Volume 14 | Issue 6 |

 Abstract

Background: Chronic toxicity is an emerging public health concern marked by the gradual accumulation of low-level toxic substances within human tissues leading to progressive multisystem dysfunction. While modern toxicology effectively identifies individual toxins and acute mechanisms, it often struggles to address the cumulative and synergistic effects of long-term, low-dose exposure. The Ayurvedic concept of Dushivisha which is defined as a partially neutralized toxin that persists in the body, offers a complementary, systems-based framework for understanding chronic toxicity. Objective: To explore and analyze the concept of Dushivisha from Ayurveda in the context of chronic toxicity and to correlate its classical descriptions with contemporary toxicological mechanisms such as oxidative stress, bioaccumulation, enzyme inhibition and mitochondrial dysfunction. The study further aims to evaluate the relevance of an integrative approach combining Ayurvedic detoxification principles with modern medical perspectives for a more comprehensive understanding and management of chronic low-dose toxic exposure. Methods: A systematic literature review has been undertaken utilizing multiple academic databases including PubMed, Google Scholar, Science direct , classical ayurvedic texts (eg.Charak Samhita, Sushrut Samhita) and Ayurveda focused repositories such as Ayush Research Portal. Result: Dushivisha represents a chronic, low-grade toxic state distinct from acute poisoning, where accumulated toxins weaken digestion (Agni) and generate Ama, promoting inflammation. Long-term exposure to toxins causes oxidative stress and multi-organ damage. It leads to tissue dysfunction, channel blockage and chronic conditions, paralleling modern toxins like pollutants, chemicals and heavy metals. Discussion: The Ayurvedic concept of Dushivisha explains chronic toxicity by addressing cumulative, low-dose exposures often overlooked in modern toxicology. It correlates with bioaccumulation and systemic dysfunction seen in chronic diseases. Ayurvedic therapies, including detoxification and rejuvenation, along with formulations like Dushivishari Agada, offer a holistic approach for managing long-term toxic effects. Conclusion: The concept of Dushivisha remains highly relevant today in explaining chronic toxicity and lifestyle diseases. Gradual toxin accumulation can disrupt multiple body systems over time. Combining Ayurvedic insights with modern science supports better understanding and management. Preventive care, detoxification and rejuvenation therapies offer practical strategies while further research can strengthen integrative public health approaches.

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Keywords: Chronic toxicity, Dushivisha, Bioaccumulation, Oxidative stress, Heavy metals, Ayurveda, Detoxification, Chronic disease, Environmental toxins, Systems-based toxicology

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Parkinson's disease (PD) is a progressive neurodegenerative disorder characterized by motor and non-motor impairments, including balance issues, cognitive decline, and a high risk of falls. Falls significantly reduce quality of life and increase disability in individuals with PD. CoDuSe (Core stability, Dual-tasking, and Sensory strategies) and Square Stepping Exercise (SSE) have individually shown potential in improving balance and cognitive function.

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Parkinson's disease, CoDuSe, Square Stepping Exercise, balance, cognition, fall risk, physiotherapy

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This study examines the risk and return performance of selected large-cap and small-cap stocks listed on the National Stock Exchange (NSE) of India. The research focuses on 12 companies from the Banking, Oil & Energy, Textiles, and Chemicals & Pharmaceuticals sectors. The main objective is to analyze the relationship between risk and return and to construct an optimal portfolio through diversification. Secondary data were collected from NSE reports, company financial statements, Yahoo Finance, and other reliable financial sources. Statistical tools such as Mean Return, Standard Deviation, Beta, Sharpe Ratio, Correlation Analysis, T-Test, ANOVA, and the Sharpe Single Index Model were used for analysis. The findings reveal significant variations in risk and return among the selected stocks. Vardhman Textiles recorded the highest average return, while Trident Ltd showed the highest risk. SBI demonstrated better return performance among banking stocks, whereas Indian Oil Corporation exhibited a balanced risk-return profile in the Oil & Energy sector. Correlation analysis indicated that only a few stocks showed significant relationships, supporting diversification benefits. The optimal portfolio constructed using the Sharpe Single Index Model allocated higher weights to SBI, ICICI Bank, and Vardhman Textiles. The study concludes that a combination of large-cap and small-cap stocks improves portfolio diversification and enhances risk-adjusted returns. The findings provide useful insights for investors, portfolio managers, and financial researchers in making informed investment decisions

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Portfolio Construction, Risk Analysis, Return Analysis, Large-Cap Stocks, Small-Cap Stocks, National Stock Exchange (NSE), Portfolio Management, Risk-Return Relationship, Equity Investment, Stock Performance, Beta, Sharpe Ratio, Investment Decision Making.

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Migraine is one of the most prevalent and disabling neurological disorders worldwide, significantly affecting quality of life, daily functioning, and productivity. In addition to recurrent headaches, individuals with migraine commonly experience neck pain, sleep disturbances, anxiety, depression, and musculoskeletal dysfunction. Although pharmacological management remains the primary treatment approach, increasing attention has been directed toward non-pharmacological interventions such as physiotherapy and exercise-based rehabilitation.

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Clinical Pilates, Migraine, Headache Disorders, Physiotherapy, Exercise Therapy, Cervicogenic Headache, Tension-Type Headache, Rehabilitation.

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The article examined the commercial networks and trade commodities between Indian state of Manipur and Burma of the colonial era. The paper intends to present in the first place, a brief account of the mountain chains, which sits between Brahmaputra valley of Assam and Burma, upon which the trade routes located in this isolated region of north east India. Secondly, a description of the nature of the routes and countries which pass through these mountain chains is proposed, and may be discussed under two heads: Firstly, of those routes which start from the frontiers of the Sylhet and Cachar district of Assam to the territory of Manipur and then to the centre of the northern province of Ava through the Ningthee river in Manipur and Chindwin river in Burma. Secondly, those line of communication leading from the northern borders of the Brahmaputra valley of Assam into the Manipur valley and thence to the northern limit of the Burmese region. And also, an attempt is made to examined the utility of the many routes extending between the territory of British India into Burma. Finally, this article seeks to provides brief information about the traded items between the two countries from the pre-colonial to colonial period.

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Trade routes, Mountain chains, Brahmaputra valley, Manipur, Burma

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Proper energy demand forecasting is an important process in energy production, distribution, and usage. Traditional methods of energy demand forecasting are unable to cope with changes in consumption influenced by environmental and demographic factors. The proposed study is focused on presenting an Intelligent Energy Forecasting Framework based on Machine Learning (ML) technology that can help forecast energy consumption. Various types of supervised learning are implemented in the framework, including Linear Regression, Decision Trees, and Support Vector Machine (SVM). Environmental parameters (temperature, humidity), geographical position, and demographic parameters are used as input variables. The comparison of ML models reveals that SVM predicts the energy consumption more precisely and provides forecasts with almost no error rate. The framework integrates predictive analytics and dashboard that offers demand forecast, pattern analysis, and scenarios prediction.

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Energy forecasting , Machine learning , Regression tree , Smart Grid , Linear regression , Decision Tree , Support vector machine

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Abstract: Low-light image enhancement (LLIE) remains a fundamental challenge in computer vision, with critical implications for surveillance, autonomous navigation, medical imaging, and consumer photography. This literature survey systematically examines the evolution of LLIE methodologies from classical histogram-based and Retinex-inspired techniques through supervised deep learning to zero-reference unsupervised paradigms. The survey evaluates contemporary performance benchmarks, outlines the mathematical formulations driving zero-reference non-reference loss functions, and highlights data domains spanning paired indoor settings and unpaired real-world datasets. A comprehensive research gap analysis identifies critical deficiencies in existing zero-reference implementations, specifically focusing on the lack of integrated post-enhancement quality metrics, computational profiles for resource-constrained architectures, and multi-benchmark generalization capabilities.

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Low-light image enhancement, Zero-DCE, deep curve estimation, visibility classification, unsupervised learning, PSNR, SSIM, image quality assessment, Retinex theory, histogram equalization, computer vision.

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ABSTRACT ; The widespread adoption of electric vehicles (EVs) has introduced new challenges for stakeholders ranging from grid operators to EV owners. A critical challenge is to develop an effective and economical strategy for managing EV charging while considering the diverse objectives of all involved parties. In this study, we propose a context-aware EV smart charging system that leverages deep reinforcement learning (DRL) to accommodate the unique requirements and goals of participants. Our DRL-based approach dynamically adapts to changing contextual factors such as time of day, location, and weather to optimize charging decisions in real time. By striking a balance between charging cost, grid load reduction, fleet operator preferences, and charging station energy efficiency, the system offers EV owners a seamless and cost-efficient charging experience. Through simulations, we evaluate the efficiency of our proposedDeepQ-Network(DQN)systembycomparingitwithotherdistinctDRLmethods:ProximalPolicy Optimization (PPO), synchronous Advantage Actor-Critic (A3C), and Deep Deterministic Policy Gradient (DDPG). Notably, our proposed methodology, DQN, demonstrated superior computational performance compared to the others. Our results reveal that the proposed system achieves a remarkable, approximately 18% enhancement in energy efficiency compared to traditional methods. Moreover, it demonstrates about a 12% increase in cost-effectiveness for EV owners, effectively reducing grid strain by 20% and curbing CO2 emissions by 10% due to the utilization of natural energy sources. The system's success lies in its ability to facilitate This position paper introduces a novel Cyber Physical - Socio-Environmental Systems (CPSES) framework for Smart Water Networks (SWN). The proposed framework introduces environmental aspects as a key constituent, which is important for addressing the interconnected challenges of water systems, such as climate change impacts, contamination risk, and sustainability. The CPSES framework dynamically incorporates interactions between physical components (sensors, actuators), cyber systems (control, monitoring, digital twins), social factors (policy, demand management, crisis response), and environmental impacts (emissions, resource availability). The significance of this framework lies in its potential to enable more resilient, sustainable, and secure SWNs by incorporating feedback loops among the CPSES constituents. Key CPSES research challenges are outlined, such as modeling the impacts of climate change, dynamic risk estimation, and ethical and fairness aspects within SWN. A realworld use case on water contamination crisis management demonstrates the framework's practical application and relevance for researchers. Index Terms--Cyber-physical systems, environmental factors, smart water networks, social factors, system of systems, water monitoring, water pollution, water resources

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nternet of Things (IoT) Smart Water Distribution System Rural Water Supply 55 LPCD (Liters Per Capita per Day) Automated Water Distribution Water Resource Management Smart Village / Rural Development Water Monitoring System Flow Sensor Water Level Sensor Wireless Sensor Network (WSN) Embedded Systems Arduino / Microcontroller Real-Time Monitoring Water Conservation

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Renal failure, also referred to as kidney failure, is the incapacity of the kidney to remove waste materials and extra fluid from the blood. Hemodialysis (HD) is a treatment for kidney failure. This treatment has major drawback, as it needs to be performed frequently, often lasting several hours and requiring at least session per week. Hemodialysis poses a significant physical, mental, and social strain on the patients as it is very demanding, stressful, and challenging. To assess the functional status and psychosocial adjustment of renal failure patients undergoing hemodialysis a descriptive correlational research study was done. A total of 150 renal failure patients undergoing hemodialysis were selected by using a consecutive sampling technique from the Hemodialysis units of GMCH, Assam. Data were collected using a sociodemographic profile, structured functional status and psychosocial adjustment questionnaire through face-to-face interviews. The study found that out of 150 samples, 103 patients (69%) had an average functional status, 26 patients (17%) had good functional status and 21 patients (14%) had poor functional status. The majority of the subjects 106 (71%) had a moderately adequate psychosocial adjustment, followed by 23 (15%) with adequate psychosocial adjustment and 21(14%) with inadequate psychosocial adjustment. A moderate positive correlation was found between functional status and psychosocial adjustment of renal failure patients undergoing hemodialysis (r=0.60, p<0.001). Hemodialysis as a therapy option for renal failure has a significant impact on the patient's functional level and mental well-being in addition to their physical health. To enhance the general quality of life of hemodialysis patients, healthcare providers should take these factors into account.

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Renal failure, hemodialysis, functional status, psychosocial adjustment

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