Dynamic Node Research Articles

Uncertainty quantification of multi-scale resilience in networked systems with nonlinear dynamics using arbitrary polynomial chaos

Complex systems derive sophisticated behavioral dynamics by connecting individual component dynamics via a complex network. The resilience of complex systems is a critical ability to regain desirable behavior after perturbations. In the past years, our understanding of large-scale networked resilience is largely confined to proprietary agent-based simulations or topological analysis of graphs. However, we know the dynamics and topology both matter and the impact of model uncertainty of the system remains unsolved, especially on individual nodes. In order to quantify the effect of uncertainty on resilience across the network resolutions (from macro-scale network statistics to individual node dynamics), we employ an arbitrary polynomial chaos (aPC) expansion method to identify the probability of a node in losing its resilience and how the different model parameters contribute to this risk on a single node. We test this using both a generic networked bi-stable system and also established ecological and work force commuter network dynamics to demonstrate applicability. This framework will aid practitioners to both understand macro-scale behavior and make micro-scale interventions.

Time-frequency fusion features-based incremental network for smartphone measured structural seismic response classification

Smartphone as a portable device can be used for public participatory structural seismic response acquisition. This data acquisition approach not only reduces the acquisition cost but also facilitates the possibility to obtain city-scale building seismic responses. In this paper, we propose a novel structural seismic response classification method using time–frequency fusion features-based incremental network, namely SeismicNet, that can automatically classify the signals acquired through smartphones into structural response under normal operation conditions (structural normal response) or structural response under strong ground motion (structural seismic response). In this method, the time-domain signals of structural response are converted into time–frequency maps, which will be fed into a lightweight convolutional network for the extraction of the initial fusion features. Subsequently, these features are mapped by random weights to dynamic nodes. This not only improves the capability of feature expression, but also allows incremental updating of the model. Finally, a simulated structural response dataset and some real structural seismic responses are used to verify the accuracy and practicability of the proposed method. Furthermore, the results showed that the proposed method is more than 84 times in training speed and 2 times in inference speed than some well-known deep learning networks (VGG16 and ResNet50).

H∞Bipartite Synchronization of Double-Layer Markov Switched Cooperation-Competition Neural Networks: A Distributed Dynamic Event-Triggered Mechanism.

In this article, the H∞ bipartite synchronization issue is studied for a class of discrete-time coupled switched neural networks with antagonistic interactions via a distributed dynamic event-triggered control scheme. Essentially different from most current literature, the topology switching of the investigated signed graph is governed by a double-layer switching signal, which integrates a flexible deterministic switching regularity, the persistent dwell-time switching, into a Markov chain to represent the variation of transition probability. Considering the coexistence of cooperative and antagonistic interactions among nodes, the bipartite synchronization of which the dynamics of nodes converge to values with the same modulus but the opposite signs is explored. A distributed control strategy based on the dynamic event-triggered mechanism is utilized to achieve this goal. Under this circumstance, the information update of the controller presents an aperiodic manner, and the frequency of data transmission can be reduced extensively. Thereafter, by constructing a novel Lyapunov function depending on both the switching signal and the internal dynamic nonnegative variable of the triggering mechanism, the exponential stability of bipartite synchronization error systems in the mean-square sense is analyzed. Finally, two simulation examples are provided to illustrate the effectiveness of the derived results.

MB-CIM: a multi-round budgeted competitive influence maximisation

Influence maximisation is trying to select a small set of seed users in the social network to maximise the spread of influence. An individual's decision to adopt a product or innovation will be highly dependent on the choices made by the individual's neighbours in the social network. In CIM, competitors need to decide which nodes would be an influential one and how many resources should be allocated to this member. Identifying the best algorithm for the influence maximisation under budget constraint has become a demanding task. Predicting, and controlling social influence is an exciting topic in social network analysis. We are interested in multi-round CIM where each competitor needs to decide the location and the amount of budget to invest in the most influential members simultaneously and repeatedly under a given total budget. This paper proposes a tree-approximate game-theoretical framework and introduces the new measurement as a dynamic node weight.

MemCA: All-Memristor Design for Deterministic and Probabilistic Cellular Automata Hardware Realization

Inspired by the behavior of natural systems, Cellular Automata (CA) tackle the demanding long-distance information transfer of conventional computers by the massive parallel computation performed by a set of locally-coupled dynamical nodes. Although CA are envisioned as powerful deterministic computers, their intrinsic capabilities are expanded after the memristor’s probabilistic switching is introduced into CA cells, resulting in new hybrid deterministic and probabilistic memristor-based CA (MemCA). In the proposed MemCA hardware realization, memristor devices are incorporated in both the cell and rule modules, composing the very first all-memristor CA hardware, designed with mixed CMOS/Memristor circuits. The proposed implementation accomplishes high operating speed and reduced area requirements, exploiting also memristor as an entropy source in every CA cell. MemCA’s functioning is showcased in deterministic and probabilistic operation, which can be externally modified by the selection of programming voltage amplitude, without changing the design. Also, the proposed MemCA system includes a reconfigurable rule module implementation that allows for spatial and temporal rule inhomogeneity.

Trustability for Resilient Internet of Things Services on 5G Multiple Access Edge Cloud Computing

Billions of Internet of Things (IoT) devices and sensors are expected to be supported by fifth-generation (5G) wireless cellular networks. This highly connected structure is predicted to attract different and unseen types of attacks on devices, sensors, and networks that require advanced mitigation strategies and the active monitoring of the system components. Therefore, a paradigm shift is needed, from traditional prevention and detection approaches toward resilience. This study proposes a trust-based defense framework to ensure resilient IoT services on 5G multi-access edge computing (MEC) systems. This defense framework is based on the trustability metric, which is an extension of the concept of reliability and measures how much a system can be trusted to keep a given level of performance under a specific successful attack vector. Furthermore, trustability is used as a trade-off with system cost to measure the net utility of the system. Systems using multiple sensors with different levels of redundancy were tested, and the framework was shown to measure the trustability of the entire system. Furthermore, different types of attacks were simulated on an edge cloud with multiple nodes, and the trustability was compared to the capabilities of dynamic node addition for the redundancy and removal of untrusted nodes. Finally, the defense framework measured the net utility of the service, comparing the two types of edge clouds with and without the node deactivation capability. Overall, the proposed defense framework based on trustability ensures a satisfactory level of resilience for IoT on 5G MEC systems, which serves as a trade-off with an accepted cost of redundant resources under various attacks.

Application of Edge Computing in Structural Health Monitoring of Simply Supported PCI Girder Bridges.

This study proposes an innovative method for structural health monitoring of simply supported PCI girder bridges based on dynamic strain and edge computing. Field static and dynamic load tests were conducted on a bridge consisting of a span with newly replaced PCI girders and numerous spans with old PCI girders. Both the static and dynamic test results showed that the flexural rigidity of the old PCI girders decreased significantly due to deterioration. To improve the efficiency of on-site monitoring data transmission and data analysis, this study developed a smart dynamic strain gauge node with the function of edge computing. Continuous data with a sampling frequency of 100 Hz were computed at the sensor node. Among the computed results, only the maximum dynamic strain data caused by the passage of the heaviest vehicle within 1 min were transmitted. The on-site monitoring results indicated that under routine traffic conditions, the dynamic strain response of the new PCI girder was smaller than that of the deteriorated PCI girder. When the monitored dynamic strain response has a tendency to magnify, attention should be paid to the potential prestress loss or other deterioration behaviors of the bridge.

Design of energy efficient domino logic circuit using lector technique

ABSTRACT Calculating power and delay in VLSI circuits are two main challenges in designing CMOS VLSI circuits. The manuscript proposes a lector technique-based foot-driven stack transistor domino logic for power and delay reduction. The lector technique uses two leakage-controlled transistors, PMOS and NMOS. The gate terminal of PMOS is connected to the source of NMOS, and the gate terminal of NMOS is connected to the source of PMOS. NMOS transistor N5 is used in the proposed circuit, driven by a dynamic node, which helps to reduce the power. This manuscript uses the proposed technique to design a buffer, two-input – AND gate, OR gate, and XOR gate circuits. The logic gates are simulated on the gpdk 45 nm cadence virtuoso software tool. The simulation result shows that the proposed domino logic circuit significantly reduces power, delay, energy, and leakage current. Monte Carlo analysis is performed to study the mean and standard deviation of the proposed circuit with 1000 samples.

Stochastic synchronisation of nonlinear networks with additive measurement noise

Stochastic synchronisation of complex networks has recently attracted considerable attention because of the ubiquitousness of random noise in real-world complex systems. Previous works have mainly considered noise-corrupted communication by ignoring intrinsic dynamics of nodes. This work provides a systematic study of complex networks by taking into account both additive noise and nonlinear dynamics of nodes, examining their stochastic synchronisation problems. We model the deterministic diffusive coupling and noisy additive coupling with the time-invariant strength and the time-varying strength, respectively. Using the stochastic Lyapunov technique, this framework enables an analytical treatment of the problem, ultimately leading to sufficient conditions for the onset of synchronisation. For symmetrical/asymmetrical connections, the synchronisation condition for the time-invariant coupling strength can be stated elegantly in terms of node dynamics and network topology, whereas that for the time-varying coupling strength is described as a robust condition. Finally, two simulation examples are carried out to illustrate the effectiveness of theoretical findings.

Secure wireless network system based on deep reinforcement learning network

In order to improve the security of the secure wireless network system, this paper studies the security of the wireless network system based on the deep reinforcement learning network. Moreover, this paper divides the entire network into a domain structure, and each domain structure includes ordinary nodes, council nodes, key nodes, and dynamic gateway nodes. The key node selects the dynamic gateway node in the domain, and after the dynamic gateway node processes the message, it is forwarded to the gateway node in the adjacent domain. In addition, this paper studies the application of multi-objective optimization in trust management mechanism in secure routing. Furthermore, this paper builds a secure wireless network system based on the deep reinforcement learning network based on the deep reinforcement learning network, and builds a system architecture. Finally, this paper tests the secure wireless network system based on deep reinforcement learning network through simulation experiments. The research results show that the safety factor of the system is relatively high.

DHGEEP: A Dynamic Heterogeneous Graph-Embedding Method for Evolutionary Prediction

Current graph-embedding methods mainly focus on static homogeneous graphs, where the entity type is the same and the topology is fixed. However, in real networks, such as academic networks and shopping networks, there are typically various types of nodes and temporal interactions. The dynamical and heterogeneous components of graphs in general contain abundant information. Currently, most studies on dynamic graphs do not sufficiently consider the heterogeneity of the network in question, and hence the semantic information of the interactions between heterogeneous nodes is missing in the graph embeddings. On the other hand, the overall size of the network tends to accumulate over time, and its growth rate can reflect the ability of the entire network to generate interactions of heterogeneous nodes; therefore, we developed a graph dynamics model to model the evolution of graph dynamics. Moreover, the temporal properties of nodes regularly affect the generation of temporal interaction events with which they are connected. Thus, we developed a node dynamics model to model the evolution of node connectivity. In this paper, we propose DHGEEP, a dynamic heterogeneous graph-embedding method based on the Hawkes process, to predict the evolution of dynamic heterogeneous networks. The model considers the generation of temporal events as an effect of historical events, introduces the Hawkes process to simulate this evolution, and then captures semantic and structural information based on the meta-paths of temporal heterogeneous nodes. Finally, the graph-level dynamics of the network and the node-level dynamics of each node are integrated into the DHGEEP framework. The embeddings of the nodes are automatically obtained by minimizing the value of the loss function. Experiments were conducted on three downstream tasks, static link prediction, temporal event prediction for homogeneous nodes, and temporal event prediction for heterogeneous nodes, on three datasets. Experimental results show that DHGEEP achieves excellent performance in these tasks. In the most significant task, temporal event prediction of heterogeneous nodes, the values of precision@2 and recall@2 can reach 30.23% and 10.48% on the AMiner dataset, and reach 4.56% and 1.61% on the DBLP dataset, so that our method is more accurate at predicting future temporal events than the baseline.

Inverse Optimal Impulsive Neural Control for Complex Networks Applied to Epidemic Diseases

This paper proposes an impulsive control scheme for a complex network that helps reduce the spread of two epidemic diseases: influenza type A and COVID-19. Both are respiratory infections; thus, they have a similar form of transmission, and it is possible to use the same control scheme in both study cases. The objective of this work is to use neural impulsive inverse optimal pinning control for complex networks to reduce the effects of propagation. The dynamic model is considered unknown, for which we design a neural identifier that, through training using the extended Kalman filter algorithm, provides the appropriate nonlinear model for this complex network. The dynamics of the network nodes are represented by the Susceptible-Infected-Removed (SIR) compartmental model in their discrete form. The results of the simulations are presented and addressed, applying the same control scheme but with different parameter values for each case study.

A Novel Multifaceted Trust Management Framework for Vehicular Networks

Maintaining interconnectivity between dynamic vehicular nodes and ensuring trust in vehicular networking is still a challenging issue. It is crucial for the vehicular environments to maintain a stable interconnectivity between the vehicular nodes and further to prevent the vehicular nodes from broadcasting fake messages and simultaneously protect the vehicles against tracking attacks. An efficient Trust Management System (TMS) is proposed in this paper that will ensure trustworthiness and stable interconnectivity between vehicular entities to assure road safety and reliable communication. The proposed approach incorporates a novel timestamp mechanism and block chain concepts to ensure the steady inter-connectivity between the dynamic vehicular nodes. Further, the framework leverages block chain concepts to verify the correctness of the events stored in the Road Side Units (RSUs). It also proposes a versatile hybrid trust model that uses innovative direct and recommended trust evaluation techniques to compute trust between the vehicular entities. Moreover, the framework integrates a threading mechanism to schedule message execution in direct trust evaluation, and clustering techniques to group similar messages in indirect trust evaluation. Various experimental and comparative analyses with other related studies are carried out in a simulated environment to evaluate the performance of the proposed hybrid trust model. The findings show that the proposed trust model produces an accuracy of 92% in identifying malicious nodes.

Impulsive Pinning Control of Discrete-Time Complex Networks with Time-Varying Connections

Complex dynamical networks with time-varying connections have characteristics that allow a better representation of real-world complex systems, especially interest in their not static behavior and topology. Their applications reach areas such as communication systems, electrical systems, medicine, robotic, and more. Both continuous and discrete-time complex dynamical networks and the pinning control technique have been studied. However, even with interest in the research on complex networks combining characteristics of discrete-time, time-varying connections, pinning control, and impulsive control, there are few studies reported in the literature. There are some previous studies dealing with impulsively pin-controlling a discrete-time complex network. Nevertheless, they neglect to deal with time-varying connections; they deal with these systems by experimentally using continuous-time methods or linearizing the node dynamics. In this manner, this paper presents a control scheme that not only deals with pin control on discrete-time complex networks but also includes time-varying connections. This paper proposes an impulsive pin control to a zero state using passivity degrees considering a discrete-time complex network with undirected, linear, and diffusive couplings. Additionally, a corresponding mathematical analysis, which allows the representation of the dynamics as a set of symmetric matrices, is presented. With this, certain kinds of time-varying connections can be integrated into the analysis. Moreover, a particular criterion for selecting nodes to pin is also presented. The behavior of the controller for the non-varying and time-varying coupling cases is shown via numeric simulations.

Sentinel Node Identification with Hybrid Tracer-guided and Conventional Dynamic Sentinel Node Biopsy in Penile Cancer: A Prospective Study in 130 Patients from the Two National Referral Centres in Sweden

BackgroundStudies suggest that a hybrid indocyanine green (ICG)-99mTc-nanocolloid tracer improves sentinel node (SN) identification compared to conventional dynamic sentinel node biopsy (DSNB). ObjectiveTo investigate hybrid tracer–guided SN identification in a multicentre setting and determine false-negative (FN) and complication rates. Design, setting, and participantsA total of 130 patients with penile cancer scheduled for DSNB were prospectively included between February 2016 and December 2017 at two national Swedish referral centres. ICG-99mTc-nanocolloid hybrid tracer was used in the standard DSNB protocol. InterventionSNs were identified intraoperatively using radioguidance, fluorescence imaging, and blue dye. Outcome measurements and statistical analysisThe number of SNs identified by each tracer and the rates of complications and nodal recurrence during median follow-up of 34 mo were recorded. Differences in proportions between groups were compared using χ2 and McNemar’s tests. Results and limitationsOverall, 453 SNs were identified preoperatively via single-photon emission computed tomography/computed tomography. Among the 425 SNs excised, radioguidance, fluorescence, and blue dye identified 414 (97%), 363 (85%), and 349 (82%), respectively. Fluorescence imaging helped to detect six SNs that were negative using the other tracers, two of which were from the same patient and contained metastases. Histopathological examination detected 33 metastatic SNs in 20/130 patients (15%). The FN rate was 12% per groin (95% confidence interval 8–16%). ConclusionsIdentification of SNs in patients with penile cancer relies mainly on radioguidance, while fluorescence (ICG) and blue dye methods for optical SN identification are comparable. However, the value of fluorescence imaging should be further evaluated in studies with long-term follow-up. Patient summaryIn this study, we investigated addition of a dye called indocyanine green (ICG) for assessment of lymph nodes in patients with cancer of the penis. ICG did not improve the rate of detection of nodes most likely to harbour cancer because of their location in the drainage pathway for lymphatic fluid, but did help in identifying additional metastases.

Dual Threshold Voltage Pseudo Domino Logic - Based Buffer with Reduced Power

In this paper, a new technique of power reduction in pseudo domino logic-based buffer is proposed with dual threshold voltage. The proposed method employs the dual threshold voltage technique to reduce the power. During the precharge stage, we have used the high threshold voltage transistor in the non-critical path of the circuit, to reduce the leakage current due to which the average power dissipation is minimized. In critical path, low threshold voltage transistors are used. The low threshold voltage transistors are used in evaluation stage, for the performance enhancement of the circuit. Keeper circuit is used with two PMOS transistor which helps in maintaining voltage of dynamic node and improves the noise immunity and speed of the circuit. Also, a NMOS transistor, which is operated by the dynamic node is used which minimizes the leakage by creating a stacking impact. In addition, the clock inverter is enabled via low voltage NMOS which significantly reduces the current in the inverter due to direct connection between the supply voltage (Vdd) to ground.

Blockchain-Enabled Approach for Big Data Processing in Edge Computing

The integration of blockchain with edge computing technology has emerged as a popular topic in recent years. Edge computing offers many advantages to IoT data computing by reducing the bandwidth in the cloud, whereas blockchain offers a promising solution to the data integrity issues of dynamic edge nodes. There is considerable technological mismatch, however, when employing a blockchain protocol for edge computing. Standard edge nodes usually deal with big data and require constant data exchange over time, whereas traditional blockchains prevent the storage of big data in a distributed network. This study presents a novel blockchain architecture called Recordchain, which is fully adapted to the edge computing environment. The innovative feature of Recordchain is its ability to locate actual data separately while providing security, scalability, and performance, thus addressing the storage utilization issues related to big data. The efficiency and performance of the proposed architecture is further demonstrated experimentally.

SC209 - Bilateral inguinal lymph-node dissection versus unilateral inguinal lymph-node dissection and contralateral dynamic sentinel node biopsy in clinical N1 squamous cell carcinoma of the penis

SC209 - Bilateral inguinal lymph-node dissection versus unilateral inguinal lymph-node dissection and contralateral dynamic sentinel node biopsy in clinical N1 squamous cell carcinoma of the penis

Motor learning drives dynamic patterns of intermittent myelination on learning-activated axons.

Myelin plasticity occurs when newly formed and pre-existing oligodendrocytes remodel existing patterns of myelination. Myelin remodeling occurs in response to changes in neuronal activity and is required for learning and memory. However, the link between behavior-induced neuronal activity and circuit-specific changes in myelination remains unclear. Using longitudinal in vivo two-photon imaging and targeted labeling of learning-activated neurons in mice, we explore how the pattern of intermittent myelination is altered on individual cortical axons during learning of a dexterous reach task. We show that behavior-induced myelin plasticity is targeted to learning-activated axons and occurs in a staged response across cortical layers in the mouse primary motor cortex. During learning, myelin sheaths retract, which results in lengthening of nodes of Ranvier. Following motor learning, addition of newly formed myelin sheaths increases the number of continuous stretches of myelination. Computational modeling suggests that motor learning-induced myelin plasticity initially slows and subsequently increases axonal conduction speed. Finally, we show that both the magnitude and timing of nodal and myelin dynamics correlate with improvement of behavioral performance during motor learning. Thus, learning-induced and circuit-specific myelination changes may contribute to information encoding in neural circuits during motor learning.

Observer‐based bipartite Mittag‐Leffler time‐varying formation for fractional multi‐agent systems with discontinuous inherent dynamics

SummaryIn this article, the bipartite Mittag‐Leffler time‐varying formation issue of fractional multi‐agent systems is discussed, where the dynamical nodes of followers are modeled to be discontinuous and nondecreasing. A observer‐based adaptive distributed control scheme, which is dependent on output information, is designed to realized the bipartite formation control goal. Meanwhile, the efficient algorithm with respect to the control protocol also is presented. Under fractional Filippov differential inclusion framework, by constructing Lur'e Postnikov‐type Lyapunov functional and applying Clarke's non‐smooth analysis technique, the bipartite Mittag‐Leffler time‐varying formation conditions are derived in terms of linear matrix inequalities. At last, a simulation example is carried out to verify the effectiveness of the proposed scheme.