SIMULATION AND PREDICTION OF DEBRIS FLOW USING ARTIFICIAL NEURAL NETWORK

Debris flow is one of the most destructive phenomena of natural hazards. Recently, major natural haz-ard, claiming human lives and assets, is due to debris flow in the world. Several practical methods for forecasting de-bris flow have been proposed, however, the accuracy of these methods is not high enough for practical use because of the stochastic and non-linear characteristics of debris flow. Artificial neural network has proven to be feasible and use-fill in developing models for nonlinear systems. On the other hand, predicting the future behavior based on a time se-ries of collected historical data is also an important tool in many scientific applications. In this study we present a three-layer feed-forward neural network model to forecast surge of debris flow according to the time series data collect-ed in the Jiangjia Ravine, situated in north part of Yunnan Province of China. The simulation and prediction of debris flow using the proposed approach shows this model is feasible, however, further studies are needed.     

Three Super Active Regions in the Descending Phase of Solar Cycle 23

We analyze the magnetic configurations of three super active regions, NOAA 10484, 10486 and 10488, observed by the Huairou Multi-Channel Solar Telescope (MCST) from 2003 October 18 to November 4. Many energetic phenomena, such as flares (including a X-28 flare) and coronal mass ejections (CMEs), occurred during this period. We think that strong shear and fast emergence of magnetic flux are the main causes of these events. The question is also of great interest why these dramatic eruptions occurred so close together in the descending phase of the solar cycle.     

类太阳色球活动星εEri磁场的观测研究

本文给出１９９３—１９９４年期间，用云南天文台１米望远镜折轴摄谱仪观测获得类太阳色球活动星。εＥｒｉ的５个波段高分辨、高信噪比光谱，运用两种不同方法：谱线轮廓塞曼致宽分析和多重线统计分析法成功地测定了εＥｒｉ的表面磁场强度和磁场的覆盖因子，并对这两种磁场测定方法作了简单的讨论．     

Magnetic Reconnection, Turbulence, and Collisionless Shock

A short summary of recent progress in measuring and understanding turbulence during magnetic reconnection in laboratory plasmas is given. Magnetic reconnection is considered as a primary process to dissipate magnetic energy in laboratory and astrophysical plasmas. A central question concerns why the observed reconnection rates are much faster than predictions made by classical theories, such as the Sweet–Parker model based on MHD with classical Spitzer resistivity. Often, the local resistivity is conjectured to be enhanced by turbulence to accelerate reconnection rates either in the context of the Sweet–Parker model or by facilitating setup of the Pestchek model. Measurements at a dedicated laboratory experiment, called MRX or Magnetic Reconnection Experiment, have indicated existence of strong electromagnetic turbulence in current sheets undergoing fast reconnection. The origin of the turbulence has been identified as right-hand polarized whistler waves, propagating obliquely to the reconnecting field, with a phase velocity comparable to the relative drift velocity. These waves are consistent with an obliquely propagating electromagnetic lower-hybrid drift instability driven by drift speeds large compared to the Alfven speed in high-beta plasmas. Interestingly, this instability may explain electromagnetic turbulence also observed in collisionless shocks, which are common in energetic astrophysical phenomena.     

Identification of physical processes inherent in artificial neural network rainfall runoff models

The emergence of artificial neural network (ANN) technology has provided many promising results in the field of hydrology and water resources simulation. However, one of the major criticisms of ANN hydrologic models is that they do not consider/explain the underlying physical processes in a watershed, resulting in them being labelled as black‐box models. This paper discusses a research study conducted in order to examine whether or not the physical processes in a watershed are inherent in a trained ANN rainfall‐runoff model. The investigation is based on analysing definite statistical measures of strength of relationship between the disintegrated hidden neuron responses of an ANN model and its input variables, as well as various deterministic components of a conceptual rainfall‐runoff model. The approach is illustrated by presenting a case study for the Kentucky River watershed. The results suggest that the distributed structure of the ANN is able to capture certain physical behaviour of the rainfall‐runoff process. The results demonstrate that the hidden neurons in the ANN rainfall‐runoff model approximate various components of the hydrologic system, such as infiltration, base flow, and delayed and quick surface flow, etc., and represent the rising limb and different portions of the falling limb of a flow hydrograph. Copyright © 2004 John Wiley & Sons, Ltd.     

形变大地测量学的进展、问题与地震预报

简要概括了形变大地测量学的革命性进展，研讨了它的科学特色、功能和定义以及对地球科学和防灾减灾的推动。形变大地测量学有助于从根本上击破多年来制约地震预报的“瓶颈”，但也存在不少急待解决的问题。着重研讨了在21世纪前10年，形变大地测量学如何依托多年的学科积累并充分受益于人造卫星和数字化等新技术，开展创新性研究和试验以推进地震预报。为此，对当前的研究工作提出了12条科学技术途径。最后对学科名称提出了建议。     

On the Line Formation in Stellar Magnetized Atmospheres

1 INTRODUCTIONIn the previous paper (on et al. 1999, hereafter Paper I), we investigated the wavelength-dependence of four colltribution functions (CFs) derived from dmerent formal solutions andreferring to different emergellt quantities in the unpolarized case. Because one cannot generallyassign a single formation region to the whole line band in a real stellar atmosphere, e.g., the solaratmosphere, instead, the line formation region can be defined as the layers deviating farthestfrom t…     

几个活动区精细H_α结构的物理浅析

本文利用紫金山天文台赣榆站太阳精细结构望远镜拍得的高分辨率色球Ｈ_α照片，分析了三个典型活动区。文中利用并检验了七十年代获得的磁图推导法则，应用自己的数值模拟知识和Ｚｗａａｎ对ＡＦＳ系新浮流区概括的特征，参考七十和九十年代总结的有关耀斑和ＥｌｌｅｒｍａｎＢｏｍｂ的出现规律，逐日分析活动区发展，定出其内部的中性线位置，提出简单和复合中性线的区分，由ＡＦＳ系和亮谱斑同时出现判断新浮流区，从近离带图找等离子体不稳定点，从远离带图找普遍的磁场流场分布，由Ｈ_α结构的综合迹象推测磁场变化。总结出有关活动区Ｈ_α结构、磁场及等离子体性质关系的几点启示。