海底电缆交叉鬼波化双检合并技术改进及应用

双检合并技术在海底电缆地震资料处理中被广泛应用,可衰减电缆鬼波的影响。传统方式以水、陆检记录的波场记录相同为假设,电缆鬼波压制效果差,合并效果并不理想。在分析交叉鬼波化双检合并技术基本原理的基础上,综合利用水检资料与陆检鬼波褶积的结果和陆检资料与水检鬼波褶积的结果求取刻度算子,同时在地震资料处理中利用陆检资料标定水检资料,从而优化了交叉鬼波化双检合并技术。实际资料处理结果表明,改进后的技术可有效衰减电缆鬼波,分离出上行波场,大幅度拓宽地震资料频带,提高地震资料分辨率,从而提供更丰富的构造细节信息。     

纳米氧化物对水泥基料化学收缩的影响

采用ASTM化学收缩试验方法,研究纳米SiO2、MgO、Al2O3对水泥基材料化学收缩变化的影响,明确纳米材料种类的差异性。结果表明,各实验组水泥基材料化学收缩均经历三阶段:急速变化区、缓慢变化区、平稳区,7d始水泥基材料化学收缩基本稳定。几种纳米材料的最佳掺量对水泥基材料的化学收缩改善作用大小为:SiO2>Al2O3>MgO。研究发现,纳米材料种类和掺量的不同对化学收缩的影响也较大,在选用时,需综合考虑。     

Broad-Spectral-Range Sustainability and Controllable Excitation of Hyperbolic Phonon Polaritons in α-MoO3

Hyperbolic phonon polaritons (HPhPs) in orthorhombic-phase molybdenum trioxide (α-MoO₃) show in-plane hyperbolicity, great wavelength compression, and ultralong lifetime, therefore holding great potential in nanophotonic applications. However, its polaritonic response in the far-infrared (FIR) range remains unexplored due to challenges in experimental characterization. Here, monochromated electron energy loss spectroscopy (EELS) in a scanning transmission electron microscope (STEM) is used to probe HPhPs in α-MoO₃ in both mid-infrared (MIR) and FIR frequencies and correlate their behaviors with microstructures and orientations. It is found that low structural symmetry leads to various phonon modes and multiple Reststrahlen bands (RBs) over a broad spectral range (over 70 meV) and in different directions (55–63 meV and 119–125 meV along the b-axis, 68–106 meV along the c-axis, and 101–121 meV along the a-axis). These HPhPs can be selectively excited by controlling the direction of swift electrons. These findings provide new opportunities in nanophotonic and optoelectronic applications, such as directed light propagation, hyperlenses, and heat transfer.     

Disease Ionomics: Understanding the Role of Ions in Complex Disease

Ionomics is a novel multidisciplinary field that uses advanced techniques to investigate the composition and distribution of all minerals and trace elements in a living organism and their variations under diverse physiological and pathological conditions. It involves both high-throughput elemental profiling technologies and bioinformatic methods, providing opportunities to study the molecular mechanism underlying the metabolism, homeostasis, and cross-talk of these elements. While much effort has been made in exploring the ionomic traits relating to plant physiology and nutrition, the use of ionomics in the research of serious diseases is still in progress. In recent years, a number of ionomic studies have been carried out for a variety of complex diseases, which offer theoretical and practical insights into the etiology, early diagnosis, prognosis, and therapy of them. This review aims to give an overview of recent applications of ionomics in the study of complex diseases and discuss the latest advances and future trends in this area. Overall, disease ionomics may provide substantial information for systematic understanding of the properties of the elements and the dynamic network of elements involved in the onset and development of diseases.     

改进HSR-FCN的服装图像识别分类算法研究

目前网络上的服装图像数量增长迅猛，对于大量服装图像实现智能分类的需求日益增加。将基于区域的全卷积网络（Region-Based Fully Convolutional Networks，R-FCN）引入到服装图像识别中，针对服装图像分类中网络训练时间长、形变服装图像识别率低的问题，提出一种新颖的改进框架HSR-FCN。新框架将R-FCN中的区域建议网络和HyperNet网络相融合，改变图片特征学习方式，使得HSR-FCN可以在更短的训练时间内达到更高的准确率。在模型中引入了空间转换网络，对输入服装图像和特征图进行了空间变换及对齐，加强了对多角度服装和形变服装的特征学习。实验结果表明，改进后的HSR-FCN模型有效地加强了对形变服装图像的学习，且在训练时间更短的情况下，比原来的网络模型R-FCN平均准确率提高了大约3个百分点，达到96.69%。