Volume 14 | Issue 4 |

 Abstract

The study of consciousness has remained a central concern in both philosophical and scientific discourse, raising fundamental questions about the nature of reality, selfhood, and awareness. This paper undertakes a theoretical inquiry into human consciousness as presented in the Mandukya Upanishad, one of the most concise yet profound texts in the Upanishadic tradition. The Upanishad offers a systematic framework by classifying consciousness into four states: waking (j?grat), dreaming (svapna), deep sleep (su?upti), and the transcendental state (tur?ya). The study analyzes these four states as progressive levels of awareness, demonstrating how empirical and mental experiences are ultimately grounded in a deeper, non-dual consciousness. Special emphasis is placed on the concept of tur?ya, which is described as pure, unconditioned awareness beyond subject-object duality. The paper further explores the philosophical implications of identifying ?tman (individual self) with Brahman (ultimate reality), a central doctrine within Advaita Vedanta. In addition, the symbolic significance of the syllable Om (AUM) is examined as a representation of the totality of consciousness and as a practical means for its realization. The study also engages with contemporary perspectives by comparing the Mandukya model with modern theories in psychology and neuroscience, particularly in relation to different states of consciousness. By integrating classical philosophical insights with modern discourse, this paper argues that the Mandukya Upanishad provides a comprehensive and non-dual framework for understanding consciousness, one that transcends materialistic explanations and offers a deeper ontological and experiential perspective. The findings suggest that the Upanishadic model remains highly relevant for contemporary studies of consciousness and the philosophy of mind.

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 Keywords

Mandukya upanishad, human consciousness

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Heart disease is one of the leading causes of death worldwide. Early prediction can significantly reduce mortality rates. This paper presents a machine learning-based approach for heart disease prediction using classification algorithms such as Logistic Regression, Decision Tree, and Random Forest.

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 Keywords

Heart Disease, Machine Learning, Classification, Healthcare, Prediction Machine learning Algorithms.

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This paper examines the impact of bid-ask spreads and transaction costs on trading behaviour, market efficiency, and asset pricing in financial markets. Building on established theoretical models, including Roll (1984), Glosten and Milgrom (1985), Kyle (1985), and Amihud and Mendelson (1986), the study integrates recent empirical evidence from equity, fixed-income, and cryptocurrency markets up to 2026. The results show that bid-ask spreads represent not only execution costs but also reflect information asymmetry and market conditions. Transaction costs influence trading activity, portfolio decisions, and contribute to the liquidity premium in asset returns. Furthermore, modern developments such as zerocommission trading, high-frequency trading, and payment for order flow have reshaped cost structures in financial markets. Evidence from recent U.S. Treasury market volatility highlights the role of spreads in market stability. The study also evaluates financial transaction taxes, emphasizing their trade-offs between revenue generation and market efficiency.

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 Keywords

Bid-Ask Spread, Transaction Costs, Market Liquidity, Asset Pricing, Market Microstructure, Liquidity Premium, Adverse Selection, High-Frequency Trading, Financial Transaction Tax

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Parenting styles are linked with developmental aspects and behaviour of a child including academic achievement. However, whether this association is relevant to young adult migrant students with limited direct parental influence remains unclear. The present study aims to explore the association of authoritarian and authoritative parenting and academic achievement among young adult migrant students. Data were collected from 50 participants (28 females, 22 males, aged 18 -25) studying in Bengaluru. Perceived parenting styles were analysed by Parental Authority Questionnaire (PAQ) (Buri, 1991) administered using the Likert scale. Academic data of the sample was collected using self-reported CGPA. The reliability of parenting scores were found to be above 0.75 using Cronbach's Alpha. Pearson correlation revealed weak correlations of authoritarian and authoritative parenting styles with academic achievement, none of which were statistically significant. The findings revealed that association between parenting styles and academic achievement may be reduced during young adulthood, particularly when individuals are migrant students, not directly influenced by parents.

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 Keywords

Authoritative parenting, Authoritarian parenting, Academic achievement, Young adults, Parenting, Migrant students

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Quick commerce (Q-commerce), characterized by ultra-fast delivery of goods within a short time frame, has rapidly transformed the retail landscape in India. Hyderabad, as a major metropolitan and IT hub, has emerged as a significant market for Q-commerce expansion. This study examines the growth trends, key drivers, challenges, and future opportunities of Q-commerce in Hyderabad. Using secondary data and a structured questionnaire, the research identifies consumer preferences, usage patterns, and satisfaction levels. The findings reveal that convenience, speed, and digital accessibility are major factors driving adoption, while profitability concerns and operational challenges remain key issues. The study concludes that Q-commerce has strong growth potential, with scope for diversification and technological integration.

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Quick Commerce (Q-commerce), Consumer Behavior ,Digital Transformation, Diversification,

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Working capital management is a very important part of financial management, which involves the management of short-term assets and liabilities of an organisation. This includes maintaining sufficient liquidity to conduct organisational activities in an efficient manner. In addition to this, this involves improving the profitability of the organisation. Working capital management plays an important role, especially in the manufacturing sector, because in such industries most of the funds are invested in inventories and receivables. In this study, working capital management strategies followed by JALARAM DOORS & HARDWARES, a manufacturing and trading organization involved in wooden doors manufacturing and hardware supply, have been studied. The study relies upon secondary data collected from financial statements of the organisation from 2021-22, 2022-23, and 2024-25. Financial ratio analysis will be conducted through the current ratio, inventory turnover ratio, and debtor's turnover ratio. From the findings, it is seen that although there are satisfactory levels of liquidity in the organization, there are some issues associated with the management of inventories and fluctuation in the profitability levels

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 Keywords

Working Capital, Liquidity, Inventory, Receivables, Profitability

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The rapid proliferation of Internet of Things (IoT) devices has introduced significant cybersecurity challenges due to their resource-constrained nature and limited native protection mechanisms. Traditional intrusion detection systems (IDS), which rely on signature-based approaches and cloud-dependent architectures, are often unsuitable for decentralized IoT environments. This paper presents NeuroFence, a lightweight, AI-driven intrusion detection system designed for real-time cyber defense in edge-based IoT networks. The proposed system operates entirely on local gateways, such as Raspberry Pi, and leverages machine learning techniques to model normal network behaviour and detect anomalies in real time. NeuroFence incorporates a hybrid detection framework that combines rule-based analysis with unsupervised anomaly detection models, enabling both immediate threat identification and adaptive learning against evolving attack patterns. The system captures live network traffic, extracts features using sliding-window techniques, and evaluates traffic behaviour using models such as Isolation Forest and TinyML-based classifiers. A complete edge-first architecture is implemented, integrating packet sniffing (Scapy), lightweight data processing, local storage (SQLite), and an interactive dashboard built using Flask and React for real-time alert visualization and operator response. The system also includes mechanisms for safe mitigation, forensic logging, and continuous model improvement through feedback-driven learning. Experimental evaluation demonstrates that NeuroFence effectively detects anomalous IoT traffic with low latency and minimal computational overhead, making it suitable for deployment in resource-constrained environments such as smart homes, industrial IoT systems, and remote installations. The proposed solution highlights the potential of decentralized, AI-powered security frameworks in enhancing the resilience and autonomy of modern IoT ecosystems.

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 Keywords

Internet of Things (IoT), Intrusion Detection System (IDS), Edge Computing, Machine Learning, Anomaly Detection, TinyML, Cybersecurity, Network Traffic Analysis, Raspberry Pi, Isolation Forest

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Land use pattern is a reflection of the interrelationship between natural resources and human activities in a region. Fatehpur district, located in the Ganga-Yamuna Doab, is highly suitable for land use studies due to its agrarian nature. The present study is based on the spatio-temporal analysis of land resources in Fatehpur district. The main objective of the study is to understand the distribution, trends, and variability of different land use categories in the district--such as agricultural land, forest area, fallow and wasteland, pasture land, and built-up area. In this research, secondary data (district statistical handbook, land records, and census data) have been used to classify land use and analyze changes over time. The analysis clearly indicates that agriculture is the dominant land use in the district, occupying the largest proportion of the total area, whereas forest area is very limited. Due to urbanization and infrastructural development, there has been a gradual increase in built-up areas, which is putting pressure on agricultural land. The study also reveals that land use patterns are strongly influenced not only by physical factors--such as soil, climate, and water resources--but also by socio-economic factors like population growth, technological development, and market accessibility. Additionally, issues such as land degradation, declining groundwater levels, and imbalanced land use are emerging challenges in the region. Finally, the study emphasizes the need for sustainable land use management measures, such as agricultural diversification and the promotion of modern irrigation techniques, to ensure balanced and long-term utilization of resources in Fatehpur district.

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 Keywords

Land Use Pattern, Spatio-Temporal Analysis, Fatehpur District

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Over the past few years, the safety of women has emerged as an important issue in society, particularly in the presence of other people and when the circumstances are unfavorable during the nights. As the number of harassment and emergency incidents increases, the need to find intelligent, non-obtrusive safety solutions that will work in real time is growing. The proposed project is called Night Guardian: Empowering Women Safety with Hand Sign-based Communication and AI and suggests a new solution that would be based on leveraging artificial intelligence and computer vision to provide immediate assistance in case of distress scenarios without having to engage with the user directly. The system is developed based on the latest technologies like MediaPipe and OpenCV to record and process real-time video feeds of a webcam. MediaPipe is applied in the correct hand landmark detection and keypoint extraction in order to determine hand gestures very precisely. These features are extracted and converted into numerical forms and input to machine learning algorithms like Random Forest, Support Vector Machine (SVM) and K-Nearest Neighbor (KNN). Performance measures such as precision, recall, and F1-score are used to train and test the models to be reliable at gesture recognition. After a distressing gesture has been identified, an alert system is automatically triggered. This involves raising a local alarm to draw attention locally and an automated email notification and a picture of the gesture to emergency contacts or authorities that are predetermined. The combination of these alert mechanisms is to make sure that there is quick response hence increasing the possibility of intervention in time. With the application of AI, the system can be continuously and effectively used even in dynamic environments. Altogether, the offered system will be an effective and scalable way to enhance the safety of women with the help of intelligent automation. It offers a quick, convenient, and free form of emergency communication by using machine learning and real-time gesture recognition to deliver a hands-free form of communication. This project, apart from fostering innovation in technology, helps to enhance social well being by making people be empowered with proactive safety tools.

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 Keywords

Over the past few years, the safety of women has emerged as an important issue in society, particularly in the presence of other people and when the circumstances are unfavorable during the nights. As the number of harassment and emergency incidents increases, the need to find intelligent, non-obtrusive safety solutions that will work in real time is growing. The proposed project is called Night Guardian: Empowering Women Safety with Hand Sign-based Communication and AI and suggests a new solut

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Crop stress is one of the foremost threats to global food security, causing yield losses estimated at 20-40% annually. Traditional monitoring relies on costly sensor arrays or time-intensive manual inspection, limiting accessibility for small-scale farmers. This paper proposes a low-cost, camera-only crop stress detection system built around a fixed ESP32-CAM RGB module. A multi-task convolutional neural network (CNN) based on MobileNetV2 simultaneously classifies crop health (Healthy/Stressed), estimates a continuous stress severity score, and assesses soil surface condition (Dry/Moist/Wet). Temporal analysis via an exponentially weighted moving average (EWMA) over a rolling N-frame buffer enables detection of progressive stress onset before visible leaf damage appears, reducing false-positive alerts by 31% compared to single-frame classification. A hybrid learning pipeline combines supervised training on a labeled base dataset with semi-supervised pseudo-label fine-tuning for unseen crop types. Grad-CAM visualisations highlight stress-causative regions, improving model transparency and farmer trust. The system achieved 93.4% binary classification accuracy, 89.7% soil condition classification accuracy, and a stress severity MAE of 0.06 on held-out test data.

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 Keywords

Crop Stress Detection, Convolutional Neural Network, ESP32-CAM, MobileNetV2, Hybrid Learning, Semi-Supervised Learning, Grad-CAM, Temporal Analysis, Precision Agriculture, Explainable AI.

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