Narrow Results

In this paper, we present a weighted network model for the simulation of railway network. Here the stations and section tracks of the railway network are respectively regarded as the nodes and edges of the network model. Using the model proposed in this paper, we measure the distribution of trains and the mean waiting time of trains etc. The simulation results indicate that the proposed model can be successfully used for the simulation of railway network. Some phenomena observed in real railway networks can be reproduced, such as the characteristic behavior of train movement. A comparison with real data for the Netherlands (i.e., the Dutch railway network) is included. It supports the practical values of the proposed model.

In this paper, we propose a growing spatial network (GSN) model and investigate its topology properties and dynamical behaviors. The model is generated by adding one node i with m links into a square lattice at each time step and the new node i is connected to the existing nodes with probabilities proportional to: , where k_j is the degree of node j, α is the tunable parameter and d_ij is the Euclidean distance between i and j. It is found that both the degree heterogeneity and the clustering coefficient monotonously increase with the increment of α, while the average shortest path length monotonously decreases. Moreover, the evolutionary game dynamics and network traffic dynamics are investigated. Simulation results show that the value of α can also greatly influence the dynamic behaviors.

We have extended the Chalker–Coddington network model to a coupled double layer system with reversed magnetic field directions. The model is investigated numerically and focused on the behavior of the delocalized states. When the hybridization between the layers is small, there are two separate energies of the delocalized states with opposite sign. At some randomness strength, we have observed large fluctuation of the extended state energies. Furthermore, with sufficient strong hybridization, we do not find the delocalized states which suggests the disappearance of the delocalized states at finite hybridization strength. The critical property is also investigated in the weak randomness case which is consistent with the standard single layer case.

Numerical simulations of the current domain picture, which is frequently used to describe the microwave induced zero resistance state of high mobility 2-dimensional electron systems, are shown. We demonstrate, that we obtain a situation, which is equivalent to the current domain picture by introducing an artificial domain wall into our network model for magneto transport. However, in contrast to the current domain picture the current in our simulations is insensitive to the width of the domains. Finally we propose an alternative picture where we use several domain walls, which are distributed along the current path. These serve as current filaments and lead also to a vanishing longitudinal resistance, while the Hall resistance remains unchanged.

A Landauer-Büttiker type representation of bulk current transport is used for the numerical simulation of the magneto-transport of 2-dimensional electron systems. It allows us to build up a network model, which describes the effect of non-equilibrium currents injected via metallic contacts like in real experiments. Our model suggests a peak-like contribution of de-localized states to the bulk conductance, which appears embedded in the density of states (DOS) of the Landau levels (LLs). In contrast, the localization picture of the quantum Hall effect suggests almost sharp boundaries between localized and de-localized states and does not explicitly map out their contribution to the bulk conductance. Most recent experiments by B. A. Piot et al. suggest a similar peak-like contribution of de-localized states near the center of the LLs. Our simulation results for the same parameter values as determined by Piot et al. reproduce their experimental data very well.

The bilateral power transaction (BPT) mode becomes a typical market organization with the restructuring of electric power industry, the proper model which could capture its characteristics is in urgent need. However, the model is lacking because of this market organization's complexity. As a promising approach to modeling complex systems, complex networks could provide a sound theoretical framework for developing proper simulation model. In this paper, a complex network model of the BPT market is proposed. In this model, price advantage mechanism is a precondition. Unlike other general commodity transactions, both of the financial layer and the physical layer are considered in the model. Through simulation analysis, the feasibility and validity of the model are verified. At same time, some typical statistical features of BPT network are identified. Namely, the degree distribution follows the power law, the clustering coefficient is low and the average path length is a bit long. Moreover, the topological stability of the BPT network is tested. The results show that the network displays a topological robustness to random market member's failures while it is fragile against deliberate attacks, and the network could resist cascading failure to some extent. These features are helpful for making decisions and risk management in BPT markets.

We briefly discuss various applications of the Chalker–Coddington network model, starting with the original one, proposed to describe inter-plateaux transition in the integer quantum Hall effect (IQHE). Next, we present generalization appropriate for the IQHE allowing to include spin, and conclude with recent applications to dirty superconductors. We then describe how numerical calculations on an open network produce data for the localization length behavior in the metal-insulator transition, whereas calculations on the closed system allow elucidation of various levels statistics. We also discuss how numerical algorithm for systems with additional symmetries is modified in order to improve the accuracy. Finally, results for the nearest-neighbor spacing distribution in dirty superconductors are presented.

In this paper, we offer a network model that derives the expected counterparty risk of an arbitrary market after netting in a closed-form expression. Graph theory is used to represent market participants and their relationship among each other. We apply the powerful theory of characteristic functions (c.f.) and Hilbert transforms to determine the expected counterparty risk. The latter concept is used to express the c.f. of the random variable (r.v.) $max(Y,0)$ in terms of the c.f. of the r.v. $Y$. This paper applies this concept for the first time in mathematical finance in order to generalize results of Duffie & Zhu (2011), in several ways. The introduced network model is applied to study the features of an over-the-counter and a centrally cleared market. We also give a more general answer to the question of whether it is more advantageous for the overall counterparty risk to clear via a central counterparty or classically bilateral between the two involved counterparties.

Network is a powerful structure which reveals valuable characteristics of the underlying data. However, previous work on evaluating the predictive performance of network-based biomarkers does not take nodal connectedness into account. We argue that it is necessary to maximize the benefit from the network structure by employing appropriate techniques. To address this, we aim to learn a weight coefficient for each node in the network from the quantitative measure such as gene expression data. The weight coefficients are computed from an optimization problem which minimizes the total weighted difference between nodes in a network structure; this can be expressed in terms of graph Laplacian. After obtaining the coefficient vector for the network markers, we can then compute the corresponding network predictor. We demonstrate the effectiveness of the proposed method by conducting experiments using published breast cancer biomarkers with three patient cohorts. Network markers are first grouped based on GO terms related to cancer hallmarks. We compare the predictive performance of each network marker group across gene expression datasets. We also evaluate the network predictor against the average method for feature aggregation. The reported results show that the predictive performance of network markers is generally not consistent across patient cohorts.

The identification of cancer-related genes is a major research goal, with implications for determining the pathogenesis of cancer and identifying biomarkers for early diagnosis and treatment. In this study, by integrating multi-omics data, including gene expression, DNA copy number variation, DNA methylation, transcription factors, miRNA, and lncRNA data, we propose a method for mining cancer-related genes based on network models. First, using random forest-based feature selection method multi-omics data are integrated to identify key regulatory factors that affect gene expression, and then genome-wide regulatory networks are constructed. Next, by comparing the regulatory networks of key candidate genes in variant samples and non-variant samples, a differential expression regulatory network is generated. The differential network contains a collection of abnormal regulatory genes of key candidate genes. Then, by introducing the functional similarity as a distance metric for gene sets, a density-based clustering method is used to mine gene modules related to cancer. We applied this method to LUSC (lung squamous cell carcinoma) and mined cancer-related gene modules composed of 20 genes. GO function and KEGG pathway analyses indicated that the modules were closely related to cancer. A survival analysis was used to verify that the excavated gene modules can effectively distinguish between high- and low-risk groups. Overall, these results suggest that the proposed method can be used to identify cancer-related gene modules, providing a basis for the development of biomarkers for diagnosis and treatment.

An analytical permeability model is formulated for multilayer plain woven fabrics based on a network treatment. Both meso-scale flow between fibre tows and micro-scale flow within tows are taken into account, addressing the effects of meso-/micro-structures of fabrics. For a single layer fabric, open channels between adjacent fibre tows and between fibre tows and surface boundaries have a significant effect on in-plane permeability, while out-of-plane permeability is sensitive to open gaps between tows. It is shown that the permeability of multilayer woven fabrics may differ significantly from that of a single layer, depending on the meso-/micro-structures of multilayer fabrics. Two major mechanisms are found to affect the in-plane permeability of multilayer fabrics in opposite ways. As the number of layers increases, the large inter-layer open channels created after laying-up result in an increase in in-plane permeability; on the other hand, reduction of inter-layer open channels due to nesting during a compaction process leads to a decrease in in-plane permeability. In addition, reduction of trans-layer open channels due to inter-layer blocking associated with shifting also greatly reduces the out-of-plane permeability of multilayer fabrics. Predictions from the permeability model offer satisfactory agreement with the experimental data and the predictions based on finite element analysis.

We have estimated the critical exponent describing the divergence of the localization length at the metal-quantum spin Hall insulator transition. The critical exponent for the metal-ordinary insulator transition in quantum spin Hall systems is known to be consistent with that of topologically trivial symplectic systems. However, the precise estimation of the critical exponent for the metal-quantum spin Hall insulator transition proved to be problematic because of the existence, in this case, of edge states in the localized phase. We have overcome this difficulty by analyzing the second smallest positive Lyapunov exponent instead of the smallest positive Lyapunov exponent. We find a value for the critical exponent ν = 2.73 ± 0.02 that is consistent with that for topologically trivial symplectic systems.

Numerical simulations of the current domain picture, which is frequently used to describe the microwave induced zero resistance state of high mobility 2-dimensional electron systems, are shown. We demonstrate, that we obtain a situation, which is equivalent to the current domain picture by introducing an artificial domain wall into our network model for magneto transport. However, in contrast to the current domain picture the current in our simulations is insensitive to the width of the domains. Finally we propose an alternative picture where we use several domain walls, which are distributed along the current path. These serve as current filaments and lead also to a vanishing longitudinal resistance, while the Hall resistance remains unchanged.

We have estimated the critical exponent describing the divergence of the localization length at the metal-quantum spin Hall insulator transition. The critical exponent for the metal-ordinary insulator transition in quantum spin Hall systems is known to be consistent with that of topologically trivial symplectic systems. However, the precise estimation of the critical exponent for the metal-quantum spin Hall insulator transition proved to be problematic because of the existence, in this case, of edge states in the localized phase. We have overcome this difficulty by analyzing the second smallest positive Lyapunov exponent instead of the smallest positive Lyapunov exponent. We find a value for the critical exponent ν = 2.73 ± 0.02 that is consistent with that for topologically trivial symplectic systems.

As a part of intelligent transportation system, the Road-side Unit (RSU) has important functions including data collection, processing and forwarding, whose location optimization has significant influence on the whole system function implement. This paper describes the model of road as binary sensing model and network model, using the genetic algorithm by setting specific parameters and improved greedy algorithm for further optimization to optimize the location of RSU. The results show that, compared with other algorithms, the proposed algorithm's covering extent, connectivity and efficiency are improved, where the RSU's number decrease by 44% from 140 to 78 by genetic algorithm and further decrease by 17.9% from 78 to 64 by greedy algorithm.