Volume 13 | Issue 8 |

 Abstract

Abstract Introduction: In today's fast-paced world, where health often takes a backseat, the knowledge of nutritional deficiencies among school children is slipping through the cracks -- and who better to turn the tide than school teachers, the torchbearers of young minds? In the selected schools of Sagar, Madhya Pradesh, educators stand at the crossroads, holding the key to early detection and prevention of malnutrition-related issues. This study aims to leave no stone unturned in assessing how well-equipped these mentors are in identifying and addressing the dietary pitfalls that can stunt a child's growth and learning. After all, a stitch in time saves nine, and empowering teachers with nutritional awareness could be the first step toward healthier, brighter futures for countless children. Aims: The primary aim of this study is to assess the level of knowledge regarding nutritional deficiencies among school teachers in selected schools of Sagar, Madhya Pradesh. It seeks to identify gaps in awareness, understand teachers' roles in early recognition of nutritional issues among students, and promote the integration of basic nutrition education into the school environment. Ultimately, the study aspires to empower teachers as frontline advocates for child health and nutrition. Objectives: 1) To assess the existing knowledge of school teachers regarding common nutritional deficiencies among school children. 2) To identify the types of nutritional deficiencies most prevalent among school children as perceived by school teachers. 3) To determine the association between knowledge level and selected demographic variables of school teachers (e.g., age, gender, qualification, teaching experience, training in nutrition/health, etc.). 4) To provide recommendations for educational interventions or training programs based on the findings. Materials and Methods: A descriptive study was conducted among 40 school teachers from Amreen English Medium Higher Secondary School, Sagar (M.P.), selected through simple random sampling. Inclusion criteria were teachers who were present, willing to participate, and had some knowledge of nutritional deficiencies. Data was collected using a structured questionnaire comprising two sections: demographic variables and multiple-choice questions assessing knowledge on causes, symptoms, prevention, and management of nutritional deficiencies in children. The tool's validity was ensured through expert review, and reliability was confirmed with a Spearman-Brown coefficient of 0.96. A pilot study on 10 participants established the feasibility of the study. Ethical approval and informed consent were obtained. Data was analyzed using descriptive statistics (mean, percentage, standard deviation) and inferential statistics (Chi-square test) to determine associations between knowledge levels and demographic variables. Results: The findings of the study revealed that the majority of school teachers (63%) were aged between 20-30 years, with a higher proportion being male (70%). Most held a bachelor's degree (70%) and had 0-5 years of teaching experience (63%), predominantly teaching science (56%) in rural private schools. Regarding knowledge of nutritional deficiencies, most teachers demonstrated an average level of knowledge, with a few showing poor or good knowledge scores. The Chi-square analysis indicated no statistically significant association between the pre-test knowledge levels and demographic variables such as age, gender, education qualification, teaching experience, subject area, grade level taught, type and location of school, marital status, number of children, socio-economic status, training related to child nutrition, attendance at workshops/seminars, or personal/family history of nutritional deficiency disorders. This suggests that the knowledge level of school teachers regarding nutritional deficiencies in school children was generally independent of their demographic characteristics. Conclusion: The present study concluded that while the majority of school teachers had an average level of knowledge regarding nutritional deficiencies among school children, there remains a significant gap in comprehensive understanding, especially in specific areas such as symptoms, prevention, and long-term impacts. Despite varying demographic characteristics, no significant association was found between knowledge levels and factors such as age, gender, educational qualification, teaching experience, or participation in training or workshops. This indicates the need for structured, targeted educational interventions and regular in-service training programs to enhance teachers' awareness and equip them with the necessary knowledge to identify and address nutritional issues among students effectively. Strengthening teachers' understanding can contribute to early detection, better guidance, and promotion of healthier practices among school children.

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 Keywords

Nutritional Deficiencies, School Children, School Teachers, Knowledge Level, Health Education, Micronutrient Deficiency, Malnutrition, Teacher Awareness, Descriptive Study, Sagar, Madhya Pradesh, Training and Workshops, Child Nutrition, Educational Interventions, Pre-test Knowledge, Demographic Variables

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In today's high-demand, digitally driven workplaces, organizations face mounting challenges in monitoring and enhancing employee well-being while maintaining optimal productivity. Traditional management tools such as annual performance reviews, feedback forms, and wellness surveys often fall short in identifying real-time stress or burnout in employees. These conventional mechanisms are reactive and lag behind the rapid pace of workplace changes. To bridge this gap, this research introduces the concept of Neuro Management Systems -- an innovative model that utilizes Brain Computer Interfaces (BCI) to detect and respond to employee stress and productivity levels in real time. Brain-Computer Interfaces are neurotechnological systems that interpret electrical brain signals (such as EEG waves) to understand an individual's cognitive state. In the proposed framework, employees wear lightweight, non-invasive neural headsets that transmit data about their focus, mental fatigue, and emotional stress. This raw data is analyzed by machine learning algorithms that translate brain signals into actionable insights on an employee's cognitive workload and emotional resilience. These insights are then visualized in a real-time management dashboard, accessible to authorized supervisors and HR personnel. This system not only enables the early detection of stress and cognitive overload but also supports dynamic decision-making. For instance, if the system identifies a consistent decline in attention span or a rise in stress levels in a specific team, the manager can intervene immediately by adjusting task distribution, providing support resources, or introducing wellness breaks. Thus, instead of waiting for signs of burnout to become visible, the organization can proactively adapt workplace conditions to sustain employee performance and mental health.A key advantage of this neuro-management model lies in its personalized and adaptive capability. Every individual's brain responds differently to workload and stimuli. Over time, the system learns employee-specific neural patterns and tailors recommendations accordingly. This eliminates one-size-fits-all strategies and fosters a culture of precision management. Moreover, this research emphasizes strong ethical safeguards. As neuro-data is sensitive, the framework includes robust privacy protocols, employee consent mechanisms, and data anonymization standards. The goal is not surveillance but support -- using technology to empower rather than control. The implications of this model are far-reaching. In high-stress sectors like finance, healthcare, customer service, and software development, real-time neuro-feedback can significantly reduce attrition, boost job satisfaction, and enhance output. The integration of neuroscience with management transforms the traditional HR paradigm into a neuro-intelligent decision support system that is empathetic, efficient, and forward-looking.this paper lays the foundation for a new frontier in management -- one where the human brain becomes an active feedback tool for leadership decisions. By merging cognitive data with organizational strategy, Neuro Management Systems offer a sustainable path to managing talent in complex, high-performance environments. This research provides a blueprint for building stress-aware, neuro-responsive workplaces that place employee wellness at the core of productivity and performance optimization..

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Computer Interface (BCI),Neuro-Management Systems,Employee Stress Monitoring,Cognitive Load eal-Time Productivity Tracking,Workplace Neurotechnology,EEG-Based Feedback,Human-Computer Interaction (HCI)

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Phishing has emerged as a significant cybersecurity challenge, evolving across multiple communication platforms such as SMS, email, instant messaging apps, voice calls, and malicious web links. To address this growing concern, this paper presents a unified and modular phishing detection framework capable of analyzing diverse data types including textual content, URLs, call transcripts, and mobile metadata. The system integrates natural language processing with traditional and ensemble machine learning algorithms--such as BERT, LSTM, Random Forest, and XGBoost--alongside advanced feature selection techniques like PCA and RFE to enhance detection accuracy and efficiency. Each data input type is routed through a dedicated pipeline designed to extract semantic, structural, and behavioral patterns indicative of phishing attempts. Additionally, the model incorporates image-based website analysis and speech-to-text interpretation for detecting visually deceptive pages and voice scams. Evaluated on a curated dataset of over 30,000 instances, the proposed system achieves strong detection performance while maintaining low false positive rates. The real-time processing and centralized threat visualization make it suitable for deployment in both enterprise networks and consumer-facing applications. This research contributes to scalable, cross-platform phishing prevention through intelligent, adaptable detection mechanisms.

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Phishing detection, Machine learning, URL analysis, Speech processing, Text classification, Cyber threat intelligence.

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Creative Commons Attribution 4.0 and The Open Definition

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This study explores how urban consumers in Southern India weigh ethical product attributes such as organic certification, fair-trade labels, biodegradable packaging, and local sourcing when making purchase decisions. Using a choice-based conjoint (CBC) analysis, it investigates the role of environmental concern and trust in certification labels in shaping consumer preferences and willingness to pay (WTP). Focusing on Tier-2 cities, the research addresses gaps in ethical consumption studies within emerging markets. Findings reveal that while price remains influential, ethical features significantly impact consumer choices, especially among younger and higher-income groups. The study also highlights a narrowing gap between ethical intentions and actual behaviour, offering practical insights for ethical branding and sustainable marketing strategies. By integrating psychological and demographic dimensions, this research provides a culturally contextualised framework for understanding ethical consumerism in transitional economies and offers actionable implications for policymakers, marketers, and certification agencies promoting sustainability.

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Ethical consumption, Conjoint analysis, Willingness to pay, Certification trust, Environmental concern, Sustainable marketing, India, attitude-behaviour gap, Tier-2 cities

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This paper presents an innovative, art-integrated pedagogical method for teaching complex geographical concepts through multisensory engagement. The case study highlights a classroom demonstration on volcanic eruptions incorporating music, hands-on modeling, and creative expression. Conducted as part of the SAMMRIDHI National Event organized by NCERT-CIET, this approach aligns with the National Education Policy (NEP) 2020, emphasizing experiential, interdisciplinary learning. The overwhelmingly positive response underscores the potential of art-integrated teaching to enhance conceptual clarity and student engagement in geography.

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Art-Integration, Geography Teaching, Volcanic Eruptions, Experiential Learning, NEP 2020, SAMMRIDHI, Multisensory Education, NCERT-CIET

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Artificial intelligence (AI) is fundamentally reshaping the landscape of professional softwaredevelopment, moving beyond mere automation to redefine workflows, roles, and strategiccapabilities across the entire software development lifecycle (SDLC). This report provides acomprehensive analysis of AI's pervasive integration, detailing its transformative impact onrequirements gathering, design, code generation, testing, debugging, code review,documentation, security, and deployment. It explores the significant benefits, includingenhanced productivity, improved code quality, accelerated time-to-market, and augmenteddecision-making, while also critically examining the inherent challenges such as accuracyconcerns, security vulnerabilities, intellectual property complexities, the risk of over-reliance, andcrucial ethical considerations. Furthermore, the report delves into the evolving roles andessential skill sets for software engineers in an AI-driven era, highlighting the shift towards anintelligence-centric development paradigm. Finally, it forecasts emerging trends, including therise of agentic AI, multimodal and customized models, self-healing software, and thedemocratization of AI tools through open-source initiatives, concluding with actionablerecommendations for practitioners and organizations navigating this profound technological evolution.

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Artificial Intelligence (AI), Software Development Lifecycle (SDLC), Large Language Models (LLMs), Generative AI (GenAI), Code Generation, AI Coding Assistants, Automated Testing, Debugging and Error Resolution, Code Review, Security Analysis, Intellectual Property (IP), Algorithmic Bias, Agentic AI Systems, Self-Healing Software, Open-Source AI

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Creative Commons Attribution 4.0 and The Open Definition

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The shift towards online education, driven by technological advancements and the global pandemic, has introduced innovative teaching-learning practices including online teaching and it needs a different assessment method such as the Online Unrestricted-Type Open Book Examination (OUTOBE). The growing reliance on online education has led to the adoption of various assessment formats, including the OUTOBE, which allows students access to unlimited resources during the examination. This study explores the attitudes of student-teachers studied in the B.A. B.Ed. / B.Sc. B.Ed. (4 Year Integrated) Programme of Doctor Harisingh Gour Vishwavidyalaya (MP) India towards OUTOBE, focusing on their attitude of its fairness, effectiveness, and impact on learning outcomes. Purposive sampling technique was used for selection of the sample. Total 66 student-teachers in which 24 female and 42 male student-teachers who had both the experience of online unrestricted type open book and traditional (closed book) examination, were included in this study. Using a quantitative survey to capture diverse perspectives of student-teachers. To compare the means of scores, t-test is used for analysis of data. The findings revealed that while many student-teachers appreciate the flexibility and reduced anxiety associated with OUTOBE, there are some concerns about its effectiveness in evaluating true understanding and critical thinking skills. Despite these concerns, the majority of student-teachers expressed a preference for OUTOBE over traditional examinations, citing its potential to assess applied knowledge and practical skills. It offers valuable insights for educators and policymakers seeking to enhance the assessment strategies in higher education, ensuring them align with 21st-century teaching and learning paradigms.

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OUTOBE, Online Unrestricted-Type Open Book Examination, Attitude, Student-teacher and Innovative Assessment.

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The cement industry in India faces constant pressure to enhance productivity, workplace safety, and cost efficiency. This case study presents the implementation of the 5S methodology ata mid-sized cement manufacturing plant in India. It highlights how the 5S approach -- Sort, Set in Order, Shine, Standardize, and Sustain -- was used to improve housekeeping, reduce waste, and create a culture of continuous improvement. The case study offers insights into practical challenges, solutions, and measurable outcomes.This case study examines the systematic implementation of 5S methodology in a cement manufacturing plant in India to improve workplace organization, safety, and operational efficiency. The research investigates the challenges and benefits of implementing Sort (Seiri), Set in Order (Seiton), Shine (Seiso), Standardize (Seiketsu), and Sustain (Shitsuke) principles in a complex industrial environment. The study was conducted over an eight-month period at ABC Cement Limited, focusing on critical production areas including raw material handling, kiln operations, and cement grinding sections. Results demonstrate significant improvements in workplace safety (45% reduction in minor accidents), equipment efficiency (18% improvement in Overall Equipment Effectiveness), and employee productivity (22% increase in output per man-hour). The implementation achieved substantial cost savings of 38 million annually through waste reduction, improved equipment reliability, and enhanced operational efficiency.

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5S Methodology, Workplace Organization, Cement Manufacturing, Lean Manufacturing, Safety Improvement, Operational Efficiency, India

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The rapid development in commercial and social areas of modern society has affected the air quality adversely. The pollutant concentration due to industries and transport continue to grow and have an effect on human life. According to the global pollution database maintained by the World Health Organization (WHO), India has the greatest number of polluted cities globally. Higher levels of the Air Quality Index (AQI), which indicates increased risk due to pollutant exposure, are an important indicator that directly affects public health. Thus, tracking air quality index is essential in developing countries so that measures to curb air pollution could be tackled. In this research paper the primary aim is to anticipate the levels of Air Quality Index using classification techniques namely Decision Tree, Random Forest, Linear Regression and Support Vector Machines. For the experimental work the dataset of Delhi: Ashok Vihar and 2 stations of Kolkata: Ballygunge and Victoria has been gathered from Central Pollution Control Board Website. It has been concluded that Random Forest depicts maximum performance than the other classification techniques based on accuracy, precision and recall and SVM exhibits the lowest prediction accuracy.

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Classification, Support Vector Machine, Decision Tree, Linear Regression ,Random Forest, Air Quality Index

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This study investigates and compares user preferences, satisfaction levels, and usability factors between mobile banking applications and online banking platforms in public sector banks in India. In the world of banking, where services appear to be digitalized at a rapid pace, it is important to know customers' behavior in each of the channels as such knowledge can propel service delivery and user experience. A stratified random sample of 150 respondents from urban and semi urban areas that have diverse demographic profile and banking habits was formed. The structured questionnaire was used to collect the primary data, concerning the platform usage, service satisfaction, as well as the security perceptions and the influential factors of adoption. Descriptive and inferential statistical tools, i.e. t-tests and regression analysis were used to analyze the data. Specifically, findings show that mobile banking is the favored platform since it is easy to use, fast, accessible, and with a better user interface, more particularly younger and more experienced users. Key determinants of preference of platform have been security, convenience, and service range. Despite both platforms being commonly used, mobile banking has a lot more user satisfaction and engagement. Based on the study, strategic investments are made in mobile technologies and user centered amendment. In future, there_is going to be more personalized, secure, AI integrated mobile services and at the same time there will be inclusive and innovative digital banking experience.

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Creative Commons Attribution 4.0 and The Open Definition

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Mobile Banking, Online Banking, User Preference, Public Sector Banks, Digital Adoption

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Creative Commons Attribution 4.0 and The Open Definition