Volume 13 | Issue 10 |

 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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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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Hypertension is extremely common among patients on maintenance hemodialysis (HD) and is a major driver of cardiovascular complications and mortality. Effective blood pressure (BP) management depends not only on appropriate drug therapy but also on consistent adherence to prescribed regimens. In HD patients, adherence is frequently inadequate due to factors such as treatment complexity, large pill burdens, emotional distress, and financial or logistical barriers. The Adherence to Refills and Medications Scale (ARMS) is a validated questionnaire that evaluates both medication-taking and refill behaviors, making it especially suitable for individuals with chronic health conditions such as end-stage renal disease (ESRD). This review outlines the epidemiology and pathophysiology of hypertension in HD, explores adherence-related challenges, compares available assessment tools, and highlights the advantages of ARMS for evaluating and improving antihypertensive medication adherence. It also summarizes available evidence and proposes directions for future clinical and research initiatives.

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Hypertension, Hemodialysis, Medication Adherence, ARMS

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The advent of digital humanities has revolutionized the study of literature by integrating computational tools with traditional interpretive methods. This paper explores the application of digital humanities methodologies including text mining, computational linguistics, and digital archiving to analyse literary texts and uncover patterns, trends, and hidden structures that remain elusive through conventional close reading. By employing large-scale text mining techniques, scholars can identify recurring motifs, stylistic markers, and intertextual relationships across extensive corpora. Computational linguistics enables nuanced examinations of language, semantics, and sentiment, offering insights into authorial style, cultural influences, and socio-political contexts. Digital archives further enhance accessibility, enabling researchers to curate, annotate, and preserve vast bodies of literary work while fostering collaborative scholarship. The study emphasizes the complementary nature of distant and close reading, arguing that digital humanities tools do not replace humanistic inquiry but rather augment it with scale, precision, and new interpretive possibilities. Ultimately, this paper demonstrates how computational approaches extend the boundaries of literary analysis, paving the way for innovative scholarship that bridges technology and the humanities.

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Digital humanities, text mining, computational linguistics, digital archives, literary analysis, distant reading, digital scholarship

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Tourism has multi-dimensional impacts, influencing the social, cultural, economic, political, environmental, and other aspects of society. Among these, environmental impact has emerged as a significant concern, as the rapid growth of tourism over the years has brought both opportunities and challenges for tourism and society. The objectives of this study are to assess both the positive and negative impacts of tourism on the environment and to analyse the community's awareness of, and response to, these changes. The research design was descriptive, and the study was conducted in the key areas of Dharamshala. Purposive sampling was used to select 50 respondents. Data collection involved the semi-structured interview schedule method. The present study findings highlight that tourism in Dharamshala has led to both positive and negative environmental impacts. Consequently, local communities, authorities, and tourism-related commercial establishments are actively working towards sustainable tourism practices.

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Dharamshala, Tourism, Environmental Impacts, Sustainable Tourism, Society

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The proliferation of space debris in low Earth orbit (LEO) poses a significant threat to operational satellites, space missions, and the sustainability of space exploration. This paper proposes an innovative artificial intelligence (AI)-based framework for tracking and removing space debris. By leveraging machine learning algorithms for real-time debris detection and predictive orbital modeling, coupled with autonomous robotic systems for debris capture, the proposed system aims to enhance the efficiency and safety of space operations. Simulated results demonstrate a 92% accuracy in debris identification and a 78% success rate in debris removal operations, highlighting the potential of AI-driven solutions to address the growing challenge of orbital debris.

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Artificial Intelligence (AI),Machine Learning (ML),Deep Learning (DL),Reinforcement Learning (RL),Satellite Collision Avoidance System (AISCAS),Space Situational Awareness (SSA), Conjunction Analysis, Orbit Prediction, Trajectory Forecasting, Autonomous Decision Making, Real-Time Detection, Predictive Modeling, Maneuver Planning, Space Debris Mitigation, Orbital Safety, Low Earth Orbit (LEO),Medium Earth Orbit (MEO),Kessler Syndrome, Space Traffic Management, Satellite Autonomy, Data Fusion, O

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This study focuses on the development of a cost-effective smart solar tracking system aimed at improving the efficiency of photovoltaic (PV) energy harvesting. The system is designed to automatically orient itself toward the sun throughout the day, unlike conventional fixed panels that capture limited sunlight. The design employs Light Dependent Resistors (LDRs) as sensors, an LM393 comparator for signal processing, and a motor driver (L298N) to control the motion of DC gear motors. A small solar panel is used for power generation and tested under varying sunlight conditions. The research follows a descriptive design supported by quantitative analysis, where the tracker's output performance is compared with that of a fixed panel. Findings indicate that the smart tracker enhances energy output significantly, demonstrating its potential for scalable and sustainable applications in renewable energy.

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Solar tracking system, Light Dependent Resistor (LDR), LM393 comparator, L298N motor driver, Renewable energy, Efficiency improvement, Photovoltaic system, Sun tracking, Low-cost automation

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Chola religion and worship

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Chola religion and worship

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In the rapidly evolving corporate landscape of Mysuru, managing human capital through effective performance management and appraisal systems (PMAS) has become a strategic imperative for organizational success. This comprehensive study explores the design, implementation, and perception of PMAS within corporate entities, emphasizing their role in aligning individual performance with organizational goals, fostering continuous improvement, and enhancing employee motivation. The article highlights how modern performance management transcends traditional appraisal methods, incorporating ongoing goal setting, regular feedback, coaching, and data-driven decision-making enabled by digital transformation and HR analytics. The global shift precipitated by the COVID-19 pandemic, which brought remote and hybrid working models to the forefront, has fundamentally changed appraisal dynamics, focusing on output-based evaluation and augmenting the importance of trust, communication, and flexibility. The literature review underscores challenges such as bias, employee engagement, and the need for cultural and contextual adaptation to optimize PMAS effectiveness. Based on survey data from 40 corporate employees, the study reveals that while formal appraisal systems and perceived fairness are prevalent, gaps remain in communicating appraisal objectives clearly, delivering effective feedback, linking appraisals transparently to career progression, and leveraging rewards to motivate staff. The findings advocate for organizations to enhance clarity, feedback quality, and transparency to transform appraisal systems into vital tools for talent development and organizational excellence. The article concludes that successful PMAS necessitate employee involvement, managerial commitment, and continuous adaptation to technological and contextual changes. By fostering alignment, accountability, and motivation, organizations can unlock the full potential of their workforce, ensuring sustained competitive advantage in an increasingly complex global economy. This abstract synthesizes the article's comprehensive research, key themes, and tangible recommendations, offering a precise reflection of the work with a focus on its applicability in contemporary corporate environments. If further specific excerpts or elaborations are desired, please indicate.

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Key words: Performance Management, Performance Appraisal, Employee Performance, Goal Alignment, Performance Development, Organizational Excellence, Digital Transformation, Workforce Optimization, Succession Planning, Coaching and Mentoring, Employee Engagement, Output-Based Evaluation

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