Ion flux dropout observed near dipolarization front

An ion flux dropout near the dipolarization front （DF） at around XGSM =- 11 RE in the Earth＇ s plasma sheet was observed by Time History of Events and Macroscale Interaction during substorms （THEMIS） on March 31, 2009. The ion differential energy fluxes at energies from 450 eV to 150 keV measured by the ESA and SST instruments from THC began to decrease about 2 s before the detection of the DF and reached a local minimum 6 s later. Then, the ion fluxes gradually increased to form a dropout around the DF. The spatial extent of the dropout was about 4,000 km. For energies above 20 keV, the ion fluxes after the dropout are greater than those before it, contrary to the fluxes at energies below 20 keV. The associated ion density variation indicates that the ion flux dropout coincides with the ion density dropout. Taking advantage of multipoint observations, THD, THC, and THE detected the same DF consecutively. Only THC detected an obvious ion flux dropout; THD observed an indistinct one about 2 s before THC; no high-energy （E 〉 30 keV） ion flux dropout was observed by THE. Our study suggests that the ion flux dropout may evolve with the earthward-propagating DF, and its properties can depend on locations relative to the DF.     

Benthic foraminiferal δ~(13)C minimum events in the southeastern Okhotsk Sea over the last 180 ka

A total of six d13C minimum events,i.e.,VI,V,IV,III,II,and I,were observed via a stable carbon and oxygen isotope analysis of infaunal benthic foraminifera Uvigerina spp.in gravity core OS03-1 in the southeastern Okhotsk Sea over the last 180 ka.These events occurred at112–109,102–90,85–76,57–54,44–40,and 17–10 ka BP.The largest negative excursions reached 2.5%in event V and were greater than 1%in the other events.We proposed that all d13C minimum events were caused by the increase in sea surface water productivity,the weakened formation of Okhotsk Sea intermediate water,and the enhancement of the oxygen minimum zone.The negative excursions were unaffected by methane hydrate destabilization and subsequent methane release based on the results obtained by using archaeal lipid markers.     

对合法有序语境下的网络监督思考

网络监督是以网民为主体,以互联网为平台,通过网络技术和各种网络形式对公共权力进行监督的方式。它需要加强网络系统的建设,建立部门齐全的法律相互协作,并协调网络政治参与和网络有序发展的关系,只有这样,才能提高网民的网络监督意识与能力。     

民办高校党组织与董事会关系探究

随着民办高校的发展,民办高校党建工作在逐渐步入正轨的同时面临许多发展难题,其中最核心的是民办高校党组织与董事会的关系问题。从理论来看,二者之间的关系为决策与参与决策、监督与被监督、引导与被引导、分工协作,但在实践中却出现分权不清、失衡、权力运作失范、制度缺失严重、机制运作失调等问题,因此,只有转变观念,准确定位,建立框架科学、细节完备的制度体系,打造良性互动的科学机制,夯实基础、循序渐进才能处理好二者的关系,推进民办高校健康发展。     

一种环境激励下斜拉桥模型损伤识别方法

虚拟脉冲响应函数可表征环境激励下结构系统固有的动力特性,但桥梁试验模型空间变异的模糊性和测量过程中环境激励的随机性,使得虚拟脉冲响应函数同时具有随机不确定性和模糊不确定性,适用于随机-模糊统计原理处理.而Hilbert-Huang变换(HHT)是一种新的适用于非线性、非平稳信号且具有自适应性的数据处理方法.提出了基于HHT与虚拟脉冲响应函数随机模糊均值的结构损伤识别方法,并将4个Hilbert边际谱能量指标应用到室内斜拉桥模型的损伤识别中.结果表明:主梁损伤前后4种能量指标都发生了变化,且体现出相似的规律性,准确指示了较大程度损伤的损伤位置和程度,为大型桥梁结构在线监测提供了一种方法.     

Template/surfactant free and UV light irradiation assisted fabrication of Mn-Co oxides composite nanorings: Structure and synthesis mechanism

A facile, green, cost effective, and template or surfactant free strategy via a two-step route i.e. UV irradiation followed by dissolution-growth for the controlled synthesis of Mn-Co composite nanorings at room temperature was proposed. The nanorings structures and the process were systematically studied via TEM, HRTEM, EDX and XRD. Bir-MnO_2 nanosheets first formed through the direct photolysis of KMnO_4 under UV light irradiation. This was followed by a dissolution-growth process via the dissolution of Bir-MnO_2 nanosheets by the generated H~+ from the redox reaction between the added Co(NO_3)_2 and residual KMnO_4, as well as the accompanied formation of CoOOH, and Mn-Co composite nanorings. It was found that a balance between the dissolution and growth rate was needed. Suitable pH, KMnO_4 presence and the starting nanosheets like Bir-MnO_2 were the key factors for the formation of nanorings. The obtained Mn-Co nanorings/TiO_2 showed enhanced efficiency for the catalytic oxidation of HCHO. Based on the above understandings, it is believed that the developed dissolution-growth method can be adopted for the designing and preparation of other Mn-Co composite nanostructures which can be further applied for environmental pollution remediation.     

Distance-Based Regression Analysis for Measuring Associations

Distance-based regression model, as a nonparametric multivariate method, has been widely used to detect the association between variations in a distance or dissimilarity matrix for outcomes and predictor variables of interest in genetic association studies, genomic analyses, and many other research areas. Based on it, a pseudo-F statistic which partitions the variation in distance matrices is often constructed to achieve the aim. To the best of our knowledge, the statistical properties of the pseudo-F statistic has not yet been well established in the literature. To fill this gap, the authors study the asymptotic null distribution of the pseudo-F statistic and show that it is asymptotically equivalent to a mixture of chi-squared random variables. Given that the pseudo-F test statistic has unsatisfactory power when the correlations of the response variables are large, the authors propose a square-root F-type test statistic which replaces the similarity matrix with its square root. The asymptotic null distribution of the new test statistic and power of both tests are also investigated. Simulation studies are conducted to validate the asymptotic distributions of the tests and demonstrate that the proposed test has more robust power than the pseudo-F test. Both test statistics are exemplified with a gene expression dataset for a prostate cancer pathway.

     

A Layer-Stripping Method for 3D Near-Surface Velocity Model Building Using Seismic First-Arrival Times

In order to improve the efficiency of 3D near-surface velocity model building, we develop a layer-stripping method using seismic first-arrival times. The velocity model within a Common Mid-Point (CMP) ...     

基于变量选择-神经网络模型的复杂路网短时交通流预测

摘要： 针对传统交通流预测模型正在由单断面历史数据处理向多断面、多时刻历史数据处理转变,但在考虑各断面间的影响时,多变的交通状况往往会使预测模型复杂化的问题,引入一种多元线性回归最小绝对收缩和选择算子方法(Lasso),并利用其优秀的变量选择能力,在复杂路网多断面中选出相关性较高的断面；结合神经网络(NN)的非线性特性,提出了Lasso NN组合模型.结果表明：Lasso NN模型在路网交叉口对未来15 min交通流数据预测的误差率低于9.2%；在非交叉口的误差率低于6.7%,总体优于各自单独使用得出的结果.     

齿轮检测装置及焦化设备结构优化的研究

文章主要研究了捣固焦炉设备中的齿轮检测装置,装煤车后挡板卷扬缓冲装置及摇动给料机漏煤现象的结构优化,目的为提高焦炉设备的性能,保障其质量,提高焦化设备的结构性能和生产效率。