Mit zweierlei Maß: Die mangelnde Verbindlichkeit von Patientenverfügungen bei untergebrachten Personen mit psychischen Störungen in der Schweiz

Definition of the problem

Full age patients with mental disorders and preserved decision-making capacity are increasingly interested in drafting advance directives. Whereas in Switzerland (as well as in Germany) advance directives are binding in principle, an advance directive is not binding and has only to be considered in case of involuntary admission due to a mental disorder. Thereby, the virtual aim of an advance directive – the anticipated self-determination – is questioned.

Arguments

From an ethical perspective, patients with mental disorders have the same rights as patients with somatic diseases. Therefore, the question arises if there are good reasons for this unequal treatment. In the present contribution, four possible reasons are analyzed: (1) persons with mental disorders are “different”; (2) protection from self-harm; (3) often not present end-of-life context; (4) reduced bindingness as the more appropriate standard in general.

Conclusion

The authors come to the conclusion that patients with mental disorders are often capable of composing a valid advance directive, and just as any other human being, they have a principal and binding right for self-determination, even though they are particularly vulnerable in crisis situations.

     

An Improved Neural Network Application for Short-Term Load Forecasting in Power Systems

In this paper an improved neural network application for short-term load forecasting purposes is presented. To speed up the learning process on one side, and not to jeopardize the stability performance of the learning process on the other side, the adaptive approach to the learning-rate parameter has been employed. Also, instead of learning overall load characteristics, the preprocessing of input data has been designed with the idea to learn only load demand behavior that is important for a certain period. The proposed neural network has shown good performance, even in the case of the incomplete data temperature set and at high irregularities in weekly load data.     

Influence of processing on stability,microstructure and thermoelectric properties of Ca3Co4 − xO9 + δ

Due to high figure of merit, Ca₃Co_4 ? xO_9 + δ (CCO) has potential as p-type material for high-temperature thermoelectrics. Here, the influence of processing including solid state sintering, spark plasma sintering and post-calcination on stability, microstructure and thermoelectric properties is reported. By a new post-calcination approach, single-phase materials were obtained from precursors to final dense ceramics in one step. The highest zT of 0.11 was recorded at 800 °C for CCO with 98 and 72% relative densities. In situ high-temperature X-ray diffraction in air and oxygen revealed a higher stability of CCO in oxygen (～970 °C) than in air (～930 °C), with formation of Ca₃Co₂O₆ which also showed high stability in oxygen, even at 1125 °C. Since achievement of phase pure high density CCO by post-calcination method in air is challenging, the phase stability of CCO in oxygen is important for understanding and further improvement of the method.     

The effect of composition of a polymeric coating on the biofilm formation of bacteria and filamentous fungi

Functionality of polymeric coating, especially in terms of anti-corrosive properties and stability, can be negatively influenced by formation of either bacterial or fungal biofilm on its surface. Herein, the epoxy-ester resin based polymeric coating was filled with pigments (natural silicon dioxide diatomite, natural wollastonite, tungstate and molybdate). Pigments was modified by conducting polymers (polyaniline phosphate, polypyrrole phosphate, poly(p-phenylenediamine) phosphate and ZnFe₂O₄). Impact of modified pigments on the surface energy and formation of biofilm were tested. The use of various biofilm forming species of both the bacteria and fungi filled a knowledge gap about their behavior on polymeric coatings.     

SkaSim – Skalierbare HPC‐Software für molekulare Simulationen in der chemischen Industrie

This article outlines advances in molecular modeling and simulation using massively parallel high‐performance computers (HPC). In the SkaSim project, partners from the HPC community collaborated with users from science and industry. The aim was to optimize the prediction of thermodynamic property data in terms of efficiency, quality and reliability using HPC methods. In this context, various topics were dealt with: atomistic simulation of homogeneous gas bubble formation, surface tension of classical fluids and ionic liquids, multicriteria optimization of molecular models, the development of the molecular simulation codes ls1 mardyn and ms2, atomistic simulation of gas separation processes, molecular membrane structure generators, transport resistors and the evaluation of predictive property data models based on specific mixture types.     

A case of using the Semantic Interoperability Framework for custom orthopedic implants manufacturing

The efficiency and effectiveness of the daily practice in orthopedic surgery depend on the availability, interoperability and unique access to a wide set of information, related to the patient’s medical record and diagnosis, domain knowledge and available resources and staff. The most important of the tangible resources, needed for the therapeutic or preventive actions are orthopedic implants. In some cases, the implants may be highly complex and customized products, which need to be manufactured (assembled) on basis of the above information in a shortest possible timeframe. In this paper, the case of the custom orthopedic implants manufacturing is described from the perspective of the collaborative enterprising, with special consideration of the interoperability issues of the involved enterprise collaboration. It is shown how the previously developed Semantic Interoperability Framework can be used to improve the efficiency of the manufacturing and other relevant processes.     

Image coding algorithm based on Hadamard transform and simple vector quantization

Transform coding is commonly used in image processing algorithms to provide high compression ratios, often at the expense of processing time and simplicity of the system. We have recently proposed a pixel value prediction scheme in order to exploit adjacent pixel correlation, providing a low-complexity model for image coding. However, the proposed model was unable to reach high compression ratios retaining high quality of reconstructed image at the same time. In this paper we propose a new segmentation algorithm which further utilizes adjacent pixel correlation, provides higher compression ratios and it is based on application of Hadamard transform coding. Additional compression is provided by using vector quantization for a low number of quantization levels and by simplifying generalized Lloyd’s algorithm where the special attention is paid to determination of optimal partitions for vector quantization, making a fixed quantizer. The proposed method is quite simple and experimental results show that it ensures better or similar rate-distortion ratio for very low bit-rates, comparing to the other similar methods that are based on wavelet or curvelet transform coding and support or core vector machine application. Furthermore, the proposed method requires very low processing time since the proposed quantizers are fixed, much less than the required time for the aforementioned methods that we compare with as well as much less than the time required for fractal image coding. In the end, the appropriate discussion is provided comparing the results with a scheme based on linear prediction and dual-mode quantization.

     

混合的SAR图像变化检测算法

为尽可能多地消除遥感图像变化检测过程中“伪变化”信息的影响,获得比较客观的感兴趣区域变化检测结果,针对遥感图像中SAR图像的特点,提出一种混合的SAR图像变化检测算法。对已配准好的图像进行Frost滤波,用邻域比值的方法构造差异图,对得到的差异图进行非下采样轮廓波变换（NSCT）,对变换得到的高频子带和低频子带分别处理,用模糊C均值（FCM）聚类算法得到变化检测的结果。实验结果表明,该算法模型很好地保留了图像变化区域的细节,提高了变化检测准确性。     

Preference-based topology optimization for vehicle concept design with concurrent static and crash load cases

In the simulation-based design process of automotive structures, an increasing amount of multi-disciplinary requirements have to be considered. Methods of topology optimization can be used to devise structural concepts early in the design process to obtain the best possible structural layout as starting point for further development steps. Especially relevant for the vehicle design process is the concurrent consideration of static load requirements representing normal operating conditions and energy absorption requirements targeting passive safety in crash events. When the disciplines are considered separately, the heuristic Hybrid Cellular Automaton topology optimization is a suitable method. However, in practical applications, both disciplines are usually addressed sequentially. This complicates the overall process and may reduce the quality of the final optimization result, since optimization objectives may be conflicting. We propose a preference-based Scaled Energy Weighting approach to address the topology optimization of both disciplines concurrently. The main idea is to decouple the user preference from the scaling of the different magnitudes of energies. This enables a multi-objective optimization and ultimately the selection of the desired trade-off solution. We first validate the capability of the method to provide structures optimized for stiffness and energy absorption objectives on beam examples. Finally, the method is applied to optimize a concept structure of an industrial vehicle body, demonstrating its practical feasibility.