Volume 14 | Issue 4 |

 Abstract

The impact of sustainable practices and ESG (Environmental, Social, and Governance) disclosure on investor decision-making in the Indian auto industry is examined in this study. The research aims to determine whether investors' understanding of ESG concerns effects their capital allocation decisions in light of the expanding global emphasis on responsible investment. The automotive industry, which is well-known for having a significant social and environmental impact, offers a pertinent context for this investigation. Using information gathered from 365 respondents with a range of educational backgrounds, the study uses a quantitative methodology. ANOVA, Pearson correlation, and linear regression are instances of statistical analyses. The results show that investors with more education are much more aware of sustainable practices and ESG disclosures. The influence of ESG scores on investing choices is positively correlated with knowledge levels in a statistically meaningful way. Interestingly, investment decisions are more influenced by knowledge of industry-specific sustainable practices than by broad ESG disclosure. The results highlight the increasing significance of ESG awareness in influencing investing behaviour, even if the regression model only accounts for a small percentage of the variance. In order to encourage sustainability-driven investment strategies, this study emphasizes the necessity of better ESG communication by automakers as well as the significance of investor education.

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 Keywords

ESG Disclosure, Sustainable Practices, Investor Decision-Making, India's Automobile Industry

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The increase in industrialization and urbanization has led to higher risks of exposure to harmful gases, which can pose serious health hazards and environmental threats. This paper presents the design and implementation of a Harmful Gas Detection and Alert System that continuously monitors the presence of toxic gases such as carbon monoxide (CO), methane (CH?), and ammonia (NH?) in the environment. The system integrates gas sensors, a microcontroller, and wireless communication modules to detect gas leakage in real-time. Upon detecting gas concentrations above predefined safety thresholds, the system triggers an audible alarm, visual indicators, and sends an instant alert via SMS or mobile application to concerned personnel. Experimental results demonstrate that the proposed system can efficiently detect harmful gas levels and provide timely warnings, thereby preventing accidents and enhancing workplace safety. This system is cost-effective, scalable, and suitable for deployment in homes, industries, and public spaces.

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 Keywords

Harmful gas detection, gas sensors, microcontroller, IoT alert system, safety monitoring.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Confessional Poetry is a type of narrative and lyric verse dealing with the facts and intimate mental and physical experiences of the poet's own life. In such poetry, like a mirror, the self is a primary concern which is treated with utmost frankness, and lack of restraint, written in ordinary speech and using open forms. The readers can connect their lives with the confessional poetry as it represents different aspects of human life. A group of poets who have become known as confessional has also engendered much debate. For the female writers, confessional poetry is a medium where they can expose their psychological feelings. Kamala Das has mostly been assessed as a writer in this genre of confessional poetry. She projects her emotion as well as represents the reality of the world through portraying her own experience. She has created a permanent place for herself in the contemporary Indo - Anglican Poetry by taking the literary world in the mid-sixties. She became one of the pioneering Post-Independence Indian English Poets to have contributed immensely to the growth and development of Modern Indian English Poetry.

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 Keywords

Confessional, engendered, psychological, contemporary, pioneering

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Lithium-ion batteries are widely used in electric vehicles and electronic devices, but they are prone to thermal runaway, which can lead to safety hazards. This paper presents an AI-based battery thermal runaway prediction system that monitors key parameters such as temperature, voltage, and current in real time. The system uses machine learning algorithms to analyze sensor data and predict potential failures before they occur. By integrating IoT technology, the system enables remote monitoring and timely alerts. This approach improves battery safety, enhances performance, and reduces the risk of accidents, making it suitable for modern energy storage and electric vehicle applications.

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 Keywords

Battery Management System, Thermal Runaway, Machine Learning, IoT, Predictive Maintenance, Lithium-ion Battery

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Heart disease remains a leading cause of death globally, necessitating early detection for effective intervention. This study introduces HeartFL, a scalable and efficient machine learning system that predicts heart disease risk using patient health data. Leveraging a Decision Tree algorithm, HeartFL analyzes critical medical factors--including age, cholesterol, blood pressure, and heart rate--to assess disease likelihood. The system supports privacy-preserving federated learning, enabling decentralized model training without exposing sensitive patient information. A user-friendly interface allows input of health parameters or medical report uploads for automated prediction. Experimental evaluation confirms the model's reliability and accuracy, facilitating timely diagnosis. By combining intelligent prediction with privacy-aware methods, HeartFL aims to enhance clinical decision-making and improve patient outcomes. The system is deployed as a web application and can be accessed at: https://adityaindana.pythonanywhere.com/

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 Keywords

Heart disease prediction, Machine learning, Decision Tree algorithm, Patient health data, Federated learning, Data privacy, Medical diagnosis, Early detection, Healthcare technology, Predictive modeling

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Creative Commons Attribution 4.0 and The Open Definition

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ESG investing will be the subject of this study, with a particular emphasis on non-financial aspects of the Indian investment sector, such as environmental, social, and governmental factors. To determine whether investors and investment managers in the Indian investment sector are likely to adopt these practices or do they possibly pose a threat to maximize returns and increase financial wealth for their clients and themselves? Investment managers and investors from different Indian investment organisations provided a total of 385 replies to the survey used to collect the primary data for the study. The sample is chosen using a random sampling technique. In order to collect data pertinent to the Indian investment sector and show a link between organisational success and sustainable investment, the study employed quantitative methodologies and a descriptive statistical approach. Journals, papers, newsletters, books, government portals, regulatory websites, stock exchanges, and annual reports are all sources of secondary data. Every question in the questionnaire is analysed using SPSS software. Here, the coefficient correlation between two variables is determined using Pearson's correlation analysis. It was discovered that there was a significant correlation between the factors influencing investors' interest in sustainable investments and the benefits they provided. The conclusion is that sustainable investments will increase economic transactions, financial responsibility, and transparency, all of which contribute to the long-term success of Indian organisations.

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 Keywords

Words: Environmental, Social, and Governance, Indian Investment, Investment managers and investors

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The problem of financial fraud detection is one of the major problems that are faced by the digital banks. The financial fraud losses incurred by the countries across the globe are estimated to be over 485 billion dollars annually. In this paper, the authors are proposing a production-grade real-time financial fraud detection system known as FraudGuard based on ensemble machine learning, behavioral DNA profiling, and graph-based network analysis for financial fraud detection with 88.1% model confidence. In the proposed system, the transactions are processed within 10ms with a 22-feature XGBoost model, synthetic transactions, and Indian banking patterns. Additionally, the proposed financial fraud detection system uses the WebSocket protocol. As part of the proposed system, the authors are able to attain a 0.941 AUC-ROC score, precision of 0.891, recall of 0.863, and F1-score of 0.877 along with multi-channel financial fraud alerting via SMS with the help of Twilio. Additionally, the proposed financial fraud detection system uses six different financial fraud detection patterns. Six financial fraud detection patterns are card testing, AML structuring, geographic impossibility, account takeover, velocity attacks, and cryptocurrency conversion fraud.

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 Keywords

Financial fraud detection, Machine learning, XGBoost, Behavioral analysis, Real-time systems, AML, SHAP, WebSocket streaming

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Creative Commons Attribution 4.0 and The Open Definition

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Independent navigation and social interaction remain challenging for individuals with visual impairments, necessitating the development of assistive technologies that enhance safety and independence. This work proposes a smart wearable system designed to provide real-time facial recognition and location-based support. The system utilizes a computer vision model, YOLOv8, deployed on a Raspberry Pi 5 platform to identify familiar individuals and deliver immediate audio feedback to the user. To ensure user safety, a GPS module is incorporated for continuous location tracking, while a GSM module enables emergency communication by sending alerts and initiating calls to predefined contacts during critical situations. All components are integrated into a compact and lightweight wearable configuration consisting of camera-equipped glasses connected to a portable processing unit. By focusing on essential functionalities such as facial recognition, real-time tracking, and emergency connectivity, the proposed system offers an efficient and reliable assistive solution, thereby promoting greater autonomy, confidence, and social interaction for visually impaired individuals.

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 Keywords

Assistive Technology, Computer Vision, Raspberry Pi, YOLOv8, Visual Impairment.

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Creative Commons Attribution 4.0 and The Open Definition

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 Keywords

SDG 11 (Sustainable Cities and Communities), Urban unemployment, Sustainable urban development, State-level analysis, India, Urbanization, Population density, Per capita NSDP.

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The present study aims to explore the relationship between interpersonal communication and psychological resilience among professionals working in crime scene investigations. The study seeks to understand communication patterns, identify challenges faced during collaborative work, and examine how resilience contributes to effective teamwork in high-pressure situations. A mixed-method research design was adopted for the study. Data were collected from approximately 50 participants, including crime scene experts, Forensic scientists, and Police officers, using purposive sampling. Information was gathered through structured questionnaires and socio-demographic forms to assess communication practices, stress experiences, and resilience levels. In addition, semi-structured interviews were conducted to obtain deeper insights into communication dynamics, coping mechanisms, and collaborative experiences in investigative settings. The study emphasizes the importance of strong interpersonal communication and psychological resilience in enhancing teamwork, improving coordination, and supporting mental well-being among Forensic and Police professionals. The findings are expected to provide valuable insights for developing training programs and workplace interventions aimed at strengthening communication skills and resilience among personnel working in high-stress investigative environments

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Interpersonal Communication, Psychological Resilience, Forensic Science, Police Personnel, Crime Scene Investigation, Occupational Stress, Team Collaboration, Mental Health in Law Enforcement.

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Creative Commons Attribution 4.0 and The Open Definition