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.

The security environment is experiencing unprecedented dynamics, absorbing and counteracting elements that generate challenges and threats, favoring aspects (apparently) beneficial to development and responding to the most unique threats that manage to have an increased impact on new areas of public interest, thus weakening national security. This paper aims to highlight the variety of effects on the various areas of the global social system that can be generated by a vital infrastructure that has become dysfunctional as a result of a cyber attack. Also, the article discusses the way in which various cyber attacks on the energy sector were orchestrated against the background of alleged rivalries between states.

In the present paper we describe an original installation for microwave hydrodistillation at low pressure that we constructed in order to obtain volatile oils from different plants. The results that we have obtained are encouraging meaning that the main components of these oils were found in correspondence with the data presented in literature. A novelty of this process is the fact that the hydrodistillation time was shorter than in the case of a classic process of hydrodistillation at atmospheric pressure. The aim of our research was to find a new and modern way of obtaining volatile oils from plants.

This paper propose an automatic method for facial features detection and then the image quality improvement methods to increase the rate of good recognition of a classifier based on appearance.