Volume 13 | Issue 11 |

 Abstract

The study explores the level of public awareness of social marketing campaigns implemented in Kerala's health sector. Although social marketing has emerged globally as an effective approach to promote positive health behaviors, limited research has examined its effectiveness in the Indian context, particularly in Kerala. This study addresses that gap by assessing the extent of public awareness of government-led health campaigns aimed at tackling major health issues such as diabetes and hypertension. Based on both primary and secondary data, a structured questionnaire was administered to 800 respondents, yielding 707 valid responses selected through multi-stage sampling. The findings reveal that younger populations, urban residents, and government employees exhibit higher levels of awareness compared to other demographic groups. The study highlights that despite the wide reach of health campaigns, variations in awareness persist across demographic segments. These insights emphasize the importance of designing more inclusive and targeted social marketing strategies to enhance public awareness and participation in health initiatives. The study contributes valuable evidence to guide policymakers in strengthening communication efforts for improved public health outcomes in Kerala.

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 Keywords

Public Awareness, Social Marketing, Health Campaigns, Kerala, Health Communication, Government Initiatives

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Creative Commons Attribution 4.0 and The Open Definition

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Creative Commons Attribution 4.0 and The Open Definition

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Chronic Obstructive Pulmonary Disease, Physiotherapy, Pulmonary Rehabilitation, Exercise Tolerance, 6-Minute Walk Test, Borg Dyspnea Scale, Breathing Exercises, Quality of Life.

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Creative Commons Attribution 4.0 and The Open Definition

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Psychological trauma often leads to long-term emotional and behavioral challenges such as anxiety, depression, and post-traumatic stress disorder (PTSD). Virtual Reality (VR) therapy has emerged as an effective tool for exposure-based interventions; however, most existing systems lack adaptability to the user's emotional state during therapy sessions. This study proposes a conceptual model for an Adaptive AI-Virtual Reality Therapy System (AAVRTS) designed to support trauma survivors through real-time emotional regulation. The framework integrates AI-driven emotional inference, biofeedback sensors, and dynamically responsive virtual environments that adapt instantly to physiological and psychological cues. Additionally, the system includes a therapist dashboard enabling live monitoring and personalized session adjustments. Although the system is conceptual and not yet physically implemented, it provides a foundational model for developing interactive, safe, and emotion-responsive digital therapies aimed at enhancing engagement and improving therapeutic outcomes for trauma-affected individuals.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Adaptive Artificial Intelligence; Virtual Reality Therapy; Emotion Regulation; Psychological Trauma; Biofeedback Sensors; Mental Health Technology

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Creative Commons Attribution 4.0 and The Open Definition

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The automobile industry is a major global market player and ranked third in overall automobile undergoing a significant transformation with the rise of Electric Vehicles (EVs). S As automobile industry accounts 7.1% of total India's GDP and exported 4.13million of vehicles according in 2021 report. The industry also move a good strength of 4lakhs jobs annually. In the present context around 31.03 million production units in 2025. EVs are emerging as a sustainable alternative to traditional internal combustion engine vehicles due to their environmental benefits and technological advancements. This research paper explores the market growth, adoption trends, challenges, and future prospects of electric vehicles in India. The study highlights the role of government policies, consumer preferences, and infrastructural development in shaping the EV ecosystem. It also discusses the challenges faced by the industry, including charging infrastructure, cost competitiveness, and consumer perception. The research concludes that with continued innovation and policy support, EVs will play a pivotal role in achieving a cleaner and more sustainable transportation system in the coming decade.

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Creative Commons Attribution 4.0 and The Open Definition

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Electric Vehicles (EVs), Sustainability, Renewable Energy, Green Mobility, Government Policy, Market Growth

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Creative Commons Attribution 4.0 and The Open Definition

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Creative Commons Attribution 4.0 and The Open Definition

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Multiple factors, Virechana, Ayurveda, Infertility, Shatapushpa, Dashamoola

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Creative Commons Attribution 4.0 and The Open Definition

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Creative Commons Attribution 4.0 and The Open Definition

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Anti-Inflammatory, Egg Albumin, Homoeopathy, Eucalyptus globulus

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This paper scrutinizes the psychological dimensions of bereavement and trauma in Sudha Murty's twin novellas, The Mother I Never Knew (2014), through the lens of John Bowlby's Attachment Theory and Pauline Boss's Ambiguous Loss Theory. Sudha Murty's eponymous heroes, Venkatesh and Mukesh endure profound emotional turmoil upon discovering hidden familial truths that subvert their sense of identity and belonging. Their anguish is not for any physical loss by death but for the emotional and symbolic loss of the mother figure and of interpersonal trust. Application of Bowlby's view of attachment as a groundwork for psychological stability and Boss's concept of unresolved grief in the face of ambiguous loss, this research paper interprets Murty's narratives as psychological explorations of separation, guilt, and reconciliation. The paper also situates Murty's empathetic storytelling within the larger psychoanalytic discourse of literature as a space of healing and catharsis. Ultimately, Murty's work represents angst as both a wound and a route toward self-realization, exemplifying how the search for the maternal presence becomes a metaphor for regaining emotional wholeness.

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Sudha Murty, bereavement, psychological trauma, attachment theory, ambiguous loss, psychoanalysis, motherhood, identity ________________________________________

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The present study investigated the occurrence of Salmonella and Shigella species in raw milk samples collected from 20 areas of Sikar district across three seasons--summer, rainy, and winter--to assess seasonal and geographical variations in microbial contamination. A total of 300 milk samples were analyzed using Salmonella-Shigella (SS) agar. Out of all samples examined, 262 (87.33%) showed no bacterial growth, whereas 38 samples (12.67%) exhibited the presence of Salmonella, Shigella, or both. The occurrence of these pathogens varied seasonally, with the highest contamination observed during the rainy season. During summer, 6% of samples were contaminated--2% with Salmonella, 2% with Shigella, and 2% with both species. In contrast, the rainy season recorded 19% contamination--5% with Salmonella, 5% with Shigella, and 9% with both. Winter samples showed 13% contamination--5% with Salmonella, 5% with Shigella, and 3% with both. Spatial analysis revealed that contaminated samples were distributed across multiple areas, indicating the influence of local hygiene and environmental conditions on milk quality. The detection of these enteric pathogens, prohibited under the Prevention of Food Adulteration Rules (1956) and the Food Safety and Standards Act (2011), highlights lapses in hygiene during milking, handling, and storage. Factors such as poor washing practices, inadequate utensil sanitation, and lack of milk testing facilities likely contributed to contamination. The findings emphasize that raw milk collected during the rainy season, followed by winter, carries a higher microbial load, posing potential public health risks. Seasonal variation and regional differences play a crucial role in shaping the microbial composition of milk, underscoring the need for improved hygienic practices and monitoring to ensure milk safety throughout the year.

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 Keywords

Raw milk contamination; Salmonella; Shigella; seasonal variation; microbial load; Sikar district; food safety; hygienic practices; milk quality rainy season.

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Creative Commons Attribution 4.0 and The Open Definition

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The indigenous tribes of the Andaman and Nicobar Islands, classified as Particularly Vulnerable Tribal Groups (PVTGs) under Article 342 of the Constitution of India, embody a unique socio-cultural heritage intertwined with their ancestral lands. Governed by statutory frameworks such as the Andaman and Nicobar Islands (Tribal Councils) Regulation, 2009, and protected under the Scheduled Tribes and Other Traditional Forest Dwellers (Recognition of Forest Rights) Act, 2006 (FRA), these communities face existential threats from developmental encroachments, ecological disruptions, and policy ambiguities. This study undertakes a doctrinal legal analysis of the Tribal and Village Councils' institutional efficacy in Nicobar District and the Andaman and Nicobar Administration's welfare mechanisms, including the Andaman Adim Janjati Vikas Samiti (AAJVS) and PM-JANMAN scheme. Drawing on legislative texts, judicial precedents, and administrative reports from provided sources, it evaluates compliance with constitutional mandates under Articles 244 and 275, and international obligations under the UN Declaration on the Rights of Indigenous Peoples (UNDRIP), 2007. Findings indicate robust statutory safeguards for self-governance but persistent implementation gaps, particularly in consent-based land diversion under FRA. The analysis proposes amendments to enhance participatory jurisprudence, ensuring tribal autonomy and sustainable welfare. As a law student's inquiry, this work underscores the judiciary's pivotal role in reconciling development with indigenous rights, advocating for a rights-based paradigm in tribal administration.

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A Legal Analysis of Tribal Governance and Welfare Frameworks in Andaman and Nicobar Islands

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Creative Commons Attribution 4.0 and The Open Definition

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Generative Artificial Intelligence (AI) represents a significant evolution in machine learning, characterized by its ability to create novel, realistic content. This paper investigates the foundational models of generative AI and proposes a structured framework to guide their application across diverse industries. By examining the core mechanisms of models such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Transformers, and Diffusion Models, this study provides a technical overview of the current landscape. It addresses the critical gap between the rapid advancement of these models and the need for standardized, ethical, and scalable deployment strategies. The central thesis posits that a multi-layered architectural approach is necessary to harness the full potential of generative AI while mitigating inherent risks like algorithmic bias and data privacy violations. This research culminates in a proposed framework designed to enhance transparency, security, and efficiency in sectors including healthcare, finance, and surveillance.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Generative AI, Machine Learning Models, GAN, VAE, Transformer, Diffusion Model, System Architecture, Ethical AI, Data Privacy, Cybersecurity.

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Creative Commons Attribution 4.0 and The Open Definition