This paper encapsulates the Chinese Airline Network (CAN) into multi-layer infrastructures via the “k-core decomposition” method. The network is divided into three layers: Core layer, containing airports of provincial capital cities, is densely connected and sustains most flight flow; Bridge layer, consisting of airports in Tier 2 and Tier 3 cities, mainly connects two other layers; and Periphery layer, comprising airports of remote areas, sustains little flight flow. Moreover, it is unveiled that CAN stays the most robust when low-degree nodes or high flight flow links are removed, which is similar to the Worldwide Airline Network (WAN), albeit less redundantPeer ReviewedPostprint (author's final draft

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The aim of this study was to explore the incidence and risk factors for posterior cage migration (PCM) following decompression and instrumented fusion for degenerative lumbar disorders, and hope to provide references in decision making and surgical planning for spine surgeons.By retrieving the medical records from January 2011 to December 2015, 286 patients were retrospectively reviewed. According to the occurrence of PCM, patients were divided into 2 groups: PCM group and non-PCM (N-PCM). To investigate risk values for PCM, 3 categorized factors were analyzed statistically: patient characteristics: age, sex, body mass index, bone mineral density, duration of disease, diagnosis, comorbidity, smoke; surgical variables: surgery time, blood loss, surgical strategy, cage morphology, cage size, surgical segment, fusion number, source of bone graft, surgeon experience; radiographic parameters: preoperative lumbar lordosis, correction of lumbar lordosis, preoperative lumbar mobility, preoperative intervertebral height, change of intervertebral height, Modic changes, paraspinal muscle degeneration.PCM was detected in 18 of 286 patients (6.3%) at follow-up. There was no statistically significant difference between the 2 groups in patient characteristics, except diagnosis, as lumbar spondylolisthesis was more prevalent in PCM group than that in N-PCM group. There was no difference between the 2 groups in surgical variables, except cage size and surgeon experience, as size of cage was smaller in PCM group than that in N-PCM group, and the surgeons with less experience (less than 3 years) were more prevalent in PCM group than that in N-PCM group. There was no statistically significant difference between 2 groups in radiographic parameters. Logistic regression model revealed that less than 3 years of surgeons’ experience, small cage size, and lumbar spondylolisthesis were independently associated with PCM.For patients with lumbar spondylolisthesis, they should be fully informed about the risk of PCM before operation. While for spinal surgeons, large cage should be preferred, and careful manipulation should be adopted, especially for new learners with less than 3-year experience of fusion surgery.

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Abstract-With more and more vehicles equipped with GPS tracking devices, there is increasing interest in building and updating maps using vehicular GPS traces. But commodity GPS devices have lower accuracy and lower sampling frequency, which made it more difficult to infer road network than most existing approaches that using highprecision and high-frequency GPS devices. As a key component of road network, intersection plays the role of transport hub. So, if the intersections are detected in advance, the road network can be then constructed conveniently by connecting the intersections. In this paper, we propose a novel algorithm for recognizing intersections with coarse-grained GPS traces based on data preprocessing and clustering. The algorithm first prune low quality GPS points, then find out the turning points around intersections and the converging points in the preprocessing step, and finally cluster these converging points to find out the cluster centers, i.e. the intersection positions. In addition, we introduce a simple road network construction algorithm based on the identified intersections. We evaluate our method using GPS data gathered from 2,827 taxis in Shenyang, Liaoning, China. Evaluation results demonstrate that our algorithm is able to find most of the road intersections effectively.