Asian Journal For Convergence In Technology (AJCT) ISSN -2350-1146

ORGANIZATIONAL AMBIDEXTERITY AND INNOVATION: BALANCING EXPLOITATION OF EXISTING CAPABILITIES WITH EXPLORATION OF NEW OPPORTUNITIES FOR LONG-TERM SUCCESS

2026-04-17T07:10:44-04:00

Organizational ambidexterity has emerged as a critical capability for sustained competitive advantage in today's rapidly evolving business environment. This research examines how organizations balance exploitation of existing capabilities with exploration of new opportunities to achieve long-term innovation success. Through analysis of contemporary empirical data and performance metrics, this study demonstrates that ambidextrous organizations exhibit significantly higher performance outcomes (r = 0.76, p < 0.01) compared to their non-ambidextrous counterparts. The research reveals that 77% of organizations now utilize structured innovation frameworks to balance exploration-exploitation tensions, while companies implementing ambidextrous strategies show 25% higher innovation performance and 19% better financial outcomes. Key findings indicate that successful ambidextrous organizations employ contextual differentiation, dynamic resource allocation, and integrated innovation management systems to navigate the inherent paradoxes between stability and change, efficiency and flexibility.

THE IMPACT OF DIGITAL TRANSFORMATION ON STRATEGIC BUSINESS PERFORMANCE AMONG FORTUNE 500 COMPANIES

2026-04-17T07:16:49-04:00

This research examines the relationship between digital transformation initiatives and strategic business performance outcomes among Fortune 500 companies from 2020 to 2024. Through comprehensive analysis of realworld data from 147 Fortune 500 implementations, this study reveals that companies investing 5% or more of their total budget in systematic digital transformation achieve an average 340% ROI within 18 months. The research demonstrates that 73% of Fortune 500 companies have moved beyond pilot programs to full-scale implementation, with enterprise AI transformation generating an average of $2.4 million in annual savings. Key findings indicate that top-performing organizations achieve 300-500% ROI within 24 months through strategic focus and systematic execution, while companies failing to embrace digital transformation face significant competitive disadvantages, with 52% of Fortune 500 companies since 2000 either going bankrupt, being acquired, or ceasing to exist due to digital disruption.

ARTIFICIAL INTELLIGENCE IN STRATEGIC DECISION-MAKING COMPARED TO TRADITIONAL PLANNING PROCESSES

2026-04-17T07:22:30-04:00

This research examines the transformative impact of artificial intelligence (AI) on strategic decision-making processes compared to traditional planning methodologies. Through analysis of recent empirical data, case studies, and performance metrics, this study reveals that AI-enhanced decision-making offers significant improvements in speed, accuracy, and scalability over conventional approaches. The research demonstrates that organizations adopting AI in strategic planning achieve up to 50% improvement in forecast accuracy and 95% enhancement in decision precision. However, traditional methods still maintain relevance in contexts requiring human intuition and ethical considerations.

CRISIS LEADERSHIP AND ORGANIZATIONAL RESILIENCE DURING GLOBAL DISRUPTIONS

2026-04-17T07:28:41-04:00

This research examines the relationship between crisis leadership effectiveness and organizational resilience during global disruptions from 2020-2024, with particular focus on the COVID-19 pandemic, supply chain crises, and geopolitical conflicts. Using a mixed-methods approach analyzing data from 301 organizations across diverse sectors, this study identifies critical leadership competencies and organizational capabilities that enable successful navigation of unprecedented challenges. Results demonstrate that organizations with transformational crisis leadership achieved 52% better recovery performance and 34% higher resilience scores compared to those with traditional leadership approaches. Key findings include the identification of seven core crisis leadership dimensions: compassion and care, openness and communication, adaptiveness, resilience and courage, decisiveness, consultation and collaboration, and employee empowerment. The study reveals that 93% of senior executives now prioritize supply chain flexibility and resilience, while 82% have increased IT spending for digital transformation. These insights provide evidence-based frameworks for developing crisis-ready leadership and building organizational resilience capabilities essential for thriving amid future global disruptions.

INNOVATION STRATEGY IMPLEMENTATION IN MATURE INDUSTRIES THROUGH MULTI-CASE ANALYSIS

2026-04-17T07:34:15-04:00

This paper investigates innovation strategy implementation patterns across mature industries through comprehensive multi-case analysis. Drawing from recent data spanning 2020-2024, this study examines how traditional sectors including automotive, manufacturing, and pharmaceuticals navigate digital transformation challenges. The research analyzes innovation investment trends, strategic frameworks, and implementation outcomes across 45 case studies from established industries. Findings reveal that mature industries face distinct challenges in innovation adoption, requiring specialized approaches that balance legacy system integration with emerging technologies. The study identifies four key innovation archetypes: Digital Integrators, Progressive Adapters, Strategic Innovators, and Legacy Modernizers. Results indicate that successful innovation implementation depends on organizational readiness, strategic alignment, and stakeholder engagement rather than technological sophistication alone.

Integrating Policy-Based Access Control with Chaotic Cryptography for Enhanced Security

2026-05-26T05:39:18-04:00

These Current cryptographic techniques are based on number theoretic or algebraic concepts. Chaos is another paradigm, which seems promising. Chaos is an offshoot from the field of nonlinear dynamics and has been widely studied. A large number of applications in real systems, both man-made and natural, are being investigated using this novel approach of nonlinear dynamics. The important characteristics of chaos are its extreme sensitivity to initial conditions of the system. The policy-based cryptography allows performing of the policy enforcement while respecting the data minimization principle. Such ’privacy-aware’ policy enforcement is enabled by two cryptographic primitives: policy-based encryption and policy-based signature.

Internet of Things (IoT) in Healthcare: Applications, Challenges, and Future Prospects

2026-05-26T05:47:08-04:00

By enabling cutting-edge technologies that improve patient care, expedite medical workflows, and encourage individualized treatment, the Internet of Things (IoT) has emerged as a significant force revolutionizing healthcare. A variety of IoT applications in healthcare are examined in this study, such as asset management, intelligent healthcare facilities, smart medical equipment, and remote patient monitoring. Important topics like data security and privacy dangers, interoperability problems, financial limitations, and ethical concerns are also covered. The study presents a fair assessment of how IoT may impact healthcare in the future by taking into account both its advantages and disadvantages. It also suggests ways to deal with present issues.

Development of a Virtual Learning Environment for Enhancing User Engagement and Course Completion Rates

2026-05-26T06:04:44-04:00

This paper presents the design and implementation of a Virtual Learning Environment (VLE) aimed at enhancing user engagement and improving course completion rates in higher education institutions. The proposed system integrates user management, course handling, learning and assessment tools, certificate generation, and payment gateway integration into a secure and scalable web-based platform. Developed using modern web technologies and Agile methodology, the system ensures flexibility, usability, and performance optimisation. Experimental testing demonstrates strong usability scores, stable performance under concurrent load, and improved administrative efficiency. The developed VLE addresses limitations in existing platforms by providing an intuitive, modular, and data-driven digital learning ecosystem.

Optimizing Convolutional Neural Networks with Nature-Inspired Algorithms for Diabetic Retinopathy Classification

2026-05-26T06:19:16-04:00

Diabetic Retinopathy (DR) is a progressive eye disorder caused by prolonged diabetes and is recognized as one of the leading causes of vision impairment and blindness worldwide. The disease affects the small blood vessels of the retina, resulting in structural damage that gradually deteriorates visual capability. Early detection and continuous monitoring of DR are essential to prevent severe complications and permanent vision loss. However, manual examination of retinal fundus images by ophthalmologists is time-consuming and requires significant expertise, especially when dealing with large-scale screening programs. Consequently, automated diagnostic systems based on artificial intelligence have gained considerable attention in recent years. Advancements in medical imaging and machine learning have enabled the development of intelligent models capable of detecting retinal abnormalities with high precision. The classification of DR severity levels from digital fundus images. To further enhance the performance of the CNN model, Nature-Inspired Algorithms (NIAs) are incorporated as optimization techniques. These algorithms mimic natural evolutionary and behavioral processes to improve model parameters and learning efficiency. Several NIAs are investigated in order to identify the most effective optimization strategy for improving classification performance.

Among the evaluated approaches, Particle Swarm Optimization (PSO) demonstrated superior capability in optimizing the CNN architecture by effectively adjusting network parameters and improving feature learning. The proposed hybrid CNN–PSO model achieved an overall classification accuracy of 98.83%, outperforming several existing state-of-the-art methods reported in the literature. The results highlight the effectiveness of integrating nature-inspired optimization strategies with deep learning frameworks for medical image analysis. This approach offers a reliable and efficient solution for automated DR screening and can significantly support ophthalmologists in early diagnosis and clinical decision-making.

Privacy-Preserving Data Analysis: Perform data Analysis on Encrypted Data Stored in the Cloud

2026-05-26T06:32:56-04:00

Balancing data security with analytical performance in the analysis of encrypted data has proven to be a challenging task. An initiative called Privacy-Preserving Data Analysis uses Python to conduct safe data analysis on cloud-stored encrypted data. The purpose of this project is to reduce the possibility that private data may be discovered when analyzing the data. Many methods are used to do this, including secure multiparty computation and homomorphic encryption. The project entails putting in place a safe data processing pipeline that protects the confidentiality of the data that is stored. With homomorphic encryption, sensitive data can be computed directly on encrypted data, eliminating the need for decryption and revealing it. In this project, the Python programming language serves as the primary development tool. It's a great option because of its widespread libraries, community support, and ease of use. The project entails utilizing Python to create privacy-preserving protocols and encryption techniques and to integrate them with cloud storage providers. By employing methods like homomorphic encryption and secure multiparty computation and implementing them with the Python programming language, this project seeks to offer a safe and private way to analyze sensitive data that is kept in the cloud.

Resume Builder and Analyzer

2026-05-26T07:05:44-04:00

The increasing adoption of digital technologies in recruitment has fundamentally altered the way organizations evaluate job applications. With employers receiving large volumes of resumes for each vacancy, automated screening mechanisms have become essential for maintaining efficiency and consistency in the hiring process. Applicant Tracking Systems (ATS) are now widely used to parse, filter, and rank resumes based on predefined criteria such as structural organization, keyword relevance, and alignment with job requirements. While these systems significantly reduce recruiter workload and accelerate shortlisting, they also contribute to the rejection of many qualified candidates whose resumes fail to meet automated evaluation standards rather than reflecting a lack of skills or experience.

A major challenge faced by job seekers is limited awareness of how ATS platforms interpret resume content. Conventional resume-writing practices often emphasize visual design and creative formatting, which may interfere with machine-based parsing. Elements such as complex layouts, nonstandard section headings, and insufficient keyword optimization can prevent ATS software from accurately extracting candidate information. As a result, resumes that are otherwise strong may be filtered out during the initial screening stage. This growing dependence on automated recruitment has created a demand for intelligent tools that assist candidates in producing resumes optimized for ATS evaluation while maintaining professional quality and clarity.

This paper presents the design and development of a Smart AI Resume Builder and Analyzer, a web-based platform intended to support structured resume creation and intelligent resume evaluation. The proposed system integrates resume generation, ATS-focused analysis, quantitative scoring, and personalized feedback within a unified framework. The resume builder component guides users through a standardized data entry process that captures personal details, educational background, professional experience, skills, certifications, and career objectives. This structured approach reduces formatting inconsistencies and ensures compliance with commonly accepted ATS-friendly resume layouts. Resumes generated by the system are exported in widely used formats such as PDF and DOCX to ensure compatibility with online job portals and recruitment platforms.

The analyzer component employs a hybrid evaluation strategy that combines rule-based assessment with Artificial Intelligence and Natural Language Processing techniques. Rulebased analysis focuses on formatting simplicity, section ordering, header consistency, and structural compliance with ATS parsing constraints. AI-driven analysis examines resume content for keyword relevance, semantic similarity to target job descriptions, grammatical accuracy, readability, and overall coherence. The system produces objective metrics including ATS compatibility scores, keyword match percentages, and overall resume quality ratings, offering users clear insights into resume effectiveness.

A distinguishing feature of the proposed system is its emphasis on personalized and role-specific feedback. Instead of providing generic suggestions, the analyzer identifies missing or underrepresented skills, weak experience descriptions, and content gaps relative to selected job roles. Actionable recommendations are generated to help users revise and enhance their resumes in a targeted manner. The platform also provides a centralized dashboard that enables users to track resume versions, compare evaluation scores across iterations, and assess measurable improvements over time. This feedback loop supports continuous optimization and informed decisionmaking during the job application process.

To evaluate system performance, experimental testing was conducted using a dataset of sample resumes across multiple job profiles, including technical and entry-level roles. Each resume was analyzed before and after applying system-generated recommendations. The results indicate a substantial improvement in ATS compatibility scores, keyword relevance, and readability metrics following optimization. These findings suggest an increased likelihood of resumes successfully passing automated screening stages. User observations further indicated reduced time spent on resume preparation and greater confidence in job applications.

The proposed Smart AI Resume Builder and Analyzer offers a practical, accessible, and scalable solution for modern erecruitment challenges. By combining structured resume creation with intelligent analysis and transparent feedback, the system enhances resume quality and supports equitable participation in automated hiring processes. The platform is suitable for individual job seekers as well as institutional deployment in academic and training environments. Future enhancements include multilingual support, advanced ATS simulation, and integration with job portals to further strengthen job-role matching and employability outcomes.

Enhancing Accessibility with Gesture Controlled Virtual Input

2026-05-26T07:05:09-04:00

This paper focuses on the development of a gesturecontrolled virtual mouse and keyboard system that allows users to interact with a computer without using physical input devices. The system is designed using a standard webcam to capture realtime hand movements. These movements are processed with the help of computer vision techniques using MediaPipe and OpenCV to detect hand landmarks and recognize different gestures. The recognized gestures are then converted into mouse and keyboard actions such as cursor movement, clicking, scrolling, and text input using the PyAutoGUI library. The main aim of this work is to provide a simple and affordable solution that can improve accessibility, especially for users who face difficulty in using traditional mouse and keyboard devices. The system works in real time and does not require any special hardware or pre-trained datasets. Experimental results show that the proposed system performs well under normal lighting conditions and provides smooth and responsive control for basic computer operations. This project demonstrates that gesturebased virtual input can serve as a practical and low-cost alternative to conventional input devices.

Smart Expense Tracker with OCR

2026-05-27T01:30:29-04:00

Effective personal finance management has become increasingly important in the modern digital environment. However, many existing expense tracking systems still depend on manual data entry, which can be time-consuming and prone to human errors. To overcome these challenges, this work proposes a Smart Expense Tracker that integrates Optical Character Recognition (OCR), voice input, and intelligent data analysis to automate the expense tracking process. The proposed system allows users to upload receipt images, from which the OCR module automatically extracts important information such as the transaction date, amount, and merchant details. In addition, voice commands provide an alternative method for quickly recording expenses without manual typing. The application presents financial insights through interactive visualizations including pie charts and trend graphs, enabling users to better understand their spending behaviour. Additional features such as budget alerts, data export in CSV or PDF format, and AI-based spending insights further enhance financial awareness and decision-making. By combining automation with intelligent analysis, the proposed system simplifies the process of managing personal expenses while improving accuracy and user convenience.

A systematic Survey on Brain Tumor Detection using Deep Learning Techniques

2026-05-27T03:38:07-04:00

Technological advancements have significantly transformed various domains, particularly the field of healthcare. The integration of advanced computational techniques in medical applications has improved the diagnosis and treatment of critical diseases such as brain tumors. Brain tumors are among the most serious and life-threatening neurological disorders, requiring accurate and early detection for effective treatment. Automated brain tumor detection systems are designed to differentiate between normal and abnormal brain tissues using medical imaging data. In recent years, medical image processing techniques, especially those based on Magnetic Resonance Imaging (MRI), have played a vital role in automating tasks such as feature extraction, segmentation, and classification. These approaches enable faster and more reliable tumor identification. A large number of studies have explored various methods for brain tumor detection using machine learning and deep learning techniques, with a primary focus on segmentation and classification tasks. This paper aims to provide a comprehensive analysis of brain tumor detection and classification methods developed between 2019 and 2025. The study evaluates widely used approaches and examines the effectiveness of Computer-Aided Diagnosis (CAD) systems in improving diagnostic performance. To ensure a broad and unbiased review, relevant research articles were collected from multiple scientific databases, including IEEE Xplore, ScienceDirect, PubMed, Google Scholar, and ResearchGate.

Public–Private Key Cryptography Based Secure Identity and Transaction Management in Blockchain

2026-05-27T03:52:53-04:00

A Blockchain is a new type of applications and solutions for identity management, trust, and data monetization. Blockchain is fundamentally a conveyed database of records or digital occasions that are executed and shared among taking an interest parties. The most theory is that the blockchain provides the basis for a collective consensus in the electronic digital environment. It opens the entryway for creating a majority rule open and versatile advanced economy from a centralized one. Blockchain is that the technology backbone of Bitcoin. The blockchain provides a complete and testable database of any single exchange that's ever been generated. Bitcoin, the digital money anonymous peer-to - peer, is the latest indication that utilizes blockchain technologies.The explanation bitcoin's going to be disturbing beginner point is which mathematic work and innovation behind it redefines the construct of possession. This paper purposes the mathematical solution behind Blockchain technology and created a few instinct regarding the mathematical relationship that exists between public and private keys get to a private-key & public-key ECDSA combination is essence of how Bitcoin and other apps of blockchains work. The ECDSA takes isolated strategies for signing, verification as well as confirmation. Every method it such calculation comprising a few mathematical functions of numbers. The able to sign algorithm makes use of private-key & public-key is utilized during confirmation process.

Alzheimer’sclassification Using Deep Learning

2026-05-27T04:13:20-04:00

Alzheimer's disease gradually damages brain functions such as memory, putting enormous load on healthcare systems. This study uses deep learning algorithms on MRI brain images to create a system for detecting Alzheimer's disease in its early stages and aiding in quick treatment. The model employs trained convolutional neural networks (CNNs) for greyscale MRI images of the brain and categorises patients into four categories: No Impairment, Very Mild Impairment, Mild Impairment, and Moderate Impairment. To improve the framework's learning and lessen the consequences of class imbalance, data augmentation, class weighting, batch normalization, dropout regularization, and other approaches are utilized. Brain Alzheimer scans captured in a Stream lit-based system are predicted by the algorithm. The scans are normalised, scaled, converted to greyscale, and put through a number of processes before being categorised..The scans are grayscaled, normalised, shrunk, and put through a number of adjustments before categorisation. Both the Alzheimer's disease stage and the prediction's confidence score are output by the system. With this integrated structure, the model reduces the bias common in manual patient diagnosis for timely patient treatment by using AI's ability to provide appropriate and quick clinical recommendations.

Real-Time E-Commerce Customer SegmentationUsing Hybrid Clustering and Deep Learning Techniques

2026-05-27T04:27:47-04:00

Customer segmentation plays a crucial role in ecommerce personalization, marketing optimization, and customer retention. Traditional segmentation techniques, such as K-Means clustering and RFM modeling, often fail to handle real-time data, dynamic customer behavior, and scalability challenges. Moreover, existing methods lack predictive capabilities, limiting their ability to anticipate future customer actions.

This research proposes a real-time AI-driven customer segmentation framework that integrates hybrid clustering (KMeans, DBSCAN, and GMM) with predictive analytics (XGBoost, LSTM) to enhance segmentation accuracy. Apache Kafka and Spark Streaming enable real-time customer segmentation, while Google BigQuery and Dask ensure scalability for largescale datasets. The framework dynamically selects the best clustering algorithm using Silhouette Score and Davies-Bouldin Index, addressing the limitations of singlemodel approaches. Additionally, machine learning models predict customer lifetime value, future purchases, and retention probabilities, providing actionable insights for personalized marketing strategies.

Experimental results demonstrate that the proposed hybrid clustering approach outperforms traditional methods, reducing segmentation errors by 40%, improving predictive accuracy by 15%, and enabling real-time customer insights. The findings indicate that integrating streaming analytics, hybrid clustering, and AI-driven predictive modeling can significantly enhance customer segmentation strategies for modern e-commerce businesses.

DeFake StyleGAN Deepfake Detector for Facial Images

2026-05-27T05:12:50-04:00

This research introduces DeFake, a system designed to detect highly realistic facial images generated by advanced AI techniques like StyleGAN. As AI-generated images become increasingly convincing, identifying fakes has become a vital challenge. DeFake employs deep learning models that analyze subtle patterns and artifacts left behind during the image generation process. By examining image details and hidden frequency patterns, our system can accurately distinguish between real images and AI-created fakes, even when they look almost identical to the human eye. This technology is especially useful for verifying digital content, supporting law enforcement, and enhancing cybersecurity efforts. Our approach not only detects fake images but also helps trace their origin, addressing the critical need to protect the integrity of visual media in the digital age. As AI technologies evolve, tools like DeFake are essential to maintaining trust and authenticity in digital content.

Automated Wheat Disease Detection Using Deep Learning: An Object Detection and Classification Approach

2026-05-27T05:53:25-04:00

Wheat is among the key staple crops in the world, greatly influenced by plant diseases. As global population grows, there is urgency to establish sustainable agricultural methods requiring early and accurate diagnosis of wheat diseases. This paper examines deep learning solutions for automated wheat head disease detection using two benchmark datasets: the Global Wheat Head Detection (GWHD) dataset for object detection, and the Large Wheat Disease Classification Dataset (LWDC) for disease classification. YOLOv4 trained on GWHD achieves a mean Average Precision (mAP) of 91%. Transfer learning with COCO pre-trained weights improves multi-class detection on LWDC. Among five CNN models evaluated — VGG19, ResNet50, EfficientNet-B0, NASNetMobile, and NASNetLarge — VGG19 achieves the best F1 score of 95%. The combined YOLOv4 + VGG19 pipeline demonstrates strong potential for real-time wheat disease monitoring in precision agriculture.

Improved Framework for Identifying Lung Nodules in CT Images Using Deep Learning Techniques

2026-05-27T06:28:24-04:00

This article details a sophisticated deep learning paradigm engineered for the autonomous localization and characterization of pulmonary nodules in Computed Tomography (CT) imaging. Whereas traditional computer-aided diagnosis (CAD) systems are frequently limited by elevated false-positive rates and a failure to integrate global spatial dependencies, the proposed architecture employs a synergistic hybrid approach. Specifically, it leverages Enhanced Convolutional Neural Networks (CNN) for fine-grained local feature extraction in conjunction with Vision Transformers (ViT) to facilitate comprehensive global contextual modeling.

Validated against the LIDC-IDRI and LUNA16 benchmarks, the methodology incorporates rigorous preprocessing protocols, including anisotropic diffusion filtering and the Synthetic Minority Oversampling Technique (SMOTE) to mitigate class imbalance. Empirical evaluations yield a classification accuracy of 98.34%, representing a substantial reduction in diagnostic discrepancies and providing a robust foundation for early-stage clinical intervention.

AI Driven Deep Neural Network forGesture and Sign Language Recognition-A Survey Approach

2026-05-27T06:36:09-04:00

Sign language recognition plays a key role in bridging communication gaps between hearing-impaired individuals and society. This survey paper offers a comprehensive examination of AI-driven deep neural network (DNN) architectures developed for the purpose of gesture recognition and sign language identification. Human communication relies profoundly on non-verbal channels, and for individuals with hearing or speech impairments, sign language constitutes the central mode of expressing thought, need, and emotion. Bridging the communication divide between the Deaf community and the hearing world through intelligent automated systems represents one of the most socially meaningful directions in contemporary artificial intelligence research. This paper consolidates findings from a wide spectrum of studies spanning classical machine learning, convolutional neural networks, recurrent sequence models, attention-based transformers, graph convolutional networks, and multimodal fusion architectures.

Integrating Machine Learning and Deep Learning for Multiclass Mortality Prediction in Healthcare Data

2026-05-27T06:45:46-04:00

The prediction of mortality in cardiovascular patients demands models that balance accuracy, interpretability, and robustness. The current research work implementation presents a unified system that integrates ensemble machine learning (ML) approaches with complex deep learning (DL) architectures for multiclass mortality prediction. The ML component employs Random Forest, Gradient Boosting, and XGBoost classifiers, each evaluated through stratified sampling, confusion matrices, and precision–recall analysis. These models establish strong baselines, with XGBoost achieving the highest accuracy among the ensemble group. Building upon this foundation, the DL component introduces fully connected neural networks (FCNNs) enhanced with dropout and batch normalization, convolutional neural networks (CNNs) adapted for tabular data, and an FCNN augmented with attention mechanisms to capture feature importance. The CNN model demonstrated superior generalization, attaining validation accuracy above 96%, while the attention‑based FCNN provided interpretability without compromising predictive strength. Comparative visualization of accuracy curves and error distributions underscores the complementary strengths of ML and DL approaches. This hybrid pipeline not only advances methodological rigor but also contributes to reproducible AI practices in healthcare, offering a scalable result for clinical choice validation in mortality risk assessment.

Waste Segregation through AI

2026-05-27T06:56:27-04:00

Growing volumes of municipal solid waste, driven by rapid urbanization, have intensified demands for scalable and intelligent material classification tools. This paper introduces a purely browser-based waste sorting application that leverages Artificial Intelligence to categorize discarded items into three groups: Biodegradable/Wet, Recyclable/Dry, and Hazardous. The system deploys a TensorFlow.js inference model trained via Google Teachable Machine, executing all predictions locally within the user’s browser without transmitting any data to an external server. The front-end stack, built with HTML5, CSS3, Tailwind CSS, and Daisy UI, supports live webcam capture and static image uploads, delivering results within two to five seconds accompanied by color-coded disposal advisories. An embedded feedback channel enables users to flag incorrect predictions, supporting iterative model improvement. Testing across multiple browsers and device categories confirmed reliable classification performance, with plastic materials achieving confidence levels approaching 97.6%. The application is lightweight, privacy-conscious, and deployable without backend infrastructure, making it suitable for municipal, educational, and community-level waste management programs.

Fake News Detection Using Web Scraping

2026-05-27T07:05:59-04:00

Fake news has become a major challenge in the digital age due to the swift distribution of information through social media and online platforms. The increasing volume of deceptive or inaccurate content can influence public opinion, create confusion, and damage trust in reliable sources. Traditional manual verification methods are time-consuming and inefficient when dealing with large volumes of data. This paper presents a Fake News Detection System that utilizes machine learning techniques to automatically classify news content as genuine or fake. The system analyzes textual features from news content through NLP-based methods and applies classification algorithms to determine credibility. By leveraging automated detection mechanisms, the system aims to improve information reliability and reduce the spread of misinformation across digital platforms.

Edge AI for Real-Time Sign Language Translator

2026-05-27T07:06:14-04:00

Communicating with individuals who are deaf can often be difficult. This research aims to create a system that translates sign language into text and speech in real time. Using a camera, the system captures hand movements and converts them into written or spoken language. It relies on computer vision and a specialized computational model to analyze the hand gestures, interpret their meaning, and translate them accurately.

All processing occurs directly on the device, ensuring fast performance and protecting user privacy, since data does not need to be sent to external servers. In tests, the system was able to correctly recognize about 90% of gestures, even under poor lighting or noisy conditions. This sign language translation system provides an effective tool to help people who are deaf communicate more easily with others.

ASIC Design flow of Image Signal Processor

2026-05-27T07:14:42-04:00

ASIC Design flow of Image Signal Processor is discussed in detail in this paper. ISP is built to deliver high quality videos and images. Architecture is developed as per the specification. The RTL code is written for digital blocks and verified with verification techniques. Synthesis generates netlist of the design. Backend layout plan is carried out and GDSII is generated.

Real-Time Grain Storage Monitoring System

2026-05-27T07:17:25-04:00

Efficient storage of food grains is essential for maintaining food quality and reducing post-harvest losses. Improper storage conditions such as high temperature, humidity, and gas accumulation often lead to spoilage, fungal growth, and pest infestation. This paper presents the design and implementation of an IoT-based Smart Food Grain Storage Monitoring System that continuously monitors environmental conditions inside the grain storage unit. The system utilizes an Arduino Uno microcontroller integrated with DHT22 temperature and humidity sensors and MQ135 gas sensors to detect variations in environmental parameters that may affect grain quality. The collected data is displayed on an LCD module and warning indications are provided through LED alerts when parameters exceed predefined threshold values. The proposed system reduces manual monitoring efforts and helps farmers maintain proper storage conditions. The developed prototype demonstrates a cost-effective and reliable solution for small-scale grain storage facilities, enabling improved grain preservation and reduced post-harvest losses.

A Comprehensive Survey on Deep Learning Methods for Automated Spinal Fracture Detection and Classification

2026-05-27T07:23:09-04:00

Trauma, osteoporosis, or metastatic diseases are the most prevalent causes of spinal fractures, a dangerous medical condition. These fractures have the potential to cause serious neurological problems, if they are misdiagnosed or misclassified, leading to diminished quality of life and persistent pain. Conventional diagnostic methods, including X-rays, computed tomography (CT), and magnetic resonance imaging (MRI), rely on expert interpretation by radiologists. However, manual assessment is time-consuming, subject to inter-observer variability, and may lead to diagnostic inconsistencies, particularly in subtle or complex cases. With advancements in artificial intelligence (AI), deep learning has revealed abundant possibility in automating spinal fracture detection and classification, improving both speed and accuracy. In recent years, deep learning techniques have developed as a dominant tool for automated spinal fracture detection and classification, offering great precision and effectiveness. This survey delivers a broad review of state-of-the-art deep learning models applied to spinal fracture analysis, covering CNNs, transformer-based architectures, and hybrid approaches. We analyze several publicly accessible datasets, preprocessing techniques, model architectures, and evaluation metrics used in the literature. The research gap is examined in the outcome section of this study. Finally, we outline future research directions, emphasizing the need for improved generalization, explainability, and integration with clinical workflows. This survey aims to serve as a useful reference for researchers and clinicians seeking to advance automated spinal fracture diagnosis using deep learning.

IOT-Based Smart Healthcare Data Transmission Using Enhanced Clustering Strategies

2026-05-27T07:23:18-04:00

The rapid growth of Internet of Things (IoT) technologies has significantly transformed healthcare monitoring systems by enabling continuous collection and transmission of patient data. However, efficient and reliable data transmission remains a major challenge due to network congestion, limited energy resources, and scalability issues in IoT environments. This paper proposes an enhanced clustering-based framework to improve healthcare data transmission in IoT networks.In the proposed approach, sensor nodes are initially organized into clusters using a middle-order clustering mechanism to optimize network structure and reduce communication overhead. An efficient data transmission strategy is then applied to select optimal paths within clustered networks. Additionally, a Routing Protocol for Low-Power and Lossy Networks (RPL) based routing mechanism with handshake computation is implemented to enhance communication reliability and extend the network lifetime in multimedia IoT environments.The proposed MOOR strategy is evaluated through simulation under multiple network conditions. Performance metrics such as control overhead and packet delivery ratio (PDR) are analyzed to measure the efficiency of the system. The simulation results demonstrate that the proposed approach achieves improved packet delivery performance and reduced control overhead compared with conventional methods, making it suitable for reliable healthcare data communication in IoT-based monitoring systems.

Thermosense: Dual-Mode Thermoelectric Soldier Suit with Real-Time Ambient Sensing

2026-05-27T07:41:36-04:00

Modern military operations often expose soldiers to extreme environmental conditions such as intense heat, severe cold, and unpredictable weather variations. These conditions can negatively affect the health, endurance, and operational efficiency of military personnel. This paper presents ThermoSense, an advanced wearable soldier suit designed to provide adaptive thermal regulation along with real time monitoring of ambient environmental conditions. The proposed system integrates thermoelectric modules based on the Peltier effect that enable both heating and cooling functions within a single wearable platform. Environmental sensors are incorporated to continuously measure parameters such as temperature and humidity, allowing the system to dynamically adjust thermal conditions according to the surrounding environment. A microcontroller-based control unit processes the sensor data and automatically regulates the thermoelectric modules to maintain optimal comfort for the wearer. The system is designed to be lightweight, energy efficient, and suitable for deployment in harsh environments such as deserts, high altitude regions, and cold mountainous terrains. By combining wearable thermoelectric technology with intelligent sensing and automated control mechanisms, ThermoSense enhances soldier safety, improves thermal comfort, and supports better operational performance in challenging battlefield conditions.

Biomedical Image Processing: Techniques, Applications and Recent Research Trends – A Review

2026-05-27T07:46:01-04:00

Biomedical image processing is an necessary component of modern healthcare systems. Images of medical acquireed from various imaging modalities provide important diagnostic information, but these images often face issues from noise, artifacts, and low contrast. Image processing techniques help improve image quality and enable automated disease detection and clinical analysis. This review paper outlines the fundamental stages in biomedical image processing such as feature extraction, segmentation, preprocessing feature classification and image acquisation. It looks at both modern deep learning methods and conventional processing of images. A comparative discussion of different segmentation approaches and commonly used evaluation metrics is also presented. The paper further highlights current challenges and emerging research directions in biomedical imaging systems.

Smart Automatic Garden Watering System Using Soil Moisture Sensors

2026-05-27T07:51:10-04:00

This project presents a decorative automatic garden watering system implemented without a microcontroller, using only discrete electronic components on a breadboard. The system detects soil moisture using conductive probes and automatically activates a water pump when the soil becomes dry through a transistor-based switching circuit. Once sufficient moisture is restored, the pump is turned off, preventing overwatering and conserving water. LED indicators provide visual status of soil conditions, adding a decorative feature. The circuit uses simple components such as transistors, resistors, relays, and diodes, making the system low-cost, energy-efficient, and suitable for small home gardens and educational applications. The design demonstrates how analog electronics can effectively perform automation tasks without complex programming or digital controllers.

Adaptive IoT‑Enabled Node for Multi‑Parameter Environmental Sensing and Cloud‑Based Analytics

2026-05-27T08:00:31-04:00

Environmental monitoring is increasingly important due to rising pollution levels, climate variability, and sustainability concerns. This paper presents a compact IoT enabled environmental sensing node that integrates a Beetle ESP32‑C6 microcontroller with a DF Robot Fermion multifunctional sensor for continuous measurement of temperature, humidity, barometric pressure, ambient light, and ultraviolet index. The sensed data are periodically transmitted over Wi‑Fi 6 to a cloud analytics platform, where they are stored, visualized, and analyzed for anomaly detection and user notification via web or mobile dashboards. Along with a description of the hardware architecture, communication pipeline, and software implementation, the paper briefly reviews recent smart environmental monitoring solutions that leverage IoT, wireless sensor networks, and data analytics for air quality monitoring, water quality supervision, and precision agriculture. Experimental results from a prototype deployment demonstrate that the proposed design simplifies the realization of distributed, low power monitoring nodes and supports scalable, data driven environmental management in both urban and rural settings.

Multipurpose Smart Gloves for Deaf and Dumb Using Raspberry Pi Pico

2026-05-27T08:21:43-04:00

Communication barriers faced by individuals with hearing and speech impairments significantly limit their ability to interact in society. This paper presents the design and development of a multipurpose smart glove system that translates hand gestures into both text and speech in real time. The system utilizes flex sensors to detect finger movements, and a Raspberry Pi Pico microcontroller to process gesture data. Recognized gestures are mapped to predefined messages, which are displayed on an LCD and converted into audio using a voice module. The proposed solution is cost-effective, portable, and user-friendly, making it suitable for daily use. Experimental results demonstrate reliable gesture recognition with low latency, enhancing communication and independence for differently-abled individuals.

Lora Based Wireless Post Border Security System

2026-05-28T03:03:03-04:00

Border surveillance is one of the most critical and challenging responsibilities in ensuring national security. In situations involving threats such as terrorist infiltration, unauthorized entry, and other illegal activities across borders, the need for advanced and intelligent monitoring systems becomes essential. The proposed project focuses on the development of a smart border security system that incorporates modern technologies to enhance surveillance capabilities. The primary aim of this work is to explain the functioning of the technologies used in the system and how they assist defense personnel in maintaining border security. Continuous monitoring of border areas is necessary to detect and prevent such threats. However, relying solely on human surveillance requires significant manpower and constant vigilance, which may not always be efficient. To address this issue, an automated border surveillance system is proposed to reduce human effort while improving monitoring accuracy. The system is designed to identify suspicious activities and respond by generating alert signals and triggering necessary security mechanisms. A control center can be located at a safe distance from the border, where operators receive alerts and make informed decisions regarding further action.

Empower Her: IoT-Driven Smart Women’s Safety System Using Arduino for Real-Time Protection

2026-05-28T03:15:08-04:00

Women’s safety remains critical amid rising harass-ment cases against physically challenged women. This paper presents EmpowerHer, a wearable IoT-driven safety device using Arduino Uno that integrates GPS tracking, GSM emergency SMS alerts, ESP32-CAM live video streaming, APR33A voice distress, playback, and panic button activation. The compact wristwatch-like system sends precise GPS coordinates.

LONG 15.0995483), plays “Help!” audio, activates vibration feed-back, and provides local buzzer deterrence. Hardware includes
Node MCU WiFi, NEO-6M GPS (162 dBm sensitivity), SIM900A. GSM, relay, LCD I2C display, and vibration motor. Testing confirms reliable SMS delivery, stable video streaming, and continuous system monitoring. This Arduino-based IoT solution delivers cost-effective real-time protection for women, elderly, and physically challenged individuals in urban environments

Neoraksha: Smart Incubator For Preterm Infant Safety

2026-05-28T05:23:23-04:00

Preterm infants are highly vulnerable to environmental fluctuations and medical complications that can arise within minutes if not continuously monitored. Neoraksha is a smart, technology- integrated incubator designed to enhance the safety, stability, and quality of care for premature neonates. This project presents the design and development of an intelligent incubator system that combines real- time physiological monitoring, environmental control, and automated alert mechanisms to support neonatal well-being.

The system incorporates sensors to track critical parameters such as temperature, humidity, heart rate, and oxygen saturation, ensuring the infant remains within clinically recommended thresholds. An embedded microcontroller processes sensor data and regulates thermal and humidity levels through a closed-loop feedback mechanism. Additionally, Neoraksha features IoT-based connectivity, enabling remote monitoring by healthcare professionals and immediate notification in case of abnormalities. A user-friendly interface provides caregivers with comprehensive, actionable insights, thereby reducing response time during emergencies.

Solar Wireless Electric Vehicle Charging System

2026-05-28T06:05:06-04:00

Electric vehicles (EVs) are increasingly being adopted worldwide and are gradually becoming a significant part of modern transportation. In addition to their environmental advantages, EVs help reduce travel costs by replacing conventional fuels with electricity, which is comparatively more economical. However, to further enhance their reliability and practicality, improvements in battery charging technologies are essential. The proposed “solar-based wireless EV charger” utilizes renewable energy to address these challenges. In this system, solar energy is converted into electrical energy and stored in a lead-acid battery. A battery management unit is employed to regulate and manage the stored energy, which is then used to charge electric vehicles. Wireless charging technology operates by transferring power through an electromagnetic field across a certain cases, eliminating the need for physical connections. can be charged either through conventional plug-in methods at charging stations or via wireless power transfer systems. These wireless systems can be implemented as either static charging, where the vehicle is stationary or dynamic charging, which enables charging while the vehicle is in motion. The underlying principle of wireless power transfer in this system is inductive coupling, where energy is transmitted from the source to the vehicle battery through transformer-like windings. This approach offers a promising solution for efficient, contactless, and sustainable charging of electric vehicles.

Solar Agro-Tech Rover

2026-05-28T06:24:14-04:00

The Solar-Based Multi-Proposal Agriculture Robot marks a groundbreaking advancement in modern farming, designed to address the diverse needs of agriculture in India. In response to the demographic significance of agriculture in the country's economy, the robot employs an Arduino UNO R3 as its central control unit. This multifunctional robotic system integrates a range of essential components, including DC motors, relays, ultrasonic sensors, and application-specific tools such as grass cutters, seed sower and water/chemical spray systems. Aiming to propel agricultural mechanization and improve overall productivity, the system control is meticulously orchestrated by the Arduino UNO R3, providing a cost-effective and versatile solution. The integration of DC motors facilitates precise control over wheel movement and cutting operations, thereby enhancing efficiency in the field. Recognizing the need for sustainable energy sources, the robot incorporates solar panels to charge a dedicated battery. This not only ensures uninterrupted operation but also aligns with environmentally friendly practices. This underscores the commitment to safety and reliability, crucial considerations for the widespread adoption of agricultural automation.

Joint 3D Trajectory and High-Dimensional PhaseShift Optimization for Reconfigurable Intelligent Surface (RIS)-Assisted UAV Networks: A Deep Reinforcement Learning Approach

2026-05-28T06:35:02-04:00

This paper investigates the joint optimization of a three-dimensional (3D) rotary-wing unmanned aerial vehicle (UAV) trajectory and the high-dimensional phase-shift vector of a large reconfigurable intelligent surface (RIS) deployed on offshore energy infrastructure. Motivated by India’s emerging offshore wind program, a cascaded air-to-ground/ground-to-air channel model and a propulsion-aware energy consumption model are combined to formulate a long-horizon cumulative energy-efficiency (EE) maximization problem under mobility and unit-modulus RIS constraints. A deep deterministic policy gradient (DDPG) framework is adopted to learn continuous control policies for both UAV motion and RIS phases. Representative plots demonstrate convergence behavior, trajectory characteristics, EE scalability with the number of RIS elements, and throughput sensitivity to UAV speed. A real-world case study is constructed around India’s planned 500 MW offshore wind project site in the Gulf of Khambhat (Gujarat), consistent with national policy and tender documents.

Relay-Based Cooperative Routing for Enhanced Energy Efficiency and Reliability in Wireless Sensor Networks

2026-05-28T06:40:56-04:00

Wireless sensor networks (WSNs) consist of sensor nodes with limited resources, which cooperatively monitor environmental conditions and transmit data to a node at sink. Efficient routing is essential in WSNs to ensure reliable data delivery while minimizing energy consumption. In this work, a Relay-Based Cooperative Routing (RBCR) protocol is proposed to enhance communication reliability and energy efficiency in WSNs. The proposed protocol employs cooperative communication where selected relay nodes assist in forwarding data packets from the source to the destination. To identify optimal relay nodes for packet transmission, an effective relay selection mechanism is used based on residual energy, link quality and distance to the sink. The RBCR protocol performance is evaluated using the NS-2 simulation environment and compared with a conventional routing protocol. Simulation results shows that the proposed protocol achieves high packet delivery ratio (PDR) and throughput by reducing energy consumption and end-to-end delay, thereby extending the overall network lifetime. The results show that the RBCR protocol provides an effective and reliable routing solution for improving performance in the wireless sensor networks.

Experimental Study on PLC–VFD Integration for Real-Time Motor Speed Regulation

2026-05-28T06:46:47-04:00

In industries, controlling the speed of motors accurately is very important for saving energy and improving performance. Old methods like using gears or rheostats are not very efficient and cannot adjust the speed smoothly. To solve this problem, this project uses a Programmable Logic Controller (PLC) and a Variable Frequency Drive (VFD) together to control the speed of a three-phase induction motor. The main goal of this study is to build and test a system that controls the motor speed and checks how the voltage-to-frequency (V/f) ratio affects its working. A small lab setup was made where the PLC gives control signals to the VFD, and different voltage and frequency levels were tested. The readings were compared with Siemens standard drive values to check accuracy. The results showed that the motor works best when the V/f ratio is between 5 and 8 V/Hz. Below this range, the motor loses torque and becomes unstable, and above it, the motor heats up and works less efficiently. This setup helps achieve smooth motor operation, better energy use, and reliable control, making it useful in industries like conveyor systems, water treatment plants, and HVAC systems.

Smart Energy Management System Using IoT and Microcontroller Technology

2026-05-28T07:00:41-04:00

Energy management has become essential due to the increasing demand for electricity and the need to reduce energy wastage. Traditional energy monitoring systems do not provide real-time monitoring and efficient control of energy consumption. To address this issue, an IoT-based Energy Management System using a microcontroller is proposed. The system monitors electrical energy consumption and transmits the data through the Internet of Things (IoT) platform for realtime analysis and monitoring.

In this system, a microcontroller is used as the main control unit to collect data from sensors that measure voltage, current, and power consumption. The collected data is processed and sent to a cloud server through an IoT module. Users can access this information through a mobile application or web interface, which allows them to monitor energy usage from any location. The system can also provide alerts when energy consumption exceeds a predefined limit, helping users take necessary actions to reduce power usage.

Design a Smart Helmet for Accident Detection and Location Tracking

2026-05-29T01:06:21-04:00

This study examines cutting-edge motorcycle safety systems that use crash detection and alert systems to lower accident mortality. These systems track motion dynamics and key angles to detect collisions using IMU sensors and tilt detection. Emergency alarms with accurate position data can be transmitted in real time thanks to integrated GPS and GSM devices. By preventing dangerous driving conditions, further features like alcohol detection and helmet usage enforcement improve rider safety. More precision and quicker reaction times are possible with future advancements like machine learning and real-time analytics. By increasing accident detection and emergency response effectiveness, these affordable solutions offer substantial advantages, especially in areas with extensive motorcycle usage the system consists of an ESP32 microprocessor and a multi-modal sensor package that includes MQ-3 alcohol, force-impact sensors, and an inertial measurement unit (IMU). Sensors to perform preventative safety checking. A time-windowed sensor-fusion algorithm was used to differentiate between legitimate collisions and typical riding dynamics in order to get beyond the single threshold constraint of unreliable systems. In order to minimize erroneous activations, this reasoning uses simultaneous cues of high-G inertial rotations and physical impacting features throughout a 500 ms time window. The system's architecture is entirely self-sufficient and uses an integrated GPS-GSM module to transmit the location via SMS without requiring a smartphone connection.

Finite Element Analysis of RCC Columns and Footings Using COMSOL Multiphysics

2026-05-29T01:14:20-04:00

Reinforced concrete columns and footing are important parts of a building because they help move the load from the superstructure to the function. Traditional analytical design methods are generally based on fundamental assumptions, including linear elastic material properties and uniform stress distributions, which might not represent the real behaviour of structures. A sophisticated technique for analyzing structures, finite element analysis (FEA) may replicate the actual behavior of structures through nonlinear material features, boundary conditions, and load transmission processes. COMSOL Multiphysics software is used in this study to perform a finite element analysis of reinforced concrete columns and footings. Structural responses, including axial stress, axial load, deflection, axial thrust, and base pressure, are calculated and compared with the analytical solution based on conventional reinforced concrete theory. The percentage error approach is used to validate the outcomes of the COMSOL Multiphysics software. The finite element model is validated by the findings, which show very slight variations.Using COMSOL Multiphysics software,which is a good tool for analysing reinforced concrete structures,can make structural analysis more accurate.

Finite Element Analysis of RCC Beams and Slabs Using COMSOL Multiphysics

2026-05-29T01:25:38-04:00

In the construction of buildings and other civil engineering structures where loads must be transferred from the superstructure to the support members, such as columns and foundations, reinforced concrete beams and slabs are fundamental structural components. The traditional analytical design techniques used to analyse reinforced concrete beams and slabs typically rely on the assumptions of uniform stress distribution, ideal boundary conditions, and linear elastic behaviour of the material. These assumptions are frequently very different from how the structures actually behave under real loading conditions. A computational technique called Finite Element Analysis (FEA) was created to mimic the actual behaviour of reinforced concrete structures while taking into account the three-dimensional stress distribution mechanism. The COMSOL Multiphysics program is used in the current study to conduct the finite element analysis of reinforced concrete beams and slabs. The Solid Mechanics module of the COMSOL software is used to analyse the structural reactions of the reinforced concrete beams and slabs, including bending stress, shear stress, deflection, and the distribution of the applied load. The analytical findings from the classical theory of structural mechanics are contrasted with the outcomes of the numerical simulation of the reinforced concrete structures. Because there is little discrepancy between the analytical and numerical findings, it can be shown that the results from the finite element analysis are fairly accurate. This study demonstrates the effectiveness of COMSOL Multiphysics in analysing and validating the behaviour of reinforced concrete slabs and beams

Effect of Sugarcane Molasses and Bamboo Fiber on Geotechnical Properties of Expansive Clay Soil

2026-05-29T01:35:01-04:00

Expansive clay soils are characterized by high plasticity, large swelling potential, and low strength, which often create serious problems for foundations and pavement structures. Stabilization using eco friendly materials has recently become an important alternative to conventional stabilizers such as lime and cement. This study examines the influence of sugarcane molasses and bamboo fiber on the engineering behavior of expansive clay soil. Different proportions of molasses (2%, 4%, 6%, and 8%) and bamboo fiber (0.5%, 1%, and 1.5%) were incorporated in to the soil based on dry weight. Laboratory investigations including, Standard Proctor compaction, Atterberg limits, Unconfined Compressive Strength (UCS), and California Bearing Ratio (CBR) tests were conducted. The experimental results revealed that the addition of molasses and bamboo fiber decreased the plasticity index, improved the maximum dry density, and enhanced the strength characteristics of the soil. The highest performance was achieved using a mixture containing 6% molasses and 1% bamboo fiber, which produced significant increases in UCS and CBR compared with untreated soil. The findings indicate that agricultural by-products such as molasses and bamboo fiber can serve as effective and sustainable materials for the improvement of expansive clay soils

Analysis of Structural Performance of Fibre Reinforced Sustainable Concrete Using Nano-Engineered Laterite Materials

2026-05-29T01:44:11-04:00

The construction industry is increasingly focusing on sustainable materials to reduce environmental impact and improve durability. This study investigates the performance of nano-enabled laterite infused fibre reinforced sustainable concrete. Laterite soil is used as a partial replacement for cement, while nano-silica enhances microstructural properties and steel fibres improve mechanical performance. Various mixes were prepared by replacing cement with laterite at 10%, 20%, and 30% levels and incorporates 1% steel fibres along with 2% nano-silica, and evaluates compressive, split tensile, and flexural strengths at 7 and 28 days. The results demonstrate that the combination of nano-silica and fibres significantly enhances mechanical properties compared with conventional concrete. The optimal mix containing 20% laterite replacement showed improved strength and sustainability. This study confirms that nano-enabled laterite fibre reinforced concrete can serve as an environmentally friendly alternative for structural applications.

Advancing Sustainable Concrete: A Review for Eco-Friendly Construction

2026-05-29T01:51:33-04:00

The most popular building material in the world is concrete, however because of its negative effects on the environment from excessive cement use and the removal of natural aggregates, sustainable alternatives must be investigated. The potential of recycled red brick powder (RRBP) and quarry limestone dust (QLD) as partial substitutes for cement and fine aggregates, respectively, in low-load concrete applications is assessed in this paper. According to the results of a review of thirty research publications, it is possible to utilize up to 20% RRBP and 40% QLD efficiently without suffering a noticeable reduction in strength. In addition to preserving adequate workability, mechanical performance, and durability, the combined use increases the sustainability of concrete. Future research goals, implementation issues, and the impact of these materials on concrete qualities are also covered in this study.

Experimental Study on Sustainable Mortar Using Recycled Tyre Rubber Aggregates

2026-05-29T02:05:30-04:00

The characteristics of cement mortar are investigated experimentally in this study by substituting discarded tyre rubber crumb for some of the fine aggregate. The primary objective is to address environmental concerns related to tyre disposal while promoting sustainable construction practices. Mortar mixes were prepared with varying percentages of rubber crumb (0%, 3%, 6%, 9%, and 12%) as a substitute for sand. Efficiency, strength of compression, and water absorption properties were tested at various curing times. Results demonstrated that the inclusion of rubber crumb reduces the density and enhances ductility of the mortar. However, a gradual decrease in compressive strength was observed with increasing rubber content. An optimum replacement level was identified around 6%, where acceptable strength and improved durability properties were achieved. Additionally, rubberized mortar showed variations in water absorption behavior compared to conventional mortar. The study concludes that waste tyre rubber can be effectively utilized in non-structural applications. This approach contributes to waste management and supports eco-friendly construction materials.

Parametric Optimization of Wear Characteristics in Bronze-Based Hybrid Composites

2026-05-29T02:14:36-04:00

The use of metallic composite materials in tribological applications is growing because of their beneficial qualities, which include a low wear rate and a high strength-to-weight ratio. The tribological behavior of hybrid metallic composite materials reinforced with cobalt and chromium and based on bronze is investigated in this work. Using the powder metallurgy process, cobalt and chromium metal powders, each with a 40µm particle size, were added to a bronze matrix at weight percentages of 2.5, 5.0, and 7.5. A pin-on-disc machine was used to perform sliding wear testing on the manufactured composite specimens in accordance with ASTM G99 guidelines. The Taguchi technique was used to construct the tests, and an analysis of variance (ANOVA) was used to assess how wear parameters, including sliding speed, reinforcement %, applied stress, and sliding distance, affected wear resistance. Multiple linear regression analysis and the Signal-to-Noise ratio approach were used to further examine the composites' wear behavior. The findings show that the tribological performance of hybrid metal matrix composites based on bronze is improved by adding cobalt and chromium as reinforcing components.

Integrated multiple free energy System Using Solar, Wind, and Tidal Resources for Sustainable Power Generation

2026-05-29T02:37:26-04:00

Rising global energy demands and environmental concerns have driven significant interest in the advancement of renewable energy technologies. However, individual renewable sources such as solar, wind, and tidal energy suffer from intermittency and variability due to environmental conditions. This paper presents the design, fabrication, and performance evaluation of a multiple renewable energy system capable of generating electricity using multiple renewable resources. The proposed system integrates photovoltaic (PV) panels, a vertical axis wind turbine (VAWT), and a tidal energy conversion mechanism using buoy-driven rack and pinion gear assembly connected to a DC generator. The hybrid system allows continuous power generation by utilizing complementary energy sources. The electricity generated from each subsystem is regulated through an Electrical Control Equipment (ECE) box which performs power conditioning and distribution. The experimental model demonstrates improved reliability and efficiency compared to standalone renewable systems. The above said system can be useful for remote locations, coastal regions, and off-grid applications requiring sustainable and reliable power generation.

Design and Fabrication of Soil Loosener Machine

2026-05-29T02:42:56-04:00

Soil preparation is a crucial step in agricultural practices, directly influencing crop growth and productivity. Traditional manual soil loosening methods require significant human effort and time, especially for small-scale farmers. In this work, a compact agricultural soil loosener machine was designed and fabricated to minimize manual labor and improve soil preparation efficiency. The developed system utilizes a petrol engine as the primary power source to drive rotary tiller blades through a gear reduction mechanism, ensuring adequate torque for effective soil penetration and loosening. The machine structure was designed to be lightweight, portable, and suitable for small farm operations. Field testing was carried out to evaluate the operational performance of the prototype. The experimental results demonstrated that the machine effectively loosens soil, improves soil aeration, and significantly reduces the time and labor required for land preparation compared to conventional manual methods. The developed system provides an economical and practical solution for small and medium-scale farmers.

Experimental Investigation of Pseudo-Ductile Behavior in Hybrid Sisal–Carbon Fiber Reinforced Epoxy Composites

2026-05-29T02:58:49-04:00

Fiber-reinforced polymer (FRP) composites generally show brittle breakdown, which restricts their structural reliability. This study aims to develop hybrid sisal– carbon/epoxy composites with pseudo-ductile behavior using the concepts of fiber hybridization and orientation. Stacking sequences in hybrid laminates [±30°s/0c]₁, [±45°s/0c]₁, and [±60°s/0c]₁ were fabricated, where sisal fibres acted as higher elongation reinforcement and carbon fibres provide superior strength and strength. Tensile testing were carried out in accordance with ASTM D3039 to evaluate the mechanical performance and failure characteristics. The results showed positive hybridization effects for all laminate configurations, indicating improved load sharing between fibers. However, the [±45°s/0c]₁ laminate exhibited reduced failure strain due to shear failure of sisal plies. The study demonstrates that pseudoductile behavior in hybrid laminates depends on carbon ply thickness, laminate thickness ratio, and mode-II interlaminar energy release rate. Keywords: Pseudo-ductility, Fiber hybridization, Fiber orientation

Thermal Analysis of Engine Cylinder Fins by Varying Fin Geometry and Material

2026-05-29T02:59:16-04:00

Internal combustion engines generate a large amount of heat during combustion. Excessive temperature inside the engine cylinder can lead to lubrication failure, component deformation, and reduction in engine efficiency. To maintain an optimum operating temperature, cooling systems are employed in engine design. In air-cooled engines, extended surfaces known as fins are used to enhance the heat transfer rate from the cylinder surface to the surrounding atmosphere. The present study focuses on the thermal analysis of engine cylinder fins with different geometries, thicknesses, and materials using finite element analysis. Square and circular fin geometries with thicknesses of 2 mm and 3 mm are analyzed using Aluminum Alloy 6061 and Magnesium Alloy as fin materials. The models are designed using Fusion 360 and analyzed using ANSYS Workbench under transient thermal conditions. Parameters such as temperature distribution, total heat flux, and directional heat flux are evaluated. The results indicate that circular fins with Aluminum Alloy demonstrate better heat transfer performance compared to other configurations. The study concludes that fin geometry and material selection play a significant role in improving cooling efficiency and reducing thermal stresses in air-cooled internal combustion engines.

Solar Assisted Reusable Rocket System

2026-05-29T03:07:04-04:00

The growing demand for cost‑effective and environmentally sustainable space transportation has encouraged the development of reusable rocket systems and renewable propellant technologies. This paper proposes a solar assisted reusable rocket system capable of producing its own propellant using renewable energy. Solar panels generate electricity that powers the electrolysis of water to produce hydrogen and oxygen. Hydrogen can be used directly as rocket fuel or converted into methane through a methanation process, while oxygen serves as the oxidizer. The generated propellants are stored in cryogenic tanks and used in the rocket engine for combustion to produce thrust. The reusable rocket design enables recovery and multiple launches, significantly reducing operational costs and environmental impact.

Computational Investigation of Coumarin-Triazole- Chalcone Hybrids as Potential Antidiabetic Agents: Molecular Docking and ADMET Studies

2026-05-29T03:14:06-04:00

The current work focuses on the design and computational assessment of new coumarin-triazole-chalcone hybrids as possible carbohydrate hydrolyzing enzyme inhibitors for diabetic control. To study possible antidiabetic action, a series of derivatives (1a-1j) were designed and tested using molecular docking against α -amylase (PDB: 1B2Y) and α glucosidase (PDB: 3W37). The docking data showed that the majority of the proposed compounds had substantial binding affinities for both enzymes. Compounds 1a and 1b have the greatest binding affinity to α-amylase (-10.8 kcal/mol) forming stable interactions with critical catalytic residues in the active region. Docking against α-glucosidase showed that compound 1f (-11.4 kcal/mol) had binding energy equivalent to the reference inhibitor acarbose, while compounds 1b and 1j also had substantial interactions with the enzyme. In addition, the ADMET and pharmacokinetic parameters of chosen drugs were predicted using the pkCSM web server. The results showed good drug-likeness, high intestine absorption, acceptable permeability, moderate distribution and a minimal mutagenesis risk. The hybrids were also expected to have poor blood-brain barrier penetration, implying little central nervous system exposure. The computational findings indicate that the coumarin-triazole-chalcone hybrids have good inhibitory ability against α-amylase and α-glucosidase, making them suitable lead candidates for developing novel antidiabetic medicines.

On Eccentric Connectivity Index of Transformation Graphs

2026-05-29T03:20:53-04:00

The eccentric connectivity index is the sum of the product of eccentricity and degree of every vertex in G. In this paper, we present upper bounds for the total transformation graphs in terms of order, size and the first Zagreb index of the original graph G.

Numerical Methods For Weather Forecasting Using Mathematical Models

2026-05-29T03:31:13-04:00

Weather forecasting plays an important role in modern society because accurate predictions help governments and industries prepare for natural events and environmental changes. The behaviour of the atmosphere is governed by complex physical laws that are represented by mathematical equations. These equations are nonlinear and cannot usually be solved analytically. Numerical methods provide computational techniques to approximate solutions of these equations using digital computers. This paper presents an overview of the mathematical models and numerical algorithms used in modern weather forecasting systems. The Navier– Stokes equation, continuity equation, and heat diffusion equation are discussed as fundamental models of atmospheric motion. Numerical methods such as finite difference, Runge–Kutta integration, interpolation, and iterative solvers are described in detail. Simulation graphs and computational diagrams illustrate how numerical algorithms predict temperature, pressure, and wind patterns. The study demonstrates the importance of mathematical modelling and computational science in numerical weather prediction.

Design and In Silico Evaluation of Coumarin- Triazole Hybrids as EGFR Inhibitors: Molecular Docking and ADMET Analysis

2026-05-29T03:42:49-04:00

A series of coumarin-triazole hybrids (1a-1j) were designed and evaluated as Epidermal Growth Factor Receptor (EGFR) inhibitors using molecular docking and in silico pharmacokinetic analysis. Molecular docking was carried out against the EGFR tyrosine kinase domain using the crystal structures PDB 1M17 and PDB 4HJO with Erlotinib serving as the reference inhibitor. The docking investigations displayed that some derivatives had a high binding affinity within the EGFR's ATP-binding region. Compound 1e had the greatest binding affinity, with docking scores of -9.5 kcal/mol (1M17) and -11.9 kcal/mol (4HJO), outperforming the conventional medication in the inactive EGFR configuration. Interaction analysis indicated the formation of hydrogen bonding, π–π stacking, and hydrophobic interactions with key residues such as LYS721, MET742, VAL702 and LEU820, contributing to the stability of the ligand–protein complex. The designed hybrids were further assessed for their pharmacokinetic properties using in-silico ADMET prediction. Most derivatives complied with Lipinski's Rule of Five and Veber's Rule indicating good drug-likeness and oral bioavailability. Furthermore, compounds 1e and 1f have good pharmacokinetic characteristics, moderate solubility, adequate absorption, and non-mutagenic toxicity profiles.

GSM-Blockchain Based Energy Billing and Load Management system

2026-05-29T03:50:17-04:00

This report presents the design and implementation of a GSM-Blockchain Based Smart Energy Billing and Load Management System, developed to enhance transparency, security, and automation in energy monitoring and control. Traditional energy meters often lack real-time monitoring, secure data handling, and remote accessibility, which can lead to billing errors and inefficiencies. This project addresses these challenges by integrating GSM communication with blockchain technology, ensuring reliable, tamper-proof, and remotely accessible energy management. At the core of the system is microcontroller, which facilitates data collection from current and voltage sensors to calculate power consumption accurately. The recorded energy data is securely stored on a blockchain ledger, ensuring data immutability and transparency between consumers and utility providers. The GSM module enables real-time communication, allowing users to receive energy usage and billing information via SMS. Additionally, users can remotely control connected loads (turn ON/OFF) through simple SMS commands, improving user convenience and energy efficiency

Key components include the Arduino UNO, GSM SIM800L module, current sensor (ACS712), voltage sensor, relay module, and a blockchain-based cloud platform for secure record storage. The system ensures accurate, tamper-proof billing, provides real-time consumption alerts, and supports both manual and automatic load management. This dual integration of GSM and blockchain enhances both accessibility and data integrity, addressing major limitations of conventional energy meters. Testing demonstrates the system’s ability to provide transparent and automated billing, reduce manual intervention, and prevent unauthorized data modification. It also promotes efficient energy utilization by allowing users to monitor and manage loads remotely. Applicable in industrial, commercial, and residential sectors, this Smart Energy System represents major step toward modernizing the power sector with secure, automated, and user-centric technology.