In an increasingly competitive global market, organizations are seeking to enhance both cost efficiency and regulatory compliance in their supply chains. This paper explores the integration of Quality at Source (Q@S) into supplier management as a strategic pathway to achieve these goals. Quality at Source is a proactive approach that emphasizes defect prevention and quality assurance at the earliest stages of production, specifically at the supplier level. By embedding Q@S principles within supplier management, companies can not only reduce the financial burden associated with defect detection and correction but also mitigate risks associated with non-compliance with industry regulations.

The study identifies key benefits of Q@S, including cost savings through reduced waste, improved production efficiency, and minimized need for extensive downstream quality control. Additionally, it demonstrates how Q@S facilitates regulatory compliance by ensuring that quality standards are met consistently, thus simplifying audits and reducing the likelihood of penalties. The paper details a framework for Q@S implementation, covering essential steps such as supplier selection, establishing quality metrics, conducting regular audits, and fostering a culture of continuous improvement among suppliers.

Through a combination of case studies and data-driven insights, the paper illustrates the tangible benefits of Q@S integration. Real-world examples showcase successful applications of Q@S across different industries, highlighting significant cost reductions and enhanced compliance. Furthermore, this research discusses the challenges companies may face, such as initial investment costs and resistance to change from suppliers, and provides strategies for overcoming these barriers.

The paper concludes by examining the role of advanced digital technologies—such as artificial intelligence (AI), Internet of Things (IoT), and blockchain—in furthering Q@S initiatives. These technologies enable real-time monitoring and data transparency, enhancing the capacity of organizations to track supplier performance and maintain quality standards. The findings underscore the importance of Quality at Source as an essential component of modern supplier management, providing a roadmap for organizations aiming to achieve cost-effective, compliant, and sustainable supply chains.

This article outlines the importance of cybersecurity for modern engine control systems. Much emphasis is placed on how fast-paced the technologies in the automotive industry are and the several risks involved with their use. Due to high connectivity and heavy dependency on software, serious vulnerabilities are present in trucks' engine control units (ECUs) that pose a severe threat regarding cybersecurity in the automotive industry. The article discusses how engine control systems have been developing over time, enumerates the most common cyber-attacks they face, and points out the consequences that may influence vehicle safety and performance, as well as environmental compliance. Attention is given to regulatory laws, industry standards, and strategies regarding how to handle these challenges. The article also discusses how artificial intelligence influences ECU security and the challenges and opportunities arising in this fast-changing sector. Overall, the article provides a broad overview of the relationship between cybersecurity and ECUs, underlining the fact that strong security is urgently needed to provide safety, reliability, and trustworthiness to modern vehicles.

The categorization of intricate emotional phenomena in news texts permits the assessment of the impact of such news on society. In this context, it is evident that the existing body of research on emotion analysis in Turkish texts is insufficient. The objective of this study is to identify the emotions of "fear," "happiness," "anger," "sadness," and "surprise" in Turkish news texts using machine learning methods. In consideration of the Turkish language structure, the adjectives and nouns were parsed on a specially prepared corpus and dictionary groups specific to this study were created with the use of the TF-IDF and Double Normalization methods. Subsequently, these lexicon groups were employed for the classification of sentiment in news texts using a range of machine learning techniques, including Multilayer Perceptron (MLP), Support Vector Machines (SVM), Random Forest (RF), LightGBM, XGBoost, GPT-2, BERT, DistilBERT, and XLM ROBERTa. The evaluation metrics employed were F1-score, accuracy, recall, and precision. In the training phase of the model, the dictionary group created with Double Normalization, the GPT-2 model, the Grid Search optimization method, and cross-validation were employed in order to achieve the highest F1-score of 0.99 and accuracy of 0.99. It is anticipated that the methods and dataset utilized in this study will contribute to the existing literature on sentiment classification in Turkish texts and provide insights that will inform future similar studies.

Artificial intelligence (AI) has bloomed in recent years and is gradually becoming an irreplaceable asset in finance,
among other sectors. Personal finance is a subset of finance, which too is being revolutionized due to changing times
and technological advancements, much like AI. Security and proper financial guidance have never been more
important with such significant change. In this study, we use FinBERT, a modern large language model specialized
in the financial domain, for our AI-powered personal finance coach. However, FinBERT, although a cut above the
rest, still has room for growth, so we aim to improve its flaws and enhance its efficiency. We established that
FinBERT succeeded in detecting sentiments in explicit sentiments, but was not usually successful in doing so
correctly for implicit sentiments. FinBERT, despite its limitations, has a high accuracy and is the best model to use
in our study. This model can also be utilised to provide accurate results regarding the overall trend (positive or
negative) of the global stock market. Our results demonstrate that integrating AI in personal finances is feasible and
can successfully aid individuals in making decisions regarding their finances.

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.