用Network Meta分析系统评价GLP-1受体激动剂类降糖药的心血管安全性

目的使用Network Meta分析系统评价胰高血糖素样肽1(GLP-1)受体激动剂类降糖药的心血管安全性。方法系统检索Medline、Embase、Clinical Trials.gov和Cochrane Library数据库(截止2011年10月)中比较GLP-1受体激动剂与其他降糖药物或安慰剂的心血管安全性的随机对照研究(RCT),采用传统Meta分析和NetworkMeta分析方法对纳入的RCT的研究结果进行合并。结果共纳入45项研究,15883例糖尿病患者,包括八种干预措施(六种GLP-1类药:艾塞那肽、利拉鲁肽、他司鲁肽、阿必鲁肽、利西拉来和LY2189265,以及安慰剂和传统降糖药),研究总臂数为95。传统Meta分析和Network Meta分析结果相近,均未显示GLP-1受体激动剂与其他降糖药物或安慰剂之间心血管疾病安全性有统计学差异(P均>0.05)。此外,结合直接和间接比较的Network Meta分析显示六种GLP-1类药之间两两比较的心血管安全性也均无统计学差异(P均>0.05)。基于贝叶斯理论的Network Meta分析可对八种干预措施进行排序,显示安慰剂心血管风险最大。结论尽管单个研究报道GLP-1类药有潜在的心血管保护效应,但目前Network Meta分析仍无法定论,仍有待专门设计的大型前瞻性研究加以验证。

Three-Dimensional Planning of Arrival and Departure Route Network Based on Improved Ant-Colony Algorithm

In order to improve safety,economy efficiency and design automation degree of air route in terminal airspace,Three-dimensional(3D)planning of routes network is investigated.A waypoint probability search method is proposed to optimize individual flight path.Through updating horizontal pheromones by negative feedback factors,an antcolony algorithm of path searching in 3Dterminal airspace is implemented.The principle of optimization sequence of arrival and departure routes is analyzed.Each route is optimized successively,and the overall optimization of the whole route network is finally achieved.A case study shows that it takes about 63 sto optimize 8arrival and departure routes,and the operation efficiency can be significantly improved with desirable safety and economy.

Mapping the small-world properties of brain networks in Chinese to English simultaneous interpreting by using functional near-infrared spectroscopy

The aim of this study is to examine the small-world properties of functional brain networks inChinese to English simultaneous interpreting(SI)using functional near-infrared spectroscopy(INIRS),In particular,the fNIRS neuroimaging combined with complex network analysis wasperformed to extract the features of functional brain networks underling three translationstrategies associated with Chinese to English SI:"transcoding"that takes the"shortcut"linkingtranslation equivalents between Chinese and the English,code-mixing"that basically does notinvolve blingual procesing,and"transphrasingn that takes the long route"involving amonolingual processing of meaning in Chinese and then another monolingual processing ofmeaning in English.Our results demonstrated that the small-world net work topology was able todistinguish well bet ween the transcoding,code-mixing and transphrasing strategies related toChinese to English SI.

Interaction Energy Prediction of Organic Molecules using Deep Tensor Neural Network

The interaction energy of two molecules system plays a critical role in analyzing the interacting effect in molecular dynamic simulation.Since the limitation of quantum mechanics calculating resources,the interaction energy based on quantum mechanics can not be merged into molecular dynamic simulation for a long time scale.A deep learning framework,deep tensor neural network,is applied to predict the interaction energy of three organic related systems within the quantum mechanics level of accuracy.The geometric structure and atomic types of molecular conformation,as the data descriptors,are applied as the network inputs to predict the interaction energy in the system.The neural network is trained with the hierarchically generated conformations data set.The complex tensor hidden layers are simplified and trained in the optimization process.The predicted results of different molecular sys tems indica te that deep t ensor neural net work is capable to predic t the interaction energy with 1 kcal/mol of the mean absolute error in a relatively short time.The prediction highly improves the efficiency of interaction energy calculation.The whole proposed framework provides new insights to introducing deep learning technology into the interaction energy calculation.

基于GAN和MS-ResNet的房颤自动检测模型

房颤是一种常见的心律失常疾病,针对现有研究工作大多依赖于单尺度信号段而忽略了不同尺度下潜在的互补信息和数据不平衡问题导致诊断性能下降的问题,提出了一种新颖的基于生成对抗网络(generative adversarial network, GAN)和多尺度残差网络(multiscale residual net, MS-ResNet)的房颤自动检测模型,该网络使用GAN合成具有高形态相似性的单导联心电数据来解决数据的隐私和不平衡问题。同时,设计了MS-ResNet特征提取策略,从不同尺度提取不同大小信号段的特征,从而有效地捕捉P波消失和RR间期不规则特征。该模型联合这两种策略不仅为房颤自动检测生成高质量心电图(electrocardiogram,ECG)数据,还可以利用多尺度网格提取不同波之间的时序特征。在PhysioNet Challenge2017公开ECG数据集上以及平衡后的数据集上评估了MS-ResNet的性能,并将其与现有的房颤分类模型进行了比较。实验结果表明,MS-ResNet在平衡后的数据集上平均F1值和精确率分别达到0.914 1和91.56%,与不平衡数据集相比,F1提高了4.5%,精确率提高了3.5%。

Exploration of the Potential Mechanism of Gancao Yangyin Decoction on Aging Based on Network Pharmacology

Objective:To explore the targels and molecular mechanism of Gancao Yangyin Decoction(甘草养阴汤,GCYYD)based on network pharmacology.Methods:The effective chemical components of 7 kinds of Chinese materia medica in GCYYD and their relevant targets were obtai ned through the traditional Chinese medicine systems pharmacology database and analysis platform(TCMSP)and the encyelopedia of traditional Chinese medicine(ETCM).The aging-related targets were obtained through GeneCards database.The targets related to the effective chemical components were mapped with the aging-related targets,and the gene targets of GCY YD for intervening in aging were obtained.The protein interaction network diagram of GCY YD interfering with aging was drawn through String database and Cytoscape 3.7.1 software,and the core target genes were screened.The potential targets obtained were analyzed by gene ontology(GO)biological function enrichment analysis and kyoto encyelopedia of genes and genomes(KEGG)pathway enrichment analysis.Results:Totally 130 effective chemical components of the 7 kinds of Chinese materia medica of GCYYD and 276 related targets were obtained from TCMSP and ETCM databases.Totally 216 aging-related targets were obtained through GeneCards database.There were 63 target genes intervening in aging in GCYYD,with core target genes ALB,AKTI,TNF,L 6,MMP-3,VEGFA,CASP5,etc.Through biological function and signaling pathway enrichment analyses for the target genes with R software,147 KEGG signaling pathways were found,mainly related to age-RAGE signaling pathway in diabetic complications,proteoglycans in cancer,fluid shear stress and atherosclero-sis,HIF-1 signaling pathway,human cytomegalovirus infection,celluar senescence,prostate cancer,bladder cancer,elte.Conclusion:GCYYD can intervene in aging through"multicomponents-mulitargets-multipath-ways",which lays foundation for further experimental research.

A Combining Call Admission Control and Power Control Scheme for D2D Communications Underlaying Cellular Networks

As device-to-device(D2D) communications usually reuses the resource of cellular networks, call admission control(CAC) and power control are crucial problems. However in most power control schemes, total data rates or throughput are regarded as optimization criterion. In this paper, a combining call admission control(CAC) and power control scheme under guaranteeing QoS of every user equipment(UE) is proposed. First, a simple CAC scheme is introduced. Then based on the CAC scheme, a combining call admission control and power control scheme is proposed. Next, the performance of the proposed scheme is evaluated. Finally, maximum DUE pair number and average transmitting power is calculated. Simulation results show that D2 D communications with the proposed combining call admission control and power control scheme can effectively improve the maximum DUE pair number under the premise of meeting necessary QoS.

IUKF neural network modeling for FOG temperature drift

A novel neural network based on iterated unscented Kalman filter （IUKF） algorithm is established to model and com- pensate for the fiber optic gyro （FOG） bias drift caused by temperature. In the network, FOG temperature and its gradient are set as input and the FOG bias drift is set as the expected output. A 2-5-1 network trained with IUKF algorithm is established. The IUKF algorithm is developed on the basis of the unscented Kalman filter （UKF）. The weight and bias vectors of the hidden layer are set as the state of the UKF and its process and measurement equations are deduced according to the network architecture. To solve the unavoidable estimation deviation of the mean and covariance of the states in the UKF algorithm, iterative computation is introduced into the UKF after the measurement update. While the measure- ment noise R is extended into the state vectors before iteration in order to meet the statistic orthogonality of estimate and mea- surement noise. The IUKF algorithm can provide the optimized estimation for the neural network because of its state expansion and iteration. Temperature rise （-20-20℃） and drop （70-20℃） tests for FOG are carried out in an attemperator. The temperature drift model is built with neural network, and it is trained respectively with BP, UKF and IUKF algorithms. The results prove that the proposed model has higher precision compared with the back- propagation （BP） and UKF network models.

Optimization of Laser Ablation Technology for PDPhSM Matrix Nanocomposite Thin Film by Artificial Neural Networks-particle Swarm Algorithm

A new thermal ring-opening polymerization technique for 1, 1, 3, 3-tetra-ph enyl-1, 3-disilacyclobutane （TPDC） based on the use of metal nanoparticles produced by pulsed laser ablation was investigated. This method facilitates the synthesis of polydiphenysilylenemethyle （PDPhSM） thin film, which is difficult to make by conventional methods because of its insolubility and high melting point. TPDC was first evaporated on silicon substrates and then exposed to metal nanoparticles deposition by pulsed laser ablation prior to heat treatment.The TPDC films with metal nanoparticles were heated in an electric furnace in air atmosphere to induce ring-opening polymerization of TPDC. The film thicknesses before and after polymerization were measured by a stylus profilometer. Since the polymerization process competes with re-evaporation of TPDC during the heating, the thickness ratio of the polymer to the monomer was defined as the polymerization efficiency, which depends greatly on the technology conditions. Therefore, a well trained radial base function neural network model was constructed to approach the complex nonlinear relationship. Moreover, a particle swarm algorithm was firstly introduced to search for an optimum technology directly from RBF neural network model. This ensures that the fabrication of thin film with appropriate properties using pulsed laser ablation requires no in-depth understanding of the entire behavior of the technology conditions.

Orchestrating Network Functions in Software-Defined Networks

Software.defined networking(SDN) enables third.part companies to participate in the network function innovations. A number of instances for one network function will inevitably co.exist in the network. Although some orchestration architecture has been proposed to chain network functions, rare works are focused on how to optimize this process. In this paper, we propose an optimized model for network function orchestration, function combination model(FCM). Our main contributions are as following. First, network functions are featured with a new abstraction, and are open to external providers. And FCM identifies network functions using unique type, and organizes their instances distributed over the network with the appropriate way. Second, with the specialized demands, we can combine function instances under the global network views, and formulate it into the problem of Boolean linear program(BLP). A simulated annealing algorithm is designed to approach optimal solution for this BLP. Finally, the numerical experiment demonstrates that our model can create outstanding composite schemas efficiently.

Adaptive coupled synchronization of non-autonomous systems in ring networks

The adaptive coupled synchronization method for non-autonomous systems is proposed. This method can avoid estimating the value of coupling coefficient. Under the uniform Lipschitz assumption, we derive the asymptotical synchronization for a general coupling ring network with N identical non-autonomous systems~ even when N is large enough. Strict theoretical proofs are given. Numerical simulations illustrate the effectiveness of the present method.

Synchronization in a fractional-order dynamic network with uncertain parameters using an adaptive control strategy

This paper studies synchronization of all nodes in a fractional-order complex dynamic network. An adaptive control strategy for synchronizing a dynamic network is proposed. Based on the Lyapunov stability theory, this paper shows that tracking errors of all nodes in a fractional-order complex network converge to zero. This simple yet prac- tical scheme can be used in many networks such as small-world networks and scale-free networks. Unlike the existing methods which assume the coupling configuration among the nodes of the network with diffusivity, symmetry, balance, or irreducibility, in this case, these assumptions are unnecessary, and the proposed adaptive strategy is more feasible. Two examples are presented to illustrate effectiveness of the proposed method.

Mobile user forecast and power-law acceleration invariance of scale-free networks

This paper studies and predicts the number growth of China＇s mobile users by using the power-law regression. We find that the number growth of the mobile users follows a power law. Motivated by the data on the evolution of the mobile users, we consider scenarios of self-organization of accelerating growth networks into scale-free structures and propose a directed network model, in which the nodes grow following a power-law acceleration. The expressions for the transient and the stationary average degree distributions are obtained by using the Poisson process. This result shows that the model generates appropriate power-law connectivity distributions. Therefore, we find a power-law acceleration invariance of the scale-free networks. The numerical simulations of the models agree with the analytical results well.

Nonlinear properties of the lattice network-based nonlinear CRLH transmission lines

The nonlinear properties of lattice network-based（LNB） composite right-/left-handed transmission lines（CRLH TLs）with nonlinear capacitors are experimentally investigated.Harmonic generation,subharmonic generation,and parametric excitation are clearly observed in an unbalanced LNB CRLH TL separately.While the balanced design of the novel nonlinear TL shows that the subharmonic generation and parametric processes can be suppressed,and almost the same power level of the higher harmonics can be achieved over a wide bandwidth range,which are difficult to find in conventional CRLH TLs.

Residential Community Open-Up Strategy Based on Prim’s Algorithm and Neural Network Algorithm

“Open community” has aroused widespread concern and research. This paper focuses on the system analysis research of the problem that based on statistics including the regression equation fitting function and mathematical theory, combined with the actual effect of camera measurement method, Prim’s algorithm and neural network to “Open community” and the applicable conditions. Research results show that with the increasing number of roads within the district, the benefit time gradually increased, but each type of district capacity is different.

MobileNetV3与互易点损失函数相结合的雷达波形开集识别

基于深度学习的雷达波形识别方法通常假定待识别信号属于一个已知并且种类有限的集合,但在实际场景中可能存在大量未知雷达信号,导致此类闭集识别方法难以适用。针对此问题,提出一种MobileNetV3与互易点损失函数相结合的开集识别方法。利用神经网络提取信号时频图像的高维特征向量,通过特征向量和互易点的距离来衡量已知信号和未知信号之间的差异,使模型在正确识别已知信号波形的同时也能对未知信号波形进行判别,实现雷达波形的开集识别功能。实验结果表明,在6~15 dB的信噪比范围内,该方法对已知信号波形的识别准确率接近100%,对未知信号波形的判别准确率达到90%以上。

基于.Net的工作流管理系统设计

工作流管理系统在企业、电子政务、银行、图书馆等行业领域有着广泛的需求和大量的应用,目前正向基于Web、分布式和异步处理的方向发展。探讨利用.Net技术改善传统工作流的开发和部署过程,研究如何利用.Net技术实现办公工作流应用;提出了基于.Net的工作流总体框架;通过.Net组件实现COM+服务,实现工作流流转;结合数据库与XML技术,研究如何通过Asp.net页面提供管理及流程、规则定义支持。

基于改进LeNet-5网络的交通标志识别方法

针对传统LeNet-5卷积神经网络用于交通标志等多种类识别任务中,存在识别正确率低、网络容易过拟合以及梯度消失等问题进行改进。引入Inception卷积模块组来提取目标丰富的特征,同时增加网络的深度。引入BN (batch normalization)层对输入批量样本进行规范化处理;同时改用性能更好的Relu激活函数,并使用全局池化层代替全连接层,合理改变卷积核的大小和数目。研究结果表明,改进LeNet-5网络能够有效解决过拟合和梯度消失等问题,具有较好的鲁棒性;网络识别率达到98. 5%以上,相比CNN (convolutional neural network)+SVM (support vector machine)提高了约5%,比传统的LeNet-5网络提高了3%。可见,改进后的LeNet-5网络图像识别的准确率得到显著提高。

高职院校ASP.NET程序设计教学改革与思考

ASP.NET程序设计是一门实践性极强的职业技能型课程,在计算机专业课中处于非常重要的地位.文章分析了高职ASP.NET程序设计课程的教学现状及存在的问题,总结提出"大案例贯穿——做中学"教学设计思路,将工业制造过程(毛胚—成品—精品)运用于程序设计教学过程,培养学生"做学"一体的学习意识,提高其自学能力,并在教学过程中进行实践检验,对出现的问题进行了有益探索与思考.