Search

Interactions among atmospheric, root‐soil, and vegetation processes drive carbon dioxide fluxes (Fc) from land to atmosphere. Eddy covariance measurements are commonly used to measure Fc at sub‐daily timescales and validate process‐based and data‐driven models. However, these validations do not reveal process interactions, thresholds, and key differences in how models replicate them. We use information theory‐based measures to explore multivariate information flow pathways from forcing data to observed and modeled hourly Fc, using flux tower data sets in the Midwestern U.S. in intensively managed corn‐soybean landscapes. We compare multiple linear regressions, long‐short term memory, and random forests (RF), and evaluate how different model structures use information from combinations of sources to predict Fc. We extend a framework for model predictive and functional performance, which examines a suite of dependencies from all forcing variables to the observed or modeled target. Of the three model types, RF exhibited the highest functional and predictive performance, correctly capturing strong dependencies between radiation and temperature variables with Fc. Regionally trained models demonstrate lower predictive but higher functional performance compared to site‐specific models, suggesting superior reproduction of observed relationships at the expense of predictive accuracy. This study shows that some metrics of predictive performance encapsulate functional behaviors better than others, highlighting the need for multiple metrics of both types. This study improves our understanding of carbon fluxes in an intensively managed landscape, and more generally provides insight into how model structures and forcing variables translate to interactions that are well versus poorly captured in models. Plain Language Summary: In an agricultural landscape, exchanges of carbon dioxide between the land and atmosphere occur due to photosynthesis and respiration and depend on weather, soil, and vegetation conditions. Traditional model performance metrics focus on the relationship between observed and modeled outputs, while functional performance considers the relationships between interacting inputs and outputs. We compare several performance measures for three different machine learning (ML) models that simulate sub‐daily carbon fluxes. We look at how drivers such as solar radiation, soil moisture, temperature, humidity, and rainfall provide information to carbon fluxes, and whether different ML models also capture these interactions. In other words: Air, soil, and plants drive carbon's upward path,Models are detectives, interpreting their math.With information theory, we map data's travel courses,To see how models find or miss carbon's causal sources. Key Points: Information theory measures describe individual and joint causal relationships in observed versus modeled vertical carbon dioxide fluxesThree machine learning models overestimate unique information from sources at the expense of synergistic, or joint informationRegionally trained models have improved functional but not predictive performances, indicating a trade‐off [ABSTRACT FROM AUTHOR]

Breast cancer is ranked as a significant cause of mortality among females globally. Its complex nature poses principal challenges for physicians and researchers for rapid diagnosis and prognosis. Hence, machine learning algorithms are employed to forecast and identify diseases. This study discusses the comparative analysis of seven machine learning models, e.g., logistic regression (LR), support vector machine (SVM), k-nearest neighbor classifier (KNN), decision tree classifier (DT), random forest classifier (RF), Naïve Bayes (NB), and artificial neural network (ANN) to predict breast cancer using Wisconsin breast cancer and breast cancer datasets. In the Wisconsin breast cancer dataset, KNN depicted 99% accuracy, followed by RF (98%), SVM (96%), NB (96%), LR (96%), ANN (93%), and DT (92%). On the contrary, in the breast cancer (BC) dataset, the highest accuracy was achieved by LR at 83%, and the lowest was achieved by DT (65%), which depicted that the numeric dataset WBC has better accuracy than the breast cancer dataset. [ABSTRACT FROM AUTHOR]

Copyright of Plateau Meteorology is the property of Plateau Meteorology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As the economic level of individuals rises, so too does the demand for mutton. Enhancing the breeds of mutton sheep not only boosts production efficiency and economic benefits but also fosters the sustainable growth of the mutton sheep breeding industry. Thus, this study examines the early growth and reproductive traits of Tianmu Sainuo sheep, analyzing the genetic interactions among these traits to furnish a theoretical foundation for refining breeding strategies and expediting the genetic advancement of this breed. The investigation compiled 29,966 data entries, involving 111 sires for birth weight (BWT) and 113 for other metrics. The data encompassed 10,415 BWT records from 1,633 dams, 12,753 weaning weight (WWT) records from 1,570 dams, 12,793 average daily gain (ADG) records from 1,597 dams, and 13,594 litter size (LS) records from 1,499 dams. Utilizing the GLM procedure in SAS 9.2 software, the study analyzed the non-genetic influences on lamb BWT, WWT, ADG, and LS. Concurrently, DMU software estimated the variance components across various animal models for each trait. Employing the Akaike Information Criterion (AIC) and likelihood ratio test (LRT), six models were tested, incorporating or excluding maternal inheritance and environmental impacts, to identify the optimal model for deriving genetic parameters. The findings reveal that birth year (BY), birth quarter (BQ), birth type (BT), age of mother (AM), and birth sex (BS) exerted significant impacts on BWT, WWT, and ADG (p < 0.01). Additionally, BQ and AM significantly influenced LS (p < 0.01). The most accurate genetic evaluation model determined the heritability of BWT, WWT, ADG, and LS to be 0.0695, 0.0849, 0.0777, and 0.1252, respectively. [ABSTRACT FROM AUTHOR]

Due to the rapid economic development of globalization and the intensification of economic and trade exchanges, cross-international and regional carbon emissions have become increasingly severe. Governments worldwide establish laws and regulations to protect their countries' environmental impact. Therefore, selecting robustness evaluation models and metrics is an urgent research topic. This article proves the reliability and scientific of the assessment data through literature coupling evaluation, multidisciplinary coupling mathematical model and international engineering case analysis. The innovation of this project's research lies in the comprehensive analysis of the complex coupling effects of various discrete data and uncertainty indicators on the research model across international projects and how to model and evaluate interactive effects accurately. This article provides scientific measurement standards and data support for governments worldwide to formulate carbon tariffs and carbon emission policies. Case analysis data shows that the carbon emission ratio of exporting and importing countries is 0.577:100; the carbon trading quota ratio is 32.50:100. [ABSTRACT FROM AUTHOR]

The upper ocean mixed‐layer depth (MLD) has been recognized as a crucial factor in understanding the impact of climate change on tropical cyclone (TC) intensity, while the specific MLD variations in the western North Pacific (WNP) basin have received comparatively little attention. Using two ocean reanalysis datasets and the output of 28 CMIP6 models, the climatological mean MLD and its trend in the TC active region are examined. Compared to the ORAS4 and EN4 datasets, the spatial pattern of the climatological mean MLD can be well simulated in the multi‐model ensemble (MME). The mean MLD decreases with increasing latitude and maintains the maximum in the equatorial region. The spatial patterns of MLD long‐term (1958–2017) and short‐term (1979–2017) trends are deepening in the TC active region, with the relatively large positive trends occurring to the southeastern WNP primarily between 5°N and 15°N. However, the simulated trend in the MME is dominant with shoaling MLD, and the shoaling of the MLD is also projected under the SSP5‐8.5 scenario. It is suggested that the bias on the simulated shoaling of the MLD in CMIP6 models may result from the overestimated enhancement of the upper‐ocean stratification. Thus, there is a discrepancy between the observed and simulated MLD trends in the WNP basin, which may have implications for TC intensity change under global warming scenarios. [ABSTRACT FROM AUTHOR]

In order to evaluate the simulation effect of the model on the dynamic characteristics of the climate system, the approximate entropy method was used to analyze the complexity of surface air temperature (SAT) based on observational data (CN05.1) and the model-simulated data by the Beijing Climate Central Climate Model (BCC-CSM2-MR) in this study. We investigated whether the simulated climate model data exhibited complexity characteristics similar to real-world meteorological elements. In this scenario, we could assess the climate model's performance by comparing the complexity of observed and simulated data. The results indicate that the BCC-CSM2-MR model can accurately reflect the complexity of SAT in China. When considering the entire year, the model exhibited greater complexity in the simulated SAT than the observations in most regions of China, indicating an overestimation of complexity by the model. When examining different seasons, the model's complexity was similar to the observations in spring, summer, and winter, indicating a better performance during these seasons. In contrast, the simulated performance is poor during autumn, mainly due to an overestimation of complexity in Qinghai, south-central Xinjiang, and western Xizang. [ABSTRACT FROM AUTHOR]

Statistical approaches in linguistics seem to have gained in importance in recent times, especially in the field of Corpus Linguistics. In particular, the last ten years have seen an upsurge of linguists being dedicated to statistical methods and the improvement of statistical knowledge. This has repeatedly been described as 'the quantitative turn' in linguistics. In the present paper, we assess how real this quantitative turn actually is and whether statistics can be considered the 'new normal' in (corpus) linguistics. To this end, we have analyzed the contributions to six high-impact journals (Corpora, Corpus Linguistics and Linguistic Theory, ICAME Journal, English World-Wide, Journal of English Linguistics, and Language Variation and Change) for a period of eleven years (January 2011 until December 2021). Our results suggest that, indeed, statistical methods seem to be on the rise in linguistic studies. However, their frequency strongly varies between the journals, and, in general, we have identified some room for improvement in the use of advanced statistical methods, in particular the discussion of true prediction. [ABSTRACT FROM AUTHOR]

Plume rising from wildfire due to the buoyancy generated by the heat released from the fire is a crucial phenomenon to model for a correct description of smoke dispersion within the atmosphere. During the rise, the plume experiences the drag of external air which limits the rising itself. In this work, we investigate the dependence of the hybrid Eulerian-Lagrangian plume rise scheme embedded in the Lagrangian stochastic particle model SPRAY-WEB on the drag coefficient and on the horizontal resolution of the plume rise grid. We test four different drag coefficient models depending on the Reynolds number of the cells as well as a constant drag coefficient. As for the horizontal resolution, we use three different horizontal cell sizes: 200 m, 400 m, and 600 m, namely roughly a quarter, a half, and three-quarters of the source size. We compare the simulation results with the observations taken during a field experiment performed in Idaho organized by the US Environmental Protection Agency, where they collected lidar data and aircraft CO concentration measurements. We found that the drag coefficient influences mainly the plume near the source, where the drag role is more important due to the higher vertical velocities. It has also turned out that the best cell-to-source size ratio for our purpose is one to two. [ABSTRACT FROM AUTHOR]

In machine learning or scientific computing, model performance is measured with an objective function. But why choose one objective over another? According to the information‐theoretic paradigm, the "best" objective function is whichever minimizes information loss. To evaluate different objectives, transform them into likelihoods. The ratios of these likelihoods represent how strongly we should prefer one objective versus another, and the log of that ratio represents the relative information loss (or gain) from one objective to another. In plain terms, minimizing information loss is equivalent to minimizing uncertainty, as well as maximizing probability and general utility. We argue that this paradigm is well‐suited to models that have many uses and no definite utility like the complex Earth system models used to understand the effects of climate change. Furthermore, the benefits of "maximizing information and general utility" extend beyond model accuracy to other important considerations including how efficiently the model calibrates, how well it generalizes, and how well it compresses data. Key Points: A basic problem in modeling is the choice of objective function (or performance metric)According to information theory, the "best" objective function minimizes information loss, which we evaluate using the AICLike friction or inefficiency in a system, information loss incurs additional cost however the model is used [ABSTRACT FROM AUTHOR]

The rapid expansion of Earth system model (ESM) data available from the Coupled Model Intercomparison Project Phase 6 (CMIP6) necessitates new methods to evaluate the performance and suitability of ESMs used for hydroclimate applications as these extremely large data volumes complicate stakeholder efforts to use new ESM outputs in updated climate vulnerability and impact assessments. We develop an analysis framework to inform ESM sub‐selection based on process‐oriented considerations and demonstrate its performance for a regional application in the US Pacific Northwest. First, a suite of global and regional metrics is calculated, using multiple historical observation datasets to assess ESM performance. These metrics are then used to rank CMIP6 models, and a culled ensemble of models is selected using a trend‐related diagnostics approach. This culling strategy does not dramatically change climate scenario trend projections in this region, despite retaining only 20% of the CMIP6 ESMs in the final model ensemble. The reliability of the culled trend projection envelope and model response similarity is also assessed using a perfect model framework. The absolute difference in temperature trend projections is reduced relative to the full ensemble compared to the model for each SSP scenario, while precipitation trend errors are largely unaffected. In addition, we find that the spread of the culled ensemble temperature and precipitation trends includes the trend of the "truth" model ∼83%‐92% of the time. This analysis demonstrates a reliable method to reduce ESM ensemble size that can ease use of ESMs for creating and understanding climate vulnerability and impact assessments. Plain Language Summary: This study provides an updated and rigorously tested method for evaluating the performance of climate models for applications relevant to water managers and other stakeholders. Using traditional metrics of climate model performance, both regional and global, as well as newly developed metrics based on processes important for the simulation of precipitation, we have created a generalizable, systematic, and succinct method for reducing the number of models to be considered for climate change impact applications. By reducing the number of relevant models to around 20% of the total models and not having a significant impact on future temperature and precipitation trend projections in doing so, we strongly reduce the computational effort needed to gain a realistic simulation of the future climate for a given regional impact. This method is tested with a variety of statistical tests and found to be reliable for our application over the Pacific Northwest United States. Key Points: A method for evaluation of climate models over the PNW using a combination of global and regional metrics has been developedThis allows for a reduced envelope of ESMs for impact applications without significantly affecting the future trend projections [ABSTRACT FROM AUTHOR]

Due to the high cost of repairing defective programs, many researches focus on automatic program repair (APR). In recent years, the new trend of APR is to apply neural networks to mine the relations between defective programs and corresponding patches automatically, which is known as neural program repair (NPR). The community, however, ignores some important properties that could impact the applicability of NPR systems, such as robustness. For semantic‐identical buggy programs, NPR systems may produce totally different patches. In this paper, we propose an evaluation tool named RobustNPR, the first NPR robustness evaluation tool. RobustNPR employs several mutators to generate semantic‐identical mutants of defective programs. For an original defective program and its mutant, it checks two aspects of NPR: (a) Can NPR fix mutants when it can fix the original defective program? and (b) can NPR generate semantic‐identical patches for the original program and the mutant? Then, we evaluate four SOTA NPR models and analyze the results. From the results, we find that even for the best‐performing model, 20.16% of the repair success is unreliable, which indicates that the robustness of NPR is not perfect. In addition, we find that the robustness of NPR is correlated with model settings and other factors. [ABSTRACT FROM AUTHOR]

Structural equation modeling (SEM) is a widespread and commonly used approach to test substantive hypotheses in the social and behavioral sciences. When performing hypothesis tests, it is vital to rely on a sufficiently large sample size to achieve an adequate degree of statistical power to detect the hypothesized effect. However, applications of SEM rarely consider statistical power in informing sample size considerations or determine the statistical power for the focal hypothesis tests performed. One reason is the difficulty in translating substantive hypotheses into specific effect size values required to perform power analyses, as well as the lack of user-friendly software to automate this process. The present paper presents the second version of the R package semPower which includes comprehensive functionality for various types of power analyses in SEM. Specifically, semPower 2 allows one to perform both analytical and simulated a priori, post hoc, and compromise power analysis for structural equation models with or without latent variables, and also supports multigroup settings and provides user-friendly convenience functions for many common model types (e.g., standard confirmatory factor analysis [CFA] models, regression models, autoregressive moving average [ARMA] models, cross-lagged panel models) to simplify power analyses when a model-based definition of the effect in terms of model parameters is desired. [ABSTRACT FROM AUTHOR]

The impact of climate change on rainfall patterns and intensity necessitates an accurate estimation of runoff from rainfall to effectively manage water resources. This study utilized the MIKE HYDRO River software to evaluate rainfall-runoff in the upper Vaigai sub-basin. Input parameters, including rainfall, evaporation, and stream discharge data, were collected over a period of 16 years from 2000 to 2015. The auto-calibration capabilities of the software were employed to calibrate the model from 2000 to 2010, which was validated using data sets collected from 2011 to 2015. The optimal model and operating parameters were determined, and hydrographs were compared for simulated and observed runoff. The reliability and accuracy of the MIKE HYDRO River model were assessed using various goodness-of-fit indicators, including the coefficient of determination (R2), root-mean-square error (RMSE), and index of agreement. The R2 values demonstrated a strong correlation of 0.79 and 0.87 with the observed and simulated runoff for the calibration and validation period, respectively. The attainment of an RMSE of 4.05 m3/s and 2.20 m3/s and an index of agreement of 0.93 and 0.96 during the calibration and validation period, respectively, confirmed the model's excellent performance. The results of the rainfall-runoff simulation indicate that the MIKE HYDRO River tool is suitable for rainfall-runoff studies and integrated water supply management in the upper Vaigai. [ABSTRACT FROM AUTHOR]

One of the effects of droughts on the catchment area is the impact on sediment production. The purpose of this study was to investigate the effect of drought on the amount of sediment production estimated by the sediment gauge curve located at the Chamriz hydrometric station. In this study, using the long-term data of the Chamriz hydrometric station located upstream of the Darudzen dam, the rate of changes in the produced sediment discharge in each year was calculated using different methods of the sediment curve. The evaluation of the measuring curve construction methods was done with three Index, RMSE, MAE and ME, and the evaluation results showed that based on the RMSE, MAE and ME Index,non-parametric correction methods, single-line method and FAO have the most efficiency. In this study, two drought indices, SPI and RDI, were calculated by using the meteorological data of the station in question and compared and analyzed with the average sediment discharge produced in each year. Finally, by examining the situation of sediment production in many years with drought and drought, we can conclude that on average, after the occurrence of drought, with the occurrence of drought, the amount of sediment production has increased by 3.4 times, if In the second case, the amount of sediment production has increased by 1.7 times on average. It can be concluded that a detailed examination of the effect of drought indicators on sediment control and water resource management can be an effective start to reduce the effects of drought. [ABSTRACT FROM AUTHOR]

The Tucker-Lewis index (TLI; Tucker & Lewis, 1973), also known as the non-normed fit index (NNFI; Bentler & Bonett, 1980), is one of the numerous incremental fit indices widely used in linear mean and covariance structure modeling, particularly in exploratory factor analysis, tools popular in prevention research. It augments information provided by other indices such as the root-mean-square error of approximation (RMSEA). In this paper, we develop and examine an analogous index for categorical item level data modeled with item response theory (IRT). The proposed Tucker-Lewis index for IRT (TLIRT) is based on Maydeu-Olivares and Joes (2005) [Formula: see text] family of limited-information overall model fit statistics. The limited-information fit statistics have significantly better Chi-square approximation and power than traditional full-information Pearson or likelihood ratio statistics under realistic situations. Building on the incremental fit assessment principle, the TLIRT compares the fit of model under consideration along a spectrum of worst to best possible model fit scenarios. We examine the performance of the new index using simulated and empirical data. Results from a simulation study suggest that the new index behaves as theoretically expected, and it can offer additional insights about model fit not available from other sources. In addition, a more stringent cutoff value is perhaps needed than Hu and Bentlers (1999) traditional cutoff criterion with continuous variables. In the empirical data analysis, we use a data set from a measurement development project in support of cigarette smoking cessation research to illustrate the usefulness of the TLIRT. We noticed that had we only utilized the RMSEA index, we could have arrived at qualitatively different conclusions about model fit, depending on the choice of test statistics, an issue to which the TLIRT is relatively more immune.

Business failure prediction has become crucially important for today's firms, enabling them to reduce financial risks and make informed decisions. This study uses a dataset of 6819 companies and 96 financial and macroeconomic variables to present a comparative analysis of machine learning (ML) models for predicting corporate bankruptcies. Behind this research is to improve the accuracy of bankruptcy prediction, which can help companies make more informed decisions and reduce financial risks. This study aims to evaluate the effectiveness of 16 ML algorithms in terms of accuracy, sensitivity, and other relevant metrics. The work uses methodologies that include data collection and cleaning, exploratory data analysis, model preprocessing and training, and model performance evaluation. Data preprocessing and hyperparameter optimization techniques were used to improve model performance. The evaluated algorithms include Classifiers such as Stacking Classifier (SCC), Randomized Search Classifier (RCV), Historical Gradient Boosting Classifier (HGBC), MLP Classifier (MLPC), K-Neighbors Classifier (KNC), Decision Tree Classifier (DTC), XGBRF Classifier (XGBRFC), Support Vector Classifier (SVC), Logistic Regression Classifier (LR), Linear SVC Classifier (LSVC). With an accuracy of 97.63 %, recall of 97.63 %, and F1-score of 97.63 %, the results show that the SCC algorithm was the best. Other models, such as RCV and DTC, also showed good results, with accuracies above 97 %. However, models such as PAC and BNB had lower performance and accuracy below 90 %. Finally, this study compares the results of ML models in predicting business failures and highlights their effectiveness. The SCC algorithm is considered the most suitable model for this task, as it suggests that it can help economic actors make more informed decisions and reduce financial risks in the context of firms.

Abstract This study assesses a 40‐day 3.25‐km global simulation of the Simple Cloud‐Resolving E3SM Model (SCREAMv0) using high‐resolution ground‐based observations from the Atmospheric Radiation Measurement (ARM) Green Ocean Amazon (GoAmazon) field campaign. SCREAMv0 reasonably captures the diurnal timing of boundary layer clouds yet underestimates the boundary layer cloud fraction and mid‐level congestus. SCREAMv0 well replicates the precipitation diurnal cycle, however it exhibits biases in the precipitation cluster size distribution compared to scanning radar observations. Specifically, SCREAMv0 overproduces clusters smaller than 128 km, and does not form enough large clusters. Such biases suggest an inhibition of convective upscale growth, preventing isolated deep convective clusters from evolving into larger mesoscale systems. This model bias is partially attributed to the misrepresentation of land‐atmosphere coupling. This study highlights the potential use of high‐resolution ground‐based observations to diagnose convective processes in global storm resolving model simulations, identify key model deficiencies, and guide future process‐oriented model sensitivity tests and detailed analyses.

Differentially private (DP) synthetic data has emerged as a potential solution for sharing sensitive individual-level biomedical data. DP generative models offer a promising approach for generating realistic synthetic data that aims to maintain the original data’s central statistical properties while ensuring privacy by limiting the risk of disclosing sensitive information about individuals. However, the issue regarding how to assess the expected real-world prediction performance of machine learning models trained on synthetic data remains an open question. In this study, we experimentally evaluate two different model evaluation protocols for classifiers trained on synthetic data. The first protocol employs solely synthetic data for downstream model evaluation, whereas the second protocol assumes limited DP access to a private test set consisting of real data managed by a data curator. We also propose a metric for assessing how well the evaluation results of the proposed protocols match the real-world prediction performance of the models. The assessment measures both the systematic error component indicating how optimistic or pessimistic the protocol is on average and the random error component indicating the variability of the protocol’s error. The results of our study suggest that employing the second protocol is advantageous, particularly in biomedical health studies where the precision of the research is of utmost importance. Our comprehensive empirical study offers new insights into the practical feasibility and usefulness of different evaluation protocols for classifiers trained on DP-synthetic data.

Automatic text summarization (ATS), alongside neural machine translation or question answering, is one of the leading tasks in Natural Language Processing (NLP). In recent years, ATS has experienced significant development, especially in the English NLP world. Modern approaches are mainly based on the versatile Transformer architecture proposed by Vaswani et al. in 2017, which has revolutionized the field, and was later tuned and adjusted to various needs of different tasks. Non-mainstream languages, with Czech taken as a representative, on the other hand, are a little bit behind these efforts and tend to use lighter or heuristic methods. With the new CzeGPT-2 model and abstractive summarizer, we would like to take a step forward detailing the process of training a GPT-2 generative transformer model for a new language with a comprehensive evaluation of the task of Czech summarization and pointing out the benefits of this approach. We also present an in-depth analysis of the errors in generated summaries, allowing to locate the model’s weak spots.

Sustainability assessment of dairy systems is necessary to understand bottlenecks and to identify measures to enhance sustainability of the systems. The Dairy Sustainability Assessment Tool (DSAT) was developed with a focus on the facilitation of discussion on sustainability among dairy sector actors in East Africa. DSAT has been applied in several case studies, but the tool has not been systematically evaluated. The objectives of this study were to evaluate the strengths and weaknesses of DSAT and to compare its main features with similar dairy assessment tools for Africa. This evaluation consisted of a literature search to identify relevant assessment tools and an online survey about the tool among its users. DSAT was different from other tools compared with as it does not require input data from farm surveys as the assessments rely on expert opinion, and it can conduct assessments at farm, regional and national scales. Results also showed that DSAT was appropriate for holistic assessments of the sustainability of dairy systems in Africa by providing a good overview of threats to sustainability. Weaknesses were mainly the scoring of indicators, which tends to be subjective and the absence of numerical reference values for indicators. Therefore, improving DSAT requires addressing the identified weaknesses.

The resistance to chloride diffusion is one of the most crucial durable properties of concrete. However, traditional methods to evaluate this property are time-consuming and inefficient. In this research, backpropagation-artificial neural network (BP-ANN), support vector regression (SVR), genetic programming (GP), extreme gradient boost (XGBoost), and random forest (RF) models were optimized using particle swarm optimization (PSO) to predict the chloride diffusion coefficient of concretes containing silica fume. A database was also compiled, consisting of various features related to materials composition, curing, and exposure conditions. Statistical assessments were made to evaluate the predictive efficacy of every model. In addition, the distribution of errors and the consistency of each model were scrutinized. The findings indicate that the XGBoost model outperformed the standard models, achieving an R2 value of 0.9382 and an MSE of 3.0162. The models' predictive precision was notably enhanced following their integration with PSO. The PSO algorithm can also decrease the occurrence of significant error points in the predicted values and enhance the consistency of predictive performance across the range of experimental data. Finally, the PSO-XGBoost demonstrated the best comprehensive performance and proved to be the most efficient among the other PSO-synthesized (PSOS) models.

Observation-based climate model evaluation and future projections help policymakers in developing action plans for efficient management of water resources and mitigation of the impacts of hazardous extremes. Apart from this socioeconomic importance, the scientific value cannot be overstated, especially in light of the upcoming Fifth U.S. National Climate Assessment (NCA) report. In this study, we evaluate the realism of hydroclimate variability in the historical simulations of a suite of coupled general circulation models (CGCMs) participating in the fifth and sixth phases of the Coupled Model Intercomparison Project (CMIP5 and CMIP6). Our results demonstrate systematic biases in the simulated seasonal precipitation-most prominently, wet bias over the mountainous western United States in winter, and dry bias over the U.S. central plains in summer. A distinctive feature of this work is our focus on the examination of the atmospheric water budget, in particular the relative importance of remote and local contributions-convergence of moisture fluxes and local land surface processes (evapotranspiration) respectively-in helping produce precipitation. This diagnosis reveals that the leading contribution of the remote influence in winter is overestimated by the CMIP6 multimodel mean (MMM), whereas the local influence, which is more influential in summer, is underestimated. Our results aid in understanding the drivers of seasonal precipitation over the United States, where precipitation will likely increase by the end of the century but with significant model disagreement for the summer and fall. In support of ongoing NCA efforts, our study aims to contribute a comprehensive, regional-level analysis of the moisture budget and emphasizes the importance of realistically simulating its major components in CGCMs.

A realistic representation of top‐of‐the‐atmosphere (TOA) radiation response to surface warming is key for trusting climate model projections. We show that coupled models with freely evolving ocean‐atmosphere interactions systematically underestimate the observed global TOA radiation trend during 2001–2022 in 552 simulations. Locally, even if a simulation spontaneously reproduces observed surface temperature trends, TOA radiation trends are more likely under‐ than overestimated. This response bias stems from the models' inability to reproduce the observed large‐scale surface warming pattern and from errors in the atmospheric physics affecting short‐ and longwave radiation. Models with a better representation of the TOA radiation response to local surface warming have a relatively low equilibrium climate sensitivity. Our bias metric is a novel process‐based approach which links a model's current response to climate change to its behavior in the future. Plain Language Summary: A realistic representation of the radiation balance at the top of the Earth's atmosphere (TOA) by coupled climate models is essential for trust in future climate projections. Despite that relevance, it is still not clear whether the models correctly simulate the coupling between surface warming and TOA radiation because of the short observational record. We show that climate models systematically underestimate the observed increase in global TOA radiation during 2001–2022 in 552 simulations. Locally, even if a simulation reproduces observed changes in surface temperature, changes in TOA radiation are more likely under‐ than overestimated. This response bias stems from the models' inability to reproduce the observed large‐scale patterns of surface warming and from errors in the atmospheric physics which suppress the communication of the surface information to the TOA. Models that better represent the TOA radiation response to local surface warming have a relatively low equilibrium climate sensitivity, that is, a weak global‐mean surface warming in response to a doubling of the atmospheric CO2 concentration above pre‐industrial levels. Our new bias metric links a model's current response to climate change to its behavior in the future. Key Points: Climate models underestimate the observed global TOA radiation trend during 2001–2022Surface warming patterns and atmospheric physics matter for the observation‐model discrepancyModels with a small bias in the coupling between surface warming and TOA radiation trends have a relatively low climate sensitivity [ABSTRACT FROM AUTHOR]

Background: ORIN1001, a first-in-class oral IRE1-α endoribonuclease inhibitor to block the activation of XBP1, is currently in clinical development for inhibiting tumor growth and enhancing the effect of chemical or targeted therapy. Early establishment of a population pharmacokinetic (PopPK) model could characterize the pharmacokinetics (PK) of ORIN1001 and evaluate the effects of individual-specific factors on PK, which will facilitate the future development of this investigational drug. Methods: Non-linear mixed effect model was constructed by Phoenix NLME software, utilizing the information from Chinese patients with advanced solid tumors in a phase I clinical trial (Register No. NCT05154201). Statistically significant PK covariates were screened out by a stepwise process. The final model, after validating by the goodness-of-fit plots, non-parametric bootstrap, visual predictive check and test of normalized prediction distribution errors, was further applied to simulate and evaluate the impact of covariates on ORIN1001 exposure at steady state up to 900 mg per day as a single agent. Results: A two-compartment model with first-order absorption (with lag-time)/ elimination was selected as the best structural model. Total bilirubin (TBIL) and lean body weight (LBW) were considered as the statistically significant covariates on clearance (CL/F) of ORIN1001. They were also confirmed to exert clinically significant effects on ORIN1001 steady-state exposure after model simulation. The necessity of dose adjustments based on these two covariates remains to be validated in a larger population. Conclusion: The first PopPK model of ORIN1001 was successfully constructed, which may provide some important references for future research. [ABSTRACT FROM AUTHOR]

Vertical exchange between the atmospheric boundary layer (ABL) and free troposphere (FT) is a key link in coupling the earth's surface and upper atmosphere. This process is usually quantified by numerical simulations, while its reliability is not well assessed until now. Using space‐time intensified ABL observations, we evaluate the ABL‐FT air mass exchange flux derived from the Weather Research and Forecast (WRF) model. A six‐site sounding experiment is conducted in the North China Plain during the wintertime of 2019. The measured data is processed to provide enough information to derive the vertical exchange flux corresponding to the model‐based result, so that a systematic comparison is conducted. Three physical processes involved in ABL‐FT vertical exchange are quantitatively evaluated, that is, temporal variation of ABL height, advection across the inclined ABL top, and vertical motion at the ABL‐FT interface. Results show that the model‐based and observation‐based fluxes are generally agreed in temporal evolution (R = 0.67, p < 0.01), both characterized by 4–6 days periodicity and diurnal cycle. Their relative mean error was about 45% during the whole study period, mainly stemming from the vertical motion term and the advection crossing term. The model inaccuracy in representing these relevant processes at the ABL top is largely responsible for the discrepancy. Besides, the difference may also be attributed to the observational uncertainty (∼22%) that is caused by the measurement's difficulties in determining ABL spatial variation and acquiring vertical velocity. Through this study, the credibility and limitation of the WRF model in deriving ABL‐FT exchange flux are quantified. Plain Language Summary: The vertical exchange between the atmospheric boundary layer (ABL) and free troposphere (FT) plays a crucial role in atmospheric matter transport and energy flow, affecting weather systems, atmospheric chemistry, air pollution and climate change. This process is usually quantified by numerical simulation. This work aims to evaluate the reliability of the air exchange flux derived from a meteorological model, using comprehensive observation data in 1 month. The result shows that the model‐based exchange flux can reproduce the temporal evolution of the observation‐based exchange flux, including 4–6 days periodicity and diurnal variation. The overall relative difference between simulated and observed fluxes is about 45% during the whole study period. A more detailed comparison of each component term is conducted to investigate the causes of the discrepancy. We found that the difference is partly attributed to the model inaccuracy in representing the ABL top spatial variation and the vertical velocity at this layer. Additionally, a 22% uncertainty in observation‐based flux introduced by the relevant data processing is also an important reason. The evaluation result provides a quantitative reference to understand the biases in the meteorological model and their impact on ABL‐FT vertical exchange simulation study. Key Points: The air exchange flux between the boundary layer and free troposphere is reproduced by the WRF model with synoptic and diurnal variationsThe model‐derived exchange flux deviates from observation‐derived flux with a relative difference of 45%The deviation is mainly attributed to simulation‐observation differences in vertical motion and spatial gradient at the boundary layer top [ABSTRACT FROM AUTHOR]

Machine learning is increasingly and ubiquitously being used in the medical domain. Evaluation metrics like accuracy, precision, and recall may indicate the performance of the models but not necessarily the reliability of their outcomes. This paper assesses the effectiveness of a number of machine learning algorithms applied to an important dataset in the medical domain, specifically, mental health, by employing explainability methodologies. Using multiple machine learning algorithms and model explainability techniques, this work provides insights into the models' workings to help determine the reliability of the machine learning algorithm predictions. The results are not intuitive. It was found that the models were focusing significantly on less relevant features and, at times, unsound ranking of the features to make the predictions. This paper therefore argues that it is important for research in applied machine learning to provide insights into the explainability of models in addition to other performance metrics like accuracy. This is particularly important for applications in critical domains such as healthcare. [ABSTRACT FROM AUTHOR]

Non-coding variants associated with complex traits can alter the motifs of transcription factor (TF)–deoxyribonucleic acid binding. Although many computational models have been developed to predict the effects of non-coding variants on TF binding, their predictive power lacks systematic evaluation. Here we have evaluated 14 different models built on position weight matrices (PWMs), support vector machines, ordinary least squares and deep neural networks (DNNs), using large-scale in vitro (i.e. SNP-SELEX) and in vivo (i.e. allele-specific binding, ASB) TF binding data. Our results show that the accuracy of each model in predicting SNP effects in vitro significantly exceeds that achieved in vivo. For in vitro variant impact prediction, kmer/gkm-based machine learning methods (deltaSVM_HT-SELEX, QBiC-Pred) trained on in vitro datasets exhibit the best performance. For in vivo ASB variant prediction, DNN-based multitask models (DeepSEA, Sei, Enformer) trained on the ChIP-seq dataset exhibit relatively superior performance. Among the PWM-based methods, tRap demonstrates better performance in both in vitro and in vivo evaluations. In addition, we find that TF classes such as basic leucine zipper factors could be predicted more accurately, whereas those such as C2H2 zinc finger factors are predicted less accurately, aligning with the evolutionary conservation of these TF classes. We also underscore the significance of non-sequence factors such as cis -regulatory element type, TF expression, interactions and post-translational modifications in influencing the in vivo predictive performance of TFs. Our research provides valuable insights into selecting prioritization methods for non-coding variants and further optimizing such models. [ABSTRACT FROM AUTHOR]

In many modern machine learning applications, changes in covariate distributions and difficulty in acquiring outcome information have posed challenges to robust model training and evaluation. Numerous transfer learning methods have been developed to robustly adapt the model itself to some unlabeled target populations using existing labeled data in a source population. However, there is a paucity of literature on transferring performance metrics, especially receiver operating characteristic (ROC) parameters, of a trained model. In this paper, we aim to evaluate the performance of a trained binary classifier on unlabeled target population based on ROC analysis. We proposed S emisupervised T ransfer l E arning of A ccuracy M easures (STEAM), an efficient three-step estimation procedure that employs (1) double-index modeling to construct calibrated density ratio weights and (2) robust imputation to leverage the large amount of unlabeled data to improve estimation efficiency. We establish the consistency and asymptotic normality of the proposed estimator under the correct specification of either the density ratio model or the outcome model. We also correct for potential overfitting bias in the estimators in finite samples with cross-validation. We compare our proposed estimators to existing methods and show reductions in bias and gains in efficiency through simulations. We illustrate the practical utility of the proposed method on evaluating prediction performance of a phenotyping model for rheumatoid arthritis (RA) on a temporally evolving EHR cohort. [ABSTRACT FROM AUTHOR]

The constitutive model and its material parameters largely determine the effectiveness and accuracy of the finite element (FE) simulation for metal cutting processes. However, even for the same workpiece material, there are multiple constitutive models with different predictive abilities that are applicable to the cutting simulation. Therefore, a method for evaluating the constitutive models based on the coupled Eulerian-Lagrangian (CEL) orthogonal cutting model incorporating the experimentally determined Coulomb friction coefficient is proposed. The evaluation method can effectively avoid the influences of chip separation criteria, damage models, and adaptive remeshing on the evaluation results with the ability to simulate material side flow. This paper evaluates Johnson-Cook (JC) and Zerilli-Armstrong (ZA) constitutive models, which are often applied in cutting simulations. Material parameters for these constitutive models are identified from the constitutive data of the primary or secondary shear zone in cutting tests. A series of FE simulations for orthogonal cutting of 42CrMo4 steel is carried out under various cutting conditions using different constitutive models. The simulated cutting forces, chip thickness, and average temperature over the tool-chip interface are compared with experimental results under the same cutting conditions. The conclusion is that no constitutive model is the most accurate for all predictions. Nevertheless, for 42CrMo4 steel, the ZA model with material parameters identified from constitutive data of the secondary shear zone has the best comprehensive predictive performance. The evaluation method contributes to selecting the most suitable constitutive model and conducting efficient cutting simulations. Furthermore, several feasible approaches for improving the constitutive models are presented through error analysis. [ABSTRACT FROM AUTHOR]

El Niño-Southern Oscillation (ENSO) exhibits a substantial impact on sea surface height (SSH), and some climate models are significantly biased in reproducing the observed ENSO impact on SSH. This study aims to assess the performance of 26 Coupled Model Intercomparison Project Phase 6 (CMIP6) models for global SSH response to ENSO using their historical simulations from 1958 to 2014 during December‒February (DJF). The observation data for SSH and sea surface temperature (SST) are derived from Ocean Reanalysis System 5 (ORAS5). The fidelity of models is evaluated based on skill metric parameters such as root mean square error, interannual variability skill score, and M-Score. In CMIP6 models, El Niño plays a dominant role to simulate enhanced SSH across eastern Pacific Ocean (Region I) and southwest tropical Indian Ocean (Region III). In contrast, El Niño induces cooler SSTs over the region bounded between 100°E–133°E, 5°S–20°N (Region II), which is further associated with reduced SSH. The inter-model relationship of ENSO amplitude with the area mean regression coefficient for SSH and SST is investigated for these three hotspot regions. Overall, models with stronger ENSO amplitude simulate higher values of SSH and SST over Region I, III, and weaker amplitude over Region II. [ABSTRACT FROM AUTHOR]

This paper presents a pioneering methodology for refining product recommender systems, introducing a synergistic integration of unsupervised models—K-means clustering, content-based filtering (CBF), and hierarchical clustering—with the cutting-edge GPT-4 large language model (LLM). Its innovation lies in utilizing GPT-4 for model evaluation, harnessing its advanced natural language understanding capabilities to enhance the precision and relevance of product recommendations. A flask-based API simplifies its implementation for e-commerce owners, allowing for the seamless training and evaluation of the models using CSV-formatted product data. The unique aspect of this approach lies in its ability to empower e-commerce with sophisticated unsupervised recommender system algorithms, while the GPT model significantly contributes to refining the semantic context of product features, resulting in a more personalized and effective product recommendation system. The experimental results underscore the superiority of this integrated framework, marking a significant advancement in the field of recommender systems and providing businesses with an efficient and scalable solution to optimize their product recommendations. [ABSTRACT FROM AUTHOR]

Copyright of Food Research & Development is the property of Food Research & Development Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objective To explore the establishment of a subclinical hypothyroidism（SCH） rat model during pregnancy using total thyroidectomy and L-T4 subcutaneous injection method and evaluate the stability of the model.Methods A total of 26 female SD rats weighing 180-200 g were randomly divided into the control group（n=10） and the model group（n=16）.The control group was treated with total thyroidectomy, while the model group was treated with total thyroidectomy and postoperative subcutaneous injection of L-T4 1.0 μg/（100 g·d）.The levels of TSH,TT4 and body weight were observed at three time points: one month after operation, after total thyroidectomy+L-T410 d after operation and 13 d after pregnancy.Results One month after operation, the serum TSH level of the rats in the model group was significantly higher than that of the control group, and the difference was statistically significant（P＜0.01）.After total thyroidectomy +L-T410 d, the serum TSH level of the rates in the model group was higher than that in the control group, and the difference was statistically significant（P＜0.01）.At the 13th day of pregnancy, the serum TSH level of the rates in the model group was significantly higher than that in the control group（P＜0.01）.The serum TT4 level in the model group was lower than that in the control group one month after operation, and the difference was statistically significant（P＜0.01）.There was no statistically significant difference in serum TT4 levels of the rats in two groups after total thyroidectomy+L-T410 d（P＞0.05）.At the 13th day of pregnancy, there was no statistically significant difference in TT4 levels between the two groups（P＞0.05）.There was no significant difference in preoperative weight between the two groups（P＞0.05）.The weight of the model group was significantly lower than that of the control group after non-pregnancy surgery and drug administration, and the difference was statistically significant（P＜0.01）.At 13th day of pregnancy, the weight of model group was significantly lower than that of control group, and the difference was statistically significant（P＜0.01）.There was no significant difference in the average number of offspring between the two groups（P＞0.05）.Conclusion The method of subcutaneous injection of L-T4 can successfully establish a SCH rat model during pregnancy, with good stability.  [ABSTRACT FROM AUTHOR]

Understanding the present and future features of atmospheric rivers (ARs) is critical for effective disaster prevention and mitigation efforts. This study comprehensively assesses the performance of ARs in Phase 6 of the Coupled Model Intercomparison Project (CMIP6) models on both seasonal and interannual timescales within the historical period and investigates the future projection of ARs under different emission scenarios on a global scale. The multi‐model mean results obtained using the PanLu detection algorithm consistently exhibit agreement with the observational AR climatology and capture interannual fluctuations as well as the relationships with large‐scale drivers. The future projections reveal increased AR frequency, intensity, duration, and spatial extent and decreased landfall intervals with regional variations and seasonal fluctuations. Besides, the AR frequency increase will accelerate around the middle of the century, attributed to a non‐linear rise in surface temperature. Furthermore, mid‐latitude ARs are gradually shifting toward higher latitudes in both hemispheres under SSP585, with Greenland experiencing a substantial increase in AR frequency and AR‐induced precipitation. The hydrological implications arising from more frequent ARs are manifested more prominently in AR‐induced heavy precipitation (HP), with regions historically characterized by lower AR occurrence also receiving a higher percentage of precipitation from ARs. At last, an incremental decomposition highlights the dominant role of thermal effects and relatively limited contributions from dynamical effects in AR changes. Besides, the interplay between regionally divergent temperature amplification results in different dynamically driven AR responses across the globe. Plain Language Summary: Understanding atmospheric rivers (ARs) is crucial for disaster prevention and mitigation. This study evaluates the performance of CMIP6 simulation on historical ARs on seasonal and interannual timescales, assesses their future projection under different emission scenarios, and examines their hydrological implications. The seasonal and interannual AR patterns in models are consistent with the observations. Future projections reveal that ARs will become more frequent, intense, and longer‐lasting while the time between landfall AR events will be shorter, with the magnitude varying by region and season. Interestingly, AR frequency is expected to increase even faster after the middle of the century due to a non‐linear rise in Earth surface temperature. The study also shows that mid‐latitude ARs are shifting toward higher latitudes, bringing more precipitation to the polar regions. The changes in AR characteristics can lead to increased risks of heavy precipitation, especially in regions where ARs are relatively not frequent in the past time. The study also shows that the changes in AR characteristics are primarily contributed by the increase in atmospheric moisture. The contribution from the changes in wind patterns is limited and varies across different regions as a result of regionally divergent temperature amplification due to global warming. Key Points: CMIP6 simulates atmospheric rivers (ARs) well in seasonal and interannual timescales globallyARs will play a more significant role in the hydrological cycle as global warming alters various AR characteristicsThermal effects dominate AR changes globally, while dynamical effects associated with temperature rise vary regionally [ABSTRACT FROM AUTHOR]

Both the attribution of historical change and future projections of droughts rely heavily on climate modeling. However, reasonable drought simulations have remained a challenge, and the related performances of the current state-of-the-art Coupled Model Intercomparison Project phase 6 (CMIP6) models remain unknown. Here, both the strengths and weaknesses of CMIP6 models in simulating droughts and corresponding hydrothermal conditions in drylands are assessed. While the general patterns of simulated meteorological elements in drylands resemble the observations, the annual precipitation is overestimated by ∼33% (with a model spread of 2.3%–77.2%), along with an underestimation of potential evapotranspiration (PET) by ∼32% (17.5%–17.2%). The water deficit condition, measured by the difference between precipitation and PET, is 50% (29.1%–71.7%) weaker than observations. The CMIP6 models show weaknesses in capturing the climate mean drought characteristics in drylands, particularly with the occurrence and duration largely underestimated in the hyperarid Afro-Asian areas. Nonetheless, the drought-associated meteorological anomalies, including reduced precipitation, warmer temperatures, higher evaporative demand, and increased water deficit conditions, are reasonably reproduced. The simulated magnitude of precipitation (water deficit) associated with dryland droughts is overestimated by 28% (24%) compared to observations. The observed increasing trends in drought fractional area, occurrence, and corresponding meteorological anomalies during 1980–2014 are reasonably reproduced. Still, the increase in drought characteristics, associated precipitation and water deficit are obviously underestimated after the late 1990s, especially for mild and moderate droughts, indicative of a weaker response of dryland drought changes to global warming in CMIP6 models. Our results suggest that it is imperative to employ bias correction approaches in drought-related studies over drylands by using CMIP6 outputs. [ABSTRACT FROM AUTHOR]

Aim: To develop a visual prediction model for gestational diabetes (GD) in pregnant women and to establish an effective and practical tool for clinical application. Methods: To establish a prediction model, the modelling set included 1756 women enrolled in the Zunyi birth cohort, the internal validation set included 1234 enrolled women, and pregnant women in the Wuhan cohort were included in the external validation set. We established a demographic–lifestyle factor model (DLFM) and a demographic–lifestyle–environmental pollution factor model (DLEFM) based on whether the women were exposed to environmental pollutants. The least absolute shrinkage and selection lasso–logistic regression analyses were used to identify the independent predictors of GD and construct a nomogram for predicting its occurrence. Results: The DLEFM regression analysis showed that a family history of diabetes (odd ratio [OR] 2.28; 95% confidence interval [CI] 1.05‐4.71), a history of GD in pregnant women (OR 4.22; 95% CI 1.89‐9.41), being overweight or obese before pregnancy (OR 1.71; 95% CI 1.27‐2.29), a history of hypertension (OR 2.61; 95% CI 1.41‐4.72), sedentary time (h/day) (OR 1.16; 95% CI 1.08‐1.24), monobenzyl phthalate (OR 1.95; 95% CI 1.45‐2.67) and Q4 mono‐ethyl phthalate concentration (OR 1.85; 95% CI 1.26‐2.73) were independent predictors. The area under the receiver operating curves for the internal validation of the DLEFM and the DLFM constructed using these seven factors was 0.827 and 0.783, respectively. The calibration curve of the DLEFM was close to the diagonal line. The DLEFM was thus the more optimal model, and the one which we chose. Conclusions: A nomogram based on preconception factors was constructed to predict the occurrence of GD in the second and third trimesters. It provided an effective tool for the early prediction and timely management of GD. [ABSTRACT FROM AUTHOR]

In this study, an ensemble forecasting model for in situ wind speed and wave height was developed using the Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST) model. This model utilized four bias correction algorithms—Model Output Statistics (MOS), Back Propagation Neural Network (BPNN), Long Short-Term Memory (LSTM) neural network, and Convolutional Neural Network (CNN)—to construct ensemble forecasts. The training data were derived from the COAWST simulations of one year and observations from three buoy stations (Laohutan, Zhifudao, and Lianyungang) in the Yellow Sea and Bohai Sea. After the optimization of the bias correction model training, the subsequent evaluations on the ensemble forecasts showed that the in situ forecasting accuracy of wind speed and wave height was significantly improved. Although there were some uncertainties on bias correction performance levels for individual algorithms, the uncertainties were greatly reduced by the ensemble forecasts. Depending on the dynamic weight assignment, the ensemble forecasts presented a stable performance even when the corrected forecasts by three algorithms had an obvious negative bias. Specifically, the ensemble forecasting bias was found with a mean reduction of about 96%~99% and 91%~95% for wind speed and wave height, and a reduction of about 91%~98% and 16%~54% during the period of Typhoon "Muifa". For the four correction algorithms, the performance of bias correction was not directly related to the algorithm complexity. However, the strategies with more complex algorithms (i.e., CNN) were more conservative, and simple algorithms (i.e., MOS) might have induced unstable performance levels despite their lower bias in some cases. [ABSTRACT FROM AUTHOR]

Southwest China is a tectonically and seismically active region, witnessing strong deformation due to the collision between the Indian and Eurasian plates. Constraining the subsurface velocity structure of this region is thus important in understanding the tectonics and geodynamic processes of continental collision and in mitigating seismic hazards. Numerous studies have provided various 3D seismic velocity models in southwest China. However, discrepancies exist among these models, and less effort has been made to quantify the reliability and accuracy of these existing velocity models. In this study, we use regional 3D waveform simulation to evaluate the performance of various regional crustal 3D velocity models in reproducing observed seismograms. We particularly focus on two recent earthquake sequence in the region, the 2021 Yunnan Yangbi MS6.4 earthquake sequence and the 2022 Sichuan Luding MS6.8 earthquake sequence. The tested 3D velocity models include the Southwest China Community Velocity model V1.0, the Unified Seismic Tomography Models for Continental China Lithosphere V2.0, the adjoint full waveform tomography model of the crustal and upper mantle beneath Eastern Tibetan Plateau, and the shallow seismic structure model beneath continental China. Our results show that the tested 3D velocity models generally capture well long-period (<0.2 Hz) waveforms, indicating that the 3D models adequately resolve overall large-scale subsurface structures. However, the 3D synthetics show discrepancies in higher frequencies (0.05–0.3 Hz) and the performance of the 3D velocity models varies from region to region, suggesting that smaller scale heterogeneities are not well constrained. Including shallow velocity structures (<10 km) can improve the waveform fitting, emphasizing the importance of incorporating shallow structures in waveform modeling. The full-waveform tomography model shows a slighter better performance than the other models, especially for the body-waves, highlighting the advantages of full-waveform method in achieving sub-wavelength resolution despite the usage of very long-period waveforms. In light of these comparison results of model performance, we identify the advantages and limitations of different seismic tomography models and methods, and we propose to incorporate different tomography methods and datasets to better constrain subsurface structures. While our target region in this study is southwest China, the analysis that we have conducted can be applied to other regions of various scales and tectonic settings for quantitative seismic model evaluation. [ABSTRACT FROM AUTHOR]

Maintenance and safety inspection of trains is a critical element of providing a safe and reliable train service. Checking for the presence of bolts is an essential part of train inspection, which is currently, typically carried out during visual inspections. There is an opportunity to automate bolt inspection using machine vision with edge devices. One particular challenge is the implementation of such inspection mechanisms on edge devices, which necessitates using lighter models to ensure efficiency. Traditional methods have often fallen short of the required object detection performance, thus demonstrating the need for a more advanced approach. To address this challenge, researchers have been exploring the use of deep learning algorithms and computer vision techniques to improve the accuracy and reliability of bolt detection on edge devices. High precision in identifying absent bolts in train components is essential to avoid potential mishaps and system malfunctions. This paper presents "BoltVision", a comparative analysis of three cutting-edge machine learning models: convolutional neural networks (CNNs), vision transformers (ViTs), and compact convolutional transformers (CCTs). This study illustrates the superior assessment capabilities of these models and discusses their effectiveness in addressing the prevalent issue of edge devices. Results show that BoltVision, utilising a pre-trained ViT base, achieves a remarkable 93% accuracy in classifying missing bolts. These results underscore the potential of BoltVision in tackling specific safety inspection challenges for trains and highlight its effectiveness when deployed on edge devices characterised by constrained computational resources. This attests to the pivotal role of transformer-based architectures in revolutionising predictive maintenance and safety assurance within the rail transportation industry. [ABSTRACT FROM AUTHOR]

Extreme precipitation is typical in the Indian subcontinent, and these occurrences might cause human fatalities, property damage, and the environment. Understanding regional extreme precipitation in the global Coupled Model Intercomparison Project Phase 6 (CMIP6) models is challenging. The present study evaluates the performance of 20 CMIP6 models for daily precipitation across India from 1980 to 2014 (35 years) during the Indian Summer Monsoon (ISM) season (June-July–August-September (JJAS)) and ranked 20 models based on four metrics. In this analysis, the extreme precipitation over India is determined by using Generalized Extreme Value (GEV) distribution. The observation data is collected from the Indian Meteorological Department (IMD) over India during JJAS. The performance of CMIP6 models is determined by using four different model evaluation metrics as root mean square error (RMSE), standard deviation (SD), correlation coefficient (CC), and interannual variability score (IVS). A total rank is estimated based on the four skill metrics and the resulting top ten models, i.e., AWI-ESM-1–1-LR, BCC-ESM1, IPSL-CM6A-LR, MPI-ESM1-1–2-HAM, EC-Earth3-Veg, IITM-ESM, INM-CM4-8, GISS-E2-1-G, MIROC6, and NESM3 are found for mean precipitation. In extreme precipitation, the ten best-performing models are given as MIROC6, EC-Earth3-CC, CMCC-CM2-SR5, EC-Earth3-Veg, CMCC-CM2-HR4, BCC-ESM1, FGOALS-g3, INM-CM5-0, CanESM5, and INM-CM4-8. However, a strong uncertainty in CMIP6 models has been observed for extreme precipitation as compared to mean patterns over India. Overall, CMIP6 models perform well for mean precipitation and have a strong bias for extreme precipitation obtained from GEV over India during the ISM season.. [ABSTRACT FROM AUTHOR]

This paper addresses a problem of optimal error quantification in the framework of robust control theory in the 1 setup. The upper bounds of biased external disturbance and the gains of coprime factor perturbations in a discrete-time linear time invariant SISO plant are assumed to be unknown. The computation of optimal data-consistent upper bounds under a known bias of external disturbance has been simplified to linear programming. This allows for the computation of optimal estimates in real-time and their application to achieve optimal robust steady-state tracking even when facing an unknown bias in the external disturbance. The presented results have been illustrated through computer simulations. [ABSTRACT FROM AUTHOR]

Regional climate models (RCMs) are widely used to assess future impacts associated with climate change at regional and local scales. RCMs must represent relevant climate variables in the present‐day climate to be considered fit‐for‐purpose for impact assessment. This condition is particularly difficult to meet over complex regions such as the Andes‐Amazon transition region, where the Andean topography and abundance of tropical rainfall regimes remain a challenge for numerical climate models. In this study, we evaluate the ability of 30 regional climate simulations (6 RCMs driven by 10 global climate models) to reproduce historical (1981–2005) rainfall climatology and temporal variability over the Andes‐Amazon transition region. We assess spatio‐temporal features such as spatial distribution of rainfall, focusing on the orographic effects over the Andes‐Amazon "rainfall hotspots" region, and seasonal and interannual precipitation variability. The Eta RCM exhibits the highest spatial correlation (up to 0.6) and accurately reproduces mean annual precipitation and orographic precipitation patterns across the region, while some other RCMs have good performances at specific locations. Most RCMs simulate a wet bias over the highlands, particularly at the eastern Andean summits, as evidenced by the 100%–2,500% overestimations of precipitation in these regions. Annual cycles are well represented by most RCMs, but peak seasons are exaggerated, especially at equatorial locations. No RCM is particularly skillful in reproducing the interannual variability patterns. Results highlight skills and weaknesses of the different regional climate simulations, and can assist in the selection of regional climate simulations for impact studies in the Andes‐Amazon transition zone. Plain Language Summary: Regional climate models (RCMs) are useful numerical tools to investigate future climate change impacts (e.g., future water availability, frequency of floods and droughts, regional warming). Regarding regional scale, RCMs are expected to perform better than global climate models due to finer spatial resolution. However, in the Andes‐Amazon transition region, assessing the performance of RCMs is challenging due to complex terrain and scarcity of observations. This region is of critical importance for the water cycle of local and regional ecological systems, but has been often overlooked in RCM assessments. Here, we evaluate how 30 regional climate simulations perform in representing precipitation regional contrasts, wet‐dry seasons, and year‐to‐year changes over the Andes‐Amazon transition region. We find that models perform differently over specific regions, with prominent overestimations at high altitudes by most RCMs. However, Eta RCM has the best performance regarding regional patterns of precipitation and its wet‐dry fluctuations. Besides overestimations during austral summer and spring, wet‐dry seasonal fluctuations are well simulated by most RCMs, but none excels in representing wet‐dry yearly fluctuations. Strengths and weaknesses of different regional climate simulations are shown, and can help choose the most appropriate simulations for distinct impact studies in this region. Key Points: Precipitation output from 30 regional climate simulations is assessed over the Andes‐Amazon in terms of climatology and temporal variabilityThe spatio‐temporal behavior of seasonality is well reproduced by most simulations, with overestimations during austral summer and springWhile orographic precipitation is a major challenge for most regional climate models, Eta satisfactorily reproduces climate patterns in the Andes‐Amazon region [ABSTRACT FROM AUTHOR]

With the warming and humidification process in the Northwest Arid Zone over the past 30 years, the runoff of a vast majority of rivers has been affected to different degrees. In this paper, the runoff from the Weigan River, a typical inland river in the arid zone of Northwest China, is taken as an example, and seven types of CMIP6 data are selected with the help of a SWAT model to predict the runoff volume of the Weigan River in the next 30 years under climate change. The results show that (1) the SWAT model can simulate the runoff from the Weigan River well and has good applicability in this study area. (2) With an increase in radiative forcing, the temperature, precipitation and runoff in the study area show an increasing trend. (3) Under the four radiative forcing scenarios in 2030 and 2050, the runoff from the Weigan River out of the mountain is predicted to be maintained at 25.68 to 30.89 × 108 m3, which is an increase of 1.35% to 21.91% compared with the current runoff, of which the contribution to the increase in future runoff caused by the changes in temperature and precipitation is 68.71% and 27.24%, respectively. It is important to explore the impact of climate change on the runoff from the Weigan River to understand the impact of climate change on the Northwest Arid Region scientifically and rationally, and to provide a scientific basis for evaluating the risk of climate change and formulating policies to deal with it. [ABSTRACT FROM AUTHOR]