光照、温度对黑鲷(♀)×真鲷(♂)杂交子代受精卵孵化效果的影响

为了研究不同光照强度、温度对黑鲷(Acantho pagrusschlegelii)(♀)×真鲷(Pagrosomus major)(♂)杂交F1代受精卵孵化效果及仔鱼存活的影响,作者采用试验生态学的方法比较不同温度(16、18、20、22、24、26℃)下杂交鲷受精卵的孵化率、畸形率、孵化时间、7日龄仔鱼的存活率;比较不同光照强度下(10、100、300、800、1500、4 000、10 000 lx)受精卵孵化率、畸形率、孵化时间;比较不同光照强度下(200、1 000、2 000、5 000、10 000、15 000、20 000 lx)7日龄仔鱼的存活率。结果表明:在盐度为22条件下,受精卵最高孵化率(34.00%)和最低畸形率(16.65%)的孵化温度为20℃;在16～26℃范围内,温度与孵化时间呈负相关,与7日龄仔鱼存活率也呈负相关。不同的光照强度对受精卵孵化率和孵化时间的影响不显著,孵化率为65.88%～71.88%;但大于800lx的光照会令畸形率上升(大于15.21%);另外,5000～10000lx的光照强度更适宜仔鱼生存,7日龄仔鱼存活率达到38%～40.75%。黑鲷(♀)×真鲷(♂)杂交子代最佳孵化温度为20℃,最适光照强度为5 000～10 000 lx。     

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.     

Predictability of Chinese Summer Extreme Rainfall Based on Arctic Sea Ice and Tropical Sea Surface Temperature

Chinese summer extreme rainfall often brings huge economic losses, so the prediction of summer extreme rainfall is necessary. This study focuses on the predictability of the leading mode of Chinese summer extreme rainfall from empirical orthogonal function(EOF) analysis. The predictors used in this study are Arctic sea ice concentration(ASIC) and regional sea surface temperature(SST) in selected optimal time periods. The most important role that Arctic sea ice(ASI) plays in the appearance of EOF1 may be strengthening the high pressure over North China, thereby preventing water vapor from going north. The contribution of SST is mainly at low latitudes and characterized by a significant cyclone anomaly over South China. The forecast models using predictor ASIC(PA), SST(PS), and the two together(PAS) are established by using data from 1980 to 2004. An independent forecast is made for the last 11 years(2005-2015). The correlation coefficient(COR) skills between the observed and cross-validation reforecast principal components(PC) of the PA, PS, and PAS models are 0.47, 0.66, and 0.76, respectively. These values indicate that SST is a major cause of Chinese summer extreme rainfall during 1980-2004. The COR skill of the PA model during the independent forecast period of 2004-2015 is 0.7, which is significantly higher than those of the PS and PAS models. Thus, the main factor influencing Chinese summer extreme rainfall in recent years has changed from low latitudes to high latitudes. The impact of ASI on Chinese summer extreme rainfall is becoming increasingly significant.     

南极罗斯海沉积物中可培养菌株的分离及胞外水解酶活性检测

研究了南极罗斯海地区沉积物中可培养微生物多样性,并检测了菌株胞外酶活性,为开发利用南极资源奠定基础。采用Zobell2216E培养基、土豆培养基(PDA)及稀释平板划线法分离并获得纯培养的细菌和真菌,分别通过16SrDNA、18S/ITS序列鉴定分析了可培养微生物的多样性和系统发育。从南极罗斯海沉积物样品中获得78株细菌和54株真菌,细菌、真菌的优势菌株分别为嗜冷杆菌属(Psychrobacter)、枝孢属(Cladosporium);78株细菌菌株中有2株与模式菌株的16SrDNA基因序列相似性小于97%,可能是潜在的细菌新物种。细菌API20NE生理生化和真菌产胞外酶活性结果表明,获得的细菌和真菌大多数可检测到胞外水解酶活性。本研究丰富了南极可培养微生物资源,为南极罗斯海地区可培养微生物的多样性研究以及南极微生物资源的应用研究奠定了基础。     

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.     

ENVIRONMENTAL SUSTAINABILITY AND SCENARIOS OF URBANIZATION IN ARID AREA——A Case Study in Wuwei City of Gansu Province

1IN T R O D U C T IO N The western China isfacinggrowing problems of eco- logicaland economic development. The disparitiebse- tween the socio-economy of the western and eastern China were enlarging, and the major ecologicalprob- lems stilelvolve as past(H…     

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°