Volume 14 | Issue 4

 Abstract

Pratishyaya, a commonly encountered disorder in Ayurvedic clinical practice, is primarily characterized by nasal discharge, sneezing, nasal obstruction, and headache. It closely resembles rhinitis in modern medicine, which can be allergic or infective in origin. Ayurveda attributes the pathogenesis of Pratishyaya to the vitiation of Vata and Kapha Doshas, often precipitated by improper dietary habits and environmental exposure. Laxmivilas Rasa, a classical herbo-mineral formulation, is widely used in respiratory disorders due to its Kapha-Vata pacifying, anti-inflammatory, and Rasayana properties. This study aims to evaluate the efficacy of Laxmivilas Rasa in a diagnosed case of Pratishyaya. A 40year old male patient presenting with classical symptoms was treated with Laxmivilas Rasa (125 mg twice daily) for 14 days. Symptom severity was assessed using a grading scale before and after treatment. Significant improvement was observed, with an overall relief of approximately 75%. The findings suggest that Laxmivilas Rasa is effective and safe in the management of Pratishyaya. However, further large-scale clinical trials are necessary to establish its efficacy scientifically.

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Pratishyaya, Laxmivilas Rasa, Rhinitis, Ayurveda, Nasal Disorders

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Mental well-being is a growing concern in modern society, with individuals seeking holistic approaches to achieve psychological balance. Jainism, an ancient Indian philosophy, offers profound insights into mental equanimity through its ethical and spiritual framework. This paper explores Jainism's contributions to mental well-being by examining its core principles, such as Ahimsa (non-violence), Aparigraha (non-possessiveness), and Anukampa (compassion), and their psychological benefits. The concept of Samata (equanimity) in Jain philosophy emphasizes inner stability, emotional regulation, and resilience. Jain practices like Samayika (meditation), Pratikraman (self-reflection), and Sallekhana (philosophical acceptance of life and death) play a crucial role in fostering mindfulness, reducing stress, and enhancing cognitive clarity. Ethical living, as prescribed by Jain vows (Mahavratas and Anuvratas), helps mitigate cognitive dissonance and cultivates emotional harmony. This paper also draws parallels between Jain meditative techniques and contemporary mindfulness-based therapies, highlighting the relevance of detachment (Vairagya) in managing anxiety and depression. Through case studies and anecdotal evidence, the discussion underscores how Jain principles contribute to mental resilience and emotional stability. However, challenges such as adapting Jain ideals to fast-paced modern lifestyles and potential misinterpretations of detachment are also examined. The paper concludes by proposing a Jain-inspired framework for mental health that integrates traditional wisdom with modern psychological practices, positioning Jainism as a timeless guide to achieving inner peace and well-being.

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Keywords: Jainism, mental well-being, equanimity, Ahimsa, Aparigraha, Anukampa, mindfulness, meditation, cognitive clarity, detachment, resilience, psychological balance.

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Hyperspectral image classification is a critical aspect of remote sensing that is applied in land cover mapping and an environmental response but is not without such issues as a high-dimensional dimension, spectral redundancy, and limited labelled observations. The current graph-based representations, i.e., S3CLLEWGCN, are useful in the sense that they capture spatialspectral- relations, but have the disadvantage of sequential optimization, fixed adjacency rules, and scalability. To solve them, this paper comes up with Hybrid Spatial Spectral Optimal Transport Graph Framework that combines supervised manifold embedding with adaptive graph learning. The model is built on entropic regularized Wasserstein distance in constructing the neighborhood at every dynamic level, as well as a bilateral-branch multi-scale Graph Convolutional Network with attention-based aggregation. The combination of cross-entropy with manifold preservation and graph smoothness makes the loss function unified, guaranteeing the robustness of feature learning. It has been proven superior in classification accuracy, increased in generalization and decreased reliance on manual parameter filtering.

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Hyperspectral Image Classification, Graph Convolutional Network, Optimal Transport, Wasserstein Distance, Spatial-Spectral Learning, Manifold Embedding, Semi-Supervised Learning

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Palladium nanoparticles (A), as a result of APTMS and THF-HPO with Pd salt, have been modelled using SwissADME server and physicochemical, pharmacokinetics, drug-likeness and medicinal properties were screened. The results show that A possess high gastrointestinal (GI) absorption with no blood brain barrier (BBB) permeation. It displays worthy bioavailability score and moderate synthetic accessibility.

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APTMS, THF-HPO, Pd nanoparticles, Medicinal properties and SwissADME

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Calotropis procera is a medicinal plant known for its rich phytochemical composition and therapeutic potential. The present study evaluates the antibacterial activity of Calotropis procera extracts against three pathogenic bacteria: Escherichia coli, Staphylococcus aureus, and Bacillus subtilis. The plant extracts exhibited significant inhibitory effects against both Gram-negative and Gram-positive bacteria, indicating broad-spectrum antibacterial potential. The observed activity may be attributed to the presence of bioactive compounds such as alkaloids, flavonoids, tannins, and phenolics. These findings support the traditional use of Calotropis procera and suggest its potential as a natural source for developing effective antibacterial agents.

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Calotropis procera, antibacterial activity, alkaloid, flavonoid

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Among the leading contributors to global cancer mortality, lung cancer stands out due to the difficulty of de tecting it before it reaches an advanced stage. Manual review of Computed Tomography (CT) scans demands considerable radiologist effort and remains susceptible to perceptual errors. This work introduces an end-to-end deep learning framework that automates the classification of lung CT images into benign and malignant categories. The system builds on MobileNetV2 --acompact yet powerful convolutional architecture pretrained on ImageNet -- and adapts it to the medical imaging do main through a structured two-phase fine-tuning procedure. A dataset of 495 CT images was used for training, validation, and testing, with class imbalance managed via adaptive sample weighting. Comprehensive image preprocessing and controlled augmentation were applied to improve model generalization. The resulting model attained 99.38% accuracy, 99.08% precision, 100.00% sensitivity, 98.15% specificity, a 99.54% F1-score, and a ROC-AUC of 0.9935 on 162 held-out test images. A browser accessible Streamlit application was built around the trained model, enabling clinicians to obtain real-time predictions directly from uploaded scans without any machine learning background. The findings confirm that combining transfer learning with carefully designed class-balancing strategies yields substantially better outcomes than naive from-scratch CNN training.

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Lung Cancer Detection, Convolutional Neural Networks, MobileNetV2, Transfer Learning, CT Scan Analysis, Medical Imaging, Clinical Decision Support, Web Deployment, Deep Learning

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Abstract: This review paper presents a comprehensive analysis of bridge piers with varying geometric shapes, focusing on their structural performance under standard loading conditions for a 12.3 m span as per RDSO-B-11775 guidelines. The primary aim is to compare different pier configurations and identify the most efficient shape based on structural behavior. Key parameters such as load-bearing capacity, displacement, bending moments, and stress distribution are critically reviewed from existing studies. Different geometric shapes, including rectangular, circular, and hammerhead piers, are evaluated considering dead load, live load, and impact load conditions. The review highlights how geometry significantly influences stiffness, stress concentration, and overall structural response. Circular piers are found to provide uniform stress distribution and improved performance, whereas rectangular piers exhibit higher stiffness but suffer from stress concentration at corners. Hammerhead piers offer advantages in load transfer efficiency for superstructures. The findings from various studies indicate that selecting an optimal pier geometry is essential for enhancing stability, durability, and safety. This review contributes to better design understanding by comparing different pier shapes and identifying circular piers as a more effective and reliable option for bridge construction.

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Keywords: Bridge Piers, Geometric Shapes, Structural Performance, STAAD Pro, Cyclic Loading, RDSO-B-11775 etc.

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Keywords: Prevalence, Diabetes mellitus, Rural and urban population

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Ischemicstrokesubtypingwasnotonlyhighlyvaluableforeffec tiveinterventionandtreatment, but also important to the prognosis of ischemic stroke. The manual adjudication of disease classification was time-consuming, error-prone, and limits scaling to large datasets. In this study, an integrated machine learning approach was used to classify the subtype of ischemic stroke on The International Stroke Trial (IST) dataset. We considered the common problems of feature selection and prediction in medical datasets. Firstly, the importances of features were ranked by the Shapiro-Wilk algorithm and Pearson correlations between features were analyzed. Then, we used Recursive Feature Elimination with Cross-Validation (RFECV), which incorporated linear SVC, Random-Forest-Classifier, Extra-Trees-Classifier, AdaBoost-Classifier.

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We considered the common problems of feature selection and prediction in medical datasets.

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Vaginal infections are among the most common health issues affecting women of reproductive age, primarily caused by an imbalance in the normal vaginal microflora. Conditions such as bacterial vaginosis, candidiasis, and trichomoniasis are often associated with symptoms like itching, irritation, inflammation, and abnormal discharge. Conventional treatments, including antibiotics and antifungal agents, though effective, are frequently associated with side effects, recurrence, and the development of drug resistance. Therefore, there is a growing need for safer, more effective, and natural therapeutic alternatives. The present study focuses on the formulation and evaluation of a herbal vaginal gel incorporating medicinal plant extracts known for their antimicrobial, anti-inflammatory, and soothing properties. Selected herbal ingredients such as Aloe vera, Azadirachta indica (Neem), Ocimum sanctum (Tulsi), Curcuma longa (Turmeric), and pomegranate peel extract were utilized due to their well-documented pharmacological activities. The extracts were prepared using suitable extraction techniques and incorporated into a gel base formulated with appropriate gelling agents to ensure optimal viscosity, spreadability, and bioadhesion. The formulated gels were evaluated for various physicochemical parameters including pH, viscosity, homogeneity, spreadability, and extrudability to ensure suitability for vaginal application. Microbiological studies were conducted to assess antimicrobial activity against common vaginal pathogens such as Candida albicans and Escherichia coli. Stability studies were also performed under different storage conditions to determine the shelf life and consistency of the formulation. The results indicated that the herbal vaginal gel exhibited satisfactory physicochemical characteristics and significant antimicrobial activity, suggesting its potential effectiveness in managing vaginal infections. The formulation also demonstrated good stability and compatibility of herbal constituents within the gel base. The use of herbal ingredients provides additional advantages such as reduced side effects, improved patient compliance, and enhanced therapeutic efficacy.

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Bacterial vaginosis, Punica Granatum, Antimicrobial and antifungal activity, Antimicrobial study

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Buddhist medicine, monastic hospitals, faith-based pandemic response, integrative medicine, public health, Vietnam Buddhist Sangha, narrative scoping review

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????? ????: ????????? ?????????, ???????, ??????? ????, ??? ????????, ??????? ????, ???????????, ?? ?? ????, ???????, ??? ???, ?????????? ???????, ?????? ??????, ?????? ?????????, ??????? ??????, ?????????? ?????, ?????? ????????

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This project presents an Automated Fake News Identification System using machine learning techniques to detect misleading information in digital content. It addresses the rapid spread of fake news on social media and online platforms. The system applies Natural Language Processing for text preprocessing, including tokenization and stopword removal. TF-IDF feature extraction converts text into numerical form for classification. A Flask-based interface allows users to input news and receive instant predictions. The model is evaluated using accuracy, precision, and recall to ensure reliable and scalable misinformation detection.

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Fake News Detection, Machine Learning, Natural Language Processing, Text Preprocessing, TF-IDF, Classification, Social Media, Flask Interface, Accuracy Precision Recall, Automated Fact-Checking

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This study looks at how college students use educational mobile apps in their daily academic lives. The research is based on primary data from 100 students who answered a structured questionnaire. It focuses on factors like age, gender, academic level, time spent on apps, types of apps used, and students' opinions. The data was analysed using basic business analytics tools like percentages and simple tables. Most students prefer to use smartphones for educational apps. Video-based learning platforms are the most popular. Many students also say these apps help them understand their subjects better and improve their academic performance. The study also tests three ideas about how often students use apps, how much time they spend, and what types of apps they use. The results show these factors are linked to student engagement and academic level. These findings can help educators, app developers, and institutions improve digital learning systems.

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Mobile apps, educational apps, college students, usage patterns, business analytics.

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Education being the backbone of any society has always been the main subject of that society's rulers, philosophers, and intellectuals. From time to time in every society, there have been arguments, debates, and discussions on how education should be conducted so that the objectives of that society can be achieved. The final task of formulating these arguments, debates, and discussions in the form of policies and implementing them among their students is done by the teachers, who also determine the form of education of that society. With time, along with traditional/conventional mediums, with the help of technology, a large section is being educated according to their social, and professional needs through distance or online mediums. All types of academic education of the society have been included in the National Education Policy 2020, in this, efforts have been made to fulfil the objectives related to education while using the methods of distance education / online mediums. In the NEP 2020, policy efforts and suggestions have also been made regarding professional competence in teacher education for teachers of traditional/ conventional medium, but there seems to be a lack of such efforts in NEP 2020 for teachers and employees working in distance education. Even big education and teacher education platforms like DIKSHA (Digital Infrastructure for Knowledge SHAring) and SWAYAM (Study Webs of Active- learning for Young Aspiring Minds) have not been able to provide any courses in this direction. In this research paper, an attempt has been made to clarify what is there for teacher education in this policy and what kind of provisions have been made for the needs of teacher education, especially for distance education and to suggest solutions from our side.

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National Education Policy 2020, Distance Education, DIKSHA (Digital Initiative for Knowledge SHAring), CPD (Continuous Professional Development), MOOC (Massive Open Online Courses), CRC (Cluster Resource Centre)

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Digital health awareness has emerged as a crucial determinant in improving patient outcomes, treatment adherence, and self-care practices. This study aims to assess the effect of digital health awareness among patients attending selected hospitals. A quantitative research approach with a quasi-experimental design was adopted. The study evaluates pre- and post-awareness levels and associated patient outcomes. Statistical analysis was performed using descriptive and inferential statistics. The findings revealed a significant improvement in digital health awareness and associated patient outcomes following the intervention (p < 0.001). The study concludes that structured digital health education significantly enhances patient knowledge, self-care practices, and healthcare utilization.

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Digital Health Awareness, Patient Outcomes, Health Literacy, Nursing

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Cissus quadrangularis, a member of the Vitaceae family, is a traditional medicinal plant used to treat several illnesses, including bone repair and inflammation. Its phytochemical composition has been described in various research with in vitro support as antioxidants, antibacterials, and antifungals against specific microbial species. Although it shown mild antifungal effects against Penicillium spp., Trichoderma viride, and Candida albicans, there is no scientific proof of its effectiveness against Fusarium species. Therefore, this gap is essentially filled by using Veldt grapes as a medicinal or biocontrol agent against Fusarium infection. This study highlights the gaps and limitations in the existing literature.

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A REVIEW ARTICLE ON VELDT GRAPE AGAINST FUSARIUM

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The rapid growth of healthcare data from electronic health records, wearable devices, and medical imaging has led to the emergence of Big Data Analytics in healthcare. This paper explores how big data techniques are used to predict diseases, improve patient outcomes, and enhance decision-making. It highlights technologies, methodologies, applications, and challenges associated with healthcare prediction systems. The study concludes that predictive analytics plays a vital role in transforming modern healthcare systems.

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Big data analytics

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Agriculture may be a major supply of bread and butter throughout the planet particularly for the bulk of poor individuals living in rural areas in developing countries. A key challenge for the bulk of those farmers is- access to finance. Lack of access to finance may be a key impediment to Farmers in rising the potency of their production and adopting higher technologies Agriculture is that the most significant sector of the Indian economy. Indian agriculture sector accounts for concerning eighteen of India's gross domestic product and provides employment to concerning five hundredth of the country's personnel. The paper focuses on (i) to review the impact of termination on agricultural finance for the farmers. (ii) to look at the extent of awareness farmers have towards the varied agricultural finance obtainable for them. (iii) to know the satisfaction level of farmers from the varied policies enforced by the government. Statistical tools like ANOVA and Chi-square test is used to test the hypotheses.

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Agricultural Finance, Farmers

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Withania somnifera is being extensively used from ancient times in India especially for rejuvenation of the body as well as longevity enhancement. This herb is considered as Indian ginseng in Ayurveda. As Ayurvedic preparations, various parts of the Withania somnifera are being used to treat variety of ailments that affect the human health, especially dried roots are widely used for the treatment of nervous and sexual disorders. Presently green synthesis of nanoparticles has emerged as extensive research area to develop new drugs using noble metals, zinc. In this study, Methanolic extract of Withania somnifera were evaluated for phytochemical screening as well as antimicrobial activity against gram negative bacteria (E.coli and Salmonella), gram positive bacteria (S. aureus) and fungi (Candida albicans) before the green synthesis ZnO nanoparticles. ZnO Nanoparticles were synthesized using Zinc acetate. ZnO nanoparticals were characterized by using various techniques UV- Visible, XRD and FTIR. Later these ZnO green nanoparticles were evaluated for antioxidant activity & antimicrobial activity.

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 Keywords

- Withania sonifera, Green synthesis, Phyrtochemicals, Antimicrobial activity and Antioxidant activity.

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