International Journal of Advanced Research and Interdisciplinary Scientific Endeavours
The International Journal of Advanced Research and Interdisciplinary Scientific Endeavours (IJARISE) (E-ISSN: 3048-7021) is an international, peer-reviewed, open-access scholarly journal committed to publishing high-quality research across diverse academic disciplines. The journal provides a dynamic platform for undergraduate and postgraduate researchers, early-career scholars, and experienced academicians to disseminate innovative and impactful research. IJARISE publishes original research articles, review papers, and technical contributions in the fields of science, engineering, technology, management, and interdisciplinary studies. The journal strongly emphasizes the integration of multiple disciplines to address contemporary global challenges and foster advanced research solutions. The journal adheres to the UGC 2025 guidelines for academic publishing, ensuring transparency, quality, and ethical scholarly communication. The journal is indexed in recognized databases including Google Scholar, Scilit, Semantic Scholar, CORE, ResearchGate, WorldCat, DRJI, and other relevant indexing platforms. IJARISE follows a fully Open Access (OA) model, providing immediate and unrestricted access to all published content without subscription or registration barriers. The editorial and peer-review process is managed by an international editorial board, ensuring high standards of quality and integrity. The journal operates using the Open Journal Systems (OJS) platform developed by the Public Knowledge Project (PKP), Simon Fraser University. Journal Scope and Subject Coverage: The International Journal of Advanced Research and Interdisciplinary Scientific Endeavours (IJARISE) aims to promote innovative, high-impact, and interdisciplinary research by providing a global platform for knowledge exchange. The journal encourages submissions that bridge multiple disciplines and foster collaboration among researchers from diverse academic and professional backgrounds. It particularly supports early-career researchers and students by offering an accessible and credible platform for publishing their work. The scope of the journal is broad and inclusive, covering areas such as engineering and technology, physical and applied sciences, computer science, artificial intelligence and data science, management, commerce and economics, environmental and sustainability studies, interdisciplinary and emerging technologies, as well as social sciences and humanities with interdisciplinary relevance. Publication Ethics and Policies: 1] Journal Ethics: The journal follows the ethical standards prescribed by the Committee on Publication Ethics (COPE) and adheres to best practices in scholarly publishing, including policies on authorship, plagiarism, conflict of interest, and data integrity. 2] Open Access Policy: This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge. 3] Repository Policy: Authors are permitted to deposit all versions of their manuscripts in institutional repositories, preprint servers, and personal websites. 4] Licensing Policy: All articles are published under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license. Authors retain copyright of their work. 5] Plagiarism Policy: The journal maintains a strict zero-tolerance policy towards plagiarism. All manuscripts are screened using plagiarism detection tools prior to peer review and publication. 6] AI Usage Policy: Authors must disclose any use of AI tools in manuscript preparation in a dedicated “AI Usage Disclosure” section. AI tools cannot be listed as authors. 7] Retraction Policy: The journal follows COPE Retraction Guidelines and may issue corrections, expressions of concern, or retractions in cases of ethical violations. 8] Digital Archiving Policy: All content is preserved through recognized archiving systems such as LOCKSS, CLOCKSS, CORE, PKP Preservation Network, and Internet Archive. 9] Advertising Policy: The journal accepts only ethical and academically relevant advertisements, clearly separated from editorial content. 10] DOI and Content Registration: All published articles are registered with Crossref and assigned a unique Digital Object Identifier (DOI) to ensure persistent identification and discoverability.
Acceleron Aerospace Sciences Private Limited
Circular Economy in Waste Reduction, Income and Employment Creation Potentials: The Case of Assosa Town Municipal Solid Waste Management, Western Ethiopia
The circular economy principle is applied in manufacturing, industries and services provision to minimize waste, through waste recycling and reusing, and recovering, making wise of resources, and changing waste –to- income. However, the application a circular economy to municipal solid waste (MSW) management services has been scarcely reported. The objectives of the study were to determine the potential of recoverable, recyclable, and reusable municipal solid waste, and evaluate how much income and employment can be created if the circular economy is applied. The study was conducted in Assosa Town using survey questionnaires that involved 385 randomly selected households. Samples from public institutions, commercial centers, and small- scale enterprises were systematically selected. The SW generated from these four major sources were monitored and recorded for three consecutive months. The study results showed that 73% of the average annual SW generated from HHs, 67.7% of institutions, 69.6% of commercial centers, and 77.1% of small enterprises were recyclable and reusable, as well as, recoverable. Based on the current local market value, the recyclable, reusable, and recoverable SW can generate an average annual income of 49,972,996.31 ETB / y, which is possible to create employment opportunities for 375,585 persons /year. Hence, it is concluded that applying a circular economy principle helps reducing wastes moving to landfill site, enabling them to convert waste-to-money and create employment. Thus, it is suggested that municipalities should shift from linear approach to SW management and deploy existing waste resources through implementing a circular economy principle in provision of MSW management service.
Copyright (c) 2026 International Journal of Advanced Research and Interdisciplinary Scientific Endeavours https://creativecommons.org/licenses/by/4.0
An Integrative Model of Chronic Disease Management Combining Ayurvedic Therapeutics and Modern Scientific Evidence
Chronic diseases such as diabetes mellitus, cardiovascular disorders, arthritis, neurodegenerative conditions, and metabolic syndromes represent a major global health burden, characterized by long duration, complex etiology, and substantial impact on quality of life. Conventional biomedical approaches, while effective in acute management and symptom control, often face limitations in addressing the holistic, preventive, and lifestyle-oriented dimensions of chronic illness. Ayurveda, the traditional system of medicine originating in India, offers a comprehensive framework emphasizing individualized care, balance of bodily systems, diet, lifestyle, and mind–body harmony. This paper proposes an integrative model of chronic disease management that synergistically combines Ayurvedic therapeutics with modern scientific evidence. By mapping Ayurvedic concepts such as Dosha, Dhatu, Agni, and Ojas onto contemporary biomedical constructs including metabolism, immunity, inflammation, and homeostasis, the study develops a scientifically grounded, patient-centered framework. Evidence from clinical trials, pharmacological studies, systems biology, and lifestyle medicine is reviewed to support the feasibility and effectiveness of this integrative approach. The proposed model aims to enhance therapeutic outcomes, improve patient adherence, reduce long-term complications, and contribute to sustainable healthcare practices.
Optimizing Human Resource Management to Promote Sustainable Educational Development: Insights from Bageshwar District
Human resources play a central role in achieving sustainable development in the education sector, as teacher quality, motivation, and professional growth directly influence institutional effectiveness and student learning outcomes. Effective Human Resource Management (HRM) practices are therefore essential for ensuring teacher performance, retention, and long-term organizational sustainability. This study examines HRM practices in schools across Bageshwar District, India, with the objective of assessing their impact on sustainable educational development. A mixed-methods research approach was adopted, combining quantitative data from structured surveys with qualitative insights from interviews and institutional records. Key HRM dimensions analyzed include recruitment and selection processes, professional development and training opportunities, teacher motivation, performance appraisal mechanisms, and retention strategies. The findings reveal notable gaps between HRM policies and their practical implementation, particularly in areas related to continuous professional development, systematic performance evaluation, and incentive-based motivation. The study highlights the need for context-specific HRM frameworks and policy interventions to strengthen human resource practices in school education. The recommendations offered aim to support teacher effectiveness, improve institutional efficiency, and contribute to the sustainability and quality of education in the region.
Copyright (c) 2025 International Journal of Advanced Research and Interdisciplinary Scientific Endeavours https://creativecommons.org/licenses/by/4.0
Optimizing Physical Performance and Mental Resilience through Yoga in Educational Sports
Yoga, an ancient mind–body discipline rooted in Indian philosophical traditions has seen renewed interest for its potential benefits when integrated into contemporary educational and sports systems. This paper explores the scientific foundations underpinning the integration of yoga into school and university sports programs, including physiological, psychological, biomechanical, and cognitive evidence. Through systematic review of existing research and theoretical frameworks, we highlight mechanisms by which yoga enhances athletic performance, reduces injury risk, and bolsters mental health. We also discuss practical implementation strategies, curricular design, teacher training needs, and future research directions.
Nanotechnology in Modern Physics: Materials, Devices, and Applications
Nanotechnology has emerged as a transformative field within modern physics, enabling the manipulation and understanding of materials at scales between 1 and 100 nanometers. At these dimensions, classical physical laws often fail, giving way to quantum mechanical phenomena, size-dependent properties, and surface-dominated effects. This paper provides a comprehensive overview of nanomaterials, including zero, one, two, and three-dimensional systems, highlighting their unique electrical, optical, magnetic, and mechanical properties. Fundamental nanoscale physics, such as quantum confinement, ballistic transport, and enhanced surface interactions, is discussed to explain the deviations from bulk behavior. The role of nanotechnology in device applications is examined through nanoelectronic devices like Single Electron Transistors and MOSFETs, nanosensors, and optoelectronic components, demonstrating how quantum effects and nanoscale interactions influence performance. Additionally, the paper explores the integration of nanomaterials in energy systems, including nanostructured solar cells, lithium-ion batteries, and supercapacitors, as well as in medical and biological applications such as targeted drug delivery, nanoparticle-based imaging, and biosensing. Environmental and space applications are also addressed, emphasizing the importance of nanostructures in pollution detection, lightweight composites, and radiation-resistant materials. Experimental techniques, including Scanning Tunneling Microscopy, Atomic Force Microscopy, electron microscopy, and spectroscopic characterization, are discussed for their essential role in analyzing nanomaterials and validating theoretical models. Overall, this paper highlights the interplay between nanoscale physics, material properties, and practical applications, providing a detailed understanding of the capabilities of nanotechnology in modern physics.
Ion Propulsion in Deep Space: Enabling Multi-Target Exploration with NASA’s Dawn Mission
NASA’s DAWN mission was a great mission which have been done in deep space mission. By using ion propulsion in mission to explore the asteroid belt between mars and Jupiter planet. Originally this mission was made for 11 years , it showed how xenon powered engine can support more duration in space mission. In this study, we explore how the DAWN mission can be redesigned to complete its goals within five years of life span. We look into improvement the propulsion system and overall mission design using technologies. Our key idea is replacing solar power with kilo power nuclear reactors, which can provide a steady and reliable energy source to generate ion population thrust. we also consider boosting the performance by increasing their accelerating voltages . Additionally, we explore the use of Bismuth as a fuel instead of use xenon to increase its efficiency in thrust, weight and power as well as cost too . This paper discuss about how these changes can make the improvements in the efficiency, and overall success in less life span. we also look at better trajectory planning methods to reduce time travel without losing scientific values . as future concept , we have also suggested the next generation fuel as antimatter assist ion driven system which give more efficiency than all other fuels and even faster in space mission.
Physiological and Biomechanical Aspects of Aqueous Humor Flow in Glaucoma
Glaucoma is a progressive optic neuropathy characterized by irreversible vision loss and is one of the leading causes of blindness worldwide. Elevated intraocular pressure (IOP), primarily governed by the production and drainage of aqueous humor, remains the most significant modifiable risk factor in glaucoma management. Understanding the dynamics of aqueous humor flow is therefore central to elucidating the pathophysiology of glaucoma. This review presents a comprehensive analysis of aqueous humor formation, circulation, and outflow mechanisms under normal and glaucomatous conditions. Emphasis is placed on the biomechanical, hydrodynamic, and molecular alterations that impair aqueous humor drainage, particularly in the trabecular meshwork and uveoscleral pathways. Advances in experimental studies, imaging techniques, computational fluid dynamics (CFD), and mathematical modeling of aqueous humor flow are critically reviewed. The paper also discusses therapeutic implications and future research directions integrating biomechanics, nanotechnology, and personalized medicine.
Mathematical and Computational Modeling of Thermal Dynamics in Human Skin and Skeletal Muscle During Rhythmic Contractions
Thermal dynamics in human skin and skeletal muscle are central to understanding physiological responses during exercise, thermoregulation, and clinical interventions such as hyperthermia therapy or rehabilitation. Rhythmic muscle contractions, as observed during physical activity, induce complex heat generation, transfer, and dissipation processes that interact with blood perfusion, metabolic activity, and environmental conditions. This review synthesizes current mathematical and computational models that describe thermal behavior in skin and skeletal muscle, emphasizing mechanisms such as conduction, convection, perfusion, and metabolic heat production. We discuss models ranging from simplified bioheat equations to advanced Multiphysics simulations, highlight the role of boundary conditions, heterogeneity of tissue properties, and evaluate their applications in sports science, medical diagnostics, and therapeutic interventions. Finally, we identify key challenges and future directions for accurately capturing the coupled thermomechanical dynamics during rhythmic muscular activity.
Enhancing Human Resource Management for Sustainable Educational Development: A Study of Tehri District
Human Resource Management (HRM) plays a pivotal role in enhancing the quality, efficiency, and sustainability of educational institutions. In developing regions such as Tehri District of Uttarakhand, educational progress is closely linked with the effective management of human resources, including teachers, administrators, and support staff. This study examines the existing HRM practices in the education sector of Tehri District and evaluates their impact on educational development. Using a mixed-methods approach involving survey data, interviews, and secondary sources, the research identifies critical gaps in recruitment, training, performance appraisal, motivation, and retention of educational personnel. The paper proposes a strategic HRM framework aimed at strengthening institutional capacity, improving teacher performance, and fostering inclusive educational growth in the district. The findings offer valuable insights for policymakers, educational planners, and administrators seeking sustainable reforms in human resource management within the education sector.
Empowering Women for Resilient Futures: Gender-Inclusive Disaster Risk Reduction in South Asia
This research paper critically examines the role of women in disaster risk reduction (DRR) within the South Asian context, exploring how gender-inclusive strategies can strengthen resilience and reduce vulnerability. South Asia is among the world’s most disaster-prone regions, and gender inequality amplifies women’s exposure to risk while limiting their participation in preparedness, response, and governance. Drawing on empirical evidence, policy frameworks, and case studies from across South Asia, this paper highlights barriers women face, evaluates gender-responsive DRR practices, and proposes a comprehensive approach for empowering women as agents of resilience.
AI-based Deployment of Efficient Custom Backdoor in Virtual Environment
The aim of this paper is to deploy a custom backdoor on to a target machine (Metasploit/Windows) from the source machine (Kali Linux) in a virtual environment (Hypervisor -Windows). For the Virtual system, a wired network is used. The overall goal of the project is to deploy malware such as a backdoor, on devices connected to the infected network, to showcase the threat posed by such malicious software. This work proposes a framework for the AI-based deployment of effective custom backdoors within a virtualized environment. The local virtualization platforms have leveraged to create scalable, isolated, and reproducible sandboxes for backdoor research. This is achieved by carrying out an ARP (Address Resolution Protocol) spoofing attack. It is followed by manipulation of DNS (Domain Name System) server response to redirect the victim to a malicious site or intercept HTTP (Hyper-Text Transfer Protocol) response to enable downloading of malicious files on the target system. Hence, the trojan downloaded can be of any form, like a key-logger or Backdoor. The Backdoor will help us get full system access to the target site and we will be able to download and upload files on or from the target machine, thus effectively creating a backdoor.
Economic Conditions of Women Domestic Workers Across the Life Course in Urban Informal Employment
Women domestic workers constitute a significant yet often invisible component of urban economies in the Global South. Their labor supports households and urban growth but remains largely informal, precarious, and under-compensated. This paper examines the economic realities faced by women domestic workers across different age groups in informal urban settings. Using a mixed-methods approach that combines quantitative surveys with qualitative life-history interviews across three metropolitan regions, this study investigates earnings, job security, work conditions, access to social protection, and intra-household responsibilities. Results demonstrate age-differentiated economic challenges: younger workers face unstable earnings and limited autonomy; mid-aged workers carry highest workload with inadequate compensation; older workers confront declining health without pension or safety nets. The study underscores the need for age-sensitive policies, inclusion of domestic workers in labor protections, and targeted financial literacy and social security programs.
Computational Free Energy Approaches to Trimethoprim Resistance: Progress, Challenges, and Clinical Relevance
The rapid emergence of antimicrobial resistance (AMR) has necessitated the development of predictive tools capable of identifying drug-resistant mutations in clinically relevant timeframes. Trimethoprim, a widely prescribed antibiotic targeting bacterial dihydrofolate reductase (DHFR), has been compromised by the accumulation of resistance-conferring point mutations. Conventional phenotypic assays and sequence-based diagnostics often fail to capture the functional impact of novel or rare variants. In recent years, alchemical free energy methods rigorous, physics-based computational approaches rooted in statistical mechanics have emerged as promising tools for predicting the effect of mutations on drug binding affinity. This review critically examines the theoretical foundations, methodological advancements, validation studies, and clinical translational potential of alchemical free energy calculations for classifying trimethoprim-resistance mutations. We discuss challenges related to accuracy, computational cost, and integration into clinical workflows, and highlight emerging strategies that may enable routine deployment of these methods in precision antimicrobial therapy.
Reproducibility by Construction: Open Algorithms, Public Benchmarks, and Cloud-Native Artifact Pipelines
Contemporary computer science research often publishes high-level algorithm descriptions without sufficient implementation details, relies on closed or non-portable code, and uses custom or non-standard benchmarks. These practices undermine reproducibility and prevent fair comparison across tools and studies. This paper proposes a practice-driven model for reproducible research software that focuses on implementable algorithm specifications, open-source licensing, explicit software lineage and citation, and standardized data and benchmark formats to support transparent reuse and replication across computer science domains. The model recommends integrating version control, build tools, and automated testing with cloud- or VM-backed continuous integration systems, ensuring that code pushes automatically trigger dependency pinning and benchmark execution across agreed-upon suites. This produces shareable, executable artifacts instead of static, text-based descriptions. The approach highlights the importance of community-curated public benchmark repositories and artifact evaluation processes to replace ad-hoc comparisons with repeatable, platform-agnostic measurements aligned with software engineering best practices. By treating artifacts as first-class research outputs, the model reduces bit-rot, clarifies implementation choices, and improves portability and verification across systems, programming languages, and heterogeneous hardware. The work concludes with a proposal for a community-endorsed platform that isolates environments, automates builds and tests, and continuously validates results as dependencies evolve, ultimately aligning incentives for sustainable, verifiable computer science research software.
India’s Defence Modernization and the Pursuit of Technological Autarky: Policy Reforms, Persistent Gaps, and Strategic Pathways
India’s defence sector is undergoing a profound strategic transformation driven by the ambition to achieve self-sufficiency (autarky) in arms development, which is a cornerstone of its pursuit of great power status. This imperative is rooted in the doctrine of techno-nationalism and the need to deter ongoing security threats. Historically characterized by deficiencies and heavy reliance on imports, the strategic policy shift toward Atmanirbhar Bharat (Self-reliant India) has led to demonstrable successes. Indigenous defence production achieved a historic high of ₹1.27 lakh crore in 2023-24, and defence exports surged significantly, reaching ₹23,622 crore in 2024-25. Nonetheless, modernization efforts are hindered by critical technological gaps in core areas, such as propulsion and advanced electronics, as well as an insufficient financial commitment to Research and Development (R&D), which currently accounts for around 4% of the defence budget. The continued dependence on external sources for highly technical subsystems creates an "autarky gap". Achieving genuine autonomy requires escalating R&D spending to a recommended 8-10% of the total defence budget, implementing structural R&D reforms (such as creating a Department of Defence Science, Technology, and Innovation-DSTI), and institutionalizing the proactive, in-house design and integrated acquisition model successfully pioneered by the Indian Navy.
Ceres as a Strategic Node for Interplanetary Communication: Assessing Its Potential for a Deep Space Network Hub
The dwarf planet Ceres, located in the central Main Asteroid Belt, has attracted scientific attention as a potential logistics, resource, and communication hub for future interplanetary missions. Its unique orbital position, low gravity, hypothesized water-ice inventory, and stable environment suggest that it could serve as a relay point for a next-generation Deep Space Network (DSN). This short review evaluates the scientific, engineering, and infrastructural potential of Ceres as a communication node. It examines orbital dynamics, advantages for long-range communication, resource availability for constructing communication infrastructure, and key challenges that must be addressed for operational viability. The study concludes that Ceres is a compelling intermediate outpost for relay operations between inner planets, outer planets, and deep-space probes, provided significant technological and logistical advancements occur.
A Review of Fruit Disease Detection Using Deep Learning Models: Trends, Challenges, and Future Direction
Fruits are the important nutrition in human life. Different diseases occur in the Fruit quality that affect the economic growth. Disease detection is important for ensuring crop health, yield, and food security. Traditional methods rely on manual inspection, which is time- consuming and error-prone. Deep learning (DL) models are the powerful tool for identifying disease in various fruits. Convolutional Neural Networks (CNNs) are highly effective for detecting and classifying fruit diseases using image data, offering automated, accurate, and scalable solutions for agricultural diagnostics. Fruit disease dataset such as Kaggle for classification and roboflow dataset for identifying the disease in fruits. There are so many Challenges that include restricted data diversity, poor generalization, and lack of interpretability. Future directions for identifying fruit diseases using deep learning include explainable AI, multimodal data fusion, and real-time mobile deployment. This review aims to guide future research toward robust, scalable, and interpretable solutions.
Integrating Ayurvedic Therapeutics with Modern Science: A Holistic Approach to Chronic Disease Management
Chronic diseases such as diabetes mellitus, cardiovascular disorders, metabolic syndrome, arthritis, and neurodegenerative conditions represent a growing global health burden. Conventional biomedical approaches, while effective in symptom control and acute management, often fall short in addressing the multifactorial, long-term nature of these disorders. Ayurveda, the ancient Indian system of medicine, offers a holistic framework emphasizing balance among body, mind, and environment. This paper explores the integration of Ayurvedic therapeutics with modern scientific approaches for comprehensive chronic disease management. By synthesizing classical Ayurvedic principles with contemporary biomedical evidence, systems biology, pharmacology, and lifestyle medicine, the study highlights how integrative models can enhance therapeutic efficacy, patient adherence, and quality of life. The paper underscores the relevance of personalized medicine, preventive care, and bioinspired therapeutics in addressing chronic diseases through an evidence-informed integrative paradigm.
Workload-Driven Perspectives on Networked Filesystems: Benchmarking NFS/SMB and Version-Level Trade-offs
This paper surveys network file system protocols with a systems focus on NFS and SMB/CIFS, explaining how transparency to applications interacts with protocol design, client caching, and failure handling in distributed storage stacks. It consolidates measurement methodology via widely used tools (Postmark for small-file, mail-server-like workloads and LADDIS/SPEC SFS for NFS server micro-operations), detailing metrics, load generation, and interpretability limits across client/server configurations. A versioned protocol comparison highlights, when NFSv4.1’s stateful features and read delegations reduce request volume and improve throughput over NFSv3 for small-file access patterns, while also identifying workloads where added chattiness can degrade performance. Complementary production CIFS trace analysis quantifies real-world I/O volumes, read-write ratios, reopen distributions, and low sharing rates, yielding design implications for client-side caching, tiering, and write-optimized filesystems. Together, these benchmark-anchored and trace-driven perspectives provide practical guidance for performance evaluation and protocol selection in computer systems and storage research.
Artificial Intelligence and Data Science
Phase Resolved Observational Study and Imaging Analysis of the September 2025 Total Lunar Eclipse
This paper gives observations and analysis of the 2025 September total lunar eclipse as witnessed from Thiruvananthapuram, Kerala, India. The six major phases of the eclipse were captured with high-resolution images using a 130 mm Newtonian reflector telescope, including the complete Moon prior to the eclipse, penumbral onset, partial umbral stages, and totality. The duration of each phase was meticulously noted, giving an accurate timeline account of the event. Relative brightness of the Moon was recorded at every phase, decreasing from 100% when full to 10–15% at totality, indicative of the shadow effect of the Earth and atmospheric scattering of sunlight. Ambient temperature was also recorded, decreasing from 28.0°C to at totality, showing minimal environmental impact related to the eclipse. The observations show the definite trend of lunar shadowing, progression of red "Blood Moon" coloration, and relation between qualitative observation, quantitative brightness, and change in temperature. The study indicates the necessity of methodical observation and consolidation of various measurements to interpret lunar eclipses and their subtle impact on local conditions.
Thermo-Electro-Mechanical Modeling of SMA–Piezoelectric Adaptive Structures Using MATLAB
This paper presents a MATLAB-based study of smart materials, focusing on Shape Memory Alloys (SMA) and piezoelectric materials and their combined behavior in adaptive structures. SMA shows large deformation due to temperature changes, while piezoelectric materials produce small and precise deformation when voltage is applied. In this work, mathematical models for both materials are developed and simulated to understand their behavior. The stress–strain and displacement–temperature results clearly show the nonlinear response of SMA, while the displacement–voltage results confirm the linear behavior of piezoelectric materials. A coupled model is also studied to analyze the combined effect of temperature and voltage. The results show that SMA provides the main deformation, and the piezoelectric material adds fine control. A table is used to clearly present this combined behavior. Overall, the study shows that combining these materials can improve the performance of adaptive structures in applications like aerospace, robotics, and vibration control systems.
Analytical Investigation of Synovial Fluid Motion Across Cartilage Layers in the Knee Joint
Understanding the flow behaviour of synovial fluid is essential for analysing lubrication, nutrient transport, and mechanical functionality within the knee joint. This study presents a mathematical model describing axial and transverse velocity components under pressure driven flow with variable viscosity and porous-like resistance parameters. The governing equations were solved to obtain velocity profiles and volumetric flow rate, enabling a detailed assessment of fluid transport under physiological conditions. The results show that axial velocity follows a smooth parabolic pattern typical of laminar lubrication flow, while transverse velocity is highly sensitive to viscosity ratios and structural resistance, reflecting the influence of synovial membrane permeability. The computed flow rates demonstrate that elevated viscosity and decreased permeability conditions associated with osteoarthritis—significantly reduce fluid transport capacity. The study provides mechanistic insight into synovial fluid dynamics and offers a theoretical basis for improving visco supplementation strategies, developing biomimetic joint lubricants, and informing therapeutic approaches for degenerative knee disorders.
Toward Fintech Adoption Framework for Developing Countries (India) - A Literature Review Based on the Stakeholder Perspective
Financial technology (fintech) has emerged as a transformative force in developing economies, driving financial inclusion and economic growth. India, with its robust digital initiatives such as Unified Payments Interface (UPI) and Digital India, has made remarkable progress in building a vibrant fintech ecosystem. However, challenges such as regulatory ambiguities, digital infrastructure gaps, low financial literacy, and trust deficits continue to hinder its potential for inclusive and sustainable growth. This study examines India’s fintech landscape from a stakeholder perspective, identifying the roles of key players - government, financial institutions, technology providers, and consumers in shaping the ecosystem. Additionally, it draws lessons from other developing nations, such as Indonesia, Kenya, and Nigeria, to propose actionable strategies for enhancing fintech adoption in India. A comprehensive framework for fintech adoption is proposed, focusing on five key pillars: policy and regulation, infrastructure development, stakeholder collaboration, education and awareness, and localized financial inclusion strategies. The study emphasizes the importance of adaptive regulations, scalable infrastructure, public-private partnerships, and targeted consumer education in addressing existing barriers. Insights from Indonesia’s localized solutions, Kenya’s mobile-based innovations, and Nigeria’s agent banking networks provide practical approaches for India to bridge its rural-urban divide and reach underserved populations. India’s phased fintech roadmap: pilot innovation & literacy, build infrastructure & partnerships, and strengthen regulation for sustainable, inclusive digital financial growth.
Informational Substance Theory: From Substantial Motion to Conscious Fields
Informational Substance Theory (IST) proposes a novel metaphysical framework where dynamic, structured information constitutes the fundamental ontological substrate of reality. Drawing on Mulla Sadra’s doctrine of substantial motion (al-harakat al-jawhariyya), which envisions existence as perpetual becoming, IST integrates quantum field theory (QFT), loop quantum gravity (LQG), and Integrated Information Theory (IIT) to unify physics, metaphysics, and philosophy of mind. We redefine substance as a self-organizing informational continuum, where physical fields emerge as excitations, spacetime as quantized relational networks, and consciousness as a highly integrated, self-reflexive informational field. Contrasting IST with panpsychism, neutral monism, and process philosophy, we argue it avoids traditional dualisms and reductionism, offering a non-reductive solution to the “hard problem” of consciousness. Grounded in empirical frameworks like IIT’s Φ metric and neural field theories (e.g., active inference), IST suggests testable implications, including experiments on informational integration in consciousness and quantum cosmology. By formalizing the informational continuum via information geometry and category theory, IST bridges Sadrian metaphysics with modern science, proposing that the universe is a self-evolving narrative of information. We outline future research to model this ontology and explore its ethical and cosmological implications.
Physics-Augmented Neural Network for Predicting Corrosion Inhibition Efficiency of Green Inhibitors
Material degradation, particularly corrosion, poses a persistent engineering challenge that impacts infrastructure, transportation, energy, and manufacturing sectors. In India alone, corrosion-related losses are estimated to exceed several billion dollars annually, affecting pipelines, bridges, marine vessels, and industrial plants. This study presents a physics-augmented artificial neural network (ANN) framework for predicting the performance of eco-friendly corrosion inhibitors in acidic environments. The approach integrates experimentally measured process parameters concentration, temperature, immersion time, and pH together with adsorption equilibrium constants obtained from independent literature sources, thereby enhancing mechanistic consistency and interpretability. A curated dataset of 60 inhibitor–condition combinations was compiled from peer-reviewed studies. The ANN, implemented in MATLAB and trained using the Levenberg–Marquardt algorithm with L2 regularization and early stopping, achieved high predictive fidelity (R² ≈ 0.99 across training, validation, and testing). Predictive robustness was further assessed through repeated random splits and bootstrapped ensembles. Beyond corrosion, the framework is generalisable to other degradation phenomena such as coating breakdown, oxidation, and galvanic corrosion relevant to aerospace, marine, and automotive sectors. Real-time deployment via a lightweight graphical interface enables engineers to rapidly estimate inhibitor performance, reduce reliance on time-consuming experiments, and support integration with digital twin platforms for predictive maintenance. The method offers a pathway to accelerate green materials selection, cut experimental screening costs substantially, and enhance the sustainability and resilience of critical infrastructure.
Reverse Engineering UFO, UAP, USO and Vimanas Advanced Space Technology Devices
This comparative study examines the striking parallels between modern Unidentified Aerial Phenomena (UAP), Unidentified Flying Objects (UFO), and Unidentified Submerged Objects (USO) with advanced aerospace technologies described in the ancient Sanskrit text Vimana Shastra. Through systematic analysis of documented sightings from 1947 to 2025 and detailed examination of four key Vimana technologies, the Shaktyakarshana Yantra (energy harvesting system), Vishwakriya Darshana Darpana Yantra (3D observation device), Ganapa Yantra (solar energy collector), and Parivesha Kriya Yantra (protective shield system), this research reveals remarkable technological convergences across millennia. The study identifies consistent behavioral patterns including luminous appearances, sudden directional changes, instantaneous disappearance capabilities, shape-shifting phenomena, and trans-medium travel between air and water. Modern UAP characteristics such as rapid acceleration, electromagnetic effects, and advanced stealth capabilities demonstrate substantial alignment with ancient Vimana descriptions of energy manipulation, holographic projection, and plasma-based shielding technologies. Notable parallels include the ancient "Gudha" invisibility technique with modern cloaking observations, "Chhaya Grahana" cloning methods with contemporary reports of multiplying objects, and multi-source energy harvesting systems resembling current renewable energy integration concepts. These findings suggest that ancient civilizations possessed sophisticated understanding of aerospace engineering principles that parallel modern technological developments in solar propulsion, electromagnetic shielding, holographic imaging, and hybrid energy systems. The research implications extend to contemporary aerospace innovation, offering potential insights for next-generation spacecraft design, defense systems, and sustainable energy technologies. This interdisciplinary analysis bridges ancient knowledge with modern science, providing a framework for understanding unexplained aerial phenomena while inspiring future technological advancement through historical wisdom.
Aerodynamic Analysis of a Rigid Honeybee-Inspired Drone Using CFD and Static Simulation
In the development of bio-inspired Micro Air Vehicles (MAVs), flapping-wing configurations derived from natural fliers such as honeybees offer unique advantages in maneuverability and stability within complex environments. However, modeling the unsteady aerodynamic behavior of flapping wings is computationally intensive. In this study, a simplified quasi-steady approach is adopted by simulating the aerodynamics of a rigid, honeybee-inspired drone at multiple static orientations using a virtual inflow rotation technique. Computational Fluid Dynamics (CFD) simulations are performed at various effective angles of attack by altering the direction of the incoming flow while keeping the wing geometry fixed. Key vortex structures, including leading-edge and wingtip vortices, are analyzed to evaluate their role in lift and thrust generation. Aerodynamic forces (lift, drag, and thrust) are quantified across multiple static orientations to gain insights into aerodynamic performance. In parallel, a basic static structural analysis of the rigid wing is conducted using the aerodynamic pressure loads obtained from the CFD simulations. The structural response under representative aerodynamic loads is evaluated to assess stress distribution and potential wing deformation. This combined aerodynamic and structural analysis provides a comprehensive understanding of wing performance and structural integrity, offering valuable data for optimizing bio-mimetic MAV design.
Comprehensive Observation of the 21 June 2020 Annular Solar Eclipse in Northern India: Phase Evolution, Atmospheric Response, and the Ring of Fire Phenomenon
On 21,june,2020, annular solar eclipse was witnessed from Nakodar City, situated in the northern part of India. The rare celestial phenomenon created the clear "Ring of Fire" effect when the Moon passed well in front of the Sun but, because it had a smaller angular size, did not block the Sun completely. With a Celestron AstroMaster 130EQ telescope and a certified solar filter, we effectively recorded the full sequence of eclipse stages, from the beginning contact, to maximum annularity, to the end contact, under secure and stable observational conditions. This study offers a critical observational analysis based on ground-level photographic records of the eclipse without automated image analysis tools. Instead, phase tracking was manually done using a controlled time-sequenced observation protocol, while ensuring the proper detection of all important stages, including annular ring formation. Additional atmospheric conditions like visible light obscuration and temperature variation were recorded during the eclipse interval to determine its immediate terrestrial impacts. The article explains the geometrical and physical concepts that occur during the eclipse, such as the conditions required for annularity, magnitude of the eclipse, and obscuration ratio. The article also notes the capability of amateur-level astronomical machinery to produce high-quality observational data adequate for scientific reporting. This research seeks to add to the general field of solar astronomy by offering an organized instance of eclipse recording from a rural setting in India, confirming the relevance of local ground observations to global eclipse science.
Case Study on Air India Flight -171 Crash
This case study shows the detailed analysis of the recent devastating crash of Air India AI-171 , a Boeing 787-8 Dreamliner in Ahmedabad on June 12,2025 just after 5 to 10 seconds after takeoff at 1.38 pm from Sardar Vallabhai Patel International Airport enroute to London which have 242 people onboard including 230 passengers , 10 crew members and 2 pilots surviving only 1 passengers with fatal injuries , crash also resulted in multiple fatalities on ground , including medical students and residents near the impact site resulting in the death toll of 294. Preliminary investigations revealed that the aircraft lost altitude shortly after takeoff with the issuing of Mayday call by the pilot before the plane crashed into a residential area and exploded into flames. The Aircraft Accident Investigation Bureau (AAIB), supported by international agencies promptly initiated a multidisciplinary probe analyzing the Black Box Data and considering all possible causes such as Engine Failure and sabotage. The tragedy has exposed critical regulatory and safety shortcomings within the Indian Aviation Sector prompting calls for urgent systematic reforms.
Artificial Super Intelligence (ASI), A Revolutionary and Hypothetical Potential for Artificial Intelligence
AI can be described as the versatile field that merge computer science, mathematics, cognitive psychology, neuroscience, and other disciplines to create intelligent systems capable of reasoning, learning, and decision-making. These systems often engage advanced techniques such as machine learning, deep learning, natural language processing, computer vision, and robotics to mimic human-like cognitive functions.AI in its complex form involves the development of algorithms and models that can process vast amounts of data, extract meaningful insights, and adapt to changing environments autonomously.AI can be applied to various tasks such as problem-solving, speech recognition, natural language processing, and image recognition, among others.