Volume 14 | Issue 4 |

 Abstract

With consequences for both teachers and students, teacher burnout is becoming more widely acknowledged as a systemic problem in education. This study examines burnout, job satisfaction, coping mechanisms, and occupational stress in 22 Indian in-service teachers. Results show that almost 75% of teachers deal with ongoing stress, but coping strategies like self-care and social support maintain job satisfaction. The majority of teachers report feeling good about their jobs despite having a lot on their plates. To improve teacher well-being and classroom effectiveness, institutional reforms--such as cutting paperwork, bolstering collegial support, and encouraging autonomy--are advised.

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 Keywords

Teacher Burnout, Coping Strategies, Job Satisfaction, Occupational Stress, Teacher well-being, Indian Education

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BACKGROUND: Adolescence, the second decade of life, is a critical time that requires substantial adaptation to social and physical changes. Adolescents are vulnerable to a various psychosocial issues, which have long term effect if adequate care and attention are provided during this time of transitional phase. AIM: The main aim of the study is to estimate the prevalence of school going adolescents with psychosocial dysfunctions. METHODOLOGY: The objective of our investigation was to evaluate the psychosocial dysfunction of adolescents (12-18 years old) in a few Mohali schools and to ascertain whether psychosocial dysfunction and a few demographic factors are related. Both a quantitative research approach and a descriptive cross-sectional research design were utilized in this investigation. A pediatric symptoms checklist (Y-PSC) was used to measure psychosocial dysfunction in the 100 adolescents who were chosen using a convenience sampling strategy. RESULT: According to the study's findings, 29% of participants experienced psychosocial disorder, whereas 71% did not. With a standard deviation of 7.76 and a mean Y-PSC score of 23.63, the group appears to have mild to moderate symptoms. No significant association was found between psyschosocial dysfunction and selected demographic variables. CONCLUSION: Although it seems to be unaffected by socio demographic variables, the study finds that psychosocial dysfunction is common among a significant proportion of adolescents. Therefore, it is advised that early screening and support be provided to meet the emotional and behavioral requirements and that more proactive steps such as educating and raising awareness among students and providing training to parents and educators.

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 Keywords

Adolescents, Awareness, Psychosocial Dysfunction, and Y-PSC.

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The global shift toward electric mobility faces persistent obstacles, notably limited driving range, extensive charging wait times, and the massive financial investment required for stationary charging networks. This review explores the transformation of Electric Vehicle (EV) ecosystems, shifting from conventional Grid-to-Vehicle (G2V) frameworks toward more integrated, bidirectional models such as Vehicle-to-Home (V2H), Vehicle-to-Grid (V2G), and Vehicle-to-Vehicle (V2V). Central to this evolution is the V2V concept, which reimagines EVs as mobile energy hubs capable of distributing power to other vehicles in both stationary and dynamic environments. To support in-motion energy sharing, the study highlights innovations in Wireless Power Transfer(WPT) systems. Specifically, it examines specialized hardware--such as triangular transmitter coils--designed to maintain high efficiency even when vehicles are misaligned during platooning. These physical power links are supported by sophisticated communication protocols, including Dedicated Short-Range Communication (DSRC) and Vehicular Ad-hoc Networks (VANETs), which ensure secure, real-time coordination between moving units. Additionally, the review evaluates various optimization algorithms--from dynamic programming for fiscal efficiency to "virtual demand" modelling --aimed at maximizing the use of renewable energy. Together, these advancements represent a move toward a decentralized, resilient vehicular network that minimizes grid strain and reduces the need for permanent infrastructure.

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 Keywords

DC-DC Converter, Emergency EV Power Transfer, Vehicle-to-Vehicle Charging, Electric Vehicles, Energy Sharing System

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Late detection of academically struggling pupils is a longstanding difficulty in secondary and tertiary education. By the time poor grades appear in formal records, the window for meaningful support has typically already closed. Our aim in this study was to build a working tool that flags at-risk students early enough for staff to act on it. We assembled a labelled dataset of 649 secondary school pupils covering attendance, family circumstances, study habits, and term-by-term marks, then ran six supervised classifiers against it: Logistic Regression, Decision Tree, SVM, AdaBoost, XGBoost, and a bagged tree ensemble. We then stacked all six behind a trained meta-learner, which pushed test accuracy to 93%, ahead of every individual model. Precision, recall, and F1 all matched that figure for the proposed stacker. The trained model was incorporated into a lightweight Flask application that lets non-technical staff enter a pupil's details and receive an immediate pass-or-fail forecast. This paper covers the full workflow: data cleaning, encoding, model selection, stacking architecture, and web deployment.

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 Keywords

Student Academic Performance Prediction, Machine Learning, Supervised Classification, Ensemble Learning, Stacking Model, XGBoost, AdaBoost, Decision Tree, Support Vector Machine, Logistic Regression, Educational Data Mining, Predictive Analytics in Education, Student Risk Assessment, Feature Engineering, Data Preprocessing, Flask Web Application, Early Intervention System, Academic Outcome Forecasting

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Core Proposal The system uses an extended Kalman filter (EKF) for on-board diagnosis of soft short circuits (SCs) in lithium-ion batteries (LIBs). EKF estimates state of charge (SOC) differences from real-time voltages to detect faults and assess severity via resistance values. Experiments on battery packs validate quick detection and accurate estimation. Key Challenges Lithium-ion batteries provide high energy density but pose thermal stability risks, including fire and explosion hazards in EVs. The paper reviews thermal runaway, fire dynamics in cells and packs, and fault detection for components like PMSMs and batteries. It stresses needs for accurate, fast, sensitive, and cost-effective methods. System Design Data from sensors (voltage, current, temperature, vibration) feeds machine learning models trained on normal behavior to spot anomalies in battery, motor, or electrical systems. Real-time alerts enable predictive maintenance, cutting downtime and costs while boosting safety. Applications Targets EV manufacturers, fleet managers, and maintenance providers for integration into vehicles. Advantages include enhanced safety, reliability, and efficiency.

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Primary Keywords Anomaly Detection Electric Vehicles (EVs) Lithium-ion Batteries (LIBs) Soft Short Circuits (SCs) Extended Kalman Filter (EKF) State of Charge (SOC) Thermal Runaway Fault Diagnosis Predictive Maintenance Machine Learning Secondary Keywords Permanent Magnet Synchronous Motors (PMSMs) Battery Management Systems Real-time Monitoring Voltage Abnormality Sensor Data (voltage, current, temperature, vibration)

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The rapid growth of digital fashion and personalized styling platforms has created a strong demand for intelligent virtual appearance recommendation systems. This paper presents an AI-Based Virtual Look Recommendation System that enables users to virtually try hairstyles and eyewear before making real-world decisions. The proposed system uses Google MediaPipe FaceMesh to detect facial landmarks from a single uploaded image and classifies the user's face into six common face shapes: Oval, Round, Square, Heart, Diamond, and Oblong. Based on the detected face shape and selected gender, the system recommends suitable hairstyles and glasses through a rule-based recommendation engine. Selected styles are rendered onto the user image using image overlay and compositing techniques. The backend is developed using Python FastAPI, while the frontend uses React.js to provide an interactive user experience. Experimental evaluation on a 50-image dataset achieved 94% face detection success rate, 83% face shape classification accuracy, and positive user satisfaction for generated results. The system demonstrates that effective virtual try-on experiences can be delivered using open-source technologies without expensive hardware or GPU infrastructure.

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Artificial Intelligence, Virtual Try-On, Face Shape Detection, MediaPipe FaceMesh, Hairstyle Recommendation, Eyewear Recommendation, Computer Vision, FastAPI

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This paper posits that editing as a practice in the cinema of Hirokazu Kore-eda is an ethical practice that redefines the image of death through absence and not visualisation. By referring to Maborosi and Still Walking, the paper presents the idea of editorial ethics of absence to explain how off-screen death, dilation of time, withheld flashback, and narrative ellipsis create a spectatorship mode, which is based on uncertainty and attention. Based on the realism of Andre Bazin, the time-image of Gilles Deleuze, and ethics of alterity by Emmanuel Levinas, the article shows that the editing of Kore-eda does not want the spectacle and explanatory closure. The paper examines the cut as a moral boundary that indicates the boundaries of representation through close examination of key sequences to demonstrate how the cut does not act as a device of narrative control. Temporality of grief, in which loss has not been resolved, makes the spectators align themselves with the lived in-betweenness of grief in the cinema of Kore-eda, which turns absence into an ethical form. The article adds to film theory by rebranding editing as a place of ethical containment and a broadening of debates on cinematic death beyond representation to the politics of not showing.

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Editing, Absence, Death, Ethical Cinema, Hirokazu Kore-eda, Time-Image, Spectatorship

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The IT training industry in India is characterized by intense competition, rapidly evolving technology demands, and an increasingly discerning student population. In this context, brand positioning has emerged as a critical determinant of student choice and institutional growth. This study examines the brand positioning and competitive strategies of Pantech Solutions, a well-established IT training organization operating across South India. Primary data were collected from 144 students and trainees through a structured questionnaire via Google Forms. Statistical tools including percentage analysis, mean score analysis, Pearson correlation, and Chi-Square tests were employed for analysis. Key findings reveal that Pantech Solutions enjoys strong brand awareness (Mean = 3.81) and is widely recognized for superior practical exposure (56.9% agreement), though critical gaps exist in training quality consistency (Mean = 3.10) and reliability perception (38.2% disagreement). The study concludes that while the institute operates from a meaningful brand foundation, strategic investment in quality delivery consistency, reliability communication, and value articulation is essential to translate awareness into durable competitive advantage.

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Brand Positioning, Competitive Strategy, IT Training Industry, Pantech Solutions, Brand Awareness, Student Satisfaction, Service Quality.

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ABSTRACT: The increasing demand for environmentally sustainable materials has encouraged the utilization of agricultural wastes as alternative sources of industrial raw materials. This study focuses on the production and characterization of silica extracted from coconut husk ash (CHA), an abundant agro-waste byproduct. Coconut husks were cleaned, dried and subjected to controlled combustion at varying temperatures (4000C, 6000C & 8000C) to obtain ash. The resulting ash was processed through acid leaching and alkaline extraction to isolate amorphous silica. The silica yield and purity were analyzed using X-ray fluorescence (XRF), X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM). Results were revealed that the optimized process produced silica with purity exceeding 90% exhibiting amorphous characteristics and fine particle morphology suitable for industrial applications. The findings demonstrate that coconut husk ash is a promising and sustainable source of high-purity silica, offering potential use in glass, ceramics and composite materials industries while contributing to waste minimization and value addition.

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Key Words: Coconut husk ash, Amorphous silica, XRD, Agro waste

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