Good polynomials for optimal LRC of low locality

Designs, Codes and Cryptography - According to a magnific method due to I. Tamo and A. Barg, a class of polynomials over finite fields, called good polynomials, was introduced and used to construct...     

Nonlinear compressed sensing-based adaptive modal shapes selection approach for efficient dynamic response analysis of flexible multibody system

Nonlinear Dynamics - The modal truncation is usually used in the dynamic response analysis of flexible multibody systems, in which, appropriate selection of modal shapes is very critical to achieve...     

Interaction induced non-reciprocal three-level quantum transport

Besides its fundamental importance, non-reciprocity has also found many potential applications in quantum technology. Recently, many quantum systems have been proposed to realize non-reciprocity, but stable non-reciprocal process is still experimentally difficult in general, due to the needed cyclical interactions in artificial systems or operational difficulties in solid state materials. Here, we propose a new kind of interaction induced non-reciprocal operation, based on the conventional stimulated-Raman-adiabatic-passage(STIRAP) setup, which removes the experimental difficulty of requiring cyclical interaction, and thus it is directly implementable in various quantum systems. Furthermore, we also illustrate our proposal on a chain of three coupled superconducting transmons, which can lead to a non-reciprocal circulator with high fidelity without a ring coupling configuration as in the previous schemes or implementations. Therefore, our protocol provides a promising way to explore fundamental non-reciprocal quantum physics as well as realize non-reciprocal quantum device.     

Two-dimensional coordination polymer-based nanosensor for sensitive and reliable nucleic acids detection in living cells

A reliable and sensitive strategy which can assess nucleic acid levels in living cells would be essential for fundamental research of biomedical applications. Some nanomaterial-based fluorescence biosensors recently developed for detecting nucleic acids, however, are often with expensive, complicated and timeconsuming preparation process. Here, by using a facile bottom-up synthesis method, a two-dimensional(2 D) coordination polymer(CP) nanosheet, [Cu(tz)](Htz = 1,2,4-triazole), was successfully...     

MUC1 vaccines using β-cyclodextrin grafted chitosan (CS-g-CD) as carrier via host-guest interaction elicit robust immune responses

We construct MUC1 vaccines using β-cyclodextrin grafted chitosan (CS-g-CD) as carrier via host-guest interaction. These vaccines based on non-covalent assembling can provoke robust immune responses, including high level of specific antibodies and cytokines. The induced antibodies can specifically recognize tumor cells and mediate cytotoxicity against tumor cells. These results indicate that CS-g-CD with strong immunostimulatory activities can be a straightforward platform for peptide-based vaccine construction.     

光纤基底TiNi形状记忆合金薄膜制备工艺

将TiNi基记忆合金薄膜与光纤相结合可制成智能化、集成化且成本经济的微机电系统和微传感器件.本文采用磁控溅射法在二氧化硅光纤基底上制备TiNi记忆合金薄膜,系统讨论了溅射工艺参数以及后续退火处理对薄膜质量的影响.采用自研制光纤镀膜掩膜装置在直径为125μm的光纤圆周表面上形成均匀薄膜.实验表明:在靶基距、背底真空度、Ar气流量和溅射时间一定的条件下,溅射功率存在最佳值;溅射压强较大时,薄膜沉积速率较低,但薄膜表面粗糙度较小.进行退火处理后,薄膜形成较良好的晶体结构,Ti_49.09Ni_50.91薄膜中马氏体B19′相和奥氏体B2相共存,但以B19′为主.根据本文研究结果,在玻璃光纤基底上制备高质量的TiNi基记忆合金薄膜是可实现的,本工作为下一步研制微机电系统和微型传感器做了基础准备.     

A Factor-GARCH Model for High Dimensional Volatilities

Acta Mathematicae Applicatae Sinica, English Series - This paper proposes a method for modelling volatilities (conditional covariance matrices) of high dimensional dynamic data. We combine the...     

Network analysis based on low-rank method for mining information on integrated data of multi-cancers

The noise problem of cancer sequencing data has been a problem that can’t be ignored. Utilizing considerable way to reduce noise of these cancer data is an important issue in the analysis of gene co-expression network. In this paper, we apply a sparse and low-rank method which is Robust Principal Component Analysis (RPCA) to solve the noise problem for integrated data of multi-cancers from The Cancer Genome Atlas (TCGA). And then we build the gene co-expression network based on the integrated data after noise reduction. Finally, we perform nodes and pathways mining on the denoising networks. Experiments in this paper show that after denoising by RPCA, the gene expression data tend to be orderly and neat than before, and the constructed networks contain more pathway enrichment information than unprocessed data. Moreover, learning from the betweenness centrality of the nodes in the network, we find some abnormally expressed genes and pathways proven that are associated with many cancers from the denoised network. The experimental results indicate that our method is reasonable and effective, and we also find some candidate suspicious genes that may be linked to multi-cancers.     

Structural damage detection using convolutional neural networks combining strain energy and dynamic response

Meccanica - Based on the classification ability of a convolutional neural network (CNN), this paper proposes a structural damage detection method in which a CNN is used to classify the location and...