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Training in the Occupational Safety and Health (OSH) sector is crucial for minimizing workplace hazards and ensuring employee well-being. Virtual Reality (VR) emerges as a training tool that can enhance learning outcomes and simulate hazardous scenarios safely. However, several aspects must be taken into consideration when implementing VR-based training solutions. The paper investigates how to effectively design, develop, integrate, and validate a VR OSH training tool. To this aim, a comprehensive guideline of 9 key elements articulated in 29 items is proposed. Every element and item is retrieved from analyzing the existing literature on the topic using a systematic approach. The result is a comprehensive guide to consider all these aspects from the outset of design, in a cohesive, complete, and tailored manner. This formalization is intended to facilitate the advancement of research and implementation of these solutions, which to date have been largely confined to prototypes or lack real practical application.

Background: Endogenous and exogenous factors impact the operational characteristics of supply chains, affecting wholesale warehouses. The survival of a warehouse is often threatened by disruptive events that alter infrastructure and performance. The emergence of COVID-19 exemplified the need for adaptability in retail goods supply chains, emphasizing the necessity for responding to external shocks. Methods: The MARLIN (Method wArehouse ResiLience dIstruptioN) method, founded on theories and models of resilience engineering is introduced. MARLIN is a practical tool designed to identify key areas requiring intervention in response to disruptive events. An empirical test was conducted in an Italian warehouse. Results: The conducted test yielded tangible results, demonstrating the efficacy of the method. It successfully pinpointed areas necessitating intervention and identified Key Performance Indicators (KPIs) associated with disruptions. The study not only underscores the importance of data collection but also highlights the often-overlooked significance of warehouse management. Conclusions: The study establishes MARLIN as a valuable asset for stakeholders involved in disruption management. Its application has proven instrumental in recognizing areas of intervention and identifying KPIs related to disruptions. Ongoing research endeavors to broaden its applicability across diverse supply chain scenarios, aiming to enhance situational awareness and enable proactive risk assessment through what-if analysis.

The accessibility of advanced Artificial Intelligence-based tools, like ChatGPT, has made Large Language Models (LLMs) readily available to students. These LLMs can generate original written content to assist students in their academic assessments. With the rapid adoption of LLMs, exemplified by the popularity of OpenAI's ChatGPT, there is a growing need to explore their application in education. Few studies examine students' use of LLMs as learning tools. This paper focuses on the application of ChatGPT in engineering higher education through an in-depth case study. It investigates whether engineering students can generate high-quality university essays with LLMs assistance, whether existing LLMs identification systems can detect essays produced with LLMs, and how students perceive the usefulness and acceptance of LLMs in learning. The research adopts a deductive/inductive approach, combining conceptualization and empirical evidence analysis. The study involves mechanical and management engineering students, who compose essays using LLMs. The essay assessment showed good results, but some recommendations emerged for teachers and students. Thirteen LLMs detectors were tested without achieving satisfactory results, suggesting to avoid LLMs ban. In addition, students were administered a questionnaire based on constructs and items that follow the technology acceptance models available in the literature. The results contribute to qualitative evidence by highlighting possible future research and educational practices.

In the field of industrial process monitoring, scholars and practitioners are increasing interest in time-varying processes, where different phases are implemented within an unknown time frame. The measurement of process parameters could inform about the health state of the production assets, or products, but only if the measured parameters are coupled with the specific phase identification. A combination of values could be common for one phase and uncommon for another phase; thus, the same combination of values shows a high or low probability depending on the specific phase. The automatic identification of the production phase usually relies on clustering techniques. This is largely due to the difficulty of finding training fault data for supervised models. With these two considerations in mind, this contribution proposes the Latent Dirichlet Allocation as a natural language-processing technique for reviewing the topic of clustering applied in time-varying contexts, in the maintenance field. Thus, the paper presents this innovative methodology to analyze this specific research fields, presenting the step-by-step application and its results, with an overview of the theme.

Healthcare providers play a fundamental role in evaluating pain. Several issues about how nurses are educated remain unsolved. The aim of our study was to address how Italian nurses manage patients suffering from pain in daily practice. A cross-sectional survey was administered among Italian registered nurses. Data were collected using a 34-item questionnaire that had been previously validated during a pilot study. The lowest level of participation/education/information events was observed in the South (p=0.0001). A significant difference among the four areas was found in the department affiliation of responders (p=0.0001). Pain assessment at patients’ admission was most frequent in the Northeast (32.9%), whereas the lowest frequency was found in the South (15.1%) (p=0.0001). The prevalence of nurses’ knowledge of pain scales and their distribution in usual applications was similar in the Northwest and -east, and Central Italy, but lower in the South (p=0.0001). This study underlines the need for change in the clinical approach to pain treatment in healthcare. Pain assessment is a fundamental step for preparing individualized therapeutic plans, and nurses play a crucial role in improving the quality of life of suffering patients.