The complex network of global cargo ship movements

Transportation networks play a crucial role in human mobility, the exchange of goods and the spread of invasive species. With 90 per cent of world trade carried by sea, the global network of merchant ships provides one of the most important modes of transportation. Here, we use information about the itineraries of 16 363 cargo ships during the year 2007 to construct a network of links between ports. We show that the network has several features that set it apart from other transportation networks. In particular, most ships can be classified into three categories: bulk dry carriers, container ships and oil tankers. These three categories do not only differ in the ships'' physical characteristics, but also in their mobility patterns and networks. Container ships follow regularly repeating paths whereas bulk dry carriers and oil tankers move less predictably between ports. The network of all ship movements possesses a heavy-tailed distribution for the connectivity of ports and for the loads transported on the links with systematic differences between ship types. The data analysed in this paper improve current assumptions based on gravity models of ship movements, an important step towards understanding patterns of global trade and bioinvasion.     

一种面向复杂嵌入式系统的互连网络研究

提出了一种结构简单,具有分形特征的基三分层互连网络.讨论了适合于基三分层网络的节点编码方案,给出了网络中任意两个节点之间最短路径的求解算法和网络平均最短距离的估计值.针对复杂嵌入式系统的特点,分析了基三分层网络的成本、可靠性和实时性.将基三分层网络与常见的嵌入式实时互连拓扑结构进行了比较,结果表明基三分层网络可靠性较高,具有较高的性价比,适合作为通信局部性较高的嵌入式实时互连网络.     

基于复杂网络理论的产品结构模块划分方法

针对现有的产品结构模块划分方法的不足,将复杂网络理论中的社团结构发现方法应用于产品结构模块的划分,提出了一种新的结构模块划分方法.产品的结构单元作为网络的节点,有关联的结构单元对应的节点之间为网络的边,从而构建产品结构的网络图,使用复杂网路理论中的社团结构发现方法——GN算法实现结构模块的划分.论文阐述了基于GN算法的模块划分的方法与步骤,在此基础上以汽车发动机的结构模块划分为例验证了该方法的有效性和实用性,并对模块划分结果进行了分析,最后指出了今后进一步研究的内容与方法.     

Modelling of cluster supply network with cascading failure spread and its vulnerability analysis

In this paper, a new entropy approach is developed to study the vulnerability of cluster supply chain network during the cascading failure spread from a holistic point of view. We use the tools of complex network theory and social network analysis to obtain the network representation of cluster supply chain system and explain its cascading phenomenon. Then we build a new cascading model for cluster supply chain network, and further introduce the concept of network load entropy, to analyse and predict the dynamic behaviours of the vulnerability during the process of failure spreading. Using a cluster supply chain network in an industrial district as a case study, we employ the modelling approach to explore its vulnerability. The simulation results demonstrate that the vulnerability of cluster supply chain network with cascading failures can be identified and predicted efficiently by using the modelling approach. In addition, this work may have practical implications for reducing the cluster supply chain network vulnerability in the cascade control and defence, then obtaining its healthy evolution.     

Modelling of plasma etching using a generalized regression neural network

Plasma etching was modelled by using a generalized regression neural network (GRNN). The etching process was characterized with a statistical experimental design. Three etch responses were modelled, which include two etch rates of aluminium and silica and etching profile. GRNN prediction ability was optimized as a function of training factor. Three types of models were constructed depending on the type of prepared data. Type I model corresponds to the model constructed with the original, non-classified data. Type II and III models were built for the classified data without and with the control of data interface, respectively. Compared to type I models, type II models for two etch rates demonstrated more than 25% improvement. By the control of data interface, type III models exhibited more than 15% improvement over type II models. Classification-based models in conjunction with data control thus illustrated much improved prediction of GRNN over those for non-classified models.     

Modelling and analysis of a cooperative production network

In this study, we examine the cooperative production business model for a group of producers serving their own customers and also have access to external customers who can make an agreement to buy products at a lower price if a desired service level can be guaranteed. When the producers cannot meet the desired service level requirement of the external customers at the offered price on their own, they participate in a cooperative network. The network consolidates the external customers for its members and routes an arriving external customer to one of the participants. We determine the optimal production and rationing policies for each participating manufacturer as well as the optimal routing policy for the network. We also propose an accurate approximate method to analyse a network with a high number of homogeneous producers using a single queue approximation method. We show that, based on the parameters of the producers and the external market, the network can provide the desired service level for the external customers at the offered price and makes all the members increase their profit by better utilising their capacity and serving more external customers.     

Identification of the influential parts in a complex mechanical product from a reliability perspective using complex network theory

Due to the difference in the numbers and strengths of physical relationships among parts, complex mechanical products (CMPs) have community structure characteristics. There are often some influential parts in the community. Failures of these influential parts spread rapidly along the physical relationships between parts in the community, which seriously affects the reliability of a product. Therefore, identifying the influential parts in the community and adopting targeted measures can effectively improve the reliability and service life of a product. However, identifying the influential parts within each community in a collection of parts with complex relationships is very difficult. Thus, from the perspective of reliability, a method for identifying the influential parts of a CMP based on complex network theory is proposed and used to identify the influential parts in each community of products. First, weighted complex network (WCN) theory is employed to construct a CMP into a WCN model. Second, the complex network community detection method is employed to detect the community structure of the WCN model. Third, a modified LocalRank algorithm is employed to identify the influential nodes in each community, ie, the influential parts in each community of a CMP. Fourth, a modified susceptible-infectious-recovered (SIR) model is employed to evaluate the impacts of the influential parts. An analysis of a company's DC drill planetary gearbox shows that the proposed method is accurate and effective.     

A complex network approach to environmental impact assessment

This article proposes a complex network methodology for the process of Environmental Impact Assessment (EIA) that limits subjectivity and reduces uncertainty by incorporating elements of complex systems theory in the stages of identification and assessment of the significance of environmental impacts. The proposed methodology reduces the sources of uncertainty, which emerge from the use of simplified models that analyse the environment-activity interactions in a unidirectional fashion. This proposal determines the significance of environmental impacts through multidirectional or complex causal relationships. Likewise, it limits the subjectivity of the evaluator by using these causality relationships instead of criteria based on the impacts’ attributes. The application of the proposed methodology demonstrates the advantages of (i) prioritizing the impacts according to their capacity to interact with other impacts, and (ii) the possibility to redirect the environmental management plans towards the prevention of impacts of higher complexity and to reduce the importance of derived impacts.

The application of the proposed methodology reveals that the percentage of irrelevant and moderate impacts is reduced, whereas the percentage of severe and critical impacts increase, in comparison to the conventional methodologies.     

Network-analysis approaches to deal with causal complexity in a supply network

Large integrated supply networks can exhibit several complex system characteristics. In such systems, researchers tend to misperceive feedback relationships and have difficulty in identifying dynamic causal behaviour, even when they have an understanding of the underlying structural relationships within a system. Supply-network researchers often make use of simulation models, but this is only appropriate if a high degree of knowledge concerning the supply network is available. Many disciplines, including a limited number of supply network researchers, have used network analyses to represent complex systems, and several advanced graph theory techniques exist to support such studies. The aim of this paper is to demonstrate the use of network-analysis approaches in order to analyse supply networks. The research was carried out in four case-study areas within a sugarcane-production-and-processing environment and demonstrates two network-analysis approaches. Semi-structured interviews with stakeholders from different sectors were carried out, and issues (or problems) in the supply network were incorporated into a single coherent network. An energy-transformation approach as well as transitivity produced valuable information. A cause-and-effect network-analysis approach could depict suitable key performance indicators as well as leverage points within the supply network. These methodologies enable researchers to achieve a high degree of understanding in a relatively short time span. The analyses of the supply network occur at a higher degree of abstraction, hence obviating any need to model and understand the intricate detail of the system before any conclusion can be reached.     

Incomplete and noisy network data as a percolation process

We discuss the ramifications of noisy and incomplete observations of network data on the existence of a giant connected component (GCC). The existence of a GCC in a random graph can be described in terms of a percolation process, and building on general results for classes of random graphs with specified degree distributions we derive percolation thresholds above which GCCs exist. We show that sampling and noise can have a profound effect on the perceived existence of a GCC and find that both processes can destroy it. We also show that the absence of a GCC puts a theoretical upper bound on the false-positive rate and relate our percolation analysis to experimental protein–protein interaction data.     

Sickle-shaped crack in a round bar under complex Mode I loading

A sickle‐shaped surface crack in a round bar under complex Mode I loading is considered. First, the stress‐intensity factor (SIF) along the front of the flaw is numerically determined for five elementary Mode I stress distributions (constant, linear, quadratic, cubic and quartic) directly applied on the crack faces. The finite element method and linear elastic fracture mechanics concepts are employed. Then, a numerical procedure to calculate approximate values of SIF for a complex Mode I stress distribution on the crack faces is proposed based on both the power series expansion of the function describing such a stress distribution and the superposition principle. In order to validate the results obtained through the above procedure, a comparison with numerical data available in the literature is made.     

基于优化NRS和复杂网络的柴油发电机组故障诊断

为解决柴油发电机组故障诊断中复合故障难以识别的问题,设计了一种变分模态分解、优化邻域粗糙集和社团层次聚类相结合的故障诊断方法。采用变分模态分解方法对采集的声信号进行分解,并形成初始特征集。考虑到冗余特征的影响,使用优化邻域粗糙集进行特征筛选,达到属性简约的目的。利用复杂网络中的社团结构建立故障诊断网络,通过设计社团区分准则函数找出社团结构,同时实现了故障诊断分类。试验表明,所提方法的故障诊断率达到了99.17%,其有效性及优越性得到了充分证实。     

Sick and edgy: walk-counting as a metric of epidemic spreading on networks

A network structure metric is herein suggested for the investigation of the behaviour of epidemic spreading processes in general network-structured populations. This simple measure, based on the algebraic powers of the adjacency matrix associated with the network in question, is shown to admit a heuristic interpretation as a representation of a spreading process similar to standard epidemic models. It is further shown that the values of this metric may be of use in understanding the dynamic pattern of epidemic spread on networks of greatly varying structural properties (e.g. the degree distribution, the assortativity/dissortativity and the clustering).     

Modeling the satellite placement problem as a network flow problem with one side constraint

Summary The placement of telecommunication satellites in the geostationary orbit (GSO) gives rise to NP-hard optimization problems usually approached with iterative neighborhood (possibly tabu) search schemes. A typical iteration thereof consists in fixing an order for the satellites and determining their actual locations by linear programming. In such procedures it is crucial to efficiently solve the very large number of arising special linear programs. We describe those linear programs, characterize their duals as special network flow problems with one side constraint and then present an efficient network simplex method to solve them. Since these problems can be highly degenerate, we generalize Cunningham's concept of strongly feasible bases to our case and present a procedure based thereupon which prevents cycling. Computational experience with our algorithms substantiates our efficiency claims.     

基于复杂网络社团聚类的复合故障特征分离诊断方法

针对复合故障多种故障特征相互叠加彼此干扰,给全面准确诊断带来困难,提出了基于复杂网络社团聚类的复合故障特征分离诊断方法。该方法首先应用EMD将复合故障信号分解为若干个IMF分量,由于不同单一故障的特征会在不同频段得以体现,提取每个IMF分量的特征量,建立故障数据网络模型;然后将每个IMF分量视为网络中的社团,根据复杂网络社团结构的特性,进行同类社团合并,合并所得每个社团与单一故障相对应,最后对合并的信号进行分析,实现复合故障特征分离。本文以转子不平衡和轴承内圈、轴承内圈和滚动体复合故障特征分离与诊断为例,验证了该方法的可行性。     

Green electroluminescent device with a terbium β-diketonate complex as emissive center

In this study, a terbium complex, Tb(acac)₃bath (acac: acetylacetone, bath: 4,7-diphenyl-1,10-phenanthroline), was synthesized and its luminescent properties were investigated compared with the reported terbium complex, Tb(acac)₃phen (phen: phenanthroline). When it was used as an emitting material in organic electroluminescent (EL) device, the triple-layer-type device with a structure of glass substrate/ITO (indium-tin oxide)/TPD (N,N^′-diphenyl-N,N^′-bis(3-methylphenyl)-1,1^′-biphenyl-4,4^′-diamine)/Tb(acac)₃bath/Alq₃ (tris (8-hydroxyquinolinato) aluminum)/Al (aluminum) exhibited bright characteristic emission of terbium ion upon applying DC voltage. An apparent difference was observed between the photoluminescence spectrum and the EL spectrum. The EL device exhibited some characteristics of diode and the maximum luminance of 77 cd/m² was obtained at 17 V.     

Resiliency as a component importance measure in network reliability

This paper seeks to define the concept of resiliency as a component importance measure related to network reliability. Resiliency can be defined as a composite of: (1) the ability of a network to provide service despite external failures and (2) the time to restore service when in the presence of such failures. Although, Resiliency has been extensively studied in different research areas, this paper will study the specific aspects of quantifiable network resiliency when the network is experiencing potential catastrophic failures from external events and/or influences, and when it is not known a priori which specific components within the network will fail. A formal definition for Category I resiliency is proposed and a step-by-step approach based on Monte-Carlo simulation to calculate it is defined. To illustrate the approach, two-terminal networks with varying degrees of redundancy, have been considered. The results obtained for test networks show that this new quantifiable concept of resiliency provides insight into the performance and topology of the network. Future use for this work could include methods for safeguarding critical network components and optimizing the use of redundancy as a technique to improve network resiliency.     

The addition of Si to Fe-based alloys as a means of enhancing sulphidation resistance in complex mixed atmospheres

Abstract

Of the three principal oxide-forming elements present in most high temperature engineering alloys (Cr, Al and Si), silicon has been the poor relation, largely as a result of its potentially adverse effect upon fabricability and weldability. However, several advanced heat resistant alloys containing between 2 and 3.5% Si have come onto the market in recent years with reported good resistance to corrosive atmospheres [1–8]. However, these alloys contain high levels of Cr, Ni and/or Co and are therefore relatively expensive.

The present study explores the “Si-effect” using a cheaper 12%Cr ferritic, Type 410 class of steel. A commercial alloy, HCM 12, containing 0.2% Si has been selected as a base composition to which Si has been added in amounts ranging from nominally 1 to 4%. Samples of this commercial alloy have been exposed alongside similar specimens machined from specially cast higher-Si model alloys in highly reducing, non-equilibrated CO-based gas mixtures containing 4% CO2, 0–3% H₂O and 0.1 and 0.8% H₂S, balance H₂. These atmospheres simulate those found in a dry-feed entrained slagging coal gasifier where corrosive degradation in the form of sulphidation attack limits operating temperature and therefore plant efficiency. Constructional costs remain an important issue, however, and comparisons are made with the performance of higher alloyed materials, HR 3C and Alloy 800H, exposed at the same time.

Initially, experiments were carried out for up to 2,000 hours at 450°C in a 0.8%H₂S containing atmosphere. Based upon the findings of these tests, a second series was carried out at a higher temperature, i.e. 550°C, for 1,000 hours in a 0.1%H₂S gas mixture. Specimens were subjected to periodic interruptions for weight change measurements and visual inspection with selected samples also being discontinued for more in-depth surface and cross-sectional microstructural studies as well as metal loss measurements. The influence of Si upon corrosion kinetics and in modifying the nature and extent of attack has been established.     

Modelling the initial phase of an epidemic using incidence and infection network data: 2009 H1N1 pandemic in Israel as a case study

This paper presents new computational and modelling tools for studying the dynamics of an epidemic in its initial stages that use both available incidence time series and data describing the population''s infection network structure. The work is motivated by data collected at the beginning of the H1N1 pandemic outbreak in Israel in the summer of 2009. We formulated a new discrete-time stochastic epidemic SIR (susceptible-infected-recovered) model that explicitly takes into account the disease''s specific generation-time distribution and the intrinsic demographic stochasticity inherent to the infection process. Moreover, in contrast with many other modelling approaches, the model allows direct analytical derivation of estimates for the effective reproductive number (R_e) and of their credible intervals, by maximum likelihood and Bayesian methods. The basic model can be extended to include age–class structure, and a maximum likelihood methodology allows us to estimate the model''s next-generation matrix by combining two types of data: (i) the incidence series of each age group, and (ii) infection network data that provide partial information of ‘who-infected-who’. Unlike other approaches for estimating the next-generation matrix, the method developed here does not require making a priori assumptions about the structure of the next-generation matrix. We show, using a simulation study, that even a relatively small amount of information about the infection network greatly improves the accuracy of estimation of the next-generation matrix. The method is applied in practice to estimate the next-generation matrix from the Israeli H1N1 pandemic data. The tools developed here should be of practical importance for future investigations of epidemics during their initial stages. However, they require the availability of data which represent a random sample of the real epidemic process. We discuss the conditions under which reporting rates may or may not influence our estimated quantities and the effects of bias.     

Thermal stability of R-134a, R-141b, R-13I1, R-7146, R-125 associated with stainless steel as a containing material

An experimental apparatus for assessing the thermal stability threshold of refrigerant working fluids is described and results for R-134a (1,1,1,2-tetrafluoroethane), R141b (1,1-dichloro-1-fluoroethane), R-13I1 (trifluoromethyl iodide), R-7146 (sulphur hexafluoride), R-125 (pentafluoroethane) are presented. The information is a concern for the design of refrigeration systems, high temperature heat pumps and Organic Rankine Cycles (ORC), for which the above refrigerants are proposed. The method aims to identify a maximum temperature for plant operation in contact with stainless steel and involves the evaluation of four indicators: (1) pressure variation while the fluid is maintained at set temperature; (2) saturation pressure comparison after heat treatment; (3) chemical analysis; and (4) vessel visual inspection after the test session. The highest temperatures at which no evident degradation occured are: 368°C for R-134a; 102°C for R-13I1; 90°C for R-141b; 204°C for R-7146; and 396°C for R-125.