TBP－（X－5）反相色谱分离m－NPF分光光度法测定铀

本文研究了以磷酸三丁酯为固定相，大孔聚乙烯苯（Ｘ－５型）为担体的浸渍树脂、垂直型反相色谱柱，对铀分离富集的基本性能，以及铀（Ⅳ）－间硝基苯基萤光酮（ｍ－ＮＰＦ）一氨三乙酸（ＮＴＡ）－吐温－８０显色体系的反应条件，提出了ＴＢＰ（Ｘ－５）分离，ｍ－ＮＰＦ－ＮＴＡ－吐温－８０光度法测定铀，经标准样品验证，结果满意。     

Turbulence fields in the lee of two‐dimensional transverse dunes simulated in a wind tunnel

The turbulence field of airflow in the lee of a dune has significant impacts on dune dynamics and related processes. We used particle image velocimetry in a wind tunnel simulation to obtain detailed velocity measurements in the lee of two‐dimensional transverse dune models, then used the results to analyse their turbulence fields. The dune models used in this study had a single lee angle of 30°, and a total of six stoss angles: 3°, 5°, 10°, 15°, 20° and 25°. We used vorticity, turbulence intensity, Reynolds stress and turbulent kinetic energy to characterize the turbulence fields. These parameters were functions of stoss angle, wind velocity, distance from the dune crest and height above the ground surface. The stoss angles could generally be divided into two groups based on the profiles of mean velocity, turbulence and Reynolds stress. Stoss angles of 3° and 5° usually had similar profiles, and angles of 15°, 20° and 25° formed a second group with similar profiles. The profiles for the stoss angle of 10° were usually transitional and were intermediate between the two groups. Vorticity, Reynolds stress and turbulent kinetic energy increased monotonically with increasing free‐stream wind velocity, but their variations with respect to the stoss angle were complex. The stoss angles of 15° and 20° had the maximum values of these three parameters, thus these angles may have special significance in dune development given the characteristics of the mean velocity fields and turbulence fields they produce within the lee airflow. It is the streamwise velocity component and its turbulence that determine the surface shear stress. Copyright © 2008 John Wiley and Sons, Ltd.     

2000~2010年长江三角洲土地利用/覆盖空间格局动态变化研究

土地利用/覆盖变化与空间格局间的相互作用机制是全球环境变化的研究热点。以2000年、2005年及2010年遥感影像为基础数据源,通过面向对象的分类方法,对长江三角洲的土地覆盖进行分类。利用分类结果,通过数学模型和景观指数探讨了土地利用变化对长江三角洲景观格局的影响。结果表明：1)2000~2010年长江三角洲土地利用变化明显,人工表面“涨势”明显,耕地与湿地则呈现下降趋势;2)10年间长江三角洲土地利用多样化指数增加,土地利用多样性程度逐年提高;3)2000~2010年长江三角洲景观破碎化程度增加,优势度减小,景观形状复杂性与异质性不断加大。这些研究可以为土地利用结构的调整、生态环境的恢复重建提供依据,促进长江三角洲的可持续发展。     

庆阳地区超高层巨厚层黄土地基工程地质特征

为了更深层次地了解黄土的地质特征,以庆阳地区某超高层巨厚层黄土地基为研究对象,对陇东黄土的物理和力学性能进行了深入探究。主要通过原位试验与室内试验,从地层结构、分布深度、液限、塑限、塑性指数、孔隙比、湿陷变形、剪切波速、地基土承载力、抗剪强度等方面对该地区黄土的工程性能进行了综合分析与评价,结果表明：5.0 m以内的马兰黄土湿陷性较强烈,黏聚力小,压缩性大;随着黄土厚度逐渐增大,离石黄土及午城黄土黏聚力增大,压缩性减小,呈轻微-中等湿陷性;受压超过400 kPa的马兰黄土、受压大于600 kPa的离石黄土及受压大于200 kPa的午城黄土均无湿陷性;15.0 m以内土体最大干密度为1.67~1.76 g/cm³,最优含水率为16.6%~17.7%;含水率高值相对集中在地下水位62.0 m以下、饱和度90.0%以上地区。     

美国全球变化研究现状

美国的全球变化研究主要由美国全球变化研究计划（ＵＳＧＣＲＰ）支持,重点资助季节—年际尺度气候变率,十年—百年尺度的气候变化,臭氧、ＵＶ辐射以及大气化学的变化,土地利用以及陆地、海洋生态系统的变化等４个领域。当前,水汽与云仍是全球变化研究中不确定性较大的一个方面,因而受到关注。关于气候变化的信号检测以及成因分析也是一个研究热点。气候模拟研究是全球变化研究的一个主要方法。卫星资料在全球变化研究中的应用取得了大量成果。近期美国在全球变化研究领域的重点是气候模拟,短期气候预测,十年—百年尺度的气候变化,臭氧、ＵＶ辐射以及大气化学的变化,地表以及陆地、海洋生态系统变化,对全球变化的区域尺度估计,卫星资料的应用,气候变化影响的国家级评估等８个方面。     

Modified sublimation to isolate phenanthrene-degrading bacteria of the genera Sphingomonas and Burkholderia from Xiamen oil port

Sublimation was developed by Alley and Brown (2000) in order to isolate bacterial strains that were capable of degrading water insoluble compounds. In this study, sublimation was modified by the use of nutritional agar plates, instead of mineral salt agar, to isolate phenanthrene-degrading bacteria from a mixed culture that had been enriched under the selective pressure of high phenanthrene content. Five strains were obtained with different morphology and degradation ability. Based on the 16S rDNA sequence, two of them were classified as species of the genus Sphingomonas; the others as species of the genus Burkholderia. Denaturing gradient gel electrophoresis (DGGE) was introduced to detect dynamic changes in the bacterial community during enrichment batch culture, and to determine any correlation between the five isolates and the phenanthrene-degrading consortium. The DGGE profile indicated that these five isolates corresponded to four dominant bands of the consortium. Compared to traditional means of isolation, we concluded that modified sublimation is effective and more convenient.     

Semi-supervised least squares support vector machine algorithm: application to offshore oil reservoir

At the early stages of deep-water oil exploration and development, fewer and further apart wells are drilled than in onshore oilfields. Supervised least squares support vector machine algorithms are used to predict the reservoir parameters but the prediction accuracy is low. We combined the least squares support vector machine (LSSVM) algorithm with semi-supervised learning and established a semi-supervised regression model, which we call the semi-supervised least squares support vector machine (SLSSVM) model. The iterative matrix inversion is also introduced to improve the training ability and training time of the model. We use the UCI data to test the generalization of a semi-supervised and a supervised LSSVM models. The test results suggest that the generalization performance of the LSSVM model greatly improves and with decreasing training samples the generalization performance is better. Moreover, for small-sample models, the SLSSVM method has higher precision than the semi-supervised K-nearest neighbor (SKNN) method. The new semisupervised LSSVM algorithm was used to predict the distribution of porosity and sandstone in the Jingzhou study area.     

地震数据IP传输及存储技术应用

介绍一种IP传输方案。该方案通过地震数据IP传输及存储单元(ITU)、IP流多串口服务器完成，能实现单个台站与多个台网中心的Internet传输、单个台站与多个台网中心的专线传输、地震台网中心之间的Internet连接、远程数据下载及参数设置等多种传输方式，该传输方案具备实时性、可压缩性、多路服务、安全性、数据在线保存、传输费用低、操作简单等特点。     

Semantic segmentation of high spatial resolution images with deep neural networks

Availability of reliable delineation of urban lands is fundamental to applications such as infrastructure management and urban planning. An accurate semantic segmentation approach can assign each pixel of remotely sensed imagery a reliable ground object class. In this paper, we propose an end-to-end deep learning architecture to perform the pixel-level understanding of high spatial resolution remote sensing images. Both local and global contextual information are considered. The local contexts are learned by the deep residual net, and the multi-scale global contexts are extracted by a pyramid pooling module. These contextual features are concatenated to predict labels for each pixel. In addition, multiple additional losses are proposed to enhance our deep learning network to optimize multi-level features from different resolution images simultaneously. Two public datasets, including Vaihingen and Potsdam datasets, are used to assess the performance of the proposed deep neural network. Comparison with the results from the published state-of-the-art algorithms demonstrates the effectiveness of our approach.