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双语推荐：阵列类型

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采用具有倾角的轮型阵列能消除平面阵列对其后方背景声源无抑制能力的缺点，降低对测试环境的要求。通过仿真计算获得了三维轮型传声器阵列波束形成指向图及典型最大旁瓣水平随阵列倾角的变化曲线，分析了阵列倾角对其声源识别性能的影响。在此基础上，提出了阵列多倾角测量声级平均的声源识别改进方法，三种类型声场声源识别的模拟计算结果表明：该方法在准确计算目标声源位置和幅值的同时，相比于一定倾角阵列的单次测量结果可以更有效地同时衰减阵列前方声波和背后背景噪声在聚焦方向上产生的旁瓣干扰，显著地提高了声源识别精度。
A planar array nearly has no ability to suppress background noise from its back side. In order to overcome the drawback of a planar array and lessen requirements to the test environment, the wheel array with a tilted angle is utilized. The beamforming patterns are simulated and calculated, as well as the typical maximal sidelobe level curves versus the tilted angles. The effects of the tilted angle on the sound source identification performance are analyzed. On this basis, the improved sound source identification method is given via averaging the pressure levels measured by the wheel array with different tilted angles. The simulation and calculation results of three types of sound fields indicate that this method can not only calculate the location and the strength of the source accurately, but also suppress the sidelobes more effec-tively than the single-measurement method by the wheel array with a given tilted angle. In conclusion, the improved method makes the precision of sound sou

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光伏阵列多安装在较恶劣的室外环境中，因此在运行过程中常会发生故障。为辨别光伏阵列故障类型，提出了基于L-M算法的BP神经网络的故障诊断方法。在深入分析不同故障状态下光伏阵列输出量变化规律的基础上，确定了故障诊断模型的输入变量。本方法无需额外的设备支持，具有简便、成本低的优点；可以在线实时地进行故障诊断。仿真和初步实验结果验证了基于BP神经网络的故障诊断方法可以有效地检测出光伏阵列短路、断路、异常老化及局部阴影等四种故障。
Because PV arrays are always installed in poor outdoor environment, a variety of faults often occur during the operation. In order to obtain the types of fault, a fault diagnosis method of the BP neural network based on L-M algorithm is proposed. Through the in-depth analysis of the output of the PV array under normal state and fault states, the input variables of the diagnosis model are obtained. Compared with other fault diagnosis methods for the PV array, the proposed method does not need additional equipments, so the cost is reduced and the system can be run online and real-time. Finally, the simulation and experimental results show that the fault diagnosis method for the PV array based on the BP neural network can effectively detect four types of fault for PV array such as short-circuit, open-circuit, abnormal degradation and partial shading.

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利用数值模拟方法分析了基于小波变换的方向谱估计分析方法的适用性，测波阵列类型、波浪入射方向、方向集中度以及采用长度对小波方向谱估计分析方法分析结果的影响。结果表明：小波方向谱的方法适用条件较广泛，实用性更强。
The applicability of the estimation of the directional spectrum based on wavelet transform is discussed using the data generated from the numerical simulations. The analysis focusing on the influences of measured array types, wave incident direction, directional concentration and sampling lengths on the directional spectrum based on wavelet transform is carried out, which shows that the wavelet direction spectrum is more widely applicable.

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染色体微阵列芯片分析（CMA）包括比较基因组杂交微阵列（array CGH）和单核苷酸多态微阵列（SNP array），可以在全基因组范围内高分辨检测染色体的微缺失和微重复，与传统染色体核型分析和荧光原位杂交（FISH）检测相比，具有高通量、高分辨率和高自动化检测的优势，同时可以一次性同步检测许多与出生缺陷和先天性疾病相关的基因组异常，近年来已经开始应用于侵入性产前诊断。回顾近年来多个大样本和多中心的临床试验对CMA技术用于产前诊断的研究结果，借鉴美国妇产科医师协会（ACOG）和母婴医学协会（SMFM）发布的CMA在产前诊断应用中的建议，对CMA在应用过程中如何选择微阵列芯片类型、检测的适用对象和检测的时期、检测结果的解释以及相关的遗传咨询等关键问题进行了详细讨论，并指出CMA在产前诊断应用中面临的机遇和挑战，以及检测前和检测后遗传咨询在实际应用中的重要性和必要性。
Chromosome microarray analysis(CMA), including array CGH and SNP array, was used in the invasive prenatal diagnosis of chorionic villus or amniocentesis samples to detect microdeletion and microduplica-tion in the whole genome. Compared with the conventional karyotyping and FISH methods, CMA with advantages of high throughput, high resolution and high automation can detect synchronously many abnormalities in genome related to birth defects and congenital diseases. This review summarized the recent large multicenter prospective studies about use of CMA in prenatal diagnosis, and recommendations from the American College of Obstetri-cians and Gynecologists (ACOG) and the Society for Maternal-Fetal Medicine(SMFM). Some essential issues were addressed, such as the microarray type, the appropriate women for testing, sampling and the detection window, the data analysis and interpretation in genetic counseling. In addition, the opportunity and challenge during ap-plication of CMA in prenatal

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以光伏阵列为研究对象，分析了辐照强度、温度以及日类型对光伏阵列出力的影响。建立了光伏短期功率预测最小二乘支持向量机LS-SVM模型。依据实验数据对模型进行了验证计算．并与BP神经网络模型做了比较，其中LS-SVM模型最大相对误差值为10．54％，平均绝对百分比误差（MAPE）为8．18％，绝对误差平方和平均值的均方根（RMSE）为0．4884，表明模型预测值离散化程度较小，所有预测点均与实际值非常接近，模型具有较好的拟合效果和泛化能力．可以有效地预测短期光伏发电功率。
The photovoltaic array is used as the object of study to analyze the effect of irradiation intensity and temperature on the output of photovoltaic array. The least squares support vector machine (LS-SVM) model for short-term photovoltaic power prediction is established ,verified and calculated on the basis of experimental data,and compared with BP neural network model. The maximum relative error of LS-SVM model is 10.54%, the mean absolute percentage error (MAPE) is 8.18%and the root-mean-square error (RMSE) of mean value of absolute error sum of squares is 0.4884,which means that the discretization level of predicted value of model is relatively low,all predicted points are close to their actual value and the model has a good imitative effect and generalization ability and can predict the short-term photovoltaic power effectively.

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该文提出一种用于多类型癌症分子分型的演化超网络模式识别方法。首先采用“一对多”方法，将一个多类分型问题转化为多个二类分型问题；然后利用信噪比方法对DNA微阵列数据进行信息基因选择；经过超网络对训练集的演化学习，构造一系列二类分类器并进行集成，最终构建一个多类型癌症分型系统并对待测样本进行分类。对急性白血病、儿童小圆蓝细胞肿瘤和GCM数据集实验结果表明：演化超网络留一交叉验证(LOOCV)识别率分别为：98.61%，100%和85.35%。演化超网络有利于挖掘癌症相关基因，具有良好的学习结果可读性。
This paper presents a pattern recognition method for multiclass cancer molecular classification using evolutionary hypernetworks. A multiclass classification issue is decomposed into a set of binary classification issues by One-Versus-All (OVA) approach. The signal-to-noise ratio method is employed for informative genes selection from the DNA microarray. A series of binary classifiers are evolved and used to build a final ensemble classifier for multiclass classification through an evolutionary learning procedure of the hypernetwork. The test sample is classified by using the ensemble classifier. Experimental results show that the Leave One Out Cross Validation (LOOCV) accuracy of the acute leukemia dataset, the small, round blue cell tumor dataset, and the GCM dataset is 98.61%, 100%and 85.35%, respectively. The evolutionary hypernetworks is fit to find cancer-related genes and has a good readability of the learned results.

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研究了忆阻器的电荷控制和磁通量控制模型,构建了忆阻器Simulink模型,并对其仿真结果进行特性分析.提出了一种用于图像存储的忆阻器交叉阵列,可以实现黑白、灰度图像的存储和输出,并设计了基于Matlab的图形用户界面,通过选择和设定输入电压和图像类型、参数大小,便能直观准确地反映出忆阻器的行为特性及在二值和灰度图像上的存储特性,为更加方便地研究忆阻器及应用提供了有效的方法.
A memristor is a passive nonlinear two-terminal circuit element with memory capacity .Its resist-ance can be changed by controlling the current or voltage .In this paper ,the charge-controlled model and the magnetic flux-controlled model of HP memristor are studied .A Simulink model is built and the charac-teristics of the memristor are analyzed by the corresponding simulation results .A memristor-based cross-bar array is proposed ,which can realize storage and output for binary ,grayscale and color images .A graphical user interface for studying the memristor''s features is designed based on Matlab software .Some input signals ,pictures and parameters can be chosen or set to observe the corresponding output .This is an effective method for studying memristors and their application .

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人类基因组中蛋白质非编码基因占大多数，这些非编码基因与癌症的发生、发展有密切联系。非编码基因转录形成的 RNA 称作非编码 RNA。其中的长链非编码 RNA（long non-coding RNA，lncRNA）具有重要的基因调控功能并由此受到重视。由于部分 lncRNA 在各类型的癌症中表达异常，因此可以作为癌症的标志物用以诊断癌症。本文对 lncRNA 的分类、基因调控机制以及在癌症中的作用进行介绍，并对现有的以及部分潜在的癌症标志物lncRNA进行详细阐述。随着基因测序技术及微阵列技术的发展，更多的 lncRNA 会被发现并且能够成为重要的诊断肿瘤的标志物。
[Large numbers of the genomic transcripts do not encode proteins but play critical role in development and progress of cancer.Those non-coding transcripts are called non-coding RNA.Among the non-coding RNA, the long non-coding RNA (lncRNA) are increasingly recog-nized as participators of gene regulation.Parts of the lncRNA are found to be dysregulated in cancer and believed to be potential biomarkers for cancer diagnosis .In this paper, the classification, regu-lation mechanisms and biological functions of the lncRNA are introduced.Besides, some existing and potential biomarker lncRNA are also elaborated here.More lncRNA will be investigated and be-come the significant biomarkers for cancer diagnosis with the advancement of gene sequence and mi-croarray technologies.

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目的 探讨全基因组高分辨率染色体微阵列分析(CMA)技术对超声心动图检查提示为先天性心脏病(CHD)的胎儿进行遗传病因学诊断的临床价值.方法 收集2012年1月至2014年1月在广州医科大学附属广州市妇女儿童医疗中心就诊并接受侵入性产前诊断的176例超声心动图检查提示为CHD胎儿的临床资料,在其中158例染色体核型分析结果正常的CHD胎儿中,有88例(50.0％,88/176)胎儿进行了CMA检测.同时收集所有CHD胎儿的父母外周血标本,用于不明确临床意义的拷贝数变异(vOUS)的协助诊断.88例行CMA检测胎儿分为两组,68例单纯心脏结构异常的CHD胎儿为单纯心脏结构异常组;20例合并心外结构异常的CHD胎儿为合并心外结构异常组,对两组胎儿的CHD表型进行分类,对检出的染色体拷贝数变异(CNV)性质按致病性CNV、VOUS及良性CNV进行分类.结果 (1)88例行CMA检测的胎儿中,单一类型CHD胎儿共58例(66％,58/88),复合类型CHD胎儿共30例(34％,30/88).其中,单纯心脏结构异常组单一类型CHD胎儿45例,其致病性CNV检出率为11％(5/45);复合类型CHD胎儿23例,其致病性CNV检出率为17％ (4/23).合并心外结构异常组单一类型CHD胎儿13例,其致病性CNV检出率为5/13;复合类型CHD胎儿7例,其致病性CNV检出率为0.(2)88例行CMA检
Objective To explore the clinical value of genome-wide high resolution chromosomal microarray analysis (CMA) in etiological study of fetuses with congenital heart disease (CHD) diagnosed by fetal echocardiography.Methods A total of 176 fetuses diagnosed CHD by fetal echocardiography were analyzed,and invasive prenatal diagnosis was performed at Guangzhou Women and Children''s Medical Center from January 2012 to January 2014.Among them,158 fetuses were proved to have normal karyotype,and 88 fetuses (50.0％,88/176) underwent CMA testing.The parental blood specimens were also collected for assisting the diagnosis of variants of uncertain clinical significance (VOUS).The 88 fetuses were divided into two groups:isolated CHD (n=68) and CHD with extra-cardiac structural abnormalities (n=20).The phenotypes of the two groups were subclassified.Copy number variations (CNV) were classified as benign CNV,pathogenic CNV (pCNV) or VOUS.Results (1) 58 fetuses (66％,58/88) were with simple C

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目的研究Twist在中晚期宫颈癌组织中的表达及其与临床病理特征的关系,探讨Twist在宫颈癌侵袭、转移和进展中的作用.方法采用免疫组化法检测中、晚期宫颈癌组织微阵列芯片78例组织标本中Twist的表达情况;分析其表达与临床病理间的关系.结果 (1)78例宫颈癌组织中Twist的阳性表达率为56/78(71.8%);(2)Twist在宫颈癌组织中表达与肿瘤组织学类型、FIGO分期、淋巴转移、分化程度密切相关,随着FIGO分期的变晚、淋巴转移的出现、分化程度的降低其表达逐渐增高(P0.05).结论 Twist在宫颈癌组织中表达可能与宫颈癌的侵袭、转移和进展密切相关.
Objective The purpose of this study was to research the expression of Twist in cervical cancer tissues and the relationship between its expression and clinicopathologic characteristic, and to investigate the role of twist in cervical cancer invasion, metastasis and progress. Methods Immunohistochemistry was used to detect the expression of the Twist in 78 cases of middle and advanced stages cervical cancer tissue microarray chip. The relationship between its expression and clinicopathological characteristic was analyzed. Results (1) The positive expression of twist in cervical cancer was 56/78 (71.8%);(2) The expression of Twist in cervical cancer was significantly related to tumor histology type, FIGO stage, lymph node metastasis and differentiation degree. With the increase of FIGO stage, the emergence of lymph node metastasis and the decrease of differentiation degree, the expression of Twist increased gradually ( <0.05) . Conclusion The expression of Twist may be closely related to

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