Effect of glycerol on the separation of nucleosomes and bent DNA in low ionic strength polyacrylamide gel electrophoresis

This paper seeks to understand extreme public transit riders in Beijing using both traditional household surveys and emerging new data sources such as Smart Card Data (SCD). We focus on four types of extreme transit behaviors: public transit riders who (1) travel significantly earlier than average riders (‘early birds’); (2) ride in unusual late hours (‘night owls’); (3) commute in excessively long distance (‘tireless itinerants’); and (4) make significantly more trips per day (‘recurring itinerants’). SCD are used to identify the spatiotemporal patterns of these four extreme transit behaviors. In addition, household surveys are employed to supplement the socioeconomic background and tentatively profile extreme travelers. While the research findings are useful to guide urban governance and planning in Beijing, our methodology and procedures can be extended to understand travel patterns elsewhere

Big data for intrametropolitan human movement studies : A case study of bus commuters based on smart card data

Unlike the data from traditional sources, there have not been standard ways to validate the quality and reliability of information derived from big data. This article argues that the theory of urban formation can be used to do the validation. In addition, the information derived from big data can be used to verify and even extend existing theories or hypotheses of urban formation. It proposes a general framework regarding how the theory of urban formation can be employed to validate information derived from smart card data and how the validated information can supplement other data to reveal spatial patterns of economic agglomeration or human settlements. Through a case study of Beijing, it demonstrates the usefulness of the framework. Additionally, it utilizes smart card data to delineate characteristics of subcenters defined by bus commuters of Beijing

Featured graphic. visualizing the minimum solution of the transportation problem of linear programming (TPLP) for beijing’s bus commuters

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Mapping Growing Stem Volume of Chinese Fir Plantation Using a Saturation-based Multivariate Method and Quad-polarimetric SAR Images

For the planning and sustainable management of forest resources, well-managed plantations are of great significance to mitigate the decrease of forested areas. Monitoring these planted forests is essential for forest resource inventories. In this study, two ALOS PALSAR-2 quad-polarimetric synthetic aperture radar (SAR) images and ground measurements were employed to estimate growing stem volume (GSV) of Chinese fir plantations located in a hilly area of southern China. To investigate the relationships between forest GSV and polarization characteristics, single and fused variables were derived by the Yamaguchi decomposition and the saturation value of GSV was estimated using a semi-exponential empirical model as a base model. Based on the estimated saturation values and relative root mean square error (RRMSE), the single and fused characteristics and corresponding models were selected and integrated, which led to a novel saturation-based multivariate method used to improve the GSV estimation and mapping of Chinese fir plantations. The new findings included: (1) All the original polarimetric characteristics, statistically, were not significantly correlated with the forest GSV, and their logarithm and ratio transformation fused variables greatly improved the correlations, thus the estimation accuracy of the forest GSV. (2) The logarithm transformation of surface scattering resulted in the greatest saturation, value but the logarithm transformation of double-bounce scattering resulted in the smallest RRMSE of the GSV estimates. (3) Compared with the single transformations, the fused variables led to more reasonable saturation values and obviously reduced the values of RRMSE. (4) The saturation-based multivariate method led to more accurate estimates of the forest GSV than the univariate method, with the smallest value (29.64%) of RRMSE achieved using the set of six variables. This implied that the novel saturation-based multivariate method provided greater potential to improve the estimation and mapping of the forest GSV

Estimating the Growing Stem Volume of the Planted Forest Using the General Linear Model and Time Series Quad-Polarimetric SAR Images

Increasing the area of planted forests is rather important for compensation the loss of natural forests and slowing down the global warming. Forest growing stem volume (GSV) is a key indicator for monitoring and evaluating the quality of planted forest. To improve the accuracy of planted forest GSV located in south China, four L-band ALOS PALSAR-2 quad-polarimetric synthetic aperture radar (SAR) images were acquired from June to September with short intervals. Polarimetric characteristics (un-fused and fused) derived by the Yamaguchi decomposition from time series SAR images with different intervals were considered as independent variables for the GSV estimation. Then, the general linear model (GLM) obeyed the exponential distribution were proposed to retrieve the stand-level GSV in plantation. The results show that the un-fused power of double bounce scatters and four fused variables derived from single SAR image is highly sensitive to the GSV, and these polarimeric characteristics derived from the time series images more significantly contribute to improved estimation of GSV. Moreover, compared with the estimated GSV using the semi-exponential model, the employed GLM model with less limitations and simple algorithm has a higher saturation level (nearly to 300 m3/ha) and higher sensitivity to high forest GSV values than the semi-exponential model. Furthermore, by reducing the external disturbance with the help of time average, the accuracy of estimated GSV is improved using fused polarimeric characteristics, and the estimation accuracy of forest GSV was improved as the images increase. Using the fused polarimetric characteristics (Dbl×Vol/Odd) and the GLM, the minimum RRMSE was reduced from 33.87% from single SAR image to 24.42% from the time series SAR images. It is implied that the GLM is more suitable for polarimetric characteristics derived from the time series SAR images and has more potential to improve the planted forest GSV

Reliability Prediction of Tunnel Roof with a Nonlinear Failure Criterion

Based on the kinematics-based upper bound theorem and reliability theory, the stability of deep tunnel roofs in nonlinear Hoek-Brown media is investigated. The performance functions of rectangular and circular tunnels are proposed according to the roof collapse mode, respectively, with support pressure and pore water pressure being considered. With the proposed performance function of the rectangular tunnels, the first-order reliability method is utilized to perform reliability analysis. The rock strength parameters are regarded as random variables following the normal or lognormal distribution. To assess the validity of the obtained results, reliability indexes for different support pressure values are calculated and compared with solutions using the response surface method and Monte-Carlo simulation. The agreement shows that the first-order reliability method effectively evaluates the reliability index with the proposed performance function. Sensitivity analysis is performed to throw light on the significance of different random variables, and the impact of the variation coefficient on reliability indexes is discussed. For circular tunnels, MCS is utilized to evaluate the roof stability with the proposed performance function. The influences of the support pressure on the reliability index and the corresponding design points are investigated. The parametric study shows that the normal distribution of random variables has more influence on the failure probability than that of the lognormal distribution. However, the difference between the two distributions is small. σt is the major factor that influences the reliability index compared to the B and ru. The supporting pressure for circular tunnels is smaller than that of rectangular tunnels when a target reliability index of 2.5 (failure probability equals 0.62%) is given