A biodiversity evaluation framework for restoration of aquatic macrophyte communities in shallow lakes driven by hydrological process management

Macrophyte community diversity and composition respond to ecosystem conservation and local environmental factors. In this study, we developed a multidimensional diversity framework for macrophyte communities, including the taxonomic and functional alpha and beta diversity. We used the framework to explore the relationships among water level regimes and these diversity parameters in a case study of China's Baiyangdian Lake. Analysis of indicators of hydrologic alteration divided the water level from 1959 to 2019 into four regimes (dry, <6.42 m; low, 6.42–7.23 m; medium, 7.23–8.19 m; high, >8.19 m). Alpha and beta diversity were significantly higher in the medium regime than in the low and high regimes. Redundancy analysis indicated that the maximum water depth significantly affected taxonomic alpha diversity, and total nitrogen (TN) and chemical oxygen demand (COD) concentration significantly affected functional alpha diversity, respectively. Mantel tests showed that TN, Secchi depth (SD), and water depth in the high water level regime significantly increased the total beta diversity and turnover components. TN was the main factor that increased total taxonomic beta diversity. Water level regime mainly influenced interspecific relationships by changing the TN and COD concentration. The water level should be maintained between the medium and high water level regimes to promote restoration of the macrophyte community and improve ecosystem stability. The biodiversity evaluation framework would provide a deeper insight into the hydrological process management for restoration of aquatic macrophyte communities in shallow lakes.     

万维望远镜平台下基于ASSURE模式的天文教学研究

随着教学手段和教学方法的现代化,信息技术与课程整合正成为教育改革的研究热点。文章以万维望远镜(WWT)平台为教学媒体,以ASSURE模式为教学设计的理论依据,设计并构建用以指导天文教学准备、实施与评价的教学模式,并将该模式应用于“四季星空”的教学案例中,使用基于信息技术的互动分析编码系统和学生课程反馈调查对教学效果进行分析和评价。研究表明,万维望远镜的使用可以降低教师言语比率,改变传统天文教学中以讲授式为主的教学方式。它适合起点能力较高、对新知识和新技能掌握能力较强的学生,能提高其学习兴趣和积极性,激发其探索宇宙的兴趣,有助于培养学生的科学素养。     

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.     

Determination of the regional deformation rates of Shanghai and Kashima VLBI stations based on ITRF97

The vertical deformation rates (VDRs) and horizontal deformation rates (HDRs) of Shanghai VLBI station in China and Kashima and Kashima34 VLBI stations in Japan were re-analysed using the baseline length change rates from Shanghai to 13 global VLBI stations, and from Kashima to 27 stations and from Kashima34 to 12 stations, based on the NASA VLBI global solution glb1123 (Ma, 1999). The velocity vectors of the global VLBI stations were referred to the ITRF97 reference frame, and the Eulerian vectors of different models of plate motion were used for comparative solutions. The VDR of Shanghai station is estimated to be −1.91±0.56 mm/yr, and those of Kashima and Kashima34 stations, −3.72±0.74mm/yr and −8.81±0.84mm/yr, respectively. The difference between the last two was verified by further analysis. Similar estimates were also made for the Kokee, Kauai and MK_VLBA VLBI stations in mid-Pacific.     

青藏高原隆升的非线性动态有限元仿真研究

根据青藏高原的地质特征建立分析模型，采用3维动态有限元方法，在计算仿真板块速度场的基础上，计算在青藏高原的隆升过程中该地区地壳岩石的等效应力和位移随时间的变化，计算仿真得到的速度场与1998年GPS观测的速度场吻合良好；与过去一贯的假设相反，计算结果反映出地壳应力场不是静态的，而是此起彼伏，不断变化的，应力值最大且变化最剧烈的地区在克什米尔地区、鄂尔多斯地区和鲜水河－小江断裂带，与地震多发区域吻合。     

ECOLOGICAL SERIES OF SOIL ANIMALS IN DARLIDAI MOUNTAIN

The ecological series of soil animals under the broad-leaved and pine mixed forest in Darlidai Mountainwas studied. Seven sample plots were selected according to different altitude gradients, which belong to different vegeta-tion types. By investigating and analyzing soil animals in every sample plot it is found that there are 45 groups and 1956individuals, which axe involved in 3 phylums, 7 classes, 16 orders, respectively. The altitude is a key factor which af-fects ecological series of soil animals. Both the groups and individuals of soil animals increase with altitude increasingunder certain conditions, which contrastes with ordinary cases, resulting from special micro-climate in studied area. Thegroups and individuls of soil animals are the most under the broad-leaved and pine forest on the top of the mountain, andthe least under Picea-Abies forest in the foot of the mountain.     

大同—阳高Ms6.1地震前兆综合加权信息熵的变化

利用“地震预报计算机专家系统”的思想对大同 -阳高 Ms6 .1地震前每一前兆异常事件进行综合评估 ,以每一异常的最可能发震时间来计算发震概率 ,利用地震前兆综合加权信息熵研究了系统熵值与地震的关系。对华北地区的地震前兆综合加权信息熵研究表明 ,在大同 -阳高 Ms6 .1地震前 ,信息熵出现了明显的减熵有序变化     

对青藏东北缘现今块体划分、运动及变形的初步研究

利用2维非连续变形分析方法（DDA），以位移代替围压作为边界约束力，研究青藏东北缘现今块体划分及其运动变形。根据该地区地质构造及地震活动，以GPS点测量位移作为模拟结果约束点，得出了较合理的块体划分模型和随时间演化的主应变分布图，并把应变高值区与近几年来发生的5级以上地震作对比，得出了研究区内地震危险性可能较大的区域。另外，对模拟的甘青块体与阿拉善块体的边缘带断裂左旋运动做了大概计算。     

抽油机悬点投影测量仪

油田抽油机的抽油杆幌动幅度过大，是引起抽油杆断裂的一个重要因素，检测这种幌动幅度是防止抽油杆断裂的一种有效手段。设计了一种基于面阵CCD和普通半导体激光器（LD）测量这种幌动的悬点投影测量方法，通过数字卷积滤波，达到了范围为0－40mm，误差＜0.2mm的技术检测指标。