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29th Digital Avionics Systems Conference on Improving Our Environment through Green Avionics and ATM Solutions

作者： Wang, Yao Sridhar, Banavar NASA Ames Res Ctr Moffett Field CA 94035 USA

ISBN: (纸本)9781424466177

This paper presents a detailed convective forecast accuracy analysis at center and sector levels. The study is aimed to provide more meaningful forecast verification measures to aviation community, as well as to obtain useful information leading to the improvements in the weather translation capacity models. In general, the vast majority of forecast verification efforts over past decades have been on the calculation of traditional standard verification measure scores over forecast and observation data analyses onto grids. These verification measures based on the binary classification have been applied in quality assurance of weather forecast products at the national level for many years. Our research focuses on the forecast at the center and sector levels. We calculate the standard forecast verification measure scores for en-route air traffic centers and sectors first, followed by conducting the forecast validation analysis and related verification measures for weather intensities and locations at centers and sectors levels. An approach to improve the prediction of sector weather coverage by multiple sector forecasts is then developed. The weather severe intensity assessment was carried out by using the correlations between forecast and actual weather observation airspace coverage. The weather forecast accuracy on horizontal location was assessed by examining the forecast errors. The improvement in prediction of weather coverage was determined by the correlation between actual sector weather coverage and prediction. The analysis was accomplished by using observed and forecasted Convective Weather Avoidance model (CWAM) data collected from June to September in 2007. CWAM zero-minute forecast data with aircraft avoidance probability of 60% and 80% are used as the actual weather observation. All forecast measurements are based on 30-minute, 60-minute, 90-minute, and 120-minute forecasts with the same avoidance probabilities. The forecast accuracy analysis for times und

关键词： ZDC center aerospace engineering air traffic aircraft avoidance probability aviation community avionics binary classification convective forecast accuracy analysis convective weather avoidance model data analysis forecast errors forecast verification measures multiple linear regression model multiple sector forecast validation analysis regression analysis traditional standard verification measure weather forecast accuracy analysis weather forecasting weather intensity assessment weather observation airspace coverage weather translation capacity

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modelling the impact of infection control measures on the incidence of vancomycin-resistance enterococci: a time-series analysis

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BMC Proceedings 2011年第6期5卷 1-1页

作者： Y-C Chen Y-C Chuang National Taiwan University Hospital Taipei Taiwan China

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HFE Gene Variants Modify the Association between Maternal Lead Burden and Infant Birthweight: A Prospective Birth Cohort Study in Mexico City, Mexico

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ENVIRONMENTAL HEALTH 2010年第2010期9卷 43-43页

作者： Cantonwine, David Hu, Howard Maria Tellez-Rojo, Martha Sanchez, Brisa N. Lamadrid-Figueroa, Hector Ettinger, Adrienne S. Mercado-Garcia, Adriana Hernandez-Avila, Mauricio Wright, Robert O. Univ Michigan Sch Publ Hlth Dept Environm Hlth Sci Ann Arbor MI 48109 USA Harvard Univ Sch Publ Hlth Dept Environm Hlth Boston MA 02115 USA Harvard Univ Brigham & Womens Hosp Sch Med Dept MedChanning Lab Boston MA 02115 USA Natl Inst Publ Hlth Div Stat Ctr Evaluat Res & Surveys Cuernavaca Morelos Mexico Univ Michigan Sch Publ Hlth Dept Biostat Ann Arbor MI 48109 USA Natl Inst Publ Hlth Div Environm Hlth Ctr Populat Hlth Res Cuernavaca Morelos Mexico Minist Hlth Mexico City DF Mexico

Background: Neonatal growth is a complex process involving genetic and environmental factors. Polymorphisms in the hemochromatosis (HFE) iron regulatory genes have been shown to modify transport and toxicity of lead which is known to affect birth weight. Methods: We investigated the role of HFE C282Y, HFE H63 D, and transferrin (TF) P570 S gene variants in modifying the association of lead and infant birthweight in a cohort of Mexican mother-infant pairs. Subjects were initially recruited between 1994-1995 from three maternity hospitals in Mexico City and 411 infants/565 mothers had archived blood available for genotyping. multiple linear regression models, stratified by either maternal/infant HFE or TF genotype and then combined with interaction terms, were constructed examining the association of lead and birthweight after controlling for covariates. Results: 3.1%, 16.8% and 17.5% of infants (N = 390) and 1.9%, 14.5% and 18.9% of mothers (N = 533) carried the HFE C282Y, HFE H63D, and TF P570 S variants, respectively. The presence of infant HFE H63 D variants predicted 110.3 g (95% CI -216.1, -4.6) decreases in birthweight while maternal HFE H63 D variants predicted reductions of 52.0 g (95% CI -147.3 to 43.2). Interaction models suggest that both maternal and infant HFE H63 D genotype may modify tibia lead's effect on infant birthweight in opposing ways. In our interaction models, maternal HFE H63 D variant carriers had a negative association between tibia lead and birthweight. Conclusions: These results suggest that the HFE H63 D genotype modifies lead's effects on infant birthweight in a complex fashion that may reflect maternal-fetal interactions with respect to the metabolism and transport of metals.

关键词： Birth Weight multiple linear regression model Iron Status Lead Exposure Blood Lead Level

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KSHV activation, human and viral IL-6 production, and other cytokine dysregulation: Association with the symptomatology of KSHV-associated multicentric Castleman's disease

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Infectious Agents and Cancer 2010年第1期5卷 1-2页

作者： Mark N Polizzotto Thomas S Uldrick Victoria Wang Karen Aleman Kathleen M Wyvill Dierdre O'Mahony Richard F Little Robert Yarchoan Stefania Pittaluga Vickie Marshall Denise Whitby Giovanna Tosato Seth M Steinberg HIV and AIDS Malignancy Branch Center for Cancer Research National Cancer Institute Bethesda MD USA Laboratory of Pathology Center for Cancer Research National Cancer Institute Bethesda MD USA Viral Oncology Section AIDS and Cancer Virus Program National Cancer Institute Bethesda MD USA Laboratory of Cellular Oncology Center for Cancer Research National Cancer Institute Bethesda MD USA Biostatistic and Data Management Section Center for Cancer Research National Cancer Institute Bethesda MD USA

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A comparison of artificial neural networks with other statistical approaches for the prediction of true metabolizable energy of meat and bone meal

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POULTRY SCIENCE 2010年第7期89卷 1562-1568页

作者： Perai, A. H. Moghaddam, H. Nassiri Asadpour, S. Bahrampour, J. Mansoori, Gh. Ferdowsi Univ Mashhad Excellence Ctr Anim Sci Res Mashhad Iran Ferdowsi Univ Mashhad Dept Anim Sci Fac Agr Mashhad Iran Ferdowsi Univ Mashhad Dept Chem Fac Sci Mashhad Iran Shahid Bahonar Univ Kerman Dept Anim Sci Jiroft Fac Agr Jiroft Iran Ghasr e Shirin Branch Islamic Azad Univ Dept Chem Fac Sci Kermanshah Iran

There has been a considerable and continuous interest to develop equations for rapid and accurate prediction of the ME of meat and bone meal. In this study, an artificial neural network (ANN), a partial least squares (PLS), and a multiple linear regression (MLR) statistical method were used to predict the TME(n) of meat and bone meal based on its CP, ether extract, and ash content. The accuracy of the models was calculated by R(2) value, MS error, mean absolute percentage error, mean absolute deviation, bias, and Theil's U. The predictive ability of an ANN was compared with a PLS and a MLR model using the same training data sets. The squared regression coefficients of prediction for the MLR, PLS, and ANN models were 0.38, 0.36, and 0.94, respectively. The results revealed that ANN produced more accurate predictions of TME(n) as compared with PLS and MLR methods. Based on the results of this study, ANN could be used as a promising approach for rapid prediction of nutritive value of meat and bone meal.

关键词： meat and bone meal metabolizable energy neural network model partial least squares model multiple linear regression model

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Prediction model of Secondary Substances in Anthocyanins Synthesis of Purple Corn

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Agricultural Science & Technology 2010年第8期11卷 153-156,182页

作者：朱敏史振声李凤海王志斌沈阳农业大学特种玉米研究所沈阳110866

The aim of this study was to assay the polyphenols,flavonoid,polyphenol oxidase and phenylalnine ammonialyase which were relative to the anthocyanins synthesis of purple corn. The optimization of multiple linear regression model of anthocyanins synthesis was y=4.383 86-0.205 45x1＋5.479 638x2＋0.195 575x4. According to standard partial regression coefficient testing,the result indicated that polyphenols content was negatively correlated with anthocyanins and the relative influence to anthocyanins synthesis was-42.7%; flavonoid content and activity of polyphenol oxidase were positively correlated with anthocyanins of purple corn and the relative influence to anthocyanins synthesis were 71.45% and 73.32% respectively. There was no positive correlation between the activity of phenylalnine ammonialyase and anthocyanins of purple corn. The establishment of multiple linear regression model of anthocyanins synthesis was to provide theory foundation of producing anthocyanins in laboratory.

关键词： Anthocyanins Flavonoid multiple linear regression model Purple corn Polyphenols Polyphenol oxidase Phenylalnine ammonialyase

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REFERENCE EVAPOTRANSPIRATION ESTIMATION USING ADAPTIVE NEURO-FUZZY INFERENCE SYSTEMS

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IRRIGATION AND DRAINAGE 2009年第5期58卷 617-628页

作者： Dogan, Emrah Sakarya Univ Dept Civil Engn Fac Engn TR-54187 Sakarya Turkey

Evapotranspiration, an important component in terrestrial water balance and net primary productivity models, is difficult to measure and estimate. In this Study, the potential of the adaptive neuro-fuzzy inference system (ANFIS) is investigated in modelling of daily grass crop reference evapotranspiration (ETo) obtained using the Penman-Monteith equation. Various combinations of daily climatic data, namely solar radiation, air temperature, relative humidity and wind speed, are used as inputs to the ANFIS so as to evaluate the degree of effect of each of these variables on daily Penman-Monteith estimated ETo. The results of the ANFIS model are compared with a multiple linear regression model. Mean square error, average absolute relative error and determination coefficient statistics are used as comparison criteria for evaluation of the model performance. The ANFIS technique whose inputs are solar radiation, air temperature, relative humidity and wind speed, gave mean square errors of 0.016, average absolute relative errors of 6.4%, and determination coefficients of 0.996 for Morgan Hill 139 station (San Francisco Bay, USA). Based on the comparisons, it was found that the ANFIS model could be successfully employed in estimating the daily ETo. Copyright (C) 2008 John Wiley & Sons, Ltd.

关键词： daily evapotranspiration adaptive neuro fuzzy inference systems Penman-Monteith model multiple linear regression model

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Potentialities of quantile regression to predict ozone concentrations

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ENVIRONMETRICS 2009年第2期20卷 147-158页

作者： Sousa, S. I. V. Pires, J. C. M. Martins, F. G. Pereira, M. C. Alvim-Ferraz, M. C. M. Univ Porto LEPAE Dept Engn Quim Fac Engn P-4200465 Oporto Portugal

This paper aims: (i) to analyse the influence of ozone precursors (both meteorological variables and pollutant concentrations) on ozone concentrations at different ozone levels;and (ii) to predict next day hourly ozone concentrations using a new approach based on quantile regression (QR). The performance of this model was compared with multiple linear regressions (MLR) for the three following periods: daylight, night time and all day. QR as proven to be an useful mathematical tool to evidence the heterogeneity of ozone predictor influences at different ozone levels. Such heterogeneity is generally hidden when an ordinary least square regression model is applied. The influence of previous concentrations of ozone and nitrogen monoxide on next day ozone concentrations was higher for lower quantiles. When QR was applied, the wind direction (WD) was found to be significant in the medium quantiles and the relative humidity (RH) in the higher quantiles. On the contrary, using the MLR models, both variables were not statistically significant. Moreover, QR allowed more efficient previsions of extreme values which are very useful once the forecasting of higher concentrations is fundamental to develop strategies for protecting the public health. Copyright (C) 2008 John Wiley & Sons, Ltd.

关键词： ozone concentration prediction quantile regression multiple linear regression model

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Exhaled nitric oxide and airway hyperresponsiveness in workers: A preliminary study in lifeguards

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BMC Pulmonary Medicine 2009年第1期9卷 1-9页

作者： Demange, Valérie Bohadana, Abraham Massin, Nicole Wild, Pascal INRS Département Epidémiologie en Entreprise 54519 Vandœuvre-lès-Nancy Cedex Rue du Morvan CS 60027 France INSERM U954 Faculté de Médecine 54500 Vandoeuvre- lès-Nancy 9 avenue de la forêt de Haye France Service de Pneumologie CHU de Nancy hôpital d'adultes de Brabois 54511 Vandoeuvre-lès-Nancy Avenue de Bourgogne France

Background: Airway inflammation and airway hyperresponsiveness (AHR) are two characteristic features of asthma. Fractional exhaled nitric oxide (FENO) has shown good correlation with AHR in asthmatics. Less information is available about FENO as a marker of inflammation from work exposures. We thus examined the relation between FENO and AHR in lifeguards undergoing exposure to chloramines in indoor ***: 39 lifeguards at six indoor pools were given a respiratory health questionnaire, FENO measurements, spirometry, and a methacholine bronchial challenge (MBC) test. Subjects were labeled MBC+ if the forced expiratory volume (FEV1) fell by 20% or more. The normalized linear dose-response slope (NDRS) was calculated as the percentage fall in FEV1 at the last dose divided by the total dose given. The relation between MBC and FENO was assessed using logistic regression adjusting on confounding factors. The association between NDRS and log-transformed values of FENO was tested in a multiple linear regression ***: The prevalence of lifeguards MBC+ was 37.5%. In reactors, the median FENO was 18.9 ppb (90% of the predicted value) vs. 12.5 ppb (73% predicted) in non-reactors. FENO values ≥ 60% of predicted values were 80% sensitive and 42% specific to identify subjects MBC+. In the logistic regression model no other factor had an effect on MBC after adjusting for FENO. In the linear regression model, NDRS was significantly predicted by log ***: In lifeguards working in indoor swimming pools, elevated FENO levels are associated with increased airway responsiveness. © 2009 Demange et al;licensee BioMed Central Ltd.

关键词： Asthma Airway Inflammation multiple linear regression model Airway Hyperresponsiveness Airway Responsiveness

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Conceptual Data model-Based Software Size Estimation for Information Systems

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ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY 2009年第2期19卷 4-4页

作者： Tan, Hee Beng Kuan Zhao, Yuan Zhang, Hongyu Nanyang Technol Univ Singapore 639798 Singapore Tsinghua Univ Sch Software Beijing 100084 Peoples R China

Size estimation plays a key role in effort estimation that has a crucial impact on software projects in the software industry. Some information required by existing software sizing methods is difficult to predict in the early stage of software development. A conceptual data model is widely used in the early stage of requirements analysis for information systems. Lines of code (LOC) is a commonly used software size measure. This article proposes a novel LOC estimation method for information systems from their conceptual data models through using a multiple linear regression model. We have validated the proposed method using samples from both the software industry and open-source systems.

关键词： Measurement Software sizing line of code (LOC) conceptual data model multiple linear regression model

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