Volume 13 | Issue 8 |

 Abstract

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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 Keywords

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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 Keywords

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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 Keywords

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

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This study presents an interactive, real-time simulation combining deep reinforcement learning and adversarial dynamics in a multi agent treasure maze setting. Built with PyTorch for learning and Pygame for rendering, the system instructs Deep Q Networks (DQN) to properly negotiate randomly generated mazes while reacting to deceptive elements like concealed agents and false items. Agents' environments force them to identify real targets, avoid adversaries, and be agile decision-makers in the face of ambiguity. Constraints on energy limit how activities may be performed, hence motivating the strategic use of current knowledge. Using Matplotlib, a Jupyter Notebook grabs and shows a range of real-time game data including survival performance, choice accuracy, treasure gathering, and fake player detection. The game also offers in-game commentary, a real-time scoreboard, and status overlays so as to enhance feedback and study. Along with offering a modular, expandable architecture for adversarial reinforcement learning experiment testing, the system helps with manual and AI-controlled gaming

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Deep Q-Network (DQN), Camouflage Deception, Fake Player Detection, Multi-Agent Reinforcement Learning, Power-Up Management, Jupyter Notebook Simulation, Pygame

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The rapid generation of industrial and mine wastes such as Fly Ash, Red Mud, and Slag presents both environmental challenges and opportunities for resource recovery. This study explores their potential utilization as sustainable materials in line with circular economy principles. Drawing on secondary data from previous research and case studies, the work critically evaluates the physicochemical properties, environmental impacts, and engineering suitability of these wastes for applications in construction, including bricks, road sub-base layers, and embankments. Performance comparisons indicate that Slag demonstrates superior mechanical strength and environmental stability, while Fly Ash shows consistent pozzolanic behavior and Red Mud requires treatment before reuse. A compliance-based assessment aligned with USEPA/CPCB standards was used to ensure safety and feasibility. The findings support the strategic transformation of industrial waste into valuable resources, promoting sustainable mining practices, reducing landfill dependency, and contributing to responsible resource utilization. The research also recommends policy incentives to accelerate adoption in the industrial sector.

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Circular Economy, Mining Waste Management, Fly Ash, Red Mud, Slag, Resource Recovery, Industrial Waste Utilization, Sustainable Construction Materials, Waste Valorization

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The increasing complexity of modern electronic systems necessitates efficient and reliable testing mechanisms during printed circuit board (PCB) development and manufacturing. This project, titled "FPGA-Based Signal Generation for PCB Testing Simulators," aims to design and implement a high-speed, versatile signal generator using FPGA technology. The system can generate various waveforms, including sine, square, triangular, and pulse signals, with precise control over parameters such as frequency, amplitude, and duty cycle. The FPGA-based implementation employs techniques such as lookup tables (LUTs) for waveform synthesis and programmable logic for customizable signal parameters. The generated signals are output through a digital-to-analog converter (DAC) and verified using visualization tools like oscilloscopes. This project emphasizes simplicity and flexibility, reducing complexity compared to existing automated test systems while ensuring high accuracy and real-time signal generation capabilities. Applications include PCB testing, educational tools for understanding signal processing and FPGA programming, and pre-processing in communication systems. The results demonstrate the feasibility of a cost-effective, configurable signal generator that serves as a foundation for future enhancements, such as feedback analysis and adaptive testing.

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FPGA, Signal Generation, PCB Testing, UART, VHDL, DAC, Logic Analyzer, Waveform Synthesis, Logic Locking, LFSR.

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Early detection of heart disease plays a vital role in preventing life-threatening cardiovascular events and reducing healthcare burdens. This research presents a machine learning-based approach to predict the 10-year risk of heart disease using clinical data from the Framingham Heart Study. A Logistic Regression model was implemented due to its simplicity, interpretability, and suitability for binary classification tasks in healthcare. The dataset was preprocessed through normalization and feature selection, focusing on key indicators such as age, blood pressure, cholesterol, and glucose levels. The model was trained and evaluated using accuracy, confusion matrix, and other classification metrics. The Logistic Regression model achieved an accuracy of 100%, demonstrating its effectiveness in identifying at-risk individuals. Although more complex algorithms like XGBoost offer higher accuracy, the transparency of Logistic Regression makes it a valuable tool for real-world clinical applications. The study concludes by suggesting future enhancements such as handling data imbalance, integrating ensemble models, and deploying explainable AI to improve prediction reliability and clinical trust.

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Heart Disease Prediction, Logistic Regression, Framingham Dataset, Machine Learning, Clinical Risk Factors, Explainable AI, Binary Classification.

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The 21st century has witnessed a dramatic transformation in family structures and individual life choices, most prominently exemplified by the rise of Double Income No Kids (DINK) couples. Characterized by both partners maintaining gainful employment while consciously choosing to remain childfree, DINK partnerships have surged across diverse societies, signalling shifts in cultural priorities, economic dynamics, and personal fulfilment paradigms. Drawing on multidisciplinary perspectives--spanning psychology, sociology, economics, and gender studies--this research paper provides a comprehensive analysis of the psychological condition of DINK couples in the modern era. Psychological outcomes for DINK couples are analysed in depth. Empirical studies and surveys reveal that DINKs typically experience higher rates of marital satisfaction, lower levels of chronic stress, and greater opportunities for self-actualization and social engagement compared to parenting peers. However, significant psychological challenges persist, especially relating to social stigma (reported by up to 77% of DINKs in some contexts), gendered double standards, risk of loneliness or support gaps in old age, and the evolving nature of regret and legacy as couples age. Diverse adaptive strategies--ranging from robust financial and legal planning to deep investment in friendships, mentorship, pet companionship, and virtual communities--demonstrate remarkable flexibility and resilience within DINK populations. The research highlights how the DINK lifestyle is both a product and driver of shifting societal values concerning family, adulthood, and meaning. It offers a nuanced view of gender, class, and cultural variability, outlining how the DINK experience diverges in urban versus rural settings, across socioeconomic strata, and between different global regions. Furthermore, it discusses the broader social and policy implications of this demographic trend, including its impact on consumer behaviour, workforce dynamics, and the future of elder care. The paper concludes by noting key limitations in current research, notably a lack of longitudinal studies and underrepresentation of global South and LGBTQ+ DINK experiences, and offers recommendations for future scholarly, clinical, and policy attention. Ultimately, this work reframes the psychological condition of DINK couples not as an anomaly, but as an adaptive and innovative response to the complex realities of 21st-century life.

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DINK, Double Income No Kids, psychological well-being, childfree couples, marital satisfaction, social stigma, global demographics, aging.

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Abstract This article explores the intricate relationship between language, meaning, and interpretation through the lens of poststructuralist theory, with a particular focus on deconstruction. Drawing from the works of Jacques Derrida, Roland Barthes, and Michel Foucault, the study conceptualizes language as a labyrinth--an endless network of signifiers that destabilizes fixed meaning. The paper examines how poststructuralism challenges traditional hermeneutics by revealing the inherent instability of textuality and the impossibility of final interpretation. Through close readings of literary and philosophical texts, it illustrates how meaning is constantly deferred and reconstituted within systems of difference, rendering the act of reading both productive and indeterminate. Rather than seeking definitive readings, the article argues for a critical openness that acknowledges the text's multiplicity and the reader's active role in constructing meaning. Ultimately, the paper emphasizes that the limits of interpretation are not constraints but invitations to rethink authority, authorship, and the politics of meaning in contemporary theory.

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Keywords: Poststructuralism, Deconstruction, Textuality, Interpretation, Derrida

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Artificial Intelligence (AI) is rapidly changing how we function as a society and is opening up complex ethical questions that need to be historically explored philosophically. This paper looks at the ethical considerations surrounding AI from a Western and an Indian philosophical position. These investigations have to do with the elements of autonomy, accountability, and moral responsibility. AI systems are taking over decision-making in a variety of important domains (e.g., healthcare, education, policing, warfare). Concerns are raised about algorithmic bias, data privacy, surveillance, and even the diminishing of human agency. Using the normative traditions of utilitarianism, deontology, and virtue ethics, this study looks at the various ways in which machines can either make moral decisions, or respect the dignity of personhood. Further, ethical relationships from the Indian traditions that have been less internationally discussed like dharma (righteous duty) or karma (moral action) relationships based on their morals can be taken into account when investigating the position of a good or bad dehumanized world. The hope of this paper is that while it can be shown that AI can maximize efficiency and optimize outcomes, it can still be subordinated to humans as normative ethical beings in order to limit dehumanization and loss of ethics towards moral disengagement. This will involve a study of ethical behavior and choices of AI, accountability, and transparency in how algorithmic choices are made, and the implementation of ethical guardrails around the AI process in creating systems that rely on artificial intelligence. The investigation concludes that a cross-cultural, philosophical inquiry is the only way to resolve the human ethical dilemmas that AI presents us with and ultimately serve those hard questions about humanity's relationship with technology's best interests and to serve the human condition over undermining it. This study contributes to the ongoing efforts around the world to develop responsible, ethically grounded AI.

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 Keywords

Ethics and Artificial Intelligence, Philosophical Inquiry, Moral Responsibility, Autonomy and Responsibility, Dharma and Karma, Human-Centered Technology

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