The European Entrepreneurship Competence (EntreComp) framework has been proposed by the European Commission in order to build consensus around a common understanding of entrepreneurship competence (knowledge, skills, and attitude). This study reports a bibliometric review of the research to date, addressing the EntreComp framework, based on documents published during the period from 2016 to June 2022. The research dataset was collected from Web of Science Core Collection (WoS) and included 37 articles. The bibliometric analysis includes citation, cooccurence and network analysis to represent the between countries and interorganizational collaboration, to identify relationships between documents topics. Along with the bibliometric analysis, a detailed description of characteristics for each article (study design and objectives, instruments, research findings, etc.) was also presented. Overall, this study provides insights regarding research addressing EntreComp framework, that could guide future research directions of practitioners/policy makers/researchers/etc. in the entrepreneurial competencies field and other interconnected domains.

Recent progress in machine learning (ML) has fueled the emergence of intelligent clinical decision support systems (CDSSs) designed to optimize diagnostic and prognostic accuracy through the analysis of complex and heterogeneous medical data. The analysis provides a comprehensive perspective on the use of machine learning in the medical field by integrating a bibliometric assessment of the recent literature and a detailed examination of the algorithms used in current studies. The bibliometric component highlights the evolution of publications, the thematic distribution of research and emerging directions within various medical specialties. In addition, the evaluation of selected articles sheds light on the concrete ways of applying ML algorithms, as well as the methodological limitations encountered in clinical practice. Random forest and gradient boosting are commonly used in internal medicine and cardiology, while convolutional neural networks (CNNs) dominate neuroimaging in neurology and image-based analyses in oncology and radiology.

Background and Objectives: Assessing the national burden of chronic wounds is a complex data analytics challenge. Robust estimates in Eastern Europe are scarce, highlighting the need for computational methods to validate cases in large-scale health databases. Materials and Methods: We applied a large-scale data analytics approach to Romania’s National Inpatient Database (public hospitals, 2017–2022). A computational case-ascertainment algorithm (validated “≥2 admissions” rule) was used to identify recurrently hospitalized patients, establishing a cohort of 18,856 patients (65,771 hospitalizations). We computed annual prevalence, incidence, and mortality per 100,000 adults, stratified by ulcer categories, age, and sex. Results: Hospital-treated prevalence and incidence showed a clear pre-pandemic peak followed by a marked decline in 2020–2021 and only partial rebound by 2022, consistent with pandemic-related disruption of inpatient care. Population-level mortality remained low, but pressure ulcers, although least frequent, accounted for the highest mortality burden. Venous ulcers were the most common category, and the hospital-treated burden was concentrated in adults aged ≥ 65 years and in men. Conclusions: This nationwide data-analytics framework provides the first validated inpatient indicators of chronic ulcer burden in Romania and demonstrates substantial hospital-treated disease burden with pronounced sensitivity to healthcare access constraints. Clinical Implications: The findings can support health-policy and prevention strategies by prioritizing early detection and integrated hospital–community wound care pathways for high-risk groups (men and older adults) and by strengthening outpatient services to reduce avoidable admissions.

In this paper we consider two continuous functions f, g : [a, b] → ℝ and we study for these ones, under which circumstances the intermediate point function is four order di erentiable at the point x = a and we calculate its derivative.

In this paper, we provide an overview of Human-Computer Interaction (HCI) in the context of Artificial Intelligence (AI), focusing on Machine Learning (ML), Deep Learning (DL), Artificial Neural Networks (ANN), and applications of these techniques in the field of Healthcare. This review highlights the critical role of the human factor in AI-driven systems with discussions on AI ethics and expectations. Artificial Intelligence techniques, including ML and DL enhance gesture and speech recognition in HCI, while ANN models are particularly effective for hand gesture recognition. In Human-Computer Interaction, AI techniques bring value and understanding to Healthcare in real life. Despite the benefits that Artificial Intelligence brings to HCI, challenges remain. The future promises new applications and perspectives in HCI, where AI with ML, DL, and ANN have an effective impact.