Abstract Earthquakes, fire, and floods often cause structural collapses of buildings. However, the inspection of such damaged buildings poses a high risk for emergency forces or is even impossible. We present three recently selected missions of the Robotics Task Force of the German Rescue Robotics Center (DRZ), where both ground and aerial robots were used to explore destroyed buildings. We describe and reflect the missions as well as the lessons learned that have resulted from them. To make robots from research laboratories fit for real operations, realistic outdoor and indoor test environments were set up at the DRZ and used for tests in regular exercises by researchers and emergency forces. On the basis of this experience, the robots and their control software were significantly improved. Furthermore, expert teams of researchers and first responders were formed, each with realistic assessments of the operational and practical suitability of robotic systems.

Early detection and diagnosis of dementia is a major challenge for medical research and practice. Hence, in the last decade, digital drawing tests became popular, showing sometimes even better performance than their paper-and-pencil versions. Combined with machine learning algorithms, these tests are used to differentiate between healthy people and people with mild cognitive impairment (MCI) or early Alzheimer’s disease (eAD), commonly using data from the Clock Drawing Test (CDT). In this investigation, a Random Forest Classification (RF) algorithm is trained on digital Tree Drawing Test (dTDT) data, containing socio-medical information and process data of 86 healthy people, 97 people with MCI, and 74 people with eAD. The results indicate that the binary classification works well for homogeneous groups, as demonstrated by a sensitivity of 0.85 and a specificity of 0.9 (AUC of 0.94). In contrast, the performance of both binary and multiclass classification degrades for groups with het erogeneous characteristics, which is reflected in a sensitivity of 0.91 and 0.29 and a specificity of 0.44 and 0.36 (AUC of 0.74 and 0.65), respectively. Nevertheless, as the early detection of cognitive impairment becomes increasingly important in healthcare, the results could be useful for models that aim for automatic identification

Der Datenjournalismus wird gleichermaßen stark in der Nachrichtenbranche beobachtet und in der Journalismusforschung reflektiert. Dieser Beitrag beschreibt das Phänomen zunächst im Kontext des Megatrends der Automatisierung des Journalismus. Anschließend wird die erste Trendstudie zum Da-tenjournalismus in Deutschland vorgestellt: Die Berufsfeldstudie war 2012 und 2019 im Feld. Die ge-wählten Items ermöglichen einen Längsschnitt-Vergleich der Entwicklung des Datenjournalismus. Bei einem Vergleich mit den nationalen Daten der „Worlds of Journalism Study“ werden weitere Gemein-samkeiten und Unterschiede deutlich. Die Ergebnisse zeigen, dass sich der Datenjournalismus in Deutschland zunehmend institutionalisiert hat und Datenjournalist:innen sich stark einem investigati-ven politischen Journalismus verpflichtet fühlen.

Nowadays, robots are found in a growing number of areas where they collaborate closely with humans. Enabled by lightweight materials and safety sensors, these cobots are gaining increasing popularity in domestic care, where they support people with physical impairments in their everyday lives. However, when cobots perform actions autonomously, it remains challenging for human collaborators to understand and predict their behavior, which is crucial for achieving trust and user acceptance. One significant aspect of predicting cobot behavior is understanding their perception and comprehending how they “see” the world. To tackle this challenge, we compared three different visualization techniques for Spatial Augmented Reality. All of these communicate cobot perception by visually indicating which objects in the cobot’s surrounding have been identified by their sensors. We compared the well-established visualizations Wedge and Halo against our proposed visualization Line in a remote user experiment with participants suffering from physical impairments. In a second remote experiment, we validated these findings with a broader non-specific user base. Our findings show that Line, a lower complexity visualization, results in significantly faster reaction times compared to Halo, and lower task load compared to both Wedge and Halo. Overall, users prefer Line as a more straightforward visualization. In Spatial Augmented Reality, with its known disadvantage of limited projection area size, established off-screen visualizations are not effective in communicating cobot perception and Line presents an easy-to-understand alternative.