The characteristics, distribution, and preservation of pores are vital in controlling the storage and distribution of shale gas. The Qiongzhusi Formation shales taken from different members with similar tectonic and thermal evolutions were used to evaluate the response of pore characteristics to minerals and sealing systems using field-emission scanning electron microscopy and gas adsorption. Because of differences in mineral structure and arrangement, feldspar, organic matter (OM)–clay, OM–rutile, and OM–apatite aggregates facilitate multiple types of pores in the shale and influence the relative proportions of surface porosity for different types of pores owing to differences in mineral structure and arrangement. Rigid frameworks and pressure shadows formed by rigid minerals and OM–mineral aggregates preserved OM and pores to some extent. The sealing capacity of the floor controls the sealing system and hydrocarbon expulsion efficiency of the Qiongzhusi Formation in different members. During thermal evolution, the amount of hydrocarbons generated and expelled affected the stress equilibrium state between the pore pressure and external stress, influencing the compaction intensity of shales. The OM pore development characteristics were evolved with variation in the stress equilibrium state in different sealing systems. Once the stress equilibrium state was disrupted, the OM pores deformed, narrowed, or even closed under the influence of compaction owing to the loss of overpressure support. The pore characteristics of the Qiongzhusi Formation shales responded significantly to different sealing systems. A few OM pores are flat and slitlike in the open system, whereas numerous OM pores are round and elliptical in the semiopen system. Meanwhile, the average diameter of the OM pores in the open system was reduced by approximately 40.2% compared with that of the semiopen system. Furthermore, the pore volume and specific surface area of the mesopores for open system shales were reduced by 38.4% and 37.7%, respectively, compared to the semiopen system. These findings will improve the understanding of the distribution and preservation of pore in shale and help assess the sweet-spot members for the Qiongzhusi Formation shale gas.

Influence maximization is an important problem, which seeks a small set of key users who spread the influence widely into the network. It finds applications in viral marketing, epidemic control, and assessing cascading failures within complex systems. The current studies treat nodes in social network with equal weights, and the influence possibility mainly decide by node degree. In this paper, we study the influence maximization problem in social networks and we improve the independent cascade model to realize the goal of different weights for different users, and the differentiation of influence probability. Meanwhile, We take advantage of the community structure to speed up the algorithm. Then, we propose a method called the reverse reachable index method based on random walk (RSRW) to select potential high-impact nodes from those communities. The experimental result on four actual data set shows that these improvements can greatly reduce the calculation time while ensuring the accuracy of the results.

This study uses deep learning theory into the character recognition technology for Shui characters in ancient books, with the objectives of overcoming the instability of the high-pixel ancient Shui characters generative model and the need for large scale handwritten text data annotation among other issues. By constructing a multilayer adversarial neural network with a Laplacian structure, a clear generative model is established for original image data of Shui characters and a stable adversarial network model with multiple mapping relationships from coarse to fine is formed. Based on the analysis of the feature distance of Shui character image samples, the minimum inter-class spacing value and the optimal number of clusters are calculated. Combined with feedback from the classifier model, the optimal number of clusters in the clustering model is adjusted, an evaluation function with information entropy adjustment and clustering threshold convergence is constructed for the unsupervised labelling of Shui character image samples. In this paper, the feedback from the convolutional neural network is used to determine the algorithmic model of the hyperparameters for clustering annotation, and this structure is also designed to improve the recognition rate of handwritten Shui characters in ancient books. INDEX TERMS Shui characters, generative adversarial network, unsupervised labeling.

This paper proposed an agent communication model including communication language specification based on OWL-communication language and communication actions, as well as formal communication process. So in the process of the interaction of multi-agent, agent must take the same communication language and use common understanding about the content of communication. In the open environment, the object that agent communicate with is different time by time and every agent may have different understanding about the same thing. The proposed solution prevents the misunderstanding during the business process though the agents' communications.