一种新型极性脲丙基-C30固定相的制备和性能评价

以3-脲丙基三甲氧基硅烷为偶联剂，制备了一种新型极性脲丙基-C30（TPU-C30）反相色谱固定相。采用扫描电子显微镜、元素分析、红外光谱和热分析等对该固定相进行表征。结果表明，TPU-C30固定相已成功制备，连续制备3次固定相，其元素含量的相对偏差均小于5%，说明该合成工艺重复性良好。以不同极性、位置异构、碱性化合物为溶质探针，以传统的C18色谱柱与C30色谱柱为参比，对制备的固定相的色谱性能进行了研究。研究结果表明，TPU-C30固定相具有不同于传统C18柱、C30柱的选择性和更优的择形性，明显改善了碱性物质的峰形，其具有广阔的应用空间。     

Research on Multi-Terminal’s AC Offloading Scheme and Multi-Server’s AC Selection Scheme in IoT

Mobile Edge Computing (MEC) technology and Simultaneous Wireless Information and Power Transfer (SWIPT) technology are important ones to improve the computing rate and the sustainability of devices in the Internet of things (IoT). However, the system models of most relevant papers only considered multi-terminal, excluding multi-server. Therefore, this paper aims at the scenario of IoT with multi-terminal, multi-server and multi-relay, in which can optimize the computing rate and computing cost by using deep reinforcement learning (DRL) algorithm. Firstly, the formulas of computing rate and computing cost in proposed scenario are derived. Secondly, by introducing the modified Actor-Critic (AC) algorithm and convex optimization algorithm, we get the offloading scheme and time allocation that maximize the computing rate. Finally, the selection scheme of minimizing the computing cost is obtained by AC algorithm. The simulation results verify the theoretical analysis. The algorithm proposed in this paper not only achieves a near-optimal computing rate and computing cost while significantly reducing the program execution delay, but also makes full use of the energy collected by the SWIPT technology to improve energy utilization.     

EGFAFS: A Novel Feature Selection Algorithm Based on Explosion Gravitation Field Algorithm

Feature selection (FS) is a vital step in data mining and machine learning, especially for analyzing the data in high-dimensional feature space. Gene expression data usually consist of a few samples characterized by high-dimensional feature space. As a result, they are not suitable to be processed by simple methods, such as the filter-based method. In this study, we propose a novel feature selection algorithm based on the Explosion Gravitation Field Algorithm, called EGFAFS. To reduce the dimensions of the feature space to acceptable dimensions, we constructed a recommended feature pool by a series of Random Forests based on the Gini index. Furthermore, by paying more attention to the features in the recommended feature pool, we can find the best subset more efficiently. To verify the performance of EGFAFS for FS, we tested EGFAFS on eight gene expression datasets compared with four heuristic-based FS methods (GA, PSO, SA, and DE) and four other FS methods (Boruta, HSICLasso, DNN-FS, and EGSG). The results show that EGFAFS has better performance for FS on gene expression data in terms of evaluation metrics, having more than the other eight FS algorithms. The genes selected by EGFAGS play an essential role in the differential co-expression network and some biological functions further demonstrate the success of EGFAFS for solving FS problems on gene expression data.     

静态傅里叶变换光谱仪的机理及干涉条纹的校正

针对高速、瞬时光谱测量要求光谱仪的结构简单、装备方便、实时性强的特点,文章介绍了一种静态傅里叶变换光谱仪,并对其原理进行了推导与剖析。针对其调整中出现的干涉条纹的倾斜校正问题,进行了详细的理论分析和干涉条纹模型的数学推导,得出倾斜镜旋转参数和光学元件最小通光口径之间的数学关系,以及倾斜镜旋转参数和干涉条纹旋转参数之间的数学关系。通过利用Matlab7.0数学工具,对所总结推导的干涉条纹模型进行模拟,对所得的各种参数之间的数学关系进行验证。分析结果表明据此数学关系所求得的β角校正精度达到1.4%,是一种有效可行的校正方法。     

多阶段M-SV投资组合优化的离散近似迭代法研究

文章提出了离散近似迭代法,用该方法求解具有交易成本和交易量限制的多阶段均值一半方差(M-sV)投资组合模型.离散近似迭代方法的基本思路为:首先,将连续型状态变量离散化,根据网络图的构造方法将上述模型转化多阶段赋权有向图;其次,运用嘉量原理求出起点至终点的最长路程,即获得模型的一个可行解;最后,以该可行解为基础,继续迭代直到前后两个可行解非常接近.文章还证明了该方法的收敛性和复杂性.     

基于随机耦合模型的系统级电磁环境效应分析

通过宽频带电磁环境下计算机机箱关键部位感应电压统计分布的实验验证和基于随机矩阵理论的耦合模型的理论计算研究,给出了随机耦合模型(RCM)的应用流程和计算方法;就散射系数、归一化散射和归一化阻抗矩阵的统计特性进行了理论和实验研究,实验测量结果与理论分析结果非常一致;针对RCM的应用进行了初步分析,验证了随机耦合模型在复杂电磁环境下系统级效应分析和预测的适用性。结果证明此方法尤其适用于大型的具有非平行面封装腔体电子设备的电磁兼容、效应及评估等研究。     

短脉冲激光加热模型的遴选原则

 利用推导出的1维半空间光致热弹性应力响应的精确解,对激光与材料相互作用的加热模型的选取进行了研究。理论分析和计算结果表明：常用的表面加热模型只有在热穿透深度远大于光吸收长度时有效,若将其应用于超短脉冲激光（ps以下量级）加热的情况,可能会导致较大的误差;皮秒和飞秒激光辐照的热效应须用体加热模型计算。     

可见-近红外光谱的模型转移分类方法

基于光谱技术建立的多元校正模型通常条件下只适用于同一台仪器、相同的测试条件及同批次或同类别的样品.在仪器、测试环境、样品发生变化后,已建光谱模型不再适配,需要进行模型转移.模型转移是限制光谱技术推广应用的关键技术瓶颈,模型转移是否成功直接影响到可见-近红外光谱技术的推广应用,为此,综述了其研究现状,并探讨了其未来发展方...     

Single-Round DNA Aptamer Selection by Combined Use of Capillary Electrophoresis and Next Generation Sequencing: An Aptaomics Approach for Identifying Unique Functional Protein-Binding DNA Aptamers

In DNA aptamer selection, existing methods do not discriminate aptamer sequences based on their binding affinity and function and the reproducibility of the selection is often poor, even for the selection of well-known aptamers like those that bind the commonly used model protein thrombin. In the present study, a novel single-round selection method (SR-CE selection) was developed by combining capillary electrophoresis (CE) with next generation sequencing. Using SR-CE selection, a successful semi-quantitative and semi-comprehensive aptamer selection for thrombin was demonstrated with high reproducibility for the first time. Selection rules based on dissociation equilibria and kinetics were devised to obtain families of analogous sequences. Selected sequences of the same family were shown to bind thrombin with high affinity. Furthermore, data acquired from SR-CE selection was mined by creating sub-libraries that were categorized by the functionality of the aptamers (e. g., pre-organized aptamers versus structure-induced aptamers). Using this approach, a novel fluorescent molecular recognition sensor for thrombin with nanomolar detection limits was discovered. Thus, in this proof-of-concept report, we have demonstrated the potential of a “DNA Aptaomics” approach to systematically design functional aptamers as well as to obtain high affinity aptamers.