Linking watershed terrain and hydrology to soil chemical properties, microbial communities and impacts on soil organic C in a humid mid-latitude forested watershed

Understanding the response of humid mid-latitude forests to changes in precipitation, temperature, nutrient cycling, and disturbance is critical to improving our predictive understanding of changes in the surface-subsurface energy balance due to climate change. Mechanistic understanding of the effects of long-term and transient moisture conditions are needed to quantify
linkages between changing redox conditions, microbial activity, and soil mineral and nutrient interactions on C cycling and greenhouse gas releases. To illuminate relationships between the soil chemistry, microbial communities and organic C we established transects across hydraulic and topographic gradients in a small watershed with transient moisture conditions. Valley bottoms tend to be more frequently saturated than ridge tops and side slopes which generally are only saturated when shallow storm flow zones are active. Fifty shallow (~36”) soil cores were collected during timeframes representative of low CO2, soil winter conditions and high CO2, soil summer conditions. Cores were subdivided into 240 samples based on pedology and analyses of the geochemical (moisture content, metals, pH, Fe species, N, C, CEC, AEC) and microbial (16S rRNA gene
amplification with Illumina MiSeq sequencing) characteristics were conducted and correlated to watershed terrain and hydrology. To associate microbial metabolic activity with greenhouse gas emissions we installed 17 soil gas probes, collected gas samples for 16 months and analyzed them for CO2 and other fixed and greenhouse gasses. Parallel to the experimental efforts our data is being used to support hydrobiogeochemical process modeling by coupling the Community Land Model (CLM) with a subsurface process model (PFLOTRAN) to simulate processes and interactions from the molecular to watershed scales. Including above ground processes (biogeophysics, hydrology, and vegetation dynamics), CLM provides mechanistic water, energy, and organic matter inputs to the surface/subsurface models, in which coupled biogeochemical reaction
networks are used to improve the representation of below-ground processes. Preliminary results suggest that inclusion of above ground processes from CLM greatly improves the prediction of moisture response and water cycle at the watershed scale.

Statistical Monitoring of Dynamic Multivariate Processes: Part I: Modeling Auto- and Cross-Correlation

The work in this paper is of particular significance since it considers the problem of modelling cross- and auto-correlation in statistical process monitoring. The presence of both types of correlation can lead to fault insensitivity or false alarms, although in published literature to date, only autocorrelation has been broadly considered. The proposed method, which uses a Kalman innovation model, effectively removes both correlations. The paper (and Part 2 [2]) has emerged from work supported by EPSRC grant GR/S84354/01 and is of direct relevance to problems in several application areas including chemical, electrical, and mechanical process monitoring.

Neural Network Modeling of Rheological Parameters of Grouts Containing Viscosity-Modifying Agent

The development of artificial neural network (ANN) models to predict the rheological behavior of grouts is described is this paper and the sensitivity of such parameters to the variation in mixture ingredients is also evaluated. The input parameters of the neural network were the mixture ingredients influencing the rheological behavior of grouts, namely the cement content, fly ash, ground-granulated blast-furnace slag, limestone powder, silica fume, water-binder ratio (w/b), high-range water-reducing admixture, and viscosity-modifying agent (welan gum). The six outputs of the ANN models were the mini-slump, the apparent viscosity at low shear, and the yield stress and plastic viscosity values of the Bingham and modified Bingham models, respectively. The model is based on a multi-layer feed-forward neural network. The details of the proposed ANN with its architecture, training, and validation are presented in this paper. A database of 186 mixtures from eight different studies was developed to train and test the ANN model. The effectiveness of the trained ANN model is evaluated by comparing its responses with the experimental data that were used in the training process. The results show that the ANN model can accurately predict the mini-slump, the apparent viscosity at low shear, the yield stress, and the plastic viscosity values of the Bingham and modified Bingham models of the pseudo-plastic grouts used in the training process. The results can also predict these properties of new mixtures within the practical range of the input variables used in the training with an absolute error of 2%, 0.5%, 8%, 4%, 2%, and 1.6%, respectively. The sensitivity of the ANN model showed that the trend data obtained by the models were in good agreement with the actual experimental results, demonstrating the effect of mixture ingredients on fluidity and the rheological parameters with both the Bingham and modified Bingham models.

Modeling Multivariate Interest Rates Using Time-Varying Copulas and Reducible Nonlinear Stochastic Differential Equations

We propose a new approach for modeling nonlinear multivariate interest rate processes based on time-varying copulas and reducible stochastic differential equations (SDEs). In the modeling of the marginal processes, we consider a class of nonlinear SDEs that are reducible to Ornstein--Uhlenbeck (OU) process or Cox, Ingersoll, and Ross (1985) (CIR) process. The reducibility is achieved via a nonlinear transformation function. The main advantage of this approach is that these SDEs can account for nonlinear features, observed in short-term interest rate series, while at the same time leading to exact discretization and closed-form likelihood functions. Although a rich set of specifications may be entertained, our exposition focuses on a couple of nonlinear constant elasticity volatility (CEV) processes, denoted as OU-CEV and CIR-CEV, respectively. These two processes encompass a number of existing models that have closed-form likelihood functions. The transition density, the conditional distribution function, and the steady-state density function are derived in closed form as well as the conditional and unconditional moments for both processes. In order to obtain a more flexible functional form over time, we allow the transformation function to be time varying. Results from our study of U.S. and UK short-term interest rates suggest that the new models outperform existing parametric models with closed-form likelihood functions. We also find the time-varying effects in the transformation functions statistically significant. To examine the joint behavior of interest rate series, we propose flexible nonlinear multivariate models by joining univariate nonlinear processes via appropriate copulas. We study the conditional dependence structure of the two rates using Patton (2006a) time-varying symmetrized Joe--Clayton copula. We find evidence of asymmetric dependence between the two rates, and that the level of dependence is positively related to the level of the two rates. (JEL: C13, C32, G12) Copyright The Author 2010. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org, Oxford University Press.

One Dimensional Modeling of a Turbogenerating Spark Ignition Engine Operating on Biogas

Turbocompounding is generally regarded as the process of recovering a proportion of the exhaust gas energy from a reciprocating engine and applying it to the output power of the crankshaft. In conventional turbocompounding, the power turbine has been mechanically connected to the crankshaft but now a new method has emerged. Recent advances in high speed electrical machines have enabled the power turbine to be coupled to an electric generator. Decoupling the power turbine from the crankshaft and coupling it to a generator allows the power electronics to control the turbine speed independently in order to optimize the turbine efficiency for different engine operating conditions.

The Influence of Friction Stir Welding Process Idealization on Residual Stress and Distortion Predictions for Future Airframe Assembly Simulations

The ability to accurately predict residual stresses and resultant distortions is a key product from process assembly simulations. Assembly processes necessarily consider large structural components potentially making simulations computationally expensive. The objective herein is to develop greater understanding of the influence of friction stir welding process idealization on the prediction of residual stress and distortion and thus determine the minimum required modeling fidelity for future airframe assembly simulations. The combined computational and experimental results highlight the importance of accurately representing the welding forging force and process speed. In addition, the results emphasize that increased CPU simulation times are associated with representing the tool torque, while there is potentially only local increase in prediction fidelity.

Dynamic grey-box modeling for online monitoring of extrusion viscosity

Melt viscosity is a key indicator of product quality in polymer extrusion processes. However, real time monitoring and control of viscosity is difficult to achieve. In this article, a novel “soft sensor” approach based on dynamic gray-box modeling is proposed. The soft sensor involves a nonlinear finite impulse response model with adaptable linear parameters for real-time prediction of the melt viscosity based on the process inputs; the model output is then used as an input of a model with a simple-fixed structure to predict the barrel pressure which can be measured online. Finally, the predicted pressure is compared to the measured value and the corresponding error is used as a feedback signal to correct the viscosity estimate. This novel feedback structure enables the online adaptability of the viscosity model in response to modeling errors and disturbances, hence producing a reliable viscosity estimate. The experimental results on different material/die/extruder confirm the effectiveness of the proposed “soft sensor” method based on dynamic gray-box modeling for real-time monitoring and control of polymer extrusion processes. POLYM. ENG. SCI., 2012. © 2012 Society of Plastics Engineers

Physical and numerical constraints in source modeling for finite difference simulation of room acoustics

In finite difference time domain simulation of room acoustics, source functions are subject to various constraints. These depend on the way sources are injected into the grid and on the chosen parameters of the numerical scheme being used. This paper addresses the issue of selecting and designing sources for finite difference simulation, by first reviewing associated aims and constraints, and evaluating existing source models against these criteria. The process of exciting a model is generalized by introducing a system of three cascaded filters, respectively, characterizing the driving pulse, the source mechanics, and the injection of the resulting source function into the grid. It is shown that hard, soft, and transparent sources can be seen as special cases within this unified approach. Starting from the mechanics of a small pulsating sphere, a parametric source model is formulated by specifying suitable filters. This physically constrained source model is numerically consistent, does not scatter incoming waves, and is free from zero- and low-frequency artifacts. Simulation results are employed for comparison with existing source formulations in terms of meeting the spectral and temporal requirements on the outward propagating wave.

Modeling target bulk heating resulting from ultra-intense short pulse laser irradiation of solid density targets

Isochoric heating of solid-density matter up to a few tens of eV is of interest for investigating astrophysical or inertial fusion scenarios. Such ultra-fast heating can be achieved via the energy deposition of short-pulse laser generated electrons. Here, we report on experimental measurements of this process by means of time-and space-resolved optical interferometry. Our results are found in reasonable agreement with a simple numerical model of fast electron-induced heating. (C) 2013 AIP Publishing LLC.

Kinetics of levoglucosan and formaldehyde formation during cellulose pyrolysis process

The mechanisms and kinetics studies of the formation of levoglucosan and formaldehyde from anhydroglucose radical have been carried out theoretically in this paper. The geometries and frequencies of all the stationary points are calculated at the B3LYP/6-31+G(D,P) level based on quantum mechanics, Six elementary reactions are found, and three global reactions are involved. The variational transition-state rate constants for the elementary reactions are calculated within 450-1500 K. The global rate constants for every pathway are evaluated from the sum of the individual elementary reaction rate constants. The first-order Arrhenius expressions for these six elementary reactions and the three pathways are suggested. By comparing with the experimental data, computational methods without tunneling correction give good description for Path1 (the formation of levoglucosan); while methods with tunneling correction (zero-curvature tunneling and small-curvature tunneling correction) give good results for Path2 (the first possibility for the formation of formaldehyde), all the test methods give similar results for Path3 (the second possibility for the formation of formaldehyde), all the modeling results for Path3 are in good agreement with the experimental data, verifying that it is the most possible way for the formation of formaldehyde during cellulose pyrolysis. © 2012 Elsevier Ltd. All rights reserved.

Modeling Klebsiella pneumoniae pathogenesis by infection of the wax moth Galleria mellonella

The implementation of infection models that approximate human disease is essential for understanding pathogenesis at the molecular level and for testing new therapies before they are entered into clinical stages. Insects are increasingly being used as surrogate hosts because they share, with mammals, essential aspects of the innate immune response to infections. We examined whether the larva of the wax moth Galleria mellonella could be used as a host model to conceptually approximate Klebsiella pneumoniae-triggered pneumonia. We report that the G. mellonella model is capable of distinguishing between pathogenic and nonpathogenic Klebsiella strains. Moreover, K. pneumoniae infection of G. mellonella models some of the known features of Klebsiella-induced pneumonia, i.e., cell death associated with bacterial replication, avoidance of phagocytosis by phagocytes, and the attenuation of host defense responses, chiefly the production of antimicrobial factors. Similar to the case for the mouse pneumonia model, activation of innate responses improved G. mellonella survival against subsequent Klebsiella challenge. Virulence factors necessary in the mouse pneumonia model were also implicated in the Galleria model. We found that mutants lacking capsule polysaccharide, lipid A decorations, or the outer membrane proteins OmpA and OmpK36 were attenuated in Galleria. All mutants activated G. mellonella defensive responses. The Galleria model also allowed us to monitor Klebsiella gene expression. The expression levels of cps and the loci implicated in lipid A remodeling peaked during the first hours postinfection, in a PhoPQ- and PmrAB-governed process. Taken together, these results support the utility of G. mellonella as a surrogate host for assessing infections with K. pneumoniae.

Modeling of titanium alloys by using artificial neural networks

Titanium alloy exhibits an excellent combination of bio-compatibility, corrosion resistance, strength and toughness. The microstructure of an alloy influences the properties. The microstructures depend mainly on alloying elements, method of production, mechanical, and thermal treatments. The relationships between these variables and final properties of the alloy are complex, non-linear in nature, which is the biggest hurdle in developing proper correlations between them by conventional methods. So, we developed artificial neural networks (ANN) models for solving these complex phenomena in titanium alloys.

In the present work, ANN models were used for the analysis and prediction of the correlation between the process parameters, the alloying elements, microstructural features, beta transus temperature and mechanical properties in titanium alloys. Sensitivity analysis of trained neural network models were studied which resulted a better understanding of relationships between inputs and outputs. The model predictions and the analysis are well in agreement with the experimental results. The simulation results show that the average output-prediction error by models are less than 5% of the prediction range in more than 95% of the cases, which is quite acceptable for all metallurgical purposes.