Chaos Solitons & Fractals最新文献

{"title":"Graph convolutional network for structural equivalent key nodes identification in complex networks","authors":"Asmita Patel,&nbsp;Buddha Singh","doi":"10.1016/j.chaos.2025.116376","DOIUrl":"10.1016/j.chaos.2025.116376","url":null,"abstract":"<div><div>Identifying key influential nodes in complex networks is crucial for applications such as social network analysis, epidemiology, and recommendation systems. This paper proposes SE_GCN (Structural Equivalence with Graph Convolutional Network), a method that combines structural equivalence with Graph Convolutional Networks (GCNs) to identify key nodes in complex networks. SE_GCN leverages structural similarities among nodes at various hop distances to construct a comprehensive feature matrix, which is directly used for node embedding. GCNs are employed to process this feature matrix, learning effective representations of nodes within the network. The fully connected layer of SE_GCN computes the embedded score of each node, and a sigmoid function predicts the influential probabilities of nodes. The performance of SE_GCN is evaluated by comparing it with the Susceptible-Infected-Recovered (SIR) epidemiological model, Kendall's tau correlation, and Jaccard similarity. The proposed method is assessed using baseline methods in terms of infection rate, seed set size, correlation coefficient, and similarity index across several synthetic and real-world networks. The results demonstrate that SE_GCN outperforms existing methods, highlighting its effectiveness and robustness in identifying influential nodes.</div></div>","PeriodicalId":9764,"journal":{"name":"Chaos Solitons & Fractals","volume":"196 ","pages":"Article 116376"},"PeriodicalIF":5.3,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143746964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Strontium ferrite nanoparticles based broadband nonlinear optical modulator for ultrafast pulse generation in fiber lasers","authors":"Mamoon Asghar ,&nbsp;M. Hamza Younes ,&nbsp;Qaisar Hayat ,&nbsp;Asma Noor ,&nbsp;Tahani A. Alrebdi ,&nbsp;Haroon Asghar","doi":"10.1016/j.chaos.2025.116395","DOIUrl":"10.1016/j.chaos.2025.116395","url":null,"abstract":"<div><div>Nanomaterials exhibiting broadband nonlinear optical responses have garnered considerable interest in the field of ultrafast photonics. These materials have been extensively validated as effective saturable absorbers (SAs), demonstrating their capability to facilitate the generation of broadband optical pulses. In this study, strontium ferrite nanoparticles (SrFeO-NPs), were synthesized using the sol-gel method and were then employed as SA to initiate ultrafast pulse operation in fiber lasers. The prepared NPs exhibited excellent nonlinear absorption properties, enabling the measurement of modulation depth and saturation intensities across the 1–2 μm spectral range. The measured results showed that the SrFeO-NPs as SA in fiber lasers yielded pulse durations in the femtosecond (fs) domain in Yb, Er, and Tm-doped fiber lasers, with pulse durations of 801 fs, 857 fs, and 671 fs, respectively. The performance characteristics of the output pulses, containing wavelength, repetition rates, output power, pulse energy, and stability, were also examined and discussed in detail. This study paves the way for novel approaches in integrating diverse materials, thereby advancing the development of broadband laser systems utilizing nanomaterials for applications in the extended near to mid-infrared spectral region.</div></div>","PeriodicalId":9764,"journal":{"name":"Chaos Solitons & Fractals","volume":"196 ","pages":"Article 116395"},"PeriodicalIF":5.3,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143746965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Josephson “flying qubit” revival: Flux-based control optimization","authors":"M.V. Bastrakova ,&nbsp;D.S. Pashin ,&nbsp;P.V. Pikunov ,&nbsp;I.I. Soloviev ,&nbsp;A.E. Schegolev ,&nbsp;N.V. Klenov","doi":"10.1016/j.chaos.2025.116353","DOIUrl":"10.1016/j.chaos.2025.116353","url":null,"abstract":"<div><div>A decade ago, Josephson “flying qubits” based on adiabatic superconducting logic cells showed promise as quantum data buses, but their development stalled due to the incompatibility of traditional qubit control methods with their design. We revisit this concept by exploring the control of the inductively shunted two-junction superconducting interferometer (adiabatic quantum flux parametron, AQFP) in the quantum regime using unipolar magnetic field pulses generated by adiabatic superconducting electronics. Our research demonstrates the feasibility of high fidelity quantum operations (fidelity more than 99.99%) in this system via Landau–Zener tunneling. To this end, a method is proposed for selecting the duration and shape of control pulses to eliminate unwanted leakage into high-lying states in AQFP-based systems.</div></div>","PeriodicalId":9764,"journal":{"name":"Chaos Solitons & Fractals","volume":"196 ","pages":"Article 116353"},"PeriodicalIF":5.3,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143746966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Remote synchronization induced by external signals in star networks","authors":"Siyu Huo ,&nbsp;Nan Yao ,&nbsp;Zi-Gang Huang ,&nbsp;Zonghua Liu","doi":"10.1016/j.chaos.2025.116345","DOIUrl":"10.1016/j.chaos.2025.116345","url":null,"abstract":"<div><div>Remote synchronization (RS) is an emerging focus in nonlinear dynamics and complex networks, as it provides insights into how functional connections in the brain arise from its structural network. While previous studies have extensively explored RS under intrinsic coupling conditions, the combined influence of external stimuli and coupling strength, a critical factor in real-world systems, remains unexplored. In this study, we investigate RS in a star network of coupled Stuart–Landau oscillators, a simplified model of hierarchical brain structures, under varying external stimuli and coupling strengths. Using numerical simulations and theoretical analysis, we identify two distinct mechanisms driving RS: direct resonance between external stimuli and leaf nodes, and hub-mediated synchronization facilitated by resonance between the hub node and the stimuli. We further reveal that RS occurs within specific coupling strength ranges and is maximized when the stimulus frequency aligns with the natural frequencies of either the hub or leaf nodes. These results emphasize the delicate interplay between coupling strength and external perturbations in shaping RS, providing a theoretical framework for understanding the mechanisms of RS emergence under external stimuli.</div></div>","PeriodicalId":9764,"journal":{"name":"Chaos Solitons & Fractals","volume":"196 ","pages":"Article 116345"},"PeriodicalIF":5.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generating Mandelbrot and Julia sets using PV iterative technique","authors":"Pragati Gautam ,&nbsp;Vineet","doi":"10.1016/j.chaos.2025.116346","DOIUrl":"10.1016/j.chaos.2025.116346","url":null,"abstract":"<div><div>In this study, we utilize the PV iteration method to generate Mandelbrot and Julia sets for the function <span><math><mrow><mi>G</mi><mrow><mo>(</mo><mi>z</mi><mo>)</mo></mrow><mo>=</mo><msup><mrow><mi>z</mi></mrow><mrow><mi>k</mi></mrow></msup><mo>+</mo><mi>c</mi></mrow></math></span>. We establish escape criterion conditions for the PV iteration and provide a variety of graphical examples for different parameter settings. We also compare the graphs with those generated by other well-known iterations, such as the Picard-Mann and M iterations. Furthermore, we investigate the dependency between the iteration’s parameters and three numerical measures: the average escape time (AET), the non-escaping area index (NAI), and the fractal generation time. A comparative analysis is conducted with the renowned Mann, Picard-Mann, and M iteration methods. The results demonstrate that the fractals generated by the PV iteration exhibit distinct characteristics compared to those generated by other iterations, with non-linear dependencies that vary between different methods. These findings highlight the unique properties and potential applications of PV iteration in fractal generation.</div></div>","PeriodicalId":9764,"journal":{"name":"Chaos Solitons & Fractals","volume":"196 ","pages":"Article 116346"},"PeriodicalIF":5.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143746962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Aging in coevolving voter models","authors":"Byungjoon Min ,&nbsp;Maxi San Miguel","doi":"10.1016/j.chaos.2025.116344","DOIUrl":"10.1016/j.chaos.2025.116344","url":null,"abstract":"<div><div>Aging, understood as the tendency to remain in a given state the longer the persistence time in that state, plays a crucial role in the dynamics of complex systems. In this paper, we explore the influence of aging on coevolution models, that is, models in which the dynamics of the states of the nodes in a complex network is coupled to the dynamics of the structure of the network. In particular we consider the coevolving voter model, and we introduce two versions of this model that include aging effects: the Link Aging Model (LAM) and the Node Aging Model (NAM). In the LAM, aging is associated with the persistence time of a link in the evolving network, while in the NAM, aging is associated with the persistence time of a node in a given state. We show that aging significantly affects the absorbing phase transition of the coevolution voter model, shifting the transition point in opposite directions for the LAM and NAM. We also show that the generic absorbing phase transition can disappear due to aging effects.</div></div>","PeriodicalId":9764,"journal":{"name":"Chaos Solitons & Fractals","volume":"196 ","pages":"Article 116344"},"PeriodicalIF":5.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143746961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Temporal correlation-based neural relational inference for binary dynamics","authors":"Haowei Zhang ,&nbsp;Yuexing Han ,&nbsp;Gouhei Tanaka ,&nbsp;Bing Wang","doi":"10.1016/j.chaos.2025.116350","DOIUrl":"10.1016/j.chaos.2025.116350","url":null,"abstract":"<div><div>Binary-state dynamics are prevalent in nature, from societal dynamics to dynamical systems in physics. Reconstructing a network structure behind interacting binary-state dynamical systems is essential, as it can facilitate understanding of these dynamical systems and improve the accuracy of predicting dynamical behavior. So far, few works have focused on correlation information in binary-state temporal data to help reconstruct networks. In this study, we propose temporal correlation-based neural relational inference for binary dynamics (TCNRI), inspired by the maximum likelihood estimation of activation events in binary dynamics processes. TCNRI constructs instantaneous correlation features and long-term correlation features by analyzing activation events in the time series data. These features capture the correlation information and help TCNRI reconstruct the network structure. We treat the binary-state dynamical process as a Markov process and use neural networks to reproduce node dynamics based on the reconstructed network structure. We conduct simulations on the classic susceptible–infected–susceptible (SIS) dynamics and Ising dynamics. The results show that TCNRI significantly outperforms baseline models and can accurately reconstruct the network structure for both typical synthetic networks and real networks.</div></div>","PeriodicalId":9764,"journal":{"name":"Chaos Solitons & Fractals","volume":"196 ","pages":"Article 116350"},"PeriodicalIF":5.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143746963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effect of complex vortices generated by asymmetrically distributed induced charges on fluid flow and mass transfer in pressure driven micromixers","authors":"Shuai Yuan ,&nbsp;Tao Peng ,&nbsp;Xiaodong Liu","doi":"10.1016/j.chaos.2025.116373","DOIUrl":"10.1016/j.chaos.2025.116373","url":null,"abstract":"<div><div>Microscale electric field-induced vortical structures, arising from spatial accumulation of induced charges, demonstrate significant potential for augmenting mixing performance in low Reynolds number (<em>Re</em>) laminar flows. This investigation establishes a computational framework incorporating an asymmetric conductive plate with hybrid linear-curvilinear edges to systematically elucidate the coupling mechanisms between inhomogeneous charge distributions, complex vortex generation, and pressure-driven molecular transport. Our findings reveal that under balanced pressure-driven flow (PDF) and induced-charge electroosmotic flow (EOF), expanding the polarization area promotes bilateral charge accumulation along curvilinear boundaries, creating localized electric field minima that suppress interfacial slip velocity and mixing efficiency. In contrast, linear edges exhibit maximum slip velocities, generating vortex pairs with enhanced convection capacity. Notably, plate reorientation and increased curvature radius amplify surface electric field asymmetry, achieving superior mass transfer through either expanded vortex perturbation domains or intensified rotational intensity. When PDF dominates EOF by 25-fold, the weakened charge heterogeneity minimally stimulates vortex development, rendering interfacial perturbation the primary mixing driver. This work advances fundamental understanding of asymmetric charge-polarization dynamics in microscale flow manipulation, offering critical insights for designing active micromixers.</div></div>","PeriodicalId":9764,"journal":{"name":"Chaos Solitons & Fractals","volume":"196 ","pages":"Article 116373"},"PeriodicalIF":5.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143746960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multifractal analysis of temperature in Europe: Climate change effect","authors":"Borko Stosic ,&nbsp;Tatijana Stosic ,&nbsp;Ivana Tošić ,&nbsp;Vladimir Djurdjević","doi":"10.1016/j.chaos.2025.116386","DOIUrl":"10.1016/j.chaos.2025.116386","url":null,"abstract":"<div><div>We investigated multifractality of temporal series of daily mean air temperature anomalies over the entire European continent by applying the method Multifractal detrended fluctuation analysis (MFDFA) on high resolution E-OBSv26.0e dataset. The spatial resolution is 0.1⁰ which corresponds to 120,866 grid cells, and the time span of daily temperature series is from 1961 to 2020, which permits to compare two climate periods (1961–1990 and 1991–2020) and evaluate the impact of climate change. For each grid cell and each of the two periods we calculated the multifractal spectrum <span><math><mi>f</mi><mfenced><mi>α</mi></mfenced></math></span> which contains the information about specific properties of temperature series: persistence (described by the position of the maximum of the spectrum <span><math><msub><mi>α</mi><mn>0</mn></msub></math></span>), strength of multifractality (described by the spectrum width <span><math><mi>W</mi></math></span>) and the contribution of large/small fluctuations (described by the spectrum skew parameter <span><math><mi>r</mi><mo>)</mo><mo>.</mo></math></span>We found that in both periods temperature series exhibit properties of a multifractal process, with persistent long-term correlations (<span><math><msub><mi>α</mi><mn>0</mn></msub><mo>&gt;</mo><mn>0.5</mn><mo>)</mo><mspace></mspace></math></span>and the dominance of scaling of small fluctuations (<span><math><mi>r</mi><mo>&gt;</mo><mn>0</mn></math></span>). The spatial distribution of multifractal parameters exhibits a distinct gradient during the first period. The parameter <span><math><msub><mi>α</mi><mn>0</mn></msub></math></span> (persistence) increases from west to east and from south to north. In contrast, <span><math><mi>W</mi></math></span> (strength of multifractality) decreases from south to north in regions outside Eastern Europe, whereas it increases from south to north within Eastern Europe. In the second period, the area with stronger multifractality (higher <span><math><mi>W</mi></math></span> values) of temperature anomalies expanded significantly in Eastern Europe. The spatially averaged value and the range of the width <em>W</em> of the multifractal spectrum increased in the second period following the increase of the mean daily temperature. These results reveal the impact of climate change on multifractal properties of air temperature.</div></div>","PeriodicalId":9764,"journal":{"name":"Chaos Solitons & Fractals","volume":"196 ","pages":"Article 116386"},"PeriodicalIF":5.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data analysis of dynamical system for the optimization of disease dynamics through Neural Networks Paradigm","authors":"Aatif Ali ,&nbsp;Mei Sun ,&nbsp;Mohamed R. Ali","doi":"10.1016/j.chaos.2025.116284","DOIUrl":"10.1016/j.chaos.2025.116284","url":null,"abstract":"<div><div>Vaccine coverage and non-pharmaceutical interventions have great importance relative to public health in the current scenario of pandemic throughout the world. A compartmental model for assessing the vaccine and community contact rate (in light of social-distancing and isolation) coverage in symptomatic and asymptomatic public. In most biological phenomena, particularly infectious diseases, fractional models capture crossover behavior and provide deeper insight. Also, the disease informed neural network embedded with the proposed model to deduce the temporal evolution dynamics of the COVID-19 model. The Reproduction number determines the severity of disease computed by the next-generation approach. The mathematical model assesses the dynamics of Corona-virus based on biological parameters, which are estimated from recorded data by the least square curve technique. The proposed model shows precise predictions of the real cases. The COVID-19 data of Pakistan, suggest that the vaccine efficacy is found to be useful with adoption of moderately (50% reduction of baseline value) could prevent 70%–80% of the projected infected persons over 100 days. While the contact rates impact on epidemiological outcomes is highly nonlinear, which indicates the high value to eradicate the pandemic if the underlying contact rate is relatively low. Our study urge that the contact rates (social distancing and isolation etc.) and vaccine coverage with high efficacy has probably high value in curtailing the burden of the pandemic. Additionally, we discuss how neural networks may predict disease spread and do so with the robustness and effectiveness of neural networks. The deep learning method predicts the dynamics with forecast their progression and demonstrates the high potential in combination with compartmental model. Furthermore, the results demonstrate that neural networks outperform traditional approaches in forecasting complex disease dynamics, determining crucial thresholds, and refining suppression strategies, offering important public health insights. Additionally, this strategy opens the door for more extensive artificial intelligence integration in healthcare optimization.</div></div>","PeriodicalId":9764,"journal":{"name":"Chaos Solitons & Fractals","volume":"196 ","pages":"Article 116284"},"PeriodicalIF":5.3,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}