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.     

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.     

影响鸢乌贼手钓上钩率的因子探讨

根据印度洋西北部鸢乌贼（Sym plectoteuthis oualaniensis）作业渔场的现场调查数据．分析了作业水深、作业时段、摄食等级和月相等因子对手钓上钩率的影响。统计表明．晚上、午夜和凌晨3个不同时段和不同作业水深对手钓上钩率均有显著性影响。3个时段中，其摄食等级呈先低后高再低的现象。午夜和凌展，上钩率的变化趋势与摄食等级的变化趋势呈显著的负相关。在月相的影响下．离朔日三四天之前，手钓产量呈上升趋势；离望日五六天之前。手钓产量呈明显的下降趋势。     

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°     

电离氢区S140,S141和S142的红外发射研究

本文使用红外天文卫星（IRAS）巡天数据的最新版本IRAS天空巡天图（ISSA），经过进一步处理，得到了S140S141和S142SharplessHII区－分子云复合体的红外发射强度、温度及其光深的分布．在此基础上对各HII区的一些物理参量进行了统计分析，得到了分子云复合体的红外发射总光度以及复合体中尘埃的分布情况，对小尺度尘埃（VSG）的丰度进行了分析．并对各恒星形成区中的致密团块进行了研究，揭示出其中一些可能的恒星形成区域．同时，对S140区中的有关红外点源作出了能谱分析，并对S141区的激发星进行了讨论．     

Silicon limitation on primary production and its destiny in Jiaozhou Bay, China——Ⅳ：Study on cross-bay transect from estuary to ocean

The authors analyzed the data collected in the Ecological Station Jiaozhou Bay from May 1991 to November 1994, including 12 seasonal investigations, to determine the characteristics, dynamic cycles and variation trends of the silicate in the bay. The results indicated that the rivers around Jiaozhou Bay provided abundant supply of silicate to the bay. The silicate concentration there depended on river flow variation. The horizontal variation of silicate concentration on the transect showed that the silicate concentration decreased with distance from shorelines. The vertical variation of it showed that silicate sank and deposited on the sea bottom by phytoplankton uptake and death, and zooplankton excretion. In this way, silicon would endlessly be transferred from terrestrial sources to the sea bottom. The silicon took up by phytoplankton and by other biogeochemical processes led to insufficient silicon supply for phytoplankton growth. In this paper, a 2D dynamic model of river flow versus silicate concentration was established by which silicate concentrations of 0.028–0.062 μmol/L in seawater was yielded by inputting certain seasonal unit river flows (m³/s), or in other words, the silicate supply rate; and when the unit river flow was set to zero, meaning no river input, the silicate concentrations were between 0.05–0.69 μmol/L in the bay. In terms of the silicate supply rate, Jiaozhou Bay was divided into three parts. The division shows a given river flow could generate several different silicon levels in corresponding regions, so as to the silicon-limitation levels to the phytoplankton in these regions. Another dynamic model of river flow versus primary production was set up by which the phytoplankton primary production of 5.21–15.55 (mgC/m²·d)/(m³/s) were obtained in our case at unit river flow values via silicate concentration or primary production conversion rate. Similarly, the values of primary production of 121.98–195.33 (mgC/m²·d) were achieved at zero unit river flow condition. A primary production conversion rate reflects the sensitivity to silicon depletion so as to different phytoplankton primary production and silicon requirements by different phytoplankton assemblages in different marine areas. In addition, the authors differentiated two equations (Eqs. 1 and 2) in the models to obtain the river flow variation that determines the silicate concentration variation, and in turn, the variation of primary production. These results proved further that nutrient silicon is a limiting factor for phytoplankton growth. This study was funded by NSFC (No. 40036010), and the Director's Fund of the Beihai Sea Monitoring Center, the State Oceanic Administration.     

EFFECT OF NITRATE AND PHOSPHATE ON ACCUMULATION OF β-CAROTENE ISOMERS IN DUNALIELLA SALINA

DunaIiella salina, a halotolerent unicellular green alga, can accurmulate a Iarge amount of β-caroteneunder environmental conditions. The isorners of β-carotene extIacted from D. salina culturedin medium with different nitrate and phosphate concentrations were analysed by HPLC with Alox-Talumins column. At least six isomers were found in different proportions depending on the culture me-dia's nitrate and/or phosphate concentrations. Nitrate and/or phosphae defidency was conducive tothe accumulation of totaI cis isomers but not of al1 trans isomer. lt is sUggeSted that 1 mmol/L KNO_3and 0.1 mmol/L KH_2, PO_4 are favourable for accumulation of total cis β-carotene.     

解决西部水资源匮乏的设想

1.我国西部能矿资源丰富,煤、石油、有色金属矿产等等,在我国占有举足轻重的地位。如果加以开发.将成为21世纪经济发展的支柱。但是,由于干旱缺水,这些资源无从开发。 农业是我国的基本问题,粮食是关键。按目前亩产400公斤粮食的全国平均水平,只能养活12.5亿人,到下个世纪人口高峰期,现有耕地将无法满足生活基本需要。     

基于Chandra的活动星系核观测

活动星系核（AGN）是宇宙中最奇特的天体之一。它是真正意义上的全波天体，其中X射线波段的发射功率占到全波段功率的50％左右。AGN的X射线辐射研究涉及天体物理中的最基本问题，例如能量产生、辐射机制和宇宙论等，而Chandra X射线卫星的高分辨率图像和光谱对这一研究有着重要作用。以Chandra卫星的部分观测结果为例，简要介绍了几类不同类型AGN的X射线辐射研究进展：（1）宽吸收线类星体APM08279＋5255（z=3．91）的X射线谱分析，以及高红移类星体的观测概况；（2）Seyfert星系NGC 4151延展X射线发射问题的解决，及NGC 1068X射线辐射与光学波段的高激发态发射线（[OⅢ]λ5007）有很强相关性的发现；（3）6个BLLac天体样本的X射线环境分析；（4）射电星系X射线喷流的观测等。