Aim/Purpose: This paper examines the transformative impact of Artificial Intelligence (AI) on professional skills in organizations and explores strategies to address the resulting challenges. Background: The rapid integration of AI across various sectors is automating tasks and reducing cognitive workload, leading to increased productivity but also raising concerns about job displacement. Successfully adapting to this transformation requires organizations to implement new working models and develop strategies for upskilling and reskilling their workforce. Methodology: This review analyzes recent research and practice on AI's impact on human skills in organizations. We identify key trends in how AI is reshaping professional competencies and highlight the crucial role of transversal skills in this evolving landscape. The paper also discusses effective strategies to support organizations and guide workers through upskilling and reskilling processes. Contribution: The paper contributes to the existing body of knowledge by examining recent trends in AI's impact on professional skills and workplaces. It emphasizes the importance of transversal skills and identifies strategies to support organizations and workers in meeting upskilling and reskilling challenges. Our findings suggest that investing in workforce development is crucial for ensuring that the benefits of AI are equitably distributed among all stakeholders. Findings: Our findings indicate that organizations must employ a proactive approach to navigate the AI-driven transformation of the workplace. This approach involves mapping the transversal skills needed to address current skill gaps, helping workers identify and develop skills required for effective AI adoption, and implementing processes to support workers through targeted training and development opportunities. These strategies are essential for ensuring that workers' attitudes and mental models towards AI are adaptable and prepared for the changing labor market. Recommendation for Researchers: We emphasize the need for researchers to adopt a transdisciplinary approach when studying AI's impact on the workplace. Given AI's complexity and its far-reaching implications across various fields including computer science, mathematics, engineering, and behavioral and social sciences, integrating diverse perspectives is crucial for a holistic understanding of AI's applications and consequences. Future Research: Looking ahead, further research is needed to deepen our understanding of AI's impact on human skills, particularly the role of soft skills in AI adoption within organizations. Future studies should also address the challenges posed by Industry 5.0, which is expected to bring about even more extensive integration of new technologies and automation.

Bicycle–motorised vehicle (BMV) collisions account for the majority of the recorded bicyclists’ fatalities and serious injuries. This systematic review intends to examine the main factors contributing to BMV collisions. We performed a comprehensive literature review on Scopus, TRID, ProQuest, and Web of Science databases. Fifty-nine English-language peer-reviewed articles met the eligibility criteria and were included in the final analysis. The main factors contributing to BMV collisions identified were classified in accordance with a recently published conceptual framework for road safety. The majority of studies have identified factors related to road users’ behaviour (59.3%) and infrastructure characteristics (57.6%). A minority of studies identified variables related to exposure (40.7%) and vehicles (15.3%) as contributor factors to BMV collisions. A small but significant proportion of studies (20.3%) provided evidence that environmental factors may also play a role, although to a lesser extent, in determining BMV collisions. In addition to the three factors comprised in the applied conceptual framework for road safety, we identified environmental conditions as a category of factors contributing to BMV collisions.

Collaborative robots are revolutionising the manufacturing industry and the way workers perform their tasks. When designing shared workspaces between robots and humans, human factors and ergonomics are often overlooked. This study assessed the relationship between cognitive workload, workstation design, user acceptance and trust in collaborative robots. We combined subjective and objective data to evaluate the cognitive workload during an assembly task in three different scenarios in which we manipulated various features of the workstation and interaction modalities. Our results showed that participants experienced a reduction in cognitive workload in each of the three trials, indicating an improvement in cognitive performance. Additionally, we found that user acceptance predicted perceived stress across the trials but did not significantly impact the cognitive workload. Trust was not found to moderate the relationship between cognitive workload and perceived stress. This study has the potential to make a significant contribution to the field of collaborative assembly systems by providing valuable insights and helping to bridge the gap between researchers and practitioners. This study can potentially impact companies looking to improve safety, productivity and efficiency.

Accident analysis and studies on traffic revealed that cyclists' violation of red-light regulation is a typical infringement committed by cyclists. Furthermore, an association between cyclists' crash involvement and red-light violations has been found across different countries. The literature on red-light running cyclists' behavior in relation to their characteristic is still scarce. The present study, adopted an eye-observational methodology to investigates differences in cyclists' crossing behavior at intersections, with a particular attention to their demographical characteristics. The classification of cyclists' red-light behavior in risk-taking, opportunistic and law-obeying, was adopted and re-adapted to reflect more objective behaviors, eliminating any inference or judgment. Two researchers at a time observed unobtrusively at four different intersections, during morning and late afternoon peak hours, 1381 cyclists approaching the traffic light during the red phase. More than 60% of the observed cyclists violated the traffic control. Results showed that the visual search strategy displayed by the cyclists and the presence of other cyclists at the intersection are important factors in predicting the probability of red-light running behavior.