Artificial Intelligence (AI) in education has both positive and negative impacts, particularly in term of increasing plagiarism. This research analyzes Indonesia’s plagiarism regulations and offers solutions. It uses doctrinal methods with legislative, case, and comparative studies, revealing that plagiarism is regulated but not specifically for AI involvement. The results show that plagiarism in scientific work has actually been regulated through several regulations. On the other hand, there is no regulation governing the involvement of AI in the process of preparing scientific articles. Comparative studies show that the US, Singapore, and the EU have advanced regulations for AI in education. The US has copyright laws for AI works and state regulations, Singapore’s Ministry of Education has guidelines for AI integration and ethics, and the EU has the Artificial Intelligence Act. To tackle AI-related plagiarism in Indonesia, the study suggests enacting AI-specific laws and revising existing ones. Ministerial and Rector statutes should address technical aspects of AI use and plagiarism checks. The Ministry should issue guidelines for universities to develop Standard Procedures for Writing and Checking Scientific Work, using reliable AI-checking software. These measures aim to prevent plagiarism in Indonesia’s educational sector.

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.

This study conducted a systematic review of the existing literature on rhythmic gymnastics. Through searching databases such as PubMed, Web of Science, and Scopus, 37 out of 2319 articles were selected, covering training and physical fitness, nutrition and metabolism, as well as sports injuries and rehabilitation. The findings revealed that: (1) Core physical training significantly enhanced athletes’ performance; (2) Inadequate nutritional intake was prevalent; (3) The incidence of sports injuries was high, particularly those resulting from overtraining. The conclusion emphasizes the need to enhance strength training, optimize nutritional management, and further investigate injury prevention and rehabilitation measures to enhance athletes’ performance and health status.

The Heating, Ventilation, Air Conditioning, and Refrigeration (HVAC&R) industry is pivotal to Europe’s goals for energy efficiency, sustainability, and technological advancement. As demand for skilled HVAC&R professionals rises, the effectiveness of educational programs in this field has become a focal point. This article explores the Portuguese case to analyze how pedagogical strategies and student motivation contribute to the quality of HVAC&R training across Europe. The study highlights innovative teaching methodologies such as active and competency-based learning, as well as the use of laboratory training and digital simulations to provide hands-on experience. Additionally, it emphasizes Bloom’s Taxonomy as a framework for curriculum development, ensuring that students advance from foundational knowledge to complex problem-solving abilities. Motivation is also identified as a critical factor for student engagement and long-term career commitment. The article concludes that a balanced integration of theoretical knowledge, practical skills, and motivational support is essential for producing highly qualified HVAC&R professionals. This approach not only meets current industry needs but also aligns with Europe’s broader environmental and technological objectives, offering valuable insights for educators, policymakers, and industry stakeholders.

The purpose of this study is to examine how financial slack and board gender diversity affect carbon emission disclosure and how that disclosure affects firm value in energy sector companies that are listed on the Indonesian stock exchange between 2017 and 2021. Annual reports and sustainability sources provide secondary data for this quantitative study. Purposive sampling was employed in this investigation, including nine companies and a five-year observation period. Thus, 45 samples altogether were employed in the present study. The partial least squares approach is the data analysis strategy used in this investigation. The study’s findings indicate that the Gender Diversity Board does not significantly affect carbon emission disclosure and significantly influences firm value. Financial slack significantly affects carbon emission disclosure but does not directly affect firm value. Financial slack and board gender diversity through carbon emission disclosure have no significant effect on firm value.

This study aims to explore the relationship between online marketing dimensions and customer satisfaction within Jordanian companies and distributors. Utilizing a descriptive analytical methodology, the research focused on customers of Jordanian pharmaceutical companies and distributors. A survey was conducted using a questionnaire distributed to a target sample; out of 75 questionnaires, 61 were returned and valid, yielding a response rate of 81.3%. Data from the questionnaires were analyzed using AMOS and SPSS software. The findings indicated a statistically significant correlation between the collective dimensions of online marketing (attraction, engagement, retention, learning, and communication) and customer satisfaction. However, regression analysis showed no significant relationship between the individual dimensions of attraction, engagement, and retention with customer satisfaction. The study found that Jordanian pharmaceutical companies practice digital marketing at a high level, according to the sample responses. A key recommendation from the study is for pharmaceutical products to be promoted through various online marketing channels, including sponsored ads on social media and websites targeting both local and international audiences.