Volume 13 | Issue 4

 Abstract

This study investigates the spatio-temporal changes in vegetation, water availability, and urbanization from 2020 to 2025 in the Mahandai and Kharun River region near Nava Raipur, Chhattisgarh, using remote sensing indices--NDVI (Normalized Difference Vegetation Index), NDWI (Normalized Difference Water Index), and NDBI (Normalized Difference Built-up Index). Results indicate a marginal rise in NDVI values for dense vegetation; however, a concurrent reduction in its spatial extent suggests ecological degradation and declining vegetation health. NDWI values exhibit a notable increase, linked to erratic climatic events such as off-season cyclonic rainfall, temporarily enhancing surface moisture but signaling rising climate volatility. NDBI analysis reveals significant urban expansion, reflecting conversion of natural and agricultural lands into built-up areas, contributing to urban heat island effects and hydrological disruption. These land use and land cover (LULC) changes pose serious environmental challenges including habitat loss, rising land surface temperatures, and reduced groundwater recharge. The findings underscore the urgent need for sustainable land management, preservation of riparian buffers, and climate-resilient urban planning to mitigate the adverse impacts of unregulated development and climatic instability in the region.

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Mahanadi River, Kharun River, GIS, NDVI, NDWI, NDBI

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The formulation and evaluation of herbal effervescent powders for constipation were conducted to develop a natural, effective, and patient-friendly treatment option. Four formulations were prepared using triphala , trikatu , chitrak , fennel , sendhav , citric acid , tartaric acid , sodium bicaebonate , and other excipients . Effervescent powders were designed to produce solutions that release carbon dioxide simultaneously. The main advantages of effervescent powder are quick production of solution. Thus, it is faster and better to absorb. This powder was evaluated for various parameters like angle of repose, dissolution studies, and effervescent cessation time. The formulated effervescent powder exhibited excellent flow properties which showed good angle of repose, Carr's index, Hausner's ratio, bulk density and tapped density.

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Effervescent Powder , Constipation , triphala , trikatu , chitrak , fennel , sendhav , Carr's index, Hausner's ratio, bulk density and tapped density.

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This study explores the evolution of gender stereotypes in the portrayal of women's representation through artistic canvases, drawing on feminist theory as its theoretical framework in Indian art. The research highlights the shift in perspectives and representations of femininity by examining how women's roles have been depicted across historical and contemporary artistic periods in India. Employing a qualitative methodology, specifically thematic analysis of select artworks, the study uncovers how artists have transitioned from typifying women as symbols of sexuality or domesticity to celebrating their multifaceted identities. Contemporary artists, through their intuitive styles, delve into women's suppressed emotions and resilience, portraying their triumph over societal prejudices. The paper argues that such contemporary female visual narratives challenge traditional stereotypes, and redefine women's roles across social, economic, and cultural dimensions. This analysis enhances the discourse on gender representation in art, highlighting its capacity to foster equity and empower society.

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Indian art, Visual art, Female Visual Narratives, Diaspora art, Women artists

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Agadtantra focuses on toxicology which includes study of different types of toxins, their ill -effects on the body and management for the same through Ayurveda as well as modern medicine. Various Agada kalpa or yoga serve as an antidote for poisoning, comprises combination of antitoxic medications, along with antioxidant, hepato-protective and immune-modulator substances, etc. Cancer is a group of diseases involving abnormal cell growth with the potential to invade or spread to other parts of the body. Toxic carcinogens are the substances that can cause cancer. These substances can be found in the environment, workplace, and even in food. The concepts explained in Agadtantra such as Dooshivisha (latent or denatured poisons), Garavisha (artificial poisons), sthavara visha (Plant based poisons) and also Viruddhahara (Incompatible food) can be considered as the etiological factors and responsible for pathology of cancer. These contribute to cellular damage and mutations that lead to cancer. Chemotherapy and radiotherapy are two distinct cancer treatments that share the goal of killing or slowing the growth of cancerous cells. As these treatments also cause toxic effects on body, Ayurveda focuses on detoxification of the body i.e. eliminating all these toxins by shodhan karma such as vaman, virechan, basti etc. Shaman chikitsa can also be given such as various agada kalpas (formulations) that are useful to reduce or to eliminate the toxicity of chemotherapy and radiotherapy.

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Agadtantra, Cancer, Toxic Carcinogens, Dooshivisha, Detoxification, Agada Yoga

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Foodborne pathogens remain a critical public health challenge worldwide, responsible for severe illness, hospitalizations, and economic losses. Key bacterial culprits such as Escherichia coli O157:H7, Salmonella spp., Listeria monocytogenes, and Bacillus cereus are frequently linked to contaminated food and water sources. While culture-based microbiological methods have long been used for pathogen identification, they are often time-consuming and lack the speed required for rapid response. This review highlights recent advancements in molecular and immunological detection methods that have revolutionized food safety monitoring. Molecular approaches--including polymerase chain reaction (PCR), real-time PCR (qPCR), multiplex PCR, loop-mediated isothermal amplification (LAMP), and reverse transcription PCR (RT-PCR)--offer rapid, sensitive, and specific identification of pathogens even at low concentrations. Immunological methods, particularly enzyme-linked immunosorbent assay (ELISA) and lateral flow assays, are valuable for their ease of use, cost-effectiveness, and potential for on-site testing. While these methods are powerful, each has limitations such as cross-reactivity, inability to differentiate viable from non-viable organisms, or dependence on specialized equipment. The integration of these approaches with biosensors, isothermal systems, and portable platforms shows promise for enhancing real-time detection capabilities. Ultimately, a combined strategy leveraging both traditional and next-generation methods is essential to prevent foodborne outbreaks and protect consumer health. This paper underscores the importance of ongoing innovation and interdisciplinary collaboration in ensuring food safety.

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Foodborne pathogens; PCR; ELISA; molecular diagnostics; food safety; pathogen detection.

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: This paper explores the application of Natural Language Processing (NLP) and Machine Learning (ML) techniques to automate threat intelligence, a critical component in modern cybersecurity defense systems. As cyber threats grow in scale, complexity, and frequency, traditional manual methods of threat detection and analysis are no longer sufficient to ensure timely and accurate responses. The research leverages a diverse dataset comprising unstructured threat reports, dark web communications, and security blogs to train a variety of models including Support Vector Machines, Random Forests, clustering algorithms, Recurrent Neural Networks, and transformer-based architectures like BERT. Through extensive experimentation and evaluation using metrics such as precision, recall, F1-score, and accuracy, the study finds that deep learning models, particularly BERT, outperform other methods in extracting and interpreting contextual threat information. The results demonstrate that NLP and ML not only enhance the speed and accuracy of threat identification but also enable scalable, automated analysis suitable for real-time cybersecurity operations. This work contributes to the growing body of research on intelligent threat detection and provides a foundation for integrating advanced AI-driven tools into existing cybersecurity infrastructures

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Threat Intelligence, Natural Language Processing, Machine Learning, Cybersecurity Automation, Deep Learning

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William Congreve's play The Double Dealer published in 1694, stands as a remarkable work of Restoration comedy, distinguished by its incisive satire and sophisticated critique of aristocratic society. While the play adheres to the formal conventions of the comedy of manners, its deeper function lies in its sharp dissection of the hypocrisy, moral ambiguity, and performative behavior of the elite class in late 17th-century England. This article explores the satirical dimensions of The Double Dealer, focusing on how Congreve uses wit, irony, and theatrical deception to expose the moral contradictions of his time. At the center of the satire is the character of Maskwell, a master manipulator who exemplifies the duplicity of the social order. Through his calculated betrayals, Congreve satirizes a culture in which success is achieved not through virtue but through deception. The play also targets the pretensions and vanity of the aristocracy, particularly through characters like Lady Plyant and Lady Touchwood, whose exaggerated behaviours reveal the absurdities of class-based social expectations and gender roles. The article examines the way Congreve critiques the institution of marriage, portraying it as a transaction governed more by property and inheritance than by love or compatibility. Female characters, though often constrained by patriarchal norms, also become vehicles of satire as they subvert or conform to social expectations in revealing ways. The play's meta-theatrical elements reinforce its satire by highlighting the performative nature of identity and truth in elite society. The Double Dealer emerges as a complex satire that not only entertains but interrogates the values of its cultural moment. Congreve's biting wit and strategic use of deception position the play as both a product of and a commentary on the Restoration ethos, making it a timeless reflection on human duplicity and societal performance.

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Deception, Elite society, Restoration, Marriage, Patriarchal, Absurdity

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Lung cancer is the most common and deadliest cancer worldwide, where early detection is essential in improving patient outcomes. Machine learning (ML) has emerged as a groundbreaking healthcare technology with enormous potential in optimizing the accuracy, efficiency, and accessibility of lung cancer diagnosis. This paper explores various ML algorithms for the early detection of lung cancer from clinical and medical imaging data. Different approaches, including Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and ensemble models, are assessed based on their capacity to classify and predict malignancy in lung nodules [1] to [5]. The work utilizes public datasets such as Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI) for training and validation models [6], [7]. Data preprocessing tasks like noise removal, feature extraction, segmentation, and increasing the quality and pertinence of the input data are performed [8]. The feature selection methods use dimensionality reduction techniques to ensure efficient performance and minimal computational cost [9]. Research has demonstrated that CNNs are more sensitive and specific for the detection of cancerous lesions than traditional ML approaches [10]-[12]. Deep learning algorithms are also more capable of detecting subtle imaging features that may not be detectable by the naked eye, and this improves the reliability of diagnosis. The addition of clinical parameters such as age, smoking status, and genetic predispositions improves predictive ability [13], [14]. In conclusion, ML use in lung cancer detection is a significant step toward early diagnosis, with high potential for enhanced mortality rates and personalized treatment planning.

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Lung cancer detection, Machine learning (ML), Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), Medical imaging, Lung nodules, Deep learning, Feature extraction ,Early diagnosis ,Predictive modeling

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One potentially significant idea that contributes to achieving global sustainable development is green technology. A fresh, significant idea that would improve the environment is needed in the globe today. Realizing the need for creative green products in today's global market and attempting to determine the detrimental effects of non-green products are the study's main goals. A specific city (Coimbatore) has been chosen for the study, and the necessary data has been gathered from a variety of sources, examined using appropriate statistical techniques, and facts have been discovered. According to the study, so-called green or organic items benefit humanity more and aid in the eradication of some problems related to green technology. It contributes to sustainable growth. The study also sheds information on potential directions for future research.

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Green technology, Sustainable, Environment, Organic, Eradicate, potential directions.

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Increase in the number of potholes have serious impact on road safety and infrastructure, leading to increased costs for vehicle repairs and accidents. Why? Even with manual inspections and sensor-based systems, pothole detection is not an option. A real-time pothole detection system using deep learning techniques, built on the YOLO (You Only Look Once) ONNX model is presented in this article. This involves gathering data, generating model data and testing mobile and vehicle-mounted applications over the course of several months. It was 92% accurate in detection and had an adequate high confidence level estimate (ROC-AUC) score, while also maintaining proper balance between precision and recall. Other concerns we tackle include differences in environment between samples, inaccurate data detection systems, and hardware failures.

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Road safety, object detection, YOLO, real-time, machine learning, image processing.

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Androsace globifera contains significant phytochemicals such as saponins and is utilized for treating liver and kidney diseases, amenorrhea, skin allergies, leucorrhoea, and as an abortifacient. Morphological studies reveal that the leaves are diverse in shape, ranging from speculating to elliptical. Organoleptic analysis indicates an astringent taste, aromatic odor, and brittle fracture, with the stem being straight and colored brownish-green. The flowers are pink with 12-15 blooms, five petals, seven sepals, a 1.5 mm style, and a 3 mm capsule. The powdered leaves and roots are greenish and brown, respectively, with an astringent and aromatic odor, and a bitter and acrid taste. Microscopic and physicochemical studies identify vascular bundles and upper and lower epidermal cells. The moisture content of roots and leaves is 2.5% and 3%, respectively. The total ash content of roots and leaves is 25% and 22.5%, acid-insoluble ash is 12.5% and 9%, and water-soluble ash is 10% and 8.9%. The extractive values for roots and leaves are as follows: water (0.8% and 1%), ethanol (4% and 2.25%), chloroform (8% and 8.5%), ethyl acetate (9% and 7%), and methanol (11% and 13%). Leaf constants include a stomatal number of 5, a stomatal index of 2.5-7, a vein islet number of 11-17, a vein termination number of 9-12, and a palisade ratio of 2:6. Fluorescent studies show the leaves and roots as light brown and dark brown, respectively. Histochemical analysis reveals the presence of lignified cellulose and cuticular cell walls, aleurone grains, calcium oxalate, fatty acids, resins, inulin, mucilage, tannins, and hydroxyl anthraquinones.

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Androsace globifera, Characteristics, Evaluation, Microscopy, Screening

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This paper presents the design and implementation of a versatile floor cleaning robot, controlled through an Android application. The robot integrates multiple cleaning functionalities--vacuuming, mopping, spraying, and drying--while ensuring effective navigation and obstacle avoidance. It is equipped with a modular cleaning platform, adjustable cleaning pads, and real-time resource and battery monitoring, all controlled through a user-friendly mobile interface. Building on previous research in smart home systems, intelligent path planning, and sensor fusion, the proposed system maximizes cleaning efficiency and adaptability, while significantly reducing user effort. The robot's ability to adapt to various floor types, optimize cleaning routes, and enable remote operation via mobile scheduling and monitoring is demonstrated through experimental tests. This work advances the development of energy-efficient cleaning devices and smart home automation.

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Autonomous cleaning robot, Smart home, Obstacle detection, Android control, Vacuuming, mopping, Adaptive navigation

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Creative Commons Attribution 4.0 and The Open Definition

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This report investigates the existing work on optimizing house price estimation with machine learning and deep learning techniques. Focusing on its base data types structured and then multi-modal (price, geospatial etc.) it runs through essential algorithms such as Linear Regression, XGBoost and Neural Network and compares their capabilities pros and cons. From the results, it emphasizes the ability of these methods to enhance predictive accuracy based on heterogeneous data sources, whilst challenges such as interpretability of models and integration of data persist. Promising future directions to move the field forward, such as hybrid models and multi-modal approaches, are discussed.

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Deep learning, machine learning, house price prediction, multi-modal data, neural networks, regression analysis, feature engineering, hybrid models

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For domestic, agricultural, and industrial purposes, groundwater is an essential supply of fresh water in India, particularly in regions with inadequate surface water infrastructure. Groundwater quality in Coimbatore, Tamil Nadu, a fast-growing metropolis renowned for its textile industries, is being weakened by pollution from sewage, automobile emissions, industrial discharges, and agricultural runoff. The physicochemical properties of borewell water from western Coimbatore's non-traffic and traffic-congested areas are compared in this study. To evaluate water quality and comprehend the impact of human activity, parameters including pH, TDS, hardness, chloride, and microbiological content were examined. The study emphasises the influence of urbanisation and traffic-related pollution on groundwater, pointing out notable variations in water quality between the two zones.

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Groundwater quality, Borewell water, Physicochemical analysis, Urban pollution, Traffic areas, Coimbatore, Water contamination, Sustainable water management, Industrial effluents, Microbial content.

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The rapid advancement of Artificial Intelligence (AI) has introduced new complexities into the traditional framework of Intellectual Property Rights (IPRs). This research explores the legal challenges posed by AI-generated works in the Indian context, focusing on three primary domains of IP law--copyright, patents, and trademarks. It critically analyzes whether the existing legal structure, which assumes human authorship and inventorship, is equipped to handle autonomous or semi-autonomous outputs generated by AI systems. Drawing upon doctrinal research, comparative analysis with jurisdictions such as the United States, the European Union, and the United Kingdom, and policy reports by international organizations like WIPO, this paper reveals significant doctrinal and enforcement gaps in Indian IP law. It argues for the introduction of sui generis rights for AI-generated creations and recommends legislative amendments to accommodate AI's growing role in innovation and branding. By proposing a reform-oriented legal framework rooted in accountability, transparency, and global compatibility, the paper advocates for India to lead a proactive IP law transformation suitable for the AI era.

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Artificial Intelligence, Intellectual Property Rights, Copyright, Patents, Trademarks, India, Legal Reform, Sui Generis Rights, AI-Generated Works, Innovation Law Acknowledgement

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Agriculture forms the foundation of numerous nations, including India, sustaining millions by overcoming challenges like climate shifts and outbreaks of plant ailments. Innovative research has led to the creation of a web-based platform offering real-time guidance on optimal crop choices, considering points such as soil health, temperature, humidity, ph levels.This platform brings together advanced machine learning and deep learning techniques to address critical areas of precision agriculture. It comprises five key modules: Crop Recommendation, Yield Prediction, Plant Disease Detection, Smart Farming Guidance, and Weather Forecasting .The Crop Recommendation module suggests the most suitable crops for cultivation based on parameters such as soil type, pH, nutrient content, and regional agro-climatic conditions. This promotes sustainable crop planning and resource optimization. The Yield Prediction engine uses historical yield data, meteorological records, and agricultural inputs to forecast potential productivity, aiding in economic planning and supply chain management. Through the Plant Disease Detection module, farmers can identify diseases early by uploading images of affected crops, which are analyzed using convolutional neural networks to suggest accurate diagnoses and treatments. The Smart Farming Guidance system delivers dynamic recommendations for irrigation scheduling, nutrient management, and pest control tailored to current crop conditions. Additionally, the Weather Forecasting component offers hyper-local predictions, enabling timely interventions to mitigate climate-related risks. Collectively, AgriSense stands as a holistic advisory platform that empowers farmers, enhances crop productivity, and contributes to the advancement of smart and sustainable agriculture globally.

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crop recommendation, machine learning, plant disease identification, random forest, weather-forecast ,fertilizer recommendation.

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This paper proposes a Smart Industrial Real-Time Water Quality Monitoring and Prediction System that integrates the Internet of Things (IoT) and machine learning to improve industrial water management and environmental safety. The system monitors key water parameters -Total Dissolved Solids (TDS), ammonia concentration, pH, turbidity, and temperature via dedicated sensors connected to an Arduino microcontroller, with data transmitted to the Thing-Speak cloud platform using an ESP8266 Wi-Fi module. Real-time alerts are facilitated through an on-site buzzer and a Telegram bot to notify users of abnormal conditions. For predictive analytics, the system employs machine learning algorithms such as Random Forest, Naive Bayes, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), XG-Boost, Logistic Regression, and Decision Tree to classify water quality status based on historical data. This unified framework provides a scalable and cost-effective solution for continuous monitoring, early warning, and data-driven decision-making across industries such as manufacturing, agriculture, and wastewater treatment.

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Water Quality Monitoring, Internet of Things (IoT), Machine Learning, Real-Time Prediction, Industrial Water Management, Environmental Safety.

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Creative Commons Attribution 4.0 and The Open Definition

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Psidium guajava also known as common guava belonging to the Myrtaceae family. Their leaves accelerate follicular activities, diminish dandruff formation, strengthen hair also acts as mild cleansers. Guava leaves are fortified with bioactive composition such as flavonoids, tannins and essential oils. Guava leaves is a potential natural ingredient due to their sustainable content of favorable compounds using Psidium guajava extract in formulation of hair mask can shield hair follicles from damage, can strengthen hair follicles, reduce hair breakage. Also can assist to softer, shinier and healthy hair. Guava leaves if incorporated to hair mask stimulate hair follicles and boost hair growth. The antioxidants present in guava leaves minimize oxidative stress which can damage hair follicle and result to hairfall. Guava leaves are also sustainable because of being abundant in nature also the extraction process is environmental friendly.

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guava leaves extract, hair follicle, hair mask, antioxidant, sustainable, environmental friendly

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Creative Commons Attribution 4.0 and The Open Definition

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In today's constantly shifting global context, informal education is an essential complement to conventional educational systems. Unlike structured classroom education, which has predefined curricula and evaluations, informal education comes naturally from daily experiences, social interactions, media engagement, travel, observation, and self-directed discovery. It is a lifelong, adaptable, and self-motivated process that is highly responsive to individual interests, cultural contexts, and real-world situations. Informal education fosters adaptability, creativity, teamwork, emotional intelligence, empathy, and critical problem-solving abilities--all of which are required for success in today's complicated world. It frequently fills gaps left by formal education, strengthening underprivileged populations while encouraging lifelong learning and inclusive progress. However, the lack of established frameworks and certification creates difficulties in analysis, validation, and integration. This study investigates these issues and presents a paradigm for bridging informal and formal educational approaches, emphasizing informal learning's transformative significance in developing well-rounded, resilient, and innovative individuals. As the world grows more linked and complex, the importance of informal education must be recognized, promoted, and strategically integrated to ensure a future-ready society.

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Education, Learning, Informal education, formal education

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Creative Commons Attribution 4.0 and The Open Definition

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The Syrian Civil War, which began in 2011, is rooted in decades of authoritarian rule, economic mismanagement, and social inequality under President Bashar al-Assad's regime. Widespread unrest emerged from political repression, economic hardships, and sectarian favoritism, particularly against marginalized Sunni Muslims. As the conflict escalated, extremist factions like ISIS gained prominence, complicating the opposition landscape and prompting significant foreign interventions. This multifaceted crisis has not only created a severe humanitarian disaster but has also profoundly altered the balance of power in the Middle East. The war has notably affected Kurdish aspirations for autonomy, as historically marginalized groups sought self-governance amidst the ethnic and sectarian divides exacerbated by the conflict. The Assad regime's dual strategy of repression and tactical alliances with Kurdish forces against ISIS further complicated the dynamics, leading to shifting alliances that reshaped the region's political landscape. Sectarian divides have been a critical factor in transforming the Middle East's balance of power. The Assad regime's brutal repression of Sunni protests and its framing of the conflict in sectarian terms rallied Alawite support while alienating Sunnis. Foreign interventions have further polarized the conflict, with Iran backing Assad and Sunni-majority states supporting opposition groups. This sectarian strife has intensified regional rivalries, complicating peace efforts and contributing to broader geopolitical instability. The emergence of ISIS, exploiting the chaos to enforce its radical Sunni ideology through severe human rights violations, has further deepened sectarian divides and complicated prospects for lasting peace in the region. This report examines the governance strategies of the Assad regime during the Syrian Civil War and their impact on the balance of power in the Middle East. It highlights the regime's brutal repression of dissent, reliance on external alliances with Russia and Iran, and the resulting human rights abuses that fragmented authority amid rising opposition and extremist factions like ISIS. Iran's military and logistical support has reinforced Assad's position while deepening sectarian tensions, as it seeks to bolster Shia influence against Sunni adversaries. Russia's military intervention since 2015 has further solidified Assad's regime, enhancing its geopolitical interests and complicating U.S.-Russia relations. Meanwhile, Saudi Arabia's support for opposition groups reflects its efforts to counter Iranian influence, though challenges such as fragmentation among rebel factions and evolving U.S. policies have complicated its objectives. Overall, the interplay of these dynamics underscores a shifting balance of power in the region, characterized by increased foreign intervention, sectarian strife, and a humanitarian crisis that complicates prospects for peace and stability. The fall of President Bashar al-Assad's regime in Syria signifies a crucial shift in the Middle East's balance of power, following over a decade of civil war. The regime's weakened military, compounded by diminished support from key allies Russia and Iran, has led to significant territorial losses, including the recent rebel offensive that captured Damascus. This collapse has created a precarious state in Syria, characterized by escalating violence and a dire humanitarian crisis affecting over 17 million people. UN officials stress the urgent need for a credible political transition and the preservation of state institutions amidst widespread instability. Iran's influence is notably waning as Hezbollah reallocates resources to confront Israel, complicating its regional strategy and weakening its military posture. The emergence of Islamist rebel leadership poses new security challenges for Israel and risks further destabilizing Iran's proxy networks. As regional and global powers vie to fill the power vacuum, the implications for foreign policy and stability in the Middle East remain profound and complex, necessitating increased international support and funding to address the humanitarian crisis and facilitate aid delivery across conflict lines.

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syrian civil war,ISIS, Middle East, Russia, USA, Humanitarian crisis, UN.

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Creative Commons Attribution 4.0 and The Open Definition