CALL FOR PAPER - Vol 10 - Issue 1

This study examines the socio-demographic profile and Labour Partition patterns of rural architectural labourers in the Savanur block, Karnataka, based on primary data collected in 2025. The sample of 153 respondents revealed that male labourers constitute 53.6% and female labourers 45.8%, with a few Shares in other gender categories. Age distribution shows the highest participation among those aged 40- 50 (29.4%). Followed by 30 to 40 (24.2%), while younger (15-30) and older (50+) Age groups are less well represented. Maruti status beta indicates that 71.9% are married, 15.7% single, and 11.8% widowed, with widowers being extremely rare. Family structure in the sample is predominantly joined, with 68 0% of households being joined families and 32.0% nuclear. Drawing comparison with Karnataka state, a rising female labour participation rate in rural areas, A growing share of middle-aged labourers and changing family marital dynamics. The findings highlight both alignment and unique local variances in Savanur. The study suggests targeted interventions for underrepresented groups (youth, widowed, female labourers), Improvements in social support and infrastructure and policy measures that recognise the role of marital status and family type in shaping labour force participation.

The increasing demand for sustainable, biocompatible, and environmentally friendly materials has intensified research interest in naturally derived polysaccharides for industrial and biomedical applications. Among these biopolymers, alginate and hyaluronic acid have gained significant attention due to their remarkable physicochemical properties, biodegradability, non-toxicity, and versatile functional characteristics. This paper reviews the emerging roles of alginate and hyaluronic acid as sustainable biomaterials in the food and pharmaceutical industries, with emphasis on their extraction, structural properties, modification techniques, and industrial applications. Alginate, primarily obtained from brown algae, exhibits excellent gel-forming and encapsulation abilities, making it valuable in food preservation, edible coatings, controlled drug delivery, and tissue engineering. Hyaluronic acid, naturally present in animal connective tissues and microbial sources, possesses exceptional moisture retention, viscoelasticity, and biocompatibility, which support its growing utilization in wound healing, cosmetic formulations, ophthalmic preparations, and targeted therapeutic systems. Recent advances in green extraction technologies, nano-formulation, and polymer blending have further expanded the functional applications of these biomaterials. The study also highlights the economic and environmental benefits associated with the replacement of synthetic polymers by natural polysaccharides in industrial processes. Despite their enormous potential, challenges such as production cost, stability, purification efficiency, and large-scale commercialization remain major concerns requiring further scientific attention. The integration of sustainable extraction methods, biotechnology, and advanced material engineering is expected to enhance the industrial competitiveness of alginate and hyaluronic acid in the global bioeconomy. This presentation demonstrates that natural polysaccharides represent promising alternatives for the development of safer, sustainable, and multifunctional biomaterials capable of addressing current challenges in food preservation, drug delivery, and biomedical innovation

This study investigated the effects of online resources, specifically YouTube and WhatsApp, on students’ academic achievement and retention in Financial Accounting at Abia State College of Education (Technical), Arochukwu (ASCETA). The study adopted a quasi-experimental pre-test, post-test, and control group design. The population comprised all second-year students in the Department of Business Education (Accounting option), from which a sample of 90 students was drawn using purposive and simple random sampling techniques. Data were collected and analyzed using mean and standard deviation to answer the research questions, while Analysis of Covariance (ANCOVA) tested the hypotheses at a 0.05 level of significance. The findings revealed that both YouTube instructional videos and WhatsApp-mediated discussions significantly enhanced students’ academic achievement compared to the conventional lecture method, with YouTube showing the strongest effect. YouTube also proved most effective for retention of Financial Accounting concepts, while WhatsApp facilitated collaborative learning and improved understanding. Gender was found to have no significant influence on achievement or retention, indicating that both male and female students benefited equally from these online resources. The study concluded that YouTube and WhatsApp have substantial potential to improve learning outcomes in Financial Accounting, with YouTube being most effective for both achievement and retention. Recommendations include integrating YouTube videos into instruction, using WhatsApp for collaborative discussion and revision, providing institutional support for digital learning, guiding students on responsible use of online resources, and conducting capacity-building workshops for lecturers.

Nigeria is still striving to achieve the Millennium Development Goals (MDGs) four (4) and five (5) which calls for reducing maternal and child mortality. The excessive mortality is as a result of preventable and treatable diseases which are compounded by endemic failure in health system, socio economic factors, heterogeneous religion, diverse cultural and linguistic background among others. Past researchers have made a great effort to address these problems through the use of information and communication Technologies (ICT) in revolutionizing health sector. More so, the MDGs eight (8) calls upon the exploitation of benefits of the existing new technologies, especially those related to information and communication. With the advancement in ICT, many forms of eHealth have emerged to tackle these health challenges in maternal and child health services. The ICT- based solution enhances maternal delivery, minimize delay and cost effective as well as eliminate geographical barriers. The paper explores the role of ICT in reducing maternal-child mortality; existing solutions were carefully reviewed and necessary recommendations were provided.

This study examines the dynamic effects of oil revenue on revenue generation in Nigeria using a Vector Error Correction Model (VECM) to capture both long-run and short-run relationships. The Augmented Dickey-Fuller (ADF) unit root test shows that all variables are integrated of order one, I(1). The lag selection criteria indicate an optimal lag of one, and the cointegration test confirms a long-run equilibrium relationship among the variables. The VECM results reveal that oil revenue has a significant long-run effect on revenue generation in Nigeria, with a coefficient of -0.88845 and t-statistic value of [-30.2226]. Exports also significantly influence revenue generation in the long run with a coefficient of 0.60397 and t-statistic value of [4.39911]. The error correction term indicates that about 40.36% of short-run deviations from equilibrium are corrected annually. Diagnostic tests show no evidence of serial correlation, although residuals are not perfectly normally distributed. The study recommends that government should adopt effective forecasting and monitoring strategies to enhance revenue generation in Nigeria.

cardiovascular disease (CVD) is one of the leading causes of mortality worldwide, and early detection is essential for reducing its clinical and economic burden. Obstructive sleep apnea (OSA), a common sleep-related breathing disorder, has been identified as a significant risk factor for various cardiovascular conditions, including hypertension, arrhythmias, heart failure, and coronary artery disease. This project proposes a novel end-to-end deep learning framework that utilizes sleep apnea-related physiological signals for the early prediction of cardiac disease. The proposed model integrates one-dimensional Convolutional Neural Networks (1D-CNN), Bidirectional Long Short-Term Memory (BiLSTM) networks, and a Multi-Head Self-Attention mechanism to automatically extract spatial and temporal features from electrocardiogram (ECG), heart rate variability (HRV), blood oxygen saturation (SpO₂), and respiratory signals. The self-attention module enhances the model by identifying the most informative signal segments associated with cardiovascular abnormalities, thereby improving prediction accuracy and interpretability. The framework is evaluated using publicly available sleep apnea and cardiac datasets, with performance assessed through accuracy, precision, recall, F1-score, specificity, and ROC-AUC. The proposed system aims to provide an intelligent clinical decision-support tool for early cardiovascular risk assessment, enabling timely intervention, continuous patient monitoring, and improved healthcare outcomes through AI-driven predictive analytics.