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Association of dietary fat composition with kidney function decline in patients with type 2 diabetes

Diabetology & Metabolic Syndrome 2015年第1期7卷 1-1页

作者： Ana Luiza Teixeira dos Santos Camila Kümmel Duarte Maira Zoldan Manoella Freitas Santos Lorenzo Catucci Boza Jorge Luiz Gross Mirela Jobim de Azevedo Themis Zelmanovitz HCPA Porto Alegre Brazil

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Comparison of questionnaire responses with biomarkers of tobacco smoke exposure in a Canadian birth cohort at three months of age

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Allergy, Asthma & Clinical Immunology 2014年第1期10卷 1-2页

作者： Kathleen McLean Bruce Lanphear Ryan Allen Tim Takaro Amanda J Wheeler Jeff Brook James Scott Michael Brauer Malcolm Sears Padmaja Subbarao Stuart Turvey Allan Becker Piush Mandhane Faculty of Health Sciences Simon Fraser University Burnaby Canada Air Health Science Division Health Canada Ottawa Canada Air Quality Research Division Environment Canada Toronto Canada Dalla Lana School of Public Health University of Toronto Toronto Canada School of Population and Public Health University of British Columbia Vancouver Canada Department of Medicine McMaster University Hamilton Canada Department of Pediatrics Hospital for Sick Children Toronto Canada Department of Pediatrics University of British Columbia Vancouver Canada Department of Pediatrics & Child Health University of Manitoba Winnipeg Canada Faculty of Medicine and Dentistry University of Alberta Edmonton Canada

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Analysis of the acoustic environment in an ICU using patient information as a covariate

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Critical Care 2014年第1期18卷 1-182页

作者： M Park A Kohlrausch B Vlaskamp P Vos AW Oldenbeuving Philips Research Laboratories Eindhoven the Netherlands St Elisabeth Hospital Tilburg the Netherlands

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Bio-informatics assessment schema (BIAS) to improve myocardial perfusion image diagnostic and prognostic value: the NHLBI-sponsored women's ischemia syndrome evaluation (WISE) study

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Journal of Cardiovascular Magnetic Resonance 2014年第SUPPL 1期16卷 P201-P201页

作者： Mark Doyle Gerald Pohost Leslee J Shaw Diane V Thompson Sheryl F Kelsey B Delia Johnson William J Rogers Geetha Rayarao Barry L Sharaf Carl J Pepine C Noel Bairey Merz Robert W Biederman Cardiac MRI Allegheny General Hospital Pittsburgh Pennsylvania USA Cardiology Univ. of Southern California Los Angeles California USA Cardiology Atlanta Cardiovascular Research Inst. Atlanta Georgia USA Cardiology University of Pittsburgh Pittsburgh Pennsylvania USA Cardiology Univ. of Alabama Birmingham Alabama USA Cardiology Brown University Providence Rhode Island USA Cardiology University of Florida Gainesville Florida USA Cardiology Cedars-Sinai Medical Ctr Los Angeles California USA

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regression-Based models for Bus Dwell Time

Regression-Based Models for Bus Dwell Time

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IEEE International Conference on Intelligent Transportation Systems

作者： Xinkai JIANG Xiaoguang YANG IK4-Ikerlan Arabako Teknologi Parkea Miñao Spain

ISBN: (纸本)9781479960798

Bus dwell time contribute to much of the bus travel time and headway variations, meanwhile influence bus service reliability and restrict bus stop capacity directly. But in terms of how passenger passenger characteristics affect bus dwell time under different bus stop types, many of researches found in the literature are not enough. Firstly numbers of passenger alighting/boarding, bus arrival and depart rate were collected via videos, including linear and non-linear bus stops, also dwell time distribution was test respectively. Secondly quantitative analysis of dwell time vs various variables have been done. Based on field data, multiple linear regression model were proposed and estimated. At last the dwell time equations were compared with equations found in the literature, meanwhile comparison between measured value and theoretical value has been done to test the accuracy of the regression model. The results show dwell time follow Erlang-distribution, and in non-linear bus stop average boarding time is 1.5~3.2s higher than linear bus stop. Among all the regression models, using the maximum of boarding and alighting passengers as independent variable show the most accuracy.

关键词： regression analysis road vehicles Erlang-distribution bus arrival rate bus depart rate bus dwell time bus service reliability bus stop capacity bus stop types bus travel time dwell time distribution dwell time equation headway variation multiple linear regression model nonlinear bus stop passenger alighting passenger boarding passenger characteristic Analytical models Data models Equations linear regression Mathematical model Software Videos bus stop dwell time model comparison passenger data regression fit

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Research on the influencing factors of the dividend policy of China's petroleum and petrochemical listed companies

Research on the influencing factors of the dividend policy o...

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2014 International Conference on Artificial Intelligence and Industrial Application (AIIA2014)

作者： Y.T.Zhang Y.Q.Chang F.L.Li School of Economics and Management China University of Petroleum (Huadong) School of Geosciences China University of Petroleum (Huadong)

ISBN: (纸本)9781784660529

Dividend policy is one of the three-core content of financial management of modern companies, and it is not only related to the company's background, but also associated with the company's industry. China has a strict control on the petroleum and petrochemical industry, so there is serious state-owned monopoly capital in petroleum and petrochemical market, which makes the petroleum and petrochemical companies completely different characteristics from other industries. This paper selects the latest data of petroleum and petrochemical listed companies in the securities market of China, and then adopts empirical research methods to study what factors influence the dividend policy of listed companies using a multiple linear regression model, in order to put forward suggestions for the improvement of the dividend policy. The research we have done suggests that cash flow from operations per share, net assets per share, net assets growth rate and ownership concentration have an important influence on cash dividend per share for petroleum and petrochemical listed companies. The results of the paper indicate that there is serious speculation in China's securities market, and the majority shareholders expropriate minority shareholders and the petroleum and petrochemical listed companies are lack of growth.

关键词： petroleum and petrochemical listed companies dividend policy influencing factors multiple linear regression model

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Temperature Error Compensation for Open-Loop Fiber Optical Gyro using Back-Propagation Neural Networks with Optimal Structure

Temperature Error Compensation for Open-Loop Fiber Optical G...

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2014中国制导、导航与控制学术会议

作者： Limei Song Wei Zhang AVIC XI''AN Fight Automatic Control Research Institute Xi''an710065 China Delegation of Air ForceXi''an710065 China

ISBN: (纸本)9781479946983

Open-Loop Fiber Optic Gyroscopes (FOG) is widely used,which is easily affected by the temperature around *** temperature model has a very complicated nonlinear characteristic.A BP neural network model with advantage of approximating the nonlinear function was developed to simulate outputs of an open-loop FOG and then compensate the FOG's temperature error in full temperature range??–50??~ +70????.With experimental data,the networks with one-hidden-layer structure adopted the temperature and the temperature change rate as network inputs,and the outputs of FOG as network *** results showed that the number of hidden-layer neurons plays an important role in simulation performance,and the network with 11 hidden-layer neurons offered better precision and ***,the comparison of 4 different training algorithms demonstrated that the Levenberg-Marquardt algorithm resulted in a better convergence during training *** the chosen structure and training algorithm,the BP neural network model was used to compensate the temperature error of the *** was found that the compensated outputs of the FOG became more accurate and more *** addition,the neural network model further proved its superiority of precision and robustness by comparison with a multiple linear regression model and a quadratic curve fitting model.

关键词： backpropagation computerised instrumentation curve fitting error compensation fibre optic gyroscopes neural nets regression analysis BP neural network model FOG Levenberg-Marquardt algorithm backpropagation neural network model hidden-layer neuron multiple linear regression model nonlinear function approximation one-hidden-layer structure open-loop fiber optical gyroscope optimal structure quadratic curve fitting model temperature error compensation training algorithm Biological neural networks Neurons Stability analysis Temperature distribution Temperature measurement Thermal stability Training

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An improved algorithm for missing data in wireless sensor networks

An improved algorithm for missing data in wireless sensor ne...

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International Conference on Software Intelligence Technologies and Applications & International Conference on Frontiers of Internet of Things 2014

作者： Hongguo Zhang Liang Yang Harbin University of Science and Technology China 150080

In the field of wireless sensor networks, missing data has been an issue of widespread concern. In this paper, an improved algorithm based on different data types, is proposed. Missing data is estimated by a new algorithm ,which is based on a variety of data types. Data, which is dynamic and transitional, is a correlative of missing data. And the multiple linear regression model is used to solve the problem of missing data whose type of value is different from the others. The proposed algorithm can not only ensures the normal data transmission, but also improves the traditional methods in dealing with the problem of the incompleteness of multiple data. Furthermore, the proposed algorithm avoids the limitations of the traditional methods for estimating real-time data.

关键词： correlation data missing data multiple data multiple linear regression model missing data Wireless sensor networks data types multiple regression model TRADITIONAL METHODS data association algorithms

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Robustness of thermal error compensation modeling models of CNC machine tools

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2013年第9-12期69卷 2593-2603页

作者： Miao, En-Ming Gong, Ya-Yun Niu, Peng-Cheng Ji, Chang-Zhu Chen, Hai-Dong HeFei Univ Technol Sch Instrument Sci & Optoelect Engn Hefei 230009 Anhui Peoples R China

In order to achieve effective control of thermal error compensation of computer numerical control (CNC) machine tools, the prediction accuracy and robustness of the compensation model is particularly important. In this paper, the temperature of sensitive points and thermal error of the spindle in Z direction are measured. Using a combination of fuzzy clustering analysis and gray correlation method to select temperature-sensitive points and then using multiple linear regression of least squares and least absolute estimation methods, distributed lag model, and support vector regression machine to establish prediction models of the relationship between temperature of sensitive points and the thermal error. Also, the temperature values of sensitive points and the thermal error in the experimental conditions of different ambient temperatures and different spindle speeds are measured. By comparing the prediction accuracy of various prediction models under different experimental conditions verify the robustness of the models. Experimental results show that when the modeling data are less, the prediction accuracy of multiple linear regression of least squares and least absolute estimation methods and distributed lag model is declined, and their robustness are poor, while support vector regression model has good prediction accuracy and its robustness remains strong when changing the experimental conditions. However, when modeling data are rich, the prediction accuracy of various algorithms is improved, but the robustness of support vector regression model is volatile. The robustness analysis of different models provides a useful reference for the thermal error compensation model, selection of CNC machine tools, and has good engineering applications.

关键词： Machine tools Thermal error multiple linear regression model Distributed lag model Support vector regression model Robustness

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Characteristics of the urban heat island in a high-altitude metropolitan city, Ulaanbaatar, Mongolia

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ASIA-PACIFIC JOURNAL OF ATMOSPHERIC SCIENCES 2013年第4期49卷 535-541页

作者： Ganbat, Gantuya Han, Ji-Young Ryu, Young-Hee Baik, Jong-Jin Seoul Natl Univ Sch Earth & Environm Sci Seoul 151742 South Korea Korea Inst Atmospher Predict Syst Seoul South Korea

Ulaanbaatar, the capital city of Mongolia, with a population of 1.1 million is located at an altitude of about 1350 m and in a valley. This study is the first to document the characteristics of the urban heat island (UHI) in Ulaanbaatar. Data from two meteorological stations, an urban site and a rural site, for the 31-year period 1980-2010 are used for UHI analysis. The average UHI intensity is 1.6A degrees C. The UHI intensity exhibits a large seasonal dependence, being strongest in winter (3.3A degrees C) and weakest in summer (0.3A degrees C). The average daily maximum UHI intensity is 4.3A degrees C. The strongest daily maximum UHI intensity occurs in winter with an average intensity of 6.4A degrees C, and the weakest one occurs in summer with an average intensity of 2.5A degrees C. The occurrence frequency of the daily maximum UHI intensity in the nighttime is 5.6 times that in the daytime. A multiple linear regression analysis is undertaken to examine the relative importance of meteorological parameters (previous-day maximum UHI intensity, wind speed, cloudiness, and relative humidity) that affect the daily maximum UHI intensity. The half of the variance (49.8%) is explained by the multiple linear regression model. The previous-day maximum UHI intensity is the most important parameter and is positively correlated with the daily maximum UHI intensity. Cloudiness is the second most important parameter and is negatively correlated with the daily maximum UHI intensity. When the data are classified into daytime/nighttime and season, the relative importance of the meteorological parameters changes. The most important parameter in spring and summer is cloudiness, while in autumn and winter it is the previous-day maximum UHI intensity.

关键词： Urban heat island Ulaanbaatar daily maximum urban heat island intensity multiple linear regression model

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