SUMMARYBased on the differential quadrature (DQ) rule, the Gauss Lobatto quadrature rule and the variational principle, a DQ finite element method (DQFEM) is proposed for the free vibration analysis of thin plates. The DQFEM is a highly accurate and rapidly converging approach, and is distinct from the differential quadrature element method (DQEM) and the quadrature element method (QEM) by employing the function values themselves in the trial function for the title problem.The DQFEM, without using shape functions, essentially combines the high accuracy of the differential quadrature method (DQM) with the generality of the standard finite element formulation, and has superior accuracy to the standard FEM and FDM, and superior efficiency to the p-version FEM and QEM in calculating the stiffness and mass matrices.By incorporating the reformulated DQ rules for general curvilinear quadrilaterals domains into the DQFEM, a curvilinear quadrilateral DQ finite plate element is also proposed. The inter-element compatibility conditions as well as multiple boundary conditions can be implemented, simply and conveniently as in FEM, through modifying the nodal parameters when required at boundary grid points using the DQ rules. Thus, the DQFEM is capable of constructing curvilinear quadrilateral elements with any degree of freedom and any order of inter-element compatibilities. A series of frequency comparisons of thin isotropic plates with irregular and regular planforms validate the performance of the DQFEM.

In this paper, a smart aggregate-based approach is proposed for the structural health monitoring of a concrete shear wall structure. The piezoceramic-based smart aggregates were distributed in predetermined locations prior to the casting of the concrete structure to form an active-sensing system for the health monitoring purpose. To evaluate the damage in different areas, the concrete shear wall was sectioned into sub-domains and a wavelet-packet-based damage index matrix is proposed to evaluate the health status in these sections. A cyclic loading procedure was applied to gradually fail the concrete shear wall and the proposed structural health monitoring approach was used to perform structural health monitoring during this loading procedure. The experimental results have shown that the proposed smart aggregate-based approach effectively evaluated the damage status in different areas and detected the precautionary point to predict the structural failure. The proposed approach has the potential to be applied to the structural health monitoring of large-scale concrete shear wall structures.

Existing enhancement methods are empirically expected to help the high-level end computer vision task: however, that is observed to not always be the case in practice. We focus on object or face detection in poor visibility enhancements caused by bad weathers (haze, rain) and low light conditions. To provide a more thorough examination and fair comparison, we introduce three benchmark sets collected in real-world hazy, rainy, and lowlight conditions, respectively, with annotated objects/faces. We launched the UG 2+ challenge Track 2 competition in IEEE CVPR 2019, aiming to evoke a comprehensive discussion and exploration about whether and how low-level vision techniques can benefit the high-level automatic visual recognition in various scenarios. To our best knowledge, this is the first and currently largest effort of its kind. Baseline results by cascading existing enhancement and detection models are reported, indicating the highly challenging nature of our new data as well as the large room for further technical innovations. Thanks to a large participation from the research community, we are able to analyze representative team solutions, striving to better identify the strengths and limitations of existing mindsets as well as the future directions.Index Terms-Poor visibility environment, object detection, face detection, haze, rain, low-light conditions *The first two authors Wenhan Yang and Ye Yuan contributed equally. Ye Yuan and Wenhan Yang helped prepare the dataset proposed for the UG2+ Challenges, and were the main responsible members for UG2+ Challenge 2019 (Track 2) platform setup and technical support. Wenqi Ren, Jiaying Liu, Walter J. Scheirer, and Zhangyang Wang were the main organizers of the challenge and helped prepare the dataset, raise sponsors, set up evaluation environment, and improve the technical submission. Other authors are the group members of winner teams in UG2+ challenge Track 2 contributing to the winning methods.

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Objective: To identify potential risk factors of unsatisfactory screw position during robot-assisted pedicle screw fixation.Methods: A retrospective analysis of robot-assisted pedicle screw fixation performed in Beijing Jishuitan Hospital from March 2018 to March 2019 was conducted. Research data was collected from the medical record and imaging systems. Univariate tests were performed on the potential risk factors (patient’s characteristics and surgical factors) of unsatisfactory screw position during robot-assisted pedicle screw fixation. For statistically significant variables in univariate tests, a logistic regression test was used to identify independent risk factors for unsatisfactory screw position.Results: A total of 780 pedicle screws placed in 163 robot-assisted surgeries were analyzed. The rate of perfect screw positions was 93.08%, and the unsatisfactory rate was 6.92%. In patients with severe obesity (body mass index ≥ 30 kg/m²) (odds ratio [OR], 2.459; 95% confidence interval [CI], 1.199–5.044; p = 0.014), osteoporosis (T ≤ -2.5) (OR, 1.857; 95% CI, 1.046–3.295; p = 0.034), and the segments 3 levels away from the tracker (OR, 2.216; 95% CI, 1.119–4.387; p = 0.022), robot-assisted pedicle screw placement has a higher risk of screw malposition.Conclusion: During robot-assisted pedicle screw placement for patients with severe obesity, osteoporosis, and segments 3 levels away from the tracker, vigilance should be maintained during surgery to avoid postoperative complications due to unsatisfactory screw position.