Variation Of Model Parameters Research Articles

Evaluating the Correlation Between Stimulus Frequency Otoacoustic Emission Group Delays and Tuning Sharpness in a Cochlear Model.

A theoretical framework based on coherent reflection and filter theory predicts that the phase-gradient delays of stimulus frequency otoacoustic emissions (SFOAEs) are correlated with tuning sharpness in the mammalian cochlea. In this paper, we use a computational model of the cochlea to test this theory and to evaluate how SFOAE phase-gradient delays may be used to estimate the sharpness of cochlear tuning. This study is based on a physiologically motivated model which has been previously shown to predict key aspects of cochlear micromechanics. Cochlear roughness is introduced to model the reflection mechanism which underlies SFOAE generation. We then examine how varying the values of key model parameters or of the sound pressure level of the stimulus affects the relation between cochlear tuning and SFOAE delays. Finally, we quantify the ability of model simulations of SFOAE phase-gradient delays to provide reliable estimates of the tuning sharpness of the model. We find that variations of model parameters that cause significant broadening of basilar membrane (BM) tuning typically give rise to a sizeable reduction in SFOAE phase-gradient delays. However, some changes in model parameters may cause a significant broadening of BM tuning with only a moderate decrease in SFOAE delays. SFOAE delays can be used to estimate the tuning sharpness of the model with reasonable accuracy only in cases where broadening of cochlear tuning is associated with a significant reduction in SFOAE delays. The numerical results provide key insights about the correlations between cochlear tuning and SFOAE delays.

A comparison of stochastic and deterministic dynamics of tuberculosis model

Tuberculosis is a deadly infectious disease leading to a major health concern in Southern India while there are major obstacles to controlling its spread. Here, we present a mathematical model with five compartments. We categorize the infected compartment into two subcategories: latent TB-infected individuals and active TB-infected individuals. By introducing white noise in a deterministic system, we formulate a stochastic system. We investigate the model in deterministic and stochastic framework, followed by data calibration using TB infection data from Kanyakumari District in South India from 2019 to 2023. In the deterministic model, we derive the disease-free and endemic equilibrium points, compute the basic reproduction number, and examine their stability. Moreover, we perform sensitivity analysis to evaluate how variations in model parameters affect TB prevalence. In addition, we study the uniqueness of solutions of stochastic model. After that we derive the conditions for disease extinction and stochastic permanence, and execute extensive simulations to capture the variability and randomness in TB transmission dynamics. This study signifies that stochastic dynamics is richer than deterministic one in the way of mitigation TB transmission.

Phenotypic homogenization and potential fitness constraints following non-native introgression in an endemic sportfish.

Introgressive hybridization may lead to contrasting evolutionary outcomes that are difficult to predict, since they depend on the fitness effects of endogenous genomic interactions and environmental factors. Conservation of endemic biodiversity may be more effective with require direct measurement of introgressed ancestry and fitness in wild populations, especially for keystone taxa at risk of hybridization following species introductions. We assessed the relationship of non-native ancestry with growth and body condition in the basin-restricted Neosho Bass (Micropterus velox; NB), focusing on two streams in the NB native range that are admixed extensively with non-native Smallmouth Bass (M. dolomieu; SMB). We quantified genetic composition of 116 fish from Big Sugar Creek (N=46) and Elk River (N=70) at 14 microsatellite loci. Using back-calculated total length-at-age estimated from sagittal otoliths, we assessed whether genetic ancestry explained variation in von Bertalanffy growth model parameters, accounting for sex and stream effects. We then assessed the relationship of ancestry and body condition. We found no differences in growth parameters by sex, stream, or ancestry, suggesting phenotypic homogenization which could be mediated by selection on body size. We found a negative correlation between SMB ancestry and condition, including lower condition in Big Sugar Creek, possibly reflecting a trade-off between maximum length and condition with respect to overall fitness. We show that ongoing non-native introgression, which may be augmented by anthropogenic SMB introductions, may attenuate evolutionary differentiation between species and directly influence fitness, possibly having critical implications for long-term persistence and management of adaptive potential in a popular and ecologically important endemic sportfish.

The Variation of Several Parameters in the Premature Ovarian Failure Model by D-Galactose and the Effect of Some Drugs on the Model: Fundamental Study

The Variation of Several Parameters in the Premature Ovarian Failure Model by D-Galactose and the Effect of Some Drugs on the Model: Fundamental Study

Thin accretion disk around black hole in Einstein–Maxwell-scalar theory

We examine the accretion process in a thin disk surrounding a supermassive black hole within the framework of Einstein–Maxwell-scalar (EMS) gravity. Our investigation aims to elucidate how variations in model parameters affect different physical properties of the disk. When keeping EMS parameters β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\beta $$\\end{document} and q constant, we observe a reduction in radiation flux and temperature as α\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\alpha $$\\end{document} increases. However, the luminosity and radiative efficiency exhibit relatively minor variation. Conversely, under fixed α\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\alpha $$\\end{document} and q, an escalation in β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\beta $$\\end{document} leads to heightened levels of radiation flux, temperature, luminosity, and radiative efficiency. These results underscore the diverse influences of model parameters on observable metrics, providing valuable insights for the astronomical study of distinct black holes.

Cost analysis of technological vs. conventional upper limb rehabilitation for patients with neurological disorders: an Italian real-world data case study.

Most patients suffering from neurological disorders endure varying degrees of upper limb dysfunction, limiting their everyday activities, with only a limited number regaining full arm use. Robotic and technological rehabilitation has been demonstrated to be a feasible solution to guarantee an effective rehabilitation to recover upper limb performance or to prevent complications of upper limb immobility. However, there is currently a lack of studies which analyze the sustainability of robotic and technological rehabilitation by comparing its costs to conventional rehabilitation pathways. Since technology-based and conventional rehabilitation of the upper limb have been demonstrated to have comparable efficacy when the rehabilitation dose is matched, our study concentrates on a cost minimization analysis. The aim of the study is to compare the costs of a "mixed" rehabilitation cycle, which combines conventional and technology-based treatments (the latter delivered with a single therapist supervising several patients), with a cycle of purely conventional treatments. This has been done by developing a cost model and retrospectively analyzing the costs sustained by an Italian hospital which has adopted such a mixed model. A sensitivity analysis has been done to identify the parameters of the model that have the greatest influence on cost difference and to evaluate their optimal values in terms of efficiency of mixed rehabilitation. Finally, probabilistic simulations have been applied to consider the variability of model parameters around such optimized values and evaluate the probability of achieving a given level of savings. We found a cost difference of 49.60 € per cycle in favor of mixed rehabilitation. The sensitivity analysis demonstrated that, in the situation of the hospital under investigation, the parameter having the largest influence on the cost difference is the number of robotic treatments in a mixed rehab cycle. Probabilistic simulations indicate a probability higher than 98% of an optimized mixed rehabilitation cycle being less expensive than a pure conventional one. Through a retrospective cost analysis, we found that the technology-based mixed rehabilitation approach, within a specific organizational model allowing a single physiotherapist to supervise up to four patients concurrently, allowed cost savings compared to the conventional rehabilitation model.

A radar backscatter simulation of a forest canopy using 3D physical structures derived from LiDAR scanning

ABSTRACT This study aims to explore the forest aboveground biomass relationship to C-band backscatter. A one-hectare area in Wytham Woods, located west of Oxford, was selected for this research. The area has a total of 525 trees of seven different tree species. The Michigan Microwave Canopy Scattering Model (MIMICS), a two-layer radiative transfer model, was applied to simulate canopy backscatter responses in this deciduous UK forest. Model parameters related to forest structure were derived from previously published terrestrial laser (LiDAR) data. Simulated backscatter was performed for co-polarized and cross-polarized modes at a C-band frequency range and incidence angles (20° to 45° at 5° increments). The research includes five objectives: i) backscatter sensitivity analysis from the variation of different model parameters; ii) backscatter seasonal effect under spring–summer (leaf-on) and autumn–winter (leaf-off) periods; iii) backscatter comparison between species as well as between simulated and satellite observations in a spatial pattern; iv) relationship between simulated backscatter and aboveground biomass; and v) relationship between MIMICS forest structure parameters and simulated backscatter. Sensitivity analysis results showed significant differences in backscatter from changes in leaf distribution, leaf thickness and water content (leaf, trunk and soil) for both polarization modes in leaf-on scenarios. Leaf-off scenarios presented significant differences from changes in branch distribution but only for the co-polarized mode. Seasonal variations presented significant backscatter differences between spring–summer and autumn–winter scenarios; additionally, backscatter differences among species for each seasonality in the co-polarized mode were observed. The computation of the grid resolution (20 m × 20 m) showed a range of backscatter values depending on the grid and incidence angle. Higher backscatter values were observed for the satellite data than for the simulated data. Finally, the aboveground biomass and the MIMICS input parameters presented high random variability and little systematic co-variation with the total backscatter on both simulated seasons, suggesting that more robust allometric equations for biomass estimation are required.

Induction of subject-ventilator asynchrony by variation of respiratory parameters in a lung injury model in pigs

BackgroundSubject-ventilator asynchrony (SVA) was shown to be associated with negative clinical outcomes. To elucidate pathophysiology pathways and effects of SVA on lung tissue histology a reproducible animal model of artificially induced asynchrony was developed and evaluated.MethodsAlterations in ventilator parameters were used to induce the three main types of asynchrony: ineffective efforts (IE), auto-triggering (AT), and double-triggering (DT). Airway flow and pressure, as well as oesophageal pressure waveforms, were recorded, asynchrony cycles were manually classified and the asynchrony index (AIX) was calculated. Bench tests were conducted on an active lung simulator with ventilator settings altered cycle by cycle. The developed algorithm was evaluated in three pilot experiments and a study in pigs ventilated for twelve hours with AIX = 25%.ResultsIE and AT were induced reliably and fail-safe by end-expiratory hold and adjustment of respiratory rate, respectively. DT was provoked using airway pressure ramp prolongation, however not controlled specifically in the pilots. In the subsequent study, an AIX = 28.8% [24.0%-34.4%] was induced and maintained over twelve hours.ConclusionsThe method allows to reproducibly induce and maintain three clinically relevant types of SVA observed in ventilated patients and may thus serve as a useful tool for future investigations on cellular and inflammatory effects of asynchrony.

Quasinormal modes, thermodynamics and shadow of black holes in Hu–Sawicki f(R)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\varvec{f(R)}$$\\end{document} gravity theory

We derive novel black hole solutions in a modified gravity theory, namely the Hu–Sawicki model of f(R) gravity. After obtaining the black hole solution, we study the horizon radius of the black hole from the metric and then analyse the dependence of the model parameters on the horizon. We then use the 6th order WKB method to study the quasinormal modes of oscillations (QNMs) of the black hole perturbed by a scalar field. The dependence of the amplitude and damping part of the QNMs are analysed with respect to variations in model parameters and the error associated with the QNMs are also computed. After that we study some thermodynamic properties associated with the black hole such as its thermodynamic temperature as well as greybody factors. It is found that the black hole has the possibility of showcasing negative temperatures and is thermodynamically unstable for feasible values of model parameters. Then we analyse the geodesics and derive the photon sphere radius as well as the shadow radius of the black hole. The photon radius is independent of the model parameters while shadow radius showed fair amount of dependence on the model parameters. We tried to constrain the parameters with the help of Keck and VLTI observational data and obtained some bounds on m and c2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$c_{2}$$\\end{document} parameters.

Rapid Inverse Parameter Inference Using Physics-Informed Neural Networks

As Li-ion batteries become more essential in today's economy, tools need to be developed to accurately and rapidly diagnose a battery's internal state-of-health. Using a Li-ion battery's (high-rate) voltage response, it is proposed to determine a battery's internal state through Bayesian calibration.However, Bayesian calibration is notoriously slow and requires thousands of model runs. To accelerate parameter inference using Bayesian calibration, a surrogate model is developed to replace the underlying physics-based Li-ion model. Developing a surrogate model for rapid Bayesian calibration analysis is discussed for both the single particle model (SPM) and the pseudo two-dimensional (P2D) model.Surrogate models are constructed using physics-informed neural networks (PINNs) that encode the influence of internal properties on observed voltage responses. In practice, a neural network can be trained by: 1) using simulation results of the physics-based model (i.e., a data-loss approach); 2) using the residuals of the governing equations themselves (i.e., a physics-loss approach); or 3) using a combination of simulation results and governing equation residuals. In the present work, PINNs are developed using a variety of training losses and neural network architectures. In this analysis, it is shown that a PINN surrogate model can be reliably trained with only physics-informed loss. However, using a coupled data-informed and physics-loss approach produced the most accurate PINNs. Figure~\\ref{fig:spm_2d} illustrates the absolute relative errors of trained PINN networks using several different training losses and neural network architectures.After determining a consistent training strategy for both the SPM and P2D PINN surrogate models, the PINNs are extended to determine additional internal state-of-health parameters. As more and more parameters were introduced, the PINN training suffered from ``the curse of dimensionality", which was mitigated by using a hierarchical training approach (where a PINN trained with fewer variable model parameters was used to train a PINN with more variable model parameters).Next, the high-dimensionality PINN surrogates are then integrated into Bayesian calibration schemes to identify internal Li-ion battery properties from experimentally measured voltages. Interpreting the high-dimensional parameter posteriors is discussed with respect to model error, parameter prior choices, and experimental errors. Figure 1

Reconstructing the Pseudo-2-Dimensional (p2D) Model in Comsol Multiphysics Using Mathematical Interfaces

The classic ‘pseudo-2-dimensional’ (p2D) model of Newman and co-workers (also known as the DFN model), with its different efficient implementations, is the state-of-the-art physics-based electrochemical model for lithium-ion batteries. COMSOL Multiphysics (COMSOL) is a popular modeling software for multiphysics simulations, and its Battery Design Module for Li-ion batteries is widely used in the battery modeling community. This work presents a step-wise model development for reconstructing the detailed p2D model equations. This includes variable model parameters and weak-form implementation for the solid-phase diffusion, eliminating the need for non-local couplings and approximations for modeling the pseudo radial-dimension in the solid-phase.To model and understand the effect of capacity degradation on lithium-ion cells, the battery models in COMSOL need to incorporate additional degradation mechanisms. In this work, we have followed the approach of equation-based modeling in COMSOL, which enables us to formulate the p2D model using the mathematical PDE interfaces. This approach provides us with a lot of flexibility in terms of battery degradation model formulation and helps develop a clearer understanding of the p2D model equation in light of the assumptions involved.The steps in the model development involved writing the conservation equations in the generic mathematics interfaces coupled with the additional equations for the degradation mechanisms. The efforts were centered on developing cycling simulations based on Events for practically relevant operating conditions. This custom model was validated against the results from COMSOL’s Battery Interface, in-house codes, and baseline cycle data from the experiments and further developed as a physicochemical degradation model for automotive, large-format Lithium-ion batteries.This equation-based COMSOL model with cycling works without the requirement for Battery Design Module, LiveLink and Application Builder and can be adopted for a variety of electrochemical modeling applications by the electrochemical modeling community.

Interacting quark star with pressure anisotropy and recent astrophysical observations

This paper investigates the internal structure and equation of state (EoS) of quark stars (QS) with pressure anisotropy, incorporating recent astrophysical observations. Utilizing perturbative QCD corrections and color superconductivity, the EoS is expressed as a dimensionless function dependent on a single parameter, providing a comprehensive exploration of strong interaction effects. By rescaling the EoS and introducing dimensionless variables, the study encompasses scenarios ranging from noninteracting quark matter to extreme stiffness characterized by a parameter, λ̄. Anisotropic QS configurations are constructed using a spherically symmetric metric, with locally anisotropic matter described by a generalized EoS for tangential pressure. The Tolman–Oppenheimer–Volkoff equations governing star structure are presented in dimensionless form, and numerical solutions explore the parameter space defined by effective bag constants and λ̄. Mass–radius relations and the compactness of anisotropic QSs are analyzed under various model parameter variations, considering constraints from pulsar masses and gravitational wave phenomena. The study highlights the influence of pressure anisotropy on the maximum mass of QSs and discusses the impact of model parameters on internal properties. Additionally, the paper considers static stability, adiabatic index, and sound velocity profiles, providing a comprehensive understanding of QS behavior. Overall, this investigation contributes valuable insights into the properties of QSs and their compatibility with observational constraints, offering a detailed exploration of their EoS and internal structure.

Modeling the dependence of electrolyte design on lithium-sulfur battery performance

Herein, we offer two electrochemical models, a zero-dimensional (0-D) and a one-dimensional (1-D) model, to mechanistically explore the impact of the electrolyte-to-sulfur (E/S) ratio and electrolyte properties on the discharge behavior of a lithium-sulfur (Li-S) cell. The electrochemically active area in both models is defined based on the carbon weight fraction and reference porosity, so the influence of the carbon loading and E/S ratio on the reaction kinetics is included. Mass transfer of the species must be considered to model the reliance of the discharge capacity on the current density. Instead, the 0-D model is more responsive to the influence of the E/S ratio on the discharge capacity. The effect of electrolyte properties can be modeled implicitly through model parameters. The 0-D model is less sensitive to the variance in model parameters than the 1-D model in terms of the predicted capacity.

A Robust Approach using M-Estimation for Dynamic Panel Autoregressive Model

This paper presents a robust M-estimation approach for first-order panel autoregressive models, addressing the challenges posed by high persistence levels of the autoregressive parameter and individual heterogeneity. Generalized method of moments estimators widely used in dynamic panel models exhibit substantial finite sample biases and are sensitive to weak instruments, particularly as the autoregressive parameter gets close to unity. Our proposed weighted M-estimator, which uses a power function for the scale parameter in Huber’s loss function, offers a robust alternative. By minimizing the variance of model parameters through an optimal tuning parameter, our method enhances the efficiency and robustness of parameter estimates. We demonstrate the superiority of the proposed approach through several Monte-Carlo simulations and an application to hydro-electric power output data, providing comprehensive comparisons with existing generalized method of moments estimators.

Decrease in soil test phosphorus levels under omitted phosphorus fertilizer application

AbstractMany European cropped soils have high soil test P (STP) values in the top soil because of P accumulation over many years of fertilizer application. This should allow to save P fertilizer applications for some years without STP values decreasing to a level that might negatively impact crop yield. However, the way STP develops under omitted P fertilizer application is not well understood. We examined STP development under omitted P fertilizer application for timeframes between 7 and 46 years on 96 unfertilized treatments (P0 treatments) of 43 European long‐term P field experiments, using five different STP methods. For comparability, values obtained by different STP methods were converted to Olsen‐P concentrations. We fitted exponential decay curves to Olsen‐P data of each P0 treatment defined by initial Olsen‐P values (Olsen‐Pi), rates of decrease (k) and asymptotes (A), reflecting minimum obtainable STP. Subsequently, we analysed whether the variables most commonly recorded in experiments, are sufficient to explain the variation in model parameters, these variables being P export, clay content, Corg and pH as well as average annual temperature and precipitation. We found that out of our predictor variables, soil clay content, precipitation and temperature were showing the most prominent effects on the parameters Olsen‐Pi, A or k. However, the amount of variation explained by the considered variables was too low to potentially facilitate a prediction of STP decrease, and various P0 treatments showed no clear Olsen‐P decrease or unexpectedly high asymptotes. This hints at a strong influence of the P sorption capacity of the soil with often high potential for replenishment from less available P pools. In connection with P introduction from the subsoil or possibly from surrounding plots, the extension of timeframes of omitted P fertilizer application without reaching critical STP values for crop production, might be explainable. Corresponding effects could not be analysed because of lack of data for most P0 treatments, calling for the additional determination of, for example, the maximum P sorption capacity, total P and subsoil P in future experiments.

Quantifying Biogeochemical Controls of Open Ocean CDOM From a Global Mechanistic Model

AbstractChromophoric dissolved organic matter (CDOM) is an important part of ocean carbon biogeochemistry with relevance to long‐term observations of ocean biology due to its dominant light absorption properties. Thus, understanding the underlying processes controlling CDOM distribution is important for predicting changes in light availability, primary production, and the cycling of biogeochemically important matter. We present a biogeochemical CDOM model for the open ocean with two classes of biological lability and uncertainty estimates derived from 43 ensemble members that provide a range of model parameter variations. Ensemble members were optimized to match global ocean in situ CDOM measurements and independently assessed against satellite CDOM estimates, which showed good agreement in spatial patterns. Based on the ensemble median, we estimate that about 7% of open‐ocean CDOM is of terrestrial origin, but the ensemble range is large (<0.1–26%). CDOM is rapidly removed in the surface ocean (<200 m) due to biological degradation for short‐lived CDOM and photodegradation for long‐lived CDOM, leading to a net flux of CDOM to the surface ocean from the dark ocean. This deep‐water source (ensemble median 0.001 m−1 yr−1) is similar in magnitude to the riverine flux (0.005 m−1 yr−1) into the surface ocean. Though discrepancies between the model and observational data remain, this work serves as a foundational framework for a mechanistic assessment of global CDOM distribution that is independent of satellite data.

Parameters estimation and sensitivity analysis of lithium-ion battery model uncertainty based on osprey optimization algorithm

To advance the field of lithium-ion battery (LIB) research, this paper unveils an accurate modelling of LIB that primarily relies on the equivalent circuit model, backed by the Osprey Optimization Algorithm (OOA). In the modelling stage, both single and double resistance-capacitance models are evaluated to depict the charge dynamics, incorporating the effects of fading, load, and temperature variations. The OOA approach is utilized to minimize integral squared errors between the actual measured and model-predicted battery voltages under constraints imposed by the model design variables. This approach is applied to a commercial 2.6 Ahr Panasonic LIB, with the performance of the OOA-based model being benchmarked against models developed by means of other optimization algorithms for further validation. Moreover, the robustness of the OOA method is assessed under battery uncertainty conditions or model parameter variation. A sensitivity analysis is performed on the battery model by employing a proposed approach that evaluates the impact of varying each parameter of the battery model by ±5 %, in a sequence that ascends and descends from 0 to 5 %. The single resistance-capacitance model is selected for in-depth validations. Notably, the OOA approach excels in estimating parameters for LIB modeling under both normal and abnormal operating conditions.

Parameter estimation and uncertainty quantification of a fiber-reinforced concrete model by means of a multi-level Bayesian approach

The paper presents a procedure for the stochastic calibration of a cracked hinge model on the basis of an extensive experimental campaign performed on a large group of nominally identical fiber-reinforced specimens. The calibration is carried out in a multi-level Bayesian framework that allows to quantify and separate several uncertainty contributions affecting model parameters. Indeed, the variability in the experimental response for nominally identical specimens due to the material heterogeneity represents a significant uncertainty contribution as well as model error. The former can be quantified at the hyper-parameter level of the multi-level framework. The presented results highlight the good agreement of the numerical predictions with the experimental data and the superior performance of the multi-level framework compared to that of the classical single-level framework. We also perform analyses to explore the impact of the prior parameter model conditioned on hyper-parameters and assess the minimum number of specimen datasets needed to quantify the inherent variability of model parameters.

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).

Spatial-temporal variability in nitrogen use efficiency: Insights from a long-term experiment and crop simulation modeling to support site specific nitrogen management

Within-field soil heterogeneity can lead to large variation in nitrogen use efficiency (NUE). Crop simulation models provide a multi-faceted approach to management considering both soil and plant interactions. However, research using crop models for investigating within field variation in NUE is limited, in part because of challenges quantifying spatially variable soil model parameters. Here soil apparent electrical conductivity (ECa) and measured soil properties were used to map spatial variations in soil characteristics across a Long-Term Experiment in Norfolk, England. The relationship between plot ECa across the 3 ha experiment and agronomic data across three different nitrogen rates (0, 110, and 220 kg N ha-1) over five wheat years (2010–2020) was quantified. The Sirius crop model was parameterized for two soils representing the extremes of ECa. Sirius was validated using recorded plot data. Site-specific optimal nitrogen and associated leaching risks were simulated across 29 years of weather data. Variation in soil properties had significant impact on measured NUE. At 220 kg N ha-1 mean observed yields across 5 years ranged from 9.0 to 10.7 t ha-1 and grain protein from 11.6% to 11% on the low EC and high EC plots, respectively. On average fertiliser grain N recovery was 19.7 kg N ha-1 lower on the low ECa plots. Sirius simulated the variation in yield, grain protein and grain N recovery to a good level of accuracy with RRMSE of 19.5%, 15.4% and 19.5%, respectively. Simulated optimal nitrogen on the low EC soils was on average 12 kg N ha-1 lower, with >1 in 4 years with optimal nitrogen <200 kg N ha-1. Our work demonstrated that using a combination of proximal soil EC scans and targeted soil sampling we can optimize the data requirements for model parameterisation to support site-specific N management.