Volume 14 | Issue 2 |

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Abstract: Education has always been the foundation of civilization and progress. It is not merely the transmission of knowledge but the cultivation of human potential, creativity, and social consciousness. In the twenty-first century, humanity faces rapid technological changes, environmental crises, global inequalities, and shifting socio-political landscapes. To address these challenges, educational reform must move beyond rote learning and outdated paradigms to embrace inclusivity, innovation, and holistic development. This paper explores the necessity of educational reform, the shortcomings of current systems, and the pathways toward a more equitable, future-ready, and transformative model of education. By integrating digital literacy, experiential learning, ethical values, and sustainability, education can truly become the vehicle for building a better tomorrow.

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Education, Educational Reform, Digital Literacy, Holistic Development.

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 Abstract

Lokbhashela Dharamik Va Sahityik Darja Denyat Mahanubhav Panthachi Bhumika

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Lokbhashela Dharamik Va Sahityik Darja Denyat Mahanubhav Panthachi Bhumika

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ABSTRACT Thyroid hormones, including thyroxine (T4) and triiodothyronine (T3), play a crucial role in regulating growth, metabolism, and reproduction in fish. Studies also indicate that thyroid hormones can affect gonadal steroid syntheses and sexual maturation in fish. This study enhances our understanding of the multi hormonal control of teleost metabolism and provides a baseline for molecular investigations. The present experiment was done during the off period of their breeding season. Testosterone is an anabolic steroid, and research on its impact on thyroid function, especially in fish, is pretty-specific generally, steroids can influence thyroid function, but outcomes vary based on species, dosage, and duration. Testosterone and thyroxine have significant effects on the thyroid gland of Clarias batarachus.

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Keywords: - Clarias Batarachus, Testosterone Propionate, Thyroxine, Thyroid Hormones, and Metabolism.

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The rapid adoption of electric vehicles (EVs) has significantly increased the importance of reliable battery health monitoring to ensure operational safety, performance optimization, and extended battery lifespan. Conventional battery management systems (BMS) primarily rely on threshold-based and model-driven techniques, which often lack adaptability to dynamic operating conditions and early fault detection. To address these limitations, this paper presents an intelligent EV battery health monitoring system using TinyML, designed for real-time, edge-level inference on resource-constrained embedded hardware The proposed system integrates voltage, current, and temperature sensing using a lithium-ion battery pack interfaced with an ESP32 microcontroller. A lightweight TinyML model is trained offline using experimentally collected battery parameters and deployed on the microcontroller using TensorFlow Lite for Microcontrollers. The model performs on-device inference to classify battery health conditions into predefined states such as healthy, warning, and critical, enabling proactive fault detection without dependency on cloud-based computation. Real-time battery parameters and health status are transmitted to a cloud platform using Wi-Fi connectivity for remote monitoring and visualization. Experimental results demonstrate that the proposed system achieves reliable health classification with low inference latency and minimal memory footprint, making it suitable for embedded EV applications. The system offers a scalable, low-cost, and energy-efficient solution for intelligent battery health monitoring, highlighting the potential of TinyML-based edge intelligence in next-generation EV battery management systems.

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TinyML, Electric Vehicle Battery Health, Edge Intelligence, Internet of Things (IoT), ESP32, Lithium-Ion Battery

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India's gig economy is expanding at a fast rate as it is providing certain flexibilities to workers and now emerged as a point of discussion among policymakers. However, the compatibility of the gig economy with sustainable development goals is a largely untouched area of research in terms of policy compliance. The existing research is primarily focused on the implications of the gig economy in terms of jobs and working conditions, without addressing the research gap on the compatibility of gig economy governance with the SDGs, leaving the area ripe for further inquiry. This paper addresses this gap by examining the policy dealing with the gig economy in India against the selected SDG targets, namely SDG 8.5, SDG 8.8, SDG 10.4, and SDG 16.6. The study uses a qualitative analysis of secondary data sources, which includes labour legislation, government policy documents, SDG indicator frameworks, and international labour standards. Major policy guidelines for gig economy or platform workers were identified against the Sustainable Development targets through an alignment scorecard. The result shows that the Indian policy for the gig economy was able to identify gig economy work, but there is only partial and uneven alignment as per the SDGs. Social protection measures are divided and lack unity; labour laws are not enforceable, and institutional coordination is poor. The paper suggests that without specific integration of SDG into gig economy governance, serious risks regarding regulation will continue instead of contributing to sustainable development. It concludes with the targeted policy recommendations to improve the SDG compatibility of gig economy regulation in India.

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Gig economy; Sustainable Development Goals; Labour regulation; Social protection.

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English pronunciation plays a critical role in effective communication, yet many learners face challenges due to regional accents, lack of feedback, and limited access to guided learning. With advancements in artificial intelligence and speech processing, this paper presents AuraVoice, an AI- powered application designed to detect and correct pronunciation errors in real-time. The system integrates speech recognition, phoneme analysis, and feedback delivery using tools like the CMU Pronouncing Dictionary, Levenshtein Distance, Librosa, and gTTS. A mobile-friendly interface is developed using Flutter, with backend support from Flask and SQLite. This platform offers an interactive, scalable solution for self-paced spoken English improvement. It promotes accessible learning through intuitive feedback mechanisms and offline capability. The paper also discusses limitations in existing systems and proposes enhancements using intelligent algorithms, aiming to transform digital language learning into a more engaging, efficient, and personalized experience.

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Pronunciation, Speech Recognition, AI in Education, CMUdict, Flutter, Levenshtein Distance

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Leadership is a fundamental element in the effective functioning of educational institutions. School heads, principals, and academic administrators influence institutional success through their leadership approach, decision-making style, and interaction with teachers and staff. This review paper examines different leadership styles practiced in educational institutions, such as transformational, transactional, democratic, autocratic, instructional, and distributed leadership. The study is based on secondary data collected from published research articles, books, and academic reports. The review highlights the influence of leadership styles on teacher motivation, organizational culture, and institutional performance. The study concludes that participative and adaptive leadership styles are more effective in achieving sustainable educational outcomes.

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Educational Leadership, Leadership Styles, Institutional Effectiveness, Instructional Leadership, Academic Administration

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Chadha, Satbir. Hills and Lakes. Virasaat Art Publication, 2024. Hills and Lakes by Satbir Chadha: Care, Consciousness, and the Fragile Architecture of Human Bonds Dr. Ritu Kamra Kumar Abstract This paper offers a critical reading of Satbir Chadha's Hills and Lakes (2024), situating the novel within the ethical and psychological traditions of the English domestic novel. Drawing upon E. M. Forster's theory of character and Virginia Woolf's representation of caregiving consciousness, the study examines how Chadha privileges moral attentiveness over formal complexity. The novel's episodic, picaresque structure foregrounds lived experience across multiple geographies while maintaining care as its central thematic concern. The paper argues that Satya's transformation--from nurturer to the nurtured--constitutes the novel's most significant ethical reversal, foregrounding mental health, vulnerability, and reciprocity. While the novel engages with social concerns such as terrorism, ecological degradation, and gendered precarity, it resists sensationalism through restrained diction and diary-like narration. The paper also interrogates the relative flatness of secondary characters, noting the absence of surprising psychological development as both a limitation and a deliberate ethical choice. Ultimately, the study positions Hills and Lakes as a compassionate realist narrative that affirms care as both sustaining force and existential burden in contemporary life.

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Keywords: Ethical, psychological, caregiving, consciousness, mental wellness

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Abstract The Mahanubhava Panth, a lesser-known monotheistic tradition originating in 13th-century Maharashtra, offers a unique blend of moral discipline, spiritual devotion, and social reform. Founded by Bhagwan Shri Chakradhar Swami, the Panth emphasizes ethical living, equality, simplicity, and unwavering devotion to Lord Krishna in his five divine incarnations. Its teachings advocate detachment from worldly desires, self-control, and adherence to seven prohibitions that safeguard moral and social harmony. The Panth's rich cultural heritage includes rituals, festivals, and community practices that foster unity, spiritual growth, and ethical awareness. In contemporary Telangana, ashrams and youth programs continue to propagate these values, highlighting the Panth's enduring relevance. This study explores the moral, spiritual, historical, and ritualistic dimensions of the Mahanubhava Panth, demonstrating its potential contribution to modern ethical and spiritual life.

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Mahanubhava Panth, Chakradhar Swami, Moral and Spiritual Philosophy, Devotional Practices, Societal Well-being

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Early detection of Alzheimer's disease (AD) is crucial for enabling timely intervention and improving patient outcomes. This project proposes a Hybrid ANN-Ensemble Model for Early Alzheimer's Disease Detection that integrates Artificial Neural Networks (ANN) with advanced ensemble learning techniques to enhance predictive accuracy and robustness. The proposed system utilizes multi-model input data, including neuro imaging features, cognitive assessment scores, and relevant clinical parameters, to perform comprehensive analysis and early-stage classification. The ensemble component is incorporated to reduce overfitting and improve generalization, while the ANN model captures complex nonlinear relationships within high-dimensional medical data. Advanced preprocessing techniques such as feature selection, normalization, and dimensionality reduction are applied to optimize model performance while maintaining computational efficiency. Model validation is performed using cross-validation techniques to ensure reliability and stability. Performance is evaluated using standard metrics, including accuracy, precision, recall, F1-score, and Area Under the Receiver Operating Characteristic Curve (AUC-ROC). Compared to conventional single-model approaches, the proposed hybrid framework demonstrates improved sensitivity in detecting Mild Cognitive Impairment (MCI), a critical precursor to Alzheimer's disease. The system functions as an intelligent decision-support tool, assisting clinicians in early diagnosis, risk assessment, and timely clinical intervention.

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Alzheimer's Disease, Early Detection, Artificial Neural Network (ANN), Ensemble Learning, Hybrid Model, Mild Cognitive Impairment (MCI), Machine Learning, Neuro imaging Analysis, Clinical Decision Support System, Predictive Modeling.

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