基于Python和Requests快速获取网页数据的方法研究

随着“互联网+”和大数据时代的到来,网络上充斥着各种各样的数据,过滤并获取有用的数据在当今环境下至关重要。文章提出一种基于Python和Requests模块的快速获取网页数据的方法,使用该方法可以获取解析前的网页源代码文本和图片数据,并保存为本地文件,为之后的数据分析和深入学习大数据技术奠定基础。实验结果表明,该方法步骤和代码编写简单易学,运行结果较好,具有一定的实用性。

Jointly Optimized Request Dispatching and Service Placement for MEC in LEO Network

Integrating Multi-access Edge Computing(MEC) in Low Earth Orbit(LEO) network is an important way to provide globally seamless low-delay service. In this paper, we consider the scenario that MEC platforms with computation and storage resource are deployed on LEO satellites, which is called "LEO-MEC". Service request dispatching decision is very important for resource utilization of the whole LEO-MEC system and Qo E of MEC users. Another important problem is service placement that is closely coupled with request dispatching. This paper models the joint service request dispatching and service placement problem as an optimization problem, which is a Mixed Integer Linear Programming(MILP). Our proposed mechanism solves this problem and uses the solved decision variables to dispatch requests and place services. Simulation results show that our proposed mechanism can achieve better performance in terms of ratio of served users and average hop count compared with baseline mechanism.

Blockchain-Based Power Transaction Method for Active Distribution Network

A blockchain-based power transaction method is proposed for Active Distribution Network(ADN),considering the poor security and high cost of a centralized power trading system.Firstly,the decentralized blockchain structure of the ADN power transaction is built and the transaction information is kept in blocks.Secondly,considering the transaction needs between users and power suppliers in ADN,an energy request mechanism is proposed,and the optimization objective function is designed by integrating cost aware requests and storage aware requests.Finally,the particle swarm optimization algorithm is used for multi-objective optimal search to find the power trading scheme with the minimum power purchase cost of users and the maximum power sold by power suppliers.The experimental demonstration of the proposed method based on the experimental platform shows that when the number of participants is no more than 10,the transaction delay time is 0.2 s,and the transaction cost fluctuates at 200,000 yuan,which is better than other comparison methods.

基于网络药理学探讨山奈酚-7-O-新橘皮糖苷抗前列腺癌的作用机制

目的 探讨山奈酚-7-O-新橘皮糖苷(kaempferol-7-O-neohesperidoside, K7ON)抗前列腺癌细胞(prostate cancer, PCa)的作用及潜在分子机制。方法 采用CCK-8法检测K7ON对PCa细胞PC3、DU145、C4-2和LNCap增殖的影响;应用细胞划痕实验检测K7ON对DU145细胞迁移能力的影响;SuperPred等数据库获取K7ON和PCa的靶点;从Venny在线平台获取K7ON与PCa的共同靶点,应用String和Cytoscape构建蛋白相互作用(protein-protein interaction, PPI)网络;通过DAVID数据库进行GO和KEGG功能富集分析,构建“药物-靶点-疾病-通路”网络模型。通过流式细胞术检测K7ON对PCa细胞周期的影响;采用Western blot法检测周期相关蛋白Skp2、p27和p21蛋白的表达;应用Sybyl X2.0将Skp2与K7ON进行分子对接。结果 K7ON可显著抑制PCa细胞的增殖和迁移能力。筛选出药物与疾病的交集靶点共34个,其中Skp2、p27等是K7ON治疗PCa的关键靶点,进一步的GO和KEGG功能功能富集表明其机制主要与细胞周期相关。流式细胞术结果表明,K7ON处理可使DU145细胞周期阻滞在S期。与对照组相比,Skp2蛋白表达水平明显下调,p27和p21的蛋白表达水平上调。分子对接结果显示K7ON与受体Skp2具有较好的结合能力。结论 K7ON可抑制PCa细胞的增殖和迁移,使细胞周期阻滞在S期,其机制可能与调控Skp2-p27/p21信号通路相关。

基于BP神经网络模型优化Fe_(1-x)O基氨合成催化剂

运用BP神经网络建立了助催化剂含量与催化剂活性之间的预测模型,对Fe_(1-x)O基氨合成催化剂的助催化剂进行优化。首先将前期实验数据整理归纳为含有3、4、5、6和7个助催化剂等5类催化剂,以助催化剂含量(体积分数)为输入变量,以425℃反应器出口氨浓度(活性)为输出变量,对助催化剂进行优化。结果表明,BP神经网络预测模型拟合值均方误差最高为0.2784,预测值均方误差最高为0.1592,构建的BP神经网络模型准确度较高。在该模型的基础上,运用多种群遗传算法进行极值寻优,求解最优的催化剂配方,并进行实验验证。结果表明,根据优化结果制备5个样品的实验测定值与预测值的相对误差最高为2.88%,优化结果较为准确;含有7个助催化剂的催化剂活性最高为18.83%,比原样本的统计平均活性值(17.52%)高1.31%,相对提高7.48%,助催化剂含量优化取得满意的结果。

基于YAML的iOS应用开发框架

为解决初学者使用Swift语言编写iOS程序上手难,公司开发多平台应用程序成本高等问题,本文基于跨平台开发理念实现一种通过编写简单易学的解释性配置文件YAML进行苹果应用开发的iOS框架。首先调研第三方语言开发苹果应用的可行性,其次在YAML语法中对UI控件、样式渲染、网络请求等进行抽象及结构定义,框架底层根据结构定义依次完成资源加载、数据处理、控件创建及用户交互等核心功能。并基于该框架设计开发一个在线问诊应用(AitmedCare),且成功上线Apple Store,配合Web和Android框架可以达到开发一套代码发布在多平台的目的。应用的成功发布表明框架从功能性、安全性等角度符合iOS软件的开发规范,可以被应用于iOS开发。

Workload-aware request routing in cloud data center using software-defined networking

Large latency of applications will bring revenue loss to cloud infrastructure providers in the cloud data center. The existing controllers of software-defined networking architecture can fetch and process traffic information in the network. Therefore, the controllers can only optimize the network latency of applications. However, the serving latency of applications is also an important factor in delivered user-experience for arrival requests. Unintelligent request routing will cause large serving latency if arrival requests are allocated to overloaded virtual machines. To deal with the request routing problem, this paper proposes the workload-aware software-defined networking controller architecture. Then, request routing algorithms are proposed to minimize the total round trip time for every type of request by considering the congestion in the network and the workload in virtual machines（VMs）. This paper finally provides the evaluation of the proposed algorithms in a simulated prototype. The simulation results show that the proposed methodology is efficient compared with the existing approaches.

Effect of soil archaea on N_(2)O emission in alpine permafrost

Soil microbial communities are pivotal in permafrost biogeochemical cycles,yet the variations of abundant and rare microbial taxa and their impacts on greenhouse gas emissions in different seasons,remain elusive,especially in the case of soil archaea.Here,we conducted a study on soil abundant and rare archaeal taxa during the growing and non-growing seasons in the active layer of alpine permafrost in the Qinghai-Tibetan Plateau.The results suggested that,for the archaeal communities in the sub-layer,abundant taxa exhibited higher diversity,while rare taxa maintained a more stable composition from the growing to non-growing season.Water soluble organic carbon and soil porosity were the most significant environmental variables affecting the compositions of abundant and rare taxa,respectively.Stochastic and deterministic processes dominated the assemblies of rare and abundant taxa,respectively.The archaeal ecological network influenced N_(2)O flux through different modules.Rare taxa performed an essential role in stabilizing the network and exerting important effects on N_(2)O flux.Our study provides a pioneering and comprehensive investigation aimed at unravelling the mechanisms by which archaea or other microorganisms influence greenhouse gas emissions in the alpine permafrost.

A PSO Based Multi-Domain Virtual Network Embedding Approach

This paper proposed a multi-domain virtual network embedding algorithm based on multi-controller SDN architecture. The local controller first selects candidate substrate nodes for each virtual node in the domain. Then the global controller abstracts substrate network topology based on the candidate nodes and boundary nodes of each domain, and applies Particle Swarm Optimization Algorithm on it to divide virtual network requests. Each local controller then embeds the virtual nodes of the divided single-domain virtual network requests in the domain, and cooperates with other local controllers to embed the inter-domain virtual links. Simulation experimental results show that the proposed algorithm has good performance in reducing embedding cost with good stability and scalability.

Research on Rice Leaf Disease Recognition Based on BP Neural Network

To solve the problem of mistake recognition among rice diseases, automatic recognition methods based on BP(back propagation) neural network were studied in this paper for blast, sheath blight and bacterial blight. Chose mobile terminal equipment as image collecting tool and built database of rice leaf images with diseases under threshold segmentation method. Characteristic parameters were extracted from color, shape and texture. Furthermore, parameters were optimized using the single-factor variance analysis and the effects of BP neural network model. The optimization would simplify BP neural network model without reducing the recognition accuracy. The finally model could successfully recognize 98%, 96% and 98% of rice blast, sheath blight and white leaf blight, respectively.

High Temperature Flow Stress Prediction of Nano-Al_2O_3/Cu Composite Using an Artificial Neural Network

Alumina dispersion strengthened copper composite （nano-Al2O3/Cu composite） was recently emerged as a kind of potentially viable and attractive engineering material for applications requiring high strength, high thermal and electrical conductivities and resistance to softening at elevated temperatures. The nano-Al2O3/Cu composite was produced by internal oxidation. The microstructures of the composite were analyzed by the TEM and its hot deformation behavior was investigated by means of continuous compression tests performed on a Gleeble 1500 thermo-simulator. Making use of the modified algorithm-Levenberg-Marquardt （L-M） algorithm BP neural network, a model for predicting the flow stresses during hot deformation was set up on the base of the experimental data. Results show that the microstructures of the composite are characterized by uniform distribution of nano-Al2O3 particles in Cu-matrix. The sliding of dislocations is the main deformation mechanism. The dynamic recovery is the main softening mode with the flow stress decreasing gently from 500℃ to 850 ~C. The recrystallization of Cu-matrix can be retarded late into as high as 850 ℃, when it happens only partially. The well-trained BP neural network model can accurately describe the influence of the temperature, strain rate, and true strain on the flow stresses, therefore, it can precisely predict the flow stresses of the composite under given deforming conditions and provide a new way to optimize hot deforming process parameters.

HAS Dynamic Buffer-Driven Resource Management to Enhance QoE in Mobile Network

Hypertext transfer protocol(HTTP) adaptive streaming(HAS) plays a key role in mobile video transmission. Considering the multi-segment and multi-rate features of HAS, this paper proposes a buffer-driven resource management(BDRM) method to enhance HAS quality of experience(QoE) in mobile network. Different from the traditional methods only focusing on base station side without considering the buffer, the proposed method takes both station and client sides into account and end user's buffer plays as the drive of whole schedule process. The proposed HAS QoE influencing factors are composed of initial delay, rebuffering and quality level. The BDRM method decomposes the HAS QoE maximization problem into client and base station sides separately to solve it in multicell and multi-user video playing scene in mobile network. In client side, the decision is made based on buffer probe and rate request algorithm by each user separately. It guarantees the less rebuffering events and decides which HAS segment rate to fetch. While, in the base station side, the schedule of wireless resource is made to maximize the quality level of all access clients and decides the final rate pulled from HAS server. The drive of buffer and twice rate request schemes make BDRMtake full advantage of HAS's multi-segment and multi-rate features. As to the simulation results, compared with proportional fair(PF), Max C/I and traditional HAS schedule(THS) methods, the proposed BDRM method decreases rebuffering percent to 1.96% from 11.1% with PF and from 7.01% with THS and increases the mean MOS of all users to 3.94 from 3.42 with PF method and from 2.15 with Max C/I method. It also guarantees a high fairness with 0.98 from the view of objective and subjective assessment metrics.

Equilibrium Analysis for Anycast in WDM Networks

In this paper, the wavelength-routed WDM network was analyzed for the dynamic case where the arrival of anycast requests was modeled by a state-dependent Poisson process. The equilibrium analysis was also given with the UWNC algorithm.

Analysis models and methods on the network storage system I/O performance

Network storage provides high scalability, availability and flexibility for storage systems, and is widely applied to many fields. Particularly, I/O performance is of great significance. Its application is wide and expanding rapidly. I/O performance has already become the bottleneck of the whole performance of computer systems for a long time, and under the condition of the present computer technology, I/O performance optimization method looks especially important. In the paper, I/O performance model was analyzed based on the combination of quasi birth, death process and queuing model, and then solved the model. A number of important related performance indicators and the relationship between them were given. By the way of example, this method can show the I/O performance more accurately. Finally, we got some useful conclusions, which may be used to evaluate network storage performance, and are the basis of confirming I/O scheduling strategy.

Survey on Single Path and Multipath Energy Efficient Routing Protocols for Wireless Sensor Networks

Wireless sensor networks (WSN) are one of the most evolving technologies applicable in various fields where sensitive information is required. However, the constraints of limited resources and requirements for environment-dependent connectivity and life cycle have urged designers to seek more efficient WSN infrastructures. In this paper, we have discussed and compared the results of various single path protocols i.e. LEACH, PAGASIS and VGA. Performance analysis of energy efficient multipath routing protocols incorporating wireless interference has also been performed.

Predicting the Mechanical Properties of BHA-Li2O Composites Using Artificial Neural Networks

In this study the mechanical properties of bovine hydroxyapatite (BHA)-Li2O composites are predicted using artificial neural networks (ANN) and then compared with obtained experimental values. BHA was mixed with lithium carbonate (Li2CO3) and sintered at various temperatures between 900-1300°C. Selected experimental values obtained for the compression strength, microhardness and density were used to define and train the ANN system. Intermediate data values not used to train the ANN model were then used to compare and determine the reliability of the ANN system. The results demonstrate the viable potential in using the ANN approach in predicting mechanical properties even with limited data sets.

O(t^(-β))-SYNCHRONIZATION AND ASYMPTOTIC SYNCHRONIZATION OF DELAYED FRACTIONAL ORDER NEURAL NETWORKS

This article explores the O(t^(-β))synchronization and asymptotic synchronization for fractional order BAM neural networks(FBAMNNs)with discrete delays,distributed delays and non-identical perturbations.By designing a state feedback control law and a new kind of fractional order Lyapunov functional,a new set of algebraic sufficient conditions are derived to guarantee the O(t^(-β))Synchronization and asymptotic synchronization of the considered FBAMNNs model;this can easily be evaluated without using a MATLAB LMI control toolbox.Finally,two numerical examples,along with the simulation results,illustrate the correctness and viability of the exhibited synchronization results.

A ＂cluster＂ based search scheme in peer-to-peer network

This paper presents a "cluster" based search scheme in peer-to-peer network. The idea is based on the fact that data distribution in an information society has structured feature. We designed an algorithm to cluster peers that have similar interests. When receiving a query request, a peer will preferentially forward it to another peer which belongs to the same cluster and shares more similar interests. By this way search efficiency will be remarkably improved and at the same time good resilience against peer failure (the ability to withstand peer failure) is reserved.

SFES: A scalable, fault-tolerant, efficient search scheme in a peer-to-peer network

This paper presents SFES: a scalable, fault-tolerant, efficient search scheme in a peer-to-peer network. The idea is based on the fact that data distribution in an information society has structured features. We designed an algorithm to cluster peers that have similar interests. When receiving a query request, a peer will preferentially forward it to another peer which belongs to the same cluster and shares more similar interests. By this method, search efficiency will be remarkably improved and at the same time good resistance against peer failure (the ability to withstand peer failure) is reserved. Keyword partial-match is supported, too.

RESEARCH ON BUS-ROUTE NETWORK PLANNING

A new method of bus-route network planning is presented by analyzing theservice characteristics of bus-route network.The following optimization objects areadopted:to minimize the total number of transfers from one route to another,to minimizethe total number of passenger-kilometers(or passenger-hours),to even passenger distri-bution on the network,and to make the directions of bus-routes consistent with the direc-tions of passenger flows.The algorithm is very simple.This method is suitable forlarge-sized and medium-sized cities and has been adopted in the transportation planningof Nanjing and Zhengzhou.The result is satisfactory.