Industry 4.0 emphasizes the importance of sustainable manufacturing practices. Accordingly, engineering education has increasingly incorporated virtual reality (VR) technology. This study aims to identify the potential usage of VR as an educational tool to enhance manufacturing sustainability within Industry 4.0. The methodology used an experimental design, a structured survey, and a multilevel modeling analysis to measure the effects of VR interventions. The findings show that the learning outcomes of VR treatments and post-test scores are significantly impacted by two crucial factors: age (β = 0.35, p < 0.01) and VR experience (r = 0.42, p < 0.01). Furthermore, sustainability attitudes moderately affected academic development (β = 0.25, p < 0.05). According to descriptive statistics, virtual reality treatment resulted in a 25% improvement in the post-test results. According to MLM, the combined effects of virtual reality experience and sustainability attitudes on learning outcomes account for 45% of the total variation (R² = 0.45, p < 0.01). The study’s findings highlight the importance of VR in enhancing educational outcomes and fostering sustainable production practices within Industry 4.0. This research is unique in its simultaneous investigation of VR technology, engineering education, and sustainability, providing valuable insights into how VR can contribute to preparing engineers for the challenges of Industry 4.0 and advancing sustainable initiatives in the manufacturing field.

The research focuses on incorporating microgrids into engineering curricula for achieving voltage stability in today’s power systems. This helps to meet the increasing demand for engineers to integrate distributed power generation and renewable energy sources. Some limitations of the current literature include the absence of models outlining approaches to microgrid education and limited insight into teaching strategies for electrical power systems. The research used a quantitative methodology to survey 100 engineering students enrolled in a microgrid modeling class to achieve the study’s objectives. The data analysis involved machine learning models such as Random Forest, Gradient Boosting, K-Means, hierarchical clustering, and regression models. The major findings identified exam score as the most significant determiner of student performance (weight ≈ 0.40). Based on the clustering analysis, it was found that microgrid systems can be grouped into four operational states. It was also seen that linear regression models were highly accurate and better than other highly complex models, like Decision Tree, with a model accuracy of R² ≈ 0.4. One of the study’s major strengths is the potential impact of the proposed framework for integrating microgrids into engineering education on the professional training of engineers. This framework, based on theoretical knowledge and practical experience as well as on developing advanced analytical skills, can significantly enhance the professional training of engineers to deal with the complexities of contemporary power systems, including microgrids and sustainable energy progress.

Digital evolution is a computer-based instantiation of Darwinian evolution in which short self-replicating computer programs compete, mutate, and evolve. It is an excellent platform for addressing topics in long-term evolution and paleobiology, such as mass extinction and recovery, with experimental evolutionary approaches. We evolved model communities with ecological interdependence among community members, which were subjected to two principal types of mass extinction: a pulse extinction that killed randomly, and a selective press extinction involving an alteration of the abiotic environment to which the communities had to adapt. These treatments were applied at two different strengths, along with unperturbed control experiments. We examined how stability in the digital communities was affected from the perspectives of division of labor, relative shift in rank abundance, and genealogical connectedness of the community's component ecotypes. Mass extinction that was due to a Strong Press treatment was most effective in producing reshaped communities that differed from the pre-treatment ones in all of the measured perspectives; weaker versions of the treatments did not generally produce significant departures from a Control treatment; and results for the Strong Pulse treatment generally fell between those extremes. The Strong Pulse treatment differed from others in that it produced a slight but detectable shift towards more generalized communities. Compared to Press treatments, Pulse treatments also showed a greater contribution from re-evolved ecological doppelgangers rather than new ecotypes. However, relatively few Control communities showed stability in any of these metrics over the whole course of the experiment, and most did not represent stable states (by some measure of stability) that were disrupted by the extinction treatments. Our results have interesting, broad qualitative parallels with findings from the paleontological record, and show the potential of digital evolution studies to illuminate many aspects of mass extinction and recovery by addressing them in a truly experimental manner.

Cloud computing, a superset of heterogeneous distributed computing, allows sharing of geographically dispersed resources across multiple organizations on a rental basis using virtualization as per demand. In cloud computing, workflow allocation to achieve the optimum schedule has been reported to be NP-hard. This paper proposes a Levelized Multiple Workflow Heterogeneous Earliest Finish Time (LMHEFT) model to optimize makespan in the cloud computing environment. The model has two phases: task prioritization and task allocation. The task prioritization phase begins by dividing workflows into the number of partitions as per the level attribute; after that, upward rank is employed to determine the partition-wise task allocation order. In the allocation phase, the best-suited virtual machine is determined to offer the lowest finish time for each task in partition-wise mapping to minimize the workflow task’s completion time. The model considers the inter-task communication between the cooperative workflow tasks. A comparative performance evaluation of LMHEFT has been conducted with the competitive models from the literature implemented in MATLAB, i.e., heterogeneous earliest finish time (HEFT) and dynamic level scheduling (DLS), on makespan, flowtime, and utilization. The experimental findings indicate that LMHEFT surpasses HEFT and DLS in terms of makespan 15.51% and 85.12% when varying the number of workflows, 41.19% and 86.73% when varying depth levels, and 13.74% and 80.24% when varying virtual machines, respectively. Further statistical analysis has been carried out to confirm the hypothesis developed in the simulation study by using normality tests, homogeneity tests, and the Kruskal–Wallis test.