Model Parameters Research Articles

Analysis of the Utility Tunnel Joint's Stress Characteristics Under Well Point Precipitation Conditions

The method by which groundwater influences subsurface buildings is relatively complex in the groundwater-rich regions of southern China. Notably, it substantially affects the pipe corridor junctions, which are crucial to impermeability. In order to analyze the forces on the utility tunnel joints during well-point precipitation, numerical modeling of the utility tunnel joints using the well-point precipitation method based on Finite element software was carried out, and a precipitation model tank was made to verify the model parameters and boundary restrictions. The results indicate that under well-point precipitation, the force variation of the bottom plate is more significant than that of the top plate at the same port and that in the joint section close to the precipitation point, the force variation of the bottom plate can reach 2-3 times that of the top plate. The spigot joint's bottom plate has a maximum tensile stress of 319.52 kPa, which raises the possibility of tensile damage. The well-point dewatering method has a higher likelihood of bottom plate cracks, which makes it a critical component to be watched during construction and operation.

Simulation of fuel cell membrane durability under vehicle operation

In this work, a statistical fuel cell chemo-mechanical membrane degradation model is developed based on the ionomer fibrillar morphology as a framework for use-level membrane durability prediction for fuel cell electric vehicles. The mechanical and chemical degradation modes are separately calibrated with pressure differential-accelerated mechanical stress tests and accelerated membrane durability tests, respectively. Finite element simulations are used to estimate the initial stress distribution across the membrane, while the genetic algorithm with the least squares method is employed to calibrate the model parameters with experimental results, thus reaching a good agreement. Next, the validated model is utilized for a case study of fuel cell electric transit bus operation in the city of Victoria, B.C., Canada. The initial cell voltage profile is obtained using a dynamic fuel cell power system model applied to the transit bus drive cycle recorded during real-world operation. According to the model predictions, reducing the stack nominal power from 396 to 132 kW results in a 148% membrane lifetime enhancement, whereas decreasing the cell temperature from 90 to 70°C results in an 11-fold increase in the membrane lifetime under simulated transit bus operation, thereby exceeding the 25,000-h lifetime target for this application.

Bubble Point Measurements of cis-1,1,1,4,4,4-Hexafluorobutene [R-1336mzz(Z)] + trans-1,2-Dichloroethene [R-1130(E)] mixtures

Saturation pressures of pure R-1336mzz(Z) and R-1130(E) and bubble point pressures of three R-1336mzz(Z)/1130(E) blends were measured from 265 K to 360 K. For each pure refrigerant or refrigerant blend, a total of twenty unique saturation pressures or bubble points were measured. In total 100 unique state points were obtained. Presently, no Helmholtz-energy-explicit type equation of state (EoS) is available for R-1130(E). While an extended corresponding states EoS for R-1130(E) is available to estimate the properties of the R-1336mzz(Z)/1130(E) blend, this model does not resolve the azeotropic behavior of the mixture. Therefore, the perturbed-chain statistical-associating fluid theory (PC-SAFT) EoS is used to model the vapor–liquid equilibria of the R-1336mzz(Z)/1130(E) blend. PC-SAFT model parameters are reported, and the overall performance of the model is characterized by deviations from the experimental data.

Dynamical system analysis of LRS-BI Universe with [formula omitted] gravity theory

Considering the Universe as a dynamic system, the understanding of its evolution is an interesting aspect of study in cosmology. Here, we investigate the anisotropic locally rotationally symmetric (LRS) Bianchi type-I (LRS-BI) spacetime under the f(Q) gravity of symmetric teleparallel theory equivalent to the GR (STEGR) as a dynamical system and try to understand the role of anisotropy in the evolution of its various phases. For this work, we consider two models, viz., f(Q)=−(Q+2Λ) and f(Q)=−βQn to study the critical points and stability of the LRS-BI Universe. In both the models, it is found that the various phases like radiation-dominated, matter-dominated, and dark energy-dominated phases are heteroclinically connected, and there is some role of anisotropy in the evolution of these phases of the Universe. However, for both the models, there are some unphysical solutions too depending upon the values of model parameters β and n along with the anisotropic parameter α.

Accuracy and uncertainty of predicted maximum and residual displacements of RC bridge columns under earthquake excitations

Fundamental to the performance-based seismic design is the accuracy of predicted responses under earthquake excitations. This study evaluates the accuracy and uncertainty of predicted maximum and residual displacements of concrete bridge columns subjected to different types of ground motions. A series of nonlinear time-history analyses considering a wide range of element formulations and model parameter combinations was performed to reproduce measured responses from previous shake table tests. Variations in element formulation had little influence on the accuracy and uncertainty of predicted maximum displacements. The gradient inelastic force-based element, however, predicted bar tensile strain profiles across the plastic hinge with higher resolution, but at a higher computational cost than displacement-based and beam with hinges elements. Models with tangent stiffness-based Rayleigh damping produced the most accurate predictions of the maximum drift ratios with an RMSE of 0.011, indicating that the maximum displacement (or drift ratio) can be predicted with reasonable accuracy. Residual displacements, on the other hand, were predicted with unreasonable levels of error and uncertainty. A data-driven model was thus proposed to correct predicted residual displacement. Following correction, the RMSE of the predicted residual drift ratios was reduced by 43 % to 0.006.

Sensitivity analysis and calibration for a two-dimensional state-space model of metal hydride storage tanks based on experimental data

In this paper, a state-space model of a metal hydride tank is formulated and analyzed in detail. Firstly, a three-dimensional state-space model of the metal hydride tank is simplified by assuming that the tank temperature can be measured. Secondly, the model is completed with a pipe model, which allows to compute the input flow from the pipe pressure that can be easily measured. The proposed model solves the problem that the conventional metal hydride tank models usually take the mass flow rate as input and ignore the pressure as system input. Thirdly, the first-order trajectory sensitivity analysis method is adopted to determine the sensitivity of selected unknown parameters. Latter, the particle swarm optimization algorithm is used to estimate the unknown model parameters from experimental data. Finally, a comparison between experimental data and simulation results demonstrates that the proposed model can reflect the dynamic characteristics of the metal hydride tank.

Identifying low-dimensional trajectories of mechanically-ventilated patient systems: Empirical phenotypes of joint patient+care processes to enhance temporal analysis in ARDS research.

Refined management of mechanically ventilation is an obvious target for improving patient outcomes, but is impeded by the nature of data for study and hypothesis generation. The connections between clinical outcomes and temporal development of iatrogenic injuries current lung-protective ventilator settings remain poorly understood. Analysis of lung-ventilator system (LVS) evolution at relevant timescales is frustrated by data volume and multiple sources of heterogeneity. This work motivates, presents, and validates a computational pipeline for resolving LVS systems into the joint evolution of data-conditioned model parameters and ventilator information. Applied to individuals, the workflow yields a concise low-dimensional representation of LVS behavior expressed in phenotypic breath waveforms suitable for analysis. The effectiveness of this approach is demonstrated through application to multi-day observational series of 35 patients. Individual patient analyses reveal multiple types of patient-oriented dynamics and breath behavior to expose the complexity of LVS evolution; less than 10% of phenotype changes related to ventilator settings changes. Dynamics are shown to including both stable and unstable phenotype transitions as well as both discrete and continuous changes unrelated to ventilator settings. At a cohort scale, 721 phenotypes constructed from individual data are condensed into a set of 16 groups that empirically organize around certain settings (positive end-expository pressure and ventilator mode) and structurally similar pressure-volume loop characterizations. Individual and cohort scale phenotypes, which may be refined by hypothesis-specific constructions, provide a common framework for ongoing temporal analysis and investigation of LVS dynamics.

Equilibrating the impact of fluid scattering attenuation on underwater optical imaging via adaptive parameter learning

Fluid scattering attenuation is a pervasive and intricate physical phenomenon in underwater environment, which challenges optical imaging for various visual tasks. This paper presents a Learnable Physical Imaging Model (LPIM) for equilibrating the impact of the fluid scattering attenuation on underwater optical imaging. Whereas the imaging medium has the liquidity and diversity, an adaptive physical imaging framework is proposed to learn the nonlinear degradation parameter for various complex underwater environment. This adaptive imaging model is jointed by adversarial competition and directional learning strategy, which makes our model learn more excellent properties from the unpaired referring image, and guides the restoration network to directionally adjust the imaging model parameters. To describe the real scenarios better, a multi-branch network, i.e., R-Net, D-Net and BN-net, is developed to learn and capture the color, light intensity and attenuation information. Experimental results on several datasets demonstrate that our approach outperforms the state-of-the-arts in both color balance and visual effect. The LPIM provides an innovative framework designed to counteract image degradation resulting from fluid scattering and attenuation.

Model parameter estimation with imprecise information

ABSTRACT Model parameter estimation is a well-known inverse problem, as long as single-value point data are available as observations of system performance measurement. However, classical statistical methods, such as the minimization of an objective function or maximum likelihood, are no longer straightforward, when measurements are imprecise in nature. Typical examples of the latter include censored data and binary information. Here, we explore Approximate Bayesian Computation as a simple method to perform model parameter estimation with such imprecise information. We demonstrate the method for the example of a plain rainfall–runoff model and illustrate the advantages and shortcomings. Last, we outline the value of Shapley values to determine which type of observation contributes to the parameter estimation and which are of minor importance.

Study of self-bound compact stars in f(T)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$f(\\mathscr {T})$$\\end{document} gravity and observational constraints on the model parameters

The present study is based on f(T)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$f(\\mathscr {T})$$\\end{document} gravity, where we impose possible observational constraints on the model parameters to obtain physically plausible features of compact stars, specifically neutron stars. To do so, as a first step, we consider the field equations in the f(T)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$f(\\mathscr {T})$$\\end{document} gravity framework. We then solve the field equations to generate a set of new exact solutions in f(T)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$f(\\mathscr {T})$$\\end{document} gravity where model parameters are found by using boundary conditions. A few tests are performed to asses the stability of the model and compare the obtained results with observations of compact stars, in particular with the compact binary merger event GW190814. We observe the maximum mass of the star beyond the 3M⊙\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$3M_{\\odot }$$\\end{document} when the surface density is of the order 1014\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$10^{14}$$\\end{document}gm/cm3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^3$$\\end{document} for the higher torsion parameter, which implies that an anisotropic solution in teleparallel gravity is more suitable for modeling of massive compact objects in lower mass-gap\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$mass-gap$$\\end{document}, and thus the model presented herein provides a satisfactory physical scenario with respect to the observational signature.

Diagnostically distinct resting state fMRI energy distributions: A subject-specific maximum entropy modeling study.

Objective Existing neuroimaging studies of psychotic and mood disorders have reported brain activation differences (first-order properties) and altered pairwise correlation based functional connectivity (second-order properties). However, both approaches have certain limitations that can be overcome by integrating them in a pairwise maximum entropy model (MEM) that better represents a comprehensive picture of fMRI signal patterns and provides a system-wide summary measure called energy. This study examines the applicability of individual-level MEM for psychiatry and identifies image-derived model coefficients related to model parameters. Method MEMs are fit to resting state fMRI data from each individual with schizophrenia/schizoaffective disorder, bipolar disorder, and major depression (n=132) and demographically matched healthy controls (n=132) from the UK Biobank to different subsets of the default mode network (DMN) regions. Results The model satisfactorily explained observed brain energy state occurrence probabilities across all participants, and model parameters were significantly correlated with image-derived coefficients for all groups. Within clinical groups, averaged energy level distributions were higher in schizophrenia/schizoaffective disorder but lower in bipolar disorder compared to controls for both bilateral and unilateral DMN. Major depression energy distributions were higher compared to controls only in the right hemisphere DMN. Conclusions Diagnostically distinct energy states suggest that probability distributions of temporal changes in synchronously active nodes may underlie each diagnostic entity. Subject-specific MEMs allow for factoring in the individual variations compared to traditional group-level inferences, offering an improved measure of biologically meaningful correlates of brain activity that may have potential clinical utility.

Emergent cosmology in 4D Einstein Gauss Bonnet theory of gravity

In this paper, in an FLRW background and a perfect fluid equation of state, we explore the possibility of the realization of an emergent scenario in a 4D regularized extension of Einstein-Gauss-Bonnet gravity, with the field equations particularly expressed in terms of scalar-tensor degrees of freedom. By assuming non-zero spatial curvature (k = ± 1), the stability of the Einstein static universe (ESU) and its subsequent exit into the standard inflationary scenario is tested through different approaches. In terms of dynamical systems, a spatially closed universe rather than an open universe shows appealing behaviour to exhibit a graceful transition from the ESU to standard cosmological history. We found that under linear homogeneous perturbations, for some constraints imposed on the model parameters, the ESU is stable under those perturbations. Moreover, it is noted that for a successful graceful transition, the equation of state ω must satisfy the conditions −1 < ω < 0 and ω < − 1 for closed and open universes, respectively. Furthermore, the ESU is seen to be neutrally stable under matter perturbation in the Newtonian gauge.

An Agent-based Approach to Study Spatial Structure Effects on Estimated Distance Deterrence in Commuting

AbstractWe provide an experimental design to study how estimates of the distance deterrence parameter in a standard doubly-constrained gravity model respond to different patterns of spatial structure. The design is defined as an agent-based simulation framework that simultaneously considers the response of commuting and migration to changes in the spatial structure, accounting for labor and housing market issues. The agents are equipped with a utility function and are expected to maximize their utilities corresponding to their disposable income net of commuting costs and house value. We study how the estimates of the distance deterrence parameter respond to where the central business district is located, to the clustering of local and basic sector jobs, to changes in the road transportation network, and to the compactness in the system of towns in the geography.

Diffuse reflectance spectroscopy for optical characterizations of orthotopic head and neck cancer models in vivo

We demonstrated an easy-to-build, portable diffuse reflectance spectroscopy device along with a Monte Carlo inverse model to quantify tissue absorption and scattering-based parameters of orthotopic head and neck cancer models in vivo. Both tissue-mimicking phantom studies and animal studies were conducted to verify the optical spectroscopy system and Monte Carlo inverse model for the accurate extraction of tissue optical properties. For the first time, we reported the tissue absorption and scattering coefficients of mouse normal tongue tissues and tongue tumor tissues. Our in vivo animal studies showed reduced total hemoglobin concentration, lower tissue vascular oxygen saturation, and increased tissue scattering in the orthotopic tongue tumors compared to the normal tongue tissues. Our data also showed that mice tongue tumors with different sizes may have significantly different tissue absorption and scattering-based parameters. Small tongue tumors (volume was ∼60 mm3) had increased absorption coefficients, decreased reduced-scattering coefficients, and increased total hemoglobin concentrations compared to tiny tongue tumors (volume was ∼18 mm3). These results demonstrated the potential of diffuse reflectance spectroscopy to noninvasively evaluate tumor biology using orthotopic tongue cancer models for future head and neck cancer research.

The New Strange Generalized Rayleigh Family: Characteristics and Applications to COVID-19 Data

In this paper, we introduce a novel family of continuous distributions known as the Odd Generalized Rayleigh-G Family. Within this family, we present a special sub-model known as the odd Generalized Rayleigh Inverse Weibull (OGRIW) distribution. The OGRIW distribution is derived by combining the T-X family and the Generalized Rayleigh distribution. We provide a comprehensive expansion of the (PDF) and (CDF) for the OGRIW distribution. Additionally, we investigate several mathematical properties of the OGRIW distribution, including moments, moment-generating function, incomplete moments, quantile function, order statistics and Rényi entropy. To estimate the model parameters, we employ the maximum likelihood method, aiming to identify the parameter values that maximise the likelihood of the observed data. Finally, we apply the proposed OGRIW distribution to two real COVID-19 datasets from Mexico and Canada. The results of these applications demonstrate that the new distribution exhibits remarkable flexibility and outperforms other comparative distributions in terms of accurately modelling the COVID-19 data.

Quantitative determination of cadmium content in peanut oil using microwave detection method combined with multivariate analysis

This research introduced an innovative application of microwave detection technology to quickly and precisely quantify cadmium concentration in peanut oil. Using the principle of free space measurement, a compact microwave detection device was fabricated, and obtain a transmission index with a frequency range of 2.5 GHz to 11.5 GHz and a step size of 10 MHz. Firstly, the preprocessed transmission index is selected by using three feature extraction algorithms: competitive adaptive reweighting sampling (CARS) algorithm, boot­strapping soft shrinkage (BOSS) algorithm, and iteratively variable subset optimization (IVSO) algorithm, and quantified them with the least square support vector regression (LSSVR) model. Then, two intelligent optimization algorithms, sine cosine algorithm (SCA) and firefly algorithm (FA) were used to optimize the parameters of the LSSVR model. Compared with the original LSSVR model, the coefficient of determination (RP2) is improved after three feature extraction algorithms and two parameter optimization algorithms, and the root average square error (RMSEP) is also decreased. Among these algorithms, feature extraction algorithm CARS and parameter optimization algorithm FA have the best prediction results. When the two are coupled, the CARS-FA-LSSVR model shows the best generalization performance, with RP2 of 0.9944 and RMSEP of 1.3075 mg∙kg-1. Through this research, it can be seen that high-precision detection of heavy metal cadmium content in peanut oil can be achieved by using microwave detection method combined with the stoichiometric model and can be extended to the detection of heavy metal content in other foods, providing a new way of idea for food safety detection.

Disparate nonlinear neural dynamics measured with different techniques in macaque and human V1

Diverse neuro-imaging techniques measure different aspects of neural responses with distinct spatial and temporal resolutions. Relating measured neural responses across different methods has been challenging. Here, we take a step towards overcoming this challenge, by comparing the nonlinearity of neural dynamics measured across methods. We used widefield voltage-sensitive dye imaging (VSDI) to measure neural population responses in macaque V1 to visual stimuli with a wide range of temporal waveforms. We found that stimulus-evoked VSDI responses are surprisingly near-additive in time. These results are qualitatively different from the strong sub-additive dynamics previously measured using fMRI and electrocorticography (ECoG) in human visual cortex with a similar set of stimuli. To test whether this discrepancy is specific to VSDI—a signal dominated by subthreshold neural activity, we repeated our measurements using widefield imaging of a genetically encoded calcium indicator (GcaMP6f)—a signal dominated by spiking activity, and found that GCaMP signals in macaque V1 are also near-additive. Therefore, the discrepancies in the extent of sub-additivity between the macaque and the human measurements are unlikely due to differences between sub- and supra-threshold neural responses. Finally, we use a simple yet flexible delayed normalization model to capture these different dynamics across measurements (with different model parameters). The model can potentially generalize to a broader set of stimuli, which aligns with previous suggestion that dynamic gain-control is a canonical computation contributing to neural processing in the brain.

A Functional Coefficients Version of a Generalized Orr–Sherby–Dorn Creep Model: An Application to 2.25Cr–1Mo Steel

It is important to be able to predict the creep life of materials used in power plants. This paper illustrates the inadequacies of the Orr–Sherby–Dorn (OSD) creep model in achieving this aim for 2.25Cr–1Mo steel. This failure is explained in terms of non-constant model parameters—which in turn is the result of changing creep mechanisms. The paper introduces a semi-parametric estimation procedure for a variant of the OSD model (a structural coefficients version) that can be used to deal with such changing creep mechanisms while maintaining the structure of the model and consequently producing more reliable long-term predictions compared to the unmodified OSD model and the recently introduced LOESS technique. For 2.25Cr–1Mo steel, it was found that the model parameters varied in line with changing creep mechanisms, but in a modified way compared to that already suggested in the literature for this material. The models used suggested that with diminishing stress and increasing temperature, dislocation creep within the crystal structure morphs into grain boundary dislocation motion and finally Nabarro-Herring creep.

Bridging the Covid-19 data and the epidemiological model using the time-varying parameter SIRD model

This paper extends the canonical model of epidemiology, the SIRD model, to allow for time-varying parameters for real-time measurement and prediction of the trajectory of the Covid-19 pandemic. Time variation in model parameters is captured using the score-driven modeling structure designed for the typical daily count data related to the pandemic. The resulting specification permits a flexible yet parsimonious model with a low computational cost. The model is extended to allow for unreported cases using a mixed-frequency setting. Results suggest that these cases’ effects on the parameter estimates might be sizeable. Full sample results show that the flexible framework accurately captures the successive waves of the pandemic. A real-time exercise indicates that the proposed structure delivers timely and precise information on the pandemic’s current stance. This superior performance, in turn, transforms into accurate predictions of the death cases and cases treated in Intensive Care Units (ICUs).

First-order multivariate integer-valued autoregressive model with multivariate mixture distributions

The univariate integer-valued time series has been extensively studied, but literature on multivariate integer-valued time series models is quite limited and the complex correlation structure among the multivariate integer-valued time series is barely discussed. In this study, we proposed a first-order multivariate integer-valued autoregressive model to characterize the correlation among multivariate integer-valued time series with higher flexibility. Under the general conditions, we established the stationarity and ergodicity of the proposed model. With the proposed method, we discussed the models with multivariate Poisson-lognormal distribution and multivariate geometric-logitnormal distribution and the corresponding properties. The estimation method based on EM algorithm was developed for the model parameters and extensive simulation studies were performed to evaluate the effectiveness of proposed estimation method. Finally, a real crime dataset was analysed to demonstrate the advantage of the proposed model with comparison to the other models.