Metallurgical and Materials Engineering (E-Journal)
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Investigation of Neuromuscular Concept by Bosu Ball and Trampoline Training in Athletes Sustaining Soft Tissue Injuries of Knee using Randomized Controlled Study Design
Get PDFIntroduction: In this study the effects of unstable platform as neuromuscular concept to gain balance and strength are investigated. Individuals sustained Anterior cruciate ligament injury with and without isolated meniscal injuries are recruited for the study.
Objective: The objective of the study has centralised the concept of studying the effects of unstable platform following ACL injury.
Materials and Methods:
This study records the outcomes after exploration of the individual in unstable platform for the establishment of Neuromuscular control. Balance and strength are the output by Vertical jump test after training of eight weeks. 30 Male athletes in the age group of 18-28 were recruited for the research recorded the means with the t value and p value -18.500 and 0.000 value respectively.
Results: P value <.05 records the significant value through outcomes after both the groups underwent training on unstable platform. A minimum difference exists between the two groups as Bosu Ball training records the mild significant value in comparison with Trampoline exercises.
Conclusion: A positive influence of unstable surface develops the motor skills required for sport perspective. There by unstable surfaces as equipment in ACL Rehabilitation are quite hard for adaptability but in still the motor skills in Sport activities
The Impact Of Social Media On Adolescents: A Study On Psychological, Academic, And Social Effects
Get PDFThis investigation is delving into the different sides of social media usage on adolescents by examining their psychological, academic, and social states. By using a mixed-methods research design, the researchers conducted interviews with people from diverse regions of India and also obtained survey data from over 200 participants. The results of the study point out the existence of strong relationships between social media use and higher levels of anxiety, boredom with academic work, and alienation among people. The research also brings to light the necessity of digital literacy and the idea of organized steps to reduce the ill effects of social media. The paper ends by giving some suggestions on policies that are directed to educators, parents, and policymakers who are responsible for taking care of the health of adolescents by proposing healthier online engagement among adolescents
Decoding The Confluence Of Green Finance & Climate Finance On Environmental Performance To Reinforce Sustainability
Get PDFThe world is grappling with serious challenges resulting from climate change and its far-reaching effects, such as extreme weather conditions, earthquakes, floods, and droughts. Amid these challenges, green finance & climate finance emerge as a sustainability-focused beacon of hope. It functions as a financial tool that supports governments and businesses in achieving economic growth while maintaining environmental sustainability. This mechanism channels financial resources toward climate action and sustainable development objectives. This research objective is to explore the confluence of green finance & climate finance on environmental performance, which is essential for fostering ecologically sustainable growth. This qualitative research employed a grounded theory approach to derive insights inductively, utilizing existing literature to assess the relationship between green finance& climate finance with ecological performance. The literature review revealed that green finance and climate finance is crucial in enhancing corporate and economic environmental performance. Investments in sustainable projects contribute to lowering carbon emissions, improving environmental quality, and reinforcing climate action efforts. The findings suggest that green finance& climate finance could be instrumental in improving ecological performance and advancing sustainability goals. Furthermore, both governmental and private sector entities must take a proactive role in advocating for green financial resources to overcome financial barriers. They should integrate green finance& climate finance considerations into policymaking, budget planning, and strategic decision-making to promote a more sustainable and environmentally responsible society
Surveillance Video Anomaly Detection With Multi-Branch Gan
Get PDFIdentifying irregularities in surveillance videos is essential for maintaining public safety in various settings. Conventional methods, which often depend on human oversight, can be inefficient and susceptible to errors. Nevertheless, with the advancement of deep learning technologies, the task of detecting anomalies in real-time can now be automated. Generative Adversarial Networks (GANs) have demonstrated their effectiveness in video analysis, enabling the automated identification of anomalies. This project introduces an innovative approach that employs a Multi-Branch GAN (M-GAN) model specifically crafted for the detection of abnormal events in surveillance footage. The M-GAN utilizes a two-phase approach: it initially learns the patterns of typical activities and subsequently identifies any deviations as potential anomalies. A key advantage of this model is its ability to function without requiring labelled anomaly data, which increases its flexibility across different environments and conditions. Experimental results demonstrate that the M-GAN outperforms conventional GAN-based methods, achieving superior precision and recall rates in detecting abnormal occurrences. This outstanding performance positions M-GAN as an excellent solution for instantaneousabnormal event detection in surveillance systems, enhancing safety and security while reducing dependence on manual monitoring
Design And Simulation Of The Wave Performance Of A Nanocomposite RF MEMS Microswitch Reinforced With Gold Nanoparticles Under The Influence Of Time-Varying Electrostatic Energy
Get PDFCapacitive RF MEMS switches have emerged as a prominent and technologically simple solution in modern microelectromechanical systems, garnering substantial research focus. This work presents a novel exploration of incorporating gold nanoparticles into the fabrication of RF MEMS microswitches, marking the first comprehensive assessment of their potential in this application. The investigation employs a combined analytical-numerical approach to analyze the dynamic response of these devices under time-dependent electrostatic actuation. The microswitch architecture is modeled as a bifurcated microbeam structure. Nonlinear governing equations describing its mechanical deformation are formulated through non-local extensions of Euler-Bernoulli beam theory, incorporating von Kármán's geometric nonlinearity to account for large-deflection strain-displacement relationships. Mechanical properties of the gold-reinforced nanocomposite are derived using homogenization techniques based on mixture laws. The resulting equations are discretized via the Galerkin weighted residual method, enabling numerical solutions. Key findings reveal that the integration of gold nanoparticles substantially enhances the structural stability of microbeams. Specifically, a minimal nanoparticle concentration of 0.1% elevates the pull-in voltage threshold by approximately 18%. This improvement suggests that gold nanoparticle integration offers a viable strategy for achieving higher actuation voltages in compact microswitch designs. Such advancements could enable the miniaturization of MEMS devices while maintaining or enhancing operational performance, positioning nanocomposite engineering as a critical tool in microsystem optimization
Chatbot For Government Employees In Education Department
Get PDFGovernment employees in the education sector often encounter delays and inefficiencies due to inadequate systems for task scheduling and query resolution. This paper proposes a chatbot-based helpdesk to address these issues by providing instant answers to frequently asked questions and integrating task management features like desktop reminders. Leveraging advanced NLP techniques and the Hugging Face Zephyr-7B model via API, the system delivers accurate, AI-powered responses even for complex queries not covered in the FAQ dataset. Built using Gradio and Python, the chatbot offers a user-friendly interface, aiming to streamline workflows and enhance communication within the sector.  
Temporal Progression Analysis of Diabetic Retinopathy Using Recurrent-CNN Architectures
Get PDFDiabetic Retinopathy (DR) is a progressive eye disease that requires timely and accurate detection to prevent vision impairment. While convolutional neural networks (CNNs) have shown high efficacy in detecting DR from retinal images, they often fall short in capturing temporal changes across longitudinal patient data. This research proposes a hybrid deep learning framework integrating Recurrent Neural Networks (RNNs) with CNNs—termed Recurrent-CNN (R-CNN)—to analyze the temporal progression of DR. The model leverages sequential retinal images and clinical metadata to model disease evolution over time, enabling more granular stage prediction. We train and validate our approach on publicly available and proprietary longitudinal DR datasets, achieving notable improvements in progression prediction accuracy and temporal consistency compared to baseline CNN models. Our findings suggest that incorporating temporal dynamics significantly enhances the interpretability and clinical relevance of DR grading systems, providing a robust tool for ophthalmologists in proactive patient management
Statistical Learning for High-Dimensional Data: A Comprehensive Approach to Dimensionality Reduction in Machine Learning
Get PDFDimensionality reduction is a crucial process in machine learning, particularly when dealing with high-dimensional data. As the number of features increases, models often suffer from overfitting, computational complexity, and a lack of interpretability. This paper explores statistical methods for dimensionality reduction, focusing on techniques such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and t-SNE. These methods aim to preserve the underlying structure of data while reducing its dimensions for better model performance. By analyzing the mathematical foundations of these techniques, we evaluate their application across various machine learning models, demonstrating their utility in improving model efficiency and interpretability. Experimental results validate the effectiveness of these statistical methods in practical machine learning tasks
AI-Driven Innovation For A Sustainable Future: Transforming Healthcare
Get PDFThe integration of Artificial Intelligence (AI) into healthcare is reshaping the delivery and sustainability of medical services worldwide. This study investigates the impact of AI-driven innovation on promoting a sustainable future in healthcare, focusing on its effects on diagnostic accuracy, operational efficiency, environmental performance, and patient satisfaction. Utilizing a mixed-methods approach, data were collected from five countries through structured surveys, expert interviews, and secondary databases. Statistical analyses—including regression models, independent t-tests, and MANOVA—revealed significant associations between AI adoption and improvements in healthcare efficiency, cost reduction, and carbon footprint mitigation. Visual data further illustrated rising trends in patient satisfaction and strategic allocation of AI functions, particularly in diagnostics, imaging, and monitoring. The findings affirm that AI, when ethically and inclusively implemented, can serve as a cornerstone for transforming healthcare into a more sustainable, equitable, and technologically advanced system. This study provides strategic insights for policymakers, healthcare institutions, and technology developers aiming to align digital health innovations with long-term sustainability goals
Assessing Work-Life Balance And Teaching Outcomes In Self-Financing Colleges In Kerala
Get PDFThe study investigates work-life balance as a predictor of teaching performance among self-financing college teachers in Ernakulam district, Kerala. As private higher education institutions are growing at a rapid pace, teachers experience job insecurity, overwork, and lack of institutional support, impeding their well-being and academic performance. With a descriptive-analytic approach supported by Work-Life Border Theory and the Job Demands-Resources Model, the responses of 91 teachers were compared quantitatively using descriptive and inferential statistics. The findings report a young, female-dominated workforce balancing professional and heavy caregiving responsibilities, often confronted with role overload, stress, and moderate institutional support. Quality of Work Life (QWL) dimensions—i.e., work quality, work-life balance, stress management, and financial management—were significantly connected to personality development and career progression but less connected to teaching and research skills. Regression analysis identified these dimensions as predictors of teacher development outcomes, reflecting the importance of supportive work culture in enhancing career progression and personal well-being. The study highlights the ongoing negotiation between professional and personal requirements that the faculty are in, with many experiencing conflicts that intrude into personal time, as envisaged in Life Border Theory. With some flexibility in institutions, wellness programs are still insufficient to completely negate worklife tensions. Gender, household responsibilities, and educational levels had significant roles in predicting the outcomes of QWL, with female faculty and those having organized home roles having more work-life congruence. The research highlights the need for selected institutional policies to enhance the working conditions of faculty members, offer better opportunities for professional development, and facilitate well-being to ultimately enhance teaching performance and professional development in Kerala's self-financing college system