Eight-node conforming straight-side quadrilateral element with high-order completeness (QH8-C1)

A new eight-node conforming quadrilateral element with high-order completeness, denoted as QH8-C1, is proposed in this article. First, expressions for the interpolation displacement function satisfying the requirements for high-order completeness in the global coordinate system are constructed. Second, the displacement function expression in global coordinates is transformed into isoparametric coordinates, and the relationships between the two series of coefficients for the two kinds of displacement function expressions are found. Third, the displacement function expression is modified to satisfy the requirements of nodal freedom and interelement boundary continuity. The key to the new element construction is the derivation of the linear relationship expressions among 12 coefficients of element displacement interpolation polynomials in the global and isoparametric coordinate systems. As a result, the relationship between quadratic completeness and interelement continuity is explicitly given, and a proof of the completeness and the continuity was conducted to theoretically guarantee the validity of the derivation results. Furthermore, in order to verify the correctness of the theoretical work, nine numerical examples were performed. The computation results from these examples demonstrate that QH8-C1 exhibited excellent performance, including high simulation accuracy, fast convergence, insensitivity to mesh distortion, and monotonic convergence.     

Mineralization of Para-Chlorobenzoic Acid in Water by Cobalt-Incorporated MCM-41 Catalyzed Ozonation

Cobalt-incorporated MCM-41(Co-MCM-41) was used as a heterogeneous catalyst for the ozonation of para-chlorobenzoic acid (p-CBA) in aqueous solution. Cobalt oxide supported on MCM-41(Co/MCM-41) was synthesized for comparison. Their textural properties were elucidated by various characterization techniques to understand the relationship between surface texture and catalytic activity. TOC removal at 60 min reached 91% with Co-MCM-41, 83% with Co/MCM-41 and only 52% with ozone alone, respectively. Observations from diffuse reflection spectroscopy demonstrated that different metal phases were formed in these cobalt-modified molecular sieves samples. Radical scavenger experiments indicated the formation of hydroxyl radicals that were responsible for the effective degradation of p-CBA. An integrated approach to the catalytic mechanism was proposed by considering the variation of pH in the course of ozonation as well as its subsequent influence on the dissociation of targeted compounds and surface charge of the catalyst. In the reusability experiments, the reused Co-MCM-41 was able to regain the same catalytic capability as the fresh one within 5 cycles. X-ray photoelectron spectroscopy results indicated that a part of Co²⁺ was oxidized to Co³⁺ after oxidation reaction.     

TiO2/MXene-Assisted LDI-MS for Urine Metabolic Profiling in Urinary Disease

Cancer remains an intractable medical problem. Rapid diagnosis and identification of cancer are critical to differentiate it from nonmalignant diseases. High-throughput biofluid metabolic analysis has potential for cancer diagnosis. Nevertheless, the present metabolite analysis method does not meet the demand for high-throughput screening of diseases. Herein, a high-throughput, cost-effective, and noninvasive urine metabolic profiling method based on TiO₂/MXene-assisted laser desorption/ionization mass spectrometry (LDI-MS) is presented for the efficient screening of bladder cancer (BC) and nonmalignant urinary disease. Combined with machine learning, TiO₂/MXene-assisted LDI-MS enables high diagnostic accuracy (96.8%) for the classification of patient groups (including 47 BC and 46 ureteral calculus (UC) patients) from healthy controls (113 cases). In addition, BC patients can also be identified from noncancerous UC individuals with an accuracy of 88.3% in the independent test cohort. Furthermore, metabolite variations between BC and UC individuals are investigated based on relative quantification, and related pathways are also discussed. These results suggest that this method, based on urine metabolic patterns, provides a potential tool for rapidly distinguishing urinary diseases and it may pave the way for precision medicine.     

特高压换流站监控参数的异常判据研究

研究了某±800 kV换流站内直流、交流电压参量,探索参量间的运行边界及可能存在的相互关联,分析各电气量不同工况下存在的特征,考察在控制保护系统中设置报警或功能响应的可行性.     

应变速率对Q460连铸坯高温力学性能的影响

摘要：采用Gleeble-3500热模拟试验机和金相法测试了不同应变速率下建筑用钢Q460连铸坯的高温力学性能,获得了600~1200℃范围内Q460连铸坯的高温强度、热塑性和最终室温组织随拉伸温度和应变速率的变化规律。结果表明,当Q460连铸坯在较高的应变速率（10s-1）下进行高温拉伸时,试样的断面收缩率随着拉伸温度的升高而升高,没有出现高温脆性区;在较低的应变速率（10-3s-1）下进行高温拉伸时,试样的断面收缩率出现了2个脆性区,第1个在1100℃至熔点温度,第2个脆性区间在700℃附近。总体来说,实验钢种的高温断面收缩率均大于65%,表明建筑用钢Q460连铸坯具有较好的高温热塑性。此外,同一应变速率下,Q460连铸坯的抗拉强度随着拉伸温度的升高而降低,而伸长率随着拉伸温度的升高而升高。     

Discovering small-world in association link networks for association learning

Association Link Network (ALN) is a kind of Semantic Link Network built by mining the association relations among multimedia Web resources for effectively supporting Web intelligent application such as Web-based learning, and semantic search. This paper explores the Small-World properties of ALN to provide theoretical support for association learning (i.e., a simple idea of “learning from Web resources”). First, a filtering algorithm of ALN is proposed to generate the filtered status of ALN, aiming to observe the Small-World properties of ALN at given network size and filtering parameter. Comparison of the Small-World properties between ALN and random graph shows that ALN reveals prominent Small-World characteristic. Then, we investigate the evolution of Small-World properties over time at several incremental network sizes. The average path length of ALN scales with the network size, while clustering coefficient of ALN is independent of the network size. And we find that ALN has smaller average path length and higher clustering coefficient than WWW at the same network size and network average degree. After that, based on the Small-World characteristic of ALN, we present an Association Learning Model (ALM), which can efficiently provide association learning of Web resources in breadth or depth for learners.     

基于深度学习的电子鼻食品新鲜度检测与识别技术研究

实际生活中,食品储存情况复杂,对食品新鲜度的准确检测十分重要.将常见食品分为水果、蔬菜、肉类三个类别,并根据实际情况,对不同食品储存情况设计相应实验,利用设计的电子鼻系统对食品样本进行检测,将测试得到的样本数据用于建立线性判别分析(Linear Discriminant Analysis,LDA)、支持向量机(Support Vector Machine,SVM)、卷积神经网络(Convolutional Neural Network,CNN)模型,最终利用不同模型对食品样本的状态:新鲜、水果腐败、蔬菜腐败、肉类腐败,作出分类识别.对于实际测试样本,LDA、SVM、CNN识别准确率分别为:45.00％、85.00％、90.00％,结果证明基于深度学习的电子鼻系统可对不同储存情况下的食品新鲜状态作出有效判断,可在一定程度上为受主观因素影响的感官评价提供客观参考,提高食品新鲜度判断准确性.     

基于Innography平台的高校专利情报分析研究

本文以中国国家知识产权局1985—2015年某北京市属高校的专利数据为基础,首先从专利申请趋势、IPC分布、授权量、有效专利量、高强度专利量等方面,分析该校专利的现状,找出存在的问题.然后利用Innography专利分析平台,对该校高强度专利的高产出学科领域进行挖掘,并针对其中某一领域,在世界范围内进行专利数据的挖掘,分析核心专利权人及技术热点.最后结合专利情况对该校的科研创新能力进行总结分析.     

基于迁移学习的电能替代节能量在线估计方法

电能替代成为能源转型发展的重要趋势和关键路径,节能量的快速准确估计有利于电能替代项目的推广。为充分利用少量电能替代项目调试期数据快速估计节能量,提出了一种基于迁移学习的单位节能量在线估计方法。首先利用回归算法对大量基期样本展开训练,获得基期能耗模型;其次,利用基于迁移学习的回归算法对大量基期样本、少量调试期样本展开训练,并通过不同的权重更新策略迭代调整基期样本、调试期样本权重,获得调试期能耗模型;最后,采用归一法获得参考条件下能耗差值,即单位节能量。针对干燥领域的电能替代进行仿真分析,证明了所提方法的有效性,并研究了迭代次数、样本数目和样本组合对所提算法预测误差的影响。     

Human fall detection in surveillance video based on PCANet

Fall incidents have been reported as the second most common cause of death, especially for elderly people. Human fall detection is necessary in smart home healthcare systems. Recently various fall detection approaches have been proposed., among which computer vision based approaches offer a promising and effective way. In this paper, we proposed a new framework for fall detection based on automatic feature learning methods. First, the extracted frames, including human from video sequences of different views, form the training set. Then, a PCANet model is trained by using all samples to predict the label of every frame. Because a fall behavior is contained in many continuous frames, the reliable fall detection should not only analyze one frame but also a video sequence. Based on the prediction result of the trained PCANet model for each frame, an action model is further obtained by SVM with the predicted labels of frames in video sequences. Experiments show that the proposed method achieved reliable results compared with other commonly used methods based on the multiple cameras fall dataset, and a better result is further achieved in our dataset which contains more training samples.