Comparative Study of Meniscal Injuries among Kho-Kho and Basketball Players
Authors: Dr. B. Veera Kumar Reddy, Dr. K. Sivananda
Abstract
The frequency of sports-related meniscal injuries is rising, particularly in high-impact games played on hard surfaces. The study of aimed to compare the prevalence of meniscal injuries between Kho-Kho and Basketball players, focusing on the effect of hard surfaces in Basketball. A sample of 80 collegiate-level players 40 Kho-Kho and 40 Basketball aged 18–25 years was studied. Data were collected through injury history questionnaires, clinical screening, and statistical analysis. Results indicated that Basketball players had a significantly higher rate of meniscal injuries (35%) compared to Kho-Kho players (12.5%). The hard playing surface in Basketball was a key point to note of, contributing factor, whereas Kho-Kho, largely played on soil- based grounds, showed fewer injury incidences. The study highlights the need for preventive measures, including surface modifications, footwear adaptations, and injury-prevention training programs in proper methods.
The Split Phase Economic Load Dispatch Algorithm
Authors: Jyoti Jain
Abstract
In this paper, a new algorithm named as Split Phase Economic Load Dispatch- algorithm (SPELDA) has been developed. This algorithm works in two phases. In the first phase, a population of points is randomly generated. The feasible points which satisfy the equality as well as inequality constraints of the Economic Load Dispatch problem are selected after each iteration of the algorithm. These points are copied to the External set and are not allowed to participate in future iterations of PSO. This phase is complete after ten such points have been copied to the External set and then the algorithm switches over to the second phase. In the second phase, the points of External set become initial points for Economic Load Dispatch of IEEE 5, 14 and 30 bus systems. In both the phases, the velocities of the points are modified by the use of clamping factors which slows down the movement of individual particles/points. It results in faster convergence. Also a new measure represented by KOUNTS has been devised to measure the computational effort. It indicates the number of times a function has been evaluated during the optimization process. The results of Split Phase Economic Load Dispatch (SPELDA) algorithm have been compared with basic particle swarm optimization and Lambda iteration method. Analysis of results show that the cost of generation obtained by SPELDA is found to be lesser compared to basic PSO and lambda iteration method.
MODELING AND EXTRACTION OF ESSENTIAL OIL FROM LAVANDULA OFFICINALIS L. AS BIOMASS USING GREEN SOLVENT
Authors: Linda LOUCIF SEIAD, Naoual BERROUANE, Hassina BOUSSAK, Khelifa BOUACEM
Abstract
This study aims to valorize natural substances using green solvents through the extraction of essential oil (EO) from Lavandula officinalis L. via hydrodistillation. A two parameter factorial design was employed to assess individual factor effects on extraction efficiency. EO yield was calculated to evaluate extraction process performance under different experimental conditions, and factor interactions were determined. The resulting characteristic model is a first-order model, statistically validated through ANOVA. The coefficient of determination (R²) indicates that the fitted model explains 98.99% of the variability in EO yield, while the adjusted R² value of 97.98%, which is more appropriate for comparing models with varying numbers of independent variables, confirms the model’s robustness. The model accurately describes the studied system. These preliminary results are promising and warrant further investigation. Algerian Lavandula officinalis L. represents a valuable biomass resource and demonstrates significant potential as a source of bioactive compounds.
Preparation of Copper Oxide Nanoparticles Using Green Synthesis using Guava Leaf (Psidium guajava) Extract
Authors: G. Punithakumari
Abstract
Green synthesis plays the vital role for the production of the nanoparticle for researchers nowadays. Copper sulphate 0.1M and guava leaf extract was made as a bulk solution. 50ml of copper sulphate and 150ml of guava leaf is taken in a beaker and mixed well. Psidium guajava (guava leaf) aqueous extract can reduce copper ions into copper nano particles with a reaction time of 5 minutes and form the brown colored precipitate. This precipitate is settling down in the bottom. These green synthesized nanoparticles of copper oxide are characterized by UV- Vis spectrophotometer, FT-IR, and EDX analysis. The peak present in 462 nm in the UV-Vis spectrum due to the inter band transition of core electrons and band gap energy of 2.6 eV was observed through tauc plot. The sharp peak shows at 692.97 and 752.62 cm-1 in FT-IR spectrum is owing to Cu-O bond. CuO Nano particle is flower in shape. The particle sizes of CuO NPs are found in different sizes range of 49 nm, 87 nm, 120 nm and 324 nm.
A SYSTEMATIC REVIEW OF LEVERAGING TECHNOLOGY TO NURTURE PROSPECTIVE TALENT IN HIGHER EDUCATION
Authors: K.M.Chethana
Abstract
Talent management is a well-thought-out, all-encompassing plan for attracting, training, engaging, and retaining a diverse workforce composed of the best talent. In this paradigm, talent development is crucial because it focuses on finding, developing, and enhancing people’s skills so that they are in line with the demands of the market. the institution’s objectives in a line. Talent development in higher education involves the intentional process of identifying, developing, and enhancing the abilities of instructors in order to meet institutional objectives and the difficulties presented by a constantly evolving world. It is essential for attracting and retaining the best personnel, improving the institution’s performance, and adapting to challenges such as technological innovation. It encompasses activities such as coaching, training, and succession planning. This study offers a methodical review of the literature on how technology, specifically artificial intelligence (AI), is used in higher education institutions to foster and cultivate potential talent. To determine research trends, significant subject clusters, and future directions, the review combines bibliometric and systematic analysis. The findings emphasize the quick advancement of AI-driven talent development research between 2021 and 2025, with an emphasis on governance, digital transformation, and pedagogy. The results demonstrate interdisciplinary connections between educational, psychological, and computational domains. emphasizing the necessity of institutional frameworks that harmonize innovation with ethical AI behaviors in the fields of psychology, management, and technology.
A STUDY TO ASSESS THE KNOWLEDGE REGARDING VITAMIN D DEFICIENCY AMONG STUDENTS OF SELECTED COLLEGE OF NURSING, SHIMLA
Authors: Shilpa, Chanchal Sharma, Geeta Thakur, Deepa Gautam, Uravashi
Abstract
Vitamin D is one of the major vitamins and very essential for maintenance of normal growth and development of strong bones. It is often referred to as “sunshine vitamin” because it can be synthesized in the body from the ultraviolet rays provided by the sun. The main action of vitamin D is to help calcium and phosphorus in our diet to be absorbed from the gut. It is soluble in the fats and fat solvents and insoluble in water. Vitamin D is a prohormone and it can be activated in our bodies to a complete hormone form, calcitriol. Vitamin D has long been known for its role in regulating body levels of calcium and phosphorus, and bone mineralization. Vitamin D can be administered orally or by injection. Orally administered vitamin D is absorbed from the small intestines. Fat and bile also help in absorption of vitamin D. Vitamin D passes to the general circulation via lymph and distributed throughout the body. There are two forms of vitamin D i.e., D2 (Ergocalciferol) and D3 (Cholecalciferol). Vitamin D is synthesized in the epidermis of skin from 7-dehydrocholestrol on exposure to ultraviolet rays of the sun. The personated study aimed primarily to assess the knowledge regarding vitamin D deficiency among nursing students of selected college of nursing, Shimla. The data were collected by using knowledge questionnaire from 100 students selected by non- probability purposive sampling technique to assess the knowledge regarding vitamin D deficiency among nursing students. To find out association between the knowledge score with their social demographic variables. Level of knowledge was more in nursing students of age group 20-21 years on the other hand knowledge was lesser in age group of 17-18 years. According to the samples, students who belong to the sunny area have more knowledge than students who belongs to the shady area. Students who follow vegetarian dietary habit have more knowledge than students who follows non-vegetarian and vegan diet.
CHALLENGES AND FACILITATORS OF EVIDENCE- BASED PRACTICE ADOPTION AMONG NURSES
Authors: Sheela prabhudas, Navpreet kaur, Fazl Ur Rahman Wani
Abstract
Evidence-based practice (EBP) is a relatively new concept in nursing that has shown to lead to positive results. It’s important to look into what helps or hinders its use, especially the way research findings can be combined with everyday nursing. Many nurses have said they need more training in research and EBP ideas. While some are eager to use EBP to enhance the quality of care and improve patient results, many feel they don’t have the necessary tools and resources. This study highlights the importance of making EBP a priority at both the institutional and government levels. For many years, health service providers have aimed to adopt evidence- based practice. The World Health Organization (WHO) emphasizes that healthcare should be based on the best available evidence. Healthcare workers are expected to connect with evidence and adjust their practice accordingly. Regulatory bodies, like the Nursing and Midwifery Council, expect nurses to apply EBP in all environments. However, there is still a gap between available research, practical changes, and better patient outcomes.
AI-SUPPORTED DECISION-MAKING IN INTENSIVE CARE UNITS (ICU): IMPLICATIONS FOR NURSING AND MEDICAL PRACTICE
Authors: Fazl Ur Rahman Wani, P. Vanaja, Sudhir Singh
Abstract
Artificial Intelligence (AI) is transforming healthcare delivery, particularly in high acuity environments such as intensive care units (ICUs), where rapid, evidence based decisions are critical for patient survival. AI supported decision making in ICUs encompasses a range of technologies including predictive analytics, machine learning algorithms, natural language processing, and clinical decision support systems (CDSS) that enhance clinicians’ ability to assess, monitor, and treat critically ill patients. This review article explores the development, applications, and implications of AI assisted decision making in intensive care medicine, emphasizing its role in improving diagnostic accuracy, optimizing resource allocation, and supporting precision care. The integration of AI into critical care nursing and medical practice presents opportunities for enhanced clinical outcomes, but it also raises challenges related to ethical governance, data bias, accountability, and the evolving roles of healthcare professionals. Drawing on global evidence and case studies from both developed and developing nations, including India, this article synthesizes current literature on the technological, ethical, and professional dimensions of AI in ICUs. The review concludes with recommendations for ethical implementation, workforce preparedness, and interprofessional collaboration to ensure safe, equitable, and effective use of AI in critical care settings.