Volume 14 | Issue 7 |

 Abstract

Abstract Potency selection is a fundamental component of homeopathic prescribing and is traditionally regarded as an important factor in individualized treatment. Selecting an appropriate potency requires consideration of multiple patient- and disease-related factors, including the nature and duration of the condition, patient sensitivity, age, general health, and the degree of similarity between the remedy and the patient's symptom profile. This article presents an overview of commonly described principles of potency selection in classical homeopathic literature. It discusses the characteristics of low, medium, and high potencies, practical approaches to initiating treatment, monitoring patient response, and adjusting potency when appropriate. The article also emphasizes the importance of individualized prescribing and professional clinical judgment in homeopathic practice. By providing a structured overview of traditional potency selection concepts, this review aims to serve as a useful educational resource for homeopathy students and practitioners while recognizing that recommendations may vary among different schools of homeopathy.

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Keywords Homeopathy Homeopathic Potency Potency Selection Individualized Treatment Homeopathic Remedies Acute Conditions Chronic Conditions Patient Sensitivity Homeopathic Prescribing Classical Homeopathy Potentization Clinical Practice

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Floods are among the most devastating natural hazards, causing severe loss of life, infrastructure damage and environmental degradation. Accurate flood risk assessment crucial for effective disaster management and sustainable planning. This study develops a deep learning framework using a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model integrated with remote sensing and GIS to assess flood risk in the Lower Godavari basin, India. Spatial datasets including DEM, land use/land cover, slope, drainage density, distance to river, soil and NDVI derived from Sentinel-2, along with temporal data such as daily rainfall and river discharge (2015-2024), were used. The CNN component extracts spatial features, while the LSTM component learns temporal dependencies. The model achieved high performance with an AUC of 0.92, accuracy of 0.89 and F1-score of 0.87. The final flood risk map categorizes the basin into low, moderate, high and very high risk zones. The study demonstrates that the integration of deep learning with geospatial data significantly improves flood risk assessment and can support decision makers in planning and mitigation.

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Flood risk, CNN-LSTM, Remote sensing, GIS, Deep learning, Flood susceptibility, Lower Godavari basin

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The Indian automobile industry has undergone a significant transformation over the last decade, with Sport Utility Vehicles (SUVs) emerging as the fastest-growing and most preferred passenger vehicle segment. Earlier, the Indian market was dominated by small hatchbacks due to affordability, fuel efficiency, and ease of driving. However, changing consumer preferences, rising disposable incomes, rapid urbanization, improved road infrastructure, and technological advancements have led to a remarkable shift toward SUVs. Automobile manufacturers have responded by introducing compact, mid-size, and premium SUVs across various price ranges, making them accessible to a wider section of consumers. The rapid rise of SUVs has not only influenced buying behaviour but has also reshaped production strategies, competition, employment opportunities, and investment patterns within the Indian automobile industry. Companies such as Maruti Suzuki, Hyundai, Tata Motors, Mahindra, Kia, Toyota, and MG Motor have significantly expanded their SUV portfolios to capture growing demand. At the same time, the increasing popularity of SUVs has raised concerns regarding fuel consumption, carbon emissions, urban traffic congestion, and environmental sustainability. This research paper examines the factors responsible for the rapid growth of SUVs in India and analyses their impact on the Indian automobile market from economic, social, and environmental perspectives. The paper also discusses the challenges facing the SUV segment and highlights future opportunities, particularly the emergence of electric and hybrid SUVs. The study is based on secondary data collected from industry reports, government publications, company reports, and academic journals.

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SUV, Indian Automobile Industry, Consumer Behaviour, Passenger Vehicles, Market Growth, Electric SUVs.

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Garbhopanishad is a Upanishad that presents a comprehensive account of human embryological development by integrating anatomical, physiological, psychological, and spiritual dimensions. This review critically analyses the embryological concepts described in Garbhopanishad and evaluates their relevance in relation to classical Ayurvedic literature and modern embryology through a qualitative literary review of the text along with comparative references from the Sushruta Samhita, Harita Samhita, and contemporary embryology literature. The text explains embryogenesis through the principles of Panchamaha bhuta, Sapta Dhatu, Beeja, and Jeevatma, describing the sequential stages of foetal development from Kalila (gelatinous stage) to Budbuda, Pinda, and complete foetal formation over nine months. It further discusses linga niryana, yamala garbha, congenital anomalies, foetal consciousness, and Sankhya Sharira (anatomical enumeration). Several developmental milestones and concepts described in Garbhopanishad demonstrate notable conceptual similarities with Ayurvedic embryological doctrines and certain observations of modern embryology. Thus, Garbhopanishad offers a holistic and multidimensional understanding of embryogenesis that remains relevant to Ayurveda, developmental biology, prenatal psychology, and integrative medicine, highlighting the enduring significance of ancient Indian embryological knowledge in contemporary scientific discourse.

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Garbhopanishad, Garbha Vigyana, Embryology, Ayurveda, Foetal Development, Foetal Consciousness.

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This research analyses how psychological influences-a trader's behaviour regarding stock trades-derive from cognitive distortions that lead to faulty methods of reasoning, which ultimately disrupt their normal decision-making processes, therefore revealing damaging results. Previous research as well as theoretical constructs regarding investment decision-making and other behavioural influences on trader behaviour are illustrated using traditional financial theory, which assumes that all investment decisions are based solely upon rationality; however, in practice, emotional factors, belief systems, peer pressures, and mental shortcuts known as heuristics comprise significant parts of making investment decisions. Therefore, how investment decisions are made may differ from what is theoretically postulated by these traditional financial theories. This paper presents three primary cognitive biases that can affect a trader's buying and selling decisions: overconfidence bias, herd behaviour, and loss aversion. Overconfidence, due to an investor believing they are more knowledgeable and capable than they actually are, may cause an investor to trade excessively or take risks disproportionately; therefore, overconfidence also influences their degree of willingness to pursue investment decisions even during increased risk periods. Herd behaviour is defined as an investor following the direction of another investor rather than using independent analysis during periods of economic uncertainty. Loss aversion bias refers to the fact that investors are much more afraid of losing money than they value making money, and so they frequently hold onto losing stock longer than is necessary or avoid taking investments that could lead to loss, regardless of whether they can profit by taking such an investment. This research will provide a detailed description and analytical analysis of retail investors and traders. The Quantitative method will be applied to obtain primary data from respondents using structured questionnaires. Secondary data will include journals, books, and prior research that have studied behavioural finance. Statistical tools will be utilized to analyse the data and identify any relationship between psychological biases and the production of investment decisions. Statistical techniques will consist of descriptive analysis, correlational analysis, regression analysis, SPSS, and Excel. The purpose of this research is to raise awareness among investors of the existence of hidden psychological influences impacting their trading behaviours. Additionally, this research will add to the behavioural finance literature about investors in a developing market, which in this case is Ranchi. The result of this research will assist investors, financial advisors, and policymakers in formulating effective financial strategies through the provision of financial literacy and to provide investors with better informed and derived decision making in their investments.

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Key words: - Psychological Biases, Overconfidence Bias, Herd Behaviour, Loss Aversion, Behavioural Finance, Stock Market, Investment Decision-Making.

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The continued build-out of online infrastructure and digital services has greatly increased the threat of both passive and aggressive cyber attacks and thus threatens the accessibility, privacy and integrity of Sensitive data. A hybrid encryption and analysis based security framework to mitigate both passive and active attacks The system combines RSA and AES cryptographic algorithms for providing strong data security during transmission. A genetic algorithm based optimization technique is used to improve the detection of suspicious patterns in web usage. In addition, the model gives importance to early threat detection and prevention through web usage mining and access log analysis. The approach supports secure data flow, user behavior profiling, and real-time response to unauthorized activities with results to demonstrate improved accuracy, encryption strength.It contributes to more resilient, privacy-aware digital communication environments in dynamic threat landscapes.

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Cybersecurity, Passive Attacks, Active Attacks, RSA, AES, Genetic Algorithm, Web Usage Mining, Data Encryption, Attack Detection, Secure Communication, Real-Time Monitoring, Intrusion Prevention System

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With growing interdependence among countries, there is an acute need for cross-lingual communication to bring people together around the globe. This paper presents the design and testing of an automated multilingual document translation system built entirely within UiPath Studio, a leading Robotic Process Automation (RPA) platform, without relying on external translation APIs. The proposed architecture combines UiPath's Document Understanding, Optical Character Recognition, and native automation activities to read, extract, and translate text from multilingual documents while preserving structure and formatting. Eliminating third-party API dependence yields faster processing, more uniform translation quality, and stronger data privacy. Following an applied, experimental research design, the workflow was tested across Hindi, Marathi, French, Tamil, and Telugu. Results indicate that translation tasks can be automated with minimal human intervention while retaining document structure and reasonable accuracy, and that the resulting interface is usable by both technical and non-technical users. The findings position UiPath as a secure, scalable, and cost-effective tool for automated multilingual document translation, and as a viable contributor to bridging language barriers in the digital era.

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UiPath, Language Translation, Robotic Process Automation, Workflow Automation, Document Understanding, No-API Translation

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Abstract - Trigeminal Neuralgia(TN) is a painful condition that causes sharp, shooting, electric shock like pain along one or more division of trigeminal Nerve, often starting with cold exposure or chewing .In this case report a 49-year-old man who has been experiencing right-sided facial neuralgia for one and a half years. The pain starts from the forehead and spreads to the upper jaw area, lasting about 1 -2 minutes.The pain comes suddenly and is sharp in nature,gets worst when exposed to cold,during runny nose, in winter or when chewing hard food substances and improves with warmth.The patient was clinically diagnosed with right sided facial Neuralgia.Based on symptoms reportorial analysis andindividulaizedassessment. Homoeopathic medicineNatriumMuriaticum 1M was prescribed. There are no Neurological deficitand the purpose of this case report is to showhow a case of idiopathic trigeminal neuralgia improved significantly, with complete relief of symptoms after follow - up.This case demonstrates how personalized homoeopathic treatment can effectively managechronic facial neuralgia.

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Keywords - Homoeopathy, Individualised Homoeopathic medicine, trigeminal neuralgia, pain intensity score.

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Structural deterioration caused by cement seepage, moisture penetration, and reinforcement corrosion is one of the major challenges in civil engineering. Traditional inspection methods are time-consuming, expensive, and dependent on human expertise. This research presents an Artificial Intelligence (AI)-based image processing system for detecting reinforcement exposure, cement seepage, and surface defects in reinforced concrete structures. Images collected from buildings and bridges are analyzed using AI techniques to identify damaged regions and classify their severity. The proposed system helps engineers detect structural problems at an early stage, reducing maintenance costs and improving structural safety. The developed approach can also be integrated with mobile applications and web dashboards for real-time monitoring.

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Keywords: Artificial Intelligence, Reinforcement Corrosion, Cement Seepage, Structural Health Monitoring, Image Processing.

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Healthy eating during childhood is essential for optimal growth, development, and the prevention of nutrition-related diseases. However, increasing consumption of junk food, inadequate nutritional awareness, and sedentary lifestyles have led to unhealthy eating habits among school-aged children. Schools provide an ideal environment for nutrition education, and innovative teaching strategies such as game-based learning have shown promise in improving children's knowledge, attitudes, and dietary behaviors. High-quality evidence, including the randomized controlled trial by Kato-Lin et al. (2020), demonstrated that mobile game-based interventions can positively influence children's food choices. Furthermore, systematic reviews by Nora Suleiman-Martos et al. (2021) and Mohapatra and Mohandas (2024) support the effectiveness of gamification in nutrition education, while recent Indian school-based studies (2024) indicate its relevance in culturally similar settings. This study aims to assess the effectiveness of game-based learning in promoting healthy eating habits among school children at Santhapeta School, Chittoor District, Andhra Pradesh. Specifically, it evaluates changes in students' knowledge, attitudes, and healthy eating practices following a game-based educational intervention. The findings are expected to provide evidence for integrating gamified nutrition education into school health programs, thereby encouraging healthier food choices and contributing to the prevention of childhood obesity and other diet-related health problems.

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Game-Based Learning, Healthy Eating Habits, Nutrition Education, School Children, Gamification, Dietary Behaviour, School Health Promotion, Childhood Nutrition

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