Study of the pyrolysis mechanism of SiBCN polymer precursor

The pyrolysis mechanisms occurring during the conversion of polyborosilazane (PBSZ) into amorphous SiBCN cerasmic have been investigated. TGA–TDG experiment have been applied to investigate the mass loss behaviour during ceramization. Solid-state 11B, 13C and 29Si NMR spectroscopy has been applied to probe the local environment of all NMR active nuclei in the precursor, the thermolysis intermediates and the ceramic residue. IR spectroscopy has been performed to receive valuable information on the chemical bonding in all materials. At temperature below 400oC, Si-N bonds are formed via condensation reaction involving N-H and Si-H units with hydrogen released. It is followed by evolution of hydrocarbons due to the cleavage of bonds and formation of methane and hydrogen at 600 oC. After heating to 1000 oC, ceramization complete and free carbon, BN3 domains as well as Si–C–N units coexist SiCxN4-x,x=0,1,2,3. And BN3 keep unchanged during the whole ceramization stage

Let Segment Anything Help Image Dehaze

The large language model and high-level vision model have achieved impressive performance improvements with large datasets and model sizes. However, low-level computer vision tasks, such as image dehaze and blur removal, still rely on a small number of datasets and small-sized models, which generally leads to overfitting and local optima. Therefore, we propose a framework to integrate large-model prior into low-level computer vision tasks. Just as with the task of image segmentation, the degradation of haze is also texture-related. So we propose to detect gray-scale coding, network channel expansion, and pre-dehaze structures to integrate large-model prior knowledge into any low-level dehazing network. We demonstrate the effectiveness and applicability of large models in guiding low-level visual tasks through different datasets and algorithms comparison experiments. Finally, we demonstrate the effect of grayscale coding, network channel expansion, and recurrent network structures through ablation experiments. Under the conditions where additional data and training resources are not required, we successfully prove that the integration of large-model prior knowledge will improve the dehaze performance and save training time for low-level visual tasks

The preparation and properties of novel structural carbon foams derived from different mesophase pitches

As a novel porous multi-functional carbon material, carbon foams have high bulk thermal conductivity and low density, making them as excellent materials for thermal management systems applications, such as heat exchangers, space radiators, and thermal protection systems. In this paper, the carbon foams with high thermal conductivity, derived from three kinds of mesophase pitches, were fabricated by the process of foaming, carbonization and graphitization. The microstructures of the foams were examined by scanning electron microscopy. It was found that the pores were uniformly distributed, and the pore wall thickened with increasing foams’ density. The properties of the foams were studied, including compressive strength and thermal conductivity. The results showed that lower density and higher thermal conductivity were achieved for the foams using the two kinds of pitches with higher volatile components. The bulk thermal conductivity of carbon foams were up to 179 W/(m·K) and 201 W/(m·K), for the densities of 0.66 g/cm3 and 0.83 g/cm3, respectively. The foams’ compressive strength was in the range of 1.6 MPa to 3.4 MPa

Toward Real Flare Removal: A Comprehensive Pipeline and A New Benchmark

Photographing in the under-illuminated scenes, the presence of complex light sources often leave strong flare artifacts in images, where the intensity, the spectrum, the reflection, and the aberration altogether contribute the deterioration. Besides the image quality, it also influence the performance of down-stream visual applications. Thus, removing the lens flare and ghosts is a challenge issue especially in low-light environment. However, existing methods for flare removal mainly restricted to the problems of inadequate simulation and real-world capture, where the categories of scattered flares are singular and the reflected ghosts are unavailable. Therefore, a comprehensive deterioration procedure is crucial for constructing the dataset of flare removal. Based on the theoretical analysis and real-world evaluation, we propose a well-developed methodology for generating the data-pairs with flare deterioration. The procedure is comprehensive, where the similarity of scattered flares and the symmetric effect of reflected ghosts are realized. Moreover, we also construct a real-shot pipeline that respectively processes the effects of scattering and reflective flares, aiming to directly generate the data for end-to-end methods. Experimental results show that the proposed methodology add diversity to the existing flare datasets and construct a comprehensive mapping procedure for flare data pairs. And our method facilities the data-driven model to realize better restoration in flare images and proposes a better evaluation system based on real shots, resulting promote progress in the area of real flare removal

Real-world Deep Local Motion Deblurring

Most existing deblurring methods focus on removing global blur caused by camera shake, while they cannot well handle local blur caused by object movements. To fill the vacancy of local deblurring in real scenes, we establish the first real local motion blur dataset (ReLoBlur), which is captured by a synchronized beam-splitting photographing system and corrected by a post-progressing pipeline. Based on ReLoBlur, we propose a Local Blur-Aware Gated network (LBAG) and several local blur-aware techniques to bridge the gap between global and local deblurring: 1) a blur detection approach based on background subtraction to localize blurred regions; 2) a gate mechanism to guide our network to focus on blurred regions; and 3) a blur-aware patch cropping strategy to address data imbalance problem. Extensive experiments prove the reliability of ReLoBlur dataset, and demonstrate that LBAG achieves better performance than state-of-the-art global deblurring methods without our proposed local blur-aware techniques

Natural Killer Cells for Cancer Immunotherapy: Opportunities and Challenges

Natural killer (NK) cells are advantaged immune cells and play a pivotal role in both innate and adaptive immune responses. To date, autogenous and allogenic NK cells have been generated from a variety of origins, including perinatal blood (e.g., umbilical cord blood and placental blood), peripheral blood, and even stem cells (hematopoietic stem cells and pluripotent stem cells). NK cells function mainly via antibody-dependent cell-mediated cytotoxicity (ADCC), direct cytolytic effect, and paracrine effects (e.g., IFN-γ, GM-CSF, granzyme, and perforin). Distinguishing from the adaptive immunizing cells (e.g., T and B lymphocytes), NK cells, and chimeric antigen receptor-transduced NK (CAR-NK), cell-based cytotherapy is adequate to fulfill the biofunction of eliminating pathogenic infection, combating hematological malignancies and metastatic solid tumors, and delaying aging. In this chapter, we mainly focus on the state-of-the-art renewal of NK cell-based cytotherapy for cancer immunosurveillance and immunotherapy from the view of high-efficient in vitro preparation (e.g., candidate cell sources and ex vivo cultivation) and preclinical and clinical investigation. Furthermore, we also figure out the promising prospects and the concomitant challenges of NK cell-based remedies for cancer management in future, which will collectively benefit the development of NK cell-based cancer immunotherapy in future