Research of Inclusion Complexation of 4,4＇-Thiodiphenol Effected by β-Cyclodextrin in Aqueous Solutions and the Effect on Photodegradation

The inclusion behavior of 4, 4＇ -Thiodiphenol （TDP）, a typical bisphenol and endocrine disruptor, reacts with β-cyclodextrin （β-CD） in aqueous solutions has been investigated by means of UV absorption spectrum and quantum-chemical calculation with Gaussian 98 software. The results show that the inclusion behavior of TDP is quite different in acidic solutions （pH 5.9） from that in alkaline solutions （pH 10.0）. This behavior difference is attributed to the different formula structures in aqueous solutions at acidic and alkaline pH values that are demonstrated by quantum- chemical modeling and calculation. TDP forms a 1 ： 1 fl-CD inclusion complex in aqueous solutions. The equiiibrium constant K was calculated to be 553.49 L/mol at pH 5.9 and 1 318.20 L/mol at pH 10.0 respectively for the inclusion complex reaction by using the modified Benesi-Heldbrand equation. After inclusion TDP＇s structure is changed especially at the inclusion part with the bond order becoming larger, which results in inhibitive photodegradation during direct photooxidation and H2O2 assisted photooxidation.     

Optimizing Conditions of Enhancing Granule Sludge Concentration and Performance

An expanded granular sludge bed （EGSB） reactor was adopted to study the influence factors and rule of enhancing granular sludge concentration and performance. The experiment was performed at 33 ℃, pH 6.0-8.0 with continuous flow by adding proper quantity of nutritional trace elements. The results show that SLR was the key of steady operation of EGSB reactor. The increment of the granular sludge was influenced by volume loading rate （VLR）, liquid up-flow velocity and sludge loading rate （SLR）. Concentration of granular sludge increased rapidly when liquid up-flow velocity was over 0.94 m · h^-1 with SLR being at 1.0-2.0 d ^-1. With the propriety parameters： liquid up-flow velocity 2.52 m · h^-1, SLR 1.0-2.2 d^-1 and VLR 8.2-13.1 kg · m ^3 · d^-1, 23 days＇ continuous operation resulted in an increment by over 80% of granular sludge concentration in the EGSB reactor, plus good granular sludge property.     

Soil Organic Carbon Loss under Different Slope Gradients in Loess Hilly Region

Based on field runoff plots observation and sample analysis, the effect of slope gradient on soil organic carbon loss was studied under natural rainfall conditions in loess hilly region. The results showed that with slope gradient increasing （from10° to 30°）, the changing trend of soil erosion intensity （A） was A20°〉A30°〉A15°〉A10°〉A25°, suggesting that slope gradient between 20° and 25° was a critical degree ranger to exist. Soil organic carbon loss was mainly influenced by soil erosion intensity, appearing the same trend as did soil erosion intensity with increasing slope gradient. Soil erosion results in organic carbon enrichment in sediment. Average enrichment ratios of five runoff plots varied from 2.27 to 3.74, and decreased with increasing erosion intensity and slope gradient. The decrease of surface runoff and soil erosion is the key to reduce soil organic carbon loss.     

A New Generalized Similarity-Based Topic Distillation Algorithm

The procedure of hypertext induced topic search based on a semantic relation model is analyzed, and the reason for the topic drift of HITS algorithm was found to prove that Web pages are projected to a wrong latent semantic basis. A new concept-generalized similarity is introduced and, based on this, a new topic distillation algorithm GSTDA（generalized similarity based topic distillation algorithm） was presented to improve the quality of topic distillation. GSTDA was applied not only to avoid the topic drift, but also to explore relative topics to user query. The experimental results on 10 queries show that GSTDA reduces topic drift rate by 10% to 58% compared to that of HITS（hypertext induced topic search） algorithm, and discovers several relative topics to queries that have multiple meanings.     

Research on Bayesian Network Based User＇s Interest Model

It has very realistic significance for improving the quality of users＇ accessing information to filter and selectively retrieve the large number of information on the Internet. On the basis of analyzing the existing users＇ interest models and some basic questions of users＇ interest （representation, derivation and identification of users＇ interest）, a Bayesian network based users＇ interest model is given. In this model, the users＇ interest reduction algorithm based on Markov Blanket model is used to reduce the interest noise, and then users＇ interested and not interested documents are used to train the Bayesian network. Compared to the simple model, this model has the following advantages like small space requirements, simple reasoning method and high recognition rate. The experiment result shows this model can more appropriately reflect the user＇s interest, and has higher performance and good usability.     

A Deep Web Query Interfaces Classification Method Based on RBF Neural Network

This paper proposes a new approach for classification for query interfaces of Deep Web, which extracts features from the form＇s text data on the query interfaces, assisted with the synonym library, and uses radial basic function neural network （RBFNN） algorithm to classify the query interfaces. The applied RBFNN is a kind of effective feed-forward artificial neural network, which has a simple networking structure but features with strength of excellent nonlinear approximation, fast convergence and global convergence. A TEL_8 query interfaces＇ data set from UIUC on-line database is used in our experiments, which consists of 477 query interfaces in 8 typical domains. Experimental results proved that the proposed approach can efficiently classify the query interfaces with an accuracy of 95.67%.     

KDS-CM： A Cache Mechanism Based on Top-K Data Source for Deep Web Query

Caching is an important technique to enhance the efficiency of query processing. Unfortunately, traditional caching mechanisms are not efficient for deep Web because of storage space and dynamic maintenance limitations. In this paper, we present on providing a cache mechanism based on Top-K data source （KDS-CM） instead of result records for deep Web query. By integrating techniques from IR and Top-K, a data reorganization strategy is presented to model KDS-CM. Also some measures about cache management and optimization are proposed to improve the performances of cache effectively. Experimental results show the benefits of KDS-CM in execution cost and dynamic maintenance when compared with various alternate strategies.     

Automatic Question Answering from Web Documents

A passage retrieval strategy for web-based question answering （QA） systems is proposed in our QA system. It firstly analyzes the question based on semantic patterns to obtain its syntactic and semantic information and then form initial queries. The queries are used to retrieve documents from the World Wide Web （WWW） using the Google search engine. The queries are then rewritten to form queries for passage retrieval in order to improve the precision. The relations between keywords in the question are employed in our query rewrite method. The experimental result on the question set of the TREC-2003 passage task shows that our system performs well for factoid questions.     

Research on the User Interest Modeling of Personalized Search Engine

At present, how to enable Search Engine to construct user personal interest model initially, master user＇s personalized information timely and provide personalized services accurately have become the hotspot in the research of Search Engine area. Aiming at the problems of user model＇s construction and combining techniques of manual customization modeling and automatic analytical modeling, a User Interest Model （UIM） is proposed in the paper. On the basis of it, the corresponding establishment and update algorithms of User lnterest Profile （UIP） are presented subsequently. Simulation tests proved that the UIM proposed and corresponding algorithms could enhance the retrieval precision effectively and have superior adaptability.     

Analysis of Conditional Value-at-Risk for Newsvendor with Holding and Backorder Cost under Market Search

We consider a distribution system with one supplier and two retailers. For the two retailers, they face different demand and are both risk averse. We study a single period model which the supplier has ample goods and the retailers order goods separately. Market search is measured as the fraction of customers who unsatisfied with their ＂local＂ retailer due to stock-out, and search for the goods at the other retailer before leaving the system. We investigate how the retailers game for order quantity in a Conditional Value-at-Risk framework and study how risk averse degree, market search level, holding cost and backorder cost influence the optimal order strategies. Furthermore, we use uniform distribution to illustrate these results and obtain Nash equilibrium of order strategies.