Insight-HXMT observations of Swift J0243.6+6124 during its 2017-2018 outburst

The recently discovered neutron star transient Swift J0243.6+6124 has been monitored by {\it the Hard X-ray Modulation Telescope} ({\it Insight-\rm HXMT). Based on the obtained data, we investigate the broadband spectrum of the source throughout the outburst. We estimate the broadband flux of the source and search for possible cyclotron line in the broadband spectrum. No evidence of line-like features is, however, found up to $\rm 150~keV$. In the absence of any cyclotron line in its energy spectrum, we estimate the magnetic field of the source based on the observed spin evolution of the neutron star by applying two accretion torque models. In both cases, we get consistent results with $B\rm \sim 10^{13}~G$, $D\rm \sim 6~kpc$ and peak luminosity of $\rm >10^{39}~erg~s^{-1}$ which makes the source the first Galactic ultraluminous X-ray source hosting a neutron star.Comment: publishe

Overview to the Hard X-ray Modulation Telescope (Insight-HXMT) Satellite

As China's first X-ray astronomical satellite, the Hard X-ray Modulation Telescope (HXMT), which was dubbed as Insight-HXMT after the launch on June 15, 2017, is a wide-band (1-250 keV) slat-collimator-based X-ray astronomy satellite with the capability of all-sky monitoring in 0.2-3 MeV. It was designed to perform pointing, scanning and gamma-ray burst (GRB) observations and, based on the Direct Demodulation Method (DDM), the image of the scanned sky region can be reconstructed. Here we give an overview of the mission and its progresses, including payload, core sciences, ground calibration/facility, ground segment, data archive, software, in-orbit performance, calibration, background model, observations and some preliminary results.Comment: 29 pages, 40 figures, 6 tables, to appear in Sci. China-Phys. Mech. Astron. arXiv admin note: text overlap with arXiv:1910.0443

Optimization of Cardiac Magnetic Resonance Synthetic Image Based on Simulated Generative Adversarial Network

The generative adversarial network (GAN) has advantage to fit data distribution, so it can achieve data augmentation by fitting the real distribution and synthesizing additional training data. In this way, the deep convolution model can also be well trained in the case of using a small sample medical image data set. However, some certain gaps still exist between synthetic images and real images. In order to further narrow those gaps, this paper proposed a method that applies SimGAN on cardiac magnetic resonance synthetic image optimization task. Meanwhile, the improved residual structure is used to deepen the network structure to improve the performance of the optimizer. Lastly, the experiments will show the good result of our data augmentation method based on GAN

Development and validation of a brief diabetic foot ulceration risk checklist among diabetic patients: a multicenter longitudinal study in China

Abstract The study aims to develop and assess and validate a brief diabetic foot ulceration risk checklist among diabetic patients through a longitudinal study. Patients who had diabetes mellitus and had no foot ulceration and severe systematic disorders were recruited from eleven tertiary hospitals in nine provinces or municipalities of China. Internal consistency reliability, construct validity, concurrent validity, item property, and measurement invariance of the tool were assessed. The predictive capability of the tool was validated by the follow-up data using the receiver operating characteristic curve. At baseline, 477 valid cases were collected. Twelve items were remained after initial selection. Cronbach’s alpha was 0.56. Confirmatory factor analysis showed that the model had acceptable goodness-of-fit yet local dependency between two items. Item response theory showed that most items had acceptable discrimination and difficulty parameters. Differential item functioning showed that tool had measurement invariance. 278 were followed up one year after the baseline. Follow-up showed that one-year incidence of ulceration among the patients was 3.6%, and the area under the receiver operating characteristic curve was 0.77 (95% confidence interval: 0.61–0.93). The cut-off point of the tool was 4, when sensitivity and specificity were 0.62 and 0.75 respectively. The checklist has good psychometric properties according to mixed evidences from classical and modern test theory, and has good predictive capability