Brandenburg University of Applied Sciences

Zusammenfassung Psychische Erkrankungen gehen für die Betroffenen und ihre Angehörigen mit erheblichen Belastungen und für die Gesellschaft mit besonders hohen Kosten einher. Früherkennung und Frühintervention haben das Potential, die Krankheitsverläufe und die soziale Inklusion der Betroffenen substanziell zu verbessern und die gesellschaftlichen Kosten dramatisch zu reduzieren

Patients, as central actors in healthcare, should be enabled to actively participate in health services research processes. In addition, other stakeholders, such as professionals from healthcare practice, are also essential for a comprehensive participatory approach. This DNVF memorandum focuses on participatory approaches in the context of health services research. It begins by outlining the key characteristics of participatory health services research and describing its current development and institutionalization in Germany. The DNVF memorandum also highlights the potential and benefits of participatory research. Finally, it addresses two cross-cutting topics that are particularly relevant for further development in this field: the theoretical and conceptual foundations, and the investigation of effects and effectiveness of participatory approaches.

Anecdotal reports in angling media suggest that using fluorescent lures may increase catch rates in dim light or at high turbidity. We conducted a controlled angling experiment, comprising 501 30-min experimental fishing trials in three meso- to eutrophic waterbodies and assessed catch rates and sizes of European perch ( Perca fluviatilis) caught when offered two soft plastic lures (fluorescent vs. nonfluorescent) with similar reflective spectra. We also examined fluorescent properties of a range of market-available lures and modeled the experimental lure’s fluorescing effects under natural lake light. Considering the specific light environment of the study waters, the experimental fluorescent lure could get excited by downwelling visible daylight and fluoresce at depths of up to 3 m. Based on a sample catch of 331 perch, and after controlling for interactions with illuminance, cloud cover, water depth and daytime, the fluorescence of the experimental lure did, however, neither affect the catch rate nor the size of perch caught. Lure fluorescence maybe less important than many anglers believe, but further studies in different lake conditions are needed.

This paper presents the first circuit-level analysis of a two-stage GaN-HEMT LNA with an integrated switch, comparing two distinct technologies: one with a standard passivation layer (Type A) and another featuring an advanced passivation layer (Type B). Through a detailed evaluation of their performance in both amplification and isolation modes, the study highlights Type B as a significant advancement over Type A. Leveraging technological enhancements, including an improved passivation layer and slightly optimized component matching, Type B demonstrates superior performance. The results are compelling, with Type B achieving a gain of 19 dB, a low noise figure of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">${1.17 \text{dB}}$</tex>, and small-signal isolation exceeding 30 dBm at 5.2 GHz. These achievements establish Type B as a formidable option for future 5G telecommunication systems, offering a performance that challenges traditional designs that use separate switches.

<title>Abstract</title> When dense granular gases are continuously excited under microgravity conditions, spatial inhomogeneities of the particle number density can emerge. A significant share of particles may collect in strongly overpopulated regions, called clusters. This dynamical clustering, or gas-cluster transition, is caused by a complex interplay and balance between the energy influx and dissipation in particle collisions. Particle number density, container geometry, and excitation strength influence this transition. We perform Discrete Element Method (DEM) simulations for ensembles of frictional spheres in a cuboid container and apply the Kolmogorov–Smirnov test and a caging criterion to the local packing fraction profiles to detect clusters. Machine learning can be used to study the gas-cluster transition, and can be a promising alternative to identify the state of the system for a given set of system parameters without time-consuming complex DEM simulations. We test various machine learning models and identify the best models to predict dynamical clustering of frictional spheres in a specific experimental geometry.

The design of fan-blisks is a multi-criterion optimisation challenge primarily involving the aerodynamic shape optimisation of blade profiles and subsequent blade balancing, i.e., shifting profiles in axial and circumferential directions to avoid stress hotspots. Although it is already known that blade balancing affects aerodynamic properties, this correlation is often not taken into account, which is why it is usually performed as an independent step after solving the aerodynamic design problem. However, this assumption is questionable because such shifts should be used to control both secondary flow effects and resulting stress. Therefore, the paper proposes problem formulations combining both aspects. Since optimisation requires costly numerical evaluations, machine learning methods are investigated to predict aerodynamic performance and stress constraints more efficiently. This enables a global optimisation process by reducing computational costs. Various different surrogate types are investigated, where stress constraints are formulated either as regression task predicting stress maxima, or as classification problem directly assessing design feasibility.

Abstract When granular gases in microgravity are continuously excited mechanically, spatial inhomogeneities of the particle distribution can emerge. At a sufficiently large overall packing fraction, a significant share of particles tend to concentrate in strongly overpopulated regions, so-called clusters, far from the excitation sources. This dynamical clustering is caused by a complex balance between energy influx and dissipation. The mean number density of particles, the geometry of the container, and the excitation strength influence cluster formation. A quantification of clustering thresholds is not trivial. We generate ‘synthetic’ data sets by Discrete Element Method simulations of frictional spheres in a cuboid container and apply established criteria to classify the local packing fraction profiles. Machine learning approaches that predict dynamic clustering from known system parameters on the basis of classical test criteria areoposed and tested. It avoids the necessity of complex numerical simulations.

Random experiments that are simple and clear enough to be performed by human agents feature prominently in the teaching of elementary stochastics as well as in games. We present Alea, a domain-specific language for the specification of random experiments. Alea code can either be analyzed statically to obtain and inspect probability distributions of outcomes, or be executed with a source pseudo-randomness for simulation or as a game assistant. The language is intended for ease of use by non-expert programmers, in particular students of elementary stochastics, and players and designers of games of chance, by focusing on concepts common to functional programming and basic mathematics. Both the design of the language and the implementation of runtime environments are work in progress.