铁磁偶合的Gd（Ⅲ）Cu（Ⅱ）配合物──组装分子基铁磁体的一种重要元件

铁磁偶合的Ｇｄ（Ⅲ）Ｃｕ（Ⅱ）配合物──组装分子基铁磁体的一种重要元件缪明明，廖代正，王耕霖（南开大学化学系天津３０００７１）摘要分子基磁体的设计是自然界向化学家挑战的一个热点课题。铁磁偶合的Ｇｄ（Ⅲ）Ｃｕ（Ⅱ）配合物是组装分子基磁体的一种重要元件。...     

辐射敏感综合症--AT病研究进展

共济失调性毛细血管扩张综合症(AT)是一种单基因突变引起的遗传性疾病，表现为高辐射敏感性、进行性神经退变、免疫缺陷、早衰及易患癌症等。本文综述了AT病的研究概况，包括AT临床症状、AT基因的定位、结构与功能、辐射对AT细胞信号传导的影响、AT基因组不稳定性及基因治疗。     

Studies on the treatment of brilliant green solution by combination microwave induced oxidation with CoFe2O4

In this work the synergistic effects of microwave (MW) irradiation induced oxidation processes and CoFe₂O₄ were studied for the degradation of brilliant green (BG) from aqueous solutions. Under the optimum condition, the ratio of solid to liquid was 1:167 (0.3 g CoFe₂O₄ mixed with 50 mL of 20 mg L⁻¹ BG solution), MW power 600 W, and the time of the irradiation 2 min. And the decolorization rate could reach up to 100%. Further investigation showed that BG was degraded by MW-induced oxidation combined with CoFe₂O₄ surface adsorption. The CoFe₂O₄ could increase the efficiencies of MW degradation and be used repeatedly. The experimental results indicated that the method of MW degradation BG in the presence of CoFe₂O₄ could reduce reaction time and increase product yield.     

Semisupervised Encrypted Traffic Identification Based on Auxiliary Classification Generative Adversarial Network

The rapidly increasing popularity of mobile devices has changed the methods with which people access various network services and increased network traffic markedly. Over the past few decades, network traffic identification has been a research hotspot in the field of network management and security monitoring. However, as more network services use encryption technology, network traffic identification faces many challenges. Although classic machine learning methods can solve many problems that cannot be solved by port- and payload-based methods, manually extract features that are frequently updated is time-consuming and labor-intensive. Deep learning has good automatic feature learning capabilities and is an ideal method for network traffic identification, particularly encrypted traffic identification; Existing recognition methods based on deep learning primarily use supervised learning methods and rely on many labeled samples. However, in real scenarios, labeled samples are often difficult to obtain. This paper adjusts the structure of the auxiliary classification generation adversarial network (ACGAN) so that it can use unlabeled samples for training, and use the wasserstein distance instead of the original cross entropy as the loss function to achieve semisupervised learning. Experimental results show that the identification accuracy of ISCX and USTC data sets using the proposed method yields markedly better performance when the number of labeled samples is small compared to that of convolutional neural network (CNN) based classifier.     

纯铜动态再结晶的Monte Carlo法模拟

本文建立了一种基于能量判据的确定动态再结晶各个阶段临界应变的方法，并基于此种方法Monte Carlo(MC)法进行了纯铜动态再结晶的二维模拟，定量计算了变形引起的存储能与变形之间的关系，计算出了发生动态再结晶的临界应变。模拟结果表明，采用MC法可以揭示动态再结晶的形核生长过程与应变历史之间的关系。结合本文所建立的基于能量判据的确定动态再结晶各个阶段临界应变的方法，可以预测开始发生动态再结晶的临界变形条件。     

四氯化硅的应用现状研究

综述了四氯化硅的主要处理方法和应用研究进展。详细介绍了四氯化硅在制备二氧化硅、多晶硅、三氯硅烷及其他硅烷产品方面的应用及其制备原理,并对各制备工艺技术进行了分析。     

Construction of a key-dependent message secure symmetric encryption scheme in the ideal cipher model

Key-dependent message （KDM） security is an important security issue that has attracted much research in recent years. In this paper, we present a new construction of the symmetric encryption scheme in the the ideal cipher model （ICM）; we prove that our scheme is KDM secure against active attacks with respect to arbitrary polynomialtime challenge functions. Our main idea is to introduce a universal hash function （UHF） h as a random value for each encrypfion, and then use s = h（sk） as the key of the ideal cipher F, where sk is the private key of our symmetric encryption scheme. Although many other schemes that are secure against KDM attacks have already been proposed, in both the ideal standard models, the much more significance of our paper is the simplicity in which we implement KDM security against active attacks.     

基于溶液图像时序特征的元素组分含量动态监测系统

针对稀土萃取过程中组分含量难以实时监测以及现有组分含量检测方法耗时、耗内存的现状,设计了一种基于溶液图像时序特征的元素组分含量动态监测系统。首先使用图像采集装置获取萃取槽体溶液的时序图像,考虑萃取液颜色特性和单一颜色空间的不全面性,采用主成分分析（PCA）方法在HSI和YUV融合的颜色空间提取图像的时序特征,并结合生产指标构造基于鲸鱼优化算法（WOA）的最小二乘支持向量机（LSSVM）分类器来对工况状态进行判断。然后当工况处于非最佳状态时,在HSV颜色空间对图像提取颜色直方图和颜色矩特征,并开发以溶液图像间的混合特征差值的线性加权值为相似度度量的图像检索系统,从而获取组分含量值。最后进行镨/钕萃取槽体混合溶液测试,结果表明该系统能够实现元素组分含量的动态监测。     

基于隐马尔可夫模型的调频广播信号整点报时特性识别方法

由于整点报时与不报时是区分正常广播与“黑广播”的重要指标之一,因此文章提出一种基于隐马尔可夫模型的调频广播信号整点报时特性识别方法。利用静音识别算法将调频广播整点时刻频谱数据表示为0-1序列并在0-1序列中分析调频广播信号的静音序列变化规律,得到调频广播频谱数据的隐藏状态和观察状态的随机序列;通过子区间划分,分别获得初始隐藏状态转移矩阵和初始观察状态概率矩阵;采用Baum-Welch迭代学习算法,在调频广播整点时刻频谱数据训练样本集上对隐藏状态转移矩阵和观察状态概率矩阵进行训练学习,分别得到调频广播整点报时和不报时的隐马尔可夫模型,并给出调频广播信号整点报时特性识别算法。采用实测调频广播整点报时频谱数据进行整点报时特性识别实验,其结果表明,该方法可有效地识别调频广播信号整点报时特性,其整点报时特性的平均识别率为87.5%。该方法在快速发现“黑广播”方面具有一定的实用价值。