International Research Journal on Advanced Science Hub
International Research Journal on Advanced Science Hub (IRJASH) (eISSN 2582-4376) is an open access journal that was launched by RSP Science Hub in 2019. It aims to publish high quality peer reviewed articles to disseminate the progress in the field of science and technology therby bridging the gap between researchers, Academicians and industrial expert to remain up-to-date with the latest developments. Upon acceptance all the articles are free to read and download.
Dual Prediction of Blood Pressure and Blood Glucose Level using PPG Signals: Explore Deep Learning Models through Comparative Study
Monitoring blood pressure (BP) and blood glucose levels is vital for managing unremitting conditions such as hypertension and diabetes, which affect millions of individuals around the world. Conventional strategies for measuring BP, which depend exclusively on pulse amplitudes, often fail to provide exact readings, especially in patients with atherosclerosis or those who are obese, where pulse amplitudes may be frail or mutilated. Moreover, customary strategies for blood glucose estimation involve invasive strategies, causing discomfort. This research presents a novel approach to non-invasive dual prediction of BP and blood glucose levels utilizing photoplethysmogram (PPG) signals. Leveraging the power of LSTM network and XGBoostRegressor enables accurate and efficient forecast of both BP and blood glucose levels. LSTM is utilized to find time related patterns within PPG signal, while XGBoost enhances model performance by identifying the most relevant features for prediction. Then, the two pre-trained deep learning models (one for glucose prediction and another for blood pressure prediction) are loaded, and a test sample is reshaped to match the required input format for each model. These models are then used to make predictions on the reshaped sample. The results demonstrate promising performance, resulting in an RMS error of 8.014 mg/dL for blood glucose estimation and a Mean Absolute Error (MAE) of 25.336 mmHg for BP estimation. The model's performance reflects high potential for practical, non-invasive monitoring, offering a more comfortable and accessible alternative for patients requiring regular checking of both BP and glucose levels.
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Economic Dynamics Model Using q-Laplace’s Transform
Here, for the first time, we introduce the quantum Laplace transform as a tool to analyze dynamic models in economic theory. In order to do it, at first, we revisit some useful essential preliminaries of q-calculus as well as q-Laplace transform and then apply this framework to three important cases: the Walrasian price stability model, the measurement of input response in an inflationary setting, and the model of farm sponsored job training. The solutions derived through this approach are studied numerically that shows this method provides a clear and systematic way to interpret dynamic behavior in economics. A noteworthy feature of this framework is its consistency, and it is notable that as the parameter q→1, all the outcomes reduce smoothly to those obtained via the classical Laplace transform. This convergence highlights the robustness of the method that opens a promising pathway for deploying q-calculus in economic dynamics. In short, this approach opens new research opportunities for both theoretical and applied economics..
Performance Assessment of a Designed RC Slab Under Sequential Fire and Blast Loading Using a Novel Etabs-Ansys Framework
This research develops a novel, integrated workflow to evaluate the performance of a code-compliant RC slab panel designed for gravity and lateral loads in ETABS under consecutive thermal and blast loading. A prototype one-way slab panel is designed and detailed as per Indian Standards (IS 456:2000). The critical reinforcement data and geometry are then translated into a high-fidelity finite element model in ANSYS Workbench for advanced nonlinear analysis. A sequentially coupled thermo-mechanical simulation is performed. First, a Transient Thermal analysis applies the ISO-834 standard fire curve for one hour to induce thermal degradation. Subsequently, an Explicit Dynamics analysis subjects the fire-damaged slab to a blast load, modeled using the CONWEP methodology with a centrally-located charge. The results quantify the severe performance degradation in the combined fire-blast scenario compared to isolated events. The study concludes by deriving resilience-oriented design and detailing recommendations for RC slabs in disaster-prone urban environments. The primary contribution of this work is the established ETABS-to-ANSYS workflow, providing a practical tool for engineers to assess the multi-hazard resilience of as-designed structural elements.
Review Paper On: Array Manipulation Techniques
This review presents a systematic analysis of four fundamental array manipulation techniques— traversal, searching, two-pointer approach, and sliding window paradigm—that are critical for developing efficient algorithms. We examine linear and specialized traversal methods, compare brute-force and binary search algorithms, and analyse two-pointer variations (same-direction and opposite-end) for optimization problems. The sliding window technique is explored for both fixed and dynamic window scenarios in subarray problems. Beyond theoretical foundations, this work synthesizes practical insights, performance trade-offs, and implementation challenges, bridging the gap between algorithmic principles and real-world applications. By providing a structured framework for selecting and optimizing array-based solutions, this review serves as an essential resource for researchers and practitioners advancing algorithm design.
AI-Powered Lost Object and Entity Tracking Across Live and Recorded Camera Feeds
The rapid growth of video surveillance systems in urban, industrial, and institutional areas has resulted in a huge increase in visual data streams. Manually monitoring these large video feeds has become inefficient, prone to errors, and labor-intensive. This situation calls for the development of smart, automated surveillance systems. This paper proposes an AI-driven framework that can detect, identify, and track lost objects and individuals in both live and recorded CCTV footage. The system uses advanced deep learning models, in-cluding YOLOv8 for real-time multi-class object detection and CNN-based face recognition for re-identification and tracking across different camera views. The framework includes modules for data prepro-cessing, feature extraction, and temporal tracking. These components work together to ensure continuous identity tracking and object localization. It also allows for multi-camera synchronization, which enables smooth cross-view tracking of individuals and misplaced items in complicated environments. We conducted experimental analysis on benchmark datasets and real-world surveillance footage. The results showed an av-erage precision of 94.2% and a recall of 91.6%, highlighting the model's strength under various lighting, oc-clusion, and motion conditions. In addition to high detection accuracy, the framework offers real-time per-formance, scalability, and flexibility for use in airports, railway stations, shopping malls, and law enforce-ment agencies. This research bridges the gap between traditional manual surveillance and modern autono-mous monitoring systems, contributing to the field of intelligent security solutions.
Attendifyx: Smart Attendance System Using Deep Learning
With the advent of the digital age, old attendance systems like manual registers and simple biometric verification tend to be susceptible to inefficiency, human mistakes, and proxy abuse. AttendifyX mitigates these issues by an intelligent, deep learning–powered face recognition method for automated and accurate classroom attendance. The framework utilizes the Buffalo-L version of InsightFace model together with a randomized multi-capture verification that flags a student present only if identified in a minimum of 75% of sampled captures within a session—hence reducing impersonation and short-term attendance fraud. Architecturally, AttendifyX incorporates a Python-based recognition module, a Node/Express backend store MySQL, and a React + Vite frontend that includes dashboards for administrators and instructors. Key features encompass automated reconciliation of attendance, reporting analytics, schedule management, and modular APIs ready for ERP integration. By integrating cutting-edge recognition, smart verification, and an intuitive admin interface, AttendifyX guarantees precision, transparency, and scalability—translating traditional attendance monitoring into a smooth, reliable digital process.
Protection and Control Measures for Fire Safety in High-Risk Industrial Areas
The evaluation of protection & control measures for fire safety in high-risk industrial areas is a critical aspect with far-reaching implications for the safety & safeguards of personnel and the preservation of various valuable assets in an industrial establishment. This project aims to layout an overview of the comprehensive evaluation process that consists of protection & control measures for fire safety & similar incidents. This study emphasizes the significance of regulatory compliance, employee training, and the cultivation of a strong safety culture within different industrial establishments & facilities. This project underscores the importance of critical evaluation process, which is essential for wellbeing of living beings & safeguarding of assets & property, and even for the sustainable functioning of various industrial operations across different high-risk industries.
Sustainable Wildlife Movement Detection and Crop Protection System
Wildlife intrusion into farmlands poses a major threat to agricultural productivity and farmer safety, particularly in regions bordering forest areas. Traditional preventive measures such as fencing, ultrasonic repellents, and surveillance cameras are often costly, energy-intensive, and unreliable under low-light or complex environmental conditions. To address these limitations, this project proposes a Sustainable Wildlife Movement Detection and Crop Protection System that integrates IoT and AI for intelligent, eco-friendly, and automated crop protection. The proposed system employs a YOLO-based object detection algorithm for real-time identification and classification of animals from live camera feeds. The detected animal behavior and movement patterns are analyzed to assess threat levels. Based on this analysis, appropriate eco-friendly deterrents including sound, light, or eco-friendly fog/spray mechanisms are automatically activated to drive away the animals without causing harm. An offline alert mechanism ensures that nearby farmers receive notifications even in areas with limited or no internet connectivity. Additionally, a farmer-friendly dashboard provides live intrusion alerts, detection history, and manual control options. By integrating YOLO-based AI detection, IoT-based automation, and sustainable deterrent mechanisms, the proposed system offers a low-cost, scalable, and intelligent solution that minimizes crop losses, enhances farmer safety, and fosters coexistence between humans and wildlife.
Client-Side Threats in SPAs: Modeling Security Risks in Popular JavaScript Framework
Single Page Applications (SPAs) have reshaped web development by improving responsiveness and interactivity, but the shift of application logic and data handling to the client side has introduced security challenges that traditional server-centric models do not adequately address. This study proposes and validates a threat model specifically designed for SPAs, focusing on two widely adopted JavaScript frameworks, React and Vue.js. Two prototype applications with equivalent functionality were developed and evaluated using a modified STRIDE methodology, combining static analysis tools (ESLint, SonarQube, Snyk), dynamic testing tools (OWASP ZAP, Burp Suite), and manual inspection of client-side code and runtime behavior. The analysis identified common vulnerabilities across both frameworks, including DOM-based XSS, insecure token storage, broken route guards, and exposed API endpoints. React showed higher risk when unsafe rendering practices such as dangerously SetInnerHTML were used, while Vue’s vulnerabilities were linked to insecure use of v-html and un validated dynamic imports. Mitigation strategies, including input sanitization, Http Only cookie-based token storage, Content Security Policy (CSP), and strict route guards, significantly reduced vulnerabilities. This work delivers a structured SPA-specific threat model and reproducible methodology, providing developers and security practitioners with actionable guidance for building more secure client-side applications.
Single Page Applications (SPAs Threat Modeling)
Mastering Enterprise Cloud at Scale: Architecture, Compliance, and Automation in AWS and Hybrid Environments
Scalability of cloud solutions is becoming a key factor in agility, efficiency, and regulatory compliance in the fast-paced digital ecosystem as enterprises turn to cloud solutions. The review will examine the meeting point of architecture, automation, and compliance in AWS and hybrid cloud setups. It proposes theoretical frameworks and experimental outcomes to examine the transformation of cloud operations at scale due to infrastructure as code (IaC), policy-as-code, and DevSecOps pipelines. Major conclusions have been made that the complete implementation of cloud-native strategies saves a lot of time in terms of deployment, non-compliance, as well as the operation expenses. This research introduces Enterprise Cloud Capability Integration (ECCI) model which is a convergent model to align cloud architecture to business and compliance objectives. The paper ends by outlining future research directions in the areas of AI-enforced automation, quantum safe cloud security, and real time regulatory frameworks to meet the emerging challenges to the enterprise.
Humanization in the AI Era: Why it Should be the Focus of EdTech 4.0?
Artificial Intelligence (AI) is redefining education through EdTech 4.0, introducing adaptive platforms, intelligent tutoring systems, and data-driven personalization. While these innovations promise efficiency and scale, they risk sidelining the very essence of learning—human connection, empathy, and ethical engagement. This paper emphasizes that humanization must be the guiding principle of EdTech 4.0, ensuring technology serves as an enabler of meaningful relationships rather than a substitute for them. Drawing on global policy frameworks (UNESCO, OECD, SDG 4), critical pedagogy, and transformative learning theory, the study proposes a layered framework that embeds empathy, ethics, agency, and emotional intelligence into AI-driven education. Through thematic analysis of scholarly literature and real-world case studies, the paper demonstrates that human-centered design is not optional—it is essential for equity, inclusion, and learner well-being. The findings call for a paradigm shift from technology-centered innovation to human-centered transformation, creating learning ecosystems that are not only smart but profoundly human.
Real-Time Data Engineering for Smart Applications
The digital transformation era is characterized by intelligent applications that increasingly rely on rapid processing and analysis of large, non-homogeneous data streams. These smart systems are built on real-time data engineering, enabling seamless data ingestion, transformation, and decision-making across industries such as healthcare, transportation, manufacturing, and finance. The paper will look into the architectural principles, enabling technologies, practical uses, and the future trend of real-time data engineering. It underscores that frameworks of stream processing, edge computing, and cloud-native processing and AI integration are critical to the development of scalable and responsive intelligent systems. The paper also lists popular traps like latency, scalability, data quality, and security, and also provides a projection of the future trend, which has been brought about by federated learning, data meshes, and quantum computing. The provided insights make a detailed picture of the development of the operational and strategic landscape of smart technologies in real-time by applying data engineering.
Ecological Significance and Aquatic Diversity of Daya River in Odisha: Conservation Prospectiveness
The study aims to evaluate the ethiofaunal richness of the Daya River in Odisha, highlighting its ecological value and function in maintaining regional diversity. Being a distributary of the Kuakhai River, the Daya passes through Khordha and Puri districts before joining Chilika Lake, creating a crucial freshwater ecosystem that hosts a wide range of aquatic organisms. The research examines the diversity, distribution, and abundance of aquatic organisms including fish, molluscs, crustaceans, planktons, and amphibians, and how these correlate with the physicochemical properties of the river water. Field surveys at different locations along the upstream and downstream reaches of the river were undertaken. Parameters such as temperature, pH, dissolved oxygen (DO), biological oxygen demand (BOD), nitrates, and phosphates were measured to determine their impact on faunal diversity. The findings reflect a diversified community of water life, suggesting that the Daya River is still biologically active and ecologically rich. Nevertheless, the presence of tolerant and sensitive species concurrently indicates non-uniform water quality at different sites. The study warns of anthropogenic pressures like agricultural effluent run-off, sewage outflow, overfishing, and land development degrading habitats and water quality, causing sedimentation, eutrophication, and destruction of riparian cover. It advocates integrated river management, control of pollution, riverbank restoration, sustainable aquaculture, and community engagement to preserve biodiversity and ecological integrity and livelihoods of the Daya River.
Biodiversity Patterns and Conservation Strategies in Chandaka Wildlife Sanctuary
The study examines biodiversity of Chandaka Wildlife Sanctuary, a crucial ecological region near Bhubaneswar in Odisha, India. Extending over the Khordha and Cuttack districts, the sanctuary area measures approximately 193 square Kilometers and constitutes a region of the Eastern Ghats topography. It is mostly a dry deciduous forest community with Sal (Shorea robusta) and mixed species as the dominant one, offering a natural environment to a diverse array of flora and fauna. The goal of this research work is to record and examine the species composition, diversity, its distribution in sanctuary and determine the ecological processes and also conservation issues influencing in distinctive habitat. Field surveys were carried out in various zones of the sanctuary with conventional ecological techniques like transect walks, quadrat sampling, and camera trapping. The research documented a diversified richness of plant species consisting of medicinal plants, shrubs, grasses, and climbers supporting the livelihood of various animal species. The faunal diversity comprises large mammals like the Indian elephant (Elephas maximus indicus), leopard (Panthera pardus), spotted deer (Axis axis), wild boar (Sus scrofa), and a number of species of primates and small mammals. The sanctuary also has a diverse range of birdlife, reptiles, amphibians, insects, adding to its ecological richness and productivity. The study recognizes some of the major threats to biodiversity, mostly emanating from anthropogenic influences like deforestation, poaching, encroachment, cattle grazing, and urbanization because of the close location of Bhubaneswar city. Human-wildlife conflict, particularly with elephants, and habitat fragmentation have turned out to be serious problems for the ecological integrity of the sanctuary. Water body degradation and riparian vegetation loss have also affected the habitat quality for aquatic and semi-aquatic organisms. The research also emphasizes to intensive conservation efforts to protect Chandaka Wildlife Sanctuary's biodiversity. The proposed steps are restoration of habitat through afforestation, intensification of anti-poaching patrols, establishment of wildlife corridors, and augmentation of water resources. In addition, community participation and environmental education are necessary to ensure sustainable cohabitation between people and wildlife. In conclusion, the current study offers useful baseline data regarding the status of biodiversity at Chandaka Wildlife Sanctuary, which can provide insights for future conservation research, policy, and ecosystem management. Conservation of this sanctuary is important, not only for regional biodiversity maintenance but also for ecological balance sustenance as well as environmental resilience upholding in Odisha.
Study of Walrasian Model Using Quantum Calculus
In this work, for the first time, we present the Walrasian Model in view of q-calculus as a novel perspective of economic dynamics. Though q-methods have been widely applied in mathematical physics, their use in microeconomic theory remains unexplored. In order to study this model, at first, we begin by outlining the foundational concepts of q-calculus, and then reformulate the Walrasian framework within this q-calculus set up. This approach is not only applied to solve the one-dimensional dynamic equation that illustrates the effectiveness through a classic example of microeconomics but also the numerical validation against classical datasets confirms the robustness of this method. Most importantly, we show, if the deformation parameter q tends to 1, the results converge seamlessly to those obtained via the classical calculus that bridges the new formulation with established theory. This study also highlights the potential of q-calculus as a powerful tool for advance research in economic dynamics and opens new directions for theoretical as well as applied economics.
A Study On Online Shopping Dissonance Among College Girls with Reference to HEI'S
access to a vast array of goods and services. However, this digital landscape frequently givesrise to cognitive dissonance, a psychological discomfort stemming from a mismatch between acustomer's pre-purchase expectations and their post-purchase reality. In the context of onlineshopping, this "dissonance" manifests as a feeling of regret, uncertainty, or dissatisfaction after apurchase, often referred to as "buyer's remorse." It occurs when the actual product or experience doesn’t align with the consumer's initial beliefs, values, or expectations. This study specifically investigates how dissonance affects women, a significant demographic in e-commerce who often prioritize aesthetic, fit, and tactile attributes. We identified key sources of dissatisfaction, including misleading product visuals, inconsistent sizing, perceived quality discrepancies, and inadequate product information. These issues, combined with the inherent limitations of the online medium and challenging post-purchase processes like returns, are major drivers of cognitive dissonance. Consumer behavior is always dynamic and changes with time, with each global revolution bringing about some changes in how people shop and how marketers adapt. The current major revolution is the emergence of universal global connectivity, which has had a significant impact on digital marketing and consumer choices. As a result, consumer behavior has changed unrecognizably over time and will continue to do so in the future. To address these challenges, we propose solutions such as leveraging advanced visual technologies, implementing sophisticated fit prediction tools, streamlining logistics, and fostering transparent communication. By addressing these critical areas, e-commerce platforms can significantly mitigate cognitive dissonance, thereby enhancing customer satisfaction, fostering loyalty, and ensuring sustainable growth in the dynamic online retail environment.
Intelligent Automation Adoption in Manufacturing Supply Chains: A Systematic Literature Review of RPA and AI Integration Frameworks, Performance Impacts, and Implementation Strategies
Manufacturing supply chains are becoming more tangled and unpredictable, with mounting pressure to adapt quickly, revealing just how inadequate old, step-by-step planning can be when shipments change overnight. By blending Robotic Process Automation with Artificial Intelligence, intelligent automation has become a game changer—sharpening predictions, speeding up decisions, and giving supply chains clearer visibility, like spotting delays before a truck even leaves the warehouse. This review pulls together studies on how RPA and AI are being adopted in manufacturing supply chains, zeroing in on integration frameworks, performance benchmarks, key enablers and obstacles, and how they shape forecasting and day-to-day logistics, from warehouse scheduling to delivery routes. Following PRISMA guidelines, we systematically screened peer reviewed studies from 2017 to 2025 across Web of Science, Scopus, IEEE Xplore, and ScienceDirect, combing through titles like flipping through well-worn index cards. Out of 381 records reviewed, just 50 studies made the cut. We pulled data on implementation strategies, outcomes, and contextual factors, checked quality with the Mixed Methods Appraisal Tool, and uncovered themes through careful, line by line coding. Research shows that using structured adoption frameworks that blend RPA with AI makes supply chains quicker to adapt and easier to see end-to-end, delivering clear gains—costs drop by 24.8%, lead times shorten by 21.3%, forecasting improves by 23.1%, and errors fall by more than half, down 52.6%. Start with RPA, then bring in AI, and you’ll hit about 85% success—far better than the 70% you get when you try both at the same time. Sharp leadership, talented teams, and savvy change management are needed, but spreadsheets, system linkages, upfront fees, and ethical issues remain. Intelligent automation, based on evidence-based, human-centered, scalable frameworks, may improve operations, strategy, and performance. Businesses should launch plans in stages, support them with solid data governance, and lead staff through each step of a new system until every click and screen is familiar to make significant progress.
Integrated AI Model for Traffic Density Management and Emergency Vehicle Prioritization
This paper is concerned with a Smart Traffic Management System based on AI that operates traffic lights dynamically via real-time multi-modal data. The system combines computer vision, audio recognition, and RFID detection to give precedence to traffic movement, particularly for emergency responders. Camera inputs are analyzed with the YOLO object detection model to estimate traffic density in multiple lanes, while an audio classifier processes siren audio features using Fast Fourier Transform (FFT) features. Furthermore, RFID tags on emergency vehicles allow correct detection even in adverse conditions. A decision module implements hierarchical priority rules, in which the passage of emergency vehicles is given priority over calendar-based event handling, lane congestion control, and regular signal cycling. The ultimate decision is implemented by a Raspberry Pi-based signal controller, which controls traffic lights using GPIO or relay connections. The system also updates a dashboard interface to offer real-time monitoring for authorities. The invention greatly decreases traffic jams, provides quicker response in emergencies, and dynamically adjusts to urban traffic patterns by integrating several sensing modalities with smart decision -making. The inventive concept of the invention is in its combination of image, audio, and RFID data for real-time prioritization and rule-based decision hierarchy that makes it a more robust and adaptive traffic management solution than traditional timer-based systems.
Heritage Ornamentation of Garli–Pragpur Translating Tradition into Modern Design
Garli and Pragpur, India’s first recognized heritage villages, embody a unique decorative language where arches, jaalis, murals and woodwork tell stories of cultural exchange and craftsmanship. Their façades, alive with Gothic, Colonial and Himachali influences, form an architectural identity that is both practical and poetic. However, modernization threatens the integrity and visibility of these traditional motifs; this research seeks to bridge the gap between heritage conservation and contemporary design by exploring how these patterns can be meaningfully adapted in modern architecture. This study employs a mixed-method approach combining visual surveys, documentation, and spatial analysis of heritage buildings with interviews of local residents, artisans, and architects. The findings reveal that the ornamentation of Garli and Pragpur is not a static relic but a hybrid vocabulary where Western stylistic cues are seamlessly blended with vernacular traditions. However, this research also highlights urgent conservation challenges like many buildings lie vacant or dilapidated due to changing demographics, economic migration and complex ownership issues, raising the stakes for documenting and adapting traditional patterns before they disappear. By analyzing and translating these motifs, the research demonstrates their potential for meaningful adaptation in modern architecture. The study argues that these decorative traditions can inspire design solutions that are culturally rooted, aesthetically rich and environmentally responsive. In doing so, Garli and Pragpur are positioned not only as living museums of heritage but also as active sources of design innovation for contemporary practice.
Interior Choreography in Design Practice
A shaft of sunlight pierces through a carved jaali. A devotee walks barefoot across the cold stone floor. Each step echoes like a drumbeat in the sanctum. The temple does not stand still, it performs. The idea of interior choreography is examined in this study, which frames interior design as a dynamic performance influenced by movement, light, narrative, and historical memory. Interiors are often seen as fixed, designed objects—containers for life. Yet the truth is more fluid: they perform. They guide, they pause, and they crescendo. By suggesting interiors as choreographed spaces where narrative, sensory perception, and cultural memory converge, this study fills that gap. A qualitative interpretative methodology was adopted, combining case studies of temples, heritage buildings, schools, and museums with literature on spatial sequencing and environmental psychology. Fieldwork included site observations, photographic documentation, and analysis of spatial components to trace how interiors guide behavior and construct narratives. The results show how interior spaces serve as experiential scripts, with materials maintaining continuity with the past, movement defining rhythm, and light directing atmosphere. Reinterpreting these ideas in contemporary interior design results in improved cultural relevance, emotional resonance, and memory retention. The study concludes that a transformative design methodology can be achieved by approaching interiors as staged performances. For architects and interior designers, this approach provides a framework to integrate cultural memory, sensory experience, and narrative into built spaces—fostering environments that move beyond static form into living, performative encounters.
Adaptive Reuse for Community Development
Garli and Pragpur, a heritage village cluster in Himachal Pradesh, are known for their fading havelis, narrow lanes, and a distinctive blend of colonial, Sikh, and vernacular Himachali architecture. Once flourishing under the enterprise of the Sood community, these settlements today face neglect as many historic structures remain abandoned or underutilized, leading to a gradual erosion of cultural identity. This research explores adaptive reuse as a sustainable strategy to revitalize void heritage buildings for social welfare and community development. Through site observations, architectural documentation, and analysis of reuse approaches, the study examines how abandoned structures can be assigned new functions that respond to present societal needs. The findings reveal that heritage conservation must extend beyond physical restoration to address the social and functional dimensions of space. Buildings that lose their original utility risk falling into disrepair; however, adaptive reuse bridges the past and present by providing renewed purpose while respecting original character. The study concludes that integrating modern, community-oriented functions within heritage buildings can foster inclusive development, preserve cultural continuity, and revitalize neglected environments, positioning adaptive reuse as a key strategy for ensuring the sustainable survival of cultural assets and enhancing the quality of life for local communities.
Modernizing PeopleSoft Financial Systems: Automation, Cloud Integration, and Workflow Optimization
Enterprise Resource Planning (ERP) systems have been a part of the financial and operational processes of contemporary institutions. Although a strong and mature platform, Oracle PeopleSoft Financials was developed based on the old architectures that are becoming less and less fit to meet the modern business requirements of agility, automation, and cloud-native integration. This literature review indicates the need and ways of modernising PeopleSoft Financial systems with three key pillars such as automation, cloud integration, and workflow optimisation. The paper presents a theoretical framework backed by literature in the industry and the scholarly world to inform the strategies of modernisation. The main issues of customisations of the past, data integrity, and the readiness of the organisation are addressed. Further research directions and recommendations include a focus on establishing a consistent modernisation framework, transitioning to the use of hybrid clouds, and the use of advanced analytics. The presented insights will help to inform research and practical implementation to implement sustainable ERP transformation.
Zero Trust Architectures for Responsible AI in Enterprise Applications
The current-day business decisions have been centered on artificial intelligence (AI). Nonetheless, obtaining them without compromising transparency and ethics is becoming a problem. The paper has explored how the application of Zero Trust Architecture (ZTA) and the principles of Responsible AI (RAI) can be used to build AI systems that are both secure and ethically right. We research existing frameworks, field testing, and design techniques that deploy ZTA to the AI lifecycle - collecting data to training, deployment, use, monitoring, and auditing. We have found that ZTA does not only make security to be more secure through hard access control and privacy, but it also increases the level of fairness, transparency, and resistance to adversarial attacks. We finish by a couple of suggestions to future research on ZTA-powered Responsible AI and conclude with the argument that one model of governance can unite both cybersecurity and AI ethics.
DineWise RMS: A QR Code-Based Restaurant Management System for Table Booking and Queue Management
The restaurant industry faces challenges such as long waiting times, inefficient table management, and high operational costs due to manual processes. In this study, developed mobile and Windows-based a QR code-based restaurant management system which provides the facility to streamline table booking along with automated queue management and order processing, named “DineWise RMS”. By leveraging QR code technology, customers can scan codes to access digital menus, reserve tables, join virtual queues, and place orders directly from their smartphones. The system integrates with a central database to provide real-time updates on table availability, queue status, and order progress. This work highlights the potential of QR code technology to enhance operational efficiency and customer satisfaction in the dining sector.
Solar Energy System Simulation with Respect to Cybersecurity Using Decoy Device
The rapid expansion of renewable energy deployment has placed solar photovoltaic (PV) at the forefront of the clean power supply for the residential, commercial and industrial users. With their insertion into smart Grids, though these systems are facing increasing exposure to cyber physical threats to stability simulation and protection system that embedded with solar panels, inverters and battery management units along with smart sensing using LDR, PIR, IR, temperature sensors, and actuator responses through LED and buzzer notifications. A multi-layered cybersecurity framework is designed, including AI-powered anomaly detection for timely threat detection, blockchain-enabled decentralized logging to provide tamper-proof audit records, honeypot-derived decoy devices to divert attackers, and a Moving Target Defence (MTD) function for adaptive system reconfiguration against dynamic threats. The system is implemented on Raspberry Pi and ESP32 boards to facilitate real-time data acquisition, secure processing, and visualization of solar performance data, environmental parameters, and intrusion attempts. Experimental validation confirms the system's accurate estimation of solar irradiance, effective inverter–battery interaction, and strong intrusion resilience based on coordinated responses. Furthermore, the decoy-based deception method proposed in this work effectively misdirects adversaries from important resources while ensuring operational continuity. The findings identify a new paradigm for incorporating sustainable energy management with sophisticated cybersecurity measures and provide a resilient, scalable, and smart model for future decentralized smart grid infrastructures.
Adaptive Traffic Signal Control System Using Machine Learning and Neural Evolution: Multi-Case Study for Intersection Optimization and Emergency Vehicle Prioritization
Urban traffic congestion poses persistent challenges, aggravated by insufficient integration of adaptive control and emergency vehicle prioritization. Existing AI models largely focus on either traffic flow optimization or emergency response, rarely combining both. This research fills this gap by developing a unified framework that predicts optimal green light durations using real-time traffic density, vehicle heterogeneity, and emergency prioritization. A four-way intersection traffic simulation was developed using the pygame library to closely mimic real traffic dynamics, lane configurations, vehicle types, and turning movements. Five case studies were conducted: (1) a mathematical green-time prediction model incorporating weighted vehicle classes, clearance times, arrival rates, and queue spillover controls; (2) reinforcement learning (RL) trained on varied traffic conditions; (3) RL enhanced with emergency vehicle priority; (4) application of the Neuroevolutionary of Augmenting Topologies (NEAT) algorithm for model architecture optimization; and (5) comparative analysis of model performance. The mathematical model reduced mean vehicle delay by 23% (p < 0.05), standard RL achieved 31% improvement (p < 0.01), emergency-aware RL maintained a 28% reduction while ensuring emergency vehicle clearance within 18.7 seconds on average, and the NEAT-based system improved throughput by 34% with superior adaptability to traffic fluctuations. The integrated framework significantly enhances traffic efficiency compared to fixed-time signals while guaranteeing rapid emergency vehicle response. Its extensibility supports integration into smart city traffic management platforms, offering scalable and adaptive solutions for urban mobility challenges.
Adaptive Mask Optimization-Driven Hybrid Gan with Swarm-Based Tuning for High-Fidelity Image Inpainting: Results and Performance Analysis
Image inpainting is a computer vision technique that aims to fill in missing or damaged areas of an image with plausible content. This paper presents the results and analysis of an Adaptive Mask Optimization-Driven Hybrid GAN designed to enhance image inpainting quality. The model integrates dynamic mask refinement with swarm-based hyperparameter tuning and a multi-component loss formulation to improve structural accuracy, texture realism, and computational efficiency. Evaluated on the Paris Street View dataset with cross-validation on three additional datasets, the proposed model achieved a PSNR of 34.72 dB, SSIM of 0.942, and EPI of 0.087, surpassing GAN-Inpaint, Painter Net, Hybrid Swarming Algorithm, and Swarm-Optimized Transformer GAN. Cross-dataset evaluation confirmed generalization across urban, artistic, and natural scenes. Ablation studies revealed that adaptive mask optimization and swarm-based tuning significantly improve perceptual quality, while computational analysis showed a balanced trade-off between accuracy and inference time. These results establish the model as a robust, efficient, and generalizable solution for applications in digital restoration, medical imaging, and remote sensing.
Assessment of Ethylene-Vinyl Acetate Material Properties and Compliance Testing for Complete EVA Footwear
Ethylene-vinyl acetate (EVA) is widely used in the footwear industry due to its excellent cushioning, flexibility, and lightweight characteristics. This study investigates the performance properties of EVA material for complete footwear applications, focusing on critical tests including sole abrasion, tear strength, compression resistance, full shoe flexing, and chemical safety parameters such as cadmium, lead, phthalates, and pH value. The analysis combines empirical testing with a review of regulatory compliance standards and existing literature. The results demonstrate (white shoe of density 0.19 g/cc and black shoe of density 0.17g/cc) versatility and potential to meet functional and environmental safety requirements for footwear manufacturing.
An IoT Based Smart Home System for Fault Detection
This paper includes an IoT-based smart house system to detect faults in real time with the help of embedded platforms including. It incorporates the environmental, electrical, and operation sensors to detect abnormalities and failures of the home facilities and equipment. NODE to NODE communication is facilitated with BLE, Zigbee, Z-Wave and Wi-Fi that is optimized to support different power and bandwidth demands. Firmware is run on microcontrollers, which have a capability to interface low-level sensors and do edge processing in C/C++, or Python. Advanced diagnostics are done on complex components based on embedded Linux and either Python or JavaScript. The cloud services such as AWS IoT offer access at a distance, data recordings, and fault classification based on machine learning. Scalable and modular system architecture ensures powerful and effective fault detection in a smart home setting. Isolation Forest and LSTM neural networks for sensor problem diagnosis and predictive maintenance. Network adaptability is increased by local fallback strategies, watchdog timers, and secure firmware updates. This research supports the development of smart home systems which are scalable, safe, and intelligent.
Stress Detection in It Workers Using Image Processing and AI
In today’s fast-paced IT industry, employees frequently experience stress due to prolonged computer usage, tight deadlines, and constant mental pressure. Often, individuals may not realize they are under stress until it negatively impacts their health, focus, or performance. Chronic stress can lead to serious consequences such as burnout, increased absenteeism, and reduced productivity. This project proposes a non-intrusive, real-time stress detection system using a widely available tool the computer’s built-in webcam. The system passively observes facial expressions and behaviours during regular work activities without requiring the user to wear any sensors or interrupt their workflow. Through image processing and artificial intelligence techniques, it analyses live webcam footage to detect subtle cues like a furrowed brow, clenched jaw, or eye fatigue, which are indicative of stress. Trained AI models assess these cues in real time and provide immediate feedback, such as suggesting short breaks or relaxation exercises, when elevated stress levels are detected. Unlike traditional methods involving surveys or medical equipment, this approach is contactless, effortless, and integrated seamlessly into the user’s routine. Its passive operation ensures minimal disruption while still offering meaningful support. By identifying stress at an early stage, the system enables timely intervention, helping individuals manage stress before it escalates. This real- time monitoring promotes workplace wellness by encouraging healthier habits and self- awareness. Over time, it can contribute to reduced burnout, fewer stress-related health issues, and improved employee performance. Ultimately, this AI-powered webcam-based system offers a smart, effective solution for supporting mental well-being in high-pressure IT environments without adding extra burden on users.