High-risk pregnancies are a global concern, with maternal and fetal well-being at the forefront of clinical care. Pregnancy’s three trimesters bring distinct changes to mothers and fetal development, impacting maternal health through hormonal, physical, and emotional shifts. Fetal well-being is influenced by organ development, nutrition, oxygenation, and environmental exposures. Effective management of high-risk pregnancies necessitates a specialized, multidisciplinary approach. To comprehend this integrated approach, a comparative literature analysis using Atlas.ti software is essential. Findings reveal key aspects vital to high-risk pregnancy care, including intervention effectiveness, case characteristics, regional variations, economic implications, psychosocial impacts, holistic care, longitudinal studies, cultural factors, technological influences, and educational strategies. These findings inform current clinical practices and drive further research. Integration of knowledge across multidisciplinary care teams is pivotal for enhancing care for high-risk pregnancies, promoting maternal and fetal well-being worldwide.

The practice of ethical management has gained traction due to its role in enhancing stakeholder relations, which can have severe repercussions for organisations. By prioritising ethics, companies not only uphold moral principles but also gain a competitive advantage. This is particularly true in societies that value socially responsible business and give preference to companies that go beyond the requirements of the law. Understanding the significance of ethical management practices is therefore becoming key to creating a responsible and sustainable business environment that benefits both an organisation and its stakeholders, such as employees, consumers and society. The purpose of this article is to present a comprehensive exploration of the impact of selected aspects of ethical management in Slovak companies with foreign participation on the ethicality of their relationships with stakeholders. By examining a range of factors related to ethical management, the article seeks to identify statistically significant differences among companies with different approaches to managing business ethics. Employing this analysis, the article contributes to the understanding of ethical practices in Slovak companies and provides insights for academics and practitioners of business ethics. The data used for this analysis was collected through an online questionnaire survey, resulting in a sample size of 179 monitored subjects, all of whom are Slovak companies with foreign participation. The research design included two groups of factors: “general factors of business ethics” or “ethical management approaches” and “ethicality of company-stakeholder relationships.” The statistical analysis included the Shapiro-Wilk normality test, followed by the non-parametric Kruskal-Wallis H test, and post hoc analysis using the Bonferroni adjustment for previously identified significances. The results of the research presented in the article indicate a predominantly positive ethical stance towards employees, suppliers, customers and other stakeholders among Slovak companies. Statistically significant differences were found in the levels of ethicality in relation to legal form, with limited liability and joint-stock companies showing different perceptions towards supplier ethics. The research also proves that an ethical organisational climate is a major determinant of the ethicality of Slovak companies and suggests that a robust integration of ethics into strategic planning significantly improves their stakeholder relations. It can also be concluded that the scope of a code of ethics is particularly significant for community relations, whereas the frequency with which it is updated has less impact. This research holds significant value because it explores the impact of ethical management practices on stakeholder relations and ethical issues in Slovak companies with foreign participation. By focusing on the specific context of Slovak companies, the research offers unique insights into the relationship between ethical management factors and stakeholder dynamics. This research aims to bridge a gap by shedding light on the intricate dynamics between ethical management and stakeholder relations. The findings provide valuable guidance to organisational leaders, policymakers and stakeholders in fostering ethical behaviour and mitigating ethical risks within companies.

"Kappa", as a late work of Ryunosuke Wasagawa, contains the strong feelings of Ryusuke Wasagawa's criticism of society, Sukekawa depicts a bizarre and fantasy Kappa country world, using the art of mapping, showing the darkness of Japanese society and the ugly side of society, this article analyzes the world-weary thoughts contained in the story through the storyline in the novel "Kappa" and the main characters in the story.

With the rapid development of artificial intelligence (AI) technology, its application in the field of auditing has gained increasing attention. This paper explores the application of AI technology in audit risk assessment and control (ARAC), aiming to improve audit efficiency and effectiveness. First, the paper introduces the basic concepts of AI technology and its application background in the auditing field. Then, it provides a detailed analysis of the specific applications of AI technology in audit risk assessment and control, including data analysis, risk prediction, automated auditing, continuous monitoring, intelligent decision support, and compliance checks. Finally, the paper discusses the challenges and opportunities of AI technology in audit risk assessment and control, as well as future research directions.

The aim of this study is to investigate the effect of tourist resources, conditions and opportunities of sacral tourism in Kazakhstan using panel data (time series and cross-sectional) regression analysis for a sample of 14 regions of Kazakhstan observed over the period from 2004 to 2022. The article presents an overview of modern methods of assessment of the tourist and recreational potential of sacral tourism, as used by national and foreign scientific works. The main focus is on the method of estimating the size and effectiveness of the tourist potential, which reflects the realization and volume of tourist resources and their potential. The overall results show a significant positive effect in that the strongest impact on the increase in the number of tourist residents is the proposed infrastructure and the readiness of regions to receive tourists qualitatively. This study is expected to be of value to firm managers, investors, researchers, and regulators in decision- making at different levels of government.

In this paper, we assess the results of experiment with different machine learning algorithms for the data classification on the basis of accuracy, precision, recall and F1-Score metrics. We collected metrics like Accuracy, F1-Score, Precision, and Recall: From the Neural Network model, it produced the highest Accuracy of 0.129526 also highest F1-Score of 0.118785, showing that it has the correct balance of precision and recall ratio that can pick up important patterns from the dataset. Random Forest was not much behind with an accuracy of 0.128119 and highest precision score of 0.118553 knit a great ability for handling relations in large dataset but with slightly lower recall in comparison with Neural Network. This ranked the Decision Tree model at number three with a 0.111792, Accuracy Score while its Recall score showed it can predict true positives better than Support Vector Machine (SVM), although it predicts more of the positives than it actually is a majority of the times. SVM ranked fourth, with accuracy of 0.095465 and F1-Score of 0.067861, the figure showing difficulty in classification of associated classes. Finally, the K-Neighbors model took the 6th place, with the predetermined accuracy of 0.065531 and the unsatisfactory results with the precision and recall indicating the problems of this algorithm in classification. We found out that Neural Networks and Random Forests are the best algorithms for this classification task, while K-Neighbors is far much inferior than the other classifiers.