Variation Of Model Parameters Research Articles

Efficient Modeling of Electrodialysis Process for Waste Water Treatment through Systematic Parameter Estimation

Electrodialysis is a promising electrochemical process for separation and recovery of useful ions from waste waters. The large number of parameters involved in electrodialysis models makes their estimation challenging. This work proposes a systematic sensitivity analysis approach to identify the impact model parameter variations exert on multiple electrodialysis performance indicators, within a wide operating range. This enables the robust mapping of electrodialysis performance toward the direction of maximum variability in the multi-dimensional parametric space and the identification of operating regimes, where the input parameters exhibit the highest sensitivity. Such regimes are used to determine upper and lower limits in the parameter estimation problem. Parameters and ranges exerting insignificant changes on the output electrodialysis indicators are omitted from evaluation, which reduces the associated complexity. The approach is validated against published experimental results obtained for a lead ions removal electrodialysis recirculation batch process. Only four out of nine parameters need to be estimated, while excellent match is observed between experimental and predicted data.

A Pharmacokinetics-Informed Approach to Navigating Hydroxychloroquine Shortages in Patients With Rheumatic Disease During the COVID-19 Pandemic.

ObjectiveThe recent hydroxychloroquine (HCQ) shortage due to use in coronavirus disease 2019 (COVID‐19) has forced some rheumatic disease patients to choose between continuing their current dose of HCQ but exhaust their supply early or ration it in order to prolong its use. Blood HCQ concentrations are directly correlated with disease activity in rheumatic diseases such as systemic lupus erythematosus. We sought to model how changes in HCQ dosage will best maintain sufficient blood HCQ concentrations for the longest period of time in order to avoid potential future flares.MethodsA one‐compartment pharmacokinetic model was used to predict mean blood HCQ concentrations. Monte Carlo simulations with 10‐fold inflated model parameter variance was utilized to assess the impact of variability.ResultsMaintenance of 400 mg/d resulted in mean therapeutic whole‐blood HCQ concentrations that exceeded 700 ng/ml for 10.5 days, whereas HCQ rationing by reducing the dose by half resulted in the mean concentration remaining above 700 ng/ml for 2.4 days (net gain = 8 days). Variability analysis demonstrates that results may differ at the individual level, dependent on baseline blood HCQ concentrations.ConclusionAlthough mean blood concentrations exceed 700 ng/ml for a longer time if patients maintain their full dose of HCQ, more information is needed to fully understand the elimination of HCQ at the patient level, particularly the contribution of tissue stores of HCQ transiting back into the blood.

Modeling and active control of chatter vibrations of piezoelectric stacked machining arm in micro-turning process

Self-excited vibrations known as chatter are considered as the most detrimental issue in micro-turning processes. Occurring unpredictably, they adversely affect the tool life, productivity rate and surface quality of the machining processes. In this paper, a novel machining arm is modeled as a piezoelectric stacked rod which is subjected to a chatter force in the orthogonal micro-turning process. Due to the fact that machining processes are affected by various sources of uncertainties, H∞ robust control approach is used to suppress the chatter vibrations of the machining arm in the presence of tool wear and dynamic model parameter variations. Also, input control force of the system is provided by exciting the input voltage of piezoelectric layers of the rod. In order to be certain that the designed controller succeeds in suppressing vibrations of the effective structural modes, behavior of the first three modes of vibrations are considered in the final response of the machining arm. In the following, performance of the robust H∞ controller is compared with a modified PID controller. Simulation results show that the H∞ controller improves the robustness and performance of the system against uncertainties. The PID controller extends the stability region of the sharp tool and fails to achieve this purpose for the worn tool although its performance is acceptable in suppressing chatter vibrations.

Cost Effectiveness of External Beam Radiation Therapy versus Percutaneous Image-Guided Cryoablation for Palliation of Uncomplicated Bone Metastases

PurposeTo evaluate the cost effectiveness of incorporating cryoablation in the treatment regimens for uncomplicated bone metastases using radiation therapy (RT) in single-fraction RT (SFRT) or multiple-fraction RT (MFRT) regimens. Materials and MethodsA Markov model was constructed using 1-month cycles over a lifetime horizon to compare the cost effectiveness of multiple strategies, including RT followed by RT (RT-RT) for recurrent pain, RT followed by cryoablation (RT-ablation), and cryoablation followed by RT (ablation-RT). RT-RT consisted of 8 Gy in 1 fraction/8 Gy in 1 fraction (SFRT-SFRT) and 30 Gy in 10 fractions/20 Gy in 5 fractions (MFRT-MFRT). Probabilities and utilities were extracted from a search of the medical literature. Costs were calculated from a payer perspective using 2017 Medicare reimbursement in an outpatient setting. Incremental cost effectiveness ratios (ICERs) were calculated using strategies evaluated for willingness-to-pay threshold of $100,000 per quality-adjusted life-year (QALY). To account for model uncertainty, one-way and probabilistic sensitivity analyses were performed. ResultsIn the base case analysis, SFRT-ablation was cost effective relative to SFRT-SFRT at $96,387/QALY. MFRT-ablation was cost effective relative to MFRT-MFRT at $85,576/QALY. Ablation-SFRT and ablation-MFRT were not cost effective with ICERs >$100,000/QALY. In one-way sensitivity analyses, results were highly sensitive to variation in multiple model parameters, including median survival (base: 9 months), with SFRT-SFRT favored at median survival ≤8.7 months. Probabilistic sensitivity analysis examining SFRT-based regimens showed that SFRT-ablation was preferred in 36.9% of simulations at WTP of $100,000/QALY. ConclusionsCryoablation is a potentially cost-effective alternative to reirradiation with RT for recurrent of pain following RT; however, no strategy incorporating initial cryoablation was cost effective.

Do model parameters change under changing climate and land use in the upstream of the Lancang River Basin, China?

ABSTRACT Hydrological model parameters may vary under the impacts of climate and land-use change. This study proposes a hydrological modelling framework based on the Soil and Water Assessment Tool (SWAT) to investigate the variability of model parameters in three different experiments and to assess the impacts of climate and/or land-use change on these parameters in the upstream of the Lancang River Basin, China. In Experiment 1 (E1), most parameters show clear temporal trends under changing climate and land use, implying that model parameters are strongly influenced by their combined effects. Experiments 2 (E2) and 3 (E3) investigate the separate impacts of land-use change and climate change, respectively. Due to the almost invisible changes in land use in E2, there is no change detected in the model parameters. Temporal trends are found in most parameters in E3 and over half of them show consistent trends with E1, which indicates that climate change has greater impacts on model parameter variability. The simulated extreme streamflow and sediment fluxes vary substantially with time-variant parameters, implying that the variations in model parameters do matter for hydrological prediction.

Data-Driven Model-Free Adaptive Current Control of a Wound Rotor Synchronous Machine Drive System

The lack of precise mathematical model of nonlinear systems, such as electric machines, brings certain limits to design a powerful controller for such systems. This article presents a model-free-adaptive-control (MFAC) scheme as a good alternative to the commonly used model-based-control (MBC) methods in order to improve the controller design limitations due to the need for system model information. In this regard, an MFAC is developed in this article for current control of a wound rotor synchronous machine (WRSM) drive system and compared with a proportional-integral (PI) controller as a well-known model-based controller. Since the controller performance can be affected by unexpected phenomena, such as model errors, parameter variations, and changes in operating point, beforehand, the robustness of developed MFAC is first tested under the mentioned conditions in the simulation area. Then, through experimental validation on a specifically designed test bench, a satisfactory performance was obtained for the MFAC scheme.

A Sensitivity Analysis of Two Mesoscale Models: COAMPS and WRF

AbstractA sensitivity analysis methodology recently developed by the authors is applied to COAMPS and WRF. The method involves varying model parameters according to Latin Hypercube Sampling, and developing multivariate multiple regression models that map the model parameters to forecasts over a spatial domain. The regression coefficients and p values testing whether the coefficients are zero serve as measures of sensitivity of forecasts with respect to model parameters. Nine model parameters are selected from COAMPS and WRF, and their impact is examined on nine forecast quantities (water vapor, convective and gridscale precipitation, and air temperature and wind speed at three altitudes). Although the conclusions depend on the model parameters and specific forecast quantities, it is shown that sensitivity to model parameters is often accompanied by nontrivial spatial structure, which itself depends on the underlying forecast model (i.e., COAMPS vs WRF). One specific difference between these models is in their sensitivity with respect to a parameter that controls temperature increments in the Kain–Fritsch trigger function; whereas this parameter has a distinct spatial structure in COAMPS, that structure is completely absent in WRF. The differences between COAMPS and WRF also extend to the quality of the statistical models used to assess sensitivity; specifically, the differences are largest over the waters off the southeastern coast of the United States. The implication of these findings is twofold: not only is the spatial structure of sensitivities different between COAMPS and WRF, the underlying relationship between the model parameters and the forecasts is also different between the two models.

Variable Infiltration-Capacity Model Sensitivity, Parameter Uncertainty, and Data Augmentation for the Diyala River Basin in Iraq

AbstractTo construct a valid model, hydrologists face challenges in determining sensitivity to the forcing data and uncertainty in model parameters. These require basin data and forcing data from d...

Evolution of the disintegration breakage of red-bed soft rock using a logistic regression model

Abstract The disintegration of red-bed soft rock is closely associated with a series of geological disasters. However, research on the evolution of the disintegration breakage has not yet been sufficiently executed. To compensate for this insufficiency, slake durability tests are carried out by three widely used experimental methods to explore the disintegration of red-bed soft rock. Then, based on the logistic regression model, the evolution model of disintegration breakage is derived, and the mathematical meaning of the model parameters (i.e., β 0 and β 1 ) is analysed. The results show that the variation in model parameters corresponds to the process of disintegration breakage. Moreover, the new formulas based on the established evolution model to calculate the disintegration index (including the slake durability index and the disintegration ratio) are presented to replace the traditional expression. Finally, the effects of the test device, drying temperature, and disintegration index on the disintegration breakage of soft rock are studied and discussed. Compared with the slake durability index, the disintegration ratio considering all mesh sizes is a more reasonable index to quantify the disintegration characteristics. The results of the present study are helpful for understanding the evolutionary process of disintegration breakage.

Performance comparison of GRG algorithm with evolutionary algorithms in an aqueous electrolyte system

Excel solver is a powerful tool for optimization of linear and nonlinear problems. With this unique tool, the user can achieve an optimal value for the desired objective function in Excel cell. This solver acts on a group of cells that are directly or indirectly associated with the function; thus, the user-defined values will be optimized. In the present work, 13 existing species in an electrolyte solution have been considered to predict the activity coefficient of inorganic ions in the electrolyte solution, which includes H2O, CO2(aq), H+, Na+, Ba2+, Ca2+, Sr2+, Mg2+, OH–, Cl−, SO4, CO3, HCO3. In this study, to predict the activity coefficient of species in the system, Extended UNIQUAC activity coefficient model was considered and its parameters optimized using Excel solver tool based on GRG algorithm. Total error for optimization of adjustable parameters of Extended UNIQUAC model for 13 desired ions at three temperatures 298.15 K, 323.15 K and 373.15 K with the Excel solver tool was 0.0087. The results of GRG algorithm were favorable than those of ICA, PSO and ABC algorithms. The results of this optimization are intended to predict mineral deposition. The number of adjustable variables (model parameters for optimization) is over 200, and the number of target functions is 39.

Neural networks-based model predictive control for precision motion tracking of a micropositioning system

Micropositioning systems are widely employed in industrial applications. Nonminimum-phase (NMP) is a normal phenomenon in micropositioning system, which leads to a great challenge for control system design. Model predictive control (MPC) is effective in handling the NMP problem. However, the parameter tuning of MPC is quite complicated and time-consuming using traditional methods for motion tracking control implementation. In this paper, an efficient neural networks (NN) model is established to optimize the MPC controller parameters including the prediction horizon, control horizon, and weighting factor. With the developed NN model, the motion tracking process of the micropositioning system is more intelligent and adaptive. The effectiveness of the presented novel NN-MPC control strategy has been verified by conducting extensive simulation studies. Furthermore, the results demonstrate that the NN-MPC scheme has good robustness under model parameter variation and noise condition.

Electro Pneumatic Control System for Inverted Pendulum

Abstract The paper concerns the inverted pendulum control system with using pneumatic cylinder. A mathematical model of the pendulum used to derive the LQG controller was presented. Prepared laboratory stand was presented and described in detail. The main purpose of the work was experimental researches. A number of control process tests were conducted with variable model parameters such as additional mass, injected disturbances and so on. The results were shown on the time plots of the control object states.

Quantification of embodied energy and carbon footprint of pervious concrete pavements through a methodical lifecycle assessment framework

This study provided a systematic lifecycle assessment (LCA) methodology that was used to quantify the total embodied energy and greenhouse gas (GHG) emissions involved in construction of a new pavement. Furthermore, a comparative LCA was performed to adjudge the benefits offered by pervious concrete pavement (PCP) over Portland cement concrete pavement (PCCP) for different mixing procedures, and distinct base layers. The system boundaries included the following phases of pavement LCA: raw material production, transportation, and pavement construction. The total embodied energy and GHG emissions were quantified for a functional unit of 1 km single lane road, 3.5 m wide, and 0.35 m thickness. The relative contribution of each component to energy consumption and GHG emissions was estimated and the variability of LCA model parameters was understood using sensitivity analysis. The results dictated that the total embodied energy and GHG emissions in PCP with aggregate base layer were reduced by up to 3% and 2.7%, respectively, compared to that of PCCP with similar configuration. Further, a capital cost analysis was conducted due to non-availability of maintenance and end-of-life data, which indicated that PCP is almost 1.21% costlier than PCCP for RMC mixing, and 4.13% cheaper for in-situ mixing. The conceptual approach developed in this study provided a generalized methodical framework that could be used to assess the environmental credibility of pavement systems for site-specific conditions.

Prediction and parameter uncertainty for winter wheat phenology models depend on model and parameterization method differences

Crop phenological development models are fundamental tools that can be used for scheduling agricultural practices and predicting crop yields. In previous research, different crop phenology models were compared, and different parameterization methods for crop model calibration were evaluated, which revealed that both model structures and parameterization methods were important for crop modeling. However, few studies have considered the combination of both factors and compared these influences simultaneously. Therefore, information regarding the extent of variation in model accuracy and uncertainty depending on model structure and parameterization method is lacking. In this study, we developed three winter wheat phenology models with different structures, i.e., the Agricultural Production System Simulator model for wheat (APSIM-wheat), Wang and Engel model, and sigmoid and exponential function based model, to predict the heading date as a case study. We calibrated these models using three different parameterization methods (augmented Lagrange multiplier method, Nelder-Mead method, and Bayesian optimization with Gaussian process) to investigate their effects on model accuracy and uncertainty. Six-fold cross validation of nine combinations of model calibration (3 models × 3 parameterizations) and their validation revealed that accuracies ranged mostly from 2 to 7 days in the root mean square error (RMSE). The coefficient of variation of RMSE varied widely in among model structures and parameterization methods (∼0.01–0.6). Furthermore, the coefficient of variation of model parameters also varied substantially depending both on model structure and parameterization method. Especially for the model with more parameters, we found that the prediction and parameter stability varied depending on parameterization methods. These findings suggest that both prediction and parameter uncertainty varied with model structure and parameterization method and emphasize the importance of which models and parameterization methods modelers use for robust crop phenology model.

A combined reduced order‐full order methodology for the solution of 3D magneto‐mechanical problems with application to magnetic resonance imaging scanners

SummaryThe design of a new magnetic resonance imaging (MRI) scanner requires multiple numerical simulations of the same magneto‐mechanical problem for varying model parameters, such as frequency and electric conductivity, in order to ensure that the vibrations, noise, and heat dissipation are minimized. The high computational cost required for these repeated simulations leads to a bottleneck in the design process due to an increased design time and, thus, a higher cost. To alleviate these issues, the application of reduced order modeling techniques, which are able to find a general solution to high‐dimensional parametric problems in a very efficient manner, is considered. Building on the established proper orthogonal decomposition technique available in the literature, the main novelty of this work is an efficient implementation for the solution of 3D magneto‐mechanical problems in the context of challenging MRI configurations. This methodology provides a general solution for varying parameters of interest. The accuracy and efficiency of the method are proven by applying it to challenging MRI configurations and comparing with the full‐order solution.

EEG classification across sessions and across subjects through transfer learning in motor imagery-based brain-machine interface system

Transfer learning enables the adaption of models to handle mismatches of distributions across sessions or across subjects. In this paper, we proposed a new transfer learning algorithm to classify motor imagery EEG data. By analyzing the power spectrum of EEG data related to motor imagery, the shared features across sessions or across subjects, namely, the mean and variance of model parameters, are extracted. Then, select the data sets that were most relevant to the new data set according to Euclidean distance to update the shared features. Finally, utilize the shared features and subject/session-specific features jointly to generate a new model. We evaluated our algorithm by analyzing the motor imagery EEG data from 10 healthy participants and a public data set from BCI competition IV. The classification accuracy of the proposed transfer learning is higher than that of traditional machine learning algorithms. The results of the paired t test showed that the classification results of PSD and the transfer learning algorithm were significantly different (p = 2.0946e-9), and the classification results of CSP and the transfer learning algorithm were significantly different (p = 1.9122e-6). The test accuracy of data set 2a of BCI competition IV was 85.7% ± 5.4%, which was higher than that of related traditional machine learning algorithms. Preliminary results suggested that the proposed algorithm can be effectively applied to the classification of motor imagery EEG signals across sessions and across subjects and the performance is better than that of the traditional machine learning algorithms. It can be promising to be applied to the field of brain-computer interface (BCI). Graphical abstract.

Steady-State Analysis of Microgrid Distributed Control Under Denial of Service Attacks

Distributed control is an effective method to coordinate multiple components in a microgrid. It usually has mandatory requirements for communication graph topology; changing the topology unexpectedly could disrupt the operation of a distributed controller. A denial of service (DoS) attack blocks communication channels to cause variations in communication graph. Unforeseen topology changes render a controller to receive incomplete system information. Control decisions made based on incorrect information can lead to significantly reduced grid supporting capability. As a result, the vital power balance between generation and load may be challenged. Determining power balance requires steady-state power flow analysis; however, the cyber-induced steady-state analysis is not well studied in the literature. In this article, we show that a cyber–physical microgrid with unknown DoS attacks has augmented uncertain power flow equations, whose special structure creates new computational issue to determining power balance. To deal with the issue, we develop a novel cyber-induced microgrid model: the cyber–physical system is transformed into a pure physical model, where the disruption effects of DoS in the cyber layer are shown to be equivalent to the variations of the physical model parameters. With the transformed system, we develop a sufficient condition to ascertain the system power balance under unknown DoS attacks. In addition, practical insights for resilient distributed control design are shown as well.

Dual Stage Normalization Approach Towards Classification of Breast Cancer

ABSTRACTBreast cancer is a major concern among women that causes high risk of death. Early diagnosis of such cancer becomes challenging due to alterations in the color of the histopathological breast images. This study uses a publicly available dataset of breast cancer histopathology images. This paper introduces a dual stage normalization approach, to address the color variation problem of biopsy specimen collectively caused by incompatible staining in biopsy process and bizarre imaging quality. The dual stage normalization proposed here consists of a stain normalization unit and a light normalization unit. This system addresses the variations of both imaging and staining of specimen that are caused by a microscopic imaging setup. Later on, eight features have been extracted from the normalized images and used for the classification of breast cancer (benign and malignant). The overall accuracy of the back propagation algorithm (BPA) classifier is obtained as 81.8%. After comparison with other classifier accuracies, BPA classifier is found to be acceptable. Recall and precision values are approximately 89% and 90%, respectively, which is acceptable. The saturation-weighted hue statistics produces balanced and uniform color hues for stain normalization. This statistic is powerful against variations in model parameters and unsusceptible to image subjects and achromatic colors. This normalization technique retains all histological data with an enhanced performance.

Approach to Numerical Implementation of the Drift-Diffusion Model of Field Effects Induced by a Moving Source

An approach to numerical implementation of the diffusion-drift model is described with application to problem of estimating field effects induced in an object by a concentrated moving source. The mathematical model is formalized using initial boundary-value problem for multidimensional evolution equation of convection-reaction-diffusion type. The computational algorithm is based on modification of the predictor-corrector scheme intended for solving the diffusion problem and approximation of the drift component by the Roberts–Weiss scheme. The mathematical model has been implemented in the Matlab software package. The results of computing experiments attendant to variations of the control model parameters are presented.

Improving Hydrological Models With the Assimilation of Crowdsourced Data

AbstractSmall streams often lack reliable hydrological data. Environmental agencies play a key role in providing such data; however, these agencies are often challenged by the growing monitoring needs and lack of funding. Given the spatial mismatch between observed data and small watersheds/headwaters, local volunteers can act as potentially valuable research partners. We examine how CrowdHydrology, a citizen science program that collects stream stage and stream temperature observations, improves a hydrologic model of the Boyne River, Michigan, USA. Volunteers provided observations at four calibration sites with different interarrival times of the observations. We tested whether stream stage and stream temperature observations (measured by volunteers) improved the performance of a Soil and Water Assessment Tool (SWAT) model of the Boyne River. Observations were integrated into the model using the ensemble Kalman filter. This framework allowed us to integrate observation error, track the variability of model parameters, and simulate daily streamflow and stream temperature across the watershed. Measures of daily model performance included the Nash‐Sutcliffe efficiency, modified Nash‐Sutcliffe efficiency (Ef‐mod), refined index of agreement (dr), and relative bias (Bias). For all calibration sites, estimates of streamflow improved after data assimilation compared to simulations based on initial/default SWAT parameters. Different measures of model performance emerged based on the interarrival times of the observations. Results demonstrate that observations collected by local volunteers, with a certain temporal resolution, can improve SWAT hydrological models and capture central tendency.