Physica A: Statistical Mechanics and its Applications最新文献

In the natural and social sciences, multifractal properties between two non-stationary time series are influenced not only by each other, but also by exogenous variables and historical data. However, traditional multifractal detrended cross-correlation analysis did not realize this problem, but directly explored the multifractal nature of time series. To eliminate the influence of exogenous variables and historical data as much as possible, the deep multifractal detrended cross-correlation analysis (DMF-DCCA) is developed to research the multifractal cross- correlation nature between two non-stationary time series. Furthermore, the effectiveness of DMF-DCCA has been validated using a simulated dataset and two real-world datasets.

Connected vehicles (CVs) will gradually replace traditional vehicles to become the main components of traffic flow. Studying the car-following behavior characteristics is crucial for improving traffic flow stability and safety in CVs environment. Additionally, the radius of road curvature significantly impacts vehicle driving behavior, making it necessary to consider it for the car-following models of CVs. The artificial potential field (APF) theory can more accurately and comprehensively depict various microscopic driving behaviors, offering a new approach for modeling vehicle microscopic behavior. Firstly, this paper constructs the attractive and repulsive potential fields considering horizontal curve curvature based on a road coordinate transformation model. Secondly, an Artificial Potential Field-Based Car-Following Model Considering Curvature (APFCCM) in connected vehicles environment is proposed. Finally, the model is calibrated and validated using the Hangzhou - Xifu Freeway dataset from the Tongji Road Trajectory Sharing (TJRD TS) platform, and compared with the full velocity difference model(FVDM), the Intelligent Driver Model (IDM) and the Driving Risk Potential Field Model (DRPFM). The results show that the APFCCM performs well in trajectory simulation, model accuracy, and scenario adaptability, and it has the lowest mean absolute error(MAE) and root mean square error(RMSE) in position, speed, and acceleration metrics.

In the event of an emergency, the crowd may exhibit behaviors that are not normally present due to panic and tension. These behaviors are collectively referred to as non-adaptive behaviors, mainly including reentry behavior, companion behavior, and herd behavior. To study the impact and degree of influence of these behaviors on crowd evacuation results, this study selected a university auditorium as the research object, and convened school volunteers to conduct on-site evacuation experiments in the auditorium. Real time observation and data analysis were conducted on the herd effect, companion behavior, and reentry behavior during the experiment, and compared with basic experiments. The focus was on indicators such as evacuation time, evacuation speed, and exit decision-making behavior. The experimental results indicate that reentry behavior, companion behavior, and herd effect are all unfavorable for crowd evacuation.

With the ongoing evolution of scientific research collaboration in depth and breadth, the age distribution among paper collaborators is becoming increasingly varied. The question of whether this age distribution affects the quality of collaborative papers has emerged as a significant topic in recent research. In this study, we have compiled a comprehensive scientific database comprising more than 3.5 million papers from over 180 countries. These papers include multiple attribute information about their authors. We define the age diversity of a paper based on the academic ages of its co-authors and examine the relationship between age diversity and citation impact using regression analyses. Our findings reveal a clear upward trend in the age diversity of authors over time. The results indicate that the average age diversity of highly cited papers is consistently higher than that of general papers across the entire timeline, spanning four disciplines: economics, engineering, computer science, and physics. Moreover, we demonstrate a significantly positive correlation between age diversity and citation impact. In the aforementioned four disciplines, an increase in the collaborators’ age diversity is associated with a corresponding rise in the paper’s citation impact. This study contributes to a deeper understanding of age-related dynamics in scientific collaboration. It also offers insights into the age distribution of research teams, providing practical suggestions for researchers engaged in collaborative endeavors.

Most studies considered metal matrix nanocomposites (NCs) because of their excellent mechanical and electrical properties. In recent years, external electric fields (EEFs) in the aforementioned NCs were identified as a crucial role in modulating mechanical behavior. The EEF may affect strength, hardness, ductility, and fracture toughness. The explanation for these changes is the interaction of EEF with the nanoparticles in the metal matrix. In the present study, the effects of various EEF values on the mechanical properties of Al/Cu/Al three-layer NCs (TLNCs) were assessed using the molecular dynamics (MD) modeling method and LAMMPS software. MD findings predicted that the EEF reduced the physical stability and mechanical strength of modeled samples. Physically, this performance resulted from a decrease in attraction force among distinct particles inside the computing box in the presence of EEF. The proposed samples' ultimate tensile strength (UTS) and Young's modulus (YM) decreased to 2.587 GPa and 20.19 GPa, respectively, when the EEF value increased to 0.05 V/Å. Finally, it was determined that EEF is a crucial parameter in the mechanical development of MMNC structures and should be used in mechanical bacterial design in industrial applications.

This paper identifies the cryptocurrency market crashes and analyses its dynamics using the complex network. We identify three distinct crashes during 2017–20, and the analysis is carried out by dividing the time series into pre-crash, crash, and post-crash periods. Partial correlation based complex network analysis is carried out to study the crashes. Degree density (${\rho }_D$), average path length ($\bar{l}$), and average clustering coefficient ($\overline{cc}$) are estimated from these networks. We find that both ${\rho }_D$ and $\overline{cc}$ are smallest during the pre-crash period, and spike during the crash suggesting the network is dense during a crash. Although ${\rho }_D$ and $\overline{cc}$ decrease in the post-crash period, they remain higher than pre-crash levels for the 2017–18 and 2018–19 crashes suggesting a market attempt to return to normalcy. We get $\bar{l}$ is minimal during the crash period, suggesting a rapid flow of information. A dense network and rapid information flow suggest that during a crash uninformed synchronized panic sell-off happens. However, during the 2019–20 crash, the values of ${\rho }_D$, $\overline{cc}$, and $\bar{l}$ did not vary significantly, indicating minimal change in dynamics compared to other crashes. The findings of this study may guide investors in making decisions during market crashes.

The connected and automated vehicle (CAV) is able to acquire the global intelligence in advance by communicating with other CAVs and roadside units (RSU), thus integrated speed control has the potential of easing the traffic wave, reducing the fuel consumption and emissions. In this paper, a dynamic temporal and spatial speed control framework is proposed to optimize the travel speed of CAVs along the signalized arterial under the mixed traffic flow including CAVs and human driven vehicles (HDVs). A speed control optimization method is proposed to minimize the number of stops of the ego CAV and its follower HDV with considering the signal status and queuing. A secondary speed control method based on the dynamic control areas is introduced in the mentioned framework to guide the CAV to the targeted positions. The corresponding dynamic variable parameter model is then designed to optimize the operational parameters of the corresponding control area to minimize the total fuel consumption of all vehicles under different market penetration rates. Finally, the simulation platform of Urban Mobility (SUMO) is used to test the proposed speed control strategy. The results indicate that the total stop delays are reduced by 60.9 % and saving 6.5 % total fuel consumption under the 30 % penetration rate.

This paper proposes a new method called the “Special Neural Network” to solve the HIV infection model of CD4(+) cells using a novel approximation approach. Unlike traditional methods that involve constructing loss functions and performing inverse matrix operations, our method discretizes the differential equations at configuration points, combines them, and transforms the system into a set of nonlinear equations. Parameters in the neural network are then iteratively solved using optimization to obtain an approximate solution. Additionally, when using the neural network as an approximate solution to the differential equations, we provide a form that satisfies the initial conditions through construction, eliminating the need to handle initial conditions during the solving process and thus streamlining the method. Finally, by comparing with other numerical methods using two sets of models and parameters, the Special Neural Network achieves high precision results and further demonstrates the advantages of our approach.

There are reasons to suggest that a number of mental disorders may be related to alteration in the neural complexity (NC). Thus, quantitative analysis of NC could be helpful in classifying mental and understanding conditions. Here, focusing on a methodological procedure, we have worked with young individuals, typical and with attention-deficit/hyperactivity disorder (ADHD) whose NC was assessed using q-statistics applied to the electroencephalogram (EEG). The EEG was recorded while subjects performed the visual Attention Network Test (ANT) and during a short pretask period of resting state. Time intervals of the EEG amplitudes that passed a threshold were collected from task and pretask signals from each subject. The data were satisfactorily fitted with a stretched $q$-exponential including a power-law prefactor(characterized by the exponent c), thus determining the best $(c,q)$ for each subject, indicative of their individual complexity. We found larger values of $q$ and $c$ in ADHD subjects as compared with the typical subjects both at task and pretask periods, the task values for both groups being larger than at rest. The $c$ parameter was highly specific in relation to DSM diagnosis for inattention, where well-defined clusters were observed. The parameter values were organized in four well-defined clusters in $(c,q)$-space. As expected, the tasks apparently induced greater complexity in neural functional states with likely greater amount of internal information processing. The results suggest that complexity is higher in ADHD subjects than in typical pairs. The distribution of values in the $(c,q)$-space derived from $q$-statistics seems to be a promising biomarker for ADHD diagnosis.

This study presents calculations of a multiparticle system within the framework of fractional quantum mechanics. We specifically explore the energy levels of a bosonic system with repulsive interactions confined in a hard-wall box. The impacts of fractional parameters on the system’s thermodynamic properties are meticulously analyzed. Furthermore, utilizing this model, we construct a quantum Otto cycle and discover that the system exhibits Bose–Fermi duality under varying fractional parameters. Intriguingly, the introduction of fractional parameters enables to optimize the performance of the quantum heat engine, edging it closer to the Carnot efficiency.