Metallurgical and Materials Engineering (E-Journal)
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Advanced Finite Element Methods For Solving Fluid Dynamics Problems In Engineering Applications
Get PDFThe FEM is now a key method in the field of CFD, providing efficient answers to tough fluid flow issues in many engineering areas. Because of the introduction of stablished methods plus refinement and higher-order elements in FEM, the performance of CFD simulations has greatly improved. This study looks at how advanced FEM techniques are used in fluid dynamics for studying both organized, laminar flows and chaotic, turbulent flows in different engineering applications. The study compares standard FEM to advanced FEM techniques, focusing on their computational accuracy, how they converge and how well they match with reality. In this section, the authors describe the mathematical equations, the boundary rules and the numerical routines used. The performance of the advanced FEM framework is demonstrated through its use in solving lid-driven cavity flow, flow over a cylinder and internal pipe flow problems. Finally, the paper points out that FEM-based solvers may play a key role in future Multiphysics and real-time engineering situations
Job Satisfaction Of Police Personnel’s Through Emotional Labour And Occupational Stress
Get PDFPurpose: This research endeavor delves into the intricate dynamics between emotional labour, job satisfaction, and the well-being of police personnel, seeking to elucidate the nuanced mechanisms by which emotional labour influences job satisfaction in this unique occupational context.
Design: A mixed-methods approach was employed, combining primary data collection through a survey of 23/7 police personnel members, with secondary data gathered from reputable sources such as Google Scholar, Science Direct, and Semantic Scholar.
Findings:The findings reveal a significant negative relationship between emotional labour and job satisfaction, with high levels of occupational stress exacerbating this effect. The study's conclusions underscore the critical impact of job satisfaction and occupational stress on police personnel performance.
Originality Value: The contribution of the research to the existing body of knowledge by highlighting the importance of addressing emotional labour in police personnel, with a view to improving job satisfaction and overall well-being. The study's findings have significant implications for policy-makers and police administrators seeking to mitigate the negative effects of emotional labour. (EL
Self-Adaptive Wireless Communication: Leveraging ML And Agentic AI In Smart Telecommunication Networks
Get PDFSmart wireless telecommunication networks are increasingly incorporating various Machine Learning (ML) techniques for enhanced performance. These algorithms are anticipated to continue in the post-5G/6G era. Current mainstream telco networks rely on rules, thresholds, and simple heuristics for controlling complex processes and behaviors. As a result, many applications, such as forecasting key performance metrics, detecting unusual performance patterns (anomalies), and doing root cause analyses now require more elaborate AI algorithms for their automated realization. Although they have been successfully deployed in inspection tasks, ML and AI-enabled functions still mainly work in the “pilot frame”, meaning that when a function works well on a specific case, it needs to be re-trained, re-tested, or re-tuned for handling different instances. Deep Learning (DL) techniques are replacing traditional data-centric architectures, pipelines, and algorithms in many industries. They enable automatic feature extraction, state-of-the-art performance, and more interpretable results. However, it is also important to investigate novel DL architectures or training pipelines that can adapt themselves to very large, changing models and topological structures and be trained and evaluated continuously without stopping services. Enabling Self-Adaptive (SA) AI is among the next big challenges in digital telecommunications, including but not limited to the following endeavors and questions. What monitoring metrics, strategies, and methodologies are effective in inspection tasks of large ray algorithms or ML models? How can the potential cause space of Managerial Performance Confidentiality (MPCC)-related anomalies be narrowed down or partitioned for fault detection and root cause localization? Clustering and classification algorithms with clear interpretability characteristics will be investigated for this endeavor. In addition, Enabled State Estimation (ESE) is one of the most critical building blocks for enabling proactive, efficient, and powerful management and control of telecommunication networks . By modeling the spatio-temporal SST behavior of the entire network, it is possible to synchronize many important tasks in the time and data domains, which makes some complex-to-explain and complex-to-controlled scenarios manageable. Meanwhile, this paradigm also raises probing questions of how to implement ESE in low-cost and on-demand modes in flexible, multi-dimensional SaaS cases
Artificial Intelligence in Ultrasound Medicine: Technological Innovations, Clinical Integration, and Ethical Challenges
Get PDFThe application of artificial intelligence (AI) algorithms in clinical practice faces a number of obstacles, despite the fact that AI has shown promise in improving ultrasound diagnosis. In order to highlight important factors for creating and deploying AI solutions in breast cancer imaging, this scoping review attempts to identify these obstacles and enablers. Six databases (PubMed, Web of Science, CINHAL, Embase, IEEE, and ArXiv) were searched for relevant material between 2014 and 2024. Articles that exclusively focused on performance or that used data that was not gathered in a clinical radiology context and did not involve actual patients were removed; instead, articles that described some of the challenges or facilitators in the development or implementation of AI in clinical imaging were included. This study underlines the value of patient-centered design, efficient governance, and interdisciplinary collaboration in guaranteeing that all demographic groups have equal access to innovative AI-enabled ultrasound technology that has been ethically manipulated
Construction And Analysis Of Hyper Block Graeco Latin Sudoku Square Design
Get PDFA novel experimental design called Hyper Block Graeco Latin Sudoku Square Design (Hyper Block GLaSS Design) has been released. It applies the row blocking and column blocking features of Subramani and Pormuswamy Sudoku Square Designs. The Error Sum of Square is decreased by implementing the Block Sum of Square for both rows and columns. Hyper Block GLaSS Design aims to investigate eight parameters and test three sets of treatments concurrently in a single experiment. For Hyper Block GLaSS Design a numerical example is used to analyze the fixed effect model's construction, compare it to the Hyper Graeco Latin Sudoku Square Design, and determine how efficient it is.
By adding row and column blocking, the suggested new design outperforms the Hyper Graeco Latin Sudoku Square Design in terms of lowest mean squares error
Role Of Social Risks And Customer Trust In Shaping Purchase Intentions Through Digital Media Marketing In OTT Platforms
Get PDFThis study investigates the impact of Digital Media Marketing (DMM) on the purchase intentions of consumers in OTT environments, considering the mediating roles of customer trust and social risks. A descriptive research design was employed, with primary data collected through an online survey from 454 respondents, focusing on their viewing patterns, perceptions of digital marketing, and related variables. The findings indicates that digital marketing significantly influences product awareness and convenience, but the concerns on over privacy and information security is hindering the full engagement of the customers. Customer trust emerged as a critical factor influencing purchase intentions, while social risks, such as societal pressures, were found to have a negative impact. Regression analysis estimated that customer trust and social risks mediate the influence existing between digital marketing and purchase intentions. The study suggests that OTT platforms should focus on enhancing trust-building measures, addressing privacy concerns, and creating culturally sensitive campaigns to reduce social risks, thereby fostering greater consumer engagement and driving purchase decisions
An Experimental Investigation Of The Seismic Behavior Of Brick Walls Reinforced With Hybrid Steel-Glass Fiber Concrete
Get PDFThe existing masonry walls are highly vulnerable to earthquakes. One of the new methods of seismic improvement of existing buildings is the use of concrete coating. This study investigated the effectiveness of enhancing unreinforced masonry (URM) walls using hybrid concrete coating (concrete with steel-glass fibers). This study was experimental and tested two sample walls on a real scale and under an in-plane cyclic loading. One sample was a reinforced wall coated with hybrid fiber concrete (HFC) on one side, and the other was a reinforced wall coated on both sides; meanwhile, an unreinforced wall served as the reference wall. The results revealed that enhancing URM walls with HFC coating provides economic benefits and work simplicity, and most importantly, avoids crack formation by creating a hardened panel. Besides, an HFC layer could considerably enhance the wall’s lateral bearing capacity, energy, and stiffness
One-Pot MCR Of 3,4-Dihydropyrimidine-2-(1H)-One Derivatives By Silica Supported Cu-Zn Nanoparticle
Get PDFA rapid and environmentally sustainable one-pot MCR for the formation of 3,4-dihydropyrimidine-2-(1H)-one analogues, utilizing a silica-supported Cu-Zn nanocomposite as an efficient catalyst under 40 kHz ultrasound conditions with ethanol as the solvent. This one-pot multicomponent reaction, which integrates aromatic aldehydes, urea, and ethyl acetoacetate, yields remarkable results in a very short time, demonstrating high selectivity. The synthesized silica-supported nano catalyst was thoroughly characterized using SEM-EDX. Various spectroscopic techniques, including 1H NMR, 13C NMR, and mass spectrometry has been to confirm the structural information of formed molecules. This method additional advantages like as operational simplicity and environmentally viable, a nano-stable catalyst with excellent reusability (up to 8-10 cycles), rapid reaction times (<10 min), and the elimination of high-cost purification. Because of these attributes, the used method plays dual role sustainable and cost-effective
A Comprehensive Machine Learning Framework For Predicting The Energy And Economic Impact Of Electric City Buses
Get PDFThe research work currently attempts to reduce the carbon emissions and make energy-efficient urban public transportation with electric buses. This research sets up a big data analytics framework with machine learning to forecast and optimize the energy consumption of electric city buses. Such system offers accurate prediction of energy economy by utilizing real-time large-scale telematics and operational information processed via batch and stream through Apache Spark. As per the objective of fast-paced transit environment subjected to continuous disturbances by traffic, weather, and vehicle load, the scalability of the framework serves as a crucial capacity for distributed computation and in-memory processing. Energy consumption can be viewed in a holistic manner charged with heterogeneous data sources. Such predictive insights enable transit agencies to undertake proactive energy strategies, model optimization on routes, and introduction of batteries that last longer into lower operational costs with reduced environmental impact. Future work will be targeted towards real-time integrated streaming tools such as Apache Kafka and Flink and deploy advanced models like LSTM and Reinforcement Learning while developing visual analytics and cloud scale. The research will explore how NLP can be subjected to use for unstructured data analysis, for instance through driver logs and maintenance reports. From intelligent transport systems, this framework is considered a great major step and indeed becomes a crucial building block towards the vision of smart energy-efficient cities