In general, teacher educators are considered to be educational specialists whose main task is to communicate content-based concepts to prospective teachers. However, unfortunately, most studies on teacher professional development overlook this specific language-oriented aspect of content-based teaching. Therefore, we address the aforementioned research gap and argue that teacher educators’ evaluation of their language-oriented performance in educational communication enhances the quality of their content-based teaching. Accordingly, we examine how the language-oriented performance of teacher educators is evaluated by both individual teacher educators (sample size N=3) and their students (N=32) in a small-scale intervention study. The findings of the study reveal that there is a relationship between the order of application of five language focus areas (i.e., language awareness, active listening, formalizing interaction, language support, and language and learning development, as noticed by the students), and teacher educators’ ability to apply these areas in accordance with their objectives related to content-based teaching.
Background & aims: Accurate diagnosis of sarcopenia requires evaluation of muscle quality, which refers to the amount of fat infiltration in muscle tissue. In this study, we aim to investigate whether we can independently predict mortality risk in transcatheter aortic valve implantation (TAVI) patients, using automatic deep learning algorithms to assess muscle quality on procedural computed tomography (CT) scans. Methods: This study included 1199 patients with severe aortic stenosis who underwent transcatheter aortic valve implantation (TAVI) between January 2010 and January 2020. A procedural CT scan was performed as part of the preprocedural-TAVI evaluation, and the scans were analyzed using deep-learning-based software to automatically determine skeletal muscle density (SMD) and intermuscular adipose tissue (IMAT). The association of SMD and IMAT with all-cause mortality was analyzed using a Cox regression model, adjusted for other known mortality predictors, including muscle mass. Results: The mean age of the participants was 80 ± 7 years, 53% were female. The median observation time was 1084 days, and the overall mortality rate was 39%. We found that the lowest tertile of muscle quality, as determined by SMD, was associated with an increased risk of mortality (HR 1.40 [95%CI: 1.15–1.70], p < 0.01). Similarly, low muscle quality as defined by high IMAT in the lowest tertile was also associated with increased mortality risk (HR 1.24 [95%CI: 1.01–1.52], p = 0.04). Conclusions: Our findings suggest that deep learning-assessed low muscle quality, as indicated by fat infiltration in muscle tissue, is a practical, useful and independent predictor of mortality after TAVI.
This paper investigates strategies to generate levels for action-adventure games. For this genre, level design is more critical than for rule-driven genres such as simulation or rogue-like role-playing games, for which procedural level generation has been successful in the past. The approach outlined by this article distinguishes between missions and spaces as two separate structures that need to be generated in two individual steps. It discusses the merits of different types of generative grammars for each individual step in the process. Notably, the approach acknowledges that the online generation of levels needs to be tailored strictly to the actual experience of a player. Therefore, the approach incorporates techniques to establish and exploit player models in actual play.
The Hereon team has expressed interest in the use of the PO platform for the virtualization of the (hydro)dynamic behavior of offshore wind farms, in particular regarding turbidity around wind turbines. BUas has developed the Procedural Ocean (PO) platform. The platform uses procedural content generation (AI) for data-driven 3D virtualization of complex marine and maritime environments, with elements such as geo-environment (bathymery, etc.), geo-physics (weather conditions, waves), wind farms, aquaculture, shipping, ecology, and more. The virtual and immersive environment in the game engine Unreal supports advanced (game-like) user interaction for policy-oriented learning (marine spatial planning), ocean management, and decision making. We therefore propose a joint pilot Research and Development (R&D) project to explore, demonstrate and validate how a gridded dataset provided by Hereon can show the dynmics around wind farm monopiles. Furthermore, we can explore interactivity with the engineering and design of the turbine and the multiplication of the turbine design to compose a wind farm. Client: Hereon (The Helmholtz-Zentrum Hereon is a non-profit making research institute )
