Water quality data for Fall Creek,New York,USA: 1972–1995

The 33 086 ha mixed land use Fall Creek watershed in upstate New York is part of the Great Lakes drainage system. Results from more than 3500 water samples are available in a data set that compiles flow data and measurements of various water quality analytes collected between 1972 and 1995 in all seasons and under all flow regimes in Fall Creek and its tributaries. Data is freely accessible at https://ecommons.cornell.edu/handle/1813/8148 and includes measurements of suspended solids, pH, alkalinity, calcium, magnesium, potassium, sodium, chloride, nitrate nitrogen (NO3-N), sulphate sulphur (SO4-S), phosphorus (P) fractions molybdate reactive P (MRP) and total dissolved P (TDP), percent P in sediment, and ammonium nitrogen (NH4-N). Methods, sub-watershed areas, and coordinates for sampling sites are also included. The work represented in this data set has made important scientific contributions to understanding of hydrological and biogeochemical processes that influence loading in mixed use watersheds and that have an impact on algal productivity in receiving water bodies. In addition, the work has been foundational for important regulatory and management decisions in the region.     

Developing constitutive models from EPR‐based self‐learning finite element analysis

A constitutive model that captures the material behavior under a wide range of loading conditions is essential for simulating complex boundary value problems. In recent years, some attempts have been made to develop constitutive models for finite element analysis using self‐learning simulation (SelfSim). Self‐learning simulation is an inverse analysis technique that extracts material behavior from some boundary measurements (eg, load and displacement). In the heart of the self‐learning framework is a neural network which is used to train and develop a constitutive model that represents the material behavior. It is generally known that neural networks suffer from a number of drawbacks. This paper utilizes evolutionary polynomial regression (EPR) in the framework of SelfSim within an automation process which is coded in Matlab environment. EPR is a hybrid data mining technique that uses a combination of a genetic algorithm and the least square method to search for mathematical equations to represent the behavior of a system. Two strategies of material modeling have been considered in the SelfSim‐based finite element analysis. These include a total stress‐strain strategy applied to analysis of a truss structure using synthetic measurement data and an incremental stress‐strain strategy applied to simulation of triaxial tests using experimental data. The results show that effective and accurate constitutive models can be developed from the proposed EPR‐based self‐learning finite element method. The EPR‐based self‐learning FEM can provide accurate predictions to engineering problems. The main advantages of using EPR over neural network are highlighted.     

基于噪声谱比法的内蒙古西部地区台站场地响应研究

利用内蒙古西部12个台站的地脉动噪声数据，采用噪声谱比法研究台站的场地响应情况。分析表明，台站场地响应按曲线形态可分为3类，且可能受地形地貌、局部构造和台基状况等条件影响。对比分析噪声谱比法与Moya方法的场地响应结果发现，场地响应曲线形态基本一致，只有极少数台站存在明显差异。结果表明，内蒙古西部区域大部分台站的台基状况总体较好，场地响应曲线较为平坦，无明显频率放大点。     

Pulsar magnetic field for an infinitely conductive accretion disk

The magnetic-field distribution outside a flat, infinitely conductive unbounded disk in the field of a point magnetic dipole is determined. A relationship is established between the problem of magnetic-field determination and the problem of the flow of an ideal incompressible fluid around an infinitely thin disk.     

轴对称稳恒磁场的一种解法

本文依据稳恒磁场中的高斯定理和安培环路定理，用迭代法推导出对称轴上的磁感应强度与空间任意一点磁感应强度的关系式，从而可得出轴对称稳恒磁场的无电流分布空间的解式。     

Evolutionary Processes in Low-Mass Binary Systems

We consider the evolution of certain low-mass binaries, incorporating models of (a) internal evolution, (b) tidal friction, (c) dynamo activity driven by an elementary α,Ω dynamo, (d) stellar wind driven by the activity, and (e) magnetic braking as a consequence of wind and poloidal dynamo-generated magnetic field. In some circumstances the stellar wind is found to remove mass on a nuclear timescale, as is necessary to explain some observed systems. We can hope that various uncertainties in the model may be clarified by a careful comparison of the models with such observed quantities as rotation periods. These are modified by processes (a), (b) and (e). Assuming that stellar evolution is slow, rotation rate should in some circumstances represent a balance between magnetic braking trying to slow the star down and tidal friction trying to spin it up. Preliminary attempts are promising, but indicate that some fine tuning is necessary. When there is a third body present, in an orbit which is inclined but not necessarily of short period, the eccentricity of a close binary can be strongly modified by ‘Kozai cycles’. We show that this may complicate attempts to account for spin rates of stars in close binaries.     

宇宙磁场中的中微子

本文讨论了有质量的Dirac粒子在宇宙磁场中的演化。宇宙磁场使空间度规出现各向异性。通过求解Dirac方程,得到了中微子在宇宙磁场中的表观磁矩。     

MATLAB在运用系统建模处理南海气象数据过程中应用

介绍了MATLAB语言特点和系统建模方法的基本理论.根据南海气象数据的实际建模处理过程,给出了建模的详细步骤及其MATLAB实现过程以及MATLABTM的主要实现程序.试验讨论和结果表明利用MATLAB语言可以方便地对南海气象数据用系统建模方法进行建模和处理,MATLAB在运用系统建模法处理南海气象数据方面具有明显的优越性.