Values Of Model Parameters Research Articles

A deterministic transmission model for analytics-driven optimization of COVID-19 post-pandemic vaccination and quarantine strategies.

This study developed a deterministic transmission model for the coronavirus disease of 2019 (COVID-19), considering various factors such as vaccination, awareness, quarantine, and treatment resource limitations for infected individuals in quarantine facilities. The proposed model comprised five compartments: susceptible, vaccinated, quarantined, infected, and recovery. It also considered awareness and limited resources by using a saturated function. Dynamic analyses, including equilibrium points, control reproduction numbers, and bifurcation analyses, were conducted in this research, employing analytics to derive insights. Our results indicated the possibility of an endemic equilibrium even if the reproduction number for control was less than one. Using incidence data from West Java, Indonesia, we estimated our model parameter values to calibrate them with the real situation in the field. Elasticity analysis highlighted the crucial role of contact restrictions in reducing the spread of COVID-19, especially when combined with community awareness. This emphasized the analytics-driven nature of our approach. We transformed our model into an optimal control framework due to budget constraints. Leveraging Pontriagin's maximum principle, we meticulously formulated and solved our optimal control problem using the forward-backward sweep method. Our experiments underscored the pivotal role of vaccination in infection containment. Vaccination effectively reduces the risk of infection among vaccinated individuals, leading to a lower overall infection rate. However, combining vaccination and quarantine measures yields even more promising results than vaccination alone. A second crucial finding emphasized the need for early intervention during outbreaks rather than delayed responses. Early interventions significantly reduce the number of preventable infections, underscoring their importance.

BAYESIAN CALIBRATION WITH ADAPTIVE MODEL DISCREPANCY

We investigate a computer model calibration technique inspired by the well-known Bayesian framework of Kennedy and O'Hagan (KOH). We tackle the full Bayesian formulation where model parameter and model discrepancy hyperparameters are estimated jointly and reduce the problem dimensionality by introducing a functional relationship that we call the full maximum a posteriori (FMP) method. This method also eliminates the need for a true value of model parameters that caused identifiability issues in the KOH formulation. When the joint posterior is approximated as a mixture of Gaussians, the FMP calibration is proven to avoid some pitfalls of the KOH calibration, namely missing some probability regions and underestimating the posterior variance. We then illustrate two numerical examples where both model error and measurement uncertainty are estimated together. Using the solution to the full Bayesian problem as a reference, we show that the FMP results are accurate and robust, and avoid the need for high-dimensional Markov chains for sampling.

Not all subhaloes are created equal: modelling the diversity of subhalo density profiles in TNG50

ABSTRACT In this work, we analyse the density profiles of subhaloes with masses Msh ≥ 1.4 × 108 M⊙ in the TNG50 simulation, with the aim of including baryonic effects. We evaluate the performance of frequently used models, such as the standard Navarro–Frenk–White (NFW), the Einasto, and a smoothly truncated version of the NFW profile. We find that these models do not perform well for the majority of subhaloes, with the NFW profile giving the worst fit in most cases. This is primarily due to mismatches in the inner and outer logarithmic slopes, which are significantly steeper for a large number of subhaloes in the presence of baryons. To address this issue, we propose new three-parameter models and show that they significantly improve the goodness of fit independently of the subhalo’s specific properties. Our best-performing model is a modified version of the NFW profile with an inner log-slope of −2 and a variable truncation that is sharper and steeper than the slope transition in the standard NFW profile. Additionally, we investigate how both the parameter values of the best density profile model and the average density profiles vary with subhalo mass, Vmax, distance from the host halo centre, baryon content, and infall time, and we also present explicit scaling relations for the mean parameters of the individual profiles. The newly proposed fit and the scaling relations are useful to predict the properties of realistic subhaloes in the mass range 108 M⊙ ≤Msh ≤ 1013 M⊙ that can be influenced by the presence of baryons.

Identification of switched dynamic system for electric multiple unit train modeling

A new iterative algorithm is proposed to identify the high-speed train model which is described as a switched dynamic system including a fast switched nonlinear static subsystem followed by a non-switched linear dynamic subsystem. The non-switched property of the linear part leads to a more complex dynamics of the whole system, compared to the switched Hammerstein system which linear part switches synchronously with its nonlinear part. The proposed iterative algorithm alternates between identifying the switched nonlinear static subsystem and the linear dynamic subsystem. The switched nonlinear subsystem identification incorporates a quadratic error bound constraint with respect to nominal values of model parameters, and is formulated as a second-order cone program. Properties of convergence and asymptotic unbiasedness of the obtained estimates are proved under some necessary assumptions. With real data and simulation examples, the proposed iterative algorithm produces convergent and asymptotically unbiased estimates. In the simulation results, incorporating the quadratic error bounds results in a fast convergence rate in the presence of a small noise variance, and also produces a smaller identification error in the presence of colored noises with a non-zero mean and a large variance.

Bifurcation analysis of multistability and hysteresis in a model of HIV infection

Bifurcation analysis of multistability and hysteresis in a model of HIV infection

Adapting a process-oriented cold hardiness model to conifers

Plant phenology has been and continues to be impacted by climate change. Process-oriented modeling of phenology reveals biological characteristics through interpretation of model results and parameter values. This paper aims to implement a cold hardiness model using historical Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) data, determine if model results improve with data clustering by seed source or growing location, and interpret model results into biological meaning. These interpretations have applications for reforestation and studies of plant phenological response to climate change.Cold hardiness data were compiled through literature review. A dynamic process-oriented cold hardiness model driven by thermal time based on daily temperatures was applied to multiple data clustering scenarios. Model results were analyzed for goodness of fit to determine model error, efficiency, and bias. A sensitivity analysis was performed to determine parameter sensitivity, and further validate the model calibration.Data clustering by seed source improved fit compared to clustering by growing location or no clustering, when applied to the full dataset. Using only temperature inputs, model results had low error when data modeled were similar. Results show that for cold hardiness acclimation a linear growing degree function with a threshold of 10 °C was adequate across testing data, as was an upper limit cold hardiness temperature of –3 °C.Interpretation of model results show that both acclimation to growing sites and seed source genetics impact cold hardiness response, though clustering by seed source improved model performance. This model could be applied to mitigate cold related risks to seedlings during production and establishment, and serve as a template for predicting phenological responses in simulated future climate scenarios.

Multiresolution Representation of Geoelectric Model and Parameter Operation in Wavelet Domain

In this paper, an algorithm for wavelet domain geoelectric model representation and parameter manipulation is introduced, which can be utilized for multiresolution inversions of geophysical data based on tree structure. With proper wavelet choice, the model can be represented by much less parameters than traditional model representation, expecting to reduce the computational cost for inversion remarkably. The use of tree structure to represent the wavelet coefficients makes the parameter operations (e.g., perturbating model parameter and adding more details in a model) in inversion much simpler. Introducing detail in a model or removing detail from a model can be simply realized by adding nodes to or deleting nodes from the existing tree structure. The model parameter value can be perturbated by changing the tree node values. Our algorithm is general and can be applied to develop any geophysical inversions in wavelet domain with trivial additional work once the forward modeling and inversion algorithm is available.

Technique for Iterative Refinement of Parameter Values in Analytical Models of Microelectronic Devices

Technique for Iterative Refinement of Parameter Values in Analytical Models of Microelectronic Devices

Non-Parametric Model-Based Estimation of the Effective Reproduction Number for SARS-CoV-2

Describing viral outbreaks, such as the COVID-19 pandemic, often involves employing compartmental models composed of ordinary differential equation (ODE) systems. Estimating the parameter values for these ODE models is crucial and relies on accessible data. To accurately represent realistic pandemic scenarios with diverse situations, it is necessary to consider model parameters as time dependent. However, estimating such time-dependent parameters, like transition rates in compartmental models, is notoriously challenging due to the unknown function class of these parameters. In this study, we propose a novel approach by using an Augmented Kalman Smoother (AKS) combined with an Expectation-Maximization (EM) algorithm to simultaneously estimate all time-dependent parameters in an SIRD compartmental model. Our approach can be applied to general ODE systems with time-varying parameters, requiring no prior knowledge of model parameters or additional assumptions on the function class of the ODE time dependencies. A key advantage of our method compared to other methods is that it does not require assumptions about the parameterization of the serial interval distribution for estimating SIRD model parameters. Applying our approach to COVID-19 data in Germany, we adequately describe time-series data with strong fluctuations and multiple waves, obtaining non-parametric model-based time-course estimates for the effective reproduction number.

Simulation of gravity- and pump-driven perfusion techniques for measuring outflow facility of ex vivo and in vivo eyes.

Aqueous humor dynamics are commonly assessed by infusing fluid into the eye and measuring intraocular pressure (IOP). From the pressure-flow relationship, conventional outflow facility is estimated to study glaucomatous processes that lower facility or identify therapeutics that enhance facility in hopes of restoring healthy IOP levels. The relative merits and limitations of constant flow (CF), gravity-driven constant pressure (CPg), and pump-driven constant pressure (CPp) infusion techniques were explored via simulations of a lumped parameter viscoelastic model of the eye. Model parameter values were based on published perfusion system properties and outflow facility data from rodents. Step increases in pressure or flow were simulated without and with IOP noise recorded from enucleated eyes, anesthetized animals, and conscious animals. Steady-state response levels were determined using published window and ratio criteria. Model simulations show that all perfusion techniques estimate facility accurately and that ocular fluid dynamics set a hard limit on how fast measurements can be taken. This limit can be approached with CPg and CPp systems by increasing their gain but not with CF systems, which invariably take longest to settle. Facility experiment duration is further lengthened by inclusion of IOP noise, and data filtering is needed for steady-state detection with in vivo noise. The ratio criterion was particularly affected because noise in the flow data is amplified by the higher gain of CPg and CPp systems. A recursive regression method is introduced, which can ignore large transient IOP fluctuations that interfere with steady-state detection by fitting incoming data to the viscoelastic eye model. The fitting method greatly speeds up data collection without loss of accuracy, which could enable outflow facility measurements in conscious animals. The model may be generalized to study response dynamics to fluid infusion in other viscoelastic compartments of the body and model insights extended to optimize experiment design.

A domain-dependent stability analysis of reaction–diffusion systems with linear cross-diffusion on circular domains

In this study, we present theoretical considerations of, and analyse, the effects of circular geometry on the stability analysis of semi-linear parabolic PDEs of reaction–diffusion type with linear cross-diffusion for a two-component system on circular domains. The highlights of our theoretical and computational findings are: (i) By employing linear stability analysis for a two-component reaction–diffusion system with linear cross-diffusion on circular disc domains, we derive necessary and sufficient conditions for the system to exhibit cross-diffusion driven-instability, dependent on the length scale of the geometry. These analytical studies involve cross-diffusion and circular geometry to unravel analytical conditions for the full computational classification of the parameter spaces that allow the system to exhibit Turing, Hopf and transcritical patterns. (ii) We compute parameter spaces on which patterns are formed only due to linear cross-diffusion as well as due to a critical domain length. These spaces do not exist in the absence of cross-diffusion nor when the conditions on the domain length are violated. (iii) To support our theoretical findings, finite element simulations illustrating the formation of spot patterns on circular domains are presented. Model parameter values are selected from parameter spaces that are induced by cross-diffusion, thereby supporting linear cross-diffusion coupled with reaction–diffusion theory as a candidate mechanism for pattern formation. (iv) A by-product of this study, is that an activator-depleted reaction–diffusion system with linear cross-diffusion on circular domains, appears to favour the formation of spot patterns for most of the parameter values chosen. Such patterns are reminiscent of those observed on stingrays, which form on approximately circular domains during growth development.

Comparative analysis of kinetic realizations of insulin signaling

Several studies have developed dynamical models to understand the underlying mechanisms of insulin signaling, a signaling cascade that leads to the translocation of glucose, the human body’s main source of energy. Fortunately, reaction network analysis allows us to extract properties of dynamical systems without depending on their model parameter values. This study focuses on the comparison of insulin signaling in healthy state (INSMS or INSulin Metabolic Signaling) and in type 2 diabetes (INRES or INsulin RESistance) using reaction network analysis. The analysis uses network decomposition to identify the different subsystems involved in insulin signaling (e.g., insulin receptor binding and recycling, GLUT4 translocation, and ERK signaling pathway, among others). Furthermore, results show that INSMS and INRES are similar with respect to some network, structo-kinetic, and kinetic properties. Their differences, however, provide insights into what happens when insulin resistance occurs. First, the variation in the number of species involved in INSMS and INRES suggests that when irregularities occur in the insulin signaling pathway, other complexes (and, hence, other processes) get involved, characterizing insulin resistance. Second, the loss of concordance exhibited by INRES suggests less restrictive interplay between the species involved in insulin signaling, leading to unusual activities in the signaling cascade. Lastly, GLUT4 losing its absolute concentration robustness in INRES may signify that the transporter has lost its reliability in shuttling glucose to the cell, inhibiting efficient cellular energy production. This study also suggests possible applications of the equilibria parametrization and network decomposition, resulting from the analysis, to potentially establish absolute concentration robustness in a species.

Modelling faecal microbe dynamics within stormwater constructed wetlands

Modelling faecal microbe levels is performed widely in natural and wastewater wetlands, yet these predictions can be challening due to highly stochastic storm events. In our study, a coupled hydrodynamic and microorganism model was developed and tested to predict the long-term faecal microbial removal in stormwater constructed wetlands. The microorganism model simulates the fate and transport of the faecal indicator organism Escherichia coli (E. coli), resolving advection-dispersion, sedimentation, resuspension and die-off based on temperature and UV exposure. The model was tested using a two-year monitoring dataset collected from Troups Creek wetland, a multiple-inflow stormwater wetland in Melbourne, Australia. The model parameter values applied in the coupled model were based on a mixture of site-specific data and values obtained from literature. The only adjusted parameter in our microorganism model was the die-off rate in dark conditions in the stormwater wetlands. An urban stormwater microorganism model, MOPUS, was used to generate continuous catchment E. coli loading rates as input to the wetland. The hydrodynamic model was evaluated using flow rate monitored at the outlet weir, achieving Nash- Sutcliffe Efficiency (E) values of 0.86 over the two-year monitoring period. The E. coli model was tested using outflow E. coli concentration data and achieved an overall E of 0.37. The performance of the microbial model was variable across the 22 monitored events, with E ranging from <0 to 0.8. Sensitivity tests were performed to evaluate the model outputs and the results indicated that (a) the importance of collecting high-quality data for stormwater inputs into wetlands and (b) the importance of accurate estimation of the die-off rate in wetland microbial removal models. Our research showed that this model can be used to help design and rectify stormwater constructed wetlands for better faecal microbial removal, vegetation maintenance and support future real-time decision-making.

A machine learning approach to automate ductile damage parameter selection using finite element simulations

Ductile damage models require constitutive model parameter values that are difficult to derive experimentally or analytically. The calibration procedure for ductile damage model parameters, typically performed manually, is labour-intensive. In this work we detail a fully autonomous framework that integrates Bayesian optimisation and finite element analysis to identify ductile damage model parameters. The framework detailed here selects ductile damage model parameters from inputs that can be derived from a simple tensile test. This framework has been successfully deployed to three datasets of P91 material tested at ambient (20°C) and higher (500°C) temperatures. The Bayesian optimisation derived material model parameters result in simulated output with less than 2% error compared to experimental data. This research demonstrates that algorithm hyperparameters can significantly affect the Bayesian optimised ductile damage parameter values resulting in non-unique ductile damage parameters. We show that the non-unique solutions can be further assessed using a second test geometry.

Application of Generalized Poisson Regression for the development of modeling motorcycle accidents based on rider characteristics

Motorcycles are a mode of transportation that has experienced many traffic accidents in developing countries. This incident can occur intentionally or not and cause great loss of life and property. Therefore, this work aims to develop a model of the number of motorcycle accidents based on driver characteristics using Generalized Poisson Regression in East Java Province, Indonesia. The modeling can identify the relationship between the number of motorcycle accidents with variables that contribute to accident based on driver characteristics. This characteristic is a combination of socioeconomic factors and movements. Research analysis includes descriptive statistics and accident modeling using Generalized Poisson Regression Models. Model parameter values are estimated using the Maximum Likelihood Estimation (MLE) approach. The optimal solution for parameter estimation uses the Newton-Raphson algorithm. The results found two models and then selected the best model. Based on the parameter significance test, the population density, the percentage of low education, the gender ratio, and the percentage of accident perpetrators without a driver’s license significantly impact the number of motorcycle accidents in East Java Province.

Studying a time‐fractional moving boundary diffusion model of solvent through a glassy polymer: A computational approach

AbstractIn this article, a time‐fractional nonlinear moving boundary problem is considered to express the mathematical model of diffusion of a solvent into a glassy polymeric device. A numerical method, including an iterative process based on a non‐standard implicit finite difference method with variable time‐step is developed to solve this problem. The purpose of solving this problem is to study and predict the solvent diffusion behavior in the polymer and the position of the moving boundary. We analyze the monotonicity and positivity of the numerical solution. To show the effectiveness and robustness of the numerical method, two test problems are considered and the numerical results are reported for different values of model parameters.

Region and cloud regime dependence of parametric sensitivity in E3SM atmosphere model

The Department of Energy (DOE)’s Energy Exascale Earth System Model (E3SM), including its atmosphere model (EAM), has many relatively new features. In a previous study we conducted a systematic parametric sensitivity analysis for EAM based on short, perturbed parameter ensemble (PPE) simulations, mainly focusing on global mean climate features and metrics. While parameter values in global climate models are generally invariant in space and time, model response to parameters perturbation may vary by regions and climate regimes, which motivates the need to better understand the EAM model behaviors and physics at regional scale and process level. In this study, using the same set of PPE simulations and a similar sensitivity analysis framework, we identify parameters that cause largest sensitivities over different regions and compare model responses in fast atmospheric processes to the parameters across different cloud regimes for several important cloud-related fidelity metrics. We find that cloud forcing has opposite response to some parameters over mid-latitude vs. tropical land. We also analyze how the parametric sensitivity varies as stratocumulus transitions to shallow convection and to deep convection over ocean. Low cloud forcing and shortwave cloud forcing in the subtropical eastern Pacific are most sensitive to the parameters controlling the width of the probability density function (PDF) of the subgrid vertical velocity (w’) (gamma) and the damping of the w’ skewness (c8) near the coast but become more sensitive to the parameter affecting the damping of the w’ variance (c1) further offshore. Detailed interpretation of the spatial dependence of parametric sensitivity is provided. We also investigate how the parametric sensitivity evolves with prediction duration. This study improves our process-level understanding of cloud physics and parameterization and provides insights for developing more advanced regime-aware parameterization schemes in global climate model.

Optimal mass variables for semivisible jets

Strongly coupled hidden sector theories predict collider production of invisible, composite dark matter candidates mixed with standard model hadrons in the form of semivisible jets. Classical mass reconstruction techniques may not be optimal for these unusual topologies, in which the missing transverse momentum comes from massive particles and has a nontrivial relationship to the visible jet momentum. We apply the artificial event variable network, a semisupervised, interpretable machine learning technique that uses an information bottleneck, to derive superior mass reconstruction functions for several cases of resonant semivisible jet production. We demonstrate that the technique can extrapolate to unknown signal model parameter values. We further demonstrate the viability of conducting an actual search for new physics using this method, by applying the learned functions to standard model background events from quantum chromodynamics.

Parameter identification of solar cells using improved Archimedes Optimization Algorithm

The parameters of solar cells for five PV models are identified using an Improved Archimedes Optimization Algorithm (IAOA) in this paper. Two modifications are made to the original Archimedes Optimization Algorithm (AOA). To control the unequal exploration and exploitation phases, the initial adjustment is to incorporate an augmented density decreasing factor. A random average calculation between the current object position and the best object position is implemented for the second modification to solve the local optima issue. The proposed IAOA is then used to tackle the problem of identifying PV model parameters from experimental I-V data. Different PV models, such as the one-diode model (ODM), the two-diode model (TDM), and the PV module model (PMM), have been distinguished using the suggested IAOA. The proposed IAOA outperforms other present algorithms and even outperforms the original AOA based on the revealed results. As closely as feasible to the experimental I-V data of real PV solar cells and module models, the proposed IAOA can choose the best parameter values for PV models.

Bayesian inference of the impulse-response model of athlete training and performance

ABSTRACT The Banister impulse-response (IR) model was designed to predict an athlete’s performance ability from their past training. Despite its long history, the model’s usefulness remains limited due to difficulties in obtaining precise parameter estimates and performance predictions. To help address these challenges, we developed a Bayesian implementation of the IR model, which formalises the combined use of prior knowledge and data. We report the following methodological contributions: 1) we reformulated the model to facilitate the specification of informative priors, 2) we derived the IR model in Bayesian terms, and 3) we developed a method that enabled the JAGS software to be used while enforcing parameter constraints. To demonstrate proof-of-principle, we applied the model to the data of a national-class middle-distance runner. We specified the priors from published values of IR model parameters, followed by estimating the posterior distributions from the priors and the athlete’s data. The Bayesian approach led to more precise and plausible parameter estimates than nonlinear least squares. We conclude that the Bayesian implementation of the IR model shows promise in addressing a primary challenge to its usefulness for athlete monitoring.