Modelling water infiltration into macroporous hill slopes using special boundary conditions

The formulation of suitable boundary conditions is a very crucial task when modeling water infiltration into macroporous hill slopes. The processes of water infiltration and exfiltration vary in space and time and depend on the flow on the surface as well as in the subsurface. In this contribution we have purposed special system process dependent boundary conditions can be formulated for a two-phase dual-permeability model to simulate infiltration and exfiltration processes. The presented formulation analyses the saturation conditions of the dual-permeability model (e.g. saturation) at the boundary nodes and adopts the boundary conditions depending on the processes at the soil surface such as rainfall intensity. Using a simplified macroporous hill slope and a heavy rainfall event we demonstrate the functionality of our formulation

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

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

Signatures of polaronic excitations in quasi-one-dimensional LaTiO3.41_{3.41}

The optical properties of quasi-one-dimensional metallic LaTiO$_{3.41}$ are studied for the polarization along the $a$ and $b$ axes. With decreasing temperature modes appear along both directions suggestive for a phase transition. The broadness of these modes along the conducting axis might be due to the coupling of the phonons to low-energy electronic excitations across an energy gap. We observe a pronounced midinfrared band with a temperature dependence consistent with (interacting) polaron models. The polaronic picture is corroborated by the presence of strong electron-phonon coupling and the temperature dependence of the dc conductivity.Comment: 5 pages, 5 figure

Inhibition of Macrophage Migration Inhibitory Factor Activity Attenuates Haemorrhagic Shock-Induced Multiple Organ Dysfunction in Rats

OBJECTIVE: The aim of this study was to investigate (a) macrophage migration inhibitory factor (MIF) levels in polytrauma patients and rats after haemorrhagic shock (HS), (b) the potential of the MIF inhibitor ISO-1 to reduce multiple organ dysfunction syndrome (MODS) in acute (short-term and long-term follow-up) HS rat models and (c) whether treatment with ISO-1 attenuates NF-κB and NLRP3 activation in HS. BACKGROUND: The MODS caused by an excessive systemic inflammatory response following trauma is associated with a high morbidity and mortality. MIF is a pleiotropic cytokine which can modulate the inflammatory response, however, its role in trauma is unknown. METHODS: The MIF levels in plasma of polytrauma patients and serum of rats with HS were measured by ELISA. Acute HS rat models were performed to determine the influence of ISO-1 on MODS. The activation of NF-κB and NLRP3 pathways were analysed by western blot in the kidney and liver. RESULTS: We demonstrated that (a) MIF levels are increased in polytrauma patients on arrival to the emergency room and in rats after HS, (b) HS caused organ injury and/or dysfunction and hypotension (post-resuscitation) in rats, while (c) treatment of HS-rats with ISO-1 attenuated the organ injury and dysfunction in acute HS models and (d) reduced the activation of NF-κB and NLRP3 pathways in the kidney and liver. CONCLUSION: Our results point to a role of MIF in the pathophysiology of trauma-induced organ injury and dysfunction and indicate that MIF inhibitors may be used as a potential therapeutic approach for MODS after trauma and/or haemorrhage

Characterization of Reachable Attractors Using Petri Net Unfoldings

International audienceAttractors of network dynamics represent the long-term behaviours of the modelled system. Their characterization is therefore crucial for understanding the response and differentiation capabilities of a dynamical system. In the scope of qualitative models of interaction networks, the computation of attractors reachable from a given state of the network faces combinatorial issues due to the state space explosion. In this paper, we present a new algorithm that exploits the concurrency between transitions of parallel acting components in order to reduce the search space. The algorithm relies on Petri net unfoldings that can be used to compute a compact representation of the dynamics. We illustrate the applicability of the algorithm with Petri net models of cell signalling and regulation networks, Boolean and multi-valued. The proposed approach aims at being complementary to existing methods for deriving the attractors of Boolean models, while being %so far more generic since it applies to any safe Petri net