The integration of Advanced Artificial Intelligence (AI) technologies in Printed Circuit Board (PCB) manufacturing has revolutionized the industry, offering significant improvements in defect prevention and yield optimization. As PCB manufacturing processes become increasingly complex and intricate, traditional methods of defect detection and quality assurance are often insufficient to meet the high demand for precision and efficiency. This paper explores the application of cutting-edge AI methodologies, including machine learning, computer vision, and neural networks, to address critical challenges in PCB production. The primary focus of this research is on the role of AI in enhancing automated defect detection systems and optimizing production yield. AI-based systems such as predictive maintenance, real-time process monitoring, and intelligent decision-making have been shown to drastically reduce defects while improving overall production throughput. Machine learning algorithms can identify subtle defects in real time, often undetectable by conventional methods, while neural networks can analyze historical data to predict potential failures before they occur. Additionally, AI-driven optimization techniques help manufacturers adjust production parameters dynamically, ensuring higher yields and minimizing waste.

Through a combination of theoretical analysis and case studies, this paper highlights the effectiveness of AI-driven solutions in PCB manufacturing. Results from industry applications indicate significant improvements in both quality control and yield rates, providing a competitive edge to manufacturers adopting these technologies. Furthermore, this paper discusses future trends, including the integration of AI with the Internet of Things (IoT), edge computing, and sustainable manufacturing practices, which will further enhance the capabilities of AI systems in this field. The findings suggest that advanced AI technologies are not only capable of overcoming existing challenges in defect prevention but also hold the potential to reshape the future of PCB manufacturing by offering highly adaptive, precise, and efficient solutions. This research underscores the transformative impact of AI in modernizing manufacturing processes, making it an indispensable tool for the future of the PCB industry.

The integration of the Internet of Things (IoT) and Edge Computing is revolutionizing the manufacturing industry, ushering in the era of smart manufacturing as part of Industry 4.0. This paper explores the synergy between IoT and Edge Computing, focusing on their combined architecture and the future trends driving innovation in smart factories. IoT enables the connection and communication of machines, sensors, and systems, allowing for real-time data collection and monitoring. However, traditional cloud-based approaches face challenges such as latency, bandwidth limitations, and security risks, which can hinder real-time decision-making in fast-paced manufacturing environments. Edge Computing addresses these issues by processing data closer to the source, minimizing latency and reducing dependence on cloud infrastructures. By combining IoT and edge solutions, smart manufacturing systems can make faster, data-driven decisions, leading to improved efficiency, reliability, and operational flexibility. This paper delves into the architectural design of IoT and edge computing in manufacturing, outlining how data flows from IoT devices to edge nodes and cloud services. Several real-world use cases and industry examples are analyzed to highlight the practical benefits of these technologies.

Additionally, this research identifies key challenges such as security vulnerabilities, the need for robust network infrastructures (e.g., 5G), and issues related to data standardization. The future of smart manufacturing is also explored, emphasizing trends like the adoption of artificial intelligence (AI) and machine learning (ML) at the edge, digital twins for real-time monitoring, and the role of IoT and edge computing in fostering sustainability through energy-efficient production processes.This study provides a comprehensive overview of IoT and edge computing architectures in smart manufacturing and offers insights into future technological trends that will shape the industry.

Abstract

This paper discusses a case study of a residential building in Benghazi that features slab-on-grade foundations supported by expansive soils. Although such structures are uncommon in the area, they can suffer serious damage due to volumetric changes in the soil caused by wetting and drying. The building has a total plan area of 200 m², with tie beams located 1.2 m above the formation level, and expansive soil used as backfill. After being moistened and lightly compacted, 100 mm thick concrete slabs were poured between the tie beams, resulting in varying slab sizes.

Following a month of heavy rainfall, significant cracking—longitudinal, diagonal, and warping—occurred in the slabs due to uneven deformation from the expansive soil. The paper includes photographs of these deformations and cracks, along with an analysis of the geotechnical properties of the expansive soil, including its swelling and shrinking characteristics. It also employs an approach to predict the pressure exerted on the slabs due to differential heave.

In a world where 95% of cybersecurity breaches are due to human error, and a rapidly evolving cybersecurity landscape, the effectiveness of employee’s proper security awareness stands as a pivotal concern [1]. Especially when the employees are the first line of defense for any organization. This highlights the importance of having robust cybersecurity awareness that is dynamic to face the ever-changing threats in the world. However, traditional employee security awareness training programs often fall short, perceived as dull, ineffective, and unable to keep pace with the dynamic cybersecurity landscape [2]. These shortcomings not only diminish engagement but also lead to decreased knowledge retention, rendering employees more vulnerable to cyber threats.

In fiber communication system, delay elements are essential for coherent receivers and in the situation of time division multiplexing. Optical communication is transmitted with light. It has a propagation delay. So, in this system, the performance of optical delay element is demonstrated on wavelength conversion, effects of single mode fiber (SMF) dispersion and fiber length. A numerical simulation is used to predict the performance of delay element for different experimental conditions. It has been proven that when the wavelength is changed and its element covers the entire C band. The system is used with the suitable optical pump power and various fiber lengths. It is accomplished with the various parameters by using Opti-system Software. The system operates near 1550 nm and generates delays time approximately 2.65 nanoseconds. In this paper, the performance value is also stated the table which is comparison of theorical result and practical result of delay time.