase, and catalase-like functions, with performance often surpassing their biological counterparts under physiological conditions. Moreover, 2D nanomaterials and Prussian blue analogues demonstrate significant promise as tunable catalytic platforms. Functionally, nanozymes are proving integral in areas such as tumor theranostics, antibacterial therapy, antioxidation, and bioorthogonal catalysis. Applications range from in situ ROS modulation for cancer treatment to programmable catalysis in cellular imaging and drug activation. Despite these advances, challenges remain in enhancing substrate specificity, minimizing cytotoxicity, and fully elucidating mechanistic pathways. In conclusion, nanozymes hold substantial potential to reshape therapeutic and diagnostic modalities. Future research must focus on integrating simulation-driven design, expanding the scope of enzyme mimicry, and ensuring biosafety in complex biological environments. Addressing these gaps could accelerate the clinical translation of nanozyme-based technologies, establishing them as cornerstone tools in next-generation biomedical applications.

Anaemia, a condition characterized by a deficiency in red blood cells or haemoglobin, remains a global public health challenge affecting nearly a third of the world's population. Traditional management strategies primarily focus on nutrient supplementation (e.g., iron, vitamin B12, folate) and treating underlying causes. However, variable efficacy and side effects of these approaches have prompted the exploration of novel adjuvants. The human gut microbiome, a complex ecosystem of trillions of microorganisms, is increasingly recognized as a critical regulator of host physiology, including nutrient absorption and immune function. This literature review synthesizes current evidence on the mechanisms by which gut microbes and their therapeutic derivatives, probiotics, influence the pathogenesis and management of anaemia. We explore the triad relationship between the gut microbiota, iron homeostasis, and inflammation, detailing how specific bacterial taxa can enhance or inhibit iron absorption. Furthermore, we examine the direct role of microbes in the synthesis of folate and vitamin B12, essential cofactors for erythropoiesis. Evidence from preclinical and clinical studies demonstrating the efficacy of various probiotic strains, particularly Lactobacillus and Bifidobacterium, in improving haemoglobin status is critically appraised. The review also discusses the potential of synbiotics and postbiotics as next-generation therapeutic tools. Finally, we identify key research gaps and future directions, concluding that targeted modulation of the gut microbiome represents a promising, multifaceted strategy for the prevention and management of various forms of anaemia, moving beyond conventional nutrient-replacement paradigms.

ROC curves are useful for comparing two independent observations and the outcomes of their assessments for the same disease in a given study.  In general, the test with the higher AUC may be considered better and is an effective way to summarize the overall diagnostic accuracy of the test. AUC scores are convenient to compare multiple classifiers. Nonetheless, it is also important to check the actual curves especially when evaluating the final model. In the ROC curve analysis, the choice of the optimal cutoff value depends both on probabilistic and clinical considerations. From a probabilistic standpoint, one can use the coordinates of the ROC curve to identify the cutoff that maximizes the discrimination between true-positive rate and false-positive rate(1). The name "Receiver Operating Characteristic" came from. ROC analysis is part of a field called "Signal Detection Theory" (3) developed during World War II for the analysis of radar images. It was not until the 1970's that signal detection theory(4) was recognized as useful for interpreting medical test results.

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In today’s fast-paced world influenced by digital innovation, globalization, and evolving workforce expectations, organizations must continuously adapt to survive and succeed. Change is no longer a one-time event; it has become a constant feature of organizational life. This study systematically reviews 39 peer-reviewed research papers published between 2000 and 2025 to explore how organizations manage change and, more importantly, how employees respond to it. Using the PRISMA method, the review examines internal factors such as leadership style, organizational culture, employee perspectives on change drivers and personality traits, along with external influences like technological advancements and market dynamics. Findings reveal that supportive leadership, transparent communication, and participative decision-making significantly improve employee acceptance of change and reduce resistance. Leaders who actively involve their teams and build trust create a more positive environment for transformation. The study also highlights that employees react to change differently based on their adaptability and generational background, showing that a one-size-fits-all approach is ineffective. Organizations must adopt flexible and inclusive change strategies that consider the diversity of their workforce. Moreover, the review points out that organizational commitment, peer support, and innovation-friendly cultures play vital roles in successful change implementation. Clear communication and emotional support throughout the process further enhance readiness for change. By synthesizing key insights from two decades of research, the study offers practical guidance for leaders and decision-makers seeking to build resilient, forward-looking organizations. It emphasizes the importance of integrating individual, organizational, and external factors into a cohesive change management strategy that can help organizations navigate ongoing disruption and prepare for future challenges.

Self-help groups have evolved as effective grassroots tools for fostering social and economic development, particularly among marginalised groups. Poverty, financial illiteracy, a lack of education, and women’s disempowerment continue to be difficulties for rural women. Microfinance has been shown to be a transforming force in the fight against poverty, driving economic development. In India, self-help groups are at the forefront of these initiatives, as a crucial tool for empowerment and long-term change. Using a case study approach, this paper investigates the important role of SHGs in accomplishing the Sustainable Development Goals. The study examines real-world examples to demonstrate how SHGs help alleviate poverty, promote gender equality, increase financial inclusion, and empower women. This study employs a robust mixed-methods approach, combining an extensive literature review with rich qualitative insights gathered through focus group discussions and in-depth interviews. The research engages directly with women SHG leaders across six talukas of Navsari district, providing a comprehensive understanding of their experiences and the transformative impact of SHGs. The study concludes that Self-Help Groups have effectively contributed to the achievement of several Sustainable Development Goals, including SDG 1 (No Poverty), SDG 3 (Good Health and Well-Being), SDG 4 (Quality Education), SDG 5 (Gender Equality), SDG 8 (Decent Work and Economic Growth), SDG 10 (Reduced inequality) and, SDG 12 (Responsible Consumption & Production). It highlights the pivotal role of SHGs as key drivers of inclusive growth, emphasizing their significance in fostering a more equitable and sustainable future.

Phishing attacks are performed by writing and forwarding falsified body of email messages which look legitimate or real from an undisputed origin to a victim or different category of victims. They focus at acquiring the sensitive data of users or by transferring and loading malware on the user's computers. Consequently, this study aims to implement an AI-driven approach to detect emails that seems to be phishing while the features are analyzed. This project leverage three machine learning models namely: MLP, a deep learning algorithm, Naïve Bayes and Logistic Regression. The following performance metrics were obtained -> for Multilayer Perceptron (MLP) model: accuracy: 98.57%, precision: 100.00%, recall: 90.00% while the f1_score metrics was 94.74%. For Naïve Bayes (NB) model: accuracy: 96.95%, precision: 100.00%, recall: 78.75% while the f1_score metrics was 88.11%. For Logistic Regression (LR) model: accuracy: 94.71%, precision: 99.03%, recall: 63.75% while the f1_score metrics was 77.57%. The result shows that MLP Classifier may better capture complex patterns in phishing emails, leading to higher detection rates. Naive Bayes is still a strong choice, especially for simpler or smaller datasets due to its speed and efficiency. Logistic Regression is reliable but slightly less accurate on this particular task. For this project, a phishing email dataset from the Kaggle Machine Learning Repository was utilized. This dataset contains 5000+ instances of phishing and ham emails.

Contemporary marketing thoughts seem too rigidly glued to the view that consumer purchase decision-making processes are primarily motivated by extrinsic variables and rational drivers more than anything else. However, neuroscientific evidence from archived and current sources shows that intrinsic brain processes activated by subtle sensory cues significantly influence consumer purchase decisions. Exploring that illuminating discourse, this study employs a meta-analytical approach, systematically searching databases such as Scopus and Web of Science to synthesize existing literature to bring out insights that enrich our understanding of consumer behaviour. The analysis culminates in the conceptualisation of a five-construct neurological model that embody the sequential consumer decision-making process as comprised of: the challenge, inquiry, experiment, experience, and content. Rigorous inclusion-exclusion criteria narrowed an initial pool of 457 articles to 98 relevant studies, assessing sample sizes, demographic characteristics, and effect sizes. The DerSimonian-Laird random-effects model evaluated between-study variance, supplemented by sensitivity and stratified analyses to refine insights. Key takeaways from the model highlight the importance of addressing intrinsic consumer needs during the neural challenge stage, delivering clear information during neural inquiry, and enhancing brand loyalty through experiential learning in neural experimentation. This theoretical framework enriches our understanding of the interplay between emotion and rationality, offering practical implications for marketing strategies and encouraging insights into evolving consumer psychology.

With the rapid growth of information technology and the widespread use of digital devices, social engineering has become one of the most pressing challenges in today’s cybersecurity landscape. In simple terms, social engineering involves manipulating people by exploiting human psychology to carry out harmful activities. This review explores different types of social engineering threats across various environments, emphasizing how people rather than systems or technologies are often the weakest link in security. As a result, there's a growing need to boost awareness among users. One of the most effective ways to tackle this issue is through targeted training and educational programs. This paper discusses how creative and well-structured information security education can significantly improve user awareness and help reduce the number of cyber incidents.

Nigeria is home to significant lithium resources, particularly in regions such as Nasarawa, Kaduna, Oyo, Kwara, Kogi, Ekiti, and Plateau. These lithium-bearing pegmatites, mainly consisting of spodumene and lepidolite, contain high-grade lithium oxide (Li₂O) concentrations that are in demand globally due to the crucial role lithium plays in the development of clean energy technologies. As the world transitions away from fossil fuels, lithium is at the forefront of this shift, particularly in applications such as electric vehicle (EV) batteries, renewable energy storage systems, and consumer electronics. With the global market for lithium growing at an unprecedented rate, Nigeria stands poised to become a key player in the global lithium supply chain. However, while Nigeria’s lithium resources have the potential to bring significant economic benefits, the country’s lithium industry faces multiple challenges. Illegal mining practices, particularly in regions like Nasarawa’s Wamba community, have resulted in environmental degradation and human rights violations, including child labor. Additionally, foreign involvement in illegal mining activities, especially from countries such as China, complicates efforts to regulate the sector. Despite these challenges, there are substantial opportunities for growth and development in Nigeria’s lithium industry. To fully realize the economic potential of its lithium resources, Nigeria must focus on several key areas: the establishment of robust regulatory frameworks, the adoption of sustainable mining technologies, the development of critical infrastructure, and the promotion of public-private partnerships. With the right policies and investments, Nigeria can become a leading supplier of lithium for global clean energy technologies. This paper provides an overview of Nigeria’s lithium resources, exploring the current state of the industry, challenges faced, and strategies for leveraging these resources to contribute to the global clean energy transition while ensuring the country’s long-term economic growth and environmental sustainability.

The Automatic Room Comfort Controller was developed using a fuzzy logic–based control system to regulate indoor environmental conditions. The system uses three primary inputs—temperature, humidity, and occupancy—to generate two control outputs, namely Fan/AC speed and Window Position. The controller was designed and simulated using MATLAB’s Fuzzy Logic Designer, following defined membership functions and IF–THEN rule sets. Simulation results demonstrated that the fuzzy controller was able to adapt system responses according to varying room conditions, maintaining comfort while minimizing excessive energy use. The behavior observed across multiple input test scenarios showed that the system responded smoothly and consistently, avoiding abrupt switching common in traditional fixed threshold HVAC systems. Overall, the fuzzy logic model proved effective for real-time intelligent indoor climate regulation. 

In the twenty-first century, agriculture industry faces several critical challenges, including the need to enhance crop immunity, reduce the dependance on chemical fertilizers, synthetic pesticides and herbicides and most importantly, improve overall crop yield and quality. Onion is one of the significant crops which is widely cultivated in Maharashtra. Maharashtra produces around 8.6 million tons of onions annually. Onion is commonly used as an edible vegetable and essential role in everyday cooking. Nowadays it is important to increase quality and quantity of onion is the need of society. The am fungi significantly enhanced the yield and quality and quantity of crop. The aim of present study to understand the impact of salinity and AM fungi on production of Onion for this purpose we use arbuscular mycorrhizal (AM) fungal species such as Acaulospora spinosa, Glomus fasciculatum, Glomus mosseae, and Gigaspora candida to increase the productivity of onion to examined how salt affects the growth of onion.  The treated plots are compared with untreated plots. The treatments included onion crop with AM fungi with salt (NaCl) and crop without AM fungi with salt, to see how each group responded to salt stress. The experiment evaluated the effects of mycorrhizal fungi under NaCl-induced salinity on onion plants compared to non-mycorrhizal (non-AM) plants exposed to the same conditions. The findings revealed that non-AM plants exhibited significantly lower yields than those inoculated with AM fungi. The findings of study show positive effects of treatment on Onion crop showed consistently better growth than non-inoculated plants under all levels of salt stress. Increasing NaCl concentrations reduced leaves, roots, shoot/root length, and biomass, but AM-treated plants maintained significantly higher values. AM fungi notably improved bulb fresh and dry weight compared to plants without AM under the same salt levels. Overall, AM inoculation effectively mitigated the negative impact of salinity on onion plant growth. The present study was conducted during the period from October 2023 to January 2024. During the winter season, characterized by moderate daytime temperatures, distinctly cooler nights and low humidity.

Manufacturing companies are highly concerned with non-value added (NVA) activities that are unnecessary or wasteful from the customer’s perspective, and the employees who are responsible for these activities are considered as NVA headcounts. Consequently, this study aims to use the Define – Measure – Analyze – Improve – Control (DMAIC) process to solve this problem for an Electronics Manufacturing Company. It showed how to analyze the data and then defined that the Inventory Control process has the most NVA headcounts to start with the improvement, the SIPOC Model Diagram (SIPOC) was brought out to measure the target process. Useful methods like Cause and Effect Diagram (CED), 5 Whys, and Time Study Snapback (TSS) were used to analyze and indicate the root causes. In the improving stage, a new Inventory Control matrix was developed to reduce 13 NVA headcounts and save approximately 78000$ per year for the company. The study also listed out the activities that would have to do to maintain the project’s effectiveness.

This study focuses on the development of the GUnify Information Management System as a platform to validate the effectiveness of the Deflate compression algorithm in optimizing storage requirements for associations with limited resources. GUnify addresses the challenges of managing memberships, events, web portals, and financial reports, providing a cost-effective solution for organizations unable to procure high-end computing resources. Integrating the Deflate algorithm significantly reduces data storage and transmission costs while maintaining performance and functionality. Developed iteratively using Agile Scrum methodology, GUnify adapts to evolving organizational needs and fosters stakeholder collaboration. Its architecture includes Membership Management, Event Management, Web Portal, and Finance Management modules, enabling process automation and enhanced data integrity. The Deflate algorithm ensures efficient storage and retrieval of key data such as member profiles, event schedules, financial reports, and transaction histories. Workflow diagrams illustrate how automation reduces manual tasks and supports decision-making. Additionally, the system's software quality was rigorously evaluated using the ISO 25010 standard, ensuring compliance with criteria such as functional suitability, performance efficiency, usability, reliability, security, and maintainability. This study demonstrates that the Deflate algorithm is a viable solution for optimizing storage and operational efficiency in resource-constrained environments, making GUnify a practical and scalable tool for association management.

We study a three-species ecological model in which the first species (x) preys on the second species(y) and the second species exerts an amensal effect on the third species (z). Interactions between predator and prey include an explicit time delay(τ) and the system is formulated as a set of delay differential equations. We identify the positive coexistence equilibrium and perform a local stability analysis about this steady state. we derive sufficient conditions for a Hopf bifurcation driven by the delay parameter (τ). By treating (τ) as the bifurcation parameter, we determine critical delay values at which the coexistence equilibrium loses stability and periodic oscillations emerge. Numerical simulations implemented in MATLAB confirm the analytical predictions and illustrate the instability regimes and bifurcating limit cycles.

Talent retention is a huge challenge the Information Technology Enabled Service sector faces, and it is a debatable issue. The attrition rate in the IT sector in India is 40-45 per cent. Observing the rate at which multinational companies are setting up subsidiaries in India, there is a possibility of greater and heavier demand for talent in multinational and domestic companies. This positive situation has suddenly increased the demand for skilled and talented employees. Employee job mobility was frequent in the Information Technology sector. Now, this trend is gradually spreading to other industries. Experts liken this to a virus because it signals that employers should focus on retaining talented and skilled staff, considering the costs of recruitment, selection, onboarding, training, and development. This shift has moved attention from simple personnel management to strategic human resources management, especially in IT, where the workforce is the key resource. Today, the main concern for human resources managers is keeping talented employees within the organization. This paper offers a comprehensive overview of employee retention and examines the main factors that influence an employee's likelihood of leaving the IT sector.

In the present work density measurements are carried out on P-Cyanobenzylidene P- Nonyloxyaniline ,  liquid crystalline compound. The density is observed to drop with grow of temperature in mesogic phases expect in the vicity of stage transition it shows steep grow before it attains equilibrium value of the next part. Using density facts the various thermodynamic parameters like Moelwin Hughes parameter, Isochoric hotness coordinated of internal pressure, Sharma parameter Huggin’s parameter, Gruneisen parameter, etc. are assessed. The Bayer’s nonlinearity parameter is also estimated. All variables exhibit discontinuity at phase transformation. The outcome is discussed with instance to the literature facts accessible on number of samples.

Educational administrators operate in dynamic institutional environments where interpersonal effectiveness is as important as managerial and technical competence. This study explores the critical role of leadership soft skills in fostering successful educational administration. It identifies essential soft skills, examines their influence on institutional outcomes, and proposes a Leadership Soft Skill Model tailored for educational leaders. Findings from existing research highlight emotional intelligence, communication, collaboration, adaptability, and ethical decision-making as core competencies that enhance school climate, teacher motivation, and administrative efficiency. The study concludes with a conceptual model and suggests future research directions to expand empirical understanding of soft-skill–based educational leadership.

Polycyclic Aromatic hydrocarbons constitute a large class of organic compound that are compose of two or more fused aromatic rings.  They are mostly produced by the pyrolysis or incomplete combustion of organic materials. The level of polycyclic aromatic hydrocarbons (PAHs) were determined in spent motor-cycle engine oil, spent sport car engine oil, Talinium triangulare (water leaf) grown on soil contaminated with spent sport car engine oil, and spent motor-cycle oil. The PAHs were analyzed using gas chromatograph-Flame Ionation Detector (GC-FID). Additionally, the growth factors were established using Talinum Triangulare grown in the unpolluted soil and exposed to both spent sport car engine oil and motor-cycle oil. In terms of their bio-tolerance, the uncontaminated talinium triangulare displayed a superior growth performance in comparison to the exposed plant. Nonetheless, there was a notable difference between uncontaminated and contaminated plants (P<0.05). The PAHs results indicated that spent sport car engine oil had higher total PAHs concentration of 5.5518 mg/L than spent motor-cycle engine oil, which had a total PAHs concentration of 2.7490 mg/L.      Talinium Triangulare grown on soil contaminated with spent sport car engine oil accumulated 0.9108 mg/kg of total PAHs, while spent motor-cycle oil accumulated a total PAHs concentration of 0.5276 mg/kg. In spent sport car engine oil benzo (b) flouoranthene had the highest concentration, while in spent motor-cycle engine oil, the highest concentration was benzo (a) pyrene. Out of the sixteen PAHs USEPA targets, kylene was the only one that wasn't found in any of the samples. These findings demonstrated the presence of hazardous PAHs in the spent engines oil of both motor-cycles and sport car, which are carelessly discarded into the environment and can bioaccumulate in edible plants. It implies that eating vegetables grown on soil tainted with used engine oil poses an inherent risk and danger to consumers.

This qualitative study explores the pervasive issue of gender discrimination within Bangladesh's garment industry, where women make up the majority of the workforce yet continue to face significant barriers to equality. Through in-depth interviews and surveys with 100 female workers from various garment factories in Dhaka, Chittagong, and other hubs, the research examines key forms of gender discrimination, including wage disparity, limited career advancement, workplace harassment, and the emerging threat of automation. The findings reveal that female workers earn significantly lower wages than their male counterparts, experience limited promotion opportunities, and endure high levels of sexual harassment, often compounded by a culture of silence and fear. Additionally, the rise of automation in the sector poses a unique challenge for women, with many fearing job displacements due to their lower skill levels in handling new technologies. The study concludes with actionable recommendations for addressing these issues, including implementing equal pay policies, establishing career development programs, enforcing anti-harassment measures, and providing gender-sensitive automation training. By highlighting the lived experiences of women workers, this research underscores the urgent need for comprehensive reforms to promote gender equality in Bangladesh’s garment sector.

Electricity theft, a type of non-technical loss (NTL), reduces utility revenues, distorts feeder loadings, and hampers investments in reliability and clean energy. This study presents an integrated economic framework to maximise recoverable value from theft within distribution networks. The framework combines mechanism design to align customer incentives through pricing, amnesty programmes, and credible penalties with a portfolio approach to detection and remediation strategies. These include advanced metering, feeder/DT balancing, analytics-driven audits, and standardised legal evidence packs, all within budget, legal, and equity constraints. It models recoveries as the discounted net present value of past back-billing and legitimate future consumption, guided by key utility KPIs such as recovered NPV per cost, relapse rates, and alarm accuracy. Implementation follows a three-phase plan: initial pilots, scaling and optimisation, and institutionalisation, supported by governance structures (RACI, due process, independent audits) and measurement & verification. Examples from India and Brazil demonstrate that integrating technology, legal processes, and social measures is vital for shifting incentives away from theft and towards compliance. Ultimately, this approach provides regulators with a justifiable method to reduce NTL while upholding affordability and public trust.

With the rapid development of short video technologies and the intelligent evolution of tourism e-commerce platforms, promotional videos have become a critical medium for attracting customers and driving conversion. However, due to the experiential and emotional nature of tourism products, consumers are highly sensitive to the embedded cues of “e-commerce politeness” in video content. Missteps in tone, cultural representation, or perceived authenticity can trigger trust deficits and adversely affect purchase decisions. This study investigates the impact of e-commerce politeness in tourism promotional videos on customer conversion behavior. To achieve this goal, we adopted the Critical Incident Technique, a qualitative method well-suited for capturing specific and influential user experiences. Through an online survey, we collected 263 valid incidents, including 126 positive and 137 negative cases. These incidents were systematically categorized into five positive dimensions—emotional engagement, formal innovation, audiovisual expression, cultural core, and technology-driven design—and six negative dimensions—content authenticity, perceived experience, scene rationality, cultural representation, stylistic rendering, and production quality. Data analysis revealed that audiovisual expression was the most influential positive factor (23.81%), effectively stimulating travel intention. Conversely, content authenticity emerged as the most prominent negative factor (27.94%), underscoring the risk of expectation gaps. Inter-coder reliability scores (R = 0.923 for positive incidents; R = 0.874 for negative incidents) validated the robustness of the classification framework. This study contributes to the theoretical understanding of e-commerce politeness in digital tourism marketing and offers actionable insights for video content creators, destination marketers, and platform operators. By aligning video content with customer expectations and cultural sensitivities, tourism e-commerce stakeholders can enhance trust, improve conversion rates, and foster more meaningful customer engagement.

The retail industry in India has witnessed remarkable growth in recent years, with organised retail formats like D’Mart playing a pivotal role in transforming consumer shopping behaviour. This study investigates the factors influencing consumer buying decisions toward D’Mart products in Bangalore. The research aims to identify key determinants such as price perception, product quality, brand image, store ambience, promotional strategies, and customer service that shape consumer preferences and purchase intentions. A structured questionnaire was administered to 120 respondents across different demographic segments within Bangalore city using a stratified random sampling method. Both descriptive and inferential statistical analyses, including correlation and regression, were applied to evaluate the relationship between demographic variables and buying behaviour. The findings indicate that price affordability and product quality are the most significant influencers of consumer choice, followed by promotional offers and store accessibility. The study also reveals that younger consumers and working professionals are more inclined toward D’Mart’s private-label brands due to perceived value for money and convenience. Customer satisfaction is strongly correlated with store layout, billing efficiency, and staff behaviour, which collectively contribute to brand loyalty. The research contributes to understanding urban retail consumer psychology and offers strategic insights for D’Mart management to enhance customer engagement and retention. By identifying dominant behavioural factors, this study helps develop targeted marketing strategies, optimise store operations, and improve the overall customer experience. Future research could expand to comparative analyses with other retail chains or explore digital shopping trends within India’s organised retail sector.

Several river ecosystems are undergoing varied land-use practices, whose monitoring should be continuous. This study evaluated the influence of land-use practices on water quality and macro-invertebrate taxa, specifically the mayfly (order Ephemeroptera) assemblage, along the River Nzoia in Kenya. Four dominant land-use activities were identified as undisturbed, sugarcane growing, settlement, and industrial activities. All the physicochemical water quality parameters displayed significant (P < 0.05) spatial variations. Areas with industrial activities had low DO, as well as high BOD, TA, pH and conductivity, settlement and sugarcane growing areas had high levels of phosphates and nitrates. Land use patterns dictated the macro-invertebrate community structure, where sites with low disturbances had high composition, abundance and diversity and were dominated by order Ephemeroptera, Plecoptera, and Trichoptera (EPT). The distribution of mayfly was significant relative to land-use practice (P < 0.05), where undisturbed sites followed by industrial sites had the highest occurrence and abundance of mayfly taxa, suggesting the occurrence of more tolerant species of mayfly in sites near industrial areas. Dominance of Baetis, and Caenis in undisturbed sites and settlement areas, coupled with Heptagenia and Ephemerella dominance in the sugarcane growing region, but none of the mayfly taxa dominated industrial sites, suggests that they are influenced by anthropogenic activities. PCA plots showed a clear distinction between land-use practices, with ephemeroptera taxa composition being clearly distinguished in the tri-plot. The present study indicates that different types of land-use practices within the study area caused changes in the abundances of the macro-invertebrates and, particularly, mayfly taxa. Thus, all stakeholders should formulate immediate policies that will reduce human impacts on the water quality in River Nzoia. There is also a need to sensitize the local community members to avoid harmful activities along the River Nzoia.

This article focuses on the Economic Theory of the Health Market, based on the contributions generated by David Ricardo's Economic Theory, commonly known as Ricardian Theory, which for this article will be referred to as Ricardian Economics (RE). Methodologically, documentary research was carried out, using Google Scholar, open websites and academic institutions, based on the search for keywords such as Ricardian theory, health market. It was also possible to visit articles and dissertations, which allowed us to verify that RE cannot find a standard capable of measuring the value of all things [a fact that can be inferred in the analysis of the health market, through health economics] and that the market has its specificities to respect in order to implement a particular economic policy.

ABSTRACT Aim/Purpose This study investigates customer sentiment toward telecom Quality of Service Delivery (QoSD) in Ghana by integrating dual sentiment, that is, an expressed emotional sentiment from customer narratives and experienced service sentiment derived from SERVQUAL ratingsinto a unified service quality evaluation framework. Background Although SERVQUAL-based research consistently links service quality dimensions to customer satisfaction, far less is known about how emotional expressions interact with cognitive service evaluations in developing markets. This gap limits understanding of how users interpret service failures and form satisfaction judgments in resource-constrained telecom environments. Methodology A nationwide survey of 536 mobile users was analyzed using a hybrid analytical approach: (1) natural language processing (NLP) sentiment scoring of open-ended responses, and (2) construction of a Service Sentiment Index (SSI) based on standardized SERVQUAL dimension scores. Mediation analysis using Hayes’ PROCESS tested whether experienced sentiment serves as a psychological pathway linking reliability to overall satisfaction. Contribution The study introduces and empirically validates a dual-sentiment framework for telecom QoSD assessment, demonstrating significant divergence between customers’ expressed emotional sentiment and their experienced service sentiment. It extends the SERVQUAL model by incorporating affective evaluation processes and provides empirical evidence that sentiment partially mediates the reliability–satisfaction relationship. Findings Expressed sentiment was predominantly negative (56.5%), whereas SSI reflected more moderate evaluations. Reliability, assurance, and empathy emerged as key predictors of both sentiment and satisfaction. Mediation results confirmed that experienced sentiment explains a substantial portion of the total effect of reliability on satisfaction, supporting a dual cognitive–affective interpretation of QoSD. Recommendations for Practitioners Telecom providers should prioritize network reliability, transparent communication, and empathetic customer engagement to address emotional dissatisfaction. The presence of strong negative expressed sentimenteven among customers reporting moderate satisfactionsignals potential reputational and churn risks requiring proactive management.   Recommendations for Researchers Researchers should integrate text analytics with psychometric models in service quality studies and further examine sentiment as a mediator across diverse service industries and cultural settings. Impact on Society Understanding sentiment divergence enhances the design of customer-centric QoS interventions, strengthens regulatory oversight, and supports the development of more responsive telecom service policies in emerging markets. Future Research Longitudinal studies should explore sentiment trajectories over time, while cross-market comparative research should validate the generalizability of the dual-sentiment framework across other developing and developed economies.

Trapping rats has become a practical and environmentally safe method of control as well as monitoring the presence of rats in a place. The study aims to determine the presence of house rats (Rattus tanezumi Temminck) and the trap success rate around the IPB Darmaga Campus, Bogor, Indonesia, using a single live trap. There were 10 traps installed incrementally over 3 consecutive nights at 10 locations both indoor and outdoor. The number of rats caught, the number of rat footprints, the size and sex of the rats, and the location of the trapped rats became an observed parameter. The results showed that house rats around the IPB Darmaga Campus caused damage to facilities, physical appearance, faeces, and urine odor. The trapping success around the IPB Darmaga Campus from 300 trappings reached 9%, which is 3.67% of trapped rats remained in traps and 5.33% rats escaped from traps. The trapping success was affected by trap shyness by 72.16%.

The growing need for rapid situational awareness in outdoor environments has highlighted the demand for lightweight, real-time hazard-detection systems deployable on unmanned aerial vehicles (UAVs). This study presents EdgeAI-Drone, a novel MobileNetV3-SSD–based framework optimized for real-time detection of abandoned weapons in natural terrains. A fully custom dataset of 2,350 images was developed using Philippine outdoor environments, capturing various weapon replicas under diverse lighting, terrain, and occlusion conditions. Images were manually annotated in Pascal VOC format and augmented with geometric and photometric transformations to enhance robustness. The proposed model was trained using transfer learning and optimized through structured pruning and INT8 quantization, enabling deployment on resource-constrained edge devices such as the NVIDIA Jetson Nano and Coral Edge TPU. Experimental results demonstrate that EdgeAI-Drone achieved high detection accuracy, with a Precision of 0.91, Recall of 0.94, F1-score of 0.92, mAP@0.5 of 0.87, and mAP@0.5:0.95 of 0.71. Real-time inference speeds were recorded at 22–24 FPS on the Jetson Nano and 55–60 FPS on the Coral Edge TPU. The system remained operationally robust across UAV flight altitudes of 5 m, 10 m, and 15 m, with graceful performance degradation at higher altitudes. Qualitative results further confirmed the model’s ability to identify partially occluded weapon replicas in cluttered outdoor settings. The findings indicate that integrating lightweight CNN architectures with edge-optimized deployment pipelines can enable practical, reliable UAV-based hazard detection systems. EdgeAI-Drone demonstrates strong potential for supporting search-and-rescue missions, post-conflict site assessments, border monitoring, and disaster response operations. Future work includes expanding to multi-class hazard detection, incorporating thermal/infrared sensing, and integrating autonomous UAV navigation for fully automated field hazard assessment.

The study examines the role of artificial intelligence (AI) is transforming the accounting profession in this dynamic world of business and accounting. The focus is on the challenges and opportunities for professional accountants in Bangladesh. The purpose is to understand how AI is reshaping skills, jobs, and ethics in this emerging economy like Bangladesh. The study used a qualitative research design. Data was collected through a single Focus Group Discussion (FGD). Seven experts participated. They represented accounting, IT, human resources, and corporate world. Thematic analysis revealed that AI drives efficiency but also causes acute job displacement fear of job loss among staff. This is a novel finding for the Bangladeshi context. Secondly, there is a prevalent skill and education gap in Bangladesh. This is a retraining crisis for senior professionals. The study suggests that the speed of automation is highly disruptive in emerging markets. Policy recommendations include mandatory AI literacy training, assessing current level of AI adoption and find out the ways to implement AI in major sectors to gain efficiency and effectiveness. To get maximum benefit from this a complete regulatory reforms is necessary.

This study explores how green marketing influences the destination choice of shipping tourists, a segment where traveller’s increasingly seek sustainable experiences, but empirical evidence remains limited. This quantitative research employed a structured questionnaire and used R-based statistical techniques, including logistic regression, ROC analysis, and mediation analysis, on a sample of 325 shipping tourists to predict destination choice. The analysis confirmed excellent instrument reliability (alpha = 0.90) and found strong positive correlations between Green Marketing Awareness and Destination Choice (r=0.832); however, the logistic regression model found no statistically significant individual effects for the predictors, and the Causal Mediation Analysis failed to detect any significant direct or indirect effects. While the model showed fair overall discrimination (AUC = 0.7845) and an adequate fit (Hosmer-Lemeshow p > 0.05), its practical utility is severely limited by poor sensitivity (only 0.92% correctly classified true positives); therefore, future research must re-evaluate the binary measurement of Destination Choice and incorporate unobserved factors to enhance the predictive power of the model and generalizability of findings.

Artificial Intelligence (AI) has rapidly integrated into daily human activity, reshaping learning, working, and healthcare practices. While its potential to optimize efficiency is celebrated, there are rising concerns about its subtle yet significant effects on the human brain. This qualitative study explores how continuous reliance on AI influences cognitive load, memory retention, and attentional control. Data were gathered through semi-structured interviews with 40 participants, including students, educators, corporate professionals, health workers, and members of the general public. Four core themes emerged: (1) Cognitive Offloading and Dependency, (2) Memory Transformation, (3) Attention Fragmentation and Focus Shifts, and (4) Health and Wellbeing Concerns. Participants reported that while AI reduced task burden, it simultaneously created dependency, reshaped recall strategies, fragmented attention, and raised anxiety regarding overreliance. Interpretations suggest a paradox: AI lightens mental strain but erodes certain cognitive practices. The study highlights underexplored dimensions of how AI is reconfiguring mental functions, raising implications for education, workplace policies, and public health. Findings stress the importance of balancing AI use with active mental engagement to protect long-term cognitive health.

This paper deals with an environmental impact assessment of the Peruru Banda Hill comprising of granite, located at around 8 km west of Tiruchanur, Tirupati District, Andhra Pradesh, India before and after the construction of the Vakulamatha Temple by the Tirumala-Tirupati Devasthanams (TTD) in June 2022. Studies using multi-date Google Earth Pro (GEP) satellite images generated from the software available in the public domain revealed a remarkable improvement in the environmental condition of the temple portion of the Hill, while the remaining portion of the hill is either highly water-logged or marked with rugged hilly region at varying elevations owing to illegal mining by local artisans. This paper provides a methodology helpful to the TTD Engineering Department to transform the entire rocky and water-logged areas of the hill into a high-value prime land through appropriate mining of Peruru granite and conjunctive use of surface water and groundwater in the nearby Peruru Tank.     

The use of technology in the Digital Era in the world of education is needed to facilitate the assessment of students, be it cognitive competence, psychomotor and even character assessment contained in affective competence. SIHEBAT is a digital platform developed by researchers in learning to conduct character assessments in students. The researcher conducted research on the development of the SIHEBAT platform on the learning process of students at SDN 2 Sukamurni. This study aims to 1) how to develop this SIHEBAT platform in the assessment of the 7 habits of great Indonesian children in elementary school, and 2) how this SIHEBAT platform can be applied in the assessment of the 7 habits of Indonesian children in elementary school. The type of research used is Research and Development (R&D), and the research model uses D&D which contains the EDDIE stage, namely Design and Development with a qualitative approach. The population in this study is 8 teachers at SDN 2 Sukamurni, Cilawu district, Garut regency using purposive sampling. The data collection technique used was non-test with documentation studies, interviews and questionnaires. The technical data analysis carried out in this study is a quantitative descriptive analysis, with validation tests, feasibility tests and acceptance tests. The results of the study show that the SIHEBAT digital platform obtained a "feasible" assessment in the content domain and "very feasible" in the construction domain and has a very high level of acceptanceof the user. From the material experts, seven habits of great Indonesian children obtained a score of 91.25% with the interpretation of the material very feasible to use. This proves that the results of the research can answer the problem and make a positive contribution to the assessment of the seven habits of great Indonesian children in elementary schools.

Ayurveda is the most ancient system of medicine among the different system of medicine existing in the world. In the classification the disease Pandu felt in the group of Varna in which there are significant changes in the normal color of the body. The detail description of Pandu Roga and its classification starts form Ayurvedic Samhitas only. Most of the Acharyas have described five types of PanduRoga, ie. Vatika, Paittika, Kaphaja, Tridoshaja and Mridabhakshhanajanya Pandu. Acharya Charaka, Vagbhatta and Madhava have given Mridakabhakshanjanya type of Pandu. Major etiological factor is Mridabhakashana (soil eating). It may be considered as either oral ingestion of soil or use articles contaminated with soil as unclean hands and fingers or as improperly washed vegetables or eatables. At least one of the Dosha of the individual who has a habit of eating soil gets aggravated. The intake of astringent soil promotes Vata, the salty soil would provoke Pitta, while the intake of sweet soil generates Kapha. Due to the dry nature of the soil, it makes the tissues of the body dry. It will not be digested properly and as a result, it would block various channels of the body. This retards the power of the sensory and motor organs and also the bodily luster, potency and Ojas, thereby producing Pandu Roga which in turn destroys strength, color, and digestion of the patient.

The study found that farmers' awareness of crop insurance schemes has a positive and insignificant influence on their attitude toward the scheme. However, a lack of awareness hurts farmers' attitudes. Social norms considerably impact behavioural intention, with a C.R. of 1.868. Perceived behaviour control (PBC) has an insignificant influence on behavioural intention, while risk awareness has less impact. Awareness about crop insurance schemes significantly affects farmer behavioural intentions, with a positive attitude (3.45/5.0) and a significant impact on crop insurance behaviour. Perceived behaviour control has less impact on farmer crop insurance behaviour. The analysis considers income, attitude towards crop insurance, behavioural intention, and insurance behaviour as dependent variables. Results show no significant difference in education, attitudes, or insurance behaviour among different groups. Low-income farmers showed significant differences from middle- and high-income farmers, as per Scheffe's post-hoc tests. crop insurance and risk awareness explained a 34% (R squared) variance in farmers' attitude toward crop insurance towards the scheme.  Attitude, Perceived behaviour control, social norms, Awareness about crop insurance schemes, and Knowledge about crop insurance, variables explain 43% variance in the behavioural intention of the farmers.

Electronic Human Resource Management (E-HRM) plays a crucial role in enhancing operational efficiency, decision-making and coordination within higher education institutions. Open universities, especially those offering skill-based programs, face challenges due to decentralized operations, geographically dispersed staff and varied administrative processes. This study presents a conceptual digital E-HRM framework for such institutions using secondary data, integrating student assessment (theory, practical, viva), course content delivery and evaluation processes. The framework draws on E-HRM typologies, technology acceptance concepts and insights from higher education practices and Indian skill development policies. Findings indicate that combining operational, relational and transformational E-HRM approaches with a structured skill-based course roadmap improves administrative efficiency, faculty management and program delivery quality. The framework also addresses digital readiness, process fragmentation and user adoption challenges, offering a strategic guide for implementing digital HR systems in open universities. The study provides a systematic approach to enhancing HR effectiveness in skill-based education and informs future empirical research.

Animal Enchroachment is a major threat to the productivity of the crops, which affects food security and reduces the farmer’s profit. Machine learning-based solutions are used to overcome this problem. Convolu- tional Neural Network (CNN), ResNet-50, and Inception v3 are the three methods used to identify the animals. The proposed model classifies the detected animals and alerts humans through a message to avoid animal intru- sions into properties. Hence, minimising the dangerous consequences caused by the intrusion. The Inception v3 model provides more accurate results compared to the other two models, and it is considered the main method for the proposed model.

This paper explores the dynamic intersection of traditional knowledge systems and women-led entrepreneurship through a case study of Phulkari, the iconic embroidery art form of Punjab. Rooted in the cultural and social fabric of Punjabi life since the 15th century, Phulkari has historically been practiced by women to commemorate significant life events, transmitted through generations via oral and experiential traditions. The study traces Phulkari’s evolution from a domestic and ritualistic craft to a commercially viable industry, examining how this transformation has opened new avenues for women of rural and semi-urban areas to engage in entrepreneurship. Women artisans in the 21st century, through cooperatives, Self-Help Groups (SHGs), and digital platforms such as Amazon Karigar and GoCoop, have leveraged Phulkari not only as a means of socio-cultural reclamation and innovation but also as a sustainable source of income. This paper also discusses the critical challenges faced by the sector, including limited market access, exploitation by intermediaries, digital illiteracy, and the erosion of authenticity due to mass production, all at their times. To combat these, the Geographical Indication (GI) tag was awarded to Phulkari in 2009 is evaluated as a potential tool for safeguarding community intellectual property. The study argues that Phulkari-based women’s entrepreneurship serves as a replicable model for inclusive and sustainable development, proving how indigenous traditions can be harnessed for both economic empowerment and cultural preservation.

Three munia birds, javan munia (Lonchura leucogastroides), white-headed munia (Lonchura maja), and scaly-breasted munia (Lonchura punctulata), are important pests at rice fields in Indonesia. These seed-eating birds can consume up to 10% of their body weight in seeds, causing significant damage. Botanical repellents, which use active ingredients derived from plants, are an eco-friendly method to deter pests. This study examined the potential of jengkol (Pithecollobium lobatum) fruit peel as a natural repellent to control munia birds in the rice fields. The research was conducted in Cicadas Village, Ciampea District and Situ Gede Village, West Bogor District. Repellent concentrations of 0, 400, 600, and 800 g jengkol fruit peel per liter water, then it diluted within water with 10 ml of jengkol fruit peel concentrate per liter water. Each concentration were applied to 75 m² plots within spray volume of 3.75 liter. Bird populations were observed over seven consecutive days. The results showed that no concentration approached the 70% repellent standard, with the effect lasting only one day after spraying due to various factors including rain, heat, and the surrounding environment. These findings suggest that the jengkol fruit peel for bird repellent is ineffective in its current form, therefore it need a sticker. Keywords : Efficacy, important pest, seed-eating birds

The Internet of Things (IoT) introduces new security considerations because connected devices typically have limited computational resources, making traditional cryptographic algorithms inefficient. RSA, a widely used public-key cryptosystem, is particularly resource-intensive due to its large key sizes and heavy arithmetic operations. This study investigates the inefficiency of standard RSA in resource-constrained IoT environments and evaluates optimization strategies aimed at improving performance without compromising security. Enhancements include key size reduction, the use of the Chinese Remainder Theorem (CRT) for decryption, and precomputation techniques. Performance was assessed in a simulated IoT environment, measuring execution time, memory consumption, and energy efficiency. Security considerations, including potential vulnerabilities of CRT-based RSA such as differential fault attacks, were addressed through redundancy and verification mechanisms. Comparative insights with Elliptic Curve Cryptography (ECC) and post-quantum lightweight schemes (e.g., Ascon, Kyber-SLH-DSA) were provided to contextualize the results. The findings show that optimized RSA achieves significant reductions in computational overhead and energy consumption while maintaining correctness, demonstrating its feasibility for low-power IoT devices. Hybrid cryptography approaches, combining RSA key exchange with symmetric AES payload encryption, are recommended for future implementations. These results reinforce that efficient and practical public-key encryption is achievable in constrained IoT systems while preserving strong security.

This paper examines the comparative analysis of maize and sorghum yields under different fertilizer application. Maize and sorghum were planted on a land area of 50m2 each. The 50m2 of land for maize and sorghum was further divided into two. NPK fertilizer was applied to one half while urea fertilizer was applied to the other half of land for maize and sorghum. The yields of maize and sorghum were calculated. The Physicochemical characteristics of the soil were analyzed using Association of Official Analytical Chemists (AOAC) Methods. Descriptive statistic, mean, Analysis of variance (ANOVA) and t- test statistic were used in data analysis. The result of the Laboratory analysis of the soil reveals that the soil is sandy and slightly acidic with low Nitrogen, Organic carbon, Organic Matter, and Potassium. The result of the Analysis of variance (ANOVA) shows that there is no significant difference in the values of the physicochemical parameters selected. This implies that the soil in the study area is relatively the same in term of physicochemical characteristics. In term of crop yield, the result of the study reveals that the average yield of maize under NPK and urea fertilizer is 1.69t/ha and 1.30t/ha respectively. Similarly, the yields of sorghum under NPK and urea fertilizer is 1.58t/h and 1.22t/h respectively. This implies that, in term of yields, maize and sorghum performed best when treated with NPK fertilizer than urea fertilizer. However, the result of t-test statistic shows that there is a significant difference in yields of maize and sorghum under NPK and Urea fertilizers because the p values for maize and sorghum, 0.0319 and 0.0222 respectively, are less than 0.05. Therefore, the study recommend application of NPK fertilizer for both maize and sorghum crops for better productivity.

Background: Ugandan farmers are susceptible to the changing climate with unpredictable rainfall distribution and severe rising temperatures. Climatic change is likely to affect the basic elements of life for people around the world resulting into no access to water and food production, hence hundred millions of people could suffer hunger and water shortages leading to severe death.  This paper is aimed at exploring farmers’ perceptions of climate change. Material and method: The study employed a case study design where several instrumental bound cases are examined. We utilized multiple data collection methods to explore perceptions of climate change. We also chose the qualitative-focused enquiry for this study because this approach allows the comparison of different stakeholders’ perspectives drawn primarily from a qualitative approach. Data were collected using semi-structured interviews from fifteen purposefully selected local farmers from selected sub counties. Results: Findings revealed that farmers perceive climate change news as a source of negative excitements such as fear, worry and intimidation. Similarly, findings revealed that bush burning, deforestation, bush burning and over grazing are the major causes of climate change. In addition to ignorance amongst the people who are not clearly aware of what climate change is and they don’t know why they receive little rain fall, why very hot days. Conclusion: This research study revealed a number of key ideas and it is concluded that some people are not informed clearly of what is causing climate change and the inadequate information about climate change needed to be improved to help the entire generation understand climate change. And it was thus concluded that people’s awareness on climate change is vital for a sustainable livelihood.

Artificial intelligence is a technology that allows computers or machines to think like humans and accomplish tasks that are similar to those performed by the human brain. Artificial Intelligence and technological advancements are now being applied in practically in all facets of life. It has been used in conjunction with digital marketing to help firms reach out to customer at the proper time. “Marketers have the ability to process large amounts of data and meet client expectations. It has been that AI has been used to ensure customer satisfaction”. The use of the internet is growing in India, which opens up new commercial options. Artificial Intelligence (AI) in the modern era is a broad field that employs advanced techniques to derive viewpoints from vast volumes of data. The basic idea behind AI is to teach robots to practice, resolve problem that is faced on a regular basis. The AI is enabled in supporting the management to create and monitor various digital content, assist in understanding the overall needs of the customers and enable in providing the necessary information to the management for taking key decisions. The AI helps the marketers to create distinct advantage and enable in providing better products and services to the customers.

Fingerprints are unique dermatoglyphic patterns found on the volar surfaces of the fingertips, characterized by ridges and furrows. Poroscopy, the systematic examination of sweat pores located along these ridges, is a significant technique in forensic identification due to the individual specificity of pore configurations. While previous research has established the uniqueness and consistency of pore patterns, the potential influence of aging on these characteristics remains insufficiently studied. Current literature offers limited insight into agerelated changes in pore size, morphology, or visibility, and there is a notable absence of longitudinal data and diverse age cohort studies to fully address this issue. This study aims to address this gap by systematically analysing pore features across four distinct age cohorts: adolescents and young adults (13–24 years), middleaged adults (25–48 years), older adults (49–60 years), and senior adults (60+ years). A total of 400 fingerprint samples were collected from these groups. Fingerprint impressions were obtained using an inkless ink pad on glossy paper, selected for its ability to produce high-resolution prints with clearly visible pores. Adequate pressure was applied to ensure optimal ridge and pore detail. The collection sequence commenced with the right thumb and concluded with the left little finger, with ten prints gathered per age cohort. A mirrorless camera equipped with a fixed focal length and zoom lens was employed for print documentation. The number of pores per ridge segment was manually quantified to calculate the average pore diameter. The results demonstrate the considerable potential of poroscopy in forensic age estimation, highlighting its utility as a supplementary tool for approximating the age of an individual from latent fingerprint evidence recovered at crime scenes.

Sustainable Human Capital (SHC) has become central to organizational sustainability, emphasizing the long-term preservation of employees’ health, motivation, and capacity to contribute (Dyllick & Hockerts, 2002). Sustainable human resource management (SHRM) views employees as a very important resource for the organisation, while paying close attention to their preferences, needs, and perspectives. Sustainable Human Capital (SHC) has become a critical priority as organizations seek to build resilient, healthy, and high-capacity workforces. However, existing research has not fully explained how individual-level psychological and motivational processes contribute to long-term human capital sustainability. This conceptual paper proposes an integrative framework in which employee voice serves as a foundational driver that enhances motivation, which in turn strengthens mental well-being, work–life integration, and workplace productivity. Drawing on Sustainable HRM theory, Social Exchange Theory, Job Demand Resource Theory, Stakeholder Theory, and motivation-based perspectives, the model argues that when employees feel able to express ideas and concerns, their intrinsic and extrinsic motivation increases, leading to improved psychological health, reduced stress, greater balance between work and personal life, and higher engagement and performance. These individual outcomes accumulate to form Sustainable Human Capital, characterized by a healthy, motivated, and productive workforce capable of supporting long-term organizational sustainability. The paper concludes that employee voice strengthens sustainable human capital by increasing employee motivation, which enhances mental well-being, work–life integration, and workplace productivity—ultimately creating a healthy, motivated, and future-ready workforce.

Cultural diversity strongly influences dietary habits, nutrition perceptions, and health outcomes. Nutrition education lacking cultural awareness often fails to engage communities or sustain behaviour change. Drawing on frameworks such as cultural competence, community-based participation, and culturally responsive pedagogy, it shows how tailoring interventions to local languages, food preferences, and traditions enhances effectiveness. Culture is presented not as a barrier but as a foundation for building trust, relevance, and empowerment in nutrition education, with recommendations for educators and policymakers to incorporate cultural adaptation at every stage.

Cloud computing has revolutionized the IT industry by offering scalable and on-demand resources under a pay-as-you-go model. However, the same flexibility that makes cloud computing attractive also introduces challenges in managing costs effectively. Cloud Cost Management (CCM) focuses on monitoring, analyzing and optimizing expenditure to achieve an ideal balance between performance and affordability. This paper reviews existing cost management strategies, techniques, and tools used across different cloud environments. It highlights traditional budgeting and monitoring methods, advanced optimization models using machine learning, and automation through FinOps practices. There view also identifies current challenges such as complex pricing structures, lack of cost visibility in multi-cloud environments, and unpredictable billing patterns. Finally, the paper discusses emerging trends and future research directions aimed at intelligent and sustainable cost optimization in cloud ecosystems.

Mahatma Gandhi’s philosophy of Swadeshi emphasized economic self-reliance, decentralized industries, and rural empowerment. This study examines the relevance of Swadeshi in modern rural development, particularly through small-scale industries (SSIs). It explores how Gandhi’s vision countered colonial exploitation, revived indigenous industries, and promoted sustainable livelihoods. The research highlights contemporary rural development strategies, including government policies, technological advancements, and financial inclusion, that align with Swadeshi principles. Findings indicate that decentralized industrialization fosters employment, reduces rural-to-urban migration, and enhances socio-economic resilience. Additionally, eco-friendly practices and renewable energy integration in SSIs contribute to sustainable development. The study concludes that modernizing Swadeshi-based industries through technology, policy support, and financial assistance ensures inclusive and sustainable rural growth, making Gandhi’s vision highly relevant in today’s globalized economy.

The objective of this paper is to study a special case of connectedness in bitopological spaces by considering   open sets and examining their relationships with connected space and pre-connected space.

Augmented Reality has become an important tool for improving how students learn complex science lessons. In many schools, instructional materials are often limited, outdated or not suited for interactive teaching. This affects subjects such as Earth Science where learners need clear visual explanations to understand processes inside the Earth. Teachers often rely on drawings and verbal descriptions which may not show the full structure or behaviour of natural events. Studies have also shown that inadequate learning resources contribute to lower academic performance, which highlights the need for better teaching tools. EsAR was developed as a response to these challenges. It is a Markerless AR application that presents 3D volcano models, types of volcanoes and simple eruption simulations. By using a mobile device, learners can view these models in real space and see how volcanoes form and erupt. This makes abstract ideas easier to understand. Modern AR systems such as EasyAR use surface detection and SLAM to track the environment, which allows digital models to blend naturally with the physical world. The application was created using the Waterfall Model. Each step, from gathering requirements to deployment, was completed in sequence. Unity 3D and C# were used to build the models and interactions. Storyboard and interface designs ensured the app remained simple and accessible to users. The system was evaluated using selected ISO 25010 quality characteristics. IT experts, science teachers and students rated functional suitability, usability, reliability, performance efficiency, compatibility and portability. The overall rating of 3.77, interpreted as Excellent, showed that the application worked as intended and was easy to use on different devices. The results show that AR can help address gaps in science education. EsAR offers a practical way to improve visual understanding and make learning more engaging. With further development, AR can continue to support better science instruction in schools.

Planetary landings can only be autonomous by having control systems that are able to balance accuracy and efficiency under nonlinear conditions. This research project examines the use of reinforcement learning (RL) which in this case is Proximal Policy Optimization (PPO) algorithm to simulate and optimize the dynamics of lunar descent in a one dimensional environment with Newtonian physics. The agent was trained to successfully complete soft landings at different fuel and thrust-cost settings, where a reward function punished the use of fuel and high terminal velocity. On 40 000 training episodes, the agent always shared similar stable policies that reduced fuel usage and yet reached safe touchdown speeds (less than 2 m/s-1). Quantitative analysis showed the existence of a strong negative correlation between available fuel and final velocity and unique dual-phase kinetics of fuel-use patterns were found which were similar to real-world powered-descent sequences. These findings confirm that reinforcement learning is a physically consistent, adaptive control technique to optimize descent, and has the potential to be useful in autonomous guidance systems in future trips to the moon and other planets.

Purpose - The complexities of recognizing student depression make it a promising area for exploring the effectiveness of various text vectorization techniques such as `CountVectorizer`, `TfidfVectorizer`, and `HashingVectorizer` in machine learning applications. These techniques can help identify patterns and symptoms by analyzing large datasets like social media activity which may reveal indicators of depression that traditional diagnostic methods might miss. Method - In recognizing student depression through machine learning, the methodology follows the Knowledge Discovery in Databases (KDD) process which guides each stage of data handling and analysis. Results - Based on the result of the evaluation of different text vectorization techniques, the `CountVectorizer` achieved the highest classification accuracy at 87%, ranking it first. `TfidfVectorizer` follows closely with an accuracy of 86%, ranking second. Lastly, `HashingVectorizer` achieved a lower accuracy of 77%, placing it third in performance. Conclusion – The study reaffirms that traditional vectorization methods like `CountVectorizer` and `TfidfVectorizer` remain highly effective for text classification tasks and that method selection should be guided by the specific needs of the application and characteristics of the data. Recommendation - Future work will center on combining these techniques or probing more advanced embedding methods which better capture deeper semantic relations. The generalization of such vectorizers on other datasets and domains will be further illustrated in detail by their robustness and performance.

Static routing is one of the most fundamental routing techniques used in computer networks to manually define paths between source and destination networks. Unlike dynamic routing protocols, which automatically update routes using network topology changes, static routing provides network administrators with complete control over the routing table. This research paper presents a detailed study on the implementation, advantages, limitations, and performance of static routing in modern network environments. The study further evaluates the suitability of static routing in enterprise and small-scale networks, emphasizing security, reliability, and ease of configuration. Using Cisco routers and simulation tools, the paper demonstrates the configuration and behavior of static routes and highlights how manually configured paths can significantly enhance stability in predictable network environments.

This study examines the effects of performance allowances and work environment on employee performance at the Regional Secretariat of Merauke Regency, Indonesia. The research was motivated by the need to enhance public sector productivity through effective human resource management in a decentralized administrative context. Using a quantitative explanatory design, data were collected from 129 civil servants selected through purposive sampling. Data analysis employed multiple linear regression using IBM SPSS Statistics 26. The findings reveal that both performance allowances and the work environment significantly and positively influence employee performance, both individually and simultaneously. Performance allowances function as extrinsic motivators that enhance accountability and work enthusiasm, whereas a supportive work environment strengthens psychological comfort, collaboration, and commitment. Among the two factors, the work environment demonstrated a stronger influence, underscoring the importance of non-financial determinants in sustaining performance. The study concludes that a synergistic combination of fair compensation policies and conducive workplace conditions is essential for optimizing employee performance in public organizations. Practical implications include policy reform in allowance structures, workplace modernization, and integrated capacity development strategies.

This study examines the influence of social media on the purchasing decisions of Generation Z regarding organic cosmetic products. Gen Z, a digitally native and socially conscious group, relies significantly on platforms like Instagram, YouTube, and Facebook to engage with beauty-related content, find new products, and validate buying decisions via reviews and influencer endorsements. The study uses a quantitative research design, collecting primary data from roughly 60 Gen Z respondents via a structured questionnaire with multiple-choice and Likert scale items. The data was examined using descriptive statistics, frequency distributions, and correlation analysis. The study found that Gen Z consumers have a high level of environmental and health awareness, with an average awareness score of 4.2 on a five-point scale. Trust in online reviews showed a large positive connection (r = +0.65) with purchasing intent, demonstrating that reliable web information had a major impact on their purchasing behaviour. Influencer posts were the most effective sort of material, followed by detailed product reviews. Traditional commercials and lectures proved to be less compelling. Overall, the survey emphasizes the relevance of original, relatable, and trustworthy online content in influencing Gen Z consumer choices. These insights provide useful information for brands looking to connect with this environmentally conscious and media-savvy generation.

The exponential growth of governmental documentation in India has created an urgent need for efficient automated summarization systems. This research presents eSummarizer AI Service, a custom Transformer-based machine learning model designed specifically for summarizing Indian government documents such as policy papers, circulars, legislative texts, and departmental reports. The study employs advanced Natural Language Processing (NLP) techniques, including BART (Bidirectional and Auto-Regressive Transformer), reinforcement learning, and a custom dataset curated from official government portals. Evaluation using ROUGE metrics demonstrates significant improvements over existing baseline models, achieving high coherence, contextual relevance, and factual consistency. The proposed system has practical applications for policymakers, researchers, and citizens, enhancing the accessibility and comprehension of complex governmental information.

This study examines how MRI and ML can improve cancer detection and early detection. Better cancer diagnostics and earlier detection are the goals. Test and evaluate machine learning methods for different cancers. These include CNNs, SVMs, RFs, and ensemble approaches. Public datasets were analyzed. The AUC-ROC, sensitivity, specificity, and detection rate were used to evaluate the results. Using radiomics features with deep learning architectures to improve diagnostic skills is also considered. Ensemble systems that use many machine learning (ML) methods outperform solo algorithms with an average precision of 92.7 percent. A list of the primary issues that must be addressed for practical translation is also provided. This requires larger and more diverse datasets and improved understanding and application of learned information. Multimodal integration and federated learning approaches may be studied in the future to solve current issues.

This study aimed to document the diversity, distribution, and feeding habits of insect pests associated with the crop. a total of 17 insect species from seven orders (coleoptera, hemiptera, diptera, hymenoptera, lepidoptera, orthoptera, and thysanoptera) were recorded, with coleoptera (41.18%) and hemiptera (29.41%) being the most dominant. the family chrysomelidae had the highest representation with four species, while other families had one species each. The pests were categorized by feeding behavior: defoliators (11 species, 64.71%), sap-suckers (5 species, 29.41%), and tissue feeders (1 species, 5.88%). chewing insects, especially coleopteran defoliators, were the main cause of crop damage, while sap-sucking pests caused indirect damage through nutrient depletion and pathogen transmission. These findings provide a baseline for understanding pest dynamics, which is essential for developing effective integrated pest management (ipm) strategies to reduce crop losses while highlighting the need for regular monitoring and management of both defoliators and sap-sucking pests.

The purpose of this research is to evaluate how blended or hybrid learning of CAD instruction serves the students of Manufacturing Engineering better. In engineering programs, with the acceleration of digital transformation in the post-pandemic era, online lectures and virtual demonstrations are increasingly combined with face-to-face laboratory activities to support design-based learning. This study used a descriptive comparative research approach and compared two groups: students who were enrolled purely in face-to-face CAD classes versus those enrolled in hybrid/blended classes. Academic statistics regarding student engagement, motivation, and satisfaction were collected using measurements of student performance combined with a student engagement survey. Hybrid learners performed marginally better than traditional learners on their course assessments and more significantly on final assessments and design projects. Student engagement measurements were consistently greater among hybrid students when compared with traditional students, indicating that the hybrid learning model, which integrates multimedia and flexible instructional approaches into the learning environment, enhances motivational and participatory aspects of learning. Feedback from surveys corroborates the impact of digital resources on students' ability to learn at their own pace and also notes issues related to internet connectivity and software limitations. Results provide strong support for the implementation of hybrid instructional formats in skills-based engineering courses (CAD). Hybrid instructional formats were noted to enhance cognitive and affective (motivational) learning outcomes of CAD.

This study developed and evaluated the Innovative Car Air Conditioning Trainer as an instructional prototype designed to enhance technical-vocational education in refrigeration and air conditioning technology. Guided by a developmental research design, the project progressed through three phases: design, construction, and evaluation. The trainer was conceptualized to integrate electrical protection devices, subsystem isolation switches, and digital monitoring instruments, thereby simulating the operational dynamics of an automotive air conditioning system. Fabrication involved the assembly of mechanical and electrical subsystems to replicate real-world RAC operations, while evaluation was conducted by faculty experts from the Bachelor of Science in Refrigeration and Air Conditioning Technology and Bachelor in Industrial Technology major in Heating, Ventilation, and Air Conditioning Technology (BIndTech HVACRT) programs at Zamboanga Peninsula Polytechnic State University. Using a structured survey instrument and four-point Likert scale, results revealed high levels of acceptability in terms of design, functionality, and instructional relevance, with mean ratings ranging from 3.8 to 4.0. Findings confirm that the trainer effectively bridges theoretical knowledge with hands-on practice, aligns with TESDA competency standards, and supports modular learning and fault simulation for enhanced student engagement. The study concludes that the prototype is both acceptable and effective as an instructional tool, with potential for replication, scaling, and further innovation to strengthen competency-based technical-vocational education.

Two subclasses of starlike and convex functions analytic in the unit open disk using q-derivative operator have been investigated in the present paper. The necessary and sufficient condition for the function belonging to these classes have been obtained. We further examine various properties, such as the Hadamard product and the quasi-Hadamard product. The coefficient estimates for the function belonging to these classes are also found. Mathematics Subject Classification 2020: 30C45, 30C50

The COVID-19 pandemic has profoundly impacted all aspects of national education. It has not only highlighted the inadequacies of existing care arrangements but also spurred new care demands. Policymakers and educational institutions increasingly recognize the need to more effectively understand and support the well-being of their domestic students and provide them with more comprehensive care. This paper draws on theories of social material care to explore the possibility of using care as a guiding principle to steer national education in a more productive direction. The core argument is that a focus on care is crucial for promoting the well-being of international students and creating a more effective institutional environment for attracting, retaining, and supporting them. Furthermore, it is essential for redefining and achieving the grand goals of national education itself. This paper demonstrates these points through three "drafts," illustrating how a focus on care can reshape international education policy and institutional support and point to new directions for research on international student mobility.

With the surge of the Covid-19 pandemic, online sellers faced challenges particularly in managing their businesses amidst the uncertainties of the pandemic. This study aims to delve into the experiences of online sellers, focusing on the challenges and mitigating measures used by online sellers during pandemic. This qualitative study employs a phenomenological research design to explore the lived experiences of food online sellers during the Covid-19 pandemic. A total of five participants, consisting of food online sellers based in the local community, were selected through purposive sampling to provide rich and diverse insights into the challenges and mitigating measures associated with online selling during the pandemic. The study takes place in Tacloban City, a first-class, highly urbanized city in the Eastern Visayas region of the Philippines. Using semi-structured interviews food online sellers shared their experiences related to the challenges faced and the strategies employed in navigating the complexities of online selling during the pandemic. The findings of the study revealed eight emergent themes that encapsulate the informants' experiences, challenges, and coping strategies in food online selling during the pandemic. Specifically, three themes described their experiences: Low in Sales, Struggling in Buying Stocks, and Resorting to Online Business, which illustrate their initial difficulties and adaptive responses in transitioning to digital platforms. Regarding the challenges they encountered, three themes surfaced: Purchasing Ingredients, Dealing with Complaints and Addressing It Properly, and Relying on Delivery Drivers, pointing to supply chain issues, customer service concerns, and dependence on third-party logistics. Lastly, two themes represented the mitigating measures employed: SellerCustomer Relationship and Advertise the Product on Social Media, emphasizing the strategies informants used to maintain customer trust and boost visibility in a competitive online marketplace.

This article provides a self-contained description of linear regression, covering both the necessary linear algebra concepts and their implementation in Python. Linear regression remains one of the most interpretable and widely used tools in the data scientist’s toolbox. By mastering both its theoretical foundations and practical applications, one can build robust and explainable models. In this paper, we explain the fundamentals of linear regression, outline how it works, and guide the reader through the implementation process step by step. We also discuss essential techniques such as feature scaling and gradient descent, which are crucial for improving model accuracy and efficiency. Whether applied to business trend analysis or broader data science applications, this paper serves as a comprehensive introduction to linear regression for beginners and practitioners alike.

This work presents the design and simulation of a universal smart battery charger capable of charging different types of rechargeable batteries, including Nickel-Cadmium (NiCd), Nickel-Metal Hydride (NiMH), Sealed Lead-Acid (SLA), and rechargeable alkaline batteries. The system is designed to automatically or manually select the appropriate charging algorithm and parameters for each battery chemistry, ensuring safe and efficient charging performance. A microcontroller-based control system is used to monitors battery parameters such as voltage, current, and temperature in real-time. The system applies the correct charging method depending on the type of battery selected —constant-current (CC), constant-voltage (CV), or trickle charge—along with intelligent termination techniques such as −ΔV detection, temperature rise monitoring (ΔT/Δt), or current tapering. Overvoltage, overcurrent, reverse polarity, and thermal shutdown safeguards were integrated for safety and reliability. A user interface with indicators and selection buttons was incorporated for easy operation and status monitoring. The developed system provides a cost-effective solution for charging a wide range of rechargeable batteries.

Reinforcement learning (RL) provides a flexible framework for optimizing personalized healthcare treatment options. In this work, we implemented a diabetes management problem using actor-critic reinforcement learning from a matrix game perspective, with the goal of maximizing long-term health outcomes. We simulated a diabetic patient over 20 weeks, with the actor recommending treatment plans (e.g., insulin and dietary interventions), while the critic determined the patient's benefit. The model was able to learn patient-specific plans that recommended treatment with normal blood sugar levels more frequently (75%) compared to using a fixed baseline (50%). We also developed a Python application to simulate diabetes models, providing visualizations of policy evolution, health outcomes, and value estimates. The results of this work demonstrate promising signs for integrating reinforcement learning into healthcare precision medicine, while highlighting future implementation challenges, addressing relevant safety constraints and appropriate data use.

This study aimed at estimating variance components and genetic parameters for reproductive traits of Large White sows in Kenya, in order to facilitate genetic improvement of reproductive efficiency in sows through selective breeding. 1145 records comprising 1129 records of litter size at birth (LSB), 1101 records of number of piglets born alive (NPBA), 1114 records of litter size at weaning (LSW) and 681 records of inter-farrowing interval (IFI) were obtained from 4 farms in western Kenya. After editing, a total of 1138 records of at least 2 traits were available for analysis. Genetic variance components were 0.10±0.03 for LSB, 1.33±1.80 for NPBA, 0.01±0.21for LSW and 23.28±22.71 for IFI.  Phenotypic variance components were 7.63±0.33 for LSB, 6.67±0.29 for NPBA, 6.03±0.26 for LSW and 589.40±25.48 for IFI. Heritability estimates for reproductive traits were generally low. The estimates were 0.014±0.040 for LSB, 0.011±0.039 for NPBA, (0.001±0.035 for LSW and 0.039±0.038 for IFI. The standard errors for estimates were high. This is because, reproductive traits are strongly influenced by environmental factors. High environmental variability relative to genetic variability can make it difficult to accurately estimate heritability. The genetic correlation coefficients were 0.227.for LSB and NPBA, -0.555 for LSB and LSW, 0.865 for LSB and IFI, -0.924 for NPBA and LSW, -0.283 for NPBA and IFI as well as -0.079 for LSW and IFI. Phenotypic correlation coefficients were 0.810±0.011 for LSB and NPBA, 0.655±0.018 for LSB and LSW, 0.019±0.031 for LSB and IFI, 0.821±0.010 for NPBA and LSW, 0.004±0.031 for NPBA and IFI as well as 0.034±0.031 for LSW and IFI, The study concluded that, the low genetic variance compared to phenotypic variance across traits indicates a strong influence of environmental factors on reproductive traits, limiting the genetic contribution to variability. The very low heritability values with high standard errors highlight the limited potential for genetic improvement through selection and emphasize the use of other sources of information like progeny and ancestors. The genetic correlations with both positive and negative relationships, suggest the need for careful, balanced selection to avoid unfavorable genetic trade-offs between traits. Strong positive phenotypic correlations between traits like LSB and NPBA  suggest shared environmental influences, underscoring the importance of improved management for overall reproductive performance.

In contemporary society, there is increasing emphasis on personal appearance and image. Within the rapidly growing cosmetics market, foundation as a core product in base makeup has garnered heightened attention. This study investigates the key factors influencing consumer satisfaction with foundation products, focusing on product knowledge (measured by self-awareness of skincare needs) and personality traits (such as openness to new experiences, enjoyment of life, and preference for variety) a questionnaire survey among 200 consumers with prior foundation usage experience. The survey included three primary constructs: product knowledge, personality traits, and satisfaction with foundation use. Results from factor analysis and correlation analysis using SPSS indicated a significant positive correlation between product knowledge and foundation satisfaction. Personality traits such as novelty-seeking also significantly affect product satisfaction. Another analysis revealed that respondents with high product knowledge and personality traits reported significantly higher product satisfaction. These findings suggest that product knowledge and personality traits affect consumer satisfaction with foundation products.

Lifestyle factors affect female consumers' satisfaction with facial mask usage. As modern life accelerates and stress levels rise, women's demand for high quality and convenient skincare products grows. Facial masks, as a key component of daily skincare routines, are subject to variations in satisfaction levels depending on individual characteristics and lifestyle patterns. This research employed purposive sampling and conducted a questionnaire survey targeting Taiwanese women with prior experience using facial masks. The results indicate that certain demographic variables such as marital status, age, and educational level significantly affect satisfaction with facial masks. Specifically, unmarried women and those with a university education reported higher satisfaction with the efficacy of facial masks. Self-image, brand orientation, and trend consciousness were analyzed regarding lifestyle dimensions. Except for trend consciousness, the other two lifestyle dimensions showed a significant positive correlation with satisfaction regarding product design and functional performance. Respondents strongly emphasized self-image and demonstrated the highest level of satisfaction with the product's appearance and effectiveness.

A further understanding into the interplay of the reference rate, inflow of capital from non-residents of the country and unemployment within the Ghanaian economy was sought by this study. To achieve this, secondary data from 2003 to 2023 were utilized. Three specific objectives were addressed by the research work. To begin with, the effect of the base rate and prior capital inflows from foreigners on future investments in Ghana was assessed. The results indicated that while a direct influence on future investments was made by the reference rate and past investments from non-residents, a more deterministic significance was by previous investments. Second, the effect of prior capital inflows by foreigners and previous levels of unemployment on the current levels of the base rate was probed. The reference rate that was set by the central bank of Ghana was found to be more dependent on lagged foreign direct investments than on past unemployment levels. This showed that the central bank was more willing to mitigate the risk associated with inflation than to stabilize the labor market. Lastly, the influence of the reference rate and unemployment from previous years on future unemployment was determined. An analysis of the impact of prior levels of unemployment and the rate set by the central bank on the unemployment rate revealed that a notable effect on the level of unemployment in Ghana was had by the monetary policy rate.

Prostate cancer remains one of the most prevalent malignancies among men worldwide, with early detection being crucial for effective treatment and improved survival outcomes. Traditional diagnostic procedures, such as prostate-specific antigen (PSA) testing, digital rectal examination (DRE), and biopsy, often suffer from limitations including subjectivity, low specificity, and inconsistent accuracy. This study presents the development of a predictive model for prostate cancer using a machine learning-based classification algorithm, specifically the Support Vector Machine (SVM). The dataset utilised was obtained from a publicly available prostate cancer repository, containing relevant biomedical and demographic features. Preprocessing procedures, including normalisation and data transformation, were applied to enhance model quality and ensure robustness. Experimental results revealed that the SVM model achieved a high predictive accuracy of 84.8% under crossvalidation and 87% on full dataset evaluation, with a corresponding error rate of less than 0.17. These results demonstrate the model’s ability to accurately distinguish between malignant and non-malignant cases, validating its suitability for clinical decision support. The model’s performance further confirms the potential of SVM as an effective classification technique for medical diagnostics, especially where datasets exhibit complex, nonlinear feature interactions. This study emphasises the significance of machine learning in enhancing diagnostic precision and reliability within the medical domain. The outcomes provide valuable insights into integrating artificial intelligence into healthcare systems for early cancer detection, reducing diagnostic delays, and supporting medical professionals in clinical decision-making.

Based on the stimulus-organism-response (SOR) model, this study aims to investigate the effect of anthropomorphism and initial trust on customer experience. It also assesses the moderating effect of technology anxiety on the relationship between anthropomorphism and customer experience, which remains underexplored. Data were collected from 385 Tunisian students and analyzed using partial least squares structural equation modeling. Results indicate that anthropomorphism has a positive influence on both initial trust and customer experience, and that initial trust has a positive effect on customer experience. Findings also reveal that technology anxiety negatively moderates the relationship between anthropomorphism and customer experience. This study provides valuable insights for managers and system developers.

The prevalence of diet-related chronic diseases such as Type 2 Diabetes Mellitus (T2DM) and hypertension necessitates the development of intelligent nutritional systems that go beyond user preference to prioritize clinical safety. Traditional food recommender systems often suffer from a "health-blind" bias, optimizing primarily for taste or popularity. This paper proposes a novel Risk-Aware Diet Recommendation System (RADRS) that integrates Logistic Regression (LR) for interpretable health risk assessment with Linear Programming (LP) for dietary optimization. By training LR models on the NHANES and Pima Indians Diabetes datasets, the system calculates individual disease probabilities. These probabilities dynamically configure nutritional constraints—specifically modifying sodium, carbohydrate, and saturated fat limits—within an LP solver. The proposed architecture bridges the gap between predictive health analytics and personalized meal planning, ensuring that recommendations are both palatable and medically compliant. Keywords—Health Informatics, Logistic Regression, Recommender Systems, Linear Programming, Personalized Nutrition, Chronic Disease Management.

This study investigates the impact of knowledge conversion on innovation and organizational performance through a multi-layered moderated mediation framework. Drawing on the SECI model (socialization, externalization, combination, and internalization), the research examines how different modes of knowledge conversion influence both incremental and radical innovation outcomes, which subsequently shape overall organizational performance. The study also explores how organizational culture and digital knowledge infrastructure moderate the relationships within the framework. Data were collected from 412 IT professionals working in knowledge-intensive and innovation-driven environments across multiple organizations. Using structural equation modelling (SEM), the study empirically validates the proposed framework and reveals that knowledge conversion significantly enhances innovation capability, and innovation acts as a strong mediator between knowledge conversion processes and performance outcomes. Furthermore, supportive organizational culture and robust digital infrastructure significantly strengthen the indirect effects, confirming a multi-layered moderated mediation effect. The findings highlight the strategic importance of investing in knowledge conversion mechanisms to foster innovation and drive sustainable organizational performance. This study contributes to the knowledge management and innovation literature by offering a deeper understanding of how knowledge processes interact with contextual factors to influence performance in IT-driven environments. It also provides practical implications for managers seeking to enhance innovation capability through effective knowledge management practices.

This study examined the implementation of the Alternative Learning System (ALS) for Indigenous Peoples (IP) learners in the Division of Sultan Kudarat. Specifically, it assessed the extent of (a) attainment of program objectives, (b) adequacy of resources in terms of instructional materials and mobilization, (c) management functions including planning, organizing, coordinating, and monitoring, and (d) skills development among IP learners. A descriptive survey research design was employed. Data were collected using researcher-developed questionnaires and analyzed using descriptive statistical methods. The respondents consisted of 35 ALS personnel, including mobile teachers, instructional managers, and district coordinators assigned to learning centers predominantly serving IP learners in selected districts of Sultan Kudarat. Findings revealed that the ALS program was implemented at a highly effective level across all indicators. The attainment of program objectives obtained a mean rating of 4.00, while adequacy of instructional materials and mobilization yielded mean ratings of 3.85 and 3.95, respectively. Management functions—planning (3.97), organizing (3.97), coordinating (3.89), and monitoring (4.00)—were likewise rated as highly effective. Learners’ skills development also registered a high level of effectiveness with a mean of 3.90. Overall, the results indicate that the ALS program for IP learners in the Division of Sultan Kudarat is effectively implemented, supported by adequate resources, sound management practices, and positive learner outcomes. These findings underscore the importance of sustained program support to ensure continuous access to quality education for Indigenous Peoples.

E-retailing has changed the retail environment in India. Consumer's attitude towards shopping has been changed with the introduction of internet. Due to Internet world has become a small village. The e-retailing website is the primary door of the online store that interacts between the e-retailer and consumers. The electronic retailing (e-retailing, internet retailing etc.) is the way of selling goods using electronic media, in particular, the internet. Providing the customers more comfort and convenience to purchase the goods. In India, the use of mobile phones, internet adoption, and the availability of low cost data plans have contributed in the growth of e-retailing. The close of physical stores in COVID-19 epidemic has also increase the growth of eretailing in India. As a result, customers start shopping through online. However, the expansion of e-retailing has also brought many difficulties, such as the effect on conventional brick-and-mortar merchants, worries about data privacy, and the requirement for a strong logistics and supply chain infrastructure. In this research paper the authors propose to explore the profile of e-retailing customer’s and to investigate conceptually the challenges and the avenues involved in e- Retailing. 

This qualitative study explores the pervasive issue of gender discrimination within Bangladesh's garment industry, where women make up the majority of the workforce yet continue to face significant barriers to equality. Through in-depth interviews and surveys with 100 female workers from various garment factories in Dhaka, Chittagong, and other hubs, the research examines key forms of gender discrimination, including wage disparity, limited career advancement, workplace harassment, and the emerging threat of automation. The findings reveal that female workers earn significantly lower wages than their male counterparts, experience limited promotion opportunities, and endure high levels of sexual harassment, often compounded by a culture of silence and fear. Additionally, the rise of automation in the sector poses a unique challenge for women, with many fearing job displacements due to their lower skill levels in handling new technologies. The study concludes with actionable recommendations for addressing these issues, including implementing equal pay policies, establishing career development programs, enforcing anti-harassment measures, and providing gender-sensitive automation training. By highlighting the lived experiences of women workers, this research underscores the urgent need for comprehensive reforms to promote gender equality in Bangladesh’s garment sector.

India is also known as the ‘Land of Snakes’. as there are  Out of venomous species main are Cobra, Russell’s Viper, Saw Scaled Viper , and Krait, the remaining species are either semi-venomous or non-venomous. But still most  people are afraid of snakes as they don’t know much about them, is the prime reason for decline of this fascinating creature.  Vidarbha has an arid topography with high temperatures, and other climatic conditions that favour the reptilian fauna. Very few researcher studied snakes diversity in Vidarbha, and hence there is a need to study ophidian fauna.   The specimens were observed during the visit of the probable habitats or the rescuers  associated with different NGOs, photographed, and identified with the help of keys, and methods suggested by Daniel (2002) Whitaker, and Captain (2004) , and Khaire (2010). In the present study, diversity of this ophidian fauna is identified, and found six families of snakes namely Typhlopidae, Pythonidae, Boidae, Colubridae, Elapidae, , and Viperidae , and  represent 19 species. Out of the total identified species four venomous snakes, thirteen non-venomous, and two semi- venomous are reported during the month from August 2018 to March 2019. The habitats, and other ecological parameters along with the existence of ophidian form we can correlate in the future part of the study.          

This study analyzes exponentially accelerating fluid through vertically porous plate with a heat source. The effects of rotation, porosity, and Thermal Radiation on velocity (Speed), temperature (Warmth), and concentration(accumulation) profiles are investigated. Results indicate that velocity rises with heat source increase but decreases with radiation and rotation. Temperature increases with heat generation raises while concentration drops with higher Schmidt numbers. Also, Skin friction, Nusselt & Sherwood values tabulated.

Gum Arabic (GA), a natural, edible hydrocolloid obtained predominantly from Acacia senegal and Acacia seyal, is a highly branched heteropolysaccharide composed mainly of arabinose, galactose, rhamnose, glucuronic acid, and small proportions of protein. Its unique molecular architecture comprising arabinogalactan (AG), arabinogalactan protein (AGP), and glycoprotein (GP) fractions confers exceptional solubility, emulsification, film-forming capacity, and stability, which underpin its long-standing relevance in food, pharmaceutical, cosmetics, and industrial applications. Renewed scientific interest in GA is driven by its biodegradability, safety, and functional versatility, as well as its growing importance as a sustainable biomaterial. Recent advances have focused on modifying GA to enhance its physicochemical and functional properties. Chemical, physical, and enzymatic approaches including oxidation, cross-linking, esterification, graft-copolymerization, and nanoparticle functionalization have produced derivatives with improved rheological behavior, stability, and targeted performance. Modified GA has demonstrated significant potential in Nano chemistry as a stabilizer and reducing agent for metal nanoparticles, in drug delivery through pH-responsive hydrogels and polysaccharide drug conjugates, and in environmental technologies such as wastewater remediation and semiconductor development. In construction materials, GA acts as a natural binder that improves compressive strength, durability, and water resistance of stabilized earth blocks, offering a sustainable alternative to conventional stabilizers. Beyond industrial applications, GA provides notable health benefits. As a fermentable dietary fiber, it functions as a prebiotic, enhancing mineral absorption and supporting gut microbiota. Its antioxidant, anti-inflammatory, antimicrobial, and detoxification properties contribute to renal, cardiovascular, and gastrointestinal protection. Modified GA derivatives, including aldehyde-functionalized and cross-linked forms, have also shown promise for controlled drug release, tissue engineering, and biomedical therapeutics. This review synthesizes current knowledge on the composition, structural characteristics, physicochemical properties, and applications of GA, with emphasis on recent modification strategies that broaden its utility across scientific and industrial domains. The growing development of GA-based materials highlights its potential as a renewable, biocompatible platform for next-generation technological innovations.

Concrete structures in marine environments are vulnerable to chloride and sulphate attack, leading to corrosion and reduced mechanical performance. This study examines the effect of partially replacing cement with microsilica at 5 w%, 10 w%, and 15 w% on the compressive and tensile strengths of concrete before and after 28 days of seawater exposure. Concrete mixes were designed with a ratio of 1:2:4 and a water/cement ratio of 0.4. The inclusion of microsilica significantly enhanced compressive strength before exposure, with the optimum performance observed at 10 w% replacement, showing a 26.1% increase after seawater exposure. Conversely, tensile strength declined across all mixes following exposure, with reductions between 18.45% and 27.19%, attributed to the ingress of chloride and sulphate ions that weakened the interfacial transition zone. Despite this, microsilica-modified concretes retained higher residual tensile strength than the control mix. The findings indicate that microsilica improves the strength and durability of concrete exposed to marine environments, making it an effective supplementary cementitious material for sustainable coastal and offshore applications.

This paper presents a comparative analysis of saving and investing behaviour to examine how financial literacy, income level, risk perception and demographic characteristics influence individual financial decision-making. The study is based on primary data collected from 120 respondents in selected areas of Pune city through a structured questionnaire. Descriptive statistical tools, including percentages, tables and graphical analysis, were employed to analyse and interpret the data. The results reveal that traditional saving instruments such as bank savings accounts and fixed deposits remain the most preferred avenues due to their safety, liquidity and low risk. However, an emerging inclination towards investment options such as mutual funds and equities is observed, particularly among younger and well-educated respondents. Despite this trend, investment participation is restricted by factors such as inadequate financial knowledge, fear of market volatility and limited disposable income. The study finds that a majority of respondents consider a balanced combination of saving and investing to be the most effective approach for long-term wealth creation and financial security. The paper emphasizes the need for enhanced financial literacy, early investment awareness and accessible investment products to promote informed and sustainable financial behaviour. The findings provide valuable insights for policymakers, financial institutions and educators in developing strategies to improve individual financial well-being.

This paper explores the personality characteristics of the five Pandava brothers—Yudhishthira, Bhima, Arjuna, Nakula, and Sahadeva—from the Mahabharata through the lens of Alfred Adler’s Birth Order Theory. The study situates this psychological analysis within the framework of Indigenous Knowledge Systems (IKS), emphasizing how ancient Indian epics convey implicit psychological insights. Through qualitative content analysis, the research identifies patterns aligning with Adler’s conceptualization of first-born, middle-born, and youngest child traits. The findings reveal a synthesis between cultural narratives and universal psychological principles, demonstrating the continuing relevance of indigenous literature to modern psychology.

Software-Defined Networks (SDN) provide centralized control for programmable routing, yet traditional algorithms like OSPF and ECMP struggle with dynamic traffic patterns, congestion hotspots, and QoS demands in large-scale deployments. This paper conducts a systematic review of machine learning (ML) techniques— including supervised classifiers, reinforcement learning (RL) agents, and graph neural networks (GNNs)— applied to SDN routing and performance optimization, highlighting their roles in traffic classification (up to 99.81% accuracy), predictive KPI forecasting, and adaptive path selection. We propose the Hybrid Causal-RL-GNN (HCRG) framework, which fuses Graph Attention Networks (GAT) for topology-aware state encoding with a causality-enhanced Soft Actor-Critic (SAC) agent to quantify action impacts and maximize a composite reward function balancing latency, packet loss, and throughput. Trained offline on Mininet-emulated NSFNET and Fat-Tree topologies with Ryu controllers, HCRG deploys via OpenFlow for real-time flow rule installation, incorporating hyperparameters like learning rate 0.001 and discount factor 0.99 over 20,000 episodes. Extensive evaluations under normal, congested, and failure scenarios demonstrate HCRG's superiority: 28% latency reduction (22 ms vs. 45 ms baselines), 22% throughput increase (2.2 Gbps), and 35% loss mitigation (1.6%), outperforming ROAR, RouteNet, and ECMP by 15-35% while maintaining <5 ms inference latency at scale. This work advances autonomous SDN traffic engineering, with implications for 5G/6G and edge computing, paving the way for federated extensions in multi-domain environments.

Supply chain and logistics optimization has become increasingly important as India’s e-commerce sector grows rapidly. To meet growing customer expectations of speed and reliability, e-commerce companies are turning to technology-driven solutions including AI-enabled route optimisation, machine learning for demand forecasting and real-time tracking. The focus of this project is to analyse the challenge, which are faced by e-Kart, Delhivery or other companies regarding logistics and supply chain operations. It is expected that through the research on operational performance, technology, and customer satisfaction, the companies will be able to solve logistical issues like poor infrastructure, disturbances from external parties, high operating costs, etc. Through quantitative and qualitative data, the study will determine how technology and sustainable practices can be implemented to enhance supply chain performance in India’s e-commerce landscape. We will use various statistical methods like regression, anova, and factor analysis to pinpoint which logistics problems, which consumer satisfaction, and which tech integration issues matter most. The proposed study intends to develop an integrated framework linking technology adoption- operation performance- and sustainability which the e-commerce firms can utilize to improve operations. The expected results are that technology solutions increased efficiency and decreased costs while maximizing customer satisfaction. But it is estimated that the constraints on infrastructure, the scalability of green logistics, and external disruption will be large hindrances to further optimization. This study will add to the literature gap by assessing the scalability of sustainable logistics practices with technological advancement. Further, it will provide recommendations for company executives who want to optimize the supply chain and logistics functions in India.

In recent times, the rapid growth in construction waste generation has raised significant environmental and economic concerns in recent times. However, Building Information Modelling (BIM) has emerged as a promising solution for managing construction waste and promoting sustainability. BIM offers advanced capabilities for visualization, simulation, and data-driven decision-making, making it a valuable tool for optimizing waste reduction strategies in construction projects. This paper offers a thorough and in-depth review that examines the present and prospective trends of BIM research and its implications in the realm of construction waste management (CWM). Through a Bibliometric analysis, a total of 637 publications were collected from the "Web of Science" core database. Employing VOSviewer for analysis and visualization, co-occurrence, co-word analysis, cluster analysis, and co-citation analyses were conducted to explore influential authors and journals, high-frequency keywords, recent research trends, and potential future research directions in the field. The findings shed light on crucial topics in BIM for CWM, such as circular economy, recycling, waste estimation, and waste reduction. The study systematically analyzes and categorizes the existing literature, mapping the knowledge landscape, and highlights the main future trends in academic research on the integration of BIM and CWM. Looking ahead, future research is anticipated to focus on integrating BIM with Internet of Things (IoT) models, incorporating circular economy BIM systems, exploring green building using BIM models, implementing BIM-based design for deconstruction, and adopting multi-dimensional BIM frameworks. This comprehensive review provides innovative insights into the unique contributions of BIM for CWM, differentiating it from prior research and enhancing the paper's scholarly impact.

While generative artificial intelligence (GenAI) technologies are revolutionising content production, they also pose serious privacy and data security issues. The potential of privacy violations, biases, and cyberattacks rises as these models process large datasets, many of which contain sensitive or private data. These issues are examined in this book, especially in important fields like cybersecurity, healthcare, and finance. The potential for GenAI models to reproduce or infer sensitive data from training datasets is a major problem that raises ethical and intellectual property issues. Data protection techniques like encryption, tokenisation, and anonymisation are crucial to reducing these dangers. This study assesses the efficacy of these techniques by looking at how they affect the functional performance and privacy risk reduction of GenAI systems. It evaluates the impact of tokenisation and anonymisation on a state-of-the-art large language model (LLM) through experimental analysis. Empirical results offer insights into the trade-offs between protecting model performance and data privacy using open-source tools such as Microsoft Presidio. The goal of the research is to help create safe and morally sound GenAI applications, making sure that advancements in AI are in line with data security guidelines while preserving accuracy and efficiency in practical applications.

Organizations must maintain sustainable employee well-being while maintaining performance at a time of growing job demands, digital overload, and the erosion of work-life boundaries. This conceptual study investigates the potential of mindfulness, which is often characterized as present-moment, nonjudgmental awareness, as a long-term professional approach. The study offers a conceptual framework that links mindfulness practices to improved personal resources (emotional regulation, attention, resilience), which in turn lead to better mental health, a better work-life balance, and increased productivity. It does this by drawing on important theories (such as Conservation of Resources, Job Demands–Resources, Attention Restoration, and Self-Regulation) and a broad body of empirical and theoretical literature.The research presents mindfulness as a long-term corporate resource that supports workplace culture, sustainable performance, and employee resilience rather than only as a personal health tool. Future research directions and practical implications are highlighted.