对低压接地故障保护几种不同方式的探讨

依据《低压配电设计规范》所认定的3种不同接地故障保护方式对其工作原理及条件进行分析，并对其所应采取的各类接地措施作了较为详尽的说明与分析，以便在实际工作中更好的确保配电线路在发生接地故障时，保护元件及时动作，防止人身电击伤亡、电气火灾和线路损害，减少工程造价。     

农用聚乙烯调光膜的研制

本文介绍农用聚乙烯调光膜的制造、性能和应用。通过将稀土络合物调光剂和其他功能助剂添加到聚乙烯树脂中，经挤出塑化吹胀成型制得调光膜。膜的拉伸强度（纵／横）、断裂伸长率（纵／横）和直角撕裂强度（纵／横）分别为１７．２／１６．１ＭＰａ、４１３／４５７％、８１／８６ｋＮ；用红、兰、绿色调光剂可分别制得主峰波长为６１６ｎｍ的红光膜、４３６ｎｍ的兰光膜和５４０ｎｍ的绿光膜，其中以红光膜最佳。经农田实际应用表明     

Thermal Explosions in the Early Evolution of the Earth

Exothermic regimes in the Earth's interior at an early stage of evolution are considered. Conditions are found that lead to selfaccelerating development of gravitational differentiation—separation of the heavier material forming the Earth's core from silicates in extended and heated local areas.     

基于NPSI方法的西安市地裂缝灾害链地表形变监测与演化态势分析

受构造背景、地下水采掘、活断层等因素的综合作用,西安市地裂缝于近年间不断加剧,引发多处不均匀地表沉降,并演化为对地表及地下建筑物均有强烈破坏作用的城市地质灾害链。为探明西安市地裂缝灾害链区域近年间的地表沉降态势、辨析不均匀沉降与地裂缝发育的联系,引入在城市地表形变监测精度和可靠性方面具有显著优势的网络化永久散射体时序雷达干涉测量（NPSI）方法,以2017年3月至2018年3月间成像的15期Sentinel-1A卫星SAR影像为数据源针对西安地区开展了时序监测分析,结合水准测量数据验证发现NPSI监测结果的精度达到 ±4.75 mm。实验结果表明：西安市地裂缝正向西南郊发育,地下水采掘及地上地下工程的建设在地裂缝发育趋势下加剧了不均匀沉降灾害,在形成地裂缝灾害链的鱼化寨、电子城、曲江新区以及地铁3号线等危害严重区域需要实时监测地裂缝发育趋势,并合理规划地下水开采及工程建设活动。相关研究结果可为路政、城建等部门的业务工作及相关研究提供参考信息。     

地球敏感器成像仿真与检测方法

针对地球敏感器的设计与仿真需求,研究了地球椭球成像、大气轮廓、晨昏线及背景星空成像,实现了高精度地球敏感器成像仿真;并在此基础上比较实验了圆拟合、椭圆拟合等不同地球边界拟合及地心检测算法的性能,根据系统在轨运行特点,提出了分步地球图像检测算法.一方面克服单纯圆拟合方法带来的地球扁率误差,另一方面利用轨道先验知识克服椭圆拟合过程中的长轴不稳定性.仿真实验证明,该方法有效地提高了敏感器检测精度及系统鲁棒性,并且时间复杂度低,满足在轨要求,能够有效促进各型地球敏感器的性能提升.     

添加微量稀土元素改善矿用硬质合金性能

此文研究了添加微量稀土元素对矿用硬质含金性能的影响。研究表明，添加一定量一定形态的稀土元素，能提高矿用硬质合会使用寿命，提高合金抗奇强度和抗冲击动性，增加合会的热塑性和高温强韧性，而对硬度、密度、磁力没有多大影响。     

塑性混凝土防渗墙与防渗土工膜联合应用分析研究

该文主要是通过朝面山水库防渗设计采用塑性混凝土防渗墙与防渗土工膜结合使用,根据模型计算分析可知,该方案防渗效果较好,是可行的、有效的防渗措施,可在水库加固中加以推广。     

土坝在设计和运行中防治白蚁危害的研究

以往在水库大坝发生白蚁危害病情后,方采取工程措施进行除险加固。为把白蚁控制在危害之前,本文结合工程典型实例,根据气候、土壤、植被等建坝外部环境条件和白蚁生存需具备的土、水、食料基本因素,考虑白蚁具有喜温好湿、畏光避风、蔓延等生物特性,通过填筑坝料土质分析,研究了水库蓄水坝体浸润性、坝土级配及湿度与白蚁生存之间的关系,并提出相应对策以达到防患于未然。结果表明,坝体结构设计时,通过截断白蚁吸水水源,增加坝体干燥区,缩限白蚁生存空间;通过调整坝料级配,防止白蚁筑巢;合理采用护坡,切断白蚁食源。同时,结合水库运行管理,采取"预防为主,综合防治"的措施,使坝体周边一定范围内成为无蚁区,防止白蚁蔓延至坝,维护水库大坝的安全。     

A scalable approach to mapping annual land cover at 250 m using MODIS time series data: A case study in the Dry Chaco ecoregion of South America

Land use and land cover (LULC) maps from remote sensing are vital for monitoring, understanding and predicting the effects of complex human-nature interactions that span local, regional and global scales. We present a method to map annual LULC at a regional spatial scale with source data and processing techniques that permit scaling to broader spatial and temporal scales, while maintaining a consistent classification scheme and accuracy. Using the Dry Chaco ecoregion in Argentina, Bolivia and Paraguay as a test site, we derived a suite of predictor variables from 2001 to 2007 from the MODIS 250 m vegetation index product (MOD13Q1). These variables included: annual statistics of red, near infrared, and enhanced vegetation index (EVI), phenological metrics derived from EVI time series data, and slope and elevation. For reference data, we visually interpreted percent cover of eight classes at locations with high-resolution QuickBird imagery in Google Earth. An adjustable majority cover threshold was used to assign samples to a dominant class. When compared to field data, we found this imagery to have georeferencing error < 5% the length of a MODIS pixel, while most class interpretation error was related to confusion between agriculture and herbaceous vegetation. We used the Random Forests classifier to identify the best sets of predictor variables and percent cover thresholds for discriminating our LULC classes. The best variable set included all predictor variables and a cover threshold of 80%. This optimal Random Forests was used to map LULC for each year between 2001 and 2007, followed by a per-pixel, 3-year temporal filter to remove disallowed LULC transitions. Our sequence of maps had an overall accuracy of 79.3%, producer accuracy from 51.4% (plantation) to 95.8% (woody vegetation), and user accuracy from 58.9% (herbaceous vegetation) to 100.0% (water). We attributed map class confusion to limited spectral information, sub-pixel spectral mixing, georeferencing error and human error in interpreting reference samples. We used our maps to assess woody vegetation change in the Dry Chaco from 2002 to 2006, which was characterized by rapid deforestation related to soybean and planted pasture expansion. This method can be easily applied to other regions or continents to produce spatially and temporally consistent information on annual LULC.