Al-Iraqia Journal for Scientific Engineering Research

Heat sinks, a type of heat exchanger used due to the simplicity of fabrication, low cost, and reliable heat dissipation, are widely employed to cool electronic devices. In recent decades, heat dissipation has remained a critical challenge in optimizing the thermal performance of heat sinks. Therefore, to ensure optimal performance and reliability, effective heat removal from these devices is essential across various operational conditions. This comprehensive review examines various methods for enhancing the thermal design of heat sinks. It includes investigations into both passive and active techniques that modify the solid or fluid domain to improve heat removal. The study encompasses different approaches, focusing on perforated plate-fin heat sinks, phase change materials (PCM) enhanced by metal foam, active cooling techniques, and cost optimization. The literature revealed a strong preference for plate fins as the primary heat sink design, comprising 70% of the investigated studies. Pin fins were utilized in 15% of cases, while a hybrid configuration incorporating both plate and pin fins was observed in the remaining 15%. The integration of fins with the chosen PCM significantly accelerated the melting process, leading to an average improvement in thermal performance. The composite incorporating foam and PCM demonstrated a substantial improvement in thermal conductivity. This study aims to summarize existing research efforts, identify limitations, and explore potential solutions for advancing the thermal performance of heat sinks.

The review delves into creating an understandable framework for machine learning in robotics. It stresses the significance ofmachine learning in materials science and robotics highlighting how it can transform industries by boosting efficiency and deepening our knowledge of materials on levels. The review also discusses the hurdles posed by attacks on machine learning and the increasing relevance of machine learning in software development. It outlines the approach used in the review,including the search strategy criteria for inclusion and exclusion and the process for selecting studies, including adherence to research published in English only. The classification section organizes the chosen studies into six areas: reinforcement learning, adversarial techniques, applications of learning,and image recognition. In the Discussion section,challenges like critical learning models in robotics unsupervised learning, adversarial attacks on datasets,and limited data for polyp detection are identified. Recommendations for research are provided along with insights into motivations behind these studies; topics covered include reinforcement learning, adversarial examples, domain alignment,and world adversarial attacks on industrial systems

Incorporating Internet of Things (IoT) technology into load control systems has changed how we acquire regulatorsand monitordevices, making it more convenient and safer.The proposed systemfocuseson designing an intelligent indicator and control system solution for demanding industrial environments where equipment experiences heavy-duty cycles. The Arduino microcontroller dictates myriad safety features and controls and monitors various devices' functions. This allows for easier mobile and Wi-Fi integration to remotely operate electronic devices, providing the system with an additional level of efficiency that increases safety in various applications such as agriculture, home automation, and industrial uses. Based on themulti-segment framework, the system processes input signals managed by an Arduino-based microcontroller that controls output devices such as motors, fans, andlights. It can be monitored 24/7 via a mode based on IoT (Internet of Things) technology to receive feedback and perform operations fromanywhere in the world. This systemsupports system anomaly detection-e.g., motor stallsandovervoltage conditions. Itcan alert users if something malfunctions, improve visibility of day-to-day operations, and allowfor swift responses. The results show that the designed system has improved the flexibility and usability of the operational load control using the Arduino microcontroller. It was also found that upon detecting a stall, the system feedback is sent as a notification to the user by the Blynk mobile app, thus preventing the motor operations from serious damage. Therefore, this system was able to offer sustainablesolutions in contrast tooverloading or overheating that may be faced bythe system or the motors.It is also recommended that load frequency control (LFC) be implemented with Proportional-Integral-Derivative (PID)controllers and optimizedtechniques to make interconnected power systems more stable and efficient

Human Emotion Expression isdifficult to define and categorize, making it a difficult research topic. Teaching machines to decipher facial expressions is difficult. Dynamic facial expressions include modest muscular movements that shape their appearance. Advanced algorithms and methods are needed to capture and analyze these minute differences. Deep learning\'s popularity has expanded emotion recognition applications.The ability to recognizefacial expressions has several practical uses. In the medical field, it can help doctors better understand their patients\' mental health issues and formulate effective treatment plans. Emotion recognition may be used in the field of customer service to get a feel for how satisfied customersare and adjust strategies accordingly. User experiences may be improved through human-computer interaction when emotion detection allows for more natural and individualized interactions with technology. Gaming and entertainment could employ emotional reaction recognition to tailor and engage players based on their moods. Emotion detection can also improve situational awareness and identify high-stress situations in law enforcement and security. Recognition of pupils\' facial expressions can assist teachers in improvingclassroom instruction and comprehendingtheiremotions. Deep learning models provide an advantage over prior emotion-recognition systems, but they need additional research for high-robust expression detection

Communication systems are constantly evolving to keep pace with the increasing number of users and their digital transactions, and the need to increase the capacity of communication networks has emerged. However, hacking and phishing operations have increased to obtain or modify users\' private data. As a result, it has become necessary to enhance network security measures to protect systems and users from hacking attempts by employing encryption techniques to secure important data. In the proposed system, a communication system using advanced Li-Fi technology was developed and tested in different weather conditions and at different distances, and the system was protected using RSA technology to encrypt data and,transmit it over the network and then decrypt it at the receiving end. This approach has shown positive results with the use of large prime numbers with a high level of resistance to hacking and decryption, ensuringthe secure flow of network data

This paper presents a performance analysis of a three-phase interleaved buck-boost converter integrated with a Maximum Power Point Tracking (MPPT) algorithm using the Perturb and Observe (P&O) method for an independent wind energy generation system. The P&O method effectively identifies and tracks the maximum power point (MPP) under varying wind conditions. The use of a three-phase interleaved converter improves the power conversion efficiency and reduces input current ripple while distributing thermal stress across multiple phases. Simulation results, conducted via MATLAB, demonstrate a significant increase in overall systemefficiency, with power extraction efficiency improving by up to 20% compared to traditional single-phase converters. The converter also reduces current ripple by approximately 30%, enhancing stability during variable wind profiles. The system responds accurately to wind speed changes, ensuring optimal energy extraction across variousoperational conditions. This study provides practical insights into the design and application of advanced power converters and MPPT controllers in wind energy systems, offering apathway toward improved energy management and reliability in wind energy conversion systems

The heat exchanger was invented in thermal contact and at different temperatures for the transfer of enthalpy (thermal energy) between two or more liquids or a liquid and solid surface (one medium to another medium). Basic units called heat exchangers are used to extract and recover heat in the industrial process globally. Various applications and devices use the heat exchanger to exchange heat between two media, including cooling towers, car radiators, condensers, evaporators, etc. An improved techniqueinthe field of heat transfer serves as a research task and is classified as an active, passive, and large-scale composite technique. Active methods need an external power source for the input process and do not need an additional power source or power to improve heat transfer. Passive methods are generally used in both numerical and experimental applications to save energy and cost in enhancing heat transfer and friction losses. The inclusion of wire coils, internal threads, and nanofluid comes under passivemethods, which areused to improve the heat exchanger's overall performance and thermal efficiency. The current review investigates the enhancement of the heat transfer rate using wire coils as a means of optimization fortwo types of laminar and turbulent flow.The current work proposal for future studies of the same field is to use two techniques to improve heat transfer, including a wire coil with ahybrid nanofluid to enhance the thermal conductivity coefficient

Today, human life is threatened by road accidents inside the vehicle especially when driving in bad weather conditions such as rainy, icy, and foggy weather. These weather conditions can be treacherous if the driver does not have enough preparation and awareness of his vehicle for bad weather on the road. The driver must have preparations such as slowing down, increasing the following distance, keeping the light ON, breaking gently, and preparing for unexpected things happening on the road; all these need more attention on the road. Therefore, most vehicles today have a controller (ECU) (Engine Control Unit); it is like the human brain; the main job of the ECU is to collect the vehicle's information from sensors such as tire pressure, engine, brake, light, airbag, and collision. However, the important information for our project comes from collision sensors fixed on the front and back bumpers of the vehicle. The development of technology such as Visible Light Communication (VLC) is widely used for high-speed wireless data communication with a high data rate for different applications. The VLC technique is less affected by bad weather conditions and transmits data of different types over long distances at high speed. Therefore, this technology is used for numerous promising applications and is found suitable for outdoor and indoor applications. This article project uses visible light techniques to exchange information for all vehicle sensors with data centers through road sign grid networks. The main source of a transmitter and receiver by using a matrix LED light fixed on the Headlight or tail-light of the vehicle. In addition, all this information collected from different vehicles is processed in the Network Road Data Centre. The result will be a signal of attention and order to the Driver and ECU of the vehicle to reduce the speed and stop the vehicle. Finally, a new VLC transmission method is proposed in this article for communication between vehicles and environments to make intelligent roads and save human life. Moreover, the design uses VLC encoded by complex modulation techniques called recursive optical OFDM with a low FPGA power consumption, leading to an increase in vehicle response and a reduction in accidents compared with other methods