Community detection by label propagation with compression of flow

The label propagation algorithm (LPA) has been proved to be a fast and effective method for detecting communities in large complex networks. However, its performance is subject to the non-stable and trivial solutions of the problem. In this paper, we propose a modified label propagation algorithm LPAf to efficiently detect community structures in networks. Instead of the majority voting rule of the basic LPA, LPAf updates the label of a node by considering the compression of a description of random walks on a network. A multi-step greedy agglomerative strategy is employed to enable LPAf to escape the local optimum. Furthermore, an incomplete update condition is also adopted to speed up the convergence. Experimental results on both synthetic and real-world networks confirm the effectiveness of our algorithm

The effects of overtaking strategy in the Nagel-Schreckenberg model

Based on the Nagel-Schreckenberg (NS) model with periodic boundary conditions, we proposed the NSOS model by adding the overtaking strategy (OS). In our model, overtaking vehicles are randomly selected with probability $q$ at each time step, and the successful overtaking is determined by their velocities. We observed that (i) traffic jams still occur in the NSOS model; (ii) OS increases the traffic flow in the regime where the densities exceed the maximum flow density. We also studied the phase transition (from free flow phase to jammed phase) of the NSOS model by analyzing the overtaking success rate, order parameter, relaxation time and correlation function, respectively. It was shown that the NSOS model differs from the NS model mainly in the jammed regime, and the influence of OS on the transition density is dominated by the braking probability $p$Comment: 9 pages, 20 figures, to be published in The European Physical Journal B (EPJB

Community Detection in Dynamic Networks via Adaptive Label Propagation

An adaptive label propagation algorithm (ALPA) is proposed to detect and monitor communities in dynamic networks. Unlike the traditional methods by re-computing the whole community decomposition after each modification of the network, ALPA takes into account the information of historical communities and updates its solution according to the network modifications via a local label propagation process, which generally affects only a small portion of the network. This makes it respond to network changes at low computational cost. The effectiveness of ALPA has been tested on both synthetic and real-world networks, which shows that it can successfully identify and track dynamic communities. Moreover, ALPA could detect communities with high quality and accuracy compared to other methods. Therefore, being low-complexity and parameter-free, ALPA is a scalable and promising solution for some real-world applications of community detection in dynamic networks.Comment: 16 pages, 11 figure

Multifractal and Network Analysis of Phase Transition

Many models and real complex systems possess critical thresholds at which the systems shift from one sate to another. The discovery of the early warnings of the systems in the vicinity of critical point are of great importance to estimate how far a system is from a critical threshold. Multifractal Detrended Fluctuation analysis (MF-DFA) and visibility graph method have been employed to investigate the fluctuation and geometrical structures of magnetization time series of two-dimensional Ising model around critical point. The Hurst exponent has been confirmed to be a good indicator of phase transition. Increase of the multifractality of the time series have been observed from generalized Hurst exponents and singularity spectrum. Both Long-term correlation and broad probability density function are identified to be the sources of multifractality of time series near critical regime. Heterogeneous nature of the networks constructed from magnetization time series have validated the fractal properties of magnetization time series from complex network perspective. Evolution of the topology quantities such as clustering coefficient, average degree, average shortest path length, density, assortativity and heterogeneity serve as early warnings of phase transition. Those methods and results can provide new insights about analysis of phase transition problems and can be used as early warnings for various complex systems.Comment: 23 pages, 11 figure

Loss of ATF3 exacerbates liver damage through the activation of mTOR/p70S6K/ HIF-1α signaling pathway in liver inflammatory injury.

Activating transcription factor 3 (ATF3) is a stress-induced transcription factor that plays important roles in regulating immune and metabolic homeostasis. Activation of the mechanistic target of rapamycin (mTOR) and hypoxia-inducible factor (HIF) transcription factors are crucial for the regulation of immune cell function. Here, we investigated the mechanism by which the ATF3/mTOR/HIF-1 axis regulates immune responses in a liver ischemia/reperfusion injury (IRI) model. Deletion of ATF3 exacerbated liver damage, as evidenced by increased levels of serum ALT, intrahepatic macrophage/neutrophil trafficking, hepatocellular apoptosis, and the upregulation of pro-inflammatory mediators. ATF3 deficiency promoted mTOR and p70S6K phosphorylation, activated high mobility group box 1 (HMGB1) and TLR4, inhibited prolyl-hydroxylase 1 (PHD1), and increased HIF-1α activity, leading to Foxp3 downregulation and RORγt and IL-17A upregulation in IRI livers. Blocking mTOR or p70S6K in ATF3 knockout (KO) mice or bone marrow-derived macrophages (BMMs) downregulated HMGB1, TLR4, and HIF-1α and upregulated PHD1, increasing Foxp3 and decreasing IL-17A levels in vitro. Silencing of HIF-1α in ATF3 KO mice ameliorated IRI-induced liver damage in parallel with the downregulation of IL-17A in ATF3-deficient mice. These findings demonstrated that ATF3 deficiency activated mTOR/p70S6K/HIF-1α signaling, which was crucial for the modulation of TLR4-driven inflammatory responses and T cell development. The present study provides potential therapeutic targets for the treatment of liver IRI followed by liver transplantation

Investigation of Electrode Electrochemical Reactions in CH3 NH3 PbBr3 Perovskite Single-Crystal Field-Effect Transistors.

Optoelectronic devices based on metal halide perovskites, including solar cells and light-emitting diodes, have attracted tremendous research attention globally in the last decade. Due to their potential to achieve high carrier mobilities, organic-inorganic hybrid perovskite materials can enable high-performance, solution-processed field-effect transistors (FETs) for next-generation, low-cost, flexible electronic circuits and displays. However, the performance of perovskite FETs is hampered predominantly by device instabilities, whose origin remains poorly understood. Here, perovskite single-crystal FETs based on methylammonium lead bromide are studied and device instabilities due to electrochemical reactions at the interface between the perovskite and gold source-drain top contacts are investigated. Despite forming the contacts by a gentle, soft lamination method, evidence is found that even at such "ideal" interfaces, a defective, intermixed layer is formed at the interface upon biasing of the device. Using a bottom-contact, bottom-gate architecture, it is shown that it is possible to minimize such a reaction through a chemical modification of the electrodes, and this enables fabrication of perovskite single-crystal FETs with high mobility of up to ≈15 cm2 V-1 s-1 at 80 K. This work addresses one of the key challenges toward the realization of high-performance solution-processed perovskite FETs

Quantitative Effect of Zr Content on the Structure and Water–Gas Shift Reaction Activities of Gold Supported on Ceria–Zirconia

Effect of Zr content on the structure and water–gas shift reaction catalytic activities of Au-CeO2-ZrO2 catalysts were quantitatively analyzed in detail. For the low ZrO2 content (0–15 wt. %), the Ce-Zr-O solid solutions were formed through the substitutional incorporation of Zr cations into CeO2 lattice, resulting in the contraction of cell parameters a and d-spacing (i.e., lattice distortion) and the increase of microstrain and oxygen vacancies. Quantitatively, the enhanced WGS activities have good linear correlation with the cell parameters a, microstrain, Raman shift and oxygen vacancies. Whereas, for the rich-zirconia (45 wt. %) sample, Au-CeZr-45 has some isolated t-ZrO2 and fluorite CeO2 instead of solid solution. The isolated t-ZrO2 crystallites block the contact between Au and CeO2, resulting in the agglomeration of gold clusters and, as a consequence, poor WGS activity of Au-CeZr-45 catalyst

Advances in the Synthesis of Ferrierite Zeolite

As one of the most important porous materials, zeolites with intricate micropores have been widely employed as catalysts for decades due to their large pore volume, high surface area, and good thermal and hydrothermal stabilities. Among them, ferrierite (FER) zeolite with a two-dimensional micropore structure is an excellent heterogeneous catalyst for isomerization, carbonylation, cracking, and so on. In the past years, considering the important industrial application of FER zeolite, great efforts have been made to improve the synthesis of FER zeolite and thus decrease the synthesis cost and enhance catalytic performance. In this review, we briefly summarize the advances in the synthesis of FER zeolite including the development of synthesis routes, the use of organic templates, organotemplate-free synthesis, the strategies of morphology control, and the creation of intra-crystalline mesopores. Furthermore, the synthesis of hetero-atomic FER zeolites such as Fe-FER and Ti-FER has been discussed