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.     

强碱三元复合驱色谱分离及其影响因素——以大庆油区喇嘛甸油田为例

三元复合驱油体系中驱油剂的协同效应,会使油水间的界面张力大幅度下降,原油采收率大幅度提高;但三元复合驱油体系的色谱分离现象也会破坏其协同效应和完整性,揭示色谱分离现象及其影响因素对于改善三元复合驱油效果具有十分重要意义。以油藏工程和分析化学理论为指导,以化学分析和物理模拟为技术途径,并以大庆喇嘛甸油田典型区块地质特征和流体性质为研究对象,开展强碱三元复合驱色谱分离现象及其影响因素研究。结果表明,采用前置高浓度聚合物段塞后,可以有效扩大波及体积,但表面活性剂与碱和聚合物之间无因次等浓距增加,色谱分离现象加剧。大庆油区喇嘛甸油田不同区块油水性质差异对色谱分离现象存在影响,原因在于部分重烷基苯石油磺酸进入油相,影响水相中浓度检测值。强碱三元复合驱油体系中表面活性剂与碱和聚合物之间色谱分离程度比较严重,碱与聚合物之间较弱。     

Effects of Undercooling and Transformation Time on Microstructure and Strength of Fe–C–Mn–Si Superbainitic Steel

Strength of Materials - A metallographic method, dilatometry, and X-ray diffraction were applied to investigate the effects of undercooling and holding time on bainitic transformation,...     

采用信息增益率的混合入侵检测模型设计

针对现有混合入侵检测模型仅定性选取特征而导致检测精度较低的问题，同时为了充分结合误用检测模型和异常检测模型的优势，提出一种采用信息增益率的混合入侵检测模型.首先，利用信息增益率定量地选择特征子集，最大程度地保留样本信息；其次，采用余弦时变粒子群算法确定支持向量机参数构建误用检测模型，使其更好地平衡粒子在全局和局部的搜索能力，然后，选取灰狼算法确定单类支持向量机参数构建异常检测模型，以此来提高对最优参数的搜索效率和精细程度，综合提高混合入侵检测模型对攻击的检测效果；最后，通过两种数据集进行仿真实验，验证了所提混合入侵检测模型具有较好的检测性能.     

Decorative near-infrared transmission filters featuring high-efficiency and angular-insensitivity employing 1D photonic crystals

We present a new scheme for visibly-opaque but near-infrared-transmitting filters involving 7 layers based on one-dimensional ternary photonic crystals, with capabilities in reaching nearly 100% transmission efficiency in the near-infrared region. Different decorative reflection colors can be created by adding additional three layers while maintaining the near-infrared transmission performance. In addition, our proposed structural colors show great angular insensitivity up to ±60° for both transverse electric and transverse magnetic polarizations, which are highly desired in various fields. The facile strategy described here involves a simple deposition method for the fabrication, thereby having great potential in diverse applications such as image sensors, anti-counterfeit tag, and optical measurement systems.

     

Controlled Secure Direct Communication Protocol via the Three-Qubit Partially Entangled Set of States

In this paper, we first re-examine the previous protocol of controlled quantum secure direct communication of Zhang et al.’s scheme, which was found insecure under two kinds of attacks, fake entangled particles attack and disentanglement attack. Then, by changing the party of the preparation of cluster states and using unitary operations, we present an improved protocol which can avoid these two kinds of attacks. Moreover, the protocol is proposed using the three-qubit partially entangled set of states. It is more efficient by only using three particles rather than four or even more to transmit one bit secret information. Given our using state is much easier to prepare for multiqubit states and our protocol needs less measurement resource, it makes this protocol more convenient from an applied point of view.     

基于多尺度双线性卷积神经网络的多角度下车型精细识别

针对多角度下车辆出现一定的尺度变化和形变导致很难被准确识别的问题，提出基于多尺度双线性卷积神经网络（MS-B-CNN）的车型精细识别模型。首先，对双线性卷积神经网络（B-CNN）算法进行改进，提出MS-B-CNN算法对不同卷积层的特征进行了多尺度融合，以提高特征表达能力；此外，还采用基于中心损失函数与Softmax损失函数联合学习的策略，在Softmax损失函数基础上分别对训练集每个类别在特征空间维护一个类中心，在训练过程中新增加样本时，网络会约束样本的分类中心距离，以提高多角度情况下的车型识别的能力。实验结果显示，该车型识别模型在CompCars数据集上的正确率达到了93.63%，验证了模型在多角度情况下的准确性和鲁棒性。