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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 Keywords

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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 Keywords

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

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Creative Commons Attribution 4.0 and The Open Definition

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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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 Keywords

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

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Creative Commons Attribution 4.0 and The Open Definition

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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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Creative Commons Attribution 4.0 and The Open Definition

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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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 Keywords

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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Creative Commons Attribution 4.0 and The Open Definition

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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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Creative Commons Attribution 4.0 and The Open Definition