珠江口硒的形态分布特征

硒属于硫族元素,在化学和生物化学性质上,硒与硫、碲相似.它和砷一样也是一种类金属物质.硒广泛存在于自然界中,岩石、土壤、沉积物、水体、生物体以及大气环境都含有不同程度的硒.硒是生物必需的一种微量元素,一方面表现出重要的生物功能,另一方面在较高浓度下也表现出毒性,摄入过多可导致动物和人硒中毒[1].在陆源硒向海洋的输送中河流占重要贡献,河流可能是溶解硒输入海洋的主要来源[2,3].河口区是陆地径流与海水相互混合的地带,其中发生的反应直接影响元素的入海通量,因此研究河口化学元素的行为对估计河流向海洋的输入及建立全球生物地球化学循环模型有十分重要的意义.     

Undrained monotonic shear behavior of marine soft clay after long-term cyclic loading

Abstract

In the coastal area, nearshore and offshore structures have been or will be built in marine soft clay deposits that have experienced long-term cyclic loads. Therefore, the mechanical behavior of marine clay after long-term cyclic loading needs to be investigated. In this research, a series of monotonic and cyclic triaxial tests were carried out to investigate the postcyclic mechanical behavior of the marine soft clay. The postcyclic water pore pressure, shear strength and secant stiffness are discussed by comparing the results with the standard monotonic test (without cyclic loading). It is very interesting that the postcyclic behavior of marine soft clay specimen is similar to the behavior of overconsolidated specimen, that is, the specimen shows apparent overconsolidation behavior after long-term cyclic loading. Then relationship between the overconsolidation ratio and the apparent overconsolidation ratio is established on the basis of the theory of equivalent overconsolidation. Finally, a validation formula is proposed which can predict the postcyclic undrained shear strength of marine soft clay.     

基于Stacking集成学习的恒星/星系分类研究

机器学习在当今诸多领域已经取得了巨大的成功,但是机器学习的预测效果往往依赖于具体问题.集成学习通过综合多个基分类器来预测结果,因此,其适应各种场景的能力较强,分类准确率较高.基于斯隆数字巡天(Sloan Digital Sky Survey,SDSS)计划恒星/星系中最暗源星等集分类正确率低的问题,提出一种基于Stacking集成学习的恒星/星系分类算法.从SDSS-DR7(SDSS Data Release 7)中获取完整的测光数据集,并根据星等值划分为亮源星等集、暗源星等集和最暗源星等集.仅针对分类较为复杂且困难的最暗源星等集展开分类研究.首先,对最暗源星等集使用10折嵌套交叉验证,然后使用支持向量机(Support Vector Machine,SVM)、随机森林(Random Forest,RF)、XGBoost(eXtreme Gradient Boosting)等算法建立基分类器模型;使用梯度提升树(Gradient Boosting Decision Tree,GBDT)作为元分类器模型.最后,使用基于星系的分类正确率等指标,与功能树(Function Tree,FT)、SVM、RF、GBDT、XGBoost、堆叠降噪自编码(Stacked Denoising AutoEncoders,SDAE)、深度置信网络(Deep Belief Network,DBN)、深度感知决策树(Deep Perception Decision Tree,DPDT)等模型进行分类结果对比分析.实验结果表明,Stacking集成学习模型在最暗源星等集分类中要比FT算法的星系分类正确率提高了将近10%.同其他传统的机器学习算法、较强的提升算法、深度学习算法相比,Stacking集成学习模型也有较大的提升.     

土地利用视角下城市交通事故驱动机理的研究进展

城镇化与机动交通的快速发展,引发了城市土地利用与交通系统的重塑,导致城市交通安全问题日益严峻。为了优化土地利用布局并改善交通安全,需要从土地利用视角开展交通事故的驱动机理研究。国内外该方面研究形成了以交通流量和交通速度为主要中介因素联系土地利用与交通事故的经典理论框架,却忽略了源于土地利用并深刻影响交通安全的交通需求因素,导致缺乏“土地利用—交通需求—交通事故”完整路径链的研究。论文通过综述该领域文献,在归纳总结城市交通事故影响因素的基础上,揭示土地利用视角下交通事故的驱动机理并探讨未来研究方向。研究指出,土地利用的多维属性特征对交通事故具有重要影响,土地利用与交通系统的动态匹配关系及其对出行行为的影响是揭示交通事故驱动机理的关键突破口,对于构建交通安全导向的城市土地利用模式具有重要的理论与实践意义。     

社区生活圈的新时间地理学研究框架

社区生活圈从居民日常活动及行为视角考察城市社区,是城市地理学和城市相关学科的研究前沿,也是中国国土空间规划体系创新的重要组成部分,以及中国城市社会可持续发展的重要抓手。伴随着流动性和信息化的不断深入,社区生活圈的主体日益多元化、社区活动和居民时空行为日益多样化、社区空间的功能与意义日益丰富化,亟需城市地理学的研究创新与实践引导。时间地理学是理解人与环境关系的社会—技术—生态综合方法,为早期基于时空行为与生活空间的社区生活圈研究提供了重要基础。新时间地理学重视家庭及其他组织企划的交互与时空组合,可为社区生活圈内个体—家庭—社区之间的复杂互动关系研究、时空行为的社会文化制约与多情境分析及模拟提供重要支撑。论文基于新时间地理学方法,从理论、方法和实证3个维度提出社区生活圈的新时间地理学研究框架,具体包括构建社区生活圈的时空行为理论,揭示社区生活圈的时空间结构;创新社区生活圈的时空行为分析和模拟方法;从社区生活圈时空行为优化、社区交往生活圈、社区安全生活圈等方面创新中国城市规划与管理等研究内容。     

Detection of coffee berry necrosis by digital image processing of landsat 8 oli satellite imagery

Coffee berry necrosis is a fungal disease that, at a high level, significantly affects coffee productivity. With the advent of surface mapping satellites, it was possible to obtain information about the spectral signature of the crop on a time scale pertinent to the monitoring and detection of plant phenological changes. The objective of this paper was to define the best machine learning algorithm that is able to classify the incidence CBN as a function of Landsat 8 OLI images in different atmospheric correction methods. Landsat 8 OLI images were acquired at the dates closest to sampling anthracnose field data at three times corresponding to grain filling period and were submitted to atmospheric corrections by DOS, ATCOR, and 6SV methods. The images classified by the algorithms of machine learning, Random Forest, Multilayer Perceptron and Naive Bayes were tested 30 times in random sampling. Given the overall accuracy of each test, the algorithms were evaluated using the Friedman and Nemenyi tests to identify the statistical difference in the treatments. The obtained results indicated that the overall accuracy and the balanced accuracy index were on an average around 0.55 and 0.45, respectively, for the Naive Bayes and Multilayer Perceptron algorithms in the ATCOR atmospheric correction. According to the Friedman and Nemenyi tests, both algorithms were defined as the best classifiers. These results demonstrate that Landsat 8 OLI images were able to identify an incidence of the coffee berry necrosis by means of machine learning techniques, a fact that cannot be observed by the Pearson correlation.     

农户对禁牧政策的行为响应及其影响因素研究——以新疆策勒县为例

以新疆策勒县为例,对当地农牧民在封育禁牧影响下的经济行为响应及其影响因素利用对数线性模型进行定量分析,以研究不同背景条件的农牧民在进行行为选择时的规律性因素。研究表明,农户所属区域及其拥有的资本水平是农户对禁牧行为响应的显著影响因素;农户所属区域及当地的技术服务培训等政策配套水平是农户对禁牧态度的显著影响因素;农牧民畜牧生产观念的转变和草场承包责任制的落实是实施禁牧政策的先决条件。建议政府针对区域及农户的资本差异进行分类补偿;当地政府可整合利用各相关部门的项目资源和资金进行禁牧的政策前期和推广工作。     

Study of Star/Galaxy Classification Based on the XGBoost Algorithm

Machine learning has achieved great success in many areas today. The lifting algorithm has a strong ability to adapt to various scenarios with a high accuracy, and has played a great role in many fields. But in astronomy, the application of lifting algorithms is still rare. In response to the low classification accuracy of the dark star/galaxy source set in the Sloan Digital Sky Survey (SDSS), a new research result of machine learning, eXtreme Gradient Boosting (XGBoost), has been introduced. The complete photometric data set is obtained from the SDSS-DR7, and divided into a bright source set and a dark source set according to the star magnitude. Firstly, the ten-fold cross-validation method is used for the bright source set and the dark source set respectively, and the XGBoost algorithm is used to establish the star/galaxy classification model. Then, the grid search and other methods are used to adjust the XGBoost parameters. Finally, based on the galaxy classification accuracy and other indicators, the classification results are analyzed, by comparing with the models of function tree (FT), Adaptive boosting (Adaboost), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Stacked Denoising AutoEncoders (SDAE), and Deep Belief Nets (DBN). The experimental results show that, the XGBoost improves the classification accuracy of galaxies in the dark source classification by nearly 10% as compared to the function tree algorithm, and improves the classification accuracy of sources with the darkest magnitudes in the dark source set by nearly 5% as compared to the function tree algorithm. Compared with other traditional machine learning algorithms and deep neural networks, the XGBoost also has different degrees of improvement.     

URANS investigation of the interaction between the free surface and a shallowly submerged underwater vehicle at steady drift

An axisymmetric underwater vehicle (UV) at a steady drift angle experiences the complex three-dimensional crossflow separation. This separation arises from the unfavorable circumferential pressure gradient developed from the windward side toward the leeward side. As is well known, the separated flow in the leeward side gives rise to the formation of a pair of vortices, which affects considerably the forces and moments acting on the UV. In this regard, the main purpose of the present study is to evaluate the role of the leeward vortical flow structure in the hydrodynamic behavior of a shallowly submerged UV at a moderate drift angle traveling beneath the free surface. Accordingly, the static drift tests are performed on the SUBOFF UV model using URANS equations coupled with a Reynolds stress turbulence model. The simulations are carried out in the commercial code STARCCM+ at a constant advance velocity based on Froude number equal to F_n = 0.512 over submergence depths and drift angles ranging from h = 1.1D to h = ∞ and from β = 0 to β = 18.11°, respectively. The validation of the numerical model is partially conducted by using the existing experimental data of the forces and moment acting on the totally submerged bare hull model. Significant interaction between the low-pressure region created by the leeward vortical flow structure and the free surface is observed. As a result of this interaction, the leeward vortical flow structure appears to be largely responsible for the behavior of the forces and moments exerted on a shallowly submerged UV at steady drift.