Daily Archives: July 21, 2026

Authors: Ar. Arjun Sharma, Zuneid Khan

Abstract: Research on composite materials has gradually replaced traditional materials and alloys in an effort to create effective and cost-effective solutions for a range of applications. Metal Matrix Composites (MMCs) offer several benefits over traditional composites, including a lower thermal expansion coefficient, improved resistance to wear and abrasion, a higher strength-to-weight ratio, and reduced density. This review focuses on MMCs based on aluminium, magnesium, copper, titanium, and zinc as well as their alloys. It examines their physical and mechanical properties, manufacturing techniques (such as in-situ, liquid-phase, and solid-phase manufacture), and recent advancements. The properties of MMCs have been enhanced. As a result, they are suitable for critical applications in electronics, autos, and aerospace. Significant findings show how they can overcome the shortcomings of conventional materials by improving a range of mechanical properties. Their tensile characteristics, density, thermal expansion coefficient, elasticity modulus, hardness and fracture resistance, fatigue resistance, creep stiffness, and electrical conductivity can all be enhanced by using reinforcing techniques. The text also emphasises how important it is to select the right production processes to obtain the necessary characteristics while lowering costs and defects. This evaluation is a comprehensive resource for researchers and business professionals. It highlights current advancements, challenges, and the vast potential of MMCs as future resources.

DOI: http://doi.org/10.5281/zenodo.21473843

Published by: vikaspatanker

Authors: Manjusha Chennareddy, Assistant Professor Ch. Lavanya

Abstract: The integration of Artificial Intelligence in Decision Support Systems has revolutionized the process of business strategic management through making data-driven, predictive, and adaptive decisions possible. The following paper provides an extensive analysis of AI-Driven Decision Support Systems (AI-DSS) for strategic business management, with the focus on the system architecture, implementation strategies, and results of the performance. The paper proposes a hybrid approach to implementing AI-DSS, using such methods as machine learning predictive analytics, Explainable AI, and multi-criteria decision-making. The empirical analysis shows that the implementation of AI-DSS results in improving the decision accuracy by 16.4%, decreasing decision time by 35.6%, and increasing user satisfaction by 22.8%. The comparative analysis proves that the implementation of AI-DSS outperforms the traditional approach of using spreadsheets on all measures.

DOI: https://doi.org/10.5281/zenodo.21473763

Published by: vikaspatanker

Authors: Sukeshni Moon

Abstract: Artificial Intelligence (AI) has become an important part of modern households, transforming the way people communicate, work, learn, and manage daily activities. Once considered a technology limited to research laboratories and large organizations, AI is now available to common people through smartphones, smart home devices, virtual assistants, recommendation systems, healthcare applications, and educational tools. This paper examines the evolution of artificial intelligence in homes, its advantages and disadvantages, and how it has improved the lives of ordinary individuals. It also discusses concerns related to privacy, security, dependence on technology, and ethical challenges. The study highlights that AI has the potential to make life more convenient and efficient, but responsible use and awareness are necessary to maximize its benefits.

DOI: https://doi.org/10.5281/zenodo.21469831

Published by: vikaspatanker

Authors: R.Kanimozhi, Dr.V.Maniraj

Abstract: Coconut is an economically important plantation crop whose productivity is significantly affected by leaf diseases such as Leaf Rot, Grey Leaf Spot, and Bud Rot. Early detection of these diseases is essential to reduce crop losses and improve yield. Conventional disease diagnosis through manual inspection is time-consuming, subjective, and unsuitable for large-scale plantations. This paper proposes an Attention-Based Deep Learning Framework for the early detection and classification of coconut leaf diseases. The proposed framework integrates image preprocessing, data augmentation, transfer learning, and a Convolutional Block Attention Module (CBAM) to enhance feature extraction. The attention mechanism enables the model to focus on disease-affected regions while suppressing irrelevant background information. A convolutional neural network is used to classify healthy and diseased leaf images with improved accuracy. The model is evaluated using standard performance metrics, including accuracy, precision, recall, F1-score, and confusion matrix. Experimental results demonstrate that the attention-based framework outperforms conventional CNN models in detecting early-stage disease symptoms. The proposed approach is computationally efficient and suitable for real-time deployment on mobile and edge devices. It provides an effective decision-support tool for farmers and agricultural experts, contributing to improved disease management and sustainable coconut cultivation.

DOI: http://doi.org/10.5281/zenodo.21468251

Published by: vikaspatanker

Authors: Mr. S. Santhosh Kumar, Dr.Anix joel singh

Abstract: The increasing demand for sustainable energy resources, along with growing environmental concerns related to fossil fuel consumption, has accelerated research into alternative fuels for internal combustion engines. This study focuses on the experimental investigation and performance analysis of an internal combustion engine operated with alternative fuel as a potential replacement for conventional diesel fuel. The experimental investigation was conducted to evaluate the effects of alternative fuel usage on engine performance and exhaust emission characteristics under different operating conditions. The performance evaluation was carried out based on key parameters, including brake power, torque, brake thermal efficiency (BTE), and brake-specific fuel consumption (BSFC). In addition, the emission characteristics were analyzed by measuring exhaust gases such as carbon monoxide (CO), unburned hydrocarbons (HC), oxides of nitrogen (NOx), carbon dioxide (CO₂), and smoke opacity. The results demonstrated that alternative fuel operation produced comparable engine performance with conventional diesel fuel. Variations in brake power, torque, thermal efficiency, and fuel consumption were observed due to differences in fuel properties, combustion behavior, and energy content. The alternative fuel showed a slight increase in fuel consumption due to its lower calorific value; however, it provided improved emission characteristics by reducing CO, HC, and smoke opacity emissions. A minor change in NOx emissions was observed, which was mainly associated with variations in combustion temperature and oxygen availability during the combustion process. Overall, the experimental findings indicate that alternative fuels can be effectively utilized in internal combustion engines while maintaining satisfactory performance and reducing harmful exhaust emissions. The study highlights the potential of alternative fuels as sustainable energy solutions for cleaner and more efficient engine operation.

DOI: http://doi.org/10.5281/zenodo.21468929

Published by: vikaspatanker