高职院校有机化学网络自选模块化教学模式研究

随着产教融合和校企合作的不断深化,在得到协同发展的同时也暴露了一些问题。高职教育培养下的学生,依然存在专业与岗位不匹配的情况。企业找不到匹配岗位的员工,毕业学生找不到匹配的岗位。基于此,在产教融合的基础上,对高职教育进行改革,对于提高高职教育的教学质量十分重要。以高职有机化学为例,对其进行网络自选模块化的教学改革研究,在产教融合背景下提出专岗专培的培养模式,从而提高高职有机化学的教学效率。

自选式网络调查的统计推断研究

自选式网络调查作为网络调查的形式之一,得到了越来越广泛的应用,但由于其入样概率未知,传统的概率抽样推断理论难以解决自选式网络调查的统计推断问题。为此,给出自选式网络调查总体均值、总量的估计及其性质,并对相应的性质进行推导证明,最后采用倾向得分的方法对估计进行加权调整,以修正之前的估计。

探究基于产教融合的高职有机化学网络自选模块化教学改革

随着职业教育产教融合、校企合作的深化,也暴露出一些需要解决的问题.比如,高等职业教育培养的学生依旧存在专业与岗位匹配度不够高,企业不易招到能直接胜任岗位的员工;职业院校的毕业生难于找到心仪的工作.[1]因此,进行职业教育教学改革,提高教育教学质量已经迫在眉睫.本文通过教学平台的搭建、培养模式的改革、改革的措施,提出专企专培,专岗专培,普岗共培'的'两专一共'多样性培养模式,来进行有机化学教学质量提升的改革.

Improved Adaptive Random Convolutional Network Coding Algorithm

To address the issue of field size in random network coding, we propose an Improved Adaptive Random Convolutional Network Coding (IARCNC) algorithm to considerably reduce the amount of occupied memory. The operation of IARCNC is similar to that of Adaptive Random Convolutional Network Coding (ARCNC), with the coefficients of local encoding kernels chosen uniformly at random over a small finite field. The difference is that the length of the local encoding kernels at the nodes used by IARCNC is constrained by the depth; meanwhile, increases until all the related sink nodes can be decoded. This restriction can make the code length distribution more reasonable. Therefore, IARCNC retains the advantages of ARCNC, such as a small decoding delay and partial adaptation to an unknown topology without an early estimation of the field size. In addition, it has its own advantage, that is, a higher reduction in memory use. The simulation and the example show the effectiveness of the proposed algorithm.

Always-optimally-coordinated candidate selection algorithm for peer-to-peer files sharing system in mobile self-organized networks

In order to improve the performance of peer-to-peer files sharing system under mobile distributed en- vironments, a novel always-optimally-coordinated （AOC） criterion and corresponding candidate selection algorithm are proposed in this paper. Compared with the traditional min-hops criterion, the new approach introduces a fuzzy knowledge combination theory to investigate several important factors that influence files transfer success rate and efficiency. Whereas the min-hops based protocols only ask the nearest candidate peer for desired files, the selection algorithm based on AOC comprehensively considers users＇ preferences and network requirements with flexible balancing rules. Furthermore, its advantage also expresses in the independence of specified resource discovering protocols, allowing for scalability. The simulation results show that when using the AOC based peer selection algorithm, system performance is much better than the rain-hops scheme, with files successful transfer rate improved more than 50% and transfer time re- duced at least 20%.

Complex Behavior in a Selective Aging Neuron Model Based on Small World Networks

Complex behavior in a selective aging simple neuron model based on small world networks is investigated. The basic elements of the model are endowed with the main features of a neuron function. The structure of the selective aging neuron model is discussed. We also give some properties of the new network and find that the neuron model displays a power-law behavior. If the brain network is small world-like network, the mean avalanche size is almost the same unless the aging parameter is big enough.

Policy Optimization Study Based on Evolutionary Learning

In order to achieve an intelligent and automated self-management network,dynamic policy configuration and selection are needed.A certain policy only suits to a certain network environment.If the network environment changes,the certain policy does not suit any more.Thereby,the policy-based management should also have similar "natural selection" process.Useful policy will be retained,and policies which have lost their effectiveness are eliminated.A policy optimization method based on evolutionary learning was proposed.For different shooting times,the priority of policy with high shooting times is improved,while policy with a low rate has lower priority,and long-term no shooting policy will be dormant.Thus the strategy for the survival of the fittest is realized,and the degree of self-learning in policy management is improved.

AFAR:adaptive fuzzy ant-based routing for communication networks

We propose a novel approach called adaptive fuzzy ant-based routing (AFAR), where a group of intelligent agents (or ants) builds paths between a pair of nodes, exploring the network concurrently and exchanging obtained information to up-date the routing tables. Routing decisions can be made by the fuzzy logic technique based on local information about the current network state and the knowledge constructed by a previous set of behaviors of other agents. The fuzzy logic technique allows multiple constraints such as path delay and path utilization to be considered in a simple and intuitive way. Simulation tests show that AFAR outperforms OSPF, AntNet and ASR, three of the currently most important state-of-the-art algorithms, in terms of end-to-end delay, packet delivery, and packet drop ratio. AFAR is a promising alternative for routing of data in next generation networks.