地质雷达快速检测运营公路隧道隐患

为了评价运行多年隧道的衬砌质量，查找隐患，开展了拱顶的地质雷达无损快速检测。在雷达波形剖面图上分析空洞、不密实异常，推断出大量隐患。物探成果经过开凿验证，证明其隐患定位成功率在85％以上。地质雷达快速检测方法为隧道病害治理提供了可靠的基础资料。     

茯苓种植用材代用品

比较攀西地区茯苓种植用材代用1品(简称新配方2)生长的茯苓的菌核、菌丝与商品茯苓的显微特征及多糖成分的含量差异。分别提取茯苓水溶性及碱溶性多糖,酚-硫酸法测定茯苓多糖的含量。结果表明:新配方2 生长的茯苓的菌核、菌丝与商品茯苓的显微特征相似,多糖含量相近,但提取率有较大差异。与商品茯苓比较,新配方2生长的茯苓菌核多糖提取率基本一致,菌丝的多糖提取率约低1/4。由于新配方2生长的茯苓菌丝培育成本远低于商品茯苓,提高了栽培茯苓的经济效益。可以考虑将茯苓菌丝作为茯苓替代品入药。     

基于DOM的宁波市城市用地调查

介绍以DOM，进行城市用地调查方式，极大的提高了调查的效率。     

沙坡头没蒿和柠条叶片δ13C 和 N 含量的季节变化特征

通过对腾格里沙漠东南缘沙坡头人工固沙区油蒿和柠条叶片稳定碳同位素分辨率（△）和N 含量的测定，研究了不同种植方式下油蒿和柠条叶片△ 和 N 含量的季节变化及其关系。结果表明：在不同种植方式下，两种植物的△ 与 N 含量在不同月份之间均存在显著差异。油蒿叶片 △ 极显著高于柠条的：在不同种植方式下，油蒿单种下 △ 显著高于混种，柠条则反之。 油蒿叶片 N 含量极显著低于柠条的：不同种植方式下柠条叶片 N 含量无显著差异，而单种油蒿叶片 N 含量则显著低于混种。在 △ 与 N 含量的关系中，柠条叶片 △ 与 N 在两种生境、单种和混种下均极显著正相关，雨明柠条叶片 N 含量可以作为其 △ 与 N 含量的季节变化及其关系在不同微生境有所差异。     

Assessing storm surge impacts on coastal inundation due to climate change: case studies of Baltimore and Dorchester County in Maryland

Natural Hazards - Hurricane Isabel (2003) generated record flooding around Chesapeake Bay and caused extensive damage in rural Eastern Shore of Maryland and metropolitan cities like Baltimore....     

Generating GPS satellite fractional cycle bias for ambiguity-fixed precise point positioning

With the development of precise point positioning (PPP), the School of Geodesy and Geomatics (SGG) at Wuhan University is now routinely producing GPS satellite fractional cycle bias (FCB) products with open access for worldwide PPP users to conduct ambiguity-fixed PPP solution. We provide a brief theoretical background of PPP and present the strategies and models to compute the FCB products. The practical realization of the two-step (wide-lane and narrow-lane) FCB estimation scheme is described in detail. With GPS measurements taken in various situations, i.e., static, dynamic, and on low earth orbit (LEO) satellites, the quality of FCB estimation and the effectiveness of PPP ambiguity resolution (AR) are evaluated. The comparison with CNES FCBs indicated that our FCBs had a good consistency with the CNES ones. For wide-lane FCB, almost all the differences of the two products were within ±0.05 cycles. For narrow-lane FCB, 87.8 % of the differences were located between ±0.05 cycles, and 97.4 % of them were located between ±0.075 cycles. The experimental results showed that, compared with conventional ambiguity-float PPP, the averaged position RMS of static PPP can be improved from (3.6, 1.4, 3.6) to (2.0, 1.0, 2.7) centimeters for ambiguity-fixed PPP. The average accuracy improvement in the east, north, and up components reached 44.4, 28.6, and 25.0 %, respectively. A kinematic, ambiguity-fixed PPP test with observation of 80 min achieved a position accuracy of better than 5 cm at the one-sigma level in all three coordinate components. Compared with the results of ambiguity-float, kinematic PPP, the positioning biases of ambiguity-fixed PPP were improved by about 78.2, 20.8, and 65.1 % in east, north, and up. The RMS of LEO PPP test was improved by about 23.0, 37.0, and 43.0 % for GRACE-A and GRACE-B in radial, tangential, and normal directions when AR was applied to the same data set. These results demonstrated that the SGG FCB products can be produced with high quality for users anywhere around the world to carry out ambiguity-fixed PPP solutions.     

Classifying Human Activity Patterns from Smartphone Collected GPS data: A Fuzzy Classification and Aggregation Approach

Smartphones have emerged as a promising type of equipment for monitoring human activities in environmental health studies. However, degraded location accuracy and inconsistency of smartphone‐measured GPS data have limited its effectiveness for classifying human activity patterns. This study proposes a fuzzy classification scheme for differentiating human activity patterns from smartphone‐collected GPS data. Specifically, a fuzzy logic reasoning was adopted to overcome the influence of location uncertainty by estimating the probability of different activity types for single GPS points. Based on that approach, a segment aggregation method was developed to infer activity patterns, while adjusting for uncertainties of point attributes. Validations of the proposed methods were carried out based on a convenient sample of three subjects with different types of smartphones. The results indicate desirable accuracy (e.g. up to 96% in activity identification) using of this method. Two examples are provided in the Appendix to illustrate how the proposed methods could be applied in environmental health studies. Researchers could tailor this scheme to fit a variety of research topics.