Network Topology Research Articles

Optimal Scheduling of Integrated Energy System Considering Virtual Heat Storage and Electric Vehicles

Integrated energy systems (IESs) are complex multisource supply systems with integrated source, grid, load, and storage systems, which can provide various flexible resources. Nowadays, there exists the phenomenon of a current power system lacking flexibility. Thus, more research focuses on enhancing the flexibility of power systems by considering the participation of IESs in distribution network optimization scheduling. Therefore, the optimal scheduling of IESs considering virtual heat storage and electric vehicles (EVs) is proposed in this paper. Firstly, the basic structure of IESs and mathematical models for the operation of the relevant equipment are presented. Then, an optimal scheduling strategy of an IES considering virtual heat storage and electric vehicles is proposed. Finally, an IES with an IEEE 33-node distribution network, 20-node Belgian natural gas network, and 44-node heating network topologies is selected to validate the proposed strategy. The proposed models of integrated demand response (IDR), EV orderly charging participation, virtual heat storage, and actual multitype energy storage devices play the role of peak shaving and valley filling, which also helps to reduce the scheduling cost from CNY 11,253.0 to CNY 11,184.4. The simulation results also demonstrate that the proposed model can effectively improve the operational economy of IESs, and the scheduling strategy can promote the consumption of renewable energy, with the wind curtailment rate decreasing from 63.62% to 12.50% and the solar curtailment rate decreasing from 56.92% to 21.34%.

The endoplasmic reticulum as an active liquid network

The peripheral endoplasmic reticulum (ER) forms a dense, interconnected, and constantly evolving network of membrane-bound tubules in eukaryotic cells. While individual structural elements and the morphogens that stabilize them have been described, a quantitative understanding of the dynamic large-scale network topology remains elusive. We develop a physical model of the ER as an active liquid network, governed by a balance of tension-driven shrinking and new tubule growth. This minimalist model gives rise to steady-state network structures with density and rearrangement timescales predicted from the junction mobility and tubule spawning rate. Several parameter-independent geometric features of the liquid network model are shown to be representative of ER architecture in live mammalian cells. The liquid network model connects the timescales of distinct dynamic features such as ring closure and new tubule growth in the ER. Furthermore, it demonstrates how the steady-state network morphology on a cellular scale arises from the balance of microscopic dynamic rearrangements.

Assembly of Eosin Y in-situ intercalated Zr-MOF composite for decontaminating Cr(VI) and reactive dyes via physical capture and photochemical purification

Post-modification based on highly water stable Zr-MOF introduces a novel dimension of functional regulation, offering a fresh perspective for the advancement of pragmatic photocatalysts in the water treatment field. In this study, an entirely novel Zr-MOF (NU-1400-py), featuring a topological network identical to that of NU-1400, has been assembled. The main difference is the replacement of TPTC's central benzene ring with a pyrimidine ring. Utilizing the inherent porosity and semiconducting properties, this Zr-MOF possesses the capability to physically trap Cr2O72- ions (136.43 mg/g), achieving photochemical purification of Cr(VI) and K-, X-, M-type reactive dyes from water. Moreover, another novel heterojunction material EY@Zr-MOF, has been fabricated via in-situ intercalation of luminescent Eosin Y (EY) species within Zr-MOF. Due to the sensitization of embedded EY molecules, EY@Zr-MOF exhibits significantly optimized interface resistance, transient photocurrent intensity and carrier migration efficiency compared to pristine Zr-MOF. Notably, the photochemical purification efficiencies of EY@Zr-MOF towards Cr2O72- (99.66 %, 30 min), RB13 (90.18 %, 120 min), RR2 (92.40 %, 300 min) and RR11 (84.80 %, 90 min) have been dramatically elevated, offering the considerably accelerated reaction kinetics of 1.24, 4.82, 2.48 and 6.23 times, respectively, compared to pristine Zr-MOF, the photochemical purification efficiencies of Zr-MOF towards Cr2O72- (97.88 %, 30 min), RB13 (85.55 %, 450 min), RR2 (71.38 %, 450 min) and RR11 (53.16 %, 240 min). Additionally, its photocatalytic REDOX mechanisms in eliminating target contaminants were proposed tentatively. This contribution offers a viable pathway for designing multi-functional materials aimed at solving some intractable environmental issues caused by Cr(VI) and reactive dyes.

Tetraphenylethene-Based Ni8-Pyrazolate Metal-Organic Framework for Photoredox/Nickel Dual Catalysis of C-S Cross-Coupling.

As a prototypical aggregation-induced emission luminogen (AIEgen), the tetraphenylethene (TPE) moiety has been judiciously modified as organic linkers for constructing various functional metal-organic frameworks (MOFs). However, these AIEgen-based MOFs have rarely received research attention in photocatalytic applications due to their limited stability in harsh reaction conditions. In this work, we report a robust Ni8-pyrazolate-based MOF (denoted as TPE4Pz-Ni) under the guidance of reticular chemistry, which is assembled by an AIE-active tetratopic linker of 1,1,2,2-tetrakis(4-(1H-pyrazol-4-yl)phenyl)ethane (H4-TPE4Pz) with a 12-connected Ni8-cluster of [Ni8(OH)4(H2O)2Pz12] (Pz = pyrazolate) in a (4,12)-connected ftw-a topological network. Notably, MOF TPE4Pz-Ni exhibits excellent stability in a wide range of solvents and even in a saturated NaOH solution. Moreover, its luminescent emission is effectively quenched via a ligand-to-metal charge transfer (LMCT) process originating from the TPE-cored linker to the Ni8 cluster, which enables TPE4Pz-Ni to act as an efficient photoredox/nickel dual catalyst for light-mediated C-S cross-coupling reactions between various aryl iodides and thiols.

An immuno-epidemiological model with non-exponentially distributed disease stage on complex networks

Most of epidemic models assume that duration of the disease phase is distributed exponentially for the simplification of model formulation and analysis. Actually, the exponentially distributed assumption on the description of disease stages is hard to accurately approximate the interplay of drug concentration and viral load within host. In this article, we formulate an immuno-epidemiological epidemic model on complex networks, which is composed of ordinary differential equations and integral equations. The linkage of within- and between-host is connected by setting that the death caused by the disease is an increasing function in viral load within host. Mathematical analysis of the model includes the existence of the solution to the epidemiological model on complex networks, the existence and stability of equilibrium, which are completely determined by the basic reproduction number of the between-host system. Numerical analysis are shown that the non-exponentially distributions and the topology of networks have significant roles in the prediction of epidemic patterns.

Assessing movement-specific resilience of a signalized road network under lane-level cascading failure

Accurately assessing the resilience of the road network is crucial for responding to emergencies and enhancing public safety. Signal control plays a significant role in managing traffic flow. However, its impact is often overlooked in resilience assessments, where traffic flow and signal control are usually considered separately. A Movement-Specific Resilience (MSR) assessment model is proposed to integrate signal timing into resilience analysis. To accurately represent traffic flow paths under phase control, a dual graph is used to depict the topological network, allowing the assessment of relationships among all movements at an intersection. Based on this, a cascading failure model is developed to analyze the impact of signal control on traffic flow reassignment, reflecting how signal timing influences traffic flow propagation after failures. The method is validated using data collected from a sub-road network in Xi’an city. Results reveal the cumulative resilience of single lanes is not equivalent to the resilience of road segments. The MSR is higher when the network’s failure degree is low and decreases as the failure level increases. Furthermore, road saturation is inversely related to MSR, while MSR is proportional to capacity. MSR remains unaffected by failures and oversaturation when capacity exceeds a certain threshold. These insights could be a theoretical foundation for bolstering resilience via signal control adjustments.

Disaster vulnerability in road networks: a data-driven approach through analyzing network topology and movement activity

The rise in natural disasters and climate-induced events, such as wildfires, hurricanes, and flooding, has significantly affected urban life. These events can disrupt daily activity and flows of individuals and goods on road and transit networks. To enhance urban resilience against disasters, it’s crucial to study and understand road network vulnerability, utilizing data-driven insights to inform planning and preparedness efforts. The aim of this paper is to develop a data-driven exploratory approach to assess vulnerability in road networks in response to a disruption. To accomplish this, we compare the centrality of road segments before, during, and after disaster, considering the network topological structure and movement activity as it is observed through large tracking data of cellphone traces on the network. The novelty of our approach lies in inferring the impact from movement data, instead of manually removing links from the network. The results obtained from this study suggest that incorporating movement data into the assessment of network functionality provides a more realistic estimation of the road network vulnerability in response to a disruption, compared to solely using network topology.

When does the mean network capture the topology of a sample of networks?

The notion of Fréchet mean (also known as “barycenter”) network is the workhorse of most machine learning algorithms that require the estimation of a “location” parameter to analyse network-valued data. In this context, it is critical that the network barycenter inherits the topological structure of the networks in the training dataset. The metric–which measures the proximity between networks–controls the structural properties of the barycenter. This work is significant because it provides for the first time analytical estimates of the sample Fréchet mean for the stochastic blockmodel, which is at the cutting edge of rigorous probabilistic analysis of random networks. We show that the mean network computed with the Hamming distance is unable to capture the topology of the networks in the training sample, whereas the mean network computed using the effective resistance distance recovers the correct partitions and associated edge density. From a practical standpoint, our work informs the choice of metrics in the context where the sample Fréchet mean network is used to characterize the topology of networks for network-valued machine learning.

Investigating the role of structural connectivity in the individuals with moderate hearing loss

Hearing loss has been identified as a risk factor for cognitive decline. The processing of auditory information relies on a well-connected neuronal network. Previous studies have found white matter abnormalities in individuals with hearing loss, but the structural connectivity and microstructural properties of white matter in individuals with moderate hearing loss remain unclear.To examine the integrity of white matter, identify vulnerable structural connectivity, and assess network topology, we examined major white matter tracts in elderly individuals with moderate hearing loss (MHL) (>30 dB) and compared them to age-matched controls with good hearing (GH).We observed that the fractional anisotropy of the right corticospinal tract and left superior longitudinal fasciculus – parietal was higher in subjects with MHL. Additionally, we identified a disrupted network centered on the left putamen in MHL, involving eight brain regions. Network topology analysis showed reduced betweenness centrality and small-world network in MHL. Interestingly, the fractional anisotropy of the forceps major tract and left uncinate tracts were correlated with hearing status.Our findings suggest that MHL is associated with putamen-centered disruptions in structural connectivity. The increased fractional anisotropy of the right corticospinal tract may be a compensatory mechanism, as it projects fibers to the right putamen. Overall, these results provide valuable insights into the impact of hearing loss on white matter integrity and may inform the development of new treatments to prevent its progression.

Explaining Protest Participation in Semi-authoritarian Regimes: The Power of Social Networks

AbstractThis study uses the case of ecological protests in the Russian Federation and the social network of 903 263 VKontakte users to investigate the unique characteristics of protesters’ social networks and assess the effects of network topology on the likelihood of participation. By applying Bayesian structural equation modeling to the data, the study finds that social network topology plays a more nuanced and complex role in predicting protest participation beyond simple exposure to information. Notably, individuals situated as brokers in lower-density and higher-closure networks exhibit a higher likelihood of participation, while central nodes participate less. The results highlight the importance of strategic positioning as brokers for increasing the likelihood of participation. The paper discusses these and other findings, contributing to a deeper understanding of the interplay between social network structures and protest participation.

Eigenmode and eigenpropagation of the electromagnetic waves in Möbius and Klein networks

To explore the distribution of characteristic frequencies and the propagation properties of eigenmodes in topological networks at the zero-energy level, we design optical waveguide networks with two typical topologies: Möbius network and Klein network, inspired by the Möbius strip and Klein bottle, respectively. We investigate the degeneracy at characteristic frequencies and the propagation properties of the eigenmodes of these networks, both theoretically and experimentally. We discovered an intriguing eigenpropagation in the Möbius network and multiple degenerate eigenmodes in the Klein network, analyzing the propagation characteristics and distribution patterns of electromagnetic waves within them. In our experiments, we utilize coaxial cables as one-dimensional waveguides to construct transmission line networks for the two networks. We observe the distinct transmission paths of the Möbius network’s eigenmode and the two degenerate eigenmodes of the Klein network. Our findings provide a theoretical foundation for new optical modal transmission devices and novel nanoarrays, with potential implications for theoretical and experimental research in other quantum systems and topological networks.

An Efficient Time‐Sensitive Networking Traffic Scheduling Method for Train Communication Network

As a critical system of rail train, the train communication network (TCN) is an integrated central platform that is used to realize the train operation control, condition monitoring, and data transmission. With the development of train intelligence, the scale of data flow in TCN continues to expand, and the control data and monitoring data must be real‐time transmitted to ensure the safe and reliable operation of the train. Time‐sensitive network (TSN) can be applied as the next‐generation TCN solution, which provides end‐to‐end data transmission with extremely low delay and high reliability based on Ethernet. For the application of TSN technology in TCN, a single frame simple scheduling method is proposed to effectively reduce the worst end‐to‐end delay of the entire network, which improve the real‐time performance of TSN‐based TCN. The network topology and data flow characteristics in TCN are analyzed, a series of scheduling constraints are constructed, and the satisfiability modulo theories (SMT) is used to obtain a gate control list (GCL) to ensure the real‐time performance of time‐trigger streams. Finally, the TCN simulation model is established using OMnet++ software, the experiment results show that the proposed method can effectively reduce the overall delay and jitter, and meet the real‐time requirement of TCN. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Realized Random Graphs, with an Application to the Interbank Network

Abstract We introduce a new inferential methodology for dynamic network models driven by latent state variables. The main idea is to obtain a noisy representation of the state variables dynamics by computing a sequence of cross-sectional estimates of the network model at each point in time. The dynamic modeling of these cross-sectional estimates, that we name realized random graphs, transforms the original nonlinear state-space network model into a linear time-series model that can be easily estimated. Under the assumption of a mixed-membership blockmodel structure, the model parameters and the unobservable state variables can be consistently estimated when both the size of the network and the time-series length are large. By allowing for an extremely rich parameterization of the model in high dimensions, the proposed methodology describes the heterogeneous topology of real-world networks. We corroborate our findings by using this novel framework to estimate and forecast the dynamic common factors driving the evolution of the Italian electronic market of interbank deposits, and we show that the interbank lending rate is a key factor determining the network topology.

Light-Driven, Reversible Spatiotemporal Control of Dynamic Covalent Polymers.

Dynamic covalent polymer networks exhibit a cross-linked structure like conventional thermosets and elastomers, although their topology can be reorganized through externally triggered bond exchange reactions. This characteristic enables a unique combination of repairability, recyclability and dimensional stability, crucial for a sustainable industrial economy. Herein the application of a photoswitchable nitrogen superbase is reported for the spatially resolved and reversible control over dynamic bond exchange within a thiol-ene photopolymer. By the exposure to UV or visible light, the associative exchange between thioester links and thiol groups is successfully gained control over, and thereby the macroscopic mechanical material properties, in a locally controlled manner. Consequently, the resulting reorganization of the global network topology enables to utilize this material for previously unrealizable advanced applications such as spatially resolved, reversible reshaping as well as micro-imprinting over multiple steps. Finally, the presented concept contributes fundamentally to the evolution of dynamic polymers and provides universal applicability in covalent adaptable networks relying on a base-catalyzed exchange mechanism.

Enhancing interconnection network topology for chiplet-based systems: An automated design framework

Chiplet-based systems integrate discrete chips on an interposer and use the interconnection network to enable communication between different components. The topology of the interconnection network poses a significant challenge to overall performance, as it can greatly affect both latency and throughput. However, the design of the interconnection network topology is not currently automated. They rely heavily on expert knowledge and fail to deliver optimal performance. To this end, we propose an automated design framework for chiplet interconnection network topology, called CINT-AD. To implement CINT-AD, we first investigate topology-related properties from the perspective of design constraints and structural symmetry. Then, using these properties, we develop an automated framework to generate the topology for interposer interconnections between different chiplets. A deadlock-free routing scheme is proposed for the topologies generated by CINT-AD to fully utilize the resources of the interconnection network. Experimental results show that CINT-AD achieves lower average latency and higher throughput compared to existing state-of-the-art topologies. Furthermore, power and area analysis show that the overhead of CINT-AD is negligible.

Higher-order network information propagation model based on social impact theory

User relationships are becoming increasingly diverse in current online social networks. This paper utilizes simplicial complexes in higher-order networks to depict multiple user relationships and characterize the topology of social networks. By employing social impact theory and factors such as user interest and information aging to define state transition rules among users, we establish a UAPR information propagation model based on simplicial complexes to simulate the information propagation process in higher-order social networks. The proposed model is simulated on three types of synthetic networks and four real-world simplicial complexes, demonstrating its ability to accurately describe the dynamics and influences of information propagation. Furthermore, our results indicate that different network structures, user interest values, information aging speeds, and intimacy levels significantly influence the information transmission process.

Neural relational and dynamics inference for complex systems

Many complex processes in the real world can be viewed as complex systems and their evolution is governed by underlying nonlinear dynamics. However, one can only access the trajectories of the system without knowing the underlying system structure and dynamics in most cases. To address this challenge, this paper proposes a model called Neural Relational and Dynamics Inference (NRDI) that combines graph neural networks (GNNs) and ordinary differential equation systems (ODEs) to handle both continuous-time dynamics prediction and network topology inference for complex systems. Our model contains two modules: (1) the network inference module, which infers system structure from input system trajectories using GNNs, and (2) the dynamics learning module, which employs GNNs to fit the differential equation system for predicting future trajectories. We tested NRDI’s performance on system trajectory prediction and graph reconstruction separately. Experimental results show that the proposed NRDI outperforms many baseline models on continuous-time complex network dynamics prediction, and can explicitly infer network structures with high accuracy.

A Method for Quantifying Global Network Topology Based on a Mathematical Model

To facilitate a direct comparison of the differences in network resources among countries worldwide, this paper proposes a method for quantifying the relationship between autonomous systems and territorial networks from the perspective of network topology. Using global router-level network topology data as the foundational data for the network resources of various countries, we abstract the dual mapping information of router geographic distribution and operational ownership into a matrix-form mathematical model. By employing relevant indicators from both network scale and border connectivity, we compare matrix model data from different periods to quantitatively assess changes in the network structures of countries globally. The study results show that internet resources are concentrated in the United States, which owns 38.04% of the global routers, distributed across 87.88% of the countries, significantly impacting the global network. Compared to the average quantitative indicators of each country, 67.00% of the countries exhibit higher deployment consistency, 37.30% show higher border connection consistency, 23.81% perform prominently in terms of impact, and 46.20% have outstanding border node degrees. From a continental perspective, the analysis indicates that Asian and African countries have a closer relationship between AS and territorial networks, while Europe’s connections are relatively sparse. Over time, we observe a slight decline in deployment consistency in Asia, Africa, and Europe, a slight increase in border connection consistency in Asia, Africa, and North America, and enhanced impact in Asia, Africa, Europe, and South America. These trends suggest that the integration between AS and territorial networks is intensifying in most countries.

Spatially Varying Effect Mechanism of Intermodal Connection on Metro Ridership: Evidence from a Polycentric Megacity with Multilevel Ring Roads

Understanding the spatially varying effect mechanism of intermodal connection on metro ridership helps policymakers develop differentiated interventions to promote metro usage, especially for megacities with multiple city sub-centers and ring roads. Using multiple datasets in Shanghai, this study combines Light Gradient Boosting Machine (LightGBM) with Shapley additive explanations (SHAP) to explore these effects with the consideration of the built environment and metro network topology. Results show that the collective impacts of intermodal connection are positive, not only within the main city but also alongside the main commuting corridors, while negative effects occur in the peripheral area. Specifically, bike sharing trips increase metro ridership within the inner ring of the city, while bus services lower metro usage at stations alongside the elevated ring roads. Parking facilities enable metro usage at city sub-centers, and the small pedestrian catchment area increases metro riders alongside the main commuting corridors. Empirical findings help policymakers understand the effect mechanism of intermodal connection for stations in different regions and prioritize customized planning strategies.

Aberrant individual large-scale functional network connectivity and topology in chronic insomnia disorder with and without depression

Insomnia is increasingly prevalent with significant associations with depression. Delineating specific neural circuits for chronic insomnia disorder (CID) with and without depressive symptoms is fundamental to develop precision diagnosis and treatment. In this study, we examine static, dynamic and network topology changes of individual large-scale functional network for CID with (CID-D) and without depression to reveal their specific neural underpinnings. Seventeen individual-specific functional brain networks are obtained using a regularized nonnegative matrix factorization technique. Disorders-shared and -specific differences in static and dynamic large-scale functional network connectivities within or between the cognitive control network, dorsal attention network, visual network, limbic network, and default mode network are found for CID and CID-D. Additionally, CID and CID-D groups showed compromised network topological architecture including reduced small-world properties, clustering coefficients and modularity indicating decreased network efficiency and impaired functional segregation. Moreover, the altered neuroimaging indices show significant associations with clinical manifestations and could serve as effective neuromarkers to distinguish among healthy controls, CID and CID-D. Taken together, these findings provide novel insights into the neural basis of CID and CID-D, which may facilitate developing new diagnostic and therapeutic approaches.