宁夏生态移民村空间剥夺测度及影响因素

生态移民村是一种具有扶贫脱贫与生态保护双重意义的特殊乡村类型,是乡村振兴不可或缺的重要对象。在空间重构过程中形成的空间剥夺现象,是当前生态移民村全面振兴面临的一个新问题。为探究生态移民村空间剥夺规律,以宁夏70个生态移民村2017年相关数据为基础,构建了以收入就业、教育培训、社会生活、居住环境及公共服务可达性为主要内容的空间剥夺指标体系并进行水平测度,进而运用地理加权回归和地理探测器对其影响因子进行探测。结果显示：① 宁夏生态移民村空间剥夺整体水平较低,但总指数均值相对于非生态移民村显著高出0.023;② 宁夏生态移民村空间剥夺时空分异特征明显,总指数均值在时间上呈现“先较低—后上升—再下降”趋势,在空间上呈现“北部低—中部高—南部次高”分布;③ 民族构成、地形地貌、移民时段、依托资源和经济区带是影响生态移民村空间剥夺的重要因子,前3项对空间剥夺总指数的解释力分别达到了22.4%、10.6%和14.0%。空间剥夺水平测度为生态移民生产生活评价和政策调整完善提供新的客观依据,相关部门可据此优化调节生态移民村空间资源配置,增加其获取资源的能力和机会,推动生态移民村全面振兴。     

基于卫星遥感数据的森林病虫害监测与评价

森林病虫害由于在森林资源中造成的重大破坏而被人们称为“不冒烟的火灾”,其对生态系统的研究具有重要意义。现有基于遥感数据的病虫害研究多集中在森林病虫害的监测、爆发原因以及发病区域内生产力的变化情况,而对于森林病虫害发生后森林中植被指数与叶面积指数之间的相关性的变化情况还相对较少,处于需要持续性深入探讨的阶段。以加拿大不列颠哥伦比亚地区2002—2012年森林病虫害数据为基础,分析不同严重程度的病虫害对叶面积指数（LAI）与归一化植被指数（NDVI）和增强型植被指数（EVI）的影响。结果表明：① 受病虫害感染的像元在轻度（Light）、中度（Moderate）和重度（Severe）三个严重级别中,NDVI与LAI之间的相关性由弱变强,又由强变弱;② EVI与LAI之间的相关性,在轻度（Light）、中度（Moderate）和重度（Severe）三个严重级别的像元中则依次变强。这一研究将为今后利用遥感数据识别病虫害、评价生态系统影响提供基础。     

一种潜艇装备作战能力比较研究方法

对潜艇装备作战能力评估问题进行了研究，发现运用定量评估方法的过程中存在指标评价所需数据无法完全获取的问题。结合美弗吉尼亚潜艇改进情况，分析了影响潜艇作战能力的一些关键因素，重点基于潜艇武器装载能力，对国外典型潜艇进行了作战能力比较分析，最后对分析结果进行了评价，并讨论了未来潜艇作战能力评估方法的发展方向。     

Discovery of Four New Clusters in the Cygnus Cloud

We report the discovery of four new open clusters(named QC 1,QC 2,QC 3 and QC 4)in the direction of Cygnus Cloud and select their members based on five astrometric parameters(l,b,ω,μα*,μδ)of Gaia DR2.We also derive their astrophysical parameters for each new cluster.Structure parameters are generated by fitting the radial density distribution with a King’s profile.Using solar metallicity,we performed isochrone-fitting on their purified color-magnitude diagrams(CMDs)to derive the age of the clusters.The known cluster NGC 7062 in an adjacent area is chosen to verify our identification process.The estimated distance,reddening and age of NGC 7062 are in good agreement with the literature.     

Classification of stellar spectra with SVM based on within-class scatter and between-class scatter

Support Vector Machine (SVM) is a popular data mining technique, and it has been widely applied in astronomical tasks, especially in stellar spectra classification. Since SVM doesn’t take the data distribution into consideration, and therefore, its classification efficiencies can’t be greatly improved. Meanwhile, SVM ignores the internal information of the training dataset, such as the within-class structure and between-class structure. In view of this, we propose a new classification algorithm-SVM based on Within-Class Scatter and Between-Class Scatter (WBS-SVM) in this paper. WBS-SVM tries to find an optimal hyperplane to separate two classes. The difference is that it incorporates minimum within-class scatter and maximum between-class scatter in Linear Discriminant Analysis (LDA) into SVM. These two scatters represent the distributions of the training dataset, and the optimization of WBS-SVM ensures the samples in the same class are as close as possible and the samples in different classes are as far as possible. Experiments on the K-, F-, G-type stellar spectra from Sloan Digital Sky Survey (SDSS), Data Release 8 show that our proposed WBS-SVM can greatly improve the classification accuracies.     

赤潮治理方法综述

文章综述目前赤潮治理的主要方法,其中物理法重点介绍机械搅动法、超声波法、吸附法和气浮法,化学法重点介绍无机药剂法、有机药剂法和胶体絮凝沉淀法,生物法主要介绍引入赤潮藻类生物天敌、微生物技术和化感技术,分析各自优势和不足,采用微生物技术和化感技术具有重要意义和发展前景。     

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.     

Fast genetic mapping in barley: case studies of cuticle mutants using RNA-sequencing

Barley(Hordeum vulgare L.) is one of the earliest domesticated crop species and ranked as the fourth largest cereal production worldwide. Forward genetic studies in barley have greatly advanced plant genetics during the last century; however, most genes are identified by the conventional mapping method. Array genotyping and exome-capture sequencing have also been successfully used to target the causal mutation in barley populations, but these techniques are not widely adopted because of associated costs and partly due to the huge genome size of barley. This review summarizes three mapping cases of barley cuticle mutants in our laboratory with the help of RNA-sequencing. The causal mutations have been successfully identified for two of them and the target genes are located in the pericentromeric regions. Detailed information on the mapping-by-sequencing, mapping-and-sequencing, and RNA-sequencing assisted linkage mapping are presented and some limitations and challenges on the mapping assisted by RNA sequencing are also discussed. The alternative and elegant methods presented in this review may greatly accelerate forward genetics of barley mapping, especially for laboratories without large funding.     

游客-环境互动视角下旅游者目的地环境恢复性效应

日常生活环境和旅游目的地环境都可以为个体提供恢复的机会，不同环境的属性特征导致不同程度的恢复。为了探究环境类型与恢复性效应之间的关系及停留时间的调节作用，文章采用问卷调查方式，利用配对样本t检验和混合设计方差分析相结合的方法，探讨游客在不同环境中的恢复性效应，并对这一现象提供心理上的解释。结果表明：1）恢复体验的4个维度（放松体验、心理脱离、掌握体验、控制体验）在旅游目的地环境中的得分均高于日常生活环境。2）掌握和控制体验维度在城市旅游目的地中的变化程度高于乡村旅游目的地，两种类型目的地的放松和心理脱离维度差异较小。3）随着在目的地停留时间的延长，乡村旅游目的地放松体验和心理脱离维度变化程度高于城市旅游目的地，两种类型目的地环境恢复性效应会相互靠近。因此，游客的环境恢复性效应不仅受到环境和目的地类型的影响，同时还与游客的适应程度有关，且受到其停留时间的调节作用。     

观驾山—黑岛一带信息量法找金研究

本文以成矿地质模型为依据，对观驾山—黑山岛一带的化探、物探、遥感及地质等信息变量进行了详细的分析，选取了１０个信息变量２４个标志状态；在此基础上，运用信息量法对研究区内４５８个单元的信息量值进行计算，并由此确定了研究范围内的成矿远景区。事实表明，研究区信息量成矿预测是非常有效的