The objective of the present study is to observe the surface morphology, structure and elemental composition of the ash particles produced from some thermal power stations of India using scanning electron microscopy (SEM) and energy dispersive X-ray analysis (EDXA). This information is useful to better understand the ash particles before deciding its utility in varied areas.

This paper presents a coupling of the Monte Carlo method with computational fluid dynamics (CFD) to analyze the flow channel design of an irradiated target through numerical simulations. A novel series flow channel configuration is proposed, which effectively facilitates the removal of heat generated by high-power irradiation from the target without necessitating an increase in the cooling water flow rate. The research assesses the performance of both parallel and serial cooling channels within the target, revealing that, when subjected to equivalent cooling water flow rates, the maximum temperature observed in the target employing the serial channel configuration is lower. This reduction in temperature is ascribed to the accelerated flow of cooling water within the serial channel, which subsequently elevates both the Reynolds number and the Nusselt number, leading to enhanced heat transfer efficiency. Furthermore, the maximum temperature is observed to occur further downstream, thereby circumventing areas of peak heat generation. This phenomenon arises because the cooling water traverses the target plates with the highest internal heat generation at a lower temperature when the flow channels are arranged in series, optimizing the cooling effect on these targets. However, it is crucial to note that the pressure loss associated with the serial structure is two orders of magnitude greater than that of the parallel structure, necessitating increased pump power and imposing stricter requirements on the target container and cooling water pipeline. These findings can serve as a reference for the design of the cooling channels in the target station system, particularly in light of the anticipated increase in beam power during the second phase of the China Spallation Neutron Source (CSNS Ⅱ).

This study provides empirical data on the impact of generative AI in education, with special emphasis on sustainable development goals (SDGs). By conducting a thorough analysis of the relationship between generative AI technologies and educational outcomes, this research fills a critical gap in the literature. The insights offered are valuable for policymakers seeking to leverage new educational technologies to support sustainable development. Using Smart-PLS4, five hypotheses derived from the research questions were tested based on data collected from an E-Questionnaire distributed to academic faculty members and education managers. Of the 311 valid responses, the measurement model assessment confirmed the validity and reliability of the data, while the structural model assessment validated the hypotheses. The study’s findings reveal that New Approaches to Learning Outcome Assessment (NALOA) significantly contribute to achieving SDGs, with a path coefficient of 0.477 (p < 0.001). Similarly, the Use of Generative AI Technologies (UGAIT) has a notable positive impact on SDGs, with a value of 0.221 (p < 0.001). A Paradigm Shift in Education and Educational Process Organization (PSEPQ) also demonstrates a significant, though smaller, effect on SDGs with a coefficient of 0.142 (p = 0.008). However, the Opportunities and Risks of Generative AI in Education (ORGIE) study did not find statistically significant evidence of an impact on SDGs (p = 0.390). These findings highlight the potential opportunities and challenges of using generative AI technologies in education and underscore their key role in advancing sustainable development goals. The study also offers a strategic roadmap for educational institutions, particularly in Oman to harness AI technology in support of sustainable development objectives.

The presence of a crisis has consistently been an inherent aspect of the Supply Chain, mostly as a result of the substantial number of stakeholders involved and the intricate dynamics of their relationships. The objective of this study is to assess the potential of Big Data as a tool for planning risk management in Supply Chain crises. Specifically, it focuses on using computational analysis and modeling to quantitatively analyze financial risks. The “Web of Science—Elsevier” database was employed to fulfill the aims of this work by identifying relevant papers for the investigation. The data were inputted into VOS viewer, a software application used to construct and visualize bibliometric networks for subsequent research. Data processing indicates a significant rise in the quantity of publications and citations related to the topic over the past five years. Moreover, the study encompasses a wide variety of crisis types, with the COVID-19 pandemic being the most significant. Nevertheless, the cooperation among institutions is evidently limited. This has limited the theoretical progress of the field and may have contributed to the ambiguity in understanding the research issue.

Introduction, purpose of the study: In Central Europe, in Hungary, the state guarantees access to health care and basic health services partly through the Semmelweis Plan adopted in 2011. The Health Plan aims to optimize and transform the health system. The objectives of hospital integration, as set out in the Plan, started with the state ownership of municipal hospitals in 2012, continued with the launch of integration processes in 2012–2013 and culminated today. The transformation of a health system can have an impact on health services and thus on meeting the needs of the population. We aim to study the effectiveness of integration through access to CT diagnostic testing. Our hypothesis is that integration has resulted in increased access to modern diagnostic services. The specialty under study is computed tomography (CT) diagnostic care. Our research shows that the number of people receiving CT diagnostic care has increased significantly because of integration, which has also brought a number of positive benefits, such as reduced health inequalities, reduced travel time, costs and waiting lists. Test material and method: Our quantitative retrospective research was carried out in the hospital of Kalocsa through document analysis. The research material was comparing two time periods in the Kalocsa site of Bács-Kiskun County, Southern Hungary. The number of patients attending CT examinations by area of duty of care according to postal codes was collected: Pre-integration period 2014.01.01–2017.11.30. (Kalocsa did not have CT equipment, so patients who appeared in Kecskemét Hospital but were under the care of Kalocsa), post-integration period 2017.12.01–2019.12.31. (period after the installation of CT in Kalocsa). The target group of the study consisted of women and men together, aged 0–99 years, who appeared for a CT diagnostic examination. The study sample size was 6721 persons. Linear regression statistics were used to evaluate the results. Based on empirical experience, a SWOT analysis was carried out to further investigate the effectiveness of integration. Results: As a result of the integration, the CT scan machine purchased in the Kalocsa District Hospital has enabled an average of 129.7 patients per month to receive CT scans on site without travelling. The model used is significant, explaining 86% of the change in the number of patients served (F = 43.535; p < 0.001, adjusted R² = 0.860). The variable of integration in the model is significant, with an average increase in the number of patients served of 129.7 per month (t = 22.686; p < 0.001) following the introduction of CT due to integration. None of the month variables representing seasonal effects were found to be significant, with no seasonal effect on care. The SWOT analysis has clearly identified the strengths, weaknesses, opportunities and threats related to the integration, the main outcome of which is the acquisition of a CT diagnostic tool. Conclusions: Although we only looked at one segment of the evidence for the effectiveness of hospital integration, integration in the study area has had a positive impact on CT availability, reducing disparities in care.

Natural forests and abandoned agricultural lands are increasingly replaced by monospecific forest plantations that have poor capacity to support biodiversity and ecosystem services. Natural forests harbour plants belonging to different mycorrhiza types that differ in their microbiome and carbon and nutrient cycling properties. Here we describe the MycoPhylo field experiment that encompasses 116 woody plant species from three mycorrhiza types and 237 plots, with plant diversity and mycorrhiza type diversity ranging from one to four and one to three per plot, respectively. The MycoPhylo experiment enables us to test hypotheses about the plant species, species diversity, mycorrhiza type, and mycorrhiza type diversity effects and their phylogenetic context on soil microbial diversity and functioning and soil processes. Alongside with other experiments in the TreeDivNet consortium, MycoPhylo will contribute to our understanding of the tree diversity effects on soil biodiversity and ecosystem functioning across biomes, especially from the mycorrhiza type and phylogenetic conservatism perspectives.