Entrepreneurship can provide a creative, disruptive, problem-solving-oriented approach to the current economic, environmental, and social challenges of the world. This article aims to provide an analysis about the way universities can have an impact on developing entrepreneurial competence in students through extracurricular activities. The research relies on a questionnaire survey of students at the University of Petrosani, who participated in a range of entrepreneurial activities both online during the COVID-19 pandemic and face-to-face afterwards. The methodology consisted of applying principal component analysis to reduce the dimensionality of the indicators, followed by classification of the respondents through cluster analysis and training of a feedforward neural network. After finishing the network-training process, the error was minimized, resulting in three classes of respondents. Furthermore, based on the three classes, follow-up conclusions, policies, and decisions can be issued regarding the perception of entrepreneurship at the societal level, which is beneficial for academia and entrepreneurs, as well as for future research undertaken in this field. The key conclusion of our research is that entrepreneurship education is a real facilitator of the transition to sustainable entrepreneurship. Students perceived meeting successful entrepreneurs as being among the most effective extracurricular activities, assessing online activities as useful, and the field of study proved to be an important factor in their entrepreneurial intention.

Researchers from different fields have studied the effects of the COVID-19 pandemic and published their results in peer-reviewed journals indexed in international databases such as Web of Science (WoS), Scopus, PubMed. Focusing on efficient methods for navigating the extensive literature on COVID-19 pandemic research, our study conducts a content analysis of the top 1000 cited papers in WoS that delve into the subject by using elements of natural language processing (NLP). Knowing that in WoS, a scientific paper is described by the group Paper = {Abstract, Keyword, Title}; we obtained via NLP methods the word dictionaries with their frequencies of use and the word cloud for the 100 most used words, and we investigated if there is a degree of similarity between the titles of the papers and their abstracts, respectively. Using the Python packages NLTK, TextBlob, VADER, we computed sentiment scores for paper titles and abstracts, analyzed the results, and then, using Azure Machine Learning-Sentiment analysis, extended the range of comparison of sentiment scores. Our proposed analysis method can be applied to any research topic or theme from papers, articles, or projects in various fields of specialization to create a minimal dictionary of terms based on frequency of use, with visual representation by word cloud. Complementing the content analysis in our research with sentiment and similarity analysis highlights the different or similar treatment of the topics addressed in the research, as well as the opinions and feelings conveyed by the authors in relation to the researched issue.

The concept of well-being is multifaceted, influenced by a range of factors, including economic, social, perceptual, and personal, and varies considerably between individuals, countries, and continents. The objective of this research paper is to achieve an efficient grouping of countries according to several indicators that measure overall happiness. The indicators utilized in this study, derived from the World Happiness Report, are as follows: Life Ladder, Log GDP (Gross Domestic Product) per capita, Social Support, Healthy Life Expectancy at Birth, Freedom to Make Life Choices, Generosity, Perceptions of Corruption, Positive Affect, and Negative Affect. Consequently, following the identification, filtering, and collection of data on the countries in the study, cluster analysis, discriminant analysis, and neural network training were employed to obtain a classification of the countries into heterogeneous groups with a minimum error rate. Finally, for Romania, scenarios and policies were developed to facilitate ascent from the initial class to a class with a superior level of well-being.

Uranium mine closure represents a complex, multi-stage process encompassing technical decommissioning, environmental remediation, and socio-economic adjustment, with impacts extending beyond the mining sector. In countries operating nuclear power plants, such as Romania, the closure of domestic uranium mining activities also affects regional labour markets and long-term energy system configuration. This study assesses the medium- and long-term impacts of uranium mine closure from a process-oriented perspective, focusing on environmental cost internalization, workforce transition, and institutional coordination. The research adopts a mixed-method approach combining documentary analysis of regulatory and strategic frameworks with a quantitative survey conducted among 50 experts in mining engineering, environmental management, and energy-related fields. Descriptive statistical techniques are used to evaluate expert perceptions regarding the effectiveness and coherence of mine closure processes, including environmental protection measures, social compensation mechanisms, and post-closure development strategies. The results show that technical closure and environmental remediation measures are partially implemented and generally aligned with regulatory requirements. However, significant shortcomings persist in labour reconversion programs, inter-institutional coordination, and the integration of mine closure with regional development and renewable energy initiatives.

Milk and its by-products offer a concentrated source of proteins and nutrients that are essential for life and that can be challenging to obtain from other foods. There has been growing interest in the production, enhancement, and effective utilization of milk over time. The objective of this research paper is to contribute to ongoing efforts to enhance the production and collection of milk and dairy derivatives in Romania. In a study analyzing the dairy industry in the European Union, various indicators were examined with the aim of classifying countries and determining Romania’s position. To gain a comprehensive understanding of the dairy industry in the European Union, several indicators were considered, including milk production; different dairy products, such as butter and cheese; and data on bovine populations in various age groups. To efficiently classify the countries and identify Romania’s position, advanced data mining techniques were employed, including cluster analysis and neural network training. To enhance and advance the dairy industry in Romania, this study proposes the exploration of the potential advantages of implementing Industry 4.0 solutions, particularly on a larger scale, with Enterprise Resources Planning (ERP) software.

In recent years, the e-commerce sector has undergone continuous adaptation to both consumer needs and the economic context. This adaptation is driven by technological advances and the development of new software products. The present study aimed at achieving two primary objectives. First, it sought to assess the current state of e-commerce development in Romania within the broader European context. Second, it identified the use of AI-driven automation as a potential strategy for improving e-commerce in the country. To this end, e-commerce indicators were extracted from the questionnaire “ICT Usage and E-commerce in Enterprises,” which was conducted by National Statistical Authorities and centralized at the level of the European Commission. The questionnaire was carried out on a sample of 157,000 companies. A range of sophisticated techniques were employed to these indicators with the aim of reducing their dimensionality and classification error, with the objective of achieving a robust classification with the lowest possible error rate. We then proceeded to analyze Romania’s position in this ranking and, given the structure of e-commerce companies in the country, proposed the use of AI-driven automation as a potential strategy for enhancing activity in this sector.