Generating urban road intersection models from low-frequency GPS trajectory data

Detailed real-time road data are an important prerequisite for navigation and intelligent transportation systems. As accident-prone areas, road intersections play a critical role in route guidance and traffic management. Ubiquitous trajectory data have led to a recent surge in road map reconstruction. However, it is still challenging to automatically generate detailed structural models for road intersections, especially from low-frequency trajectory data. We propose a novel three-step approach to extract the structural and semantic information of road intersections from low-frequency trajectories. The spatial coverage of road intersections is first detected based on hotspot analysis and triangulation-based point clustering. Next, an improved hierarchical trajectory clustering algorithm is designed to adaptively extract the turning modes and traffic rules of road intersections. Finally, structural models are generated via K-segment fitting and common subsequence merging. Experimental results demonstrate that the proposed method can efficiently handle low-frequency, unstable trajectory data and accurately extract the structural and semantic features of road intersections. Therefore, the proposed method provides a promising solution for enriching and updating routable road data.     

链霉菌FR—008中线性DNA复制子的检测

采用菌丝 原位包埋脉冲电泳的方法从链霉菌ＦＲ－００８中分离出三条ＤＮＡ带,双向电泳及脉冲条件变换实验证明,这些ＤＮＡ带都是一分子外切核酶酶酶解发现,它们的５‘末端封闭。这三条ＤＮＡ带分别代表了链霉菌ＦＲ－００８中线性染色体和两条线性质粒。     

江西省浅水湖泊大型底栖无脊椎动物多样性特征和影响因素

2019年春、秋两季,对江西省76个湖泊的底栖动物进行了调查,在分析其群落结构和多样性的基础上,探讨了多样性与环境因子间的关系,旨在确定影响大型底栖无脊椎动物多样性的重要环境因子,以期为江西省湖泊的精准化管理提供科学依据和技术支持。两次调查共检出140个分类单元,以水生昆虫和软体动物为主,摇蚊类物种数占水生昆虫物种数的53.62%。富营养指示种(长足摇蚊属一种和摇蚊属一种)为春、秋两季的优势分类单元,湖沼典型种(长足摇蚊属一种、摇蚊属一种、石田螺属一种、苏氏尾鳃蚓和霍甫水丝蚓)的丰度变化导致了群落结构的季节变化。大型底栖无脊椎动物α多样性水平较低,基于PLSR和PLS-SEM的分析结果表明,影响大型底栖无脊椎动物多样性指标的环境因子既与藻类种群动态密切相关,也与水质状态有关。大型底栖无脊椎动物多样性与藻类种群状态存在稳定的联系,不受季节因素的影响,过高的藻类丰度不利于大型底栖无脊椎动物多样性的提高,而较好的水质状况有利于大型底栖无脊椎动物多样性的提高。为保护江西省湖泊大型底栖无脊椎动物资源,应在具有渔业养殖功能的湖泊和城镇湖泊中进行水生植物的修复工作,使藻型湖泊向草型湖泊方向演化;对一...     

乌江磷浓度与通量变化及来源解析

磷(P)是长江流域备受关注的污染物。乌江是长江八大支流之一,位于三峡水库近库尾江段。武隆断面是乌江入长江控制断面。对1998—2019时期武隆断面径流量、悬浮泥沙浓度(SS)与输沙量、磷浓度与通量(包括总磷(TP)、溶解态磷(DP)和颗粒态磷(PP))年际变化及季节特征进行研究,并基于河流基流分割原理对磷的来源进行了解析。结果表明,(1)1998—2019年,乌江武隆断面径流量在一定幅度内上下波动,而悬浮泥沙浓度和输沙量下降剧烈。(2)22年来,乌江TP和DP浓度与通量总体上呈先升高后下降的趋势,2009—2013年为磷污染峰值期,TP和DP浓度与通量远高于其它时期。(3)2007年是一个重要的时间节点,该节点前,TP的赋存形态以颗粒态为主,颗粒态磷在总磷中的占比均值为65%;该节点后,TP的赋存形态转变为以溶解态为主,颗粒态磷占比均值下降为16%,相应地,溶解态磷占比由35%上升为84%。水沙条件改变是磷形态发生显著变化的主要驱动力,磷污染程度亦是磷形态变化的重要影响因素。(4)磷通量在年内的季节分布发生了显著变化,丰水期磷通量减少,枯水期磷通量增加。(5)1998—2012、2009—2013和2014—2019年3个时期点源负荷占比分别为23.5%、36.8%和62.1%,呈增加趋势。(6)建议制定适宜的总磷控制目标,结合目前所存在的磷污染风险点,进一步强化监管,侧重源头治理。     

富硫型水库沉积物AVS和SEM空间分布特征及重金属风险评价

随着湖库外源性污染的有效控制,沉积物污染特征成为影响湖库水环境质量的关键因素。本文以富硫型水库——汤河水库为研究对象,分析探讨水库沉积物的理化性质、重金属总量、酸可挥发性硫化物(acid volatile sulfide,AVS)和同步提取重金属(simultaneously extracted metals,SEM)的空间分布特征,采用沉积物基准法(sediment quality guidelines, SQGs)和AVS与SEM关系法对重金属可能诱发的生态风险和毒性效应进行评估。结果表明:汤河水库沉积物中AVS含量在0.03~51.75 μmol/g之间,并呈现坝前深水区>东支流库区>西支流库区的空间分布特征。根据Pearson相关性分析可知,汤河水库沉积物AVS含量与间隙水中SO₄^2-浓度、沉积物烧失量(LOI)含量呈显著正相关,与沉积物间隙水中NO₃^-浓度和沉积物pH呈现显著负相关。间隙水中SO₄^2-浓度和沉积物LOI含量是控制AVS产量的重要因素。水库沉积物中ΣSEM变化范围为0.52~2.75 μmol/g,呈现出东支流库区>坝前深水区>西支流库区的分布规律。水库沉积物中ΣSEM/AVS和ΣSEM-AVS的变化范围分别为0.06~22.73和-49.18~2.44 μmol/g,显示出水库坝前深水区表层0~10 cm沉积物不具有生态风险,而东、西支流库区沉积物中的SEM因为不能完全被AVS所固定而存在潜在生态风险。就单一重金属而言,汤河水库坝前深水区和东西支流中部区域沉积物Ni、Cu、Pb和Zn含量均介于临界效应含量(threshold effects level, TEL)值和可能效应含量(probable effect level, PEL)值之间,可产生低级风险毒性效应,但Ni的毒性效应风险接近中级,今后应予以重点关注。富硫型水库沉积物中容易出现高含量AVS,沉积物间隙水中SO₄^2-和NO₃^-的浓度是决定沉积物AVS净产量的重要因素。流域内重金属土壤背景值以及工业、采矿产业排放的废水类型直接影响着诱发生态风险的重金属种类。     

福建海坛岛青峰老红砂旋回沉积的粒度特征及其古环境意义

福建海坛岛青峰老红砂剖面以棕红色砂层和黄橙色砂层的交替旋回变化为显著特征。通过野外考察、采样和测定分析,对青峰老红砂剖面的粒度特征及其古气候意义进行深入研究。结果表明：（1）青峰剖面棕红色砂层以细砂为主,黄橙色砂层以中砂为主,细砂-中砂在剖面上呈现明显的交替旋回变化;（2）棕红色砂层和黄橙色砂层的粒度频率曲线均以单峰加一细尾为主,含有一定量的粉砂、黏土;概率累积曲线以滚-跳-悬三段式为主,但两种砂层在组分含量、斜率等方面存在差异;（3）粒度参数呈现明显的波动旋回变化,可能指示沉积时期区域风力、搬运动力、化学风化强度方面发生了周期性变化,即棕红色砂层显示了暖湿气候条件下的红壤化过程,黄橙色砂层则显示凉干气候条件下的黄壤化过程。因此,青峰老红砂旋回沉积表明第四纪晚期以来,福建东部沿海的古气候古土壤可能经历了南亚热带（赤红壤）和暖温带（黄壤）的交替变迁。     

Leak-off mechanism and pressure prediction for shallow sediments in deepwater drilling

Deepwater sediments are prone to loss circulation in drilling due to a low overburden gradient. How to predict the magnitude of leak-off pressure more accurately is an important issue in the protection of drilling safety and the reduction of drilling cost in deep water. Starting from the mechanical properties of a shallow formation and based on the basic theory of rock-soil mechanics, the stress distribution around a borehole was analyzed. It was found that the rock or soil on a borehole is in the plastic yield state before the effective tensile stress is generated, and the effective tangential and vertical stresses increase as the drilling fluid density increases; thus, tensile failure will not occur on the borehole wall. Based on the results of stress calculation, two mechanisms and leak-off pressure prediction models for shallow sediments in deepwater drilling were put forward, and the calculated values of these models were compared with the measured value of shallow leak-off pressure in actual drilling. The results show that the MHPS (minimum horizontal principle stress) model and the FIF (fracturing in formation) model can predict the lower and upper limits of leak-off pressure. The PLC (permeable lost circulation) model can comprehensively analyze the factors influencing permeable leakage and provide a theoretical basis for leak-off prevention and plugging in deepwater drilling.     

Color dependence of clustering properties of Luminous Red Galaxies (LRGs)

From the approximately volume-limited Luminous Red Galaxy (LRG) sample of the Sloan Digital Sky Survey Data Release 6 (SDSS DR6), we construct three LRG samples with different g-r color, which have the nearly same number density, to investigate the color dependence of clustering properties of LRGs. It is found that the blue galaxies have a more filamentary distribution than red galaxies, and that the bluest LRGs preferentially inhabit the dense groups and clusters. But in three LRG samples with different u-g color, we do not observe the tendency for clustering properties of LRGs to significantly change with color. We preferentially conclude that the clustering properties of LRGs are not strongly correlated with colors.     

Spatial Association Between Orogenic Gold Mineralization and Structures Revealed by 3D Prospectivity Modeling: A Case Study of the Xiadian Gold Deposit,Jiaodong Peninsula,China

Natural Resources Research - The Xiadian orogenic deposit with?~?100 t of gold resources, located in the Jiaodong Peninsula, Eastern China, shows an economically attractive gold...     

Exploring Spatially Non-stationary Relationships in the Determinants of Mineralization in 3D Geological Space

Exploring the spatial relationships between various geological features and mineralization is not only conducive to understanding the genesis of ore deposits but can also help to guide mineral exploration by providing predictive mineral maps. However, most current methods assume spatially constant determinants of mineralization and therefore have limited applicability to detecting possible spatially non-stationary relationships between the geological features and the mineralization. In this paper, the spatial variation between the distribution of mineralization and its determining factors is described for a case study in the Dingjiashan Pb–Zn deposit, China. A local regression modeling technique, geological weighted regression (GWR), was leveraged to study the spatial non-stationarity in the 3D geological space. First, ordinary least-squares (OLS) regression was applied, the redundancy and significance of the controlling factors were tested, and the spatial dependency in Zn and Pb ore grade measurements was confirmed. Second, GWR models with different kernel functions in 3D space were applied, and their results were compared to the OLS model. The results show a superior performance of GWR compared with OLS and a significant spatial non-stationarity in the determinants of ore grade. Third, a non-stationarity test was performed. The stationarity index and the Monte Carlo stationarity test demonstrate the non-stationarity of all the variables throughout the area. Finally, the influences of the degree of non-stationary of all controlling factors on mineralization are discussed. The existence of significant non-stationarity of mineral ore determinants in 3D space opens up an exciting avenue for research into the prediction of underground ore bodies.