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.

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.

This article refers to Hallstatt in Austria and Ioannina in Greece. The goals analyze the two locations that have similarities in geometric shape, digital elevation model (DEM), and geomorphology. Firstly, Hallsatt’s advances were more technical than aesthetic. There is a general tendency towards extravagance and baroque and Greco-Oriental influences. Secondly, Ioannina is a mountainous city located around Lake Pamvotis. The geometry develops parallel to the lake. The city experiences many cultures. The ancient city had an urban planning that characterized the Ottoman Empire. In the old part, there is the castle, old stone streets, wooden houses, and the house of the Greek Muslim Ali Pasha. The author obtains numerous aerial photographs using Google Earth software. The photographs were received dynamically for all the perimeters of the regions. In short, the cartographer has between 15 and 20 photographs. The next step is to align the photographs in Zephyr photogrammetry software. Configuring resolutions, distance, camera locations, contrast, and brightness is essential. The final products are the 3D texture, 3D model, and orthophotos from Hallstatt and Ioannina. Digital products are suitable for measuring areas, circumferences, and heights. Furthermore, digital products represent a digital archiving practice: conservation and visualization are crucial factors today as they share, represent, promote, and document urban planning, historical memory, and the natural environment.

In China, ideological and political education is currently the hot direction of teaching reform in various colleges and universities, yet the development of appropriate teaching evaluation methods needs to catch up. This study addresses the pressing need for a preliminary investigation into the complex relationships among ideological and political education, the students’ learning satisfaction and teaching quality. This research examines the influence of teaching and ideological and political education quality on students’ satisfactions by designing a set of scales, collecting about 3800 questionnaires. Utilizing Structural Equation Modeling (SEM) and qualitative interviews, this study reveals that the teaching quality directly affects students’ learning satisfaction and ideological and political education. Notably, ideological and political education can also affect students’ learning satisfaction. The findings underscore the importance of including ideological and political education assessments in evaluating courses. This research contributes to the ongoing dialogue on effective teaching evaluation methods in the context of evolving educational practices.