Metallurgical and Materials Engineering (E-Journal)
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Why you Leave? An approach to understand Job Embeddedness, Staffing & Employee Performance
Get PDFThe worldwide labor force has undergone substantial changes in the last century. These changes are mostly dependent on the level of job embeddedness and the variety of staffing strategies that businesses employ. Our current reality necessitates uniqueness, adaptability, and creativity. It goes without saying that companies will need to modify their thought processes to keep up with this shift, as well as their staff retention management protocols. This study looked at the impact of job embeddedness as an independent factor on employee performance as well as the relationship between staffing practices and performance. The hypothesis was accepted since there was a strong and positive association between staffing and employee performance. However, the effect of work embeddedness on employee performance was not supported by statistics and the hypothesis was not maintained. Policymakers in human resources will find this research to be very helpful
Autonomous Nano Drones for Suspicious Activity Detection and Tracking of Doubtful Individuals
Get PDFThis paper proposes a novel framework that leverages autonomous nano drones equipped with advanced AI capabilities for real-time detection and tracking of suspicious individuals in dynamic environments. The system integrates lightweight object detection using YOLOv7, temporal behavior analysis via Long Short-Term Memory (LSTM) networks, and swarm-based coordination driven by Particle Swarm Optimization (PSO). Drones collaboratively monitor and analyze human activity, while onboard edge processing ensures low-latency decision-making without reliance on centralized computation. Kalman filters are employed for accurate and continuous target tracking, and a secure mesh communication protocol facilitates real-time alert generation to the control center. Experimental evaluation demonstrates superior performance of the proposed system over traditional surveillance approaches, achieving higher detection accuracy (92.3%), improved activity classification (89.5%), and reduced latency (45 ms). The results affirm the effectiveness and scalability of autonomous nano drone swarms for intelligent surveillance applications in smart cities, critical infrastructure, and defense operations
Aqueous Method Synthesis and Characterization of SiO2@CsPbBr3 Thin Film Structured Materials
Get PDFNanocrystalline CsPbBr3 and CsPbBr3 phosphor layers were successfully deposited onto monodisperse, spherical, and non-aggregated SiO2 particles using an aqueous synthesis method. This process led to the formation of SiO2@CsPbBr3 thin-film structured materials. The structural, morphological, and optical properties of the resulting materials were systematically investigated through X-ray diffraction (XRD), scanning electron microscopy (SEM), photoluminescence (PL) spectroscopy, and UV-visible absorption spectroscopy. XRD results confirmed the successful coating of CsPbBr3 layers on the SiO₂ surfaces, this finding further verified by SEM images. The photoluminescence spectra revealed that the SiO2@CsPbBr3 thin film materials exhibited a prominent green emission band, with maximum at 507 nm, indicating their potential in optoelectronic and photonic applications
A Hybrid Explainable AI Framework for Early Lung Cancer Detection Using CTGAN-Augmented Clinical Data, Gene Biomarkers, and Transformer-CNN Networks
Get PDFDue to delayed diagnosis and restricted access to early screening, lung cancer continues to be a major cause of cancer-related death. In order to improve early lung cancer detection, this study suggests a hybrid AI-driven diagnostic system that integrates transformer-CNN-based deep learning, synthetic data generation using CTGAN, and gene expression profiling. The Kruskal-Wallis statistical approach is utilized to identify important gene biomarkers, while CTGAN is employed to address class imbalance and enrich the dataset. A new explainable AI architecture is created to accurately classify patient outcomes by combining a bespoke CNN with a Pyramid Vision Transformer (PVT). The suggested model achieves 98.93% accuracy with full explainability via GradCAM, outperforming conventional classifiers. The findings show that there is a great deal of promise for better clinical oncology diagnosis and individualized care
Emotional Intelligence in Artificial Agents: Leveraging Deep Multimodal Big Data for Contextual Social Interaction and Adaptive Behavioral Modelling
Get PDFAs artificial agents develop beyond mere tools and begin to perform roles traditionally associated with humans, expectations of their performance are equally evolving. Not only must agents be able to accomplish their tasks; but they must also be able to do so in a manner that observers would consider socially or contextually appropriate. For social interaction where the agent and human are co-performers, adherence to social cues that signal emergent aspects of a relationship such as intimacy or status is paramount to the experience of the interacting humans. For autonomous agents who function alone, adaptive behavioral modeling and user state awareness are critical to the impact of the agent’s actions on humans. Such contextual social behavior is a requirement for complex applications including physically located social robots, virtual avatars emerging in gaming, online social environments, or customer service interactions, and proactive virtual assistants. Humans have sophisticated socio-emotional capacities that enable them to behaviorally coordinate their interactions with others, inferring mental states that may lie far beyond explicit observable cues. Furthermore, emotional expressions are multimodal and are the result of a complex interaction between inherent affective states and contextual interaction. The Human Centered Intelligent Systems conceptual framework describes a pathway whereby artificial agents may also achieve aspects of this intelligence through rich user state modeling based on deep multimodal analysis of big data that can capture the social behavior and interaction context. In this chapter, we describe this "user-state" modeling approach and exemplify its applicability to a spectrum of agent applications
Impact of Post-Weld Heat Treatment on the Microstructural and Mechanical Behavior of API X70 Steel
Get PDFThe global advancement in scientific research within the petroleum sector, particularly in transportation methods, has driven interest in understanding the welding characteristics of petroleum pipelines. This study provides an experimental evaluation of the effects of heat treatments on the mechanical and microstructural properties of welded API X70 steel. Specifically, it investigates the influence of varying heat treatment conditions on the fusion zone (FZ) of API X70 steel welds. The heat treatment process was carried out at temperatures ranging from 450℃ to 650℃, in increments of 50℃, with each temperature maintained isothermally for 2 hours. Comprehensive characterization techniques, including optical microscopy, scanning electron microscopy (SEM), and X-ray diffraction (XRD), were employed to analyze the microstructural evolution. Additionally, Vickers hardness testing was conducted to evaluate the mechanical response. The results demonstrated that increasing the heat treatment temperature promoted grain coarsening and growth within the fusion zone, accompanied by a progressive decline in the hardness of the weld joints. These findings highlight the critical role of heat treatment parameters in tailoring the performance of welded pipelines
Design of a Microstrip Patch Antenna for Drone Detection and Tracking
Get PDFWith drones rapidly proliferating, opportunities — and challenges — have arisen in the area of surveillance, logistics, and security. But, since their usage is increasing, a robust detection and tracking system is needed to mitigate any possible threat. This work studies drone detection and tracking based on microstrip patch antennas. For this application, these antennas are preferred, with their low profile, low-weight structure, and low cost. Theoretical foundation, design methodology, simulation results, and potential applications related to signal processing and system integration for improved tracking accuracy are discussed
Artificial Intelligence Based Domestic Plant Selection for Optimum Sustainability
Get PDFIn the era of environmental awareness and sustainability, the concept of "Go Green" has gained significant momentum. Urban households and indoor gardening enthusiasts often struggle to select suitable plants that can thrive in their specific environmental conditions, leading to poor plant growth and resource wastage. Existing solutions primarily rely on generic plant recommendation systems or manual selection based on experience, which may not ensure optimal sustainability. To address this challenge, this paper presents an Artificial Intelligence-based plant selection system that utilizes real-time environmental data, including temperature, humidity, and soil moisture, collected using sensors. The system employs a Random Forest algorithm to analyze these parameters and match them with a curated plant dataset, ensuring the selection of the most suitable plants for a given location. Experimental results demonstrate that the proposed approach enhances plant survival rates and promotes efficient resource utilization. This AI-driven solution provides an intelligent, automated, and sustainable method for plant selection, contributing to the broader goal of environmental conservation and urban greenery optimization
Legal Frameworks for Combating Dangerous Cyber Offenses in Vietnam
Get PDFIn recent years, Vietnam has witnessed a significant rise in cybercrime, posing serious threats to national security, economic stability, and individual privacy. The rapid digital transformation and increasing internet penetration have outpaced the development of effective legal frameworks to combat these dangerous cyber offenses. This study analyzed the existing legal frameworks in Vietnam concerning cybercrime, assessed their effectiveness, and identified gaps that hinder effective law enforcement. The study systematically examined relevant laws, regulations, and policies, including the Cybersecurity Law and the Penal Code, while also considering international best practices in combating cybercrime. Additionally, the study used specific cases and peer-reviewed journal articles on the subject of legal frameworks around cyber offenses to synthesize and compare the analysis from the laws. Findings indicate that while Vietnam has made strides in establishing a legal foundation for addressing cybercrimes, challenges remain, including vague definitions of cybercrimes, insufficient enforcement mechanisms, and a lack of public awareness regarding cybersecurity laws. This study emphasizes the urgent need for comprehensive reforms to strengthen Vietnam's legal frameworks, including clearer definitions of cyber offenses, enhanced collaboration between government agencies, and increased public education on cybersecurity. This study is relevant not only for policymakers and legal practitioners in Vietnam but also for scholars and international organizations seeking to understand the complexities of cybercrime legislation in emerging digital economies
Investigation Of Alternating Bands In Friction Stir Welds Of Aluminum Alloys
Get PDFFriction stir welding (FSW) is a solid-state welding process that involves complex phenomena influenced by welding parameters such as feed and rotation speeds, as well as the tool geometry. These parameters condition the temperature cycles, which in turn affect the microstructure and mechanical properties of the weld. This study investigates the formation of alternating bands in FSW aluminium alloys, specifically focusing on the impact of welding parameters, tool geometry, and grain boundary characteristics. The alternating bands are identified as regions of contrasting grain structures, with differences observed in grain size and boundary orientation. High-angle (HA) boundaries, predominantly (111), and low-angle (LA) boundaries, predominantly (101), are found to alternate within the bands. Hardness tests, including Vickers and nano-indentation, show no significant variation in hardness between the alternating bands. The weld core consists of larger grains with HA joints and smaller grains with LA joints. The RS, on the other hand, contains equiaxed grains with HA joints and random orientations. Furthermore, the microstructure formed by HA grain boundaries exhibits a higher concentration of particles compared to the LA boundary areas. These findings provide new insights into the relationship between welding parameters, microstructure, and mechanical properties in friction stir welds