Control of a lane-drop bottleneck through variable speed limits

In this study, we formulate the VSL control problem for the traffic system in a zone upstream to a lane-drop bottleneck based on two traffic flow models: the Lighthill-Whitham-Richards (LWR) model, which is an infinite-dimensional partial differential equation, and the link queue model, which is a finite-dimensional ordinary differential equation. In both models, the discharging flow-rate is determined by a recently developed model of capacity drop, and the upstream in-flux is regulated by the speed limit in the VSL zone. Since the link queue model approximates the LWR model and is much simpler, we first analyze the control problem and develop effective VSL strategies based on the former. First for an open-loop control system with a constant speed limit, we prove that a constant speed limit can introduce an uncongested equilibrium state, in addition to a congested one with capacity drop, but the congested equilibrium state is always exponentially stable. Then we apply a feedback proportional-integral (PI) controller to form a closed-loop control system, in which the congested equilibrium state and, therefore, capacity drop can be removed by the I-controller. Both analytical and numerical results show that, with appropriately chosen controller parameters, the closed-loop control system is stable, effect, and robust. Finally, we show that the VSL strategies based on I- and PI-controllers are also stable, effective, and robust for the LWR model. Since the properties of the control system are transferable between the two models, we establish a dual approach for studying the control problems of nonlinear traffic flow systems. We also confirm that the VSL strategy is effective only if capacity drop occurs. The obtained method and insights can be useful for future studies on other traffic control methods and implementations of VSL strategies.Comment: 31 pages, 14 figure

Determination of multifractal dimensions of complex networks by means of the sandbox algorithm

Complex networks have attracted much attention in diverse areas of science and technology. Multifractal analysis (MFA) is a useful way to systematically describe the spatial heterogeneity of both theoretical and experimental fractal patterns. In this paper, we employ the sandbox (SB) algorithm proposed by T\'{e}l et al. (Physica A, 159 (1989) 155-166), for MFA of complex networks. First we compare the SB algorithm with two existing algorithms of MFA for complex networks: the compact-box-burning (CBB) algorithm proposed by Furuya and Yakubo (Phys. Rev. E, 84 (2011) 036118), and the improved box-counting (BC) algorithm proposed by Li et al. (J. Stat. Mech.: Theor. Exp., 2014 (2014) P02020) by calculating the mass exponents tau(q) of some deterministic model networks. We make a detailed comparison between the numerical and theoretical results of these model networks. The comparison results show that the SB algorithm is the most effective and feasible algorithm to calculate the mass exponents tau(q) and to explore the multifractal behavior of complex networks. Then we apply the SB algorithm to study the multifractal property of some classic model networks, such as scale-free networks, small-world networks, and random networks. Our results show that multifractality exists in scale-free networks, that of small-world networks is not obvious, and it almost does not exist in random networks.Comment: 17 pages, 2 table, 10 figure

Multifractal analysis of weighted networks by a modified sandbox algorithm

Complex networks have attracted growing attention in many fields. As a generalization of fractal analysis, multifractal analysis (MFA) is a useful way to systematically describe the spatial heterogeneity of both theoretical and experimental fractal patterns. Some algorithms for MFA of unweighted complex networks have been proposed in the past a few years, including the sandbox (SB) algorithm recently employed by our group. In this paper, a modified SB algorithm (we call it SBw algorithm) is proposed for MFA of weighted networks.First, we use the SBw algorithm to study the multifractal property of two families of weighted fractal networks (WFNs): "Sierpinski" WFNs and "Cantor dust" WFNs. We also discuss how the fractal dimension and generalized fractal dimensions change with the edge-weights of the WFN. From the comparison between the theoretical and numerical fractal dimensions of these networks, we can find that the proposed SBw algorithm is efficient and feasible for MFA of weighted networks. Then, we apply the SBw algorithm to study multifractal properties of some real weighted networks ---collaboration networks. It is found that the multifractality exists in these weighted networks, and is affected by their edge-weights.Comment: 15 pages, 6 figures. Accepted for publication by Scientific Report

Estradiol regulates miR-135b and mismatch repair gene expressions via estrogen receptor-β in colorectal cells.

Estrogen has anti-colorectal cancer effects which are thought to be mediated by mismatch repair gene (MMR) activity. Estrogen receptor (ER) expression is associated with microRNA (miRNA) expression in ER-positive tumors. However, studies of direct link between estrogen (especially estradiol E2), miRNA expression, and MMR in colorectal cancer (CRC) have not been done. In this study, we first evaluated the effects of estradiol (E2) and its antagonist ICI182,780 on the expression of miRNAs (miR-31, miR-155 and miR-135b) using COLO205, SW480 and MCF-7 cell lines, followed by examining the association of tissue miRNA expression and serum E2 levels using samples collected from 18 colorectal cancer patients. E2 inhibited the expressions of miRNAs in COLO205 cells, which could be reversed by E2 antagonist ICI 182.780. The expression of miR-135b was inversely correlated with serum E2 level and ER-β mRNA expression in CRC patients' cancer tissues. There were significant correlations between serum E2 level and expression of ER-β, miR-135b, and MMR in colon cancer tissue. This study suggests that the effects of estrogen on MMR function may be related to regulating miRNA expression via ER-β, which may be the basis for the anti-cancer effect in colorectal cells