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Traditional pipeline magnetic flux leakage (MFL) internal technology mainly uses axial excitation method, which could not recognize the narrow crack defects in the axial direction of the pipe. In this paper, by using a linear magnetic dipole model to study the circumferential excitation method, the detection model of axial crack in pipeline is established, and the relationship between MFL signals and the geometry characteristics of axial cracks is calculated. Finally, the detection accuracy and identification method of axial cracks is analyzed. Research results show that: non-uniform magnetic field generated by circumferential excitation can effectively detect the narrow cracks in the axial direction of the pipeline and distinguish the depth and the width characteristics of cracks. However, the background magnetic fields near the magnetic poles have great influence on the detection accuracy, and the smooth interpolation method of the cubic-spline interpolation can be used to reduce the influence effectively. IntroductionPipeline MFL internal inspection technology is the mainstream technology to maintain the safe operation of long-distance oil -gas pipeline [1][2][3][4] . This technology belongs to the monopoly technology in the world, for it is only mastered by a few Highlights 1. The MFL internal inspection technology can maintain the safe operation of pipelines.2. The axial cracks are more hazardous than circumferential cracks for pipelines.3. The circumferential excitation method can improve the confidence of the MFL device. 4. The axial cracks can be identified by circumferential excitation method.5. The MFL internal inspection of defects in arbitrary direction is possible to realize.

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Excessive exploitation of the groundwater has resulted in obvious three-dimensional (3D) deformation features on the surface of the Beijing Plain. This paper, by combining Interferometric Synthetic Aperture Radar (InSAR) and Global Positioning System (GPS) technologies, has obtained time-series information of the 3D surface deformation in the Beijing Plain, analyzing its spatial distribution characteristics. On this basis, the relationship between different controlling factors with the 3D deformation of the surface has been analyzed as well. The following results are obtained: (1) From 2013 to 2018, the land subsidence, which generally showed the trend of slowing down, was mainly concentrated in the eastern, northern, and southern regions of Beijing Plain, with multiple subsidence centers. (2) Under the International Terrestrial Reference Frame 2005 (ITRF2005), the horizontal direction of all GPS points in the plain is basically the same, with the dominant movement direction being NE112.5°~NE113.8°. Under the Eurasian reference frame, the horizontal movement rate of GPS points significantly decreases. The movement rate and direction of each point are not characteristic of overall trend activity. (3) The distribution and extent of the 3D surface deformation in the Beijing Plain are controlled by the basement structure. Part of the subsided area corresponds to a Quaternary depression formed at the junction of active faults disrupting the area. Similarly, the distribution of horizontal deformation in the E-W and N-S directions of the plain is controlled by the regional basement structure comprising major faults bounding horizontal deformation. (4) Groundwater exploitation is the main cause of the 3D surface deformation in the Beijing Plain. The groundwater funnels of the second and third confined aquifer are in suitable agreement with the land subsidence. The horizontal movement in the Beijing Plain is either directed toward the center of the groundwater or the land subsidence funnel, and the deformation is directed from areas with higher to areas with lower groundwater levels.

This study investigates the effects of religion on perceived risks associated with travel to China. A sample of 223 international travelers answered questions about the travel risks associated with travel to China, their religious affiliation, and the strength of their religious beliefs-their religiosity. A principal components analysis identified five risk dimensions: "Value risk," "Physical/Psychological risk," "Health risk," "Terrorism/Social risk," and "Equipment/Satisfaction risk." Regression analyses identified statistically significant positive relationships between religiosity and perceived travel risks while holding experience constant. Multivariate analysis of variance (MANOVA) and a series of one-way analysis of variance (ANOVA) tests revealed significant differences in the perceived travel risks across religious affiliations. Across all five risk dimensions, travelers who reported a religious affiliation perceived more risk than nonreligious travelers did. Religiosity, religious affiliation, and the perceived risk dimensions also discriminated among preferred travel styles for a future trip to China. Implications of the study's results are discussed, and limitations and recommendations for future research presented.

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