One new genus and six new species of eriophyoid mite from Mountain Trusmadi, Malaysia are described and illustrated. They are Isoannulus morrisianae sp. nov. on Diospyros morrisiana (Ebenaceae), Abacarus bicolorus sp. nov. on Lespedeza bicolor (Leguminosae), Parneometaculus persicariae gen. nov. & sp. nov. on Persicaria chinensis (Polygonaceae), Shevtchenkella miscanthis sp. nov. on Miscanthus floridulus (Poaceae), Davisella nitidis sp. nov. on Artocarpus nitidus subsp. lingnanensis (Moraceae), and Vimola blastis sp. nov. on Blastus cochinchinensis (Melastomataceae). Furthermore, two new records of eriophyoid mites are found: Knorella bambusae (Kuang & Feng, 1989) rec. nov. on Bambusa sp. (Poaceae) and Diptilomiopus melastomae (Boczek & Chandrapatya, 2002) rec. nov. on Melastoma malabathricum (Melastomataceae). All these new eriophyoid mite species and new records are vagrants causing no apparent symptom to their host plants.

The present study reports four new records of eriophyid mites collected from

Six eriophyoid mites, including four new species and two new records, from Egypt are described and illustrated. They are Stenacis aegyptius sp. nov., on Cupressus sempervirens L. (Cupressaceae); Aceria donacis Mohanasundaram, 1983, rec. n. on Arundo donax L. (Poaceae); Aceria bambusae Channabasavanna, 1966, rec. n. on Bambusa vulgaris Schrad. ex J.C. Wendl; Schizacea aegyptimperata sp. nov. on Imperata cylindrical (L.) (Poaceae); Epitrimerus abousettai sp. nov. on Cupressus sempervirens L. (Cupressaceae) and Abacarus donacis sp. nov. on Arundo donax L. (Poaceae). The genus Schizacea is recorded for the first time in Egyptian fauna. These species are vagrants on leaves without any damage except the forth species (S. aegyptimperata) which causes rust on inner surface of leaves of the host plant. A key to the species of Schizacea of the world is provided.

Cloud computing is computing tasks distribution resources of a large number of computers in the subnet, to provide users with cheap and efficient computing power, storage capacity and service capabilities. Data mining is to find useful information in large data repository. Frequent flow of large amounts of data quickly and accurately find important basis for forecasting and decision, therefore, under the cloud computing environment parallelization frequent item data mining strategy to provide efficient solutions to store and analyze vast amounts of data has important theoretical significanceand application value.

construct an improved water demand prediction model for support vector machine (SVM) on the basis of principle components analysis (PCA) in order to improve the accuracy of water demand prediction and prediction efficiency. Analyze the principal components of all the index factors which affect water demand; eliminate redundant information between the indices, thus to reduce SVM input dimensions; besides, it also introduces genetic algorithm, solved the problem that the traditional SUV parameters cannot optimized dynamically. A simulated experiment proves that the predication accuracy of this model is higher than SVM, BP neural network; this model has higher generalization ability and is an effective model for predicting water demand.