Abstract Radar is a key sensor to achieve a reliable environment perception for advanced driver assistance system and automated driving (ADAS/AD) functions. Reducing the development efforts for ADAS functions and eventually enabling AD functions demands the extension of conventional physical test drives with simulations in virtual test environments. In such a virtual test environment, the physical radar unit is replaced by a virtual radar model. Driving datasets, such as the nuScenes dataset, containing large amounts of annotated sensor measurements, help understand sensor capabilities and play an important role in sensor modeling. This article includes a thorough analysis of the radar data available in the nuScenes dataset. Radar properties, such as detection thresholds, and detection probabilities depending on object, environment, and radar parameters, as well as object properties, such as reflection behavior depending on object type, are investigated quantitatively. The overall detection probability of the considered radar (Continental ARS-408-21) was found to be 27.81%. Four radar models on object level with different complexity levels and different parametrisation requirements are presented: a simple RCS-based radar model with an accuracy of 51%, a linear SVC model with an accuracy of 70%, a Random Forest model with an accuracy of 83%, and a Gradient Boost model with an accuracy of 86%. The feature importance analysis of the machine learning algorithms revealed that object class, object size, and object visibility are the most important parameters for the presented radar models. In contrast, daytime and weather conditions seem to have only minor influence on the modeling results.

Internet enabled the emergence of social media platforms, opened new markets and changed the way businesses and organizations operate, communicate with their target audience, promote their activities and achievements. Non-profit organizations worldwide use social media platforms and strive to increase user engagement and the number of reactions to achieve their goals. Although in Bosnia and Herzegovina, non-profit organizations use social media platforms, there is no empirical evidence of their success in increasing user engagement and the number of reactions. This study investigates which social media factors have an influence on user engagement, particularly on the number of reactions on Facebook posts. The real data from the period of 15 months were collected from a Facebook business profile of a non-profit organization from Bosnia and Herzegovina. SPSS and Excel were used to analyze the data. According to the research results, the period of publishing and the number of photos have an effect on the number of reactions on Facebook posts, while the amount of text and the day of publishing do not have an effect on the number of reactions.

Conjugated polymers need to be doped to increase charge carrier density and reach the electrical conductivity necessary for electronic and energy applications. While doping increases carrier density, Coulomb interactions between the dopant molecules and the localized carriers are poorly screened, causing broadening and a heavy tail in the electronic density‐of‐states (DOS). The authors examine the effects of dopant‐induced disorder on two complimentary charge transport properties of semiconducting polymers, the Seebeck coefficient and electrical conductivity, and demonstrate a way to mitigate them. Their simulations, based on a modified Gaussian disorder model with Miller‐Abrahams hopping rates, show that dopant‐induced broadening of the DOS negatively impacts the Seebeck coefficient versus electrical conductivity trade‐off curve. Increasing the dielectric permittivity of the polymer mitigates dopant‐carrier Coulomb interactions and improves charge transport, evidenced by simultaneous increases in conductivity and the Seebeck coefficient. They verified this increase experimentally in iodine‐doped P3HT and P3HT blended with barium titanate (BaTiO3) nanoparticles. The addition of 2% w/w BaTiO3 nanoparticles increased conductivity and Seebeck across a broad range of doping, resulting in a fourfold increase in power factor. Thus, these results show a promising path forward to reduce the dopant‐charge carrier Coulomb interactions and mitigate their adverse impact on charge transport.