Hazardous chemicals in textiles represent a serious health issue. This is mainly due to missing data on the used chemicals and/or on their hazard, which prevents proper chemical risk assessment. Although identifying and filling these data gaps is crucial, the myriad chemicals used for textile production and multiple data sources make it extremely difficult to manually collect and process all the data. Here, we propose a machine learning-based approach to tackle this issue. First, we identify the relevant sources and data that can be analyzed with machine learning. Then, we propose knowledge graphs as a tool to organize and analyze the data. We finally provide specific examples and detail the expected outcomes of our approach.

The eSPARK project examines the sustainability profile of the popular shared e-scooters through policy analysis, usage data analysis, surveys, and life cycle assessment. Policies and attempts to regulate e-scooters in Swedish and European cities are studied and discussed with stakeholders. The LCA-results suggest that factors such as how e-scooters are collected and distributed, and the total ridden kilometers have significant impact on their environmental impact. The project also suggests different methods that can support cities to predict the geographical area of the e-scooters and offers insights about how e-scooters are used in the cities. Usage data and the surveys show that they are used by active people in areas with a lot of activities, especially restaurants and clubs. Users are likely to have a driving license, to frequently use a car but also to have a monthly pass for public transport. Thus, escooters have a potential to mitigate congestion on roads and public transport but may lead to more traffic on bike infrastructure instead.

Vehicle-to-Vehicle communication can improve traffic safety and efficiency. This technology, however, increases the attack surface, making new attacks possible. To cope with these threats, researchers have made a great effort to identify and explore the potential of cyberattacks and also proposed various intrusion or misbehaviour detection systems, in particular machine learning-based solutions. Simulations have become essential to design and evaluate such detection systems as there are no real publicly available Vehicular Ad-Hoc Network (VANET) datasets containing a variety of attacks. The drawback is that simulations require a significant amount of computational resources and time for configuration. In this paper, we present an attack simulation and generation framework that allows training the attack generator with either simulated or real VANET attacks. We outline the structure of our proposed framework and describe the setup of a standard-compliant attack simulator that generates valid standardised CAM and DENM messages specified by ETSI in the Cooperative Intelligent Transport Systems (C-ITS) standards. Based on the introduced framework, we demonstrate the feasibility of using deep learning for the generation of VANET attacks, which ultimately allows us to test and verify prototypes without running resource-demanding simulations.