Article
Optics
Vimal Raj, M. S. Swapna, S. Sankararaman
Summary: The paper introduces a novel approach based on nonlinear time series and fractal analysis to study the beam quality of high beam intensity lasers. The study demonstrates improvements in spatial beam quality with increasing laser power, as indicated by the decreasing mean value of time-varying eccentricity. The analysis of phase portraits and nonlinear parameters (S, D, and H) reveals temporal instability of laser beams and their correlation with power fluctuations.
OPTICS AND LASER TECHNOLOGY
(2021)
Article
Mechanics
Nitin B. Burud, J. M. Chandra Kishen
Summary: The dependence of current events on past events, known as long memory phenomenon, is significant in stochastic processes. The complexity of concrete fracture process arises from long-range interactions of multi-scale cracks, which may lead to long memory effects. We quantify the long memory effect in concrete fracture process using Hurst exponent for acoustic emission measurements. The magnitude and inter-event time series are studied for three different beam sizes under monotonic and fatigue loading. The long memory effect is strongly observed under fatigue loading. Local variations in Hurst exponent reveal the fundamental difference between monotonic and fatigue loading, which can be utilized for damage detection in concrete.
ENGINEERING FRACTURE MECHANICS
(2023)
Article
Business, Finance
Ata Assaf, Avishek Bhandari, Husni Charif, Ender Demir
Summary: This paper studies the long memory behavior of Bitcoin, Litecoin, Ethereum, Ripple, Monero, and Dash during the COVID-19 period. The results show a noticeable decrease in persistence for most cryptocurrencies after the 2017 bubble and a dramatic drop after the outbreak of COVID-19. These findings have important implications for the evolution of market efficiency and the dynamics of crypto prices over time.
INTERNATIONAL REVIEW OF FINANCIAL ANALYSIS
(2022)
Article
Mathematics, Interdisciplinary Applications
Nazanin Zandi-Mehran, Sajad Jafari, Seyed Mohammad Reza Hashemi Golpayegani, Hamidreza Namazi
Summary: This paper investigates the impact of noise on the calculation of fractal dimensions in signals, comparing results obtained from different algorithms. It is observed that noise has a significant effect on fractal dimensions in continuous-time systems, while the Katz and Higuchi algorithms show more robustness in discrete-time signals. Furthermore, additional analyses on Lyapunov and Hurst analysis are provided in this study.
FRACTALS-COMPLEX GEOMETRY PATTERNS AND SCALING IN NATURE AND SOCIETY
(2021)
Article
Chemistry, Analytical
Igor Kotenko, Igor Saenko, Oleg Lauta, Alexander Kribel
Summary: This article discusses the construction and operation of a proactive system for protecting smart power grids against cyberattacks. The system utilizes computational intelligence methods to identify anomalies in network traffic and takes effective protection measures. Experimental results demonstrate the high efficiency of the system in detecting and predicting cyberattacks in real or near real-time.
Article
Physiology
Zalan Kaposzta, Akos Czoch, Orestis Stylianou, Keumbi Kim, Peter Mukli, Andras Eke, Frigyes Samuel Racz
Summary: Assessing power-law cross-correlations among processes is important in various fields. Traditional methods are computationally expensive and offline, but this study introduces a real-time formula for obtaining scaling functions. The proposed algorithm is accurate and efficient, allowing for real-time monitoring of mental state.
FRONTIERS IN PHYSIOLOGY
(2022)
Article
Mathematics, Applied
Franz Konstantin Fuss, Yehuda Weizman, Adin Ming Tan
Summary: This study investigates the similarities and differences in results when calculating D, H, and RI with different methods, finding that performance varies depending on the dataset used and that the relationship between RI and H and D is non-linear.
COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION
(2021)
Article
Mathematics, Interdisciplinary Applications
Matej Babic, Dragan Marinkovic, Miha Kovacic, Branko Ster, Michele Cali
Summary: This paper introduces a new method for quantifying the complexity of a network by representing the network nodes in Cartesian coordinates, converting to polar coordinates, and calculating the fractal dimension using the ReScaled R/S method. The results suggest that this method can be applied to determine the complexity of any type of network with fixed nodes, and it presents an application in the public transport system.
FRACTAL AND FRACTIONAL
(2022)
Article
Mathematics, Interdisciplinary Applications
Matthijs Koopmans
Summary: Various techniques are available for testing fractal patterns in time series data, but they may not always produce consistent results, requiring clear differentiation between fractal and non-fractal patterns during estimation. The study found that while most techniques identified fractality in two out of three datasets, distinguishing between seasonal and fractal patterns presented challenges.
FRACTALS-COMPLEX GEOMETRY PATTERNS AND SCALING IN NATURE AND SOCIETY
(2021)
Article
Computer Science, Artificial Intelligence
Sebastian Raubitzek, Thomas Neubauer
Summary: This study investigates the application of deep learning methods in predicting time series data, finding that for insufficient data sets, the fractal interpolation method outperforms the linear interpolation method, generating more fine-grained time series data.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Mathematics
Raoul Nigmatullin, Semyon Dorokhin, Alexander Ivchenko
Summary: The paper focuses on the generalization of the Hurst empirical law and proposes reduced parameters for quantitative description of long-time series. Analyzing three hypotheses, it demonstrates that the generalized Hurst laws can accurately describe trendless sequences associated with radiometric measurements with a relative error not exceeding 2%. This general approach can also be applied to other trendless sequences.
Article
Materials Science, Multidisciplinary
Mehrzad Alijani, Bahman Banimahd, Hashem Nikoomaram, Ahmad Yaghobnezhad
Summary: This paper explores the impact of COVID-19 on the capital market and economy in Iran by examining market efficiency, capital market indicators, and the relationship with COVID-19 data. The study reveals inefficiencies in the market during the pandemic and uses a new technique to analyze the data.
RESULTS IN PHYSICS
(2021)
Article
Mathematics, Interdisciplinary Applications
Lyudmyla Kirichenko, Roman Lavrynenko
Summary: This paper investigates the capabilities of machine learning for probabilistic forecasting of fractional Brownian motion (fBm). It focuses on predicting the probability of an fBm time series exceeding a certain threshold after a specific number of time steps, given only the knowledge of its Hurst exponent. The study aims to determine if self-similarity is preserved in forecasting time series and identify the most effective machine learning algorithms. Two types of forecasting methods are examined: methods with predefined distribution shape and those without. The results show that self-similarity properties can be reliably reproduced in the continuations of the fBm time series predicted by machine learning methods. The study also compares various probabilistic forecasting methods experimentally and discusses their potential applications in analyzing and modeling fractal time series.
FRACTAL AND FRACTIONAL
(2023)
Article
Statistics & Probability
Annika Betken, Jannis Buchsteiner, Herold Dehling, Ines Munker, Alexander Schnurr, Jeannette H. C. Woerner
Summary: This study analyzes the ordinal structure of long-range dependent time series using ordinal patterns and provides two estimators for the probabilities of ordinal patterns, proving limit theorems in different settings. It is found that in the case where the Hurst parameter is higher than 3/4, the estimation of the limit distribution is easier.
SCANDINAVIAN JOURNAL OF STATISTICS
(2021)
Article
Physics, Multidisciplinary
Maria C. Mariani, William Kubin, Peter K. Asante, Joe A. Guthrie, Osei K. Tweneboah
Summary: In this paper, a modification of the Detrended Fluctuation Analysis (DFA) using the ternary Cantor set is proposed, which helps reduce the overestimation problem of the Hurst exponent of DFA and correctly predict the memory behavior of time series. This new approach also involves investigating phenomena generated from the proof using real-world time series based on the theory of the Cantor set.
Article
Astronomy & Astrophysics
A. Vafaei Sadr, S. M. S. Movahed, M. Farhang, C. Ringeval, F. R. Bouchet
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
(2018)
Article
Physics, Condensed Matter
I. Hajzadeh, S. M. Mohseni, S. M. S. Movahed, G. R. Jafari
JOURNAL OF PHYSICS-CONDENSED MATTER
(2018)
Article
Astronomy & Astrophysics
A. Vafaei Sadr, M. Farhang, S. M. S. Movahed, B. Bassett, M. Kunz
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
(2018)
Article
Astronomy & Astrophysics
B. Mostaghel, H. Moshafi, S. M. S. Movahed
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
(2018)
Article
Environmental Sciences
Seyed Hamidreza Sadeghi, Somayeh Kazemi Kia, Mahdi Erfanian, Seyed Mohammad Sadegh Movahed
ENVIRONMENTAL MONITORING AND ASSESSMENT
(2019)
Article
Multidisciplinary Sciences
Marjan Mozaffarilegha, S. M. S. Movahed
SCIENTIFIC REPORTS
(2019)
Article
Astronomy & Astrophysics
Seyed Mohammad Sadegh Movahed, Alireza Vafaei Sadr, Marzieh Farhang
INTERNATIONAL JOURNAL OF MODERN PHYSICS D
(2019)
Article
Optics
M. Arshadi Pirlar, M. Rezaei Mirghaed, Y. Honarmand, S. M. S. Movahed, R. Karimzadeh
Article
Astronomy & Astrophysics
S. Ansarifard, E. Rasia, V Biffi, S. Borgani, W. Cui, M. De Petris, K. Dolag, S. Ettori, S. M. S. Movahed, G. Murante, G. Yepes
ASTRONOMY & ASTROPHYSICS
(2020)
Article
Astronomy & Astrophysics
Saeed Ansarifard, S. M. S. Movahed
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
(2020)
Article
Astronomy & Astrophysics
A. Vafaei Sadr, S. M. S. Movahed
Summary: This study examines the Gaussianity, asymmetry, and the impact of cosmic-string networks on the CMB observed by Planck through the clustering of local extrema. Results indicate that local extrema statistics support the Gaussianity hypothesis for most thresholds, with only a minor deviation observed in trough density.
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
(2021)
Article
Physics, Fluids & Plasmas
H. Masoomy, S. Tajik, S. M. S. Movahed
Summary: This study explores a new method to compute the Hurst exponent by analyzing the homology groups of high-dimensional point cloud data. The results show that higher embedding dimensions increase the dependency of topological measures on the Hurst exponent in regular synthetic fBm. To achieve reliable classification of fBm, smaller time delays should be considered regardless of the irregularity in the data. Additionally, the scale of path connectivity and the postloopless regime show robustness in distinguishing fBm signals, but this robustness decreases with higher embedding dimensions.
Article
Physics, Fluids & Plasmas
H. Masoomy, B. Askari, M. N. Najafi, S. M. S. Movahed
Summary: This research explores the topological properties of fractional Gaussian noise using persistent homology technique, revealing a strong dependency of topological holes on the Hurst exponent. The study explains the influences of Hurst exponent on the distribution of lifetime and persistence entropy, shedding light on the correlated behavior of electroencephalography for both healthy and schizophrenic samples.
Article
Astronomy & Astrophysics
M. Farhang, S. M. S. Movahed
Summary: The study investigates the anomalous Cold Spot in the cosmic microwave background sky, evaluating whether different sets of subvoids could have produced this anomaly. It was found that some models showed gravitational redshift amplitudes higher than expected, while the lensing imprint amplitudes were consistent with zero.
ASTROPHYSICAL JOURNAL
(2021)
Article
Astronomy & Astrophysics
I Eghdami, H. Panahi, S. M. S. Movahed
ASTROPHYSICAL JOURNAL
(2018)
Article
Physics, Multidisciplinary
Xiaoyu Shi, Jian Zhang, Xia Jiang, Juan Chen, Wei Hao, Bo Wang
Summary: This study presents a novel framework using offline reinforcement learning to improve energy consumption in road transportation. By leveraging real-world human driving trajectories, the proposed method achieves significant improvements in energy consumption. The offline learning approach demonstrates generalizability across different scenarios.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Junhyuk Woo, Soon Ho Kim, Hyeongmo Kim, Kyungreem Han
Summary: Reservoir computing (RC) is a new machine-learning framework that uses an abstract neural network model to process information from complex dynamical systems. This study investigates the neuronal and network dynamics of liquid state machines (LSMs) using numerical simulations and classification tasks. The findings suggest that the computational performance of LSMs is closely related to the dynamic range, with a larger dynamic range resulting in higher performance.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Yuwei Yang, Zhuoxuan Li, Jun Chen, Zhiyuan Liu, Jinde Cao
Summary: This paper proposes an extreme learning machine (ELM) algorithm based on residual correction and Tent chaos sequence (TRELM-DROP) for accurate prediction of traffic flow. The algorithm reduces the impact of randomness in traffic flow through the Tent chaos strategy and residual correction method, and avoids weight optimization using the iterative method. A DROP strategy is introduced to improve the algorithm's ability to predict traffic flow under varying conditions.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Chengwei Dong, Min Yang, Lian Jia, Zirun Li
Summary: This work presents a novel three-dimensional system with multiple types of coexisting attractors, and investigates its dynamics using various methods. The mechanism of chaos emergence is explored, and the periodic orbits in the system are studied using the variational method. A symbolic coding method is successfully established to classify the short cycles. The flexibility and validity of the system are demonstrated through analogous circuit implementation. Various chaos-based applications are also presented to show the system's feasibility.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Viorel Badescu
Summary: This article discusses the maximum work extraction from confined particles energy, considering both reversible and irreversible processes. The results vary for different types of particles and conditions. The concept of exergy cannot be defined for particles that undergo spontaneous creation and annihilation. It is also noted that the Carnot efficiency is not applicable to the conversion of confined thermal radiation into work.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
P. M. Centres, D. J. Perez-Morelo, R. Guzman, L. Reinaudi, M. C. Gimenez
Summary: In this study, a phenomenological investigation of epidemic spread was conducted using a model of agent diffusion over a square region based on the SIR model. Two possible contagion mechanisms were considered, and it was observed that the number of secondary infections produced by an individual during its infectious period depended on various factors.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Zuan Jin, Minghui Ma, Shidong Liang, Hongguang Yao
Summary: This study proposes a differential variable speed limit (DVSL) control strategy considering lane assignment, which sets dynamic speed limits for each lane to attract vehicle lane-changing behaviors before the bottleneck and reduce the impact of traffic capacity drop. Experimental results show that the proposed DVSL control strategy can alleviate traffic congestion and improve efficiency.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Matthew Dicks, Andrew Paskaramoorthy, Tim Gebbie
Summary: In this study, we investigate the learning dynamics of a single reinforcement learning optimal execution trading agent when it interacts with an event-driven agent-based financial market model. The results show that the agents with smaller state spaces converge faster and are able to intuitively learn to trade using spread and volume states. The introduction of the learning agent has a robust impact on the moments of the model, except for the Hurst exponent, which decreases, and it can increase the micro-price volatility as trading volumes increase.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Zhouzhou Yao, Xianyu Wu, Yang Yang, Ning Li
Summary: This paper developed a cooperative lane-changing decision system based on digital technology and indirect reciprocity. By introducing image scoring and a Q-learning based reinforcement learning algorithm, drivers can continuously evaluate gains and adjust their strategies. The study shows that this decision system can improve driver cooperation and traffic efficiency, achieving over 50% cooperation probability under any connected vehicles penetration and traffic density, and reaching 100% cooperation probability under high penetration and medium to high traffic density.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Josephine Nanyondo, Henry Kasumba
Summary: This paper presents a multi-class Aw-Rascle (AR) model with area occupancy expressed in terms of vehicle class proportions. The qualitative properties of the proposed equilibrium velocity and the stability conditions of the model are established. The numerical results show the effect of proportional densities on the flow of vehicle classes, indicating the realism of the proposed model.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Oliver Smirnov
Summary: This study proposes a new method for simultaneously estimating the parameters of the 2D Ising model. The method solves a constrained optimization problem, where the objective function is a pseudo-log-likelihood and the constraint is the Hamiltonian of the external field. Monte Carlo simulations were conducted using models of different shapes and sizes to evaluate the performance of the method with and without the Hamiltonian constraint. The results demonstrate that the proposed estimation method yields lower variance across all model shapes and sizes compared to a simple pseudo-maximum likelihood.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Przemyslaw Chelminiak
Summary: The study investigates the first-passage properties of a non-linear diffusion equation with diffusivity dependent on the concentration/probability density through a power-law relationship. The survival probability and first-passage time distribution are determined based on the power-law exponent, and both exact and approximate expressions are derived, along with their asymptotic representations. The results pertain to diffusing particles that are either freely or harmonically trapped. The mean first-passage time is finite for the harmonically trapped particle, while it is divergent for the freely diffusing particle.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Hidemaro Suwa
Summary: The choice of transition kernel is crucial for the performance of the Markov chain Monte Carlo method. A one-parameter rejection control transition kernel is proposed, and it is shown that the rejection process plays a significant role in determining the sampling efficiency.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Xudong Wang, Yao Chen
Summary: This article investigates the joint influence of expanding medium and constant force on particle diffusion. By starting from the Langevin picture and introducing the effect of external force in two different ways, two models with different force terms are obtained. Detailed analysis and derivation yield the Fokker-Planck equations and moments for the two models. The sustained force behaves as a decoupled force, while the intermittent force changes the diffusion behavior with specific effects depending on the expanding rate of the medium.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
