基于三维胞元空间的MA双向并行路由算法

针对传统移动代理(MA)在监测无线传感器网络(WSNs)的感兴趣信息时产生的延迟较大和能耗较多问题,提出了基于三维胞元空间的MA双向并行(3D-BPMA)路由算法.3D-BPMA将MA与传统的客户/服务器(c/S)模式相结合,在胞元内利用C/S模式搜集信息,在单层胞元系统和路由器与路由器之间采用MA双向并行的策略进行传输.仿真结果表明:3D-BPMA与LCF,DSG-MIP算法相比减少了平均响应时间和网络平均能耗,提高了MA发送率.     

Numerical study of entropy generation in Darcy-Forchheimer (D-F) Bödewadt flow of CNTs

Three-dimensional Bödewadt flow (fluid rotates at a large enough distance from the stationary plate) of carbon nanomaterial is examined. Single walled and multi walled CNTs are dissolved in water and gasoline oil baseliquids. Darcy-Forchheimer porous medium is considered. Stationary disk is further stretched linearly in radial direction. Heat transfer effect is examined in presence of radiation and convection. Effect of viscous dissipation is accounted. Entropy generation rate is studied. By using adequate transformation (von Kármán relations), the flow field equations (PDEs) are transmitted into ODEs. Solutions to these ODEs are constructed via implementation of shooting method (bvp4c). In addition to Entropy generation rate, Bejan number, heat transfer rate (Nusselt number), skin friction and temperature of fluid are examined through involved physical parameters. Axial component of velocity intensifies with increment in nanoparticles volume fraction and ratio of stretching rate to angular velocity parameter while it decays with higher porosity parameter. Higher nanoparticles volume fraction and porosity parameter lead to decay in radial as well as tangential component of velocity. However it enhances with higher ratio of stretching rate to angular velocity parameter. Temperature of fluid directly varies with higher ratio of stretching rate to angular velocity parameter, radiation parameter, Eckert number, Biot number and nanoparticles volume fraction. Rate of Entropy generation is reduced with higher estimations of porosity parameter, nanoparticles volume fraction and radiation parameter. Skin friction coefficient decays with higher porosity parameter and ratio of stretching rate to angular velocity parameter. Intensification in porosity parameter, nanoparticles volume fraction and Biot number leads to higher Nusselt number. Prominent impact is shown by multiple-walled CNTs with gasoline oil basefluid than single-walled CNTs with water basefluid.     

硫磷混合酸溶样重铬酸钾容量法测定全铁

介绍了硫磷混合酸溶样重铬酸钾容量法测定矿石中铁含量的方法和步骤。重点讨论了一线生产中的质量控制。     

以有机化学课程内容为引导，加强课程思政的探索与实践

有机化学是化学化工、材料、制药、食品等多个专业的基础课程,在其教学过程中,教师可以根据课程的内容及特点,分别以有机化学发展中的重大历史事件、有机化物的性质及有机反应为引导,开展学生的思想品德教育,加强课程思政建设。     

A signal adaptive diffusion filter for video coding: Mathematical framework and complexity reductions

In this paper we combine video compression and modern image processing methods. We construct novel iterative filter methods for prediction signals based on Partial Differential Equation (PDE) based methods. The mathematical framework of the employed diffusion filter class is given and some desirable properties are stated. In particular, two types of diffusion filters are constructed: a uniform diffusion filter using a fixed filter mask and a signal adaptive diffusion filter that incorporates the structures of the underlying prediction signal. The latter has the advantage of not attenuating existing edges while the uniform filter is less complex. The filters are embedded into a software based on HEVC with additional QTBT (Quadtree plus Binary Tree) and MTT (Multi-Type-Tree) block structure. In this setting, several measures to reduce the coding complexity of the tool are introduced, discussed and tested thoroughly. The coding complexity is reduced by up to 70% while maintaining over 80% of the gain. Overall, the diffusion filter method achieves average bitrate savings of 2.27% for Random Access having an average encoder runtime complexity of 119% and 117% decoder runtime complexity. For individual test sequences, results of 7.36% for Random Access are accomplished.     

论携号转网行为的私法规制

以电信用户入网协议为切入点,电信用户的携号转网行为应受到《合同法》《消费者权益保护法》等私法规范的保护与限制。该行为引起的格式条款解释、合同终止、违约责任、损失赔偿等问题,不能仅靠《电信条例》等公法规范来调整。应当积极引导广大消费者用户以私法领域相关规定为依据,转变争议解决思维,拓宽纠纷处理途径,合理选择维权手段,以保护自身合法民事权益,与行业监管部门共同推动电信行业有序发展。     

Multi-objective particle swarm optimisation based integrated production inventory routing planning for efficient perishable food logistics operations

Sustainable and efficient food supply chain has become an essential component of one’s life. The model proposed in this paper is deeply linked to people's quality of life as a result of which there is a large incentive to fulfil customer demands through it. This proposed model can enhance food quality by making the best possible food quality accessible to customers, construct a sustainable logistics system considering its environmental impact and ensure the customer demand to be fulfilled as fast as possible. In this paper, an extended model is examined that builds a unified planning problem for efficient food logistics operations where four important objectives are viewed: minimising the total expense of the system, maximising the average food quality along with the minimisation of the amount of CO₂ emissions in transportation along with production and total weighted delivery lead time minimisation. A four objective mixed integer linear programming model for intelligent food logistics system is developed in the paper. The optimisation of the formulated mathematical model is proposed using a modified multi-objective particle swarm optimisation algorithm with multiple social structures: MO-GLNPSO (Multi-Objective Global Local Near-Neighbour Particle Swarm Optimisation). Computational results of a case study on a given dataset as well as on multiple small, medium and large-scale datasets followed by sensitivity analysis show the potency and effectiveness of the introduced method. Lastly, there has been a scope for future study displayed which would lead to the further progress of these types of models.     

基于深度学习的秀丽隐杆线虫显微图像分割方法

利用计算机实现自动、准确的秀丽隐杆线虫(C.elegans)的各项形态学参数分析,至关重要的是从显微图像上分割出线虫体态,但由于显微镜下的图像噪声较多,线虫边缘像素与周围环境相似,而且线虫的体态具有鞭毛和其他附着物需要分离,多方面因素导致设计一个鲁棒性的C.elegans分割算法仍然面临着挑战。针对这些问题,提出了一种基于深度学习的线虫分割方法,通过训练掩模区域卷积神经网络(Mask R-CNN)学习线虫形态特征实现自动分割。首先,通过改进多级特征池化将高级语义特征与低级边缘特征融合,结合大幅度软最大损失(LMSL)损失算法改进损失计算;然后,改进非极大值抑制;最后,引入全连接融合分支等方法对分割结果进行进一步优化。实验结果表明,相比原始的Mask R-CNN,该方法平均精确率(AP)提升了4.3个百分点,平均交并比(mIOU)提升了4个百分点。表明所提出的深度学习分割方法能够有效提高分割准确率,在显微图像中更加精确地分割出线虫体。