网箱养殖罗非鱼综合症的病理组织学研究

于1989至1992年对水库网箱养殖罗非鱼综合症各发病期、各症状型的病鱼进行了病理组织学研究。研究结果表明:网箱养殖罗非鱼综合症的病理基础是肝肾功能障碍,病鱼的肝、肾、脾、肠、鳃、性腺、脑均有不同程度的坏死或变性。本文还对细菌继发性感染,各症状型的病理学变化及防治对策作了讨论。     

The hydrological influence of black locust plantations in the loess area of northwest China

Black locust (Robina pseudoacacia) has become one of the most important shelter species in the loess area of northwest China. This paper summarizes recent research concerning its hydrological influence, including canopy interception, litter absorption capacity, its effect on rainfall kinetic energy, infiltration rates, surface runoff, soil moisture, and evapotranspiration, and its role in soil conservation. Several predictive models are listed. on the basis of existing results, optimum characteristics for an effective plantation are defined, and problems requiring further research are identified.     

Galileo系统及其在中国的应用

较详细地介绍了Galileo系统的全面体系结构、频率设计、服务内容及其广泛的应用范围，指出了它与GPS系统相比的优点，并对开发和增强Galileo系统在我国多层次、全方位的应用提出了建议。     

Is Precipitation the Dominant Controlling Factor of High Inorganic Nitrogen Content in the Changjiang River and Its Mouth？

The main reasons for the high content of inorganic N and its increase by several times in the Changjiang River and its mouth during the last 40 years were analysed in this work. The inorganic N in precipitation in the Changjiang River catchment mainly comes from gaseous loss of fertilizer N, N resulting from the increases of population and livestock, and from high temperature combustions of fossil fuels. N from precipitation is the first N source in the Changjiang River water and the only direct cause of high content of inorganic N in the Changjiang River and its mouth. The lost N in gaseous form and from agriculture non-point sources fertilizer comprised about 60% of annual consumption of fertilizer N in the Changjiang River catchment and were key factors controlling the high content of inorganic N in the Changjiang River mouth. The fate of the N in precipitation and other N sources in the Changjiang River catchment are also discussed in this paper.     

Interpolating paleovegetation data with an artificial neural network approach

To drive an atmospheric general circulation model (AGCM), land surface boundary conditions like albedo and morphological roughness, which depend on the vegetation type present, have to be prescribed. For the late Quaternary there are some data available, but they are still sparse. Here an artificial neural network approach to assimilate these paleovegetation data is investigated. In contrast to a biome model the relation between climatological parameters and vegetation type is not based on biological knowledge but estimated from the available vegetation data and the AGCM climatology at the corresponding locations. For a test application, a data set for the modern vegetation reduced to the amount of data available for the Holocene climate optimum (about 6000 years B.P.) is used. From this, the neural network is able to reconstruct the complete global vegetation with a kappa value of 0.56. The most pronounced errors occur in Australia and South America in areas corresponding to large data gaps.