Scanning Nanowelding Lithography for Rewritable One‐Step Patterning of Sub‐50 nm High‐Aspect‐Ratio Metal Nanostructures

The development of a new nanolithographic strategy, named scanning nanowelding lithography (SNWL), for the one‐step fabrication of arbitrary high‐aspect‐ratio nanostructures of metal is reported in this study. Different from conventional pattern transfer and additive printing strategies which require subtraction or addition of materials, SNWL makes use of a sharp scanning tip to reshape metal thin films or existing nanostructures into desirable high‐aspect‐ratio patterns, through a cold‐welding effect of metal at the nanoscale. As a consequence, SNWL can easily fabricate, in one step and at ambient conditions, sub‐50 nm metal nanowalls with remarkable aspect ratio >5, which are found to be strong waveguide of light. More importantly, SNWL outweighs the existing strategies in terms of the unique ability to erase the as‐made nanostructures and rewrite them into other shapes and orientations on‐demand. Taking advantages of the serial and rewriting capabilities of SNWL, the smart information storage–erasure of Morse codes is demonstrated. SNWL is a promising method to construct arbitrary high‐aspect‐ratio nanostructure arrays that are highly desirable for biological, medical, optical, electronic, and information applications.     

Modeling and Measuring All the Elements of an Ultrasonic Nondestructive Evaluation System I: Modeling Foundations

Abstract

A comprehensive model of an ultrasonic nondestructive evaluation (NDE) flaw measurement system is developed that combines models for the electrical components (pulser/receiver, cabling), electromechanical components [transducer(s)], and the acoustic/elastic processes present in the materials being inspected, including the scattering of ultrasonic waves from a flaw. This model is called the electroacoustic measurement (EAM) model. Here, in Part I, the underlying modeling foundations of the EAM model are described in detail and the use of the EAM model is demonstrated in a transducer design study. This EAM model provides a new, powerful tool for analyzing virtually any element in the measurement process. In Part II it will be shown that this power can be extended to characterizing typical commercial measurement systems through the use of models and purely electrical measurements of the system components.     

π放面机道用凝胶防灭火的实践

鹤岗矿业集团公司大陆煤矿在-275南11层零区回掘期间,应用新型凝胶防灭火工艺,成功地完成了回采任务,有效地消灭了煤层发火,保证了正常生产。     

基于元组空间的位并行包分类算法

随着网络的发展,路由器需要完成数据包过滤、QOS、流量计费等额外服务,这要求路由器能够以线速(wirespeed)完成对数据包的分类处理。位并行算法是一种快速的包分类算法,但由于空间占用量过大,它不能扩展到大规模规则库。该文从位并行算法入手,通过引入元组空间的概念提出了一种新的算法,在时间复杂度与空间复杂度上都较位并行算法有很大改进,并具有很好的扩展性。     

基于遗传算法的受限物化视图

受限物化视图的选择是当前数据仓库研究的最重要的问题之一,且其是个NP问题。本文通过对视图与查询关系的研究,提出了一种选择物化视图的两阶段算法框架,首先利用候选视图选择算法缩小视图选择的范围,而后利用遗传算法解决受限物化视图的选择问题。     

Semi-supervised cardiac MRI image of the left ventricle segmentation algorithm based on contrastive learning

A semi-supervised convolutional neural network segmentation method of medical images based on contrastive learning is proposed. The cardiac magnetic resonance imaging(MRI) images to be segmented are preprocessed to obtain positive and negative samples by labels. The U-Net shrinks network is applied to extract features of the positive samples, negative samples, and input samples. In addition, an unbalanced contrastive loss function is proposed, which is weighted with the binary cross-entropy loss...     

UpG法用于辐射损伤的量效关系研究

应用常规染色体制作法和紫外线照射加吉姆萨染色法(Ultraviolet plus GienlSa technology,UpG)检测经60Coγ射线离体照射后的人外周血淋巴细胞染色体畸变率(双着丝粒体 环),研究其剂量-效应关系,探讨UpG法用于辐射损伤的剂量估算效果.实验结果表明:两种方法得出的染色体畸变率与照射剂量密切相关(P<0.05),均可用于辐射损伤的量效关系研究.两种方法的结果相比,在同一照射剂量下,UpG法高于常规法,具有显著性差异(P<0.05).UpG法可以很好用于辐射损伤的剂量估算中,且较为准确、灵敏.     

An effective and interpretable method for document classification

As the number of documents has been rapidly increasing in recent time, automatic text categorization is becoming a more important and fundamental task in information retrieval and text mining. Accuracy and interpretability are two important aspects of a text classifier. While the accuracy of a classifier measures the ability to correctly classify unseen data, interpretability is the ability of the classifier to be understood by humans and provide reasons why each data instance is assigned to a label. This paper proposes an interpretable classification method by exploiting the Dirichlet process mixture model of von Mises–Fisher distributions for directional data. By using the labeled information of the training data explicitly and determining automatically the number of topics for each class, the learned topics are coherent, relevant and discriminative. They help interpret as well as distinguish classes. Our experimental results showed the advantages of our approach in terms of separability, interpretability and effectiveness in classification task of datasets with high dimension and complex distribution. Our method is highly competitive with state-of-the-art approaches.