Mean Relative Prediction Error Research Articles

Mass evacuation planning for disasters management: A household evacuation route choice behavior analysis

This paper presents a multi-methodological approach to analyzing household evacuation route choice behaviors and the related decision-making for mass evacuation planning in disaster management. Specifically, the proposed multi-methodological approach integrates a syncretic risk perception conceptual model with a household-based group utility evolution model. The dynamics of household evacuation route choice are explored in two stages, namely the individual utility initiation and accommodation. The unique feature of the proposed approach is that it links individual risk perception, survival psychology, and group behavioral dynamics together to characterize household decision-making about choosing the evacuation routes. An empirical study coupled with experiments is conducted to help validate the proposed household evacuation route choice behavior model. Through various designed disaster scenarios, the applicability and strengths of the proposed model for mass evacuation planning and management are demonstrated. Particularly, the mean relative prediction errors of the proposed model with respect to household-based evacuation route switching probability and path flow splits are 3.79% and 6.88%, respectively, all within an acceptable range of ±10 %. This demonstrates that the proposed model and analytical results align closely with household choice behavior.

Diagnostic accuracy of estimated glomerular filtration rate in pediatric oncology patients.

Objective: To compare eGFR using different equations with 24-hour creatinine clearance Methodology: We collected data of standardized 24-hour creatinine clearance of pediatric oncology patients for a period of one year and compared the efficiency of various equations for estimation of creatinine clearance with it. For error estimation we used mean relative prediction error (MRPE) and root mean square error (RMSE) and for accuracy estimation we used Lin’s correlation. Results: Out of 100 study participants, 57 were males and 43 were females. Mean age of the population studied was 11.8 while mean BMI was 13.95. Most common tumor reported was osteosarcoma. Out of 18 equations evaluated only 5 showed an accuracy of 0.80 or more, while SEM of none of the equation is near to the desired value (zero). Minimum SEM was found to be for Schwartz (2012) equation. Conclusions: According to our study none of the equations in current clinical practices showed a good correlation with creatinine clearance adjusted according to body surface area. Keywords: eGFR, creatinine clarence, renal function, pediatric oncology.

Data-driven modeling of reconfigurable multi-accelerator systems under dynamic workloads

Reconfigurable multi-accelerator systems used as computing offloading platforms in edge-cloud continuum scenarios usually have to deal with highly dynamic workloads and operating conditions. In order to properly take advantage of their parallel processing capabilities and increase execution performance for a given workload, these systems need to continuously adapt their configuration (i.e., number and type of accelerators) at run time. When working at the edge, additional requirements such as energy efficiency must be also met. In this paper, Machine Learning techniques are applied to extract predictive models of the execution of different combinations of hardware accelerators on a reconfigurable multi-accelerator platform, aiming at satisfying the previously mentioned continuous optimization needs. One of the key benefits of the proposed approach is that its data-driven models can transparently estimate the impact of the complex interactions between hardware accelerators due to run-time resource contention among them and with the rest of the system, as opposed to traditional modeling approaches that cannot include that information in an easy and scalable way (e.g., analytical models). The proposed models are complemented with a complete infrastructure to generate, execute and monitor dynamic workloads in FPGA-based systems. This infrastructure has been used to (i) quantitatively analyze resource contention in reconfigurable multi-accelerator systems and (ii) produce the training and evaluation datasets for the ML-based models using annotated power consumption and execution performance traces. Experimental results obtained with a reconfigurable multi-accelerator platform based on the ARTICo3 framework running the MachSuite benchmarks show that the proposed modeling approach is highly effective, with a relative prediction error of less than 5% on average for both power consumption and execution performance. Result also show that the ML-based models achieve high accuracy levels when predicting the impact of resource contention and accelerator interaction on both metrics, with a mean relative prediction error of less than 0.6% and a standard deviation below 4.7% for the worst case.

Predictive performance of population pharmacokinetic models of imatinib in chronic myeloid leukemia patients.

Chronic myeloid leukemia is a myeloproliferative neoplasm associated with the specific chromosomal translocation known as the Philadelphia chromosome. Imatinib is a potent BCR-ABL tyrosine kinase inhibitor, which is approved as the first line therapy for CML patients. There are various population pharmacokinetic studies available in the literature for this population. However, their use in other populations outside of their cohort for the model development has not been evaluated. This study was aimed to perform the predictive performance of the published population pharmacokinetic models for imatinib in CML population and propose a dosing nomogram. A systematic review was conducted through PubMed, and WoS databases to identify PopPK models. Clinical data collected in adult CML patients treated with imatinib was used for evaluation of these models. Various prediction-based metrics were used for assessing the bias and precision of PopPK models using individual predictions. Eight imatinib PopPK model were selected for evaluating the model performance. A total of 145 plasma imatinib samples were collected from 43 adult patients diagnosed with CML and treated with imatinib. The PopPK model reported by Menon et al. had better performance than all other PopPK models. Menon et al. model was able to predict well for our clinical data where it had the relative mean prediction error percentage ≤ 20%, relative median absolute prediction error ≤ 30% and relative root mean square error close to zero. Based on this final model, we proposed a dosing nomogram for various weight groups, which could potentially help to maintain the trough concentrations in the therapeutic range.

Utility of cystatin C and serum creatinine-based glomerular filtration rate equations in predicting vancomycin clearance: A population pharmacokinetics analysis in elderly Chinese patients.

Renal function is an important factor affecting the pharmacokinetics of vancomycin. The renal function in elderly patients gradually decreases with age. An accurate estimated glomerular filtration rate (GFR) is essential in drug dosing. The study aimed to determine the most appropriate renal function estimation equations to describe vancomycin pharmacokinetics in elderly patients using population pharmacokinetic analysis. Data were obtained retrospectively from elderly patients aged ≥65years who received vancomycin for infection from September 2016 to January 2022. Renal function was estimated using the Cockcroft-Gault equation (CG), Modification of Diet in Renal Disease equation (MDRD), three Chronic Kidney Disease Epidemiology Collaboration equations (CKD-EPIcys-scr , CKD-EPIscr , and CKD-EPIcys ) and two Berlin Initiative Study equations (BIS-1 and BIS-2). The CKD-EPIcys-scr and BIS-2 equations were based on cystatin C (Cys C) and serum creatinine (Scr). The others were based on Cys C or Scr. A nonlinear mixed effects model (NONMEM) was used to develop the population pharmacokinetic model. A total of 471 serum concentrations from 313 elderly patients were used to develop the population pharmacokinetic model. Weight and GFR were identified as significant covariates affecting the pharmacokinetics of vancomycin. Cys C and Scr-based GFR (CKD-EPIcys-scr and BIS-2) yielded significant improvement performance compared with the other equations in model building. The interindividual variability of CL was reduced from 49.4% to 23.6% and 49.4% to 23.7% in CKD-EPIcys-scr and BIS-2 based models, respectively. However, greater interindividual variabilities of CL (from 26.6% to 29.0%) were represented in the other five models which were based on either Cys C or Scr. The GFR estimated by EPIcys-scr and BIS-2 equations and vancomycin CL exhibited a good correlation (r=0.834 and 0.833). In the external validation with 124 serum concentrations, the predictive performances of the CKD-EPIcys-scr and BIS-2 based models (the mean relative prediction errors were less than 1%, the mean relative absolute prediction errors were about 23%) were also superior to the other five models (the mean relative prediction errors were about 2%, the mean relative absolute prediction errors were greater than 25%) which are based on either Cys C or Scr. In this study, we determined that the equation used to estimate GFR can affect the population pharmacokinetic model fitting result. Population pharmacokinetics model with CKD-EPIcys-scr or BIS-2 can be used to optimize vancomycin dosage in elderly Chinese patients.

Dynamic Prediction with Statistical Uncertainty Evaluation of Phosphorus Content Based on Functional Relevance Vector Machine

In the steelmaking process, inaccurate composition of raw materials, experimental process parameter control, and other factors bring about large statistical uncertainty to the phosphorus content prediction of molten steel. Meanwhile, lots of models mainly predict the end‐point phosphorus content, but the composition change during the whole production process is still in the gray box condition, where the exact refining state is unknown. These two problems increase the difficulty in process parameter optimization and control. In order to improve the phosphorus content control in the steelmaking process, this work proposes the functional relevance vector machine (FRVM) method. This method smooths the time series of process parameters to continuous functions by functional data analysis to predict the phosphorus content change during the whole production process, and estimates the probabilistic distributions of regression weights based on the Bayesian framework to directly output the probabilistic confidence interval of the phosphorus content. The real‐world steelmaking dataset validates that the mean relative prediction error of phosphorus content by the FRVM method is less than 18.04% during the whole production process and the end‐point hit rate is 86.46% when the prediction error is within ±0.005 wt%.

Optimizing Excipient Properties to Prevent Aggregation in Biopharmaceutical Formulations.

Excipients are included within protein biotherapeutic solution formulations to improve colloidal and conformational stability but are generally not designed for the specific purpose of preventing aggregation and improving cryoprotection in solution. In this work, we have explored the relationship between the structure and antiaggregation activity of excipients by utilizing coarse-grained molecular dynamics modeling of protein-excipient interaction. We have studied human serum albumin as a model protein, and we report the interaction of 41 excipients (polysorbates, fatty alcohol ethoxylates, fatty acid ethoxylates, phospholipids, glucosides, amino acids, and others) in terms of the reduction of solvent accessible surface area of aggregation-prone regions, proposed as a mechanism of aggregation prevention. Polyoxyethylene sorbitan had the greatest degree of interaction with aggregation-prone regions, decreasing the solvent accessible surface area of APRs by 20.7 nm2 (40.1%). Physicochemical descriptors generated by Mordred are employed to probe the structure-property relationship using partial least-squares regression. A leave-one-out cross-validated model had a root-mean-square error of prediction of 4.1 nm2 and a mean relative error of prediction of 0.077. Generally, longer molecules with a large number of alcohol-terminated PEG units tended to interact more, with qualitatively different protein interactions, wrapping around the protein. Shorter or less ethoxylated compounds tend to form hemimicellar clusters at the protein surface. We propose that an improved design would feature many short chains of 5 to 10 PEG units in many distinct branches and at least some hydrophobic content in the form of medium-length or greater aliphatic chains (i.e., six or more carbon atoms). The combination of molecular dynamics simulation and quantitative modeling is an important first step in an all-purpose protein-independent model for the computer-aided design of stabilizing excipients.

Quantitative Analysis of Biodiesel Adulterants Using Raman Spectroscopy Combined with Synergy Interval Partial Least Squares (siPLS) Algorithms

Biodiesel has emerged as an alternative to traditional fuels with the aim of reducing the impact on the environment. It is produced by the esterification of oleaginous seeds, animal fats, etc., with short-chain alcohols in an alkaline solution, which is one of the most commonly used methods. This increases the oxygen content (from the fatty acids) and promotes the fuel to burn faster and more efficiently. The accurate quantification of biodiesel is of paramount importance to the fuel market due to the possibility of adulteration, which can result in economic losses, engine performance issues and environmental concerns related to corrosion. In response to achieving this goal, in this work, synergy interval partial least squares (siPLS) algorithms in combination with Raman spectroscopy are used for the quantification of the biodiesel content. Different pretreatment methods are discussed to eliminate a large amount of redundant information of the original spectrum. The siPLS technique for extracting feature variables is then used to optimize the input variables after pretreatment, in order to enhance the predictive performance of the calibration model. Finally, the D1-MSC-siPLS calibration model is constructed based on the preprocessed spectra, the selected input variables and the optimized model parameters. Compared with the feature variable selection methods of interval partial least squares (iPLS) and backward interval partial least squares (biPLS), results elucidate that the D1-MSC-siPLS calibration model is superior to the D1-MSC-biPLS and the D1-MSC-iPLS in the quantitative analysis of adulterated biodiesel. The D1-MSC-siPLS calibration model demonstrates better predictive performance compared to the full spectrum PLS model, with the optimal determination coefficient of prediction (R2P) being 0.9899; the mean relative error of prediction (MREP) decreased from 9.51% to 6.31% and the root--mean-squared error of prediction (RMSEP) decreased from 0.1912% (v/v) to 0.1367% (v/v), respectively. The above results indicate that Raman spectroscopy combined with the D1-MSC-siPLS calibration model is a feasible method for the quantitative analysis of biodiesel in adulterated hybrid fuels.

Adaptive Residual Useful Life Prediction for the Insulated-Gate Bipolar Transistors with Pulse-Width Modulation Based on Multiple Modes and Transfer Learning

Currently, residual useful life (RUL) prediction models for insulated-gate bipolar transistors (IGBT) do not focus on the multi-modal characteristics caused by the pulse-width modulation (PWM). To fill this gap, the Markovian stochastic process is proposed to model the mode transition process, due to the memoryless properties of the grid operation. For the estimation of the mode transition probabilities, transfer learning is utilized between different control signals. With the continuous mode switching, fractional Weibull motion (fWm) of multiple modes is established to model the stochasticity of the multi-modal IGBT degradation. The drift and diffusion coefficients are adaptively updated in the proposed RUL prediction model. In the case study, two sets of the real thermal-accelerated IGBT aging data are used. Different degradation modes are extracted from the meta degradation data, and then fused to be a complex health indicator (CHI) via a multi-sensor fusion algorithm. The RUL prediction model based on the fWm of multiple modes can reach a maximum relative prediction error of 2.96% and a mean relative prediction error of 1.78%. The proposed RUL prediction model with better accuracy can reduce the losses of the power grid caused by the unexpected IGBT failures.

Correction method of the fluorescence intensity of petroleum hydrocarbons in different soil types based on resonance scattering spectroscopy

Soil types has an obvious impact influence on the fluorescence intensity of soil petroleum hydrocarbons. To reduce the interference caused by soil types, this paper proposes a calibration method using resonance scattering spectroscopy. To establish the correction method, 100 g/kg crude oil samples from six soil types were prepared. The mission and resonance scattering spectrum under 280 nm excitation were measured for all samples. The results showed that the fluorescence spectra and resonance scattering spectra of soil crude oil vary with different soil types. And the fluorescence peak intensity and the resonance scattering peak intensity at 360 nm of soil petroleum hydrocarbons are highly correlated in different soil types. The fluorescence peak intensity at 360 nm was divided by the resonance scattering light intensity at 360 nm to obtain the corrected fluorescence intensity, which can effectively reduce the influence of soil type on fluorescence intensity. The feasibility of correction method was further verified for different types and concentrations of soil crude oil. The results showed better linearity between the fluorescence intensity and the concentration of Petroleum Hydrocarbons after the correction (with a correlation coefficient R2 of 0.96) than before the correction (with R2 of 0.72), the mean relative prediction error of all samples decreased from 31.92 % to 4.71 % after correction. The research can provide theoretical basis and technical support for the accurate and rapid detection of Petroleum Hydrocarbons in soil by fluorescence spectroscopy.

Modelling PM2.5 for Data-Scarce Zone of Northwestern India using Multi Linear Regression and Random Forest Approaches

ABSTRACT PM2.5 (Particulate matter with aerodynamic diameter <2.5 m) concentrations above permissible limit causes air quality deterioration and hampers human health. Due to the lack of a good spatial network of ground-based PM monitoring sites and systematic checking, the availability of continuous data of PM2.5 concentrations at macro and meso scales is restricted. Present research estimated PM2.5 concentrations at high (1 km) resolution over Faridabad, Ghaziabad, Gurugram and Gautam Buddha Nagar, a data-scarce zone of the highly urbanized area of northwestern India for the year 2019 using Random Forest (RF), Multi-Linear Regression (MLR) models and Hybrid Model combining RF and MLR. It included Aerosol Optical Depth (AOD), meteorological data and limited in-situ data of PM2.5. For validation, the correlation coefficient (R), Root-Mean-Square Error (RMSE), Mean Absolute Error (MAE) and Relative Prediction Error (RPE) have been utilized. The hybrid model estimated PM2.5 with a greater correlation (R = 0.865) and smaller RPE (22.41%) compared to standalone MLR/RF models. Despite the inadequate in-situ data, Greater Noida has been found to have a high correlation (R = 0.933) and low RPE (32.13%) in the hybrid model. The most polluted seasons of the year are winter (137.28 µgm−3) and post-monsoon (112.93 µgm−3), whereas the wet monsoon (44.56 µgm−3) season is the cleanest. The highest PM2.5 level was recorded in Noida followed by Ghaziabad, Greater Noida and Faridabad. The findings of the present research will provide an input dataset for air pollution exposure risk research in parts of northwestern India with sparse monitoring data.

Rapid detection of protein content in rice based on Raman and near-infrared spectroscopy fusion strategy combined with characteristic wavelength selection

Protein content is an essential index for evaluating rice quality. This work discussed the feasibility of rapid detection of protein content in rice using spectral data fusion technology. An improved binary particle swarm optimization algorithm (IBPSO) was proposed to select the characteristic wavelength of Raman and near-infrared spectroscopy fusion data, which improved the detection accuracy of the partial least squares correction model. The determination coefficient of prediction, root mean square error of prediction, and mean relative error of prediction of the protein content detection model established by IBPSO were 0.903, 0.235%, and 2.768%, respectively, which were better than the modeling performance of the other four algorithms. The research shows that IBPSO can efficiently acquire high correlation modeling wavelength variables through the guiding optimization of binary bits with a value of '1′. The combination of IBPSO and spectral data fusion strategy can realize the rapid detection of protein content in rice, which provides theoretical support for developing related online detection equipment.

A Metallurgical Dynamics-Based Method for Production State Characterization and End-Point Time Prediction of Basic Oxygen Furnace Steelmaking

Basic Oxygen Furnace (BOF) steelmaking is an important way for steel production. Correctly recognizing different blowing periods and abnormal refining states is significant to ensure normal production process, while accurately predicting the end-point time helps to increase the first-time qualification rate of molten steel. Since the decarburization products CO and CO2 are the main compositions of off-gas, information of off-gas is explored for BOF steelmaking control. However, the problem is that most of the existing research directly gave the proportions of CO and CO2 as model input but barely considered the variation information of off-gas to describe the production state. At the same time, the off-gas information can be expected to recognize the last blowing period and predict the end-point time earlier than the existing methods that are based on sub-lance or furnace flame image, but little literature makes an attempt. Therefore, this work proposes a new method based on functional data analysis (FDA) and phase plane (PP), defined as FDA-PP, to describe and predict the BOF steelmaking process from the metallurgical dynamics viewpoint. This method extracts the total proportion of CO and CO2 and its first-order derivative as dynamics features of steelmaking process via FDA, which indicate the reaction velocity and acceleration of decarburization reaction, and describes the evolution of dynamics features via PP. Then, the FDA-PP method extracts the features of phase trajectories for production state recognition and end-point time prediction. Experiments on a real production dataset demonstrate that the FDA-PP method has higher production state recognition accuracy than the classical phase space, SVM, and BP methods, which is 87.78% for blowing periods of normal batches, 90.94% for splashing anomaly, and 81.29% for drying anomaly, respectively. At the same time, the FDA-PP method decreases the mean relative prediction error (MRE) of the end-point time prediction for abnormal batches by about 10% compared with the SVM and BP methods.

Water consumption prediction and influencing factor analysis based on PCA-BP neural network in karst regions: a case study of Guizhou Province.

Water consumption prediction is an integral part of water resource planning and management. Constructing a highly precise water consumption prediction model is of great significance for promoting regional water resource planning and high-quality development of the socio-economy. This paper focuses on the case of the typical karst region in Guizhou Province in China. Based on data on water consumption and its influencing factors spanning 2000-2020, the principal component analysis method was applied to reduce the dimensionality of 16 influencing factors of water consumption in Guizhou; the principal components extracted were used as input samples of the BP neural network and a PCA-BP neural network water consumption prediction model was conducted to predict water consumption of Guizhou Province in the next 10years. The results show that the mean absolute error and mean relative error of prediction based on the constructed PCA-BP neural network were 2.8% and 2.9%, respectively, with superior performance in terms of prediction error and trends compared with other models. This paper discusses the main influencing factors of water consumption and analyzes their influence on the water consumption forecasting model so that the parameters of the water consumption forecasting model can be selected more efficiently and provide a reference for regional water consumption analysis and water resource planning and management.

Multi-element Quantitative Analysis of Single Micro-sized Suspended Particles in Air with High Accuracy Based on Random Forest and Variable Selection Strategies.

The chemical compositions of atmospheric particles have been studied for several decades, and the traditional techniques for particle analysis usually require time-consuming sample preparation. Within this study, simultaneous quantitative detection of multiple metallic species (Zn, Cu, and Ni) in single micro-sized suspended particles was investigated by combining random forest (RF) and variable selection strategies. Laser-induced breakdown spectra of 15 polluted black carbon samples were applied for establishing the RF model, and the movmean smoothing spectral pretreatment method and variable selection methods [variable importance measurement (VIM), genetic algorithm (GA), and variable importance projection (VIP)] were proposed. Finally, the optimized RF calibration model with the evaluation indicators of mean relative error (MRE), root-mean-square error (RMSE), and coefficient of determination (R2) was constructed based on the optimal input variables and model parameters. Compared with the univariate regression method, the VIP-RF (Zn) and VIM-RF (Cu and Ni) models showed a better correlation relationship (Rp2 = 0.9662 for Zn, Rp2 = 0.9596 for Cu, and Rp2 = 0.9548 for Ni). For Zn, Cu, and Ni, the values of RMSEP (RMSE of prediction) decreased by 116.44, 68.94, and 102.10 ppm, while the values of MREP (MRE of prediction) decreased by 67, 55, and 48%, respectively. The values of ratio of prediction to deviation (RPD) of VIP-RF (Zn), VIM-RF (Cu), and VIM-RF (Ni) models were 5.4, 5.0, and 4.7, respectively. The performance of this combined approach displays a notable accuracy improvement in the quantitative analysis of single particles, suggesting that it is a promising tool for real-time air particulate matter pollution monitoring and control in the future.

Dynamic prediction of multivariate functional data based on Functional Kernel Partial Least Squares

With the flourishment of sensing technology, a huge mass of functional data can be acquired to describe the manufacturing process and predict the product quality. But these data simultaneously bring in modeling challenges of multi-type data, high-dimensional variables, data irregularity, and complex correlations. To address these challenges, this work proposes the Functional Kernel Partial Least Squares (FKPLS) methodology, which is a function-on-function regression. The FKPLS method first smooths the discrete sequences to continuous functions via functional data analysis (FDA) to retain the dynamic characteristics of variables, and then relies on basis function expansion to estimate the regression coefficient functions in order to avoid the ill-posed problem in directly estimating the functional eigenequation. An artificial dataset and a real-world steelmaking process are used to validate the effectiveness of the FKPLS method. The results demonstrate lower mean relative prediction error of the FKPLS method than the traditional methods, which is no more than 0.36% on simulation data and no more than 14.88% on industrial data. As the regression coefficients are functions, the FKPLS method is also effective in predicting dynamic mapping between predictors and responses.

Evaluation of coating uniformity for the digestion-aid tablets by portable near-infrared spectroscopy

Process analysis can effectively stabilize pharmaceutical quality and optimize the control of production process. For the sustained-release digestion-aid tablets, the coating film thickness is an important indicator to measure the quality of products. Traditional method mainly spot-checks tablets and measures with visual microscopy, which is time-consuming and laborious. This study attempted to use a portable near-infrared spectroscopy for rapid detection of a Chinese medicine tablets from production line. First, PLS regression models were established for coating film at twelve different locations of the tablet section, and the results showed that the correlation coefficients of training and validation sets were all over 0.80. Subsequently, the twelve locations were divided into six groups to further establish regressions. After chemometrics optimization, the optimal of six group models were generally better than single location models, with Rc2 and Rv2 all above 0.85, and RMSEV values all below 2.0. The mean relative error of prediction of the optimal model was 9.49%. The pharmaceutical process detection based on the portable NIR spectroscopy met the demand of managing digestion-aid tablet coating data conveniently. The proposed approach can successfully realize on-site and online pharmaceutical monitoring and has a promising practical value.

A Machine Learning Approach to Predict Interdose Vancomycin Exposure.

Estimation of vancomycin area under the curve (AUC) is challenging in the case of discontinuous administration. Machine learning approaches are increasingly used and can be an alternative to population pharmacokinetic (POPPK) approaches for AUC estimation. The objectives were to train XGBoost algorithms based on simulations performed in a previous POPPK study to predict vancomycin AUC from early concentrations and a few features (i.e. patient information) and to evaluate them in a real-life external dataset in comparison to POPPK. Six thousand simulations performed from 6 different POPPK models were split into training and test sets. XGBoost algorithms were trained to predict trapezoidal rule AUC a priori or based on 2, 4 or 6 samples and were evaluated by resampling in the training set and validated in the test set. Finally, the 2-sample algorithm was externally evaluated on 28 real patients and compared to a state-of-the-art POPPK model-based averaging approach. The trained algorithms showed excellent performances in the test set with relative mean prediction error (MPE)/ imprecision (RMSE) of the reference AUC = 3.3/18.9, 2.8/17.4, 1.3/13.7% for the 2, 4 and 6 samples algorithms respectively. Validation in real patient showed flexibility in sampling time post-treatment initiation and excellent performances MPE/RMSE<1.5/12% for the 2 samples algorithm in comparison to different POPPK approaches. The Xgboost algorithm trained from simulation and evaluated in real patients allow accurate and precise prediction of vancomycin AUC. It can be used in combination with POPPK models to increase the confidence in AUC estimation.

A single Bayesian estimator for iohexol clearance estimation in ICU, liver failure and renal transplant patients.

Iohexol clearance has been proposed to estimate the glomerular filtration rate (GFR). A population pharmacokinetics (popPK) model was developed from heterogeneous patients. A Bayesian estimator (MAP-BE) based on a limited sampling strategy (LSS) was derived and evaluated in external patients. Full pharmacokinetic data (7-12 samples) from 172 patients receiving iohexol for measurement of their GFR (unstable and stable ICU patients, liver failure patients and kidney transplant patients) were split into development (n = 136) and validation (n = 36) datasets. A PopPK model was developed in Monolix and was used to develop MAP-BE based on LSS. Its performance for GFR estimation was evaluated in the validation set. A two-compartment model with first-order elimination best described the data. The final model included the type of patients on volume of distribution (Vd), clearance and intercompartmental constants, serum creatinine on clearance and body weight on Vd. The best LSS included samples at 0.1-1-9h exhibiting a relative mean prediction error (MPE) (RMSE) = -3.7% (14.3%) and better performance than the Bröchner-Mortensen formula (-3.0%/17%). Split by type of patients, the highest interindividual variability and imprecision was observed in unstable ICU patients (MPE (RMSE) = 3.7% (18.8%)) while the best performances were obtained for renal transplant patients (MPE (RMSE) = 1.0% (5.8%)). All LSS that included samples before 9hours for the third sample were associated with an increased imprecision. A single MAP-BE of iohexol based on a three-sample LSS for four heterogeneous populations was developed and allowed accurate estimation of GFR in kidney transplant patients, slightly biased in stable ICU patients and slightly imprecise in unstable ICU patients.

How much are Canadians willing to pay for clean surface and ground water? A meta-analysis of the Canadian non-market valuation literature

Three decades of non-market water quality valuation (NMWQV) studies in Canada are analyzed to generate a generic benefits transfer function. Contrary to the large valuation literature focusing on water and wilderness-based recreation in Canada, the number of studies related to water quality is limited. NMWQV studies lack a common design, including consistent adherence to a Canada-specific water quality ladder (WQL). Despite the high degree of data heterogeneity, values extracted from the literature show an increasing step function when relating them to the Resources for the Future WQL. Meta-regression models (MRMs) explain a large share of the variation in value estimates based on the type of water resources, population and methodological characteristics. Baseline water quality and the size of the water quality change are significant determinants of the estimated non-market values. With a relative mean prediction error of no more than 20 percent, the predictive power of the estimated MRMs is high. As such, they are an important step forward in the development of a policy-relevant water quality valuation model. However, there is a clear need for the development of more coherent non-market valuation guidelines in the Canadian water context.