Identifying the factors that influence project performance is an important concern in human resource and project management. This study aims to identify the profiles of employees who contribute significantly to project performance, using an approach based on data mining techniques applied to data obtained through a questionnaire addressed to people involved in project activities. The questionnaire was designed to collect information on the professional characteristics of respondents, their skills, teamwork, and perception of factors that influence project outcomes. Based on the collected data, a data mining analysis was performed, which allowed the identification of relevant patterns and relationships between the analyzed variables. The results highlight the existence of distinct employee profiles, characterized by specific combinations of skills, experience, and organizational behaviors, which differently influence project performance. The study contributes to the literature by highlighting the potential of data analysis techniques in identifying employee profiles that impact project success and offers practical implications for organizations seeking to optimize human resource management in projects.

For the European Union, the 2030 Agenda represents a comprehensive framework that aims to achieve objectives such as sustainable development, promoting economic growth, social inclusion, and environmental protection by the year 2030. One of the main strategies of the European Union’s Agenda 2030 is to implement a circular economy (CE) with the aim of supporting elements such as sustainable development, emphasizing resource efficiency, waste reduction, and a shift towards renewable materials. One of the most important tools that can help the circular economy achieve its objectives is Machine Learning (ML). Machine Learning can transform the economy by driving innovation, improving efficiency, and enabling data-driven decision-making across industries. Circular economy and machine learning intersect by leveraging data-driven insights to optimize resource use, improve recycling processes, and enhance product life cycles for greater sustainability. The research paper’s purpose is to highlight the role that the use of ML can have in the implementation of the principles and approaches specific to the circular economy. The paper describes the specific aspects of the circular economy, types of Machine Learning algorithms and the support that ML can offer EC. A data analysis using a specific ML algorithm is also performed. However, it is important to note that the research is limited by the choice of specific methods and datasets, without extending the analysis to various economic sectors or to the political and social influences that may affect the integration of these technologies. The research also does not address the possible ethical and security challenges associated with the use of machine learning algorithms in the circular economy.

One of the main elements that influences daily life is the environment. Especially in big cities, it leaves an important mark on the quality of life of its inhabitants, a quality that concerns many aspects such as their health, the quality of the air they breathe, the temperatures generated by the city's architecture, the size of green spaces, the noise level, etc. Big cities must achieve a balance between all these factors that influence the lives of their inhabitants. A lot of data and information about everything that can influence the quality of life is collected and analyzed. One of the elements that can help transform big cities by improving the quality of life, optimizing resources and reducing environmental impact for smarter and greener future is Machine Learning (ML). ML is about developing algorithms that allow computers to learn from data and make predictions or decisions without explicit programming. The purpose of this paper is to highlight the role that ML can play in improving the quality of life in big cities and the contribution it can make to their sustainable development. The sections of the paper aim to identify and describe the aspects that concern the environment in big cities, the specific aspects of ML as well as the optimization that ML can bring through its use. Finally, the use of ML for a city related data set is exemplified.

In the context of a pandemic that emerged with lightning speed, data science has become a cornerstone for governments decision-making processes. By analyzing numerous centralized databases, researchers have been able to identify trends, the spread of the virus, and run artificial intelligence (AI) simulations to anticipate crucial points of the COVID-19 pandemic. Data warehouses created during this period offer real-time monitoring of the global effects of the virus. The health databases are already common in national systems, but their usefulness rises above storing medical histories. The cross-disciplinary nature of the COVID-19 pandemic accentuates the need for collaboration between doctors, medical specialists and data analysts, data engineers, and Artificial Intelligence engineers. This article provides a comprehensive overview of how databases and data warehouses can offer different scenarios for citizens and health specialists alike.

The plan proposed within the 2030 Agenda through its specific objectives regarding sustainable development as well as through the approach to achieving a circular economy, is part of a strategy at European level for improving and preserving the environment in a sustainable manner. The recycling process as a component of the strategy has and will have a major impact on the environment. In this article, a data mining analysis is carried out regarding the recycling rate of electronic and electrical products at the European level. The analysis is carried out for two moments of time, namely the year 2018 and the year 2021. Following the clustering process of the countries from the European Union (EU), it is easier to see which countries have similar behavior from this point of view. At the same time, taking into account the two years, it is possible to observe the dynamics of the created clusters and how the European countries succeed to manage the recycling process of electronic and electrical products three years apart. The two moments were chosen taking into account the COVID-19 pandemic, the period in which, the teleworking way has occurred. For this reason, many employees had to purchase the necessary things and transform at least one room into an office. The authors' contributions consisted in the pre-processing of data sets, the application of data mining algorithms, obtaining the results and their interpretation in the given context, commenting on the results obtained and providing answers to the research questions. The work is structured in five sections, respectively Introduction, Literature review, Methodology, Results and Discussion and Conclusion.

Sustainable development (SD) represents a growth approach that tries to maintain a social, economic and environmental balance. SD aims to meet the needs of the present without compromising the needs of future generations. For a sustainable development, a key element is represented by renewable energy (RE). The use of RE on an increasingly large scale brings with it a series of advantages and positively influences aspects such as resource conservation, air and water quality improvement, climate change, greenhouse gas emissions, etc. Realization of RE through different ways such as wind energy, solar power, hydropower, etc. and by combining them, European citizens can take an important step in realizing a more sustainable and renewable energy future. The article presents a data mining analysis on the use of RE in EU countries, taking into account the share of industry use such as heating and cooling, transport and electricity. The results of the analysis aim to identify countries that show a similar behavior from the point of view of the use of RE. Based on them, policies and strategies at the EU level can be founded.

The technology innovation, especially in the case of artificial intelligence, has significantly transformed the work processes and how they are organised and performed. Even if the adoption of advanced technologies usually leads to a higher work performance, there are risks of negative disruptions in the working systems, such as non-ethical use and social negative effects. The paper presents the results of an ethnographic research conducted by the authors, with the objective to identify the impact of the artificial intelligence adoption in the workplace on the professional knowledge and skills requirements and on the upskilling and reskilling strategies. Three different domains were considered: information technology, education, and scientific research. One relevant conclusion of the research is that knowledge and skills requirements should be studied from multiple perspectives, such as profession dynamics, not only from the technology innovation perspective. The research originality mainly consists in the way in which the concept of the level of upskilling/reskilling importance is defined and applied, based on professional knowledge and skills development requirements. By using the assessed level of upskilling/reskilling importance, strategies and related actions may be defined and undertaken. By substantiating this manner of setting up the upskilling and reskilling strategies and actions, the research has a theoretical and practical impact in the domain of talent management.

Greenhouse gas emissions (GE) represent an element that influences the lives of all people on the planet. This action must be controlled and prevented because the negative effects are starting to appear more and more in everyday life, sometimes with devastating consequences from a climate point of view and not only for the inhabitants of certain regions. At the European level, one of the main measures taken was the implementation of the Green Deal as a response to the fight against GE. The purpose of this article is to offer a description of the main elements that are influencing the GE, as well as the role of the Green Deal. It also aims to identify the characteristics of the EU countries from the GE point of view before and after the Green Deal was proposed. In this regard two more cluster analyses are also carried out regarding GE at the European level. One analysis concerns the identification and evolution of the main groups of countries from this point of view for years 2018 and 2020. The second analysis concerns the main fields in the industry for year 2020. The used methodology was DM-CRISP. In the final part of the article the obtained results are analyzed, a discussion is added based on them and also a conclusion section.

The present paper tries to identify the dynamics of the unemployment for 27 countries from European Union and which countries have encountered the biggest cluster fluctuations (their behavior in this regard) during the COVID-19 pandemic.In order to obtain the results, a data mining analysis was made, using the specific CRISP-DM methodology.The data analysis is made using the EM and Simple K-Means cluster algorithms.For each analyzed period of time, a cluster analysis is made and each country is distributed in the most appropriate cluster.The main findings of the paper are indicating the dynamics of the unemployment based on the identified clusters and also the behavior of each country related to the movement from one cluster to another.The paper offers an original approach in which a cluster data mining analysis is made in order to identify correlation for pattern behavior in the data about unemployment.Knowing that several countries have a similar behavior when they are exposed to a certain situation, maybe strategies and regulation can be designed for all of them, reducing this way the resources consumption.

The health crisis generated by the COVID-19 pandemic has induced, among other things, an increase in the importance of remote work or teleworking (TL) in the current period. The objective of this research is to identify the economic and social impact of telework in changing the behavior of employees in Romania. The research was conducted approximately one year after the onset of the pandemic until the beginning of the vaccination period in Romania. The research proposed includes three main directions of analysis of the extracted data, which are related to telework efficiency, this being considered one of the most important indicators for a company. In order to obtain conclusive results, we used a mixed methodology, combining results obtained through a survey based on a self-administered electronic questionnaire, with a data mining analysis. Detailed analysis of the groups identified based on work efficiency allowed us to highlight the most common employee profiles. This analysis was doubled by a second classification experiment, which provided us a more detailed analysis of the groups identified based on job satisfaction and highlighted the most common employee profiles. The expansion of telework in various economic areas is a result of adaptation to the new economic and social conditions caused by the COVID-19 pandemic.

Climate change (CC) represents a real fact with consequences that start to be seen more and more often and that is why it cannot be ignored anymore. It affects many domains of the human activities and also the health of the people. Climate-specific actions are needed to be taken in order to protect the people and to save the environment. For each affected domain, new regulations and actions regarding climate change prevention must be designed, promoted and implemented. Besides phenomena like heat waves, storms, increased temperature, forest fires, floods, etc. which represent direct results of the CC, also indirect results like human health may be encountered. Human health is affected by elements that are having a big impact over the environment of the people and over the resources that they need (resources like water, food, air, natural resources, etc.). CC has also implications on people migration, the fight over the natural resources, political and economic environments. This paper offers an overview of the most important factors that are affecting the health of the people from the CC point of view and which are the main challenges that most affected countries from EU are dealing with.

Recommender systems (RS) represent a very important aspect for the Education 4.0, especially for the engineering education that is taking advantage from the new technologies. Personalized e-learning is very connected with this kind of systems, being important for the students but also for the teachers to be evolved in a personalized learning-teaching process. The use of RS has developed a lot for the engineering education and in the last years a lot of online learning environments were created and improved based on the new solution offered by the RS. The purpose of this paper is to offer an overview of the most recent researches from e-learning that are using RS. There are also analyzed the main recommender systems types and their issues and limitations.

Business process management (BPM) tools allow the analysis and improvement of the actual business processes in the organization, for making them more efficient and effective. The paper presents how the discipline of BPM can be applied to the project processes, by adopting the automation in different stage of the project process management, especially in the process analysis and discovery. The automated project process discovery is possible due to the extended IT infrastructure for project process implementation, available in the digital era. While the process simulation for project process analysis is already applied on large scale, the project process mining is still not so much adopted and exploited. The authors investigate how the existing BPM tools can be used for project process modeling and the automated discovery. In the last part of the paper, the authors present some proposals for future development of these applications.

Reinforcement of technology-enhanced education transformed education into a data-intensive domain. As in many other data-intensive domains, the interest for data analysis through various analytics is growing. The chapter starts by defining learning analytics (LA), with relevant views on the literature. A discussion about the relationships between LA, educational data mining, and academic analytics is included in the background section. In the main section of the chapter, the learning analytics, as an emerging trend in the educational systems is described by discussing the main issues, controversies, and problems on this topic. The final part of the chapter presents the future research directions and the conclusion.

The purpose of this paper is to analyze the main technologies of Education 4.0, which plays an important role in sustaining Industry 4.0 and has a significant impact on reshaping the engineering education itself. The concept of Education 4.0 is based on achieving a symbiosis between all educational actors: students, teachers, education managers and administrators in a common endeavour for improving the education practices. Education 4.0 designates educational settings, in which different actors cocreate value at different levels. Encouraging the development and usage of intelligent educational infrastructure is essential for implementing the Education 4.0 concept.

Reinforcement of the technology-enhanced education transformed education into a data-intensive domain. As in many other data-intensive domains, the interest for data analysis through various analytics is growing. The article starts by defining LA, with relevant views on the literature. A discussion about the relationships between LA, educational data mining and academic analytics is included in the background section. In the main section of the article, the learning analytics, as an emerging trend in the educational systems is describe, by discussing the main issues, controversies, problems on this topic. Final part of the article presents the future research directions and the conclusion.

The current paper debates upon the importance of learning analytics, by underlining its place in current research directions and its influence in reshaping education with the aid of cutting-edge technologies. Definitions, successful examples of learning analytics tools, as well as the challenges of implementing them are revised. A special attention is awarded to learning analytics performed in social learning environments, due to their popularity and well-established value. In order to investigate the importance of learning analytics to end-users (students and teachers), a survey based on two questionnaires was performed and revealed the fact that advanced learning analytics features are highly requested in online environments. The questionnaires were filled by 59 IT teachers and more than 390 IT students, from various countries, fact that demonstrates the global significance of the study. Also, a model of developing a feasible social learning solution with learning analytics features is proposed, shaping thus the feature research directions.

Reinforcement of technology-enhanced education transformed education into a data-intensive domain. As in many other data-intensive domains, the interest for data analysis through various analytics is growing. The chapter starts by defining learning analytics (LA), with relevant views on the literature. A discussion about the relationships between LA, educational data mining, and academic analytics is included in the background section. In the main section of the chapter, the learning analytics, as an emerging trend in the educational systems is described by discussing the main issues, controversies, and problems on this topic. The final part of the chapter presents the future research directions and the conclusion.

New paradigms of learning have occurred as a consequence of the emergence of virtual reality devices, big data and cloud computing, sensory and ubiquitous technologies, semantic or social ones, which can increase education efficiency. It is believed that the current technological revolution and the emergence of big data influences and reforms the labor market, as the demand of highly competent people is increasing. Based on these facts, the model of a "smarter university" can be implemented as an e-learning platform for helping people acquire specific competences or for increasing employability. The development of e-learning doesn't represent the ending of the traditional learning. E-learning should be embedded in the university ecosystem, by connecting it to the university's learning management system. E-learning can be used in parallel with face-to-face courses, combining the tradition with the novelty. Also, nowadays, the learning process is continuous; therefore the term of lifelong learning (LLL) has been created. It refers to the permanent implication in formal and informal education, on a daily basis. Education and learning do not refer only to the formal schooling anymore a person must acquire, evolve and maintain specific knowledge and skills to assure his self education. Although LLL is known to have positive impact on one's employability, its efficiency is hard to prove. Statistics related to graduate unemployment are considered indicators for quantifying educational efficiency. A platform which sustains LLL and, in the same time, connects its users with company and university environments would clearly show the efficiency of LLL for improving professional life of university graduates. In order to check the feasibility of such an idea and materialize it in a concept, a regional study on Danube countries was conducted, within the framework of a joint education project: START-SoPI Project "Feasibility Study on Implementing a Pan-European Social Platform to Support Lifelong Learning and Employability". The project was partly financed by START Danube Region Project Fund, while START was financed by the European Union and the City of Vienna. START-SoPI Project had partners from 3 countries from the Danube Region: Romania, Serbia and Austria. The research methodology of START-SoPI project was based on online questionnaires for students and professors (we had 391 validated questionnaires of students and 59 validated questionnaires of professors from various countries), structured interviews with companies' representatives, students, professors and personnel from career development centers (we performed 21 interviews in all three countries) and a focus group organized in Serbia. Qualitative and quantitative analysis was done on the collected data, both proving the benefits brought by the implementation of a lifelong learning platform which connects companies, universities and students. Also, we proposed a model of the architecture and we performed the feasibility study of it. The current paper presents the result of the START-SoPI project in the context of today's social and economical challenges, describes in detail how the visibility of university graduates towards companies will be increased in a lifelong learning platform and underlines the advantages of such a platform for Danube Region countries.

The current paper presents the results of a study on students' intention to promote intelligent technologies, meaning agent-based technologies at their workplace. The study was a face-to-face questionnaire-based survey. 158 students from master degree programme in IT were voluntarily participated at this study. The analysis of the collected data was done with several data mining algorithms. The survey reveals the main factors which determine the attitudes of the students using an attribute ranking technique based on gain ratio. The main factors identified were: the recommended didactic resources, the course content, the student income, the actual student job, the student previous knowledge and experience on agent technologies and the time spent for learning these topics. Profiles of the students interested/not interested to promote multi-agent systems and agent-based methodologies at workplace were also induced by using clustering techniques.