Parameter In Models Research Articles

Completing the picture of field-grown cereal crops: a new method for detailed leaf surface models in wheat.

The leaf angle distribution (LAD) is an important structural parameter of agricultural crops that influences light interception, radiation fluxes and consequently plant performance. Therefore, LAD and its parametrized form, the Beta distribution, is used in many photosynthesis models. However, in field cultivations, these parameters are difficult to assess and cereal crops in particular pose challenges since their leaves are thin, flexible, and often bent and twisted around their own axis. To our knowledge, there is only a very limited set of methods currently available to calculate LADs of field-grown cereal crops that explicitly takes these special morphological properties into account. In this study, a new processing pipeline is introduced that allows for the generation of realistic leaf surface models and the analysis of LADs of field-grown cereal crops from 3D point clouds. The data acquisition is based on a convenient stereo imaging setup. The approach was validated with different artificial targets and results on the accuracy of the 3D reconstruction, leaf surface modeling and calculated LAD are given. The mean error of the 3D reconstruction was below 1mm for an inclination angle range between 0° and 75° and the leaf surface could be quantified with an average accuracy of 90%. The concordance correlation coefficient (CCC) of 99.6% (p-value = [Formula: see text]) indicated a high correlation between the reconstructed inclination angle and the identity line. The LADs for bent leaves were reconstructed with a mean error of 0.21° and a standard deviation of 1.55°. As an additional parameter, the insertion angle was reconstructed for the artificial leaf model with an average error < 5°. Finally, the method was tested with images of field-grown cereal crops and Beta functions were approximated from the calculated LADs. The mean CCC between reconstructed LAD and calculated Beta function was 0.66. According to Cohen, this indicates a high correlation. This study shows that our image processing pipeline can reconstruct the complex leaf shape of cereal crops from stereo images. The high accuracy of the approach was demonstrated with several validation experiments including artificial leaf targets. The derived leaf models were used to calculate LADs for artificial leaves and naturally grown cereal crops. This helps to better understand the influence of the canopy structure on light absorption and plant performance and allows for a more precise parametrization of photosynthesis models via the derived Beta distributions.

Mechanistic Framework to Predict Maternal-Placental-Fetal Pharmacokinetics of Nifedipine Employing Physiologically Based Pharmacokinetic Modeling Approach.

Nifedipine is used for treating mild to severe hypertension and preventing preterm labor in pregnant women. Nevertheless, concerns about nifedipine fetal exposure and safety are always raised. The aim of this study was to develop and validate a maternal-placental-fetal nifedipine physiologically based pharmacokinetic (PBPK) model and apply the model to predict maternal, placental, and fetal exposure to nifedipine at different pregnancy stages. A nifedipine PBPK model was verified with nonpregnant data and extended to the pregnant population after the inclusion of the fetoplacental multicompartment model that accounts for the placental tissue and different fetal organs within the Simcyp Simulator version 22. Model parametrization involved scaling nifedipine transplacental clearance based on Caco-2 permeability, and fetal hepatic clearance was obtained from in vitro to in vivo extrapolation encompassing cytochrome P450 3A7 and 3A4 activities. Predicted concentration profiles were compared with in vivo observations and the transplacental transfer results were evaluated using 2-fold criteria. The PBPK model predicted a mean cord-to-maternal plasma ratio of 0.98 (range, 0.86-1.06) at term, which agrees with experimental observations of 0.78 (range, 0.59-0.93). Predicted nifedipine exposure was 1.4-, 2.0-, and 3.0-fold lower at 15, 27, and 39 weeks of gestation when compared with nonpregnant exposure, respectively. This innovative PBPK model can be applied to support maternal and fetal safety assessment for nifedipine at various stages of pregnancy.

Finite element solution of crack-tip fields for an elastic porous solid with density-dependent material moduli and preferential stiffness

In this paper, the finite element solutions of crack-tip fields for an elastic porous solid with density-dependent material moduli are presented. Unlike the classical linearized case in which material parameters are globally constant under a small strain regime, the stiffness of the model presented in this paper can depend upon the density with a modeling parameter. The proposed constitutive relationship appears linear in the Cauchy stress and linearized strain independently. From a subclass of the implicit constitutive relation, the governing equation is bestowed via the balance of linear momentum, resulting in a quasi-linear partial differential equation (PDE) system. Using the classical damped Newton’s method, the sequence of linear problems is then obtained, and the linear PDEs are discretized through a bilinear continuous Galerkin-type finite element method. We perform a series of numerical simulations for material bodies with a single edge-crack subject to a variety of loading types (i.e. the pure mode-I, II, and mixed-mode). Numerical solutions demonstrate that the modeling parameter in our proposed model can control preferential mechanical stiffness with its sign and magnitude along with the change of volumetric strain. This study can provide a mathematical and computational foundation to further model the quasi-static and dynamic evolution of cracks, utilizing the density-dependent moduli model and its modeling framework.

One-dimensional extended Su–Schrieffer–Heeger models as descendants of a two-dimensional topological model

The topological phase diagrams and finite-size energy spectra of one-dimensional extended Su–Schrieffer–Heeger (SSH) models with long-range hoppings on the trimer lattice are investigated in detail. Due to the long-range hoppings, the band structure of the original SSH model becomes more complicated and new phases with the large Zak phase can emerge. Furthermore, a seeming violation of bulk-edge correspondence occurs in the one-dimensional topological system whose band topology stems from the inversion symmetry. The one-dimensional models are mapped onto a two-dimensional topological model when a parameter of the one-dimensional models is regarded as an additional degree of freedom. As Fourier components of the derived two-dimensional model, phase boudaries and the finite-size spectra of one-dimensional models can be recovered from the model in the higher spatial dimensions. Then the origin of edge modes of one-dimensional models can be understood from two dimensions and we give a reasonable explanation of the violation of bulk-edge correspondence in one spatial dimension. In fact, we may give a general perspective that the topological properties of one-dimensional (lower-dimensional) systems can be found their origin from two-dimensional (higher-dimensional) systems.

Experimental Investigation of a 64 Ah Lithium-Ion Pouch Cell

This study presents a meticulous investigation and characterization of a 64 Ah commercial lithium-ion pouch cell. Notably, an exhaustive analysis of the cell’s open-circuit voltage and kinetics attributes is conducted, with particular emphasis on the temperature-dependent dynamics. Subsequently, a teardown experiment is performed, offering an incisive insight into the macro-geometrical properties underpinning the cell’s architecture. Further details about the microstructural features and formulation inherent to the cathode and anode are revealed after image processing of the electrodes’ cross sections. The details of cell balancing and cycling window of the electrodes in the pouch cell are determined and discussed based on the open-circuit-voltage measurements of the individual electrodes and a simple optimization algorithm. The methodologies presented in this work are insightful on the characterization and model parametrization of the high-capacity commercial lithium-ion cells.

A deep neural network: mechanistic hybrid model to predict pharmacokinetics in rat.

An important aspect in the development of small molecules as drugs or agrochemicals is their systemic availability after intravenous and oral administration. The prediction of the systemic availability from the chemical structure of a potential candidate is highly desirable, as it allows to focus the drug or agrochemical development on compounds with a favorable kinetic profile. However, such predictions are challenging as the availability is the result of the complex interplay between molecular properties, biology and physiology and training data is rare. In this work we improve the hybrid model developed earlier (Schneckener in J Chem Inf Model 59:4893-4905, 2019). We reduce the median fold change error for the total oral exposure from 2.85 to 2.35 and for intravenous administration from 1.95 to 1.62. This is achieved by training on a larger data set, improving the neural network architecture as well as the parametrization of mechanistic model. Further, we extend our approach to predict additional endpoints and to handle different covariates, like sex and dosage form. In contrast to a pure machine learning model, our model is able to predict new end points on which it has not been trained. We demonstrate this feature by predicting the exposure over the first 24h, while the model has only been trained on the total exposure.

System-Specific Parameter Optimization for Nonpolarizable and Polarizable Force Fields.

The accuracy of classical force fields (FFs) has been shown to be limited for the simulation of cation-protein systems despite their importance in understanding the processes of life. Improvements can result from optimizing the parameters of classical FFs or by extending the FF formulation by terms describing charge transfer (CT) and polarization (POL) effects. In this work, we introduce our implementation of the CTPOL model in OpenMM, which extends the classical additive FF formula by adding CT and POL. Furthermore, we present an open-source parametrization tool, called FFAFFURR, that enables the (system-specific) parametrization of OPLS-AA and CTPOL models. The performance of our workflow was evaluated by its ability to reproduce quantum chemistry energies and by molecular dynamics simulations of a zinc-finger protein.

Multifidelity adaptive sequential Monte Carlo for geophysical inversion

SUMMARY In the context of Bayesian inversion, we consider sequential Monte Carlo (SMC) methods that provide an approximation of the posterior probability density function and the evidence (marginal likelihood). These particle approaches build a sequence of importance sampling steps between gradually tempered distributions evolving from the prior to the posterior PDF. To automate the definition of the tempering schedule, adaptive SMC (ASMC) allows tuning the temperature increments on-the-go. One general challenge in Bayesian inversions is the computational burden associated with expensive, high-fidelity forward solvers. Lower-fidelity surrogate models are interesting in this context as they can emulate the response of expensive forward solvers at a fraction of their cost. We consider surrogate modelling within ASMC and introduce first an approach involving surrogate modelling only, in which either prior samples are used to train the surrogate, or the surrogate model is retrained by updating the training set during the inversion. In our implementation, we rely on polynomial chaos expansions for surrogate modelling, principal component analysis for model parametrization and a ground-penetrating radar cross-hole tomography problem with either an eikonal or finite-difference time-domain solver as high-fidelity solver. We find that the method based on retraining the surrogate during the inversion outperforms the results obtained when only considering prior samples. We then introduce a computationally more expensive multifidelity approach including a transition to the high-fidelity forward solver at the end of the surrogate-based ASMC run leading to even more accurate results. Both methods result in speed-ups that are larger than one order of magnitude compared to standard high-fidelity ASMC inversion.

Ionospheric equivalent slab thickness ingestion into the NeQuick model

The ionospheric equivalent slab thickness (τ), defined as the ratio of the vertical total electron content (vTEC) to the ionospheric F2-layer electron density maximum (NmF2), is a parameter providing useful information on the shape of the vertical electron density profile. However, the use of this information is of difficult practical application in empirical ionosphere models, such as the NeQuick model, because by design they do not explicitly include τ as a modelling parameter. In this work, we investigated the opportunity of using measured τ values to improve the empirical modelling of the ionosphere vertical electron density profile by NeQuick. Measured τ values were obtained through NmF2 observations and vTEC measurements obtained between 2001 and 2019 by an ionosonde and a ground-based GNSS receiver, respectively, co-located at Rome ionospheric station (41.8° N, 12.5° E; Italy). NeQuick τ was obtained as the ratio between modelled NmF2 and vTEC values, the latter obtained by integration of the vertical profile. As a first step, τ values modelled by NeQuick were compared with corresponding values measured at Rome station to highlight diurnal, seasonal, and solar activity differences. Then, measured τ values were ingested in NeQuick through a three-parameter assimilation procedure which first assimilate F2-layer peak characteristics to constrain the F2-layer anchor point, and then assimilate vTEC to optimize the F2-layer shape through the NeQuick F2-layer thickness parameter, namely B2bot. The assimilation procedure provides information on how the NeQuick B2bot has to be modified to match measured τ values, and then on how the shape of the F2-layer profile has to be changed accordingly. Our results highlight that, in many cases, the NeQuick B2bot has to be increased to match observations, which has implications on the modelling of the NeQuick bottomside and topside effective scale heights.

Impact of particle size distribution on the rest phase behavior of LIB cathodes – Model based analysis

Dynamic measurements like the galvanostatic intermittent titration technique (GITT) and other pulse response measurements methods are widely used for the parametrization of battery and electrode models. These methods are based on the analysis of the transient cell potential in response to a change in the current. Most of them focus on the development of the cell potential as a current is applied to a formerly equilibrated cell. On the contrary, the interruption of the current and the development of the cell potential during the rest phase is more challenging to analyze. This is in parts attributed to the non-uniform particle size distributions of electrodes, while many analytical methods rely on the assumption of a single, representative particle size.In this model study, we systematically analyze the impact of non-uniform particle size distributions by means of the extended Doyle-Fuller-Newman model. This is done both by analyzing varying distributions of discrete particle classes and comparing their effects on the voltage relaxation curve. From this, we derive recommendations for a more accurate design of relaxation experiments and provide a framework to interpret frequently observed deviations between electrochemical experiments and reduced order simulations.

Metal Organic Complexation in Seawater: Historical Background and Future Directions.

The speciation of most biologically active trace metals in seawater is dominated by complexation by organic ligands. This review traces the history of work in this area, from the early observations that showed surprisingly poor recoveries using metal preconcentration protocols to the present day, where advances in mass spectroscopy and stable isotope geochemistry are providing new insights into the structure, origin, fate, and biogeochemical impact of organic ligands. Many long-standing hypotheses about the specific biological origin of ligands such as siderophores in seawater are finally being validated. This work has revealed the complexity of organic complexation, with multiple ligands and, in some cases, timescales of ligand exchange that are much slower than originally thought. The influence of organic complexation on scavenging is now a key parameter in biogeochemical models of biologically essential metals, especially iron. New insights about the sources and sinks of ligands are required to enhance the usefulness of these models.

Drivers of deciduous forest near-infrared reflectance: A 3D radiative transfer modeling exercise based on ground lidar

The fraction of near infrared (NIR) solar energy reflected by forest canopies has been shown to correlate with leaf nitrogen availability and photosynthesis. But the canopy properties behind this relation are unclear, partly because whole canopy reflectance and albedo results from multiple complex interactions between photons and tree parts, and also because modeling these interactions has been challenging due to difficulties in representing forest canopies with sufficient detail. Ground lidar technology has been applied to this problem in recent years; it is used here to provide highly detailed forest canopy representations as inputs into a ray tracing radiative transfer model. These structural representations are coupled with the best available field data on leaf optical properties to produce simulated images of canopy reflectance in the NIR, as well as the detailed 3D mapping of NIR energy absorption within the canopy. The simulations performed for two deciduous forests in the northeastern USA were first compared to airborne hyperspectral observations to assess their accuracy. The radiative transfer model was then used to quantify the sensitivity of whole canopy reflectance and vertical absorption of NIR to changes in model parametrization in an attempt to identify the main drivers of canopy reflectance. The results point to the arrangement of leaves in the topmost canopy layers, both in terms of leaf angles and leaf clumping within shoots, and to the leaf-level absorptance in all canopy layers as the dominant factors affecting canopy reflectance and albedo. These findings emphasize the need for improving our understanding of leaf arrangement in canopy tops – including what it covaries with – and for including measurements of leaf transmission when measuring leaf spectral properties in the field.

Density measurement and uncertainty evaluation of elemental and alloy liquids using electrostatic levitation

Liquid density is a crucial parameter in physical models, numerical simulations, and a key thermophysical property essential for understanding various physical phenomena. Although density measurements of liquid metals have been carried out with various methods, it is still hard to give consistent and precise results, particularly at high temperatures. In this study, we systematically perform the precise density measurements for 34 elemental and alloy liquids using electrostatic levitation (ESL). Through a comprehensive review of density data in the literature, we propose recommended density values for fourteen transition metal liquids. Notably, we observe significant discrepancies in density measurements between ESL and electromagnetic levitation (EML). After conducting careful uncertainty evaluation and error analysis, the discrepancies are attributed to the shape deformation of the liquids, which seriously influences the density and its temperature coefficient measurements. The present work not only provides high-quality density data but also helps to develop standards for archiving density datasets and improve the density measurements of high-temperature molten melts.

An IDE-based nonlinear grey Bernoulli model and applications to daily traffic flow pattern identification

The nonlinear grey Bernoulli model is a powerful tool for modelling and forecasting time series exhibiting a rough inverted U-shape behaviour. Traditionally, it is considered an indirect dynamical model, informed by empirical knowledge that the accumulation of the inverted U-shape series leads to a growth behaviour, which is subsequently characterized by the Bernoulli ordinary differential equation (ODE). However, its mechanism remains incomplete, particularly concerning the modelling paradigm and parameter estimation. To this end, we propose an equivalent and simplified variant in the form of an integro-differential equation (IDE), enabling direct modelling of the inverted U-shaped series and thereby enhancing the interpretability. By taking the IDE as a state equation in the state space framework, separable nonlinear least squares (SLS) and nonlinear least squares (NLS) are respectively formulated to simultaneously estimate structural parameters and the initial condition. Then, by combining the state equation (ODE/IDE) and parameter estimation approach (SLS/NLS), the generated ODE-based indirect paradigms are comprehensively compared with the IDE-based direct ones. Numerical simulations show that (i) IDE-based models outperform ODE-based ones and (ii) NLS outperforms SLS. Real-world applications indicate that the nonlinear grey Bernoulli model combining IDE and NLS can accurately discover the underlying dynamics of daily traffic flow, outperforming the competitive models.

ARMOSA Model Parametrization for Winter Durum Wheat Cultivation under Diverse Cropping Management Practices in a Mediterranean Environment

In anticipation of climate changes, strategic soil management, encompassing reduced tillage and optimized crop residue utilization, emerges as a pivotal strategy for climate impact mitigation. Evaluating the transition from conventional to conservative cropping systems, especially the equilibrium shift in the medium to long term, is essential. ARMOSA, a robust crop simulation model, adeptly responds to varied soil management practices such as no tillage, minimum tillage, and specific straw management options such as chopping and incorporating crop residue into the soil (with or without prior nitrogen and water addition before ploughing). It effectively captures dynamic fluctuations in total organic carbon over an extended period. While challenges persist in precisely predicting grain yield due to climatic intricacies, ARMOSA stands out as a valuable and versatile tool. The model excels in comprehending and simulating wheat cultivar responses in dynamic agricultural ecosystems, shedding light on phenological patterns, biomass accumulation, and soil organic carbon dynamics. This research significantly advances our understanding of the intricate complexities associated with past wheat cultivation in diverse environmental conditions. ARMOSA’s ability to inform decisions on conservation practices positions it as a valuable asset for researchers, agronomists, and policymakers navigating the challenges of sustainable agriculture amidst climate change. Its real-world significance lies in its potential to guide informed decisions, contributing to global efforts in sustainable agriculture and climate resilience.

Rainfall distributional properties control hydrologic model parameter importance.

Study regionSemi-arid region of the Western United States of America in 16.6 km2 WS10 watershed using data from the highly instrumented Walnut Gulch Experimental Watershed managed by the USDA-Agricultural Research Services. Study focusHydrologic model parameters are generally designed to represent watershed physiographic properties. This study investigates the possible impact of climate forcing, particularly precipitation across a watershed, on model parameter identification – a topic that has historically received minimal attention. To address this gap, we conducted time-varying and time-aggregate sensitivity analysis of a physically based distributed hydrologic model on a heavily instrumented watershed under various observed and synthetic storm events to assess how the distributional properties of rainfall in space and time influence the importance model parameters. Particularly, we focused on a semi-arid watershed with an area larger than the typical convective storms in the region. New hydrologic insightResults show that there is a significant variation in parameter importance following rainfall spatial and temporal properties and that characterizing the relationship between parameter importance and rainfall properties is essential for identifying parameter values that adequately capture the watershed behaviors. We further showed that a power-law function linking parameter importance to precipitation properties can extrapolate results to other climate regimes. More analyses across different watersheds, climate forcing, and models are required to improve our understanding and strategies for parametrization and calibration of watershed models.

Improving monthly mean land surface temperature estimation by merging four products using the generalized three-cornered hat method and maximum likelihood estimation

Land surface temperature (LST) is a critical parameter associated land surface energy and water balance at the land–atmosphere interface. Monthly mean LST (MMLST) is used not only as an input parameter for land surface models, but also as an indicator for general climate trend studies. Four MMLST products (i.e., NESDC, TPDC, ZENODO, and ERA5) were generated by different research teams and institutes using different algorithms. In this study, a new method was presented to improve MMLST estimation by merging these four products using the generalized three-cornered hat (TCH) method and maximum likelihood estimation (MLE). The generalized TCH method was used to simultaneously calculate the relative uncertainties of these four MMLST products during the period 2003–2019. In general, the smallest uncertainties are obtained for the TPDC MMLST product in most areas of the globe, whereas the largest uncertainties are achieved for the ERA5 MMLST product, especially in high-latitude regions. The MLE method was used to merge these four MMLST products and to generate a new MMLST product in consideration of the uncertainty of each individual MMLST product. In situ measurements at 25 stations from the AmeriFlux and SURFRAD networks during the period 2003–2019 were used to evaluate the accuracies of the individual and merged MMLST products. The largest root mean squared error (RMSE) of approximately 2.4 K is obtained for the TPDC MMLST product, whereas the smallest RMSE of around 1.6 K is reached for the merged MMLST product. The NESDC, ZENODO, and ERA5 MMLST products have similar RMSE of approximately 2 K. The results indicate that the merged MMLST product outperforms each individual MMLST product. The merged MMLST product shows promising prospects for climate- and global-scale studies and applications.

Dark respiration in leaves, stems, and fruits of Corylus avellana

Dark respiration (Rd) is a fundamental process in plant physiology. It reduces the carbohydrates gained via photosynthesis to CO2 and produces energy that can be used to satisfy the maintenance needs of organs and contribute to their growth. Because Rd is involved in the formation of new organs, it is also driven by the size and growth stage of each organ. In this work, we aimed at investigating how changes in temperature and relative growth rate (RGR) impact Rd in leaves, stems and fruits of filbert (Corylus avellana), and to assess if there are critical stages during which respiration peaks can pose a limitation to the fruit set. Rd was measured on leaves, stems and fruits of “Tonda di Giffoni” during the spring and summer of 2021. Rd in filbert leaves, stems and fruits widely varied according to temperature and RGR. The results indicate that when the nut shell grows, the carbon demand, in terms of Rd, was two-fold compared to other organs. High carbon demands have significant consequences on carbon partitioning among organs and the occurrence of possible carbon shortages limiting nut yield. Rd relationships with temperature and RGR are fundamental for the parametrization of future Rd models for filbert, and the optimization of appropriate horticultural techniques to reduce the carbon limitation to fruit set.

Leading effect for wind turbine wake models

As wind energy expands worldwide, the demand of reliable, fast, cost-efficient wind turbine wake models is growing. This is a significant challenge as wind turbines face various inflow conditions that include turbulence, inhomogeneities/instationarities and upstream wakes. In consequence, an enormous number of engineering models, each one based on different physical concepts, has been proposed. The majority focuses on the far wake where the mean velocity recovers and turbulence decays after it built up. We argue that the most important, or the leading, parameter for wake modeling is the length scale of a virtual origin. Testing different models from the literature for data sets from laboratory wind turbines and multi-megawatt turbines obtained by LiDAR, we find that all models perform significantly better when such a virtual origin is added. Our results can therefore be used for a yet missing definition of a near wake zone.

Reading tea leaves or tracking true constructs? An assessment of personality-based latent profiles in eating disorders.

Eating disorder (ED) subtyping studies have often extracted an undercontrolled, an overcontrolled and a resilient profile based on trait impulsivity and perfectionism. However, the extent to which methodological choices impact the coherence and distinctness of resulting subtypes remains unclear. In this paper, we aimed to assess the robustness of these findings by extracting personality-based subtypes on a sample of ED patients (N = 221) under different analytic conditions. We ran four latent profile analyses (LPA), varying the extent to which we constrained variances and covariances during model parametrization. We then performed a comparative analysis also including state ED symptom measures as indicators. Finally, we used cross-method validation via k-means clustering to further assess the robustness of our profiles. Our results demonstrated a four-profile model based on variances in impulsivity and perfectionism to fit the data well. Across model solutions, the profiles with the most and least state and trait disturbances were replicated most stably, while more nuanced variations in trait variables resulted in less consistent profiles. Inclusion of ED symptoms as indicator variables increased subtype differentiation and similarity across profiles. Validation cluster analyses aligned most with more restrictive LPA models. These results suggest that ED subtypes track true constructs, since subtypes emerged method-independently. We found analytic methods to constrain the theoretical and practical conclusions that can be drawn. This underscores the importance of objective-driven analytic design and highlights its relevance in applying research findings in clinical practice.