Sri Sairam Institute of Technology

This study investigates the tribological behaviour of friction stir processed (FSPed) AA8011 surface composites reinforced with 1–3 wt

Video-based human action recognition is the popular research areas in the field of computer vision. The emergence of deep learning model is effective in extracting spatial features and local patterns within the video. However, because of the large number of parameters and resource requirements, these methods frequently face difficulties with computational inefficiency. A novel framework for the identification and categorization of human actions utilizing videos is suggested as a solution to these problems. Important steps in the process include pre-processing, segmentation, feature extraction, and activity detection. Initially, the input video is preprocessed using the Gaussian filter to reduce noise and focus on crucial visual content. Subsequently, an Unpooling layer Assisted SegNet (UASN) model is proposed to divide the preprocessed images into meaningful segments. Subsequently, relevant features such as shape, color, and an improved Pyramid Histogram of Oriented Gradients (PHoG) are extracted from the segmented outcome to enhance detection accuracy. Finally, a hybrid model is proposed that combines an Improved Bi-directional Long Short-Term Memory (Bi-LSTM) and LinkNet for action classification. This integrated approach aims to improve efficiency of action detection through advanced deep learning techniques. Furthermore, the analysis of action recognition in videos was conducted using the UCF101 Videos and HACS datasets. The suggested Improved Bi-LSTM+LinkNet method is also compared to the existing methods. As a consequence, the proposed method performs better than the existing methods, obtaining an accuracy of 0.980 and specificity of 0.983 using the UCF101 Videos dataset and an accuracy of 0.978 and specificity of 0.988 using the HACS dataset.

In critical care scenarios, timely access to accurate patient data is paramount. This paper introduces MediVault, a comprehensive, end-to-end digital solution designed to securely consolidate and manage electronic health records (EHRs). Developed as a cross-platform mobile application using Flutter with a Supabase backend, MediVault centralizes a patient's medical history, including diagnoses, prescriptions, and reports–enabling healthcare professionals to retrieve essential information swiftly. The system employs device-based biometric authentication to uphold stringent privacy standards. By providing a unified platform with role-based access for patients, doctors, and administrators, MediVault reduces data fragmentation and improves continuity of care. The core objective is to support medical personnel with immediate access to vital health data, thereby minimizing clinical errors and improving patient outcomes in both emergency and routine healthcare settings.

This research paper presents a quantitative comparative evaluation of solar photovoltaic (PV) systems in Chennai (India), wind energy systems in Sweetwater (USA), and a hybrid PV-wind system in Cornwall (UK), using a common lithium-ion battery energy storage system. The simulations use location-specific irradiance and wind profiles synthesized from real meteorological statistics to enable controlled and consistent comparison across systems. MATLAB simulations are conducted while maintaining identical load demand, battery capacity, simulation horizon, and energy management strategy for all configurations. The evaluation considers generation profiles, energy curtailment, battery state-of-charge (SOC), and the levelized cost of storage (LCOS). The results indicate that standalone PV and wind systems exhibit significant variability due to fluctuating irradiance and wind speed conditions, whereas the hybrid system demonstrates enhanced supply stability and improved demand-serving capability. The hybrid configuration achieves a load-served fraction exceeding 90%, reduced unmet energy, and more effective utilization of the battery system. Comparative LCOS analysis further demonstrates lower storage stress and improved cost effectiveness for the hybrid system, highlighting its suitability for reliable standalone energy supply under variable renewable conditions.

This paper investigates the complex function of sports as a strategic facilitator for the United Nations’ 2030 Agenda. Sports have a special "convening power" that can speed up progress on several of the Sustainable Development Goals (SDGs), especially in health, education, gender equality, and environmental sustainability. This is because sports are usually seen as a fun activity. This research examines the contributions of sports to social, economic, and environmental factors by combining global initiatives, including the Paris 2024 Olympic sustainability guidelines and community-based programs such as the Diyar Women’s Sports Unit. The report also finds systemic problems, such as broken funding, unequal access, and the environmental impact of big events. It ends by suggesting a unified policy framework that changes sports from a separate activity to an important part of national development plans, with a focus on circular economy models and universal design for infrastructure