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High time resolution observation and statistical analysis of atmospheric light extinction properties and the chemical speciation of fine particulates

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Science China Chemistry 2010年第8期53卷 1801-1808页

作者： YAO TingTing1,HUANG XiaoFeng1,HE LingYan1,HU Min2,SUN TianLe1,XUE Lian1,LIN Yun1,ZENG LiWu1 & ZHANG YuanHang2 1Key Laboratory for Urban Habitat Environmental Science and Technology,School of Environment and Energy,Peking University Shenzhen Graduate School,Shenzhen 518055,China 2State Key Joint Laboratory of Environmental Simulation and Pollution Control College of Environmental Sciences and Engineering,Peking University,Beijing 100871,China Key Laboratory for Urban Habitat Environmental Science and Technology School of Environment and Energy Peking University Shenzhen Graduate School Shenzhen China State Key Joint Laboratory of Environmental Simulation and Pollution Control College of Environmental Sciences and Engineering Peking University Beijing China

In recent years,the visibility deterioration caused by regional fine particulate pollution becomes one of the crucial air pollution problems in the urban areas of our *** rapid variation of visibility and fine particulates make it difficult to estimate the relationship between them precisely and accurately unless high time resolution observation data can be *** study aims to fill this gap in the field of atmospheric science by establishing a formula using multiple linear *** fitting goodness (R2=0.913,n=3167) was obtained using 10 min average of high-resolution real-time light scattering coefficients,light absorption coefficients,main chemical speciation concentration in PM1 and some meteorological parameters from 17 Jan to 16 Feb,*** shows that the average light extinction coefficient during the observation in the winter of Shenzhen was measured to be 290 ± 183 Mm?1,consisting of 72% of light scattering and 21% of *** terms of the percentage contribution of PM1 chemical species to the total light extinction,the organic matter was estimated to be most with an average of 45%,followed by ammonium sulfate with an average of 24%.The contributions of black carbon and ammonium nitrate were 17% and 12%,***,the diurnal variation of light extinction was investigated as well in this study.

关键词： light extinction PM1 chemical speciation high time resolution multiple linear regressions

Empirical Research on the Competence of Intellectual Property Rights in Shenzhen Based on Regional Industrial Innovation

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Journal of Electronic Science and Technology of China 2006年第4期4卷 345-349,452页

作者：李平李蓉萧延高蔡晓红 The Shenzhen Government Shenzhen 518000 Guangdong China School of Management Science University of Electronic Science and Technology of China Chengdu 610054 China Standard Patent &Trademark Agent LTD. Shenzhen 518000 Guangdong China

Based on statistical data of the regional industry and the level of intellectual property right （IPR） development, this paper adopts the ways such as regression analysis and description statistics, trying to analyze the intrinsic linkage among the regional industry innovation, regional competence and IPR competence. A conclusion was drawn that IPR competence can assuredly upgrade the industrial competence and regional competence, but the supporting from perfect institutional environment and service system of intellectual property rights （IPR） is yet needed. According to the analysis, this paper gives some suggestions on how to improve the IPR competence of Shenzhen,

关键词： regional industrial innovation intellectual property competence regional competence regressions

Selection of Influential Microfabric Properties of Anisotropic Amphibolite Rocks on Its Uniaxial Compressive Strength (UCS): A Comprehensive Statistical Study

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Journal of Applied Mathematics and Physics 2014年第12期2卷 1130-1138页

作者： Esamaldeen Ali Guang Wu Faculty of Petroleum and Minerals Al Neelain University Khartoum Sudan Faculty of Geosciences and Environmental Engineering Southwest Jiaotong University Chengdu China

Occasionally, in complex inherent characteristics of certain rocks, especially anisotropic rocks it may be difficult to measure the uniaxial compressive strength UCS. However, the use of empirical relationships to evaluate the UCS of rock can be more practical and economical. Consequently, this study carried out to predict UCS from microfabrics properties of banded amphibolite rocks using multiple regression analysis. Based on statistical results, rock microfabric parameters, which adequately represent the UCS of a given rock type have been selected. The results show that grain size, shape factor and quartz content have high significant correlation with UCS at 95% confidence level. From multiple regression model, approximately 84% of the variance of the UCS can be estimated by the linear combination of these three parameters. However, according to model performance criteria: correlation coefficient (R = 0.919), variance account for (VAF = 97%) and root mean square error (RMSE = 4.16) the study clearly indicates that the developed model is reliable to predict the UCS. Finally, this approach can be easily extended to the modeling of rock strength in the absence of adequate geological information or abundant data.

关键词： Anisotropic Rocks Microfabric UCS Multiple regressions Banded Amphibolite

Assessment of Mechanical Properties of Muscles from Multi-Parametric Magnetic Resonance Imaging

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Journal of Biomedical Science and Engineering 2014年第8期7卷 593-603页

作者： Renaud Grenier Delphine Périé Guillaume Gilbert Gilles Beaudoin Daniel Curnier Mechanical Engineering Department Ecole Polytechnique de Montréal Montreal Canada Notre Dame Hospital University of Montreal Montreal Canada Philips Healthcare Montreal Canada Research Center Sainte-Justine University Hospital Montreal Canada

We hypothesized that a relationship existed between the mechanical properties and the magnetic resonance imaging (MRI) parameters of muscles, as already demonstrated in cartilaginous tissues. The aim was to develop an indirect evaluation tool of the mechanical properties of degenerated muscles. Leg and arm muscles of adult rabbits were dissected, and tested 12 hours post mortem, in a state of rigor mortis, or 72 hours post mortem, in a state of post-rigor mortis. The tests consisted of a multi-parametric MRI acquisition followed by a uniaxial tensile test until failure. The statistical analysis consisted of multiple linear regressions and principal component analysis. Significant differences existed between the rigor mortis and post-rigor mortis groups for E but not for the MRI parameters. 78%, 60% or 33% of the Young’s modulus could be explained by the MRI parameters in the post-rigor mortis group, rigor mortis group or both groups respectively. These relationships were confirmed by the principal component analysis. The proposed multi-parametric MRI protocol associated to principal component analysis is a promising tool for the indirect evaluation of muscle mechanical properties and should be useful to find biomarkers and predictive factors of the evolution of the pathologies.

关键词： Muscles Mechanical Properties Multi-Parametric MRI Multiple regressions Principal Component Analysis

A Practical Solution to the Small Sample Size Bias and Uncertainty Problems of Model Selection Criteria in Two-Input Process Multiple Response Surface Methodology Datasets

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Open Journal of Statistics 2019年第1期9卷 109-142页

作者： Domingo Pavolo Delson Chikobvu The University of the Free State Bloemfontein South Africa

Multiple response surface methodology (MRSM) most often involves the analysis of small sample size datasets which have associated inherent statistical modeling problems. Firstly, classical model selection criteria in use are very inefficient with small sample size datasets. Secondly, classical model selection criteria have an acknowledged selection uncertainty problem. Finally, there is a credibility problem associated with modeling small sample sizes of the order of most MRSM datasets. This work focuses on determination of a solution to these identified problems. The small sample model selection uncertainty problem is analysed using sixteen model selection criteria and a typical two-input MRSM dataset. Selection of candidate models, for the responses in consideration, is done based on response surface conformity to expectation to deliberately avoid selection of models using the problematic classical model selection criteria. A set of permutations of combinations of response models with conforming response surfaces is determined. Each combination is optimised and results are obtained using overlaying of data matrices. The permutation of results is then averaged to obtain credible results. Thus, a transparent multiple model approach is used to obtain the solution which gives some credibility to the small sample size results of the typical MRSM dataset. The conclusion is that, for a two-input process MRSM problem, conformity of response surfaces can be effectively used to select candidate models and thus the use of the problematic model selection criteria is avoidable.

关键词： Multiple Response Surface Methodology All Possible regressions Model Selection Criteria Data Matrices

Automatic Abnormal Electroencephalograms Detection of Preterm Infants

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Journal of Data Analysis and Information Processing 2018年第4期6卷 141-155页

作者： Daniel Schang Pierre Chauvet Sylvie Nguyen The Tich Bassam Daya Nisrine Jrad Marc Gibaud ESEO Tech Angers France UCO-LARIS-UBL Angers France CHRU de Lille Hopital Albert Calmette Lille France Institute of Technology Lebanese University Saida Lebanon CHU-LARIS-UBL Angers France

Many preterm infants suffer from neural disorders caused by early birth complications. The detection of children with neurological risk is an important challenge. The electroencephalogram is an important technique for establishing long-term neurological prognosis. Within this scope, the goal of this study is to propose an automatic detection of abnormal preterm babies’ electroencephalograms (EEG). A corpus of 316 neonatal EEG recordings of 100 infants born after less than 35 weeks of gestation were preprocessed and a time series of standard deviation was computed. This time series was thresholded to detect Inter Burst Intervals (IBI). Temporal features were extracted from bursts and IBI. Feature selection was carried out with classification in one step so as to select the best combination of features in terms of classification performance. Two classifiers were tested: Multiple Linear regressions and Support Vector Machines (SVM). Performance was computed using cross validations. Methods were validated on a corpus of 100 infants with no serious brain damage. The Multiple Linear Regression method shows the best results with a sensitivity of 86.11% ± 10.01%, a specificity of 77.44% ± 7.62% and an AUC (Area under the ROC curves) of 0.82 ± 0.04. An accurate detection of abnormal EEG for preterm infants is feasible. This study is a first step towards an automatic analysis of the premature brain, making it possible to lighten the physician’s workload in the future.

关键词： Automatic EEG Analysis Machine Learning Multiple Linear regressions Preterm Infants Support Vector Machines

Hybrid Prediction Method for Solar Power Using Different Computational Intelligence Algorithms

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Smart Grid and Renewable Energy 2013年第1期4卷 76-87页

作者： Md Rahat Hossain Amanullah Maung Than Oo A. B. M. Shawkat Ali Power Engineering Research Group (PERG) Central Queensland University Rockhampton Australia.

Computational Intelligence (CI) holds the key to the development of smart grid to overcome the challenges of planning and optimization through accurate prediction of Renewable Energy Sources (RES). This paper presents an architectural framework for the construction of hybrid intelligent predictor for solar power. This research investigates the applicability of heterogeneous regression algorithms for 6 hour ahead solar power availability forecasting using historical data from Rockhampton, Australia. Real life solar radiation data is collected across six years with hourly resolution from 2005 to 2010. We observe that the hybrid prediction method is suitable for a reliable smart grid energy management. Prediction reliability of the proposed hybrid prediction method is carried out in terms of prediction error performance based on statistical and graphical methods. The experimental results show that the proposed hybrid method achieved acceptable prediction accuracy. This potential hybrid model is applicable as a local predictor for any proposed hybrid method in real life application for 6 hours in advance prediction to ensure constant solar power supply in the smart grid operation.

关键词： Computational Intelligence Heterogeneous regressions Algorithms Performance Evaluation Hybrid Method Mean Absolute Scaled Error (MASE)

What Makes a Greener Building? Lessons from Taiwan

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Journal of Environmental Protection 2018年第9期9卷 957-972页

作者： Chou-Yen Wu Shih-Fang Lo Center for Green Economy Chung Hua Institution for Economic Research Taiwan

Buildings consume vast amounts of resources and energy contributing to climate change. Some countries have developed green building certification systems. Compared to other developed areas, Taiwan lags behind in green building construction and in response created its own certification for sub-tropical countries called Ecology, Energy Saving, Waste Reduction, and Health (EEWH). But what economic factors truly drive green building construction and quality? Our study sample analyzes 1355 Taiwan green building cases from 2000 to June 2018 to determine the influence of various economic, structural, and policy factors. Results suggest that while public ownership led to more green building certifications, private ownership led to a higher environment rating. Proper incentives are needed to further increase the ratings of public buildings and to expand the construction of highly rated private buildings, especially for small and medium sized buildings.

关键词： Green Buildings EEWH Empirical Study regressions Certification