Fruits (follicles) of Hakea salicifolia and Hakea sericea (Proteaceae) are characterised by pronounced lignification and open via a ventral suture and the dorsal side. The opening along both sides is unique within the Proteaceae. Both serotinous species are obligate seeders, whose spreading benefits from bush fire events. The different tissues and the course of the vascular bundles must allow the opening mechanism. While their 2D-arrangements are known to some extent from light-microscopy images of cross-sections, this work presents their three-dimensional structures and discusses their contribution to the opening of Hakea fruits. For this purpose, 3D greyscale images, reconstructed from µCT-projection data of both fruits are segmented, assisted by a deep learning algorithm (AI algorithm). 3D renderings from these segmentations show strongly interconnected vascular bundles that build a double-dome shaped network in each valve of H. salicifolia and a dome shaped honeycomb-structure in each valve of H. sericea. However, the vascular bundles of both species show no interconnection between the two lateral valves of the fruit but leave gaps for predetermined fracture tissues on the ventral and dorsal side. The opening of the fruits after a fire or after separation from the mother plant can be explained by the anisotropic shrinkage in the two valves of the fruit.

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.

Competency-oriented exams offer a wide range of advantages, especially where the use and mastery of third-party applications and tools play an important role. Therefore, we developed a competency-oriented setup for both our programming classes and exams ensuring their constructive alignment. Exams were moved to the computer lab and designed to test both conceptional skills as well as the use of state-of-the-art programming tools. At the peak of the COVID-19 pandemic, when exams had to be moved from lab to online, we needed to design an online setup for our practical programming exams preserving the competency-oriented approach and its constructive alignment as well as the validity, reliability and fairness of the exams. The key was to use the same online tools that have been introduced for running lectures and practical classes offering almost the same learning experience as before the pandemic. However, to ensure the validity and fairness of the exams, some kind of online supervision needed to be implemented as technical solutions were found to be either unusable or not working properly in our case. This paper discusses the driving factors, the resulting technical and organizational setup as well as students’ feedback and lessons learned for further improvements. Therefore, COVID-19 has not been able to ruin our competency-oriented programming exams.

360° Camera at a small UAV, Viersen 8 / 2021

Combined view IR and RGB camera from UAV, Viersen 8/2021