Federal Institute for Occupational Safety and Health

Protecting humans from the inhalation of potentially hazardous fibres requires controlling their exposure by microscopic characterization of filter-sampled aerosols. Therefore, it is a crucial task to reliably recognize, characterize morphologically and count critical fibres in electron microscopic images. Since visual inspection of large greyscale image areas is a tedious and time-consuming process, an automated AI-assisted fibre instance recognition approach was developed. It combines pixelwise semantic image segmentation by a U-Net-like model with algorithms to search, trace and refine fibre-shaped segments. This way, a two-step instance segmentation is achieved that provides fibre morphology, criticality and number information even for fibres in intersecting or overlapping configurations. The model was optimized by supervised training on a dataset of more than 1,000 human-annotated (20Mpx) scanning electron micrographs. The quality of these training data as well as the reliability of fibre instance recognition of both our automated approach and of human evaluators were assessed by comparing their performance for 6 carbon nanotube materials that exhibited different degrees of morphological complexity. The results show good agreement with human evaluators in terms of the number of fibres found, length and width determination. With limitations for very complex morphological configurations where also humans face undecidable ambiguities or extremely time-consuming fibre tracing tasks, our fibre tracing algorithm proved capable to disentangle intersections and to count fibres in unambiguously countable clusters. Our approach achieves a large reduction in human workload. The resulting fibre instance data opens the possibility to develop purely AI-based instance segmentation solutions.

Today, terms such as sustainable production, industrial cyber-physical systems, cyber-physical production systems (CPPS), software-defined manufacturing, smart manufacturing, Industry 4.0, Industry 5.0, system of systems, internet of things, human-in-the-loop, and digital twins are widely used. These concepts emphasize key characteristics of modern and future production systems, including heterogeneity, structural and behavioral complexity, intelligence, autonomy, reconfigurability, and human centrism. They also highlight the growing importance of reliable and up-to-date risk assessment, safety, and reliability measures, given the significant environmental, economic, and social demands. However, current industrial risk analysis methods lag behind the rising technical sophistication of such systems. It remains unclear whether existing methods can capture complex failure scenarios of dynamic, AI-driven systems with advanced software architectures. This paper presents the main challenges faced by safety engineers in industrial automation and provides a structured classification of risk and reliability analysis methods and metrics, supported by a systematic review of 95 papers up to October 2025. The review addresses questions such as: which CPPS aspects must be considered, which methods are applicable, what are their advantages and limitations, and how can methods be combined? The findings reveal the need to extend classical approaches toward dynamic risk assessment, probabilistic model checking, AI-based techniques, digital twins, and intelligent fault injection. The study provides both a comprehensive overview of current risk and reliability assessment methods for CPPS and a roadmap for advancing their future development.

The way in which people work is changing, with workplaces characterized by greater variations in where, when, and how people work. Across two studies, we evaluated a web-based intervention introducing self-regulation strategies based on Action Regulation Theory to enable workers in hybrid working environments to organize their workday effectively, to manage work and private life demands, and thus to improve work performance (indicated by task performance and proactivity), occupational self-efficacy, and psychological detachment and reduce work-life conflict. In two randomized controlled trials, participants were assigned to an intervention group or a waitlist control group, filling out questionnaires before and after the intervention. In Study 1, using a randomized controlled trial with a convenience sample of 128 German employees (intervention group: n = 65; control group: n = 64) with baseline and two follow-up measurement points, multilevel analyses revealed positive effects on occupational self-efficacy, task performance, and proactivity. Moreover, we found a delayed effect on both work-life conflict and detachment after 2 weeks. In Study 2, in a randomized controlled trial with a sample of 125 Irish employees from one organization (intervention group: n = 59; control group: n = 66), we found positive effects directly after the intervention on occupational self-efficacy and work-life conflict. The results of both studies converge to support the effectiveness of the developed self-regulation intervention. Findings suggest that the intervention is an effective tool for promoting certain aspects of work performance. Furthermore, training self-regulation in the work context can improve occupational self-efficacy and reduce work-life conflict. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

Abstract Acinetobacter baumannii is a Gram-negative nosocomial pathogen that is notorious for its rapid development of antibiotic resistance. However, its ecology and evolution outside hospital settings remain poorly defined. Here, we demonstrate that the natural lifestyle of A. baumannii includes soil-dwelling and airborne dissemination, which helps explaining its adaptability and tolerance to desiccation, radiation and antibiotics, and thus its predisposition to establish within hospitals. Starting from white stork nestlings previously discovered as a reservoir, we studied food chains and associated environments and identified soil and decaying plants as habitats. We demonstrate that sterilized plant material is rapidly colonized by airborne A. baumannii. A set of 401 genomes were sequenced and compared to publicly available genomes, revealing numerous links between wildlife isolates and hospital strains, and disclosing intercontinental dispersal. Our pan-genome estimate of the species (~51,000 gene families) more than doubles that of previous studies. Our data further suggest massive radiation of the species early after its emergence, possibly fostered by human activity since the Neolithic. Now, it is possible to study the ecology and evolution of A. baumannii in nature at an unprecedented temporal and spatial resolution and to elucidate the adaptive evolution of environmental bacteria towards multidrug-resistant opportunistic pathogens.

Sedentary behaviour (SB) and the lack of physical activity (PA) are associated with negative health outcomes. Among desk-based workers, sitting at work contributes substantially to the daily time spent sedentary. Working environment can influence SB. Thus, we aimed to investigate the evidence on the impact of working from home/teleworking (WFH), which is now a common working environment versus working onsite on SB and PA. We conducted a systematic review comparing SB and PA of workers WFH compared to onsite work. We searched Pubmed, Embase and SPORTDiscus (last search: June 2025). At least two reviewers independently screened the studies and rated the of risk of bias based on adapted existing tools. We included studies on adult workers, which at least part-time WFH with comparison group working onsite, reporting SB or/and PA-outcomes per workday/work time. Data extraction was done by one reviewer and checked by two reviewers. Results were described qualitatively and random-effect meta-analyses for daily sedentary time (ST), sitting breaks, and steps were performed. We included 38 studies (from 42 articles, with n = 282,264 subjects) comparing WFH and onsite work. Four of these studies were rated as having a “low” risk of bias. SB was described in 23 studies (with n = 209,267 subjects). A meta-analysis of studies reporting quantitative results suggests an increase in ST of 31 min (95