广州城市流浪乞讨者的空间管治与日常生活实践

以广州流浪乞讨者为研究对象,通过深度访谈等质性研究方法,解读针对流浪乞讨者的空间管治政策与手段,分析流浪乞讨者的日常生活实践与生境协商。研究发现：在地方城市政策对流动性与公共秩序限制的双重压力下,在流浪乞讨者的日常生活实践中,不仅有针对管治行动者而刻意为之的特殊营建,亦有基于程式化生活流程对自我身份认同的重塑与真情实感的展演。地方政府基于空间目标的救助与真实存在的日常生活实践存在着一种张力。研究关注了城市边缘群体日常生活对地理意义的主动塑造过程,对全面理解并规范管理中国城市流浪乞讨现象具有借鉴意义。     

Bibliometric Analysis of Global Research Progress on Coastal Flooding 1995–2016

Global research progress on coastal flooding was studied using a bibliometric evaluation of publications listed in the Web of Science extended scientific citation index. There was substantial growth in coastal flooding research output, with increasing publications, a higher collaboration index, and more references during the 1995–2016 period. The USA has taken a dominant position in coastal flooding research, with the US Geological Survey leading the publications ranking. Research collaborations at institutional scales have become more important than those at global scales. International collaborative publications consistently drew more citations than those from a single country. Furthermore, coastal flooding research included combinations of multi-disciplinary categories, including ‘Geology' and ‘Environmental Sciences Ecology'. The most important coastal flooding research sites were wetlands and estuaries. While numerical modeling and 3 S(Remote sensing, RS; Geography information systems, GIS; Global positioning systems, GPS) technology were the most commonly used methods for studying coastal flooding, Lidar gained in popularity. The vulnerability and adaptation of coastal environments, their resilience after flooding, and ecosystem services function showed increases in interest.     

风和径流量对长江口缺氧影响的数值模拟

受自然和人类活动的影响，海洋缺氧现象日益严重，威胁着海洋生态环境，海洋缺氧问题已经引起了人们的广泛关注。本文应用区域海洋模式并耦合生态模式，对东海的生态系统进行了数值模拟和分析研究。与观测数据比较显示，该模型能较好地模拟长江口外生态变量的分布趋势。另外本文通过设置不同敏感性实验，探讨风和径流量对长江口底层缺氧现象的影响，结果分析表明，风和径流量对长江口外缺氧区的形成有显著的影响。径流量变化虽然对长江口外缺氧区的季节变化影响并不显著，但是对缺氧区域面积却存在显著的影响。径流量增加，水体层化增强，表层叶绿素浓度增加，最终导致缺氧区域范围扩展；径流量减小，水体层化减弱，表层叶绿素浓度减小，缺氧区域范围缩小。风向和风速的改变不仅影响长江口外缺氧区的季节变化，还影响缺氧区域面积。     

Implementation of a coupled plastic damage distinct lattice spring model for dynamic crack propagation in geomaterials

This paper studies dynamic crack propagation by employing the distinct lattice spring model (DLSM) and 3‐dimensional (3D) printing technique. A damage‐plasticity model was developed and implemented in a 2D DLSM. Applicability of the damage‐plasticity DLSM was verified against analytical elastic solutions and experimental results for crack propagation. As a physical analogy, dynamic fracturing tests were conducted on 3D printed specimens using the split Hopkinson pressure bar. The dynamic stress intensity factors were recorded, and crack paths were captured by a high‐speed camera. A parametric study was conducted to find the influences of the parameters on cracking behaviors, including initial and peak fracture toughness, crack speed, and crack patterns. Finally, selection of parameters for the damage‐plasticity model was determined through the comparison of numerical predictions and the experimentally observed cracking features.     

基于纹理和空间特征的中分辨率影像滨海水产养殖用地提取研究

近年来,水产养殖用地分布广泛,但由于其在影像上所表现的复杂性和不均匀性,造成该用地类型提取中的困难,尤其针对中分辨率遥感影像。对此,本文提出了一种基于纹理和空间特征的养殖用地提取方法,该方法主要包括3个步骤：首先,利用纹理熵和归一化差异水体指数NDWI实现水产养殖用地的粗提取;然后,依据相邻地物间的关系实现同类型地物合并;最后,本文构建一种相对宽度作为地物的近似宽度,再次利用NDWI实现水产养殖用地的准确识别。本文以越南万丰湾为研究区域,以Landat-8融合影像（融合后的像元大小为15 m）的目视解译结果为标准,对本文方法与最小距离法分类结果进行比较。实验结果表明,该方法的精度可达91.13%,远高于传统的面向对象方法,并且所提方法的错误率和虚假率分别为0.09%和8.87％,表明了该方法可靠性,因而该方法可为基于中分辨率影像的地物类型提取提供一种有效手段。     

A machine learning approach for predicting computational intensity and domain decomposition in parallel geoprocessing

ABSTRACT

High performance computing is required for fast geoprocessing of geospatial big data. Using spatial domains to represent computational intensity (CIT) and domain decomposition for parallelism are prominent strategies when designing parallel geoprocessing applications. Traditional domain decomposition is limited in evaluating the computational intensity, which often results in load imbalance and poor parallel performance. From the data science perspective, machine learning from Artificial Intelligence (AI) shows promise for better CIT evaluation. This paper proposes a machine learning approach for predicting computational intensity, followed by an optimized domain decomposition, which divides the spatial domain into balanced subdivisions based on the predicted CIT to achieve better parallel performance. The approach provides a reference framework on how various machine learning methods including feature selection and model training can be used in predicting computational intensity and optimizing parallel geoprocessing against different cases. Some comparative experiments between the approach and traditional methods were performed using the two cases, DEM generation from point clouds and spatial intersection on vector data. The results not only demonstrate the advantage of the approach, but also provide hints on how traditional GIS computation can be improved by the AI machine learning.     

利用自回归模型检测地球极移重力效应

同时采用4个台站的国际超导重力仪长期连续重力观测资料和国际地球自转服务中心提供的同步地球自转参数，研究了极移引起的地球重力场变化特征。利用自回归模型估计了各序列的功率谱密度和积谱密度，结果表明极移导致的重力效应的主要能量集中在Chandler摆动和周年项附近，叠积后实际重力观测与极移重力信号理论值之间的差异分别为0．4％和3．9％，说明超导重力仪可有效监测极移导致的重力变化。     

江汉盆地钻孔测井资料确定地应力最大水平主压应力方向

利用钻孔测井资料并运用地层倾角测量信息分析法，给出了江汉盆地地应力最大水平主压应力方向为ＮＥ６０～６５°     

DEVELOPMENT STRATEGIES OF WATER AND LAND RESOURCES IN THE HEXI REGION,CHINA

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法库地区大型韧性剪切带特征及成因机制

通过对辽北法库地区构造岩的观察和研究，确定了本区存在一大型推覆韧性剪切带。根据同构造侵入岩的同位素年龄，确定该韧性剪切带形成时期为海西－印支期，这与天山－兴安褶皱系最终形成时间相一致