Model Predictions Research Articles

Prediction of the fluoride contents of different crop species via the random forest algorithm.

Fluoride (F) is a trace element that is essential to the human body and occurs naturally in the environment. However, a deficiency or excess of F in the environment can potentially lead to human health issues. The pseudototal amount of F in soil often does not correlate directly with the F content in plants. Instead, the F content within plants tends to have a greater correlation with the bioavailable F in soils. In large-scale soil surveys, only the pseudototal elemental content of soils is typically measured, which may not be highly reliable for developing agricultural zoning plans. There are significant variations in the ability of different plants to accumulate F from soil. Additionally, due to variations in soil elemental absorption mechanisms among different plant species, when multiple crops are grown in an area, it is typically necessary to study the elemental absorption mechanisms of each crop. To address these issues, in this study, we examined the factors influencing F bioaccumulation coefficients in different crops based on 1:50,000 soil geochemical survey data. Using the random forest algorithm, four indicators-bioavailable P, bioavailable Zn, leachable Pb, and Sr-were selected from among 29 parameters to predict the F content within crops to replace bioavailable F in the soil. Compared with the multivariate linear regression (MLR) model, the random forest (RF) model provided more accurate and reliable predictions of the fluoride content in crops, with the RF model's prediction accuracy improving by approximately 95.23%. Additionally, while the partial least squares regression (PLSR) model also offered improved accuracy over MLR, the RF model still outperformed PLSR in terms of prediction accuracy and robustness. Additionally, it maximized the utilization of existing geochemical survey data, enabling cross-species studies for the first time and avoiding redundant evaluations of different types of agricultural products in the same region. In this investigation, we selected the Xining-Ledu region of Qinghai Province, China, as the study area and employed a random forest model to predict the crop F content in soils, providing a new methodological framework for crop production that effectively enhances agricultural quality and efficiency.

Material plasticity – Development of stiffness tetrads in fiber‐reinforced materials with large plastic deformations

AbstractThe main objective of this work is to follow the evolution of the stiffness tetrad during large plastic strains. For this purpose, the framework of a general theory of finite plasticity is developed. Some special cases are investigated and the case of a material plasticity theory is considered in more detail. Its main feature is that the elasticity law changes during plastic deformations, for which we develop an approach. We use three types of fiber‐reinforced composites as example materials. Finite element simulations of representative volume elements for uni‐, bi‐ and tri‐directionally reinforced materials with periodic boundary conditions are used for numerical experiments and verification of the model's predictions. From these, we extract the stiffness tetrads before and after large deformations of the material. We quantify the change in stiffness tetrads due to fiber reorientation. Finally, we propose an analytical evolution with three parameters that reproduces reasonably well the evolution of the stiffness tetrads.

Explainable Ensemble Learning and Multilayer Perceptron Modeling for Compressive Strength Prediction of Ultra-High-Performance Concrete

The performance of ultra-high-performance concrete (UHPC) allows for the design and creation of thinner elements with superior overall durability. The compressive strength of UHPC is a value that can be reached after a certain period of time through a series of tests and cures. However, this value can be estimated by machine-learning methods. In this study, multilayer perceptron (MLP) and Stacking Regressor, an ensemble machine-learning models, is used to predict the compressive strength of high-performance concrete. Then, the ML model’s performance is explained with a feature importance analysis and Shapley additive explanations (SHAPs), and the developed models are interpreted. The effect of using different random splits for the training and test sets has been investigated. It was observed that the stacking regressor, which combined the outputs of Extreme Gradient Boosting (XGBoost), Category Boosting (CatBoost), Light Gradient Boosting Machine (LightGBM), and Extra Trees regressors using random forest as the final estimator, performed significantly better than the MLP regressor. It was shown that the compressive strength was predicted by the stacking regressor with an average R2 score of 0.971 on the test set. On the other hand, the average R2 score of the MLP model was 0.909. The results of the SHAP analysis showed that the age of concrete and the amounts of silica fume, fiber, superplasticizer, cement, aggregate, and water have the greatest impact on the model predictions.

Robust Liu Estimator Used to Combat Some Challenges in Partially Linear Regression Model by Improving LTS Algorithm Using Semidefinite Programming

Outliers are a common problem in applied statistics, together with multicollinearity. In this paper, robust Liu estimators are introduced into a partially linear model to combat the presence of multicollinearity and outlier challenges when the error terms are not independent and some linear constraints are assumed to hold in the parameter space. The Liu estimator is used to address the multicollinearity, while robust methods are used to handle the outlier problem. In the literature on the Liu methodology, obtaining the best value for the biased parameter plays an important role in model prediction and is still an unsolved problem. In this regard, some robust estimators of the biased parameter are proposed based on the least trimmed squares (LTS) technique and its extensions using a semidefinite programming approach. Based on a set of observations with a sample size of n, and the integer trimming parameter h ≤ n, the LTS estimator computes the hyperplane that minimizes the sum of the lowest h squared residuals. Even though the LTS estimator is statistically more effective than the widely used least median squares (LMS) estimate, it is less complicated computationally than LMS. It is shown that the proposed robust extended Liu estimators perform better than classical estimators. As part of our proposal, using Monte Carlo simulation schemes and a real data example, the performance of robust Liu estimators is compared with that of classical ones in restricted partially linear models.

Student Loans, Access to Credit, and Consumer Credit Demand

Abstract This paper provides novel evidence that increased student loan debts, caused by rising tuitions, increase borrowers’ demand for additional consumer debt, while simultaneously restricting their ability to access it. The net effect of student loan debt on consumer borrowing varies by market, depending on whether the supply or demand channel dominates. In loosely underwritten credit markets, increased student loan debt causes borrowing to increase, while in tightly underwritten markets, increased student loan debt reduces credit use. These findings match predictions of a standard life cycle model of household consumption and borrowing, augmented by a realistic student loan repayment contract. (JEL G51, D15, I22, D14)

Conformal prediction for regression models with asymmetrically distributed errors: application to aircraft navigation during landing maneuver

Conformal prediction for regression models with asymmetrically distributed errors: application to aircraft navigation during landing maneuver

Data‐Driven Modeling and High‐Precision Tracking Control of a Soft Continuum Manipulator: Enabling Robotic Sorting of Multiwire Cables

As a new class of robots, soft continuum manipulators have attracted attention due to their flexibility and compliance. However, these characteristics create challenges for precise modeling and control. This study proposes a hybrid offline and online data‐driven scheme to achieve high‐precision tracking control of a soft continuum manipulator. First, a novel multiscale deep neural network learns the manipulator model offline. Specifically, the feature fusion module extracts highly discriminative features and captures long‐term dependencies from the temporal trajectory data. The self‐attention module strengthens the ability to represent fusion features and enhances the model prediction accuracy. Then, the learnt model is updated using multisensor data online, and the proposed controller further compensates for the updated model and enhances the tracking accuracy in the movement stage. Finally, the experimental results demonstrate a significant improvement in motion accuracy under different trajectory‐tracking scenarios (i.e., deviations of <1 mm in position and <0.8° in orientation). The example of the multiwire cable sorting proves the feasibility of the proposed scheme in high‐precision industrial applications.

Prediction of Wind Turbine Gearbox Oil Temperature Based on Stochastic Differential Equation Modeling

Aiming at the problem of high failure rate and inconvenient maintenance of wind turbine gearboxes, this paper establishes a stochastic differential equation model that can be used to fit the change of gearbox oil temperature and adopts an iterative computational method and Markov-based modified optimization to fit the prediction sequence in order to realize the accurate prediction of gearbox oil temperature. The model divides the oil temperature change of the gearbox into two parts, internal aging and external random perturbation, adopts the approximation theorem to establish the internal aging model, and uses Brownian motion to simulate the external random perturbation. The model parameters were calculated by the Newton–Raphson iterative method based on the gearbox oil temperature monitoring data. Iterative calculations and Markov-based corrections were performed on the model prediction data. The gearbox oil temperature variations were simulated in MATLAB, and the fitting and testing errors were calculated before and after the iterations. By comparing the fitting and testing errors with the ordinary differential equations and the stochastic differential equations before iteration, the iterated model can better reflect the gear oil temperature trend and predict the oil temperature at a specific time. The accuracy of the iterated model in terms of fitting and prediction is important for the development of preventive maintenance.

Enhancing Suicide Risk Prediction Models with Temporal Clinical Note Features.

The objective of this study was to investigate the impact of enhancing a structured-data-based suicide attempt risk prediction model with temporal Concept Unique Identifiers (CUIs) derived from clinical notes. We aimed to examine how different temporal schemes, model types, and prediction ranges influenced the model's predictive performance. This research sought to improve our understanding of how the integration of temporal information and clinical variable transformation could enhance model predictions. We identified modeling targets using diagnostic codes for suicide attempts within 30, 90, or 365 days following a temporally grouped visit cluster. Structured data included medications, diagnoses, procedures, and demographics, while unstructured data consisted of terms extracted with regular expressions from clinical notes. We compared models trained only on structured data (controls) to hybrid models trained on both structured and unstructured data. We used two temporalization schemes for clinical notes: fixed 90-day windows and flexible epochs. We trained and assessed random forests and hybrid LSTM neural networks using AUPRC and AUROC, with additional evaluation of sensitivity and PPV at 95% specificity. The training set included 2,364,183 visit clusters with 2,009 30-day suicide attempts, and the testing set contained 471,936 visit clusters with 480 suicide attempts. Models trained with temporal CUIs outperformed those trained with only structured data. The window-temporalized LSTM model achieved the highest AUPRC (0.056 ± 0.013) for the 30-day prediction range. Hybrid models generally showed better performance compared to controls across most metrics. This study demonstrated that incorporating EHR-derived clinical note features enhanced suicide attempt risk prediction models, particularly with window-temporalized LSTM models. Our results underscored the critical value of unstructured data in suicidality prediction, aligning with previous findings. Future research should focus on integrating more sophisticated methods to continue improving prediction accuracy, which will enhance the effectiveness of future intervention.

The evolutionary ecology of inbreeding depression in wild plant populations and its impact on plant mating systems

Inbreeding depression, the reduced fitness of inbred relative to outbred individuals was described more than two centuries ago, long before the development of population genetics. Its impact is central to evolutionary ecology and the evolution of mating systems, in particular self-fertilization in hermaphrodites. In the first half of the 20th century, population genetics revealed a mechanism for inbreeding depression through homozygosity. Numerous theoretical studies have modeled inbreeding depression as a function of genetic architecture and analyzed how it varies with population selfing rates. A major concept in these models is purging, i.e., the purging of recessive deleterious mutations through inbreeding. Consequently, inbreeding depression is expected to decrease with increasing population selfing rates. Along with these theoretical studies, many experimental studies, particularly on plants, have measured inbreeding depression using experimental crosses or directly in the field. The results of these studies have revealed that the evolutionary ecology of inbreeding depression is difficult to capture and that empirical data do not exactly match model predictions, specifically purging efficacy. In addition, the lability of inbreeding depression in natural populations can qualitatively affect the selective role of inbreeding depression in the evolution of mating systems. Recently, several studies have demonstrated the role of epigenetics in shedding new light on the dynamics of inbreeding depression in natural populations. This review provides a general overview of the studies on inbreeding depression and how various angles can help capture its selective role in natural populations.

Sound speed prediction of seafloor sediments in the South Yellow Sea based on BP-AdaBoost model

Based on the sound speed and physical property measurements of seafloor sediments obtained in the central part of the South Yellow Sea, a multi-parameter prediction model for the sound speed of seafloor sediments based on seven parameters, including density, porosity, water content, liquid limit, plasticity index, compression coefficient, and median grain size, was developed by using the BP-AdaBoost fusion algorithm. The results show that the model, configured with 7 input layer neurons, 10 hidden layer neurons, 1 output node, a learning rate of 0.1, and 300 training iterations, achieved a correlation coefficient of 0.962. The mean absolute error (MAE) of the predicted sound speed was 10.19 m/s, and the mean relative error (MAPE) was 0.66%. These results are better than those of single-parameter and dual-parameter prediction equations and other machine learning models. The BP-AdaBoost model prediction method of sound speed of seafloor sediments established in this paper is better than the single-parameter and dual-parameter prediction equations and other prediction models, and it can provide a new way for the prediction of sound speed of seafloor sediments in the study area.

Nonuniversality of heavy quark hadronization in elementary high-energy collisions

It has been traditionally hypothesized that the heavy quark (charm c and bottom b) fragmentation is universal across different collision systems, based on the notion that hadronization as a soft process should occur at the characteristic nonperturbative quantum chromodynamics (QCD) scale, ΛQCD. However, this universality hypothesis has recently been challenged by the observation that the c- and b-baryon production relative to their meson counterparts in minimum bias proton-proton (pp) collisions at the CERN Large Hadron Collider (LHC) energies is significantly enhanced as compared to the electron-positron (e+e−) collisions. The conception of nonuniversality is unambiguously reinforced by the latest measurement of the charged-particle multiplicity dependence of the b-baryon–to–meson yield ratio, Λb/B, by the LHCb experiment in s=13TeVpp collisions at the LHC, evolving continuously from the saturation value in minimum bias pp collisions toward the small value in e+e− collisions as the system size gradually reduces. We address the multiplicity dependence of b-baryon production in the canonical statistical hadronization model with input b-hadron spectrum augmented with many hitherto unobserved states from quark model predictions. We demonstrate that the decreasing trend of the Λb/B toward low multiplicities can be quantitatively understood from the canonical suppression on the yield of Λb, as caused by the requirement of strict conservation of baryon number in sufficiently small systems. We have therefore proposed a plausible scenario for understanding the origin of the nonuniversality of heavy quark fragmentation in elementary collisions. Published by the American Physical Society 2024

Partial Residual Plots as an Integrated Model Diagnostic Tool in Model-Based Meta-Analysis.

The use of partial residual plots (PRPs) was explored as a model diagnostic tool in Model-based Meta-Analysis (MBMA). Mathematical derivations illustrating the concepts were followed by an MBMA example using publicly available literature data of anti-depressive treatments with fluoxetine and venlafaxine. An Emax dose-response model was identified for venlafaxine while a constant drug effect combining all dose levels vs. placebo was identified for fluoxetine. The larger the mean baseline Hamilton Depression Rating (HAMD) score, the larger the expected drug effect (P = 0.0122), based on the likelihood ratio test. Mean baseline HAMD score (range) was 25.4 (23.5, 29.4) and 20.8 (15, 26) while mean placebo change from baseline (range) was -9.02 (-12.2, -4.8) and - 6.22 (-10.9, -1.3) for venlafaxine and fluoxetine, respectively. Average baseline HAMD score appeared larger for venlafaxine compared to fluoxetine, albeit a wider range for fluoxetine. Placebo response seemed lower but also more variable in fluoxetine compared to venlafaxine studies. Observed data points tended to deviate from model predictions when the mean baseline HAMD and placebo response values associated with those data points differed substantially from the corresponding values used for the model prediction. Normalizing observed data addressed this, providing a "like-to-like" comparison with model predictions in PRP when assessing the effect of one covariate (dose) after normalizing for other covariates/effects (placebo response and mean baseline). PRPs provide a robust integrated diagnostic tool in MBMA that uses all data to show the correlation between response and any covariate while controlling for other covariates included in the model.

Stimulus repetition induces a two-stage learning process in primary visual cortex.

Repeated stimulus exposure alters the brain's response to the stimulus. We investigated the underlying neural mechanisms by recording functional MRI data from human observers passively viewing 120 presentations of two Gabor patches (each Gabor repeating 60 times). We evaluated support for two prominent models of stimulus repetition, the fatigue model and the sharpening model. Our results uncovered a two-stage learning process in the primary visual cortex. In Stage 1, univariate BOLD activation in V1 decreased over the first twelve repetitions of the stimuli, replicating the well-known effect of repetition suppression. Applying MVPA decoding along with a moving window approach, we found that (1) the decoding accuracy between the two Gabors decreased from above-chance level (∼60% to ∼70%) at the beginning of the stage to chance level at the end of the stage (∼50%). This result, together with the accompanying weight map analysis, suggested that the learning dynamics in Stage 1 were consistent with the predictions of the fatigue model. In Stage 2, univariate BOLD activation for the remaining 48 repetitions of the two stimuli exhibited significant fluctuations but no systematic trend. The MVPA decoding accuracy between the two Gabor patches was at chance level initially and became progressively higher as stimulus repetition continued, rising above and staying above chance level starting at the ∼35th repetition. Thus, results from the second stage supported the notion that sustained and prolonged stimulus repetition prompts sharpened representations. Additional analyses addressed (1) whether the neural patterns within each learning stage remained stable and (2) whether new neural patterns were evoked in Stage 2 relative to Stage 1.

Water temperature disturbance alters the conjugate transfer of antibiotic resistance genes via affecting ROS content and intercellular aggregation

Spread of antibiotic resistance genes (ARGs) in aquatic ecosystems poses a significant global challenge to public health. The potential effects of water temperature perturbation induced by specific water environment changes on ARGs transmission are still unclear. The conjugate transfer of plasmid-mediated ARGs under water temperature perturbation was investigated in this study. The conjugate transfer frequency (CTF) was only 7.16 × 10−7 at a constant water temperature of 5 °C, and it reached 2.18 × 10−5 at 30 °C. Interestingly, compared to the constant 5 °C, the water temperature perturbations (cooling and warming models between 5–30 °C) significantly promoted the CTF. Intracellular reactive oxygen species was a dominant factor, which not only directly affected the CTF of ARGs, but also functioned indirectly via influencing the cell membrane permeability and cell adhesion. Compared to the constant 5 °C, water temperature perturbations significantly elevated the gene expression associated with intercellular contact, cell membrane permeability, oxidative stress responses, and energy driven force for CTF. Furthermore, based on the mathematical model predictions, the stabilization times of acquiring plasmid maintenance were shortened to 184 h and 190 h under cooling and warming model, respectively, thus the water temperature perturbations promoted the ARGs transmission in natural conditions compared with the constant low temperature conditions.

Intergranular fracture, grain-boundary structure, and dislocation-density interactions in FCC bicrystals

A dislocation-density based crystalline plasticity (DCP) and nonlinear finite element (FE) analysis were used to predict, and fundamentally understand how and why fracture nucleation and propagation are related to the interrelated microstructural mechanisms of dislocation-density pileups, GB structure, orientation, and total and partial dislocation density interactions within and adjacent for a random low angle grain boundary (LAGB) and a random high angle GB (HAGB). The GB orientations and structures were obtained from micropillar experiments, such that LAGBs and the HAGBs can be accurately represented and used for the modeling predictions. The normal stress, density of pileups, and dislocation-density accumulation along and within the GB were higher for the low angle GB bicrystal. These interrelated phenomena delineate how fracture for high angle GBs nucleate and propagate at lower nominal strains than the lower angle GB bicrystal case. These predictions underscore how fundamental mechanisms can be identified and used to understand how failure nucleates and propagates for different GB structures and orientations.

Enhancing Remote Sensing Water Quality Inversion through Integration of Multisource Spatial Covariates: A Case Study of Hong Kong’s Coastal Nutrient Concentrations

The application of remote sensing technology for water quality monitoring has attracted much attention recently. Remote sensing inversion in coastal waters with complex hydrodynamics for non-optically active parameters such as total nitrogen (TN) and total phosphorus (TP) remains a challenge. Existing studies build the relationships between remote sensing spectral data and TN/TP directly or indirectly via the mediation of optically active parameters (e.g., total suspended solids). Such models are often prone to overfitting, performing well with the training set but underperforming with the testing set, even though both datasets are from the same region. Using the Hong Kong coastal region as a case study, we address this issue by incorporating spatial covariates such as hydrometeorological and locational variables as additional input features for machine learning-based inversion models. The proposed model effectively alleviates overfitting while maintaining a decent level of accuracy (R2 exceeding 0.7) during the training, validation and testing steps. The gap between model R2 values in training and testing sets is controlled within 7%. A bootstrap uncertainty analysis shows significantly improved model performance as compared to the model with only remote sensing inputs. We further employ the Shapely Additive Explanations (SHAP) analysis to explore each input’s contribution to the model prediction, verifying the important role of hydrometeorological and locational variables. Our results provide a new perspective for the development of remote sensing inversion models for TN and TP in similar coastal waters.

A rigorous model for hydrogen response in industrial propylene polymerization loop reactors: From micro-mechanism to macro-behavior

In spite of playing a chain transfer agent role, the hydrogen activation effect in propylene polymerization with Ti-based Ziegler-Natta catalysts, so called “hydrogen response”, is usually explained by the dormant-site theory in polymer chemistry. However, how the micro-mechanism at the catalyst site scale affects the macro-behavior of industrial processes is unrevealed. This work aims at developing a rigorous model that can accurately describe hydrogen response and predict polymer chain microstructure for an industrial propylene polymerization process consisting of loop reactors in series. The model is comprised of a kinetic model, a thermodynamic model, and a macroscopic non-ideal loop reactor model. To describe hydrogen response, an elaborated kinetic model is established based on a consistent dormant site reactivation mechanism assuming a multi-site model. The model predictions are in good agreement with plant data, after taking the dormant sites reactivated by hydrogen into account. The proposed model is capable of fully assessing the effects of various process conditions, during steady-state and grade transition processes, on the macro-behavior (i.e., production, slurry density) as well as polymer properties (i.e., melt flow index, molecular weight distribution) in the industrial loop reactors. The underlying correlations are also discussed.

Prediction of elastic properties of woven polymer composites using micromechanical models and measured microstructural parameters

Prediction of elastic properties of plain weave composites using two well-known micromechanical models is addressed, along with a dedicated finite element model of the unit cell which includes details of the woven architecture. Predictions of micromechanical models are carried out using geometric input parameters statistically measured from micrographies of the unit cell of the plain weave composite and compared to finite element predictions and to measured elastic properties of an E-glass/vinyl ester plain weave composite. The micromechanical models predict that the width and thickness of the yarn in the fill and warp directions greatly influence the elastic properties and anisotropy of the plain weave composite. Good agreement between all approaches and the measured values is observed for the in-plane elastic properties, as long as the input geometric parameters of the unit cell required for the models are measured with statistical rigor. However, transverse shear moduli are not accurately predicted by the examined micromechanical models, only by the finite element method. Reasons for such discrepancies are discussed and supported by modeling findings and digital image correlation full field strain maps.

A Computational Framework to Optimize the Mechanical Behavior of Synthetic Vascular Grafts.

The failure of synthetic small-diameter vascular grafts has been attributed to a mismatch in the compliance between the graft and native artery, driving mechanisms that promote thrombosis and neointimal hyperplasia. Additionally, the buckling of grafts results in large deformations that can lead to device failure. Although design features can be added to lessen the buckling potential, the addition is detrimental to decreasing compliance (e.g., reinforcing coil). Herein, we developed a novel finite element framework to inform vascular graft design by evaluating compliance and resistance to buckling. A batch-processing scheme iterated across the multi-dimensional design parameter space, which included three parameters: coil thickness, modulus, and spacing. Three types of finite element models were created in FEBio for each unique coil-reinforced graft parameter combination to simulate pressurization, axial buckling, and bent buckling, and results were analyzed to quantify compliance, buckling load, and kink radius, respectively, from each model. Importantly, model validation demonstrated that model predictions agree qualitatively and quantitatively with experimental observations. Subsequently, data for each design parameter combination were integrated into an optimization function for which a minimum value was sought. The optimization values identified various candidate graft designs with promising mechanical properties. Our investigation successfully demonstrated the model-directed framework identified vascular graft designs with optimal mechanical properties, which can potentially improve clinical outcomes by addressing device failure. In addition, the presented computational framework promotes model-directed device design for a broad range of biomaterial and regenerative medicine strategies.