A sound evaluation of the cadmium (Cd) mass balance in agricultural soils needs accurate data of Cd leaching. Reported Cd concentrations from in situ studies are often one order of magnitude lower ...than predicted by empirical models, which were calibrated to pore water data from stored soils. It is hypothesized that this discrepancy is related to the preferential flow of water (non-equilibrium) and/or artefacts caused by drying and rewetting soils prior to pore water analysis. These hypotheses were tested on multiple soils (n = 27) with contrasting properties. Pore waters were collected by soil centrifugation from field fresh soil samples and also after incubating the same soils (28 days, 20 °C), following two drying-rewetting cycles, the idea being that chemical equilibrium in the soil is reached after incubation. Incubation increased pore water Cd by a factor 4, on average, and up to a factor 16. That increase was statistically related to the decrease of pore water pH and the increase of nitrate, both mainly related to incubation-induced nitrification. After correcting for both factors, the Cd rise was also highest at higher pore water Ca. This suggests that higher Ca in soil enlarges Cd concentration gradients among pore classes in field fresh soils because high Ca promotes soil aggregation and separation of mobile from immobile water. Several empirical models were used to predict pore water Cd. Predictions exceeded observations up to a factor 30 for the fresh pore waters but matched well with those of incubated soils; again, deviations from the 1:1 line in field fresh soils were largest in high Ca (>0.8 mM) soils, suggesting that local equilibrium conditions in field fresh soils are not found at higher Ca. Our results demonstrate that empirical models need recalibration with field fresh pore water data to make accurate soil Cd mass balances in risk assessments.
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•Soil incubation after drying and rewetting alters pore water Cd.•Incubation promotes chemical equilibrium and nitrification.•Low pore water Ca is concurrent with high Cd in pore waters from field fresh soils.•Empirical models based on incubated soils overestimate Cd in solution if equilibrium is unlikely.
•A longer calibration period is required for catchments with nonstationary rainfall-runoff relationships to achieve stable simulations.•With the increase in the length of the interval between the ...calibration and transfer periods, the transferability of the model decreases gradually.•When forecasting runoff under nonstationary rainfall-runoff relationships, the closeness of the total rainfall amount between calibration and transfer periods is more important than the similarity in rainfall processes.
Changing climatic conditions have changed the stationary rainfall-runoff relationships in many basins. In this context, the value of the model parameters will depend more on the selection of the calibration period, which directly affects the accuracy of runoff forecasting. However, systematic exploration and testing of the impact of calibration conditions on the transferability of hydrological models under stationary and nonstationary climatic conditions require more effort. The present study investigates the impact of four calibration conditions on model transferability, including the length of the calibration period, the length of the interval between the calibration and transfer periods, the difference in climate conditions as measured by the total rainfall amount between the calibration and transfer periods, and the difference in the similarity of rainfall processes between the calibration and transfer periods. Two catchments with stationary and nonstationary climatic conditions, and five models, including XAJ, HBV, IHACRES, SIMHYD and GR4J, are used in this study. The results show that (1) a longer calibration period is required for catchments with nonstationary rainfall-runoff relationships to achieve stable simulations; (2) with the increase in the length of the interval between the calibration and transfer periods, the transferability of the model decreases gradually, and the degree of reduction is greater for catchments with nonstationary climatic conditions and rainfall-runoff relationships; and (3) when forecasting future runoff under nonstationary rainfall-runoff relationships, the closeness of the total rainfall amount between the calibration and transfer periods is more important than the similarity in rainfall series between the calibration and transfer periods. This study provides insight into the impact of calibration conditions on the transferability of hydrological models in the context of climate change.
Heat related morbidity and mortality, especially during extreme heat events, are increasing due to climate change. More Americans die from heat than from all other natural disasters combined. ...Identifying the populations and locations that are under high risk of heat vulnerability is important for urban planning and design policy making as well as health interventions. An increasing number of heat vulnerability/risk models and indices (HV/R) have been developed based on indicators related to population heat susceptibility such as sociodemographic and environmental factors. The objectives of this study are to summarize and analyze current HV/R's construction, calculation, and validation, evaluate the limitation of these methods, and provide directions for future HV/R and related studies. This systematic review used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework and used 5 datasets for the literature search. Journal articles that developed indices or models to assess population level heat-related vulnerability or risks in the past 50 years were included. A total of 52 papers were included for analysis on model construction, data sources, weighting schemes and model validation. By synthesizing the findings, we suggested: (1) include relevant and accurately measured indicators; (2) select rational weighting methods and; (3) conduct model validation. We also concluded that it is important for future heat vulnerability models and indices studies to: (1) be conducted in more tropical areas; (2) include a comprehensive understanding of energy exchanges between landscape elements and humans; and (3) be applied in urban planning and policy making practice.
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•Review indicator selection, weighting method, and validation of heat vulnerability and risk models and indices (HV/R) using PRISMA framework.•Lack of consistency in theory interpretation and indicator selections•Both explicit and statistical weighting methods used in constructing HV/Rs have biases.•No standard criteria to state the efficiency of assessing or predicting heat vulnerability.•HV/R need to include relevant and accurately measured indicators, select rational weighting methods and conduct model validation.
•A novel experimental setup for zero-gravity distillation with metal foams.•First validation of a simulation model of zero-gravity distillation with finite reflux.•High separation efficiency was ...achieved in various modes of zero-gravity distillation.•Valuable knowledge on process behavior through model-based simulations.
Zero-gravity distillation (ZGD) is a small-scale distillation process, which offers high separation efficiencies and can be used as a part of modular production plants. Instead of gravity, capillary forces are utilized to realise the fluid flow. In this study, two experimental ZGD setups with different dimensions and materials were built. Furthermore, a modified ZGD unit with dual side heating and cooling was developed. Experiments were carried out with ethanol/water mixtures, both in the infinite reflux mode and with feed and product streams, while the temperature and concentration profiles were recorded under varied feed concentrations and volumetric flow rates. The experimental data was used to validate the process model comprising coupled momentum, heat and mass transfer equations for the liquid and vapour phases as well as heat conduction equations for the unit walls. The fluid dynamics was described using the hydrodynamic analogy concept. A good agreement between simulated and measured ethanol concentrations and temperatures was found. With the validated model, sensitivity studies on the influence of the feed flow rate and the metal foam porosity on the ZGD process were performed. It was found that the separation efficiency decreases with increasing flow rate and increases with lower porosity.
Assessing the calibration of methods for estimating the probability of the occurrence of a binary outcome is an important aspect of validating the performance of risk‐prediction algorithms. ...Calibration commonly refers to the agreement between predicted and observed probabilities of the outcome. Graphical methods are an attractive approach to assess calibration, in which observed and predicted probabilities are compared using loess‐based smoothing functions. We describe the Integrated Calibration Index (ICI) that is motivated by Harrell's Emax index, which is the maximum absolute difference between a smooth calibration curve and the diagonal line of perfect calibration. The ICI can be interpreted as weighted difference between observed and predicted probabilities, in which observations are weighted by the empirical density function of the predicted probabilities. As such, the ICI is a measure of calibration that explicitly incorporates the distribution of predicted probabilities. We also discuss two related measures of calibration, E50 and E90, which represent the median and 90th percentile of the absolute difference between observed and predicted probabilities. We illustrate the utility of the ICI, E50, and E90 by using them to compare the calibration of logistic regression with that of random forests and boosted regression trees for predicting mortality in patients hospitalized with a heart attack. The use of these numeric metrics permitted for a greater differentiation in calibration than was permissible by visual inspection of graphical calibration curves.
Prediction models are common in medicine for predicting outcomes such as mortality, complications, or response to treatment. Despite the growing interest in these models in arthroplasty (and ...orthopaedics in general), few have been adopted in clinical practice. If robustly built and validated, prediction models can be excellent tools to support surgical decision making. In this paper, we provide an overview of the statistical concepts surrounding prediction models and outline practical steps for prediction model development and validation in arthroplasty research. Please visit the followinghttps://www.youtube.com/watch?v=9Yrit23Rkicfor a video that explains the highlights of the paper in practical terms.
Prediction models are common in medicine for predicting outcomes such as mortality, complications, or response to treatment. Despite the growing interest in these models in arthroplasty (and ...orthopaedics in general), few have been adopted in clinical practice. If robustly built and validated, prediction models can be excellent tools to support surgical decision making. In this paper, we provide an overview of the statistical concepts surrounding prediction models and outline practical steps for prediction model development and validation in arthroplasty research. Please visit the followinghttps://www.youtube.com/watch?v=9Yrit23Rkicfor a video that explains the highlights of the paper in practical terms.
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•RMSE tends to be better for simulations that underestimate the average.•This trend is more noticeable when the correlation coefficient is appreciably lower than unity.•The issue is due to a ...dependency of the scatter component of the RMSE on the bias.•HH indicator provides a more accurate information on the accuracy of a simulation.
In order to evaluate the reliability of numerical simulations in geophysical applications it is necessary to pay attention when using the root mean square error (RMSE) and two other indicators derived from it (the normalized root mean square error (NRMSE), and the scatter index (SI)). In the present work, in fact, we show on a general basis that, in conditions of constant correlation coefficient, the RMSE index and its variants tend to be systematically smaller (hence identifying better performances of numerical models) for simulations affected by negative bias. Through a geometrical decomposition of RMSE in its components related to the average error and the scatter error it can be shown that the above mentioned behavior is triggered by a quasi-linear dependency between these components in the neighborhood of null bias. This result suggests that smaller values of RMSE, NRMSE and SI do not always identify the best performances of numerical simulations, and that these indicators are not always reliable to assess the accuracy of numerical models. In the present contribution we employ the corrected indicator proposed by Hanna and Heinold (1985) to develop a reliability analysis of wave generation and propagation in the Mediterranean Sea by means of the numerical model WAVEWATCH III®, showing that the best values of the indicator are obtained for simulations unaffected by bias. Evidences suggest that this indicator provides a more reliable information about the accuracy of the results of numerical models.
We introduce an exploratory study on Mutation Validation (MV), a model validation method using mutated training labels for supervised learning. MV mutates training data labels, retrains the model ...against the mutated data, and then uses the metamorphic relation that captures the consequent training performance changes to assess model fit. It does not use a validation set or test set. The intuition underpinning MV is that overfitting models tend to fit noise in the training data.
MV does not aim to replace out-of-sample validation. Instead, we provide the first exploratory study on the possibility of using MV as a complement of out-of-sample validation. We explore 8 different learning algorithms, 18 datasets, and 5 types of hyperparameter tuning tasks. Our results demonstrate that MV complements well cross-validation and test accuracy in model selection and hyperparameter tuning tasks. MV deserves more attention from developers when simplicity, sustainaiblity, security (e.g., defending training data attack), and interpretability of the built models are required.
Immunoglobulin A nephropathy (IgAN) is common worldwide and has heterogeneous phenotypes. Predicting long-term outcomes and stratifying risk are important for clinical decision making and designing ...future clinical trials.
Multicenter retrospective cohort study of 2,047 patients with IgAN.
Derivation and validation cohorts composed of 1,022 Chinese patients with IgAN from a single center and 1,025 patients with IgAN from 18 renal centers, respectively.
36 characteristics, including demographic, clinical, and pathologic variables.
Combined event of end-stage kidney disease or 50% reduction in estimated glomerular filtration rate within 5 years after diagnostic kidney biopsy.
A gradient tree boosting method implemented in the eXtreme Gradient Boosting (XGBoost) system was used to select the 10 most important variables from 36 candidate variables. Stepwise Cox regression analysis was used to derive a simplified scoring scale model (SSM) based on these 10 variables. Model discrimination and calibration were assessed using the C statistic and Hosmer-Lemeshow test. Risk stratification of the SSM was evaluated using Kaplan-Meier analysis.
In the derivation and validation cohorts, 74 and 114 patients reached the outcome, respectively. XGBoost predicted the outcome with a C statistic of 0.84 (95% CI, 0.80-0.88) for the validation cohort. The SSM included 3 variables: urine protein excretion, global sclerosis, and tubular atrophy/interstitial fibrosis. Using Kaplan-Meier analysis, the SSM identified significant risk stratification (P < 0.001).
Retrospective study design, application for other ethnic groups needs to be verified.
A prediction model using routinely available characteristics and based on the combination of a machine learning algorithm and survival analysis can stratify risk for kidney disease progression in the setting of IgAN. An online calculator, the Nanjing IgAN Risk Stratification System, permits easy implementation of this model.
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