Determination Of Model Parameters Research Articles

Spatiotemporal Metabolic Network Models Reveal Complex Autotroph-Heterotroph Biofilm Interactions Governed by Photon Incidences

Autotroph-heterotroph interactions are ubiquitous in natural environment and play a key role in controlling various essential ecosystem functions, such as production and utilization of organic matter, cycling of nitrogen, sulfur, and other chemical elements. Understanding how these biofilm metabolic interactions are constrained in space and time remains challenging because fully predictive models designed for this purpose are currently limited. Toward filling this gap, here we developed community metabolic network models for two autotroph-heterotroph biofilm consortia (termed UCC-A and UCC-O), which share a suite of common heterotrophic members but have a single distinct photoautotrophic cyanobacterium (Phormidesmis priestleyi str. ANA and Phormidium sp. OSCR) that provides organic carbon and nitrogen sources to support the growth of heterotrophic partners. After determining model parameters by data fitting using the spatiotemporal distributions of microbial abundances, we comparatively analyzed the resulting biofilm models to examine any fundamental differences in microbial interactions between the two consortia under the variation of key environmental variables: CO2 and photon levels. The UCC-A model predicted generally expected responses, i.e., the autotroph population increased in response to elevated levels of CO2 and photon, followed by increase in the heterotroph population. In contrast, the UCC-O model showed somewhat complicated dynamics, e.g., higher photon incidence rates resulted in the increase in autotroph population but decrease in heterotroph population due to the lowered provision of glucose from the autotroph. A further analysis showed that species coexistence was governed by the photon incidences rather than the carbon availability for UCC-O, which was the opposite for UCC-A.

Inverse Determination of the Johnson-Cook Material Model Parameters of Aluminum Al 6060 from Orthogonal Cutting Examinations

Modeling the material behavior under metal cutting conditions is a prerequisite for the successful simulation of the cutting process by means of Finite Element (FE) simulations. Here, the Downhill-Simplex-Algorithm is applied to the inverse determination of Johnson-Cook material model parameters for the Aluminum alloy Al 6060. For this purpose, orthogonal cutting experiment were conducted to set the experimental database for the inverse determination. Following, iterative FE-simulations were conducted in order to determine the material model parameters. The results of the inverse determination showed to result in a good agreement between simulated and experimental results within a small number of iterations.

Determination of wind-fed model parameters of neutron stars in high-mass X-ray binaries

Abstract We have studied several neutron star high-mass X-ray binaries (HMXBs) with super-giant (SG) companions using a wind-fed binary model associated with the magnetic field. By using the concept of torque balance, the magnetic field parameter determines the mass accretion rate. This would help us to consider the relationship between wind velocity and mass-loss rate. These parameters significantly improve our understanding of the accretion mechanism. The wind velocity is critical in determining the X-ray features. This can be used to identify the ejection process and the stochastic variations in their accretion regimes. However, even in systems with a long orbital period, an accretion disk can be created when the wind velocity is slow. This will allow the HMXB of both types, SG and Be, to be better characterised by deriving accurate properties from these binaries. In addition, we have performed segmentation in the parameter space of donors intended for several SG-HMXB listed in our sample set. The parameter space can be categorised into five regimes, depending on the possibility of disk formation associated with accretion from the stellar wind. This can give a quantitative clarification of the observed variability and the properties of these objects. For most of the systems, we show that the derived system parameters are consistent with the assumption that the system is emitting X-rays through direct accretion. However, there are some sources (LMC X-4, Cen X-3 and OAO1657-415) that are not in the direct accretion regime, although they share similar donor parameters. This may indicate that these systems are transitioning from a normal wind accretion phase to partial RLOF regimes.

Removal of Powerline Noise in Geophysical Datasets With a Scientific Machine-Learning Based Approach

The most common noise in geophysical data is probably the interference from powerlines. This noise manifests itself as a sinusoidal signal oscillating at the fundamental 50 Hz or 60 Hz frequency of the power grid and as harmonic components oscillating at integer multiples. Many different mitigation strategies, tailored for the specific geophysical method, have been developed to target powerline noise. One method that applies to fully sampled data is model-based subtraction, where a model of the powerline noise is fitted to the noisy data set and subsequently subtracted. In most cases, this leads to significant improvements in the signal-to-noise ratio. However, the determination of the powerline model parameters, in particular the fundamental powerline frequency, is computationally expensive, as it requires repeated solutions of a least-squares problem. We demonstrate that the powerline frequency can be directly predicted with a scientific machine-learning based approach. We work on both time domain induced polarization and surface nuclear magnetic resonance data. We use a different network for each method to trade-off prediction accuracy and prediction speed. In both cases, the prediction accuracy is fully on par with standard methods, and we obtain speed-ups by factors of 400 and 10 for the two types of data.

A Method for dSTEC Interpolation: Ionosphere Kernel Estimation Algorithm

Ionospheric structure is important for estimating ionospheric delay for user stations in Global Navigation Satellite System (GNSS). However, most existing parameter estimation methods suffer from challenges due to data inaccuracy and unavailability of limited and sparse scattered data at ground reference stations. The high variability of active low latitude or disturbed ionosphere leads to GNSS signal scintillation. It is critical to capture ionospheric random structure and estimate ionospheric parameter using data of disperse receivers to improve accuracy. This paper proposes a unifying method named Ionosphere Kernel Estimation Algorithm (IKEA) to retrieve the information of ionospheric spatial structure. The proposed model utilities the semi-parametric representation theorem to incorporate prior information and constraints. The multiple kernel technique is adopted firstly to include physical correlations. Additionally, a learning approach is deployed to determine model parameters. The IKEA model has been verified based on simulated and experimental data at active low latitudes from a network of ground GNSS reference stations from all visible GPS and GALILEO satellites. The IKEA model reduces approximately 19.5% and 24.2% of differential Slant Total Electron Content (dSTEC) in the root mean square error with respect to Inverse Distance Weighting (IDW) and the Kriging model during high ionospheric activities. The IKEA architecture has been demonstrated effective to make robust ionospheric estimation, which may be further extended for various GNSS applications and beyond.

Temporal trends and prediction of bovine tuberculosis: a time series analysis in the North-East of Iran.

Bovine tuberculosis (BTB) is a disease with high economic relevance. This study aimed to determine a fast alert surveillance system for bTB before the outbreak in the epidemic region of Iran. This cross-sectional study was conducted using the Auto-Regressive Integrated Moving Average (ARIMA) model for monthly bTB detections (reactors). These reactor cases result from the positive Tuberculin Purified Protein Derivative (PPD) test on cattle farms for the period between April 2007 and March 2019 in Razavi Khorasan province. Autocorrelation functions (ACF) and partial autocorrelation functions (PACF) plots were used to determine model parameters. The Akaike Information Criteria (AIC) were employed to select the best-fitted model. The root mean square error (RMSE) was applied for the evaluation of the models. Then, the best-fitted model was hired to predict the cases for 12 oncoming months. The data were analysed by STATA (ver. 14) software with a significant level at P≤0.05. ARIMA (3, 0, 3) 12 was introduced as a recommended fitted model according to white noise residual test (Q=22.87 and P=0.98), lower AIC (541.85), and more precise model RMSE (1.50). However, the forecast values were more than the observed values. The application and interpretation of ARIMA models are straightforward, and may be used as immediate tools for monitoring systems. However, we proposed an Auto-Regressive Integrated Moving Average with Exogenous Input (ARIMAX) model with some measurable exotic factors such as economic fluctuations, climate changes, and pulmonary tuberculosis to introduce a more precise and accurate model for the fast alert surveillance system.

Status of the Dielectric Constant of Sea Water at L-Band for Remote Sensing of Salinity

The model expressing the dielectric constant of sea water at microwave frequencies as a function of salinity and temperature is an important element in remote sensing of sea surface salinity. It is also important independently as a description of the physical properties of salt water. A major milestone was the development in the late 1970’s by Klein and Swift of a model based on laboratory measurements at L- and S-band and a functional form supported by theory for polar molecules and previous work on freshwater. Much of the subsequent work has focused on measurements at higher frequency and determining model parameters tuned to apply for applications such as remote sensing of sea surface temperature. Interest in the dielectric constant at 1.4 GHz (L-band) increased again with the development of SMOS and Aquarius to measure salinity from space. But there have been few new measurements at L-band and often confusion regarding the applicability of new models at 1.4 GHz. The objective of this manuscript is to compare available models in the context of how well they represent the dielectric constant of sea water at 1.4 GHz. Among the criteria applied will be the recent measurements at the George Washington University of the dielectric constant at 1.4 GHz.

Calibration of KCC model for UHPC under impact and blast loadings

Ultra-high performance concrete (UHPC) is a promising material for both civil and military protective structures against the potential impact and blast loadings. However, the existing numerical simulation studies related to the material model calibrations for precisely describing the dynamic behaviors of UHPC structural members under both impact and blast loadings are limited, and the calibration procedures are also not in consensus. This paper aims to propose a comprehensive Karagozian & Case Concrete (KCC) model parameter calibration procedure and reliable parameter generation method to well predict the dynamic behavior of UHPC members subjected to dynamic loadings. First, the failure surface, equation of state (EOS), damage evolution and strain rate effect parameters were comprehensively calibrated based on the existing experimental and analytical data of UHPC, and general guidelines on the determination of key KCC model parameters for UHPC are given. Then, the numerical simulations with both the autogenerated and the calibrated parameters were carried out to predict the dynamic response and damage evolution of UHPC members, i.e., reinforced UHPC beam and column, UHPC-FST beam, and reinforced UHPC panel, under drop hammer impact and field explosion. By comparing the test data, the validation of the proposed parameter generation approach is verified. The present work can provide helpful references for designing and evaluating the UHPC structural members under potential impact and blast loadings.

Imaging and characterization of the preferential flow process in agricultural land by using electrical resistivity tomography and dual-porosity model

Preferential flow is an important hydrological process, but the complexity of preferential flow channels and great difficulty in determining model parameters present certain challenges for its description on the field. The existing methods for parameters acquirement are mainly focus on calibrating by using observed data which show great uncertainty and non-uniqueness in results determination. The innovation of the study is that a new method of parameters acquisition is proposed by employing electrical resistivity tomography (ERT) method to solve the above problems. In our study, the process of deionized water infiltration was monitored, and characteristic parameters of preferential flow were abstract by using ERT in an agricultural land lies in Changhua Stream Basin, Zhejiang Province. To identify areas with preferential flow and matrix areas, soil resistivity, moisture as well as suction data were measured by resistivity meters and soil moisture and suction probes buried on site and thereby obtained area-specific soil water parameters for dual-porosity model. Then the infiltration process was simulated with a dual- porosity model based on the obtained parameters and then is verified by using infiltration results on site. We found that, instead of application of data fitting method, ERT might be useful for identification of preferential flow zone and matrix zone as well as providing precise parameters for preferential flow simulation. Compared with the laboratory test data, the Van Genuchten (VG) model parameters θs, θr, α, n differ by 9.4%, 19.8%,18.6% and 27.3% respectively. With an average Nash efficiency coefficient of 0.92 for 4 experimental sites, the simulation results for the dual-porosity model agreed with the field infiltration results. Hence, ERT can provide effective parameter support for water transportation under complex and challenging vadose zones, which is of great significance for the description of hydrological processes at a regional scale.

Repair Performance Landslide and Slope Using Bore pile and Ground Anchor on Cipali Toll Road Km 103

One of the causes of on-road collapse slopes is traffic load. Slope failure by road loads usually occurs due to several factors such as soil type, rainfall, land use. This study aims to determine landslide and slope repair performance using bore pile and ground anchor on Cipali Toll Road KM 103. The research method used in this study is the Finite element method. In this research, data collection, modeling parameter determination, slope stability analysis, slope reinforcement analysis, and reinforcement design were carried out with variations in bore pile and ground anchor dimensions. The software program used is a finite element program in the form of PLAXIS to analyze slope stability and estimate the slope failure area. The result of the study is that the R-Value inter is 0.25 with a 1.0341 safety factor. Best repair performance obtained from the addition of reinforcement with ground anchor 2 layer on bore pile 2 with a distance of 2 meters increased the safety factor to 1,913; Borepile capacity calculation with the calculation of normal force and moment iteration, the largest occurs in the DPT (Retaining Wall) stage with a normal load of -37.9 and a moment force of -471.15 which is still able to be borne by bore pile 1. The result of this study is expected to be benchmark and repair material to improve slope stability at km 103 Tol Cipali

An implicit integration scheme with consistent tangent modulus for Leblond\u2019s model of transformation-induced plasticity in steels

Thermomechanical treatments involving solid-state phase transformations play an important role for the manufacturing of functional and reliable components in many engineering applications. Accordingly, numerical investigation and optimization of such processes require considering thermoelastoplasticity under the influence of ongoing transformations and in particular the impact of transformation-induced plasticity (TRIP). While a number of elaborate plasticity models have been proposed for the description of TRIP, none of them seem to have received much prevalence in applications due to their complexity or hard to determine model parameters. Instead, the overwhelming majority of applied research either relies on simplistic formulations dating back to early phenomenological approaches or neglects TRIP altogether. In this work, we therefore provide an accessible, straightforward and easy-to-implement solution scheme for the TRIP model proposed by Leblond et al. which, despite being widely recognized, is hardly ever employed in full form. Specifically, we employ implicit backward-Euler integration and an elastic–plastic operator split approach to update the stresses in order to obtain a simple and concise algorithm for which we then derive the corresponding consistent tangent modulus. Furthermore, the work contains an application of the solution scheme to a symmetrically cooled plate and an in-depth discussion of the influence of TRIP by means of this tractable numerical example. Specifically, we highlight the discrepancies arising in transient and residual stresses and strains compared to the conventional J_2-plasticity approach where the phase transformation is accounted for merely by adapting the yield strength of the compound.

Justification of the structure and determination of the mathematical model parameters for a mechanical system with tribospectral optimization

The questions of determining the structure and parameters of the working bodies mechanical system model for the track renewal train with tribospectral optimization are considered. It is proposed that the tribospectral characteristics should be considered as an optimization parameter recognition source. The main attention is paid to the identification of the main regularities in the realized tribospectrum of the useful vibration signal. Based on the results of processing the vibration acceleration spectra, possible signs of optimization assessment are proposed.

Determination of model parameters of water-nutritional processes in soil for nitrogen compounds

Studies of development of an integrated model that allows calculating the whole range of processes necessary for predicting and managing the water and nutrient regimes of reclaimed soils are considered. Calculated values of the maximum concentrations of nitrate nitrogen in the topsoil during the growing season of plant for Tashkent’s region are shown. Values of field moisture capacity and wilting moisture of the main varieties of irrigated soils are defined.

Fast and Versatile Chromatography Process Design and Operation Optimization with the Aid of Artificial Intelligence

Preparative and process chromatography is a versatile unit operation for the capture, purification, and polishing of a broad variety of molecules, especially very similar and complex compounds such as sugars, isomers, enantiomers, diastereomers, plant extracts, and metal ions such as rare earth elements. Another steadily growing field of application is biochromatography, with a diversity of complex compounds such as peptides, proteins, mAbs, fragments, VLPs, and even mRNA vaccines. Aside from molecular diversity, separation mechanisms range from selective affinity ligands, hydrophobic interaction, ion exchange, and mixed modes. Biochromatography is utilized on a scale of a few kilograms to 100,000 tons annually at about 20 to 250 cm in column diameter. Hence, a versatile and fast tool is needed for process design as well as operation optimization and process control. Existing process modeling approaches have the obstacle of sophisticated laboratory scale experimental setups for model parameter determination and model validation. For a broader application in daily project work, the approach has to be faster and require less effort for non-chromatography experts. Through the extensive advances in the field of artificial intelligence, new methods have emerged to address this need. This paper proposes an artificial neural network-based approach which enables the identification of competitive Langmuir-isotherm parameters of arbitrary three-component mixtures on a previously specified column. This is realized by training an ANN with simulated chromatograms varying in isotherm parameters. In contrast to traditional parameter estimation techniques, the estimation time is reduced to milliseconds, and the need for expert or prior knowledge to obtain feasible estimates is reduced.

Electrochemical Impedance Spectroscopic Studies of PHEV2 form-factor Lithium-ion Cells for Automotive Applications

Prismatic PHEV2 form-factor lithium-ion cells with a nominal capacity of 25 Ah (as used for automotive applications) have been studied with electrochemical impedance spectroscopy (EIS). The data was evaluated using electrical equivalent circuits. Mathematical modeling, system identification, and the determination of model parameters of the lithium-ion cell were also carried out. While a study of the influence of temperature and state-of-charge (SOC) performed using pristine lithium-ion cells showed a nonlinear relationship between temperature and the fits for different RC elements. Finally, the interdependency of parameters determined by EIS has been demonstrated for cells in different aging regimes (cyclic vs. non-cyclic aging) relevant for automotive requirements use cases.

Improved Modified Symbiosis Organisms Search (IMSOS): A New and Adaptive Approach for Determining Model Parameters from Geoelectrical Data

Symbiotic Organisms Search (SOS) is a global optimization algorithm inspired by the natural synergy between the organisms in an ecosystem. The interactive behavior among organisms in nature simulated in SOS consists of mutualism, commensalism, and parasitism strategies to find the global optimum solution in the search space. The SOS algorithm does not require a tuning parameter, which is usually used to balance explorative and exploitative search by providing posterior sampling of the model parameters. This paper proposes an improvement of the Modified SOS (MSOS) algorithm, called IMSOS, to enhance exploitation along with exploration strategies via a modified parasitism vector. This improves the search efficiency in finding the global minimum of two multimodal testing functions. Furthermore, the algorithm is proposed for solving inversion problems in geophysics. The performance of IMSOS was tested on the inversion of synthetic and field data sets from self-potential (SP) and vertical electrical sounding (VES) measurements. The IMSOS results were comparable to those of other global optimization algorithms, including the Particle Swarm Optimization, the Differential Evolution and the Black Holes Algorithms. IMSOS accurately determined the model parameters and their uncertainties. It can be adapted and can potentially be used to solve the inversion of other geophysical data as well.

Analytical solution for large-deposit non-linear reactive flows in porous media

We discuss reactive flows in porous media that exhibit an irreversible chemical reaction between two components, resulting in large solid-product deposition. Previous works used the analytical solution for the linear problem with low deposition to determine model parameters from the reactant breakthrough concentrations and pressure drop growth across the core during laboratory coreflood. The present work derives an exact analytical solution for the non-linear problem with large solid-product deposition. We use the solution for interpretation of the laboratory data, and determination of the type curves for the measured values. Seven sets of experimental data are shown to closely match the data from the analytical model, which validates the analytical model.

Determination of Logistic Model Parameters From AM2 Model for Methane Production for Batch Bioreactors

Modeling the estimation of cumulated methane production generated from batch anaerobic bioreactors is of paramount importance. In this context, there are two main modeling approaches. The first approach is based on developing mathematical expressions representing the processes involved in the bioreactors. The AM1 model is known as the most complete one. However, it is a complicated model as it requires about 80 parameters to be tuned. A model named AM2 which is a simplified version of AM1 has also been developed. It is based on only two microbial growth processes and requires only 13 parameters. Nevertheless, both AM1 and AM2 models do not provide explicit mathematical expressions that enable to estimate the temporal evolution of methane production with respect to the parameters involved in the considered models. These models are much more useful for simulations and graphical visualizations of the dynamical behavior of the state variables including methane production. On the other hand, the second approach suggests semi-empirical or data-driven models which are based on simple explicit mathematical expressions that provide an estimate of the cumulated methane production (Logistic model, Gompertz model, etc.). But, this type of models require the identification of few parameters which are extracted from experimental data. Usually, they are simplistic and use only one growth process and thus cannot exhibit the influence of the many parameters involved in such complex dynamic biotechnological systems. In this paper, to overcome the complexity of the first type of models and to avoid the dependency on experimental data in the second type; an explicit analytical mathematical expression is proposed for estimating the cumulated methane production for batch anaerobic bioreactors. This analytical expression is derived via the adoption of some appropriate approximations performed on the set of differential equations characterizing the AM2 model. Therefore, the proposed analytical expression can be considered as an approximation of the AM2 model itself and this is the first contribution of this paper. Graphical profiles of the cumulated methane production are presented showing that of the proposed logistic expression and that of the AM2 model considered as a reference. To the best of the author’s knowledge, no such approach and result have been encountered in the literature. On the other hand, this expression resembles formally to the semi-empiric logistic model. However, the equivalent parameters of the proposed expression as self-defined by the parameters of the AM2 model and do not require experimental data to be identified as it is for the semi-empiric logistic model. Moreover, by comparing the proposed logistic model to the semi-empiric logistic model, an identification of the parameters of the semi-empiric model is linked to the parameters of the AM2 model, providing more insight into the methane production. This can be considered as a second contribution of this paper.

Full-field inverse identification of elasto-plastic model parameters for ductile cast iron

A key challenge in micromechanical simulation of ductile cast iron is the identification of the constitutive properties of the microstructural constituents. In recent scientific literature, ductile cast iron is predominantly modeled isotropically. Applied methods for the determination of model parameters for ductile cast iron include characterization by nanoindentation as well as inverse modeling approaches. In the present work, effective elastic and plastic model parameters for ferrite were determined by full-field inverse calibration. For that purpose, a stochastic speckle pattern was introduced in tensile specimens by focused ion beam (FIB). In-situ experiments were carried out in a scanning electron microscope and the strain in ferrite was measured by digital image correlation (DIC). A micromechanical model was built from the microstructure of the in-situ experiment. Measured and simulated strains were used in an optimization procedure to identify reliable plasticity parameters of ferrite in ductile cast iron. The results reveal the limitations of modeling the former isotropically.

Advanced Process Analytical Technology in Combination with Process Modeling for Endpoint and Model Parameter Determination in Lyophilization Process Design and Optimization

Lyophilization is widely used in the preservation of thermolabile products. The main shortcoming is the long processing time. Lyophilization processes are mostly based on a recipe that is not changed, but, with the Quality by Design (QbD) approach and use of Process Analytical Technology (PAT), the process duration can be optimized for maximum productivity while ensuring product safety. In this work, an advanced PAT approach is used for the endpoint determination of primary drying. Manometric temperature measurement (MTM) and comparative pressure measurement are used to determine the endpoint of the batch while a modeling approach is outlined that is able to calculate the endpoint of every vial in the batch. This approach can be used for process development, control and optimization.