Physica A: Statistical Mechanics and its Applications最新文献

Within this study, we analyse green cryptocurrencies versus sustainable investments dynamics by calculating a multifractal multiscale analysis (MMA) with Hurst surfaces paired with powerlaw distributional coherence tests for each series. Next, we determine multifractal cross-correlations by applying a maximal overlap discrete wavelet transform (MODWT) based trend-filtered variation of a multifractal detrended cross-correlation analysis (MF-DCCA). Finally, to determine the strength and directionality of potential causations, we determine the results of a nonlinear Granger causality test. The results for the MMA state $q$-dependent unstable multifractality for each series. The coherence tests indicate that the series follow powerlaws or exponentially nested powerlaws and yield fat-tails in some cases. Moreover, we find strong scaling and multifractal cross-correlations between the cryptocurrencies and the sustainability series. Finally, the nonlinear Granger causality tests across four lags indicate a complex interplay between some of the selected cryptocurrencies and various indices. These results suggest the existence of potential predictive powers of these cryptocurrencies on the market indices.

With the development of urban rail transit system, the complexity of the network intensifies, and its vulnerability shifts correspondingly. Understanding the characteristics and evolution of network vulnerability, as well as identifying developmental patterns, enables more scientific network planning. Current researches on network vulnerability predominantly focus on the static vulnerability assessment of existing networks, with limited studies on vulnerability evolution. This paper divides the topological evolution of the Beijing Urban Rail Transit Network (BURTN) from 2000 to 2020 into Formation and Improvement stages using the K-means++ method. By constructing a multidimensional vulnerability assessment model that considers node degree uniformity, network efficiency, and connectivity, the vulnerability evolution characteristics and patterns of BURTN are explored in cases of both Single-station failures and Multi-station consecutive failures (including random and intentional failures). Furthermore, the evolutionary relationship between network vulnerability and network structure is explored using the Ridge regression model. Calculations reveal that in the case of Single-station failures, during the Formation stage, the proportion of highly vulnerable stations (HVS) and the impact of each station failure on network performance decrease significantly, by 69.36 % and 67.67 %, respectively. During the Improvement stage, the proportion of HVS decreases significantly, while the impact of each station failure on network performance decreases slightly, by 79.10 % and 37.04 %, respectively. In the case of Multi-station consecutive failures, during the Formation stage, the network’s ability to cope with both random and intentional failures decreases, with the percentage of network nodes removed at the collapse state decreasing by 24.95 % and 11.12 %, respectively. During the Improvement stage, the network’s ability to cope with random failures remains stable, while its ability to cope with intentional failures decreases. This study helps to understand vulnerability from an evolutionary perspective and provides practical strategies for reducing vulnerability.

It is usually expected and observed that non-integrable isolated quantum systems thermalize. However, for some non-integrable spin chain models, in a numerical study, initial states with oscillations that persisted for some time were found and the phenomenon was named weak thermalization. Later, it was argued that such oscillations will eventually decay suggesting that weak thermalization was about time scales and not the size of the fluctuations. Nevertheless, the analyses of the size of the fluctuations were more qualitative. Here, using exact diagonalization we analyze how the size of the typical fluctuation, after long enough time for equilibration to happen, scales with the system size. For that, we use rigorous mathematical upper bounds on the equilibration of isolated quantum systems. We show that weak thermalization can be understood to be due to the small effective dimension of the initial state. Furthermore, we show that the fluctuations decay exponentially with the system size for both weak and strong thermalization indicating no sharp transitions between these two regimes.

Recent developments in the Internet of Vehicles (IoT) have enabled connected vehicles (CVs) to break the perceptual boundary of drivers to receive more abundant exogenous information, which provides more opportunities for optimizing the running state of the vehicles and enhancing traffic efficiency. Nevertheless, promoting CVs is a long process, which means that human-driven vehicles (HDVs) and CVs will coexist on the road during this stage of development. To this end, we comprehensively consider the differences in the communication mechanism between two different types of vehicles and introduce the penetration rate to quantify the ratio of HDVs and CVs, and then propose a novel generic continuum modelling framework. Subsequently, the stability norm and associated KdV-Burger equation are deduced with the aid of the linear and nonlinear stability analysis approach, respectively. Lastly, we provide several simulation experiments in the open or periodic boundary environment, to examine the above theoretical analysis.

Here we present an analysis of eigenvalues and eigenvectors of a correlation matrix that contains intra- and inter-frequency band correlations for narrow band filtered EEG signals of healthy subjects during sleep. We find that inter-band correlations of the same electrode are particularly suitable for distinguishing different sleep stages. However, largest eigenvalues and their corresponding eigenvectors show an extremely stable behavior during night sleep, while sleep stages manifest via specific deviations from an overall stable structure expressed by relative eigenvalues and deviations from average eigenvectors. Concluding that valuable information is encoded along the whole spectrum of eigenvalues and eigenvectors of the correlation matrix, we interpret our results in term of dynamical system theory.

We establish a simplicial empty-susceptible–infected (ESI) model with consideration of threshold policy to depict the network-based epidemic transmission, where the underlying propagation structures are expanded from edges to higher-order structures. To address the epidemic evolution in an explicit network, we formulate the quenched mean-field probability evolution about each site, which is composed of non-smooth differential equations based on network topology. Remarkably, under the combined action of non-smooth and high-order structures, a tristable state is observed in empirical social networks, which is consistent with the coexistence of three stable equilibria by analysis of the mean-field system. Moreover, we find that a sliding mode exists in empirical social networks, which is also indicated by the theoretical analysis of the mean-field probability equations. Finally, the system is divided into the free subsystem without the threshold policy and control subsystem with the threshold policy. Both subsystems admit a stable disease-free equilibrium and a stable endemic equilibrium, as well as coexistence of a stable disease-free equilibrium and a stable pseudo equilibrium in the system, thereby admitting three types of the bistable state under the policy with different critical levels.

We consider the hysteretic behavior of Ising spin glasses at $T=0$ for various modes of driving. Previous studies mostly focused on an infinitely slow speed $\dot{H}$ by which the external field $H$ was ramped to trigger avalanches of spin flips by starting with destabilizing a single spin while few have focused on the effect of different driving methods. First, we show that this conventional protocol imposes a system size dependence. Then, we numerically analyze the response of Ising spin glasses at rates $\dot{H}$ that are fixed as well, to elucidate the differences in the response. Specifically, we compare three different modes of ramping ($\dot{H}=c/N$, $\dot{H}=c/\sqrt{N}$, and $\dot{H}=c$ for constant $c$) for two types of spin glass systems of size $N$, representing dense networks by the Sherrington–Kirkpatrick model and sparse networks by the lattice spin glass in $d=3$ dimensions known as the Edwards Anderson model. Depending on the mode of ramping, we find that the response of each system, in form of spin-flip avalanches and other observables, can vary considerably. In particular, in the $N$-independent mode applied to the lattice spin glass, which is closest to experimental reality, we observe a percolation transition with a broad avalanche distribution between phases of localized and system-spanning responses. We explore implications for combinatorial optimization problems pertaining to sparse systems.

A new version of the convergent cross mapping is proposed to detect causalities from records for strongly coupled nonlinear dynamical systems, where the mutual entropy is used to measure nonlinear correlations, and the time delay stability is adopted to filter out false identifications. Calculations on various deterministic dynamic systems show that it is applicable not only to strongly coupled systems but also to non-interacting systems influenced by a common environment. Compared with the original version of convergent cross mapping, under strong couplings our proposed method has significantly higher accuracy, and is more robust to coupling strength. As a typical example, it is used to detect the causal effects between arterial blood pressure (ABP) and intracranial pressure (ICP) of patients diagnosed with traumatic brain injury (TBI). A mono-directional causality from ICP to ABP is identified.

We investigate the dynamics of quantum features in a two-qubit system subjected to acceleration in one or both subsystems while interacting with Ohmic or super-Ohmic noisy reservoirs. Metrics such as quantum discord, negativity, and entropic uncertainty are deployed to analyze the dynamical map of quantum states under various conditions. Our findings suggest that increasing the number of accelerated qubits negatively impacts quantum correlations. Furthermore, assigning non-uniform values of acceleration magnitude to the two qubits also leads to higher decay and uncertainty generation. Additionally, an increase in the bosonic reservoir frequency and ohmicity parameter exhibits opposite effects to those prevailed by the purity of states by promoting decay. Notably, discord proves to be sensitive to acceleration and non-uniformity, while negativity is strongly influenced by low purity values. The role of Ohmic noisy reservoirs is emphasized, as they display less decay compared to super-Ohmic reservoirs. Overall, the super-Ohmic noisy reservoir induced higher decoherence and uncertainty, causing larger quantum correlation decay compared to the Ohmic noise.

The non-Markovian dynamics of a two-dimensional charged harmonic oscillator linearly coupled to a neutral bosonic heat bath is investigated in a linear-polarized electric field. The analytical expressions for the time-dependent and asymptotic magnetic moment are derived for the Markovian and non-Markovian dynamics. It is predicted that the liner-polarized electric field generates strong orbital currents and magnetization in a symmetric harmonic oscillator embedded into anisotropic heat bath. The role of the mixed dissipative kernel in magnetization is analyzed.