Volume 14 | Issue 6 |

 Abstract

National Education Policy

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National Education Policy

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The National Education Policy (NEP) 2020 and the NIPUN Bharat Mission have firmly established Foundational Literacy and Numeracy (FLN) as the highest educational priority in India's school system. The NIPUN Lakshyas are grade-wise, measurable learning benchmarks for children in Balvatika, Grade 1, and Grade 2, which define the irreducible minimum competencies every child must demonstrate before transitioning to Grade 3. However, a critical challenge in realizing this vision is assessment: the Foundational Stage explicitly avoids formal written examinations, yet systematic monitoring of FLN mastery is indispensable. While several qualitative assessment tools exist, including learning diaries, rating scales, checklists, running records, and observation schedules--this article makes a focused argument for leveraging two tools that are already institutionally established and professionally familiar to every teacher: anecdotal records and student portfolios. Rather than introducing new assessment instruments, the article proposes that these existing tools, when deliberately and systematically aligned with the NIPUN Lakshyas, can serve as powerful, teacher-friendly mechanisms for monitoring FLN compliance in every child. Drawing on empirical research from the past decade and key Indian policy documents, including NEP 2020 and NCF-FS 2022, the article examines how anecdotal records and portfolios can be repositioned from generic developmental documentation tools into targeted FLN compliance trackers--ensuring that no child crosses into Grade 3 without demonstrable mastery of foundational skills.

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NIPUN Bharat Mission, NIPUN Lakshyas, foundational literacy and numeracy, FLN compliance, anecdotal records, portfolio assessment, qualitative assessment, NEP 2020, NCF-FS 2022, Foundational Stage

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The rapid adoption of artificial intelligence (AI) is transforming workplace skill requirements and increasing the importance of continuous learning and workforce upskilling. As organizations seek to develop future-ready employees, understanding workforce learning preferences has become essential for designing effective learning and development strategies. This study aims to examine the underlying dimensions of employees' learning and upskilling preferences in the AI era, identify distinct workforce segments based on these preferences, and analyse demographic differences among the identified segments. A quantitative research design was employed using data collected from 300 employees across various industries. Exploratory factor analysis was conducted to identify the key dimensions of workforce learning preferences, while cluster analysis was used to classify employees into homogeneous groups. Furthermore, analysis of variance (ANOVA) and Chi-square tests were performed to examine differences among workforce segments and their demographic characteristics. The findings revealed four major dimensions of learning and upskilling preferences: AI-related skills, technical skills, soft skills, and career-development learning. Three distinct workforce segments emerged from the analysis, namely AI Enthusiasts, Career-Oriented Learners, and Passive Learners. Significant differences were observed among the identified segments across all learning dimensions. Additionally, workforce segment membership was significantly associated with age, educational qualification, work experience, and industry, whereas gender did not exhibit a significant association.

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Artificial Intelligence, Workforce Upskilling, Employee Learning Preferences, Workforce Segmentation, Human Resource Management.

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In Mamang Dai's The Legends of Pensam (2006), the central concept of pensam--the "in-between"--serves as a critical liminal threshold where myth, history, and modern transition intersect. This paper examines how Adi women navigate this space, confronting a "gendered subaltern" condition shaped by patriarchal customary laws, rigid gender roles, and the restrictive rituals of widowhood. While characters like Pinyar and Omum experience social isolation and the silencing of their lived struggles, this study argues that they are not merely passive victims of their circumstances. Instead, the narrative articulates a profound model of relational agency that challenges Western paradigms. Drawing on Indigenous feminism and cultural memory studies, the paper highlights how this agency is rooted in collective responsibility, intergenerational continuity, and ecological custodianship. As the primary transmitters of oral tradition, women serve as the vital custodians of ancestral knowledge, myths, and rituals that sustain communal identity. Their intimate environmental wisdom--expressed through agricultural practices and healing--redefines power as a life-sustaining responsibility rather than institutional dominance. By centering the roles of these women as storytellers, mourners, and caregivers, Dai's work performs a decolonising act, restoring "narrative visibility" and "symbolic presence" to voices historically relegated to the margins. Ultimately, the paper concludes that Dai redefines Indigenous strength as the capacity to negotiate the threshold of tradition and change, transforming lived silence into a profound form of cultural authority.

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Pensam, Relational Agency, Adi Tribe, Cultural Memory, Gendered Subaltern.

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The present study aims to examine the relationship between Growth Mindset and Psychological Well-Being among prospective teachers. Growth Mindset refers to the belief that intelligence and abilities can be developed through effort, learning, and persistence, whereas Psychological Well-Being represents positive mental health, emotional balance, and life satisfaction. A descriptive survey method was adopted for the study. The sample consisted of prospective teachers selected through a simple random sampling technique. Standardized tools were used to collect the required data. The data were analyzed using descriptive statistics and Pearson's product-moment correlation. The findings of the study revealed that prospective teachers possess a moderate level of Growth Mindset and Psychological Well-Being. Further, a positive significant relationship was found between Growth Mindset and Psychological Well-Being among prospective teachers. The study highlights the importance of fostering a growth mindset in teacher education programs to enhance the psychological well-being of future teachers.

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Growth Mindset, Psychological Well-Being, Prospective Teachers, Teacher Education, Mental Health, Academic Psychology.

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This paper argues that mental health must be treated as a core national priority in India's development trajectory towards 2047. The case is based on three linked propositions: first, the burden of mental illness is not merely a clinical concern but a constraint on human capital, productivity, public safety, social cohesion and constitutional dignity; second, India's present psychiatric infrastructure remains uneven across geography, workforce, institutions, financing and data systems; and third, the country's demographic, technological and federal capacities make a 2047 transformation feasible if mental health is elevated from programme-level delivery to mission-mode governance. Using doctrinal legal analysis, secondary policy review and systems planning, the paper examines India's National Mental Health Survey findings, the Mental Healthcare Act, 2017, the National Mental Health Programme, the District Mental Health Programme, Tele-MANAS and global standards under the WHO Comprehensive Mental Health Action Plan 2013-2030. It proposes a five-tier psychiatric infrastructure model covering community prevention, primary-care mental health, district psychiatric services, regional centres of excellence and national research-surveillance platforms. The paper further recommends a National Mental Health Infrastructure Mission 2047, a dedicated psychiatric workforce pipeline, mental health budgeting norms, insurance-linked continuity of care, school and workplace mental health systems, crisis response services, suicide prevention registries, and enforceable state-level accountability indicators. It concludes that India's goal for 2047 should be not only more hospitals and psychiatrists, but an integrated mental health ecosystem that is rights-based, community-anchored, digitally enabled and institutionally accountable.

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Mental Health, Psychiatric Infrastructure, India 2047, National Mental Health Programme, Tele-MANAS, Mental Healthcare Act 2017, District Mental Health Programme, Public Health Governance, Human Capital.

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Recent advances in deep learning--particularly Convolutional Neural Networks (CNNs)--have opened transformative opportunities for automated object detection and classification in complex outdoor environments. Models from the YOLO (You Only Look Once) family, especially YOLOv8, have emerged as highly effective tools for real-time multi-class detection, offering an optimal balance between inference speed and detection accuracy. Unlike rule-based or template-matching methods, CNN-based architectures learn intricate visual features directly from data, enabling them to adapt to varying lighting conditions, occlusion from vegetation, and complex natural backgrounds [2]. This paper proposes a complete smart farm surveillance framework that combines YOLOv8-based object detection with automated Telegram Bot API notifications. The system processes live video feeds from farm cameras, identifies threats as they occur, captures annotated snapshots, and delivers instant alerts to farmers' smartphones. Key contributions include: o A fine-tuned YOLOv8 detection model trained on a diverse agricultural dataset covering humans, birds, and animals. o An integrated Telegram Bot alerting module providing real-time notifications with timestamped annotated images. o Deployment on resource-constrained edge devices (Raspberry Pi / NVIDIA Jetson Nano) demonstrating practical feasibility. o Comprehensive experimental evaluation under varied farm conditions confirming 94.7% precision and 93.1% recall.

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CNN, YOLOv8, Smart Farming, Object Detection, Telegram Alerts, Agricultural Surveillance, Real-Time Monitoring, Edge Computing, IoT, Deep Learning

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Microfinance has become an important tool for boosting rural economic development by offering financial services to low-income households often left out of the formal banking system. Through microcredit, savings, insurance, and financial education programs, microfinance helps poor households engage in productive activities, create jobs, and improve their living conditions. In India, the Self-Help Group (SHG)-Bank Linkage Program and Microfinance Institutions (MFIs) have greatly increased financial inclusion in rural areas. As of March 2024, over 144.22 lakh Self-Help Groups were connected to banks, with savings exceeding INR65,089 crore. More than 83 percent of these groups were made up of women, highlighting the significant role of microfinance in empowering women. This paper looks at how microfinance contributes to rural economic development, with a focus on reducing poverty, generating employment, developing agriculture, promoting entrepreneurship, and enhancing social empowerment. The study also addresses key challenges in the sector and offers policy recommendations for improving its effectiveness.

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Microfinance, Rural Development, Poverty Alleviation, Financial Inclusion, Self-Help Groups, Entrepreneurship, Women's Empowerment

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Heart attack remains one of the leading causes of mortality worldwide, creating a critical need for early prediction and preventive healthcare strategies. HeartIntel: A Machine Learning Framework for Early Heart Attack Risk Prediction and Clinical Decision Support presents an intelligent system designed to identify individuals who may be at elevated risk of heart attack using clinical and physiological parameters. The framework utilizes patient information such as age, blood pressure, cholesterol level, chest pain characteristics, maximum heart rate, ST depression, and related health indicators for predictive analysis. Data preprocessing, feature transformation, and normalization techniques are applied to improve data quality and model performance. Machine learning algorithms including Logistic Regression, Random Forest, and Support Vector Machine are employed for classification and risk assessment. A web based interface enables efficient data entry, prediction generation, and result visualization. Experimental evaluation demonstrates reliable predictive capability, satisfactory accuracy, and timely identification of high risk cases. The system supports clinical decision making, promotes early intervention, enhances healthcare efficiency, reduces diagnostic delays, and contributes to improved patient safety and outcomes.

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Heart Attack Risk Prediction, HeartIntel, Machine Learning, Clinical Decision Support, Logistic Regression, Random Forest, Support Vector Machine, Healthcare Analytics, Predictive Modeling, Risk Assessment.

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Electrical poles are critical components of power distribution networks, yet faults such as current leakage, conductor breakage, and pole electrification can pose serious risks to public safety and infrastructure reliability. This paper presents ElectroSentrix, an IoT-based intelligent electrical pole fault detection and accident prevention framework designed to monitor pole conditions continuously and respond to hazardous situations in real time. The proposed system integrates voltage sensors, current sensors, an ATmega328 microcontroller, a NodeMCU communication module, an LCD display, and a voice alert unit to detect abnormal electrical conditions and issue timely warnings. Upon fault identification, the framework generates local audio notifications, displays fault information, and transmits alerts to authorized personnel through an IoT platform for rapid maintenance action. Automated monitoring minimizes manual inspection efforts and enhances operational efficiency while reducing the likelihood of electrocution incidents. Experimental implementation demonstrates reliable fault detection, prompt alert generation, and effective communication capabilities. The developed framework offers a cost-effective, scalable, and practical solution for improving electrical pole safety, ensuring public protection, and supporting smarter power distribution infrastructure.

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Electrical Pole Safety, Internet of Things (IoT), Fault Detection, Accident Prevention, Smart Monitoring, NodeMCU, ATmega328, Voltage Sensor, Current Sensor, Public Safety.

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