Analysis of resistance factors for LRFD of soil nail pullout limit state using default FHWA load and resistance models

Abstract

Resistance factors for load and resistance factor design (LRFD) of pullout limit state of both permanent and temporary soil nails are calibrated against a wide design space using the current Federal Highway Administration (FHWA) nail load and resistance models. The calculated resistance factors were shown to scatter broadly among design scenarios that differ in wall face batter, soil friction angle, nail ultimate bond strength, and surcharge live load. An important lesson learned from the analysis results is that the current practice of using a single resistance factor for LRFD of nail pullout limit state could not result in uniform reliabilities across different design scenarios. Simple artificial neural network (ANN) models were developed for computation of resistance factors. Design examples demonstrated the ability of the ANN models in providing resistance factors that yield satisfactory and consistent reliabilities in different nail pullout designs.     

On the “rotational angular momentum” of the oceans and the corresponding polar motion

Periodic polar motions caused by ocean tides are predicted. In the Liouville equations for rotational motion the complete excitation functions for the ocean tides have to be used. This does not depend on the fact that hydrodynamical ocean tide models do not consider the centrifugal acceleration. The observable polar motion of the Celestial Ephemeris Pole CEP (more exactly: the terrestrial location of the CEP) is tabulated for the ten ocean tides M₂, S₂, N₂, K₁, O₁, P₁, M f, M f′, M m, Ssa. Typical amplitudes for the largest ocean tides are 0.4 milliarcseconds. This is within the reach of geodetic VLBI and SLR observations.     

A multiple shooting approach for the numerical treatment of stellar structure and evolution

We present a new numerical method for solving the system of partial differential equations describing the structure and evolution of a spherically symmetric star. As usual, we employ the transversal method of lines in order to split the equations into a coupled spatial and temporal part. The novel features of the algorithm are the following: (a) Instead of using the Lagrangian picture we formulate the system of partial differential equations in the Eulerian picture. (b) We reformulate the equations of stellar structure as a multipoint boundary-value problem. By means of this reformulation the rather clumsy iterative matching procedure of stellar atmosphere and interior is avoided. (c) The multipoint boundary-value problem is solved by the multiple shooting method. This approach not only ensures a high accuracy of the stellar models calculated at each time step but also allows the free boundaries inside the star due to different energy transport mechanisms to be located exactly. (d) The time derivatives involved in the stellar-structure equations are discretized implicitly to second order accuracy. Moreover, at each time step, the chemical abundances are determined by using a sophisticated update procedure. In this way, a high accuracy is achieved with respect to the integration in time. The algorithm has turned out to be exceedingly reliable and numerically accurate. This is shown by the evolution of a 1 M_⊙ star up to the hydrogen-shell burning phase. In this example, the virial theorem, the law of mass conservation, and the law of energy conservation is fulfilled to a hitherto unattainable degree of accuracy. Since the multiple shooting method, which is at the heart of our approach, is a perfect example of a parallel algorithm, the computational speed of the algorithm might be substantially improved provided easy-to-program, high-performance parallel computers with sufficiently many processors become available in the near future.     

Box-Behnken设计-效应面法优选百合防褐变加工工艺研究

目的：优选百合防褐变的加工工艺。方法：在单因素试验基础上,以王百合苷B含量、褐变度、浸出物为评价指标,采用Box-Behnken设计效应面法考察烫片时间、干燥温度、干燥时间对其质量的影响,优选百合的防褐变加工工艺参数。结果:百合防褐变加工工艺为烫片时间3.5 min,干燥温度90℃,干燥时间3.0 h。结论：优选的百合防褐变加工工艺合理可行,可适用于工业生产。     

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.     

月球交会对接VLBI差分时延研究

中国探月3期任务中,月球交会对接技术是任务成功的重要保障.利用嫦娥3号(CE03)绕月飞行的VLBI (Very Long Baseline Interferometry)时延数据,模拟仿真绕月交会对接过程中,同波束VLBI观测模式下,差分群时延的变化情况.仿真结果显示,在远程导引段,轨道器和上升组合体轨道距离保持100 km,持续半小时,差分群时延很好地反映了两者的轨道信息,可以用于定轨定位;自主控制段,上升组合体靠近轨道器,在轨道距离从5 km减小到20 m过程中,上升组合体加速追赶轨道器时,差分群时延快速趋近于0,上升组合体减速远离轨道器时,差分群时延绝对值快速变大.最后,利用嫦娥3号奔月段同时发射两个DOR (Differential One-Ranging)信号的VLBI时延数据,计算差分相时延,初步展示了月球交会对接过程中同波束VLBI差分相时延的误差情况.     

ASO-S卫星工程科学应用系统的数据库设计

先进天基太阳天文台(Advanced Space-based Solar Observatory, ASO-S)卫星是我国首颗太阳观测卫星, 主要观测太阳耀斑和日冕物质抛射以及产生它们的磁场结构. ASO-S卫星的科学应用系统是科学卫星工程的6大系统之一, 它连接科学用户和卫星数据, 为将卫星的科学数据转化为科学成果提供保障. 科学应用系统的数据库是连接软件与海量数据的枢纽, 为科学数据生产和用户服务及运行提供数据层的支撑. 介绍了科学应用系统的数据库架构设计、数据库的选择以及数据库性能优化和表样例. 这里的数据库包括观测计划、工程参数、运维日志、科学数据、定标数据和特征事件识别等数据库. 这些数据库的建设将为ASO-S卫星工程科学应用系统的顺利运行提供数据支撑, 也可以为未来其他科学卫星类似数据库的搭建提供参考和借鉴.     

An Automatic Method for Detecting Transients and Variable Sources in AST3 Survey Based on Image Subtraction and Random Forest

AST3-2 (the second Antarctic Survey Telescope) is located in Antarctic Dome A, the loftiest ice dome on the Antarctic Plateau. It produces a huge amount of observational data which require a more efficient data reduction program to be developed. Also the data transmission in Antarctica is much difficult, thus it is necessary to perform data reduction and detect variable and transient sources remotely and automatically in Antarctica, but this attempt is restricted by the unsatisfactory performance of the low power consumption computer in Antarctica. For realizing this purpose, to develop a new method based on the existing image subtraction method and random forest algorithm, taking the AST3-2 2016 dataset as the test sample, becomes an alternative choice. This method performs image subtraction on the dataset, then applies the principle component analysis to extract the features of residual images. Random forest is used as a machine learning classifier, and in the test a recall rate of 97% is resulted for the positive sample. Our work has verified the feasibility and accuracy of this method, and finally found out a batch of candidates for variable stars in the AST3-2 2016 dataset.     

Formation of Carbon-Enhanced Metal-Poor RR Lyrae Stars

Carbon-enhanced metal-poor (CEMP) stars are considered to be related to the first generation of stars, and responsible for the chemical evolution of the early Galaxy. More than half of them are in binaries, and could be explained by the binary evolution, but the formation channel of them is still not fully understood. Among the hundreds of CEMP stars, there are nine CEMP RR Lyrae stars identified, and at least seven of which are very likely not binaries. The usual binary star evolution channel is difficult to produce such a single star, particularly that of carbon enrichment. One way in which such a single star might be produced is the merger of a helium white dwarf with a Hertzsprung gap (HG) star. We use a stellar evolution program to calculate the models of the merger remnants, and find that the models can reproduce the observed distribution of these CEMP single RR Lyrae stars in terms of surface temperature, gravity, and carbon abundance. Hence, it is extremely possible that the helium white dwarf and HG star merger model is one of the formation channels of the metal-poor carbon-rich RR Lyrae stars.