Volume 14 | Issue 5 |

 Abstract

Sciatica can cause pain and discomfort in the lower back or lumbar spine, buttocks, and legs. This back pain makes it difficult to engage in everyday activities. However, practicing specific yoga poses can help relieve the symptoms, soothe nerve pain, and improve your overall well-being. In this article, we will guide you through a series of beginner-friendly yoga exercises designed to target sciatica, allowing you to find relief and enhance your flexibility and strength. Let's dive into the world of yoga and discover the power it holds in alleviating sciatic pain.

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Keywords : Ayurveda ,Yoga , Iyenger props

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Social media platforms, which offer communication, entertainment, and educational opportunities, have become an indispensable element of students' everyday lives. However, overuse has led to social media addiction, which has a detrimental impact on social interaction, productivity, mental health, and academic achievement. This study examines usage patterns, behavioral trends, screen time, academic impact, and emotional repercussions of social media addiction among teenagers using data analytics. Addiction levels and their impact on student performance are determined using statistical and machine learning methods. The study emphasizes how extended use of social media can lead to anxiety, poor sleep, decreased focus, and poor academic performance. The suggested analysis shows how data analytics may be used to identify addictive behavior and offers preventative strategies for kids to use social media in moderation.

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Social Media Addiction, Data Analytics, Student Behavior, Machine Learning, Academic Performance, Mental Health, Big Data

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Traditional healthcare and supply chain systems operate independently and rely heavily on manual processes, resulting in poor coordination, limited visibility, delayed decision- making, and inefficient resource utilization. In the healthcare sector, this leads to problems such as appointment congestion, lack of remote consultation facilities, delayed access to medicines, and inefficient management of medical records and supplies. Existing systems also lack intelligent support for predicting demand, managing inventory, and monitoring service usage patterns. To address these challenges, this project proposes an integrated digital platform that combines a telemedicine system with an AI- enabled digital supply chain framework. The telemedicine module allows patients to book appointments, consult doctors remotely, receive digital prescriptions, and access medical reports through an online platform. At the same time, artificial intelligence techniques are applied to analyze healthcare usage data, appointment trends, and medicine demand to support better planning and decision-making. AI-based analysis helps in predicting service demand, identifying resource shortages, and improving the availability of medical supplies and services. Compared to the existing system, the proposed solution reduces manual workload, improves accessibility to healthcare services, enhances decision accuracy, and enables efficient coordination between healthcare delivery and supply chain operations. The project demonstrates how integrating telemedicine with AI-driven digital supply chain concepts can improve healthcare accessibility, operational efficiency, and overall system reliability.

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Digital Supply Chain, Telemedicine Platform, Artificial Intelligence, Healthcare Management System, Demand Forecasting, Inventory Optimization, Digital Healthcare.

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The implementation of car damage detection automation has become a pivotal development in the automotive industry, offering substantial improvements in safety, efficiency, and cost-effectiveness. This paper provides a thorough insight into the progress and methodologies associated with automating the car damage detection process, leveraging a variety of technologies including deep learning, computer vision, and transfer learning. Conventional approaches to car damage assessment frequently hinge on manual inspections conducted by experts, introducing subjectivity, time inefficiencies, and the possibility of human errors. To address these challenges, automated systems incorporating computer vision, machine learning, and artificial intelligence methodologies have been devised. These systems amalgamate image processing algorithms and deep learning models to effectively discern and categorize various car damages, including dents, scratches, cracks, and structural deformations. The integration of advanced Convolutional Neural Networks (CNNs) enhances the system's ability to detect even minor imperfections under varying lighting and environmental conditions. Three CNN architectures -- MobileNet, ResNet50, and InceptionV3 -- are evaluated and compared on a curated vehicle damage dataset. Results demonstrate that ResNet50 achieves the highest validation accuracy of 94.1%, significantly outperforming manual inspection benchmarks. Cloud-based deployment and mobile integration further extend the system's practical reach, enabling real-time damage evaluation for insurance companies, repair shops, and individual vehicle owners.

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Deep Learning

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Globally, road accidents have been among the major causes of death and severe injuries, which has been a major challenge to the mode of transport and the safety of the people. The driver fatigue has been cited as one of the major causes of these accidents and this may be underrated. Fatigue causes a loss of alertness to a driver, decreases his or her reaction time, deteriorates judgment and predisposes the driver to making errors. Other factors like travelling long distance, lack of sleep, abnormal working hours, and tedious driving conditions further increase drowsiness, which causes the drivers to be at risk of accidents.To adequately deal with this urgent problem, this project suggests the development of a highly-developed Driving Management System which is expected to improve the road safety by continuously and real-time monitoring of the condition of the driver. The system employs sensor-based technologies and intelligent methods of monitoring to monitor physical and behavioral patients of fatigue. These parameters are the rate of blinking of the eyes, the time of closing of the eyes, the pattern of movement of the head (like nodding) and the facial expression. The system is able to identify these indicators in order to alert against early cases of drowsiness before it turns into a hazardous caseThe main characteristic of the suggested system is that it is able to offer instant feedback and warning systems. As a signal of fatigue is detected, the system automatically sets off warning systems like buzzers, vibrations or audio messages to capture the immediate attention of the driver. This is a proactive measure, which assists in avoiding accidents through ensuring that corrective measure is undertaken at the appropriate time.The system is also meant to be inexpensive, user friendly, and easily adaptable to the needs of various kinds of vehicles such as the public transport (buses), commercial vehicles (trucks, taxis) and the personal cars. It is an easy to install, and effective to use solution and thus can be used in practical implementations. Future technologies like IoT and AI can also be combined with the system to be more precise and intelligent in data analysis.In sum, the suggested Driving Management System can play a vital role in ensuring the safety of the road setting, reducing the number of risks associated with fatigue, increasing the driver awareness, and protecting the lives of the drivers, passengers, and pedestrians. This project reiterates the need to incorporate transport systems with technology to make the driving experience a safer and more reliable one

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Driver Fatigue Detection, Driving Management System, Road Safety, Drowsiness Monitoring, Automatic Alert System, Sensor-Based Technology, Real- Time Monitoring.

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The present research was conducted with the objective of understanding and assessing the emotional intelligence of secondary-level students. Through this study, an attempt was made to determine how students understand and control their emotions and how they behave toward the emotions of others. Under this, the emotional intelligence of the students was assessed through standardized tools and questionnaires included in them. For the study, 200 students were selected from secondary schools in the district of Lucknow. The researcher used the Emotional Intelligence Scale developed by A. K. Singh and Shruti Narain, which is a standardized tool, to measure the emotional intelligence of the students. In this study, the survey method was used, and the results revealed that students with a higher level of emotional intelligence appeared more balanced in their behavior, relationships, and decision-making. On the other hand, students with comparatively lower emotional understanding faced difficulties in stress management, anger control, and adjustment. The study concludes that family, school environment, peer group, and teachers’ behavior influence the emotional intelligence of students. If students are provided with proper guidance, support, and opportunities to express their emotions, their emotional intelligence can be further developed.

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Education, Holistic Development, Emotional Intelligence, Self-Control, Guidance.

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Children represent one of the most vulnerable sections of society, and increasing crimes against them pose serious social and developmental challenges in India. This study explores trends and regional differences in crimes against children across 28 Indian states from 2013 to 2023. Crimes against children are divided into three major categories: violent crimes, sexual exploitation and trafficking, and other indirect or socially embedded crimes. The analysis uses Year-to-Year (Y-O-Y) growth rates and Compound Annual Growth Rate (CAGR) to examine short-term variations and long-term trends. The results reveal that the pattern of crimes against children has changed considerably over the years, with trafficking and sexual exploitation showing a sharp increase in several states. At the same time, indirect forms of abuse, including neglect and cyber-related victimization, have also grown steadily. Significant interstate disparities are observed, indicating that socio-economic inequality, poverty, migration, lack of awareness, and weak institutional mechanisms continue to influence child vulnerability. While some states performed relatively better in controlling these crimes, others experienced persistent increases throughout the study period. The study stresses the importance of strengthening child protection policies, improving awareness, and adopting state-specific strategies to reduce crimes against children in India.

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Crimes Against Children, State Disparities, Child Safety, Sexual Offenses, Trafficking, Regional Analysis

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Marble processing industries generate substantial quantities of slurry and fine particulate matter during cutting, grinding, and polishing operations. These wastes are often regarded as chemically inert; however, growing evidence indicates that they actively influence the geochemical behaviour and environmental mobility of heavy metals. The present study evaluates the chemical behaviour, transformation mechanisms, and environmental fate of key heavy metals -- chromium (Cr), lead (Pb), cadmium (Cd), nickel (Ni), arsenic (As), and mercury (Hg) -- associated with marble processing activities in the Jabalpur Division, Central India. A comprehensive environmental assessment was conducted through systematic sampling of marble slurry, airborne particulate matter (PM?? and PM?.?), soil, and groundwater from major processing zones. Heavy metal concentrations were determined using Atomic Absorption Spectroscopy (AAS) and Inductively Coupled Plasma Mass Spectrometry (ICP-MS) following standard acid digestion protocols. The geochemical framework of the study area is dominated by carbonate-rich systems, where dissolution of calcium carbonate (CaCO?) produces alkaline conditions (pH 8.2-9.5). These alkaline environments significantly influence metal speciation, solubility, adsorption desorption equilibria, and redox transformations. The results reveal that chromium exhibits high mobility under alkaline conditions due to the predominance of hexavalent chromium [Cr(VI)] in the form of soluble chromate ions, posing a significant risk to groundwater quality. In contrast, lead and cadmium are largely immobilized through the formation of stable carbonate and hydroxide precipitates, although periodic changes in pH and redox conditions may trigger their remobilization. Nickel shows a strong association with fine airborne particulates, indicating that atmospheric transport is a major pathway for its dispersion and human exposure. Arsenic and mercury demonstrate complex redox-dependent transformations, with mercury undergoing potential methylation and arsenic exhibiting variable mobility depending on oxidation state. The study further highlights the role of marble slurry as a reactive geochemical medium that regulates heavy metal distribution through buffering capacity, mineral precipitation, surface complexation, and redox interactions. These findings challenge the conventional perception of marble waste as inert and emphasize its active role in contaminant dynamics. The research underscores the need for effective waste management strategies, continuous environmental monitoring, and regulatory interventions to mitigate long-term ecological and human health risks associated with marble processing activities.

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Transformation Mechanisms, Adsorption desorption equilibria, Redox transformations,

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Eka Kushta is a form of Kshudra Kushta that resembles psoriasis in its clinical presentations and is primarily caused by an imbalance of the Vata and Kapha Doshas. Round plaques covered in silvery scales and erythematous, clearly delineated papules are the clinical features of psoriasis, an immune-mediated illness. The elbows, scalp, knees, and gluteal cleft are the most often affected locations. Ayurveda describes a number of therapy methods, including Shamana and Shodana, for managing Eka kushta. As a result, in the current case study, an 10 -year-old boy who presented to the Kaumarabhritya Out Patient Department with elbow, knee, hand, and foot symptoms of Eka kushta was treated with internal medication (Shamana chikitsa) and Shodana chikitsa(virechana).

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Ayurveda, Ekakustha, Psoriasis, shaman chikitsa, shodhan chikitsa

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This study presents a Dual Mode Intelligent Assistant System designed to provide hands-free human-computer interaction using voice recognition and EMG-based neuro-control techniques. The proposed system enables users to control computer operations through two independent modes: Voice Mode and Neuro Mode. The Voice Mode allows users to perform tasks using speech commands, while the Neuro Mode utilizes facial Electromyography (EMG) signals, eye movement tracking and blink detection for cursor control and navigation. The system supports various operations including mouse movement, clicking, scrolling, typing and application launching without the need for conventional input devices such as a keyboard or mouse. EMG signals acquired from facial muscle activity are processed using machine learning algorithms to improve classification accuracy and system stability. The integration of multimodal interaction methods enhances accessibility, usability and flexibility especially for users with limited physical mobility. The proposed system offers a low-cost, portable, and efficient assistive technology solution for advanced human-computer interaction applications.

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EMG (Electroencephalography) signal, Brain Computer Interface (BCI), Voice Mode, Human Computer Interaction (HCI).

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