The objective of this work was to analyze the effect of the use of ChatGPT in the teaching-learning process of scientific research in engineering. Artificial intelligence (AI) is a topic of great interest in higher education, as it combines hardware, software and programming languages to implement deep learning procedures. We focused on a specific course on scientific research in engineering, in which we measured the competencies, expressed in terms of the indicators, mastery, comprehension and synthesis capacity, in students who decided to use or not ChatGPT for the development and fulfillment of their activities. The data were processed through the statistical T-Student test and box-and-whisker plots were constructed. The results show that students’ reliance on ChatGPT limits their engagement in acquiring knowledge related to scientific research. This research presents evidence indicating that engineering science research students rely on ChatGPT to replace their academic work and consequently, they do not act dynamically in the teaching-learning process, assuming a static role.

The primary school stage is the key stage for students to form good habits and lay a good learning foundation, especially in primary schools, Chinese classes account for the largest proportion of all courses, the focus of learning began to shift to understanding and mastering. Through scientific methods, teachers can effectively improve the concentration of Chinese learning of primary school students in order to improve their interest and overall level,to have a profound impact on the future study and life of primary school students. This paper analyzes the importance and strategies of teachers' attention training in the middle Chinese classroom of primary school.

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 article reports the development of an index of culturality in Chile. Fifteen quantitative variables indicative of local cultural development are used to measure the access to cultural opportunities in each Chilean district. This approach was adopted from the theoretical framework of cultural materialism theorized by Marvin Harris in the seventies. Using this framework, a ranking is developed among 164 districts to determine the degree of cultural development exists in each and the variables that are the influential on the enhancement of this indicator. The results showed that the districts of Rancagua, Providencia, La Reina, El Bosque, and Valparaíso have better cultural opportunities based on their material forms, which are mainly driven by obtaining funds for cultural projects, workers’ salaries, civic activity, and public libraries. Based on the results of this ranking, a baseline is proposed to develop it using new data. In addition, recommendations are provided regarding public policies that have promoted cultural development in the communities with unsuccessful results. The article provides significant information for decision makers in Chile and a quantitative method for exploring cultural materialism in specific territories.

Digital transformation is a significant phenomenon that affects almost every business sector, particularly the telecommunications industry, which is closely intertwined with information technology. This study is grounded in McLuhan’s concept of technological determinism and Martin Heidegger’s philosophy of technology, which asserts that media and technology shape human thoughts and interactions, benefiting individuals, society, and culture alike. The primary objective of this research is to investigate the environmental factors that influence digital transformation and to assess its impact on the strategic renewal of a company. This research employs exploratory qualitative methods, collecting in-depth information through interviews with the respondents from Indonesia’s leading telecommunications operator who can provide comprehensive and contextual insights into digital transformation. The findings reveal specific environmental factors that drive digital transformation. The major identified components of strategic renewal include advancements in information technology, the role of human resources, and interactions with external parties, including customers and partners.

The goal of this work was to create and assess machine-learning models for estimating the risk of budget overruns in developed projects. Finding the best model for risk forecasting required evaluating the performance of several models. Using a dataset of 177 projects took into account variables like environmental risks employee skill level safety incidents and project complexity. In our experiments, we analyzed the application of different machine learning models to analyze the risk for the management decision policies of developed organizations. The performance of the chosen model Neural Network (MLP) was improved after applying the tuning process which increased the Test R² from −0.37686 before tuning to 0.195637 after tuning. The Support Vector Machine (SVM), Ridge Regression, Lasso Regression, and Random Forest (Tuned) models did not improve, as seen when Test R² is compared to the experiments. No changes in Test R²’s were observed on GBM and XGBoost, which retained same Test R² across different tuning attempts. Stacking Regressor was used only during the hyperparameter tuning phase and brought a Test R² of 0. 022219.Decision Tree was again the worst model among all throughout the experiments, with no signs of improvement in its Test R²; it was −1.4669 for Decision Tree in all experiments arranged on the basis of Gender. These results indicate that although, models such as the Neural Network (MLP) sees improvements due to hyperparameter tuning, there are minimal improvements for most models. This works does highlight some of the weaknesses in specific types of models, as well as identifies areas where additional work can be expected to deliver incremental benefits to the structured applied process of risk assessment in organizational policies.