The impact of the COVID-19 outbreak on the air traffic noise at the Hannover airport region.

In this work, the impact of the COVID-19 outbreak on the environmental noise generated by the air traffic at the Hannover Airport, Germany, is assessed. For this purpose, a comparative study of the air traffic noise in the years 2019 and 2020 is conducted by means of publicly available measurement data and computational simulations. Based on environmental noise directives defined by the responsible German authorities, the comparative study is conducted in terms of A-weighted equivalent sound pressure level metrics computed for the six months of the forecast years with the largest number of flights. In comparison with the year of 2019, the measurement data indicates that the L , L, and L were reduced in average by 2.4, 4.2, and 3.7 dBA, respectively, in the year 2020. Furthermore, the results based on the computational simulations show that the isocontour areas of the L = 60 dBA and L = 55 dBA noise protection zones defined by the German federal government were reduced by ≈ 40.29 % and ≈ 54.08 %, respectively, in the year of 2020.

Design and Additive Manufacturing of Porous Sound Absorbers-A Machine-Learning Approach.

Additive manufacturing (AM), widely known as 3D-printing, builds parts by adding material in a layer-by-layer process. This tool-less procedure enables the manufacturing of porous sound absorbers with defined geometric features, however, the connection of the acoustic behavior and the material's micro-scale structure is only known for special cases. To bridge this gap, the work presented here employs machine-learning techniques that compute acoustic material parameters (Biot parameters) from the material's micro-scale geometry. For this purpose, a set of test specimens is used that have been developed in earlier studies. The test specimens resemble generic absorbers by a regular lattice structure based on a bar design and allow a variety of parameter variations, such as bar width, or bar height. A set of 50 test specimens is manufactured by material extrusion (MEX) with a nozzle diameter of 0.2 mm and a targeted under extrusion to represent finer structures. For the training of the machine learning models, the Biot parameters are inversely identified from the manufactured specimen. Therefore, laboratory measurements of the flow resistivity and absorption coefficient are used. The resulting data is used for training two different machine learning models, an artificial neural network and a k-nearest neighbor approach. It can be shown that both models are able to predict the Biot parameters from the specimen's micro-scale with reasonable accuracy. Moreover, the detour via the Biot parameters allows the application of the process for application cases that lie beyond the scope of the initial database, for example, the material behavior for other sound fields or frequency ranges can be predicted. This makes the process particularly useful for material design and takes a step forward in the direction of tailoring materials specific to their application.

Microtubules and motor proteins support zebrafish neuronal migration by directing cargo.

Neuronal migration during development is necessary to form an ordered and functional brain. Postmitotic neurons require microtubules and dynein to move, but the mechanisms by which they contribute to migration are not fully characterized. Using tegmental hindbrain nuclei neurons in zebrafish embryos together with subcellular imaging, optogenetics, and photopharmacology, we show that, in vivo, the centrosome's position relative to the nucleus is not linked to greatest motility in this cell type. Nevertheless, microtubules, dynein, and kinesin-1 are essential for migration, and we find that interference with endosome formation or the Golgi apparatus impairs migration to a similar extent as disrupting microtubules. In addition, an imbalance in the traffic of the model cargo Cadherin-2 also reduces neuronal migration. These results lead us to propose that microtubules act as cargo carriers to control spatiotemporal protein distribution, which in turn controls motility. This adds crucial insights into the variety of ways that microtubules can support successful neuronal migration in vivo.

Design, Experimental and Numerical Characterization of 3D-Printed Porous Absorbers.

The application of porous materials is a common measure for noise mitigation and in room acoustics. The prediction of the acoustic behavior applies material models, among which most are based on the Biot-parameters. Thereby, it is expected that, if more Biot-parameters are used, a better prediction can be obtained. Nevertheless, an estimation of the Biot-parameters from the geometric design of the material is possible for simple structures only. For common porous materials, the microstructure is typically unknown and characterized by homogenized quantities. This contribution introduces a methodology that enables the design and optimization of porous materials based on the Biot-parameters and connects these to microscopic geometric quantities. Therefore, artificial porous materials were manufactured using 3D-printing technology with a prescribed geometric design and the influence of different design variables was investigated. The Biot-parameters were identified with an inverse procedure and it can be shown that different Biot-parameters can be influenced by adjusting the geometric design variables. Based on these findings, a one-parameter optimization procedure of the material is set up to maximize the absorption characteristics in the frequency range of interest.