Economic growth is a pressing issue facing the global community transitioning to sustainable development. Sustainable development is impossible without rapid economic growth limited by imperfect technologies and social structure. Most often, the limit of economic growth is related not so much to the amount of natural resources as to the possibilities of the environment. The atmosphere, water reservoirs, and the earth are already at the limit of their capabilities. This forces us to look for ways to develop production in combination with the economic and environmental spheres. Advanced companies are the first environmentally oriented enterprises, because reducing the amount of primary raw and other materials and energy, switching to secondary raw materials, and processing them reduces the cost of production, and, most often, brings additional profit. This study evaluates socioeconomic approaches to the development of the environmental management system. The creation of an environmentally friendly enterprise’s field of activity is not only a solution to many economic and environmental issues but also one of the ways to transition to a normally functioning market system, given the financial capabilities of enterprises and the understanding of the necessity of state sustainable development by the company management and the population.

This paper explores the integration of Large Language Models (LLMs) and Software-Defined Resources (SDR) as innovative tools for enhancing cloud computing education in university curricula. The study emphasizes the importance of practical knowledge in cloud technologies such as Infrastructure as a Service (IaaS), Platform as a Service (PaaS), Software as a Service (SaaS), DevOps, and cloud-native environments. It introduces Lean principles to optimize the teaching framework, promoting efficiency and effectiveness in learning. By examining a comprehensive educational reform project, the research demonstrates that incorporating SDR and LLMs can significantly enhance student engagement and learning outcomes, while also providing essential hands-on skills required in today’s dynamic cloud computing landscape. A key innovation of this study is the development and application of the Entropy-Based Diversity Efficiency Analysis (EDEA) framework, a novel method to measure and optimize the diversity and efficiency of educational content. The EDEA analysis yielded surprising results, showing that applying SDR (i.e., using cloud technologies) and LLMs can each improve a course’s Diversity Efficiency Index (DEI) by approximately one-fifth. The integrated approach presented in this paper provides a structured tool for continuous improvement in education and demonstrates the potential for modernizing educational strategies to better align with the evolving needs of the cloud computing industry.

Using the Intercultural Competence and Inclusion in Education Scale (ICIES), this study examines variations in intercultural competence and inclusion between mainstream and multiethnic high schools. The sample consisted of 384 high school students, aged 17 to 18, from both rural and urban areas in Western Romania, enrolled in grades 11 and 12. The ICIES demonstrated strong reliability, with a Cronbach’s alpha of 0.721. Exploratory factor analysis revealed three distinct dimensions: Intercultural opportunities and activities, Comfort in diverse settings, and Cultural reflection and values. Independent samples t-tests identified significant differences between mainstream and multiethnic schools across several items, with students in multiethnic schools reporting higher levels of intercultural competence and inclusion. These findings highlight the critical role of multicultural educational settings in fostering students' cultural awareness and inclusive attitudes. This study provides actionable insights for enhancing multicultural education practices and policies, including teacher training programs, inclusive curricula, and extracurricular initiatives that promote intercultural engagement and reduce intergroup biases.

Falling is one of the most critical outcomes of loss of consciousness during triage in emergency department (ED). It is an important sign requires an immediate medical intervention. This paper presents a computer vision-based fall detection model in ED. In this study, we hypothesis that the proposed vision-based triage fall detection model provides accuracy equal to traditional triage system (TTS) conducted by the nursing team. Thus, to build the proposed model, we use MoveNet, a pose estimation model that can identify joints related to falls, consisting of 17 key points. To test the hypothesis, we conducted two experiments: In the deep learning (DL) model we used the complete feature consisting of 17 keypoints which was passed to the triage fall detection model and was built using Artificial Neural Network (ANN). In the second model we use dimensionality reduction Feature-Reduction for Fall model (FRF), Random Forest (RF) feature selection analysis to filter the key points triage fall classifier. We tested the performance of the two models using a dataset consisting of many images for real-world scenarios classified into two classes: Fall and Not fall. We split the dataset into 80% for training and 20% for validation. The models in these experiments were trained to obtain the results and compare them with the reference model. To test the effectiveness of the model, a t-test was performed to evaluate the null hypothesis for both experiments. The results show FRF outperforms DL model, and FRF has same accuracy of TTS.