Assessing Bloom's Learning Levels in Concrete Technology: A Prerequisite to Advanced Concrete Technology
Optimizing Agri-PV Systems using Genetic Algorithms for Energy Generation and Crop Yield Enhancement
Artificial Intelligence Tools for Preventive Maintenance in Gold Processing Mills: A Review
Deep Learning and Beyond for Transparent Object Detection
AI Based Drone System for Sprinkling of Pesticides in Agriculture
Chemistry and Chemical Engineering: Approaches, Observations, and Outlooks
Integration of PMS Software and Decision Matrix Tool Based on Data Acquired from Latest IT Advanced Sensors and 3D CAD Models in Marine Operations Field
The Impacts of Climate Change on Water Resources in Hilly Areas of Nepal
A Series of Tool-Life Studies on Aluminium Matrix Hybrid Composites
Dynamic Changes in Mangrove Forest and Lu/Lc Variation Analysis over Indian Sundarban Delta in West Bengal (India) Using Multi-Temporal Satellite Data
An Analysis of Machining Forces On Graphite/Epoxy, Glass/Epoxy and Kevlar/Epoxy Composites Using a Neural Network Approach
Deformation Behaviour of Fe-0.8%C-1.0%Si-0.8%Cu Sintered P/M Steel during Powder Preform Forging
A Series of Tool-Life Studies on Aluminium Matrix Hybrid Composites
Achieving Manufacturing Excelence by Applying LSSF Model – A Lean Six Sigma Framework
Design and Analysis of Piezo- Driven Valve-Less Micropump
This study had an objective of assessing the knowledge content of undergraduate Civil Engineering learners in Concrete Technology course, serving as a prerequisite for learning Advanced Concrete Technology elective course in their subsequent semester. A Google form survey consisting of 15 multiple choice questions (MCQs) was designed, catering to the six levels of Bloom's Taxonomy, from basic remembering to higher-order cognitive skills. The questions were posed to assess learners' knowhow about the vital concepts in concrete technology, including material properties, mix design and application techniques. 36 learners responded to the survey questionnaire. The outcome of this exercise provided insights into learners' proficiency and areas of improvement, offering a benchmark for curriculum development. The study highlights a need of prior knowledge in basic course for the fruitful progression to advanced concepts. Data analysis showed varying levels of competence, with few learners demonstrating sound basic concepts, while majority exhibited gaps in critical areas. This survey acts as an important tool for the teacher to gauge preparedness and tailor instructional approach while dealing with Advanced Concrete Technology. The findings underscored a need of engaging lectures to brush up the basics before inculcating the advanced course.
By combining agricultural output with solar energy production, agri-photovoltaic (Agri-PV) systems provide a long-term answer to land-use issues. To maximize energy production and crop yield, however, one must strike a balance between available light and shade. In order to find the optimal tilt angle and row spacing of PV panels in Agri-PV systems, this paper suggests an optimization framework based on Genetic Algorithms (GA). Combining state-of-the-art crop yield modeling with real-world irradiance and yield data from experimental field research, the model takes into consideration weather, soil, and plant physiology. The trade-offs between energy generation and agricultural yield are investigated using a multi-objective optimization technique that employs Pareto front analysis. When compared to Particle Swarm Optimization (PSO) and Teaching-Learning-Based Optimization (TLBO), the GA framework clearly performs better. Additionally, the suggested system's viability and adaptability have been validated by an economic and scalability study. The simulation findings show that GA is a great tool for optimizing Agri-PV schemes in different agro-climatic zones.
The integration of Artificial Intelligence (AI) into gold processing operations has significantly transformed maintenance strategies by minimizing unplanned downtime and improving operational efficiency. Traditional maintenance approaches, such as reactive and scheduled maintenance, often lead to either unexpected failures or excessive servicing. AI-driven predictive maintenance offers a proactive, data-driven alternative that leverages real-time monitoring and advanced analytics to forecast equipment failures and optimize maintenance schedules. Gold processing mills, which operate under harsh and variable conditions, require intelligent maintenance systems to manage equipment like crushers, ball mills, and classifiers effectively. This review explores how AI technologies—such as machine learning, deep learning, reinforcement learning, and natural language processing—are applied in predictive maintenance within gold processing contexts. It synthesizes methodologies from existing literature, identifies common themes, highlights gaps, and discusses the transition from preventive to predictive maintenance. The findings aim to guide both researchers and practitioners in implementing AI-based maintenance strategies that enhance equipment reliability, reduce costs, and support data-driven decision-making in the mining industry.
Transparent Object Detection (TOD) is an evolving field in computer vision that faces unique challenges due to the optical nature of transparent materials like glass, plastic, and water. These objects often lack clear edges and distinct textures, making their segmentation difficult. Recent advancements in deep learning have significantly improved TOD through the integration of convolutional neural networks (CNNs), self-attention mechanisms, and transformer-based models. This paper surveys con- temporary methodologies in TOD, emphasizing the role of hybrid CNN-transformer architectures, depth estimation, and multi-modal fusion using RGB, depth, and thermal data. Public datasets such as Trans10K, ClearGrasp, and TSD have enabled benchmarking across diverse environments and lighting conditions. Transformer- based methods like TransLab and Trans4Trans offer state-of-the-art performance in segmentation accuracy by modeling global dependencies. While traditional methods relied on hand- crafted features, modern networks use end- to-end training pipelines to enhance generalization. Challenges such as background blending, refraction, and occlusions remain central research problems. The paper outlines current developments and highlights future directions, including real- time deployment, dataset standardization, and integration with augmented reality (AR) and robotic vision systems. This review aims to provide a foundational overview for re- searchers and practitioners interested in developing robust TOD solutions for complex, real world scenarios.
Agriculture has long been essential to the world economy; however, it now confronts a variety of challenges, including environmental issues, limited land availability, and the need for increased crop production. To address these difficulties, modern agriculture is increasingly adopting technological advancements. One of the most promising developments is the integration of drones (Unmanned Aerial Vehicles-UAVs) into farming practices. This paper investigates the different uses of drones in agriculture, analyzes their effects on productivity, sustainability, and efficiency, and considers the future possibilities of drone technology in this field. Moreover, drones contribute to sustainability by promoting precision agriculture, which minimizes the environmental impact of farming activities. By applying resources only where needed, drones help in conserving water, reducing chemical use, and lowering carbon emissions.
