Home About us Contact
|
Home About us Contact | ||
|
|
||
Satisfactory Prediction (satisfactory + prediction)
Selected AbstractsNonlinear response of laterally loaded piles and pile groupsINTERNATIONAL JOURNAL FOR NUMERICAL AND ANALYTICAL METHODS IN GEOMECHANICS, Issue 7 2009Wei Dong Guo Abstract In spite of extensive studies on laterally loaded piles carried out over years, none of them offers an expedite approach as to gaining the nonlinear response and its associated depth of mobilization of limiting force along each pile in a group. To serve such a need, elastic,plastic solutions for free-head, laterally loaded piles were developed recently by the author. They allow the response to be readily computed from elastic state right up to failure, by assigning a series of slip depths, and a limiting force profile. In this paper, equivalent solutions for fixed-head (FixH) single piles were developed. They are subsequently extended to cater for response of pile groups by incorporating p -multipliers. The newly established solutions were substantiated by existing numerical solutions for piles and pile groups. They offer satisfactory prediction of the nonlinear response of all the 6 single piles and 24 pile groups investigated so far after properly considering the impact of semi-FixH restraints. They also offer the extent to ultimate state of pile groups via the evaluated slip depths. The study allows ad hoc guidelines to be established for determining input parameters for the solutions. The solutions are tailored for routine prediction of the nonlinear interaction of laterally loaded FixH piles and capped pile groups. Copyright © 2008 John Wiley & Sons, Ltd. [source] Shoreline tracking and implicit source terms for a well balanced inundation modelINTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN FLUIDS, Issue 10 2010Giovanni FranchelloArticle first published online: 31 JUL 200 Abstract The HyFlux2 model has been developed to simulate severe inundation scenario due to dam break, flash flood and tsunami-wave run-up. The model solves the conservative form of the two-dimensional shallow water equations using the finite volume method. The interface flux is computed by a Flux Vector Splitting method for shallow water equations based on a Godunov-type approach. A second-order scheme is applied to the water surface level and velocity, providing results with high accuracy and assuring the balance between fluxes and sources also for complex bathymetry and topography. Physical models are included to deal with bottom steps and shorelines. The second-order scheme together with the shoreline-tracking method and the implicit source term treatment makes the model well balanced in respect to mass and momentum conservation laws, providing reliable and robust results. The developed model is validated in this paper with a 2D numerical test case and with the Okushiri tsunami run up problem. It is shown that the HyFlux2 model is able to model inundation problems, with a satisfactory prediction of the major flow characteristics such as water depth, water velocity, flood extent, and flood-wave arrival time. The results provided by the model are of great importance for the risk assessment and management. Copyright © 2009 John Wiley & Sons, Ltd. [source] An evaluation of mathematical models for predicting skin permeabilityJOURNAL OF PHARMACEUTICAL SCIENCES, Issue 1 2008Guoping Lian Abstract A number of mathematical models have been proposed for predicting skin permeability, mostly empirical and very few are deterministic. Early empirical models use simple lipophilicity parameters. The recent trend is to use more complicated molecular structure descriptors. There has been much debate on which models best predict skin permeability. This article evaluates various mathematical models using a comprehensive experimental dataset of skin permeability for 124 chemical compounds compiled from various sources. Of the seven models compared, the deterministic model of Mitragotri gives the best prediction. The simple quantitative structure permeability relationships (QSPR) model of Potts and Guy gives the second best prediction. The two models have many features in common. Both assume the lipid matrix as the pathway of transdermal permeation. Both use octanol,water partition coefficient and molecular size. Even the mathematical formulae are similar. All other empirical QSPR models that use more complicated molecular structure descriptors fail to provide satisfactory prediction. The molecular structure descriptors in the more complicated QSPR models are empirically related to skin permeation. The mechanism on how these descriptors affect transdermal permeation is not clear. Mathematically it is an ill-defined approach to use many colinearly related parameters rather than fewer independent parameters in multi-linear regression. © 2007 Wiley-Liss, Inc. and the American Pharmacists Association J Pharm Sci 97:584,598, 2008 [source] Seasonal patterns of sucrose concentration in relation to other quality parameters of sugar beet (Beta vulgaris L.)JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE, Issue 1 2006Christine Kenter Abstract The chemical composition of sugar beet is the most important parameter affecting its processing. Sugar factories require beet with high concentrations of sucrose and low concentrations of melassigenic substances to maximise the amount of extractable sugar. In order to plan the processing campaign, forecasts of root and sugar yield by prediction models are possible but there are no means to predict the technical quality of the beet. In the present study, the seasonal development and physiological relationships of different parameters of sugar beet quality were analysed. In order to estimate possibilities for quality forecasts, the concentrations of beet quality variables in October were correlated with corresponding quality measurements in late summer and to weather variables during the growing season by linear regressions. In 2000 and 2001, 27 field trials were conducted on commercial farm fields in all sugar beet growing areas in Germany. From June to October, sequential samples were taken every 4 weeks and the concentrations of sucrose, potassium, sodium, total soluble nitrogen, ,-amino nitrogen, nitrate, betaine, reducing sugars and marc in the beet were determined. The sucrose concentration increased progressively until the final harvest date in autumn, whereas the concentrations of the melassigenic substances decreased markedly until late summer and remained fairly constant as the season progressed. Marc concentration was the most stable of the parameters analysed. The sucrose concentration was positively correlated with the concentrations of dry matter, betaine and marc, but negatively with nitrate concentration and leaf yield throughout the season. The correlation between the concentrations of sucrose and nitrogenous compounds measured in summer and their final concentrations in autumn was rather weak. However, it was close for potassium, sodium and marc and a satisfactory prediction of their final concentrations was possible by the end of August. Based on weather data, beet quality was not predictable. Therefore, it seems to be difficult to integrate beet quality parameters into prediction models. Copyright © 2005 Society of Chemical Industry [source] Magnetic resonance imaging as a potential surrogate for relapses in multiple sclerosis: A meta-analytic approach,ANNALS OF NEUROLOGY, Issue 3 2009Maria Pia Sormani MscStat Objective The aim of this work was to evaluate whether the treatment effects on magnetic resonance imaging (MRI) markers at the trial level were able to predict the treatment effects on relapse rate in relapsing-remitting multiple sclerosis. Methods We used a pooled analysis of all the published randomized, placebo-controlled clinical trials in relapsing-remitting multiple sclerosis reporting data both on MRI variables and relapses. We extracted data on relapses and on MRI "active" lesions. A regression analysis weighted on trial size and duration was performed to study the relation between the treatment effect on relapses and the treatment effect on MRI lesions. We validated the estimated relation on an independent set of clinical trials satisfying the same inclusion criteria but with a control arm other than placebo. Results A set of 23 randomized, double-blind, placebo-controlled trials in relapsing-remitting multiple sclerosis was identified, for a total of 63 arms, 40 contrasts, and 6,591 patients. A strong correlation was found between the effect on the relapses and the effect on MRI activity. The adjusted R2 value of the weighted regression line was 0.81. The regression equation estimated using the placebo-controlled trials gave a satisfactory prediction of the treatment effect on relapses when applied to the validation set. Interpretation More than 80% of the variance in the effect on relapses between trials is explained by the variance in MRI effects. Smaller and shorter phase II studies based on MRI lesion end points may give indications also on the effect of the treatment on relapse end points. Ann Neurol 2009;65:268,275 [source] Prediction of metabolic function from limited data: Lumped hybrid cybernetic modeling (L-HCM)BIOTECHNOLOGY & BIOENGINEERING, Issue 2 2010Hyun-Seob Song Abstract Motivated by the need for a quick quantitative assessment of metabolic function without extensive data, we present an adaptation of the cybernetic framework, denoted as the lumped hybrid cybernetic model (L-HCM), which combines the attributes of the classical lumped cybernetic model (LCM) and the recently developed HCM. The basic tenet of L-HCM and HCM is the same, that is, they both view the uptake flux as being split among diverse pathways in an optimal way as a result of cellular regulation such that some chosen metabolic objective is realized. The L-HCM, however, portrays this flux distribution to occur in a hierarchical way, that is, first among lumped pathways, and next among individual elementary modes (EM) in each lumped pathway. Both splits are described by the cybernetic control laws using operational and structural return-on-investments, respectively. That is, the distribution of uptake flux at the first split is dynamically regulated according to environmental conditions, while the subsequent split is based purely on the stoichiometry of EMs. The resulting model is conveniently represented in terms of lumped pathways which are fully identified with respect to yield coefficients of all products unlike classical LCMs based on instinctive lumping. These characteristics enable the model to account for the complete set of EMs for arbitrarily large metabolic networks despite containing only a small number of parameters which can be identified using minimal data. However, the inherent conflict of questing for quantification of larger networks with smaller number of parameters cannot be resolved without a mechanism for parameter tuning of an empirical nature. In this work, this is accomplished by manipulating the relative importance of EMs by tuning the cybernetic control of mode-averaged enzyme activity with an empirical parameter. In a case study involving aerobic batch growth of Saccharomyces cerevisiae, L-HCM is compared with LCM. The former provides a much more satisfactory prediction than the latter when parameters are identified from a few primary metabolites. On the other hand, the classical model is more accurate than L-HCM when sufficient datasets are involved in parameter identification. In applying the two models to a chemostat scenario, L-HCM shows a reasonable prediction on metabolic shift from respiration to fermentation due to the Crabtree effect, which LCM predicts unsatisfactorily. While L-HCM appears amenable to expeditious estimates of metabolic function with minimal data, the more detailed dynamic models [such as HCM or those of Young et al. (Young et al., Biotechnol Bioeng, 2008; 100: 542,559)] are best suited for accurate treatment of metabolism when the potential of modern omic technology is fully realized. However, in view of the monumental effort surrounding the development of detailed models from extensive omic measurements, the preliminary insight into the behavior of a genotype and metabolic engineering directives that can come from L-HCM is indeed valuable. Biotechnol. Bioeng. 2010;106: 271,284. © 2010 Wiley Periodicals, Inc. [source] Development of a drug,disease simulation model for rituximab in follicular non-Hodgkin's lymphomaBRITISH JOURNAL OF CLINICAL PHARMACOLOGY, Issue 4 2009David Ternant WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT , Serum concentrations of rituximab influence its clinical efficacy in follicular lymphoma (FL), but its concentration,effect relationship has not been described by pharmacokinetic,pharmacodynamic (PK,PD) modelling. , The genetic polymorphism of FCGR3A influences rituximab efficacy and its in vitro concentration,effect relationship. , Increasing rituximab dose and/or number of infusions may lead to a better clinical response in FL. WHAT THIS PAPER ADDS , This study is the first to describe the concentration,effect relationship of rituximab in populations of FL patients. , This PK,PD model relates progression-free survival with rituximab concentrations and takes into account the influence of FCGR3A polymorphism. , Clinical trials testing new dosing regimens of rituximab can be designed using this PK,PD model. AIM Rituximab has dramatically improved the survival of patients with non-Hodgkin's lymphomas (NHL), but the dosing regimen currently used should be optimized. However, the concentration,effect relationship of rituximab has never been described by pharmacokinetic,pharmacodynamic (PK,PD) modelling, precluding the simulation of new dosing regimens. The aim of this study was to develop a PK,PD model of rituximab in relapsed/resistant follicular NHL (FL). METHODS A model describing the relationship between rituximab concentrations and progression-free survival (PFS) was developed using data extracted from the pivotal study, which evaluated 151 relapsed/resistant FL patients. The influence of FCGR3A genetic polymorphism on the efficacy of rituximab was quantified using data from 87 relapsed/resistant FL patients. The predictive performance of the model was analysed using two independent datasets: a study that evaluated rituximab combined with chemotherapy [rituximab, cyclophosphamide, vincristine, adriamycin and prednisone (R-CHOP)] in 334 relapsed/resistant FL patients and a study that evaluated rituximab monotherapy in 47 asymptomatic FL patients with known FCGR3A genotype. RESULTS For R-CHOP, observed and model-predicted PFS (90% confidence interval) at 24 months were 0.50 and 0.48 (0.40, 0.56), respectively, for the observation arm, and 0.62 and 0.59 (0.50, 0.65), respectively, for the rituximab maintenance arm. For rituximab monotherapy, observed and predicted PFS at 24 months were 0.67 and 0.63, respectively, for FCGR3A -V/V patients, and 0.41 and 0.36 (0.25, 0.49), respectively, for FCGR3A -F carriers. CONCLUSIONS Our model provides a satisfactory prediction of PFS at 24 months. It can be used to simulate new dosing regimens of rituximab in populations of FL patients and should improve the design of future clinical trials. [source] Combined Pharmacophore Modeling, Docking, and 3D QSAR Studies of ABCB1 and ABCC1 Transporter InhibitorsCHEMMEDCHEM, Issue 11 2009Abstract Quinazolinones, indolo- and pyrrolopyrimidines with inhibitory effects toward ABCB1 (P-gp) and ABCC1 (MRP1) transporters were studied by pharmacophore modeling, docking, and 3D QSAR to describe the binding preferences of the proteins. The pharmacophore overlays between dual and/or highly selective inhibitors point to binding sites of different topology and physiochemical properties for MRP1 and P-gp. Docking of selective inhibitors into the P-gp binding cavity by the use of a structural model based on the recently resolved P-gp structure confirms the P-gp pharmacophore features identified, and reveals the interactions of some functional groups and atoms in the structures with particular protein residues. The 3D QSAR analysis of the dual-effect inhibitors allows satisfactory prediction of the selectivity index of the compounds and outlines electrostatics as most important for selectivity. The results from the combined modeling approach complement each other and could improve our understanding of the protein,ligand interactions involved, and could aid in the development of highly selective and potent inhibitors of P-gp and MRP1. [source] Analytical and experimental studies on fatigue crack path under complex multi-axial loadingFATIGUE & FRACTURE OF ENGINEERING MATERIALS AND STRUCTURES, Issue 4 2006L. REIS ABSTRACT In real engineering components and structures, many accidental failures are due to unexpected or additional loadings, such as additional bending or torsion, etc. Fractographical analyses of the failure surface and the crack orientation are helpful for identifying the effects of the non-proportional multi-axial loading. There are many factors that influence fatigue crack paths. This paper studies the effects of multi-axial loading path on the crack path. Two kinds of materials were studied and compared in this paper: AISI 303 stainless steel and 42CrMo4 steel. Experiments were conducted in a biaxial testing machine INSTRON 8800. Six different biaxial loading paths were selected and applied in the tests to observe the effects of multi-axial loading paths on the additional hardening, fatigue life and the crack propagation orientation. Fractographic analyses of the plane orientations of crack initiation and propagation were carried out by optical microscope and SEM approaches. It was shown that the two materials studied had different crack orientations under the same loading path, due to their different cyclic plasticity behaviour and different sensitivity to non-proportional loading. Theoretical predictions of the damage plane were made using the critical plane approaches such as the Brown,Miller, the Findley, the Wang,Brown, the Fatemi,Socie, the Smith,Watson,Topper and the Liu's criteria. Comparisons of the predicted orientation of the damage plane with the experimental observations show that the critical plane models give satisfactory predictions for the orientations of early crack growth of the 42CrMo4 steel, but less accurate predictions were obtained for the AISI 303 stainless steel. This observation appears to show that the applicability of the fatigue models is dependent on the material type and multi-axial microstructure characteristics. [source] Investigating the impact of the Chi-Chi earthquake on the occurrence of debris flows using artificial neural networksHYDROLOGICAL PROCESSES, Issue 19 2009Fi-John Chang Abstract Debris flows have caused enormous losses of property and human life in Taiwan during the last two decades. An efficient and reliable method for predicting the occurrence of debris flows is required. The major goal of this study is to explore the impact of the Chi-Chi earthquake on the occurrence of debris flows by applying the artificial neural network (ANN) that takes both hydrological and geomorphologic influences into account. The Chen-Yu-Lan River watershed, which is located in central Taiwan, is chosen for evaluating the critical rainfall triggering debris flows. A total of 1151 data sets were collected for calibrating model parameters with two training strategies. Significant differences before and after the earthquake have been found: (1) The size of landslide area is proportioned to the occurrence of debris flows; (2) the amount of critical rainfall required for triggering debris flows has reduced significantly, about half of the original critical rainfall in the study case; and (3) the frequency of the occurrence of debris flows is largely increased. The overall accuracy of model prediction in testing phase has reached 96·5%; moreover, the accuracy of occurrence prediction is largely increased from 24 to 80% as the network trained with data from before the Chi-Chi earthquake sets and with data from the lumped before and after the earthquake sets. The results demonstrated that the ANN is capable of learning the complex mechanism of debris flows and producing satisfactory predictions. Copyright © 2009 John Wiley & Sons, Ltd. [source] Frequency analysis for predicting 1% annual maximum water levels along Florida coast, USHYDROLOGICAL PROCESSES, Issue 23 2008Sudong Xu Abstract In the Coastal Flood Insurance Study by the Federal Emergency Management Agency (FEMA, 2005), 1% annual maximum coastal water levels are used in coastal flood hazard mitigation and engineering design in coastal areas of USA. In this study, a frequency analysis method has been developed to provide more accurate predictions of 1% annual maximum water levels for the Florida coast waters. Using 82 and 94 years of annual maximum water level data at Pensacola and Fernandina, performances of traditional frequency analysis methods, including advanced method of Generalized Extreme Value distribution method, have been evaluated. Comparison with observations of annual maximum water levels with 83 and 95 years of return periods indicate that traditional methods are unable to provide satisfactory predictions of 1% annual maximum water levels to account for hurricane-induced extreme water levels. Based on the characteristics of annual maximum water level distribution of Pensacola and Fernandina stations, a new probability distribution method has been developed in this study. Comparison with observations indicates that the method presented in this study significantly improves the accuracy of predictions of 1% annual maximum water levels. For Fernandina station, predictions of extreme water level match well with the general trend of observations. With a correlation coefficient of 0·98, the error for the maximum observed extreme water level of 3·11 m (National Geodetic Vertical Datum) with 95 years of return period is 0·92%. For Pensacola station, the prediction error for the maximum observed extreme water level with a return period of 83 years is 5·5%, with a correlation value of 0·98. The frequency analysis has also been reasonably compared to the more costly Monte Carlo simulation method. Copyright © 2008 John Wiley & Sons, Ltd. [source] Improving the prediction of liquid back-mixing in trickle-bed reactors using a neural network approachJOURNAL OF CHEMICAL TECHNOLOGY & BIOTECHNOLOGY, Issue 9 2002Simon Piché Abstract Current correlations aimed at estimating the extent of liquid back-mixing, via an axial dispersion coefficient, in trickle-bed reactors continue to draw doubts on their ability to conveniently represent this important macroscopic parameter. A comprehensive database containing 973 liquid axial dispersion coefficient measurements (DAX) for trickle-bed operation reported in 22 publications between 1958 and 2001 was thus used to assess the convenience of the few available correlations. It was shown that none of the literature correlations was efficient at providing satisfactory predictions of the liquid axial dispersion coefficients. In response, artificial neural network modeling is proposed to improve the broadness and accuracy in predicting the DAX, whether the Piston,Dispersion (PD), Piston,Dispersion,Exchange (PDE) or PDE with intra-particle diffusion model is employed to extract the DAX. A combination of six dimensionless groups and a discrimination code input representing the residence-time distribution models are used to predict the Bodenstein number. The inputs are the liquid Reynolds, Galileo and Eötvos numbers, the gas Galileo number, a wall factor and a mixed Reynolds number involving the gas flow rate effect. The correlation yields an absolute average error (AARE) of 22% for the whole database with a standard deviation on the AARE of 24% and remains in accordance with parametric influences reported in the literature. © 2002 Society of Chemical Industry [source] Description of the mutual solubilities of fatty acids and water with the CPA EoSAICHE JOURNAL, Issue 6 2009M. B. Oliveira Abstract Data for the mutual solubilities of fatty acid + water mixtures are scarce and so measurements for seven fatty acid (C5 -C10, C12) + water systems were carried out. This new experimental data was successfully modelled with the cubic plus association EoS. Using data from C6 to C10 and the Elliot's cross-associating combining rule a correlation for the kij binary interaction parameter, as a function of the acid chain length, is proposed. The mutual solubilities of water and fatty acids can be adequately described with average deviations inferior to 6% for the water rich phase and 30% for the acid rich phase. Furthermore, satisfactory predictions of solid-liquid equilibria of seven fatty acids (C12 -C18) + water systems were achieved based only on the kij correlation obtained from liquid,liquid equilibria data. © 2009 American Institute of Chemical Engineers AIChE J, 2009 [source] Use of mid-infrared spectroscopy in the diffuse-reflectance mode for the prediction of the composition of organic matter in soil and litterJOURNAL OF PLANT NUTRITION AND SOIL SCIENCE, Issue 3 2008Bernard Ludwig Abstract Mid-infrared spectroscopy (MIRS) is assumed to be superior to near-infrared spectroscopy (NIRS) for the prediction of soil constituents, but its usefulness is still not sufficiently explored. The objective of this study was to evaluate the ability of MIRS to predict the chemical and biological properties of organic matter in soils and litter. Reflectance spectra of the mid-infrared region including part of the near-infrared region (7000,400,cm,1) were recorded for 56 soil and litter samples from agricultural and forest sites. Spectra were used to predict general and biological characteristics of the samples as well as the C composition which was measured by 13C CPMAS-NMR spectroscopy. A partial least-square method and cross-validation were used to develop equations for the different constituents over selected spectra ranges after several mathematical treatments of the spectra. Mid-infrared spectroscopy predicted well the C : N ratio: the modeling efficiency EF was 0.95, the regression coefficient (a) of a linear regression (measured against predicted values) was 1.0, and the correlation coefficient (r) was 0.98. Satisfactorily (EF , 0.70, 0.8 , a , 1.2, r , 0.80) assessed were the contents of C, N, and lignin, the production of dissolved organic carbon, and the contents of carbonyl C, aromatic C, O-alkyl C, and alkyl C. However, the N mineralization rate, the microbial biomass and the alkyl,to,aromatic C ratio were predicted less satisfactorily (EF < 0.70). Limiting the sample set to mineral soils did generally not result in improved predictions. The good and satisfactory predictions reported above indicate a marked usefulness of MIRS in the assessment of chemical characteristics of soils and litter, but the accuracies of the MIRS predictions in the diffuse-reflectance mode were generally not superior to those of NIRS. [source] Near-infrared spectroscopy can predict the composition of organic matter in soil and litterJOURNAL OF PLANT NUTRITION AND SOIL SCIENCE, Issue 2 2006Thomas Terhoeven-Urselmans Abstract The usefulness and limitations of near-infrared reflectance spectroscopy (NIRS) for the assessment of several soil characteristics are still not sufficiently explored. The objective of this study was to evaluate the ability of visible and near-infrared reflectance (VIS-NIR) spectroscopy to predict the composition of organic matter in soils and litter. Reflectance spectra of the VIS-NIR region (400,2500 nm) were recorded for 56 soil and litter samples from agricultural and forest sites. Spectra were used to predict general and biological characteristics of the samples as well as the C composition which was measured by 13C-CPMAS-NMR spectroscopy. A modified partial least-square method and cross-validation were used to develop equations for the different constituents over the whole spectrum (1st to 3rd derivation). Near-infrared spectroscopy predicted well the C : N ratios, the percentages of O-alkyl C and alkyl C, the ratio of alkyl C to O-alkyl C, and the sum of phenolic oxidation products: the ratios of standard deviation of the laboratory results to standard error of cross-validation (RSC) were greater than 2, the regression coefficients (a) of a linear regression (measured against predicted values) ranged from 0.9 to 1.1, and the correlation coefficients (r) were greater than 0.9. Satisfactorily (0.8 , a , 1.2, r , 0.8, and 1.4 , RSC , 2.0) assessed were the contents of C, N, and production of DOC, the percentages of carbonyl C and aromatic C and the ratio of alkyl C to aromatic C. However, the N-mineralization rate and the microbial biomass were predicted unsatisfactorily (RSC < 1.4). The good and satisfactory predictions reported above indicate a marked usefulness of NIRS in the assessment of biological and chemical characteristics of soils and litter. [source] | ||||||||||||||||