Properties Of Dynamic Networks Research Articles

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.

The core of board networks and firm value

We examine the statistical and dynamic properties of board networks connected through interlocking directorates. We find that the degree distribution of a board network follows a power-law distribution. Furthermore, the evolution of the core–periphery structure of board networks over time is related to market stability. This result implies that firms, when densely connected to other boards, would like to keep their position as core groups in board networks. Firm value shows a statistically positive relationship with firms centrally positioned in a board network. Overall, we suggest that board networks can help us understand market stability and serve as an alternative information channel for increasing the firm value. These findings imply that the statistical and dynamic properties of board networks uncover information transmission during the global financial crisis. This paper contributes to interdisciplinary applications of physics in economics and finance in terms of the dynamics of a core–periphery structure of board networks.

Dynamic network link prediction based on learning continuous time events

Dynamic network link prediction is a hot research problem in complex networks. Network representation learning can effectively learn the similarity of nodes and can be used for link prediction. Existing dynamic network representation learning methods mainly discrete window dynamic networks and then model them on static network snapshot graphs, which is difficult to effectively deal with dynamic networks with fine-grained temporal characteristics. In this paper, we propose a link prediction model that can learn complex temporal properties in dynamic networks. The model uses continuous time event sequences to represent dynamic networks, learns continuous temporal information and structural evolution features in the networks, and proposes a temporal attention-based information transfer mechanism to model the diffusion and aggregation of information in the networks. Finally, it transforms link prediction into a classification problem. The experiments are conducted on four real dynamic network datasets and simulated networks, using ap and auc as evaluation metrics. The experimental results of real networks demonstrate that the model can effectively learn the continuity of network evolution and obtain an effective node representation, thus improving the link prediction effect. The experimental results of the simulated network show that the effect of link prediction is related to the network model, but the model in this paper can still obtain better prediction results.

Spectral analysis and its applications for a class of scale‐free network based on the weighted m‐clique annex operation

The spectrum of network is an important tool to study the function and dynamic properties of network, and graph operation and product are an effective mechanism to construct a specific local and global topological structure. In this study, a class of weighted ‐clique annex operation controlled by scale factor and weight factor is defined, through which an iterative weighted network model with small‐world and scale‐free properties is constructed. In particular, when the number of iterations tends to infinity, the network has transfinite fractal property. Then, through the iterative features of the network structure, the iterative relationship of the eigenvalues of the normalized Laplacian matrix corresponding to the network is studied. Accordingly, some applications of the spectrum of the network, including the Kenemy constant , Multiplicative Degree‐Kirchhoff index , and the number of weighted spanning trees , are further given. In addition, we also study the effect of the two factors controlling network operation on the structure and function of the iterative weighted network , so that the network operation can better simulate the real network and have more application potential in the field of artificial network.

Dynamic properties of a self-replicating peptide network with inhibition.

In this paper, we report an open system consisting of three self-replicating peptides, in which peptide 1 inhibits the duplex template of peptide 2, peptide 2 inhibits duplex 3, and peptide 3 inhibits duplex 1 to complete the negative feedback loop. This interacting chemical network yields oscillations in the concentrations of all species over time and establishes a possible mechanism for pre-biotic chemical systems organization. The first focus of our analysis is the effect of altering rates of duplex formation and inhibition on oscillations. We then examine the autocatalytic rate constant in the symmetric and asymmetric cases.

The induction of the mitochondrial morphofunctional pathways improves bioenergetics in Alzheimer Disease

AbstractBackgroundAlzheimer Disease (AD) currently has no effective treatments. A consistent failure of recent clinical trials focused on Aβ or pTau protein accumulation necessitates the identification of novel therapeutic targets and strategies. The loss of cellular energetics is considered as an underlying dysfunction in AD. We have identified small molecule compounds that partially inhibit mitochondrial complex I (MCI), blocking neurodegeneration via the induction of multiple neuroprotective mechanisms, including the improvement of cellular energy homeostasis in vitro and in vivo in multiple AD models. Mitochondria are highly motile organelles that respond to physiologic cues through the dynamic properties of mitochondrial networks, which are balanced by the flux of fusion and fission events. This study aims to demonstrate that treatment with partial MCI inhibitors improves cellular energetics and function via modulation of mitochondrial dynamics using multiple models, including neuronal cells generated from human iPSCs derived from ApoE4+ late‐onset AD (LOAD) patients and ApoE3+ controls.MethodChanges in protein expression in response to treatment were assessed using the Western Blot analysis. The investigation of mitochondrial morphology was done using electron microscopy. Flow cytometry was used to measure the translocation of glucose transporters to the cell surface. Cellular energetics was assessed using a Seahorse Extracellular Flux Analyzer. Results were validated in 3xTgAD mice.ResultWe found that the 2D culture of LOAD cultures had accumulation of pTau and a significant reduction in the expression of proteins involved in mitochondrial dynamics and function, including mitochondrial preprotein translocase TIM23, mitochondrial transcription factor A, transcription factor FoxO3, and mitochondrial fusion proteins MFN2 and OPA1. Treatment with partial MCI inhibitors improved mitochondrial dynamics, rescuing the expression of MFN2 and OPA1, neuronal bioenergetics, and mitochondrial morphology. MCI inhibitors increased surface translocation of GLUTs enhancing glucose uptake and utilization, which could be measured in 3xTgAD mice in vivo using translational biomarker FDG‐PET.ConclusionMild stress caused by the application of MCI inhibitors induced mitochondrial fusion, improved energy homeostasis, and enhanced glucose uptake and utilization in vitro and in vivo. These findings could be instrumental for the validation of a novel medicinal approach and the development of biomarkers for therapeutic efficacy.

Equilibration dynamics of a dynamic covalent network diluted in a metallosupramolecular polymer matrix

We investigate the viscoelastic properties of double dynamic networks (DDNs) based on side-functionalized PnBA chains. One of these networks is highly crosslinked by metal-ligand junctions characterized by a fast association/dissociation dynamics, while the other network is sparsely crosslinked with slow dynamic covalent networks (DCNs). We first show that modulating the dynamics of the metallosupramolecular networks, by playing with the temperature, the density of reversible junctions, or the stress applied, has direct consequences on the local equilibration of the DCN. The latter takes place by a constraint release Rouse process at the rhythm of the association/dissociation of the metal-ligand junctions. Then, based on creep-recovery experiments, we investigate the ability of the DDNs to recover their initial shape after a creep test and show again the important role played by the metallosupramolecular network. In particular, the sample recovery strongly depends on the network connectivity, which is enhanced if a denser metallosupramolecular network is used as it reduces the possible creep of the double dynamic network and increases its elastic memory. The sample recovery also depends on the association-dissociation dynamics of the metallosupramolecular bonds as it fixes how fast the stretched DCN can come back to its equilibrium conformation and can recover its initial shape after a large deformation has been applied. Adjusting the dynamics of the weak network is thus a key process to govern the viscoelastic response of the slow network.

Inference to shape parameter of Nash model based on dynamic transport properties of channel networks

This study investigates the relations between the shape of hydrologic responses and the dynamic transport properties of channel networks within the framework of random walks on fractal networks, focusing on the shape parameter of Nash model. To this end, we evaluate the static fractal structures and the dynamic transport properties of various channel networks and, then, validate Liu's conjecture (1992) for the shape of hydrologic responses. In the context of random walks on fractal networks, the fractal dimensions of channel networks can directly connect the static structure to the dynamic transport properties of channel networks through Horton's law of drainage composition. It is observed that the peak coordinates of hydrologic responses would have a more intimate relation to the connectivity of channel networks than the conductivity of those. The characteristic times of hydrologic responses also tend to be related to the connectivity of channel networks. Thereby, the shape of hydrologic responses would be expected directly affected by the fractal dimension of channel networks in terms of their static structure, while interpreted a combined result of the conductivity and the connectivity of channel networks in terms of their dynamic transport properties. So, the runoff hydrographs of a river basin could be considered shaped by the fractal dimensions of its channel networks following the linear hydrologic system theory.

Abnormal static and dynamic functional network connectivity in stable chronic obstructive pulmonary disease.

Many studies have explored the neural mechanisms of cognitive impairment in chronic obstructive pulmonary disease (COPD) patients using the functional MRI. However, the dynamic properties of brain functional networks are still unclear. The purpose of this study was to explore the changes in dynamic functional network attributes and their relationship with cognitive impairment in stable COPD patients. The resting-state functional MRI and cognitive assessments were performed on 19 stable COPD patients and 19 age-, sex-, and education-matched healthy controls (HC). We conducted the independent component analysis (ICA) method on the resting-state fMRI data, and obtained seven resting-state networks (RSNs). After that, the static and dynamic functional network connectivity (sFNC and dFNC) were respectively constructed, and the differences of functional connectivity (FC) were compared between the COPD patients and the HC groups. In addition, the correlation between the dynamic functional network attributes and cognitive assessments was analyzed in COPD patients. Compared to HC, there were significant differences in sFNC among COPD patients between and within networks. COPD patients showed significantly longer mean dwell time and higher fractional windows in weaker connected State I than that in HC. Besides, in comparison to HC, COPD patients had more extensive abnormal FC in weaker connected State I and State IV, and less abnormal FC in stronger connected State II and State III, which were mainly located in the default mode network, executive control network, and visual network. In addition, the dFNC properties including mean dwell time and fractional windows, were significantly correlated with some essential clinical indicators such as FEV1, FEV1/FVC, and c-reactive protein (CRP) in COPD patients. These findings emphasized the differences in sFNC and dFNC of COPD patients, which provided a new perspective for understanding the cognitive neural mechanisms, and these indexes may serve as neuroimaging biomarkers of cognitive performance in COPD patients.

Evaluation and Optimization of heat Pump Combined District Heating System: A Case Study of China

The district heating area in China is continuously increasing, which brings an increase in district heating load. In order to solve the shortage of heating power and realize the carbon neutral target, this study proposes two retrofit schemes for district heating system by integrating air source heat pump and water source heat pump, respectively. Mathematical models are established to study the performance of the integrated systems and a bilevel optimization model is proposed to optimize them. The results show that the air source heat pump combined district heating system has better performance compared to the conventional system, which reduces 50% energy consumption, 10.8% carbon emissions and achieves better economy. The dynamic coupling property of the district heating network and power grid are also considered, and the results indicate that the introduction of air source heat pump can effectively improve the efficiency and stability of power grid and reduce the seasonal fluctuation. The potential of large-scale application of air source heat pump combined system in Beijing is evaluated. The results reveal that retrofit scheme of integrating air source heat pumps into district heating system can cover 2930 MW heating load and bring 362 million Chinese Yuan profit by reducing 219,000 tons of carbon emissions and 539,000 tons of standard coal consumption in 2025.

The global neuronal workspace as a broadcasting network.

A new strategy for moving forward in the characterization of the global neuronal workspace (GNW) is proposed. According to Dehaene, Changeux, and colleagues (Dehaene, 2014, pp. 304, 312; Dehaene & Changeux, 2004, 2005), broadcasting is the main function of the GNW. However, the dynamic network properties described by recent graph theoretic GNW models are consistent with many large-scale communication processes that are different from broadcasting. We propose to apply a different graph theoretic approach, originally developed for optimizing information dissemination in communication networks, which can be used to identify the pattern of frequency and phase-specific directed functional connections that the GNW would exhibit only if it were a broadcasting network.

Heritable Deleting Strategies for Birth and Death Evolving Networks From a Queueing System Perspective

Evolving networks have always been studied a lot featuring the dynamic properties of real-life networks. Studying the mechanism of the growth and death of a network is of great significance to network modeling. Identical to many models focused on the growing process, in this article, we study the decreasing process thoroughly. A novel evolving network model considering the growing and decreasing process is established based on the queueing system. Focused on the degreasing process, we originally investigate two strategies of vertex deleting that are the brutal strategy and the heritable strategy which characterizes the heritable behavior of “dying” vertices in real networks. On the basis of our model, stochastic properties of the proposed network are analyzed, e.g., the distribution and the expectation of the stationary scale of the network are theoretically obtained. In addition to that, degree distributions with different strategies are demonstrated in simulations, which manifests the power-low distribution. The reliability of the network is also studied by different attacks, sharing the same characteristic with the scale-free network.

Machine learning partners in criminal networks

Recent research has shown that criminal networks have complex organizational structures, but whether this can be used to predict static and dynamic properties of criminal networks remains little explored. Here, by combining graph representation learning and machine learning methods, we show that structural properties of political corruption, police intelligence, and money laundering networks can be used to recover missing criminal partnerships, distinguish among different types of criminal and legal associations, as well as predict the total amount of money exchanged among criminal agents, all with outstanding accuracy. We also show that our approach can anticipate future criminal associations during the dynamic growth of corruption networks with significant accuracy. Thus, similar to evidence found at crime scenes, we conclude that structural patterns of criminal networks carry crucial information about illegal activities, which allows machine learning methods to predict missing information and even anticipate future criminal behavior.

Triple Network Model-Based Analysis on Abnormal Core Brain Functional Network Dynamics in Different Stage of Amnestic Mild Cognitive Impairment.

Amnestic mild cognitive impairment (aMCI) is considered to be a transitional stage between Alzheimer's disease (AD) and normal cognitive state because it has the same clinical symptoms as AD but with lower severity. Studies have confirmed that patients with aMCI are more likely to develop to AD. Although studies on resting state functional connectivity have revealed the abnormal organization of brain networks, the dynamic changes of the functional connectivity across the scans have been ignored. Dynamic functional connectivity is a novel method to reveal the temporal variation of brain networks. This paper aimed to investigate the dynamic characteristics of brain functional connectivity in the early and late phases of aMCI. Based on the "triple network" model, we used the sliding time window approach to construct dynamical functional networks and then analyzed the dynamic characteristics of the functional connectivity across the entire scan. The results showed that patients with aMCI had longer dwell times in weaker network connection than in the strong network. The transitions between different states become more frequent, and the stability of the patient's brain core network deteriorates. This study also found the correlation between the altered dynamic properties of the core functional networks and the patient's clinical Mini-Mental State Examination assessment scale sores. This study revealed that the characteristics of dynamic functional networks constructed by the core cognitive networks varied in distinct ways at different stages of aMCI, which could provide a new idea for exploring the neuro-mechanisms of neurological disorders.

Dynamic Mechanical Properties of Self-Assembled Bottlebrush Polymer Networks

We systematically investigate the effects of composition on the dynamic mechanical properties of bottlebrush polymer networks self-assembled by linear–bottlebrush–linear triblock copolymers. We fix the molecular architecture of the bottlebrush, which consists of 51 poly(dimethyl siloxane) (PDMS) side chains of 5 kg/mol and has a molecular weight of 255 kg/mol, and increase only the volume fraction f of the linear poly(benzyl methacrylate) (PBnMA) blocks. As f increases from 0.05 to 0.41, the network shear modulus G at room temperature increases from ∼4 to ∼100 kPa. Yet, depending on the network morphology, the relation between G and f exhibits two regimes. (i) For sphere morphology, G is nearly a constant; yet, because of a large fraction of loops, the absolute value of G is about 40% of the stiffness Gm of the PDMS bottlebrush matrix. (ii) For cylinder morphology, G increases slowly with f but remains nearly 4 orders of magnitude lower than 109 Pa for the glassy cylinders formed by the end PBnMA blocks. We explain this remarkable behavior by modeling the polymer as a polycrystalline material consisting of randomly oriented grains, and each grain is a fiber-reinforced composite. We propose a modified Halpin–Tsai model to describe the shear modulus of such a polycrystalline material: G = Gm(1+ζf)/(1–f), in which ζ is an adjustable parameter that describes the grain size relative to the fiber diameter. Above the glass-transition temperature of end blocks, the reinforcement to network modulus from the glassy fibers diminishes, such that G becomes a constant of the matrix stiffness. Our results not only reveal previously unexplored molecule–structure–property relations of self-assembled bottlebrush polymer networks but also provide a new class of soft, solvent-free, and reprocessable polymeric materials with a wide range of controllable stiffness.