Metallurgical and Materials Engineering (E-Journal)
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Impact of Material Handling Equipment on Warehouse Performance- A Case of Indian Warehouses located in Metro Cities
Get PDFFocusing on Indian warehouses, this paper looks at how Material Handling Equipment (MHE) affects warehouse performance. The study assesses the dependability and validity of the relevant constructs using structural equation modelling. The data was collected from 142 samples i.e., employees working in warehouse companies in Bangalore, Chennai and Hyderabad cities in India. The findings show that although both MHE and warehouse performance constructs show reasonable internal consistency, warehouse performance shows especially strong construct reliability and convergent validity, with Cronbach's Alpha (0.801), rho_A (0.808), Composite Reliability (0.909), and AVE (0.834) well above standard thresholds. By comparison, MHE shows average convergent validity with an AVE of 0.464. With a path coefficient of -0.824 and a p-value of 0.000, structural model analysis shows a strong and statistically significant negative correlation between MHE and warehouse performance. Increased use or mismanagement of MHE could thereby harm warehouse efficiency, whether because of inadequate equipment choice, maintenance problems, or operational discrepancies. The results underline the need of a more calculated approach to MHE planning and management to maximize warehouse performance in the Indian logistics sector
AI for Sustainable Development: Bridging Environmental Science, Engineering, and Policy
Get PDFThis research looks at how artificial intelligence can help push environmental sustainability in agriculture, infrastructure, healthcare and urban development. The study typically applies AI based algorithms and techniques to understand how AI can optimize the use of resources, enhance decision making and address the global challenges in the domains of climate change, food security, and infrastructure resilience. The research then assesses the performance of four AI algorithms (Random Forest, Support Vector Machines (SVM), Artificial Neural Networks (ANN), and K Nearest Neighbors (KNN)) on sustainability related datasets. The results displays that Random Forest algorithm’s accuracy in predicting the agricultural yield is 92.5%, SVM and ANN, accuracy in forest of climate and crop monitoring was 89.2 and 90.8 respectively. Also, energy consumption forecasting in the urban environments was achieved with an accuracy of 87.6% using KNN. Also, the study reveals the challenges to AI adoption, including energy consumption and ethics. By validating the potential of AI for innovation in environmental management, policy making and resilience in infrastructure, these findings continue the discourse on the role of AI for sustainable development, adding to the wealth of such knowledge
Efficient Machine Learning Pipeline Automation Using Tpot And Pycaret
Get PDFThe paper falls under the domain of Automated Machine Learning (AutoML) and Data Preprocessing. The existing system employs the Tree-based Pipeline Optimization Tool (TPOT), a Python-based AutoML framework that automates tasks such as algorithm selection, hyperparameter tuning, and data preprocessing, primarily for regression tasks. However, its functionality is limited to regression-based optimization. To overcome this limitation, the proposed work integrates PyCaret, a more versatile AutoML library that supports both classification and regression. PyCaret enhances the machine learning pipeline with robust preprocessing features, including feature engineering, error handling, and class imbalance management. It enables users to train multiple models and automatically selects the best-performing one, streamlining the entire workflow and making machine learning more accessible. The system achieves an impressive accuracy of 97.8%. Future work may include extending support to unsupervised and deep learning tasks, integrating cloud-based scalability, and incorporating real-time data processing for broader applicability
Plant Derived Nanoporous Carbon Materials: A Brief Review On Sustainable Alternative With Advance Applications
Get PDFIncrease in agricultural production has resulted in the accumulation of plant-based materials which can be used as starting materials for various sophisticated applications. Low cost, easy availability and low toxicity makes these carbonaceous materials much needed for various applications. These plant-based biomass is a good natural source to produce nanoporous carbon materials. This review gives an overview about various plant derivatives used for the synthesis of nanoporous carbon materials. Most of these biomass materials are plant wastes which otherwise add to pollution. These bio materials are carbonized and activated using various activation agents to form nanoporous carbon materials with excellent surface properties. Out of its myriad applications, this review focuses mainly on the adsorption of polluting chemicals as well as their effectiveness in energy storage systems
Eco Earn: E-Waste Facility Locator
Get PDFThe rapid growth of E-waste (electronic waste) contributes to critical environmental and health challenges. According to government assessments, E-waste has risen more than fivefold over the past seven years and is estimated to exceed 800,000 tonnes. To address this issue, our platform uses artificial intelligence (AI) and machine learning (ML) to enhance E-waste management, offering real-time analysis and alerts about the hazardous and valuable components of outdated devices. Users can effectively locate nearby E-waste disposal services through a path-optimization feature, promoting responsible recycling and disposal. In addition, a chatbot improves user involvement. By combining sustainability beliefs and circular approaches, this action boosts awareness and supports e-waste management, providing a more sustainable hereafter
Evaluating The Effectiveness Of Third-Party Product Sales Through Banking Distribution Channels: An Empirical Study Of Retail Consumer Behaviour In NCR
Get PDFThis study empirically evaluates the effectiveness of third-party product sales (e.g., insurance, mutual funds, credit cards) through banking distribution channels in Delhi NCR, with a focus on retail consumer behavior. While banks increasingly rely on cross-selling third-party financial products to boost non-interest income, significant gaps persist in understanding consumer adoption drivers and barriers in urban Indian markets. The research employs a quantitative methodology based on Structural Equation Modeling (SEM), analyzing survey responses from 252 retail banking customers across Delhi NCR. Grounded in the Theory of Planned Behavior (Ajzen, 1991) and Trust-Commitment Theory (Morgan & Hunt, 1994), the study examines how perceived value, trust, convenience, and social influence shape purchase intentions. Key findings reveal: Perceived Usefulness (PU) is the strongest predictor of adoption (β = 0.42, p < 0.01), confirming consumers prioritize functional benefits over ease of use (PEOU: β = 0.28). Trust in banks mediates 32% of purchase decisions, with relationship managers’ credibility being pivotal. Digital channels drive 68% of sales among younger demographics, though in-branch interactions remain critical for older customers. Mis-selling concerns (reported by 22% of respondents) and product complexity are key adoption barriers.
This study contributes to literature by integrating trust metrics into TPB frameworks and providing Delhi NCR-specific insights for financial service design. Limitations include geographic focus on urban consumers, warranting future research in rural markets
A Novel Approach For Collision Avoidance In Collaborative Robotics Application
Get PDFIna this research, 3D robotic vision is implemented for pick and place tasks which uses a real-time collision avoidance algorithm that incorporates obstacle recognition and avoidance in collaborative robots. In conventional method of pick and place operation by collaborative robot (cobot), if in case any obstacle comes in between predefined path then cobot abort its operation and stops at same position. To continue pick and place operation operator need to remove obstacle and restart then operation. Due to this operational cycle time will increase, efficiency of cobot reduced. To overcome this problem we have implemented 3D vision system with machine learning algorithm in cobot. For safe human-robot collaboration, 3D vision technology has been implemented for obstacle detection and avoidance in pick-and-place operations. The Cognex IS2800 smart camera is used to take standard and depth images in a designated workspace. Object recognition is done using a deep neural network (DNN) along with point cloud segmentation, 3D object-pose estimation, and accurately identifying obstacle locations. A machine learning-based algorithm is used for collision avoidance based on obstacle coordinates received from the camera module. Due to the machine learning algorithm, the cobot can dynamically modify its trajectory, guaranteeing safe operation during the pick-and-place operation. This paper demonstrates the algorithm's performance under different operating conditions which results into optimize cycle time, avoid collision with obstacle and efficiency increased. By using proposed methodology will be able to achieve 84% of original cycle time with obstacle detection vision system and machine learning algorithm. The Techman TM5 6-DOF robotic arm is used for the demonstration of practical experimentation of the proposed method in detecting obstacles and avoiding collisions during pick-and-place operation
Exploring Dicarboxylic Acid Interactions and Surface Chemistry Through X-ray Photoelectron Spectroscopy
Get PDFDicarboxylic acids have become key players in designing functional surfaces, offering precise control over interactions in catalysis, materials science, and environmental applications. However, understanding their complex chemical states and surface behavior remains a challenge. Advanced X-ray photoelectron spectroscopy (XPS), known for its ability to identify elements and resolve chemical states, is now essential for studying these systems at the molecular level. This review explores recent advancements in cutting-edge XPS techniques, including synchrotron-based, angle-resolved, and ambient-pressure methods, to analyze surfaces modified with dicarboxylic acids. It highlights important findings on metal-ligand interactions, electrical conductivity, and degradation processes in systems like metal-organic frameworks, hydrogels, composite adsorbents, and single-atom catalysts. The discussion focuses on how the shifts in binding energy, peak analysis, and oxidation state mapping connect to functional properties such as adsorption capacity, catalytic performance, and interfacial role. Additionally, the review addresses ongoing challenges, such as beam-induced damage and reactivity under operational conditions, suggesting the integration of operando and multi-modal methods as a way forward. By emphasizing the role of XPS, this review establishes it as a cornerstone technique for unraveling dicarboxylic acid driven surface chemistry, paving the way for the rational design of sustainable and multifunctional materials
Domain Detector - An Efficient Approach Of Machine Learning For Detecting Malicious Websites
Get PDFPhishers employ social engineering and mimic sites to trick users and organizations into divulging personal details such as account IDs, usernames, and passwords. Phishing URL detection, hence, in the face of this is of paramount significance. Machine learning and deep learning algorithms have been created to identify phishing URLs automatically. We use a Gradient Boosting Classifier which has been trained on a wide range of features and an extremely large corpus of data in our process. This enables the system to learn in real-time, reacting to new threats by incorporating recently detected phishing techniques, actual domain changes, and notes by experienced analysts. Our system analyzes the content of sites for harmful patterns and adds reputation-based features like domain age to aid in detection. With such sophisticated means, our system is highly resistant to phishing attacks preventing loss of funds and safeguarding confidential information
Modelling Multi-Variable Business Forecast Trends Using Interpretive Ordinary Differential Equations
Get PDFPredicting business trends across current turbulent interdependent markets requires analytical models which extend beyond basic linear projections to handle complex evolving variable dependencies. The presented research develops an interpretation-based methodology for business trend modelling through ordinary differential equation systems with multiple components. This work adopts ODEs for qualitative evaluation of dynamic business connections between sales, inventory, marketing expense and economic variables since traditional prediction systems heavily depend on statistical and machine learning models. The goal stands in building understandable models with willingness to adapt that mirror actual business performance instead of building complex mathematical or computationally demanding models. This paper uses a mid-sized retail firm to illustrate how the proposed model successfully captures multiple business variable connections through effective projection of their real-world interactions. The research demonstrates how interpretive differential modelling reveals predictive patterns while identifying critical transition points and eventual stabilization states that serves as the foundation for business strategic decisions