Volume 13 | Issue 4 |

 Abstract

Depression is the most common psychological problem in every student's life. Wherever belongs to you the arts or science stream. The aim of the research paper is to measure depression among students studying arts and science stream at Government Vidarbha Institute of Science & Humanities in Amravati District. The study was conducted over a sample of 60 students (30 Arts students & 30 Science students), age range from 18 to 21 years. The Mental Depression Scale developed by L. N. Dubey is used for data collection. Data was analyzed by means, standard deviation and 't' test. The result shows, There is a gender difference in depression level between the arts and science students. In Arts, girls have slightly higher depression than boys but in Science boys have slightly higher depression than girls. Also found that, there are no significant differences between arts and science students.

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 Keywords

Depression, Arts and Science Students

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Creative Commons Attribution 4.0 and The Open Definition

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Telecom forecasting is an essential task for telecommunication providers who are aiming to reduce customer deductions and improve customer loyalty. In machine learning technology, ensemble methods such as multi-model speech classifiers have shown promising results in this field. This test compares the performance of individual models with an ensemble melody approach using key metrics such as accuracy, accuracy, Recall, F1 score, and ROC-AUC. The results show that individuals using multi-model language classifiers, particularly soft adjustments, can provide more reliable predictions and contribute to communication providers to more effectively identify endangered species customers.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Prediction by using Voting Classifier, Multi-Model Approach, Predictive Analytics, Classification Models, Supervised Learning.

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Battery packs in electric vehicles and energy storage systems can suffer various faults that pose safety hazards, including cell internal short circuits (ISC), thermal runaway (TR), sensor failures, and battery management system (BMS) malfunctions. Data-driven machine learning methods have shown promise in detecting such faults early, but their black-box nature raises trust and safety concerns. Explainable Artificial Intelligence (XAI) techniques have emerged to bridge this gap by providing insight into model decisions. This review surveys recent research on XAI-based fault diagnosis in multi-cell lithium-ion battery packs. We discuss traditional model-based approaches (using first-principles battery models and state observers) versus data-driven approaches (machine learning classifiers and anomaly detectors), highlighting how XAI methods can augment the latter. Key XAI techniques including feature attribution methods like SHAP and LIME, attention-based deep learning models, and surrogate modeling are explained and compared. Recent studies have applied XAI to identify the sensor signals and features most indicative of faults (e.g. sudden drops in cell voltage or rises in internal resistance). We compile these findings, noting each method's strengths and limitations. Challenges such as the need for large labeled fault datasets, real-time explainability, and validation of XAI outputs are identified as open issues. Overall, XAI can enhance the transparency and reliability of data-driven fault diagnosis in multi-cell battery packs, enabling safer battery management through human-interpretable insights.

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 Keywords

Battery Fault Diagnosis, Explainable AI, Lithium-Ion Battery, Multi-Cell Battery Pack

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Creative Commons Attribution 4.0 and The Open Definition

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The Companies (Amendment) Bill, 2020, introduced by the Indian government, represents a significant step in simplifying corporate laws and fostering a business-friendly environment. This amendment aims to ease the compliance burden on companies, especially startups and small businesses, while decriminalizing minor offenses, streamlining corporate governance, and enabling easier access to capital markets. The reform is part of a larger initiative to boost India's position in the World Bank's Ease of Doing Business rankings, attract foreign investment, and stimulate economic growth. This article provides a comprehensive analysis of the Companies (Amendment) Bill, 2020, examining its key provisions, impact on the business landscape, and the challenges it presents. By comparing the amendment with previous reforms and analyzing its implications for various sectors, this article explores whether the 2020 amendment is a genuine step towards a more conducive business environment or a compromise on corporate governance standards. The discussion also includes an international perspective to assess how India's reforms align with global best practices and what lessons can be drawn for future amendments.

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 Keywords

Companies (Amendment) Bill, 2020, Corporate Law Reform, Ease of Doing Business, Decriminalization of Offenses, Corporate Governance, CSR Flexibility

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Creative Commons Attribution 4.0 and The Open Definition

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Sujata is a significant figure in Buddhist tradition, primarily remembered for offering milk-rice (kheer) to Siddhartha Gautama before his enlightenment. This act of generosity played a crucial role in reviving the Bodhisattva's strength after years of extreme asceticism, ultimately leading him towards the Middle Path and eventual enlightenment. Despite Sujata's prominence in Buddhist lore, there is limited historical clarity regarding her social and caste background. Scholars and historians have debated her origins, with some suggesting she belonged to the Yadava community. This paper explores textual references, historical interpretations, and socio-cultural contexts to examine this claim. P?li Canon and other early Buddhist texts provide limited information about Sujata's lineage but describe her as the daughter of a wealthy householder in the village of Senani, near Uruvela (modern-day Bodh Gaya). The D?ghanik?ya and J?takas mention her in passing, focusing more on her act of offering rather than her ancestry. The term 'Senani' is sometimes interpreted as a title for a village headman or military leader. Some interpretations of these texts argue that since she was a cowherd's daughter, she may have belonged to a pastoral community, which has led to speculations linking her to the Yadava or Gopa (cowherd) lineage. Yadavas, historically associated with cow-rearing and pastoral traditions, have long been identified with Lord Krishna's lineage in Hindu texts. The Mahabharata and Puranas describe them as a warrior and agricultural community, which aligns with descriptions of Sujata's family as wealthy landowners and cattle rearers. Some scholars argue that the association between Sujata and the Yadava community may stem from the broader historical reality that many pastoral and agricultural groups in ancient India had overlapping identities. The fact that her father was referred to as a 'Senani' could indicate an administrative or warrior class background rather than a purely pastoral one. The caste structure in ancient India was fluid, and communities were often categorized based on occupation rather than rigid birth-based hierarchy. If Sujata's family were indeed cowherds, it is very much plausible that they were associated with the Yadava tradition.

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Senani, Yadav, Agricultural, Pastoral, cowherd, Lineage. Hierarchy, Pali-canon

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Creative Commons Attribution 4.0 and The Open Definition

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The rapid growth of electronic waste (e-waste) has emerged as a significant global environmental challenge due to its toxic components, which can harm both the environment and human health if not properly handled. This study examines the dangerous materials in e-waste, their potential environmental and health impacts, and current management approaches in various countries. Advanced nations have implemented strategies such as life cycle assessment (LCA), material flow analysis (MFA), multi-criteria analysis (MCA), and extended producer responsibility (EPR) to address e-waste issues more effectively. Exposure to dangerous chemicals and e-waste can have major negative effects on the environment and human health. Research emphasizes the hazards of organophosphorus chemicals, incineration pollutants, and the existence of harmful materials in carpets. The disposal of e-waste in landfills, illicit imports, and lax laws are the main issues facing the United States and China. While large companies utilize better procedures than smaller ones, informal recycling practices in poor nations like India cause serious harm. Stakeholder participation, formal recycling systems, stricter legislation, and technology improvements are all necessary for effective management in order to minimize harmful exposure and guarantee sustainable waste processing. E-waste is a problem that is being faced by almost all countries globally but more so by developing countries as the e-waste is imported to these countries in bulk. Hence each of us must be committed to cutting down the waste and use, reuse and recycle all e-waste.

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recycling, waste management, e-waste, life cycle assessment , material flow analysis and sustainable waste.

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Creative Commons Attribution 4.0 and The Open Definition

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The rapid advancement of Smart Industrial Internet of Things (IIoT) technologies demands highly secure, scalable, and efficient architectures for data processing and management. Traditional cloud-centric IIoT models face challenges such as high latency, centralized security vulnerabilities, and inefficient resource utilization. To address these limitations, this paper proposes an integrated framework that combines Blockchain, Software-Defined Networking (SDN), and Edge/Fog Computing. Blockchain provides decentralized authentication, immutable data storage, and enhanced trust among IIoT devices. SDN enables dynamic network management, traffic optimization, and real-time policy enforcement, while Edge/Fog Computing reduces dependency on centralized cloud servers by enabling local data processing.

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Blockchain, Software-Defined Networking (SDN), Edge Computing, Fog Computing, Cloud Computing, Industrial Internet of Things (IIoT), Security, Scalability, Low Latency, Decentralized Architecture.

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Creative Commons Attribution 4.0 and The Open Definition

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The real estate industry has long driven economic expansion, yet remains highly exposed to financial irregularities and fraudulent practices. This study examines efforts to enhance fraud prevention and financial transparency among companies in the Mumbai Metropolitan Region (MMR) through the implementation of Ind-AS 8 and CARO 2020. Artificial Intelligence (AI) tools have been applied to evaluate the effectiveness of these standards in identifying and addressing fraud. Findings suggest that AI-driven financial analysis is significantly boosting fraud detection capabilities, strengthening regulatory compliance, and improving operational efficiency.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Ind-AS 8, CARO 2020, Fraud Prevention, Real Estate in Mumbai Metropolitan Region, AI in Financial Reporting

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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: Papilio demoleus, meteorological factors, population dynamics, pest management, correlation analysis.

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Abstract: Stroke remains one of the leading causes of adult disability worldwide, often resulting in significant motor impairments and loss of functional independence. Functional Electrical Stimulation (FES), a modality that uses low-frequency electrical currents to activate paralyzed or weakened muscles, has gained attention as a potential intervention to enhance motor recovery in stroke rehabilitation. This systematic review aimed to evaluate the effectiveness of FES in improving motor function, reducing spasticity, enhancing gait, and promoting overall functional independence in post-stroke individuals. A comprehensive literature search across six databases yielded 856 articles, of which 18 met the inclusion criteria after thorough screening and quality assessment. The findings consistently indicate that FES, either alone or in conjunction with conventional therapy, can significantly improve upper and lower limb function, gait performance, and neuroplasticity. However, the diversity in stimulation parameters, outcome measures, and methodological quality among studies highlights the need for standardized protocols and more rigorous trials. Overall, FES shows promise as an effective adjunctive tool in post-stroke rehabilitation programs, supporting its integration into routine clinical practice.

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 Keywords

Keywords: Functional Electrical Stimulation, Stroke Rehabilitation, Motor Recovery, Neuroplasticity, Gait Training, Spasticity Reduction, Systematic Review

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Creative Commons Attribution 4.0 and The Open Definition