A novel coupled FDM-DEM modelling method for flexible membrane boundary in laboratory tests

Difficulties are involved in discrete element method (DEM) modelling of the flexible boundary, that is, the membranes covering the soil sample, which can be commonly found in contemporary laboratory soil tests. In this paper, a novel method is proposed wherein the finite difference method (FDM) and DEM are coupled to simulate the rubber membrane and soil body, respectively. Numerical plane strain and triaxial tests, served by the flexible membrane, are implemented and analysed later. The effect of the membrane modulus on the measurement accuracy is considered, with analytical formulae derived to judge the significance of this effect. Based on an analysis of stress-strain responses and the grain rotation field, the mechanical performances produced by the flexible and rigid lateral boundaries are compared for the plane strain test. The results show that (1) the effect of the membrane on the test result becomes more significant at larger strain level because the membrane applies additional lateral confining pressure to the soil body; (2) the tested models reproduce typical stress and volumetric paths for specimens with shear bands; (3) for the plane strain test, the rigid lateral boundary derives a much higher peak strength and larger bulk dilatation, but a similar residual strength, compared with the flexible boundary. The latter produces a more uniform (or ‘diffuse') rotation field and more mobilised local kinematics than does the former. All simulations show that the proposed FDM-DEM coupling method is able to simulate laboratory tests with a flexible boundary membrane.     

The Upper Devonian Kellwasser event recorded in a regressive sequence from inner shelf to lagoonal pond,Catalan Coastal Ranges,Spain

This study, conducted in the Catalan Coastal Ranges, north‐east Spain, describes the Upper Devonian Kellwasser event in a shallowing‐upward sequence of black shales, siltstones and quartz arenites. This sequence was deposited in a progradational and regressive coastal system where the sedimentary environment evolved from the inner shelf to a lagoonal pond located landward of the shoreline. Three anomalous succeeding steps have been identified by geochemical analysis. The first one, detected on the inner shelf, was characterized by oxygen depletion and high organic productivity. The second, detected in the nearshore, was caused by hydrothermal activity occurring under normal oxic conditions. The third and most intense step was identified in the muds of the lagoonal pond and has been linked to strong anoxic conditions, elevated clastic input derived from changes in the weathering regime at the source area and moderate hydrothermal activity. The Kellwasser event is thus defined in the study area as stepwise and multi‐causal. This is the first time that the Kellwasser event has been identified in a sedimentary environment behind the shoreline. It is also the first time that it has been reported in the Catalan Coastal Ranges.     

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.     

图像目标形状匹配与视频过滤技术研究

通过研究图像目标形状特征的提取、匹配，运用随机过程，投影理论，提出用于识别目标形状统计特性的波动度特征，建立目标形状匹配函数，给出实际图像目标的匹配实现算法，实际系统测试表明该算法能准确、快速完成实际图像目标形状的匹配、过滤，为图像跟踪、识别及视频过滤等应用领域提供了新的可借鉴方法。     

有关边界层问题中一类奇异积分方程新的存在性结果

对边界层理论新结果中出现的一类奇异积分方程w（t）=∫1 t（1-s）（λ＋λs＋s）/w（s）ds＋（1-t）∫t 0s/w（s）ds,t∈（0,1）进行讨论，并得出了上述方程在λ∈（-1/2,0）上正解存在性的新结果。     

Boundary curves for families of planar orbits

For monoparametric familiesf(x,y)=c of planar orbits, created by a planar potentialV(x,y), we introduce the notion of the family boundary curves (FBC). All members of the familyf(x,y)=c are traced in an allowable region of thexy plane, defined by the corresponding FBC, with total energyE=E(c) varying along the family. Family boundary curves are also found for two-parametric familiesf(x,y,b)=c. The relation of equilibrium points and asymptotic orbits, possibly possessed by the potentialV(x,y), to be FBC is studied.     

山西省石炭系与二叠系界线

１８８２年李希霍芬在太原西山建立石炭系的太原系含煤建造以来，石炭系与二叠系的分界意见，归纳起来有三：①以Ｐｓｅｕｄｏｓｃｈｗａｇｅｒｉｎａ（ｓ．１）带之底为界，下面为石炭系，上面为二叠系；②以晋东南的灰白色“佳祥砂岩”与太原西山黄绿色“骆驼脖子砂岩”对比，砂岩底面为石炭系与二叠系的分界线，下面是石炭系山西组，上面是二叠系石盒子组，山西组的地层时代定为晚石炭世；③以晋东南灰白色第三砂岩（陵川砂岩）与太原西山“北岔沟砂岩”对比，砂岩底面为石炭系与二叠系的分界线，下面是石炭系太原组，上面是二叠系山西组，山西组的地层时代为早二叠世。笔者认为石炭系与二叠系的划分，以第③方案较适宜。     

A MESO-β SCALE SIMULATION OF THE EFFECTS OF BOREAL FOREST ECOSYSTEM ON THE LOWER ATMOSPHERE

Based on the Intensive Field Campaign(IFC-1)data of Boreal Ecosystem-Atmosphere Study(BOREAS).a three-dimensional meso-β scale model is used to simulate the effect of boreal forests onthe lower atmosphere.A fine horizontal resolution of 2 km×2 km is used in order to distinguish thevegetative heterogeneity in the boreal region.A total of 20×25 grid points cover the entire sub-modeling area in BOREAS' South Study Area(SSA).The ecosystem types and their coverage ineach grid square are extracted from the North American Land Cover Characteristics Data Base(NALCCD)generated by the U.S.Geographical Survey(USGS)and the University of Nebraska-Lincoln(UNL).The topography of the study area is taken from the Digital Elevation Map(DEM)of USGS.The model outputs include the components of the energy balance budget within the canopyand at the ground.the turbulence parameters in the atmospheric boundary layer and the wind.temperature and humidity profiles extending up to a height of 1500 m.In addition to the fine timeand spatial step,the unique feature of the present model is the incorporation of both dynamic andbiological effects of the Boreal forest into the model parameterization scheme.The model resultscompare favorably with BOREAS' IFC-1 data in 1994 when the forest was in the luxuriant growingperiod.