Volume 14 | Issue 8 |

 Abstract

भारतीय समाज में अनुसूचित जातियाँ ऐतिहासिक रूप से सामाजिक बहिष्करण, जातिगत भेदभाव, आर्थिक वंचना तथा शैक्षिक पिछड़ेपन से प्रभावित रही हैं। स्वतंत्रता के पश्चात संविधान द्वारा सामाजिक न्याय, समानता और अवसरों की समानता को सुनिश्चित करते हुए अनुसूचित जातियों के लिए विशेष संवैधानिक संरक्षण प्रदान किया गया। इसी के अंतर्गत केंद्र एवं राज्य सरकारों द्वारा शिक्षा, छात्रवृत्ति, रोजगार, स्वरोजगार, कौशल विकास, आवास तथा सामाजिक सुरक्षा से संबंधित विभिन्न कल्याणकारी योजनाएँ संचालित की जा रही हैं।

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 Keywords

समावेशी विकास, कल्याणकारी योजनाएँ, सामाजिक बहिष्करण, आर्थिक सशक्तीकरण, शिक्षा, सामाजिक गतिशीलता।

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 Abstract

Indian writing in English is a form of writing that help the thinkers, critics, and writers to voice out their thoughts, emotions in the form of essay, poetry, short stories, drama, fiction and non-fiction works. Drama plays an important role in highlighting the stark realities of the Indian society. A prominent Marathi dramatist Vijay Tendulkar, through his writings addressed various social issues: gender hegemony, patriarchy, power dynamics, societal hypocrisy, violence, man-women relationships etc. Silence! The Court is in Session is a drama written by Vijay Tendulkar in 1963. It talks about social norms and individual desire, gender inequality, morality, and victimized status of the woman. It also reflects the harsh realities of Indian patriarchal society, hypocrisy, and mental violence in the name of reforming the society. In the mock trial, the actors in the play tormented the psyche of the protagonist. This paper will analyze the psychological turmoil of Miss Leela Benare after the attack in the mock trial from the lens of Sigmund Freud's theory of repression. It will also analyze the social hypocrisy played by a group of people in Leela Banera's life; that lack power imbalance in both the victim and the perpetrator.

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Keywords: gender roles, psychological impact, mock trial, repression

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: The conservative, patriarchal state of Mizoram, situated in the northeastern states of India, has a unique demographic profile, with a high percentage of female voters. The political transformation of Mizo women represents one of the most significant social changes in contemporary Mizoram. The recent state assembly election has witnessed a remarkable and significant transformation in its dynamic political landscape. Despite the high participation rate of female voters during elections, there is a continuous lack of adequacy in the representation and involvement of women in the political sphere. This paper explores the historical transition of Mizo women from household-centred roles to increasing participation in public and political life. Political empowerment is important for inclusive and equitable democracy. It is also crucial for understanding democratic practices prevail in the state. The study explores factors influencing the participation of women, and challenges limiting their political enhancement. The findings highlight the religious and communal interference in decision-making. In the end, the paper attempts to propose possible recommendations and strategies to empower political participation and representation of women.

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Political Empowerment, Chieftainship, British missionaries, Mizo society, Zawlbuk

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Abstract--Early prediction of diabetes risk is critical for timely preventive intervention and long-term disease management. Con-ventional clinical models rely on static biomarker data, overlook-ing the significant role of daily lifestyle behaviors in disease onset. This paper presents a machine learning framework for early diabetes risk prediction that incorporates dynamic lifestyle data including physical activity, dietary patterns, and sleep quality alongside standard clinical parameters. The proposed hybrid model combines Long Short-Term Memory (LSTM) networks for temporal sequence modeling with a Random Forest classifier for clinical risk stratification. K-Means clustering is applied to create personalized patient sub-groups, while Shapley Additive exPla-nations (SHAP) ensure model transparency. Experimental eval-uation on a multi-modal lifestyle-clinical dataset demonstrates that the proposed model achieves 92.7% accuracy, surpassing standalone machine learning and deep learning baselines. A preventive recommendation engine further enhances the system's clinical utility.

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Index Terms--Diabetes Risk Prediction, Machine Learning, Deep Learning, LSTM, Random Forest, Lifestyle Data, SHAP, Explainable AI, Personalized Healthcare.

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Family-Centered Care (FCC) is a cornerstone of contemporary pediatric nursing that recognizes the family as an essential partner in the care of hospitalized children. Unlike traditional healthcare models that focus primarily on the patient, FCC promotes collaboration between healthcare professionals and families to ensure holistic, individualized, and evidence-based care. The philosophy emphasizes respect, dignity, information sharing, participation, and collaboration throughout the child's hospitalization. Research has consistently demonstrated that FCC improves clinical outcomes, reduces parental anxiety, enhances family satisfaction, shortens hospital stays, increases adherence to treatment, and promotes emotional well-being among children. Pediatric nurses play a central role in implementing FCC through effective communication, family education, shared decision-making, and advocacy. Despite its recognized benefits, several barriers such as inadequate staffing, limited institutional support, cultural differences, and communication challenges hinder successful implementation. This article reviews the principles, benefits, nursing interventions, challenges, and future directions of Family-Centered Care in pediatric nursing and highlights its significance in improving health outcomes for hospitalized children.

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Family-centered care, pediatric nursing, hospitalized children, family participation, nursing care, child health, patient-centered care

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The rapid advancement of Artificial Intelligence (AI) and generative deep learning models has significantly enhanced the capability to produce highly realistic synthetic images, commonly referred to as deepfakes. Although these technologies have enabled innovations in entertainment, healthcare, and digital content creation, they have also introduced critical challenges related to misinformation, identity theft, cybercrime, and digital media authenticity. Existing deepfake detection approaches predominantly rely on image-based analysis using Convolutional Neural Networks (CNNs), yet their performance often deteriorates when confronted with unseen datasets, sophisticated generative models, adversarial attacks, and manipulated metadata. To address these limitations, this research proposes a novel multi-modal deepfake image detection framework that integrates visual feature learning through Vision Transformers (ViTs) with fake metadata analysis to improve detection robustness and generalization. The proposed framework combines EfficientNet-based feature extraction, attention mechanisms, Vision Transformers for global contextual representation, and metadata forensic analysis to simultaneously examine image content and associated metadata. This multi-modal strategy enables the detection of subtle spatial inconsistencies, semantic artifacts, and metadata anomalies that may remain undetected when relying solely on visual information. The model is trained and evaluated using incorporating benchmark deepfake datasets, comprehensive preprocessing, data augmentation, transfer learning, and ensemble classification techniques. Performance is assessed using standard evaluation metrics including accuracy, precision, recall, F1-score, Receiver Operating Characteristic (ROC) curve, and Area Under the Curve (AUC), along with cross-dataset validation to evaluate model robustness under diverse real-world conditions. The expected outcome of this research is the development of a scalable, reliable, and computationally efficient deepfake detection framework capable of accurately identifying AI-generated images while maintaining strong generalization across different manipulation techniques. By integrating visual and metadata-based forensic evidence, the proposed system enhances digital image authentication and contributes to combating misinformation, strengthening cybersecurity, supporting digital forensic investigations, and preserving trust in digital media. The research advances the field of computer vision by presenting a comprehensive multi modal detection framework suitable for next-generation AI-generated image verification systems.

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Deepfake Detection, Artificial Intelligence, Deep Learning, Vision Transformer (ViT), Multi-Modal Learning, Fake Metadata Analysis, Computer Vision, Digital Image Forensics, EfficientNet, Transfer Learning, Image Authentication, Cyber security.

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This scoping review maps a decade of empirical research on Mindfulness-Based Stress Reduction (MBSR) and related mindfulness-based interventions (MBIs) in India, covering 2016 to 2026. PubMed, PsycINFO, Scopus, and Google Scholar were searched, and reference lists of the retrieved articles were checked by hand so that relevant studies were not missed. Because the literature draws on quite different methodological traditions, studies were sorted by research design rather than lumped together: randomized controlled trials, quasi-experimental comparisons, single-group pre-post evaluations, cross-sectional surveys, and descriptive institutional reports. For every study, design, population, setting, outcome measures, and the statistics actually reported were charted, and where a standardized effect size was simply absent from the source, that was recorded as 'not reported' rather than estimated. Indian MBI research has grown considerably since 2020, reflecting rising academic and institutional interest, but the resulting evidence map is methodologically uneven. Randomized controlled trials exist, yet they are concentrated almost entirely among clinical and health populations: chronic pain, diagnosed anxiety, pregnancy, and community adults. Research on working professionals and students depends mainly on quasi-experimental or uncontrolled pre-post designs, which constrains what the findings can actually establish. One study frequently cited as occupational evidence turns out, on inspection, to be a correlational survey with no intervention at all. Of every occupational study charted, only one, Mitra (2025), reported a usable standardised effect size for work stress (Cohen's d = 0.64), and even that came from an uncontrolled design. Taken as a whole, the literature suggests MBSR is culturally workable and practically feasible in India. What it does not yet offer is rigorous, design-appropriate evidence for occupational and corporate populations, particularly within the IT sector, and closing that gap is the clearest priority for future research.

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 Keywords

Mindfulness-Based Stress Reduction, Scoping Review, Research Design, Evidence Mapping

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Chandrayaan-2 DFSAR, lunar south pole, polarimetric SAR, CPR, degree of polarisation, volatile retention, water-ice candidates.

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Sahityakadamya Balasahityapuraskara-sammanita-samskrta-kavlnam Paricitih (Varsa 2010 tah 2023 Paryantam)

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Sahityakadamya Balasahityapuraskara-sammanita-samskrta-kavlnam Paricitih (Varsa 2010 tah 2023 Paryantam)

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Artificial Intelligence (AI) has emerged as a transformative technology in the field of cybersecurity by enabling advanced threat detection, automated incident response, malware analysis, and predictive security measures. As cyber threats become increasingly sophisticated, organizations and governments are relying on AI-driven systems to protect critical infrastructure, digital services, and sensitive information. However, the integration of AI into cybersecurity creates complex legal and regulatory challenges concerning privacy, accountability, transparency, data governance, and misuse of technology. In India, the legal framework governing AI-based cybersecurity applications is still developing. Existing laws such as the Information Technology Act, 2000, the Digital Personal Data Protection Act, 2023, cybersecurity policies, and sector-specific regulations provide partial regulation but do not comprehensively address AI-specific risks. Questions regarding liability for autonomous AI decisions, algorithmic transparency, cybersecurity obligations, and ethical use of AI remain unresolved. This research paper examines the current legal frameworks applicable to artificial intelligence in cybersecurity in India, evaluates their limitations, and explores possible future regulatory approaches. It argues that India requires a balanced AI governance model that promotes technological innovation while ensuring security, privacy, accountability, and protection of individual rights.

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Artificial Intelligence, Cybersecurity, Information Technology Law, Data Protection, AI Regulation, India, Digital Governance.

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