AlGaN/GaN high-electron-mobility transistors (HEMTs) with two different gate–drain distances (30 μm and 10 μm) were exposed to 1 MeV, 0.6 MeV, and 0.4 MeV protons at a fluence of 2.16 × 1012 cm−2. The gate–channel electron density and low-field mobility were obtained by measuring the capacitance–voltage characteristics and current–voltage characteristics. After proton irradiation, the gate–channel low-field electron mobility of the AlGaN/GaN HEMT with a 30 μm gate–drain distance increases and that with a 10 μm gate–drain distance decreases. It is studied and found that the mobility behavior is related to the polarization Coulomb field scattering, and the proton irradiation influences the intensity of the polarization Coulomb field scattering by changing the polarization/strain distribution in the barrier layer. The different gate–drain distances correspond to different variation trends of scattering intensity. The effect of 1 MeV protons on the barrier layer is smaller compared with 0.6 MeV and 0.4 MeV protons, so the mobility variation is smaller.
A laboratory confocal micro X-ray fluorescence (micro-XRF) spectrometer based on a polycapillary focusing X-ray lens (PFXRL) in the excitation channel and a polycapillary parallel X-ray lens (PPXRL) in the detection channel was used to carry out the size-resolved source apportionment of aerosol particles. The PPXRL in the detection channel both increased the collection angle of the detector and improved the signal-to-noise ratio of the X-ray spectrum. When this confocal micro-XRF spectrometer was used to carry out the quantitative XRF analysis of single aerosol particles with smaller size than that of the overlapping foci of the PFXRL and the PPXRL, the sensitivity was corrected by using a Gaussian function. The size-resolved “fingerprint database” for the air pollution sources was established based on the quantitative XRF results for single aerosol particles. The size-resolved aerosol particles on hazy-foggy days in Beijing were apportioned.
(1) Background: Injury repair is a complex physiological process in which multiple cells and molecules are involved. Tenascin-C (TNC), an extracellular matrix (ECM) glycoprotein, is essential for angiogenesis during wound healing. This study aims to provide a comprehensive review of the dynamic changes and functions of TNC throughout tissue regeneration and to present an up-to-date synthesis of the body of knowledge pointing to multiple mechanisms of TNC at different restoration stages. (2) Methods: A review of the PubMed database was performed to include all studies describing the pathological processes of damage restoration and the role, structure, expression, and function of TNC in post-injury treatment; (3) Results: In this review, we first introduced the construction and expression signature of TNC. Then, the role of TNC during the process of damage restoration was introduced. We highlight the temporal heterogeneity of TNC levels at different restoration stages. Furthermore, we are surprised to find that post-injury angiogenesis is dynamically consistent with changes in TNC. Finally, we discuss the strategies for TNC in post-injury treatment. (4) Conclusions: The dynamic expression of TNC has a significant impact on angiogenesis and healing wounds and counters many negative aspects of poorly healing wounds, such as excessive inflammation, ischemia, scarring, and wound infection.
Point cloud registration is a key task in many computational fields. Previous correspondence matching based methods require the inputs to have distinctive geometric structures to fit a 3D rigid transformation according to point-wise sparse feature matches. However, the accuracy of transformation heavily relies on the quality of extracted features, which are prone to errors with respect to partiality and noise. In addition, they can not utilize the geometric knowledge of all the overlapping regions. On the other hand, previous global feature based approaches can utilize the entire point cloud for the registration, however they ignore the negative effect of non-overlapping points when aggregating global features. In this paper, we present OMNet, a global feature based iterative network for partial-to-partial point cloud registration. We learn overlapping masks to reject non-overlapping regions, which converts the partial-to-partial registration to the registration of the same shape. Moreover, the previously used data is sampled only once from the CAD models for each object, resulting in the same point clouds for the source and reference. We propose a more practical manner of data generation where a CAD model is sampled twice for the source and reference, avoiding the previously prevalent over-fitting issue. Experimental results show that our method achieves state-of-the-art performance compared to traditional and deep learning based methods. Code is available at https://github.com/megvii-research/OMNet.
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