The results of the linear range cell migration (RCM) correction and inherent range-dependent squint angle in the case of high-resolution highly squinted synthetic aperture radar (SAR) imaging produce two-dimensional (2-D) spatial-variant RCMs and azimuth-dependent Doppler parameters (i.e., highly varying Doppler centroid and frequency modulation rates), which make highly squinted SAR imaging difficult. However, the most existing algorithms failed to consider these problems. To obtain highquality SAR image, in this study, both the 2-D spatial-variant RCMs and the azimuth-dependent Doppler parameters are stud- ied. First, a reference range linear RCM correction (RCMC) is used to remove the most of the linear RCM components and to mitigate the range-azimuth coupling of the 2-D spectrum. And then, in the azimuth time dimension, a new perturbation function is designed in the extended nonlinear chirp scaling (CS) (ENLCS) algorithm to overcome the azimuth-dependent RCM and to equalize Doppler parameters. To remove both the inherent range-dependent RCM and the linear RCM caused by the range-dependent squint angle, a modified CS (MCS) algorithm with a new scaling function isproposed, and for the residual RCMs, a bulk RCMC and second range compression (SRC) are utilized to compensate them. With the proposed ENLCS and MCS operation, the 2-D spatial-variant RCMC and the azimuth-dependent Doppler equalization are, thus, achieved. The experimental results with simulated data in the case of the high-resolution highly squinted SAR demonstrate the superior performance of the proposed algorithm.Index Terms-Azimuth-dependent Doppler Equalization, extended nonlinear chirp scaling (ENLCS) algorithm, modified CS (MCS) algorithm, synthetic aperture radar (SAR),

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BackgroundThe emergence and spread of multidrug-resistant organisms (MDROs) are causing a global crisis. Combating antimicrobial resistance requires prevention of transmission of resistant organisms and improved use of antimicrobials.ObjectivesTo develop a Web-based information system for automatic integration, analysis, and interpretation of the antimicrobial susceptibility of all clinical isolates that incorporates rule-based classification and cluster analysis of MDROs and implements control chart analysis to facilitate outbreak detection.MethodsElectronic microbiological data from a 2200-bed teaching hospital in Taiwan were classified according to predefined criteria of MDROs. The numbers of organisms, patients, and incident patients in each MDRO pattern were presented graphically to describe spatial and time information in a Web-based user interface. Hierarchical clustering with 7 upper control limits (UCL) was used to detect suspicious outbreaks. The system’s performance in outbreak detection was evaluated based on vancomycin-resistant enterococcal outbreaks determined by a hospital-wide prospective active surveillance database compiled by infection control personnel.ResultsThe optimal UCL for MDRO outbreak detection was the upper 90% confidence interval (CI) using germ criterion with clustering (area under ROC curve (AUC) 0.93, 95% CI 0.91 to 0.95), upper 85% CI using patient criterion (AUC 0.87, 95% CI 0.80 to 0.93), and one standard deviation using incident patient criterion (AUC 0.84, 95% CI 0.75 to 0.92). The performance indicators of each UCL were statistically significantly higher with clustering than those without clustering in germ criterion (P < .001), patient criterion (P = .04), and incident patient criterion (P < .001). ConclusionThis system automatically identifies MDROs and accurately detects suspicious outbreaks of MDROs based on the antimicrobial susceptibility of all clinical isolates.

Indentation size effects in poly(methyl methacrylate) (PMMA) were studied through nanoindentation. Two factors of indentation size effects in PMMA, namely yield criterion and shear transformation-mediated plasticity, were analysed in detail. The yield criterion that considers strength differential (SD) effects and pressure sensitivity was constructed by performing the combined shear-compression experiments. The relationship between hardness and normal stress can then be obtained based on Tabot’s relation. Shear transformation-mediated plasticity was also applied to model the measured hardness as a function of the indentation depth at different strain rates. Results show that the yield criterion contains the terms of SD effects and pressure sensitivity gives the best description of the yielding of PMMA. Additionally, the volume of single shear transformation zone calculated through the presented criterion agrees well with simulation and exhibits increases with increasing strain rate. Indentation size effects in PMMA under different strain rates were discussed and an appropriate indentation depth range was suggested for calculating the hardness and modulus.

Cellular foams are usually used as energy absorbers in engineering such as cushioning to alleviate the impact energy. To explore their energy absorption property, Voronoi foams with different density gradients were established. Numerical simulations were conducted by using Ls-dyna 971. Two loading conditions were considered: the constant velocity impact and the densification velocity impact. The results indicate that the energy absorption is closely related to loading conditions and profiles of density gradients. Therefore, it is of great importance to select suitable graded foams accordingly. This study paves the way for engineering applications of graded cellular materials as protective devices.

Unmanned Aerial Vehicles (UAVs) are a novel technology for landform investigations, monitoring, as well as evolution analyses of long−term repeated observation. However, impacted by the sophisticated topographic environment, fluctuating terrain and incomplete field observations, significant differences have been found between 3D measurement accuracy and the Digital Surface Model (DSM). In this study, the DJI Phantom 4 RTK UAV was adopted to capture images of complex pit-rim landforms with significant elevation undulations. A repeated observation data acquisition scheme was proposed for a small amount of oblique-view imaging, while an ortho-view observation was conducted. Subsequently, the 3D scenes and DSMs were formed by employing Structure from Motion (SfM) and Multi-View Stereo (MVS) algorithms. Moreover, a comparison and 3D measurement accuracy analysis were conducted based on the internal and external precision by exploiting checkpoint and DSM of Difference (DoD) error analysis methods. As indicated by the results, the 3D scene plane for two imaging types could reach an accuracy of centimeters, whereas the elevation accuracy of the orthophoto dataset alone could only reach the decimeters (0.3049 m). However, only 6.30% of the total image number of oblique images was required to improve the elevation accuracy by one order of magnitude (0.0942 m). (2) An insignificant variation in internal accuracy was reported in oblique imaging-assisted datasets. In particular, SfM-MVS technology exhibited high reproducibility for repeated observations. By changing the number and position of oblique images, the external precision was able to increase effectively, the elevation error distribution was improved to become more concentrated and stable. Accordingly, a repeated observation method only including a few oblique images has been proposed and demonstrated in this study, which could optimize the elevation and improve the accuracy. The research results could provide practical and effective technology reference strategies for geomorphological surveys and repeated observation analyses in sophisticated mountain environments.

BackgroundSurveillance of health care-associated infections is an essential component of infection prevention programs, but conventional systems are labor intensive and performance dependent.ObjectiveTo develop an automatic surveillance and classification system for health care-associated bloodstream infection (HABSI), and to evaluate its performance by comparing it with a conventional infection control personnel (ICP)-based surveillance system.MethodsWe developed a Web-based system that was integrated into the medical information system of a 2200-bed teaching hospital in Taiwan. The system automatically detects and classifies HABSIs.ResultsIn this study, the number of computer-detected HABSIs correlated closely with the number of HABSIs detected by ICP by department (n=20; r=.999 P<.001) and by time (n=14; r=.941; P<.001). Compared with reference standards, this system performed excellently with regard to sensitivity (98.16%), specificity (99.96%), positive predictive value (95.81%), and negative predictive value (99.98%). The system enabled decreasing the delay in confirmation of HABSI cases, on average, by 29 days.ConclusionsThis system provides reliable and objective HABSI data for quality indicators, improving the delay caused by a conventional surveillance system.