Volume 14 | Issue 4 |

 Abstract

The Mahabharata, which is the greatest epic of India shows women's complex and layered representation. In this epic woman are not only secondary character, but authority, political influence, family duty and narrative transformation which works as an important agent. But the epic has greatest focus on male heroes and dynastic conflicts, still women's. story events express ethical dilemmas, and played crucial role to guide philosophical role. Mahabharata, written by Vyasa, and further translated in different versions by various translators is one of the ancient epic narratives which offers the theme of complicated gender roles introducing female characters such as Kunti, the mother of the Pandavas; Gandhari, the mother of Kauravas and Draupadi, the wife of the Pandavas. There are other female characters as well that embodies the ideals of devotion and its balance with sacrifice within the structure of patriarchy. Although the text emphasizes on the male heroes and their dominance, this epic also portrays the autonomy of the female characters.

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The role of Women in Mahabharata

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Ayurveda considers Dhatus as the fundamental structural (Dharana) and functional (Poshana) units of the body. Dhatu Sara represents tissue excellence and reflects strength and vitality. Varna means the complexion of the skin, indicates internal health. As per Ayurveda Dhatus are very important which performs the function of Dharana and Poshana. Dhatu sara means excellence of body tissue which is the essence of Dhatu .It also indicates Bala of an individual & Varna means the complexion of the skin that is Twak. The complexion of a person is determined by the dominant of Dhatu sarata .It determines whether the Twak is lustrous or not reddish or not etc. Bala is considered as a complexion promoter. This article aims to give information about interrelationship of Varna & Dhatu sarata in a person as per Ayurveda.

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Dhatu sarata, Varna, Twak, Dhatu, Bala & Varna

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This document focuses on the Supply Chain Dashboard. It is a system that assists small and medium enterprises in managing their supply chains. The traditional method is insufficient. Those methods can lead to errors and misconceptions. So this project named Supply Chain Dashboard is made to address those issues so that the small and medium size enterprises can have transparency in their work ensuring smooth flow of goods and product. The primary element of the supply chain dashboard is AI Analytics Module. The Python Backend utilizing FastAPI manages essential functions and drives the AI Analytics Module. Our whole data is stored in PostgreSQL. Also we have used the Typescript for seamless operations on the supply chain activities.

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Supply Chain Management (SCM), AI-Powered Dashboard, Predictive Analytics, Demand Forecasting, Inventory Optimization, Real-Time Monitoring, IoT, FastAPI, PostgreSQL, React.

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This paper presents the design and implementation of a Smart Bike Safety System that enhances rider protection through automation and real-time monitoring. The system integrates multiple sensors and embedded controllers to verify helmet usage, detect alcohol levels, and control engine ignition based on safety conditions. Two independent modules, placed in the helmet and the bike, communicate wirelessly using the ESP-NOW protocol to ensure reliable and low- latency data exchange. Motion and impact sensors continuously analyze riding conditions to identify potential accidents. Upon detecting a crash, the system initiates an intelligent alert mechanism that seeks rider confirmation and, if necessary, automatically sends emergency notifications through SMS and phone calls using a GSM module. A web-based interface further enables live tracking and system supervision. By combining prevention, detection, and rapid communication features, the proposed system offers an efficient, low-cost, and practical solution to improve two- wheeler safety and emergency response.

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 Keywords

ESP-NOW Communication, Wireless Sensor Network, Accident Detection, Motion and Impact Sensors, Engine Ignition Control, Emergency Alert System, GSM Module

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 Abstract

To define culture is to chuck out a piece of its poly-semic meanings, as not only culture means differently for different people, how and to what extent people participate in this space differs accordingly. But our preoccupation here is to do two things- one, to deal with how this space gets defined in its essentialist domain, that is, how a culture creates and negotiates an identify for itself with which it chooses to define itself, for example the notion of "Englishness" as elucidated by Talal Asad, but the argument can go much deeper. Secondly, how can we study the clairvoyance that is ejected in the process of meaning formation to this identity, through which this identity is given a theoretical and conceptual and ethical validation as well as a pre-history, which is then inserted albeit anachronistically into the modern definitions of this identity. Thirdly, cultural spaces don't always correspond to physical spaces and they have the capability to transcend those spaces, though there are ways in which cultural spaces interact with these physical spaces, in determining how people who are not believed to have belonged to this space historically or culturally, enter and negotiate this space. The meaning formation in this space is layered in relations of power and hierarchy, though not necessarily hegemonic, as the language of the meaning formation in itself provides particular space, particular way to enter that cultural space and their position within the cultural space that they need to respect.

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Anthropology, Cultural Spaces, Language, Negotiation

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Maternal health plays an essential role in the field of medicine because both the mother and the infant are affected. Early identification of any possible risks associated with pregnancy can help minimize the chances of adverse consequences. In this respect, this paper provides a description of pregnancy risk and fetal health care system that can predict potential problems during pregnancy by applying machine learning. This system obtains users' input information, such as their age, blood pressure, sugar levels, temperature, and other data about their health condition. Machine learning enables the system to determine whether there is a low, medium, or high probability of the risks associated with the patient's pregnancy. This project combines various technologies like artificial intelligence, data analytics, and others in order to improve decision-making.

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Maternal Health, Machine Learning, Risk Prediction, AI, Healthcare System, Pregnancy Monitoring, Data Analysis

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The rapid growth of digital commerce has placed product recommendation systems at the center of the modern e-commerce experience. Consumers today navigate catalogs con-taining millions of items, and the ability of a platform to surface the right product at the right moment has become a direct driver of engagement and revenue. Despite their commercial im-portance, most deployed recommendation systems rely on static models trained once on historical data, utilize only numerical ratings while ignoring the qualitative information embedded in textual reviews, and lack any mechanism to adapt as user preferences shift over time.The rapid growth of digital commerce has placed product recommendation systems at the center of the modern e-commerce experience. Consumers today navigate catalogs con-taining millions of items, and the ability of a platform to surface the right product at the right moment has become a direct driver of engagement and revenue. Despite their commercial im-portance, most deployed recommendation systems rely on static models trained once on historical data, utilize only numerical ratings while ignoring the qualitative information embedded in textual reviews, and lack any mechanism to adapt as user preferences shift over time.

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Recommendation System, Collaborative Filter-ing, Sentiment Analysis, TF-IDF, XGBoost, Adaptive Retraining, NLP, Hybrid AI, Machine Learning, Dataset Versioning, MLOps, E-Commerce

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The sustainable management of agro-industrial waste has become a major global concern, necessitating the development of innovative valorization strategies. Dragon fruit peel, a significant by-product of Hylocereus spp., constitutes approximately one-third of the total fruit mass and is an abundant source of bioactive compounds including betalains, phenolics, flavonoids, and polysaccharides. The present review provides an extensive evaluation of the potential of dragon fruit peel as a raw material for the extraction of natural pigments and polymers and their application in the formulation of a probiotic-enriched anti-tan hydrogel. Betalains demonstrate remarkable antioxidant, anti-inflammatory, and photoprotective activities, while pectin serves as a biodegradable and biocompatible polymer capable of forming stable hydrogel networks. The integration of probiotics such as Lactobacillus fermentum and Lactobacillus parabuchneri enhances the functional efficacy of the formulation by modulating the skin microbiome and improving barrier function. The synergistic interaction of these components contributes to effective tyrosinase inhibition, mitigation of oxidative stress, and protection against UV-induced skin damage. This review also explores advanced extraction techniques, nano-encapsulation strategies, rheological characterization, and industrial scalability, highlighting the role of fruit waste valorization in sustainable cosmetic innovation.

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Valorization of Dragon Fruit Peel Waste: A Review on Pigment and Polymer Extraction for Probiotic-Based Anti-Tan Hydrogel Applications

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Distributed Denial of Service (DDoS) attacks continue to pose a serious threat to the availability and reliability of modern networked systems by overwhelming target resources with malicious traffic. With the increasing dependence on cloud services, e-commerce platforms, and online applications, the need for efficient real-time detection and mitigation mechanisms has become critical. This paper presents a real-time DDoS detection and mitigation framework based on packet-level traffic analysis using WinDivert and Python. The proposed system captures live network packets, extracts traffic features such as source IP activity, packet rate, and protocol distribution, and applies threshold-based anomaly detection to identify malicious behavior. Upon detection, the framework performs immediate mitigation by dropping suspicious packets and dynamically blocking malicious IP addresses. A real-time monitoring dashboard is also integrated to visualize traffic flow, incident logs, and mitigation actions for improved situational awareness. Experimental evaluation demonstrates that the proposed system achieves rapid detection, low response latency, and effective mitigation of high-volume DDoS attacks while maintaining minimal impact on legitimate traffic. The framework provides a lightweight, cost-effective, and practical solution for enhancing cybersecurity in real-world environments.

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Distributed Denial of Service (DDoS) attacks continue to pose a serious threat to the availability and reliability of modern networked systems by overwhelming target resources with malicious traffic. With the increasing dependence on cloud services, e-commerce platforms, and online applications, the need for efficient real-time detection and mitigation mechanisms has become critical. This paper presents a real-time DDoS detection and mitigation framework based on packet-level traffic analysis us

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Phishing websites pose a serious threat to online security by deceiving users into revealing sensitive information such as login credentials and financial data. Traditionalblacklist-baseddetectiontechniquesareoftenineffectiveagainstnewly created phishing sites, highlighting the need for intelligent, data-driven solutions. This work presents a phishing website detection system using the XGBoost (Extreme Gradient Boosting) algorithm, a powerful ensemble learning method known for itshigh accuracy and efficiency.The proposed model analyzes multiple website features, including URLcharacteristics, domain information, and content- based attributes, to distinguish phishing websites from legitimate ones.After preprocessing and feature selection, the XGBoost classifier is trained and evaluated on a labeled dataset of phishing and benign websites. Experimental resultsdemonstratethattheproposedapproachachieveshighdetectionaccuracy with low false-positive rates, outperforming several traditional machine learning classifiers.The findings indicate that XGBoost is a robust and effective technique for real-time phishing website detection, contributing to improved cybersecurity and user protection.

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 Keywords

User Management Module URL Submission Module DataPreprocessingModule Feature Extraction Module Feature Selection Module XGBoost Classification Module ResultDisplay/NotificationModule

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Creative Commons Attribution 4.0 and The Open Definition