Volume 13 | Issue 10

 Abstract

Hypertension is common among patients receiving hemodialysis and significantly contributes to their risk of cardiovascular complication and mortality. Managing blood pressure in these patients is challenging due to changes in drug metabolism, fluid shifts, and coexisting medical conditions. This review examines current evidence on how antihypertensive medications are prescribed in this population, highlighting the most frequently used drug classes, factors influencing prescription choices, and opportunities for improvement. Calcium channel blockers, beta-blockers, and agents targeting the renin-angiotensin system are commonly utilized, though practices vary globally. Better understanding of these patterns can enhance treatment strategies and patient outcomes.

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Antihypertensive drug, Hemodialysis Patients.

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Adherence to antihypertensive medications is essential for patients undergoing hemodialysis to prevent cardiovascular complications and maintain stable blood pressure. However, adherence is often poor due to complex medication schedules, dialysis-related fatigue, and lack of counseling. Supported by Patient Information Leaflets (PILs), adherence can improve by providing personalized education, clarifying doubts, and reinforcing medication instructions. This review explores how structured counseling combined with written information can empower patients, reduce medication errors, and promote consistent adherence. Challenges such as low literacy, psychological stress, and system-level limitations are also discussed, along with strategies to effectively integrate counseling and PILs into routine dialysis care.

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Hypertension, Hemodialysis, Patient Counselling, Patient Information Leaflet (PILs)

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In the era of Artificial Intelligence, voice assistants have become an integral part of human computer interaction. This paper presents AURA AI, an advanced voice assistant system developed using Python and OpenAI's GPT model, designed to provide natural and intelligent conversational experiences. The system integrates speech recognition, natural language understanding, and text-to-speech technologies to enable seamless interaction between humans and machines. Unlike traditional assistants with limited pre-defined responses, AURA AI leverages the power of Generative AI to deliver context-aware, human like conversations and perform multiple tasks such as web search, automation, and real-time information retrieval.

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Artificial Intelligence, Voice Assistant, Python, GPT, Speech Recognition, Natural Language Processing

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Glaucoma, Ocular Hypertension, Mitochondrial Dysfunction, Fundus Perimetry Fluorescence, Non-invasive Imaging, Oxidative Stress.

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Munnar region of Kerala popularly known as 'Kashmir of South' well known for its green vegetation, scenic beauty and pleasant climate. The location of the region is away from the busy cities. It attracts a lot of tourist from different parts of the country and also from abroad. The major tourism activities of the region are exploring the tea plantations, visiting the agricultural farms, visiting the tea museum, trekking, boating and enjoying the beauty of waterfalls. The winter season vegetable farms are the major source of income for the farmers of the region. They can raise an additional source of income by integrating tourism in to their farms without making much additional investment. Local community of the region is also benefited from the farm tourism activities in the locality, as it provides employment, income and reduce the need for migration. This paper aims to explore the economic impact of farm tourism on the local communities in Munnar.

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Farm tourism, Economic impact, Employment, Local community, Farms.

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The Partition of 1947 was more than a geopolitical event; it was a profound human tragedy that scarred the psyche of the Indian subcontinent. It displaced millions and shattered a centuries-old composite culture, leaving a legacy of trauma that continues to echo through generations. This paper explores the reflection of this tragedy in the writings of Quratul-Ain-Haider, an Urdu writer of unparalleled vision. Through a sociological and cultural analysis, particularly of her epic novel River of Fire, this study argues that Haider's work transcends the immediate horror of Partition by contextualising it within a vast historical panorama spanning two millennia. She maps the flow of a syncretic culture to understand its violent rupture, while simultaneously offering a sharp feminist critique of the patriarchal structures that intensified during the crisis. Her writing is not merely a record of events but a philosophical exploration of identity, belonging, and the enduring power of cultural memory against the forces of political division.

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Partition, Trauma, Composite Culture, Feminism, Postcolonialism, Quratul-Ain- Haider, River of Fire, Sociology, Identity, Migration, Memory.

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Contribution of the Senia Gharana: A Historical Overview

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Contribution of the Senia Gharana: A Historical Overview

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Abstract Background : Arthritis affects one or multiple joints characterized by inflammation, pain and stiffness. Two most common types of arthritis are osteoarthritis and rheumatoid arthritis. Osteoarthritis is a degenerative disease of the joints Rheumatoid Arthritis is an autoimmune disease causing inflammation in the joints. Objective of the study was to evaluate the effect of nurse led interventions on knowledge of patients with Rheumatoid Arthritis. Methods: Quasi-experimental non-equivalent time series design was used in the study. The sample consisted of 106 patients who are diagnosed as Rheumatoid Arthritis who are recruited by purposive sampling technique. Results: There was statistically significant difference in the mean pre-test and post-tests scores of knowledge among patients with rheumatoid arthritis in the intervention group (p < 0.001) Conclusions: Study concluded that the nurse led interventions were effective in enhancing the knowledge of patients with Rheumatoid arthritis.

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Keywords: Effect, Nurse led interventions, Knowledge and Rheumatoid arthritis

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A Quantitative research approach, descriptive study was conducted to assess the level of knowledge on benefits of Amla among adolescent girls in St. Antony's Matriculation Higher Secondary School at Kumbakonam in Thanjavur (DT). The Purposive sampling technique was used. Data collected by structured questionnaire from 50 samples. The results showed that 50% of them had moderately adequate knowledge, 26% of them inadequate knowledge and 24% of them having adequate knowledge.

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Knowledge, Benefits of Amla, Adolescent Girls

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This research paper investigates the dynamic relationship between stock returns, trading volume, and volatility in the Indian stock market, with a focus on the Nifty 50 index from 2018 to 2023. Employing advanced econometric models such as quantile regression and Granger causality tests, the study explores how changes in stock returns influence trading volumes and volatility, and vice versa. Findings reveal an asymmetric effect where negative returns significantly increase volatility, while positive returns tend to elevate trading volume. The study further highlights a unidirectional causality from implied volatility to stock returns, suggesting that volatility indexes can serve as effective hedging tools for investors. These insights contribute to understanding market microstructure and can assist investors in making informed decisions by anticipating market fluctuations and managing risk effectively.

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Indian stock market, stock returns, trading volume, volatility, Nifty 50, quantile regression, Granger causality, market microstructure, implied volatility, risk management.

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The accelerating threat of climate change demands innovative and data-driven solutions. Artificial Intelligence (AI), powered by machine learning and deep learning, has emerged as a transformative tool to address both climate change mitigation and adaptation. This research paper explores how AI-enabled "smart models" can analyze massive and complex datasets, enhance predictive capabilities, and optimize resource utilization for sustainable development. Applications range from renewable energy forecasting and precision agriculture to disaster prediction and carbon monitoring. The paper highlights both existing work and future opportunities, while also addressing key challenges such as energy costs, algorithmic bias, and data inequities. By proposing an integrated framework that emphasizes inclusivity, energy efficiency, and explainability, this work contributes toward shaping a sustainable and equitable AI-driven climate future.

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Artificial Intelligence (AI), Climate Change, Smart Models, Machine Learning, Climate Mitigation, Climate Adaptation, Renewable Energy, Precision Agriculture, Sustainable Development.

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Kazuo Ishiguro's fiction persistently interrogates the tensions between home, identity, and displacement, rendering exile not merely as a geographical dislocation but as a psychological and moral condition. This article explores the thematic and narrative dimensions of exile in When We Were Orphans (2000) and The Remains of the Day (1989), examining how Ishiguro reimagines the concepts of home and self through fragmented memory, emotional repression, and moral introspection. Both novels reveal that exile is as much internal as external, a state of estrangement from one's past, identity, or emotions. The protagonists, Christopher Banks and Stevens, embody the exilic consciousness that negotiates belonging in the absence of rootedness, illustrating Ishiguro's preoccupation with the ethical complexities of remembering and forgetting. Through his distinctive narrative minimalism and introspective prose, Ishiguro transforms exile into a site of moral inquiry where silence, memory, and selfhood intersect. The paper argues that Ishiguro's characters do not merely experience exile; they inhabit it as a mode of being, thereby redefining the modern literary imagination of home and identity.

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Kazuo Ishiguro; exile; memory; home; identity; displacement; silence; When We Were Orphans; The Remains of the Day; reimagined self.

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Family businesses are the foundation of economic stability, are instrumental in long-term value creation, and contribute to 70-90% of global GDP, comprising over 60% of employment base. In India, they account for more than 70% of GDP, driving growth across sectors and creating enduring social impact. From small enterprises to global conglomerates such as Tata, Reliance, Samsung, and Walmart, these organizations combine entrepreneurial spirit with intergenerational vision, making them critical to national and global prosperity. Their success stems from long-term orientation, strong governance, and shared family values that promote stability during economic uncertainty. Yet, succession planning, generational transition, and governance complexity remain persistent challenges. To address these, many businesses have institutionalized family councils and created governance frameworks that balance family unity and professional management. The modern evolution of family offices marks a significant shift--transforming traditional family enterprises into institutional investors and diversified wealth managers. With rising interest in private equity, technology, ESG, and impact investing, next-generation leaders are redefining growth through innovation and sustainability. This transition ensures not only business continuity but also broader economic resilience. Family businesses and offices today stand at the intersection of legacy and modernization--serving as economic powerhouses and custodians of enduring values. Their ability to combine trust, stewardship, and professional governance makes them principal catalysts in shaping the future of both the Indian and global economy. Future Trends : Family offices are professionalizing and scaling rapidly. Over the next 3-10 years the biggest shifts will be: heavier allocations to private/real assets and direct deals; widescale adoption of technology, data & AI for investment and ops; meaningful entry into digital assets and tokenization; growth in impact / ESG and mission-aligned strategies; and consolidation via multi-family platforms, plus stronger governance and talent models. These forces reshape risk, access, reporting and operational models for families and office teams.

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FamilyBusiness FamilyOffice Investment FutureTrend ArohiTiwary

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Handwritten digit recognition is an important application in computer vision, often utilized in banking, postal services, and educational tools. This paper presents a deep learning-based method that makes Convolutional neural network (CNN) use to appropriately classify the handwritten numbers. MNIST is one of the reference datasets used to train the model, which contain diverse samples of handwritten numerals. The CNN architecture includes multiple convolutional and pooling layers,Then come layers that are entirely connected and Regularization of dropouts to avoid overfitting. Techniques like data augmentation and transfer learning are applied to improve generalization and reduce computational load. Performance was assessed by comparing and implementing several deep learning models and machine learning models. With precision, recall, and F1-score values of 0.99%, the CNN model also earned the maximum accuracy of 99.25%. These outcomes show how reliable and efficient CNNs are in recognizing handwritten digits. The system exhibits enormous potential for Empirical world uses that require precise and quick digit categorization.

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Computer Vision,Deep Learning,MNIST Dataset ,Data Augmentation, Model Generalization, Digit Categorization, Precision ,Recall ,Classification Accuracy.

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In recent years, the recognition of mental health as a critical component of overall employee well-being has surged. Companies worldwide are increasingly implementing mental health initiatives to foster a supportive work environment, enhance productivity, and reduce absenteeism. This report analyzes the impact of such initiatives on employee well-being by exploring various dimensions including employee satisfaction, productivity, turnover rates, and overall mental health outcomes. Through comprehensive data analysis and visualization, this report aims to provide insights on the effectiveness of mental health programs and their correlation with employee performance and organizational success.

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Mental Health,Employee Well Being,Workplace Wellness Supportive Work Environment

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This project focuses on applying an end-to-end data analytics workflow to solve a key business problem providing sales teams with a clear, data-driven understanding of regional sales performance to optimize resource allocation and identify growth opportunities.

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Data Analytics, Python (for EDA and Data Wrangling),Power BI, MySQL, Exploratory Data Analysis (EDA),Data Cleaning / Data Wrangling ,Feature Engineering

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Digital Natives, Millennials, Generation Z and Alpha, Technology adoption

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 Abstract

Bhartiya sarvochnyalay Ka Mahilao Ev Kishoro Ke Prati Sakriyata Ev Savedhanshilta Ka Vishleshan

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Bhartiya sarvochnyalay Ka Mahilao Ev Kishoro Ke Prati Sakriyata Ev Savedhanshilta Ka Vishleshan

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The integration of Artificial Intelligence (AI) into healthcare is reshaping the delivery of medical services, enabling early disease detection, precise treatment planning, and real-time monitoring. This research proposes an AI-driven framework for intelligent healthcare and predictive diagnostics that leverages machine learning, deep learning, and natural language processing to extract actionable insights from heterogeneous medical data, including electronic health records, imaging, and sensor-based monitoring systems. The framework emphasizes predictive modeling to forecast disease progression, support preventive interventions, and personalize treatment strategies while ensuring scalability across diverse clinical scenarios. Key contributions include the design of adaptive algorithms capable of handling high-dimensional data, mechanisms for explainable decision-making to enhance trust among clinicians, and integration with cloud-edge infrastructures for timely and resource-efficient deployment. Experimental validation highlights improved diagnostic accuracy, reduced latency in decision support, and enhanced patient outcomes compared to conventional approaches. This work underscores the transformative potential of AI in advancing predictive diagnostics, fostering proactive healthcare, and paving the way toward sustainable, patient-centered medical ecosystems.

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Artificial Intelligence, Predictive Diagnostics, Intelligent Healthcare, Machine Learning, Deep Learning, Clinical Decision Support.

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 Abstract

The proliferation of space debris poses an existential threat to the sustainability of operations in Low Earth Orbit (LEO). While classical Artificial Intelligence (AI) solutions have improved tracking and localized debris capture, they encounter significant computational intractability when planning large-scale, multi-target Active Debris Removal (ADR) missions. This paper proposes a Hybrid Quantum-Classical (HQC) framework specifically designed to overcome these combinatorial optimization bottlenecks. The framework leverages Quantum Annealing (QA) to efficiently solve the optimal routing problem (ORP), formulated as a high-fidelity Quadratic Unconstrained Binary Optimization (QUBO) model. This optimization is integrated with Quantum Machine Learning (QML) for accelerated Space Situational Awareness (SSA) and real-time collision risk assessment (Pc). Simulation results benchmarking the HQC optimizer against classical metaheuristics, such as Genetic Algorithms (GA) and Simulated Annealing (SA), demonstrate a superior solution quality (98% near-optimal fuel consumption) and a substantial reduction in time-to-solution (a 10-fold speedup for N=50 targets). Furthermore, the application of Variational Quantum Algorithms (VQAs) for quantum-enhanced anomaly detection improves sensor data fidelity and strengthens autonomous decision-making robustness, validating the critical role of nascent quantum technologies in preserving the orbital environment against the escalating threat of Kessler Syndrome.

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Hybrid Quantum-Classical (HQC) Framework, Quantum Annealing (QA), Quantum Machine Learning (QML), Active Debris Removal (ADR), Space Situational Awareness (SSA), Low Earth Orbit (LEO), Optimal Routing Problem (ORP), Quadratic Unconstrained Binary Optimization (QUBO), Variational Quantum Algorithms (VQAs), Quantum Neural Networks (QNNs), Quantum Autoencoders (QAEs), Quantum K-Nearest Neighbor (QkNN), Space Traffic Management (STM), Collision Probability (Pc), Multi-Target Optimization, Combinator

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