This 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.
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 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.