Complex Dynamical Systems Research Articles

Ecosystem Assessment Model Based on Multi-Population Complex System Dynamics and Simulated Annealing Algorithm

Ecosystem Assessment Model Based on Multi-Population Complex System Dynamics and Simulated Annealing Algorithm

Key motifs searching in complex dynamical systems

Key network motifs searching in complex networks is one of the crucial aspects of network analysis. There has been a series of insightful findings and valuable applications for various scenarios through the analysis of network structures. However, in dynamic systems, slight changes in the choice of dynamic equations and parameters can alter the significance of motifs. The known methods are insufficient to address this issue effectively. In this paper, we introduce a concept of perturbation energy based on the system’s Jacobian matrix, and define motif centrality for dynamic systems by seamlessly integrating network topology with dynamic equations. Through simulations, we observe that the key motifs obtained by the proposed energy method present better effective and accurate than them by integrating network topology methods, without significantly increasing algorithm complexity. The finding of key motifs can be used to apply for system control, such as formulating containment policies for the spread of epidemics and protecting fragile ecosystems. Additionally, it makes substantial contribution to a deeper understanding of concepts in physics, such as signal propagation and system’s stability.

Finding emergence in data by maximizing effective information

Abstract Quantifying emergence and modeling emergent dynamics in a data-driven manner for complex dynamical systems is challenging due to emergent behaviors cannot be directly captured by micro-level observational data. Thus, it’s crucial to develop a framework to identify emergent phenomena and capture emergent dynamics at the macro-level using available data. Inspired by the theory of causal emergence (CE), this paper introduces a machine learning framework to learn macro-dynamics in an emergent latent space and quantify the degree of CE. The framework maximizes effective information, resulting in a macro-dynamics model with enhanced causal effects. Experimental results on simulated and real data demonstrate the effectiveness of the proposed framework. It quantifies degrees of CE effectively under various conditions and reveals distinct influences of different noise types. It can learn a one-dimensional coarse-grained macro-state from fMRI data, to represent complex neural activities during movie clip viewing. Furthermore, improved generalization to different test environments is observed across all simulation data.

Exploring New Traveling Wave Solutions to the Nonlinear Integro-Partial Differential Equations with Stability and Modulation Instability in Industrial Engineering

In this research article, we demonstrate the generalized expansion method to investigate nonlinear integro-partial differential equations via an efficient mathematical method for generating abundant exact solutions for two types of applicable nonlinear models. Moreover, stability analysis and modulation instability are also studied for two types of nonlinear models in this present investigation. These analyses have several applications including analyzing control systems, engineering, biomedical engineering, neural networks, optical fiber communications, signal processing, nonlinear imaging techniques, oceanography, and astrophysical phenomena. To study nonlinear PDEs analytically, exact traveling wave solutions are in high demand. In this paper, the (1 + 1)-dimensional integro-differential Ito equation (IDIE), relevant in various branches of physics, statistical mechanics, condensed matter physics, quantum field theory, the dynamics of complex systems, etc., and also the (2 + 1)-dimensional integro-differential Sawda–Kotera equation (IDSKE), providing insights into the several physical fields, especially quantum gravity field theory, conformal field theory, neural networks, signal processing, control systems, etc., are investigated to obtain a variety of wave solutions in modern physics by using the mentioned method. Since abundant exact wave solutions give us vast information about the physical phenomena of the mentioned models, our analysis aims to determine various types of traveling wave solutions via a different integrable ordinary differential equation. Furthermore, the characteristics of the obtained new exact solutions have been illustrated by some figures. The method used here is candid, convenient, proficient, and overwhelming compared to other existing computational techniques in solving other current world physical problems. This article provides an exemplary practice of finding new types of analytical equations.

ETIOPATHOGENETIC REASONING OF URETHRAL MICROBIOTA REHABILITATION IN PATIENTS WITH ACUTE INFECTIOUS URETHRITIS

The microbiota of the male genitourinary tract is a complex dynamic system that has not been sufficiently studied. Unlike the microbiome of the female urethral mucosa, which is constantly changing, the taxonomic composition and population level of the male urethral microbiome is more stable and independent of age. The aim of the study. To improve the results of treatment of patients with inflammatory processes of the urethra by determining the taxonomic composition, population level and microscopic indicators of the "macroorganism-microbiome" ecosystem of the microbiota of the urethra in men with acute urethritis.Material and methods. The taxonomic composition and population level of the first portion of urine of 69 sexually mature men (average age 34.8±7.14 years) with acute infectious (nonspecific) urethritis were investigated. Isolation of conditionally pathogenic bacteria was carried out by the bacteriological method using optimal nutrient media, their pH, temperature and cultivation time for each taxon. The identification of isolated pure cultures was carried out according to morphological, tinctorial, cultural, biochemical properties, as well as signs of pathogenicity and antigenic structure. To determine the mechanisms of colonization of the mucous membrane of the urethra by conditionally pathogenic microorganisms, the microecological method was used.Research results. According to the taxonomic composition and the level of microecological indicators of the "macroorganism-microbiome" ecosystem of the urethra microbiota (Margalef's species richness index, Whittaker's species diversity index, and Simpson's and Berger-Parker's species dominance indices), vulgar (common), hemolytic, and enteropathogenic Escherichia coli, coagulase-positive staphylococcus. The leading role in the formation of urethritis is played by ordinary enterobacteria - E. coli, a slightly smaller etiological role in the formation of urethritis in men belongs to S. aureus (less by 17.83%), E. coli Hly+ (less by 28.35%), enteropathogenic Escherichia coli (by 83.12%). Other microorganisms have a minimal etiological role. Conclusions. Acute urethritis in men is a polyetiological disease. Enterobacteria E. coli, E. coli Hly+, enteropathogenic Escherichia coli, K. pneumoniae, S. aureus, S. epidermidis, S. viridans are involved in the formation of the inflammatory process of the urethra. The leading causative agents of acute urethritis in men are E. coli, S. aureus, E. coli Hly+, enteropathogenic escherichia, which belong to uropathogenic escherichia.

(Invited) Nanoarray Configured Monoliths for Self-Monitored Multi-Phase Reactive Processes

The incorporation of multiple functions synergistically into one device or platform can enable additions of application merits such as light weight, ease for system integration, reduced system complexity, cost-effectiveness, and programmability. This is especially true for usually complex energy, environmental, and biological systems with the integration need of many active or passive devices and components of various functionalities. In this work, we demonstrated a compact nanoarray configured monolith design with the ability to function as both a catalyst and a sensor to enable the self-monitoring of the complex multi-phase catalytic reaction processes during operation. A bimodular approach has been realized using both electrical and electrochemical interrogation modes for reactive gas and solid phase monitoring directly using the built-in catalysts. This new type of compact and built-in monolithic sensor configuration can lower the catalytic energetics and detect gas/solid composition semi-quantitatively at room temperature up to elevated temperature. Robust calibration curves of built-in sensor responses can be achieved with facile data processing and analyzing. The built-in monolith sensor shows good sensitivity and good selectivity sufficient for the self-monitoring of the reaction process. Besides, the nanoarray based monolith device has shown good environmental tolerance toward practical applications. Such compact monolithic device can serve as a self-monitored reactor, filter, and other function component that can potentially allow in-situ and real time monitoring and functional process control in complex and reactive physical and chemical environments. With further refining of the design and functional integration, this new type of smart monolithic device can provide necessary data and knowledge for future computation-driven situational awareness prediction and intelligent control of various complex dynamic processes and systems.

Layer-by-layer unsupervised clustering of statistically relevant fluctuations in noisy time-series data of complex dynamical systems

Complex systems are typically characterized by intricate internal dynamics that are often hard to elucidate. Ideally, this requires methods that allow to detect and classify in an unsupervised way the microscopic dynamical events occurring in the system. However, decoupling statistically relevant fluctuations from the internal noise remains most often nontrivial. Here, we describe "Onion Clustering": a simple, iterative unsupervised clustering method that efficiently detects and classifies statistically relevant fluctuations in noisy time-series data. We demonstrate its efficiency by analyzing simulation and experimental trajectories of various systems with complex internal dynamics, ranging from the atomic- to the microscopic-scale, in- and out-of-equilibrium. The method is based on an iterative detect-classify-archive approach. In a similar way as peeling the external (evident) layer of an onion reveals the internal hidden ones, the method performs a first detection/classification of the most populated dynamical environment in the system and of its characteristic noise. The signal of such dynamical cluster is then removed from the time-series data and the remaining part, cleared-out from its noise, is analyzed again. At every iteration, the detection of hidden dynamical subdomains is facilitated by an increasing (and adaptive) relevance-to-noise ratio. The process iterates until no new dynamical domains can be uncovered, revealing, as an output, the number of clusters that can be effectively distinguished/classified in a statistically robust way as a function of the time-resolution of the analysis. Onion Clustering is general and benefits from clear-cut physical interpretability. We expect that it will help analyzing a variety of complex dynamical systems and time-series data.

Relationships between work-family conflict and family structure in the lives of working mothers in Hungary – a pilot study

BackgroundThe family, as the basic socialization environment, is a complex dynamic system that - as a whole and through its subsystems - is in relationships with other social systems (Bagdy in Family socialization and personality disorders. Nemzeti Tankönyvkiadó, Budapest, 2002; Lakatos et al. in Mentálhigiéné és Pszichoszomatika 21(1):56–85, 2020). The system with which the family system has long-term relationships is the work system/environment. Creating and maintaining a work-life balance has become a central issue in our societies, as they are two of the most organising forces, and reconciling them is a very difficult task due to the demands and expectations coming from both directions, often simultaneously (Makra et al. in Magyar Pszichológiai Szemle 67(3):491–518, 2012). This kind of “double burden” primarily affects women, but their increasing role in the labour market is not necessarily followed by an equal sharing of work within family life (Engler et al. in Work-life balance in women’s careers. In: Tardos K, Paksi V, Fábri Gy (eds) Scientific careers in the early 21st century. Belvedere Meridionale, Szeged, pp 114–126, 2021). We hypothesise that involvement in work negatively correlates with work-life balance, making it more difficult to integrate into the family. It was expected that the relationship between the number of children and mothers’ professional involvement would be negative. A positive correlation was expected between the age of the youngest child and the mothers’ work involvement. On the other hand, a family united by cohesion and resilience leads to higher job satisfaction.MethodsFor the present analysis, we analysed the relationships between work-family conflict and family structure in working mothers with children in a sample of 273 participants. The self-reported questionnaire included demographic data and 2 standard questionnaires: the Work-Family Conflict Questionnaire and the Olson-Family Test (FACES-IV.). The study was conducted in Hungary.ResultsNo significant relationship was found between work involvement and work-family conflict. A negative relationship was observed between work involvement and family involvement. Similiarily, no significant relationship was found between the number of children, the age of the youngest child and work involvement, contrary to expectations. The findings indicate a positive relationship between good family cohesion, flexibility and job satisfaction.ConclusionStriking a work-family balance is a challenging process for families with young children, especially working mothers. A mutually negative relationship between work and family involvement has been shown. The importance of a well-functioning family, with adequate cohesion and flexibility, is reflected in family and job satisfaction. The relationship between work-to-family conflict and job involvement is moderated significantly only when family flexibility is low. The results from the present pilot study indicate important relationships between variables and point to further research directions worth investigating in a larger sample in the future.

Multidecadal changes in coastal benthic species composition and ecosystem functioning occur independently of temperature-driven community shifts.

Rising global temperatures are often identified as the key driver impacting ecosystems and the services they provide by affecting biodiversity structure and function. A disproportionate amount of our understanding of biodiversity and function is from short-term experimental studies and static values of biodiversity indices, lacking the ability to monitor long-term trends and capture community dynamics. Here, we analyse a biennial dataset spanning 32 years of macroinvertebrate benthic communities and their functional response to increasing temperatures. We monitored changes in species' thermal affinities to examine warming-related shifts by selecting their mid-point global temperature distribution range and linking them to species' traits. We employed a novel weighted metric using Biological Trait Analysis (BTA) to gain better insights into the ecological potential of each species by incorporating species abundance and body size and selecting a subset of traits that represent five ecosystem functions: bioturbation activity, sediment stability, nutrient recycling and higher and lower trophic production. Using biodiversity indices (richness, Simpson's diversity and vulnerability) and functional indices (richness, Rao's Q and redundancy), the community structure showed no significant change over time with a narrow range of variation. However, we show shifts in species composition with warming and increases in the abundance of individuals, which altered ecosystem functioning positively and/or non-linearly. Yet, when higher taxonomic groupings than species were excluded from the analysis, there was only a weak increase in the measured change in community-weighted average thermal affinities, suggesting changes in ecosystem functions over time occur independently of temperature increase-related shifts in community composition. Other environmental factors driving species composition and abundance may be more important in these subtidal macrobenthic communities. This challenges the prevailing emphasis on temperature as the primary driver of ecological response to climate change and emphasises the necessity for a comprehensive understanding of the temporal dynamics of complex systems.

Global topological synchronization of weighted simplicial complexes.

Higher-order networks are able to capture the many-body interactions present in complex systems and to unveil fundamental phenomena revealing the rich interplay between topology, geometry, and dynamics. Simplicial complexes are higher-order networks that encode higher-order topology and dynamics of complex systems. Specifically, simplicial complexes can sustain topological signals, i.e., dynamical variables not only defined on nodes of the network but also on their edges, triangles, and so on. Topological signals can undergo collective phenomena such as synchronization, however, only some higher-order network topologies can sustain global synchronization of topological signals. Here we consider global topological synchronization of topological signals on weighted simplicial complexes. We demonstrate that topological signals can globally synchronize on weighted simplicial complexes, even if they are odd-dimensional, e.g., edge signals, thus overcoming a limitation of the unweighted case. These results thus demonstrate that weighted simplicial complexes are more advantageous for observing these collective phenomena than their unweighted counterpart. In particular, we present two weighted simplicial complexes: the weighted triangulated torus and the weighted waffle. We completely characterize their higher-order spectral properties and demonstrate that, under suitable conditions on their weights, they can sustain global synchronization of edge signals. Our results are interpreted geometrically by showing, among the other results, that in some cases edge weights can be associated with the lengths of the sides of curved simplices.

Quantum logarithmic multifractality

Through a combination of rigorous analytical derivations and extensive numerical simulations, this work reports an exotic multifractal behavior, dubbed “logarithmic multifractality,” in effectively infinite-dimensional systems undergoing the Anderson transition. In contrast to conventional multifractality observed in finite dimensions, logarithmic multifractality at infinite dimension introduces an algebraic behavior with respect to the logarithm of system size or time. We demonstrate this phenomenon across eigenstate statistics, spatial correlations, and wave packet dynamics. Our findings offer crucial insights into strong finite-size effects and slow dynamics in complex systems undergoing the Anderson transition, such as the many-body localization transition. Published by the American Physical Society 2024

Modelling and Diagnosis of Hybrid Dynamic Systems by a Multi-Model Approach

Fault diagnosis is an essential task in ensuring the smooth operation of complex dynamic systems. The consequences of faults can be serious, leading to loss of life, harmful emissions to the environment, high repair costs and economic losses caused by unplanned production line stoppages. The work developed in this paper concerns the modeling and diagnosis of faults (sensor faults, system faults, actuator faults) in hybrid dynamic systems using our multi-model approach (which combines two sub-models, one continuous and the other discrete). The aim is to integrate three well-known tools in the literature: the Bond Graph, the Observer and the Timed Automata, to design a global diagnostic model. The hybrid dynamic system is modeled by connecting the tools for the continuous part, i.e. the bond graph and the observer, to the timed automata for the discrete part. The resulting model is used for fault diagnosis in two stages: The first is fault detection by analyzing the residuals generated by the system output and that of the observer. The second step involves fault localization, which results from analysis of the signature matrix and temporal identification of the system. The proposed method combines the advantages of these tools to obtain the best performance, particularly in the fault location phase. The simulation results prove the effectiveness of the proposed model for the hybrid dynamic system. Moreover, these results also evaluate the performance of the proposed diagnostic approach while reducing non-detections, detection delays and false alarms.

Modeling reactive film flows down a heated fiber

We study the dynamics of an ultra-thin reactive liquid film flowing down a heated vertical fiber under the influence of gravity. A key focus is on the impact of the van der Waals attraction, which is proportional to h−3, where h denotes the film thickness. Linear stability analysis of the film flow reveals that the van der Waals attractions and the Marangoni effect enhance the instability. Furthermore, instability is reduced by exothermic chemical reactions, while it is strengthened in the case of endothermic chemical reactions. Moreover, the weakly nonlinear stability of the film flow is studied. The results indicate the possibility of both subcritical and supercritical stability in the system. Lastly, direct numerical simulations of the evolution equation are conducted for various flow parameters. These results enhance our understanding of the intricate interplay of chemical reactions, thermal effects, and intermolecular forces influencing the liquid film dynamics in complex systems.

Multifidelity graph neural networks for efficient and accurate mesh‐based partial differential equations surrogate modeling

AbstractAccurately predicting the dynamics of complex systems governed by partial differential equations (PDEs) is crucial in various applications. Traditional numerical methods such as finite element methods (FEMs) offer precision but are resource‐intensive, particularly at high mesh resolutions. Machine learning–based surrogate models, including graph neural networks (GNNs), present viable alternatives by reducing computation times. However, their accuracy is significantly contingent on the availability of substantial high‐fidelity training data. This paper presents innovative multifidelity GNN (MFGNN) frameworks that efficiently combine low‐fidelity and high‐fidelity data to train more accurate surrogate models for mesh‐based PDE simulations, while reducing training computational cost. The proposed methods capitalize on the strengths of GNNs to manage complex geometries across different fidelity levels. Incorporating a hierarchical learning strategy and curriculum learning techniques, the proposed models significantly reduce computational demands and improve the robustness and generalizability of the results. Extensive validations across various simulation tasks show that the MFGNN frameworks surpass traditional single‐fidelity GNN models. The proposed approaches, hence, provide a scalable and practical solution for conducting detailed computational analyses where traditional high‐fidelity simulations are time‐consuming.

Generating Chaos in Dynamical Systems: Applications, Symmetry Results, and Stimulating Examples

In this paper, we present a new class of extended oscillators in light of chaos theory. It is based on dynamical complex systems built on the concept of self-describing with a stopping criterion process. We offer an effective studying approach with a specific focus on learning, provoking students’ thinking through the triad of enigmatics–creativity–acmeology. Dynamic processes are the basis of mathematical modeling; thus, we can reach the goal of the above-mentioned triad by the proposed differential systems. The results we derive strongly confirm the presence of symmetry in the outcomes of the proposed models. We suggest a stochastic approach to structuring the proposed dynamical systems by modeling the coefficients that drive them by some discrete probability distribution that exhibits symmetry or asymmetry. We propose specific tools for researching the behavior of these systems.

Extending X-reality technologies to digital twin in cultural heritage risk management: a comparative evaluation from the perspective of situation awareness

DT systems, characterized by real-time capabilities, high precision, and high integration, have become essential in various domains. In the context of cultural heritage, a DT system encompasses comprehensive information about heritage sites, contextual data, and expert knowledge, forming a complex dynamic system. The substantial volume of information and diverse sources significantly increases the cognitive load for management personnel in understanding on-site situations. This study, from the perspective of situational awareness, introduces X-reality technologies (VR and AR) into DT systems for cultural heritage risk management. The aim is to evaluate the effectiveness of different X-reality technologies in cultural heritage risk perception and their impact mechanisms. A total of 184 participants were divided into two groups and experienced three different applications (2D desktop, VR, and AR). Using situational awareness rating techniques, participants' responses were measured across three dimensions: attention resource demand, attention resource supply, and understanding of the situation. SEM was employed to estimate the stability of the scale data. The results indicate that, compared to traditional 2D desktops, both VR and AR demonstrate advantages in enhancing heritage risk situational awareness. However, in AR mode, no significant advantages were found in the dimensions of attention resource demand and attention resource supply compared to traditional 2D desktop applications. Furthermore, a significant difference in immersion between VR and AR was found to affect the attention resource demand dimension. Although the results suggest differences in the interactivity of VR and AR in affecting the attention resource demand dimension, no significant differences were found. By comprehensively understanding the functional mechanisms of X-reality technologies in influencing cultural heritage risk situational awareness, this study provides design references for constructing DT systems for cultural heritage risk management. Additionally, it offers insights for heritage site managers, experts, and stakeholders to enhance risk perception efficiency, promoting more effective risk assessment, analysis, and strategic decision-making, thereby reducing damage to cultural heritage.

Adaptive Control and Synchronization of Complex Dynamical Systems with Various Types of Delays and Stochastic Disturbances

Adaptive Control and Synchronization of Complex Dynamical Systems with Various Types of Delays and Stochastic Disturbances

Learning interpretable dynamics of stochastic complex systems from experimental data

Complex systems with many interacting nodes are inherently stochastic and best described by stochastic differential equations. Despite increasing observation data, inferring these equations from empirical data remains challenging. Here, we propose the Langevin graph network approach to learn the hidden stochastic differential equations of complex networked systems, outperforming five state-of-the-art methods. We apply our approach to two real systems: bird flock movement and tau pathology diffusion in brains. The inferred equation for bird flocks closely resembles the second-order Vicsek model, providing unprecedented evidence that the Vicsek model captures genuine flocking dynamics. Moreover, our approach uncovers the governing equation for the spread of abnormal tau proteins in mouse brains, enabling early prediction of tau occupation in each brain region and revealing distinct pathology dynamics in mutant mice. By learning interpretable stochastic dynamics of complex systems, our findings open new avenues for downstream applications such as control.

Changes in ROS/RNS Levels in Endothelial Cells in Experimental Bacteremia.

A high-precision system was developed for the quantification of biological analytes in single cells (reactive oxygen species (ROS) and reactive nitrogen species (RNS)) based on the electrochemical amperometric method. The efficacy of this system was evaluated using an experimental bacteremia model. Endothelial cells exhibited increased ROS/RNS production when stimulated by Staphylococcus aureus. However, they remained inactive when exposed to either unprimed or primed neutrophils that had been pre-incubated with bacteria. It is noteworthy that the sequential stimulation of endothelial cells with bacteria followed by neutrophils resulted in a significant increase in the ROS/RNS level, which demonstrated a correlation with the number of neutrophils in contact with the endothelial cells. These results highlight the potential of our system to quantitatively assess ROS/RNS dynamics in complex biological systems. They also offer insights into the interplay between various cellular components in experimental bacteremia.

Basin stability for updating system uncertainties.

In this paper, we propose an application of the basin stability tool which allows us to update the information on the system properties under parameter uncertainties. The concept is presented using a classical mechanical setup of coupled pendula, exchanging the energy via the supporting structure. Depending on the support parameters, the model can exhibit different types of coexisting synchronous patterns as well as remaining desynchronized. We calculate basin stability maps of particular behaviors and combine them with prior parameter distributions using Bayesian inference. The obtained posterior distributions, based on the attractor occurrence, update our knowledge on the system properties in the terms of probabilities. We also underline the problem of evaluating basin stability close to the existence borders, comparing the classical approach of fixed parameters with the one involving variations. The differences between the estimation methods can have a crucial meaning for the discussed application and should be considered carefully. The results presented in this paper uncover ways of applying the basin stability concept, which can be used to study the properties of complex dynamical systems from a probability perspective.