The application of neural networks to comprehensive prediction by seismology prediction method

BP neural networks is used to mid-term earthquake prediction in this paper. Some usual prediction parameters of seismology are used as the import units of neural networks. And the export units of neural networks is called as the character parameter W_0 describing enhancement of seismicity. We applied this method to space scanning of North China. The result shows that the mid-term anomalous zone of W_0-value usually appeared obviously around the future epicenter 1~3 years before earthquake. It is effective to mid-term prediction.

Support principles of NPR bolt/cable and control techniques of large-deformation disasters

Deep mining has been paid much more attention because of the depletion of shallow mining resources.Traditional bolts could be invalid to accommodate large displacement and deformation in geomaterials.Consequently, alternative support and reinforcement bolts need to be studied and their constitutive models also need to be developed to help understanding for the complex stress-strain responses of rock masses under loadings. The effect of Negative Poisson's Ratio(NPR) that is attributed to the swelling phenomenon along the lateral direction may appear in metal materials under tensional loadings. Thence NPR materials often have an advantage over NPR ones in mechanical behavior such as impact resistance, antishearing, and energy absorbed. From the characteristics of NPR materials, a series of bolt and cable supports with the effect of NPR and constant-resistance have been recently developed. We here firstly introduce the structural features of NPR support. Then the constitutive model of NPR support is presented and its corresponding equation of energy equilibrium. Its basic principle interacted on rock masses is further discussed. Finally, NPR cables are employed to support the slope of an open-pit mine. The applications show that NPR cables can ease failure within the slope and play an important role in predicting and providing early warning of slope failure, together with a monitoring system of slope stability.

Proposal and evaluation of a new norm index-based QSAR model to predict pEC50 and pCC50 activities of HEPT derivatives

The search and development of anti-HIV drugs is currently one of the most urgent tasks of pharmacological studies. In this work, a quantitative structure-activity relationship （QSAR） model based on some new norm indexes, was obtained to a series of more than 150 HEPT derivatives （1-[（2-hydroxyethoxy）methyl]-6-（phenylthio）thymine） to find their pEC50 （the required effective concentration to achieve 50% protection of MT-4 cells against the cytopathic effect of virus） and pCC50 （the required cytotoxic concentration to reduce visibility of 50% mock infected cell） activities. The model efficiencies were then validated using the leave-one-out cross validation （LOO-CV） and y- randomization test. Results indicated that this new model was efficient and could provide satisfactory results for prediction of pECso and pCC50 with the higher R2 train and the higher Rt2est. By using the leverage approach, the applicability domain of this model was further investigated and no response outlier was detected for HEFT derivatives involved in this work. Comparison results with reference methods demonstrated that this new method could result in significant improvements for predicting pEC50 and pCC50 of anti-HIV HEPT derivatives. Moreover, results shown in this present study suggested that these two absolutely different activities pECso and pCC50 of anti-HIV HEPT derivatives could be predicted well with a totally similar QSAR model, which indicated that this model mizht have the potential to be further utilized for other biological activities of HEFT derivatives.

Meta-Path-Based Search and Mining in Heterogeneous Information Networks

Information networks that can be extracted from many domains are widely studied recently. Different functions for mining these networks are proposed and developed, such as ranking, community detection, and link prediction. Most existing network studies are on homogeneous networks, where nodes and links are assumed from one single type. In reality, however, heterogeneous information networks can better model the real-world systems, which are typically semi-structured and typed, following a network schema. In order to mine these heterogeneous information networks directly, we propose to explore the meta structure of the information network, i.e., the network schema. The concepts of meta-paths are proposed to systematically capture numerous semantic relationships across multiple types of objects, which are defined as a path over the graph of network schema. Meta-paths can provide guidance for search and mining of the network and help analyze and understand the semantic meaning of the objects and relations in the network. Under this framework, similarity search and other mining tasks such as relationship prediction and clustering can be addressed by systematic exploration of the network meta structure. Moreover, with user＇s guidance or feedback, we can select the best meta-path or their weighted combination for a specific mining task.

Development of PREDAC-H1pdm to model the antigenic evolution of influenza A/(H1N1)pdm09 viruses

The Influenza A(H1N1)pdm09 virus caused a global pandemic in 2009 and has circulated seasonally ever since.As the continual genetic evolution of hemagglutinin in this virus leads to antigenic drift,rapid identification of antigenic variants and characterization of the antigenic evolution are needed.In this study,we developed PREDAC-H1pdm,a model to predict antigenic relationships between H1N1pdm viruses and identify antigenic clusters for post-2009 pandemic H1N1 strains.Our model performed well in predicting antigenic variants,which was helpful in influenza surveillance.By mapping the antigenic clusters for H1N1pdm,we found that substitutions on the Sa epitope were common for H1N1pdm,whereas for the former seasonal H1N1,substitutions on the Sb epitope were more common in antigenic evolution.Additionally,the localized epidemic pattern of H1N1pdm was more obvious than that of the former seasonal H1N1,which could make vaccine recommendation more sophisticated.Overall,the antigenic relationship prediction model we developed provides a rapid determination method for identifying antigenic variants,and the further analysis of evolutionary and epidemic characteristics can facilitate vaccine recommendations and influenza surveillance for H1N1pdm.