基于PSR-WSVM模型的边坡位移预测

为建立高精度的边坡位移预测模型，采用相空间重构（PSR）将边坡位移时间序列数据转换为多维数据，同时构造小波核函数改进的支持向量机模型，建立PSR-WSVM模型并应用于边坡位移预测。将PSR-WSVM模型预测结果与传统支持向量机（SVM）模型、小波支持向量机（WSVM）模型和基于相空间重构的支持向量机（PSR-SVM）模型预测结果进行对比，通过平均绝对误差（MAE）、平均绝对误差百分比（MAPE）和均方根误差（RMSE）3个精度评价指标验证PSR-WSVM模型的可行性。工程实例结果表明，PSR-WSVM模型预测结果的3个精度评价指标都优于另外3种模型，边坡位移预测的精度明显提升。     

Three-dimensional finite element simulation of fracture propagation in rock specimens with pre-existing fissure(s) under compression and their strength analysis

A FORTRAN program, consistent with the commercially available finite element (FE) code ABAQUS, is developed based on a three-dimensional (3D) linear elastic brittle damage constitutive model with two damage criteria. To consider the heterogeneity of rock, the developed FORTRAN program is used to set the stiffness and strength properties of each element of the FE model following a Weibull distribution function. The reliability of the program is assessed against available experimental results for granite cylindrical specimens with a throughgoing, flat and inclined fissure. The calibration procedure of the material parameters is explained in detail, and it is shown that the compressive to tensile strength ratio can have a substantial influence on the failure response of the specimens. Numerical simulations are conducted for models with different levels of heterogeneity. The results show a smaller load bearing capacity for models with less homogeneity, representing gradual coalescence of fully damaged elements forming throughout the models during loading. The maximum load bearing capacity is studied for various combinations of inclination angles of two centrally aligned, throughgoing and flat fissures of equal length embedded in cylindrical models under uniaxial and multiaxial loading conditions. The key role of the compressive to tensile strength ratio is highlighted by repeating certain simulations with a lower compressive to tensile strength ratio. It is proven that the peak loads of the rock models with sufficiently small compressive to tensile strength ratios containing two throughgoing fissures of equal length are similar, provided that the minimum inclination angles of the models are the same. The results are presented and discussed with respect to the existing experimental findings in the literature, suggesting that the numerical model applied in this study can provide useful insight into the failure behaviour of rock-like materials.     

Implementation of a coupled plastic damage distinct lattice spring model for dynamic crack propagation in geomaterials

This paper studies dynamic crack propagation by employing the distinct lattice spring model (DLSM) and 3‐dimensional (3D) printing technique. A damage‐plasticity model was developed and implemented in a 2D DLSM. Applicability of the damage‐plasticity DLSM was verified against analytical elastic solutions and experimental results for crack propagation. As a physical analogy, dynamic fracturing tests were conducted on 3D printed specimens using the split Hopkinson pressure bar. The dynamic stress intensity factors were recorded, and crack paths were captured by a high‐speed camera. A parametric study was conducted to find the influences of the parameters on cracking behaviors, including initial and peak fracture toughness, crack speed, and crack patterns. Finally, selection of parameters for the damage‐plasticity model was determined through the comparison of numerical predictions and the experimentally observed cracking features.     

A machine learning approach for predicting computational intensity and domain decomposition in parallel geoprocessing

ABSTRACT

High performance computing is required for fast geoprocessing of geospatial big data. Using spatial domains to represent computational intensity (CIT) and domain decomposition for parallelism are prominent strategies when designing parallel geoprocessing applications. Traditional domain decomposition is limited in evaluating the computational intensity, which often results in load imbalance and poor parallel performance. From the data science perspective, machine learning from Artificial Intelligence (AI) shows promise for better CIT evaluation. This paper proposes a machine learning approach for predicting computational intensity, followed by an optimized domain decomposition, which divides the spatial domain into balanced subdivisions based on the predicted CIT to achieve better parallel performance. The approach provides a reference framework on how various machine learning methods including feature selection and model training can be used in predicting computational intensity and optimizing parallel geoprocessing against different cases. Some comparative experiments between the approach and traditional methods were performed using the two cases, DEM generation from point clouds and spatial intersection on vector data. The results not only demonstrate the advantage of the approach, but also provide hints on how traditional GIS computation can be improved by the AI machine learning.     

Bacterial productivity in the Prydz Bay and its adjacent waters,Antarctic

1　IntroductionBacteriaandtheiractivitiesplayanimportantroleintheelementalbiogeochemicalcyclesandenergytransformingintheocean (Zhenetal.1 997) .DortchandPackard(1 989) proposedthatfoodwebsintheeutrophicwatersaredominatedbythebiomassofprimaryproducerswhilefoodwebsintheoligotrophicwatersaredominatedbythebiomassofmicrobes.Heterotrophicbacteriahadbeenshowntoplayanimportantroleinthedecompositionoflarge ,rapidlysinkingorganicparticleswithinandbelowtheeuphot iczone ,andfurthertoaffecttheelementaldyn…     

Virtual Huanghe River System： Framework and Technology

1 Introduction Huanghe (Yellow) River basin is located in 32°–42°N, 96°–119°E. The area of the catchment is more than 752,000km2. The river is 5464km long with a drop in elevation of 4830m. Among the whole area, the moun- tainous and stone area accounts for 29%, loess and hills area 46%, sandy area 11% and plain area 14%, respec- tively. Different natural landscapes exist in this area. The Huanghe River flows through the Loess Plateau, where the soil is eroded seriously (Wang, 2002;…     

X-ray synchrotron emission from the oblique shock in the jet of the powerful radio galaxy 3C 346

We report the first detection, with Chandra , of X-ray emission from the jet of the powerful narrow-line radio galaxy 3C 346. X-rays are detected from the bright radio and optical knot at which the jet apparently bends by approximately 70°. The Chandra observation also reveals a bright galaxy-scale atmosphere within the previously known cluster and provides a good X-ray spectrum for the bright core of 3C 346. The X-ray emission from the knot is synchrotron radiation, as seen in lower-power sources. In common with these sources, there is evidence of morphological differences between the radio/optical and X-ray structures, and the spectrum is inconsistent with a one-component continuous-injection model. We suggest that the X-ray-bright knot is associated with a strong oblique shock in a moderately relativistic, light jet, at ∼ 20° to the line of sight, and that this shock is caused by the jet interacting with the wake in the cluster medium behind the companion galaxy of 3C 346. The general jet curvature can result from pressure gradients in the cluster atmosphere.     

Automatic Determination of Stellar Atmospheric Parameters Using Neural Networks and Instance-Based Machine Learning

In this article we show how machine learning methods can beeffectively applied to the problem of automatically predictingstellar atmospheric parameters from spectral information, a veryimportant problem in stellar astronomy. We apply feedforwardneural networks, Kohonen's self-organizing maps andlocally-weighted regression to predict the stellar atmosphericparameters effective temperature, surface gravity and metallicityfrom spectral indices. Our experimental results show that thethree methods are capable of predicting the parameters with verygood accuracy. Locally weighted regression gives slightly betterresults than the other methods using the original dataset asinput, while self-organizing maps outperform the other methods when significant amounts of noise are added. We also implemented a heterogeneous ensemble of predictors, combining the results given by the three algorithms. This ensemble yields better results than any of the three algorithms alone, using both the original and the noisy data.     

A Chandra detection of the radio hotspot of 3C 123

Chandra X-ray Observatory observations of the powerful, peculiar radio galaxy 3C 123 have resulted in an X-ray detection of the bright eastern hotspot, with a 1-keV flux density of ∼5 nJy. The X-ray flux and spectrum of the hotspot are consistent with the X-rays being inverse-Compton scattering of radio synchrotron photons by the population of electrons responsible for the radio emission ('synchrotron self-Compton emission') if the magnetic fields in the hotspot are close to their equipartition values. 3C 123 is thus the third radio galaxy to show X-ray emission from a hotspot which is consistent with being in equipartition. Chandra also detects emission from a moderately rich cluster surrounding 3C 123, with L _X(2–10 keV)=2×10⁴⁴ erg s⁻¹ and kT ∼5 keV, and absorbed emission from the active nucleus, with an inferred intrinsic column density of 1.7×10²² cm⁻² and an intrinsic 2–10 keV luminosity of 10⁴⁴ erg s⁻¹.     

紫阳滑坡治理工程实例分析

本文介绍紫阳骨坡治理工程中有关抗滑桩的设计与施工。文中详细地论述了设计中设计人员应根据工程的具体情况去灵活处理的一些技术问题，如确定有关参数的选择原则，计算上的条件简化和一些具体技术细节的处理方法等．