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.

This study rigorously investigates the Starlink Project’s impact on Thailand’s legal frameworks, regulatory policies, and national security concerns. Utilising a well-structured online questionnaire, we collected responses from 1378 Thai participants, meticulously selected to represent diverse demographics, technology usage patterns, and social media interactions. Our analytical approach integrated binary regression analysis to dissect the intricate relationships between various predictor variables and the project’s potential effects. Notably, the study unveils critical insights into how factors such as age, gender, education level, income, as well as specific technology and social media usage (including laptop, smartphone, tablet, home and mobile Internet, and TikTok), influence perceptions of Starlink’s impact. Intriguingly, certain variables like Twitter and YouTube usage emerged as non-significant. These nuanced findings offer a robust empirical basis for stakeholders to forge targeted strategies and policies, ensuring that the advent of the Starlink Project aligns with Thailand’s national security, legal, and regulatory harmony.

To save patients’ lives, it is important to go for an early diagnosis of intracranial hemorrhage (ICH). For diagnosing ICH, the widely used method is non-contrast computed tomography (NCCT). It has fast acquisition and availability in medical emergency facilities. To predict hematoma progression and mortality, it is important to estimate the volume of intracranial hemorrhage. Radiologists can manually delineate the ICH region to estimate the hematoma volume. This process takes time and undergoes inter-rater variability. In this research paper, we develop and discuss a fine segmentation model and a coarse model for intracranial hemorrhage segmentations. Basically, two different models are discussed for intracranial hemorrhage segmentation. We trained a 2DDensNet in the first model for coarse segmentation and cascaded the coarse segmentation mask output in the fine segmentation model along with input training samples. A nnUNet model is trained in the second fine stage and will use the segmentation labels of the coarse model with true labels for intracranial hemorrhage segmentation. An optimal performance for intracranial hemorrhage segmentation solution is obtained.

According to the World Health Organization (WHO), breast cancer is among the most common cancers worldwide. Most of the anticancer agents have been showing a variety of side effects. Recently, bacterial proteins have been investigated as promising anticancer agents. Azurin is a bacterial cupredoxin protein secreted from Pseudomonas aeruginosa and has been reported as a potent multi-targeting anticancer agent, which makes it an appropriate candidate for drug delivery. Azurin may be delivered to cancer cells using different carriers like polymeric micro and nanoparticles. In the present study, azurin was extracted from the bacterial host and loaded into chitosan particles. Then its effect on MCF-7 cell line was investigated. Chitosan-azurin particles were made using the ion gelation method. Results showed that chitosan-azurin particles are about 200 nm, and the loading of the protein in particles did not affect its integrity. The MTT assay showed a significant reduction in cell viability in azurin and chitosan-azurin-treated cells. The toxicity level after 5 days was 63.78% and 82.53% for free azurin and chitosan-azurin-treated cells, respectively. It seems using an appropriate carrier system for anticancer proteins like azurin is a promising tool for developing low side effect anticancer agents.

The characteristics of agricultural products are influenced by the ecosystem, from the perspective of biotic and abiotic factors, which produce in the plant physiological responses and in turn in the fruit unique physicochemical properties, which are the basis for designations of origin and strategies to add value to the product in the current market. In the present work, ten cocoa materials (Theobroma cacao L.) were selected for their outstanding productivity (FSV41, FLE3, FEAR5, FSA12, FEC2, SCC23, SCC80, SCC55, ICS95 and CCN51), which were established in the departments of Santander (931 m a.s.l.), Huila (931 m a.s.l.), Huila (931 m a.s.l.), Huila (931 m a.s.l.), Huila (931 m a.s.l.), Huila (931 m a.s.l.) and Huila (931 m a.s.l.). These were established in the departments of Santander (931 m a.s.l.), Huila (885 m a.s.l.) and Arauca (204 m a.s.l.), the main cocoa-producing areas in Colombia. For the evaluation of the physical characteristics of the collected materials, 21 quantitative descriptors were used to determine the physical variability of the fruit according to clone and place of collection. The data collected were analyzed by means of Pearson’s correlation matrix and principal component analysis, it was possible to identify those descriptors that contribute most to the variability among materials (ear index, diameter length ratio, seed weight and diameter, and fruit weight and length). In addition, it was possible to verify the effect of the place of harvest on the physical characteristics of the materials, high-lighting the importance of the adaptation study prior to the planting of the cocoa material, with the objective of guaranteeing a premium, productive and quality cocoa crop for the industry, which is competitive in the market.