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Terms modified by Root Mean Selected AbstractsWinter diatom blooms in a regulated river in South Korea: explanations based on evolutionary computationFRESHWATER BIOLOGY, Issue 10 2007DONG-KYUN KIM Summary 1. An ecological model was developed using genetic programming (GP) to predict the time-series dynamics of the diatom, Stephanodiscus hantzschii for the lower Nakdong River, South Korea. Eight years of weekly data showed the river to be hypertrophic (chl. a, 45.1 ± 4.19 ,g L,1, mean ± SE, n = 427), and S. hantzschii annually formed blooms during the winter to spring flow period (late November to March). 2. A simple non-linear equation was created to produce a 3-day sequential forecast of the species biovolume, by means of time series optimization genetic programming (TSOGP). Training data were used in conjunction with a GP algorithm utilizing 7 years of limnological variables (1995,2001). The model was validated by comparing its output with measurements for a specific year with severe blooms (1994). The model accurately predicted timing of the blooms although it slightly underestimated biovolume (training r2 = 0.70, test r2 = 0.78). The model consisted of the following variables: dam discharge and storage, water temperature, Secchi transparency, dissolved oxygen (DO), pH, evaporation and silica concentration. 3. The application of a five-way cross-validation test suggested that GP was capable of developing models whose input variables were similar, although the data are randomly used for training. The similarity of input variable selection was approximately 51% between the best model and the top 20 candidate models out of 150 in total (based on both Root Mean Squared Error and the determination coefficients for the test data). 4. Genetic programming was able to determine the ecological importance of different environmental variables affecting the diatoms. A series of sensitivity analyses showed that water temperature was the most sensitive parameter. In addition, the optimal equation was sensitive to DO, Secchi transparency, dam discharge and silica concentration. The analyses thus identified likely causes of the proliferation of diatoms in ,river-reservoir hybrids' (i.e. rivers which have the characteristics of a reservoir during the dry season). This result provides specific information about the bloom of S. hantzschii in river systems, as well as the applicability of inductive methods, such as evolutionary computation to river-reservoir hybrid systems. [source] Predicting % of crystallinity in FCC catalysts by FT-MIR and PLSJOURNAL OF CHEMOMETRICS, Issue 11-12 2008Angel Dago Abstract This paper describes an analytical procedure for prediction of percent of crystallinity of fluidized catalytic cracking catalysts (FCC) using Fourier transform mid infrared spectroscopy (FT-MIR) and partial least-squares (PLS) multivariate calibration technique. In order to make a robust regression model, multiplicative scatter correction (MSC) and smoothed second derivative pre-processing methods were tested. Root mean squared error of prediction (RMSEP) of an independent test set was used to measure the performance of the models. The comparison shows that reasonable values of RMSEP and RMSECV were obtained for PLS-MSC model (RMSEP,=,0.8% and RMSECV,=,1.3%). The accuracy of the results obtained by the PLS-MSC regression model is in accordance with the uncertainty of the XRPD reference method. The developed method can be implemented in a refinery laboratory environment with ease. Copyright © 2008 John Wiley & Sons, Ltd. [source] Dynamic Predictive Model for Growth of Salmonella Enteritidis in Egg YolkJOURNAL OF FOOD SCIENCE, Issue 7 2007V. Gumudavelli ABSTRACT:,Salmonella Enteritidis (SE) contamination of poultry eggs is a major human health concern worldwide. The risk of SE from shell eggs can be significantly reduced through rapid cooling of eggs after they are laid and their storage under safe temperature conditions. Predictive models for the growth of SE in egg yolk under varying ambient temperature conditions (dynamic) were developed. The growth of SE in egg yolk under several isothermal conditions (10, 15, 20, 25, 30, 35, 37, 39, 41, and 43 °C) was determined. The Baranyi model, a primary model, was fitted with growth data for each temperature and corresponding maximum specific growth rates were estimated. Root mean squared error (RMSE) values were less than 0.44 log10 CFU/g and pseudo- R2 values were greater than 0.98 for the primary model fitting. For developing the secondary model, the estimated maximum specific growth rates were then modeled as a function of temperature using the modified Ratkowsky's equation. The RMSE and pseudo- R2 were 0.05/h and 0.99, respectively. A dynamic model was developed by integrating the primary and secondary models and solving it numerically using the 4th-order Runge,Kutta method to predict the growth of SE in egg yolk under varying temperature conditions. The integrated dynamic model was then validated with 4 temperature profiles (varying) such as linear heating, exponential heating, exponential cooling, and sinusoidal temperatures. The predicted values agreed well with the observed growth data with RMSE values less than 0.29 log10 CFU/g. The developed dynamic model can predict the growth SE in egg yolk under varying temperature profiles. [source] Optimization of ordered distance sampling,ENVIRONMETRICS, Issue 2 2004Ryan M. Nielson Abstract Ordered distance sampling is a point-to-object sampling method that can be labor-efficient for demanding field situations. An extensive simulation study was conducted to find the optimum number, g, of population members to be encountered from each random starting point in ordered distance sampling. Monte Carlo simulations covered 64 combinations of four spatial patterns, four densities and four sample sizes. Values of g from 1 to 10 were considered for each case. Relative root mean squared error (RRMSE) and relative bias were calculated for each level of g, with RRMSE used as the primary assessment criterion for finding the optimum level of g. A non-parametric confidence interval was derived for the density estimate, and this was included in the simulations to gauge its performance. Superior estimation properties were found for g > 3, but diminishing returns, relative to the potential for increased effort in the field, were found for g > 5. The simulations showed noticeable diminishing returns for more than 20 sampled points. The non-parametric confidence interval performed well for populations with random, aggregate or double-clumped spatial patterns, but rarely came close to target coverage for populations that were regularly distributed. The non-parametric confidence interval presented here is recommended for general use. Copyright © 2004 John Wiley & Sons, Ltd. [source] Modelling of air drying of fresh and blanched sweet potato slicesINTERNATIONAL JOURNAL OF FOOD SCIENCE & TECHNOLOGY, Issue 2 2010Kolawole O. Falade Summary Effects of blanching, drying temperatures (50,80 °C) and thickness (5, 10 and 15 mm) on drying characteristics of sweet potato slices were investigated. Lewis, Henderson and Pabis, Modified Page and Page models were tested with the drying patterns. Page and Modified Page models best described the drying curves. Moisture ratio vs. drying time profiles of the models showed high correlation coefficient (R2 = 0.9864,0.9967), and low root mean squared error (RMSE = 0.0018,0.0130) and chi-squared (,2 = 3.446 × 10,6,1.03 × 10,2). Drying of sweet potato was predominantly in the falling rate period. The temperature dependence of the diffusion coefficient (Deff) was described by Arrhenius relationship. Deff increased with increasing thickness and air temperature. Deff of fresh and blanched sweet potato slices varied between 6.36 × 10,11,1.78 × 10,9 and 1.25 × 10,10,9.75 × 10,9 m2 s,1, respectively. Activation energy for moisture diffusion of the slices ranged between 11.1 and 30.4 kJ mol,1. [source] Controlling coverage of D-optimal onion designs and selectionsJOURNAL OF CHEMOMETRICS, Issue 12 2004Ing-Marie Olsson Abstract Statistical molecular design (SMD) is a powerful approach for selection of compound sets in medicinal chemistry and quantitative structure,activity relationships (QSARs) as well as other areas. Two techniques often used in SMD are space-filling and D-optimal designs. Both on occasions lead to unwanted redundancy and replication. To remedy such shortcomings, a generalization of D-optimal selection was recently developed. This new method divides the compound candidate set into a number of subsets (,layers' or ,shells'), and a D-optimal selection is made from each layer. This improves the possibility to select representative molecular structures throughout any property space independently of requested sample size. This is important in complex situations where any given model is unlikely to be valid over the whole investigated domain of experimental conditions. The number of selected molecules can be controlled by varying the number of subsets or by altering the complexity of the model equation in each layer and/or the dependency of previous layers. The new method, called D-optimal onion design (DOOD), will allow the user to choose the model equation complexity independently of sample size while still avoiding unwarranted redundancy. The focus of the present work is algorithmic improvements of DOOD in comparison with classical D-optimal design. As illustrations, extended DOODs have been generated for two applications by in-house programming, including some modifications of the D-optimal algorithm. The performances of the investigated approaches are expected to differ depending on the number of principal properties of the compounds in the design, sample sizes and the investigated model, i.e. the aim of the design. QSAR models have been generated from the selected compound sets, and root mean squared error of prediction (RMSEP) values have been used as measures of performance of the different designs. Copyright © 2005 John Wiley & Sons, Ltd. [source] ORIGINAL ARTICLE: Investigation of the prediction accuracy of vancomycin concentrations determined by patient-specific parameters as estimated by Bayesian analysisJOURNAL OF CLINICAL PHARMACY & THERAPEUTICS, Issue 5 2010Y. Hiraki BSc Summary Background/Objective:, There have been many studies of therapeutic drug monitoring (TDM) of vancomycin (VCM) based on Bayesian analysis, but there have been no reports of the accuracy of prediction based on Bayesian-estimated patient-specific parameters. This study was conducted to compare the accuracy of prediction based on the population pharmacokinetic (PPK) method and Bayesian-estimated parameters. Method:, The subjects were 22 patients who were treated with VCM for MRSA infection and whose blood was sampled twice or more during the administration period. The concentrations between the blood samples were predicted based on the concentrations in the first blood samples based on the PPK method using mean parameters for the Japanese population and Bayesian-estimated patient-specific pharmacokinetic parameters. The mean prediction error (ME), mean absolute error (MAE) and root mean squared error (RMSE) were compared to examine the accuracy of prediction based on Bayesian-estimated patient-specific parameters. Results and discussion:, The mean measured VCM concentration was 10·43 ± 5·19 ,g/mL, whereas the mean concentration predicted based on the PPK method was 8·52 ± 4·34 ,g/mL, with an ME of ,1·91, MAE of 2·93 and RMSE of 3·21. The mean concentration predicted based on patient-specific parameters was 9·62 ± 4·95 ,g/mL with ME of ,0·81, MAE of 1·38 and RMSE of 1·74. The ME and MAE for the concentrations predicted using patient-specific parameters were smaller compared with those predicted using the PPK method (P = 0·0471 and 0·0003, respectively), indicating superior prediction with a significant difference between approaches. Conclusion:, Prediction using Bayesian estimates of patient-specific parameters was better than by the PPK method. However, when using patient-specific parameters it is still necessary to fully understand the clinical status of the patient and frequently determine VCM concentrations. [source] Effect of various estimates of renal function on prediction of vancomycin concentration by the population mean and Bayesian methodsJOURNAL OF CLINICAL PHARMACY & THERAPEUTICS, Issue 4 2009Y. Tsuji BSc Summary Objective:, Renal function was estimated in 129 elderly patients with methicillin-resistant Staphylococcus aureus (MRSA) who were treated with vancomycin (VCM). The estimation was performed by substituting serum creatinine (SCR) measured enzymatically and a value converted using the Jaffe method into the Cockcroft-Gault and Modification of Diet in Renal Disease (MDRD) equations. The serum trough level was predicted from three estimates of renal function by the population mean (PM) and Bayesian methods and the predictability was assessed. Methods:, Two-compartment model-based Japanese population parameters for VCM were used, and the mean prediction error (ME) and root mean squared error (RMSE) were calculated as indices of bias and accuracy, respectively, for predictions by the PM and Bayesian methods. Results:, The PM method gave the highest correlation with the measured value using the estimate of renal function obtained by substituting the Jaffe-converted SCR into the Cockcroft-Gault equation. There was no positive or negative bias in the ME and the value was significantly smaller than for other predicted data (P < 0·05). RMSE was also the smallest, indicating that this method increases the predictability of the serum VCM trough level. While, ME showed a negative bias for all values predicted by the Bayesian method, both the ME and RMSE were very small. Conclusion:, In the application of the PM method for VCM treatment of elderly patients with MRSA, substitution of SCR based on the Jaffe method into the Cockcroft-Gault equation increases the predictability of the serum VCM trough level. The Bayesian method predicted the serum VCM trough level with high accuracy using any of the estimates of renal function. [source] Comparisons among Designs for Equating Mixed-Format Tests in Large-Scale AssessmentsJOURNAL OF EDUCATIONAL MEASUREMENT, Issue 1 2010Sooyeon Kim In this study we examined variations of the nonequivalent groups equating design for tests containing both multiple-choice (MC) and constructed-response (CR) items to determine which design was most effective in producing equivalent scores across the two tests to be equated. Using data from a large-scale exam, this study investigated the use of anchor CR item rescoring (known as trend scoring) in the context of classical equating methods. Four linking designs were examined: an anchor with only MC items, a mixed-format anchor test containing both MC and CR items; a mixed-format anchor test incorporating common CR item rescoring; and an equivalent groups (EG) design with CR item rescoring, thereby avoiding the need for an anchor test. Designs using either MC items alone or a mixed anchor without CR item rescoring resulted in much larger bias than the other two designs. The EG design with trend scoring resulted in the smallest bias, leading to the smallest root mean squared error value. [source] Forecasting the price of crude oil via convenience yield predictionsJOURNAL OF FORECASTING, Issue 7 2007Thomas A. KnetschArticle first published online: 14 NOV 200 Abstract The paper develops an oil price forecasting technique which is based on the present value model of rational commodity pricing. The approach suggests shifting the forecasting problem to the marginal convenience yield, which can be derived from the cost-of-carry relationship. In a recursive out-of-sample analysis, forecast accuracy at horizons within one year is checked by the root mean squared error as well as the mean error and the frequency of a correct direction-of-change prediction. For all criteria employed, the proposed forecasting tool outperforms the approach of using futures prices as direct predictors of future spot prices. Vis-à-vis the random-walk model, it does not significantly improve forecast accuracy but provides valuable statements on the direction of change. Copyright © 2007 John Wiley & Sons, Ltd. [source] A markup model for forecasting inflation for the euro areaJOURNAL OF FORECASTING, Issue 7 2006Bill Russell Abstract We develop a small model for forecasting inflation for the euro area using quarterly data over the period June 1973 to March 1999. The model is used to provide inflation forecasts from June 1999 to March 2002. We compare the forecasts from our model with those derived from six competing forecasting models, including autoregressions, vector autoregressions and Phillips-curve based models. A considerable gain in forecasting performance is demonstrated using a relative root mean squared error criterion and the Diebold,Mariano test to make forecast comparisons.,,Copyright © 2006 John Wiley & Sons, Ltd. [source] Multivariate calibration of covalent aggregate fraction to the raman spectrum of regular human insulinJOURNAL OF PHARMACEUTICAL SCIENCES, Issue 9 2008Connie M. Gryniewicz Abstract Insulin aggregates were prepared by exposing samples of formulated regular human insulin to agitation at 60°C. Aliquots were drawn from the samples periodically over a time range spanning 192 h, and their aggregate compositions were determined with size exclusion chromatography. The complete data set was composed of 39 separate aliquots. The Raman spectra of three separate 10 µL volumes from each aliquot were measured using the drop-coat deposition Raman (DCDR) method. The spectra were calibrated to aggregate composition by partial least squares regression (PLS), resulting in linear calibration (R2,=,0.997) with a root mean squared error of calibration (RMSEC) of 1.3% and a root mean squared error of cross validation (RMSECV) of 5.1% in aggregate composition. Though the time required for aggregates to form under stressed conditions showed substantial sample-to-sample variation, the correlation between aggregate composition and Raman spectrum was remarkably consistent, indicating that Raman spectroscopy may be a viable screening method for aggregation of protein drugs. © 2008 Wiley-Liss, Inc. and the American Pharmacists Association J Pharm Sci 97:3727,3734, 2008 [source] A new European testate amoebae transfer function for palaeohydrological reconstruction on ombrotrophic peatlands,JOURNAL OF QUATERNARY SCIENCE, Issue 3 2007Dan J. Charman Abstract Proxy climate data can be obtained from reconstructions of hydrological changes on ombrotrophic (rain-fed) peatlands using biological indicators, such as testate amoebae. Reconstructions are based on transfer functions, relating modern assemblage composition to water table and moisture content, applied to fossil sequences. Existing transfer functions in Europe and elsewhere are limited geographically and there are often problems with missing or poor analogues. This paper presents a new palaeohydrological transfer function based on sampling raised mires from across Europe. Relationships between assemblages and hydrological variables are described using ordination analyses. Transfer functions are developed for depth to water table (n,=,119) and moisture content (n,=,132) with root mean squared errors (RMSEP) of 5.6,cm and 2.7% respectively. Both transfer functions have an r2 of 0.71, based on ,leave one out' cross-validation. Comparisons with an existing transfer function for Britain show that the European transfer function performs well in inferring measured water tables in Britain but that the British data cannot be used to infer water tables for other European sites with confidence. Several of the key missing and poor analogue taxa problems encountered in previous transfer functions are solved. The new transfer function will be an important tool in developing peat-based palaeoclimatic reconstructions for European sites. Copyright © 2006 John Wiley & Sons, Ltd. [source] THE FUTURE TRAJECTORY OF U.S. CO2 EMISSIONS: THE ROLE OF STATE VS.JOURNAL OF REGIONAL SCIENCE, Issue 1 2007AGGREGATE INFORMATION ABSTRACT This paper provides comparisons of a variety of time-series methods for short-run forecasts of the main greenhouse gas, carbon dioxide, for the United States, using a recently released state-level data set from 1960,2001. We test the out-of-sample performance of univariate and multivariate forecasting models by aggregating state-level forecasts versus forecasting the aggregate directly. We find evidence that forecasting the disaggregate series and accounting for spatial effects drastically improves forecasting performance under root mean squared forecast error loss. Based on the in-sample observations we attempt to explain the emergence of voluntary efforts by states to reduce greenhouse gas emissions. We find evidence that states with decreasing per capita emissions and a "greener" median voter are more likely to push toward voluntary cutbacks in emissions. [source] NUTRIENT LOADING ASSESSMENT IN THE ILLINOIS RIVER USING A SYNTHETIC APPROACH,JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION, Issue 4 2003Baxter E. Vieux ABSTRACT: A synthetic relationship is developed between nutrient concentrations and discharge rates at two river gauging sites in the Illinois River Basin. Analysis is performed on data collected by the U.S. Geological Survey (USGS) on nutrients in 1990 through 1997 and 1999 and on discharge rates in 1988 through 1997 and 1999. The Illinois River Basin is in western Arkansas and northeastern Oklahoma and is designated as an Oklahoma Scenic River. Consistently high nutrient concentrations in the river and receiving water bodies conflict with recreational water use, leading to intense stakeholder debate on how best to manage water quality. Results show that the majority of annual phosphorus (P) loading is transported by direct runoff, with high concentrations transported by high discharge rates and low concentrations by low discharge rates. A synthetic relationship is derived and used to generate daily phosphorus concentrations, laying the foundation for analysis of annual loading and evaluation of alternative management practices. Total nitrogen (N) concentration does not have as clear a relationship with discharge. Using a simple regression relationship, annual P loadings are estimated as having a root mean squared error (RMSE) of 39.8 t/yr and 31.9 t/yr and mean absolute percentage errors of 19 percent and 28 percent at Watts and Tahlequah, respectively. P is the limiting nutrient over the full range of discharges. Given that the majority of P is derived from Arkansas, management practices that control P would have the most benefit if applied on the Arkansas side of the border. [source] Estimation of Aqueous-Phase Reaction Rate Constants of Hydroxyl Radical with Phenols, Alkanes and AlcoholsMOLECULAR INFORMATICS, Issue 11-12 2009Ya-nan Wang Abstract A quantitative structure activity relationship (QSAR) model was developed for the aqueous-phase hydroxyl radical reaction rate constants (kOH) employing quantum chemical descriptors and multiple linear regressions (MLR). The QSAR development followed the OECD guidelines, with special attention to validation, applicability domain (AD) and mechanistic interpretation. The established model yielded satisfactory performance: the correlation coefficient square (R2) was 0.905, the root mean squared error (RMSE) was 0.139, the leave-many-out cross-validated QLMO2 was 0.806, and the external validated QEXT2 was 0.922 log units. The AD of the model covering compounds of phenols, alkanes and alcohols, was analyzed by Williams plot. The main molecular structural factors governing kOH are the energy of the highest occupied molecular orbital (EHOMO), average net atomic charges on hydrogen atoms (), molecular surface area (MSA) and dipole moment (,). It was concluded that kOH increased with increasing EHOMO and MSA, while decreased with increasing and ,. [source] The properties of Jovian Trojan asteroids listed in SDSS Moving Object Catalogue 3MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY, Issue 4 2007Gy. M. Szabó ABSTRACT We analyse 1187 observations of about 860 unique candidate Jovian Trojan asteroids listed in the 3rd release of the Sloan Digital Sky Survey (SDSS) Moving Object Catalogue. The sample is complete at the faint end to r= 21.2 mag (apparent brightness) and H= 13.8 (absolute brightness, approximately corresponding to 10 km diameter). A subset of 297 detections of previously known Trojans were used to design and optimize a selection method based on observed angular velocity that resulted in the remaining objects. Using a sample of objects with known orbits, we estimate that the candidate sample contamination is about 3 per cent. The well-controlled selection effects, the sample size, depth and accurate five-band UV,IR photometry enabled several new findings and the placement of older results on a firmer statistical footing. We find that there are significantly more asteroids in the leading swarm (L4) than in the trailing swarm (L5): N(L4)/N(L5) = 1.6 ± 0.1, independently of limiting object's size. The overall counts normalization suggests that there are about as many Jovians Trojans as there are main-belt asteroids down to the same size limit, in agreement with earlier estimates. We find that Trojan asteroids have a remarkably narrow colour distribution (root mean scatter of only ,0.05 mag) that is significantly different from the colour distribution of the main-belt asteroids. The colour of Trojan asteroids is correlated with their orbital inclination, in a similar way for both swarms, but appears uncorrelated with the object's size. We extrapolate the results presented here and estimate that the Large Synoptic Survey Telescope will determine orbits, accurate colours and measure light curves in six photometric bandpasses for about 100 000 Jovian Trojan asteroids. [source] The impact of menstrual cycle phase on cardiac autonomic regulationPSYCHOPHYSIOLOGY, Issue 4 2009Paula S. Mckinley Abstract This study investigated menstrual cycle phase differences in heart rate (HR) and RR interval variability (RRV) in 49 healthy, premenopausal, eumenorrheic women (age 30.2±6.2 years). HR and RRV were computed from ambulatory 24-h electrocardiogram, collected for up to 6 days, with at least 1 day each during early to midfollicular and midluteal menstrual phases. Phase effects on HR and RRV were assessed using linear mixed effects models with a random intercept to account for the correlation of observations within each subject as well as intrasubject variation. During follicular phase monitoring, women had significantly lower average HR (,2.33 bpm), and higher standard deviation, the root mean squared successive difference, and high frequency (0.04,0.15 Hz) and low frequency (0.15,0.40 Hz) RRV than during the luteal phase. These results provide strong support for the influence of menstrual phase on cardiac autonomic regulation in premenopausal women. [source] | |||||||||||||