Flood Risk Analysis Based on Information Diffusion Theory

Due to the fact that the flood data series of small drainage basins is relatively short, available data are often not sufficient for flood risk analysis. This presents the problem of risk analysis using very small data samples. One method that can be applied is to regard the available small samples as fuzzy information and optimize them using information diffusion technology to yield analytical results with greater reliability. In this article a risk analysis method based on information diffusion theory is applied to create a new flood risk analysis model. Application of the model is illustrated taking the Jinhuajiang and Qujiang drainage basins as examples. This is a new attempt at applying information diffusion theory in flood risk analysis. Computations based on this analytical flood risk model can yield an estimated flood damage value that is relatively accurate. This study indicates that the aforementioned model exhibits fairly stable analytical results, even when using a small set of sample data. The results also indicate that information diffusion technology is highly capable of extracting useful information and therefore improves system recognition accuracy. This method can be easily applied and the analytical results produced are easy to understand. Results are accurate enough to act as a guide in disaster situations.     

An Efficient Regression Type Estimator in Survey Sampling

For the estimation of population mean in simple random sampling, an efficient regression-type estimator is proposed which is more efficient than the conventional regression estimator and hence than mean per unit estimator, ratio and product estimators and many other estimators proposed by various authors. Some numerical examples are included for illustration.     

Some Common Themes for Enzymes and Verbs

Enzymes are remarkable molecules which make metabolism possible. Their processing powers are considerable for not only are they catalysts they also contribute to information processing, integration, coherence and memory in the cell. This complex of attributes suggests that a complementary perspective to enzyme nature and activity is needed related to what enzymes and verbs have in common. The value of this kind of thinking is that it shifts the focus from objects and mechanisms to processes and information. In order to support this idea a number of features which enzymes and verbs share are discussed including, context-dependence, occurrence, cases, voice, mood and glue/integrative capacities. The paper concludes with some reflections on the utility of a view of enzymes as verbs.     

Conditional and Unconditional Simulation of Healthy Patients' Visual Fields

This paper describes a simulation problem, motivated by the study of glaucoma, a very serious and widespread ocular illness. To ascertain whether a patient suffers from glaucoma, a perimetric test is done, but the evolution of the disease is very slow, and large longitudinal sets of tests taken on the same patient are needed to study its evolution, to analyze the efficiency of existing methods to detect the progression of glaucoma and to develop new ones. Simulation can be a very useful procedure to get appropriate data sets to work with. Our aim in this work is to simulate several VFs in a healthy patient to reflect his evolution in time. We use a spatio‐temporal model to simulate from, taking into account the correlation existing between the observed (or simulated) values in space and time. Two different simulation procedures (unconditional and conditional) are studied, and applied to obtain the simulations we are interested in. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Conditional Maximum Likelihood Estimation Following a Group Sequential Test

We consider estimation after a group sequential test. An estimator that is unbiased or has small bias may have substantial conditional bias (Troendle and Yu, 1999, Coburger and Wassmer, 2001). In this paper we derive the conditional maximum likelihood estimators of both the primary parameter and a secondary parameter, and investigate their properties within a conditional inference framework. The method applies to both the usual and adaptive group sequential test designs. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)