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Featured Application: LLM integration in generating new methodologies in OHS. This paper introduces a novel generative artificial intelligence workbench specifically tailored to the field of safety sciences. Utilizing large language models (LLMs), this innovative approach significantly diverges from traditional methods by enabling the rapid development, refinement, and preliminary testing of new safety methodologies. Traditional techniques in this field typically depend on slow, iterative cycles of empirical data collection and analysis, which can be both time-intensive and costly. In contrast, our LLM-based workbench leverages synthetic data generation and advanced prompt engineering to simulate complex safety scenarios and generate diverse, realistic data sets on demand. This capability allows for more flexible and accelerated experimentation, enhancing the efficiency and scalability of safety science research. By detailing an application case, we demonstrate the practical implementation and advantages of our framework, such as its ability to adapt quickly to evolving safety requirements and its potential to significantly cut down development time and resources. The introduction of this workbench represents a paradigm shift in safety methodology development, offering a potent tool that combines the theoretical rigor of traditional methods with the agility of modern AI technologies. [ABSTRACT FROM AUTHOR]

In the context of socio-technical systems, traditional engineering approaches are inadequate, calling for a fundamental change in perspective. A different approach encourages viewing socio-technical systems as complex living entities rather than through a simplistic lens, which enhances our understanding of their dynamics. However, these systems are designed to facilitate human activities, and the goal is not only to comprehend how they operate but also to guide their function. Currently, we lack the appropriate terminology. Hence, we introduce two principal concepts, simplexity and complixity, drawing inspiration from how nature conceals intricate mechanisms beneath straightforward, user-friendly interfaces. [ABSTRACT FROM AUTHOR]

This paper presents an analysis on information disorder in social media platforms. The study employed methods such as Natural Language Processing, Topic Modeling, and Knowledge Graph building to gain new insights into the phenomenon of fake news and its impact on critical thinking and knowledge management. The analysis focused on four research questions: 1) the distribution of misinformation, disinformation, and malinformation across different platforms; 2) recurring themes in fake news and their visibility; 3) the role of artificial intelligence as an authoritative and/or spreader agent; and 4) strategies for combating information disorder. The role of AI was highlighted, both as a tool for fact-checking and building truthiness identification bots, and as a potential amplifier of false narratives. Strategies proposed for combating information disorder include improving digital literacy skills and promoting critical thinking among social media users. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Healthcare facilities require flexible layouts that can adapt quickly in the face of various disruptions. COVID-19 confirmed this need for both healthcare and manufacturing systems. Starting with the transfer of decision support systems from manufacturing, this paper generalizes layout re-design activities for complex systems by presenting a simulation framework. Through a real case study concerning the proliferation of nosocomial cross-infection in an intensive care unit (ICU), the model developed in systems dynamics, based on a zero order immediate logic, allows reproducing the evolution of the different agencies (e.g., physicians, nurses, ancillary workers, patients), as well as of the cyber-technical side of the ICU, in its general but also local aspects. The entire global workflow is theoretically founded on lean principles, with the goal of balancing the need for minimal patient throughput time and maximum efficiency by optimizing the resources used during the process. The proposed framework might be transferred to other wards with minimal adjustments; hence, it has the potential to represent the initial step for a modular depiction of an entire healthcare facility. [ABSTRACT FROM AUTHOR]

Solid waste management is critical to sustainable, healthy, and eco-friendly cities and societies. In developing countries, only a small percentage of municipal solid waste is disposed safely, while the majority remains in the streets or disposed in open landfills. Most countries seek to establish effective and efficient solid waste management system (SWMS) that can handle and dispose of the daily generated waste at minimum cost and in a sustainable manner. Those systems usually consist of waste sources, waste collection stations, landfills, incinerators, and recycling plants, in addition to the transportation system that integrates the different sub-systems. The problem facing decision-makers while designing or reconfiguring a SWMS is to determine the optimal supply chain network design for such systems in a way that ensures the treatment and disposal of all daily generated waste at the lowest cost. In this context, this paper aims to develop a generic optimization model suitable for application in SWMS optimization in developing countries. A new mixed-integer linear programming (MILP) model is formulated for a SWMS configuration that integrates waste generation sources, collection/transfer stations, recycling plants, incinerators, and landfills. The proposed MILP model is formulated to determine the optimal number and locations of the different facilities, and the optimal flow of waste in the system that minimizes the net daily cost incurred in the system. The model has been applied in a case study on the SWMS in Fayoum Governorate, Egypt. The main contribution of this research refers both to the theoretical development of a generic MILP model that can be applied to optimally design the SWMS in developing countries, and to its operational counterpart, as per the design solutions provided in the SWMS of Fayoum Governorate (Egypt). [ABSTRACT FROM AUTHOR]

Rationale, aims, and objectives: The application of resilience in health care requires the shift from a cause‐effect approach to a systemic approach, yet few tools have been developed to measure resilience potential in this specific context. This study tests a resilience assessment grid (RAG) questionnaire to measure the resilience of anaesthesiologists, with a cross‐country survey. Method: A study was conducted with an analytic hierarchy process (AHP) questionnaire containing 57 detailed questions; 16 nations and 172 respondents were involved in the study. The data were statistically analysed to identify insights from the questionnaire, main improvements for further assessment, and confirmation of the design of the questionnaire. The questionnaire reliability was assessed by Cronbach analysis. Weak items were identified by a detailed correlations analysis and through a weight‐polarization matrix. Construct validity was confirmed by principal component analysis (PCA) and factor analysis (FA). Results: The α level of Cronbach analysis is 0.910. PCA and FA confirmed the absence of underlying unexpected factors, with less than 8% from the first factor and a total of just 54% of variability explained by 17 factors. Suggestions for revising the questionnaire ensue from the analysis, with improvements for the questionnaire's significance. Conclusion: The questionnaire shows the potential to assess proxy measures of resilience, even confirming the relevance of a structured weighting approach based on the AHP. The exemplar statistical cross‐country analyses encourage the widespread use of a centralized resilience questionnaire to support standardized analyses and the diffusion of best practices among organizations. [ABSTRACT FROM AUTHOR]

The Fourth Industrial Revolution is radically reshaping the procedures and the manufacturing environments through the digitalization process. The digitalization process can change according to the context and to specific solutions, and it is able to modify manufacturing systems and production areas. All the employees are directly affected by the transformation of the working environment, manufacturing tools, and working conditions and by the increasing need for new competencies. In this context, it is crucial to identify new and emerging hazards concerning the health and safety of the employees to ensure a conscious and safe digital transformation for everyone involved. In this regard, the paper presents the state of the research and defines seven areas of interest for a safe and harmless digital transformation for the employees, drawing attention to the hazards in the different technological areas. The state of the research unveils the absence of detailed analysis to identify specific hazards of 4.0 technologies. Therefore, every specific 4.0 technologies is analyzed by an extensive review to provide a comprehensive matrix of new and emerging hazards for health and safety within digitalized manufacturing systems. The results can help manufacturing organizations to perform robust risk assessments for worker when introducing specific 4.0 technologies. [ABSTRACT FROM AUTHOR]