Main Factors Affecting the Distribution of the Macroscopic Destruction Field of Earthquake

It is proposed that some possible macroseismic epicenters can be determined quickly from the relationship that the microseismic epicenters located by instruments bear with faults. Based on these so-called macroseismic epicenters, we can make fast seismic hazard estimation after a shock by use of the empirical distribution model of seismic intensity. In comparison with the method that uses the microseismic epicenters directly, this approach can increase the precision of fast seismic hazard estimation. Statistical analysis of 133 main earthquakes in China was made. The result shows that the deviation distance between the microseismic epicenter and macroseismic epicenter falls within the range of 35 km for 88 % earthquakes of the total and within the range of 35 to 75 km for the remaining ones. Then, we can take the area that has the microseismic epicenter as its center and is 35 km in radius as the area for emphatic analysis, and take the area within 75 km around the microseismic epicenter as the area for general analysis. The relation between the 66 earthquake cases on the N-S Seismic Belt in China and the spatial distribution characteristics of faults and the results of focal mechanism solution were analyzed in detail. We know from the analysis that the error of instrumental epicenter determination is not the only factor that gives effects to the deviation of the macroseismic epicenter. In addition to it, the fault size, fault distribution, fault activity, fault intersection types, earthquake magnitude, etc. are also main affecting factors. By sorting out, processing and analyzing these affecting factors, the principle and procedures for quickly determining the possible position of the macroseismic epicenter were set up. Taking these as a basis and establishing a nationwide database of faults that contains relevant factors, it is possible to apply this method in practical fast estimation of seismic hazard.

Potentially Toxic Element Concentration in Fruits Collected from Markazi Province(Iran): A Probabilistic Health Risk Assessment

Objective This study was conducted to evaluate the concentration of potentially toxic elements(PTEs)such as arsenic(As),cadmium(Cd),mercury(Hg),and lead(Pb)in fruit samples collected from Markazi Province,Iran.A probabilistic health risk assessment due to ingestion of PTEs through the consumption of these fruits was also conducted.Methods The concentration of PTEs in 90 samples of five types of fruits(n=3)collected from six geographic regions in Markazi Province was measured.The potential health risk was evaluated using a Monte Carlo simulation model.Results A significant difference was observed in the concentration of PTEs between fruits as well as soil and water samples collected from different regions in Markazi Province.The order of PTE concentration in the soil and water samples was as follows:Pb>As>Hg>Cd.Furthermore,the highest level of transfer factor for Cd and Hg correlated with the grape.The estimated daily intake for adults and children was lower than the recommended tolerable daily intake.Conclusion The population in Markazi Province,Iran,is not at considerable noncarcinogenic or carcinogenic risk due to the ingestion of PTEs through the consumption of the examined fruits.

The Proportional Hazards Model for Multiple Type Recurrent Gap Times

Recurrent events data and gap times between recurrent events are frequently encountered in many clinical and observational studies,and often more than one type of recurrent events is of interest.In this paper,we consider a proportional hazards model for multiple type recurrent gap times data to assess the effect of covaxiates on the censored event processes of interest.An estimating equation approach is used to obtain the estimators of regression coefficients and baseline cumulative hazard functions.We examine asymptotic properties of the proposed estimators.Finite sample properties of these estimators are demonstrated by simulations.

Maximum Likelihood Estimator for the Proportional Hazards Model with Incomplete Information

Right randomly censored data with incomplete infor-mation are frequently met in practice.Although much study about right randomly censored data has been seen in the proportional hazards model,relatively little is known about the inference of regression parameters for right randomly censored data with in-complete information in such model.In particular,theoretical properties of the maximum likelihood estimator of the regression parameters have not been proven yet in that model.In this paper,we show the consistency and asymptotic normality of the maxi-mum likelihood estimator of unknown regression parameters.

ASYMPTOTIC NORMALITY OF THE NEAREST NEIGHBOR HAZARD ESTIMATES

The nearest neighbor (n.n.) and its related methods are widely used in density and hazard function estimations. Even though the asymptotic normality of the n.n. density estimate is well known (see [1]), similar results for the n.n. hazard estimate have not been shown in the literature. In this paper, we develop a different approach to deal with the n.n. type estimator. For a mixed censorship-truneation model, we show that, under mild conditions, the n. n. estimate can be approximated by an estimate formed with a proper fixed bandwidth sequence and derive the asymptotic normality as a consequence.