Good postural control is considered to be a key component of an active lifestyle, and numerous studies have investigated the Center of Pressure (CoP) as a way of identifying motor deficits. However, the optimal frequency range for assessing CoP variables and the effect of filtering on the relationships between anthropometric variables and CoP are unclear. The aim of this work is to show the relationship between anthropometric variables and different ways of filtering the CoP data. CoP was measured in 221 healthy volunteers using a KISTLER force plate in four different test conditions, both mono and bipedal. The results show no significant changes in the existing correlations of the anthropometric variable values over different filter frequencies between 10 Hz and 13 Hz. Therefore, the findings with regard to anthropometric influences on CoP, with a reasonable but less than ideal filtering of the data, can be applied to other study settings.

The load‐shedding scenario describes an unscheduled load reduction in a power plant so that it produces only the electricity that is needed by the plant itself. The reason for such a scenario is a collapse of power supply in the transmission network. In the subsequent restoration of the electrical supply, different options are distinguished. An essential part of each option is island operating or black start capable thermal power plants. The load‐shedding scenario is complex and multilayered. If process steam is also decoupled during the load shedding, high exhaust steam temperatures in the turbine stages can lead to plant shutdown. In addition, component damage can be expected in thick‐walled components due to high temperature and pressure amplitudes. Thus, it can be shown in this paper that the lifetime losses are highest at the high‐pressure preheater 6 and at the deheater and that the process heat coupling cannot be operated with constant mass flow under all circumstances. In order to investigate these issues, a detailed model of a lignite power plant has been created, which was developed in Modelica for simulating and comparing scenarios for a variety of applications. The model comprises the entire water‐steam cycle including turbines, preheaters and pumps, as well as a very detailed boiler model including the air supply, coal mills, heating surfaces, and piping. Furthermore, the power plants' control system has been implemented in a very precise way. In addition, the study involves a calculation of lifetime consumption for specific components to evaluate the effects. In summary, it can be stated that this study examines the thermodynamic aspects during a load‐shedding scenario for the first time. It focuses on processes within the power plant and thus differs significantly from other studies on this topic, which approach the issue from the electrical grid side.

The compounds of polypropylene (PP) with paraffin wax (PR) as phase change material were fabricated by extrusion melt compounding. The compounds of PR and PP were brittle and showed PR leakage within its melting point range. The maximum 60 weight percent content of the PR was compounded with the polymer. The high amount of paraffin in the polymer plasticized and significantly decreased the melting point of the polymer. The addition of a linear triblock copolymer based on styrene and ethylene/ butylene noticeably ameliorated the workability, impact penetration and paraffin retention properties of the compounds. The compounds were further reinforced by carbon fibers and carbon nanotubes that led to an enhancement of their thermal conductivity and heat transfer efficiency. The material structure and thermophysical properties were studied by microscopy and various characterization techniques. The compounds of polymer with PR show phase change effect due to the solid-liquid phase transition of PR within its melting point range. The experimental time vs. temperature curves of the compounds were recorded within the melting range of PR. The experimental curves were compared with the theoretical calculated results. The results were in agreement except small difference that can be attributed to the experimental errors and the assumptions made during theoretical calculations. The manuscript describes the fabrication of shape enduring compounds, with improved thermal, physical, and mechanical properties and their processing by standard techniques, such as extrusion and blow molding.

In recent years, energy prices have become increasingly volatile, making it more challenging to predict them accurately. This uncertain market trend behavior makes it harder for market participants, e.g., power plant dispatchers, to make reliable decisions. Machine learning (ML) has recently emerged as a powerful artificial intelligence (AI) technique to get reliable predictions in particularly volatile and unforeseeable situations. This development makes ML models an attractive complement to other approaches that require more extensive human modeling effort and assumptions about market mechanisms. This study investigates the application of machine and deep learning approaches to predict day-ahead electricity prices for a 7-day horizon on the German spot market to give power plants enough time to ramp up or down. A qualitative and quantitative analysis is conducted, assessing model performance concerning the forecast horizon and their robustness depending on the selected hyperparameters. For evaluation purposes, three test scenarios with different characteristics are manually chosen. Various models are trained, optimized, and compared with each other using common performance metrics. This study shows that deep learning models outperform tree-based and statistical models despite or because of the volatile energy prices.