Volume 14 Issue 9

Abstract: This study employed a descriptive-quantitative design to examine the competencies and challenges of Bachelor of Technology and Livelihood Education major in Information and Communication Technology (BTLED-ICT) students in 2D animation. Specifically, it assessed students’ profiles in terms of age, sex, and socio-economic status; evaluated their competency in producing traditional key poses, creating traditional animation, developing 2D digital cut-out animation, and exporting animation to video file formats; identified the challenges encountered in learning resources, facilities and equipment, and IT infrastructure; and analyzed the relationship between competency levels and challenges met. Findings revealed that the majority of respondents were young adults, predominantly female (79.7%), with most belonging to lower socio-economic backgrounds (75.9%), which posed limitations in accessing essential resources. Competency results indicated that students were generally proficient in producing key poses and digital cut-out animation, with mean ratings of 3.10 and 3.13, respectively. However, notable gaps remained in areas requiring technical precision, such as dialogue synchronization. Learning resources (M = 4.32) and IT infrastructure (M = 4.33) were perceived favorably, although issues with accessibility, technical support, and policy clarity were noted. Facilities and equipment were also rated positively but raised concerns regarding adequacy of task-specific tools and power accessibility. The study concludes that while BTLED-ICT students possess foundational competencies in 2D animation, socio-economic constraints and technical skill gaps remain barriers to mastery. Enhancing financial support, refining technical training, upgrading learning resources and facilities, and strengthening IT infrastructure are recommended to further improve instructional delivery and student outcomes. These findings underscore the critical role of resource accessibility and institutional support in developing professional competencies in creative and technical fields.

Abstract: The frequent failure of embankment slopes along Omu-Aran-Ilorin road has resulted in the escalation of construction maintenance costs of roads. An approach to reduce the construction maintenance costs of highway maintenance is to ensure that a comprehensive assessment of embankment slopes is carried out during the initial design stage of road projects. The current study therefore provided a site-specific assessment of an embankment slope along Omuaran-Ilorin road. The site-specific soil samples were collected at four points at depth 0.5m and 1.0m and were subjected to index properties and shear strength properties. This was followed by results analysis and interpretation. The index properties reveal that the selected four soil samples are classified as fine –grained soil and examination of the embankment reveal point 3 as best location for slope stability construction while the remaining three location needs improvement and stabilization to be able to use as an embankments. Therefore, this research provided a site-specific geotechnical database for future implementation in the proposed design-recommendations for Nigerian soils, especially in the southwestern Nigeria. Further contribution of this research is to provide references for local practicing engineers.

Abstract: The rise of two-dimensional (2D) materials such as graphene, transition metal dichalcogenides (TMDs), and hexagonal boron nitride (h-BN) has opened new pathways for flexible and wearable electronic devices. However, large-area, scalable, and defect-free deposition of 2D thin films remains a critical challenge for their practical integration. In this mini-review, we focus on emerging deposition methods for 2D materials in flexible electronics, including chemical vapor deposition (CVD), atomic layer deposition (ALD), molecular beam epitaxy (MBE), solution-based methods, and printing approaches. We provide a comparative assessment of cost, scalability, film quality, and compatibility with flexible substrates, highlighting both the promise and limitations of current approaches. We also discuss controversies, such as trade-offs between scalability and performance, and identify gaps where current deposition strategies fail. Finally, perspectives on future research directions are presented, emphasizing hybrid deposition approaches and environmentally benign processing routes.

Abstract: This study examines the influence of women's entrepreneurship on their empowerment in Kolkata, India. Women entrepreneurs in Kolkata, particularly those aged 36–45, are making notable strides in establishing and managing their own enterprises. Most of these entrepreneurs are married and possess technical education, which is essential for their entrepreneurial activities. The study utilized both primary and secondary data sources, with primary data collected through a structured questionnaire and personal interviews with 208 women entrepreneurs using convenience sampling. The findings indicate that factors such as marital status, educational attainment, and training from organizations such as MSME significantly affect entrepreneurs' perceptions of business growth and success potential. Married women entrepreneurs encounter unique challenges in balancing family responsibilities with business demands. The study also underscores the substantial potential of women-led businesses in job creation, with 53.4% of survey participants employing between 5 and 10 individuals. These results highlight the need for targeted initiatives and programs to promote women's entrepreneurship and foster business growth in Kolkata. Policymakers, entrepreneurs, and support organizations can leverage these insights to enhance the overall economic environment of the region through sustained job creation and business development. Recommendations for women entrepreneurs include developing strong time-management skills, understanding lending options and financial incentives, and accessing technology and training programs.

Abstract: This study investigated the impact of mental Mathematics proficiency on job performance among secondary school teachers. The aim was to determine the influence of mental Mathematics proficiency on teachers’ cognitive skills, productivity, and instructional accuracy. The study employed a descriptive research design and it consisted of a population of 1,822 teachers from public senior secondary schools in Emohua and Port Harcourt City, Rivers State. A total of 298 teachers were selected through simple random sampling techniques. Data were collected over three weeks using a self-constructed instrument titled the Mental Mathematics Job Performance Questionnaire (MMJQ), which was structured on a modified 4-point Likert scale. The instrument was validated by experts in Measurement and Evaluation and had a high internal consistency reliability coefficient of 0.88. Data were analyzed using correlation and linear regression, following confirmation of normality via the Kolmogorov-Smirnov test. The results showed a significant positive relationship between teachers’ cognitive skills and mental Mathematics proficiency. Mental Mathematics proficiency was found to enhance teachers’ productivity significantly and improve instructional accuracy moderately. The study revealed that cognitive and mental computation skills are essential for effective job performance among teachers. It was therefore recommended that mental Mathematics modules should be designed and integrated into teacher education programmes, as well as organizing regular training to enhance teachers’ cognitive and mental Mathematical skills.

Abstract: This study examined the impact of religious tourism on women's empowerment in the Birbhum district of West Bengal, India. Renowned for its religious harmony and significant religious sites, Birbhum holds the potential to leverage religious tourism for socioeconomic development. This study aimed to evaluate the influence of religious tourism on women's empowerment within the district. Using a mixed-methods approach, primary data were collected through a structured questionnaire administered to 35 respondents using convenience sampling in the Tarapith area. Secondary data were sourced from various sources, including articles, journals, and government reports. Data were analyzed using frequency tables, simple percentages, and correlation tests using SPSS version 25. The findings suggest that religious tourism has a significant impact on women's empowerment, particularly through increased economic participation, social influence, and enhanced cultural visibility. Local women engage in entrepreneurial activities related to tourism, provide independent income, and strengthen their community influence. However, cultural norms and gender roles pose challenges for women's advancement in the tourism sector. This study recommends targeted programs and policies to support women's entrepreneurship, ensure equal opportunities, and address cultural barriers. While religious tourism positively contributes to women's empowerment in Birbhum, a comprehensive approach is necessary to fully realize its potential as a catalyst for gender equality and socioeconomic development in the region.

Abstract: Agricultural credit in Karnataka primarily comes from cooperative banks, regional rural banks, and commercial banks, often supported by government schemes offering interest subsidies and loans for various agricultural and allied activities. The Government of Karnataka mainly provides schemes such as the Kisan Credit Card (KCC) for crop loans, loans for animal husbandry and fisheries, and other schemes like Ganga Kalyana for irrigation. The progress of agricultural credit in India, especially in the Kalyana Karnataka region, has depended crucially on government intervention over the years. This empirical study attempts to examine the role of banks in providing agricultural finance and to suggest some framework changes to address farmers’ problems and build a sound financing system for the future. Agricultural Credit during 2023–24, the disbursement of agricultural credit during 2023–24 was ₹25.10 lakh crore against the target of ₹20.00 lakh crore, indicating an achievement of 125%. Commercial banks, RRBs, and cooperative banks accounted for 75%, 13%, and 12% of the total disbursement, respectively.

Abstract: This paper investigates the experience economy as a strategic pathway for inclusive and sustainable post‑oil development in Delta State, Nigeria. Despite decades of oil‑derived revenues, the state faces persistent challenges, including rising youth unemployment, environmental degradation, and overdependence on extractive industries. Adopting an ex post facto design, the study integrates qualitative fieldwork, via stakeholder observation and informal dialogues at community events, with secondary analysis of demographic and fiscal data from 2015 to 2023. The study population comprised Delta State’s youth (estimated at 3.35 million in 2023), from which a purposive sample of 120 participants aged 18–35 was engaged across four local government areas. Findings show that youth unemployment rose from 32.5% to 38.5%, underscoring systemic barriers to labour market inclusion. However, six diversification pathways emerged with significant potential: cultural and heritage tourism, eco‑tourism, wellness and lifestyle ventures, creative industries, youth entrepreneurship, and sustainable agribusiness. The study recommends targeted investments in youth‑led enterprise, infrastructure development, and cross‑sectoral partnerships to harness local capacities and reposition the state’s economy toward resilience and regeneration. These findings contribute to an evidence‑based, context‑sensitive framework for sustainable development in post‑oil contexts.

Abstract: The aim of this paper is to prove that the Diophantine exponent  is bounded by , where  is an algebraic number, and  is the Mahler measure of . In addition, a new result will be founded about transcendental number  when  is infinite.

Abstract: Sway displacement is the characteristic virtual and lateral displacement of frame structures, as a result of their response to load applications and internal actions within the structural system, and the attainment of dynamic and static equilibrium expected for the system. ( eg, ΣF = 0, and ΣM = 0 ). Similarly, non-sway frames are structures considered to have small or negligible inter-storey displacements in structural mechanics analysis, which allows for the computation of the effect of applied loads, such as, deformation, deflection, displacement and internal forces within the structure, that can also ensure the stable equilibrium condition in real life structural loadings. The paper is theoretical evaluation of cause of sway and lateral movement in structures, since such movement can affect the stability of the system. The quantum state will provide a probable assessment for the outcome of measurement on the structural system performance, such as the tolerable critical displacement and deformation limits. Quantum realm and scale, is the condition where the action or angular momentum is quantized, since the expected lateral movement and sway in structural frames are negligible and relatively very small in scalar magnitude.  Displacement is a vector, whose length is the shortest distance from the initial to a final position of point P, and the displacement vector is usually expressed as the difference between the final and initial position vectors (Vijk , Uijk ). Constraint to lateral movement can be provided using the shear wall, within the structural system to act as restraint and to reduce the sway movements to minimal, negligible and acceptable magnitude. In conclusion, action of forces on structures, must not cause significant motion (ie static equilibrium) nor excessive deformation, therefore critical evaluation of all loading conditions that could affect the performance must be ensured adequate, such as to minimized and curtailed lateral movement as a result of excessive deformation and reduction in structural ability and strength over time period.

Abstract: In this article, an attempt is made to put together developments in digital relays for protection of power transformer after 2000 year. Efforts have been made to include all the techniques and philosophies used to that end. The article includes the most recent techniques, like transformer saturation detection, harmonic usage, waveform processing and analysis techniques, various conversion methods, adding a series of electrical quantities to the differential current, and artificial neural network (ANN) and fuzzy logic concepts, as well as other conventional methods used for transformer protection. This article presents a set of references of all concerned papers and provides a brief summary of the work presented in them.

Abstract: Butterflies are one of the widely diverse and ecologically significant groups of insects, playing vital roles in pollination, biodiversity monitoring, and ecosystem balance. With the rapid advancement of artificial intelligence, deep learning has emerged as a powerful tool for solving complex image classification problems.

Abstract: This study investigates the impact of digital transformation, cybersecurity, physical capital, and unemployment on economic growth in selected African economies (Algeria, Tunisia, Morocco, Libya, Egypt, South Africa, Gabon, Botswana, and Mauritius) using panel data analysis. Covariance and correlation matrices reveal a negative relationship between economic growth (LGDP) and the variables of physical capital (LK), unemployment (LUN), digital transformation (LDT), and cybersecurity (LNCSI). These results suggest a structural shift where resources are reallocated from traditional physical capital toward human capital and digital sectors, indicating a broader transition toward technology-driven economies. Investment in cybersecurity is positively correlated with digital transformation, highlighting the necessity of cybersecurity spending alongside digital progress. The observed countercyclical relationship between economic growth and unemployment aligns with Okun’s Law, though structural unemployment persists due to skills mismatches. The findings underscore the need for policies that support sustainable digital and cybersecurity investments while addressing short-term growth trade-offs. Recommendations are provided to guide African policymakers in fostering inclusive and resilient digital economies.

Abstract— This paper introduces an image-based crowd counting method within the Audiovisual Crowd framework, designed to detect and warn against mass crowd stampedes or trampling caused by dangerous overcrowding. The approach utilizes deep convolution neural networks (CNNs) to analyze both visual and audio data, extracting meaningful features to identify anomalies in crowd behavior. The model is trained using CNN architectures and evaluated with Mean Absolute Error (MAE) and Mean Squared Error (MSE) metrics to ensure precise crowd estimation. Experimental results show that while the video-only approach effectively captures spatial information, it is more susceptible to challenges such as low-light and noisy environments. In contrast, the combined audiovisual model achieves enhanced robustness and accuracy, reaching an overall accuracy of 94% in detecting critical crowd anomalies.

Abstract: This study constructs a comprehensive Financial Inclusion Index (IFI) for Nigeria, utilising Principal Component Analysis (PCA) to capture the multi-dimensional nature of financial inclusion. To ascertain the efficient level of financial inclusion in Nigeria, this study employs Principal Component Analysis (PCA). The dimensions of financial access, financial usage, and financial quality are analysed using supply- and demand-side indicators. Post-estimation tests, including the Kaiser-Meyer-Olkin (KMO) test, are conducted to validate the adequacy of the data for PCA. The findings reveal a modest yet growing level of financial inclusion, with demand for financial services marginally surpassing supply. This study recommends broader measurement of the dimensions of the financial inclusion index; financial services should extend to rural areas with a well-functioning mobile banking to enhance financial inclusion that supports sustainable economic growth.

Abstract: Buffer overflow vulnerabilities have plagued software systems for decades and continue to pose a significant security risk. My paper provides a comprehensive analysis of buffer overflows, detailing their mechanisms, historical context, and the challenges they present to modern systems. It explores traditional and contemporary defense strategies, including compile-time and run-time defenses, and examines the potential role of emerging technologies like Artificial Intelligence (AI) in mitigating these vulnerabilities. The paper emphasizes the ongoing need for robust security practices and continuous research to address this enduring threat.

Abstract— Toxic gases and pollutants in gas plants pose serious risks to workers, equipment, and the environment. This research introduces the design and development of an Air Quality Monitoring System (AQMS) aimed at supporting preventive maintenance in such facilities. The system combines low-cost sensors, wireless communication, and data analytics to detect and measure pollutants in real time. While current monitoring systems typically offer limited coverage and track only a few key pollutants, our approach addresses these shortcomings by enabling broader, more detailed monitoring across the plant. We tested the AQMS in an operational gas plant, where it proved effective in detecting potential hazards early and supporting proactive maintenance. The findings show that the system not only reduces the risk of accidents, equipment failure, and environmental harm, but also helps streamline maintenance schedules and lower operational costs.

Abstract: Alkyd resin or polyester are polymeric compounds derived by poly-condensation reaction between triglycerides, polyols and phthalic anhydride. Their wide applications in surface-coatings and adhesives had motivated investigations into modification of  some renewable, ecofriendly and low cost seed oils as alternatives to the synthetic counterpart.The oil was manually extracted from the seed-mesocarp of palm fruits by cooking, crushing, washing, reheating, decanting and evaporating, to remove excess water molecules from the isolated oil. The percentage yield of the extracted oil was 7.51 7.51 ± 0.001, saponification value (203.57mgKOH/g; iodine value (53.01); specific gravity (0.88 g/ml); etc, were characterized by standard methods. The synthesis of the alkyd resin was carried out under a controlled temperature in a three neck-flask. Physico-chemical analysis of the palm oil modified resin compared well to the commercial grade, in relation to the viscosity, solubility, adhesion, dryness and Fourier Transform Infra-Red tests. Gardner Scale was used to examine the colour, while the refractive index of the oil was determined with Abbe’s refractometer. The impact strength and adhesion resistance of the dry alkyd resin films were determined using tubular impact tester ( ASTM D1709/ISO 7765) and Cross hatch tester (ASTM 3359) respectively. Alkyd resin from renewable source, such as palm oil is an innovative approach to mitigating against pollution from thermosetting plastic wastes, and ensure availability of raw materials for

Abstract: The role of tax education and service quality in recent year has gained renewed attention in tax policy and compliance discussions, especially within developing economies where revenue mobilization challenges persist. This study shifts from conventional approaches by exploring how quality of tax service can affect the relationship between tax education and tax compliance behaviour among Small and Medium-Sized Enterprises (SMEs) in Ghana. A cross-sectional and correlational research design was employed, using Partial Least Squares Structural Equation Modeling (PLS-SEM) to analyse data from 381 SME operators. The findings revealed a strong positive relationship between tax education and tax compliance behaviour, indicating that increased awareness of tax responsibilities enhances voluntary compliance. Additionally, tax service quality independently showed a significant positive effect on compliance behaviour. Most notably, tax service quality was found to significantly moderate the relationship between tax education and tax compliance, suggesting that the impact of education is strengthened in environments where taxpayers experience professional, responsive, and accessible service delivery. The study puts forward a recommendation for tax authorities to enhance both the clarity of educational materials and the quality of taxpayer engagement. Furthermore, integrating tax education into vocational and entrepreneurial training could foster greater understanding and willingness to comply, especially among SME taxpayers who often operate in the informal sector.

Abstract— The Self-Checkout System (SCS) is a key element of retail automation, designed to improve operational efficiency and enhance customer convenience. This systematic literature review synthesizes insights from 16 peer-reviewed Q1/Q2 studies published between 2020 and 2025, leading to the identification of four critical factors influencing efficiency: technological design, user behavior, organizational preparedness, and workforce impact. The findings suggest that a combination of advanced perception technologies (e.g., AI vision, depth cameras), user-centered interface design, process reengineering, and comprehensive staff training, including cross-training for hybrid support roles, plays a pivotal role in shaping outcomes. Recent research also highlights emerging adoption drivers such as reduced stigma around sensitive purchases, increased privacy awareness, and shifting dynamics of customer empowerment. Efficiency is not an inherent attribute of the system alone, but rather the result of interactions among technology, users, and institutional contexts. Retailers and policymakers are encouraged to pursue integrated design approaches that holistically align technological innovation, store operations, and human labor.

Abstract: Cultivating creativity is essential in early childhood education. Folk picture books, which integrate cultural heritage with artistic expression, show significant potential in stimulating young children’s creativity, though their specific mechanisms remain underexplored. This qualitative case study, conducted in a kindergarten in Guiyang, China, aimed to: (1) examine teachers’ perceptions and experiences regarding Chinese folk picture books; (2) analyze how these books stimulate creative behaviours in children aged 5–6; and (3) propose evidence-based principles for their design and application. Through in-depth interviews with 12 teachers and 2 experts, non-participant observations of experimental and control groups over two months, and systematic analysis of 20 folk picture books using a Piagetian framework, the study revealed that these books function as “cultural-cognitive scaffolds.” Results indicated that the experimental group demonstrated enhanced narrative complexity, imaginative play, and artistic expression incorporating cultural motifs. Key facilitating features included animistic characters, open-ended narratives, and distinctive artistic styles. The findings highlight the value of folk picture books in fostering creativity and suggest that educators should integrate them intentionally using interactive strategies, while designers should blend authentic cultural elements with developmentally appropriate narrative and visual techniques.

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Abstract: Moringa oleifera, commonly known as the drumstick tree or miracle tree, has received significant global attention owing to its rich nutritional profile, wide spectrum of phytochemicals, and diverse pharmacological activities. Traditionally valued in Ayurveda and African folk medicine, moringa is now recognized in modern pharmacology for its antioxidant, anti-inflammatory, antimicrobial, antidiabetic, and anticancer properties. This review synthesizes available literature on its phytochemistry, bioavailability challenges, nutritional benefits, therapeutic applications, socio-cultural barriers, safety aspects, and potential policy-level integration into public health programs. Case studies highlight its impact on combating malnutrition in developing regions. By bridging traditional knowledge with modern science, moringa emerges as a sustainable, multipurpose crop with immense relevance in global health and nutrition security.

Abstract: In a strict sense, privatization denotes the introduction of private ownership in publicly traded companies, but in a broader sense, it also denotes the introduction of private management and control in public sector companies. Involving the private sector in the ownership or management of a state-owned firm is another name for it. Thus, the phrase describes a private acquisition of all or a portion of a business. This paper focuses on critical analysis on the Indian Banking sectors and find an answer whether privatization is a major boost or a downfall. The data has been secondarily collected by the researcher in an attempt to conceptually through a light in this regard

Abstract: Kawasaki diseaseKawasaki disease (KD), often called Kawasaki syndrome, is a condition that primarily affects children under the age of five and can harm the heart and blood vessels. Boys are more likely than girls to be affected. Kawasaki disease is thought to be caused by an aberrant immunological response, probably in reaction to an infection, though the precise etiology is still unknown. There is no known technique to prevent it, and it is not communicative. Treatment with aspirin and intravenous immunoglobulin (IVIG) at an early stage is essential to lowering the likelihood of major complications, particularly heart-related ones such as coronary artery aneurysms. In India, Kawasaki disease has gained more recognition during the past 20 years and could soon overtake acute rheumatic fever as the leading cause of acquired heart disease in children. In India, the great majority of children who suffer from Kawasaki disease are still undiagnosed. A constellation of clinical symptoms with a predictable temporal sequence is used to diagnose Kawasaki disease. The subtleties involved in diagnosing Kawasaki disease must be understood by all pediatricians. The risk of coronary artery anomalies might be considerably decreased with early identification and timely treatment. KD, often called Kawasaki syndrome, is a condition that primarily affects children under the age of five and can harm the heart and blood vessels. Boys are more likely than girls to be affected. Kawasaki disease is thought to be caused by an aberrant immunological response, probably in reaction to an infection, though the precise etiology is still unknown. There is no known technique to prevent it, and it is not communicative. Treatment with aspirin and intravenous immunoglobulin (IVIG) at an early stage is essential to lowering the likelihood of major complications, particularly heart-related ones such as coronary artery aneurysms. In India, Kawasaki illness has gained more recognition during the past 20 years and could soon overtake acute rheumatic fever as the leading cause of acquired heart disease in children. In India, the great majority of children who suffer from Kawasaki disease are still undiagnosed. A constellation of clinical symptoms with a predictable temporal sequence is used to diagnose Kawasaki disease. The subtleties involved in diagnosing Kawasaki disease must be understood by all pediatricians. The risk of coronary artery anomalies might be considerably decreased with early identification and timely treatment.

Abstract: This study conducted a comprehensive biodiversity inventory of Buluan Lake, Mindanao, focusing on aquatic plants, fish, and bird species during October 2024. Field observations over 30 days documented 11 aquatic and semi-aquatic plant species, 7 fish species, and 5 bird species. Tilapia (Oreochromis niloticus) emerged as the most abundant fish, while native species such as Taruk (Libero rohita), Gourami (Trichopodus pectoralis), and Striped snakehead (Channa striata) were also present. Invasive and predatory species, including Midas cichlid and Blackbelt cichlid (Amphilophus citrinellus and Vieja melanurus) and Bighead Carp (Hypophthalmichthys nobilis), were noted, suggesting potential shifts in ecosystem dynamics. Aquatic plants such as water hyacinth (Eichhornia crassipes) and lotus (Nelumbo nucifera) provided essential habitat and contributed to water quality, while bird species such as the Great Egret (Ardea alba) and Black Bittern (Ixobrychus flavicollis) indicated overall ecosystem health. This inventory highlights the ecological richness of Buluan Lake and underscores the importance of integrating such data into conservation planning and environmental education initiatives.

Abstract—The rapid growth of online media platforms has led to the widespread spread of misinformation, resulting in an important issue which is to correctly categorize fake news to inform citizens effectively, to be a significant issue in the fields of natural language processing (NLP), and social media. Within NLP, deep learning models have become a standard and effective methodology. These models can learn rich linguistic and contextual representations with large datasets. Here contributes a comparative analysis of several Deep Learning model architectures for the identification of fake news: Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) and transformer-based models like BERT. Also compare all models based on fake news detection datasets and measures, and present their outcomes in terms of accuracy, precision, recall, F1-score, and overall computational efficiency. The analysis revealed the transformer-based models offered the best performance in academic literature due to their contextual awareness in classification, while the RNN and CNN models proffered the best computational efficiency and training times. These findings to highlight the respective advantages and disadvantages that shed light on useful design approaches for the development of effective and operationally efficient fake news detection systems for academic and practitioners alike.

Abstract: One of the pivotal moments in India's tax history would be the 56th meeting of the GST Council, which took place on September 3, 2025. Tax rates and structures are just one aspect of these improvements. They signify a dramatic change toward a more straightforward, equitable, and growth-oriented system that is in line with the goals of a Vikasit Bharat 2047. The gradual implementation of measures starting on September 22, 2025, is equally notable. The industry and customers may immediately take advantage of lower rates thanks to this sequencing, which also guarantees revenue stability. The strategy encourages demand and investment while preserving budgetary health. These announcements cover more ground than merely technical fixes. It is a reform of the people. Farmers, laborers, businesses, entrepreneurs, and people are all impacted. India's growth journey now has a stronger basis thanks to GST 2.0, which has simplified the structure, reduced essentials rates, corrected distortions, and strengthened institutions.

Abstract: The growing use of algorithmic dynamic pricing (ADP) in digital subscription services is indicative of a major change in the way companies interact with consumers and manage their revenues. By using real-time data analysis and automated decision-making techniques, companies are now able to adjust subscription prices to their clients based on their behavior, market conditions and demand. The paper describes the broad impacts of ADP on two primary market performance dimensions, namely consumer surplus and firm profitability. The results of the study show the complex and sometimes contradictory nature of the relationship between firm gains and consumer welfare. To begin with, dynamic pricing allows companies to increase their revenues to the maximum by customizing offers according to the preference of the target group, better managing churn through predictable revenue and extracting higher value from the most intensive users. However, in many cases consumer surplus decreases when the pricing is not transparent or the prices differ to a great extent for closely similar users. In this regard, personalized pricing strategies are especially problematic because they create new issues such as fairness, discrimination and the loss of trust. In addition, the paper discusses how the characteristics of the subscription as, for instance, the implementation of recurring billing, usage-based tiers, and retention dynamics which interact with algorithmic pricing strategies. The empirical findings from related sectors are also taken into consideration in order to shed light on wider behavioral and market consequences. The researchers state that algorithmic dynamic pricing can be very helpful in boosting short-term profitability, yet, the long-term effects on consumer trust, market fairness, and regulatory pressure must always be kept in mind. To be more precise, the implementation of ADP is a strategic move that has to cleverly combine data-driven personalization with the support of ethical practices that will ensure by all means the user's loyalty. The policy recommendations are presented in the form of calls for improved transparency, pricing frequency regulation, personalization limits and consumer data protections.

Abstract: The COVID-19 pandemic forced higher education institutions globally to rapidly shift from face-to-face instruction to remote and online learning. This paper examines the role of Information and Communication Technology (ICT), specifically online learning platforms, in higher education during and after the pandemic. It synthesizes empirical studies, surveys, and institutional reports from 2020 to 2024 to assess (1) how effectively online platforms met learning outcomes; (2) students’ and instructors’ perceptions and attitudes; (3) challenges and barriers in implementation; (4) innovations and strategies that improved effectiveness; and (5) recommendations for sustaining quality online education. The findings show that while ICT‐ mediated platforms enabled continuity of education, there were significant inequalities in access, varying quality in platform design and pedagogical adaptation, and instructor readiness gaps. Effective online platforms tended to feature interactive tools, good user support, frequent feedback, and alignment with instructional design principles. Hybrid learning models combining online and face-to-face instruction emerge as promising for higher education post-COVID-19. Finally, the paper discusses policy and institutional implications: investing in infrastructure, training faculty, ensuring access for students, and thoughtful pedagogical redesign. Future research directions include longitudinal studies on student outcomes, comparative analysis across countries, and the impact of emerging technologies like AI and analytics in online learning.

Abstract: This study investigates the association between personal loan applications and approvals and macroeconomic variables, including bank interest rates, the consumer price index (CPI), and employment and unemployment figures, both prior to and during the COVID-19 pandemic. Data spanning January 2018 to June 2022, were utilized for the analysis. Two analytical techniques were employed: the correlation matrix and the multiple linear regression model. The correlation analysis revealed strong relationships between the CPI and employment figures, as well as between interest rates and unemployment, in their association with both loan applications and approvals. Results from the regression model indicated that bank interest rates and employment status significantly influenced personal loan applications, regardless of the pandemic context. With respect to loan approvals, the findings demonstrated that bank interest rates, unemployment, and the CPI exerted significant effects, particularly in regions severely affected by COVID-19. These results highlight the critical role of macroeconomic conditions in shaping lending behavior and provide insights into the interaction between financial markets and economic shocks.

Abstract: This study assessed the financial management practices of Micro, Small, and Medium Enterprises (MSMEs) in the retail sector of Nueva Ecija, focusing on budgeting, working capital, record-keeping, financing decisions, and cash flow management. Using a descriptive-quantitative design, 150 MSME owners and managers were surveyed through a validated questionnaire. Results showed that most MSMEs are microenterprises under sole proprietorships with fewer than five employees. Findings revealed strengths in budgeting adjustments, managing supplier relationships, and aligning financial decisions with business goals. However, gaps were noted in budget reviews, inventory control, compliance with record-keeping standards, and cash flow forecasting.         The main challenges encountered included regulatory compliance, absence of modern accounting systems, financing constraints, and limited manpower. Correlation analysis indicated significant negative relationships between enterprise size, workforce, and effectiveness of working capital, record-keeping, and cash flow practices. The study concludes that while MSMEs value financial management, limitations in resources and knowledge hinder full implementation. Recommendations include financial literacy programs, simplified tax processes, digital adoption, and improved access to financing to enhance MSME sustainability.

Abstract: Understanding the reading preferences of academic library users is essential for enhancing fiction services and encouraging greater leisure reading engagement. This study analyzes fiction circulation data over a span of seven academic years, drawing from the prior research conducted by Berenio and Calilung on the utilization of fiction books. Through a genre-based examination of borrowing trends, the study identifies high-demand categories such as romance, fantasy, and young adult fiction. These insights inform the development of a data-driven strategy for enriching the library’s fiction collection and readers’ advisory services. Findings demonstrate that aligning collection development with actual user behavior supports a more user-centered library experience, fosters student engagement, and reinforces the academic library’s role in holistic student development. The study concludes with actionable recommendations for collection management and service improvement based on empirical borrowing patterns.

Abstract: The COVID-19 pandemic highlighted the critical importance of respiratory health and immunity, as weakened immune responses and rising pollution left young adults increasingly vulnerable to respiratory disorders, including allergies. Yogic purification techniques (Shatkriyas), such as Sutra Neti, Jal Neti, Kunjal Kriya, and Kapalbhati Pranayama, described in classical yogic texts, are traditionally used to cleanse the respiratory tract and enhance immune resilience. Inspired by the need for preventive, low-cost strategies emphasized during COVID-19, the present study evaluated the impact of selected Shatkriyas on respiratory allergy indicators in college students. A total of 60 students were assessed using the validated Respiratory Allergy Prediction (RAP) test before and after a structured three-month Shatkriya intervention program conducted under expert supervision. Pre- and post-intervention scores were compared using paired-sample t-tests and between-group analyses. Results revealed a statistically significant reduction in RAP scores in both groups: Meerut College (15.33 ± 2.62 → 9.13 ± 2.37; t = 11.64, p < 0.001, dz = −2.13) and D.N. College (16.40 ± 2.25 → 10.30 ± 2.47; t = 8.51, p < 0.001, dz = −1.55). The overall mean reduction was −6.15 points (95% CI: −7.04, −5.26; p < 0.001), with no significant difference between the two institutions (Welch’s t = −0.11, p = 0.91). These findings demonstrate that regular Shatkriya practice produces large and clinically meaningful improvements in respiratory health and immunity among college students. The study underscores the relevance of integrating yoga-based respiratory cleansing techniques into higher education as a preventive, sustainable, and non-pharmacological approach for reducing allergy burden and strengthening resilience against future respiratory challenges.

Abstract- Next Word Prediction (NWP) is essential in applications like predictive text and virtual assistants. This study explores using Long Short-Term Memory (LSTM) network for NWP, addressing the limitations of traditional models such as n-grams in capturing long-term dependencies. The model is trained on preprocessed datasets, incorporating tokenization, sequence generation, and embedding layers to represent textual data. LSTM layers are utilized to understand sequential context, followed by dense layers for prediction. Performance is evaluated through metrics like perplexity and accuracy, with the LSTM model demonstrating superior contextual understanding and predictive accuracy compared to traditional methods. The research also suggests future improvements, including hyperparameter tuning and exploring transformer architectures, to enhance NWP performance across various applications.

Abstract: Predictive Sales Analytics for Enhancing Customer Relations Using Regression Analysis focuses on utilizing data-driven techniques to inform and improve business decision-making. By applying regression analysis, the system can identify significant relationships between customer behaviors and sales performance. This allows organizations to forecast future sales outcomes and adjust strategies based on reliable predictions. The integration of predictive analytics into customer relationship management (CRM) strengthens customer retention, loyalty, and satisfaction. It also helps managers allocate resources more effectively while personalizing services to meet customer needs. The use of predictive models transforms raw customer and sales data into actionable insights. Overall, this approach combines statistical accuracy with strategic value, enabling businesses to build stronger and more sustainable customer relationships.  The goal of this project is to design and evaluate a predictive sales analytics system that leverages regression analysis to enhance customer relations. Specifically, the project aims to forecast sales outcomes, identify customer trends, and provide actionable insights for decision-makers. By integrating predictive analytics into CRM practices, the project seeks to improve customer retention, satisfaction, and loyalty. Furthermore, the system is expected to help businesses allocate resources more effectively and achieve sustainable growth. A total of 150 respondents participated in the evaluation of the system, consisting of 10 representatives from business organizations and 140 respondents from the academic community. The diverse respondent composition allowed the system to be assessed from both practical and academic perspectives. The development process was guided by the Agile methodology, ensuring iterative improvements and adaptability to user requirements. The evaluation of CRMBoost was conducted using ISO quality standards, specifically focusing on functionality, interaction capability, safety, security, and flexibility. Results showed that the system was functional in processing large amounts of customer data and flexible enough to accommodate different organizational needs. Respondents confirmed that interaction capability was intuitive and user-friendly, while safety and security measures were sufficient to protect customer information. The system outputs, which included sales forecasts, customer trend reports, and predictive insights, were validated as reliable tools for both decision-making and academic analysis. Findings revealed that regression analysis was effective in establishing relationships between independent variables such as demographics, purchasing history, and seasonal demand, and dependent variables such as sales outcomes. Business respondents highlighted the system’s practical usefulness in improving marketing strategies and customer engagement, while the academic community emphasized its contribution to advancing knowledge in predictive analytics. Overall, the system demonstrated its potential as both a technical solution and a strategic resource for organizations. In conclusion, CRMBoost bridges the gap between theory and practice by combining predictive analytics with CRM strategies. It contributes significantly to enhancing customer relationships while promoting sustainable business growth. The study recommends its adoption by organizations and further development by researchers, with continuous alignment to ISO standards to ensure long-term effectiveness and reliability.

Abstract: Tesla's pathways to autonomous mobility are investigated in this foreseeable futures report through structured scenario planning. Three internally consistent, decision-oriented scenarios to 2050 are developed by synthesizing PESTLE trends across policy, markets, society, technology, environment, and law: 1. Autonomous Mobility Future—Tesla operates Level-5 robotaxi fleets that are integrated with urban infrastructure. 2. Decarbonized Energy Leadership—Tesla's grid-scale storage, virtual power plants, and solar systems are the foundation of net-zero grids. 3. Global Infrastructure Expansion and Localization resilient, regionalized manufacturing and supply network. The strategic north star of the report is Scenario 1, which is indicative of Tesla's transition from vehicle sales to mobility-as-a-service and its AI-and-data advantages. The implications are centered on the following: regulatory engagement and compliance, safety transparency and public trust, ecosystem-scale charging and "smart road" investments, differentiated global market entry, and the development of capabilities in software, cybersecurity, pricing, and fleet operations. Adaptive portfolios and change-ready operating models are enabled for resilient long-term growth by the proposed use of signposts to monitor policy, technology, and demand inflection points.

Abstract: Potential evapotranspiration (PET) and evapotranspiration (ET) are vital components of the hydrological cycle, particularly in tropical watersheds where water management relies on accurate predictions of water availability. This study employed the Priestley-Taylor method within the Soil and Water Assessment Tool (SWAT) to model long-term PET and hydrological responses in the Oyun Watershed, Kwara State, Nigeria, over a 30-year period (1991–2021). The model was parameterized using a 90-meter Digital Elevation Model (DEM), land use and soil maps, and daily climate data from the Nigerian Meteorological Agency (NiMet). The Priestley-Taylor method was selected for its suitability in data-scarce and humid regions. Model simulations revealed a total PET of 11,807.51 mm over the simulation period, with spatial variation across sub-basins ranging from 596.51 mm to 721.96 mm. Groundwater recharge was estimated at 8,050.46 mm, while surface runoff was 3,469.06 mm. Land cover, slope, and soil permeability significantly influenced the spatial variability of these outputs. Calibration and validation results demonstrated satisfactory model performance, with Nash-Sutcliffe Efficiency (NSE) and the coefficient of determination (R²) exceeding 0.7. These findings confirm the effectiveness of the SWAT model for predicting hydrological responses in data-scarce, humid regions, providing critical insights for water management in the Oyun Watershed.

Abstract: This study investigates the effect of transformational leadership style on project performance in Nigeria’s power sector, with a particular focus on the Niger Delta Power Holding Company (NDPHC). Using a quantitative survey design, a sample of 95 respondents was selected from project managers, engineers, and administrative staff. Data were collected through structured questionnaires and analyzed using descriptive statistics, correlation, and regression analysis. Findings reveal that transformational leadership—characterized by charisma, inspiration, individualized consideration, and intellectual stimulation—has a significant positive effect on project performance, particularly in terms of innovation, teamwork, and timely delivery. By contrast, autocratic leadership styles negatively impact performance, while democratic styles foster collaboration and efficiency. The study concludes that transformational leadership enhances project success in Nigeria’s power sector. It recommends that project leaders in technical and energy-related sectors adopt transformational practices to strengthen innovation, job satisfaction, and long-term project performance. Limitations and directions for future research are also highlighted.

Abstract: Foreign direct investment (FDI) plays an essential role in the growth of developing and developed nations, making it important to explore the determinants that attract investors to a host country. While several studies have examined FDI determinants, a comprehensive and systematic review is still limited. This paper conducts a systematic literature review (SLR) of 60 articles published between 2001 and 2024 in the Scopus database to identify the key factors of FDI attractiveness. Following the PRISMA protocol, the review categorizes the findings into five major dimensions: (1) economic factors, (2) human capital and labor market conditions, (3) government policies and regulations, (4) infrastructure and logistics, and (5) political stability and institutional quality. The synthesis reveals that while traditional drivers such as market size and trade openness remain significant, institutional quality and governance are gaining prominence, particularly in emerging economies. The findings provide insights for managers and policymakers. Governments can enhance FDI inflows by improving regulatory frameworks, infrastructure, and institutional quality, while investors may use these determinants to identify favorable host-country environments.

Abstract—This paper introduces fuzzy lucky labelling and proper fuzzy lucky labeling of graphs. A fuzzy lucky labeling assigns fuzzy values from (0, 1] to vertices such that induced sums at adjacent vertices differ, while the proper version additionally requires distinct labels for neighbours. We establish key theorems, including existence results and relationships with chromatic number, and compute fuzzy lucky numbers for special graphs such as stars, bull graphs, and fork graphs.

Abstract: Rural technology adoption in emerging markets is still a very complicated problem with infrastructure, socio, economic, cultural and institutional barriers. In the case of rural financial service providers like CIFCL (Cholamandalam Investment and Finance Company Limited), the successful rollout of technology, based solutions depends on a detailed understanding of the critical success factors (CSFs) that are specific to the rural branch ecosystem. This study combines various literature works done in the areas of rural technology adoption, CSF identification and decision, making frameworks with special focus on the adoption of the Fuzzy, DEMATEL (Decision Making Trial and Evaluation Laboratory) method. The literature survey has come up with a list of the most frequently mentioned CSFs in rural technology adoption, like infrastructure readiness, leadership support, digital literacy, cost affordability, and regulatory environment, and has proposed methods for analyzing their interaction based on fuzzy causal models. The suggested use of Fuzzy, DEMATEL in the CIFCL rural branch scenario allows for the sorting of CSFs into cause and effect groups giving management the opportunity to concentrate on the root causes that trigger wider systemic reforms. Among the main findings, leadership commitment, staff training and infrastructural investment emerge as the major sources of downstream success, e.g. user adoption and client trust. Further, this review calls for more empirical research specific to CIFCL and other similar rural institutions, underlining the significance of dynamic, data, driven models in supporting inclusive, scalable, and sustainable rural digitization strategies.

Abstract: An attempt has been made to interpret ground magnetic and elevation data at Kwara State Polytechnic, Northcentral Nigeria. This study also investigates the contrasting outcomes of two groundwater boreholes situated at different elevations, part of which one is successful (good) and the other failed. The good borehole is strategically positioned at the edge of a high elevation associated with steeper slope benefiting from increasing aquifer pressure and effective water flow as highlighted by high horizontal and total gradient amplitudes. In addition, local wavelength depth estimates reveal relatively low per meter wavenumber depth, suggesting permeable subsurface with smoother variations conductive to groundwater flow. The corresponding failed borehole despite its relatively high elevation could be attributed to flatter slope, encountered challenges resulting in low horizontal and total gradient amplitudes and high local wavelength depth estimates. This study has highlighted the significance of considering elevation, gradient amplitudes, and local wavenumber depth in understanding hydrogeological conditions influencing groundwater borehole success or failure. Therefore, this study will serve as a paradigm in predicting newly target locations for abundant and sustainable groundwater supply within the study area.

Abstract—The purpose of the study was to determine the effectiveness of the teacher-developed storytelling intervention materials on the reading comprehension level of the intermediate pupils of South Cembo Elementary School for the school year 2023-2024. The study profiled the pupil-respondents in terms of sex, socioeconomic status (SES), and pupils’-parents educational attainment, and PHIL-IRI Group Screening Pre-test and post-test Results based on the comprehension levels: 1. Frustration, 2. Instructional, and 3. Independent. There were three major phases undertaken by the researcher in the development of the intervention materials: 1. Revisiting of the reading strategies and models, 2. Crafting of the teacher-developed storytelling intervention materials, and 3. Assessment of the teacher-developed storytelling intervention materials by eight (8) identified experts and one (1) user to determine the effectiveness using the following criteria: (1) User-friendliness, (2) Accessibility, (3) Flexibility, (4) Suitability, and (5) Creativity. It further aimed to find out the difference between the PHIL-IRI Group Screening pre-test and post-test results of the pupil-respondents when grouped according to sex, socioeconomic status, and parents’ educational attainment. The study employed a pre-experimental research design. It involved one group of 73 intermediate pupils purposely selected. The statistical tools used were frequency and percentage, mean and standard deviation, and paired t-test. The result of the study showed that the majority of the pupil-respondents are male, belong to the poor class, which typifies the pupils in the public elementary school in the Philippines, and whose parents have reached high school and college. The majority of the pupils were under the frustration level of comprehension based on the PHIL-IRI group screening pre-test. Eight experts assessed the teacher-developed storytelling intervention materials as effective (x̄=3.99, SD=0.519) using five major criteria, meaning “highly effective”.

Dependence on fossil fuels and wood in rural Kenya, which contributes to deforestation and increased greenhouse gas emissions, must be addressed immediately. Waste biomass is being investigated as a viable alternative, providing a potential fix for the urgent wood reliance issue and climate change. However, common biomass stoves still have shortcomings in that they discharge more flue gases into the environment due to incomplete combustion, and use charcoal as fuel. This study proposes the development of a unique batch biomass stove in the pursuit of cleaner, more effective cooking methods. Various biomass materials are investigated under different stove configurations that involve variation of primary and secondary air holes. High thermal efficiency of 67.1% for bagasse pellets and 68% for coconut husks was achieved during a standard water boiling test. A noteworthy byproduct of the stove is valuable biochar, which may be used to improve soil fertility, effluent color and odor removal, and promote sustainability. These results not only demonstrate the potential of waste biomass as a substitute energy source but also signal the beginning of environmentally friendly cooking methods and soil improvement in rural Kenya. In essence, this research represents a crucial step toward balancing human needs with environmental well-being and paves the way for a day when efficiency, environmental consciousness, and soil enrichment naturally combine.

Abstract: This paper investigates the structural properties of prime near-rings and prime rings under the action of skew derivations and generalized skew derivations. We examine how these mappings influence algebraic identities, commutativity conditions, and the behavior of ideals within such algebraic systems. By establishing new characterizations and commutativity criteria, the study extends classical results on prime rings to a broader framework that includes near-rings. Furthermore, we demonstrate how generalized skew derivations unify and extend several well-known derivation concepts, thereby providing a more flexible approach to analyzing algebraic structures. These findings contribute to the deeper understanding of prime near-rings and semiprime rings, offering new tools for further research in ring theory and its applications.

Abstract: This research on Enhanced Age, Gender, and Race Estimation Using multi-task CNN presents a comprehensive evaluation of a multi-task deep convolutional neural network (CNN) model designed to simultaneously estimate age, gender, and race from facial images. The testing utilizes real-world datasets, such as UTKFace, Adience, and MORPH II, along with a synthetic dataset that simulates ideal conditions (100% prediction accuracy) for baseline validation. The evaluation includes Mean Absolute Error (MAE) for age estimation, classification accuracy for gender and race, and one-off age accuracy to account for predictions in neighboring classes. Confusion matrices and distribution analysis provide deeper insights into the model's performance across different demographic groups. Although datasets such as UTKFace, Adience, and MORPH II present challenges due to variations in age, gender, and distributions, the proposed model demonstrates strong and high predictive accuracy. The results show that the proposed model surpasses state-of-the-art approaches, achieving an age estimation MAE of 2.95, gender classification accuracy of 98.3%, race classification accuracy of 93.1%, and one-off age accuracy of 90.7%. The addition of synthetic data proved beneficial in enhancing model robustness by mitigating demographic bias and improving prediction reliability. The findings of this study have practical implications for developing fair and reliable demographic estimation systems, with potential applications in security, human-computer interaction, and healthcare. Future work will focus on integrating attention mechanisms, fairness-aware learning, and domain adaptation techniques to enhance accuracy across diverse populations and uncontrolled environments.

Abstract: Graph theory has evolved from its origins in Euler’s 1736 solution to the Königsberg bridge problem into a foundational discipline with far-reaching applications in computer science, biology, social networks, and artificial intelligence. This literature review systematically examines the field’s historical development, theoretical advancements, algorithmic breakthroughs, and modern applications. Key contributions include Euler’s foundational work on graph traversability, Ramsey’s combinatorial insights, Erdős and Rényi’s random graph theory, and contemporary developments in complex networks (Watts-Strogatz, Barabási-Albert) and spectral methods (Chung). The review also highlights pivotal algorithmic contributions (Tarjan’s DFS, Johnson’s shortest paths) and real-world applications in machine learning (Zhou et al.), network science (Newman), and infrastructure optimization. Emerging trends such as dynamic graphs, graph neural networks (GNNs), and quantum graph algorithms are identified as critical future directions. By synthesizing classical and modern research, this review underscores graph theory’s enduring relevance in modeling and analyzing interconnected systems across disciplines.

Abstract:  Entrepreneurs plays a crucial role in an economy as an engine of economic and social development. The study tries to understand the role of entrepreneurs towards income generation based on primary data collected from two hundred respondents (200 units) using structured questionnaire from three districts of Nagaland- Kohima, Wokha and Mokokchung. The data were analysed using regression and correlation analysis to establish the relationship between units of enterprises and income generation. The study reveals that there is a statistically significant relationship between enterprise and income. Furthermore, it is also found that retail enterprises contribute the highest in terms of income generation. Promoting entrepreneurship in the state is crucial for fostering growth and development, while also helping to address the challenges of unemployment and income inequality.

Abstract: The present study examines the impact of Emotional Intelligence (EI) on job performance among employees of ICICI Bank in Agra, India. Emotional Intelligence, defined as the ability to perceive, understand, and regulate emotions in oneself and others, has increasingly been recognized as a vital factor influencing workplace effectiveness, particularly in service-oriented industries such as banking. The research adopted a descriptive and analytical design, collecting data from 150 employees across various demographic categories, including gender, age, and work experience. Statistical tools, including Chi-square tests and correlation analysis, were applied to determine associations between EI and demographic variables, as well as the relationship between EI and performance outcomes. The findings revealed that gender was significantly associated with EI levels, with female employees exhibiting relatively higher emotional intelligence than their male counterparts. In contrast, age and work experience did not show significant associations with EI. Most importantly, a very strong positive correlation (r ≈ 0.95, p < 0.001) was observed between EI and job performance, indicating that employees with higher EI consistently achieved superior performance levels. The study concludes that Emotional Intelligence is a critical predictor of employee effectiveness in the banking sector, surpassing the influence of traditional demographic factors. Based on these insights, the research recommends the integration of EI training, recruitment assessments, mentoring, and well-being programs into organizational policies to strengthen workforce capabilities. Overall, this study positions EI not as a peripheral soft skill but as a strategic competency essential for sustaining performance, customer satisfaction, and organizational growth in a competitive banking environment.

Abstract:  This study empirically investigates the relationship between Nigeria’s financial system and financial inclusion, focusing on key components such as Bank Credit to Private Sector, Stock Market Turnover Ratio, Interest Rate Spread, Ratio of Liquid Assets to Short-term Liabilities, and Inflation Rate. Employing the Autoregressive Distributed Lag (ARDL) model and Error Correction Mechanism (ECM) on data sourced from the Central Bank of Nigeria Statistical Bulletin and World Development Indicators for the period 1995–2023, the study provides robust econometric evidence. The results reveal that bank credit to the private sector and stock market liquidity significantly promote financial inclusion, while higher interest rate spreads negatively impact it. Interestingly, the inflation rate shows a positive relationship with financial inclusion, reflecting adaptive financial behaviors in response to macroeconomic instability. The diagnostic tests confirm model stability, the absence of heteroskedasticity, and the presence of a valid long-run cointegration relationship among variables. Given these findings, policymakers are recommended to enhance credit accessibility, stimulate stock market activity, reduce interest rate spreads through efficient banking reforms, and leverage financial literacy and technological innovation to improve financial inclusion and also the expansion of complete financial literacy programmes remains essential because it allows citizens to grasp financial products better and handle economic volatility with effectiveness. Future research should consider institutional and demographic dimensions to inform more inclusive financial sector policies.

Abstract: This study examines sediment dynamics at Kiri Dam, Nigeria (1982–2025), through a two-epoch bathymetric reassessment to measure hypsographic change and assess implications for water-supply reliability. In 2025, a high-resolution bathymetric survey was performed in accordance with IHO S-44 standards using Trimble RTK-GNSS and ADCP depth soundings, achieving a crossline mean absolute deviation of ≤0.12 m and a vertical RMSE of ≤0.05 m. Results were compared with the 1982 design dataset to evaluate elevation-banded losses. At NTWL (170.5 m A.M.S.L.), live storage capacity decreased from 615.00 to 344.15 MCM (−44.0%), while water spread contracted from 106.36 to 67.02 km² (−37.0%). Over 60% of storage loss occurred on depositional benches between 161 and 167 m, increasing the elevation–capacity gradient and intensifying volumetric penalties relative to areal shrinkage (elasticity ≈1.19). Multi-season analyses of particle-size distribution confirmed silty clays and clay loams dominate mid-bench and outflow zones, while sandy sediments at the right bank served as coarse-textured controls. Geochemical assays revealed moderate enrichment of Pb (16–27 mg/kg), Cu (12–19 mg/kg), and Zn (63–72 mg/kg) in fine-grained benches, with low hydrocarbon residues (ΣPAHs ≤0.8 mg/kg, ΣPCBs ≤0.04 mg/kg). A preliminary cost–benefit analysis indicated hydraulic sluicing ($0.5–1.5/m³) to be more feasible than mechanical dredging ($3–8/m³), with catchment stabilisation offering benefit–cost ratios of 1:1.3–2.0. Recommended monitoring includes resurveys every 5–10 years, event-triggered campaigns following ≥Q10 floods or >5% shoreline change, and seasonal PSD and geochemistry sampling.

Abstract: This article presents a predictive modeling framework designed to analyze and interpret complex human and environmental systems, utilizing artificial intelligence (AI) to enhance understanding and support data-driven decision-making. The framework integrates advanced machine learning techniques with dynamic data inputs, allowing the system to identify patterns, correlations, and emergent behaviors across socio-environmental datasets. By leveraging historical and real-time data, the models can classify system states, anticipate trends, and provide actionable insights that inform policy, planning, and intervention strategies. The AI-driven approach automates data processing, pattern recognition, and scenario analysis, reducing the reliance on manual interpretation and minimizing human biases in decision-making. Machine learning algorithms play a central role in capturing non-linear relationships and complex interactions, enabling the framework to adapt to evolving system dynamics and respond to new information efficiently. Results from case studies demonstrate the framework’s capability to generate accurate predictions, with improved interpretability and practical relevance for managing human-environment interactions. Performance metrics indicate that the models achieve high predictive accuracy, robustness across varied datasets, and meaningful insights that support strategic interventions. In conclusion, this AI-powered predictive modeling framework highlights the potential of combining artificial intelligence with interdisciplinary data for understanding and managing complex systems. By providing scalable, adaptable, and actionable insights, the approach offers a reliable tool for researchers, policymakers, and practitioners seeking informed decision-making in socio-environmental contexts.

Abstract: This article explores an interactive computing framework designed to transform live musical performance experiences by integrating real-time digital technologies with human creativity. The framework combines adaptive computational systems with dynamic sensory inputs, enabling performers and audiences to co-create immersive environments that respond fluidly to musical gestures, spatial contexts, and audience feedback. Through interactive interfaces and intelligent processing, the system can recognize patterns in sound and movement, adapt visual and auditory outputs, and mediate new forms of expressive dialogue between artists and participants. By leveraging both historical performance data and live feedback streams, the models anticipate variations, refine improvisational possibilities, and generate contextualized outputs that extend artistic expression. The approach automates aspects of signal analysis, gesture recognition, and multimodal synchronization, while preserving the interpretive agency of the performer and minimizing technical constraints during live settings. Computational algorithms underpin the capacity to capture subtle relationships between sound, space, and audience engagement, allowing the framework to evolve with performance dynamics and accommodate unforeseen creative directions. Case studies demonstrate the system’s capacity to enhance artistic depth, broaden participation, and support new modes of aesthetic exploration in diverse musical contexts. Evaluation metrics reveal high adaptability, interpretive richness, and audience resonance, confirming the framework’s potential for advancing both performance practice and digital creativity. In conclusion, this interactive computing framework highlights the promise of fusing technological intelligence with human artistry, offering scalable and innovative tools for musicians, researchers, and cultural practitioners seeking enriched live performance experiences.

Abstract: This article examines the expanding role of artificial intelligence (AI) in augmenting human decision-making across diverse domains, presenting a comprehensive framework that integrates advanced computational techniques with real-time and historical data inputs. The approach leverages machine learning, predictive analytics, and adaptive algorithms to capture complex cognitive, behavioral, and contextual factors influencing human choices. By systematically analyzing patterns, dependencies, and emergent dynamics within decision environments, the framework provides actionable insights that enhance strategic, operational, and policy-level decision-making. AI-driven tools automate data interpretation, scenario exploration, and risk assessment, thereby reducing cognitive overload and mitigating biases inherent in human judgment. The framework’s adaptive architecture enables continuous learning, allowing models to refine predictions, respond to evolving conditions, and support decision-makers in dynamic, uncertain contexts. Empirical evaluations across case studies illustrate the framework’s capacity to improve decision accuracy, resilience, and responsiveness, while offering interpretable outputs that facilitate stakeholder understanding and trust. Performance metrics highlight robustness across heterogeneous datasets, predictive reliability, and practical utility in guiding complex human-centric decisions. In conclusion, this study underscores the transformative potential of AI in enhancing decision-making processes by providing scalable, flexible, and evidence-driven tools that empower individuals and organizations to navigate uncertainty and optimize outcomes.

Abstract Primary hypothyroidism is a common endocrine disorder resulting from insufficient thyroid hormone production, causing fatigue, weight gain, and cold intolerance. In Ayurveda, it is associated with Kapha dosha and Meda dhatu imbalance, leading to sluggish metabolism. Kanchnar Shunti Ghanavati, combining Kanchnar (Bauhinia variegata) and Shunti (Zingiber officinale) in a concentrated tablet form, is traditionally used for Kapha balancing and thyroid support. This open-label pilot study evaluated its preliminary efficacy and safety in 10 patients with primary hypothyroidism over 12 weeks. Patients received 500 mg twice daily. Key outcomes included changes in thyroid-stimulating hormone (TSH), free thyroxine (FT4), symptom scores, and adverse events. Mean TSH decreased from 8.2 mIU/L to 4.5 mIU/L, FT4 increased from 0.7 to 1.1 ng/dL, and symptoms like fatigue improved by 45%. Mean body weight reduced by 2.3 kg. No serious adverse events occurred. These findings suggest potential benefits of Kanchnar Shunti Ghanavati, warranting larger controlled trials.

Abstract Background: Insomnia is one of the most common sleep disorders, affecting nearly one-third of adults worldwide, with significant consequences for health and occupational performance. Teachers represent a vulnerable group due to occupational stress and workload. This study evaluated the efficacy of a standardized herbal poultice pack, prepared with Ksheerbala taila, in improving insomnia severity and sleep quality among primary school teachers. Methods: A pre–post study was conducted among 20 teachers in Mysore with insomnia. Participants applied a herbal poultice pack nightly for 30 days. Validated tools including the Insomnia Severity Index (ISI), Pittsburgh Sleep Quality Index (PSQI), Perceived Stress Scale (PSS), and Regensburg Insomnia Scale (RIS) were used. Data were analyzed using paired t-tests, Wilcoxon signed-rank tests, and effect size measures. Results: Significant improvements were observed in ISI (p < 0.001), PSQI (p < 0.01), PSS (p < 0.001), and RIS (p = 0.001). Improvements were most evident in sleep duration, disturbance reduction, and daytime functioning. No adverse effects were reported. Conclusion: Herbal poultice packs are effective, safe, and culturally relevant for improving sleep quality in teachers. Larger controlled trials are recommended to validate findings.

Abstract:                                                                                                Background: Corns, or clavi, are localized areas of thickened skin that form due to repeated friction or pressure, often on the soles of the feet. They are a common dermatological concern and can significantly impact mobility and quality of life. Prevalence: General Population: Studies indicate that corns and calluses affect approximately 14–48% of individuals, with variations based on age, occupation, and footwear habits(1). Older Adults: Among individuals aged 65 and above, the prevalence of hyperkeratotic lesions, including corns, ranges from 20% to 65%, with higher rates observed in those with specific foot deformities or chronic conditions(2). Athletes and Active Individuals: Corns are prevalent in populations engaged in repetitive physical activities, such as athletes, due to increased mechanical stress on the feet(3). Homeless Populations: In homeless individuals, the prevalence of corns and calluses ranges from 7.7% to 57%, often due to inadequate footwear and prolonged periods of standing or walking(4). Introduction: Corns are small, hard, thickened patches of skin caused by repeated pressure or friction, usually on the tops or sides of toes and the balls of the feet, and can be painful. Materials and Methods: Literature search was done from standard authenticated text books, Homeopathic books, research data bases. Result: summary of the case: A 51-year-old female software employee presented with painful hard coms on the soles for 7 months, associated with pressing, tearing, and pulsating pain, worse from standing, walking, and wearing shoes, disturbing her sleep especially in the evening and night. She had a past history of chronic acidity and flatulence, particularly after onions, beans, and cabbage, along with constipation and unsatisfactory stools. She also reported craving for sweets and disturbed sleep with anxious dreams. On the mental plane, she showed lack of confidence at work, anxiety before tasks, irritability and a domineering attitude at home, and was sensitive to criticism. On local examination, tenderness was noted on pressure over the corns. Considering the totality of symptoms (gastrointestinal, mental, and particular complaints), the remedy Lycopodium clavatum, 30C was selected, which is known to cover her characteristic features and is indicated in cases of painful corns with associated digestive and psychological symptoms. Conclusion: This case illustrates that individualized homoeopathic management, including the administration of Lycopodium, in treating corns on the sole.

Abstract: This Article completely evaluates the primary data to verify objective 5 and 6 of the research. The main aim of this chapter is to sharply estimate the perceptual difference among the customers preferring brands of cellular phones. The One-way ANOVA and multiple regression analysis are used to analyze the primary data comprises of independent and dependent variables.

Abstract: Personal safety for women remains a critical concern, particularly in scenarios where immediate assistance is unavailable. Conventional safety devices often face limitations such as bulkiness, delayed activation, or passive alerting. To address these challenges, we propose a Smart Wearable Defense Strap, an innovative smartwatch accessory that integrates both emergency alerting and self-defense functionalities. The strap incorporates a compact, safely enclosed defense spray module, an SOS activation button, and a GPS-based tracking system that transmits the wearer’s live location to preconfigured contacts. The design ensures seamless integration with smartwatch sensors while enabling rapid, discreet activation during emergencies. This solution merges wearable technology with active protection mechanisms, allowing users to respond effectively to threatening situations. The Smart Wearable Defense Strap represents a practical, portable, and technologically advanced approach to enhancing personal safety, offering both preventive and reactive protection in real time.

Abstract—This study presents the design and implementation of an Internet of Things (IoT) based system for real-time monitoring of key physicochemical parameters in lake water, supporting water quality assessment and sustainable ecosystem management. Traditional water monitoring methods are often labor-intensive, infrequent, and lack spatial coverage. In contrast, the proposed system utilizes a network of sensors, including temperature sensors, pH sensors, dissolved oxygen (DO) sensors, turbidity sensors, and electrical conductivity (EC) sensors, to enable continuous, real-time data collection and analysis. The system utilizes low-power wireless communication technologies such as Wi-Fi for data transmission and integrates with cloud computing platforms for data storage, processing, and remote access. Real-time analysis and alert mechanisms enable early detection of pollution or unusual changes in water quality. The results obtained confirm the system’s effectiveness in improving monitoring accuracy and timeliness while significantly reducing the need for manual sampling. This solution provides a scalable, cost-effective, and automated approach to lake water monitoring, supporting data-driven decision-making for conservation and restoration efforts at any scale.

Abstract: The clothing industry is undergoing a significant transformation driven by the integration of Artificial Intelligence (AI) and emerging digital technologies. Traditional manual processes in clothing design are often time-consuming and restrictive, prompting the development of innovative solutions that enhance creativity and efficiency. To address this gap, the proponents introduce DesignClo, a mobile-based application that leverages a smart recommender system to deliver personalized clothing designs. The system integrates AI-Powered Design suggestions and a wide variety of motifs, enabling users to refine ideas and access trend-driven insights. Complemented by three-dimensional (3D) visualization technology, the platform allows users to preview their creations through life-like mockups, bridging the gap between concept and reality. Built using Tauri and Rust, and developed under the Agile Methodology, the application ensures adaptability, performance, and user-centered design. This inclusivity underscores the application’s broader societal value, offering opportunities for aspiring designers, professionals, and customers regardless of technological limitations. DesignClo uniquely features a virtual try-on so that the user will be able to observe their personalized clothing piece on how it would like when worn. This feature not only allow users to be able to visualize their design in a real-life setting, but it also enables them sync their own customization tailored to their preferences. Overall, DesignClo represents a breakthrough in personalized clothing production, serving as both a creative tool and an industry-aligned platform for clothing and printing businesses. By merging AI-driven recommendations, intuitive design customization, and offline usability, the system provides a sustainable and future-ready approach to enhancing user participation and shaping the evolution of the clothing industry.

Abstract: In this study, a hydrazone Schiff base ligand and its Fe(III) complex were synthesized. The hydrazone ligand was derived from hydrazone and benzaldehyde, and its antimicrobial properties were systematically evaluated. The synthesis of the Schiff base ligand was done by the condensation of 2-(2-aminothiazol-4-yl)acetohydrazide with 2-nitrobenzaldehyde, followed by complexation with FeCl₃ in a 1:2 molar ratio. Characterization was achieved using spectroscopic and analytical techniques, including ¹H-NMR, FTIR, UV-Vis, and XRD, which confirmed the successful formation of the ligand and its coordination to Fe(III). The ¹H-NMR spectrum confirmed the formation of the imine linkage (δ 8.09 ppm), while FTIR and UV-Vis data supported the presence of C=N bonding and metal-to-ligand charge transfer, respectively. XRD showed an amorphous structure consistent with flexible hydrazone linkages. Antimicrobial activity was screened against Escherichia coli, Salmonella typhi, Staphylococcus aureus, Streptococcus pyogenes, and Candida albicans using the agar well diffusion method. The Fe(III) complex demonstrated superior inhibitory activity compared to the free ligand, with the most significant enhancements observed against Staphylococcus aureus (20 mm vs. 16 mm) and Salmonella typhi (20 mm vs. 12 mm). These results suggest that metal coordination substantially enhances the antimicrobial efficacy of Schiff base ligands. The synthesized Fe(III) complex has potential as a lead candidate for the development of novel antimicrobial agents, given the rising issue of multidrug resistance.

Abstract: Corporate social responsibility is a strategy in which business gives emphasis to social responsibility and voluntarily contributes to better society and a cleaner environment. This includes sustainability strategies business employs to ensure that company’s work is carried out ethically. CSR can help improve society and promote a positive brand image for companies. India is the first country to make it compulsory for the companies under special circumstances of companies act sec 135 to contribute in CSR.  The provisions of CSR apply to every company which has a net worth of more than Rs. 500 crore, gross revenue cross Rs. 1000 crore and net profit above Rs. 5 crores in the preceding financial year. The companies should contribute in every financial year minimum 2% of its average net profits generated during preceding three financial years. The research paper deals with an understanding of CSR and its importance in the corporate world for long term sustainability and in financial performance of a business. For this purpose, previous research is reviewed from different perspective and accounting-based approach is also used to find the effects of CSR. The method applied to conduct research is regression analysis taking CSR as independent variable and main performance indicators (ROI, ROA, NBT) as dependent variable over a period of time in case of a single business house. Data for the research is collected from company’s annual reports, CSR portals, and moneycontrol.com and from other reliable sources.  The result shows almost positive relation between CSR and financial performance. But as the studied is about a single business house the research can differ from business to business working under different condition and a common inference cannot be drawn.

Abstract: Medical imaging has become a central element in modern medicine where it is used as the foundation of proper diagnosis and treatment plans. These imaging modes are desirable in terms of quality and reliability but even then, Gaussian noise continues to be a problem that reduces the crispness of the image thus leading to the inaccuracy of clinical interpretation. On the conclusions drawn in the above discussions, it is the purpose of this paper to give an exhaustive picture of Gaussian noise in medical imaging as informed by the results of this examination. It starts with naming the main origins of Gaussian noise, which are system limitations, motion of the patient and electromagnetic noise. Then the case is made to look at the effect of the Gaussian noise on the sensitivity of imaging, like ultrasound, CT, and MRI, which results in the loss of image contrast, poor yields of resolution and poor diagnostic capacity. In addition, the review also examines various mitigation measures, including measures that are software-related, including noise filters and machine learning algorithms, as well as solutions that are hardware-based and measures that are based on enhanced imaging. Finally, the present paper will also seek to provide an identifiable and concise general summary about any new information and methods on Gaussian noise in medical pictures with the goal of bettering the quality of medical pictures and provides greater accuracy to the contributions made to medical diagnosis. The results indicate that further innovation and interdisciplinary research should be conducted to create noise-reducing techniques, thus contributing to improving patient outcomes and the care they receive.

Abstract— This paper discusses Web Wizards, a mobile app designed to help users learn web development through gamified, puzzle-based learning. Compared to more traditional methods, the app introduces features like interactive challenges, real-time feedback, and two-player modes (Arcade and Story), either competitive or cooperative, to boost both engagement and understanding. The project followed a developmental research approach using the Agile methodology, with user input gathered throughout the development process. Results showed that learners felt more motivated and were able to apply practical skills more effectively. Overall, Web Wizards contributes to the field of educational technology by demonstrating how gamification can make programming concepts less intimidating and more accessible for beginners.

Abstract: This paper analyses the growing issue of online defamation targeting reputable public figures, an unintended consequence of expanded digital communication. The study situates a real-life case study, which incorporates a social media experiment involving an Instagram chat, within Jean Baudrillard's theoretical framework of hyperreality in media. Specifically, it investigates how techniques like impersonation, misrepresentation, and the fabrication of interactions are used to engineer misleading narratives within social media environments. The research illustrates that these falsified identities dismantle trust and reputation by creating a simulation that is indistinguishable from reality. The findings underscore the measurable psychological, social, and professional impacts of these defamatory practices, leading to a call for improved ethical standards, public awareness, and more robust regulatory mechanisms governing digital communication.

Abstract: The study deals with the Bibliometric study on the publication of "Knowledge Management". The records are collected from Web of Science Databases for the period of 2003-2022. A total of 3068 papers were identified in Web of Science database. The study reveals that, most of the researchers chosen to publish their research results in journal and more numbers of articles were published in the year 2022. The authorship trend shows that, out of total 3068 literature published, Single Author 7.89% and Multiple author   92.11% of the publication published under the majority  joint author. Three thousand and sixty eight authors have contributed the total of 33568 articles. Additional in this study also acknowledged to analyses coverage growth rates, most prolific authors, Degree of collaboration, Areas of research concentration, Price’s Square Root Law and Pareto Principle and citation analysis is also noted in this study.

Abstract: The need for the most fair AI systems has been overemphasized as the AI influence keeps growing and critical decisions, among others, states by the healthcare, finance, and human resources sectors, are made. AI fairness is not only about the fair distribution of results but also it involves fair processes in which decisions are made and the features of the interactions between the AI system and users. This article uses the concepts of organizational justice as a frame to explain the ways by which the design of an AI system could become a vehicle for: distributive justice (fair distribution of resources and results); procedural justice (decision, making process that is open and impartial); and interactional justice (communication that is respectful and empathetic). The conjunction of the three dimensions that the AI system can facilitate will make it possible for the latter to be more in line with human values and hence receive more trust, legitimacy, and acceptance from the stakeholders (Colquitt et al., 2013; Binns, 2018). This paper also refers to the various ways which include bias mitigation techniques, algorithmic transparency, and user, centric interfaces that bring fairness into the system. Further on, the authors explain the present continuous issues (for instance, data bias and ethical tradeoffs) and recommend future research directions for enhancing just AI systems at the end of this paper (Miller, 2017; Selbst et al., 2019).

Abstract: Blockchain technology, originally conceptualized to address the double-spending problem in digital currencies, has rapidly evolved into a transformative force with applications extending far beyond cryptocurrencies. This paper explores the multifaceted role of blockchain in enhancing cybersecurity, emphasizing its core attributes; decentralization, immutability, transparency, and resilience. Through a comprehensive literature review and analysis of real-world case studies, including the WannaCry ransomware attack and applications in healthcare and supply chain management, the study demonstrates how blockchain can secure data integrity, streamline identity verification, and improve traceability and transparency in complex systems. Additionally, the integration of blockchain with emerging technologies such as Artificial Intelligence (AI) and Machine Learning (ML) is examined, highlighting the potential for creating intelligent and adaptive cybersecurity defenses. However, the paper also identifies significant challenges, including scalability, interoperability, and regulatory compliance, that must be addressed to fully realize blockchain’s potential in cybersecurity. By proposing a holistic and interdisciplinary framework, the study underscores the necessity of collaborative efforts among developers, practitioners, and regulators to overcome these obstacles. The findings suggest that while blockchain offers substantial opportunities to revolutionize cybersecurity practices, its successful implementation requires ongoing research, standardized protocols, and adaptive regulatory measures. Ultimately, blockchain stands as a powerful tool in the cybersecurity arsenal, poised to drive the next wave of advancements in securing digital infrastructures against an increasingly sophisticated threat landscape.

Abstract: The early and accurate detection of cancer from histopathological images is crucial for the improvement patient outcomes in precision oncology because conventional diagnostic methods usually suffer from subjectivity and high variability, while traditional deep learning approaches, though effective, are limited in capturing both local morphological details and global tissue context simultaneously. In order to address this challenge, this study proposes a hybrid Convolutional Neural Network–Graph Neural Network (CNN–GNN) framework that integrates patch-level visual feature extraction with graph-based relational learning for cancer detection. The study adhered to the Agile approach and publicly available datasets, CAMELYON16 and CAMELYON17, were used, which consist of Whole-Slide Images (WSIs) and professional annotations of normal and metastatic tissue areas. Stain normalization, patch extraction, data augmentation, and graph construction were used as preprocessing steps, which provided both CNN and GNN pipelines with high-quality inputs. DenseNet121 was used in place of CNN backbone to extract patch embedding whereas Graph Convolutional Network (GCN) was used to learn the spatial and contextual relationship among patches. The last distinction came by combining CNN and GNN embedding by a multilayer perceptron classifier. The effectiveness of the given architecture was proven by experiment results. CNN model reached an accuracy of 88.9% with an F1-score of 89.2% and GNN model reached a higher accuracy of 90.7% and F1-score of 91.0%. The hybrid CNNGNN model notably outdid the two baselines, achieving a test accuracy of 95.4%, precision of 94.7%, recall of 95.9%, F1-score of 95.3% and AUC of 96.4%. Therefore, the hybrid CNNGNN model that is suggested provides a scalable, trustworthy, and clinically feasible solution to computational pathology. Along with attention mechanisms, enhanced GNN variants, and data on multiple institutions, future extensions could help to expand the overall generalizability and clinical uptake.

Abstract: The rise of online shopping has transformed consumer behavior, generating large volumes of data related to preferences, purchases, and browsing activities. Businesses now rely heavily on analytics and recommendation systems to improve customer satisfaction, drive sales, and deliver personalized shopping experiences. Among different recommendation techniques, clustering methods like K-Medoids have gained attention for their reliability in grouping users and products. Unlike K-Means, K-Medoids uses actual data points as cluster centers, making it less sensitive to outliers and more stable in identifying purchasing patterns. The proposed Online Purchasing Analytics and Recommendation System using K-Medoids clustering was designed to analyze user behavior, identify trends, and generate accurate recommendations. Its scope includes collecting and processing purchase history, browsing activities, product details, and demographics. A web-based dashboard was developed to visualize analytics and user clusters, while a recommendation engine provides personalized suggestions. The system, however, is limited to offline model training and excludes deep learning, advanced NLP, or real-time data integration. Experimental research was employed to measure the system’s effectiveness, supported by ISO 25010 quality model-based evaluations. Both qualitative and quantitative tools, including Likert scale questionnaires, ensured systematic, reliable, and meaningful results on attributes like usability, reliability, and functional suitability. The development process followed Agile (SCRUM) methodology, where tasks such as data preprocessing, feature extraction, clustering, and recommendation logic were iteratively built and refined. Regular sprint reviews and team collaboration ensured flexibility and continuous improvement. For pilot testing, Barangay 432 in Sampaloc, Manila, with 2,136 residents, was selected due to its active online shopping culture. Out of this population, 1,856 participants tested the system, alongside businesses, IT students, and faculty members, bringing the total respondents to 1,976. Evaluation results showed high usability (4.13), functional suitability (4.11), and security (4.07), while reliability (3.96) and efficiency (3.94) also performed positively. The overall weighted mean of 4.04 confirmed that users agreed the system met quality expectations, validating its readiness for real-world deployment. In conclusion, the study demonstrates that K-Medoids-based recommendation systems significantly enhance online retail by providing accurate, personalized, and meaningful product suggestions. Retail Insight not only improves user satisfaction and shopping efficiency but also supports better stock management and eco-friendly product promotion. The study recommends expanding the system with hybrid recommendation models, dynamic dashboards, real-time sentiment analysis, and integration with sustainability databases for even greater personalization and environmental impact tracking.

Abstract: The banking industry has been at the forefront of this change, as digital technology have quickly changed how businesses work throughout the world. Digitalization has improved efficiency and accessibility, but it has also created psychological problems for workers, such as technostress, which Brod (1984) originally described as a contemporary sickness caused by not being able to handle technological demands. This research investigates the differential effects of technostress among banking personnel in the public and private sectors of Himachal Pradesh, India. Utilizing primary data from 400 respondents across chosen banks (PNB, SBI, HDFC, ICICI) in four districts (Shimla, Solan, Mandi, Kangra), the study applies validated measures for technostress (Ragu-Nathan et al., 2008). A multistage sampling method was employed to carry out the research. Statistical investigation using SPSS, encompassing Mann–Whitney U and MANOVA tests, indicates that private bank personnel endure markedly elevated levels of technostress in comparison to their public sector colleagues (U = 14547.000, Z = -4.207, p < 0.05). Even though they are under a lot of stress, private bank workers say they feel like they are getting more out of life, which may be because of performance bonuses. Multivariate research substantiates that the job sector has a substantial effect on stress, work speed, and life perspective (Wilks’ Lambda = .929, F = 10.048, p < .001). These results highlight the need for specific treatments to alleviate technostress and enhance staff well-being in technology-driven banking settings.

Abstract: This document presents the development of an intelligent mobile application that uses an Image-Based Recognition System that runs on a Convolutional Neural Network (CNN) to foster sustainable behaviors and promote environmental consciousness. The system will identify and sort different materials that can be recycled such as wood, plastic, fabric, cardboard, and metal using deep learning-based image recognition. The application allows users to take or post pictures of objects after which they are processed and analyzed to determine the type of material in real-time. After this identification, the system creates rule-based suggestions, which offer curated video tutorials and step-by-step instructions on potential upcycling projects. Such customized recommendations do not only prolong the life of materials, but also encourage users to implement creative, environmentally friendly solutions in their everyday life. The application was developed on the basis of the Agile approach, which focuses on the iterative nature of development, flexibility, and constant feedback of real-life users. The data collection integrated both primary data collection techniques, including interviews and surveys to understand the preferences of the users, and secondary data collection techniques, including academic research and existing literature, to make the system consistent with the best practices in technology. The training data consisted of around 25,000 annotated images, namely around 5,000 images per material type, which was processed by the methods of normalization and augmentation to increase the recognition accuracy and reliability. Technically, the application is divided into three main parts: the input part where users post or take pictures, the processing part where the CNN-based classification takes place, and the output part where the users are shown personalized upcycling video suggestions. The front-end of the system was developed in JavaScript and React Native to provide a cross-platform mobile interface, and the back end was developed in Python and TensorFlow to provide machine learning features. PostgreSQL was used to manage databases in a reliable and secure manner. Ethical aspects were given importance such as direct user consent, balanced data set creation, and energy-efficient model training to reduce the environmental impact. In the future, the project will map out the activities of thorough testing and evaluation to further improve the accuracy of detection, usability, and scalability before the full implementation. In general, this smart mobile app shows the possibilities of integrating artificial intelligence, user-centered design, and sustainability principles to solve environmental issues and spread the culture of responsible consumption.

Abstract: The current increase in the prevalence of Autism Spectrum Disorder (ASD) and the need for early personalized intervention mandate the development of predictive systems that reach beyond static diagnosis. Machine learning models for ASD, as it exists today, are largely inflicted by limitations, including restricted data modalities, low generalization across populations, and limited interpretability — all of which become significant constraints for their clinical relevance and translational value. Furthermore, such systems often do not model the temporal development of neurodevelopmental markers and overlook the crucial interlink between diagnosis and a strategy for actionable intervention. To plug these gaps, we present the NeuroAdaptive Predictive and Interventional Framework for Autism Spectrum Disorder (NAPIF-ASD)—a multimodal, interpretable, and intervention-aware pipeline to enhance ASD prediction and intervention planning. The framework initiates with Temporal Neuro-Behavioral Graph Embedding (TNGE-Net), which jointly models individual developmental trajectories exploiting the power of graph neural networks over longitudinal neuroimaging and behavioral data. Then, Domain-Regularized Adaptive Clustering Ensemble (DRACE) identifies clinically meaningful subtypes of ASD through unsupervised clustering with domain knowledge embedded throughout the process. For interpretability, Neuro-Symbolic Causal Inference Model (NS-CIM) generates subject-specific causal maps that aid with counterfactual reasoning and explanatory insights. For generalization of the model, Federated Meta-Learning for Autism Model Transferability (FML-AMT) preserves data privacy while providing cross-institution robustness. Finally, Intervention-Driven Reinforcement Learning Engine (IDRLE) simulates long-term outcomes of early interventions towards minimizing the harm and maximizing the benefit of therapeutic strategies. By connecting prediction with actionable outcomes, this entire pipeline not only advances predictive accuracy and interpretability but constitutes a paradigm shift in the autism research landscapes. It makes significant contributions to the field of machine learning in healthcare by explicitly showing how longitudinal, causal, and federated approaches can work together towards precision psychiatry processes.

Abstract: India's economy still relies heavily on agriculture, which employs a sizable section of the workforce and makes a major GDP contribution. However, conventional farming practices have grown less sustainable because India faces more issues including climate change, water scarcity, energy inefficiency, and the requirement to provide enough food for a constantly expanding population. By facilitating more accurate and effective agricultural methods, the development of artificial intelligence (AI) presents prospective solutions to these problems. With an emphasis on optimizing resources and precision farming methods, this study explores the integration of AI to environmentally friendly agriculture in India. AI-enabled crop monitoring, soil health management, irrigation optimization, and insect control are among the main areas of interest. The research demonstrates how AI is being used to boost productivity, lessen environmental impact, and guarantee long-term viability in Indian farming by reviewing several case studies from various Indian locations. It also examines the challenges and impediments to the adoption of AI in India's rural areas and talks about prospects for expanded and deeper integration of AI technologies in agricultural practices.

Abstract: Mozambique is endowed with vast natural resources, particularly in the oil, gas, and mining sectors, yet the country continues to face challenges in translating this wealth into inclusive and sustainable development. In response, the Government of Mozambique has introduced legal instruments such as Law No. 21/2014 (Petroleum Law), Decree No. 34/2015, and more recently, Ministerial Statute DM55/2024, to operationalize local content policies aimed at maximizing the participation of Mozambican citizens and enterprises in extractive industry value chains. This article critically analyzes the design, implementation, and outcomes of Mozambique’s local content framework, comparing it with more established models in Nigeria and Angola. The study is grounded in Resource Dependence Theory, Linkage Theory, Spillover Effects, and Social Exchange Theory, providing a robust conceptual foundation for understanding how local content interventions can contribute to industrial development, employment, and skills transfer. Drawing on empirical evidence, policy documents, and case comparisons, the article finds that while Mozambique has made significant legal and institutional advances, practical outcomes remain limited due to institutional weaknesses, capacity gaps, and enforcement challenges. The paper concludes by offering policy recommendations to improve the effectiveness of local content regimes in Mozambique and across resource-rich African economies.

Abstract: This article explores how green bonds, carbon markets, and related instruments can unlock climate finance in developing countries, with a focus on Mozambique. Drawing on the Roadmap for Green Finance in Mozambique (FSDMoçambique & EED Advisory, 2021) and international evidence, it highlights the rapid growth of global sustainable debt markets -over USD 3 trillion in lifetime issuance - and the emerging role of carbon pricing and debt-for-climate swaps. Despite Mozambique’s modest participation to date, recent regulatory developments - including a draft decree on green, blue, and social bonds - and negotiations for debt swaps (e.g., with Belgium) signal growing commitment. However, structural barriers persist, including weak regulatory capacity, limited bankable projects, and macroeconomic volatility. Opportunities lie in the country’s renewable energy potential, donor-backed technical assistance, and integration into regional initiatives such as the SADC Green Bond Program and the Africa Carbon Markets Initiative. The paper synthesizes best practices from Africa and beyond - such as Kenya’s sovereign green bond, Seychelles’ debt-for-nature swap, and blended finance models - and proposes integrated policy actions to scale green finance in Mozambique. Key recommendations include finalizing sustainable bond regulation, developing a national green taxonomy, and investing in institutional capacity and transparency frameworks.

Abstract-Traditional dielectric sensors such as parallel-plate or coaxial cylindrical metal electrodes have some limitations and disadvantages, including the corrosion of electrode, the occurrence of electrode polarization, the reaction of the sensor material with dielectric samples, etc. This paper aims to investigate the influence of electrical properties of dielectric samples to the behaviors of electromagnetic field distribution inside solenoid-type dielectric sensors and the relationship between the electrical sensor parameters to be measured and material properties of dielectric samples considered, using numerical electromagnetic field analysis method. According to the numerical electromagnetic field analysis, it can be concluded that (i) In the case of high-resistivity dielectric samples such as insulating liquids and powders, the distribution capacitance is more preferable in identifying the dielectric properties of sample to the change of Q-quality; (ii) In the case of high-conductivity dielectric samples such as water and other aqueous solutions, the change of Q-quality is much more sensitive to that of the dielectric properties of sample to the distribution capacitance.

Abstract: This research examined the nexus between monetary policy and deposit money bank performance in selected sub-Saharan African nations. The study’s specific objectives were to investigate how interest rate, cash reserve ratio, and liquidity ratio would impact profit after tax of Nigerian banks. Secondary panel data from 2018 till 2023 and sourced from the central Bank of Nigeria annual bulletin was utilized for the study. Also, the selected deposit money banks were Zenith Bank PLC, First Bank PLC, GTCO, Jaiz bank and Wema bank PLC. The unit root test showed that all the variables were stationary at levels and the Hausman test revealed the usage of fixed-effect regression. Findings showed that monetary policy ratio and liquidity ratio were positively significant while cash reserve ratio was negatively significant in impacting the after-tax profit of the selected banks. The research, therefore, recommended that Nigerian banks could benefit from a greater allocation of their capital to current assets in order to improve their liquidity.

Abstract: The objective of the study was to establish the effect of equity financing on financial efficacy of listed manufacturing companies in Kenya listed in Nairobi securities exchange over a period of seven (7) years (2011 – 2017).  The study was based on Modigliani and Miller Proposition I and II, the trade-off theory, pecking order theory and the agency theory. The research adopted a descriptive research design. The target population for the study were staff members of the listed manufacturing firms in Kenya. The target constituted respondents from, accounting department, finance department, Auditing and Assurance Department and Monitoring and Evaluation Department of listed manufacturing firms in Nairobi securities exchange in Kenya. A sample of 106 respondents were selected by use of stratified random sampling. Data was collected through a structured questionnaire. Both descriptive and inferential statistics were used to analyze the data. Data presentation was done by the use of charts and tables for ease of understanding and interpretation. Pilot study was conducted by the researcher taking some questionnaires to the listed manufacturing firms head offices in Kenya. The study used Cronbach (Alpha – α) model to test the internal consistency with the alpha coefficient of above 0.7 being considered reliable. To establish the validity of the research instrument the research pursued the opinions of experts in the survey of study especially the researcher’s supervisors. Quantitative and qualitative data that were collected using questionnaires and the questionnaires were inspected for errors and gaps before issuing to the respondents. The findings revealed that equity financing positively and significantly influenced financial efficacy among the listed manufacturing firms.

Abstract: The aim of the study is to obtain a new hydrogel using HEC/SA/HA via a green chemistry approach for wound healing applications. The properties of the hydrogel have been tested which include: swelling ratio, in vitro degradation, and wound healing test. It was also characterized via UV-Vis, FTIR, TEM, and TGA.  The characteristic peak of UV-Vis spectra revealed the formation of AgNPs in the compound at 411 nm. The results from these studies showed that the hydrogel has an excellent performance in swelling ratio and vitro degradation. Furthermore, the wound healing activity of the hydrogel was tested by measuring the closure of the wound and the hydrogel used in the treated group revealed a significant healing activity compared to the control group. These results suggested that the cross-linked hydrogel based on (HEC/HA/SA) with AgNPs will be a promising dressing for wounds.

Abstract: This literature review examines the evolution, key milestones, foundational contributions, predominant models, measurement approaches, determinants, interdisciplinary intersections, and global variations in university governance. Drawing on scholarship from Europe, North America, Asia, and the Global South, this paper traces how governance emerged as a distinct research domain in higher education, identifies seminal works and contributors, compares governance frameworks across contexts, and highlights measurement tools and data‐analysis techniques. It also explores recent trends, mediating and moderating variables, implications for policy and practice, and persistent research gaps. The review concludes with recommendations for future inquiry, emphasizing under‐researched regions and longitudinal mixed‐methods studies.

Abstract: This paper examines SHGs should expand their scope and agenda from economic empowerment to social and political empowerment too. It has been recognized as an instrument to bring socio-economic transformation among the different members. In today’s interconnected finacials, SHGs plays a pivotal role in driving economic growth, particularly for women members. This article explores the various ways in which SHGs drives economic growth, evaluates its benefits and challenges, and discusses the policies and strategies. The study has said that the While carrying out income generation activities SHGs must integrate the issues relating to gender equality, access and control over resources and rights and entitlement of women into their programmes.  It explains that the status of women in society is index of its degree of civilization. As long as women are excluded from socially productive work and are restricted to household chores, the emancipation of women is impossible.

Abstract:  The aim of this study is to develop a Mobile-Based Smart Transport Route Optimization and Hazard Reporting System that could improve navigation, minimize travel time, and increase road safety in San Juan City. The complex road system of the city and the ever-changing traffic rules, such as one-way and two-way rules based on time, pose great challenges to drivers, which often result in congestion and unintended traffic violations. This study solves these problems by giving a localized and adaptive navigation solution. The proposed system uses a combination of Dijkstra's Algorithm, Google Maps Application Programming Interface and real-time traffic information to produce the most efficient and accurate routes. It also has a hazard reporting feature that allows users to share updates on accidents, road closures, police presence, and other disruptions. Other features include English and Tagalog voice navigation, a built-in speedometer with overspeed warnings, two-factor authentication for secure access, a favorites feature for easy recall of commonly traveled routes. The system was developed following an iterative prototyping approach with user feedback and validated and filtered with data collected from local drivers and the San Juan City Traffic and Parking Management Office to maintain accuracy and usability. Its area of focus is private vehicles, motorcycles, and taxis that are operating within San Juan City. Findings indicate that the system offers better route guidance, minimizes traveling time, and improves the safety of commuters by combining algorithmic route optimization and community-based hazard reporting. This study illustrates the role of localized systems in ensuring safer and efficient mobility in urban settings. This study shows the need to design navigation tools based on the actual community needs and conditions. Through the case of San Juan City, the system demonstrates how technology can directly address daily traffic issues, can offer a safer traveling experience, and can also serve as an example that can be copied in other cities with complicated traffic schemes.

Abstract- In the present study of Dang region Citrullus colocynthis plant depending upon their availability and their use in curing various health ailments for antioxidant studies. The plant have antioxidant, anti-inflammatory, anti-microbial and anti-diabetic effects. The Citrullus colocynthis plant is useful for therapeutic as well as for curing of animal diseases because of the existence of phytochemical compounds or antioxidant activity. Ethno-medicinal plants play a vibrant role in inhibiting different disorder in human-being and cattle. Due to the availability of the secondary metabolites as stated above, help in the form of anti-diuretic, anti-analgesic, anti-cancer, anti-viral, anti-malarial, anti-fungal, anti-inflammatory activities. The goal of these studies is to discover an effective treatment for a variety of illnesses that are prevalent in today,s society, as well as a way to postpone the signs of aging.

Abstract: Digital marketing was initially described as an online extension of traditional marketing, including its tools and tactics. But the unique characteristics of the digital world and how it has been used for marketing have encouraged the creation of platforms, formats, and languages that have produced tools and tactics that would be unimaginable in the offline world. The number of regular internet users in India is rising quickly, and this trend is only expected to continue. By 2020, it is predicted that there will be 550 million internet users in India, with a penetration rate of about 40%—a substantial rise from the current 35%. This study examines the impact of digitalization on India's rural marketing initiatives. This study examines the impact of digitalization on India's rural marketing initiatives. This paper will help any organization that wishes to market their products online and wants to learn about the benefits and drawbacks of rural marketing by developing an understanding of the primary advantages and disadvantages of the digitalization of rural marketing.

Abstract: This study investigated the factors influencing female students’ academic success in Physics and examined gender differences in performance at Navrongo Senior High School. Using an explanatory sequential mixed-method design, the research combined qualitative interviews with quantitative analysis of standardized Physics test scores. Quantitative results showed that although female students slightly outperformed their male counterparts (mean scores of 70.6 and 69.7, respectively), the difference was not statistically significant (t = –0.36, p = 0.717, Cohen’s d = –0.066). This finding suggests that gender alone does not determine performance in Physics. Qualitative findings highlighted the critical role of effective study habits, positive attitudes toward Physics, classroom participation, teacher support, and peer collaboration in achieving academic success. Female students particularly emphasized the importance of an inclusive and supportive classroom environment that fosters confidence and engagement in a traditionally male-dominated field. The study challenges stereotypes about girls’ participation in Physics and underscores the influence of social relationships, teaching strategies, and personal motivation on achievement. It concludes that promoting female success in Physics requires collective efforts centered on inclusive practices, responsive teaching, and sustained encouragement. The study recommends promoting structured study habits, improving students’ perceptions of Physics through positive messaging, enhancing engagement, and expanding experiential learning opportunities to support equitable achievement.

Abstract: Pulses used in radar systems, linear accelerators, and in Klystron and magnetrons require square waves with short rise times and small overshoot. However, the high-voltage pulse transformer designed and manufactured has a large leakage inductance and a constant di-stributed capacitance, which results in a long rise time and a large overshoot. In this paper, an analysis of the structure size of the pulse transformer to reduce the rise time of the pulse and propose a method to obtain the optimum result were investigated. And the correctness of the proposed design results is verified by analyzing the relationship between the influence of core material and switching speed and the Klystron (electron tube) with nonlinear inductance characteristics.

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Abstract: This paper explores the contribution of Indian cinema to Indian English literature. It contends that Indian cinema not only translates literary works but also affects the techniques of narration, themes, and popular culture in English writing in India. With a thematic framework comprising the narrative style, cultural identity, social critique, and development of genre, the research examines the relationships among these two art forms. The methodology is qualitative based on textual study, case studies, and comparative methods. Cases of Chetan Bhagat, Aravind Adiga, Rohinton Mistry, and Satyajit Ray's filmography show how literature and cinema co-operate to capture contemporary Indian realities. The paper concludes that Indian cinema has also emerged as a participative collaborator in the making of contemporary Indian English writing and reinforcing hybrid cultural identities.

Background Myofascial pain syndrome (MPS) of the upper trapezius is a common musculoskeletal condition characterized by active myofascial trigger points (MTrPs), leading to pain, disability, and diminished quality of life. Traditional physiotherapy, despite its prevalence, frequently offers merely transient alleviation. Functional Magnetic Stimulation (FMS) is an innovative, non-invasive technique that elicits painless, repeated muscle contractions using pulsed electromagnetic fields, potentially targeting underlying pathology. Objective               To evaluate the efficacy of Functional Magnetic Stimulation versus traditional physiotherapy in alleviating pain, enhancing cervical range of motion (ROM), and diminishing disability in patients with myofascial pain syndrome (MPS) of the upper trapezius. Methods This parallel-group, randomized controlled experiment enrolled 54 patients aged 18 to 45 years with active myofascial trigger points in the upper trapezius muscle. Participants were randomly allocated in a 1:1 ratio to undergo either FMS therapy (Group A) or Conventional physiotherapy (Group B). Interventions were administered thrice weekly for a duration of three weeks. The primary outcome was pain severity measured using the Visual Analogue Scale (VAS). Secondary objectives encompassed cervical range of motion assessed with a goniometer and disability evaluated using the Neck Disability Index (NDI). Evaluations were conducted at baseline and after three weeks by a blinded evaluator. Conclusion This study aims to provide protocol to evident the effectiveness of FMS relative to Conventional physiotherapy for the management of MPS. By overcoming the shortcomings of current therapies, FMS has the capacity to enhance patient outcomes in a timely and non-invasive fashion. Trial registration: The study is registered with the Clinical Trials Registry – India (CTRI/2024/05/067997).

Abstract-Web Application Firewalls (WAFs) have been characterized as essential in defending web-based systems against standard attacks of the application-layer (SQL injection and cross-site scripting). In order to consider the progress and current research tendencies in this area, this paper provides a broad bibliometric search through the literature on the topic of WAF articles during the 2010-2025 period. On 6 August 2025, the query (Web Application Firewall) OR (WAF) AND (cybersecurity OR web security OR web application security) was selected, and bibliographic data were fetched in the Scopus database. A hit list of 100 publications was eventually processed after screening 412 retrieved records at metadata level using the following filter terms; authorship, citations, keywords, abstracts, titles, year of publication, language of publication, DOI, and institution or organization of publication. Keywords co-occurrence and network effect Co-occurrence statistical analysis was done with using VOSviewer v1.6.20 and R Bibliometrix package to analyze networks and patterns of collaboration between authors and countries and citation. The findings demystify that WAF research has been continuously growing since 2016, and it becomes apparent that lack of interest in the traditional signature-based models has shifted to the machine-learning and cloud-native methods. India, China, and United States are the major contributors. Continuing gaps in research were found in encrypted-traffic inspection, real-time adaptive defence and lack of standard benchmark databases. Reproducibility resources are also given such as exported metadata, keyword mappings, and analysis scripts in the paper to assist other bibliometric research to come, and encourage the openness of future research activity.

Abstract: This paper highlights the extended applications of the Cone Penetrometer Test (CPT) for geotechnical decision support in the determination of depth of foundation embedment, optimum depth of excavation, vertical settlement distribution, determination of total consolidation settlement and the assessment of deep shear failure risk with the use of case studies in the Niger delta. These applications are more critical in site conditions where retrieval of soil sample is difficult due to extremely weak or where soil layers are very thin such that are missed during regular boring programmes. In these instances, the (CPT) serves as a vital geotechnical analysis for better and informed decisions, offering economical, high-resolution, nearly continuous data for accurate identification of soil layers and boundaries, efficient and reliable data for various applications, including pavement, swamp rig and offshore foundation design. The CPT data can often be integrated with data from other sources, processed through various analytical and modelling techniques to unravel findings that facilitate strategic and operational choices. Its advantages of rapid data acquisition, cost-effectiveness, and the ability to characterize soil conditions over large areas, which aids in foundation design and risk assessments, provide the enablement for geotechnical analysis and decision support.

Abstract: This article presents an advanced real-time churn prediction system for telecommunications utilizing Long Short-Term Memory (LSTM) networks with attention mechanisms. The system processes multimodal data, including call patterns, messaging frequency, application usage metrics, and sentiment analysis from support interactions, to identify subtle indicators of customer disengagement. Unlike traditional approaches that rely on static snapshots, this sequential learning model captures the temporal evolution of customer relationships, enabling earlier and more accurate identification of at-risk subscribers, particularly "silent churners" who gradually disengage before formal cancellation. Additionally, the integration of real-time call analytics enhances the churn prediction system by incorporating sentiment analysis and topic modeling during customer interactions, offering deeper insights into the factors driving dissatisfaction. This combination allows for real-time intervention during customer calls, preventing churn by addressing issues promptly. The dynamic retention framework incorporates adaptive threshold determination, personalized intervention selection, and multi-level adaptation mechanisms that continuously evolve based on intervention outcomes. Experimental evaluation demonstrates significant improvements in prediction accuracy, lead time, and business value compared to traditional methods, with customer support interactions and temporal features providing the highest predictive value. The architecture's real-time capabilities and multimodal integration address critical gaps in existing approaches, enabling telecommunications providers to implement timely, targeted retention strategies that substantially improve customer retention rates and overall service quality.

Abstract: The tunable physicochemical properties and environmental compatibility of deep eutectic solvents (DESs), particularly those based on choline chloride (ChCl) and glycerol, have made them a promising green alternative to conventional solvents. The refractive index behavior of ChCl:Glycerol (1:2 molar ratio) DES was comprehensively examined in this study while being modified with three structural isomers of butanol—1-butanol, 2-butanol, and 3-butanol—over a temperature range of 298.15 K to 323.15 K. Thermal expansion and diminished molecular polarizability were identified as the causes of the linear decrease in refractive index (nᴰ) observed in all systems as temperature increased. The refractive index decreased as the mole fraction of butanol increased at fixed temperatures, following the trend of 1-butanol > 2-butanol > 3-butanol. This arrangement is indicative of the impact of molecular branching on the efficiency of hydrogen bonding and intermolecular interactions in the DES matrix. The excess refractive index (nᴱ) was calculated to assess deviations from ideality. The results were consistently negative, suggesting that the DES components and the alcohols have significant associative interactions. The highest magnitude of nᴱ was observed in systems that contained 3-butanol, which indicates that steric effects were responsible for the increased structural disruption.The Redlich–Kister polynomial model was used to fit the experimental nᴱ data, resulting in an outstanding agreement with R² values exceeding 0.99 and average absolute deviations below 0.05%. These results emphasize the sensitivity of the refractive index to both molecular structure and temperature, and they establish nᴰ as a valuable probe for evaluating the intensity of interaction and non-ideality in DES–alcohol mixtures. The findings provide valuable insights for the development of green solvent systems that are optically tunable.

Abstract: The study sought to investigate the influence of digital marketing strategies adopted by private universities on student enrollment decisions in Kenya. The study employed a survey design targeting a population of 69,375 students across three private universities in Kenya. The respondents were the currently enrolled students at the undergraduate and postgraduate levels of study. The study obtained a sample size of 375 respondents. The respondents were selected using a stratified random sampling technique. Data was collected from primary sources using a structured questionnaire and analyzed through descriptive statistics (mean scores, percentages, and standard deviation) and inferential statistics (correlation and regression analysis).  The findings indicated that social media marketing improves student enrollment decisions in a significant manner. It was recommended that the private universities in Kenya should consider social media marketing in their education service promotion approach. The process of tailoring the promotional messages through social media must emphasise engaging in Q&A chatbots and short-form educational videos, like Instagram reels, Tik Tok, and YouTube shorts.

Abstract: This review evaluates the economic impact of Farmer Producer Companies (FPCs) under the Atal Bhujal Yojana (ABY) on per capita economic value in Solapur district's Gram Panchayats, a drought-prone region in Maharashtra, India. It explores the intersection of sustainable groundwater management and economic empowerment through collective farming models, addressing challenges in water-scarce agricultural systems. A systematic literature synthesis was conducted, integrating peer-reviewed articles, government reports, and case studies from 2015–2025, alongside analysis of Solapur's agricultural and economic trends. Findings indicate that FPCs, supported by ABY's water budgeting and monitoring tools (e.g., piezometers, water flow meters), enhance per capita income by 10–15% through improved agricultural productivity, with jowar yields increasing from 1.2 to 1.5 tons/ha, and cost savings of ₹5,000–7,000 per hectare via collective procurement. Water-use efficiency improved by 20%, driven by community-led water management, while market access through value-added products like organic jowar flour boosted incomes. ABY's community-driven approach fosters groundwater conservation and inclusive FPC governance, particularly for women. The study highlights policy implications for scaling FPCs, including expanded training, streamlined funding, and integration with schemes like MGNREGA. These findings contribute to sustainable agriculture and rural economic development, offering scalable solutions for drought-prone regions. Future research should explore longitudinal impacts and comparative analyses across Maharashtra's water-stressed districts.

Abstract— Predicting rice yields accurately is essential for maintaining food security, allocating resources as efficiently as possible, and promoting sustainable farming methods. This study assesses the performance of four machine learning algorithms Random Forest, Naïve Bayes, Logistic Regression, and KStar using a dataset of 180 instances with 11 attributes. WEKA (Waikato Environment for Knowledge Analysis) with 10-fold cross-validation was used to develop and evaluate the models. Confusion matrices, precision, recall, F1 score, overall accuracy, and Kappa statistics were used to evaluate performance. Confusion matrices, precision, recall, F1 score, overall accuracy, and Kappa statistics were used to evaluate performance. The results showed that Random Forest outperformed all other algorithms, achieving the highest accuracy (99.44%) with a Kappa statistic of 0.957. In both classes, it showed excellent precision, recall, and F1 scores. The minority "linear" class, on the other hand, was difficult for Naïve Bayes and KStar to handle, while Logistic Regression did reasonably well but fell short of Random Forest. These results demonstrate Random Forest's sensitivity to misclassification errors and validate its effectiveness in predicting rice yield.

Abstract: This paper examines a family of symmetric mesokurtic distributions proposed by Sebastian and James (2002), employed as error distributions in Auto Regressive models. As maximum likelihood equations become intractable under such settings, the Modified Maximum Likelihood Estimation (MML) approach is adopted to obtain parameter estimates. The asymptotic properties are discussed, and simulation experiments are carried out to evaluate the estimators. Results from the simulation show that the proposed estimates perform better than least squares estimators with respect to standard errors for small sample sizes, and the relative efficiency tends to one as n increases.

Abstract: Medication turnaround time refers to the duration between the initiation of a medication order either through manual entry or electronic prescribing and its administration to the patient. Monitoring this parameter in both outpatient (OP) and inpatient (IP) settings serves as a critical indicator for assessing the efficiency of pharmacy services and their direct impact on the quality of patient care.

Abstract: Competency is the strong area of interest for STCW Convention Code, training and education be equipped with the upgraded training materials that bring the realistic simulation on board the ship to hone their knowledge and skills. State of the arts simulator is too expensive to provide and become complacent to the real world of maintenance and operation which is the prime implication of the computer technology since total awareness and safety practices may jeopardize. The purpose of this study was to the development, designing and constructing an electronic high voltage power supply to be used in supplementing the minimum standard of competence relevant to monitoring and maintenance, the practical workshop skills in safe working practices, testing procedures, and maintenance activities for marine high voltage systems. The study was conducted at the Norwegian Training Center Manila. It was evaluated on the efficiency and effectiveness of the constructed trainer, which is an Electronic High Voltage Power Supply Device. The trainer was specifically evaluated on the technical requirements in the development on its design, construction, installation, and function; and the performance on its efficiency and effectiveness for instruction. Furthermore, it looked into the significant mean difference between efficiency of the trainer and its effectiveness for instruction upon use. The output is the technology package, which will be used upon implementation of the trainer. Descriptive method particularly survey research was employed on this study. Questionnaires were given to 10 instructors and 40 participants for evaluation. Gathered data were treated using total weighted points, weighted mean, and t-test. It was found out that the trainer is highly consistent in terms of design, construction, installation, and function; the trainer’s performance is highly efficient in terms of trainer’s output voltage and trainer's effectiveness for instructions; and there was no significant difference between the trainer’s efficiency and the trainer’s effectiveness for instruction for the instructors. And there is a significant difference between the trainer’s efficiency and the trainer’s effectiveness for instruction for the students.

Abstract: This article explores the involvement of women in Nigeria's democratic processes between 2015 and 2024, highlighting a notable decline in their political participation and representation. It underscores the connection between gender equality and sustainable development, emphasizing that true national progress is unattainable when a significant part of the population is marginalized. The Nigerian political landscape has traditionally been dominated by gender disparities, largely driven by the patriarchal framework of society. The rejection of key gender-focused legislation by a male-dominated parliament represented a missed opportunity to advance gender inclusivity and equality aligned with sustainable development goals. Utilizing a Liberal feminism perspective, the study advocates for gradual improvements through equal rights advocacy and supportive policies. Employing content analysis and drawing on secondary data sources, the research identifies various challenges—cultural, economic, and legal—that restrict women's political engagement. It concludes that despite their capabilities in public and private sectors, women often face a cycle of barriers that impede their leadership potential. These challenges are deeply rooted in societal biases, patriarchy, the struggle to balance family and career aspirations, and limited networking opportunities. The study advises that women should actively seek leadership roles and pursue higher education. Additionally, it emphasizes the importance of encouraging parents, particularly in rural areas, to prioritize the education of their daughters to prepare them for future leadership roles.

Abstract: Yoga has transcended its traditional Indian roots to become a global therapeutic practice with measurable neurobiological effects. Emerging neuroscientific research demonstrates that yogic practices that comprising asana (postures), pranayama (breath regulation), and dhyana (meditation) induce structural and functional brain changes associated with improved mental health, emotional stability, and cognitive performance. Mental health disorders such as depression, anxiety, and chronic stress have emerged as global health challenges, significantly affecting productivity, emotional stability, and overall quality of life. Despite advances in modern medicine, the neurobiological understanding of mind–body interventions such as yoga remains underexplored in mainstream neuroscience. Yoga, an ancient Indian system integrating postures (asanas), breathing techniques (pranayama), and meditation (dhyana), acts as a holistic regulator of both the central and autonomic nervous systems. By analysing recent neuroimaging and biochemical studies, this research identifies significant correlations between yoga practice and enhanced gamma-aminobutyric acid (GABA) activity, increased serotonin and dopamine levels and reduced cortisol secretion. These neurochemical modulations correspond to decreased anxiety, improved mood regulation, and enhanced cognitive performance. Functional MRI and EEG evidence further indicate that yoga strengthens the prefrontal cortex, hippocampus, and insular connectivity while reducing amygdala overactivation — mechanisms central to emotional regulation and stress resilience. Physiological parameters such as heart rate variability and blood pressure also show measurable improvements, reflecting balanced sympathetic–parasympathetic interactions. This paper concludes that yoga operates as a neurobehavioral modulator, inducing adaptive neuroplastic changes and restoring psychophysiological harmony. The integration of yoga into public health and mental wellness programs could provide a sustainable, evidence-based model for enhancing emotional stability, cognitive clarity, and resilience in an increasingly stressful world.

Abstract: This research paper presents a comprehensive review of generative artificial intelligence (AI), its underpinning architectures, real-world models, and ethical considerations. Generative AI illustrates the development from traditional classification and forecasting systems to generative systems that can generate novel and distinct outputs across various fields. This paper examined existing architectures, e.g., Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Transformers, Convolutional Neural Networks (CNNs), and Large Language Models (LLMs). The paper also surveyed realworld models, with special consideration of conversational agents and chatbots, e.g., ChatGPT. A number of ethical issues were discussed, including bias and fairness with generative AI applications, issues of privacy with use, transparency and accountability, misuse, including deep fakes and misinformation, and governance issues. Limitations and future directions were discussed in detail, with an emphasis on the need to harness generative artificial intelligence effectively and safely with human oversight and ethical responsibility, which will require interdisciplinary collaboration.