As sustainable motorized individual transport becomes essential for achieving CO2 reduction targets, this study investigates the acceptance of an eco-driving app that may provide a practical solution to improve sustainable driving. The app, building on currently available technologies, was examined using an integrated model of the Technology Acceptance Model (TAM) and Trust in Technology. The study assesses the technology's acceptance and influence on future eco-driving intentions. In addition to TAM's established variables - Perceived Ease of Use and Perceived Usefulness - Trust in Technology emerged as a significant predictor of app acceptance. The study further explored the roles of Environmental Awareness and Cognitive Flexibility, two factors previously unexamined in eco-driving research, as motivators of sustainable driving intentions. Findings indicate that app acceptance, alongside Environmental Awareness and Cognitive Flexibility, support the Intention to Perform Eco-Driving. Practical implications emphasize the importance of a design that fosters user trust, while theoretical implications suggest that future research should consider cognitive and mindset factors in addition to technology acceptance when investigating eco-driving intentions. These results underscore the relevance of our study for both practitioners and researchers in advancing sustainable motorized individual transport.
Maritime fires on container ships are an important issue in global shipping operations involving numerous actors. In these scenarios, Shared Situational Awareness (SSA) plays a critical role. This paper examines the challenges of these incidents, emphasizing the prevalence of human error and the limitations of traditional fire detection and coordination methods. It applies Endsley's three-level model of Situational Awareness (SA) and proposes a model for SSA development specific to such emergencies. The Overheat Project is introduced, highlighting the development of a Digital Solution (DS) to address enhanced SSA through real-time data integration and a collaborative platform. This platform connects vessels and on-shore data systems, providing a common operational picture (COP) for all stakeholders involved in fire response. The architecture of the DS is described, outlining the onboard and ground-based components that facilitate seamless data exchange. The paper concludes by highlighting the potential benefits of this solution in improving fire safety and response capabilities on container ships.
The increasing complexity of maritime navigation and the shift towards (semi)autonomous systems necessitate enhanced situational awareness (SA) to ensure maritime safety. This transition introduces new requirements for situation modelling and SA, particularly in busy waterways. To address these challenges, we present the Federated Evidential Learning for Anomaly Detection of Ship Trajectories (FEAST) framework, which integrates Federated Learning and Evidential Learning to provide a privacy-preserving, collaborative, and uncertainty-aware approach to out-of-distribution (OOD) anomaly detection in maritime traffic. FEAST uses data from the Automatic Identification System from the Kiel region, Germany, which exhibits unique characteristics of dynamic and heterogeneous maritime activity due to its connection with the traffic-dense Kiel Canal. Our extensive evaluations demonstrate that FEAST improves OOD anomaly detection by leveraging epistemic and aleatoric uncertainty estimates, outperforming baseline methods such as Denoise AutoEncoders and Variational AutoEncoders. Consequently, FEAST forms a solution to reliable and interpretable maritime traffic anomaly detection, supporting enhanced SA in maritime operations.
Decision-making in unknown circumstances requires balancing exploitation—leveraging past outcomes—and exploration—seeking new information—a process often constrained by cognitive load. Cognitive load may impede curiosity-driven inquiry and diminish working memory capacity, leading to less adaptive but safer selections. While previous research has shown that transcranial direct current stimulation (tDCS) applied to the dorsolateral prefrontal cortex (DLPFC) enhances cognitive flexibility and risk-taking behavior, there is limited understanding of its efficacy in mitigating the adverse impacts of higher cognitive load on the exploration-exploitation trade-off. By examining how tDCS influences decision-making under various cognitive demands, our work fills this research gap. Under low and high cognitive load settings, 20 participants, 10 receiving tDCS (tDCS group) and 10 not receiving tDCS (control group), choose between three alternatives in a binary-choice task: safe option (Option A), risky option (Option B), and exploratory option (Option R). Results revealed that those who received tDCS were more likely to engage in exploratory behavior, choosing Option $R$ in 40 % of trials as opposed to 22 % in the control group, and selected riskier alternatives (Option B) in 57.7 % of trials, which was considerably more frequent than the control group (31 %). Interestingly, even when subjected to a high cognitive load, tDCS individuals retained the choice for the option $R$, underscoring the function of neuromodulation in reducing the impact of higher cognitive load. With implications for risky situations, these findings advance our knowledge of how tDCS fosters curiosity-driven behavior through enhancement in exploratory behavior and adaptive decision-making.
Manual maintenance and repair tasks are frequently demanding and time-consuming, necessitating workers to pinpoint problems and remember intricate procedures for their resolution in contemporary industrial environments. To address these challenges we propose an innovative assistance system that leverages a semantically zoomable Digital Twin (DT) of our factory environment to provide context-sensitive assistance to factory workers thereby helping them make informed decisions in maintenance and repair tasks. Our system aids workers by providing situation-specific guidance in an intuitive user interface in Augmented Reality (AR) glasses based on dynamic inputs such as user feedback, spatial marker tracking and registration in AR, and object detection. Based on these dynamic cues the user can zoom into the multi-layered 3D DT in Unity scene and access the necessary visualization relevant to that situation. This assistance in troubleshooting and repair procedures could potentially reduce their cognitive load and minimize time and errors. A preliminary study (N = 6) is carried out to provide an initial understanding of the impact of situation-awareness and the semantic zoom-based visualizations as assistance feature and demonstrate its usability of the assistance system.
