交通网络最短路权矩阵的迭代算法

介绍了求解交通网络最短路权矩阵的3种迭代算法,从迭代思路、时间复杂度、程序可读性等方面进行了比较,指出各算法的异同点以及联系,最后通过一个具体网络模拟了各算法的迭代过程.     

Untangling process complexity in logistics delivery services with unpredictable service sequences: a mixed-method study of chemical tanker port calls

We explore the complexity issue relevant to a logistics service process characterized by unpredictable service stages and service sequences in the context of chemical tankers’ call in the Port of Houston. The operational dimensions of uncertainty and conflict surrounding process complexity are examined through a mixed-methods approach using both qualitative and quantitative data collected from the Port of Houston. We propose a taxonomy to classify several uncertainty and conflict factors emerged from our qualitative analysis, including structural conflict, structural uncertainty, infrastructural conflict, and infrastructural uncertainty. The case study findings are validated and triangulated by a quantitative analysis which quantifies the time and cost impacts of unproductive chemical tanker movement on port-call service performance. We contribute to the complexity theory by introducing four dimensions of process complexity to the stream of process-oriented logistics service research. These four complexity dimensions can be used by managers to untangle their process complexity. Our pseudo-service-stage based mapping technique will also be valuable for managers to visualize their service processes with unpredictable service sequences. Finally, we call for future research on synchronization across operational processes between service stages.     

An adaptive neural network-fuzzy linear regression approach for improved car ownership estimation and forecasting in complex and uncertain environments: the case of Iran

This paper applies a novel adaptive approach consisting of Artificial Neural Network (ANN) and Fuzzy Linear Regression (FLR) to improve car ownership forecasting in complex, ambiguous, and uncertain environments. This integrated approach is applied to forecast car ownership in Iran from 1930 to 2007. In this study, the level of car ownership is viewed as the result of demographic, politico-social, and urban structure factors including average family size, total population density, urban population density, urbanization rate, gross national product per capita, gasoline price, and total road length. To capture the potential complexity, uncertainty, and linearity relation between the car ownership function and its determinants, ANN and FLR (including eight well-known FLR) approaches are applied to the collected data. Next, the preferred ANN is selected based on sensitivity analysis results for the test data while the preferred FLR is identified with regard to ANOVA and MAPE results. The results obtained from the performance comparison demonstrate the considerable superiority of the preferred ANN over the preferred FLR regarding the nonlinear and complex nature of the car ownership function in Iran. This is the first study that presents an ANN-FLR approach for car ownership forecasting capable of handling complexity and non-linearity, uncertainty, pre-processing, and post-processing.     

航空公司动态价格竞争复杂性与延迟反馈控制

为深入研究航空公司动态价格竞争的复杂性及延迟反馈控制方法的有效性, 综合运用有限理性决策理论与非线性动力学理论,构建航空公司动态价格竞争博弈模 型,分析航空公司复杂的动力学行为,研究针对系统变量、系统参数的两类延迟反馈控制 方法的有效性与差异性.研究结果表明,航空公司价格调整速度对模型的复杂性有显著影 响;选取合适的控制因子,两类延迟反馈控制方法都能对陷入混沌的动态价格竞争模型 实施有效地混沌控制;两类延迟反馈控制方法在控制因子取值范围、收敛速度等方面存 有显著差异;控制因子取值对系统的收敛速度呈规律性影响趋势.     

一种新的分区回溯算法

在传统回溯算法及其时间和空间复杂性的研究基础上,提出了一种通用混合回溯方法——基于分区的回溯算法,在空间和时间代价较小的情况下,它能找到全部解,并且通过N皇后问题进行了实验,获得了较好的结果,说明该算法是可行的和有效的。     

基于均衡使用要求的飞机排班算法

为解决基于均衡使用要求的飞机排班问题，引入航班节的概念，并对飞机一航班节之间的匹配性约束作松弛处理，然后通过构造航班节的网络模型，将基于飞机均衡使用要求的飞机排班问题转化为寻找一个使目标函数最小的航班节编组方案问题，根据飞机的均衡使用要求构造了目标函数，设计了一种基于模拟退火算法的启发式算法。该算法的计算复杂性为O(M(m^3 mn))。     

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首先综述了当今关于小世界网络、无标度网络以及复杂加权网络的最新研究成果，并从城市交通系统的角度出发，指出了综合利用复杂网络理论、动态系统理论、现代控制理论及交通科学和工程等多学科的理论与方法，在探索城市交通网络的时空复杂性及其演化机理，揭示交通流的演化过程，缓解和预防交通拥堵，指导交通实践等方面，都具有重大的科学理论意义与实际应用价值，是值得深入研究与探讨的大问题。并进一步指出了一些今后关于城市交通网络复杂性问题的研究主题。     

NONINVASIVE DETECTION OF BRAIN ACTIVITY VARIATION UNDER DIFFERENT DEPTH OF ANESTHESIA BY EEG COMPLEXITY

Objective To detect the change of brain activity under different depth of anesthesia （DOA） noninvasively. A,fethods The Lempel-Ziv complexity C（n） was used to analyze EEG and its four components （delta, theta, alpha, beta）, which was recorded from SD rats under different DOA. The relationship between C（n） and DOA was studied. Results The C（n） of EEG will decrease while the depth of anesthesia increasing and vice versa. It can be used to detect the change of DOA sensitively. Compared with power spectrum, the change of C（n） is opposite to that of power spectrum. Only the C （n） of delta rhythm has obvious variations induced by the change of DOA, and the variations of delta is as similar as the EEG＇s. Conclusion The study shows that the desynchronized EEG is replaced by the synchronized EEG when rat goes into anesthesia state from awake, that is just the reason why complexity and power spectrum appear corresponding changes under different DOA. C（n） of delta rhythm dynamic change leads to the change of EEG, and the delta rhythm is the dominant rhythm during anesthesia for rats.     

A knowledge-transfer-based learning framework for airspace operation complexity evaluation

A sector is a component airspace whose operation is allocated to an air traffic controller. The operation complexity of a sector plays a critical role in the current Air Traffic Management system, e.g. it determines the workload volume of air traffic controllers and serves as a reliable index for airspace configuration and traffic flow management. Therefore, accurately evaluating the sector operation complexity is a problem of paramount importance in both practice and research. Due to numerous interacting factors, traditional methods based on only one single complexity indicator fail to accurately reflect the true complexity, especially when these factors are nonlinearly correlated. In light of these, the attempt to use machine learning models to mine the complex factor-complexity relationship has prevailed recently. The performance of these models however relies heavily on sufficient samples. The high cost of collecting ample data often results in a small training set, adversely impacting on the performance that these machine learning models can achieve. To overcome this problem, this paper for the first time proposes a new sector operation complexity evaluation framework based on knowledge transfer specifically for small-training-sample environment. The proposed framework is able to effectively mine knowledge hidden within the samples of the target sector, i.e. the sector undergoes evaluation, as well as other sectors, i.e. non-target sectors. Moreover, the framework can properly handle the integration between the knowledge derived from different sectors. Extensive experiments on real data of 6 sectors in China illustrate that our proposed framework can achieve promising performance on complexity evaluation when only a small training set of the target sector is available.     

三种任务类型对大学生英语写作的影响

本文以写作产出模式为基础,以广西师范大学非英语专业的学生为研究对象,研究根据同一题材编写的三种不同写作任务类型（个人写作任务、话题写作任务、概要写作任务）对非英语专业学习者在英语写作产出的准确度和复杂度上的影响。结果表明：任务类型显著影响三项写作任务语言产出的准确度和复杂度。个人任务准确度显著高于话题任务和概要任务;概要任务复杂度显著高于个人任务和话题任务。最后本文讨论了研究结果对促进写作教学的现实意义。