•First comprehensive validation of prediction models for transition to psychosis.•In external PRONIA validation sample, two models show good discrimination performance.•Combining predictions from ...raters and transition models improves performance.•Prediction of transition to psychosis is feasible on global scale.•Yet transition models need additional research efforts before clinical implementation.
A multitude of prediction models for a first psychotic episode in individuals at clinical high-risk (CHR) for psychosis have been proposed, but only rarely validated. We identified transition models based on clinical and neuropsychological data through a registered systematic literature search and evaluated their external validity in 173 CHRs from the Personalised Prognostic Tools for Early Psychosis Management (PRONIA) study. Discrimination performance was assessed with the area under the receiver operating characteristic curve (AUC), and compared to the prediction of clinical raters. External discrimination performance varied considerably across the 22 identified models (AUC 0.40−0.76), with two models showing good discrimination performance. None of the tested models significantly outperformed clinical raters (AUC = 0.75). Combining predictions of clinical raters and the best model descriptively improved discrimination performance (AUC = 0.84). Results show that personalized prediction of transition in CHR is potentially feasible on a global scale. For implementation in clinical practice, further rounds of external validation, impact studies, and development of an ethical framework is necessary.
•High-resolution PM2.5 models were developed with low-cost PurpleAir measurements.•An independent dataset was used to validate model performance at cohort locations.•A PCA-based similarity metric was ...proposed to guide low-cost monitor deployment.•Model improvement was observed after incorporating low-cost PurpleAir measurements.•PurpleAir monitors with higher PCA similarity resulted in larger model improvement.
High-resolution, high-quality exposure modeling is critical for assessing the health effects of ambient PM2.5 in epidemiological studies. Using sparse regulatory PM2.5 measurements as principal model inputs may result in two issues in exposure prediction: (1) they may affect the models’ accuracy in predicting PM2.5 spatial distribution; (2) the internal validation based on these measurements may not reliably reflect the model performance at locations of interest (e.g., a cohort’s residential locations). In this study, we used the PM2.5 measurements from a publicly available commercial low-cost PM2.5 network, PurpleAir, with an external validation dataset at the residential locations of a representative sample of participants from the Adult Changes in Thought - Air Pollution (ACT-AP) study, to improve the accuracy of exposure prediction at the cohort participant locations. We also proposed a metric based on principal component analysis (PCA) - the PCA distance - to assess the similarity between monitor and cohort locations to guide monitor deployment and data selection. The analysis was based on a spatiotemporal modeling framework with 51 “gold-standard” monitors and 58 PurpleAir monitors for model development, as well as 105 home monitors at the cohort locations for model validation, in the Puget Sound region of Washington State from June 2017 to March 2019. After including calibrated PurpleAir measurements as part of the dependent variable, the external spatiotemporal validation R2 and root-mean-square error, RMSE, for two-week concentration averages improved from 0.84 and 2.22 μg/m3 to 0.92 and 1.63 μg/m3, respectively. The external spatial validation R2 and RMSE for long-term averages over the modeling period improved from 0.72 and 1.01 μg/m3 to 0.79 and 0.88 μg/m3, respectively. The exposure predictions incorporating PurpleAir measurements demonstrated sharper urban-suburban concentration gradients. The PurpleAir monitors with shorter PCA distances improved the model’s prediction accuracy more substantially than the monitors with longer PCA distances, supporting the use of this similarity metric.
Cross-validation using randomized subsets of data—known as k-fold cross-validation—is a powerful means of testing the success rate of models used for classification. However, few if any studies have ...explored how values of k (number of subsets) affect validation results in models tested with data of known statistical properties. Here, we explore conditions of sample size, model structure, and variable dependence affecting validation outcomes in discrete Bayesian networks (BNs). We created 6 variants of a BN model with known properties of variance and collinearity, along with data sets of n = 50, 500, and 5000 samples, and then tested classification success and evaluated CPU computation time with seven levels of folds (k = 2, 5, 10, 20, n − 5, n − 2, and n − 1). Classification error declined with increasing n, particularly in BN models with high multivariate dependence, and declined with increasing k, generally levelling out at k = 10, although k = 5 sufficed with large samples (n = 5000). Our work supports the common use of k = 10 in the literature, although in some cases k = 5 would suffice with BN models having independent variable structures.
•A breakup/coalescence model is validated for 3D flow conditions.•The validation is shown for two different liquid velocities.•Two-phase flow hydrodynamics under complex flow conditions is ...analyzed.•The effects of different physical mechanisms underlying bubble breakup and coalescence are demonstrated.
In the present study, we assessed the capabilities of Eulerian-Eulerian CFD two-phase flow simulation with the homogeneous Multiple Size Group Model (MUSIG) and consideration of breakup and coalescence under three-dimensional flow conditions. We compared void fraction, bubble size and bubble velocity distributions against experimental data from vertical gas-disperse two-phase flow in a pipe with a flow obstruction. The simulation results generally agree well upstream the obstacle, where we have a typically developed pipe flow. Downstream of the obstacle void fraction is overpredicted while bubble velocity is underpredicted. The bubble size distribution has no clear trend. With higher liquid velocities, the deviations increase. As a conclusion, the simulation has difficulties to balance the gas fraction in the strong vortex in the shadow of the obstacle. Here further model improvement is needed.
While large‐scale terrestrial evapotranspiration (ET) information is essential for our understanding of the Earth's water and energy cycles, substantial differences exist in current global ET ...products due partly to uncertainties in soil‐ and vegetation‐related parameters and/or precipitation forcing. Here a calibration‐free complementary relationship (CR) model, driven purely by routine meteorological forcing (air and dew‐point temperature, wind speed, and net radiation), mainly from ERA5, was employed to estimate global ET rates during 1982–2016. Modeled ET agrees favorably with (a) monthly eddy‐covariance measurements of 129 global FLUXNET sites, and; (b) water‐balance‐derived ET of 52 basins at the multiyear mean and annual scales. Additional evaluations demonstrate that the CR is very competitive, in comparison with other 12 widely used global ET products. The 35‐years mean global land ET rate from the CR is 500 ± 6 mm yr−1 (72.3 ± 0.9 × 103 km3 yr−1) with more than 70% of the land area exhibiting increasing annual ET rates over the study period. Globally, CR ET significantly increased at a rate of 0.31 mm yr−1 during 1982–2016, suggesting a 2.2% increase in global land ET over last 35 years. Model inter‐comparisons indicate that global annual CR ET values and their trend are close to the median of not only the 12 ET products chosen but also that of 20 CMIP6 models. Since this calibration‐free CR model requires no precipitation (except in sea‐shore deserts for a subsequent ET correction), vegetation or soil data, it could be incorporated into complex hydrological and/or climate models, thereby facilitating large‐scale hydrological and climate simulations.
Key Points
A global, 30‐years‐plus complementary relationship evapotranspiration (ET) product is developed and validated at the plot and basin scales
This new ET product, derived from a minimal number of inputs without vegetation or soil data, may improve upon previous global ET estimates
Global terrestrial ET rates increased significantly during 1982–2016, particularly in the Northern Hemisphere
Modeling and simulation (M&S) is a well-known scientific tool that could be used to analyze a system or predict its behavior before physical construction. Despite being an established methodical tool ...in engineering, only a few review articles discussing emerging topics in M&S are available in open literature, especially for renewable and sustainable energy systems. This review critically examines recent advances in modeling and simulation in the energy sector, with few insights on its approaches, challenges, and prospects in selected renewable and sustainable energy systems (RSES). In addition, the concept of model validation in RSES is systematically discussed based on in-sample and out-of-sample approaches, while potential data sources with crucial elements for model validation in RSES are highlighted. Furthermore, three major groups of sustainable energy system models that play important roles in supporting national and international energy policies arepresented, to bring to light how the modeling of energy systems is evolving to meet its challenges in the design, operation, and control of RSES. This review also presents a comprehensive assessment of the current approaches, challenges, and prospects in modeling the behavior and evaluating the performance of RSES. Finally, areas that need further research and development in renewable and sustainable energy system modeling are also highlighted.
•Renewable and sustainable energy systems, modeling and simulation concepts are discussed.•New approaches for modeling and simulation of energy systems are systematically presented.•Suitable models for specific renewable and sustainable energy systems are proposed.•Updated concepts and theories in three selected energy model groups are presented.•Steps to model geothermal systems and fine-tune PV system models are introduced.
Considering the current changes and conditions, greater attention should be given to promoting the spirit of cooperation among human resources working in schools in order to achieve organizational ...goals. With this in mind, the present study aimed to validate a model for improving group cooperation among school teachers. The research employed a descriptive-correlational design. The statistical population consisted of managers and teachers in the field of educational sciences in Qom City in 2021. A sample of 129 participants was selected through accessible sampling method and Cochran's formula. Data was collected using a researcher-made questionnaire consisting of 39 statements. To validate the proposed model, structural equation modeling was performed using PLS software. The findings indicated that the tool designed to test the proposed model had adequate validity and reliability. Additionally, the overall fit of the research model was appropriate and approved. The validation of the identified components showed that all paths had a critical value higher than the critical value (1.96) at the 95% confidence level, indicating the significance of the paths, the appropriateness of the structural model, and the confirmation of all the identified components. In conclusion, the proposed model was approved, and the designed questionnaire can be used to measure the spirit of group cooperation among school teachers.
•Simultaneous fitting of multiple thermal events without prior peak discretization.•Akaike weights used to determine statistically relevant number of thermal events.•In silico and experimental ...validation of modified Sestak-Berggren methodology.
This study outlines the principles of modelling the kinetics of solid-state reactions through the simultaneous fitting of multiple peak curves using the modified Sestak-Berggren equation. This mathematical model gives an indication of the mechanism occurring and allows kinetic parameters, such as activation energy, to be estimated. This methodology is demonstrated using in silico thermo-conductivity detector (TCD) data showing the internal consistency of the Sestak-Berggren modelling approach, its applicability to noisy data and its ability to predict mechanisms occurring during a thermally induced solid state reaction. Using these in silico data it has been confirmed that this empirical model can separate overlapped peaks without a priori peak deconvolution. A rigorous statistical methodology based on the Akaike Information Criteria, is recommended to identify the optimum number of thermal events that should be applied to a system. This modified Sestak-Berggren model is then applied to an experimental dataset of temperature programmed reduction of a calcined cobalt on alumina catalyst precursor. This allows for the identification of a statistically adequate kinetic triplet for each thermal event. Recommendations on the treatment of datasets which contain “shoulders” and closely overlapped peaks are also given.
•DayCent-CR can quantify SOC across a range of agricultural crops and practices.•Cross-validation enabled model calibration and validation with a limited dataset.•Model variance parameters can be ...used in carbon credit uncertainty deductions.
Regenerative soil management practices have been shown to increase soil organic carbon in cropland previously under conventional management, and farmers that adopt regenerative practices could be eligible to participate in carbon offset programs. Due to the high cost of soil sampling at large scales, project developers of agricultural carbon offset programs may employ a hybrid measurement and modeling approach to SOC quantification. While biogeochemical models allow for carbon crediting to occur on larger scales than soil sampling alone would allow, any model used must be unbiased and shown to adequately predict SOC changes, with known uncertainty, across the crops, practice changes, and geographies of interest. The “credit-ready” version of the DayCent ecosystem model, DayCent-CR, was evaluated for performance across 14 combinations of crops and practice categories. Model calibration and validation was performed with a Bayesian Markov chain Monte Carlo approach using k-fold cross validation and 668 SOC stock change measurements from 41 agricultural research sites. Overall model performance met the guidelines established by Climate Action Reserve’s Soil Enrichment Protocol: ≥90% of model prediction intervals covered the measured value, and mean bias in all categories was less than pooled measurement uncertainty. Importantly, posterior distributions of DayCent-CR parameters and variance components enable the calculation of variance, which can then be used to calculate an uncertainty deduction that is applied to overall project credits to ensure conservatism. The calibrated model parameters are therefore valid for use in crediting programs within the domain of the validation dataset.
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