基于已知特征平面的移动测量系统安置参数检校

安置误差是影响移动测量系统激光点云精度的主要因素之一。当激光点云中存在特征平面时,安置误差会导致该平面与真实平面之间产生较大的偏差,如果存在多个空间上位置关系较好的特征平面,就能够全面反映出系统的安置误差。本文以此为依据,给出了一种基于已知特征平面的移动测量系统安置参数检校方法,并详细阐述了检校场的布设、数据获取及处理等内容。实验证明,该方法是一种简单、可靠的检校方法,具有较高的实用性。     

毛乌素沙地锦鸡儿（Caragana）根系微域细菌群落多样性特征

基于16S rRNA高通量基因测序技术，对毛乌素沙地小叶锦鸡儿（Caragana microphylla)、柠条锦鸡儿（Caragana korshinskii）根系微域（即根系、根际土、根区土、灌丛间空白土）间的细菌群落多样性和结构差异性进行表征。本研究对各根系微域间细菌群落的Alpha多样性指数进行了单因素方差分析以及基于OTU水平的PCA分析，探究其在根系微域间Alpha和Beta多样性的层级变化，证实了有关植物根系微域生态位分化的报道，并发现锦鸡儿属植物根系微域间细菌群落的多样性和结构组成随着4个微域类型由外及内呈现出显著的层级差异性（P<0.05）。通过对优势细菌群组的结构组成分析，发现锦鸡儿属植物对特定细菌群组具有显著的向根系内筛选富集的作用（P<0.05）。这种植物通过根系微域对特定细菌群组的逐级筛选富集作用，是导致锦鸡儿属植物灌丛下不同生态位间细菌群落结构和组成发生层级变异的主要原因。     

东北地区水稻扩张的海拔优势区间分析

及时掌握水稻的时空分布信息,对调整和优化农业生产结构至关重要。论文利用综合考虑植被物候和地表水变化的水稻自动制图方法,结合海拔、地表水体因素开展2001—2017年东北地区水稻分布的时空演变研究。通过889个地面调研点位对水稻分类结果验证,总体精度达90.66%,Kappa系数为0.8128。研究表明:① 21世纪初,东北地区水稻种植面积呈先略减后持续增加的趋势,2017年水稻种植面积达2001年的2.13倍。其中,水稻扩张面积的60%分布在三江平原,30%分布在松嫩平原,下辽河平原仅占不足5%。水稻扩张的海拔优势区间在200 m范围内,随着海拔的上升水稻扩张与地表水关系越来越密切。② 三江平原内,水稻扩张幅度在海拔30~70 m范围内逐渐增加,使优势区间从相对高度70 m缩减至40 m内,也使得分布优势逐渐趋向于距地表水体较远的区域。而松嫩平原和下辽河平原水稻种植分布的海拔优势区间始终分别保持在相对高度100 m、40 m内。③ 三江平原水稻的集中分布和急剧扩张,使水稻分布优势逐渐趋向于距地表水体远的区域,这将对地下水带来更大的压力;而松嫩平原水稻分布受地表水体影响较大,分布优势随着距地表水体距离的增加而减小。研究可为农业部门评估水资源承载力、保障农业可持续发展提供数据支撑及理论参考。     

Salinivibrio sp.YH4胞外丝氨酸蛋白酶EYHS耐盐性及生物信息学分析

摘要：目的 探讨菌株Salinivibrio sp.YH4分泌的丝氨酸蛋白酶EYHS的耐盐性及结构特征。方法 明胶底物酶谱法分析EYHS的耐盐性。应用生物信息学手段对EYHS及6种耐盐的S8家族丝氨酸蛋白酶结构特征进行分析。结果 EYHS在4 mol/L的NaCl溶液中仍具有活性，属于耐盐蛋白酶。EYHS及6种S8家族丝氨酸蛋白酶分子表面的loop区等无规则卷曲所占比例较高，α-螺旋与β-片层则主要位于酶分子内部。EYHS分子表面酸性氨基酸含量较高，且具有弱疏水内核。多序列比对发现蛋白酶的催化三联体两侧存在高度保守的基序和保守的极性氨基酸及芳香族氨基酸，并存在多个保守的Gly与Ala。同源模建和表面电荷分布显示，α螺旋和β片层围成了蛋白酶的催化腔，EYHS活性中心包含由Asp32、His65与Ser215组成的催化三联体，且催化位点区域表面静电势为负。结论 上述结构特征可能有助于耐盐丝氨酸蛋白酶EYHS在高盐环境下维持其稳定性和适度柔性，并有助于其催化功能的发挥，为深入研究耐盐丝氨酸蛋白酶的高盐环境适应性提供了一定的理论依据。     

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.     

Coupling numerical simulation with remotely sensed information for the study of frozen soil dynamics

The acquisition of spatial-temporal information of frozen soil is fundamental for the study of frozen soil dynamics and its feedback to climate change in cold regions. With advancement of remote sensing and better understanding of frozen soil dynamics, discrimination of freeze and thaw status of surface soil based on passive microwave remote sensing and numerical simulation of frozen soil processes under water and heat transfer principles provides valuable means for regional and global frozen soil dynamic monitoring and systematic spatial-temporal responses to global change. However, as an important data source of frozen soil processes, remotely sensed information has not yet been fully utilized in the numerical simulation of frozen soil processes. Although great progress has been made in remote sensing and frozen soil physics, yet few frozen soil research has been done on the application of remotely sensed information in association with the numerical model for frozen soil process studies. In the present study, a distributed numerical model for frozen soil dynamic studies based on coupled water-heat transferring theory in association with remotely sensed frozen soil datasets was developed. In order to reduce the uncertainty of the simulation, the remotely sensed frozen soil information was used to monitor and modify relevant parameters in the process of model simulation. The remotely sensed information and numerically simulated spatial-temporal frozen soil processes were validated by in-situ field observations in cold regions near the town of Naqu on the East-Central Tibetan Plateau. The results suggest that the overall accuracy of the algorithm for discriminating freeze and thaw status of surface soil based on passive microwave remote sensing was more than 95%. These results provided an accurate initial freeze and thaw status of surface soil for coupling and calibrating the numerical model of this study. The numerically simulated frozen soil processes demonstrated good performance of the distributed numerical model based on the coupled water-heat transferring theory. The relatively larger uncertainties of the numerical model were found in alternating periods between freezing and thawing of surface soil. The average accuracy increased by about 5% after integrating remotely sensed information on the surface soil. The simulation accuracy was significantly improved, especially in transition periods between freezing and thawing of the surface soil.     

晚第四纪MIS6以来柴达木盆地成盐作用对冰期气候的响应

气候是控制柴达木盆地盐类沉积的主要因素之一,但是其作用机制尚待明确。作者以柴达木盆地察汗斯拉图盐湖的3个含盐剖面为研究对象,采用多接收电感耦合等离子质谱(MC-ICP-MS)铀系测年测定其沉积时代,并通过X射线粉晶衍射(XRD)分析测定其盐类矿物种类。铀系测年显示D18剖面石盐和芒硝层的沉积时代为13.1±2.0 ka BP～15.9±2.5 ka BP,其中芒硝沉积年代属于末次冰期MIS2晚期;MXK2剖面芒硝层的沉积时代分别为131.7±39.5 ka BP和158.3±10.8 ka BP,D12剖面芒硝层的沉积时代分别为166.6±20.2 ka BP和198.0±20.6 ka BP,可以对应于倒数第二次冰期MIS6。XRD分析确定了3个剖面的盐类矿物主要为芒硝、石盐和石膏。综合多个盐湖晚第四纪成盐数据,本文认为倒数第二次冰期MIS6和末次冰期MIS2是柴达木盆地晚第四纪重要的成盐期,冰期的冷干气候有利于石盐和芒硝等盐类沉积。柴达木盆地"冰期成盐"的根本原因,是由于冰期环境下盆地周边山体冰川规模的扩张以及干冷的冰期气候,共同造成了盐湖补给水量的减少。此外,晚第四纪MIS6和MIS2的冰期降温也是导致盆地中冷相盐类沉积的直接原因。     

Simulation of urban expansion via integrating artificial neural network with Markov chain – cellular automata

Accurate simulations and predictions of urban expansion are critical to manage urbanization and explicitly address the spatiotemporal trends and distributions of urban expansion. Cellular Automata integrated Markov Chain (CA-MC) is one of the most frequently used models for this purpose. However, the urban suitability index (USI) map produced from the conventional CA-MC is either affected by human bias or cannot accurately reflect the possible nonlinear relations between driving factors and urban expansion. To overcome these limitations, a machine learning model (Artificial Neural Network, ANN) was integrated with CA-MC instead of the commonly used Analytical Hierarchy Process (AHP) and Logistic Regression (LR) CA-MC models. The ANN was optimized to create the USI map and then integrated with CA-MC to spatially allocate urban expansion cells. The validated results of kappa and fuzzy kappa simulation indicate that ANN-CA-MC outperformed other variously coupled CA-MC modelling approaches. Based on the ANN-CA-MC model, the urban area in South Auckland is predicted to expand to 1340.55 ha in 2026 at the expense of non-urban areas, mostly grassland and open-bare land. Most of the future expansion will take place within the planned new urban growth zone.     

基于噪声谱比法的内蒙古西部地区台站场地响应研究

利用内蒙古西部12个台站的地脉动噪声数据，采用噪声谱比法研究台站的场地响应情况。分析表明，台站场地响应按曲线形态可分为3类，且可能受地形地貌、局部构造和台基状况等条件影响。对比分析噪声谱比法与Moya方法的场地响应结果发现，场地响应曲线形态基本一致，只有极少数台站存在明显差异。结果表明，内蒙古西部区域大部分台站的台基状况总体较好，场地响应曲线较为平坦，无明显频率放大点。     

基于网络药理学探讨薤白治疗肺癌的作用机制

目的：运用网络药理学预测薤白治疗肺癌有效成分的作用靶点及通路。方法：通过TCMSP数据库获取薤白有效成分及作用靶点，并运用Cytoscape 3.7.1软件构建靶点间相互作用网络并进行关联分析，R×64 3.5.3软件及相应脚本筛选出薤白治疗肺癌的有效成分及作用靶点，生物信息学技术富集通路及生物过程。结果：预测得到薤白治疗肺癌有效成分11个和有效靶点30个，并推断其作用机制可能与AGE-RAGE、PI3K-Akt等信号通路有关，且JUN、MAPK1、MAPK3 等靶点基因可能起着关键性的作用。结论：基于网络药理学探讨了薤白治疗肺癌多成分-多靶点-多通路的作用特点，为进一步开展薤白治疗肺癌作用机制的研究提供了新的思路和方法。