Arbitrary Node Research Articles

Mobile all-light communication network

In reality, both mobile and fixed nodes are required in wireless light communication networks. Dynamic maintenance of light alignment plays a key role in mobile full-duplex light communication. Here, we merge an image identification module and a light communication system on a three-axis gimbal stabilizer. A real-time image of the other light communication system obtained by the image identification module is used as a feedback signal to control the three-axis gimbal stabilizer. Therefore, the other light communication system is automatically tracked, and the optical path between the two light communication ends is dynamically maintained, leading to mobile full-duplex light communication under the transmission control protocol/internet protocol (TCP/IP) scheme. Two green light communication apparatuses are separately deployed on two moving vehicles to establish bidirectional light transmission between moving network nodes with a maximum modulation bandwidth of 4 Mbps. Video communication across air and underwater environments is demonstrated, and internet access is illustrated via a Wi-Fi modem. To overcome environmental barriers, we combine mobile green light communication with blue laser communication, deep-ultraviolet light communication, and 850 nm laser diode communication to develop a mobile all-light communication network that enables seamless connectivity across air, land, and underwater environments. Since this network architecture is based on full-duplex communication, all communication nodes have equal and complete mapping characteristics and can facilitate bidirectional real-time data transmission between arbitrary nodes within the network, offering a promising route toward seamless mobile connectivity using light regardless of the environment.

Electronic Health Records Sharing Based on Consortium Blockchain.

In recent years, Electronic health records (EHR) has gradually become the mainstream in the healthcare field. However, due to the fact that EHR systems are provided by different vendors, data is dispersed and stored, which leads to the phenomenon of data silos, making medical information too fragmented and bringing some challenges to current medical services. Therefore, in view of the difficulties in sharing EHR between medical institutions, the risk of privacy leakage, and the lack of EHR usage control by patients, an EHR sharing model based on consortium blockchain is proposed in this paper. Firstly, the Interplanetary File System is combined with consortium blockchain, which forms a hybrid storage scheme of EHR, this technology effectively improves data security, privacy protection, and operational efficiency. Secondly, the model combines unidirectional multi-hop conditional proxy re-encryption based on type and identity with distributed key generation technology to achieve secure EHR sharing with fine grained control. At the same time, users are required to link the operation records of EHR, so as to realize the traceability of EHR usage. A dynamic Byzantine fault-tolerant algorithm based on reputation and clustering is then proposed to solve the problems of arbitrary master node selection, high latency and low throughput of PBFT, enabling the nodes to reach consensus more efficiently. Finally, the model is analyzed in terms of security and user control, showing that the model is less energy intensive in terms of communication overhead and time consumption, and can effectively achieve secure sharing between medical data.

Simultaneous bi-directional all-optical multipoint-to-multipoint switching for free-space optical communication networks

A new, to our knowledge, simultaneous bi-directional all-optical multipoint-to-multipoint switching scheme is proposed and experimentally demonstrated for free-space optical (FSO) communication networks. Through tuning the optical switch at the core node, the communication link between each two arbitrary remote nodes can be established. The multipoint transmission performance is tested in a turbulence-tunable atmospheric cell using a 6.25 Gbit/s quadrature phase-shift keying (QPSK) signal. At the bit error rate (BER) of 1×10−3, the power penalty is <3.5dB with a 250°C temperature difference of the atmospheric cell. Moreover, this scheme is compatible with all-optical wavelength conversion, which can achieve strict transparency for different modulation formats. Our proposed scheme offers the possibility to provide an efficient, low-cost, and high-speed method for simultaneous bi-directional all-optical multipoint-to-multipoint FSO communications.

Breast Cancer Classification From Digital Pathology Images via Connectivity-Aware Graph Transformer.

Automated classification of breast cancer subtypes from digital pathology images has been an extremely challenging task due to the complicated spatial patterns of cells in the tissue micro-environment. While newly proposed graph transformers are able to capture more long-range dependencies to enhance accuracy, they largely ignore the topological connectivity between graph nodes, which is nevertheless critical to extract more representative features to address this difficult task. In this paper, we propose a novel connectivity-aware graph transformer (CGT) for phenotyping the topology connectivity of the tissue graph constructed from digital pathology images for breast cancer classification. Our CGT seamlessly integrates connectivity embedding to node feature at every graph transformer layer by using local connectivity aggregation, in order to yield more comprehensive graph representations to distinguish different breast cancer subtypes. In light of the realistic intercellular communication mode, we then encode the spatial distance between two arbitrary nodes as connectivity bias in self-attention calculation, thereby allowing the CGT to distinctively harness the connectivity embedding based on the distance of two nodes. We extensively evaluate the proposed CGT on a large cohort of breast carcinoma digital pathology images stained by Haematoxylin & Eosin. Experimental results demonstrate the effectiveness of our CGT, which outperforms state-of-the-art methods by a large margin. Codes are released on https://github.com/wang-kang-6/CGT.

Retinal waves in adaptive rewiring networks orchestrate convergence and divergence in the visual system.

Spontaneous retinal wave activity shaping the visual system is a complex neurodevelopmental phenomenon. Retinal ganglion cells are the hubs through which activity diverges throughout the visual system. We consider how these divergent hubs emerge, using an adaptively rewiring neural network model. Adaptive rewiring models show in a principled way how brains could achieve their complex topologies. Modular small-world structures with rich-club effects and circuits of convergent-divergent units emerge as networks evolve, driven by their own spontaneous activity. Arbitrary nodes of an initially random model network were designated as retinal ganglion cells. They were intermittently exposed to the retinal waveform, as the network evolved through adaptive rewiring. A significant proportion of these nodes developed into divergent hubs within the characteristic complex network architecture. The proportion depends parametrically on the wave incidence rate. Higher rates increase the likelihood of hub formation, while increasing the potential of ganglion cell death. In addition, direct neighbors of designated ganglion cells differentiate like amacrine cells. The divergence observed in ganglion cells resulted in enhanced convergence downstream, suggesting that retinal waves control the formation of convergence in the lateral geniculate nuclei. We conclude that retinal waves stochastically control the distribution of converging and diverging activity in evolving complex networks.

Voltage differences as functions of a matrix eigensystem

We firstly consider an electrical network that consists of multiple subnetworks. We refer to some basic definitions which relate net current flows and voltages, the edges and nodes respectively of each subnetwork, and define matrix G, as the direct sum of the admittance matrices that correspond to each subnetwork. Then, we prove that any voltage difference at two arbitrary nodes can be explicitly written as a function only of the eigensystem of G, namely its eigenvalues and their corresponding eigenvectors. Next, we consider a DC/AC circuit network and by using duality properties of a matrix pencil related to the network, we obtain expressions for voltage differences as functions of the pencil’s eigenvalues and their corresponding eigenvectors. To validate our findings, we present illustrative numerical examples.

A federated learning model for integrating sustainable routing with the Internet of Vehicular Things using genetic algorithm

A distributed machine learning technique called federated learning allows numerous Internet of Things (IoT) edge devices to work together to train a model without sharing their raw data. Internet of Vehicular Things (IoVT) are an important tool in smart cities for moving objects, such as knowing the traffic patterns, road conditions, vehicle behavior, etc. To enhance traffic management and optimize routes, federated learning, and IoT must work jointly, which may achieve sustainable development goals (SDG) in many ways. This research outlines a system for federated learning in vehicular networks in smart cities. The suggested architecture considers the difficulties presented by such situations’ restricted network connectivity, privacy issues, and security concerns. The framework employs a hybrid methodology integrating federated learning on a centralized server with local training on individual cars. The proposed framework is assessed based on a real-world dataset from a smart city through IoT devices. The findings demonstrate that the suggested method successfully increases model accuracy while preserving the confidentiality and security of the data. In this investigation, we incorporated the Federated Learning model, which can fetch all the information between arbitrary nodes and derive the Traffic, Fuel Cost, Safety, Parking Cost, and Transportation cost for a better routing approach. The suggested framework can be utilized to increase the effectiveness of the transportation system, decrease congestion in smart cities, and improve traffic management. We employ an improved genetic algorithm (iGA) with generation-dependent even mutation to tackle the emission in the smart environment.

Simulating Fuel Assembly Bowing: A Neutron-Diffusion Method Based on Arbitrary Quadrilateral Node and Conformal Mapping Technique

Fuel assembly bowing, widely observed in a pressurized water reactor (PWR), often results in an asymmetrical power distribution. This paper proposes a neutron-diffusion method that integrates the arbitrary quadrilateral node with the conformal mapping technique to characterize the impact of fuel assembly bowing on power distribution. The proposed method involves a nonlinear iteration process to solve the neutron-diffusion equation. The global coarse-mesh finite difference equation is established on the arbitrary quadrilateral nodes, which are redivided in response to fuel assembly bowing. The local two-node nodal expansion method equation is established on the rectangular nodes, which are mapped from the original arbitrary quadrilateral nodes using the conformal mapping technique. The proposed method has improved our self-developed core code, named SPARK, for PWRs. To verify this novel method, two distinct types of fuel assembly bowing are modeled based on the mini core. The reference results for these models were obtained using the Monte Carlo code NECP-MCX. The numerical results suggest a robust agreement between the biases of keff and power distributions and their corresponding reference results.

Управление электротяговыми нагрузками на основе взаимодействия с системой электроснабжения железных дорог

Currently, the principles of electric rolling stock management do not involve consideration of the instantaneous load of traction substations and the operating mode parameters of other electric traction loads in the inter-substation zone of an electrified section of railways. The inter-train travel intervals on the section are set based on the preliminary software-based calculation of the traction power supply system for trains with a given range of train weight. The article provides the reasonable approach to managing electric traction loads based on their interaction both with each other and the railway power supply system in real time with instantaneous limitations of the energy infrastructure. The results of experimental synchronized measurements with two trains in the substation area of an electrified AC section are presented. Using a mathematical model of the pilot site and measurement data, the coefficients of the influence of electric traction loads on the voltage in arbitrary nodes and on the loading of the feeders of the contact network are calculated.

Neural Network Estimators for Optimal Tour Lengths of Traveling Salesperson Problem Instances with Arbitrary Node Distributions

It is essential to solve complex routing problems to achieve operational efficiency in logistics. However, because of their complexity, these problems are often tackled sequentially using cluster-first, route-second frameworks. Unfortunately, such two-phase frameworks can suffer from suboptimality due to the initial phase. To address this issue, we propose leveraging information about the optimal tour lengths of potential clusters as a preliminary step, transforming the two-phase approach into a less myopic solution framework. We introduce quick and highly accurate Traveling Salesperson Problem (TSP) tour length estimators based on neural networks (NNs) to facilitate this. Our approach combines the power of NNs and theoretical knowledge in the routing domain, utilizing a novel feature set that includes node-level, instance-level, and solution-level features. This hybridization of data and domain knowledge allows us to achieve predictions with an average deviation of less than 0.7% from optimality. Unlike previous studies, we design and employ new instances replicating real-life logistics networks and morphologies. These instances possess characteristics that introduce significant computational costs, making them more challenging. To address these challenges, we develop a novel and efficient method for obtaining lower bounds and partial solutions to the TSP, which are subsequently utilized as solution-level predictors. Our computational study demonstrates a prediction error up to six times lower than the best machine learning (ML) methods on their training instances and up to 100 times lower prediction error on out-of-distribution test instances. Furthermore, we integrate our proposed ML models with metaheuristics to create an enumeration-like solution framework, enabling the improved solution of massive-scale routing problems. In terms of solution time and quality, our approach significantly outperforms the state-of-the-art solver, demonstrating the potential of our features, models, and the proposed method. History: This paper has been accepted for the Transportation Science Special Issue on Machine Learning Methods and Applications in Large-Scale Route Planning Problems. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.0015 .

Free-Vibration, Buckling, and Static Analysis of Sandwich Panels with a Square Honeycomb Core Using a Meshfree Method

Meshless methods are a relatively modern approach that has garnered significant interest in analyzing and investigating complex structural problems thanks to distinctive features such as high accuracy, flexibility, rapid calculation speed, and cost–effectiveness. In the present study, the element-free Galerkin method is employed as a meshless approach, wherein a set of arbitrary nodes is distributed across the problem’s geometry, including its boundaries, to define the problem domain. The Moving Least Squares approximation is also utilized to formulate the shape functions. Given the intricate geometry of the honeycomb core within sandwich panels, the study employs the generalized method to derive the effective mechanical properties of the honeycomb core. Furthermore, to acquire displacement fields and establish relationships for the problem, the classic plate theory and the first-order shear deformation theory are independently applied. These relationships are then formulated using the Galerkin meshless method. Finally, the obtained parameters are evaluated, and the validity of these relationships is confirmed by comparing the results of this study with those presented in existing articles. The outlined procedure has been systematically simulated through a step-by-step MATLAB program. Subsequently, the impact of different boundary conditions, individual layer thicknesses, dimension ratios, and core wall spacing on the panel’s displacement, free-vibration, and buckling behaviors is thoroughly investigated. The obtained results substantiate the efficacy of the utilized methodology, demonstrating a favorable combination of accuracy and convergence rate.

Reliability evaluation for bijection-connected networks based on the super Pk-connectivity

With the need for large-scale networks evolving in a variety of fields, such as cloud/edge computing, social networks, privacy protection, etc., the network's robustness against faulty clusters has recently gained more attention. The family of bijection-connected networks includes numerous instances of remarkable network topologies, for example, hypercubes, Möbius cubes, crossed cubes, etc. For a network whose underlying topology is modeled by a graph G, its node-connectivity, κ(G), is a key performance index of network robustness, which is formalized as the total number of nodes in the smallest node-cut of G. Apparently, κ(G) happens to be bounded above by G's minimum degree. A node-cut is trivial if it is nothing but an arbitrary node's neighborhood. Then, G can be r-connected if 1≤r≤κ(G). To say the least, if each smallest node-cut of G is trivial, G is super connected. The super Pk-connectivity is an innovative assessment to quantify the connectedness level of G, where Pk notates a path consisting of k nodes (k≥1). This article aims to investigate the super Pk-connectivity for the bijection-connected networks. With regard to some specific instances, such as hypercubes, locally twisted cubes, etc., a sufficient and necessary condition is established to identify whether they are really super Pk-connected.

Algorithmic realization of exact three‐point difference scheme for singular Sturm–Liouville problem

AbstractA new algorithmic realization of exact three‐point difference schemes for a class of singular Sturm–Liouville problems is presented. It is shown that to compute the coefficients of the exact scheme in an arbitrary grid node, it is necessary to solve two auxiliary initial value problems for the second order linear ordinary differential equations: one problem on the interval (forward) and one problem on the interval (backward). Finally, the coefficient stability of the exact three‐point difference scheme is proved.

Minimal Pinning Control for Oscillatority of Boolean Networks.

In this article, minimal pinning control for oscillatority (i.e., instability) of Boolean networks (BNs) under algebraic state space representations method is studied. First, two criteria for oscillatority of BNs are obtained from the aspects of state transition matrix (STM) and network structure (NS) of BNs, respectively. A distributed pinning control (DPC) from these two aspects is proposed: one is called STM-based DPC and the other one is called NS-based DPC, both of which are only dependent on local in-neighbors. As for STM-based DPC, one arbitrary node can be chosen to be controlled, based on certain solvability of several equations, meanwhile a hybrid pinning control (HPC) combining DPC and conventional pinning control (CPC) is also proposed. In addition, as for NS-based DPC, pinning control nodes (PCNs) can be found using the information of NS, which efficiently reduces the high computational complexity. The proposed STM-based DPC and NS-based DPC in this article are shown to be simple and concise, which provide a new direction to dramatically reduce control costs and computational complexity. Finally, gene networks are simulated to discuss the effectiveness of theoretical results.

CasTformer: A novel cascade transformer towards predicting information diffusion

Predicting information diffusion cascade is an essential task in social networks. We mainly focus on predicting the size of the information cascade. The relationships inside a cascade are diverse, including global and relative spatio-temporal relationships, as well as interpersonal influence relationships. These complex relationships between nodes play a crucial role in cascade prediction, but they have not been thoroughly investigated. The Transformer's global receptive field can assist in capturing the relationships between two arbitrary nodes. However, using Transformer directly for a cascade is insufficient without considering its temporal and structural characteristics. In this paper, we propose a novel cascade Transformer for the first time, called CasTformer, specifically designed for cascade size prediction. CasTformer utilizes a global spatio-temporal positional encoding and relative relationship bias matrices on the self-attention mechanism to capture diverse cascade relationships. Moreover, self-knowledge distillation is employed for obtaining a better cascade representation to enhance prediction performance. We use four datasets with nearly millions of cascade samples to validate our model and it achieves training in 3 hours. Experimental results show that it outperforms state-of-the-art methods by an average of 11.9%, 6.1%, and 9.6% on MSLE, MAPE, and R2, respectively.

Fast and direct inversion methods for the multivariate nonequispaced fast Fourier transform

The well-known discrete Fourier transform (DFT) can easily be generalized to arbitrary nodes in the spatial domain. The fast procedure for this generalization is referred to as nonequispaced fast Fourier transform (NFFT). Various applications such as MRI and solution of PDEs are interested in the inverse problem, i.e., computing Fourier coefficients from given nonequispaced data. In this article, we survey different kinds of approaches to tackle this problem. In contrast to iterative procedures, where multiple iteration steps are needed for computing a solution, we focus especially on so-called direct inversion methods. We review density compensation techniques and introduce a new scheme that leads to an exact reconstruction for trigonometric polynomials. In addition, we consider a matrix optimization approach using Frobenius norm minimization to obtain an inverse NFFT.

Simulation of unconfined seepage in soil-rock mixture slope by virtual element method

Abstract The VEM (virtual element method) is commonly used in engineering due to its ability to solve arbitrary node meshes. In this study, we propose a method to determine the free surface of the unconfined seepage problem in soil-rock mixtures slop using the advantages of the VEM. By cutting meshes in the iteration, our method overcomes the limitation of fixed mesh in solving the free surface, and the numerical tests confirm the accuracy of the proposed method in predicting the location of the seepage surface. Moreover, the results demonstrate that the presence of rock blocks significantly impacts the unconfined seepage behavior of soil-rock mixtures slop, revealing the importance of considering rock blocks in the analysis of such systems.

SRFA-GRL: Predicting group influence in social networks with graph representation learning

Group influence evaluation is the fundamental research in social network analysis, whose main task is evaluating the influence of the group consisting of arbitrary nodes in the social network. Many methods have been proposed to measure group influence, such as centrality-based, Monte Carlo, and path-based methods. Graph Representation Learning (GRL) has profound success in social network node-level, edge-level, and graph-level tasks. GRL integrates nodes, edges, and other information in the network structure to calculate the nodes' embeddings jointly and provide embedding features with richer information than traditional methods. Group influence is related to many factors; existing methods only focus on a single aspect but ignore the various properties. This paper proposes a Subgraph Reconstruction Feature Aggregation Graph Representation Learning based (SRFA-GRL) framework to evaluate the group influence. In the SRFA-GRL framework, a subgraph reconstruction method is proposed to capture the node distribution of the group, and a vector similarity method is proposed to compute the relative distance between the maximal connected subgraph and group embeddings. Vast experiments are conducted on eight real social networks to analyze the effectiveness of the SRFA-GRL model, and the experimental results show that the SRFA-GRL framework outperforms baseline methods.

General Outage Probability Model for UAV-to-UAV links in Multi-UAV Networks

Unmanned Aerial Vehicles (UAVs) swarms have become increasingly ubiquitous in the civilian domains due to their maneuverability and scalability in collaborative flying missions. However, there is a lack of characterization of wireless communication performance in multi-UAV system without being constrained by the node spatial distributions. In this paper, the performance of a UAV swarm network is analyzed by characterizing the effects of the UAV-to-UAV (U2U) interference in terms of the Signal-to-Interference-plus-Noise Ratio (SINR). To this end, a closed-form analytical model for the Cumulative Distribution Function (CDF) of the SINR is derived by considering both deterministic and stochastic channel processes, as well as deterministic UAV mobilities. The derived model can be used to analyze U2U interference in a UAV swarm network for any arbitrary node locations. Using this model, the effects of the network parameters on various network Key Performance Indicators (KPIs) such as the outage probabilities and channel capacity rate are studied. The model is extended to incorporate hybrid LoS channels, and the general model is used to evaluate the temporal evolutions of network outage probability given the UAV trajectories. With this, the main advantage of the model in analyzing the baseline outage probabilities of multi-UAV deployments is showcased.

The fault‐tolerant beacon set of hexagonal Möbius ladder network

In localization, some specific nodes (beacon set) are selected to locate all nodes of a network, and if an arbitrary node stops working and still selected nodes remain in the beacon set, then the chosen nodes are called fault‐tolerant beacon set. Due to the variety of metric dimension applications in different areas of sciences, many generalizations were proposed, fault‐tolerant metric dimension is one of them. A resolving (beacon) set is fault tolerant, if for each is also a resolving set; it is also known as a fault‐tolerant beacon set; the minimum cardinality of such a beacon set is known as the fault‐tolerant metric dimension of a graph . In this paper, we find the fault‐tolerant beacon set of hexagonal Möbius ladder network and proved that all the different variations of and in has constant fault‐tolerant metric dimension.