氟化谱中磁化率和费尔德常数的计算

计算了自旋，轨道相互作用和晶场对顺磁性物质ＰｒＦ３中Ｆｒ＾３＋离子４ｆ和５ｄ能级的影响，考虑了间接交换作用有效场Ｈγ和外磁场Ｈｅ导致的晶场基态能级的进一步分裂，最后应用三能级模型计算了磁化率Ｘ，比法拉第旋转θＦ和费尔德常数Ｖ以及它们与温度Ｔ的依赖关系，计算表明Ｐｒ＾３＋离子之间的间接交换作用对ＰｒＦ３的磁光效应有重要影响，在１１０Ｋ〈Ｔ≤３００Ｋ范围内Ｘ＾－１，Ｖ＾－１与Ｔ成线性关系，在低温与Ｔ无     

A DEMONSTRATION OF 1×8 LINEAR ARRAY YBCO TRANSITION EDGE IR BOLOMETER

A high-T_c superconducting array microbolometer has been fabricated by using a YBCO film. Optical measurements gave moderate performance as individual detector, but singnificant improverment appears possible.     

镍催化合成二元酮化物的研究

有机锡试剂与酰氯化合物的交叉偶联反应提供了合成有机酮化物的新方法。以往有机酮化物的合成常采用锂试剂和格林试剂,它们能与生成的产物酮进一步反应,对空气也很敏感。反应条件比较苛刻,常要求在低温和无氧条件下进行,同时为防止锂试剂和格林试剂在反应时发生消除副反应,对它们的纯度要求也很高。而采用有机锡试剂,则克服和解决了由格林试剂和锂试剂合成有机酮时所无法解决的问题。其主要特点是化学     

G(F4′)条件与加强形式的停止定理

首次提出了Ｇ（Ｆ４′）条件，给出了Ｇ（Ｆ４′）条件的两个等价关系，得到了加强形式的停止定理和强鞅的一个等价关系．     

Samples Selection for Artificial Neural Network Training in Preliminary Structural Design

An artificial neural network (ANN) is applied in the preliminary structural design of reticulated shells. Major efforts are made to enhance the generalization ability of networks through well-selected training samples. Number-theoretic methods (NTMs) are adopted to generate samples with low discrepancy, i.e.uniformly scattered in the domain, where discrepancy is a quantitative measurement of the uniformity. The discrepancy of the NTM-based sample set is 1/6-1/7 that of samples with equal spacing. In a case study,networks trained by NTM-based samples are compared with those trained by equal-spaced samples in generalizing performance. The results show that both the computational precision and stability of the former ANNs are more satisfactory than those of the latter. It is concluded that the flexibility of ANNs in generalizing can be effectively increased by use of uniformly distributed training samples rather than simply piling data.More reliable uniformity should be obtained, however, through NTMs instead of equal-spaced samples.     

Evolution of Weighted Networks by Duplication-Divergence Mechanism

The duplication and divergence process is ubiquitous in nature and man-made networks. Motivated by the duplication-divergence mechanism which depicts the growth of protein networks, we propose a weighted network model in which topological evolution is coupled with weight dynamics. Large scale numerical results indicate that our model can naturally generate networks with power-law-like distributions of degree, strength and weight. The degree-strength correlation is illustrated as well. These properties are in agreement well with empirical data observed in real-world systems. Furthermore, by altering the retention probability δ, weighted, structured exponential networks are realized.