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.
A Multi-Modal Parallel Deep Learning Framework Integrating Attention U-Net and Bidirectional LSTM for Spatio-Temporal Landslide Prediction
Landslides are among the most significant natural hazards in mountainous regions, particularly under intense rainfall conditions. Accurate prediction requires the integration of both static terrain characteristics and dynamic hydrometeorological factors. However, many existing approaches rely primarily on spatial variables or aggregated rainfall indicators, limiting their ability to represent spatio-temporal relationships associated with slope failure. This study proposes a dual-stream multi-modal deep learning framework for spatio-temporal land-slide prediction in the Kerala Western Ghats, India. The framework combines an Attention U-Net branch for spatial feature extraction and a Bidirectional Long Short-Term Memory (BiLSTM) branch for temporal sequence modeling. The spatial input consists of a seven-channel tensor derived from Sentinel-2 spectral bands, the Normalized Difference Vegetation Index (NDVI), elevation, and slope layers, while the temporal input comprises a 30-day antecedent rainfall sequence obtained from the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) dataset. Feature representations extracted from the spatial and temporal branches are integrated through a late-fusion mechanism and subsequently used for binary classification. Model performance was evaluated using an independent geographic holdout dataset. The proposed framework achieved an accuracy of 94.79%, precision of 89.74%, recall of 97.22%, F1-score of 93.33%, and an AUC-ROC value of 0.9757. Comparative experiments with Random Forest, ResNet-18, and unidirectional LSTM models demonstrated improved predictive performance of the proposed architecture. The results indicate that the integration of spatial and temporal information can enhance landslide prediction in data-driven frameworks for monsoon-dominated mountainous environments.
Copyright (c) 2026 https://creativecommons.org/licenses/by-nc/4.0
A Causal-Driven Data Architecture for Block-chain Based Agricultural Supply Chains
Blockchain technology has been adopted in agricultural supply chains primarily as an immutable record-keeping mechanism. This paper argues that such usage is architecturally insufficient and proposes a four-stage causal-analytic framework repositioning blockchain as a predictive analytics platform for seed traceability. The central contribution is a data-driven methodology for determining which supply chain variables should reside on-chain versus off-chain, based on causal relevance rather than regulatory compliance or storage cost. A 600-record corpus integrates a 500-record calibrated simulation of the Indian certified seed supply chain anchored to 12 published institutional benchmarks (ICAR, NSC, NABARD, SSCA, FAOSTAT) with a 100-record real-world operational dataset covering five Indian cities. Experiment 1 applies the PC causal graph discovery algorithm, retaining 34 edges and classifying 18 variables as on-chain and 1 as off-chain. Experiment 2 computes E-values for 56 on-chain paths; 40 are significant (p<0.05), with maximum trust weight w=1.000 (E-value=1.875). Building upon this verified data topology, the ongoing scope includes Experiment 3 to derive smart contract triggers via serial mediation, targeting a Germination Rate threshold associated with localized complaint risk, and Experiment 4 to construct the Seed Traceability Risk Score (STRS) to optimize feature verification costs. This completed ledger architecture delivers an 18-variable on-chain storage schema, demonstrating that causal structure determines the predictive value of a traceability block chain.
ASeO-CNN: Active Search Optimization-enabled Convolutional Neural Network for Channel estimation on Fifth Generation wireless channels
Channel estimation remains a crucial requirement to ensure efficient spectrum usage, enhance the network performance, and reliable data transmission in fifth-generation wireless communication systems. The prevailing channel estimation methods struggle with reduced latency, pilot overhead, and higher computational complexity in dynamic wireless communication environments. Henceforth, the research proposes an Active Search Optimization-enabled Convolutional Neural Network(ASeO-CNN) for channel estimation. Through leveraging the intrinsic complex feature extraction characteristics, the model acquires the complex channel features from the orthogonal frequency division multiplexing signals. Meanwhile, the Active Search Optimization(ASeO) employed optimizes the model hyperparameters and helps in enhancing the convergence speed and channel estimation accuracy. Furthermore, the Quadrature Amplitude Modulation(QAM) scheme helps in reliable data transmission through evaluating the complex channel characteristics significantly. Experimental findings with a 20dB Signal to Noise Ratio demonstrate the improved channel estimation performance by achieving 5.32 x 10-5 Bit Error Rate, 0.97 correlation, 0.13 Mean Absolute Error, 0.040 Mean Squared Error, and 0.933 R-Squared metrics.
Design, Simulation of PMDC Motor Speed Controller in Geotechnical Uses
Field geotechnical operations demand drive systems capable of holding shaft velocity within tight bounds despite soil resistance that shifts unpredictably across strata. This paper addresses that requirement by designing, simulating, and comparing three closed-loop speed-control strategies for a Permanent Magnet DC (PMDC) motor: a classically tuned Proportional-Integral-Derivative (PID) regulator, a Mamdani Fuzzy Logic Controller (FLC) built on a compact 3×3 rule surface, and a sigmoid-blended hybrid that draws on both. The PMDC plant is derived analytically from armature-circuit and rotor-dynamics first principles and reproduced in MATLAB/Simulink with a full H-Bridge stage driven by an STM 32F103C8T6 microcontroller. Actual MATLAB/Simulink simulation results for the PID controller are presented, showing mot or voltage, speed, armature current, torque, and PWM waveforms. The proposed hybrid controller further limits the disturbance-induced speed dip to 31 RPM recovered in 0.60 s, with overshoot of 4.1 %, settling time of 1.12 s, and residual error of 0.3 %, satisfying the ±25 % velocity tolerance of ISO 22476-1 for cone penetration testing.
Design and Analysis of Avionics Rack for Space Application
The avionics rack is a critical structural component used to support and protect electronic systems in spacecraft. The design must ensure structural integrity, vibration resistance, and reliability under launch and operational conditions. In this project, an avionics rack assembly was designed and analyzed using Siemens NX. The rack structure and its components were modelled and assembled to represent a realistic system configuration. Finite Element Analysis (FEA) was performed to evaluate the structural behavior of the rack under static loading and dynamic Loading conditions. The meshing process was carried out using tetrahedral elements for structural components and 2D shell elements for printed circuit boards (PCBs). Modal analysis was carried out to identify the modes and mode shapes of the structure. Random vibration Analysis was carried out to study how structure responds under unpredictable vibrations. Static structural analysis was conducted to determine deformation and stress distribution. The results obtained from the simulations help verify that the avionics rack design is capable of withstanding mechanical loads and vibration environments typically encountered in aerospace applications.
Ultra-High Molecular Weight Polyethylene Composites for Ballistic Protection: Mechanisms, Modelling, and Performance
Ultra-high molecular weight polyethylene (UHMWPE) stands as the primary material choice for developing lightweight high-performance composites used in ballistic armour systems. This review provides a complete evaluation of UHMWPE properties which enable its outstanding energy absorption performance when subjected to high-speed impacts. The paper evaluates ballistic test results from projectiles traveling at 250 to 700 m/s speeds and examines three failure modes which include delamination and shear plugging and fibre tensile rupture. The Lambert–Jonas and Reid–Wen formulations serve as analytical and numerical models to evaluate performance predictions through ballistic limit velocity (V₅₀) and energy absorption (ΔE) and back-face signature (BFS) assessments. The review examines hybrid material structures which combine aramid with carbon fibre and polyimide and shear-thickening fluids (STF) to improve structural stiffness and multi-hit resistance. The paper examines upcoming technologies such as nano-enhanced UHMWPE and thermoplastic resin systems which represent potential advancements for future lightweight armour development. The synthesis provides design guidelines for ply orientation and layer sequencing and interfacial bonding to achieve optimal performance against diverse ballistic threats.
A Robust Hybrid Preprocessing Framework Integrating DAE, BM3D, Median Filtering, and CLAHE for Enhanced Pneumonia Chest X-Ray Analysis
For the identification of pneumonia using CXR imaging, image quality is potentially impacted by noise, low contrast, and the presence of acquisition artifacts that make it difficult to visualize pertinent or clinically relevant features. To improve the quality of CXR images associated with pneumonia diagnoses, this study recommends the use of a robust hybrid preprocessing framework that utilizes learning-based and classical preprocessing techniques for noise reduction, structural preservation, and increasing contrast, and combines these techniques into one model. The learning-based method used is a Denoising Autoencoder (DAE). The classical methods used include Median Filtering, Block-Matching and 3D Filtering (BM3D), and Contrast-Limited Adaptive Histogram Equalization (CLAHE). To evaluate how effectively each preprocessing technique or combination of techniques can enhance pneumonia CXR images, CXR images from publicly available pneumonia CXR datasets were used in this study for evaluation. The performance evaluation used a range of quantitative metrics that included Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), Mean Squared Error (MSE), Entropy, and Contrast Improvement Index (CII). The results indicate that the proposed hybrid approach is superior to all other preprocessing methods when it comes to creating high-quality images while preserving structural integrity and improving contrast. Therefore, it is concluded that the proposed hybrid preprocessing framework provides a reliable base for improved visual analysis of pneumonia CXR images and enhanced performance from deep learning-based pneumonia detection applications using CXR images.
AI-Integrated Dynamic Website for Educational Institutions Using FULL Stack
Educational institutions increasingly rely on the internet for communication, information dissemination, and stakeholder engagement. However, many institutional websites still suffer from limitations such as static content, slow performance, poor scalability, and lack of intelligent interaction. These challenges become more evident during peak usage periods such as admissions and examinations, leading to reduced accessibility and user satisfaction. This paper presents the design and implementation of an AI-Integrated Dynamic Website developed using React Js to address these issues. The proposed system enables real-time content updates, responsive multi-device access, and improved performance through a modern full-stack architecture. In addition, the system integrates an AI-powered chatbot using Dialogflow, Rasa, or OpenAI APIs to provide instant and automated responses to user queries related to admissions, academics, placements, and campus facilities. A user-friendly Content Management System (CMS) empowers non-technical administrators to manage website content efficiently without developer dependency. Cloud-based deployment with CDN caching and load balancing ensures high availability, scalability, and fault tolerance in heavy traffic conditions. The experimental evaluation demonstrates improvements in page load time, responsiveness, and user engagement. The proposed solution highlights the effectiveness of combining artificial intelligence, cloud computing, and dynamic web technologies to modernize the educational institution’s web infrastructure
Code Collab - Browser Based Real-time Code Collaboration System using WebSockets
The demand to develop efficient remote programming environment and web based learning platforms has increased gradually due to the rapid growth of the remote programming environments and collaborative coding systems in real time. Although, there are already solutions like Visual Studio Live Share, JetBrains and Code with Me which provide synchrous editing capabilities, they usually require heavy installations, authentication processes and also resource intensive environments, and limiting them in applicable to the educational and low spec environment. This research paper is the creation of a lightweight native browser based code collaboration platform known as Code Collab. A Platform to do instant multi-user and no installation coding created in React, CodeMirror, Node.js, Socket.io. System offers code matching, protected room creation and a built in chatand file/media sharing. A detailed review of modern studies of Operational Transformation (OT), Conflict Free Replicated Data Types (CRDTs), browser based IDEs and low- latency communication frameworks suggested a number of usability and performance constraints that inspired the creation of this system. Code Collab is based on WebSockets to ensure low latency communication between devices and achieves high performance in classroom-oriented scenarios. Possible extensions are AI based programming assistance and better classroom management capabilities.
YT-RAG: A Multimodal Retrieval-Augmented Generation Framework for YouTube Video Understanding
The vast body of video content on platforms such as YouTube represents one of the richest yet most query-inaccessible knowledge repositories in existence. We present YT-RAG, a multimodal Retrieval-Augmented Generation (RAG) system that enables natural-language conversation with any YouTube video by independently indexing its spoken transcript as 384-dimensional text embeddings and its visual frames as 512-dimensional image embeddings using FastEmbed, with no GPU infrastructure required. Three principal contributions define the system: (i) a parallel dual-modality ingestion pipeline running transcript and frame extraction as concurrent asyncio tasks; (ii) an agentic retrieval loop powered by Google Gemini 2.0 Flash’s native tool-call API, making retrieval conditional on model judgment rather than mandatory; and (iii) a semantic user-notes channel providing a personalised third retrieval layer absent from all prior video RAG literature. Empirical evaluation across six YouTube content categories — Comedy, Podcast, Cooking, News, Tutorials, and Coding — shows aggregate dual-modality retrieval achieves Hit@5 approximately 4× higher than text-only and 8× higher than image-only retrieval. A systematic anti-correlation in per-category modality strengths confirms that the two channels retrieve complementary evidence. Deployed as a Chrome extension operating natively alongside the YouTube player, YT-RAG is training-free, containerised, and built entirely on official APIs without agentic frameworks
OceoChat- AI- Powered Conversational Interface for Argo Ocean Data
OceoChat is an AI-powered conversational interface that revolutionizes access to ARGO ocean data, a repository exceeding 20 petabytes collected from 3000+ floats worldwide. Traditional portals are complex, English-only, and demand technical expertise, creating barriers for researchers, policymakers, and students. OceoChat’s objective is to simplify ocean data discovery and visualization through natural language queries in multiple Indian languages, with voice input/output for accessibility. The methodology integrates ARGO, NOAA, and satellite datasets with advanced AI models (Google Gemini Pro, Hugging Face) to process queries, validate data, and generate interactive visualizations such as maps, charts, heat maps, and 3D profiles. The system architecture leverages Next.js for frontend, Node.js/Express.js for backend, Sup a base for database, and cloud deployment via Vercel and Railway. Outcomes include a 70% reduction in data discovery time, on boarding of 500+ researchers and 2000+ students within six months, and integration into 50+ marine science curricula. OceoChat is positioned as a scalable, inclusive platform that accelerates research, supports education, and enables evidence-based policymaking for India’s coastal and climate challenges.
Disaster Preparedness and Response System for Rural Areas and Educational Sectors
Disaster preparedness in rural India is weak due to poor infrastructure, low digital literacy, delayed emergency response, and heavy dependence on agriculture, making communities highly vulnerable to floods, cyclones, earthquakes, droughts, and fires. This solution addresses these gaps by introducing a low-cost, community driven disaster preparedness framework that combines mobile-based training, offline awareness materials, village disaster committees, household preparedness, school-centered programs, and localized early warning systems. Using simple technologies such as Android apps, IVR, WhatsApp/SMS alerts, and community loudspeakers, the system ensures timely communication, practical training, and coordinated action. As a result, rural communities become better prepared, response time is reduced, panic during emergencies is minimized, livelihoods are protected, and long-term disaster resilience is strengthened through education and community involvement.
CIVICMIND: AI-Driven Embedded Grievances Logging System
Public grievance redressal systems play a crucial role in maintaining transparent and responsive governance. However, existing methods including written forms, phone calls, and even web or mobile applications often depend on manual input, lack intelligent prioritization, and fail to provide real-time updates. These limitations result in delayed responses, poor complaint tracking, and reduced citizen satisfaction. The proposed system, Civicmind: An Ai-Driven Embedded Grievances Logging System, introduces an innovative approach by integrating Artificial Intelligence (AI) with an IoT-based embedded framework. The system employs an ESP32 microcontroller, I2S microphone, GPS module, and OLED display to enable users to log verbal complaints conveniently in real time. The recorded data is securely stored on a microSD card and analyzed using Natural Language Processing (NLP) algorithms to evaluate the content’s urgency and assign a risk score. High-risk grievances are automatically highlighted and forwarded to relevant authorities through email alerts. A web-based dashboard further enhances the system by providing visualization, geospatial mapping, and analytical insights for administrators. This ensures faster decision-making, efficient resource allocation, and improved accountability. By combining IoT, AI, and automation, CIVICMIND aims to create a smart, transparent, and citizen-centric grievance management system that bridges the gap between the public and governing bodies.
VistaGuide: AI Travel Companion for Exploration and Insights Using VQA and Image Feature Extraction Techniques
VistaGuide is an innovative mobile application that revolutionizes travel by integrating advanced artificial intelligence and machine learning techniques with modern mobile development. In the dynamic field of mobile application development, our project utilizes state-of-the-art methods such as visual question answering (VQA) and deep learning-based image feature extraction to provide users with real-time insights and cultural immersion during their journeys. Unlike traditional rule-based systems or conventional image processing techniques, which often struggle with scalability, rigidity, and contextual understanding, our deep learning approach excels in accurately recognising landmarks and delivering rich historical narratives in the local language. The application harnesses a suite of AI-powered features to enhance user experience and safety. Real-time event updates keep travellers informed about local happenings, while an AI-driven recommendation system suggests essential services like hotels and hospitals tailored to user needs. In addition, an integrated weather forecasting module provides timely alerts to ensure preparedness for environmental changes. The safety of users is prioritised through a one-click emergency reporting feature that uses geolocation tracking, ensuring swift connectivity with local authorities during crises. Offline functionality further enhances app’s usability allowing continuous access to vital information even when internet connectivity is unavailable.
A Digital Platform Enabling Real-Time Price Discovery and Direct Market Access for Farmers
Agriculture remains a critical pillar of developing economies; however, farmers continue to face systemic challenges in accessing fair and transparent markets for their produce. A major issue within the agricultural supply chain is the exploitation caused by intermediaries, primarily due to limited access to real-time pricing information, lack of awareness of Minimum Support Prices (MSP), and insufficient logistics support. As a result, farmers often sell their produce at unfavorable prices, leading to reduced income and economic instability. This study proposes a prototype Digital Farmers Market Platform designed to bridge the gap between farmers and markets by enabling direct access to buyers and transparent price information. The system provides real-time mandi rates, MSP details, and integrated transportation support from farm to market, allowing farmers to make informed selling decisions while minimizing logistical barriers. The platform is built as a centralized, smartphone-accessible solution to ensure usability, scalability, and wider reach. By combining price transparency, streamlined logistics, and direct market connectivity, the proposed system enhances income realization, reduces dependency on middlemen, and promotes a more equitable and sustainable agricultural marketing ecosystem. The study demonstrates how digital intervention can significantly improve efficiency, fairness, and farmer empowerment across the agricultural supply chain.
An Integrated Civic Management System Enabling Crowdsourced Reporting and Transparent Governance
Cities tend to experience some or the other civic problems such as potholes, garbage overflow, water leakage, and street light malfunctions. The current complaint system is often plagued with low response rates, a lack of transparency, and inefficient tracking systems, thereby leading to low citizen engagement. The aim of this project, is to bridge the current gap between the citizens and the government by providing a complaint registration and status tracking system in a user-friendly manner. The system utilizes a crowdsourcing approach to improve the response rates of civic issues in cities. The solution design comprises a two-module approach: a Citizen Module for complaint registration with GPS and photo submission, and a specialized Authority Module for handling complaints from the respective departments. The technology stack includes Flutter for cross-platform mobile app development, Node.js for the backend API, and MongoDB with geo-location support for data storage. Google Maps or Mapbox integration provides precise location tracking. The major outcomes of this solution include optimized complaint resolution, data-driven governance, and enhanced citizen engagement in smart city development. Future plans for development include the integration of AI-based complaint classification and IoT sensors to further optimize the complaint resolution process
A Technology-Driven Gamified Approach to Environmental Education in Schools And Colleges
Environmental awareness among students is crucial for building a sustainable future. However, traditional teaching methods often fail to actively engage students or encourage real-world application of environmental concepts. This project proposes a Gamified Environmental Education Platform designed for schools and colleges to make environmental learning interactive, engaging, and effective. The primary objective of the project is to enhance students’ understanding of environmental issues such as climate change, waste management, biodiversity conservation, and sustainable practices through gamification techniques. The system uses game-based learning elements including points, levels, quizzes, challenges, rewards, and leaderboards to motivate participation and continuous learning. The platform follows a user-centric approach where students interact with educational content through tasks and simulations, while educators can track performance and engagement. The system is developed using modern web technologies, ensuring accessibility and ease of use. The outcome of the project is an interactive educational platform that improves knowledge retention, increases student participation, and promotes responsible environmental behavior. The proposed solution demonstrates how gamification can be effectively integrated into environmental education to bridge the gap between theoretical learning and practical awareness
Design and Simulation Model of Implantable Wireless Neural Stimulator
Implantable wireless neural stimulators have emerged as a critical technology in modern biomedical engineering due to their ability to eliminate battery dependency and repeated surgical procedures. This paper presents the detailed design and LTspice simulation of an implantable wireless neural stimulator powered using magnetic resonant inductive wireless power transfer. Remotely powered IMDs that enable safer and smaller neural interfaces are especially useful to freely moving animals and human subjects. Even more so for chronic applications, since rigid tethered electrodes suffer from micromotion, which results in tissue inflammation and scar formation around the electrodes. The simulation model is tailored for the development of neurostimulators characterized by swift settling times, intended for applications in the treatment of conditions such as Parkinson's disease, chronic pain, epilepsy, etc. The proposed system integrates a high-efficiency buck converter, Class-E inverter, four-coil resonant wireless power transfer link, Class-E rectifier, and a monostable multivibrator-based pulse generator. The system is designed to operate at 150 kHz to achieve optimal power transfer efficiency while maintaining safe operating conditions for biological tissues. Simulation results demonstrate stable voltage regulation, efficient wireless power delivery, and reliable generation of neural stimulation pulses with a pulse width of 50 ms and amplitude of 4.77 V. The results validate the feasibility of the proposed architecture for long-term implantable neural stimulation applications.
Efficient Resource Scheduling in Cloud Computing Based on QoS Parameters
Cloud computing has emerged as a dominant paradigm for delivering scalable and on-demand computing resources. Efficient resource scheduling plays a critical role in ensuring optimal utilization of cloud infrastructure while meeting Quality of Service (QoS) requirements such as latency, throughput, availability, and reliability. This paper proposes a QoS-aware scheduling approach that dynamically allocates resources based on user-defined service parameters. The proposed system improves performance, reduces execution time, and enhances user satisfaction compared to existing scheduling techniques. Cloud computing has become the backbone of modern digital services, offering scalable and on-demand access to computing resources. However, efficient resource scheduling remains a critical challenge due to heterogeneous workloads and diverse Quality of Service (QoS) requirements. Traditional scheduling approaches often fail to balance performance, reliability, and user satisfaction, leading to resource underutilization and frequent SLA violations.This paper proposes a QoS-aware resource scheduling framework that dynamically allocates cloud resources based on multiple service parameters, including latency, throughput, availability, and cost. The architecture integrates a monitoring feedback loop to adapt scheduling decisions in real time, ensuring responsiveness to changing workloads. Experimental evaluation demonstrates that the proposed system reduces latency by 40%, improves resource utilization by 20%, and lowers SLA violation rates by more than half compared to existing methods. The results validate the effectiveness of incorporating QoS metrics into scheduling algorithms and highlight the framework’s potential to enhance efficiency, reliability, and user satisfaction in large-scale cloud environments. This work provides a foundation for future research in intelligent, sustainable, and adaptive resource scheduling strategies.
Entre Locate: An Intelligent Business Location Recommendation System Based on Spatial Analytics and Data-Driven Decision Support
Choosing the right location is one of the most important decisions for the success of a new business. Many entrepreneurs still rely on manual surveys and personal judgment, which can be time-consuming and inaccurate. This paper presents EntreLocate, a smart business location recommendation system designed to support entrepreneurs with data-driven insights. The system uses real-world location data to analyze existing business patterns and identify areas with high business potential. After cleaning and organizing the data, similar locations are grouped to highlight promising zones for new ventures. The results are displayed using interactive maps, making them easy to understand and interpret. In addition, EntreLocate includes a profit estimation feature that considers rental costs and other expenses to help users evaluate financial feasibility. Experimental results show that the proposed system effectively supports business owners in selecting suitable and profitable locations, offering a practical and scalable solution for modern business planning
Towards Sustainable Forest Monitoring: Efficient Net-Based Animal Species Identification and Intrusion Detection
Deep learning has emerged as a powerful domain for automated wildlife monitoring by enabling hierarchical feature learning and scalable real-time analysis from visual data. However, existing wildlife monitoring approaches that rely on traditional camera traps and conventional CNN-based methods often face limitations in accurately detecting and classifying species under complex forest conditions. These methods struggle with reliable recognition of visually similar species and exhibit reduced performance when identifying rare animals. To address these drawbacks, this paper proposes a Forest Surveillance System based on EfficientNet deep learning architectures. The proposed system leverages EfficientNet compound scaling to achieve efficient feature extraction with reduced computational cost, improving the recognition of both common and rare species. Additionally, the system integrates a human intrusion detection mechanism to provide early alerts for unauthorized entry into protected forest areas, thereby enhancing wildlife conservation and forest security.
Analysis and Design of Rigid Pavement in Submerged Condition
A pavement structure causes huge expense for their rehabilitation and maintenance because of flooding or improper drainage conditions. It has been in practice from the evolution of art to science design of rigid pavements is defined by an important experimentation phenomenon. In rigid pavements the cement concrete slab is placed either on a single layer of granular material or directly on the sub grade. In rigid pavement design, sub grade strength is obtained by modulus of sub grade reaction (K). It is determined by plate load tests of the foundation defined as the pressure per unit deflection. Temperature variations during day and night time causes temperature stresses due to curling occurs for cement concrete slabs. Due to seasonal temperature variations the concrete slab is subjected to contraction and expansion. Pavement causing damage due to the cyclic load applications of vehicles stressed are induced on the slab. In this study, an analysis is carried out to determine the effect on sub grade strength and its properties during pavement submergence period. An existing pavement structure suggested by the Indian Roads Congress standard is analyzed and compared with its submerged condition. New pavement sections were designed using the material properties under submerged cases, and fatigue analysis is done through IRC method.
Post-Implementation Evaluation and System Optimization: An Analytical Study of Performance and Sustainability
Implementation is a very important step in any research and development project where ideas are put into practice by converting the theoretical knowledge into practical systems or solutions. The true gauge of success however is the effectiveness of a system after implementation. This report contains a post-implementation analysis of a system that has been implemented, and its functionality, flexibility, and sustainability. Some of the key performance indicators (KPIs) identified in the research include accuracy, user satisfaction, scalability, and responsiveness of the system. The evaluation of comparing the pre-implementation expectations and the post-implementation results has been performed to identify the areas that can be improved and optimized. Based on the findings, it is indicated that efficiency requires continuous monitoring and feedback loops that would allow reaching long-term project goals. This research finds that the post-implementation evaluation should not be termed as the final stage of any project but a continuous learning, adapting and improving process which decides the true success of any model developed.
Space structures are a common element in modern architecture because of their expressive form and intrinsic geometric complexity, which allow architects to produce designs that are both aesthetically pleasing and structurally sound. The majority of studies and research on space structures have traditionally concentrated on their structural behaviour, highlighting elements like geometric stability, load distribution, and material efficiency. However, it is becoming more and more crucial to look at space structures from both an engineering and an architectural design standpoint as they continue to acquire traction in architectural practice. In the present study. Researches on dynamic analysis of space structures were widely carried out across the world. The study on structural behavior of grid, domes, vaults which are subjected to earthquake were carried out through many analytical and experimental works. The present work focuses on the study of orientation of stiffness of the supporting roof, and the effect of peripheral cross-bracings on overall lateral response. Further, dynamic analysis has been carried out for grid space structure with different horizontal bracings. Finally, criticality and its locations in various configurations of the space structures are identified. Based on the results and discussions, it is concluded that, the presence of horizontal and vertical bracings will resist the lateral load efficiently and particularly in model 5 where cross bracings are provided. From linear time history analysis, it can be concluded that, the presence of fixed base, vertical bracings and horizontal bracings has significant effect on the vibration characteristics. Presence of horizontal and vertical bracings will contribute significantly in resisting the lateral load of grid space structures.
Improving Biometric Security Through Multimodal Fusion and Deep Hashing
Biometric security technologies are increasingly important for protecting sensitive information and securing access control. There are inherent problems related to spoofing, privacy and data security in traditional monomial biometric systems. In this paper, we proposed a novel deep learning framework to enhance the biometrics security by using multispectral face, iris and fingerprint information. Combining deep hashing into the proposed fusion framework, a strong binary multimodal latent representation is generated which is robust in presence of fake attempts. The proposed approach also integrates a hybrid security framework (combining cancellable biometrics and secure sketch method) for improving security of biometric templates. Furthermore, deep auto encoder algorithm is applied for feature extraction to get improved encoded features in order to boast security. The efficacy of the approach is demonstrated on a multimodal face, iris and fingerprint biometric database, resulting in improved performance along with enhanced privacy through cancelability and unlink ability of biometrics templates. Deep hashing function is also tested on an image retrieval dataset task as well standard one where the network structure could be applied’re used and it shows similar adaptability.
A Miniaturized High Fractional Bandwidth Microstrip Antenna for Wide-Band Application
This scholarly work presents the design of a compact microstrip antenna for transmitting and receiving signals. The antenna is optimized using parametric analysis and simulated using HFSS 15 on a 1.6-mm FR4 substrate with a dielectric constant 4.4 and a tangent angle of 0.02. It offers a wide impedance bandwidth of 8.5 GHz to 19.6 GHz, with a fractional bandwidth of 79%. The reflection coefficient of the antenna is remarkable, with values of -66.61 and -46.20 dB, and it has a gain of 3.42 dB. Its radiation patterns, both in the E- and H-plane, are observed, and it is suitable for wireless communication. The proposed antenna is ideal for various applications, including mobile communication devices, wireless networks, and satellite communication systems.
Sustainable Concrete with Bethamcherla Waste Stone and Polyvinyl-Alcohol: Mechanical Properties and NDT Evaluation
Concrete is the most widely used construction material globally, valued for its strength, durability, and versatility. With the increasing demand for sustainable construction practices, the search for alternative materials that maintain performance while conserving resources has become essential. This study explores the development of M20 and M50 grade concrete using partial replacement of conventional coarse aggregates with Bethamcherla stone at varying levels of 10%, 20%, 30%, 40%, and 50% by weight. Bethamcherla stone, known for its distinct physical characteristics, is investigated for its potential to enhance resource efficiency in concrete production. Additionally, a self-curing agent, Polyvinyl Alcohol (PVA), is incorporated at dosages of 0.03%, 0.06%, 0.12%, and 0.24% by weight of cement. PVA aids in internal moisture retention, reducing the need for external water curing particularly beneficial in water-scarce regions. The performance of the modified concrete is evaluated through tests on fresh properties such as workability and slump, as well as hardened properties like compressive strength using rebound hammer and ultrasonic pulse velocity tests conducted at 7, 14, and 28 days. The findings aim to support the development of environmentally conscious concrete that meets structural and performance standards while addressing resource and water management challenges.
Integrating Machine Learning Models with Swift for Personalized User Experiences in iOS Applications
The increased demand for the development of adaptive and intelligent mobile apps has led to the rapid implementation of machine learning models in native iOS development, specifically using Swift. In this review, Swift programming and machine learning are discussed as coming together to enable real-time personalization in iOS applications. Using the power of on-device inference, frameworks such as CoreML and SwiftData, along with powerful API solutions, developers can build user-centric experiences that are secure, responsive, and contextual. This paper focuses on recent progress in emotion-aware design, which combines machine learning methods for identifying and reacting to the emotional state of users in real time through the analysis of inputs such as facial expressions, voice tone, typing behavior, and interaction patterns. Mobile deep learning and cross-platform deployment strategies are also discussed as shaping the future of personalized applications. Through an analysis of emerging trends such as federated learning and topic modeling, this review highlights the robustness of Swift-based development in providing a solid foundation for scalable, privacy-compliant, and intelligent user experiences. The results indicate that the convergence of Swift and machine learning is likely to characterize the next generation of personalization in mobile software development.
Thermal Performance Study of an Electric-Motor Using Innovative Indirect Cooling Methods
A 70 kW Permanent Magnet Synchronous Motor (PMSM) is selected for the present thermal management study, where detailed loss calculations including stator iron losses, copper losses, rotor and magnet losses are applied as distributed heat sources in the simulation. A mesh sensitivity analysis is performed, and an optimized mesh configuration with 14 prism layers is adopted to accurately resolve near-wall heat transfer. The numerical model is validated using an energy balance, ensuring the accuracy and reliability of the thermal predictions. Two indirect liquid-cooling strategies helical and meander cooling channels are evaluated using 3D steady state CFD simulations. Although the Helical channel demonstrates approximately 20% higher thermal performance (TPF), the Meander channel is selected for further analysis due to its lower pressure drop, improved flow uniformity, and better suitability for practical motor integration. Thermal enhancement is further explored using BN and CNT nanofluids dispersed in an ethylene-glycol (EG) base fluid. Increasing the BN and CNT nanoparticle concentrations leads to a marked decrease in average temperatures across stator, windings, rotor, and magnets, indicating enhanced heat removal capability. For both nanofluids, the representative maximum reductions achieved are 8.6% at the stator, 7.7% at the slot windings, 7% at the end windings, and approximately 3.2% across the rotor and magnets. An optimal concentration range is observed for both nanofluids. BN shows a clear and substantial improvement up to 6% concentration, beyond which temperature reduction becomes marginal. Similarly, CNT exhibits strong improvement up to 1% concentration, after which further increases result in minimal additional thermal benefit. These results demonstrate that nanofluids significantly enhance PMSM cooling performance, with well-defined saturation limits guiding their effective usage in next-generation EV motor thermal management.
Copyright (c) 2025 https://creativecommons.org/licenses/by-nc/4.0
Interactive Selective Spatial Feature Extractor for Small Object Detection in Challenging Scenes
Small object detection in computer vision has made great strides in accuracy and robustness. However, practical applications are still hampered by two main issues: inaccurate detection of small objects and the difficulty of deploying these models on resource- constrained devices due to their extensive parameters and high computational demands. To address these limitations, we propose the Interactive Selective Spatial Feature Extraction method. Which is designed for efficient small object detection, utilizing a modified C2f-Darknet backbone for robust feature extraction and an attention mechanism to focus on crucial spatial areas. The core innovation of this technique lies in its ability to extract and combine multi-scale information to capture fine-grained details and border content, and an internal interaction mechanism that allows communication and information sharing to selectively attend to the most relevant features while suppressing noise. Proposed a state-of-art system that is evaluated on a publicly available DOTA dataset and VisDrone dataset. The proposed methodology detects the accurate region.
Interactive Selective Spatial Feature Extractor