In this paper, the novel study of the multilayered network model for the disrupted infrastructure of the 5G mobile network is introduced. The aim of this study is to present the new way of incorporating different types of networks, such as Wireless S...
This article highlights the issue of identifying information security threats to computer networks. The aim of the study is to increase the number of identified threats. Firstly, it was carried out the analysis of computer network models used to iden...
Many networks generated by nature have two generic properties: they are formed in the process of preferential attachment and they are scale-free. Considering these features, by interfering with mechanism of the preferential attachment, we propose a g...
The continuous uninterrupted supply of Natural Gas (NG) is crucial to today’s economy, with issues in key infrastructure, e.g., Baumgarten hub in Austria in 2017, highlighting the importance of the NG infrastructure for the supply of primary en...
The human brain is approximately a symmetric structure, although the functional brain does not exhibit symmetry. Functional brain aging process modelling is essential for the understanding of hypothesized generative mechanisms for human brain network...
An urban public traffic network is a typical high-order complex network. There are multiple types of transportation in an urban public traffic network, and each type has different impacts on urban transportation. Robustness analyses of urban public t...
The paper discusses issues concerning the occurrence of anomalies affecting the process of phase transitions. The considered issue was examined from the perspective of phase transitions in network structures, particularly in IT networks, Internet of...
Shared (electric) mobility is still facing challenges in terms of reaching its potential as a sustainable mobility solution. Low physical and digital integration with public transport, a lack of charging infrastructure, the regulatory barriers, and t...
The L-space and P-space are two essential representations for studying complex networks that contain different clusters. Existing network models can successfully generate networks in L-space, but generating networks in P-space poses significant chall...
A Bass diffusion model is defined on an arbitrary network, with the additional introduction of behavioral compartments, such that nodes can have different probabilities of receiving the information/innovation from the source and transmitting it to ot...
Background: Mathematical modeling and computer simulation are important methods for understanding complex neural systems. The whole-brain network model can help people understand the neurophysiological mechanisms of brain cognition and functional dis...
Today, most studies of audience networks analyze the landscape of the news media on the web. However, media ecology has been drastically reconfigured by the emergence of social media. In this study, we use Twitter follower data to build an online new...
The development of online social networking services provides a rich source of data of social networks including geospatial information. More and more research has shown that geographical space is an important factor in the interactions of users in s...
27 July 2021
Online social networks, as popular media and communications tools with their own extensive uses, play key roles in public opinion polls, politics, economy, and even governance. An important issue regarding these networks is the use of multiple source...
A resource network is a non-classical flow model where the infinitely divisible resource is iteratively distributed among the vertices of a weighted digraph. The model operates in discrete time. The weights of the edges denote their throughputs. The...
It is recognized that most real systems and networks exhibit a much higher clustering with comparison to a random null model, which can be explained by a higher probability of the triad formation—a pair of nodes with a mutual neighbor have a gr...
11 July 2019
We present a series of SIR-network models, extended with a game-theoretic treatment of imitation dynamics which result from regular population mobility across residential and work areas and the ensuing interactions. Each considered SIR-network model...
In the Global Navigation Satellite System, ionospheric delay is a significant source of error. The magnitude of the ionosphere total electron content (TEC) directly impacts the magnitude of the ionospheric delay. Correcting the ionospheric delay and...
High-complexity network models are challenging to execute on agricultural robots with limited computing capabilities in a large-scale pineapple planting environment in real time. Traditional module replacement often struggles to reduce model complexi...
The paper aims to present modelling the sensor network operation based on the Potts model. The authors presented own approach based on three states in which each node can be. The change in the state of a given node depends on its current state, the i...
The construction of an energy distribution network can improve the system’s ability to absorb new energy, and its stable and efficient operation has become more and more important. The security and stability analysis of a distribution network n...
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 top...
Probabilistic Boolean Networks can capture the dynamics of complex biological systems as well as other non-biological systems, such as manufacturing systems and smart grids. In this proof-of-concept manuscript, we propose a Probabilistic Boolean Netw...
11 October 2022
Risk treatment is an effective way to reduce the risk of oil pipeline accidents. Many risk analysis and treatment strategies and models have been established based on the event tree method, bow-tie method, Bayesian network method, and other methods....
In the finite control set model predictive control (FCSMPC) algorithm for a converter based on a neural network, the optimal control variables computed by neural network controllers achieve decoupling between the optimal FCSMPC algorithm design and o...
Electric vehicles (EVs) have introduced new operational challenges for distribution network service providers (DNSPs), particularly for voltage regulation due to unpredictable charging behaviour and the intermittent nature of distributed energy resou...
When applied to classification problems, Bayesian networks are often used to infer a class variable when given feature variables. Earlier reports have described that the classification accuracy of Bayesian network structures achieved by maximizing th...
Food production and energy consumption are two important factors when assessing greenhouse systems. The first must respond, both quantitatively and qualitatively, to the needs of the population, whereas the latter must be kept as low as possible. As...
This paper presents an agent-based model that performs the management of traffic flows in a network with the purpose of observing in a simulation of distinctive congestion scenarios how the automation of the monitoring task improves the network perfo...
Bioprocesses are increasingly used for the production of high added value products. Microorganisms are used in bioprocesses to mediate or catalyze the necessary reactions. This makes bioprocesses highly nonlinear and the governing mechanisms are comp...
The controllability of complex networks is a core issue in network research. Assessing the controllability robustness of networks under destructive attacks holds significant practical importance. This paper studies the controllability of networks fro...
Current network representation learning models mainly use matrix factorization-based and neural network-based approaches, and most models still focus only on local neighbor features of nodes. Knowledge representation learning aims to learn low-dimens...
A projective network model is a model that enables predictions to be made based on a subsample of the network data, with the predictions remaining unchanged if a larger sample is taken into consideration. An exchangeable model is a model that does no...
The kinetic model of Prostaglandin H Synthase-1 (PGHS-1) was developed to investigate its complex network kinetics and non-steroidal anti-inflammatory drugs (NSAIDs) efficacy in different in vitro and in vivo conditions. To correctly describe the com...
The Internet creates multidimensional and complex relationships in terms of the composition, application and mapping of social users. Most of the previous related research has focused on the single-layer topology of physical device networks but ignor...
Due to the large number of parameters and heavy computation, the real-time operation of deep learning in low-performance embedded board is still difficult. Network Pruning is one of effective methods to reduce the number of parameters without additio...
This paper analyzed urban network and regions in China using a complex network model. Data of daily migration among 348 prefectural-level cities from the Baidu Map location-based service (LBS) Open Platform were used to calculate urban network metric...
Surface defect detection is a key task in industrial production processes. However, the existing methods still suffer from low detective accuracy to pit and small defects. To solve those problems, we establish a dataset of pit defects and propose a p...
The present study compares and analyzes three risk analysis models that are applicable to shield tunnel boring machine (TBM) tunneling, and thus proposes an improved risk matrix model based on the causal networks applicable to sustainable tunnel proj...
A dynamic model that considers both linear and complex nonlinear effects extensively benefits the model-based controller development. However, predicting a detailed aerodynamic model with good accuracy for unmanned aerial vehicles (UAVs) is challengi...
The difficulty of effectively planning and assigning weekly activities has a significant influence on the long-term productivity of an underground mine. It is an especially difficult task to choose the best places for operations inside an underground...
Numerical simulation of mixed flows combining free surface and pressurized flows is a practical tool to prevent possible flood situations in urban environments. When dealing with intense storm events, the limited capacity of the drainage network cond...
Based on Beijing’s Air Quality Index (AQI) and concentration changes of the six major pollutants from 2019 to 2021, the results are visualized through descriptive statistics, and the air pollution status and influencing factors of Beijing&rsquo...
Breakdowns of two-zone random networks of the Erdős–Rényi type are investigated. They are used as mathematical models for understanding the incompleteness of the tumor network breakdown under radiochemotherapy, an incompleteness tha...
In this paper, a deep neural network based model for a set of small-scale magnetorheological dampers (MRD) is developed where relevant parameters that have a physical meaning are inputs to the model. An experimental platform and a 3D-printing rapid p...
Selecting representative groundwater monitoring wells in polluted areas is crucial to comprehensively assess groundwater pollution, thereby ensuring effective groundwater remediation. However, numerous factors can affect the effectiveness of groundwa...
Reinforcement Learning (RL) comes with the promise of automating network management. However, due to its trial-and-error learning approach, model-based RL (MBRL) is not applicable in some network management scenarios. This paper explores the potentia...
Stochastic channel network modeling is an informative tool to replicate river networks for the purpose of understanding the variability of geometry and distinguishing observed patterns in networks. In contrast, the application of stochastic network m...
This study presents an encoder–decoder neural network model to forecast storm surges on the US North Atlantic Coast. The proposed multivariate time-series forecast model consists of two long short-term memory (LSTM) models. The first LSTM model...
Flight crew performance is of great significance in keeping flights safe and sound. When evaluating the crew performance, quantitative detailed behavior information may not be available. The present paper introduces the Bayesian Network to perform fl...
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