Statistical support for the ATL program

Statistical experimental designs are presented for various numbers of organisms and agar solutions pertinent to the experiment, ""colony growth in zero gravity''. Missions lasting 7 and 30 days are considered. For the designs listed, the statistical analysis of the observations obtained on the space shuttle are outlined

Upscaling the shallow water model with a novel roughness formulation

This study presents a novel roughness formulation to conceptually account for microtopography and compares it to four existing roughness models from literature. The aim is to increase the grid size for computational efficiency, while capturing subgrid scale effects with the roughness formulation to prevent the loss in accuracy associated with coarse grids. All roughness approaches are implemented in the Hydroinformatics Modeling System and compared with results of a high resolution shallow water model in three test cases: rainfall-runoff on an inclined plane with sinewave shaped microtopography, ow over an inclined plane with random microtopography and rainfall-runoff in a small natural catchment. Although the high resolution results can not be reproduced exactly by the coarse grid model, e.g. local details of ow processes can not be resolved, overall good agreement between the upscaled models and the high resolution model has been achieved. The proposed roughness formulation generally shows the best agreement of all compared models. It is further concluded that the accuracy increases with the number of calibration parameters available, however the calibration process becomes more difficult. Using coarser grids results in significant speedup in comparison with the high resolution simulation. In the presented test cases the speedup varies from 20 up to 2520, depending on the size and complexity of the test case and the difference in cell sizes.The authors thank the Alexander von Humboldt-Foundation for the Humboldt Research Fellowship granted to Dr. Dongfang Liang.This is the accepted manuscript. The final version is available at http://link.springer.com/article/10.1007%2Fs12665-015-4726-7

Artificial neural networks for 3D cell shape recognition from confocal images

We present a dual-stage neural network architecture for analyzing fine shape details from microscopy recordings in 3D. The system, tested on red blood cells, uses training data from both healthy donors and patients with a congenital blood disease. Characteristic shape features are revealed from the spherical harmonics spectrum of each cell and are automatically processed to create a reproducible and unbiased shape recognition and classification for diagnostic and theragnostic use.Comment: 17 pages, 8 figure

Model Integration and Coupling in A Hydroinformatics System

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

Towards Business-to-IT Alignment in the Cloud

Cloud computing offers a great opportunity for business process (BP) flexibility, adaptability and reduced costs. This leads to realising the notion of business process as a service (BPaaS), i.e., BPs offered on-demand in the cloud. This paper introduces a novel architecture focusing on BPaaS design that includes the integration of existing state-of-the-art components as well as new ones which take the form of a business and a syntactic matchmaker. The end result is an environment enabling to transform domain-specific BPs into executable workflows which can then be made deployable in the cloud so as to become real BPaaSes

Reduced Order Modelling of a Reynolds Number 10⁶ Jet Flow Using Machine Learning Approaches

The extraction of the most dynamically important coherent flow structures using reduced order models (ROM) is a challenging task in various fluid dynamics applications. In particular, for high-speed round jet flows, the axisymmetric pressure mode of interest is known to be responsible for sound radiation at small angles to the jet axis and dominant contribution to the jet noise peak. In this work the axisymmetric pressure mode of the Navier-Stokes solution of a high speed jet flow at low frequency is reconstructed from simulation data using popular Machine Learning (ML) methods, whose output can later be exploited for data-driven design of effective turbulent acoustic source models. The data used as input for the ML techniques are derived from the Large Eddy Simulation database obtained by application of the high-resolution CABARET method accelerated on GPU cards for flow solutions to NASA Small Hot Jet Acoustic Rig (SHJAR) jets. The SHJAR simulation database is fed to Spectral Proper Orthogonal (SPOD), and the resulting time coefficients of the turbulent pressure fluctuations are the targets of the three machine learning methods put to test in this work. The first Machine Learning method used is the Feed-forward Neural Networks technique, which was successfully implemented for a turbulent flow over a plunging aerofoil in the literature. The second method is based on the application of Genetic Programming, which is a symbolic regression method well-known in optimisation research, but it has not been applied for turbulent flow reconstruction before. The third method, commonly known as Echo State Networks (ESNs), is a time series prediction and reconstruction method from the field of Reservoir Computing. A report on the attempts to apply these methods for approximation and extrapolation of the turbulent flow signals are discussed