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In order to solve the problems in manual analysis of regional talent demand, a quantitative analysis based on big data is proposed. Firstly, by locating expressive media of talent demand, target data sources are specified and then analysis objects defined as indices can be extracted. Secondly, indices and analysis types are classified according to functions, and then analysis solutions are presented aiming at different properties of indices. Finally, two visualization modes are provided for different users.

Wind power prediction is a key problem in optimizing power dispatching. This paper builds a wind power prediction model based on wavelet neural network which substitutes wavelet basis function for the transfer function of hidden layer. A missing data interpolation strategy is also given to improve the applicability of the model. With the wind farm data from southeast coast, the model works and the wind power in the next 30 hours is predicted. In the sense of the mean square errors this paper compared the prediction results of the model and BP neural network model, the results shows the models have a better accuracy.

Contract execution and management is the core of project management, however contractors in China failed to fully recognize its importance. In the process of contract implementation, problems like oversimplified contract, lagging contracts modification, lagging vise engineering change and letter sending and so forth are very common, which all contribute to high risk and low efficiency in contracting engineering projects. The origin of these problems were analyzed and some practical solutions were put forward in this paper .

As a new technology, Atomil Force Microscopy (AFM) is being used in the research of microscopic structure on coal surface in recent years. By this technology, we can observe the nanoscale pore and crack shape of coal surface, and meas- ure some structural parameters. Different metamorphic grades produce different feature of surface microscopic structure of coal. This paper analyzes the surface microscopic structure of different metamorphic grade coal by AFM. The results show that the coal surface microstructure has a trend from rough to smooth with the increasing of metamorphic grade. The low rank coals contain large or medium pores and the high rank coals contain micro pores. The values of surface morphology characteristic parameters (Sq and Sa) nonlinearly decrease with the increasing coal rank. That is, the coal surface becomes smoother during coalification.

This paper is innovatively to develop a new hybrid performance evaluation method in the literature of assessing the digital resources serving performances. The proposed method employs the hierarchical evaluation method based on fuzzy rules and artificial neural networks. The proposed method integrates the fuzzy logic and the artificial neural networks, which overcomes the shortcomings of redundant fuzzy rules. The evaluation index system is determined based on the universal principle and the research fruits of the former scholars home and abroad. We build a fuzzy neural network evaluation model to achieve the final evaluation goal of the digital resources. In addition, to evaluate the performance of the proposed approach, we compare its results with GRA-BPN model. The experimental results demonstrated that the proposed approach has higher accuracy and execution efficiency.

Word sense disambiguation is a crucial problem in natural language processing. While sense disambiguation of in-vocabulary words is well studied to date, few research findings are yet available concerning the prediction of unknown words' sense. In this paper, we attempt to exploit multiple features for predicting sense of Chinese out-of-vocabulary words in real text. To this end, we first take morpheme as the basic component units of Chinese words and thus investigate the relationship between Chinese unknown words' senses and their internal morphological structures. Then, we explore both word internal cues and word external contextual features, and combine them for sense prediction of Chinese unknown words using maximum entropy modeling. Our experimental results show that the incorporation of multiple features, especially the word-internal morphological features are of great value to Chinese unknown word sense prediction.

A Q-learning based network selection algorithm is proposed which is derived from the autonomy of network selection. It is based on the heterogeneous network composed of WiMax and HSDPA. The algorithm can constantly adjust its strategy for action selection according to network load conditions, traffic attribute and mobility of terminal by the constantly trial and error learning with wireless environment. Finally a suitable network can be selected for every session. Simulation results show that system blocking probability can be lowered and spectrum utility can be increased.