In this paper, we will provide an extensive analysis of how Generative Artificial Intelligence (GenAI) could be applied when handling Supply Chain Management (SCM). The paper focuses on how GenAI is more relevant in industries, and for instance, SCM where it is employed in tasks such as predicting when machines are due for a check-up, man-robot collaboration, and responsiveness. The study aims to answer two main questions: (1) What prospects can be identified when the tools of GenAI are applied in SCM? Secondly, it aims to examine the following question: (2) what difficulties may be encountered when implementing GenAI in SCM? This paper assesses studies published in academic databases and applies a structured analytical framework to explore GenAI technology in SCM. It looks at how GenAI is deployed within SCM and the challenges that have been encountered, in addition to the ethics. Moreover, this paper also discusses the problems that AI can pose once used in SCM, for instance, the quality of data used, and the ethical concerns that come with, the use of AI in SCM. A grasp of the specifics of how GenAI operates as well as how to implement it successfully in the supply chain is essential in assessing the performance of this relatively new technology as well as prognosticating the future of generation AI in supply chain planning.

The tunable conduction of coumarin-based composites has attracted considerable attention in a wide range of applications due to their unique chemical structures and fascinating properties. The incorporation of graphene oxide (GO) further enhances coumarin properties, including strong fluorescence, reversible photodimerization, and good thermal stability, expanding their potential use in advanced technological applications. This review describes the developmental evolution from GO, GO-polymer, and coumarin-based polymer to the coumarin-GO composite, concerning their synthesis, characterization, unique properties, and wide applications. We especially highlight the outstanding progress in the synthesis and structural characteristics along with their physical and chemical properties. Therefore, understanding their structure-property relations is very important to acquire scientific and technological information for developing the advanced materials with interesting performance in optoelectronic and energy applications as well as in the biomedical field. Given the expertise of influenced factors (e.g., dispersion quality, functionalization, and loading level) on the overall extent of enhancement, future research directions include optimizing coumarin-GO composites by varying the nanofiller types and coumarin compositions, which could significantly promote the development of next-generation polymer composites for specific applications.

Credit risk assessment is one of the most important aspects of financial decision-making processes. This study presents a systematic review of the literature on the application of Artificial Intelligence (AI) and Machine Learning (ML) techniques in credit risk assessment, offering insights into methodologies, outcomes, and prevalent analysis techniques. Covering studies from diverse regions and countries, the review focuses on AI/ML-based credit risk assessment from consumer and corporate perspectives. Employing the PRISMA framework, Antecedents, Decisions, and Outcomes (ADO) framework and stringent inclusion criteria, the review analyses geographic focus, methodologies, results, and analytical techniques. It examines a wide array of datasets and approaches, from traditional statistical methods to advanced AI/ML and deep learning techniques, emphasizing their impact on improving lending practices and ensuring fairness for borrowers. The discussion section critically evaluates the contributions and limitations of existing research papers, providing novel insights and comprehensive coverage. This review highlights the international scope of research in this field, with contributions from various countries providing diverse perspectives. This systematic review enhances understanding of the evolving landscape of credit risk assessment and offers valuable insights into the application, challenges, and opportunities of AI and ML in this critical financial domain. By comparing findings with existing survey papers, this review identifies novel insights and contributions, making it a valuable resource for researchers, practitioners, and policymakers in the financial industry.

Heat transfer fluids (HTFs) are critical in numerous industrial processes (e.g., the chemical industry, oil and gas, and renewable energy), enabling efficient heat exchange and precise temperature control. HTF degradation, primarily due to thermal cracking and oxidation, negatively impacts system performance, reduces fluid lifespan, and increases operational costs associated with correcting resulting issues. Regular monitoring and testing of fluid properties can help mitigate these effects and provide insights into the health of both the fluid and the system. To date, there is no extensive literature published on this topic, and the current narrative review was designed to address this gap. This review outlines the typical operating temperature ranges for industrial heat transfer fluids (i.e., steam, organic, synthetic, and molten salts) and then focuses specifically on organic and synthetic fluids used in industrial applications. It also outlines the mechanisms of fluid degradation and the impact of fluid type and condition. Other topics covered include the importance of fluid sampling and analysis, the parameters used to assess the extent of thermal degradation, and the management strategies that can be considered to help sustain fluid and system health. Operating temperature, system design, and fluid health play a significant role in the extent of thermal degradation, and regular monitoring of fluid properties, such as viscosity, acidity, and flash point, is crucial in detecting changes in condition (both early and ongoing) and providing a basis for decisions and interventions needed to mitigate or even reverse these effects. This includes, for example, selecting the right HTF for the specific application and operating temperature. This article concludes that by understanding the mechanisms of thermal degradation and implementing appropriate management strategies, it is possible to sustain the lifespan of thermal fluids and systems, ensure safe operation, and help minimise operational expenditure.

Onion (Allium cepa L.) is one of the important vegetables in Egypt. The study was conducted in the vegetable field to study the effect of different rates of phosphorus fertilizers and foliar application of Nano-Boron, Chitosan, and Naphthalene Acidic Acid (NAA) on growth and seed productivity of Onion plant (Allium cepa L., cv. Giza 6 Mohassan). The experiments were carried out in a split-plot design with three replicates. The main plot contains 3 rates of phosphorus treatments (30, 45 and 60 kg P₂O₅/feddan), Subplot includes foliar application of Nano-Boron, Nano-Chitosan and Naphthalene Acidic Acid (NAA) at a concentration of 50 ppm for each and sprayed at three times (50, 65 and 80 days after transplanting). Increasing the phosphorus fertilizers rate to 60 kg P₂O₅/fed significantly affects the growth and seed production of the Onion plant. Foliar application of nano-boron at 50 ppm concentration gave maximum values of onion seed yield in both seasons. Results stated that the correlation between yield and yield contributing characters over two years was highly significant. It could be recommended that P application at a rate of 60 kg P₂O₅ and sprayed onion plants at 50 ppm nano-boron three times (at 50, 65, and 80 days from transplanting) gave the highest seed yield of onion plants. Moreover, the maximum increments of inflorescence diameter (94.4%) were recorded to nano-boron foliar spray (60 p × nB) compared to the other treatments in both seasons.