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.     

Deep Multilabel Multilingual Document Learning for Cross-Lingual Document Retrieval

Cross-lingual document retrieval, which aims to take a query in one language to retrieve relevant documents in another, has attracted strong research interest in the last decades. Most studies on this task start with cross-lingual comparisons at the word level and then represent documents via word embeddings, which leads to insufficient structure information. In this work, the cross-lingual comparison at the document level is achieved through the cross-lingual semantic space. Our method, MDL (deep multilabel multilingual document learning), leverages a six-layer fully connected network to project cross-lingual documents into a shared semantic space. The semantic distances can be calculated when the cross-lingual documents are transformed into embeddings in semantic space. The supervision signals are automatically extracted from the data and then used to construct the semantic space via a linear classifier. The ambiguity of manual labels could be avoided and the multilabel supervision signals can be acquired instead of a single label. The representation of the semantic space is enriched by multilabel supervision signals, which improves the discriminative ability of the embeddings. The MDL is easy to extend to other fields since it does not depend on specific data. Furthermore, MDL is more efficient than the models training all languages jointly, since each language is trained individually. Experiments on Wikipedia data showed that the proposed method outperforms the state-of-the-art cross-lingual document retrieval methods.     

Tailoring Pore Structure and Morphologies in Covalent Organic Frameworks for Xe/Kr Capture and Separation

As a rising star among porous solid materials, covalent organic frameworks(COFs) with excellent properties including but not limit to facilely controllable structure, high porosity, and multi-chemical functionality represent significant potential for efficient ¹²⁷Xe/⁸⁵Kr capture and separation. In this study, through tuning the length of the organic ligands, two-dimensional(2D) COF mate-rials with identical connection group but different pore properties, denoted as ATFG-COF and TpPa-COF with AA-stacking model and TpBD-COF with AB-stacking model were synthesized and tested for Kr and Xe adsorption for the first time. Adsorption measurements indicate that the narrower pore apertures and higher porosity are conducive for COF materials to capture Xe and Kr. Furthermore, the Henry's constant, isosteric heat of adsorption(Q_st), and ideal adsorbed solution theory(IAST) selectivity of ATFG-COF, the pore size of which is closest to the kinetic diameter of the Xe atom(0.41 nm) among 2D COF materials, were carried out based on the single component sorption isotherms. The results illustrate that the high isosteric heat values of Xe/Kr adsorption on ATFG-COF are 25 and 16 kJ/mol at room temperature, respectively. Henry's law predicts that the selectivity factor of Xe to Kr is 6.07, consistent with the adsorption selectivity(ca. 6) calculated based on the IAST.     

荧光光谱法研究奥美拉唑和埃索美拉唑与牛血清白蛋白的相互作用

首次应用荧光光谱法研究外消旋体奥美拉唑和其中的一个单体S-奥美拉唑(埃索美拉唑)分别与牛血清白蛋白(BSA)的相互作用机制的异同.利用荧光猝灭反应求出了在pH7.4、pH8.0、pH9.0这三种环境中两个药物结合常数KA和结合位点数n,进一步根据热力学参数判断出它们的主要作用力类型.发现两个药物引起的BSA内源荧光的猝灭均是由静态猝灭引起的.在相同情况下埃索美拉唑对BSA的结合能力强于奥美拉唑,结合位点数部是1.两种药物分子与BSA之间的作用力类型主要为疏水作用力和静电作用力.可以得出结论:两个药物除了结合常数的差异外并无不同.     

四氨羰甲基对-叔丁基杯[4]芳烃的合成及聚集行为

对-叔丁基杯[4]芳烃与溴乙酸乙酯反应,得到锥式、1,2-交替、1,3-交替和部分锥式构象的四取代衍生物;将锥式、1,2-交替和1,3-交替构象的四乙酸乙酯取代衍生物在氨的甲醇溶液中进行氨解得到相应的酰胺产物.通过核磁研究比较了氨解产物的自聚集情况.锥式构象四氨羰甲基对-叔丁基杯[4]芳烃分子在氯仿溶剂中的分子间作用力很弱,容易溶剂化,溶剂中核磁谱图不能观察到分子聚集;而1,2-交替构象的四氨羰甲基对-叔丁基杯[4]芳烃分子间存在相当强的作用力,在氯仿中的溶解性较差,核磁谱图表明分子在氯仿溶剂中有分子聚集的现象.1,3-交替构象的四氨羰甲基对-叔丁基杯[4]芳烃分子可以形成一定的聚集体,在高浓度时是聚集体的形式;在浓度较低或者加入酸予以破坏时,溶液中是以单体的形式存在.     

硅烷化磁铁粉固定胰蛋白酶的研究

分别以硅烷化磁铁粉、硅烷磁化脱乙酰几丁质及脱乙酰几丁质作载体,甲醛或戊二醛为交联剂,对胰蛋白酶的固定化条件及所制备的固定化酶的性质进行了研究．研究了载体种类、交联剂和ｐＨ值以及载体与酶比例等因素对胰蛋白酶固定化的影响．研究了不同载体固定胰蛋白酶的特点,证明以硅烷化试剂和甲醛作交联剂的硅烷化磁铁粉作胰蛋白酶固定化的载体具有明显优点,所制备的硅烷化磁铁粉固定化胰蛋白酶酶学性质明显优于可溶性胰蛋白酶     

非稳态油膜力作用下偏角不对中碰摩转子系统振动特性分析

建立了非稳态油膜力、碰摩力作用下,偏角不对中轴系的横向振动模型,将Jeffcott转子模型推广至约束条件下的轴系振动模型,通过引入转子间的位移约束,由Lagrange方程导出了不对中轴系系统的振动方程.仿真研究表明:由于不对中的存在,轴系横向振动是强非线性、非定常、同时含有自激励、参数激励和外激励的三自由度振动系统;由于从动轴与主动轴的不对中,导致了转轴间的运动约束,使轴系横向振动系统比Jeffcott转子动力学模型所描述的振动系统少了一个自由度.随着偏角不对中量、转轴质量比的变化,系统出现周期、概周期、混沌运动特征.不对中轴系处于概周期运动时,呈现出自激振动特征.其结果可为旋转机械故障诊断、轴系对中及设备的安全运行提供更为准确的理论参考.     

波形钢板-UHPC组合桥面板结构截面优化

对实腹式波形顶板-UHPC(超高性能混凝土)组合桥面板进行了改进, 采用空腹式结构建立波形钢板-UHPC组合桥面板有限元模型, 研究UHPC层厚度、波形钢板厚度、波形长度、下缘板宽度和波形高度等截面参数变化对组合桥面板受力特性的影响, 并确定其合理取值范围. 在此基础上, 通过理想点法对参数组合进行优化, 得到合理的参数匹配. 研究结果表明 相较于实腹式组合桥面板, 优化后的组合桥面板自重减小35%, 钢板弯折处应力减小16%; 相较于正交异性钢桥面板, 桥面板用钢量减小7%, 顶板与U肋连接位置应力减小47%.     

基于关联规则和技术功效矩阵的企业技术创新机会发现和辅助决策方法

为助力科技型创新企业准确且快速地从外部捕获创新技术机会, 提出一种企业技术机会发现和辅助决策方法. 首先, 挖掘领域内的技术热点、技术重点和有潜力的技术作为领域技术创新机会. 然后, 通过关联规则分析领域技术机会和企业已有技术之间的相关性, 进一步结合技术掌握度和新颖度, 识别更适合企业的技术创新机会. 最后, 创新性地采用Sen-BERT语言模型和K-means聚类方法构建技术功效矩阵, 辅助企业从功能需求的角度进行技术创新决策. 以电动汽车领域为例验证了该方法的可行性.     

电机驱动微装配在双光子制备微结构中的应用

对微结构的制作、微装配系统进行了研究. 采用飞秒激光双光子聚合微加工技术制作有底座、精细的三维立体“拱形”微结构, 其高250μm、长300μm、厚50μm. 将此微结构与实验室自主搭建的二维微装配平台相结合, 利用自主编程的人机交互界面驱动步进电机, 远程操控微装配设备; 将荧光闪烁陶瓷粉末装配到微结构中, 对装配后的微结构进行荧光光谱表征发现, 纯荧光粉末和微结构中的荧光粉末的发射光谱在测量误差范围内基本一致, 表明荧光粉末的光学性质未发生改变. 利用该装置可以将各类微纳米级材料和微结构进行装配, 形成含有不同材料的微结构系统.