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A second-order L-stable exponential time-differencing (ETD) method is developed by combining an ETD scheme with approximating the matrix exponentials by rational functions having real distinct poles (RDP), together with a dimensional splitting integrating factor technique. A variety of non-linear reaction-diffusion equations in two and three dimensions with either Dirichlet, Neumann, or periodic boundary conditions are solved with this scheme and shown to outperform a variety of other second-order implicit-explicit schemes. An additional performance boost is gained through further use of basic parallelization techniques.
To prevent the reduction of muscle mass and loss of strength coming along with the human aging process, regular training with e.g. a leg press is suitable. However, the risk of training-induced injuries requires the continuous monitoring and controlling of the forces applied to the musculoskeletal system as well as the velocity along the motion trajectory and the range of motion. In this paper, an adaptive norm-optimal iterative learning control algorithm to minimize the knee joint loadings during the leg extension training with an industrial robot is proposed. The response of the algorithm is tested in simulation for patients with varus, normal and valgus alignment of the knee and compared to the results of a higher-order iterative learning control algorithm, a robust iterative learning control and a recently proposed conventional norm-optimal iterative learning control algorithm. Although significant improvements in performance are made compared to the conventional norm-optimal iterative learning control algorithm with a small learning factor, for the developed approach as well as the robust iterative learning control algorithm small steady state errors occur.
The Rothman–Woodroofe symmetry test statistic is revisited on the basis of independent but not necessarily identically distributed random variables. The distribution-freeness if the underlying distributions are all symmetric and continuous is obtained. The results are applied for testing symmetry in a meta-analysis random effects model. The consistency of the procedure is discussed in this situation as well. A comparison with an alternative proposal from the literature is conducted via simulations. Real data are analyzed to demonstrate how the new approach works in practice.
Muscular activity in terms of surface electromyography (sEMG) is usually normalised to maximal voluntary isometric contractions (MVICs). This study aims to compare two different MVIC-modes in handcycling and examine the effect of moving average window-size. Twelve able-bodied male competitive triathletes performed ten MVICs against manual resistance and four sport-specific trials against fixed cranks. sEMG of ten muscles [M. trapezius (TD); M. pectoralis major (PM); M. deltoideus, Pars clavicularis (DA); M. deltoideus, Pars spinalis (DP); M. biceps brachii (BB); M. triceps brachii (TB); forearm flexors (FC); forearm extensors (EC); M. latissimus dorsi (LD) and M. rectus abdominis (RA)] was recorded and filtered using moving average window-sizes of 150, 200, 250 and 300 ms. Sport-specific MVICs were higher compared to manual resistance for TB, DA, DP and LD, whereas FC, TD, BB and RA demonstrated lower values. PM and EC demonstrated no significant difference between MVIC-modes. Moving average window-size had no effect on MVIC outcomes. MVIC-mode should be taken into account when normalised sEMG data are illustrated in handcycling. Sport-specific MVICs seem to be suitable for some muscles (TB, DA, DP and LD), but should be augmented by MVICs against manual/mechanical resistance for FC, TD, BB and RA.
This paper analyzes the drag characteristics of several landing gear and turret configurations that are representative of unmanned aircraft tricycle landing gears and sensor turrets. A variety of these components were constructed via 3D-printing and analyzed in a wind-tunnel measurement campaign. Both turrets and landing gears were attached to a modular fuselage that supported both isolated components and multiple components at a time. Selected cases were numerically investigated with a Reynolds-averaged Navier-Stokes approach that showed good accuracy when compared to wind-tunnel data. The drag of main gear struts could be significantly reduced via streamlining their cross-sectional shape and keeping load carrying capabilities similar. The attachment of wheels introduced interference effects that increased strut drag moderately but significantly increased wheel drag compared to isolated cases. Very similar behavior was identified for front landing gears. The drag of an electro-optical and infrared sensor turret was found to be much higher than compared to available data of a clean hemisphere-cylinder combination. This turret drag was merely influenced by geometrical features like sensor surfaces and the rotational mechanism. The new data of this study is used to develop simple drag estimation recommendations for main and front landing gear struts and wheels as well as sensor turrets. These recommendations take geometrical considerations and interference effects into account.
The manufacturing share of laser powder bed fusion (L-PBF) increases in industrial application, but still many process steps are manually operated. Additionally, it is not possible to achieve tight dimensional tolerances or low surfaces roughness. Hence, a process chain has to be set up to combine additive manufacturing (AM) with further machining technologies. To achieve a continuous workpiece flow as basis for further industrialization of L-PBF, the paper presents a novel substrate system and its application on L-PBF machines and post-processing. The substrate system consists of a zero-point clamping system and a matrix-like interface of contact pins to be substantially connected to the workpiece within the L-PBF process.
While bringing new opportunities, the Industry 4.0 movement also imposes new challenges to the manufacturing industry and all its stakeholders. In this competitive environment, a skilled and engaged workforce is a key to success. Gamification can generate valuable feedbacks for improving employees’ engagement and performance. Currently, Gamification in workspaces focuses on computer-based assignments and training, while tasks that require manual labor are rarely considered. This research provides an overview of Enterprise Gamification approaches and evaluates the challenges. Based on that, a skill-based Gamification framework for manual tasks is proposed, and a case study in the Industry 4.0 model factory is shown.
Robust estimators for free surface turbulence characterization: A stepped spillway application
(2020)
Robust estimators are parameters insensitive to the presence of outliers. However, they presume the shape of the variables’ probability density function. This study exemplifies the sensitivity of turbulent quantities to the use of classic and robust estimators and the presence of outliers in turbulent flow depth time series. A wide range of turbulence quantities was analysed based upon a stepped spillway case study, using flow depths sampled with Acoustic Displacement Meters as the flow variable of interest. The studied parameters include: the expected free surface level, the expected fluctuation intensity, the depth skewness, the autocorrelation timescales, the vertical velocity fluctuation intensity, the perturbations celerity and the one-dimensional free surface turbulence spectrum. Three levels of filtering were utilised prior to applying classic and robust estimators, showing that comparable robustness can be obtained either using classic estimators together with an intermediate filtering technique or using robust estimators instead, without any filtering technique.
The enantioselective synthesis of α-hydroxy ketones and vicinal diols is an intriguing field because of the broad applicability of these molecules. Although, butandiol dehydrogenases are known to play a key role in the production of 2,3-butandiol, their potential as biocatalysts is still not well studied. Here, we investigate the biocatalytic properties of the meso-butanediol dehydrogenase from Bacillus licheniformis DSM 13T (BlBDH). The encoding gene was cloned with an N-terminal StrepII-tag and recombinantly overexpressed in E. coli. BlBDH is highly active towards several non-physiological diketones and α-hydroxyketones with varying aliphatic chain lengths or even containing phenyl moieties. By adjusting the reaction parameters in biotransformations the formation of either the α-hydroxyketone intermediate or the diol can be controlled.