A Mass-transfer Particle-tracking Method for Simulating Transport with Discontinuous Diffusion Coefficients

The problem of a spatially discontinuous diffusion coefficient ($D(\boldsymbol x)$) is one that may be encountered in hydrogeologic systems due to natural geological features or as a consequence of numerical discretization of flow properties. To date, mass-transfer particle-tracking (MTPT) methods, a family of Lagrangian methods in which diffusion is jointly simulated by random walk and diffusive mass transfers, have been unable to solve this problem. This manuscript presents a new mass-transfer (MT) algorithm that enables MTPT methods to accurately solve the problem of discontinuous $D(\boldsymbol x)$. To achieve this, we derive a semi-analytical solution to the discontinuous $D(\boldsymbol x)$ problem by employing a predictor-corrector approach, and we use this semi-analytical solution as the weighting function in a reformulated MT algorithm. This semi-analytical solution is generalized for cases with multiple 1D interfaces as well as for 2D cases, including a $2 \times 2$ tiling of 4 subdomains that corresponds to a numerically-generated diffusion field. The solutions generated by this new mass-transfer algorithm closely agree with an analytical 1D solution or, in more complicated cases, trusted numerical results, demonstrating the success of our proposed approach.Comment: 26 pages, 11 figure

Alien Registration- Bolster, Albert D. (Wade, Aroostook County)

https://digitalmaine.com/alien_docs/32628/thumbnail.jp

Effect of spatial concentration fluctuations on effective kinetics in diffusion-reaction systems

International audienceThe effect of spatial concentration fluctuations on the reaction of two solutes, A þ B* C, is considered. In the absence of fluctuations, the concentration of solutes decays as Adet ¼ Bdet t 1. Contrary to this, experimental and numerical studies suggest that concentrations decay significantly slower. Existing theory suggests a t d/4 scaling in the asymptotic regime (d is the dimensionality of the problem). Here we study the effect of fluctuations using the classical diffusion-reaction equation with random initial conditions. Initial concentrations of the reactants are treated as correlated random fields.We use the method of moment equations to solve the resulting stochastic diffusion-reaction equation and obtain a solution for the average concentrations that deviates from t 1 to t d/4 behavior at characteristic transition time t . We also derive analytical expressions for t as a function of Damköhler number and the coefficient of variation of the initial concentration

Survival and Predictors of Mortality in Systemic Sclerosis‐Associated Pulmonary Arterial Hypertension: Outcomes From the Pulmonary Hypertension Assessment and Recognition of Outcomes in Scleroderma Registry

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/106105/1/acr22121.pd

Filtered deterministic waves and analysis of the fractal dimension of the components of the wind velocity

The difficulty in developing models for waves in turbulent flows is a key problem in the analysis of the complexity of turbulence. We present a method to find and filter perturbations that are generated by the flow of deterministic waves from the power spectrum in the atmospheric boundary layer (ABL). The perturbation model proposed assumes that the amplitude and frequency of such waves decay with time exponentially. For illustrative purposes, we apply the technique to three time series of wind velocities obtained with a sonic anemometer. This analytical procedure allows us to filter waves of the proposed structure with a 99% significance level in the power spectrum. We have applied the same method to 540 such wind series, all painting similar results. We then compare the fractal dimension of the original series to those from which the waves have been removed. We find that the fractal dimension of the filtered waves is slightly less than that of the original series. Finally, we consider the fractal dimension of the studied series as a function of the length-scales and dissipation rate of kinetic energy per unit mass. Our results suggest an increase of fractal dimension with both length-scale and dissipation rate of kinetic energy

Two step activation of FOXO3 by AMPK generates a coherent feed-forward loop determining excitotoxic cell fate

Cerebral ischemia and excitotoxic injury induce transient or permanent bioenergetic failure, and may result in neuronal apoptosis or necrosis. We have previously shown that ATP depletion and activation of AMP-activated protein kinase (AMPK) during excitotoxic injury induces neuronal apoptosis by transcription of the proapoptotic BH3 only protein, Bim. AMPK, however, also exerts pro-survival functions in neurons. The molecular switches that determine these differential outcomes are not well understood. Using an approach combining biochemistry, single cell imaging and computational modeling, we here demonstrate that excitotoxic injury activated the bim promoter in a FOXO3-dependent manner. The activation of AMPK reduced AKT activation, and led to dephosphorylation and nuclear translocation of FOXO3. Subsequent mutation studies indicated that bim gene activation during excitotoxic injury required direct FOXO3 phosphorylation by AMPK in the nucleus as a second activation step. Inhibition of this phosphorylation prevented Bim expression and protected neurons against excitotoxic and oxygen/glucose deprivation-induced injury. Systems analysis and computational modeling revealed that these two activation steps defined a coherent feedforward loop; a network motif capable of filtering any effects of short-term AMPK activation on bim gene induction. This may prevent unwanted AMPK-mediated Bim expression and apoptosis during transient or physiological bioenergetic stress

Apparent directional mass-transfer capacity coefficients in three-dimensional anisotropic heterogeneous aquifers under radial convergent transport

        Aquifer hydraulic properties such as hydraulic conductivity (K) are ubiquitously heterogeneous and typically only a statistical characterization can be sought. Additionally, statistical anisotropy at typical characterization scales is the rule. Thus, regardless of the processes governing solute transport at the local (pore) scale, transport becomes non‐Fickian. Mass‐transfer models provide an efficient tool that reproduces observed anomalous transport; in some cases though, these models lack predictability as model parameters cannot readily be connected to the physical properties of aquifers. In this study, we focus on a multirate mass‐transfer model (MRMT), and in particular the apparent capacity coefficient (β), which is a strong indicator of the potential of immobile zones to capture moving solute. We aim to find if the choice of an apparent β can be phenomenologically related to measures of statistical anisotropy. We analyzed an ensemble of random simulations of three‐dimensional log‐transformed multi‐Gaussian permeability fields with stationary anisotropic correlation under convergent flow conditions. It was found that apparent β also displays an anisotropic behavior, physically controlled by the aquifer directional connectivity, which in turn is controlled by the anisotropic correlation model. A high hydraulic connectivity results in large β values. These results provide new insights into the practical use of mass‐transfer models for predictive purposes. &nbsp

The differential hormonal milieu of morning versus evening, may have an impact on muscle hypertrophic potential

Substantial gains in muscle strength and hypertrophy are clearly associated with the routine performance of resistance training. What is less evident is the optimal timing of the resistance training stimulus to elicit these significant functional and structural skeletal muscle changes. Therefore, this investigation determined the impact of a single bout of resistance training performed either in the morning or evening upon acute anabolic signalling (insulin-like growth factor-binding protein-3 (IGFBP-3), myogenic index and differentiation) and catabolic processes (cortisol). Twenty-four male participants (age 21.4±1.9yrs, mass 83.7±13.7kg) with no sustained resistance training experience were allocated to a resistance exercise group (REP). Sixteen of the 24 participants were randomly selected to perform an additional non-exercising control group (CP) protocol. REP performed two bouts of resistance exercise (80% 1RM) in the morning (AM: 0800 hrs) and evening (PM: 1800 hrs), with the sessions separated by a minimum of 72 hours. Venous blood was collected immediately prior to, and 5 min after, each resistance exercise and control sessions. Serum cortisol and IGFBP-3 levels, myogenic index, myotube width, were determined at each sampling period. All data are reported as mean ± SEM, statistical significance was set at P≤0.05. As expected a significant reduction in evening cortisol concentration was observed at pre (AM: 98.4±10.5, PM: 49.8±4.4 ng/ml, P0.05). Timing of resistance training regimen in the evening appears to augment some markers of hypertrophic potential, with elevated IGFBP-3, suppressed cortisol and a superior cellular environment. Further investigation, to further elucidate the time course of peak anabolic signalling in morning vs evening training conditions, are timely

Gaussian Process Modelling for Uncertainty Quantification in Convectively-Enhanced Dissolution Processes in Porous Media

Numerical groundwater flow and dissolution models of physico-chemical processes in deep aquifers are usually subject to uncertainty in one or more of the model input parameters. This uncertainty is propagated through the equations and needs to be quantified and characterised in order to rely on the model outputs. In this paper we present a Gaussian process emulation method as a tool for performing uncertainty quantification in mathematical models for convection and dissolution processes in porous media. One of the advantages of this method is its ability to significantly reduce the computational cost of an uncertainty analysis, while yielding accurate results, compared to classical Monte Carlo methods. We apply the methodology to a model of convectively-enhanced dissolution processes occurring during carbon capture and storage. In this model, the Gaussian process methodology fails due to the presence of multiple branches of solutions emanating from a bifurcation point, i.e., two equilibrium states exist rather than one. To overcome this issue we use a classifier as a precursor to the Gaussian process emulation, after which we are able to successfully perform a full uncertainty analysis in the vicinity of the bifurcation point