Volume 14 | Issue 6 |

 Abstract

The adolescents 'ability to navigate higher education successfully depends not only on academic knowledge but also on social intelligence, which enables them to manage relationships, adapt to new environments, and cope with academic pressures. Parents are the primary influence in shaping these social and emotional skills during adolescence. This study examines how parental influence affects social intelligence among higher secondary students and its implications for readiness for higher education. The study was conducted with 50 student-parent pairs (total N = 100) from Marthoma Higher Secondary School, Valakom, using structured questionnaires to assess parental influence and students' social intelligence. Data were analyzed using percentage analysis to determine levels of parental influence and Pearson's correlation coefficient to explore the relationship between parenting and social intelligence. The results indicate that 52% of students and 58% of parents reported a high level of parental influence. The correlation between parental influence and social intelligence was positive and statistically significant (r = 0.29, p < 0.05), suggesting that supportive parenting contributes meaningfully to the development of social competence. While the correlation is moderate, the findings underscore the importance of parental guidance, emotional support, and collaboration between home and school in preparing students for higher education challenges. Based on these results, the study recommends structured parental engagement, social intelligence training, and life-skill programmes within schools to enhance empathy, communication, and interpersonal skills. These initiatives can strengthen students' social competence and better equip them for academic and social demands in higher education. The study also highlights areas for further research, including examining the impact of parenting in different cultural and educational contexts.

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Parental Influence, Social Intelligence, Higher Secondary Students, Adolescent Development, Higher Education Readiness

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The adoption of new technological systems offers a revolutionary opportunity for the sustainable economic empowerment of India, especially in improving supply chain efficiency, development of MSMEs, and inclusive development. This research paper recommends the Technology-Driven Model of Sustainable Economic Empowerment, which will utilize AI, cloud computing, digital twins, IoT, and blockchain to improve efficiency and sustainability while encouraging financial inclusion and job creation. According to empirical findings by PwC, NITI Aayog and other relevant sources. AI can add USD 135-150 billion of value to MSMEs' manufacturing production by 2035. At the same time, it can help achieve the value of USD 3.13 - 3.21 trillion for the entire economy if MSMEs account for 50% of manufacturing gross value-added. Digitalization has already helped achieve over 100 billion UPI transactions, saving the country's INR 3.48 trillion through Direct Benefit Transfers. This paper employs literature review, secondary research and case studies to bring out the advantages associated with cost saving between 15% and 25% sustainability indicators, and resilience following disruptions. Obstacles in the form of digital divide, cybersecurity issues, skill gaps and expensive MSME adoption are explored in detail. The policy suggestions include partnership between government and private firms, scaling up of India AI Mission, edge AI deployment and greener computing. In this framework, technology emerging as the enabler of growth with economic empowerment and environmental conservation in India, which is aiming for a $30 - $35 trillion economy.

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 Keywords

Technology-Driven Economic Empowerment, Digital Transformation in India, Sustainable Supply Chain, AI and Digital Twins, MSME Empowerment, SDG 9, Cloud Computing, Agentic AI.

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India's post-independence democratic trajectory has long been celebrated as an extraordinary experiment in self-governance within a highly diverse, deeply stratified society. However, the first quarter of the twenty-first century has brought with it an array of structural strains, institutional recalibrations, and normative contests that compel a sober re-examination of the democratic health of the world's most populous nation-state. This article undertakes a systematic introspective analysis of recent trends shaping Indian democracy, with particular attention to the period from 2014 to 2024. Drawing on a mixed theoretical framework that incorporates Dahl's conception of polyarchy, Levitsky and Zi blatt's theory of democratic erosion, and Schedler's electoral authoritarianism paradigm, the study examines five critical dimensions: electoral integrity and the consolidation of party dominance; institutional independence, especially the judiciary, Election Commission, and investigative agencies; civil liberties, freedom of expression, and media pluralism; the relationship between majoritarianism and minority rights; and the resilience demonstrated by democratic forces in the 2024 general elections. Empirical evidence is drawn from V-Dem indices, Freedom House assessments, Economist Intelligence Unit Democracy Index data, and legislative records. The findings indicate that while India retains core features of electoral democracy, a measurable erosion of liberal-democratic norms-particularly in civil liberties, media freedom, and institutional autonomy-has occurred, warranting classification as a competitive authoritarian regime rather than a full democracy. Simultaneously, the 2024 electoral outcome signals residual democratic resilience and the continuing vitality of citizen agency. The paper concludes by mapping pathways for democratic renewal and highlights the theoretical implications of the Indian case for the comparative study of democratic backsliding in the Global South.

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Indian Democracy, Majoritarianism, Democratic Backsliding, Electoral Autocracy, Civil Liberties and Competitive Authoritarianism

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Stroke is one of the leading causes of death and long-term disability worldwide. Early detection and rapid medical intervention are essential to reduce severe neurological damage and improve patient survival rates. Recent advances in Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning have enabled automated systems capable of analyzing medical data and imaging for disease detection. This study presents a literature survey on the application of Artificial Intelligence in stroke detection and prediction using medical imaging and clinical data. Existing research demonstrates that Machine Learning algorithms and Deep Learning models can analyze Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) brain scans to identify stroke-affected regions and classify stroke types. AI techniques have also been applied to electronic health records and patient health factors to predict stroke risk. These systems assist healthcare professionals by improving diagnostic accuracy, reducing interpretation time, and supporting clinical decision-making. This survey reviews existing AI-based stroke diagnosis approaches, imaging analysis techniques, Machine Learning models, and healthcare decision support systems. The findings highlight the importance of integrating medical imaging analysis with patient health risk assessment to develop more effective stroke detection systems.

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Artificial Intelligence, Stroke Detection, Medical Image Analysis, Machine Learning, Deep Learning, CNN, Healthcare AI

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management of Iron Deficiency Anemia (Pandu Roga) Background: Iron Deficiency Anemia (IDA) is a common nutritional deficiency disorder worldwide and a major public health concern in developing countries, including India. It predominantly affects children, women of reproductive age, and pregnant women, leading to reduced physical capacity, impaired quality of life, and increased morbidity. In Ayurveda, the clinical manifestations of IDA closely resemble Pandu Roga, which is associated with Rasa Dhatu Kshaya, Agnimandya, and imbalance of Tridosha. Objective: To systematically review clinical evidence on the efficacy and safety of selected Ayurvedic formulations in the management of Iron Deficiency Anemia (Pandu Roga). Materials and Methods: A systematic review with descriptive synthesis was conducted using six peer-reviewed Indian clinical studies published between 2012 and 2025. Studies evaluating Ayurvedic herbal or herbomineral formulations in human subjects with defined hematological outcomes were included. Data on hemoglobin levels, red blood cell indices, iron parameters, clinical symptoms, and adverse drug reactions were extracted and analyzed. Results: More than 650 patients aged 2-60 years were included, with a predominance of female participants. Formulations such as Dhatri Lauha, Punarnavadi Mandura, Trikatrayadi Lauha, Loha Churna Vatakam, Yogaraja Rasayana, and Pandughni Vati showed significant improvement in hemoglobin levels, red blood cell indices, and iron parameters. Clinical symptoms improved in up to 85% of patients. No serious adverse drug reactions were reported. Conclusion: Ayurvedic formulations appear effective and safe for managing Iron Deficiency Anemia. Their multidimensional action offers a holistic alternative or adjunct to conventional iron therapy, though larger randomized controlled trials are required for confirmation. management of Iron Deficiency Anemia (Pandu Roga) Background: Iron Deficiency Anemia (IDA) is a common nutritional deficiency disorder worldwide and a major public health concern in developing countries, including India. It predominantly affects children, women of reproductive age, and pregnant women, leading to reduced physical capacity, impaired quality of life, and increased morbidity. In Ayurveda, the clinical manifestations of IDA closely resemble Pandu Roga, which is associated with Rasa Dhatu Kshaya, Agnimandya, and imbalance of Tridosha. Objective: To systematically review clinical evidence on the efficacy and safety of selected Ayurvedic formulations in the management of Iron Deficiency Anemia (Pandu Roga). Materials and Methods: A systematic review with descriptive synthesis was conducted using six peer-reviewed Indian clinical studies published between 2012 and 2025. Studies evaluating Ayurvedic herbal or herbomineral formulations in human subjects with defined hematological outcomes were included. Data on hemoglobin levels, red blood cell indices, iron parameters, clinical symptoms, and adverse drug reactions were extracted and analyzed. Results: More than 650 patients aged 2-60 years were included, with a predominance of female participants. Formulations such as Dhatri Lauha, Punarnavadi Mandura, Trikatrayadi Lauha, Loha Churna Vatakam, Yogaraja Rasayana, and Pandughni Vati showed significant improvement in hemoglobin levels, red blood cell indices, and iron parameters. Clinical symptoms improved in up to 85% of patients. No serious adverse drug reactions were reported. Conclusion: Ayurvedic formulations appear effective and safe for managing Iron Deficiency Anemia. Their multidimensional action offers a holistic alternative or adjunct to conventional iron therapy, though larger randomized controlled trials are required for confirmation.

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Keywords: Iron Deficiency Anemia; Pandu Roga; Ayurvedic formulations; Lauha Kalpa; Hematological parameters.

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The rapid growth of digital technology has significantly transformed the educational environment, particularly in higher education institutions offering media-related programs. This study examines the impact of Learning Management Systems (LMS) on media education and explores how digital learning platforms enhance teaching, learning, and student engagement. The primary aim of the research is to analyze the effectiveness of LMS platforms in improving academic performance, practical media skills, and collaborative learning among media students. The objectives of the study are to evaluate student satisfaction towards LMS usage, examine the role of LMS in media skill development, and identify the challenges faced by faculty and students in online media education. The research adopts a quantitative research methodology using survey methods among students and faculty members from media and communication departments in higher educational institutions. Data are collected through structured questionnaires and analyzed using statistical techniques. The findings reveal that LMS platforms significantly improve accessibility to learning materials, communication, flexibility, and student participation in media education. The study also identifies challenges such as limited practical exposure, technical issues, and digital divide concerns. Overall, the research concludes that Learning Management Systems play a vital role in modernizing media education and promoting technology-enabled learning environments in higher education institutions

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Digital Technology, LMS, Educational Technology, media Tecbnology, Satisfaction, Sill education, Higher education

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Stroke is one of the leading causes of death and long-term disability worldwide, making early prediction and diagnosis essential for improving patient outcomes. Recent advancements in Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) have enabled the development of intelligent systems capable of predicting stroke risk using patient healthcare data. This review analyzes various research works conducted between 2020 and 2025 on brain stroke prediction using machine learning and neural network-based approaches. Several studies employed algorithms such as Logistic Regression, Decision Tree, Random Forest, Support Vector Machine (SVM), and Artificial Neural Networks (ANN) to identify stroke risk factors and predict stroke occurrence. The reviewed works achieved prediction accuracies ranging from 88% to 92%, demonstrating the effectiveness of data-driven predictive models in healthcare applications. Furthermore, deep learning and predictive analytics approaches showed improved performance compared to traditional machine learning techniques. Despite these advancements, challenges such as limited dataset size, lack of real-time clinical validation, inadequate feature optimization, and poor generalization across diverse patient populations remain significant concerns. The findings indicate that integrating advanced deep learning models, feature engineering techniques, and real-time healthcare monitoring systems can further enhance stroke prediction accuracy and support early diagnosis. This review highlights the importance of intelligent predictive systems in reducing stroke-related mortality and assisting healthcare professionals in clinical decision-making.

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Brain Stroke Prediction, Machine Learning, Deep Learning, Artificial Intelligence, Healthcare Analytics, Neural Networks, Risk Assessment, Early Detection, Predictive Analytics, Clinical Decision Support.

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Gender equality is a fundamental principle of democratic governance and an essential prerequisite for inclusive political development. Political participation enables citizens to influence public policies, decision-making processes, and governance structures. In India, despite constitutional guarantees of equality and various policy interventions, women continue to face significant barriers to meaningful participation in political institutions. While notable progress has been made in terms of voter turnout, representation in local self-government institutions, and political awareness, gender disparities persist in legislative bodies and leadership positions. This article critically examines the status of gender equality in political participation in India by analysing constitutional provisions, contemporary trends, and major challenges affecting women's political engagement. The study is based on qualitative analysis of secondary sources, including scholarly literature, government reports, and election data. The findings indicate that socio-cultural norms, economic constraints, political party structures, and institutional barriers continue to limit women's participation. The article concludes by emphasizing the need for comprehensive reforms, greater political representation, and gender-sensitive governance to strengthen democratic inclusiveness in India.

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Gender Equality; Political Participation; Women's Representation; Political Empowerment; Democracy; India

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Mathematical modelling coupled with artificial intelligence (AI) has revolutionized materials engineering, providing predictive insights, and speeding up materials discovery. AI can be used to improve computational efficiency and accuracy, and mathematical models can be used to understand the behavior of materials. This paper summarizes several important applications such as microstructure property prediction, materials design, failure analysis and manufacturing process optimization. The synergy between physics-based modeling and machine learning algorithms can bridge the gap between theory and experiment and result in advances in materials informatics and engineering.

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Materials engineering, Machine learning, Computational design, Materials informatics

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Ayurveda is a comprehensive system of medicine that encompasses extensive knowledge related to health, disease prevention, environmental factors, and toxicology. Among its eight specialized branches, Agada Tantra is dedicated to the study of poisons (Visha), their effects on the human body, and their management. The Ayurvedic concept of Visha includes a wide spectrum of toxic substances capable of producing local as well as systemic manifestations. The skin (Twak), being the largest and most exposed organ of the body, is particularly susceptible to toxic insults resulting from direct contact, ingestion, inhalation, or chronic exposure to poisonous substances. Classical Ayurvedic texts describe various categories of poisons, including Sthavara Visha (plant and mineral poisons), Jangama Visha (animal poisons), Garavisha (artificial or compound poisons), Dushivisha (cumulative or latent toxins), and Upavisha (semi-poisonous substances). Exposure to Sthavara Visha often manifests as inflammatory skin reactions comparable to contact dermatitis, while Jangama Visha may present with urticarial and allergic skin manifestations. Furthermore, the concepts of Dushivisha and Garavisha closely resemble modern toxicological conditions arising from prolonged exposure to pesticides, industrial chemicals, environmental pollutants, cosmetic products, occupational hazards, and other cumulative toxins. Dermatological manifestations such as erythema, itching, swelling, discoloration, rashes, vesicles, ulceration, and various forms of Twak Vikara serve as important indicators in the diagnosis, prognosis, and assessment of toxic exposure. Ayurvedic literature provides detailed descriptions of these manifestations along with their pathogenesis and therapeutic management. Among the various antidotal formulations described in Agada Tantra, Panchashirisha Agada is regarded as one of the most effective remedies for Vishajanya Vyadhi due to its potent Vishaghna (anti-toxic), Kushthaghna (anti-dermatotic), Kandughna (anti-pruritic), Twachya (skin-beneficial), Varnya (complexion-enhancing), and Shothaghna (anti-inflammatory) properties. It can be administered both internally and externally depending on the clinical presentation. This review aims to explore the dermatological manifestations associated with various forms of Visha described in Ayurveda and correlate them with contemporary dermatological and toxicological concepts. Integrating classical Ayurvedic knowledge with modern scientific understanding may contribute to improved identification, prevention, and management of toxin-induced skin disorders and highlight the clinical relevance of Agada Tantra in present-day healthcare.

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Agada Tantra, Visha, Jangama Visha, Dermatological Manifestations, Twak Vikara, Panchashirisha Agada, Toxicology, Ayurvedic Dermatology, Vishaghna Chikitsa.

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