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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]

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]

In engineering applications, it is standard to consider modal parameters (e.g. natural frequencies and mode shapes), to obtain measurable changes induced by damage events. However, such changes are often too subtle to detect and interpret accurately. In order to overcome this limitation and enhance the efficacy of damage detection, this study introduces an innovative technique inspired by Prof. Lekszycki, outlined prior to his untimely departure. This technique is designed to highlight the influence of structural damage on natural frequencies. In other words, we add an external mass in a general point of a structure and calculate the set of natural frequencies before and after the occurrence of a damage event. We, therefore, repeat this procedure for all the points of the structure. The hypothesis we have verified in this paper is that if the mass is posed in the neighborhood of the place where the damage is considered, then it interacts with the undamaged and with the damaged structure in a different way. In particular, the changes of the natural frequencies between the undamaged and the damaged structure are emphasized. We verify the correctness of this idea for selected and simple examples in both 2D and 3D environments. [ABSTRACT FROM AUTHOR]

Following the severe impact of capitalist industrialization on the environment, the EU has funded several projects in the context of the European Green Deal to pursue climate neutrality by 2050. Some of these projects attempt to achieve zero emissions through political participation, while others by committing EU citizens to adopt sustainable habits in terms of both practical behavior and economic choices. The GreenSCENT project, funded by the European Union's Horizon 2020 research and innovation programme, aims to develop a Competence Framework compliant with the European Qualifications Framework and the European Green Deal. The present article documents the process of developing such a Competence Framework. Eight distinct research teams independently conducted a similar thorough literature review over an assigned topic (Climate Change; Clean Energy; Circular Economy; Green Building; Smart Mobility; From Farm to Fork; Biodiversity; Zero Pollution). The resulting documental corpora have then elicited to build the competence matrices and the corresponding European Qualification Framework levels. Once the information has been reorganized as a knowledge graph, the researchers discovered a large amount of novel interdomain connections, providing a more engaging way of interacting with the Competence Framework, and potentially apt to avoiding information overload issues while preserving the complexity, in line with the simplexity paradigm. The environmental education tools produced by this research could be useful in mitigating the repercussions of Capitalocene on the environment toward the adoption of more sustainable behaviors. [ABSTRACT FROM AUTHOR]