孤立金属尖端的电晕触发阈值

为了得到金属尖端在发生电晕放电时尖端处的电场强度,该文首先采用实验室实验得到不同高度、不同形状、不同材质的金属尖端发生电晕放电时的环境电场阈值;再采用有限元法计算二维泊松方程,得到尖端处电晕触发阈值,由此得出以下结论:环境电场阈值随金属尖端高度的增大基本呈线性减小趋势,随着尖端越来越尖,环境电场阈值呈先减小后增大的变化趋势;高度、形状对金属物尖端处电晕触发阈值无影响,尖端处电晕阈值为定值;给出尖端处电晕触发阈值为158.75 kV·m-1与空间分辨率的拟合公式,可为今后电晕放电数值模拟中判断电晕放电的起始时刻提供参考。     

冬季南黄海浮游动物群落结构及其对黄海暖流的指示

2009年12月和2010年1月对南黄海进行浮游动物采集, 以了解冬季浮游动物群落结构及其对黄海暖流的指示作用。结果表明, 南黄海冬季仍然以温带和暖温带种为主, 中华哲水蚤Calanus sinicus、强壮滨箭虫Aidanosagitta crassa、细足法Themisto gracilipes等温带和暖温带种类在浮游动物数量组成中具有较大优势。与此同时, 一些暖水种在调查海域局部出现。2009年12月暖水种仅分布在南黄海东南部黄海暖流源地附近。位于黄海中部的调查区东侧温盐层化现象明显, 近底层低温、高盐、高营养盐的水文特征体现了黄海冷水团的残留; 2010年1月在35°—36°N区域暖水种种类明显增加, 截平头水蚤Paracandacia truncata、长尾基齿哲水蚤Clausocalanus furcatus出现的位置与暖流路径相吻合, 海洋真刺水蚤Euchaeta rimana数量相比12月有明显向北推进的趋势。主成分分析显示暖水种的分布与温度有良好的相关性。Shannon-Weaver指数、丰富度指数、均匀度指数等没有呈现明显的分布规律, 对黄海暖流的指示作用不如种类明显。     

基于WBS-RBS的地铁基坑故障树风险识别与分析

针对地铁基坑工程环境复杂、影响因素多的特点,提出以故障树分析为基础结合工作分解结构（WBS）-风险分解结构（WBS）进行风险识别的方法。此方法引入WBS-RBS将地铁基坑工程工作分解结构和风险源分解结构耦合判断并说明相应风险因素或风险事件,并按照故障树建树原则根据彼此间的逻辑关系,用逻辑门连接上下层事件,形成地铁基坑工程主要故障树。在此基础上,对风险因素进行敏感性分析并提出相应的预防措施。     

The Impact of Hurricane Katrina on the Coastal Vegetation of the Weeks Bay Reserve,Alabama from NDVI Data

Normalized Difference Vegetation Index (NDVI) data were used to investigate vegetation changes after Hurricane Katrina (2005) for the Weeks Bay Reserve and surrounding area of coastal AL. Landsat 5 satellite images were acquired before landfall (March 24, 2005), after landfall (September 16, 2005), and 8 months after landfall (April 28, 2006). The March 2005 to September 2005 image comparison showed that average NDVI values decreased by 49% after landfall. Continuing into the next year, average NDVI values were −44% lower in April 2006 than they were in March 2005. Among habitat types, the estuarine emergent wetland experienced the largest average NDVI value decrease (−64%). The estuarine emergent wetland NDVI values continued to decrease by −27% from September 2005 to April 2006, whereas other habitats increased in NDVI. This continued suppression of NDVI values was attributed to increased salinity from the storm surge and to regional drought conditions that occurred after landfall. These results provide insight into the sensitivity of coastal vegetation from the interactions of both tropical cyclones and long-term environmental conditions.     

Monitoring severe aquifer-system compaction and land subsidence in Taiwan using multiple sensors: Yunlin,the southern Choushui River Alluvial Fan

During 1992–2007, excessive pumping of groundwater caused large-scale aquifer-system compaction and land subsidence in the Choshui River Alluvial Fan, especially in the area of Yunlin county. The subsidence impedes surface-water runoff and endangers the operation of Taiwan High Speed Rail. Leveling, Global Positioning System (GPS), multi-level compaction monitoring well, and Differential Interferometric Synthetic Aperture Radar (DInSAR) are used to study the extent of subsidence in Yunlin and its mechanism. These sensors complement each other in spatial and temporal resolutions. A leveling network totaling 434 km in length was deployed to derive subsidence at every 1.5 km along the routes, and the result is accurate to few mm and shows a basin-like subsidence pattern centering at Tuku Township. Four multi-level compaction monitoring wells, co-located with GPS pillars, detect compactions at different depths, showing that the aquifer-system compaction (the cause of subsidence) occurs mostly below depths >200 m, where reduction of groundwater pumping is most needed. The vertical displacements from GPS and leveling agree to within 1 cm, and are larger than the cumulative compaction detected by the compaction-monitoring wells, suggesting that compaction also occurs below 300 m (the depth of the wells). The vertical displacements derived using DInSAR and 8 ENVISAT SAR images agree with the leveling result to 1–2 cm.     

Pore to pore validation of pore network modelling against micromodel experiment results

Pore network modelling (PNM) has been widely used to study the multiphase flow and transport in porous media. Although a number of recent papers discussed the PNM validation on core-scale parameters such as permeability, relative permeability and capillary pressure; quantitative predictive potential of PNM on pore by pore basis has rarely been studied. The aim of this paper is to present a direct comparison between PNM simulations and corresponding micro-model experiments at the same scale and the same geometry. A number of well-defined and constrained two-phase flow in porous medium experimental scenarios were utilized to validate the physics solving part in PNM (filling rules, capillary and viscous pressure). This work validates that a dynamic pore network flow solver can predict two-phase flow displacements for these experiments for drainage situations at both pore and plug scales. A glass-etched micro-model is used to quantify the accuracy of a dynamic PNM solver on pore and core levels. Two-phase drainage micro fluidic experiments at different flow conditions are performed on micro-models. PNM simulations are performed on the same pattern and flow conditions as used in micro-model experiments. The two-phase distribution extracted from experiment images is registered onto rsults of PNM simulations for direct pore to pore comparison. Pore-scale matching level is found at around 75 % for all three test cases. The matching level of core-scale parameters such as S _{w c} and oil-phase permeability varies from case to case; the relative error to micro-model experiment measurements varies from 15 to 60 %. Possible reasons leading to discrepancies on core-scale parameters are discussed: missing considerations during validation of the combination of uncertainty in both simulator input parameters and experiments are seen as the principal factors.     

地图的信息表达与美学思维

长期以来人们偏重于地图的技术研究,导致地图的艺术研究进程落后于技术研究进程。近几年这种情况虽有改善,但对于地图的美学问题研究还是相对偏少。本文从地图的信息表达与美学思维角度出发,从地图的语言、技术、文化及创新4个方面探讨了地图与美学思维融合的问题,并提出了地图学与美学相结合的地图美学论,以期得到适合地图的美学设计。     

Soft soil mapping using Horizontal to Vertical Spectral Ratio (HVSR) for seismic hazard assessment of Chandigarh city in Himalayan foothills,north India

The populated and expanding city of Chandigarh is located in the foothills of Himalaya, near the potentially active Main Frontal Thrust (MFT). A hazard assessment for this city is consequently of major importance. Thick sediments underlies the city and that can potentially amplify the earthquake shaking and contribute to an earthquake disaster in the city. The present study applies the Horizontal to Vertical Spectral Ratio (HVSR) ambient noise methodology to estimate the resonance frequency of the soft sediments and to obtain a first order estimate of sediment thickness. The study indicates that the soil thickness range from 30 to 270 m and that the resonance frequencies vary from 0.236 to 1.479 Hz. A smooth correlation function between soil thickness and resonance frequency is found, indicating relatively homogeneous soil.     

Operational climate prediction in the era of big data in China: Reviews and prospects

Big data has emerged as the next technological revolution in IT industry after cloud computing and the Internet of Things. With the development of climate observing systems, particularly satellite meteorological observation and high-resolution climate models, and the rapid growth in the volume of climate data, climate prediction is now entering the era of big data. The application of big data will provide new ideas and methods for the continuous development of climate prediction. The rapid integration, cloud storage, cloud computing, and full-sample analysis of massive climate data makes it possible to understand climate states and their evolution more objectively, thus predicting the future climate more accurately. This paper describes the application status of big data in operational climate prediction in China; it analyzes the key big data technologies, discusses the future development of climate prediction operations from the perspective of big data, speculates on the prospects for applying climatic big data in cloud computing and data assimilation, and puts forward the notion of big data-based super-ensemble climate prediction methods and computerbased deep learning climate prediction methods.     

Integrated methodology for flood risk assessment and application in urban communities of Pakistan

Flood disasters and its consequent damages are on the rise globally. Pakistan has been experiencing an increase in flood frequency and severity along with resultant damages in the past. In addition to the regular practices of loss and damage estimation, current focus is on risk assessment of hazard-prone communities. Risk measurement is complex as scholars engaged in disaster science and management use different quantitative models with diverse interpretations. This study tries to provide clarity in conceptualizing disaster risk and proposes a risk assessment methodology with constituent components such as hazard, vulnerability (exposure and sensitivity) and coping/adaptive capacity. Three communities from different urban centers in Pakistan have been selected based on high flood frequency and intensity. A primary survey was conducted in selected urban communities to capture data on a number of variables relating to flood hazard, vulnerability and capacity to compute flood risk index. Households were categorized into different risk levels, such as can manage risk, can survive and cope, and cannot cope. It was found that risk levels varied significantly across the households of the three communities. Metropolitan city was found to be highly vulnerable as compared to smaller cities due to weak capacity. Households living in medium town had devised coping mechanisms to manage risk. The proposed methodology is tested and found operational for risk assessment of flood-prone areas and communities irrespective of locations and countries.