Dense and core-rim structured B4C-TiB2 ceramics with Mo-Co-WC additive

B₄C-TiB₂ ceramics (TiB₂ ranging 5~70 vol%) with Mo-Co-WC as the sintering additive were prepared by spark plasma sintering. In comparison with B₄C-TiB₂ without additive, the enhanced densification was evident in the sintered specimen with Mo-Co-WC additive. Core-rim structured grain was observed around TiB₂ grains. The interface of the rim between TiB₂ and B₄C phases demonstrated different feature: the inner borderline of the rim exhibited a smooth feature, whereas a sharp curved grain boundary was observed between the rim and the B₄C grain. The formation mechanism is discussed: the epitaxial growth of (Ti,Mo,W)B₂ rim around the TiB₂ core may occur as a result of the solid solution and dissolution-precipitation between TiB₂ phase and the sintering additive. It was revealed that the fracture toughness increased as the content of TiB₂ content increased, alongside the decreased hardness. B₄C-30 vol% TiB₂ specimen demonstrated the optimal combination of mechanical properties, reaching Vickers hardness of 24.3 GPa and fracture toughness of 3.33 MPa·m^1/2.     

基于深度学习的秀丽隐杆线虫显微图像分割方法

利用计算机实现自动、准确的秀丽隐杆线虫(C.elegans)的各项形态学参数分析,至关重要的是从显微图像上分割出线虫体态,但由于显微镜下的图像噪声较多,线虫边缘像素与周围环境相似,而且线虫的体态具有鞭毛和其他附着物需要分离,多方面因素导致设计一个鲁棒性的C.elegans分割算法仍然面临着挑战。针对这些问题,提出了一种基于深度学习的线虫分割方法,通过训练掩模区域卷积神经网络(Mask R-CNN)学习线虫形态特征实现自动分割。首先,通过改进多级特征池化将高级语义特征与低级边缘特征融合,结合大幅度软最大损失(LMSL)损失算法改进损失计算;然后,改进非极大值抑制;最后,引入全连接融合分支等方法对分割结果进行进一步优化。实验结果表明,相比原始的Mask R-CNN,该方法平均精确率(AP)提升了4.3个百分点,平均交并比(mIOU)提升了4个百分点。表明所提出的深度学习分割方法能够有效提高分割准确率,在显微图像中更加精确地分割出线虫体。     

基于USB接口的数据采集系统

提出了一种基于USB接口,采用内置USB接口的微处理器芯片来实现的数据采集系统方案,并从硬件设计和软件开发两个方面对该方案进行了探讨。提出了3种硬件设计方案,并对其中一种方案进行了详细介绍；软件开发部分介绍了USB设备固件、USB设备驱动程序和数据采集系统应用程序设计。     

High-resolution electron microscopy of the twinning and intergrowth in Ca4Al6SO16 and Ca3SrAl6SO16

The microstructures of unhydrated calcium aluminosulphate Ca₄Al₆SO₁₆ and Ca₃SrAl₆SO₁₆ have been studied by high-resolution electron microscopy (HREM). The results showed that twinning and twinned slabs could be introduced taking the [1 1 2] direction as the twin axis so that it seems to be coincident with the law of twinning formed in body-centred cubic structures. A previously reported superlattice with a repeat period twice that of the fundamental structure along the 〈1 1 0〉 direction has also been found in both matrix and twin variants. The close intergrowth of Ca₃SrAl₆SO₁₆ and another phase, possibly Sr₃Al₂O₆ existing as an inclusion between these two twin variants, was determined and clearly revealed by electron diffraction and HREM images. The coherent interphase boundaries and orientation relationship between them can also be deduced.     

高温皂碱练与酶制剂练精干绵性能比较

通过测试比较，分析了酶制剂练、高温皂碱练由精练方法不同而造成纤维在微观、外观、机械、吸湿及电学等方面的异同点。提出酶制剂练对提高纤维梳折、提高成纱品质、提高劳动生产率、降低工人劳动强度等方面均优于高温皂碱练，是精练工程改造方向之一。     

HVS：一个半现实全景图时空模型

利用实景图象组成具有空间感和时间感的虚拟空间，我们称之为半现实。这种具有空间“上下文”和时间“上下文”能力的半现实全景图可以应用于许多方面。文中描述了这种半现实ＨＶＳ的建模思想，给出了组成时空座标系的方法，并且讨论了实现中的若干问题。     

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��It is an important study subject concerning the filtration mechanics over the years to converselly determine the parameter texture (e.g. parameter recognition )in the inner system through observing the gas well pressure, production rate and water-gas ratio, in accordance with the working regulations for this gas field. By use of this method, some good results have been obtained in automatically matching the production history for eight years in the Jia-3 gas reservoir of Dengjingguan gas field.     

数据压缩的综合字典模型

文中提出一种无损的数据压缩的字典方法，被称为综合字典方法，该方法在较大程度上消除数据文件中多种形式的冗余，达到较好的压缩效果。     

A 20-Gb/s silicon bipolar 1:4-demultiplexer IC

This paper presents a 20-Gb/s 1:4-demultiplexer for future fiber-optic transmission systems. It uses an 0.4-μm emitter double polysilicon 21-GHz f_T Si bipolar foundry process. This is the highest data rate of a 1:4-DEMUX reported so far in any technology. The 1:4-DEMUX features a tree-type architecture with one frequency divider and a channel switch circuit. The circuit design was carefully optimized to achieve high speed and moderate power dissipation. It consumes 1.4 W with a single -4.5-V supply