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。     

低轨道人造卫星(CHAMP、GRACE、GOCE)与高精度地球重力场——卫星重力大地测量的最新发展及其对地球科学的重大影响

评述了卫星重力大地测量的最新发展及其对地球科学的重大影响。为了更好地理解地球内部物理构造与海洋动力学，以及大陆，冰川和海洋的相互作用，改善现有地球重力场模型（包括精度和空间解析度）是非常重要的。IUGG等国际组织对此已经强调了很多年。最近，由德国的GFZ（GeoForschungsZentrum)，美国的NASA（National Aeronautics and Space Adminitration)以及欧洲宇航局ESA（European Space Agency)开发研制了最先进的地球监测技术－SST（Satellite-to-Sateilite Tracking)。其主要特点是利用现有的GPS连续追踪新发射低轨道卫星，并由低轨道卫星对地球重力场作精密观测。已经发射和即将发射的卫星有3颗：GHAMP（Challenging Mini-Satellite Payload for Geophysical Research an Application)已经于2000年发射；GRACE（Gravity Recovery and Climate Experimert)定于2002年发射；GOCE（Gravity Field and Steady-state Ocean Cirulation Explorer)计划2004年发射，它们可以统称为重力卫星。载有SST技术的人造卫星的主要目的是获得具有前所未有的高精度和高空间解析度的全球重力场和大地水准面模型，加强人们对地球内部构造的理解并为海洋和气象研究提供更好地参考。上述3个重力卫星工作在有明显区别的不同波谱内，它们有不同的科学应用，仅有一小部分重合。所以，就应用而言它们是完全互补的。它们在地球科学中的应用将是广泛的，特别对于固体地球物理学，海洋学以及大地测量学等领域，它们将会带来革命性的变化，其意义不亚于GPS。     

THE POSITION AND ROLES OF PORTS AND RAILWAYS IN THE EURASIA LAND BRIDGE TRANSPORTATION IN NORTHEAST CHINA

This paper systematically analyzes and proves the favorable factors of utilizing the ports and the railways in Northeast China for the Asia-Europe land bridge transportation. It will be more beneficial to Dalian Port and HaDa (Harbin-Dalian) and Binzhou (Harbin-Manzhouli) railways in Northeast China to the Siberia railway in Russia than 1) to other ports and the corresponding railways in China, 2) to the Nakhodka Port in Russia and the corresponding railways and 3) to the Chongjin Port in North Korea and the corresponding railways. This paper also puts forward the reform measures to adopt the ports and the railways in the northeast region in China for the land bridge transportation and the problems in transportation policies and management systems.     

沂水崔家峪玻璃用石英砂岩矿地质特征

沂水崔家峪玻璃用石英砂岩矿床赋存于早寒武世李官组砂岩段中．呈近水平的层状产出。因其岩石坚硬．矿体呈环山的平台状分布。矿体厚度大，矿石品级高，特级品矿石二氧化硅平均含量98．47％，铁杂质平均含量0．043％(选矿后，铁杂质含量可降至0．02％以下)，为一优质玻璃硅质原料矿床。矿石为细一中粒石英砂岩，粒度以中粒为主，矿石由碎屑颗粒和胶结物组成，碎屑成分含量为97％～98％．其中绝大部分是石英颗粒．具典型的砂屑结掏。该矿床属滨海陆源沉积矿床。     

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.     

济宁市嘉祥县石灰岩矿山地质环境评价与治理

在对嘉祥县石灰岩矿进行地质灾害危险性评估、矿山环境地质问题评价的基础上,建立了一套评价体系,其中地质灾害危险性评估分为2个评价指标,矿山环境地质问题评价分为2种要素3个指标,同时将矿山恢复治理难易程度也作为一个评价因子。评价过程中对各指标危害性大、中、小程度均单独赋值,然后将各指标的分值进行叠加,确定出矿山环境地质问题的等级,将概划出的13个评价单元分为极差、差、一般3个区。提出了强化矿山管理、植树造林、科学避让、修建拦水坝等保护与治理措施及建议。