Network Topology Research Articles

Innovative High Degree of Freedom Single-Multipoint Incremental Forming System for Manufacturing Curved Thin-Walled Components

This study proposed a new design and control framework for a novel high degree of freedom single-multipoint incremental forming (HDoF-SMIF) system to manufacture curved thin-walled components. First, a novel HDoF-SMIF system was designed based on the principles of HDoF-SMIF technology (including the description of the mechanical structure and network topology). Following that, to realize the process control of the HDoF-SMIF process, this study introduced a novel control strategy for the tool deflection angle specific to robotic incremental forming. It also developed a synergistic motion control approach for the lifting platform and an accelerated computation method to determine the forming parameters for a multipoint reconfigurable die. Then, a novel HDoF-SMIF principle prototype and control program were built for the designed HDoF-SMIF system structure and control method. Finally, the feasibility of the HDoF-SMIF system was verified with the forming test on the domed component, and the geometrical accuracy and wall thickness distribution of the samples were measured using a 3D optical scanner system to evaluate the forming quality of the established HDoF-SMIF system. The results showed that compared to the conventional single-point incremental forming (SPIF) process, the developed HDoF-SMIF system can provide a more uniform thickness distribution of the formed part with a lower overall thickness thinning rate. Furthermore, by increasing the tool deflection angle with a 1mm elastomeric interpolator, the overall part's forming accuracy and surface quality can be effectively improved.

The Atypical Transport System: Natural Gas Pipeline System

The natural gas pipeline system (NGPS) is complex with a diverse network topology. This study elucidates the network characteristics of the NGPS, revealing that it shares commonalities with small-world networks, but with differences caused by its structural and operational characteristics. The combination of the low aggregation effect caused by branching structure and the offsetting effect caused by economic and geographical constraints results in a network with small world characteristics. NGPS has longer mean path than common small world network, but the aggregation factor is close to that of a standard small world network. With respect to its small-world characteristics and correlation with self-organized criticality (SOC), an in-depth study of the SOC characteristics of the NGPS was conducted utilizing a combination of traditional logarithm analysis and R/S analysis. The findings establish that there are atypical SOC characteristics, characterized by a weakened power law within a specific functional range, thereby encouraging the advancement of SCADA and other safety technologies. Because of the line-pack, which induces time lags in the NGPS, NGPS can prevent the cascade failures. The reasons for the periodicity and centrality of NGPS data are analyzed by the affecting components, with the R/S analysis verifying a weak positive correlation of the function data across different time series. The interlinking of the SOC and R/S analysis illustrates the connection between critical status, accident type, and operation index. The results prove the reasonability and importance of insurance measures, such as regular internal detection, higher grade steel pipes change and staff training et al., in a novel view of SOC. The future works in building accidents database and human factors study are proposed.

Malicious Node Detection Using Machine Learning and Distributed Data Storage Using Blockchain in WSNs

Abstract: Wireless Sensor Networks (WSNs) present unique opportunities for a broad spectrum of applications such as industrial automation, situation awareness, tactical surveillance for military applications, environmental monitoring, chemical or biological detection etc., Wireless Sensor Networks (WSNs) consist of hundreds of tiny nodes having the capability of sensing, computation and wireless communications. Deployed in a hostile environment, individual nodes of a wireless sensor network (WSN) could be easily compromised by the adversary due to the constraints such as limited battery lifetime, memory space and computing capability. It is critical to detect and isolate the compromised nodes in order to avoid being misled by the falsified information injected by the adversary through compromised nodes. However, it is challenging to secure the flat topology networks efficiently because of the poor scalability and high communication overhead. Since security and performance issues are a big concern in the case of wireless sensor networks, emphasis has been given to the scheme based on Weighted-Trust Evaluation (WTE). Extensive simulation is performed using MATLAB, to verify performance and efficiency of WTE by varying various parameters

Implementation of DOS Attack Using NS2

This paper presents a comprehensive study on the implementation of a Denial of Service (DOS) attack using NS2, a widely-used network simulator. The project involves the installation and configuration of NS2 and NAM on Ubuntu, the design of a realistic network topology, and the generation of TCP and UDP traffic to simulate a DOS attack. By evaluating the impact of the attack on network performance metrics such as throughput and latency, this study aims to enhance understanding of DOS attacks in simulated environments and propose effective mitigation strategies. The findings contribute to the field of network security by providing insights into the behavior of DOS attacks and highlighting the importance of proactive defense mechanisms.

Complex network-based risk analysis for maritime heavy casualties in China during 2012–2021

A collection of accident investigation reports for the 21 maritime heavy casualties that occurred in China during 2012–2021 is studied statistically and analytically in the present study. The basic accident factors are first extracted from these investigation reports to present the distribution features of these heavy casualties. Then, the accident factors in terms of crew, environment, ship equipment and management in each individual investigation report are logically linked to develop a causative chain. As a result, 21 chains developed from the 21 accident investigation reports are interweaved into a complex network by sharing the same factors. The accident factors involved in the complex network are quantitatively assessed in terms of activeness, connection strength and criticality based on the network topology parameters and the improved K-shell decomposition algorithm. The topology of the developed complex network implies that the occurrence of maritime heavy casualties is under control, and the results show that the seaworthiness of crew, ship equipment and cargo is critical to maintain safe operation on board; meanwhile, supervising ship operators and managers to ensure that they fulfil their responsibility is an effective way to maintain the maritime safety level.

Transcranial ultrasound stimulation effect in the redundant and synergistic networks consistent across macaques

Abstract Low-intensity transcranial ultrasound stimulation (TUS) is a non-invasive technique that safely alters neural activity, reaching deep brain areas with good spatial accuracy. We investigated the effects of TUS in macaques using a recent metric, the synergy minus redundancy rank gradient, that quantifies different kinds of neural information processing. We analyzed this high-order quantity on the fMRI data after TUS in two targets: the supplementary motor area (SMA-TUS) and the frontal polar cortex (FPC-TUS). The TUS produced specific changes at the limbic network at FPC-TUS and the motor network at SMA-TUS and altered the sensorimotor, temporal, and frontal networks in both targets, mostly consistent across macaques. Moreover, there was a reduction in the structural and functional coupling after both stimulations. Finally, the TUS changed the intrinsic high-order network topology, decreasing the modular organization of the redundancy at SMA-TUS and increasing the synergistic integration at FPC-TUS.

An Observer-Based Topology Identification and Synchronization in Finite Time for Fractional Singularly Perturbed Complex Networks via Dynamic Event-Triggered Control

This paper investigates the topology identification and synchronization in finite time for fractional singularly perturbed complex networks (FSPCNs). Firstly, a convergence principle is developed for continuously differential functions. Secondly, a dynamic event-triggered mechanism (DETM) is designed to achieve the network synchronization, and a topology observer is developed to identify the network topology. Thirdly, under the designed DETM, by constructing a Lyapunov functional and applying the inequality analysis technique, the topology identification and synchronization condition in finite time is established in the forms of the matrix inequality. In addition, it is proved that the Zeno behavior can be effectively excluded. Finally, the effectiveness of the main results is verified by an application example.

Research on multi-layer network topology optimization strategy for railway internet of things based on game theory benefits

In the railway system environment, the interconnection of a vast array of intelligent sensing devices has brought about revolutionary changes in the management and monitoring of railway transportation. However, this also poses challenges to the communication service quality within the railway Internet of Things (IoT). Through collective intelligence and collaboration, the nodes within the railway IoT can not only share data and information but also work synergistically to enhance the overall intelligence level and improve decision-making quality of the network. Therefore, this paper proposes a reconnection mechanism based on the computation of node game-theoretic benefits and optimizes this process with the concept of swarm intelligence collaboration. Initially, the game-theoretic benefit values of the nodes in the railway IoT network are calculated. Subsequently, based on the weight priority of the edges, the two edges with the larger weights are selected, and connections are established between nodes with similar game-theoretic benefit values to enhance the network’s robustness. This approach enables rapid networking and efficient communication transmission within the railway IoT, providing robust assurance for the safe and stable operation of the railway.

The Mechanism of Scopolamine’s Effect on Migration Function of Lung Cancer Cells: A Network Pharmacology and Bioinformatics Perspective

Background. This study utilized network pharmacology and bioinformatics analysis to identify the hub genes influenced by scopolamine in lung cancer. Methods. The effect of scopolamine on lung cancer was investigated by cell invasion assay and cell scratch assay. The analysis involved protein-protein interaction (PPI) networks topology analysis to identify these genes, and subsequent differential analysis and survival analysis were conducted using gene expression profile interaction analysis (GEPIA). Furthermore, the findings were supported by molecular docking experiments for verification. Results. Results from cell invasion and scratch assays suggest that scopolamine inhibits the migration of lung cancer cells. JAK2, JAK3, CCR5, and ACE were identified as the top four hub genes that have an impact on lung cancer. KEGG enrichment analysis revealed that the scopolamine response in lung cancer is significantly associated with ten pathways, including “neuroactive ligand-receptor interaction in cancer,” “PD-1 checkpoint pathway in cancer,” “chemokine signaling pathway,” “PD-L1 expression,” and others. Additionally, the expression levels of JAK2, JAK3, CCR5, and ACE were found to be correlated with survival in patients with lung cancer. Furthermore, molecular docking experiments demonstrated that scopolamine binds and forms stable complexes with the protein products of all four aforementioned genes. The main targets of scopolamine in the treatment of lung cancer are JAK2, JAK3, CCR5, and ACE. Conclusion. Scopolamine has a significant effect on various cellular functions in lung cancer cells, potentially reducing the likelihood of metastasis. Based on these findings, it is recommended to consider administering scopolamine as part of the preoperative phase for patients with lung cancer.

Intelligent fault diagnosis of machinery based on hybrid deep learning with multi temporal correlation feature fusion

AbstractWith the advent of intelligent manufacturing era, higher requirements are put forward for the fault diagnosis technology of machinery. The existing data‐driven approaches either rely on specialized empirical knowledge for feature analysis, or adopt single deep neural network topology structure for automatic feature extraction with compromise of certain information loss especially the time‐series information's sacrifice, which both eventually affect the diagnosis accuracy. To address the issue, this paper proposes a novel multi‐temporal correlation feature fusion net (MTCFF‐Net) for intelligent fault diagnosis, which can capture and retain time‐series fault feature information from different dimensions. MTCFF‐Net contains four sub‐networks, which are long and short‐term memory (LSTM) sub‐network, Gramian angular summation field (GASF)‐GhostNet sub‐network and Markov transition field (MTF)‐GhostNet sub‐network and feature fusion sub‐network. Features of different dimensional are extracted through parallel LSTM sub‐network, GASF‐GhostNet sub‐network and MTF‐GhostNet sub‐network, and then fused by feature fusion sub‐network for accurate fault diagnosis. Two fault diagnosis experimental studies on bearings are implemented to validate the effectiveness and generalization of the proposed MTCFF‐Net. Experimental results demonstrate that the proposed model is superior to other comparative approaches.

Causal network topology analysis: Characterizing causal context for risk management.

The ways that risk assessments are commonly performed in organizations have limitations that undermine their quality. They typically focus on individual risk events one at a time but are weak at integrating their relevant causal context, into decision-making processes. Network topology analysis has previously been applied to address this weakness through quantitatively characterizing the importance of the causal interactions of risk events. However, there remains a lack of both clarity and consistency in terminology, methods, and interpretation of the results of this approach. This paper presents and formalizes causal network topology analysis, a methodology that contributes to (1) characterizing the causal context of a risk event to inform its management, (2) articulating the ontological concepts underpinning a repeatable topology network analysis, and (3) justifying the selection and usage of network metrics for this purpose. The theory and methodology are discussed, and an exemplar application to a mining project feasibility study is presented.

Evolutionary dynamics of direct and indirect reciprocity on networked populations

Direct, indirect and network reciprocity are key mechanisms fostering cooperation in societies. Though intrinsically interrelated, they are often studied separately. Unravelling the evolutionary dynamics of reciprocity in structured populations remains challenging. Here, we develop a general model to explore the evolution of direct and indirect reciprocity on networks. Drawing on our model, we derive a simple rule for cooperation to evolve through reciprocity. Applying this rule to 1 million random networks, we investigate how network topology influences evolutionary outcomes. We find that, in general, direct reciprocity is easier to promote cooperation on these networks. Additionally, direct reciprocity remains robust when a population adapts to the environment through social learning. Notably, environmental fluctuations significantly lower the threshold for cooperation through indirect reciprocity. Overall, our work sheds light on a fresh framework for comprehending the evolution of reciprocity.

Discovering weak signals of emerging topics with a triple-dimensional framework

In the rapidly evolving landscape of innovation, the early identification of emerging topics is crucial across diverse research domains. This study views weak signals as the preliminary stage of emerging topics and constructs an innovative weak signal triple-dimensional analytical framework to discern nascent emerging topics. The framework uses triads to represent signals by constructing keyword citation networks and establish a collection of novel signals through network topology analysis. Weak signals are subsequently identified by examining the visibility, diffusion and social influence of signals with time-weighted attributes. An altmetrics indicator is employed to formally measure the social influence of weak signals from the perspective of public perception. We apply the proposed framework to the field of gene editing, and the outcomes of literature analysis and dynamic validation substantiate the efficacy of our approach. Compared to related methods, our framework demonstrates a more nuanced ability to distinguish between various signals, identifying more weak signals and research topics with increased potential for social impact. This research provides valuable insights for strategic decision-making, innovation management, and future foresight.

Distributed l2−l∞ filtering over wireless sensors based on adaptive event triggering

This paper investigates the design problem of a set of distributed filters. These adaptive event-triggered (AET) filters have the [Formula: see text] property and are suitable for sensor networks characterized by random measurement data loss and dynamic topology switching. In a distributed filtering network, each local filter estimates the system’s state not only relying on the node’s individual information but also using information from neighboring nodes in the network topology. The adaptive event triggering condition is determined by the combination of the latest release data of the filter itself, the estimated value of the current moment, and the latest release data of the neighboring nodes. The event triggering mechanism adopts a threshold adaptive adjustment scheme, which dynamically changes the threshold parameter according to the filtering error under the premise of guaranteeing the filter performance, thus maximally saving the network communication resources. Initially, we used a binary sequence to describe the random loss of sensor measurements, and a Markov chain is used to describe the switching of the filtering network topology. Secondly, a Lyapunov function is created to examine the [Formula: see text] performance index of the filtering error system and investigate its mean square exponential stability. Subsequently, the distributed filter is formulated, and its parameters are determined using the linear matrix inequality approach. Ultimately, the validity of the proposed method is substantiated through numerical illustrations.

Enhanced Beacons Dynamic Transmission over TSCH

Time slotted channel hopping (TSCH) has become the standard multichannel MAC protocol for low-power lossy networks. The procedure for associating nodes in a TSCH-based network is not included in the standard and has been defined in the minimal 6TiSCH configuration. Faster network formation ensures that data packet transmission can start sooner. This paper proposes a dynamic beacon transmission schedule over the TSCH mechanism that achieves a shorter network formation time than the default minimum 6TiSCH static schedule. A theoretical model is derived for the proposed mechanism to estimate the expected time for a node to get associated with the network. Simulation results obtained with different network topologies and channel conditions show that the proposed mechanism reduces the average association time and average power consumption during network formation compared to the default minimal 6TiSCH configuration.

Dynamic Perception-Based Vehicle Trajectory Prediction Using a Memory-Enhanced Spatio-Temporal Graph Network

In the realm of intelligent transportation systems, accurately predicting vehicle trajectories is paramount for enhancing road safety and optimizing traffic flow management. Addressing the impacts of complex traffic environments and efficiently modeling the diverse behaviors of vehicles are the key challenges at present. To achieve precise prediction of vehicle trajectories, it is essential to fully consider the dynamic changes in traffic conditions and the long-term dependencies of time-series data. In response to these challenges, we propose the Memory-Enhanced Spatio-Temporal Graph Network (MESTGN), an innovative model that integrates a Spatio-Temporal Graph Convolutional Network (STGCN) with an attention-enhanced Long Short-Term Memory (LSTM)-based sequence to sequence (Seq2Seq) encoder–decoder structure. MESTGN utilizes STGCN to capture the complex spatial dependencies between vehicles and reflects the interactions within the traffic network through road traffic data and network topology, which significantly influences trajectory prediction. Additionally, the model focuses on historical vehicle trajectory data points using an attention-weighted mechanism under a traditional LSTM prediction architecture, calculating the importance of critical trajectory points. Finally, our experiments conducted on the urban traffic dataset ApolloSpace validate the effectiveness of our proposed model. We demonstrate that MESTGN shows a significant performance improvement in vehicle trajectory prediction compared with existing mainstream models, thereby confirming its increased prediction accuracy.

Bayesian inference and optimisation of stochastic dynamical networks

Network inference has been extensively studied in several fields, such as systems biology and social sciences. Learning network topology and internal dynamics is essential to understand mechanisms of complex systems. In particular, sparse topologies and stable dynamics are fundamental features of many real-world continuous-time (CT) networks. Given that usually only a partial set of nodes are able to observe, we consider linear CT systems to depict networks since they can model unmeasured nodes via transfer functions. Additionally, measurements tend to be noisy and with low and varying sampling frequencies. This paper applies dynamical structure functions (DSFs) derived from linear stochastic differential equations (SDEs) to describe networks of measured nodes. A numerical sampling method, preconditioned Crank–Nicolson (pCN), is used to refine coarse-grained trajectories to improve inference accuracy. The proposed method can handle sparsely sampled data and unmeasurable nodes. Monte Carlo simulations indicate that the proposed method outperforms state-of-the-art methods with various network topologies. The developed method can be applied under a wide range of contexts, such as gene regulatory networks, social networks and communication systems.

Cyclodextrin ‘Chaperones’ Enable Quasi‐Ideal Supramolecular Network Formation and Enhanced Photodimerization of Hydrophobic, Red‐shifted Photoswitches in Water

AbstractPrecision‐engineered light‐triggered hydrogels are important for a diversity of applications. However, fields such as biomaterials require wavelength outside the harsh UV regime to prevent photodamage, typically requiring chromophores with extended π‐conjugation that suffer from poor water solubility. Herein, we demonstrate how cyclodextrins can be used as auxiliary agents to not only solubilize such chromophores, but even to preorganize them in a 2 : 2 host‐guest inclusion complex to facilitate photodimerization. We apply our concept to styrylpyrene‐end‐functionalized star‐shaped polyethylene glycols (sPEGs). We initially unravel details of the host‐guest inclusion complex using spectroscopy and mass spectrometry to give clear evidence of a 2 : 2 complex formation. Subsequently, we show that the resultant supramolecularly linked hydrogels conform to theories of supramolecular quasi‐ideal model networks, and derive details on their association dynamics using in‐depth rheological measurements and kinetic models. By comparing sPEGs of different arm length, we further elucidate the model network topology and the accessible mechanical property space. The photo‐mediated dimerization proceeds smoothly, allowing to transform the supramolecular model networks into covalent ones. We submit that our strategy opens avenues for executing hydrophobic photochemistry in aqueous environments with enhanced control over reactivity, hydrogel topology or programmable mechanical properties.

SDN Network DDOS Detection Using ML

This paper describes a technique that uses the Ryu Controller and Mininet to identify and mitigate Distributed Denial of Service (DDoS) threats in Software Defined Networks (SDN). Using Mininet, the suggested method entails building a virtual network topology with connected switches and hosts. The Ryu Controller gathers traffic data while Mininet simulates several DDoS attack types, such as ICMP flood, land assault, TCP SYN flood, and UDP attacks. The Ryu Controller collects both benign and DDoS traffic into a dataset that is used to build a machine learning (ML) model that can detect DDoS attacks in real time.

MNMST: topology of cell networks leverages identification of spatial domains from spatial transcriptomics data

Advances in spatial transcriptomics provide an unprecedented opportunity to reveal the structure and function of biology systems. However, current algorithms fail to address the heterogeneity and interpretability of spatial transcriptomics data. Here, we present a multi-layer network model for identifying spatial domains in spatial transcriptomics data with joint learning. We demonstrate that spatial domains can be precisely characterized and discriminated by the topological structure of cell networks, facilitating identification and interpretability of spatial domains, which outperforms state-of-the-art baselines. Furthermore, we prove that network model offers an effective and efficient strategy for integrative analysis of spatial transcriptomics data from various platforms.