×
The submission system is temporarily under maintenance. Please send your manuscripts to
Go to Editorial ManagerThis research provides a numerical investigation into the thermal and hydraulic performance of a pin-fin heat sink, considering the effects of fin radius, fin length, and the arrangement of the arrays. A 3D computational model was developed and solved in ANSYS Icepak for conjugate heat transfer and fluid flow under forced convection. The fin length was changed from 1 cm to 8 cm, and the fin radius from 1 mm to 6 mm. Inline and staggered configurations were investigated for the two types of arrangements to assess their effect on performance. The findings underscore an important trade-off between thermal resistance and pressure drop. As anticipated, the staggered configuration provided a consistent increase in the heat transfer coefficient because of better flow mixing and disruption of the thermal boundary layers. This improvement, however, came with a significantly higher pressure drop. From the analysis, it was evident that the best configuration is strongly influenced by the length of the fins. With shorter fins of 1-3 cm, the staggered array decreased thermal resistance far better than the in-line array. With longer fins of 6-8 cm, the in-line configuration frequently provided better overall performance because the mass flow rate was higher due to less pressure drop than the long, staggered path. In addition, the radius of the fin exhibited a nonlinear relationship with regard to performance. Increasing the radius provided a greater area for heat dissipation, but it also increased obstruction to the flow. For every specific length and arrangement combination, a thermal performance maximizing radius existed. This work gives important design rules for the thermal optimization of the heat sink geometry and emphasizes the importance of the staggered array, for short fin lengths, while revealing the in-line configuration advantage for long fin lengths when minimizing pressure drop becomes the main concern.
Internet of Things (IoT) technologies, particularly the Internet of Vehicles (IoV), have transformed transportation, enabling safer, more efficient, and intelligent mobility solutions. As mobile data and devices increase, cellular networks can support vehicular communication features for safety and non-safety purposes. This paper examines IoV integration with 5G communication technology in a smart city. With varying levels of vehicles numbers.5G efficiently supports internet vehicle communications with slicing technology offering a practical solution for IoV services. This research includes the description of the Internet of Vehicles with 5G system components. Covering the 5G with IoV in the smart cities framework for the development industry. Provide the simulation result for the IoV-5G proposed system. The results show that 5G-IoV outperforms IoV and LTE in every measured parameter, delivering up to 32% greater channel gain rate, about 65–70% lower network latency, and roughly 20–25% higher network transfer rate. The study examines and summarizes our simulation platform's performance. The analysis will be implemented by SUMO, Simu5G in the OMNeT++ simulation program.
Predictive maintenance (PdM) is a key enabler of reliability and cost reduction in Industrial Internet of Things (IIoT) systems. However, conventional centralized learning approaches require raw data aggregation, raising serious concerns regarding data privacy, communication overhead, and single points of failure. Federated Learning (FL) addresses part of this challenge by enabling distributed model training, yet centralized FL architectures still rely on a central aggregator. This paper proposes a decentralized federated learning (DFL) framework based on a ring topology for multi-class predictive maintenance in IIoT environments. The proposed approach enables collaborative model training among distributed edge nodes without relying on a central server, thereby enhancing system robustness, scalability, and data privacy. A lightweight deep neural network (DNN) is employed to classify five machine operating states using multi-sensor data, including vibration, temperature, and current measurements. Extensive experiments are conducted using a real-world sensor dataset collected from three-phase AC motors under multiple fault conditions. The performance of the proposed DFL approach is systematically compared against a non-federated baseline and a centralized FL (CFL) scheme. Experimental results demonstrate that the decentralized framework achieves clas-sification performance comparable to centralized FL, with an accuracy of up to 99.83%, while eliminating the dependency on a central coordinator. The findings confirm the feasibility of decentralized federated learning for predictive maintenance applica-tions, particularly in distributed industrial settings where data privacy, fault tolerance, and communication efficiency are critical.
Overhead crane systems are found in many industrial environments; however, controlling their motion in the presence of nonlinear and underactuated dynamics considers as a big challenge. To address this, a nonlinear control method proposed to enable the trolley to track the desired trajectory and quickly eliminate swing. First, feedback linearization is applied to the crane dynamics. Next, an energy-based compensation is implemented to ensure the boundedness of the system trajectories. Then, Particle Swarm Optimization (PSO) is utilized to optimally tune the controller parameters. The optimization relies on a multi-objective cost function formulated to simultaneously minimize steady-state error and overshoot, while improving robustness against model uncertainties and external disturbances. Finally, the robustness and validity of the proposed control method are demonstrated through the simulation of an underactuated crane system in several cases, including reference tracking, robustness against system uncertainty and external disturbances. Simulation results illustrated that the presented control method has minimum rise time, settling time with respect other control methods with zero steady state error.
The emergence of Large Language Models (LLMs) has opened new frontiers in artificial intelligence applications across multiple domains, including cybersecurity. This paper presents a comprehensive review of the role of LLMs in enhancing cyber defense mechanisms, with a particular focus on their effectiveness in identifying, mitigating, and responding to Advanced Persistent Threats (APTs) and other sophisticated cyber-attacks. We explore the integration of LLMs in threat intelligence, anomaly detection, automated incident response, and adversarial behavior analysis. By examining recent advancements, case studies, and state-of-the-art implementations, we highlight the strengths and limitations of current LLM-based approaches. Furthermore, we assess the challenges related to scalability, adversarial robustness, and ethical considerations inherent in deploying LLMs within cybersecurity infrastructures. The review concludes with future research directions, emphasizing the need for hybrid AI systems that combine LLMs with traditional rule-based and statistical methods to provide resilient and adaptive cybersecurity solutions in the face of evolving digital threats.
The rapid development of the Internet of Things (IoT) has drawn significant attention from both industry and academia, driven by the integration of cloud computing, big data analytics, machine learning, and cyber-physical systems in manufacturing. Programmable Logic Controllers (PLCs), long central to industrial control systems, have evolved from basic feedback control devices to advanced components capable of networking and data exchange through IoT technologies. The Industrial Internet of Things (IIoT) refers to intelligent automation systems that continuously monitor critical parameters and respond to changes in real time. The integration of IoT with PLCs is transforming industrial automation by enabling remote real-time monitoring, data-driven decision-making, and predictive maintenance through advanced analytics. IIoT technologies enhance manufacturing performance and offer strategic value across sectors. Understanding their impact involves examining current research, including technology assessments and application-based case studies. This study provides an overview of PLC systems evolving into IIoT frameworks, with a focus on implementing proportional-integral (PI) control using the Siemens S7-300. Designed for precise and consistent temperature regulation, this approach enhances process efficiency and product quality, making it highly suitable for industrial and manufacturing environments.
Smart city applications demand lightweight, efficient and dependable communication protocols to facilitate the functioning of resource-limited Internet of Things (IoT) devices. This work performs an extensive empirical study of the three most prominent IoT standards; Message Queuing Telemetry Transport (MQTT), the Constrained Application Protocol (CoAP) and Hypertext Transfer Protocol (HTTP) by emulating real-world smart city use cases using a Raspberry Pi based testbed. The primary metrics based on which the protocols are analyzed are latency, message overhead, delivery rate and energy consumption. ANOVA and Tukey's HSD tests are used to determine the statistical significance of experimental data. The test results indicate that CoAP under (QoS-1 reliability) shows the least latency and energy consumption and MQTT due to its support for Quality of Service (QoS) is the most reliable. Among the others, HTTP is in general performance terms certainly at the bottom of all metrics mainly for its verbosity and synchronous nature. The paper then also suggests a decision flowchart for developers to choose the suitable protocol according to application requirements. The results are more than just numbers on a graph, and the research can be deployed for advice for protocol selection in practice, where this study helps identify issues with encryption overhead (over 75\%) while showcasing multi-hop network scalability and adaptive switch mechanisms as areas that remain to be resolved. Such findings can be used as a basis for design approaches to construct secure, efficient and scalable communication protocols in urban IoT settings.
The growing sophistication of cyber threats exposes the limits of signature-based detection in Security Information and Event Management (SIEM) systems. User and Entity Behavior Analytics (UEBA) advances SIEM by enabling behavior-based anomaly detection, yet legacy approaches struggle with high false positives and poor adaptability to evolving threats. This research proposes an AI-driven UEBA framework that combines deep learning for modeling user behavior with graph-based tools to map system relationships, enhancing anomaly detection in enterprise environments. Using datasets such as CERT Insider Threat, UNSW-NB15, and TON_IoT, we simulate diverse behaviors and evaluate performance. Our Transformer-GNN ensemble achieved an F1-score of 0.90, reduced false positives by 40%, and cut incident triage time by 78% compared to rule-based SIEM. To support real-world use, we provide an open-source pipeline integrating with SIEM platforms via Kafka, Elastic search, and a modular ML inference layer. This work bridges AI research and deployable cybersecurity practice, advancing the development of adaptive, intelligent, and robust UEBA systems.