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We developed ordinary least squares regression models to predict uptake of cadmium and lead, two metals that are of public health significance because of their toxicity, in the edible tissues of lettuce. Models were parameterised using data on soil metal concentration, pH, and organic carbon. To assess the impact of physical contamination in form of aerial deposition and soil-splash on the metal concentration in lettuce, separate linear regression models were parameterised for indoor- and outdoor-grown lettuce, assuming the physical contamination to be negligible for indoor conditions. Both Cd models showed high model fit and strong predictive performance, when tested on an independent dataset, suggesting uptake via roots to be dominant. For Pb, the indoor model performed better than the outdoor model, indicating that physical contamination, contributes significantly to metal concentration in lettuce leaves. Our results highlight the importance of the parameterisation data when developing uptake models for predictions and risk assessment. Regression models for predicting Pb concentration in lettuce based on indoor data should not be used for predicting lettuce concentrations cultivated in outdoor conditions unless the contribution of physical contamination is explicitly accounted for.

Random cross-validation (CV) is often used to evaluate geospatial machine learning models, particularly when a limited amount of sample data are available, and collecting an extra test set is unfeasible. However, the prediction locations can be substantially different from the available sample, leading to over-optimistic evaluation results. This has fostered the development of spatial CV methods. Yet these methods only focus on spatial autocorrelation and cannot sufficiently guarantee that the validation subset is a good proxy of the test set with significant differences. In this paper, we propose the spatial+ cross-validation (SP-CV) method. This method, which considers both the geographic and feature spaces, is composed of two stages. The first stage addresses spatial autocorrelation issues by using agglomerative hierarchical clustering to divide the available sample into blocks. The second stage deals with multiple sources of differences. It uses cluster ensembles to split the blocks into training and validation folds based on the locations of the sample data and the values of the covariates and target variable. The proposed method is compared against random and block CV methods in a series of experiments with Amazon basin above ground biomass and California houseprice datasets. Our results show that SP-CV provided the smallest error differences with respect to the reference error. This means that SP-CV produced more representative splits and led to more reliable model evaluations. It suggests that a reliable model evaluation requires to consider both the geographic and the feature spaces in a comprehensive manner.

Extreme precipitation events are a major natural hazard and cause significant socio‐economic damages. Precipitation events are spatially extended and, thus, can cause large water accumulations, which can lead to flooding events. In order to help design flood protection infrastructure, a detailed investigation of the temporal and spatial dependencies of extreme precipitation is essential. Here, we use a statistical spatial extremes framework to systematically study the historical and projected spatial–temporal characteristics of extreme precipitation in Germany. For this purpose, we use data from 10 high‐resolution global climate model‐regional climate model (GCM‐RCM) combinations from the EURO‐CORDEX initiative and derive a statistical spatial extremes precipitation model. Our results show that there are large spreads in reproducing the temporal–spatial characters of extreme precipitation. Few climate simulations can well present the temporal clustering of observed extreme precipitation in both summer and winter. In reproducing the spatial dependencies of the observations, most GCM‐RCM combinations behave well in summer, while in winter most RCMs produce too many spatially localized extreme precipitation events. The derived statistical model, which accounts for both the spatial and temporal variability, performs well in representing the spatial dependency and intensity characteristics in summer. Furthermore, global warming will have a significant impact on the temporal and spatial dependencies of extreme precipitation in Germany. There will be more temporal‐dependent and homogeneous extreme precipitation in summer; and more temporal‐independent and localized extreme precipitation in winter. The intensity quantified by the 25‐year return level of the 10 GCM‐RCM combinations is increasing; with relative changes ranging from 5.33% to 53.24% in summer and from −15.38% to 32.33% in winter under RCP8.5. A future projection by our statistical spatial extremes model using projected temperature from GCM‐RCM combinations as a covariate shows that the 25‐year return level will increase by 3.02% under RCP2.6 and 4.16% under RCP8.5 in winter. [ABSTRACT FROM AUTHOR]

The Southern Ocean (SO) clouds exert a significant influence on the Earth's radiation budget. We analyzed ship‐based remote sensing observations of SO clouds over a 5‐month long Department of Energy (DOE) ARM Measurements of Aerosols, Radiation and Clouds over the Southern Oceans (MARCUS) field campaign to better understand cloud phase variability. We developed a method to classify eight categories of hydrometeors (ice, liquid, mixed phase, rain, drizzle, snow, aerosols, and clear sky) based on measurements of lidar, radar and radiosondes. Cloud thermodynamic phases (liquid, ice and mixed phase) at coarser scales were further derived to compare with the DOE Energy Exascale Earth System Model version 1 (E3SMv1) simulation and the Earth Model Column Collaboratory (EMC2) instrument simulator. For a scale‐aware comparison with climate model simulations, we found that spatially averaging the raw remote sensing data (e.g., backscatter and reflectivity) results in increased cloud cover and cloud liquid, whereas, directly averaging cloud phase from higher to lower resolution maintains clear air regions and is thus recommended for in‐cloud frequency comparisons. For cloud thermodynamic phases in stratiform clouds, the E3SMv1 underestimates cloud ice and overestimates cloud liquid at temperatures between −40°C and 0°C. When latitudes increase, both observations and simulations show a transition of dominant phase from liquid to ice for cloud tops as well as for the entire cloud columns, but the model underestimation of ice phase is more severe at higher latitudes. Such model bias is unlikely caused by spatial scale differences or lack of heterogeneity in cloud vertical structure in the simulation. Plain Language Summary: The Southern Ocean region of Earth consists of vast open waters with extensive cloud cover throughout the year. This cloud cover prevents the ocean water below it from absorbing solar heat and maintains global ocean and atmospheric circulation patterns. Thus, it is important to improve our understanding and our ability to model this cloud cover to better predict future changes in the Earth's climate. Here, we analyzed atmospheric data obtained by a ship traversing the open waters between Australia and Antarctica for nearly 5 months. We examined observed cloud properties such as cloud ice and liquid proportions and compared them with global climate model results. We found that the model simulates more liquid and less ice at lower temperatures when compared with observations. Such model bias becomes larger at higher latitudes, indicating that Earth's surface may absorb more solar radiation than the model estimates, especially around Antarctica. Both the observations and the model show that ice clouds are common at higher latitudes whereas liquid clouds are common at lower latitudes. Key Points: Cloud phase with respect to temperature and latitude over the Southern Ocean was identified using 5‐month ship‐based measurementsCompared with observed cloud phase, the E3SMv1 model underestimates ice and overestimates liquid phase between −40°C and 0°CObservations and simulations show similar transition from liquid to ice‐dominated cloud columns and cloud top layers from 43°S to 70°S [ABSTRACT FROM AUTHOR]

Item response theory models and applications are affected by many sources of variability, including errors associated with item parameter estimation. Metric stability analysis (MSA) is one method to evaluate the effects of item parameter standard errors that quantifies how well a model determines the latent trait metric. This paper describes how to evaluate MSA in dichotomous and polytomous data and describes a Bayesian implementation of MSA that does not require a positive definite variance–covariance matrix among item parameters. MSA analyses are illustrated in the context of an oral-health-related quality of life measure administered before and after prosthodontic treatment. The R code to implement the methods described in this paper is provided. [ABSTRACT FROM AUTHOR]

By using various skill scores and spatial characteristics of spatial verification methods and traditional techniques of the model evaluation tool, the gridded precipitation observation, known as Climate Prediction Center Morphing Technique, gauge observation and three datasets that were derived from local, Shanghai, and Grapes models, respectively, were conducted to assess the 3 lead day rainfall forecast with 0.5 day intervals during the summer of 2020 over Central East China. Results have shown that the local model generally outperforms the other two for the most skill scores but usually with relatively larger uncertainties than the Shanghai model, and it has the least displacement errors for moderate rainfall among the three datasets. However, the rainfall of the Grapes model has been heavily underestimated and is accompanied with a large displacement error. Both the local and Shanghai model can effectively forecast the large-scale convection and rainstorms but over forecast the local convection, while the local model likely over forecasts the local rainstorms. In addition, the Shanghai model slightly favors over forecasting on a broad scale range and a broad threshold range, and the local model slightly misses the rainfall exceeding 100 mm. Generally, for a broadly comparative evaluation on rainfall, the popular dichotomous methods should be recommended when considering reasonable classification of thresholds if the accuracy is highly demanding. In addition, most spatial methods are suggested to conduct with proper pre-handling of non-rainfall event cases. Especially, the verification metrics including spatial characteristic difference information should be recommended to emphasize rewarding the severe events forecast under a global warming background. [ABSTRACT FROM AUTHOR]

Topic models are a useful and popular method to find latent topics of documents. However, the short and sparse texts in social media micro-blogs such as Twitter are challenging for the most commonly used Latent Dirichlet Allocation (LDA) topic model. We compare the performance of the standard LDA topic model with the Gibbs Sampler Dirichlet Multinomial Model (GSDMM) and the Gamma Poisson Mixture Model (GPM), which are specifically designed for sparse data. To compare the performance of the three models, we propose the simulation of pseudo-documents as a novel evaluation method. In a case study with short and sparse text, the models are evaluated on tweets filtered by keywords relating to the Covid-19 pandemic. We find that standard coherence scores that are often used for the evaluation of topic models perform poorly as an evaluation metric. The results of our simulation-based approach suggest that the GSDMM and GPM topic models may generate better topics than the standard LDA model. [ABSTRACT FROM AUTHOR]

The main dilemma in the case of classification tasks is to find—from among many combinations of methods, techniques and values of their parameters—such a structure of the classifier model that could achieve the best accuracy and efficiency. The aim of the article is to develop and practically verify a framework for multi-criteria evaluation of classification models for the purposes of credit scoring. The framework is based on the Multi-Criteria Decision Making (MCDM) method called PROSA (PROMETHEE for Sustainability Analysis), which brought added value to the modelling process, allowing the assessment of classifiers to include the consistency of the results obtained on the training set and the validation set, and the consistency of the classification results obtained for the data acquired in different time periods. The study considered two aggregation scenarios of TSC (Time periods, Sub-criteria, Criteria) and SCT (Sub-criteria, Criteria, Time periods), in which very similar results were obtained for the evaluation of classification models. The leading positions in the ranking were taken by borrower classification models using logistic regression and a small number of predictive variables. The obtained rankings were compared to the assessments of the expert team, which turned out to be very similar. [ABSTRACT FROM AUTHOR]

In Morocco, agriculture is an important sector that contributes to the country's economy and food security. Accurately predicting crop yields is crucial for farmers, policy makers, and other stakeholders to make informed decisions regarding resource allocation and food security. This paper investigates the potential of Machine Learning algorithms for improving the accuracy of crop yield predictions in Morocco. The study examines various factors that affect crop yields, including weather patterns, soil moisture levels, and rainfall, and how these factors can be incorporated into Machine Learning models. The performance of different algorithms, including Decision Trees, Random Forests, and Neural Networks, is evaluated and compared to traditional statistical models used for crop prediction. The study demonstrated that the Machine Learning algorithms outperformed the Statistical models in predicting crop yields. Specifically, the Machine Learning algorithms achieved mean squared error values between 0.10 and 0.23 and coefficient of determination values ranging from 0.78 to 0.90, while the Statistical models had mean squared error values ranging from 0.16 to 0.24 and coefficient of determination values ranging from 0.76 to 0.84. The Feed Forward Artificial Neural Network algorithm had the lowest mean squared error value (0.10) and the highest R² value (0.90), indicating that it performed the best among the three Machine Learning algorithms. These results suggest that Machine Learning algorithms can significantly improve the accuracy of crop yield predictions in Morocco, potentially leading to improved food security and optimized resource allocation for farmers. [ABSTRACT FROM AUTHOR]

Testing and evaluation of select semi-empirical and black-box compressor models is carried out to quantify performance in modulation (variable speed), extrapolation, and additionally, variable superheat scenarios. Three representative literature models and an artificial neural network (ANN) model are benchmarked against the industry standard AHRI model. A methodology quantifying model performance, compared against experimental data, in said scenarios is presented. High-fidelity test data taken from either a hot-gas bypass load stand or compressor calorimeter. Scroll, screw, reciprocating, and spool compressor technologies were collected with R410A, R1234ze(E), R134a, and R32 refrigerants totaling 434 experimental points. Data is divided into training, extrapolation, variable speed, and variable superheat data splits to examine model performance. Mean Absolute Percentage Error (MAPE) is computed for mass flow rate and power after training models with training data and evaluating them against the other data splits. Two literature models are true semi-empirical formulations while the other, the ANN, and AHRI model are more empirical in nature. Neither semi-empirical model predicted all compressors. When the compressor type is predicted, the semi-empirical models yield MAPE's less than 8%, 5%, and 4% for mass flow rate and power prediction in extrapolation, modulation, and variable superheat scenarios, respectively. The exception is the Popovic and Shapiro model performing at 21% MAPE in variable superheat power prediction for the spool compressor with R1234ze(E). The ANN showed highest errors of 9.3%, 12%, and 17% in extrapolation, modulation, and variable superheat scenarios, respectively. All models outperformed the AHRI model by several orders of magnitude in these scenarios. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 pandemic has highlighted the role of infectious disease forecasting in informing public policy. However, significant barriers remain for effectively linking infectious disease forecasts to public health decision making, including a lack of model validation. Forecasting model performance and accuracy should be evaluated retrospectively to understand under which conditions models were reliable and could be improved in the future. Methods: Using archived forecasts from the California Department of Public Health's California COVID Assessment Tool (https://calcat.covid19.ca.gov/cacovidmodels/), we compared how well different forecasting models predicted COVID-19 hospitalization census across California counties and regions during periods of Alpha, Delta, and Omicron variant predominance. Results: Based on mean absolute error estimates, forecasting models had variable performance across counties and through time. When accounting for model availability across counties and dates, some individual models performed consistently better than the ensemble model, but model rankings still differed across counties. Local transmission trends, variant prevalence, and county population size were informative predictors for determining which model performed best for a given county based on a random forest classification analysis. Overall, the ensemble model performed worse in less populous counties, in part because of fewer model contributors in these locations. Conclusions: Ensemble model predictions could be improved by incorporating geographic heterogeneity in model coverage and performance. Consistency in model reporting and improved model validation can strengthen the role of infectious disease forecasting in real-time public health decision making. [ABSTRACT FROM AUTHOR]

In this paper, a set of best practice data sharing guidelines for wind turbine fault detection model evaluation is developed, which can help practitioners overcome the main challenges of digitalisation. Digitalisation is one of the key drivers for reducing costs and risks over the whole wind energy project life cycle. One of the largest challenges in successfully implementing digitalisation is the lack of data sharing and collaboration between organisations in the sector. In order to overcome this challenge, a new collaboration framework called WeDoWind was developed in recent work. The main innovation of this framework is the way it creates tangible incentives to motivate and empower different types of people from all over the world to share data and knowledge in practice. In this present paper, the challenges related to comparing and evaluating different SCADA-data-based wind turbine fault detection models are investigated by carrying out a new case study, the "WinJi Gearbox Fault Detection Challenge", based on the WeDoWind framework. A total of six new solutions were submitted to the challenge, and a comparison and evaluation of the results show that, in general, some of the approaches (Particle Swarm Optimisation algorithm for constructing health indicators, performance monitoring using Deep Neural Networks, Combined Ward Hierarchical Clustering and Novelty Detection with Local Outlier Factor and Time-to-failure prediction using Random Forest Regression) appear to exhibit high potential to reach the goals of the Challenge. However, there are a number of concrete things that would have to have been done by the Challenge providers and the Challenge moderators in order to ensure success. This includes enabling access to more details of the different failure types, access to multiple data sets from more wind turbines experiencing gearbox failure, provision of a model or rule relating fault detection times or a remaining useful lifetime to the estimated costs for repairs, replacements and inspections, provision of a clear strategy for training and test periods in advance, as well as provision of a pre-defined template or requirements for the results. These learning outcomes are used directly to define a set of best practice data sharing guidelines for wind turbine fault detection model evaluation. The guidelines can be used by researchers in the sector in order to improve model evaluation and data sharing in the future. [ABSTRACT FROM AUTHOR]

Statistical analysis of categorical data often relies on multiway contingency tables; yet, as the number of categories and/or variables increases, the number of table cells with few (or zero) observations also increases. Unfortunately, sparse contingency tables invalidate the use of standard goodness-of-fit statistics. Limited-information fit statistics and bootstrapping procedures offer valuable solutions to this problem, but they present an additional concern in their strict reliance on the (potentially misleading) observed data. To address both of these issues, we demonstrate the Bayesian model checking technique, which yields insightful, useful, and comprehensive evaluations of specific properties of a given model. We illustrate this technique using item response data from a patient-reported psychopathology screening questionnaire, and we provide annotated R code to promote dissemination of this informative method in other prevention science modeling scenarios. [ABSTRACT FROM AUTHOR]

The Iberian Peninsula is a known climate change hotspot. In the last decades, the Coupled Model Intercomparison Project (CMIP) has allowed for thousands of Global Climate Model (GCM) simulations to be conducted, an important tool to assess and understand future changes in Earth's climate. The comparison of past future projections with observations provides a robust evaluation of the models' skill according to different emission scenarios. Here, a comprehensive performance assessment of GCM‐simulated mean, maximum, and minimum temperatures, and accumulated precipitation is conducted for Iberia, by retrospectively comparing historical simulations and past future projections from CMIPs 1 to 6 with reference datasets. From means to extremes, and multi‐year intra‐annual cycles to inter‐annual trends, GCM simulations are compared with Iberia0.1 and E‐OBS observational gridded datasets, and ERA5 reanalysis. The matching between the variables' distributions is assessed through the distribution added values (DAVs), a measure of gain or loss in performance between CMIPs. Results show relevant improvements in the description of the Iberian climate throughout the CMIP effort, for historical and past future periods. While the representation of intra‐annual cycles (inter‐annual trends) is enhanced after CMIP3 (CMIP2), GCMs from all CMIPs are generally able to depict the observed warming trend. Nevertheless, until 2021, a slight detrimental effect in the performance of CMIP6 models is found, in comparison with CMIP5 ones, with positive DAVs obtained only for past future temperature projections (less than 2%). A continuous monitorization of modelling accuracy for Iberia is needed, considering the increasing relevance of climate change information for adaptation strategies. [ABSTRACT FROM AUTHOR]

A comprehensive model assessment on a 6-hourly scale from multiple dimensions is critical for model selection of dynamic downscaling to reduce uncertainties. In this study, we evaluated the performance of 13 models involved in the Coupled Model Intercomparison Project-6 (CMIP6) by comparing the simulated meteorological variables at the pressure levels of 850, 500, and 250 hPa, including 6-hourly air temperature, specific humidity, zonal wind, meridional wind, and geopotential height, to those from the ERA5 reanalysis data for the 1979–2014 period. The results indicated that the CMIP6 models could mostly reproduce the spatial pattern of the climatology. Most models underestimated air temperature and geopotential height while overestimating specific humidity and zonal and meridional wind speeds in the upper troposphere. Additionally, the interannual variability of zonal and meridional wind exhibited a relatively better performance but limited ability for specific humidity at 850 hPa. Regarding the annual and diurnal cycles, CMIP6 models reasonably captured the annual cycle shape, while an overestimation was detected in simulating the diurnal amplitude, notably at 250 hPa. Based on a comprehensive rating index and overall rankings, our findings showed that no single model was identified as suitable for the simulation of any variables and regions. The models performed well for five variables, including MPI-ESM1-2-LR, MPI-ESM1-2-HR, TaiESM1, and UKESM1-0-LL, over East Asia. Then, according to the overall performance of the five variables and model accessibility, the optimal model varied by region and shared socioeconomic pathway (SSP) scenario. The multi-model ensemble mean outperformed individual models over almost all regions when it comes to comprehensive performance. The MPI-ESM1-2-LR and MRI-ESM2-0 models were the best two out of 13 models as lateral boundary conditions applied to seven climate scenarios over East Asia. This study provides valuable scientific references for selecting the optimal CMIP6 models for the projection of dynamic downscaling over East Asia and eight subregions. [ABSTRACT FROM AUTHOR]

Structures of seven CASP13 targets were determined using cryo-electron microscopy (cryo-EM) technique with resolution between 3.0 and 4.0 Å. We provide an overview of the experimentally derived structures and describe results of the numerical evaluation of the submitted models. The evaluation is carried out by comparing coordinates of models to those of reference structures (CASP-style evaluation), as well as checking goodness-of-fit of modeled structures to the cryo-EM density maps. The performance of contributing research groups in the CASP-style evaluation is measured in terms of backbone accuracy, all-atom local geometry and similarity of inter-subunit interfaces. The results on the cryo-EM targets are compared with those on the whole set of eighty CASP13 targets. A posteriori refinement of the best models in their corresponding cryo-EM density maps resulted in structures that are very close to the reference structure, including some regions with better fit to the density.

Aim: Citizen science data are increasingly used for modelling species distributions because they offer broad spatiotemporal coverage of local observations. However, such data are often collected without experimental design or set survey methods, raising the risk that bias and noise will compromise modelled predictions. We tested the ability of species distribution models (SDMs) built from these low-structure citizen science data to match the quality of SDMs from systematically collected data and tested whether stringent data filtering improved predictions. Location: Northeastern USA. Methods: We evaluated models built from a rapidly growing dataset of avian occurrences reported by birders—eBird—against models built from four independent, systematically collected datasets. We developed SDMs for 96 species using both data sources and compared their predictive abilities. We also tested whether culling eBird data by applying stringent data filters on survey effort or observer expertise improved predictions. Results: We found that SDMs built from low-structure citizen science data matched or exceeded performance of SDMs from systematically collected datasets for 12%–31% of species ((Formula presented.) = 22%), depending on the dataset. At least one culling option produced equivalent or better performance for 40%–70% of species ((Formula presented.) = 49%). Data culling by restricting survey effort improved predictions more than restricting by observer expertise. The optimal effort restriction differed by dataset, and for three of the datasets was further informed by species traits. Main conclusions: Species distribution models developed using low-structure citizen science data sometimes performed as well as those from systematic data. Culling generally improved models, but results were heterogeneous, prohibiting clear recommendations for how to cull. Our results indicate that the growing availability of citizen science data holds potential for creating high-quality spatial predictions, but that time should be invested in determining how best to cull datasets and that one-size-fits-all solutions beyond basic outlier filtering may be hard to find.

We tailored a tropical channel configuration of the Weather Research and Forecasting (WRF) Model to study tropical cyclone (TC) activity and associated climate variabilities. This tropical channel model (TCM) covers from 308S to 508N at 27-km horizontal resolution, with physics parameterizations carefully selected to achieve more realistic simulations of TCs and large-scale climate mean states. We performed 15-member ensembles of retrospective simulations from 1982 to 2016 hurricane seasons. A thorough comparison with observations demonstrates that the TCM yields significant skills in simulating TC activity climatology and variabilities in each basin, as well as TC physical structures. The correlation of the ensemble averaged accumulated cyclone energy (ACE) with observations in the western North Pacific (WNP), eastern North Pacific (ENP), and North Atlantic (NAT) is 0.80, 0.64, and 0.61, respectively, but is insignificant in the north Indian Ocean (NIO). Moreover, the TCM-simulated modulations of El Niño–Southern Oscillation (ENSO) and the Madden–Julian oscillation (MJO) on the large-scale environment and TC genesis also agree well with observations. To examine the TCM’s potential for seasonal TC prediction, the model is used to forecast the 2017 and 2018 hurricane seasons, using bias-corrected sea surface temperatures (SSTs) from the CFSv2 seasonal prediction results. The TCM accurately predicts the hyperactive 2017 NAT hurricane season and near-normal WNP and ENP hurricane seasons when initialized in May. In addition, the TCM accurately predicts TC activity in the NAT and WNP during the 2018 season, but underpredicts ENP TC activity, in association with a poor ENSO forecast.

Abstract Background Bladder cancer poses a great burden on society and its high rate of recurrence and treatment failure necessitates use of appropriate animal models to study its pathogenesis and test novel treatments. Orthotopic models are superior to other types since they provide a normal microenvironment. Four methods are described for developing bladder cancer models inside the animal's bladder. Direct intramural injection is one of these methods and is widely used. However, its efficacy in model development has not yet been studied. We aimed to evaluate the efficacy and success rate of the direct intramural injection method of developing an orthotopic model for the study of bladder cancer. Method Tumor cell lines were prepared in four microtubes. Aliquots of 200 × 103 cells were injected through a 27 gauge needle into the ventral wall of the bladders of 4 male and 4 female BALB/c mice following a midline 1 cm laparotomy incision. In addition, 1 million cells from each microtube were injected into the flanks of control mice. To prevent infection and alleviate pain, 5 mg/kg enrofloxacin and 2.5 mg/kg flunixin meglumine, respectively, were injected subcutaneously. Results Tumors formed in all mice, resulting in 100% take rate and zero post‐operation mortality. Surgery time was ≤15 min per mouse. In two mice, tumors were found in the peritoneal space as well. Conclusion Direct intramural injection is a rapid, reliable, and reproducible method for developing orthotopic models of bladder cancer. It can be done on both male and female mice and only requires readily available surgical tools. However, needle track can result in cell spillage and peritoneal tumors.

Abstract Background Clinical prediction models are often not evaluated properly in specific settings or updated, for instance, with information from new markers. These key steps are needed such that models are fit for purpose and remain relevant in the long-term. We aimed to present an overview of methodological guidance for the evaluation (i.e., validation and impact assessment) and updating of clinical prediction models. Methods We systematically searched nine databases from January 2000 to January 2022 for articles in English with methodological recommendations for the post-derivation stages of interest. Qualitative analysis was used to summarize the 70 selected guidance papers. Results Key aspects for validation are the assessment of statistical performance using measures for discrimination (e.g., C-statistic) and calibration (e.g., calibration-in-the-large and calibration slope). For assessing impact or usefulness in clinical decision-making, recent papers advise using decision-analytic measures (e.g., the Net Benefit) over simplistic classification measures that ignore clinical consequences (e.g., accuracy, overall Net Reclassification Index). Commonly recommended methods for model updating are recalibration (i.e., adjustment of intercept or baseline hazard and/or slope), revision (i.e., re-estimation of individual predictor effects), and extension (i.e., addition of new markers). Additional methodological guidance is needed for newer types of updating (e.g., meta-model and dynamic updating) and machine learning-based models. Conclusion Substantial guidance was found for model evaluation and more conventional updating of regression-based models. An important development in model evaluation is the introduction of a decision-analytic framework for assessing clinical usefulness. Consensus is emerging on methods for model updating.

Recent upgrades to China's radar network now allow for polarimetric measurements of convective systems in central China, providing an effective data set with which to evaluate the microphysics schemes employed in local squall line simulations. We compared polarimetric radar variables derived by Weather Research and Forecasting (WRF) and radar forward models and the corresponding hydrometeor species with radar observations and retrievals for a severe squall line observed over central China on 16 March 2022. Two microphysics schemes were tested and were able to accurately depict the contrast between convective and stratiform regions in terms of the drop size distribution (DSD) and reproduce the classical polarimetric signatures of the observed differential reflectivity (ZDR) and specific differential phase (KDP) columns. However, for the convective region, the simulated DSDs in both schemes exhibited lower proportions of large drops and lower liquid water content; by contrast, for the stratiform region, the proportion of large drops was found to be too high in the Morrison (MORR) scheme. The underprediction of ice-phase processes in the convective region, particularly the riming processes associated with graupel and hail, was likely responsible for the bias toward large raindrops at low levels. In the stratiform region, raindrop evaporation in the WRF Double-Moment 6-Class (WDM6) scheme, which partially offsets the overestimation of ice-phase processes, produced ground DSDs that more closely matched the observational data, and did not exhibit the overly strong warm-rain collisional growth processes of MORR. [ABSTRACT FROM AUTHOR]

Urbanization is a phenomenon that not only contributes to the development of the country but also brings with it some problems. Sustainable urbanization, on the other hand, is the realization of the growth of cities at the economic, social and environmental levels of the society, with urban structures where long-term resources are used effectively and efficiently, and pollution can be kept at a minimum level. Turkey has been exposed to rapid urbanization in recent years and the number of people living in cities is increasing. When the literature about the studies on sustainable urbanization in Turkey examined, no study has been found that evaluates sustainable urbanization performance over the years via structural equation modelling. In this study, it is aimed to measure and evaluate the sustainable urbanization performance of Turkey between 2000 and 2019. Socio-economic and environmental variables are used in the structural equation model to measure Turkey's sustainable urbanization. Second-order confirmatory factor analysis is used to estimate the model. Based on the findings, it is observed that there has been a significant increase in environmental performance in 2016 and beyond. It is seen that environmental performance and socio-economic performance, which shows a relatively stable increase, have almost the same effect on the country's sustainable urbanization performance. [ABSTRACT FROM AUTHOR]

Digital twin is an important emerging technology for digital transformation and intelligent upgrading. Digital twin models are the prerequisite for digital twin applications, and their quality directly affects the quality of digital twin services in monitoring, simulation, prediction, optimization, and other areas. However, researchers have paid insufficient attention to the quality control of digital twin models, thus hindering their effective application. To effectively control model construction and optimize model quality in the design process, this study developed digital twin model quality optimization and control methods based on workflow management. First, a workflow process model integrating digital twin model evaluation was constructed, which integrated the design process and model evaluation methods into workflow management. Then, digital twin model quality control and optimization in different stages were achieved at the macro and micro levels. Thus, the digital twin model quality was effectively controlled during the design process, and targeted design resources were selected to optimize model quality. Finally, the validity of the proposed method of model quality optimization and control was verified using the digital twin models of a practical teaching platform and a multifunctional lift-and-slide experimental line. All evaluation indexes of the model achieved good values, and the target quality optimization of the model could be performed during the design process. The results indicate that the proposed method can effectively control and optimize the model quality, which has excellent feasibility and enables the effective application of the digital twin. [ABSTRACT FROM AUTHOR]

Asphaltenes are known to cause problems related to flocculation, precipitation, and plugging, either in the formation, production lines, and processing equipment. Different models have been proposed to predict the thermodynamic conditions under which asphaltenes precipitate over the past years. This work analyses the performance of various models on their capability to match the literature experimental data of precipitated asphaltene mass fractions. Twenty-five different models based on equation-of-state (EoS), polymer solution, and thermodynamic-colloidal theories were identified. The performance/test datasets were collected and classified according to their pressure/temperature conditions, CO2, n-C5/n-C7 gas, and liquid titrations. Statistical analysis, including residuals, parity plots, and average absolute relative deviation (AARD, %), were used to compare the adequacy of selected models. Results confirmed the need for further model development for general applications over wide pressure, temperature, and composition intervals. [ABSTRACT FROM AUTHOR]

Philosophers of science typically focus on the epistemic performance of scientific models when evaluating them. Analysing the effects that models may have on the world has typically been the purview of sociologists of science. We argue that the reactive (or "performative") effects of models should also figure in model evaluations by philosophers of science. We provide a detailed analysis of how models in financial economics created the impetus for the growing importance of the phenomenon of "passive investing" in financial markets. Considering this case motivates the position that we call contextualism about model evaluation, or model contextualism for short. Model contextualism encompasses standard analyses of the epistemic performance of the model, but also includes their reactive aspects. It entails identifying the epistemic and contextual import of the model, the ways in which a model can engender change in the world (which we call the channels of transmission), and the interactions between the epistemic and reactive import of a model. [ABSTRACT FROM AUTHOR]

This study examines the deep convection populations and mesoscale convective systems (MCSs) simulated in the DYAMOND (DYnamics of the atmospheric general circulation modeled on non‐hydrostatic domains) winter project. A storm tracking algorithm is applied to six DYAMOND simulations and a global high‐resolution satellite cloud and precipitation data set for comparison. The simulated frequencies of tropical deep convection and organized convective systems vary widely among models and regions, although robust MCSs are generally underestimated. The diurnal cycles of MCS initiation and mature stages are well simulated, but the amplitudes are exaggerated over land. Most models capture the observed MCS lifetime, cloud shield area, rainfall volume and movement speed. However, cloud‐top height and convective rainfall intensity are consistently overestimated, and stratiform rainfall area and amount are consistently underestimated. Possible causes for the model differences compared to observations and implications for future model developments are discussed. Plain Language Summary: A new class of high‐resolution global atmosphere models is emerging for Earth system modeling. These new models can directly simulate convective storm systems and hold promises to improve the simulation of hydrological extremes such as flood‐producing rainfall and how they may change in future climate. This study assesses the fidelity of simulated convective storms from six global models against high‐resolution satellite observations. We find that the models simulate widely different frequency of convective storms in the tropics, but many do not produce storms that grow as large as observed. Several important aspects of observed storms such as the diurnal cycle, land‐ocean contrast, and storm rainfall amount are reasonably captured by the models; however, precipitation intensity is consistently overestimated and the storm rainfall area is too small. We further discussed potential causes for the model differences with observations and future model development needs. Key Points: Deep convective clouds and mesoscale convective systems are tracked in global convection‐permitting simulations and satellite observationsModels produce a diverse range of tropical deep convective systems and MCS frequencies and their proportions in key climate regionsModels reasonably simulate tropical MCS diurnal cycle and some MCS characteristics, but overestimate MCS precipitation intensity [ABSTRACT FROM AUTHOR]

Fabric is produced by the weaving process through the interlacement of warp and weft yarn or knitting process through the loop formation of yarn. During these processes, there is a possibility of fabric defect formation which hinders the acceptability by the fabric consumers. Ethiopian textile factories practiced a human inspection system, a traditional means of detecting fabric quality, for monitoring textile fabric defects. Manual fabric defect detection helps to instantly correct small defects, but it is time-consuming and results in human error due to fatigue and lack of concentration. Moreover, the accuracy of recognizing the defect highly depends on the mental status of the person that checks the defects. This initiated the development of a better fabric defect identification system that helps textile experts to detect fabric defects with better precision and speed. This study proposes a vision-based fabric inspection system for plain woven gray fabrics with a uniform texture. Accordingly, a comprehensive Fabric Defect Detection Database (FDDD) is constructed. The fabric significant features were calculated using a convolutional neural network (CNN) which is a state-of-the-art technology in image processing and task analysis. The experimental result of this study shows an average accuracy of about 89% in fabric defect recognition. [ABSTRACT FROM AUTHOR]

Landfalling typhoons can cause disasters over large regions. The government and emergency responders need to take measures to mitigate disasters according to the forecast of landfall position, while slight timing error can be ignored. The reliability of operational model forecasts of typhoon landfall position needs to be evaluated beforehand, according to the forecasts and observation of historical cases. In the evaluation of landfalling typhoon track, the traditional method based on point-to-point matching methods could be influenced by the predicted typhoon translation speed. Consequently, the traditional track evaluation method may result in a large track error even if the predicted landfall position is close to observation. The purpose of this paper is to address the above issue using a simple evaluation method of landfalling typhoon track forecast based on the time neighborhood approach. In this new method, the timing error was lessened to highlight the importance of the position error during the landfall of typhoon. The properties of the time neighborhood method are compared with the traditional method based on numerical forecast results of 12 landfalling typhoon cases. Results demonstrated that the new method is not sensitive to the sampling frequency, and that the difference between the time neighborhood and traditional method will be more obvious when the moving speed of typhoon is moderate (between 15–30 km h−1). The time neighborhood concept can be easily extended to a broader context when one attempts to examine the position error more than the timing error. [ABSTRACT FROM AUTHOR]

Semi-empirical kernel-driven models have been widely used to characterize anisotropic reflectance due to their simple form and physically meaningful approximation. Recently, several kernel-driven models have been coupled with topographic effects to improve the fitting of bidirectional reflectance over rugged terrains. However, extensive evaluations of the various models' performances are required before their subsequent application in remote sensing. Three typical kernel-driven BRDF models over snow-free rugged terrains such as the RTLSR, TCKD, and the KDST-adjusted TCKD (KDST-TCKD) were investigated in this paper using simulated and observed BRFs. Against simulated data, the fitting error (NIR/Red RMSE) of the RTLSR gradually increases from 0.0358/0.0342 to 0.0471/0.0516 with mean slopes (α) increases from 9.13° to 33.40°. However, the TCKD and KDST-TCKD models perform an overall better fitting accuracy: the fitting errors of TCKD gradually decreased from 0.0366/0.0337 to 0.0252/0.0292, and the best fit from the KDST-TCDK model with NIR/Red RMSE decreased from 0.0192/0.0269 to 0.0169/0.0180. When compared to the sandbox data (α from 8.4° to 30.36°), the NIR/Red RMSE of the RTLSR model ranges from 0.0147/0.0085 to 0.0346/0.0165, for the TCKD model from 0.0144/0.0086 to 0.0298/0.0154, and for the KDST-TCKD model from 0.0137/0.0082 to 0.0234/0.0149. Using MODIS data, the TCKD and KDST-TCKD models show more significant improvements compared to the RTLSR model in rugged terrains. Their RMSE differences are within 0.003 over a relatively flat terrain (α < 10°). When α is large (20°–30° and >30°), the RMSE of the TCKD model has a decrease of around 0.01 compared to that of the RTLSR; for KDST-TCKD, it is approximately 0.02, and can even reach 0.0334 in the savannas. Therefore, the TCKD and KDST-TCKD models have an overall better performance than the RTLSR model in rugged terrains, especially in the case of large mean slopes. Among them, the KDST-TCKD model performs the best due to its consideration of topographic effects, geotropic growth, and component spectra. [ABSTRACT FROM AUTHOR]

The diagnosis and prognosis of cardiovascular disease are crucial medical tasks to ensure correct classification, which helps cardiologists provide proper treatment to the patient. Machine learning applications in the medical niche have increased as they can recognize patterns from data. Using machine learning to classify cardiovascular disease occurrence can help diagnosticians reduce misdiagnosis. This research develops a model that can correctly predict cardiovascular diseases to reduce the fatality caused by cardiovascular diseases. This paper proposes a method of k-modes clustering with Huang starting that can improve classification accuracy. Models such as random forest (RF), decision tree classifier (DT), multilayer perceptron (MP), and XGBoost (XGB) are used. GridSearchCV was used to hypertune the parameters of the applied model to optimize the result. The proposed model is applied to a real-world dataset of 70,000 instances from Kaggle. Models were trained on data that were split in 80:20 and achieved accuracy as follows: decision tree: 86.37% (with cross-validation) and 86.53% (without cross-validation), XGBoost: 86.87% (with cross-validation) and 87.02% (without cross-validation), random forest: 87.05% (with cross-validation) and 86.92% (without cross-validation), multilayer perceptron: 87.28% (with cross-validation) and 86.94% (without cross-validation). The proposed models have AUC (area under the curve) values: decision tree: 0.94, XGBoost: 0.95, random forest: 0.95, multilayer perceptron: 0.95. The conclusion drawn from this underlying research is that multilayer perceptron with cross-validation has outperformed all other algorithms in terms of accuracy. It achieved the highest accuracy of 87.28%. [ABSTRACT FROM AUTHOR]

Historical changes and possible future projections of temperature extremes in China, in terms of return values of annual extreme temperatures, are examined based on daily maximum and minimum temperatures from station observations and multiple models of the fifth and sixth phases of the Coupled Model Intercomparison Project (CMIP). The observations suggest that increases in temperature extremes are largely attributable to the changing mean climate, while the varying natural variability also has an important impact, which depends on the index of the variability. The models simulate warm extremes reasonably well but underestimate the spatial heterogeneity and temporal trend of cold extremes in China. In comparison, Coupled Model Intercomparison Project Phase 6 (CMIP6) models have higher skill in simulating temperature extremes in China, showing smaller biases and intermodel variability. MRI‐ESM2‐0 and NorESM2‐LM from CMIP6 and GFDL‐ESM2M and NorESM1‐M from CMIP5 are selected as reference models based on the better performance in reproducing observed temperature extremes in China. In the future, projections from CMIP6 multimodel ensemble (MME, represented as the multimodel median) and reference models all show a continued uptick in temperature extremes, with statistically significant increases in warm extremes mainly in the north and increases in cold extremes prominent in most parts of China. Different individual models, which have similar historical simulations, yield divergent future trends of temperature extremes, which may be associated with different climate sensitivities of models. In addition, MME usage should be treated with caution since its smoothing on spatial heterogeneity and possible information from poor models. Plain Language Summary: Given the serious hazards of extreme temperature events, understanding changes in extreme temperatures and effectively predicting their future changes are critical to climate mitigation and adaptation. According to station observations and model data from the Coupled Model Intercomparison Project Phase 5 (CMIP5) and 6 (CMIP6), we find besides the varying temperature mean, the varying temperature variance also has an important impact on changes in extreme temperatures, and CMIP6 models have overall better performance than CMIP5 models in reproducing changes in extreme temperatures in China. Our results also emphasize that good performances in historical simulation cannot guarantee better projections in the future and projections of extreme temperatures from the multimodel ensemble sacrifice the spatial heterogeneity relative to individual reference models. Key Points: The impact of the varying natural variability on changes in temperature extremes depends on the variability indexCMIP6 models perform better than CMIP5 in simulating temperature extremes changes in ChinaMultimodel ensemble projections of extreme temperatures sacrifice spatial heterogeneity [ABSTRACT FROM AUTHOR]

Causal discovery is a methodology for learning causal graphs from data, and LiNGAM is a well-known model for causal discovery. This paper describes an open-source Python package for causal discovery based on LiNGAM. The package implements various LiNGAM methods under different settings like time series cases, multiple-group cases, mixed data cases, and hidden common cause cases, in addition to evaluation of statistical reliability and model assumptions. The source code is freely available under the MIT license at https://github.com/cdt15/lingam. [ABSTRACT FROM AUTHOR]

The ability of 42 global climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6), consisting of 20 low resolution (LR) and 22 medium resolution (MR), are evaluated for their performance in simulating mean and extreme precipitation over Indonesia. Compared to Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), the model climatologies and interannual variability are investigated individually and as multimodel ensemble means (MME‐mean) at monthly and seasonal time scales for the historical simulation over the period 1988–2014. Overall, results show that both LR and MR CMIP6 model skills in simulating mean and extreme precipitation indices vary across specific Indonesian regions and seasons. The individual and MME‐mean tend to overestimate the observed climatology, being largest over drier regions, yet MR models perform better compared to the LR regarding the mean bias presumably due to increased resolution. CMIP6 models tend to simulate extreme precipitation better in the dry seasons compared to the wet season. The MME‐means of the LR and MR groups mostly outperform the individual models of each group in simulating wet extremes (R95p and Rx5d) but not for the dry extremes (CDD). Among the 42 CMIP6 models, three models consistently perform poorly in simulating Rx5d and R95p, namely FGOALS‐g3, IPSL‐CM6A‐LR, and IPSL‐CM6A‐LR‐INCA, and one model in consecutive dry day (CDD) simulation, MPI‐ESM‐1‐2‐HAM, and caution is warranted. Given the knowledge of such biases, the LR and MR CMIP6 climate models can be suitably applied to assist policy makers in their decision on climate change adaptation and mitigation action. [ABSTRACT FROM AUTHOR]

Background Assessing the performance of machine learning (ML) models requires careful consideration of the evaluation metrics used. It is often necessary to utilize multiple metrics to gain a comprehensive understanding of a trained model's performance, as each metric focuses on a specific aspect. However, comparing the scores of these individual metrics for each model to determine the best-performing model can be time-consuming and susceptible to subjective user preferences, potentially introducing bias. Results We propose the Machine Learning Cumulative Performance Score (MLcps), a novel evaluation metric for classification problems. MLcps integrates several precomputed evaluation metrics into a unified score, enabling a comprehensive assessment of the trained model's strengths and weaknesses. We tested MLcps on 4 publicly available datasets, and the results demonstrate that MLcps provides a holistic evaluation of the model's robustness, ensuring a thorough understanding of its overall performance. Conclusions By utilizing MLcps, researchers and practitioners no longer need to individually examine and compare multiple metrics to identify the best-performing models. Instead, they can rely on a single MLcps value to assess the overall performance of their ML models. This streamlined evaluation process saves valuable time and effort, enhancing the efficiency of model evaluation. MLcps is available as a Python package at https://pypi.org/project/MLcps/. [ABSTRACT FROM AUTHOR]

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Plastic trash can be found anywhere, around the marina, beaches, and coastal areas in recent times. This study proposes a trash dataset called HAIDA and a trash detector that uses a YOLOv4-based object detection algorithm to monitor coastal trash pollution efficiently. Model selection, model evaluation, and hyperparameter tuning were applied to obtain the best model for the lowest generalization error in the real world. Comparison of the state-of-the-art object detectors based on YOLOv3, YOLOv4, and Scaled-YOLOv4 that used hyperparameter tuning, the three-way holdout method, and k-fold cross-validation have been presented. An unmanned aerial vehicle (UAV) was also employed to detect trash in coastal areas using the proposed method. The performance on image classification was satisfactory. [ABSTRACT FROM AUTHOR]

This research aimed: (i) to evaluate on-farm (FARM data) multiparametric models developed under controlled experiment (INRAE data) and based on non-invasive indicators to detect subacute ruminal acidosis (SARA) in dairy cows. We also aimed to recover high discrimination capacity, if needed, by (ii) building new models with combined INRAE and FARM data; and (iii) enriching the models increasing from 2 to 5 indicators per model. For model enrichment, we focused on indicators determinable on-farm by quick and inexpensive routine analysis. Fifteen commercial dairy farms were selected to cover a wide range of SARA risk. In each farm, four Holstein early-lactating healthy primiparous cows were selected based on their last on-farm recording of milk yield and somatic cell count analysis. Cows were equipped with a reticulo-rumen pH sensor. The pH kinetics were analysed over a subsequent 7-day period. Relative pH indicators were used to classify cows with or without SARA. Milk, blood, faeces, and urine were collected for analysis of the indicators included in the models developed by Villot et al. (2020) on INRAE data that were externally evaluated using FARM data. Then, new models based on the same indicators were developed combining INRAE and FARM data to test whether a possible loss in performance was due to a limited validity domain of model by Villot et al (2020). Finally, the models developed combining INRAE and FARM data were adapted to the on-farm application and enriched by increasing indicators from 2 to 5 per model using linear discriminant analysis and leave-one-out cross-validation. The sensitivities (true-positive rate) in external evaluation on FARM data were substantially lower than those from cross-validation by Villot et al. (2020) (range: 0.1–0.75 vs 0.79–0.96, respectively), and the specificities (true-negative rate) showed a larger range with lower minimum values (range: 0.18–1.0 vs 0.62–0.97, respectively). The sensitivities of new models developed combining INRAE and FARM data ranged from 0.63 to 0.77. Models involving blood cholesterol, β-hydroxybutyrate, haptoglobin, milk and blood urea, and models involving milk fat/protein ratio, dietary starch proportion, and milk fatty acids had the highest performances, whereas models including sieved faecal residues and urine pH had the lowest. Enriching models to three indicators per model improved sensitivity and specificity, but the inclusion of more indicators was less or not effective. Larger field trials are required to validate our results and to increase variability and validity domain of models.