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Estimating the effects of meteorology on PM

Liu YANG, Ye WU, Jerry M. DAVIS, Jiming HAO

《环境科学与工程前沿（英文）》 2011年第5卷第3期页码 331-341 doi: 10.1007/s11783-011-0307-5

摘要： Particulate pollution was a critical challenge to the promise of good air quality during the 2008 Beijing Olympic Games, which took place from August 8th to 24th. To ensure good air quality for the Games, several temporary emission control measures were implemented in Beijing and surrounding areas. Ambient particulate matter concentration decreased significantly during the Olympic period; however, it is difficult to distinguish the effectiveness of those control measures since meteorology also affects ambient PM concentration. In this work, a multiple linear regression model based on continuous field monitoring at a roadside site was conducted to evaluate the effects of meteorology and emission control measures on the reduction of PM during the 2008 Olympic Games. The hourly data set was divided into two time periods, the no control period, June 22nd to July 4th, and the control period, July 28th to August 21st. The response variable was PM and the meteorology covariates used in the model were hourly temperature, dew point temperature, wind speed and precipitation. Wind direction was not a significant predictor of PM levels in either the control or the no control period. Using the meteorologically-based regression coefficients from the two time periods, meteorology was found to contribute to at least a 16% reduction in PM levels in the roadside microenvironment; while the pollution control measures contributed to at least a 43% reduction in PM levels.

关键词： meteorology emission control measures Beijing Olympic Games PM_2.5 linear statistical models

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Comparison between linear and non-linear forms of pseudo-first-order and pseudo-second-order adsorptionkinetic models for the removal of methylene blue by activated carbon

Junxiong LIN , Lan WANG ,

《环境科学与工程前沿（英文）》 2009年第3卷第3期页码 320-324 doi: 10.1007/s11783-009-0030-7

摘要： The best-fit equations of linear and non-linear forms of the two widely used kinetic models, namely pseudo-first-order and pseudo-second-order equations, were compared in this study. The experimental kinetics of methylene blue adsorption on activated carbon was used for this research. Both the correlation coefficient () and the normalized standard deviation Δ(%) were employed as error analysis methods to determine the best-fitting equations. The results show that the non-linear forms of pseudo-first-order and pseudo-second-order models were more suitable than the linear forms for fitting the experimental data. The experimental kinetics may have been distorted by linearization of the linear kinetic equations, and thus, the non-linear forms of kinetic equations should be primarily used to obtain the adsorption parameters. In addition, the Δ(%) method for error analysis may be better to determine the best-fitting model in this case.

关键词： adsorption pseudo-first order pseudo-second order kinetic model linear method non-linear method

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Enhanced empirical models for predicting the drift capacity of less ductile RC columns with flexural,

Mohammad Reza AZADI KAKAVAND, Reza ALLAHVIRDIZADEH

《结构与土木工程前沿（英文）》 2019年第13卷第5期页码 1251-1270 doi: 10.1007/s11709-019-0554-2

摘要： Capacity of components subjected to earthquake actions is still a widely interesting research topic. Hence, developing precise tools for predicting drift capacities of reinforced concrete (RC) columns is of great interest. RC columns are not only frequently constructed, but also their composite behavior makes the capacity prediction a task faced with many uncertainties. In the current article, novel empirical approaches are presented for predicting flexural, shear and axial failure modes in RC columns. To this aim, an extensive experimental database was created by collecting outcomes of previously conducted experimental tests since 1964, which are available in the literature. It serves as the basis for deriving the equations for predicting the drift capacity of RC columns by different regression analyses (both linear with different orders and nonlinear). Furthermore, fragility curves are determined for comparing the obtained results with the experimental results and with previously proposed models, like the ones of ASCE/SEI 41-13. It is demonstrated that the proposed equations predict drift capacities, which are in better agreement with experimental results than those computed by previously published models. In addition, the reliability of the proposed equations is higher from a probabilistic point of view.

关键词： flexural-shear-axial failure drift capacity reinforced concrete columns statistical analysis fragility curves

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SPT based determination of undrained shear strength: Regression models and machine learning

Walid Khalid MBARAK, Esma Nur CINICIOGLU, Ozer CINICIOGLU

《结构与土木工程前沿（英文）》 2020年第14卷第1期页码 185-198 doi: 10.1007/s11709-019-0591-x

摘要： The purpose of this study is the accurate prediction of undrained shear strength using Standard Penetration Test results and soil consistency indices, such as water content and Atterberg limits. With this study, along with the conventional methods of simple and multiple linear regression models, three machine learning algorithms, random forest, gradient boosting and stacked models, are developed for prediction of undrained shear strength. These models are employed on a relatively large data set from different projects around Turkey covering 230 observations. As an improvement over the available studies in literature, this study utilizes correct statistical analyses techniques on a relatively large database, such as using a train/test split on the data set to avoid overfitting of the developed models. Furthermore, the validity and consistency of the prediction results are ensured with the correct use of statistical measures like -value and cross-validation which were missing in previous studies. To compare the performances of the models developed in this study with the prior ones existing in literature, all models were applied on the test data set and their performances are evaluated in terms of the resulting root mean squared error ( ) values and coefficient of determination ( ). Accordingly, the models developed in this study demonstrate superior prediction capabilities compared to all of the prior studies. Moreover, to facilitate the use of machine learning algorithms for prediction purposes, entire source code prepared for this study and the collected data set are provided as supplements of this study.

关键词： undrained shear strength linear regression random forest gradient boosting machine learning standard penetration test

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Integrated uncertain models for runoff forecasting and crop planting structure optimization of the Shiyang

Fan ZHANG, Mo LI, Shanshan GUO, Chenglong ZHANG, Ping GUO

《农业科学与工程前沿（英文）》 2018年第5卷第2期页码 177-187 doi: 10.15302/J-FASE-2017177

摘要： To improve the accuracy of runoff forecasting, an uncertain multiple linear regression (UMLR) model is presented in this study. The proposed model avoids the transfer of random error generated in the independent variable to the dependent variable, as this affects prediction accuracy. On this basis, an inexact two-stage stochastic programming (ITSP) model is used for crop planting structure optimization (CPSO) with the inputs that are interval flow values under different probabilities obtained from the UMLR model. The developed system, in which the UMLR model for runoff forecasting and the ITSP model for crop planting structure optimization are integrated, is applied to a real case study. The aim of the developed system is to optimize crops planting area with limited available water resources base on the downstream runoff forecasting in order to obtain the maximum system benefit in the future. The solution obtained can demonstrate the feasibility and suitability of the developed system, and help decision makers to identify reasonable crop planting structure under multiple uncertainties.

关键词： crop planting structure optimization inexact two-stage stochastic programming runoff forecasting Shiyang River Basin uncertain multiple linear regression

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Option-like properties in the distribution of hedge fund returns

Katharina DENK, Ben DJERROUD, Luis SECO, Mohammad SHAKOURIFAR, Rudi ZAGST

《工程管理前沿（英文）》 2020年第7卷第2期页码 275-286 doi: 10.1007/s42524-020-0095-3

摘要： Hedge funds have recently become popular because of their low correlation with traditional investments and their ability to generate positive returns with a relatively low volatility. However, a close look at those high-performing hedge funds raises the questions on whether their performance is truly superior and whether the high management fees are justified. Incurring no alpha costs, passive hedge fund replication strategies raise the question on whether they can similarly perform by improving efficiency at reduced costs. Therefore, this study investigates two different model approaches for the equity long/short strategy, where weighted segmented linear regression models are employed and combined with two-state Markov switching models. The main finding proves a short put option structure, i.e., short equity market volatility, with the put structure present in all market states. We obtain an evidence that the hedge fund managers decrease their short-volatility profile during turbulent markets.

关键词： hedge funds hedge fund index segmented linear regression models regime-switching models mimicking portfolios single factor-based hedge fund replication equity long–short strategy

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Factor analysis for the statistical modeling of earthquake-induced landslides

Jeng-Wen LIN, Meng-Hsun HSIEH, Yu-Jen LI

《结构与土木工程前沿（英文）》 2020年第14卷第1期页码 123-126 doi: 10.1007/s11709-019-0582-y

摘要： Earthquake-induced landslides are difficult to assess and predict owing to the inherent unpredictability of earthquakes. In most existing studies, the landslide potential is statistically assessed by collecting and analyzing the data of historical landslide events and earthquake observation records. Unlike rainfall-induced landslides, earthquake-induced landslides cannot be predicted in advance using real-time monitoring systems, and the development of the models for these landslides should instead depend on early earthquake warnings and estimations. Hence, in this study, factor analysis was performed and the frequency distribution method was employed to investigate the potential risk of the landslides caused by earthquakes. Factors such as the slope gradient, lithology (geology), aspect, and elevation were selected and classified as influential factors to facilitate the construction of a landslide database for the area of study.

关键词： earthquake factor analysis slope landslides statistical modeling

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Design and analysis of linear oscillating motor for linear pump application-magnetic field, dynamics

Zongxia JIAO,Tianyi WANG,Liang YAN

《机械工程前沿（英文）》 2016年第11卷第4期页码 351-362 doi: 10.1007/s11465-016-0407-9

摘要：

A linear oscillating motor is an electromagnetic actuator that can achieve short-stroke reciprocating movement directly without auxiliary transmission mechanisms. It has been widely used in linear pump applications as the source of power and motion. However, because of the demand of high power density in a linear actuation system, the performance of linear oscillating motors has been the focus of studies and deserves further research for high power density. In this paper, a general framework of linear oscillating motor design and optimization is addressed in detail, including the electromagnetic, dynamics, and thermal aspects. First, the electromagnetic and dynamics characteristics are modeled to reveal the principle for optimization. Then, optimization and analysis on magnetic structure, resonant system, and thermal features are conducted, which provide the foundation for prototype development. Finally, experimental results are provided for validation. As a whole, this process offers complete guidance for high power density linear oscillating motors in linear pump applications.

关键词： linear oscillating motor linear pump magnetic field motor optimization

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Analysis on the distinguishing features of traditional Chinese therapeutics and related statistical issues

Jingqing Hu, Jie Qiao, Deying Kang, Baoyan Liu

《医学前沿（英文）》 2011年第5卷第2期页码 203-207 doi: 10.1007/s11684-011-0138-6

摘要： Traditional Chinese medicine (TCM) is one of the rarely existing ancient traditional medicines that hold systematic theories as well as preventative and therapeutic methods for diseases in practice. From the 1950s, such research methods as mathematics, statistics, and data mining (DM) have been gradually introduced to TCM studies, making it more scientific. Meanwhile, the distinct features of TCM theories and diagnostic-model have constantly challenged the methodology of statistics. This paper introduces the following scientific features of traditional Chinese therapeutics: 1) its goal is to balance the functions and conditions of human body; 2) it emphasizes on holism and individualization; 3) it stresses the longitudinal regulation and evaluation mode, which is a circle of syndrome diagnosis, treatment and evaluation; 4) the interventions of TCM are abundant, compound and natural; and 5) humanistic thought is everywhere. Some statistical problems are raised based on these features. First, complex statistical methods that can analyze subjective indexes and latent variables, multidimensional and multistage data, non-equilibrium designed studies, and longitudinal data are required. Second, comprehensive evaluation on multiple-target mechanism has been brought in by combination treatment. Third, there is a need to analyze how humanity and related cultural factors may influence the effect of interventions. Thus, promoting implemented studies of statistics as well as carrying out the TCM scientific propositions have become the common expectations of both TCM and modern medicine.

关键词： traditional Chinese therapeutics feature statistic

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Recent development on statistical methods for personalized medicine discovery

null

《医学前沿（英文）》 2013年第7卷第1期页码 102-110 doi: 10.1007/s11684-013-0245-7

摘要：

It is well documented that patients can show significant heterogeneous responses to treatments so the best treatment strategies may require adaptation over individuals and time. Recently, a number of new statistical methods have been developed to tackle the important problem of estimating personalized treatment rules using single-stage or multiple-stage clinical data. In this paper, we provide an overview of these methods and list a number of challenges.

关键词： dynamic treatment regimes personalized medicine reinforcement learning Q-learning

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Statistical considerations for genomic selection

Huimin KANG, Lei ZHOU, Jianfeng LIU

《农业科学与工程前沿（英文）》 2017年第4卷第3期页码 268-278 doi: 10.15302/J-FASE-2017164

摘要： Genomic selection is becoming increasingly important in animal and plant breeding, and is attracting greater attention for human disease risk prediction. This review covers the most commonly used statistical methods and some extensions of them, i.e., ridge regression and genomic best linear unbiased prediction, Bayesian alphabet, and least absolute shrinkage and selection operator. Then it discusses the measurement of the performance of genomic selection and factors affecting the prediction of performance. Among the measurements of prediction performance, the most important and commonly used measurement is prediction accuracy. In simulation studies where true breeding values are available, accuracy of genomic estimated breeding value can be calculated directly. In real or industrial data studies, either training-testing approach or -fold cross-validation is commonly employed to validate methods. Factors influencing the accuracy of genomic selection include linkage disequilibrium between markers and quantitative trait loci, genetic architecture of the trait, and size and composition of the training population. Genomic selection has been implemented in the breeding programs of dairy cattle, beef cattle, pigs and poultry. Genomic selection in other species has also been intensively researched, and is likely to be implemented in the near future.

关键词： genomic estimated breeding value genomic selection linkage disequilibrium statistical methods

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使用数据驱动模型优化抗体纯化策略 Article

刘松崧, Lazaros G. Papageorgiou

《工程（英文）》 2019年第5卷第6期页码 1077-1092 doi: 10.1016/j.eng.2019.10.011

摘要：

本工作致力于抗体片段纯化过程的多尺度优化。优化了生产过程中的色谱决策，包括色谱柱的数量及其大小，每批的循环数以及操作流速。使用基于微型实验数据的制造规模模拟数据集，建立了以负载质量、流速和柱床高度为输入的色谱通量数据驱动模型。与其他方法相比，分段线性回归建模方法具有简单、预测精度高的优点。提出了两种混合整数非线性规划（MINLP）模型，结合数据驱动模型，以最小化每克抗体纯化过程的总成本。然后，使用线性化技术和多参数分解将这些MINLP模型重新构造为混合整数线性规划（MILP）模型。研究了两个具有不同色谱柱尺寸替代品的工业相关案例，以证明所提出模型的适用性。

关键词：抗体纯化多尺度优化抗原结合片段混合整数规划数据驱动模型分段线性回归

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Statistical process control with intelligence using fuzzy ART neural networks

Min WANG, Tao ZAN, Renyuan FEI,

《机械工程前沿（英文）》 2010年第5卷第2期页码 149-156 doi: 10.1007/s11465-010-0008-y

摘要： With the automation development of manufacturing processes, artificial intelligence technology has been gradually employed to increase the automation and intelligence degree in quality control using statistical process control (SPC) method. In this paper, an SPC method based on a fuzzy adaptive resonance theory (ART) neural network is presented. The fuzzy ART neural network is applied to recognize the special disturbance of the manufacturing processes based on the classification on the histograms, which shows that the fuzzy ART neural network can adaptively learn the features of the histograms of the quality parameters in manufacturing processes. As a result, the special disturbance can be automatically detected when a feature of the special disturbance starts to appear in the histograms. At the same time, combined with spectrum analysis of the autoregressive model of quality parameters, the fuzzy ART neural network can also be utilized to adaptively detect the abnormal patterns in the control chart.

关键词： statistical process control (SPC) fuzzy adaptive resonance theory (ART) histogram control chart time series analysis

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Assessment of temporal and spatial variations in water quality using multivariate statistical methods

Xue LI,Pengjing LI,Dong WANG,Yuqiu WANG

《环境科学与工程前沿（英文）》 2014年第8卷第6期页码 895-904 doi: 10.1007/s11783-014-0736-z

摘要： This study evaluated the temporal and spatial variations of water quality data sets for the Xin'anjiang River through the use of multivariate statistical techniques, including cluster analysis (CA), discriminant analysis (DA), correlation analysis, and principal component analysis (PCA). The water samples, measured by ten parameters, were collected every month for three years (2008–2010) from eight sampling stations located along the river. The hierarchical CA classified the 12 months into three periods (First, Second and Third Period) and the eight sampling sites into three groups (Groups 1, 2 and 3) based on seasonal differences and various pollution levels caused by physicochemical properties and anthropogenic activities. DA identified three significant parameters (temperature, pH and ) to distinguish temporal groups with close to 76% correct assignment. The DA also discovered five parameters (temperature, electricity conductivity, total nitrogen, chemical oxygen demand and total phosphorus) for spatial variation analysis, with 80.56% correct assignment. The non–parametric correlation coefficient (Spearman R) explained the relationship between the water quality parameters and the basin characteristics, and the GIS made the results visual and direct. The PCA identified four PCs for Groups 1 and 2, and three PCs for Group 3. These PCs captured 68.94%, 67.48% and 70.35% of the total variance of Groups 1, 2 and 3, respectively. Although natural pollution affects the Xin'anjiang River, the main sources of pollution included agricultural activities, industrial waste, and domestic wastewater.

关键词： Xin'anjiang River multivariable statistical analysis temporal variation spatial variation water quality

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A review on different theoretical models of electrocaloric effect for refrigeration

《能源前沿（英文）》 2023年第17卷第4期页码 478-503 doi: 10.1007/s11708-023-0884-6

摘要： The performance parameters for characterizing the electrocaloric effect are isothermal entropy change and the adiabatic temperature change, respectively. This paper reviews the electrocaloric effect of ferroelectric materials based on different theoretical models. First, it provides four different calculation scales (the first-principle-based effective Hamiltonian, the Landau-Devonshire thermodynamic theory, phase-field simulation, and finite element analysis) to explain the basic theory of calculating the electrocaloric effect. Then, it comprehensively reviews the recent progress of these methods in regulating the electrocaloric effect and the generation mechanism of the electrocaloric effect. Finally, it summarizes and anticipates the exploration of more novel electrocaloric materials based on the framework constructed by the different computational methods.

关键词： electrocaloric effect effective Hamiltonian phase-field modeling different theoretical models

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标题作者时间类型操作

Estimating the effects of meteorology on PM

Liu YANG, Ye WU, Jerry M. DAVIS, Jiming HAO

期刊论文

Comparison between linear and non-linear forms of pseudo-first-order and pseudo-second-order adsorptionkinetic models for the removal of methylene blue by activated carbon

Junxiong LIN , Lan WANG ,

期刊论文

Enhanced empirical models for predicting the drift capacity of less ductile RC columns with flexural,

Mohammad Reza AZADI KAKAVAND, Reza ALLAHVIRDIZADEH

期刊论文

SPT based determination of undrained shear strength: Regression models and machine learning

Walid Khalid MBARAK, Esma Nur CINICIOGLU, Ozer CINICIOGLU

期刊论文

Integrated uncertain models for runoff forecasting and crop planting structure optimization of the Shiyang

Fan ZHANG, Mo LI, Shanshan GUO, Chenglong ZHANG, Ping GUO

期刊论文

Option-like properties in the distribution of hedge fund returns