Subsurface flow measurements using passive flux meters in variably-saturated cold-regions landscapes

To date, passive flux meters have predominantly been applied in temperate environments for tracking the movement of contaminants in groundwater. This study applies these instruments to reduce uncertainty in (typically instantaneous) flux measurements made in a low-gradient, wetland dominated, discontinuous permafrost environment. This method supports improved estimation of unsaturated and over-winter subsurface flows which are very difficult to quantify using hydraulic gradient-based approaches. Improved subsurface flow estimates can play a key role in understanding the water budget of this landscape.     

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.     

Optimal Filtering Algorithm for Stochastic 2-D FMM Ⅱ with Multiplicative Noise

1　Introduction　Inrecentyearstherehasbeengrowingresearchin terestinthetwo dimensional (2 D )systemtheory .The 2 Dsystemsmayfindapplicationsinareassuchasmarineseismicdataprocessingandimageprocessing .Althoughmoreandmorevaluableresultshavebeengained ,mos…     

基于蚁群算法的城市可持续发展综合评价

在可持续发展体系分类的基础上，应用蚁群算法对可持续发展各分类指数公式和综合指数公式进行优化，建立可持续发展评价模型。将该模型应用于乌鲁木齐市可持续发展水平评价，评价结果与现状分析一敛。结果表明该模型简单适用、有良好的通用性。     

分组密码的安全性与强化设计

分组密码是数据通讯中最常用的数据加密方式，以DES为例分析现有分组加密算法的安全隐患，并提出了可变密钥加密和变长密文输出两个新思路，可应用于所有现有分组加密算法以提高安全性，并就该方法的安全性、效率、具体应用做出了分析。     

滇西北和邻区均衡重力异常特征及其壳内构造

本文以爱黎-海斯卡宁均衡补偿假设模式为基础,利用圆模板编制了滇西北及邻区均衡重力异常图。分析了异常的基本特征与壳内构造的关系,并初步探讨了均衡重力异常和强震活动的相关性。     

用遗传算法对自动控制系统数学模型寻优

探讨用遗传算法对数学模型进行优化。考虑到控制系统稳健性的要求 ,用遗传算法寻找出控制系统最佳稳定域 ,实现控制系统数学模型的寻优。阐述了用遗传算法求解问题的步骤和参数的取值 ,并用仿真实验对优化结果进行了检验。结果表明 :在控制系统数学模型的优化中 ,遗传算法具有其他算法无可比拟的优越性。     

Three-dimensional discontinuous deformation analysis (3-D DDA) using a new contact resolution algorithm

This paper presents a new contact calculating algorithm for contacts between two polyhedra with planar boundaries in the three-dimensional discontinuous deformation analysis (3-D DDA). In this algorithm, all six type contacts in 3-D (vertex-to-face, vertex-to-edge, vertex-to-vertex, face-to-face, edge-to-edge, and edge-to-face) are simply transformed into the form of point-to-face contacts. The presented algorithm is a simple and efficient method and it can be easily coded into a computer program. In this paper, formulations of normal contact, shear contact and frictional force submatrices based on the new method are derived and the algorithm has been programmed in VC⁺⁺. Examples are provided to demonstrate the new contact rule between two blocks.     

The effects of gas flaring on crops in the Niger Delta,Nigeria

Flaring of associated gas from oil exploitation has several consequences on the environment. This study explores the spatial variability effects of gas flaring on the growth and development of cassava (Manihot esculenta), waterleaf (Talinum triangulare), and pepper (Piper spp.) crops commonly cultivated in the Niger Delta, Nigeria. Data was collected on soil and atmospheric temperature and moisture at a 20-m interval, starting at 40 m from the flare point to a distance of 140 m. Lengths and widths of crop leaves, height of crop plants and cassava yields were measured at the specified distances. The amino acid, ascorbic acid, starch, and sugar constituents of the cassava yields were determined. The results suggest that a spatial gradient exists in the effects of gas flares on crop development. Retardation in crop development manifests in decreased dimensions of leaf lengths and widths of cassava and pepper crops closer to the gas flare point. Statistical analysis also confirms that cassava yields are higher at locations further away from the flare point. In addition, the amount of starch and ascorbic acid in cassava decreased when the plant is grown closer to the gas flare. High temperatures around the gas flare appear to be the most likely cause of this retardation. The waterleaf crop, on the other hand, appears to thrive better around the gas flare point.