Preliminary Study on the Emission Rules of Sulfur Derivative Pollutants in Putrefactive Cyanobacteria

[Objective]The aim was to study the characteristics of the emission of sulfur derivative pollutants in cyanobacteria.[Method]Based on water odor in drink water from Gonghu Bay in Lake Taihu,the sulfur derivatives pollutants were extracted by head space solid phase microextraction（HS-SPME） and the odor substance was identified by gas chromatography and mass spectrometry（GC-MS）.Decomposed simulation was conducted in closed tube of water and cyanobacteria samples collected from Gonghu Bay in Lake Taihu.The cyanobacteria rotten odor substances was analyzed and detected by HS-SPME-GC-MS for 10 days.The sulfur derivative pollutant was expounded.[Result]The primary or secondary metabolites by cyanobacteria in water samples such as β-cyclocitral,indol,methylphenol,mercaptan and thioether were detected with scan.During the decomposition process,the emission of β-cyclocitral changed little.The maximum emission of dimethyl trisulfide appeared on the seventh and eighth day.The maximum emission of dimethyl disulfide appeared on the forth day.The maximum emission of the diethyl sulfide was on the eighth and ninth day.The maximum concentration of dimethyl trisulfide was 2 344.79 ng/L,which was much more than the olfactory threshold.[Conclusion] The sewage in Gonghu Bay in the end of May in 2007 resulted from the accumulation of cyanobacteria.

Research on Multi-robot Coordination Searching Ignition Sources Approach

For the purpose of study on forecasting forest fire behavior,a probability approach was presented to search ignition sources by multi-robot coordination. Firstly,the environment map is built based on Bayes rules. Then,the probability searching strategy based on the environment map was designed. Every grid of the searching area was assigned searching expectation value, and robots selected the grid with the highest expectation value as its searching target. The simulation results show the search time reduces greatly,which proves the feasibility and validity of the given algorithm under unknown fire condition.

Correct Classification Rates in Multi-Category Discriminant Analysis of Spatial Gaussian Data

This paper discusses the problem of classifying a multivariate Gaussian random field observation into one of the several categories specified by different parametric mean models. Investigation is conducted on the classifier based on plug-in Bayes classification rule (PBCR) formed by replacing unknown parameters in Bayes classification rule (BCR) with category parameters estimators. This is the extension of the previous one from the two category cases to the multi-category case. The novel closed-form expressions for the Bayes classification probability and actual correct classification rate associated with PBCR are derived. These correct classification rates are suggested as performance measures for the classifications procedure. An empirical study has been carried out to analyze the dependence of derived classification rates on category parameters.

Consistency of the k-Nearest Neighbor Classifier for Spatially Dependent Data

The purpose of this paper is to investigate the k-nearest neighbor classification rule for spatially dependent data.Some spatial mixing conditions are considered,and under such spatial structures,the well known k-nearest neighbor rule is suggested to classify spatial data.We established consistency and strong consistency of the classifier under mild assumptions.Our main results extend the consistency result in the i.i.d.case to the spatial case.

Bank's Self-learning Process in a Gray System of Credit Risk

A bank had better ration its credit if it first enters a gray system of credit information where it cannot distinguish between the low- and high-risk borrowers. As the bank keeps a long-term relationship with its borrowers the bank learns about the borrowers through time. With the help of logit model and Bayes ruIe, a bank can process customer's credit information and build a better credit term it gives.