The rapid evolution of e-learning platforms, propelled by advancements in artificial intelligence (AI) and machine learning (ML), presents a transformative potential in education. This dynamic landscape necessitates an exploration of AI/ML integration in adaptive learning systems to enhance educational outcomes. This study aims to map the current utilization of AI/ML in e-learning for adaptive learning, elucidating the benefits and challenges of such integration and assessing its impact on student engagement, retention, and performance. A comprehensive literature review was conducted, focusing on articles published from 2010 onwards, to document the integration of AI/ML in e-learning. The review analyzed 63 articles, employing a systematic approach to evaluate the deployment of adaptive learning algorithms and their educational implications. Findings reveal that AI/ML algorithms are instrumental in personalizing learning experiences. These technologies have been shown to optimize learning paths, enhance engagement, and improve academic performance, with some studies reporting increased test scores. The integration of AI/ML in e-learning platforms significantly contributes to the personalization and effectiveness of the educational process. Despite challenges like data privacy and the complexity of AI/ML systems, the results underscore the potential of adaptive learning to revolutionize education by catering to individual learner needs.

Recent technological advancements in e-learning platforms have made it easy to store and manage students’ related data, such as personal details, initial grade, intermediate grades, final grades, and many other parameters. These data can be efficiently processed and analyzed by intelligent techniques and algorithms to generate useful insights into the students’ performance, such as to identify the factors impacting the progress of successful students or the performance of the students who are struggling in their courses and are at risk of failing. Such a framework is scarce in the current literature. This study proposes an interpretable framework to generate useful insights from the data produced by e-learning platforms using machine learning algorithms. The proposed framework incorporates predictive models, as well as regression and classification models to analyze multiple factors of student performance. Classification models are used to systematize normal and at-risk students based on their academic performance, with high precision and accuracy. Regression analysis is performed to determine the inherent linear and nonlinear relationships between the academic outcomes of the students acting as the target or independent variables and the performance indicative features acting as dependent variables. For further analysis, a predictive modeling problem is considered, where the performance of the students is anticipated based on their commitment to a specific course, their performance for the whole course, and their final grades. The efficiency of the proposed framework is also optimized by reliably tuning the algorithmic parameters. Furthermore, the performance is accelerated by empowering the system with a GPU-based infrastructure. Results reveal that the proposed interpretable framework is highly accurate and precise and can identify factors that play a vital role in the students’ success or failure.

We are witnessing the Fourth Industrial Revolution (4IR) in which technological discoveries and changes are coming at a dizzying speed generating tremendous transformations of organizations, the environment and societies. The purpose of the present paper is to analyze a set of challenges and opportunities of the workforce and workplace ecosystems in the context of 4IR. Innovation – as key ingredient of scaling-up processes – and the widespread implementation of specific 4IR technologies will lead to substantial, sometimes even disruptive changes of the labor market, but also to the workforce and workplace ecosystems’ transformation. Among others, artificial intelligence (AI), smart robotics, and intelligent automation are emerging technologies that dramatically change the ways of work, productivity, but also the types and number of available jobs. This paper has a few specific objectives: summarizing the main technologies and tools specific to 4IR and Industry 4.0; analyzing the major shifts related to the current structure of the workforce -highlighting the external and technological component (AI, robots, automation); analyzing the hybrid workplace (physical and digital); identifying some emerging technologies and their impact on the work ecosystem. The methodology of the study relies on qualitative methods - literature review, systematization of knowledge, observation/ participation (as experts in different national and international consultancy and training programs). The paper draws conclusions and some remarks regarding the intention to address other challenging subjects in the future, such as: rethinking performance management systems to reflect and tackle these challenges; skill gaps and the role of emerging technologies in supporting Society 5.0. - a more sustainable, inclusive and human-centric society.

First introduced by Ethan Marcotte in 2010 in his article Responsive Web Design, responsive design concept refers to the ability of a website to adapt to different resolutions and devices on which it is displayed (PCs, laptops, tablets, smartphones, etc.) without undergoing degradation. The site should recognize what device it is displayed on and automatically respond to user preferences. This approach comes to the aid of those who were forced to do a lot of versions of the site, so that it can be accessed from the iPad, Blackberry, Kindle, netbook, iPhone and so on[1], and becomes today a necessity. While using the same code and URL, a responsive website can provide an optimum viewing experience - without resizing, panning or scrolling on most of the devices. Web designers approached approach the new challenge with many and various solutions. To keep up with the transformations emerging in web design, this article intends to discuss the issues applied to the Land Forces Academy’s official site.

Background/Objectives: Chronic ulcers represent a major public health concern, being associated with substantial morbidity, impaired quality of life, and significant costs to healthcare systems. Against the backdrop of an aging population and increasing prevalence of chronic comorbid conditions, this study aimed to profile hospitalized patients with chronic ulcers in Romania and to examine their differential patterns of healthcare resource utilization. Methods: We conducted a retrospective analysis of the national administrative hospitalization database between 2017 and 2022, including adult patients with at least two admissions coded with a primary diagnosis of chronic ulcer. Sociodemographic, clinical, and healthcare utilization indicators were extracted, standardized, and analyzed using the K-means clustering algorithm to derive utilization-based phenotypes. Results: Two distinct patient clusters were identified: the first comprised predominantly elderly patients with multiple comorbidities, prolonged hospitalizations, and frequent readmissions, representing a high-burden profile; the second included relatively younger patients with fewer comorbidities, shorter hospital stays, and lower readmission rates, reflecting a more stable clinical profile. The high-burden cluster accounted for a disproportionate share of inpatient resource consumption, underscoring its impact on the healthcare system. Conclusions: These findings highlight the importance of early identification of potential high-burden patients, enabling the implementation of personalized care strategies and more efficient allocation of hospital resources, with the potential to improve health outcomes and support healthcare system sustainability.

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.