The objective of this work is to propose the development of a virtual electronics laboratory with an aeronautical radar simulator using immersive technologies to help students learn. To verify whether this proposal was viable, the systematic literature review (SLR) methodology was used, whose objective was to verify whether immersive technologies were being used effectively in education and, also, what challenges, opportunities, and benefits they bring to Education 4.0. For this, eight Research Questions (RQs) were formulated to be answered by articles based on the highest SLR scores. The results presented by SLR were as follows: there was an increase in the use of immersive technologies in education, but virtual reality (VR) is still more used in education than AR, despite VR being more expensive than AR; the use of these new technologies brings new challenges, opportunities, and benefits for education; there was an increase in the quality of teaching for complex subjects; and there was an increase in students’ interest in the content presented.

Concerning the development of computational tools and solutions as a decision-making aid, this paper presents the results of the waspasWEB project, which strives to provide decision-makers with a readily accessible mechanism to employ the weighted aggregated sum product assessment (WASPAS) method. The social contribution of the project encompasses the development of a user-friendly and publicly accessible internet tool, as well as a package launched on the Comprehensive R Archive Network (CRAN) to serve the community of users of the R language. The use of operational research methodologies is crucial to justify decisions, and this effort seeks to advance the adoption of such methodologies, offering managers, researchers, and the general public an intuitive and easily accessible multi-criteria decision-making tool. In this way, we present the technical specifications, usability, and interactivity of the user with the computational platform, being validated its viability through a hypothetical case study. At the end of the research, it exposes the limitations and feasibility of the proposed computational model along with future research.

This study analyzes the role of Floating Production, Storage, and Offloading units (FPSO) in deepwater oil and gas exploration within the context of Industry 5.0, focusing on integrating social manufacturing systems. Reviewing common offshore exploration methods, the study identifies FPSOs as key catalysts for innovation, energy efficiency, and sustainability. The research concludes that combining FPSO with Industry 5.0 can optimize horizontal and vertical production processes, enhancing predictive maintenance, advanced safety, and waste management. Continuous innovation is highlighted as vital for addressing future challenges in deepwater exploration.

Distance-based multicriteria decision methods such as the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) are widely adopted in engineering, yet two limitations constrain their reliability: the dependence on subjective weighting, introducing cognitive bias, and the sensitivity of max–min normalization to outliers. This paper proposes the Comprehensive Distance-Based Ranking with Machine Learning (COBRA-ML) method, addressing both limitations through four innovations: (i) vector normalization by Euclidean norm, reducing sensitivity to extreme values; (ii) objective weighting via the Preference Selection Index (PSI); (iii) Gaussian structural balancing, compensating for the PSI’s tendency to underweight dispersed yet relevant criteria; and (iv) automatic calibration of balancing parameters through a Random Forest Regressor trained on structural problem features. The machine learning component, validated on K = 200 synthetic Multi-Criteria Decision-Making (MCDM) problems, achieves R² = 0.853 for λ, R² = 0.929 for σ, and cross-validated R² = 0.880 ± 0.015. Applied to renewable energy source selection in Brazil, the method identifies Onshore Wind as the optimal alternative (score = 0.6843), with sensitivity analysis yielding mean Kendall τ = 0.933. Comparative validation against TOPSIS, VIKOR, the Analytic Hierarchy Process (AHP), and the Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) II confirms high concordance (τ = 1.000 with PROMETHEE II; ρ = 0.975 with TOPSIS). COBRA-ML offers a transparent, reproducible alternative to subjective parameter tuning in multicriteria decision support

Diabetes is a growing global health concern, affecting approximately 422 million people and causing 1.5 million deaths annually, according to the World Health Organization (WHO). In Brazil, 15.7 million people were living with diabetes in 2021, an increase of 3.3 million over the past decade. This article presents an innovative approach using the hybrid method CRITIC-GRA-3N to select glucose meters, aiming to improve glycemic control and support diabetes management. The study evaluates criteria such as accuracy, connectivity, cost, and sustainability practices, offering a robust and replicable solution for the healthcare industry. The method reduces subjectivity and ensures consistency in ranking alternatives, contributing to the integration of academia and industry by leveraging advanced computational tools. The results highlight the importance of criteria such as energy efficiency and sustainability to promote innovation and collaboration between sectors.