3D simulation of image-defined complex internal features using the numerical manifold method

The numerical simulation of internal features,such as inclusions and voids,is important to analyze their impact on the performance of composite materials.However,the complex geometries of internal features and the induced continuous-discontinuous(C-D)deformation fields are challenges to their numerical simulation.In this study,a 3D approach using a simple mesh to simulate irregular internal geometries is developed for the first time.With the help of a developed voxel crack model,image models that are efficient when recording complex geometries are directly imported into the simulation.Surface reconstructions,which are usually labor-intensive,are excluded from this approach.Moreover,using image models as the geometric input,image processing techniques are applied to detect material interfaces and develop contact pairs.Then,the C-D deformations of the complex internal features are directly calculated based on the numerical manifold method.The accuracy and convergence of the developed3D approach are examined based on multiple benchmarks.Successful 3D C-D simulation of sandstones with naturally formed complex microfeatures demonstrates the capability of the developed approach.

Features of International Situation in the Early New Century

Along with the ring of the millennium bell, the human society has entered a new century, and China’s national rejuvenation has come into a new stage. Undoubtedly, the coming ten years are of great significance because they will serve as a link between the past and the future. During this period, further deepening of revolution in science and technology represented by information technology and

An improved deep dilated convolutional neural network for seismic facies interpretation

With the successful application and breakthrough of deep learning technology in image segmentation,there has been continuous development in the field of seismic facies interpretation using convolutional neural networks.These intelligent and automated methods significantly reduce manual labor,particularly in the laborious task of manually labeling seismic facies.However,the extensive demand for training data imposes limitations on their wider application.To overcome this challenge,we adopt the UNet architecture as the foundational network structure for seismic facies classification,which has demonstrated effective segmentation results even with small-sample training data.Additionally,we integrate spatial pyramid pooling and dilated convolution modules into the network architecture to enhance the perception of spatial information across a broader range.The seismic facies classification test on the public data from the F3 block verifies the superior performance of our proposed improved network structure in delineating seismic facies boundaries.Comparative analysis against the traditional UNet model reveals that our method achieves more accurate predictive classification results,as evidenced by various evaluation metrics for image segmentation.Obviously,the classification accuracy reaches an impressive 96%.Furthermore,the results of seismic facies classification in the seismic slice dimension provide further confirmation of the superior performance of our proposed method,which accurately defines the range of different seismic facies.This approach holds significant potential for analyzing geological patterns and extracting valuable depositional information.

Improvement of Counting Sorting Algorithm

By analyzing the internal features of counting sorting algorithm. Two improvements of counting sorting algorithms are proposed, which have a wide range of applications and better efficiency than the original counting sort while maintaining the original stability. Compared with the original counting sort, it has a wider scope of application and better time and space efficiency. In addition, the accuracy of the above conclusions can be proved by a large amount of experimental data.

Morphology of Kaloula rugifera and K. borealis Tadpoles(Anura: Microhylidae: Kaloula verrucosa Group) in China

The external morphology and internal oral features of the tadpoles of Kaloula rugifera and K. borealis are described in this paper. Similarities between these two larvae include external morphology, oral disc, internal oral features(floor and roof), terminal mouth, lateral eyes, single midventral spiracle, absence of keratinized jaw sheaths and labial teeth, absence of lingual papillae and postnarial arena, while the body of K. rugifera is bigger than that of K. borealis at stages from 36 to 39 and the number of papillae in the buccal cavity is 12–19 and 9–16, respectively in the two species. All the similarities and differences of the two species illustrate that the tadpoles within the family Microhylidae could be attributed to assistance in clarifying a certain taxonomic treatment in Microhylidae.

Effect of Cone Angle on the Hydraulic Characteristics of Globe Control Valve

Globe control valve is widely used in chemical, petroleum and hydraulic industries, and its throttling feature is achieved by the adopting of valve plug. However, very limited information is available in literature regarding the influence of valve plug on the internal and external features in globe control valves. Thus the effect of valve plug is studied by CFD and experiment in this paper. It is obtained from external features that the pressure drop between upstream and downstream pressure-sampling position increases exponentially with flow rate. And for small valve opening, the increment of pressure drop decreases with the increase of cone angle（β）. However, with the increase of valve opening, the effect of cone angle diminishes significantly. It is also found that the cone angle has little effect on flow coefficient（Cv） when the valve opening is larger than 70%. But for the cases less than 70%, Cv curve varies from an arc to a straight line. The variation of valve performance is caused by the change of internal flow. The results of internal flow show that cone angle has negligible effect on flow properties for the cases of valve opening larger than 70%. However, when valve opening is smaller than 70%, the pressure drop of orifice decreases with the increase of β, making the reduction in value and scope of the high speed zone around the conical surface of valve plug, and then results in a decreasing intensity of adjacent downstream vortex. Meanwhile, it is concluded from the results that the increase of cone angle will be beneficial for the anti-cavitation and anti-erosion of globe control valve. This paper focuses on the internal and external features of globe control valve that caused by the variation of cone angle, arriving at some results beneficial for the design and usage of globe control valve.

CPC INTERNATIONAL WORK FEATURING “POLITICAL GUIDANCE” UNDER NEW SITUATION

Since the 18th CPC National Congress,General Secretary Xi Jinping has raised new requirements for the Party’s international work under the new situation,emphasizing that on the basis of summarizing

Leading Features of the International Security Landscape in 2008

The international security situation in the past year on the whole retained the basic posture of peace and stability, but unstable and uncertain elements cast a growing shadow

A new method for the prediction of network security situations based on recurrent neural network with gated recurrent unit

Purpose-The purpose of this paper is to solve the shortage of the existing methods for the prediction of network security situations(NSS).Because the conventional methods for the prediction of NSS,such as support vector machine,particle swarm optimization,etc.,lack accuracy,robustness and efficiency,in this study,the authors propose a new method for the prediction of NSS based on recurrent neural network(RNN)with gated recurrent unit.Design/methodology/approach-This method extracts internal and external information features from the original time-series network data for the first time.Then,the extracted features are applied to the deep RNN model for training and validation.After iteration and optimization,the accuracy of predictions of NSS will be obtained by the well-trained model,and the model is robust for the unstable network data.Findings-Experiments on bench marked data set show that the proposed method obtains more accurate and robust prediction results than conventional models.Although the deep RNN models need more time consumption for training,they guarantee the accuracy and robustness of prediction in return for validation.Originality/value-In the prediction of NSS time-series data,the proposed internal and external information features are well described the original data,and the employment of deep RNN model will outperform the state-of-the-arts models.

Brief Introduction to Some Featured Speakers for the 3rd Asia TEFL International Conference

Tariq RahmanTariq Rahman earned his Ph.D in English from theUniversity of Sheffield in England.He did his post-doctoral research as a Fulbright scholar(1995-96)in the USA.He is presently National DistinguishedProfessor at the Quaid-i-Azam University in Islamabad,Pakistan.Dr.Rahman is a highly published scholar havingmore than eighty research papers in academic journals and six booksto his credit.He is considered an authority on the politics and historyof languages and language policy in Pakistan.His book Language andPolitics in Pakistan(Oxford University Press,1996)is regarded as aclassic in the field and has been reprinted four times.His latest bookDenizens of Alien Worhls(Oxford University Press,2004)is on