The present paper analyses the relevance of academic engagement in the process of students dropping out of school. Previous studies have consistently shown strong associations between engagement and students’ achievement outcomes. The increased attention given to academic engagement in recent years is also visible in the efforts of stakeholders in higher education to increase engagement and, consequently, to reduce dropout. The relationships between engagement and dropout rates are somewhat fuzzier, vigor, dedication, and absorption vary inconsistently in students at risk. Using a correlation research design, we tested several dimensions of academic engagement as predictors of early dropout intentions on a sample of first-year students (N = 1063). The results showed that psychological academic engagement of students is a significant predictor of early dropout intentions. Differences in academic engagement given by family background and academic context were also tested. The implications of the results are discussed in the light of possible interventions for increasing academic engagement of university students. Also, suggestions for including employers in academic engagement and dropout interventions are given.

The current competitive environment demands companies to be more and more efficient. In order to increase manufacturing efficiency, two apparently independent approaches have emerged: lean strategies, focusing on identifying and minimizing non-added value activities, identifying wastes in the system and their elimination, and information tools for planning and controlling activities. In this paper, a manufacturing system was considered for which it was necessary to increase the production capacity in order to respond to the customer’s increased demand. A practical case study in the automotive industry for a medium-sized enterprise was considered. In order to investigate the production process parameters and to implement lean principles, Value Stream Mapping (current analysis and optimized solution) and Anylogic agent-based simulations were carried out. Based on this, the lean performances, specific for the target VSM, were evaluated in terms of key performance indicators. The benefits of integrating agent-based simulation in the design and analysis of the value flow in the production chain are the capitalization of the information offered by Value Stream Mapping and the possibility to choose the best one from the possible scenarios. It generates important time and cost reductions without further resource waste.

The quality of product and process is one of the most important factors in achieving constructively and then functionally safe products in any industry. Over the years, the concept of Industry 4.0 has emerged in all the quality processes, such as nondestructive testing (NDT). The most widely used quality control methods in the industries of mechanical engineering, aerospace, and civil engineering are nondestructive methods, which are based on inspection by detecting indications, without affecting the surface quality of the examined parts. Over time, the focus has been on research with the fourth generation in nondestructive testing, i.e., NDT 4.0 or Smart NDT, as a main topic to ensure the efficiency and effectiveness of the methods for a safe detection of all types of discontinuities. This area of research aims at the efficiency of methods, the elimination of human errors, digitalization, and optimization from a constructive point of view. In this paper, we presented a magnetic particles inspection method and the possible future directions for the development of standard equipment used in the context of this method in accordance with the applicable physical principles and constraints of the method for cylindrical parts. A possible development direction was presented in order to streamline the mass production of parts made of ferromagnetic materials. We described the methods of analysis and the tools used for the development of a magnetic particle inspection method used for cylindrical parts in all types of industry and NDT 4.0; the aim is to provide new NDT 4.0 directions in optimizing the series production for cylindrical parts from industry, as given in the conclusion of this article.

The main objective of this research is to identify current trends in Industry 4.0 within the manufacturing sector through bibliometrics. A dataset of 1069 documents from 2020 to 2024 obtained from the Web of Science is processed. Using the R-Bibliometrix package, research trends, leading authors, and institutional contributions are identified. The accelerated growth rate of 30.77% in publications confirms research interest. Thematic exploration reveals the convergence of Industry 4.0 with sustainability, AI, the Internet of Things, smart manufacturing, and digitalization as dominant themes. The transition towards smarter and more efficient systems is evident, with an emphasis on integrating sustainability into Industry 4.0 practices. Challenges persist in management adjustment, technological integration, and strategy for digital transformation. The study identifies sustainability and machine learning as critical enabling factors for Industry 4.0, while security and collaboration have emerged as key focus areas in recent years. A wide geographic distribution of research contributions with substantial international cooperation is observed, highlighting India, Italy, and China. Major journals like Sustainability and Journal of Manufacturing Systems emerge as influential platforms for disseminating research on the topic. The analysis of citation networks, co-occurrence, and thematic evolution underscores the multidimensional impact of Industry 4.0 technologies on manufacturing.

This study conducts an Academic Literature Analysis of 75 maturity models to clarify how Industry 4.0 and Industry 5.0 are being conceptualized and assessed. We map model scope, level structures, evaluated dimensions, and enabling technologies and complement descriptive statistics with exploratory non-parametric tests on the relationship between level structure and dimensional breadth. Results show a persistent dominance of Industry 4.0 models (≈92%), alongside a recent but steady emergence of Industry 5.0 and hybrid approaches in the latest models. Structurally, five-level schemes prevail, balancing diagnostic granularity and comparability. Content-wise, Technology and Digitalization, Processes and Operations, and Management and Strategy remain core, while People and Competencies and Innovation gain relevance; Sustainability and Social Responsibility and Human–Machine Interaction appear with the rise of Industry 5.0. We contribute (i) an operational definition of “hybrid” maturity models to make the I4.0→I5.0 transition measurable, (ii) a meta-typology of maturity levels explaining the five-level preference, and (iii) an evidence-based technology cartography across models. The findings suggest that future designs should retain the digital backbone of I4.0 while integrating explicit indicators for human-centricity, sustainability, and resilience with transparent weighting and scenario-based validation.