Values Of Model Parameters Research Articles

A virtual meter-based visualization tool to present energy flows in multiple zone variable air volume air handling unit systems

Energy data estimated by virtual meters provide operational staff with an opportunity to improve building energy performance. Interactive visualization of virtual metered energy serves as a tool that can be used to identify energy consumption patterns and gain insights that can support operational decisions. This paper introduces a novel interactive visualization tool that applies visual analytics principles in developing a standalone application that illustrates equipment-level virtual metered energy trends. Users can interact with these visualizations via a user interaction panel designed to support operational decisions. Interviews with building operation professionals in two facility management companies are conducted to gain insights into visualization requirements that would assist decision-making processes. The use of the visualization tool is demonstrated using operational data collected from a commercial building located in Ottawa, Canada. The data are used to train inverse greybox models representing heat transfer mechanisms in an air handling unit and a variable air volume zone. Optimal values for model parameters are fed into the visualization tool to develop virtual meters that estimate the heat added or extracted by the air handling unit and the heat added by zone-level radiant heaters. The virtual metered energy is presented through a visualization interface that allows user interaction via processes including select, filter, explore, and compare. The potential of the visualization tool for assisting decision-making processes and improving building energy performance is demonstrated through illustrative examples.

Model Personalization in Behavioral Interventions using Model-on-Demand Estimation and Discrete Simultaneous Perturbation Stochastic Approximation.

This paper presents the use of discrete Simultaneous Perturbation Stochastic Approximation (DSPSA) to optimize dynamical models meaningful for personalized interventions in behavioral medicine, with emphasis on physical activity. DSPSA is used to determine an optimal set of model features and parameter values which would otherwise be chosen either through exhaustive search or be specified a priori. The modeling technique examined in this study is Model-on-Demand (MoD) estimation, which synergistically manages local and global modeling, and represents an appealing alternative to traditional approaches such as ARX estimation. The combination of DSPSA and MoD in behavioral medicine can provide individualized models for participant-specific interventions. MoD estimation, enhanced with a DSPSA search, can be formulated to provide not only better explanatory information about a participant's physical behavior but also predictive power, providing greater insight into environmental and mental states that may be most conducive for participants to benefit from the actions of the intervention. A case study from data collected from a representative participant of the Just Walk intervention is presented in support of these conclusions.

Calibration of cloud and aerosol related parameters for solar irradiance forecasts in WRF-solar

Model parameters are one of the sources of uncertainties in numerical weather prediction. Recently, the Weather Research and Forecasting model with Solar extensions (WRF-Solar) has been upgraded by enhancing the treatment of sub-grid scale cloud and aerosols with augmentations of a sub-grid scale cloud scheme (CLD3) and an upgraded aerosol-aware Thompson-Eidhammer scheme (TE14). However, the value of model parameters associated with these parameterizations are assigned based on limited measurements or theoretical calculations. Calibrating the sensitive parameters has the potential to improve solar irradiance predictions. In this work, we adopted a multi-objective surrogate-based optimization (SBO) framework to calibrate nine parameters used in CLD3 and TE14 that lead to the largest sensitivity in simulated irradiance. The normalized mean-absolute-error (NMAE) of global horizontal irradiance (GHI) and direct normal irradiance (DNI) are minimized by calibrating WRF-Solar over two regions including the Southern Great Plains (SGP) and Central California. We selected two cloudy cases, one over less-polluted SGP and another over Central California with high aerosol loading associated with wildfire events. The results show that generalized linear model (GLM)-based surrogate models approximate physical models well, particularly when the third order and three-way interaction terms are considered. The SBO framework efficiently searches the parameter space for optimal solutions with less computational costs than directly calibrating the physical model. We first calibrate CLD3 parameters over the less-polluted SGP region. Optimized CLD3 parameters alone result in NMAE reduction by 14% for the site-mean and up to 33% for individual cases over the SGP region. With further calibration of TE14 parameters over the Central California during active fire periods, the optimized parameters lead to over 20% reductions of NMAE.

Modeling the climate sensitivity of Patagonian glaciers and their responses to climatic change during the global last glacial maximum

Investigation of the behavior of the former ice sheet over Patagonia aids better understanding of ice-climate interactions and has important implications for deciphering how modern glaciers will respond to rapid increase in global temperature. Here, we use a 1-km resolution ice sheet model to identify the climate sensitivity of Patagonian glaciers and examine their responses to climatic change during the global Last Glacial Maximum (gLGM). Our results demonstrate that the total glacier area increases by ∼78 × 103 km2 and the equilibrium-line altitude (ELA) lowers by ∼163 m for every 1 °C of cooling under modern precipitation regime. Keeping temperature unchanged, there is an increase of total glacier area by ∼21 × 103 km2 and a ΔELA by ∼64 m for every doubling in precipitation. According to the multimodel ensemble mean of 21 PMIP models, an overall colder and drier climate prevails over Patagonia during the gLGM, with a decrease of annual mean temperature and precipitation by ∼4.7 °C and 12%, respectively. Under the modeled gLGM climate, there is an extensive ice sheet (∼387 × 103 km2) almost completely covering the Patagonian Andes, which results largely from the cooling with positive contribution from sea-level fall. The Patagonian ice sheet is drained by ice streams at the western margins and fast-flowing outlet glaciers to the east, with a north-to-south ice divide mainly extending the southern Patagonian Andes. Additionally, the erosion potential induced by the ice sheet exhibits a very strong localization over the fjords and valleys, resembling the pattern of ice velocity. The modeled ice sheet extent and ice flow pattern during the gLGM match well with the latest empirical reconstructions (though with spatial discrepancies), whereas the total ice volume is underestimated. However, the modeled Patagonian ice sheet exhibits high sensitivity to climatic forcing and the values of ice sheet model parameters used.

Accelerating expansion of the universe in modified symmetric teleparallel gravity

The fundamental nature and origin of dark energy are one of the premier mysteries of theoretical physics. In General Relativity Theory, the cosmological constant $\Lambda$ is the simplest explanation for dark energy. On the other hand, the cosmological constant $\Lambda$ suffers from a delicate issue so-called fine-tuning problem. This motivates one to modify the spacetime geometry of Einstein's GR. The $f(Q)$ gravity is a recently proposed modified theory of gravity in which the non-metricity scalar $Q$ drives the gravitational interaction. In this article, we consider a linear $f(Q)$ model, specifically $f(Q)=\alpha Q + \beta$, where $\alpha$ and $\beta$ are free parameters. Then we estimate the best fit values of model parameters that would be in agreement with the recent observational data sets. We use 57 points of the updated $H(z)$ data sets, 6 points of the BAO data sets, and 1048 points from the Pantheon supernovae samples. We apply the Bayesian analysis and likelihood function along with the Markov Chain Monte Carlo (MCMC) method. Further, we analyse the physical behaviour of cosmological parameters such as density, deceleration, and the EoS parameters corresponding to the constraint values of the model parameters. The evolution of deceleration parameter predicts a transition from decelerated to accelerated phases of the universe. Further, the evolution of equation of state parameter depicts quintessence type behaviour of the dark energy fluid part. We found that our $f(Q)$ cosmological model can effectively describe the late time cosmic acceleration without invoking any dark energy component in the matter part.

The statistical properties and applications of the alpha power Topp-Leone-G distribution

A two-parameter class of generator named the alpha power Topp-Leone-G (APTL-G) distribution for generating and engendering up to the minute family of continuous contemporary distributions is proposed. The propounded model has a bathtub and J-shapes hazard rate characteristics in data science. Thus, it has enhanced data analysis because of its productivity. A weighted function of the parent distribution was used to obtain the quantile function for the different sub-models considered. The record values of the differences of the kth lower and upper values were obtained in a closed-form to enable its applicability. The APTL-G model parameters values were captured and obtained in their simplified cases by the maximum likelihood approach. A simulation was also adopted to examine and investigate the performance, productivity, efficiency and tractability of the APTL-G sub-models. The outcomes of the real-life application and simulation study show that the performance of the goodness-of-fit of the APTL-G model is more flexible and tractable with real-life data sets concerning its goodness-of-fit.

Evaluation of 131I transfer in the environment based on the available measurements made in Belarus after the Chernobyl accident

This study evaluates the 131I transfer from ground deposition to the human thyroid gland after the Chernobyl accident using measurements of 131I concentrations in 1,252 soil, 124 grass, and 136 cow's milk samples as well as 131I thyroid activity measured in 3,100 individuals included in the Belarusian-American cohort. The following parameters of an 131I environmental transfer model used to calculate thyroid doses were evaluated in this study: (i) the interception factor of 131I by pasture grass, which was described by a purely empirical equation, (ii) the removal rate of 131I from pasture grass due to weathering and growth dilution, estimated to be 0.0676 d−1 (half-life of 10.3 d), (iii) the removal rate of 131I from cow's milk, estimated to be 0.0686 d−1 (half-life of 10.1 d), and (iv) the transfer coefficient of 131I from feed to cow's milk, arithmetic mean ± standard deviation of (6.7 ± 8.7) × 10−3 d L−1 (median = 4.0 × 10−3 d L−1). The individual model-based and measurement-based 131I thyroid activities for the Belarusian-American cohort members were calculated using different starting points of 131I transfer in the chain ‘ground deposition’ → ‘vegetation’ → ‘cow's milk’ → ‘human thyroid’, i.e., the measured 131I concentrations in soil, grass, and cow's milk. De novo thyroid doses from 131I for the 3,100 cohort members were calculated in this study using measured 131I activity concentrations in soil, grass, and cow's milk and were compared with those estimated previously for the same individuals using model-based 131I activity concentrations. It was shown that the use of measured instead of model-based 131I concentrations, in general, did not improve the measurement-based thyroid dose estimates. This is likely to be because there was already a good generic data base for the parameters used in this assessment. This finding indicates that, although the measurements of environmental samples are essential to estimate the parameter values of the 131I transfer model, the individual measurements of 131I thyroid activity are the most valuable information for estimating individual thyroid doses.

Grumpy: a simple framework for realistic forward modelling of dwarf galaxies

ABSTRACT We present a simple regulator-type framework designed specifically for modelling formation of dwarf galaxies. Despite its simplicity, when coupled with realistic mass accretion histories of haloes from simulations and reasonable choices for model parameter values, the framework can reproduce a remarkably broad range of observed properties of dwarf galaxies over seven orders of magnitude in stellar mass. In particular, we show that the model can simultaneously match observational constraints on the stellar mass–halo mass relation, as well as observed relations between stellar mass and gas phase and stellar metallicities, gas mass, size, and star formation rate, as well as general form and diversity of star formation histories of observed dwarf galaxies. The model can thus be used to predict photometric properties of dwarf galaxies hosted by dark matter haloes in N-body simulations, such as colours, surface brightnesses, and mass-to-light ratios and to forward model observations of dwarf galaxies. We present examples of such modelling and show that colours and surface brightness distributions of model galaxies are in good agreement with observed distributions for dwarfs in recent observational surveys. We also show that in contrast with the common assumption, the absolute magnitude–halo mass relation is generally predicted to have a non-power law form in the dwarf regime, and that the fraction of haloes that host detectable ultra-faint galaxies is sensitive to reionization redshift (zrei) and is predicted to be consistent with observations for zrei ≲ 9.

Exact Analytic Formula for Conductance Predicting a Tunable Sommerfeld–Arrhenius Thermal Transition within a Single‐Step Tunneling Mechanism in Molecular Junctions Subject to Mechanical Stretching

AbstractIt is shown that the conductance G of molecular tunnel junctions wherein the charge transport is dominated by a single energy level can be expressed in closed analytic form which is exact and valid at arbitrary temperature T and model parameter values. On this basis, it is shown that the single‐step tunneling mechanism is compatible with a continuous thermal transition from a weakly T‐dependent G at low T (Sommerfeld regime) to a nearly exponential ‐dependent G at high T (Arrhenius‐like regime). This Sommerfeld–Arrhenius transition can be observed in real molecular junctions and can be continuously tuned, for example, via mechanical stretching.

Integrated analysis of surface wave velocity and gravity data for the development of new density-velocity models of the crust and upper mantle in SE Iran

Inversion of Rayleigh wave dispersion curves is challenging due to its nonlinearity and multimodality. In this paper, a Simulated Annealing (SA) algorithm is applied to the nonlinear inversion of fundamental-mode Rayleigh wave group dispersion curves for shear and compressional wave velocities. In our approach, we invert thickness, velocities and densities and their vertical gradients of four layers, sediments, upper-crust, lower-crust and lithospheric mantle.At first, to determine the efficiency and stability of the method, noise-free and noisy synthetic data sets are inverted. Results from the synthetic data demonstrate that the SA applied to the nonlinear inversion of surface wave data is interesting not only in terms of accuracy but also in terms of the convergence speed. In fact, the SA method is suitable for large-scale optimization problems, especially for those in which a desired global minimum is hidden among many local minima. In a second step, real data in SE Iran are inverted to examine the usage and robustness of the proposed approach on real surface wave data. Then, we applied 3D gravity modeling based on surface wave analysis results to obtain the density structure of each layer. The reason for using both types of data sets is that the gravity anomaly does not have a good vertical resolution and surface wave group velocities are more appropriate for placing layer limits at depth, but they are not very sensitive to density variations. Therefore, the use of gravity data increases the overall resolution of density distribution. Compared with the Shuffle Complex Evolution (SCE) method that was implemented in a previous study, we found out that the SA method is more stable and has less variability of model parameter values in successive tests. In the final step, we reapplied the SA method to invert the fundamental-mode Rayleigh wave group velocities based on the results of gravity modeling. Gravity results, such as thicknesses and densities were used to limit the search space in the SA method.Our results show that the Moho depth across the Makran subduction zone is increasing from the Oman seafloor (24–30 km) and Makran forearc setting (34–42 km) to the Taftan-Bazman volcanic arc (47–49 km). Also, the results show high shear and compressional velocities under the Gulf of Oman, decreasing to the north of the Makran region. The density image shows an average crustal density with maximum values under the Gulf of Oman, decreasing northward to the Makran region.

Models Relating Individual Tree Basal Area Growth Rates to Tree Basal Areas in Even-Aged, Monoculture Forest Stands

As trees grow larger in even-aged, monoculture forests, stand density increases, taller trees shade smaller trees and a group of smaller, suppressed trees develops that may show little or no growth. Subsequent mortality is most common from this group. This work compares three models that may describe well the relationship between individual tree basal area growth rates and tree basal areas in a forest stand when a suppressed group of trees is present or not. The models are tested using a large collection of data from permanent sample plots in blackbutt (Eucalyptus pilularis Smith) forests in subtropical eastern Australia. An example is given for one stand showing how model parameter values may change as the stand ages; this illustrates how these models have potential for use in the first stage of model development to relate individual tree growth rates to tree sizes and stand characteristics. Such models may then be used widely across the forest population under consideration.

A probabilistic approach to the description of the crack kinetics and fatigue failure of structural components with allowance for the effect of crack retardation after overloads

A probabilistic approach to assessing fatigue strength and cyclic life based on Wheeler’s model is presented. The approach provides the possibility of estimating the probability of failure of the structural component under consideration, with allowance for the effect of crack retardation after the overload. The initial crack size, the parameters of the Paris equation, and the exponent of the crack retardation power function are assumed random parameters. The kinetics of a fatigue crack is described by cycle-by-cycle integration of the Paris equation written in finite differences. The probabilistic estimate is performed using the Monte Carlo statistical simulation method by repeatedly solving the Cauchy problem for various combinations of the values of random model parameters that are generated under the adopted laws of the probability distributions of those parameters. The probabilistic distributions of random parameters are selected proceeding from the level of the uncertainty of the problem using available statistical data without taking into accounting for data on the actual state of a particular structural component in real operation conditions. A computer code in Matlab environment has been developed for statistical description of the fatigue crack growth in structural components which allows accounting for the effect of crack retardation. An example of assessing the probability of the fatigue failure of a pipeline component with a longitudinal crack on the inner surface loaded by the internal pressure which varies according to a periodic law with a constant amplitude is presented for single tensile overload of the pipeline component. The dependencies of the fatigue failure probability on the number of loading cycles for various combinations of model parameters were obtained. The sensitivity of the fatigue failure probability under a given number of loading cycles to changes of the values of structural parameters was assessed.

An Integrated Machine Learning Framework for Fraud Detection

The research develops a practical Machine Learning framework with a comparative and comprehensive approach to sequence-learn and then detect the online banking payment fraud. The integrated framework introduces exploratory analysis and feature engineering, multiple modelling and performance comparison, and model robustness, uncertainty and sensitivity analysis toward a systematic approach for Machine Learning applications. For demonstration purpose, the framework is implemented on a set of real-life online banking transaction datasets obtained from a UK-based bank through three models, i.e., Support Vector Machine, Markov Model and LSTM model, with various combinational features of the datasets evidenced in the exploratory analysis and modelling with noise ratios of datasets, range values of model parameters and confidence intervals of prediction results. The modelling results show that overall, the LSTM model achieves the best performance, with outcome accuracy of 97.7%, indicating its advantage in modelling sequential data such as customer behaviours.

Analysis and numerical simulation of tuberculosis model using different fractional derivatives

The main goal of the current research is to study and explore dynamic behavior of tuberculosis by using fractional mathematical model. In this study, recently introduced fractional operator (FO) having ML non-singular kernel was used. Fixed point theory is utilized to explore the unique and existing problems in suitable model. Numerical outcomes are discovered for the verification of arbitrary fractional order derivative. These numerical outcomes are discovered from mathematical and biological perspectives by using the model parameters values. Graphical simulation shows the comparison between Fractional Caputo (Fr. Cap) method and AB Caputo (AB Cap) predictor corrector method for different fraction order. The present study suggested that AB Cap is much better than Fr. Cap.

Controls on the evolution of Cenozoic seawater chemistry

A simple process-based model of the Li, Mg, Ca, Sr and alkalinity cycles in the ocean has been developed with the aim of better understanding the information contained in the evolution of Cenozoic seawater chemistry. In the model, changes in seawater chemistry are forced by changing climatic (temperature) and tectonic (ocean crust accretion rate and continental weatherability) conditions. These drive time-varying element and isotope fluxes from continental and seafloor weathering, mid-ocean ridge hydrothermal systems and sedimentation. Inverse modelling reveals that a range of model parameter values can simultaneously reproduce the key features of the Cenozoic history of seawater chemistry for all species considered. In addition to Cenozoic cooling, a decrease in the rate of ocean crust accretion and/or an increase in continental weatherability are required to fit the data. However, paleoseawater compositions do not allow the relative roles of these tectonic forcings to be determined. While other processes (e.g., changes in surface lithology) may have played a role in generating the observed variations in seawater chemical and isotopic composition, they are not required to explain the general tends in the data. The increase in Mg/Ca over the Cenozoic in acceptable models is largely driven by a decrease in the Ca/alkalinity of the net continental, diagenetic and hydrothermal fluxes, and not by an increase in the Ca/alkalinity of the carbonate mineral sink. Thus, the increase in seawater Mg/Ca over the Cenozoic is readily explained without any decrease in dolomite formation. Notably, all acceptable models have a stronger temperaturedependence of seafloor than continental weathering. This means that climatically driven changes in chemical fluxes in the model come dominantly from seafloor, not continental, weathering.

Effectiveness of contact tracing apps for SARS-CoV-2: an updated systematic review

Objective – To systematically review evidence on effectiveness of contact tracing apps (CTAs) for SARS-CoV-2 on epidemiological and clinical outcomes Design – Update of a systematic review (https://doi.org/10.1136/bmjopen-2021-050519) Data sources - EMBASE (OVID), MEDLINE (PubMed), BioRxiv, and MedRxiv were searched up to June 9th 2021 Study selection – Studies, empirical or model-based, assessing effect of CTAs for SARS-CoV-2 on quarantine rate, reproduction number (R), total number of infections, hospitalization, mortality, and other epidemiologically and clinically relevant outcomes, were eligible for inclusion. Data extraction – Empirical and model-based studies were both critically appraised based on dedicated quality and risk of bias assessment checklists. Data on type of study (i.e., empirical or model-based), sample size, (simulated) time horizon, study population, CTA type (and associated interventions), comparator, and outcomes assessed, were extracted. Key findings were extracted and narratively summarized. Specifically for model-based studies, characteristics and values of important model parameters were collected. Results – 5123 studies were identified, of which 27 studies (five empirical, 22 model-based studies) were eligible and included in this review. All empirical studies were observational (non-randomized) studies and either at unclear or high risk of bias, mostly due to uncontrolled confounding. Risk of bias of model-based studies was considered high for 7 of 22 studies. Most studies demonstrated beneficial effects of CTAs on R, total number of infections, hospitalization, and mortality. Effect size was dependent on other model parameter values (e.g., proportion of asymptomatic individuals, testing delays), but in general a beneficial effect was observed at CTA adoption rates of 20% and over. Conclusions – CTAs are potentially effective at reducing SARS-CoV-2 related epidemiological and clinical outcomes, though effect size depends on other model parameter values. Methodologically sound comparative empirical studies on effectiveness of CTAs are lacking and would be desirable to confirm findings from model-based studies.

Validation of Calibrated Model Parameters for Jet-in-Crossflow

Previous efforts determined a set of calibrated, optimal model parameter values for Reynolds-averaged Navier–Stokes (RANS) simulations of a compressible jet in crossflow (JIC) using a turbulence model. These parameters were derived by comparing simulation results to particle image velocimetry (PIV) data of a complementary JIC experiment under a limited set of flow conditions. Here, a model using both nominal and calibrated parameters is validated against PIV data acquired from a much wider variety of JIC cases, including a realistic flight vehicle. The results from the simulations using the calibrated model parameters showed considerable improvements over those using the nominal values, even for cases that were not used in the calibration procedure that defined the optimal parameters. This improvement is demonstrated using a number of quality metrics that test the spatial alignment of the jet core, the magnitudes of multiple flow variables, and the location and strengths of vortices in the counter-rotating vortex cores on the PIV planes. These results suggest that the calibrated parameters have applicability well outside the specific flow case used in defining them and that with the right model parameters, RANS solutions for the JIC can be improved significantly over those obtained from the nominal model.

Constraining parameter uncertainty for predicting oxygen and hydrogen isotope values in fruit.

Understanding δ18O and δ2H values of agricultural products like fruit is of particular scientific interest in plant physiology, ecology, and forensic studies. Applications of mechanistic stable isotope models to predict δ18O and δ2H values of water and organic compounds in fruit, however, are hindered by a lack of empirical parameterizations and validations. We addressed this lack of data by experimentally evaluating model parameter values required to model δ18O and δ2H values of water and organic compounds in berries and leaves from strawberry and raspberry plants grown at different relative humidities. Our study revealed substantial differences between leaf and berry isotope values, consistent across the different relative humidity treatments. We demonstrated that existing isotope models can reproduce water and organic δ18O and δ2H values for leaves and berries. Yet, these simulations require organ-specific model parameterization to accurately predict δ18O and δ2H values of leaf and berry tissue and water pools. We quantified these organ-specific model parameters for both species and relative humidity conditions. Depending on the required model accuracy, species- and environment-specific model parameters may be justified. The parameter values determined in this study thus facilitate applications of stable isotope models where understanding δ18O and δ2H values of fruit is of scientific interest.

Assessing uncertainties and identifiability of foam displacement models employing different objective functions for parameter estimation

Foam injection in porous media is often used to control the gas fingering in multi-phase flow. Mathematical models of foam dynamics involve non-Newtonian formulations. To numerically simulate these complex phenomena, experimental data is gathered and used to estimate the parameter values of models via optimization techniques. The present work improves this procedure by introducing a new objective function based on the mobility reduction factor and does not require further experimental observations other than those usually obtained in core-flooding experiments. A series of numerical experiments were carried out to show the features and robustness of the proposed approach. In particular, the identifiability analysis results show that key parameters of the models are practically non-identifiable when using traditional objective functions that rely only on apparent viscosity. When the proposed objective function is used for parameter estimation, the identifiability issues are solved. In addition, the uncertainty quantification analysis revealed that lower uncertainty is achieved when using the new objective function when compared to the one that uses only apparent viscosity. In summary, we show that the new objective function generates better-calibrated models with high fidelity and low uncertainties and alleviates parameter non-identifiability issues. Therefore, the results suggest the new objective function is better suited for calibrating foam displacement models for enhanced oil recovery.

Cosmic bounce with α(e −β G − 1) + 2λ T model

We consider a model of f(G, T) gravity to study the bouncing phenomenon of our universe with FLRW metric. We take f(G, T) = α(e −β G − 1) + 2λ T and develop field equations for a particular form of Hubble parameter. The explicit forms of energy density and pressure are formulated for a perfect cosmic fluid, that gives energy conditions and well explained the bouncing. The role of deceleration parameter, jerk, and snap are explored. The graphical demonstration is also done for these terms with fixed values of model parameters. Finally, we discuss a special form of f(G, T) gravity model that ensures the bouncing universe.