Background: Simulation-based medical education is a complex learning methodology in different fields. Exposing children to this teaching method is uncommon as it is designed for adult learning. This study aimed to develop and implement simulation-based education in first aid training of children and investigate the emotions of children in post-simulation scenarios that replicate emergency situations. Methods: This was a phenomenological qualitative research study. The participants attended the modified “Little Doctor” course that aims to train children in first aid and, subsequently, completed simulation scenarios. The children attended focus groups and were asked about their experiences of the course and how they felt during the simulation scenarios. Results: 12 children (Age 8–11 years old) attended the course, and 10 completed the simulation scenarios and focus groups. The major theme derived from was the simulation experience’s effect, which was divided into two subthemes: the emotion caused by—and the behavioral response to—the simulation. The analysis revealed shock and surprise toward the environment of the simulation event and the victim. The behaviors expressed during the simulation scenarios ranged from skill application and empathy to recall and teamwork. Conclusions: Simulation scenarios were successfully implemented during the first-aid training course. Although participants reported mixed feelings regarding the experience, they expressed confidence in their ability to perform real-life skills.

The financial services industry is experiencing a swift adoption of artificial intelligence (AI) and machine learning for a variety of applications. These technologies can be employed by both public and private sector entities to ensure adherence to regulatory requirements, monitor activities, evaluate data accuracy, and identify instances of fraudulent behavior. The utilization of artificial intelligence (AI) and machine learning (ML) has the potential to provide novel and unforeseen manifestations of interconnectivity within financial markets and institutions. This can be represented by the adoption of previously disparate data sources by diverse institutions. The researchers employed convenience sampling as the sampling method. The form was filled out over the period spanning from July 2023 to February 2024, and it was designed to be both anonymous and accessible through online and offline platforms. To assess the reliability and validity of the measurement scales and evaluate the structural model, we employed Partial Least Squares (PLS) for model validation. Specifically, we have used the software package Smart-PLS 3 with a bootstrapping of 5000 samples to estimate the significance of the parameters. The results indicate a positive and direct connection between artificial intelligence (AI) and either financial services or financial institutions. On the contrary, machine learning (ML) exhibits a strong and positive association among financial services and financial institutions. Similarly, there exists a positive and direct connection between AI and investors, as well as between ML and investors.

The construction of researcher profiles is crucial for modern research management and talent assessment. Given the decentralized nature of researcher information and evaluation challenges, we propose a profile system for Chinese researchers based on unsupervised machine learning and algorithms. This system builds comprehensive profiles based on researchers’ basic and behavior information dimensions. It employs Selenium and Web Crawler for real-time data retrieval from academic platforms, utilizes TF-IDF and BERT for expertise recognition, DTM for academic dynamics, and K-means clustering for profiling. The experimental results demonstrate that these methods are capable of more accurately mining the academic expertise of researchers and performing domain clustering scoring, thereby providing a scientific basis for the selection and academic evaluation of research talents. This interactive analysis system aims to provide an intuitive platform for profile construction and analysis.

This research presents a bibliometric review of scientific production on the social and economic factors that influence mortality from tuberculosis between the years 2000 and 2024. The analysis covered 1742 documents from 848 sources, revealing an annual growth of 6% in scientific production with a notable increase starting in 2010, reaching a peak in 2021. This increase reflects growing concern about socioeconomic inequalities affecting tuberculosis mortality, exacerbated in part by the COVID-19 pandemic. The main authors identified in the study include Naghavi, Basu and Hay, whose works have had a significant impact on the field. The most prominent journals in the dissemination of this research are Plos One, International Journal of Tuberculosis and Lung Disease and The Lancet. The countries with the greatest scientific production include the United States, the United Kingdom, India and South Africa, highlighting a strong international contribution and a global approach to the problem. The semantic development of the research shows a concentration on terms such as “mortality rate”, “risk factors” and “public health”, with a thematic map highlighting driving themes such as “socioeconomic factors” and “developing countries”. The theoretical evolution reflects a growing interest in economic and social aspects to gender contexts and associated diseases. This study provides a comprehensive view of current scientific knowledge, identifying key trends and emerging areas for future research.

Over the past few years, there has been a consistent rise in the popularity of bodybuilding. This study did a bibliometric analysis to offer a systematic overview and facilitate researchers in obtaining comprehensive insights on the peculiarities of bodybuilding research. This study utilized the bibliometric analysis program Bibliometrix to identify 940 papers on bodybuilding from the Web of Science database. The publications were selected from the years 1976 to 2024 and were used for the analysis. This study provides a thorough and detailed analysis of bodybuilding research using visual representations. It includes information on the frequency of publications, the nations that have had the most impact on bodybuilding research (including institutions, sources, and authors), and notable areas of focus within the field. Furthermore, the research collaboration among nations (regions), organizations, and authors is depicted based on a set of collaboration studies. The bibliometric study of current literature offers useful and groundbreaking sources for academics and practitioners in the field of bodybuilding-related studies.

This research investigates the determinants of digital transformation among Vietnamese logistics service providers (LSPs). Employing the Technological-Organizational-Environmental framework and Resource Fit theory, the study identifies key factors influencing this process across different three stages: digitization, digitalization, digital transformation. Data from in-depth interviews with industry experts and a survey of 390 LSPs were analyzed using covariance-based structural equation modeling (CB-SEM). The findings reveal that the factors influencing the digital transformation of Vietnamese LSPs evolve across different stages. In the initial phase, information technology infrastructure, financial resources, employee capabilities, external pressures, and support services are key determinants. As digitalization progresses, leadership emerges as a crucial factor alongside the existing ones. In the final stage, the impact of these factors persists, with leadership and employee capabilities becoming increasingly important.