Properties Of Dynamic Networks Research Articles

Partitioning the contribution of bees with different traits and hoverflies to flower-visitor interaction networks

Insect pollinators are key to maintaining biodiversity and providing important ecosystem services. Among them, bee species (i.e., Apidae) and hoverflies (i.e., Syrphidae) are two of the most important and well-studied taxa worldwide. Yet, their relative contribution to the structural and dynamic properties of plant-pollinator interaction networks remains poorly understood. This is an important gap in knowledge given that these phylogenetically and functionally different groups might play different roles within their communities and respond in contrasting ways to anthropogenic perturbations.Here, we study the relative contribution of bee species (with different traits) and syrphids to maintain network properties (i.e., nestedness, network specialization (H2), and interaction diversity) and their influence on the temporal dynamics of these network metrics. To this end, we simulate species removals in community-wide flower-arthropod visitor interaction networks. These interactions were extensively sampled across four years in two different near-pristine temperate ecosystems in Japan.We found that bee species contribute significantly more than syrphids or any random combination of species to network nestedness, low specialization, and interaction diversity across months and years. In addition, bees also played a particularly important role in maintaining the observed temporal dynamics over time. Conversely, although syrphids were an abundant and species-rich group, they did not contribute so prominently to network metrics or temporal dynamics, with the exception of network complementary specialization, to which they contributed positively. In addition, smaller bee species and those that were active for longer periods were particularly important for interaction diversity and the number of interactions, respectively, and to maintain observed network temporal dynamics.Our results support that bee species, and especially the small ones, are cornerstone contributors to plant-visitor communities by shaping network properties and network temporal dynamics in natural ecosystems.

Altered resting-state functional connectivity and dynamic network properties in cognitive impairment: an independent component and dominant-coactivation pattern analyses study.

Cognitive impairment (CI) due to Alzheimer's disease (AD) encompasses a decline in cognitive abilities and can significantly impact an individual's quality of life. Early detection and intervention are crucial in managing CI, both in the preclinical and prodromal stages of AD prior to dementia. In this preliminary study, we investigated differences in resting-state functional connectivity and dynamic network properties between 23 individual with CI due to AD based on clinical assessment and 15 healthy controls (HC) using Independent Component Analysis (ICA) and Dominant-Coactivation Pattern (d-CAP) analysis. The cognitive status of the two groups was also compared, and correlations between cognitive scores and d-CAP switching probability were examined. Results showed comparable numbers of d-CAPs in the Default Mode Network (DMN), Executive Control Network (ECN), and Frontoparietal Network (FPN) between HC and CI groups. However, the Visual Network (VN) exhibited fewer d-CAPs in the CI group, suggesting altered dynamic properties of this network for the CI group. Additionally, ICA revealed significant connectivity differences for all networks. Spatial maps and effect size analyses indicated increased coactivation and more synchronized activity within the DMN in HC compared to CI. Furthermore, reduced switching probabilities were observed for the CI group in DMN, VN, and FPN networks, indicating less dynamic and flexible functional interactions. The findings highlight altered connectivity patterns within the DMN, VN, ECN, and FPN, suggesting the involvement of multiple functional networks in CI. Understanding these brain processes may contribute to developing targeted diagnostic and therapeutic strategies for CI due to AD.

Fractal and first-passage properties of a class of self-similar networks.

A class of self-similar networks, obtained by recursively replacing each edge of the current network with a well-designed structure (generator) and known as edge-iteration networks, has garnered considerable attention owing to its role in presenting rich network models to mimic real objects with self-similar structures. The generator dominates the structural and dynamic properties of edge-iteration networks. However, the general relationships between these networks' structural and dynamic properties and their generators remain unclear. We study the fractal and first-passage properties, such as the fractal dimension, walk dimension, resistance exponent, spectral dimension, and global mean first-passage time, which is the mean time for a walker, starting from a randomly selected node and reaching the fixed target node for the first time. We disclose the properties of the generators that dominate the fractal and first-passage properties of general edge-iteration networks. A clear relationship between the fractal and first-passage properties of the edge-iteration networks and the related properties of the generators are presented. The upper and lower bounds of these quantities are also discussed. Thus, networks can be customized to meet the requirements of fractal and dynamic properties by selecting an appropriate generator and tuning their structural parameters. The results obtained here shed light on the design and optimization of network structures.

Detecting periodic time scales of changes in temporal networks

Abstract Temporal networks are commonly used to represent dynamical complex systems like social networks, simultaneous firing of neurons, human mobility or public transportation. Their dynamics may evolve on multiple time scales characterizing for instance periodic activity patterns or structural changes. The detection of these time scales can be challenging from the direct observation of simple dynamical network properties like the activity of nodes or the density of links. Here, we propose two new methods, which rely on already established static representations of temporal networks, namely supra-adjacency and temporal event graphs. We define dissimilarity metrics extracted from these representations and compute their power spectra from their Fourier transforms to effectively identify dominant periodic time scales characterizing the changes of the temporal network. We demonstrate our methods using synthetic and real-world data sets describing various kinds of temporal networks. We find that while in all cases the two methods outperform the reference measures, the supra-adjacency-based method identifies more easily periodic changes in network density, while the temporal event graph-based method is better suited to detect periodic changes in the group structure of the network. Our methodology may provide insights into different phenomena occurring at multiple time scales in systems represented by temporal networks.

Temporal and topological properties of dynamic networks reflect disability in patients with neuromyelitis optica spectrum disorders

Approximately 36% of patients with neuromyelitis optica spectrum disorders (NMOSD) suffer from severe visual and motor disability (blindness or light perception or unable to walk) with abnormalities of whole-brain functional networks. However, it remains unclear how whole-brain functional networks and their dynamic properties are related to clinical disability in patients with NMOSD. Our study recruited 30 NMOSD patients (37.70 ± 11.99 years) and 45 healthy controls (HC, 41.84 ± 11.23 years). The independent component analysis, sliding-window approach and graph theory analysis were used to explore the static strength, time-varying and topological properties of large-scale functional networks and their associations with disability in NMOSD. Compared to HC, NMOSD patients showed significant alterations in dynamic networks rather than static networks. Specifically, NMOSD patients showed increased occurrence (fractional occupancy; P < 0.001) and more dwell times of the low-connectivity state (P < 0.001) with fewer transitions (P = 0.028) between states than HC, and higher fractional occupancy, increased dwell times of the low-connectivity state and lower transitions were related to more severe disability. Moreover, NMOSD patients exhibited altered small-worldness, decreased degree centrality and reduced clustering coefficients of hub nodes in dynamic networks, related to clinical disability. NMOSD patients exhibited higher occurrence and more dwell time in low-connectivity states, along with fewer transitions between states and decreased topological organizations, revealing the disrupted communication and coordination among brain networks over time. Our findings could provide new perspective to help us better understand the neuropathological mechanism of the clinical disability in NMOSD.

Altered Static and Dynamic Brain Functional Topological Organization in Patients with Dysthyroid Optic Neuropathy.

Dysthyroid optic neuropathy (DON) is a serious vision-threatening complication of thyroid-associated ophthalmopathy (TAO). Exploration of the underlying mechanisms of DON is critical for its timely clinical diagnosis. We hypothesized that TAO patients with DON may have altered brain functional networks. We aimed to explore the alterations of static and dynamic functional connectomes in patients with and without DON using resting-state functional MRI with graph theory method. A cross-sectional study. Grade A tertiary hospital. Sixty-six TAO patients (28 DON and 38 non-DON) and 30 healthy controls (HCs). Topological properties of functional networks. For static properties, DON patients exhibited lower global efficiency (Eg), local efficiency, normalized clustering coefficient, small-worldness (σ), and higher characteristic path length (Lp) than HCs. Both DON and non-DON patients exhibited varying degrees of abnormalities in nodal properties. Meanwhile, compared with non-DON, DON patients exhibited abnormalities in nodal properties in orbitofrontal cortex and visual network (VN). For dynamic properties, DON group exhibited higher variance in Eg and Lp than non-DON and HC groups. A strengthened subnetwork with VN as the core was identified in DON cohort. Significant correlations were found between network properties and clinical variables. For distinguishing DON, the combination of static and dynamic network properties exhibited optimal diagnostic performance. Functional network alterations were observed in both DON and non-DON patients, providing novel insights into the underlying neural mechanisms of disease. Functional network properties may be potential biomarkers for reflecting the progression of TAO from non-DON to DON.

Temporal dendritic heterogeneity incorporated with spiking neural networks for learning multi-timescale dynamics

It is widely believed the brain-inspired spiking neural networks have the capability of processing temporal information owing to their dynamic attributes. However, how to understand what kind of mechanisms contributing to the learning ability and exploit the rich dynamic properties of spiking neural networks to satisfactorily solve complex temporal computing tasks in practice still remains to be explored. In this article, we identify the importance of capturing the multi-timescale components, based on which a multi-compartment spiking neural model with temporal dendritic heterogeneity, is proposed. The model enables multi-timescale dynamics by automatically learning heterogeneous timing factors on different dendritic branches. Two breakthroughs are made through extensive experiments: the working mechanism of the proposed model is revealed via an elaborated temporal spiking XOR problem to analyze the temporal feature integration at different levels; comprehensive performance benefits of the model over ordinary spiking neural networks are achieved on several temporal computing benchmarks for speech recognition, visual recognition, electroencephalogram signal recognition, and robot place recognition, which shows the best-reported accuracy and model compactness, promising robustness and generalization, and high execution efficiency on neuromorphic hardware. This work moves neuromorphic computing a significant step toward real-world applications by appropriately exploiting biological observations.

Exponential stability of stochastic complex networks with multi-weights driven by second-order process based on graph theory

&lt;abstract&gt;&lt;p&gt;Stochastic complex networks with multi-weights which were driven by Brownian motion were widely investigated by many researchers. However, Brownian motion is not suitable for the modeling of engineering issues by reason of its variance, which is infinite at any time. So, in this paper, a novel kind of stochastic complex network with multi-weights driven by second-order process is developed. To disclose how the weights and second-order process affect the dynamical properties of stochastic complex networks with multi-weights driven by the second-order process, we discuss exponential stability of the system. Two types of sufficient criteria are provided to ascertain exponential stability of the system on the basis of Kirchhoff's matrix tree theorem and the Lyapunov method. Finally, some numerical examples are given to verify the correctness and validity of our results.&lt;/p&gt;&lt;/abstract&gt;

Shared and malignancy-specific functional plasticity of dynamic brain properties for patients with left frontal glioma.

The time-varying brain activity may parallel the disease progression of cerebral glioma. Assessment of brain dynamics would better characterize the pathological profile of glioma and the relevant functional remodeling. This study aims to investigate the dynamic properties of functional networks based on sliding-window approach for patients with left frontal glioma. The generalized functional plasticity due to glioma was characterized by reduced dynamic amplitude of low-frequency fluctuation of somatosensory networks, reduced dynamic functional connectivity between homotopic regions mainly involving dorsal attention network and subcortical nuclei, and enhanced subcortical dynamic functional connectivity. Malignancy-specific functional remodeling featured a chaotic modification of dynamic amplitude of low-frequency fluctuation and dynamic functional connectivity for low-grade gliomas, and attenuated dynamic functional connectivity of the intrahemispheric cortico-subcortical connections and reduced dynamic amplitude of low-frequency fluctuation of the bilateral caudate for high-grade gliomas. Network dynamic activity was clustered into four distinct configuration states. The occurrence and dwell time of the weakly connected state were reduced in patients' brains. Support vector machine model combined with predictive dynamic features achieved an averaged accuracy of 87.9% in distinguishing low- and high-grade gliomas. In conclusion, dynamic network properties are highly predictive of the malignant grade of gliomas, thus could serve as new biomarkers for disease characterization.

Exploring static and dynamic functional brain networks in adolescent depression using a co-produced novel irritability paradigm

Background Irritability is a core symptom of depression in adolescence and a risk factor for emotion regulation problems. However, its neural correlates are not well understood. Existing functional magnetic resonance imaging (fMRI) research on irritability typically overlooks its social context. Methods Here, we pilot a novel naturalistic fMRI paradigm targeting the social nature of irritability that was co-produced with young people (N = 88) and apply it in an independent sample of youth (N = 29, mean age 18.9 years, 77% female) with self-reported low mood that were aged 16 to 20 years. Participants were also fluent English speakers, free from MRI contraindications, and did not report a diagnosis of a neurological or neurodevelopmental condition. Our aim was to investigate whether graph theoretic and dynamical properties of functional brain networks differed between a resting state scan and our irritability paradigm. We also examined whether these brain features were associated with depressive symptoms and trait irritability. Results Using Leading Eigenvector Dynamics Analysis (LEiDA), we found that the dynamic properties of brain networks comprising default-mode and fronto-parietal regions differed significantly during the irritability paradigm compared to the rest condition. While no gross static topological differences were found between these two conditions, we found that some dynamic and topological features of emotion-related brain networks were related to trait irritability and depressive symptoms in our sample. Conclusions Although the current findings are preliminary due to the pilot nature of this study, this work showcases the feasibility of co-produced research in neuroimaging and lays a strong foundation for further study.

Effect of Plant Oil Derived Bio-Resin and Curing Temperature on Static and Dynamic Mechanical Properties of Epoxy Network

The depletion of petrochemical resources and greater worldwide environmental consciousness has led to a growing interest in polymers made from renewable resources during the last two decades. Hence, this work has attempted to reduce the dependence on petroleum-based epoxy by partially replacing it with epoxidized castor oil (ECO). The ECO was blended with epoxy at 10, 20, and 30% and cured with amine hardener. The effect of bio-resin on tensile, flexural, impact strength and dynamic mechanical properties were investigated. Further, the result of post-curing temperature on static and dynamic properties was examined. It was found that the addition of ECO up to 30% increased the impact strength. The 20% ECO sample showed balanced stiffness to toughness property and could be considered for semi-structural composite applications. The post-curing of samples at 150 °C showed better mechanical and dynamic properties except for impact strength.

Segmental dynamics of poly(tetrahydrofuran) cross-linked by click-chemistry: Single-chain nanoparticles vs cross-linked networks

Cross-linked poly(tetrahydrofuran) (PTHF) networks are useful as organic solvent/oil sorbents with high swelling capacity and mechanically robust solid polymer electrolyte membranes for lithium-ion batteries, as well as solid-state calcium ion batteries. Recently, the control of intra-vs intermolecular interactions has attracted significant interest to modulate the properties of dynamic networks involving non-covalent and dynamic covalent bonds. However, to the best of our knowledge, a comparison of the segmental dynamics in polymeric materials prepared exactly from the same precursor polymer through intra-vs intermolecular covalent bonds is still lacking. In this work, we report the dissimilar dynamic properties of PTHF-based materials prepared through intra-vs intermolecular click chemistry (i.e., films of PTHF-based single-chain nanoparticles (SCNPs) vs cross-linked networks). Particularly we observed remarkable similarities in the segmental relaxation time and glass transition temperature value but evident differences in the dynamic heterogeneities. These differences are attributed to the peculiar morphology of the intra-chain collapsed polymer chains (i.e., SCNPs).

Long-term Intensive Soccer Training Induced Dynamic Reconfiguration of Brain Network.

Long-term motor skill learning has been shown to impact the functional plasticity of the brain. Athletes, as a unique population, exhibit remarkable adaptive changes in the static properties of their brain networks. However, studying the differences between expert and novice athletes using a dynamic brain network framework can provide a fresh perspective on how motor skill learning affects the functional organization of the brain. In this study, we investigated the dynamic properties of brain networks in expert and novice soccer players at the whole-brain, network, and region-based levels. Our findings revealed that expert soccer players displayed reduced integration and increased segregation at the whole-brain level. As for network level, experts exhibited increased segregation and reduced flexibility in the visual network, enhanced integration between the visual and ventral attention networks, and decreased integration in the subcortical-sensorimotor and subcortical-cerebellar networks. Additionally, specific brain regions within the visual network exhibited greater recruitment in expert soccer players compared to novices at the nodal level. Furthermore, classification analyses demonstrated the critical role played by the visual network in the classification process. In conclusion, our study provides new insights into the dynamic properties of brain networks in expert and novice soccer players, and suggests that reduced integration and increased segregation in the visual network may be neuroimaging marker that distinguish expert soccer players from novices. Our findings may have implications for the training and development of athletes and advance our understanding of how motor skill learning affects brain functional organization.

Dynamic network properties of the superior temporal gyrus mediate the impact of brain age gap on chronic aphasia severity

Brain structure deteriorates with aging and predisposes an individual to more severe language impairments (aphasia) after a stroke. However, the underlying mechanisms of this relation are not well understood. Here we use an approach to model brain network properties outside the stroke lesion, network controllability, to investigate relations among individualized structural brain connections, brain age, and aphasia severity in 93 participants with chronic post-stroke aphasia. Controlling for the stroke lesion size, we observe that lower average controllability of the posterior superior temporal gyrus (STG) mediates the relation between advanced brain aging and aphasia severity. Lower controllability of the left posterior STG signifies that activity in the left posterior STG is less likely to yield a response in other brain regions due to the topological properties of the structural brain networks. These results indicate that advanced brain aging among individuals with post-stroke aphasia is associated with disruption of dynamic properties of a critical language-related area, the STG, which contributes to worse aphasic symptoms. Because brain aging is variable among individuals with aphasia, our results provide further insight into the mechanisms underlying the variance in clinical trajectories in post-stroke aphasia.

Internal hydrogen bonding of imines to control and enhance the dynamic mechanical properties of covalent adaptable networks

Covalent Adaptable Networks (CANs) have the potential to replace current thermoset materials, as the dynamic covalent bonds in CANs enable reprocessability and recycling of crosslinked polymer networks. A current limitation of CANs is, however, that they are generally susceptible towards creep, as the bond exchange reactions facilitate stress relaxation. In this work, we propose the use of internal hydrogen bonding in polyimine CANs as an efficient tool to enhance creep resistance (at operating temperature) and further control the dynamic mechanical properties, while still enabling malleability at elevated temperatures. We are able to show on a small-molecule level that ortho-substituted hydroxy groups on aromatic imines stabilise the dynamic covalent imine bonds as a result of the internal hydrogen bond that is formed between the hydroxy and imine group. Furthermore, we show that polyimine CANs with incorporated ortho hydroxy groups have significantly enhanced material properties, as can be seen in the glass transition temperature (Tg), elastic modulus (G’), creep resistance and solvent resistance. While we also consider additional steric and electronic effects that might have arisen due to installation of the hydroxy groups, we find that the stabilising effect of the internal hydrogen bond is primarily dictating the material performance.

Default mode and frontoparietal network dynamics: Associations with familial risk for depression and stress sensitivity

BackgroundMajor Depressive Disorder (MDD) is associated with alterations within the default mode (DMN) and frontoparietal (FPN) networks. However, it is unclear whether changes in these networks occur prior to onset in youth at high familial risk for MDD or are a consequence of MDD. Moreover, studies examining premorbid MDD vulnerability markers have focused on static rather than dynamic network properties, which could further elucidate DMN-FPN imbalances linked to MDD risk. MethodsEighty-nine unaffected 12–14-year-old adolescents both with (n = 27) and without (n = 62) a maternal history of MDD completed a resting state functional magnetic resonance imaging scan and self-report assessments of depressive symptoms and perceived stress at baseline and every three months across a two-year span. A coactivation pattern (CAP) analysis was conducted to examine functional network dynamic properties, including time spent in each CAP (total number of volumes), CAP persistence (number of consecutive volumes in each CAP), and number of transitions between posterior DMN-FPN and canonical DMN CAPs. Multilevel models estimated whether DMN-FPN dynamic properties predicted future depressive symptoms and stress sensitivity. ResultsHigh-risk adolescents spent more time and exhibited a longer persistence in a posterior DMN-FPN CAP. DMN-FPN CAP persistence predicted future perceived stress, but only among high-risk adolescents. High-risk adolescents characterized by high DMN-FPN persistence reported greater future perceived stress, whereas those showing low DMN-FPN persistence had reduced perceived stress over time. Unexpectedly, DMN-FPN dynamics did not predict future depressive symptoms. ConclusionsAltered DMN-FPN CAP properties among high-risk adolescents mirror alterations among individuals with MDD, suggesting that DMN-FPN dynamics may be a risk marker rather than consequence of MDD. Furthermore, longer DMN-FPN CAP persistence increases vulnerability in high-risk adolescents by predicting greater future stress sensitivity, a well-known catalyst for MDD. Replication in a larger sample is warranted.

Stability of linear Boolean networks

Stability is an important characteristic of network models that has implications for other desirable aspects such as controllability. The stability of a Boolean network (BN) depends on various factors, such as the topology of its wiring diagram and the type of the functions describing its dynamics. Linear Boolean networks can be completely described by their wiring diagram, and therefore the structure of linear networks plays a prominent role in determining their stability. In this paper, we study the stability of linear Boolean networks by computing Derrida curves and quantifying the number of attractors and cycle lengths imposed by their network topologies. Derrida curves are commonly used to measure the stability of Boolean networks and several parameters such as the average in-degree K and the output bias p can indicate if a network is stable, critical, or unstable. For random unbiased Boolean networks there is a critical connectivity value Kc=2 such that if K<Kc networks operate in the ordered regime, and if K>Kc networks operate in the chaotic regime. Here, we show that for linear networks, which are the least canalizing and most unstable, the phase transition from order to chaos already happens at an average in-degree of Kc=1. Consistently, we also show that unstable networks exhibit a large number of attractors with very long limit cycles while stable and critical networks exhibit fewer attractors with shorter limit cycles. Additionally, we present theoretical results to quantify important dynamical properties of linear networks. First, we present a formula for the proportion of attractor states in linear systems. Second, we show that the expected number of fixed points in linear systems is 2, while general Boolean networks possess on average one fixed point. Third, we present a formula to quantify the number of bijective linear Boolean networks and provide a lower bound for the percentage of this type of network.

Dual dynamic crosslinking of rubber for enhanced mechanical properties and reconfigurable shape memory behavior

AbstractConstructing dynamic covalent bonds (DCBs) in rubbers provides a promising avenue to resolve the trade‐offs between chemical crosslinking and recyclability. The reinforcement of dynamic covalent networks by nanofilling is widely adopted, which, however, inevitably hinders the exchange reaction of DCBs and network rearrangement due to the restrictions on chain mobility imposed by fillers. To date, the reinforcement of dynamic covalent networks without affecting the dynamic properties remains challenging. In this work, we report a dual dynamic crosslinking strategy by engineering reversible noncovalent bonds into dynamic covalent rubbers to improve mechanical performance and simultaneously maintain desirable malleability and reprocessability. Specifically, dynamic boronic ester‐crosslinked styrene–butadiene rubber is firstly prepared and then modified through triazolinedione (TAD) click reaction. The grafted urazole groups can form hydrogen bond interactions that further aggregate to form clusters and phase‐separation structure in the networks. Due to the reversible nature of hydrogen bonds, they can act in a sacrificial manner to dissipate mechanical energy, leading to a combination of improved strength, modulus and toughness of the networks. More importantly, the hydrogen‐bonded clusters are dissociated at elevated temperatures, enabling the network dynamic properties to be largely preserved. In addition, the dynamic features of the dual crosslinks (hydrogen bonds and boronic esters) endow the TAD‐modified networks with reconfigurable shape memory effect. © 2023 Society of Industrial Chemistry.

The Boolean constraint method application for qualitative analysis of the dynamical properties of singular Boolean networks

Qualitative study problems of trajectories behaviour for singular Boolean networks functioning on a finite time interval are solved using the method of Boolean constraints. In the form of Boolean constraints, models are built for local dynamical properties, the periodicity property of trajectories, and the property of the reachability of the tar- get state set from the initial state set. Depending on the property, the verification of Boolean models is reduced to the Boolean satisfiability problem or the problem of verifying the truth of a quantified Boolean formula. Several examples demonstrate the technology of qualitative analysis of dynamic properties in a microservice het- erogeneous computing environment. The applied software modules for constructing a Boolean model of the dynamical property of singular Boolean networks and verifying the feasibility of the model are implemented in the form of computational microser- vices. The use of this approach provides independence, reproducibility, autonomy, and scalability of modules. The developed microservices are integrated into the applied microservices package. This package is intended for the qualitative study of binary dynamic systems. The rights to launch microservices are delegated to the managing agents of this package installed in the nodes of the distributed environment. The de- veloped automation tools allow a specialist in automaton dynamics to formulate the problem statement on a computational model of the subject area in meaningful terms. С использованием метода булевых ограничений решены задачи качественного исследования динамики поведения траекторий на конечном интервале времени сингулярных булевых сетей. Получены в виде булевых ограничений модели локальных динамических свойств, свойства периодичности траекторий и свойства достижимости целевого множества состояний из множества начальных состояний. В зависимости от свойств проверка булевых ограничений сводится к задаче булевой выполнимости или задаче проверки истинности квантифицированной булевой формулы. Приведен ряд примеров, на которых продемонстрирована технология качественного исследования динамических свойств в микросервисной гетерогенной вычисли- тельной среде. Прикладные программные модули для построения булевой модели динамического свойства сингулярной булевой сети и проверки выполнимости модели реализованы в виде вычислительных микросервисов. Применение такого подхода обеспечивает независимость, воспроизводимость, автономность и масштабируемость модулей. Разработанные микросервисы расширили репозиторий пакета прикладных микросервисов для качественного исследования двоичных динамических систем. Права на запуск микросервисов делегированы управляющим агентам этого пакета, установленным в узлах распределенной среды. Разработанные средства автоматизации позволяют специалисту по автоматной динамике формулировать постановку задач на вычислительной модели предметной области в содержательных терминах.

Explosive synchronization dependence on initial conditions: The minimal Kuramoto model

Transitions from incoherent to coherent dynamical states can be observed in various real-world networks, ranging from neurons to power-grids. These transitions can be explosive or continuous, with far-reaching implications for the functioning of the affected system. It is therefore of the utmost importance to determine the conditions under which such transitions occur. While a lot of studies in literature focused on the dynamical and/or structural network properties that may generate explosive synchronization, here we report on the effects of different initial conditions. To this purpose, we consider the minimal network of Kuramoto oscillator that may display explosive synchronization, and we show that the nature of the transition changes from continuous to discontinuous as phases are differently initialized. We also determine the critical coupling strength for explosive synchronization, which also depends on the initial conditions.