A maximum-entropy length-orientation closure for short-fiber reinforced composites.

We describe an algorithm for generating fiber-filled volume elements for use in computational homogenization schemes which accounts for a coupling of the fiber-length and the fiber-orientation. For prescribed fiber-length distribution and fiber-orientation tensor of second order, a maximum-entropy estimate is used to produce a fiber-length-orientation distribution which mimics real injection molded specimens, where longer fibers show a stronger alignment than shorter fibers. We derive the length-orientation closure from scratch, discuss its integration into the sequential addition and migration algorithm for generating fiber-filled microstructures for industrial volume fractions and investigate the resulting effective elastic properties.

New results on optimal conditional error functions for adaptive two-stage designs.

Unblinded interim analyses in clinical trials with adaptive designs are gaining increasing popularity. Here, the type I error rate is controlled by defining an appropriate conditional error function. Since various approaches to the selection of the conditional error function exist, the question of an optimal choice arises.

Semi-automated title-abstract screening using natural language processing and machine learning.

Background: Title-abstract screening in the preparation of a systematic review is a time-consuming task. Modern techniques of natural language processing and machine learning might allow partly automatization of title-abstract screening. In particular, clear guidance on how to proceed with these techniques in practice is of high relevance.

Imposing Dirichlet boundary conditions directly for FFT-based computational micromechanics.

We discuss how Dirichlet boundary conditions can be directly imposed for the Moulinec-Suquet discretization on the boundary of rectangular domains in iterative schemes based on the fast Fourier transform (FFT) and computational homogenization problems in mechanics. Classically, computational homogenization methods based on the fast Fourier transform work with periodic boundary conditions. There are applications, however, when Dirichlet (or Neumann) boundary conditions are required.

Experimental Characterization of the Mechanical Properties of Filter Media in Solid-Liquid Filtration Processes.

Nonwoven filter media are used in many industrial applications due to their high filtration efficiency and great variety of compositions and structures which can be produced by different processes. During filter operation in the separation process, the fluid flow exerts forces on the filter medium which leads to its deformation, and in extreme cases damage. In order to design or select a reliable filter medium for a given application, it is essential to have a comprehensive understanding of the mechanical properties of the nonwoven material.

Phase shift of coherent magnetization dynamics after ultrafast demagnetization in strongly quenched nickel thin films.

The remagnetization process after ultrafast demagnetization can be described by relaxation mechanisms between the spin, electron, and lattice reservoirs. Thereby, collective spin excitations in form of spin waves and their angular momentum transfer play an important role on the longer timescales. In this work, we address the question whether the magnitude of demagnetization-the so-called quenching-affects the coherency and the phase of the excited spin waves.

Standardized Electric-Field-Resolved Molecular Fingerprinting.

Field-resolved infrared spectroscopy (FRS) of impulsively excited molecular vibrations can surpass the sensitivity of conventional time-integrating spectroscopies, owing to a temporal separation of the molecular signal from the noisy excitation. However, the resonant response carrying the molecular signal of interest depends on both the amplitude and phase of the excitation, which can vary over time and across different instruments. To date, this has compromised the accuracy with which FRS measurements could be compared, which is a crucial factor for practical applications.

Estimating the COVID-19 prevalence from wastewater.

Wastewater based epidemiology has become a widely used tool for monitoring trends of concentrations of different pathogens, most notably and widespread of SARS-CoV-2. Therefore, in 2022, also in Rhineland-Palatinate, the Ministry of Science and Health has included 16 wastewater treatment sites in a surveillance program providing biweekly samples. However, the mere viral load data is subject to strong fluctuations and has limited value for political deciders on its own.

Hierarchical Multicriteria Optimization of Molecular Models of Water.

Many widely used molecular models of water are built from a single Lennard-Jones site on which three point charges are positioned, one negative and two positive ones. Models from that class, denoted LJ3PC here, are computationally efficient, but it is well known that they cannot represent all relevant properties of water simultaneously with good accuracy. Despite the importance of the LJ3PC water model class, its inherent limitations in simultaneously describing different properties of water have never been studied systematically.

Statistical analysis of three data sources for Covid-19 monitoring in Rhineland-Palatinate, Germany.

In Rhineland-Palatinate, Germany, a system of three data sources has been established to track the Covid-19 pandemic. These sources are the number of Covid-19-related hospitalizations, the Covid-19 genecopies in wastewater, and the prevalence derived from a cohort study. This paper presents an extensive comparison of these parameters.

ViNe-Seg: deep-learning-assisted segmentation of visible neurons and subsequent analysis embedded in a graphical user interface.

Summary: Segmentation of neural somata is a crucial and usually the most time-consuming step in the analysis of optical functional imaging of neuronal microcircuits. In recent years, multiple auto-segmentation tools have been developed to improve the speed and consistency of the segmentation process, mostly, using deep learning approaches. Current segmentation tools, while advanced, still encounter challenges in producing accurate segmentation results, especially in datasets with a low signal-to-noise ratio.

Nondestructive material characterization and component identification in sheet metal processing with electromagnetic methods.

Electromagnetic methods for non-destructive evaluation (NDE) are presented, with which sheet metal components can be identified and their material properties can be characterized. The latter is possible with 3MA, the Micromagnetic Multiparametric Microstructure and stress Analyser. This is a combination of several micromagnetic NDE methods that make it possible to analyse the microstructure in a ferromagnetic material and to determine quantitative values of the mechanical material properties or the stress state.

Parasitic mixing in photomixers as continuous wave terahertz sources.

We present observations of parasitic frequency components in the emission spectrum of typical photomixer sources for continuous wave (CW) terahertz generation. Broadband tunable photomixer systems are often used in combination with direct power detectors, e.g.

Simulation and optimization of nutrient uptake and biomass formation using a multi-parameter Monod-type model of tobacco BY-2 cell suspension cultures in a stirred-tank bioreactor.

Introduction: Tobacco () cv Bright Yellow-2 (BY-2) cell suspension cultures enable the rapid production of complex protein-based biopharmaceuticals but currently achieve low volumetric productivity due to slow biomass formation. The biomass yield can be improved with tailored media, which can be designed either by laborious trial-and-error experiments or systematic, rational design using mechanistic models, linking nutrient consumption and biomass formation.

Methods: Here we developed an iterative experiment-modeling-optimization workflow to gradually refine such a model and its predictions, based on collected data concerning BY-2 cell macronutrient consumption (sucrose, ammonium, nitrate and phosphate) and biomass formation.

Designing spiking neural networks for robust and reconfigurable computation.

Networks of spiking neurons constitute analog systems capable of effective and resilient computing. Recent work has shown that networks of symmetrically connected inhibitory neurons may implement basic computations such that they are resilient to system disruption. For instance, if the functionality of one neuron is lost (e.

CT-based evaluation of tissue expansion in cryoablation of kidney.

Objectives: To evaluate tissue expansion during cryoablation, the displacement of markers in kidney tissue was determined using computed tomographic (CT) imaging.

Methods: CT-guided cryoablation was performed in nine porcine kidneys over a 10 min period. Markers and fiber optic temperature probes were positioned perpendicular to the cryoprobe shaft in an axial orientation.

Rapid-prototyping of microscopic thermal landscapes in Brillouin light scattering spectroscopy.

Since temperature and its spatial, and temporal variations affect a wide range of physical properties of material systems, they can be used to create reconfigurable spatial structures of various types in physical and biological objects. This paper presents an experimental optical setup for creating tunable two-dimensional temperature patterns on a micrometer scale. As an example of its practical application, we have produced temperature-induced magnetization landscapes in ferrimagnetic yttrium iron garnet films and investigated them using micro-focused Brillouin light scattering spectroscopy.

Quantum range-migration-algorithm for synthetic aperture radar applications.

The 3D range-migration algorithm (RMA) and its 2D equivalent, the omega-k algorithm, are employed in a wide range of applications where reconstruction of synthetic aperture data is required, from satellite radar imaging of planets over seismic imaging of the earth crust, down to phased-array ultrasound and ultrasonic application, and recently in-line synthetic aperture radar for non-destructive testing. These algorithms are based on Fourier transforms and share their time-complexity. This limits highly-resolved measurement data to be processed at high speeds which would be advantageous for modern production feed lines.

Sample size calculation for one-armed clinical trials with clustered data and binary outcome.

The formula of Fleiss and Cuzick (1979) to estimate the intraclass correlation coefficient is applied to reduce the task of sample size calculation for clustered data with binary outcome. It is demonstrated that this approach reduces the complexity of sample size calculation to the determination of the null and alternative hypothesis and the formulation of the quantitative influence of the belonging to the same cluster on the therapy success probability.

Evaluation of the EsteR Toolkit for COVID-19 Decision Support: Sensitivity Analysis and Usability Study.

Background: During the COVID-19 pandemic, local health authorities were responsible for managing and reporting current cases in Germany. Since March 2020, employees had to contain the spread of COVID-19 by monitoring and contacting infected persons as well as tracing their contacts. In the EsteR project, we implemented existing and newly developed statistical models as decision support tools to assist in the work of the local health authorities.

Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations.

Background: Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise the predictive performance of such forecasts if multiple models are combined into an ensemble. Here, we report on the performance of ensembles in predicting COVID-19 cases and deaths across Europe between 08 March 2021 and 07 March 2022.

Nanoval Technology-An Intermediate Process between Meltblown and Spunbond.

The idea of "Nanoval technology" origins in the metal injection molding for gas atomization of metal powders and the knowledge of spunbond technologies for the creation of thermoplastic nonwovens using the benefits of both techniques. In this study, we evaluated processing limits experimentally for the spinning of different types of polypropylene, further standard polymers, and polyphenylene sulfide, marked by defect-free fiber creation. A numerical simulation study of the turbulent air flow as well as filament motion in the process visualized that the turnover from uniaxial flow (initial stretching caused by the high air velocity directed at the spinning die) to turbulent viscoelastic behavior occurs significantly earlier than in the melt-blown process.

Calibrated simplex-mapping classification.

We propose a novel methodology for general multi-class classification in arbitrary feature spaces, which results in a potentially well-calibrated classifier. Calibrated classifiers are important in many applications because, in addition to the prediction of mere class labels, they also yield a confidence level for each of their predictions. In essence, the training of our classifier proceeds in two steps.

Direct laser writing of 3D metallic mid- and far-infrared wave components.

A method for direct fabrication of 3D silver microstructures with high fabrication throughput on virtually any substrate is presented. The method is based on laser-induced photoreduction of silver ions to silver atoms, supported by nucleation, substrate functionalization and a multiple exposure fabrication process. The combination of the novel photosensitive suspension and the novel fabrication scheme enables effective fabrication speeds of up to 1 cm per second, with a minimum structure size of less than 1 μm, a resolution of more than 750 lines/mm and a resistivity of 3.