基于GIS气候资源评价及区划研究——以陕西省苹果气候区划为例

应用模糊综合评判的理论和方法,通过陕西省优质苹果气候区划实例,探索了运用GIS技术进行气候资源评价及区划的方法:即在分析选择影响苹果品质气候因子的基础上,确定优质苹果气候资源评价指标模型;根据要素权重和隶属度,建立单因子评价栅格图层;利用GIS空间叠置功能,综合评价图按适宜度分级并与夏季水热状况矢量图叠加,得到陕西省苹果种植气候生态区划图。结果表明,渭北黄土高原海拔80 0～1 2 0 0 m冷凉半干旱、半湿润区,是陕西省优质苹果气候生态区。     

VBA的开发

介绍了能够加强Ms Office功能的二次开发工具VBA语言，以及VBA的特点与优点；介绍了在Visul Basic中利用VBA的强大功能来实现具有Office界面、功能的方法。     

青藏高原近地面层微气象学特征

利用1998年5月-7月在改则、当雄和昌都三测站获得的近地面层气象要素变化的观测资料，分析了青藏高原近地面层风速、温度和湿度日变化特征及廓线规律，发现高原近地面层微气象学特征具有自己的特点；同时还讨论了高原近地面层白天出现的逆湿现象。     

地质雷达及浅层地震在泥质粉砂岩中断裂带上的曲线特征

探讨了如何利用地质雷达及浅层地震有关原理判释泥质粉砂岩中隐伏断裂带的性质和特征。主要的工作方法是通过现场实测，分析地质雷达反映在张性断裂带和压性断裂带上的曲线特征，分析浅层地震相遇时距曲线在断裂带上的曲线特征。后者的主要特征有：（1）在断裂带上初至时间延长；（2）在断裂带上左右支时距曲线上下跳动的幅度均等；（3）在断裂带上跳动的长度不一；（4）在断裂带左右支时距曲线上下跳动或反相；（5）时距曲线呈喇叭口。阐明了地质雷达和浅层地震的原理及其在泥质粉砂岩中勘查断裂构造的效果     

Structural damage detection using the optimal weights of the approximating artificial neural networks

This work presents a novel neural network‐based approach to detect structural damage. The proposed approach comprises two steps. The first step, system identification, involves using neural system identification networks (NSINs) to identify the undamaged and damaged states of a structural system. The partial derivatives of the outputs with respect to the inputs of the NSIN, which identifies the system in a certain undamaged or damaged state, have a negligible variation with different system errors. This loosely defined unique property enables these partial derivatives to quantitatively indicate system damage from the model parameters. The second step, structural damage detection, involves using the neural damage detection network (NDDN) to detect the location and extent of the structural damage. The input to the NDDN is taken as the aforementioned partial derivatives of NSIN, and the output of the NDDN identifies the damage level for each member in the structure. Moreover, SDOF and MDOF examples are presented to demonstrate the feasibility of using the proposed method for damage detection of linear structures. Copyright © 2001 John Wiley & Sons, Ltd.     

Optimal design of passive energy dissipation systems based on H∞ and H2 performances

Passive energy dissipation devices (EDDs), such as viscous dampers, viscoelastic dampers, etc., have been used to effectively reduce the dynamic response of civil infrastructures, such as buildings and bridges, subject to earthquakes and strong winds. The design of these passive energy dissipation devices (EDDs) involves the determination of the optimal locations and the corresponding capacities. In this paper, we present two optimal design methodologies for passive EDDs based on active control theories, including H_∞ and H₂ performances, respectively. The optimal design methodologies presented are capable of determining the optimal locations and the corresponding capacities of EDDs. Emphasis is placed on the application of linear matrix inequality (LMI) for the effective design of passive EDDs using the popular MATLAB toolboxes. One important advantage of the proposed approaches is that the computation of the structural response is not needed in the design process. The proposed optimal design methodologies have been applied to: (i) a 10‐storey building and a 24‐storey building both subject to earthquake excitations, and (ii) a 76‐storey wind‐excited benchmark building, to demonstrate the advantages of the proposed design methodologies over the conventional equal capacity design. Copyright © 2002 John Wiley & Sons, Ltd.     

纤维素接枝丙烯酰胺/高岭土高吸水性复合材料研究

在高岭土的存在下，以N，N－亚甲基双丙烯酰胺作交联剂，以硝酸铈铵为引发剂微晶纤维素与丙烯酰胺进行接枝共聚反应，合成接枝纤维素／高岭土高吸水性复合材料。研究了纤维素与单体的配比、高岭土添加量、交联剂用量、引发剂用量及反应温度对吸水率的影响。用红外光谱表征复合材料的结构，用电子显微镜观察复合材料的表面与界面。结果表明，高岭土在接枝聚合物中能较好分散，复合材料吸水率达1166，对生理盐水溶液的吸水率达86。     

深海热泉生物——人类的基因资源宝库

广泛存在于大洋之中的深海热泉生物群落依靠化学自营维持生存与繁衍，构成了独特的生态系统，是对光合作用生态系统理论的一个挑战和补充，在生物学上有着重要的研究意义，对于揭示生命的起源与拓展生命的生存空间有着重要的学术价值，而且还孕育着广泛而重要的应用价值和商业前景，具体体现在新型医药开发、基因疗法、工业应用以及环境保护等方面。深海热泉生物将是人类最重要的基因资源宝库。     

A multi-scale study on land use and land cover quality change: The case of the Yellow River Delta in China

This paper presents a case study of the Yellow River Delta in China, to trace land use and land cover changes during the past 20 years, with an emphasis on land quality changes. Three sets of data are used in this case study: remote sensing data derived from satellite images; crop yield data from statistics; and soil data collected by the researchers in the field. Our study reveals that at the regional scale, LUCC has taken place in a positive direction: vegetation cover has been expanding and crop yields per hectare have been on rise. However, while the overall eco-environment has improved, the improvement is uneven across the Delta region. At local levels, some areas show signs of increased salinization and declining organic content. Both natural forces and human activities are responsible for the LUCC, but human activities play a more important role. While some impacts of human activities are positive, the damages are often long-lasting and irreversible. We also conclude that it is necessary to use both macro data (such as remote sensing data) and micro data (data collected in the field) to study land quality change. The former are efficient in examining land quality changes at the regional scale, the latter can serve to verify ground patterns revealed from macro data and help to identify local variations, so as to get a comprehensive understanding of LUCC and promote sustainable land use and land management. This revised version was published online in July 2006 with corrections to the Cover Date.     

Combination of Dependent Realizations Within the Gradual Deformation Method

Gradual deformation is a parameterization method that reduces considerably the unknown parameter space of stochastic models. This method can be used in an iterative optimization procedure for constraining stochastic simulations to data that are complex, nonanalytical functions of the simulated variables. This method is based on the fact that linear combinations of multi-Gaussian random functions remain multi-Gaussian random functions. During the past few years, we developed the gradual deformation method by combining independent realizations. This paper investigates another alternative: the combination of dependent realizations. One of our motivations for combining dependent realizations was to improve the numerical stability of the gradual deformation method. Because of limitations both in the size of simulation grids and in the precision of simulation algorithms, numerical realizations of a stochastic model are never perfectly independent. It was shown that the accumulation of very small dependence between realizations might result in significant structural drift from the initial stochastic model. From the combination of random functions whose covariance and cross-covariance are proportional to each other, we derived a new formulation of the gradual deformation method that can explicitly take into account the numerical dependence between realizations. This new formulation allows us to reduce the structural deterioration during the iterative optimization. The problem of combining dependent realizations also arises when deforming conditional realizations of a stochastic model. As opposed to the combination of independent realizations, combining conditional realizations avoids the additional conditioning step during the optimization process. However, this procedure is limited to global deformations with fixed structural parameters.