Multiphase Thermohaline Convection in the Earth’s Crust: I. A New Finite Element – Finite Volume Solution Technique Combined With a New Equation of State for NaCl–H2O

We present a new finite element – finite volume (FEFV) method combined with a realistic equation of state for NaCl–H₂O to model fluid convection driven by temperature and salinity gradients. This method can deal with the nonlinear variations in fluid properties, separation of a saline fluid into a high-density, high-salinity brine phase and low-density, low-salinity vapor phase well above the critical point of pure H₂O, and geometrically complex geological structures. Similar to the well-known implicit pressure explicit saturation formulation, this approach decouples the governing equations. We formulate a fluid pressure equation that is solved using an implicit finite element method. We derive the fluid velocities from the updated pressure field and employ them in a higher-order, mass conserving finite volume formulation to solve hyperbolic parts of the conservation laws. The parabolic parts are solved by finite element methods. This FEFV method provides for geometric flexibility and numerical efficiency. The equation of state for NaCl–H₂O is valid from 0 to 750°C, 0 to 4000 bar, and 0–100 wt.% NaCl. This allows the simulation of thermohaline convection in high-temperature and high-pressure environments, such as continental or oceanic hydrothermal systems where phase separation is common.     

Variant of Greedy Randomized Gauss-Seidel Method for Ridge Regression

The variants of randomized Kaczmarz and randomized Gauss-Seidel algorithms are two effective stochastic iterative methods for solving ridge regression problems. For solving ordinary least squares regression problems, the greedy randomized Gauss-Seidel (GRGS) algorithm always performs better than the randomized Gauss-Seidel algorithm (RGS) when the system is overdetermined. In this paper, inspired by the greedy modification technique of the GRGS algorithm, we extend the variant of the randomized Gauss-Seidel algorithm, obtaining a variant of greedy randomized Gauss-Seidel (VGRGS) algorithm for solving ridge regression problems. In addition, we propose a relaxed VGRGS algorithm and the corresponding convergence theorem is established. Numerical experiments show that our algorithms outperform the VRK-type and the VRGS algorithms when $m > n$.     

A Class of Linear Biased Estimators of Regression ParameterMatrix in the Growth Curve Model

ＡＣｌａｓｓｏｆＬｉｎｅａｒＢｉａｓｅｄＥｓｔｉｍａｔｏｒｓｏｆＲｅｇｒｅｓｓｉｏｎＰａｒａｍｅｔｅｒＭａｔｒｉｘｉｎｔｈｅＧｒｏｗｔｈＣｕｒｖｅＭｏｄｅｌ￥ＧｕｉＱｉｎｇｍｉｎｇ（ＺｈｅｎｇｚｈｏｕＩｎｓｔｉｔｕｔｅｏｆＳｕｒｖｅｙｉｎｇａｎｄＭａ...     

作物垄作的数学模型

应用高等数学中对面积的曲面积分等方法,建立了垄作种植中半椭圆形、抛物线型和三角形垄的数学模型,比较了不同垄形、垄宽、垄高在增加单位土地表面积和突出地面垄体体积的效果.     

关于线性回归模型的有偏估计

有偏估计方法是近代回归分析的常用方法．本文研究了几种常用的有偏估计方法，澄清了这些方法的区别和联系．对有偏估计的一些关键点进行研究，给出了一种新的岭参数确定法和一种新的主成分概念，并讨论了这些方法的优良性．为了提高有偏估计的效率，提出了用比例因子规范模型的方法．最后，给出了说明本文方法的数值例子．     

Some recent and new results on the maximum entropy distribution

This letter concerns the maximum entropy (ME) distribution proposed by Theil and Laitinen (1980); it summarizes some of the results obtained with this distribution and its explores the conditioning of the ME covariance matrix as the number of variables increases.     

多元协方差阵扰动模型岭估计的影响分析

本文研究了多元线性同归模型岭估计的影响分析问题.利用最小二乘估计方法,获得了多元协方差阵扰动模型与原模型参数阵之间的岭估计的一些关系式,给出了度量影响大小的基于岭估计的广义Cook距离.     

Automatic Feature Selection via Weighted Kernels and Regularization

Selecting important features in nonlinear kernel spaces is a difficult challenge in both classification and regression problems. This article proposes to achieve feature selection by optimizing a simple criterion: a feature-regularized loss function. Features within the kernel are weighted, and a lasso penalty is placed on these weights to encourage sparsity. This feature-regularized loss function is minimized by estimating the weights in conjunction with the coefficients of the original classification or regression problem, thereby automatically procuring a subset of important features. The algorithm, KerNel Iterative Feature Extraction (KNIFE), is applicable to a wide variety of kernels and high-dimensional kernel problems. In addition, a modification of KNIFE gives a computationally attractive method for graphically depicting nonlinear relationships between features by estimating their feature weights over a range of regularization parameters. The utility of KNIFE in selecting features through simulations and examples for both kernel regression and support vector machines is demonstrated. Feature path realizations also give graphical representations of important features and the nonlinear relationships among variables. Supplementary materials with computer code and an appendix on convergence analysis are available online.     

The single-longitudinal-mode operation of a ridge waveguide laser based on two-dimensional photonic crystals

An electrically driven, single-longitudinal-mode GaAs based photonic crystal (PC) ridge waveguide (RWG) laser emitting at around 850 nm is demonstrated. The single-longitudinal-mode lasing characteristic is achieved by introducing the PC to the RWG laser. The triangle PC is etched on both sides of the ridge by photolithography and inductive coupled plasma (ICP) etching. The lasing spectra of the RWG lasers with and without the PC are studied, and the result shows that the PC purifies the longitudinal mode. The power per facet versus current and current-voltage characteristics have also been studied and compared.