Volume 14 | Issue 6

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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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Underground Cable Fault Detection, Arduino Uno, GSM Module, GPS Tracking, Fault Distance Locator, Power Distribution System, Smart Monitoring, Fault Localization.

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: Road traffic accidents remain one of the major causes of fatalities, injuries, and economic losses worldwide. This project aims to reduce the increasing number of road accidents by developing an AI-powered road safety system that proactively identifies accident-prone zones and provides real-time alerts to drivers. The proposed system utilizes five years of historical accident data (2021-2025) collected across twelve accident-related parameters, including fatalities, grievous injuries, accident intensity, and yearly accident trends. The accident records are mapped to precise geographical coordinates across Pune, Maharashtra, enabling spatial analysis and hotspot identification.The system integrates machine learning and geospatial technologies to improve road safety. K-Means clustering is employed to identify and visualize accident hotspots, achieving an optimal Silhouette Score of 0.5925 with three distinct clusters. Furthermore, an XGBoost classification model is trained using historical accident data from 2021-2023 to predict future accident-prone zones for 2024-2025. Experimental evaluation demonstrates strong predictive performance, achieving an accuracy of 92.08%, precision of 91.84%, recall of 100%, F1-score of 95.74%, and ROC-AUC of 97.78%.To enhance user awareness and safety, the identified accident-prone zones are displayed on an interactive Leaflet.js map as color-coded hotspots. Drivers receive visual alerts and audio notifications through the Web Speech API whenever they approach within a predefined distance of a hazardous zone. The system is further supported by a FastAPI backend and GPS-based location tracking for real-time operation. By combining accident hotspot detection, predictive analytics, and proactive driver alerts, the proposed framework contributes toward safer transportation networks, reduced accident risks, and improved decision-making for both drivers and traffic management authorities.

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Accident-Prone Zone Identification, Road Safety, K-Means Clustering, XGBoost, Leaflet.js, Machine Learning, FastAPI, GPS Tracking, Real-Time Alerts.

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Attendance management is an essential activity in educational institutions and organizations, where accurate record keeping directly influences administration, monitoring, and performance evaluation. Traditional attendance methods such as manual registers and card-based systems are often time consuming, error prone, and vulnerable to proxy attendance. This paper presents FaceTrack: An Intelligent Vision-Based Attendance Monitoring Framework Using Deep Facial Analytics, a smart attendance solution that automates the identification and recording process through facial recognition technology. The framework utilizes OpenCV for real-time face detection, dlib-based facial feature encoding for identity matching, and a Flask-powered web interface for attendance visualization and management. Captured facial images are processed and compared with stored facial encodings to accurately identify registered individuals and automatically generate attendance records with timestamps. Attendance information is maintained in both CSV files and a MySQL database to ensure reliable storage and easy retrieval. Experimental evaluation demonstrates high recognition accuracy, reduced processing time, and effective prevention of proxy attendance under standard indoor conditions. The proposed framework enhances operational efficiency, minimizes administrative workload, and provides a scalable, contactless, and cost-effective attendance monitoring solution for modern academic environments.

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Attendance Management, Face Recognition, Computer Vision, OpenCV, Deep Facial Analytics, Flask, MySQL, Biometric Authentication, Automated Monitoring, Machine Learning.

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Breastfeeding is widely recognized as the optimal method of infant feeding, offering significant nutritional, immunological, developmental, and health benefits for both infants and mothers. Despite its advantages, breastfeeding success is often affected by challenges related to positioning, attachment, and maternal comfort, which can lead to poor milk transfer, breastfeeding complications, and early cessation. This literature review aimed to examine and synthesize existing evidence on various breastfeeding methods and their effectiveness in promoting successful breastfeeding outcomes.A narrative literature review design was adopted. Relevant studies were retrieved from electronic databases, including pubmed, google scholar, cochrane Library, and Ssciencedirect, along with guidelines from the world health organization and United Nations Children's Fund. Peer-reviewed articles, systematic reviews, meta-analyses, randomized controlled trials, observational studies, and clinical guidelines published in English were included. The literature was analyzed thematically, focusing on different breastfeeding positions, factors influencing position selection, and their impact on maternal and infant outcomes.Findings indicate that breastfeeding success is strongly influenced by proper positioning and effective attachment, which facilitate efficient milk transfer, reduce complications, and enhance maternal comfort. Evidence suggests that no single breastfeeding position is universally superior; rather, effectiveness depends on maternal anatomy, infant developmental stage, mode of delivery, and clinical conditions. Positions such as the cross-cradle and football hold are particularly beneficial during early breastfeeding initiation, while side-lying and laid-back breastfeeding improve maternal comfort and recovery. Upright and specialized positions are useful in selected clinical situations, including reflux or prematurity. The review also highlights the essential role of nurses, midwives, and lactation consultants in providing breastfeeding education and individualized support.

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breastfeeding methods, breastfeeding positions, infant attachment, maternal comfort, breastfeeding support, lactation, nursing practice.

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Allelopathy is a biological phenomenon in which plants release chemicals that affect the germination and growth of nearby plants. This study was conducted to evaluate the allelopathic effects of aqueous leaf extracts of Parthenium hysterophorus, Lantana camara, Ageratum conyzoides, and Azadirachta indica on seed germination and early seedling growth of Triticum aestivum (wheat). Leaf samples were shade-dried, powdered, and used to prepare extracts of 5%, 10%, and 15% concentrations. The experiment was carried out under controlled laboratory conditions using a completely randomized design with proper control and replications. In this study, parameters such as germination percentage, root growth, shoot growth, and seedling vigour index were evaluated. The results showed that all plant extracts had inhibitory effects, which increased with higher concentrations. Among them, Ageratum conyzoides exhibited the strongest inhibition on both root and shoot growth, followed by Lantana camara. Parthenium hysterophorus showed a moderate inhibitory effect, while Azadirachta indica showed no significant effect, indicating minimal allelopathic influence. Overall, the study demonstrates that allelopathic plants can negatively affect crop establishment and productivity. Therefore, proper management of these plants is necessary to improve crop growth and yield.

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Allelopathy, seed germination, seedling growth, inhibitory effects

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The psychological well-being of prospective teachers plays a vital role in their academic success, professional preparation, and overall development. In recent years, increasing academic demands and changing educational environments have highlighted the importance of understanding the relationship between psychological well-being and academic engagement among teacher education students. The present study investigates the level of psychological well-being and academic engagement among prospective teachers and examines the relationship between these variables. A normative survey method was employed for the study. The sample consisted of prospective teachers enrolled in teacher education institutions. Standardized tools were used to measure psychological well-being and academic engagement. Descriptive statistics, t-test, ANOVA, and Pearson's Product Moment Correlation were utilized for data analysis. The findings revealed that prospective teachers exhibited moderate to high levels of psychological well-being and academic engagement. A significant positive relationship was found between psychological well-being and academic engagement, indicating that students with higher psychological well-being tend to demonstrate greater involvement, enthusiasm, and commitment to their academic activities. The study emphasizes the need for teacher education institutions to promote psychological well-being through supportive learning environments, counselling services, and well-being-focused interventions. Enhancing psychological well-being may contribute significantly to improving academic engagement and preparing competent future teachers.

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Psychological Well-Being, Academic Engagement, Prospective Teachers, Teacher Education, Educational Psychology.

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Printmaking has shaped culture in all parts of the world. Originally used as a form of communication, printmaking is now valued as an artistic medium with unique technical qualities. To create a print, the artist creates an image on a surface usually made of metal, stone, wood, or other material. If we describe the brief history and techniques of printmaking, then its roots can be traced back to prehistoric cave paintings and prints by humans, to the emergence of modern humans, who began to record their handprints on paper. The block printing traditions of the Indus Valley Civilization represent the earliest foundations of printmaking, and their artistic and technological innovations continue to contribute to contemporary textile printing and visual culture. In addition, the carved seals, repetitive patterns, and image transfer techniques of the Indus Valley laid the foundation for modern printmaking practices. Indus Valley, Mesopotamia where carved inscriptions were repeatedly pressed into clay for printing. These inscriptions represent their identity and trade. These inscriptions later developed with woodblock printing in China, where syllabified were carved onto blocks to reproduce text and images. Chinese scholars made rubbings from carved inscriptions around 200 AD, which was an early form of printing that could be done on paper and silk. The best example of this is the Diamond Sutra. In addition, Han Dynasty seals called chops were made by carving calligraphy characters on a flat surface of jade, silver, gold, or ivory. China introduced the practice of paper currency. The principle of buying and selling through currency made trade transactions much easier. European traveler Marco Polo discussed the use of stencils on Japanese and Chinese silk and paper currency while traveling to Europe. In fourteenth-century Europe, woodcut prints became a popular way to distribute Christian imagery to the general public. In Europe, pictorial designs were being printed on cloth. Block-prints were stenciled onto thick paper. As a result, printing began to expand in Europe. If we talk about India, there is evidence of woodblock-printed textiles on the west coast of India, but the use of blocks for printing on paper was a foreign technique. Traditional Indian arts and crafts skills such as goldsmithing, wood and metal carving, and iron work already had a strong foundation in India. At that time, these seals were made of stone and metal, which have now become digital. The best example of this is the bar code scanner.

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Printmaking, Traditional, Printing Techniques, Relief Printing

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Electric vehicle (EV) charging infrastructure plays a vital role in supporting the growing adoption of sustainable transportation. This study presents the design and development of an IoT-assisted solar-powered wireless EV charging station that combines renewable energy generation, intelligent monitoring, and contactless power transfer in a single platform. Solar photovoltaic panels harvest solar energy and store it in a rechargeable battery system, ensuring reliable energy availability for charging operations. Wireless power transfer based on electromagnetic induction enables convenient charging without physical connectors, improving safety and reducing maintenance requirements. An Arduino-based controller coordinates system functions, while IoT connectivity through the ESP8266 module facilitates real-time monitoring of voltage, current, battery status, charging performance, and energy utilization. Operational data can be accessed remotely, enabling efficient supervision and timely decision-making. The proposed system promotes reduced dependence on conventional grid electricity, supports clean energy utilization, and enhances user convenience. Experimental implementation demonstrates effective wireless charging, stable system operation, and continuous monitoring capabilities, making the solution suitable for sustainable and smart electric mobility applications in diverse practical deployment scenarios.

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Electric Vehicle (EV), Wireless Charging, Solar Energy, Internet of Things (IoT), Wireless Power Transfer, ESP8266, Renewable Energy, Sustainable Transportation.

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The Gypsy Goddess, is the debut novel of Meena Kandasamy, an exploration of the plight of agriculture laborers who work in horrific conditions in the fields incessantly from minimal to no wages while undergoing constant subjugation to the upper caste landlords of Thanjavur district of Tamil Nadu. This narrative though primarily concerned with the massacre that happened on Christmas day in 1968 in Kilvenmani village, also investigates the ideas concerned with identity, politics, etc., and the narrative evokes sympathy and horror in the reader with its narrative form. Through a combination of postmodern and metafictional techniques, Kandasamy challenges traditional novelistic structures, exposing their limitations and offering a counter-narrative to the official histories of India.

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plight, subjugation, massacre, postmodern, metafiction

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The rapid proliferation of digital technology in India has given rise to an unprecedented surge in cybercrime, transforming the socio-digital landscape and presenting significant challenges for law enforcement, policymakers, and forensic practitioners. This manuscript examines the psychological dimensions of cybercrime in the Indian context, drawing on forensic psychology frameworks to analyze the cognitive, motivational, and socioenvironmental factors that drive cybercriminal behavior. The paper explores the psychological profiles of cybercriminals operating within India, including financially motivated fraudsters, ideologically driven hacktivists, and sexually motivated offenders. Using a forensic lens, the study integrates criminological theories--including routine activity theory, general strain theory, and neutralization theory--to explain the unique manifestations of cybercrime in India's demographically and culturally diverse milieu. The manuscript further investigates the psychological impact of cybervictimization on Indian individuals and communities, with special attention to gendered dimensions of online harassment and cyberstalking. Forensic investigative methodologies, digital evidence analysis, and the role of psychological profiling in cybercrime investigations are critically evaluated. Implications for forensic practice, mental health interventions, and policy recommendations are discussed. The findings underscore the urgent need for interdisciplinary collaboration among forensic psychologists, cybersecurity professionals, law enforcement agencies, and policymakers to develop culturally sensitive and empirically grounded strategies for cybercrime prevention and offender rehabilitation in India.

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cybercrime, forensic psychology, India, cybercriminal profiling, cybervictimization, digital forensics, online fraud, psychological impact

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