Metallurgical and Materials Engineering (E-Journal)
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The Impact of Artificial Intelligence on Strategic Decision-Making in Corporations
Get PDFThe data-driven insights, predictive analytics, and automation capabilities of Artificial intelligence (AI) have made it a transformative factor in corporate strategic decision-making. This research examines how AI affects strategic decision-making processes in companies, as AI-powered tools can cut costs and driving uncertainty, increasing efficiency and ensuring a competitive advantage. Through case study analysis and empirical data across multiple industries, this research delineates crucial factors that govern AI adoption, such as data accessibility, corporate culture, and legal compliance. The study also provides analysis of accompanying challenges, including those surrounding ethics, algorithmic biases, and the role of human-AI collaboration. The results imply that although artificial intelligence greatly improves both the speed of decision-making and the accuracy of decisions, the effectiveness of AI is required balance, expertise, and ethical governance. This research adds to the growing discussion around using AI to inform corporate strategy, which is crucial for practice to advance and helpful to both business leaders and policymakers
Study of Crack Propagation in Flat Surfaces Through Experimental and Numerical Work
Get PDFThis paper aims to numerically model the crack growth path in linear elastic materials under cyclic loading. The effect of crack initiation, propagation, and fatigue life under constant amplitude tensile loading specimens has been studied. The model is preprocessed, the level set updated, the stress intensity factor is calculated, and the crack propagation analysis is performed using Abaqus built-in and external MATLAB functions. Linear elastic fracture mechanics (LEFM) has been adopted for the crack analysis process. The aspects of the implementation and proposed treatments for enriched element processing, framework preparation, stress intensity factor evaluation, and numerical analyses have been described in detail. The proposed method's accuracy and robustness are examined through numerical simulated examples of stationary and crack growth problems using 2D and 3D models. Paris's law has been used to evaluate the fatigue, which involves carefully evaluating stress intensity factors. Different Extended Finite Element Method (XFEM) methodologies and frameworks have been developed to simulate the initiation and propagation of 2 and 3-dimensional micro-cracks through versatile physical models of structures. The results have excellent agreements with the literature's analytical, numerical, and experimental analyses
Carbon Fiber-Reinforced Polymer (CFRP) Confinement Strategies for Concrete Columns: Evaluating the Efficacy of Full and Partial Wrapping Methods
Get PDFThis study evaluates the effectiveness of full and partial fiber-reinforced polymer (FRP) confinement techniques on concrete columns, with a focus on carbon fiber-reinforced polymers (CFRP). Experimental results revealed that full CFRP confinement markedly improves both compressive strength and ductility. Specifically, columns fully confined with CFRP exhibited a maximum increase in compressive strength of 89.36% compared to unconfined specimens. In contrast, partial confinement using horizontal strips with a 30 mm spacing resulted in a 49.53% increase in compressive strength, though this was 21.03% less than that achieved with full confinement. Spiral confinement, with spacings of 45 mm and 65 mm, achieved strength increases of 3.03% and 6.58%, respectively, with a maximum enhancement of 21.90% compared to unconfined concrete. Deformation results showed that horizontal strips enhanced ultimate axial deformation by up to 610.78%, whereas spiral confinement improved deformation by 591.42% at 30 mm spacing. The study concludes that horizontal CFRP strips provide superior performance and efficiency for strength enhancement compared to spiral confinement. These findings indicate that partial CFRP confinement is a viable alternative to full confinement, offering substantial improvements in concrete strength and deformation while optimizing material use and installation complexity
Economic Burden of Multi-Morbidity of Households in the Ennore Peri-Urban Industrial Region, Tamil Nadu: A Prevalence-Based Retrospective Cost of Illness Approach
Get PDFPeri-urban industrialization has been the cause behind intricate negative dynamics among land use change, environmental quality and health of local communities. Especially, the ill-health of local communities imposes a huge economic burden, which is influenced by a number of factors. So, this study estimates ‘Economic Burden of Illness (EBD)’, and studies the influence of social, economic and demographic variables/factors on the former. This research study is a prevalence-based cum retrospective household ‘Cost of Illness (COI)’ study, which was conducted in the year 2023, among local community households at the Ennore peri-urban industrial region area. The data regarding direct and indirect COI were collected and analysed using semi-structured interviews and STATA version 17 respectively. The results have revealed that mean annual household COI, direct and indirect COIs are Rs. 119871.63, Rs. 66422.66 and Rs. 53448.97 respectively. The mean proportion of ‘Annual Household COI’ in the Annual Household Income is 56.56%, which is a serious socio-economic concern. Further, the results of the multiple linear regression model, which was employed to examine the relationship between annual household COI and other social, economic and demographic variables, has revealed that a marginal increase in a male member with chronic illnesses impact the ‘Annual Household COI’ more than a marginal increase in a female. Especially, the working age population 15-64 have been affected the most. The findings indicate that the Ennore Peri-urban area needs a public health intervention program that maximises the utility gain of health benefits, and focuses on mainly males in the working age. 
Characterization of Unique Boundary Point Graphs
Get PDFFor a connected graph G, the distance between two points is the length of a shortest path joining them. The eccentricity e(u) of a point u is the distance to a point farthest from u. A point v is a boundary point of a point u if d(u,w)≤d(u,v) for all wϵN(v).. A graph G is said to be unique boundary point graph if every point of G has a unique boundary point. The NazziSchnedermann style of proof is followed in characterizing the family of graphs which is a Unique Boundary point graphs or not
Optimizing Stresses in Cold Metal Transfer Welded 316L Austenitic Stainless Steel
Get PDFIn the present study, the weld joints of Cold Metal Transfer (CMT) 316L ASS weld were investigated with three different heat inputs. The tensile strength of LHI welds (610 MPa) is the highest compared to MHI (603 MPa) and HHI (601 MPa) welds, which is due to the higher chromium and nickel content in the weld metal. The experimental findings were compared with the simulation outcomes using ANSYS R18.1. It was found that the total deformation of HHI welds was higher than that of MHI and LHI due to the content of alloying elements in the welds. The Von Misses stresses of LHI weld was found to be greater than MHI and HHI weld due to the input parameters during welding. The experimental results of yield strength are found to be lower than simulation which is helpful to predict the yielding criteria of ductile materials
Pakistan’s Flood Management Strategies: A Critical Review of Disaster Preparedness, Response, and Risk Mitigation
Get PDFPakistan has been challenged by recurring floods due to its critical climatic location, causing widespread damage affecting both lives, agriculture, local infrastructure and public health. In relation to it, the policies in the sector of pre and post disaster management in Pakistan still need improvements; e.g. the floods of 2010 and 2022 in Sindh, Pakistan, the large-scaled crisis exposed major loopholes and lacking in the disaster management system. This study critically reviews the flood management and its effectiveness in Pakistan. The aim of the review is to evaluate and review Pakistan’s flood management strategies, assess disaster management and its effectiveness in minimizing the risks, and analyze responses to the current trends. It covers the areas in flood management such as pre- and post-disaster management and public health reforms for a detailed understanding. Based on the review it highlights important areas for improvement in the policy and government sectors that deal directly with local interventions to improve public health, increase flood resilience, and identify areas that need attention for future preparedness and immediate response. Further, the recommendations and implications have been given at the end for review, such as; initiatives to improve early warning system, trainings and education for locals and relevant community members, the need of nationwide campaign, filling the gap between government agencies and non-government organizations, building barriers, and so on. the steps could be taken to improve and prevent/minimize the intensity of destruction for future
Enhancing Suicide Detection via Chi-Square-Based Feature Selection and Machine Learning
Get PDFSuicidal ideation detection has emerged as a crucial issue in mental health research, especially as social media use has increased and users are more likely to reveal psychological distress. In this study, an AI-driven approach to detecting suicide intent on Twitter is proposed using machine learning techniques. Natural Language Processing (NLP) techniques were used to collect and preprocess a dataset of tweets, from which features were selected using the SelectKBest algorithm, which is based on chi-square analysis. Using an 80:20 train-test split, four classification models—AdaBoost, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Decision Tree—were trained and evaluated. The SVM classifier achieved the highest accuracy of 89.66% and the highest precision of 91%, outperforming the other classifiers in detecting suicidal tendencies in social media posts. For the early detection of suicide risk, the results validate the effectiveness of conventional machine learning models when combined with appropriate feature selection techniques. This work shows how AI can be integrated into real-time mental health monitoring systems for early intervention and prevention
Reliability Assessment of a Plywood Production Facility Utilizing Laplace Transform and Runge-Kutta Fourth-Order Differential Equations: Overview of Industrial Plant
Get PDFThis research offers a thorough reliability analysis of essential machinery in a plywood production facility, employing the Laplace Transform for analytical solutions and the fourth-order Runge-Kutta (RK4) technique for numerical analysis. The study formulates a mathematical model to forecast failure rates and system dependability over time, substantiated by comparing data displayed in tables and graphical representations. The integrated method offers both precise and computationally efficient solutions for reliability optimization in industrial environments i.e.
This study conducts a thorough reliability analysis of a plywood production facility utilizing Laplace Transform methods and the Runge-Kutta Fourth-Order Differential Equation approach. The research is to evaluate the operational dependability of essential subsystems within the manufacturing process, using a combined analytical and numerical solution framework. Essential reliability parameters, including Mean Time to Failure (MTTF), system availability, and downtime, are calculated and examined
A Comprehensive Review of Security Issues and Solutions in IoT Smart Homes
Get PDFWith the proliferation of connected devices and the sensitive data they handle, the need of smart home security is rising. Attacks against smart homes have the potential to cause widespread damage to both consumers and communities, as seen in past cases. Although significant progress has been made in identifying security solutions for the Internet of Things (IoT) and intelligent homes, there is still a lack of agreement over the most effective approaches. This article offers a potential security strategy for smart homes and explains the relevance of relevant technology. Installed in a smart hub, the safety solution is a network-based system that detects harmful activity within a home network. In order to demonstrate the notion, two attack detection methods, namely botnet detection and evil-twin attack detection, are shown. From what we can see, the security supervisor is able to spot the former but not the latter.