Multimodel combination techniques for analysis of hydrological simulations: Application to distributed model intercomparison project results

This paper examines several multimodel combination techniques that are used for streamflow forecasting: the simple model average (SMA), the multimodel superensemble (MMSE), modified multimodel superensemble (M3SE), and the weighted average method (WAM). These model combination techniques were evaluated using the results from the Distributed Model Intercomparison Project (DMIP), an international project sponsored by the National Weather Service (NWS) Office of Hydrologic Development (OHD). All of the multimodel combination results were obtained using uncalibrated DMIP model simulations and were compared against the best-uncalibrated as well as the best-calibrated individual model results. The purpose of this study is to understand how different combination techniques affect the accuracy levels of the multimodel simulations. This study revealed that the multimodel simulations obtained from uncalibrated single-model simulations are generally better than any single-member model simulations, even the best-calibrated single-model simulations. Furthermore, more sophisticated multimodel combination techniques that incorporated bias correction step work better than simple multimodel average simulations or multimodel simulations without bias correction. © 2006 American Meteorological Society

Multi-model ensemble hydrologic prediction using Bayesian model averaging

Multi-model ensemble strategy is a means to exploit the diversity of skillful predictions from different models. This paper studies the use of Bayesian model averaging (BMA) scheme to develop more skillful and reliable probabilistic hydrologic predictions from multiple competing predictions made by several hydrologic models. BMA is a statistical procedure that infers consensus predictions by weighing individual predictions based on their probabilistic likelihood measures, with the better performing predictions receiving higher weights than the worse performing ones. Furthermore, BMA provides a more reliable description of the total predictive uncertainty than the original ensemble, leading to a sharper and better calibrated probability density function (PDF) for the probabilistic predictions. In this study, a nine-member ensemble of hydrologic predictions was used to test and evaluate the BMA scheme. This ensemble was generated by calibrating three different hydrologic models using three distinct objective functions. These objective functions were chosen in a way that forces the models to capture certain aspects of the hydrograph well (e.g., peaks, mid-flows and low flows). Two sets of numerical experiments were carried out on three test basins in the US to explore the best way of using the BMA scheme. In the first set, a single set of BMA weights was computed to obtain BMA predictions, while the second set employed multiple sets of weights, with distinct sets corresponding to different flow intervals. In both sets, the streamflow values were transformed using Box-Cox transformation to ensure that the probability distribution of the prediction errors is approximately Gaussian. A split sample approach was used to obtain and validate the BMA predictions. The test results showed that BMA scheme has the advantage of generating more skillful and equally reliable probabilistic predictions than original ensemble. The performance of the expected BMA predictions in terms of daily root mean square error (DRMS) and daily absolute mean error (DABS) is generally superior to that of the best individual predictions. Furthermore, the BMA predictions employing multiple sets of weights are generally better than those using single set of weights. © 2006 Elsevier Ltd. All rights reserved

Expression levels of vascular endothelial growth factors A and C in patients with peptic ulcers and gastric cancer

Purpose: Vascular endothelial growth factor (VEGF) is one of the most important growth factors for metastatic tumors. To clarify the role of VEGF-A and C in patients with peptic ulcer disease (PUD) or gastric cancer (GC), we evaluated the expression levels of these two molecules. We also analyzed the effect of Helicobacter pylori infection on VEGF-A and C expression levels

Effect of chlorhexidine on the shear bond strength of self-etch adhesives to dentin

The aim of this study was to investigate the effect of chlorhexidine on shear bond strength of self-etch adhesives to dentin. The crowns of 60 sound human premolars were horizontally sectioned to expose the coronal dentin. Dentin surfaces were polished with 320 grit silicon carbide papers, and were randomly divided into 4 groups (n = 15). In group 1, two-step self-etch adhesive resin (Clearfil SE Bond) was used. In group 2, the surface was rinsed with chlorhexidine 2% solution prior to Clearfil SE bond application. In groups 3 and 4, the procedure was repeated similar to groups 1 and 2. In this case, onestep self-etch adhesive resin (Clearfil S3 Bond) was used. Then Z100 composite resin was applied. Subsequent to thermocycling, the shear bond strengths were measured. Data were analyzed using oneway ANOVA. In this study, p < 0.05 was considered significant. There were no significant differences between groups (p = 0.321). The use of 2% chlorhexidine had no effect on the shear bond strength of two-step and one-step self-etching adhesive resins. Moreover, there was no significant difference in the shear bond strength values between two-step and one-step self-etching adhesive resins.Key words: Chlorhexidine, shear bond strength, one-step self-etch adhesive resin, two-step self-etch adhesive resin

Growth of TiO₂ Nanotube on Titanium Substrate to Enhance its Biotribological Performance and Biocorrosion Resistance

TiO2 nanotubes (NTs) have a great potential in improving the osetointegration of titanium (Ti)-based biomaterials. Much efforts have been made to evaluate the biological performance of the TiO2 nanotube in regulating protein adsorption and cells attachments. As often used in orthopaedic applications, although biotribological performance and biocorrosion are important issues in these applications, few researches have been reported on the biotribological performance of NT layers. This paper reports the preparation of a structure-optimised TiO2 NT (SO-NT) material via a multi-step oxidation strategy, as well as its biotribological and biocorrosion behaviours. In this procedure, an interfacial bonding layer of approximately 120 nm - 150 nm was first formed on the titanium substrate, which was then joined to the NT bottoms. The mechanical testing with respect to impact, bending, and biotribological performance have demonstrated the resultant SO-NT layer possess improved mechanical stability compared to conventional NT. The uniform hyperfine interfacial bonding layer with nano-sized grains exhibited a strong bonding to NT layer and Ti substrate. It was observed that the layer not only effectively dissipates external impacts and shear stress but also acts as a good corrosion resistance barrier to prevent the Ti substrate from corrosion. Theoretical models were proposed to analyze and predict the shear performance and corrosion-resistance mechanisms of the resultant material. The obtained results demonstrated that the SO-NT material has great potential in orthopaedic applications

An Integrated Hydrologic Bayesian Multi-Model Combination Framework: Confronting Input, parameter and model structural uncertainty in Hydrologic Prediction

This paper presents a new technique--Integrated Bayesian Uncertainty Estimator (IBUNE) to account for the major uncertainties of hydrologic rainfall-runoff predictions explicitly. The uncertainties from the input (forcing) data--mainly the precipitation observations and from the model parameters are reduced through a Monte Carlo Markov Chain (MCMC) scheme named Shuffled Complex Evolution Metropolis (SCEM) algorithm which has been extended to include a precipitation error model. Afterwards, the Bayesian Model Averaging (BMA) scheme is employed to further improve the prediction skill and uncertainty estimation using multiple model output. A series of case studies using three rainfall-runoff models to predict the streamflow in the Leaf River basin, Mississippi are used to examine the necessity and usefulness of this technique. The results suggests that ignoring either input forcings error or model structural uncertainty will lead to unrealistic model simulations and their associated uncertainty bounds which does not consistently capture and represent the real-world behavior of the watershed