Dr. T. Manikandan

Scholarships and educational assistance programmes play an important role in enabling students to pursue higher education by reducing financial barriers. Tamil Nadu provides various scholarship schemes through different government departments for eligible students, including post-matric scholarships and educational assistance for different social and economic categories, along with higher-education assistance programmes. However, the availability of scholarships does not necessarily ensure that students are aware of or able to access them. This study examines the awareness and accessibility of government and private scholarship schemes among college students in Pollachi Taluk, Tamil Nadu. The study adopted a descriptive and analytical research design. Primary data were collected from 600 college students through a structured questionnaire. Frequency, percentage and Chi-square tests were used for analysis. The findings reveal that 85.0% of the respondents were aware of scholarship opportunities, while 15.0% were not aware. Government scholarships were the most widely recognised category. The college was the major source of scholarship information, reported by 89.3% of respondents. However, only 28.3% had applied for a scholarship and 24.3% reported receiving scholarship benefits. Chi-square analysis revealed significant associations between gender and scholarship awareness, age and scholarship awareness, scholarship awareness and scholarship application, government scholarship awareness and receipt of scholarship benefits, and source of information and scholarship awareness. Course of study was not significantly associated with scholarship awareness. The study concludes that there is a considerable gap between scholarship awareness and actual utilisation. Strengthening institutional guidance, timely dissemination of information and application support can improve scholarship accessibility among college students in Pollachi Taluk.  

Dr.T.Kiruthika

Emotional Intelligence (EI) has become an essential competency for employees to effectively manage workplace challenges, interpersonal relationships, and personal well-being. Among the various dimensions of emotional intelligence, self-awareness is considered the foundation for recognizing emotions, regulating behaviour, and making informed decisions. The present study examines self-awareness among working women in Coimbatore District and identifies the most significant self-awareness factors using Friedman Mean Rank analysis. Primary data were collected from 476 working women representing various occupational sectors through a structured questionnaire containing twelve self-awareness statements measured on a five-point Likert scale. Friedman Mean Rank analysis was employed to prioritize the factors influencing self-awareness. The findings indicate that all the self-awareness statements recorded mean values above the neutral value of three, indicating a satisfactory level of emotional awareness among the respondents. The statement "Try to ignore negative emotions as much as possible" obtained the highest mean score (3.93), followed by "Exhibit a sense of humour" (3.89) and "Aware of own strength and weakness" (3.85). Conversely, "Able to understand own feelings" obtained the lowest mean score (3.67), suggesting that although working women effectively regulate emotions, deeper emotional understanding requires further improvement. The study concludes that self-awareness significantly enhances emotional intelligence and contributes to better workplace behaviour, emotional stability, and professional effectiveness among working women. The findings provide valuable implications for organizations, policymakers, and human resource professionals in designing emotional intelligence development programmes that promote employee well-being, productivity, and work–life balance.  

Mrs. N. Amirtha Gowri, Dr. R. Nandhakumar

Soil fertility is a major determinant of crop productivity, resource-use efficiency, and the sustainability of intensive agricultural systems. Among soil properties, Organic Carbon (OC) is an important indicator because it influences soil aggregation, water retention, nutrient availability, microbial activity, cation exchange capacity, and root development. This paper presents a framework for incorporating soil Organic Carbon into a Genetic Algorithm–Deep Learning–Particle Swarm Optimization (GA–DL–PSO) decision framework for seasonal multicropping under drip irrigation. In the proposed approach, OC is integrated with soil pH, clay percentage, climatic variables, irrigation characteristics, crop-management variables, yield, water use, and economic indicators. A Deep Learning model is used to learn nonlinear relationships between environmental and management variables and crop performance. The predicted performance is subsequently used by a Genetic Algorithm to generate candidate cropping strategies, while Particle Swarm Optimization refines the candidate solutions toward improved yield, profitability, and water-use efficiency. The proposed framework provides a systematic computational pathway for translating soil fertility information into seasonal crop-selection and multicropping decisions. The paper also discusses OC classification, its agronomic importance, preprocessing requirements, crop-suitability implications, and its role in optimization. The framework is intended to support data-driven and sustainable decision-making in drip-irrigated agriculture.