VOLUME 2, ISSUE 7, JULY 2025

Abstract: This study examines the awareness, knowledge, satisfaction, and willingness to learn about Electrical Muscle Stimulation (EMS) among sports students in Trichy. A sample of 80 respondents was surveyed using a structured questionnaire, and the data were analyzed through descriptive statistics, ANOVA, and regression techniques. Findings reveal that 65% of students are aware of EMS, with younger students (17–20 years) reporting significantly higher knowledge levels than older cohorts (p = 0.017). 92% of respondents say they are interested in learning more, and satisfaction among EMS users is noticeably high. Formal EMS training is the best predictor of knowledge, according to regression research, but academic year, gender, and course level have no discernible effects. 38% of the variation in EMS knowledge can be explained by the model. These findings point to the necessity of focused instructional efforts, especially for senior citizens, and recommend that sports curricula include practical workshops and organised training regimens. Evidence-based sports practices, better rehabilitation results, and increased athletic performance can all be attributed to increased EMS awareness and application.

Keywords: Electrical Muscle Stimulation (EMS), Sports Training, Knowledge and Awareness, Student Perception and Learning Opportunities.

Abstract: In this paper, the direct digital frequency synthesizer (DDFS) and Quadrature direct digital frequency synthesizer (QDDFS) specifications were improved by increasing the number of accumulator bits, increasing the memory capacity of the generated signal samples, using a digital analog convertor (DAC) by increasing the number of its bits, as well as generating different types of analogue signals in a digital way.
Also, we discuss a practical mechanism of a Quadrature direct digital frequency synthesizer (QDDFS) based on a direct digital frequency synthesizer (DDFS) using Cyclone II EP2C20F484C7 FPGA from ALTERA placed on education and development board DE-1 with the following parameters: -Output waveforms: sin, cos.
-Frequency range: (3Hz…..10000 KHz ).
-Frequency Resolution (3Hz).
-Signal amplitude (5V).
-With Reset of the generator.
-Frequency of the generated signal for all types (1MHz).

Keywords: DDFS, QDDFS, FPGA, DAC.

Abstract: Beetroot has the potential to be an athlete performance booster, implying that it may enhance strength, endurance, and recovery. Beetroot supplementation, particularly in the form of beetroot juice, has gained increasing attention in sports nutrition for its potential to enhance athletic performance, endurance, and recovery. The primary bioactive compound in beetroot is dietary nitrate (NO3⁻), which is sequentially reduced to nitrite and nitric oxide (NO) in the body. This conversion promotes vasodilation, improving blood flow and oxygen delivery to muscle tissues, thereby potentially reducing fatigue and enhancing exercise efficiency. Evidence suggests that beetroot supplementation can improve cardiorespiratory endurance, delay time to exhaustion during submaximal efforts, and may enhance VO2 max performance. Additional benefits include improved mitochondrial function and modulation of hormonal responses during exercise. While optimal dosing appears to range between 6–8 mmol of nitrate, individual variability in response has been observed, influenced by factors such as genetics and training status. Despite some inconsistencies in findings, particularly concerning anaerobic performance and muscle soreness, beetroot remains a promising ergogenic aid. To maximize its benefits, ingestion should occur 90 minutes prior to activity, aligning with peak plasma nitrate levels. Further studies are warranted to refine dosage strategies and determine long-term effects across various athletic disciplines.

Keywords: Beetroot juice, Athletes, Cardiovascular endurance, Dietary nitrate, Physiological effects

Highlights • Dietary nitrate from beetroot juice enhances nitric oxide production, improving blood • flow and oxygen delivery to muscles.

Abstract: This study examines the welfare measures provided by organizations to contract employees, focusing on their awareness, effectiveness, and satisfaction levels. Organizational welfare programs play a crucial role in supporting employees’ well-being by offering benefits such as health coverage, financial security, paid leave, career development opportunities, and a safe work environment. These initiatives contribute to employee satisfaction, productivity, and retention by fostering a positive work culture and demonstrating the organization's commitment beyond just work performance. The research aims to assess the awareness of contract employees regarding available welfare facilities, evaluate the effectiveness of the welfare programs provided by the organization, and measure the level of satisfaction among contract employees with these welfare measures. Data for this study was collected through a structured questionnaire from 321 contract employees within the organization. The findings reveal that the majority of contract employees are well aware of the welfare facilities and express a high level of satisfaction with the support provided by the organization.

Keywords: Welfare Programs, Employee Well-being, Contract Employees, Employee Satisfaction

Abstract: Artificial intelligence (AI) is revolutionizing diverse sectors by transforming business models, automating decision-making, and providing unprecedented insights. This paper explores the extensive capabilities and applications of AI, including its integration with technologies like blockchain for enhanced security and the use of machine learning algorithms for continuous system improvement. We delve into AI's significant impact on healthcare, where it improves patient care, expedites diagnoses, and streamlines administrative tasks. Furthermore, the critical role of AI in information technologies and cybersecurity is examined, highlighting its ability to detect threats, automate security operations, and enhance overall cyber defences. The paper also addresses the ethical and social implications of AI deployment, such as data privacy and algorithmic bias, emphasizing the need for transparent and accountable AI systems. Finally, the application of AI in industrial automation, particularly in the food industry, is discussed, showcasing its contributions to operational efficiency, waste reduction, and sustainable practices. Despite its transformative potential, the paper acknowledges the challenges associated with AI implementation, including data security, ethical considerations, and the need for continuous system maintenance.

Keywords: Artificial Intelligence (AI), Machine Learning, Blockchain, Healthcare, Cybersecurity, Ethical Implications, Industrial Automation.

Abstract: Heart disease represents a significant health challenge worldwide, requiring efficient classification and prediction approaches for prompt diagnosis and intervention. Reliable prediction models depend on high-quality data, which calls for thorough preprocessing, particularly in areas such as imputation, outlier elimination, and feature selection to improve dataset quality and model effectiveness. Current preprocessing methods, which include managing missing data, identifying outliers, and selecting features, frequently show limitations and biases when dealing with complex datasets. In order to address these issues, this paper introduces the Data Preprocessing algorithm. This algorithm employs ensemble-based approaches to thoroughly tackle these challenges, improving dataset quality and elevating feature significance. The algorithm incorporates data merging techniques to unify various datasets and maintain uniformity. It makes use of the EMERALD algorithm for effective imputation of missing data and the RACE algorithm for successful outlier elimination. Normalization methods like Min-Max scaling are applied to standardize the data, while the DYNAMIC algorithm identifies key features essential for predictive performance.

Keywords: Data preparation, Missing data handling, Outlier elimination, Variable selection, Ensemble methods.

Abstract: In contemporary India, rural unemployment and poverty remain significant challenges despite rapid economic development and digital progress. According to CMIE data, rural unemployment stood at 7.7% as of December 31, 2022. Agriculture continues to be the primary occupation in rural India, yet many farmers, especially in rainfed areas, remain underemployed for several months due to seasonal cycles, monsoon dependency, and lack of diversification. This leads to a cycle of economic insecurity, social marginalization, and in some cases, farmer suicides. Through the lens of Tamil Nadu’s villages and with a special focus on national and state-level initiatives like PMKVY (Pradhan Mantri Kaushal Vikas Yojana) and Tamil Nadu Skill Development Corporation (TNSDC), this article analyses the root causes of rural unemployment and explores skill development as a sustainable solution for rural poverty eradication.

Keywords: Gender-Inclusive Approach, Rural unemployment, Poverty alleviation, Skill development, Climate change