Network Simulation Model Research Articles

Urban flood risk differentiation under land use scenario simulation

The frequent urban floods have seriously affected the regional sustainable development in recent years. It is significant to understand the characteristics of urban flood risk and reasonably predict urban flood risk under different land use scenarios. This study used the random forest and multi-criteria decision analysis models to assess the spatiotemporal characteristics of flood risk in Zhengzhou City, China, from 2005 to 2020, and proposed a robust method coupling Bayesian network and patch-generating land use simulation models to predict future flood risk probability. We found that the flood risk in Zhengzhou City presented an upward trend from 2005 to 2020, and its spatial pattern was "high in the middle and low in the surrounding areas". In addition, land use patterns under the sustainable development scenario would be more conducive to reducing flood risk. Our results can provide theoretical support for scientifically optimizing land use to improve urban flood risk management.

Scale transition: Pore network study of how pore structure affects the macroscopic parameters of the continuum model for drying

The scale transition from discrete pore network model (PNM) simulations to one-equation continuum model (CM) of drying has been investigated in previous studies for uniformly structured porous media. This investigation is extended in the present work to porous media with widely different pore size distributions, as well as to those with spatially correlated networks of small and large pores. The key questions examined here are how and to what extent pore-structural features can be reflected in the local macroscopic parameters of the one-equation CM derived by traditional homogenization. For this purpose, three-dimensional model capillary structures with monomodal and bimodal pore size distributions are generated and drying simulations are conducted at the limit of viscous-capillary dominated regime. By leveraging volume-averaged data obtained from PNM simulations the one-equation CM is parameterized and thus its local parameters are expressed in dependence on the pore structure. The simulation results show that for the monomodal and bimodal pore structures the profiles of the moisture transport coefficients are complex and non-unique over the entire drying process. Moreover, the deviation of the water vapor partial pressure from the saturation vapor pressure in the presence of liquid water – which is referred to as non-local equilibrium effect – is less pronounced for the bimodal pore structure compared to the monomodal pore structures. Finally, comparisons are performed between the volume-averaged data obtained at different stages of drying by the discrete simulations with monomodal and bimodal pore structures and results of the continuum model of drying.

Multielectrode Cortical Stimulation Selectively Induces Unidirectional Wave Propagation of Excitatory Neuronal Activity in Biophysical Neural Model.

Cortical stimulation is emerging as an experimental tool in basic research and a promising therapy for a range of neuropsychiatric conditions. As multielectrode arrays enter clinical practice, the possibility of using spatiotemporal patterns of electrical stimulation to induce desired physiological patterns has become theoretically possible, but in practice can only be implemented by trial-and-error because of a lack of predictive models. Experimental evidence increasingly establishes traveling waves as fundamental to cortical information-processing, but we lack an understanding of how to control wave properties despite rapidly improving technologies. This study uses a hybrid biophysical-anatomical and neural-computational model to predict and understand how a simple pattern of cortical surface stimulation could induce directional traveling waves via asymmetric activation of inhibitory interneurons. We found that pyramidal cells and basket cells are highly activated by the anodal electrode and minimally activated by the cathodal electrodes, while Martinotti cells are moderately activated by both electrodes but exhibit a slight preference for cathodal stimulation. Network model simulations found that this asymmetrical activation results in a traveling wave in superficial excitatory cells that propagates unidirectionally away from the electrode array. Our study reveals how asymmetric electrical stimulation can easily facilitate traveling waves by relying on two distinct types of inhibitory interneuron activity to shape and sustain the spatiotemporal dynamics of endogenous local circuit mechanisms.SIGNIFICANCE STATEMENT Electrical brain stimulation is becoming increasingly useful to probe the workings of brain and to treat a variety of neuropsychiatric disorders. However, stimulation is currently performed in a trial-and-error fashion as there are no methods to predict how different electrode arrangements and stimulation paradigms will affect brain functioning. In this study, we demonstrate a hybrid modeling approach, which makes experimentally testable predictions that bridge the gap between the microscale effects of multielectrode stimulation and the resultant circuit dynamics at the mesoscale. Our results show how custom stimulation paradigms can induce predictable, persistent changes in brain activity, which has the potential to restore normal brain function and become a powerful therapy for neurological and psychiatric conditions.

Financial ripple effect in complex adaptive supply networks: an agent-based model

Tightening lending standards are motivating companies to adopt supply chain financing, with invoice backed lending to remedy financial stress. These financial objects depend on company-to-company relationships. The accumulation of these dyadic relationships creates complex supply network topologies. Companies within these networks are selfish and have varying degrees of bargaining power. To remain operational, they maximise their liquidity by negotiating longer repayment terms and cheaper financing, thus distributing risk onto weaker companies and propagating financial stress. To study this phenomenon, we created an agent-based supply network simulation model capturing these behaviours. We investigate structural conditions that make supply networks vulnerable to financial stress propagation and the resultant financial ripple effects using survivability analysis. We found firms with higher bargaining power are disproportionately more exposed to network risk. In diamond-shaped networks, firms occupying lower tiers are critical in financial stress propagation, becoming deep-tier nexus suppliers. Our results are relevant to industries with heterogeneous network composition. Practitioners must mitigate the effects of vulnerable network structures with careful supply chain financing design.

Dedolomitization establishes flow pathways in Thalassinoides-bearing limestones: a quantitative case study using pore network model and flow simulation

Dedolomitization establishes flow pathways in Thalassinoides-bearing limestones: a quantitative case study using pore network model and flow simulation

A Quantitative Analysis of Chemical Plant Safety Based on Bayesian Network

Once a chemical production accident occurs in a chemical plant, it often causes serious economic losses, casualties, and environmental damage. Statistics show that many major accidents in the production and storage of chemicals are mainly caused by human factors. This article considers the influence of the human factor and proposes a quantitative analysis model of a chemical plant based on a Bayesian network. The model takes into account the main human factors in seven aspects: organization, information, job design, human system interface, task environment, workplace design, and operator characteristics. The Bayesian network modeling method and simulation were used to predict the safety quantitative value and safety level of the chemical plant. Using this model, we can quickly calculate the safe quantitative ratio of each factor in the chemical plant. Through the safety quantitative value, safety level, and sensitivity analysis, the safety hazards of chemical companies can be discovered. Immediate improvements of potential safety hazards in chemical plants are very effective in preventing major safety accidents. This model provides an effective method for chemical park managers to monitor and manage chemical plants based on quantitative safety data.

Artificial neural network based optimization for Ag grated D-shaped optical fiber surface plasmon resonance refractive index sensor

This study reports the optimization of fiber optic SPR refractive index sensor parameters with the simulation of finite element method (FEM) and artificial neural network (ANN) model. To demonstrate the applicability of the algorithm, we examined an Ag-grated D-shaped fiber optic sensor configuration with 4 basic input parameters with the aim of reaching the highest sensitivity. Through the conventional optimization, the best parameter set appeared to be a 10 nm air gap distance between the gratings (a), 20 gratings (N), 50 nm residual cladding thickness (d), and 70 nm silver layer thickness (Ag_th) at the indices of 1.35 and 1.39 yielding a sensitivity of 3775 nm/RIU. A close match is found between the actual and predicted sensitivity. 199 input data obtained from FEM are used for training by Leave One Out Cross-Validation (LOOCV) approach with R-squared value of 0.98, and the trained model with R-squared value of 0.97 is implemented in the Genetic Algorithm. We achieved the sensitivity of 3890 nm/RIU at the predicted a, N, d, and Ag_th of 10 nm, 20, 50 nm, and 75 nm, respectively. Future studies may further improve these results by integrating other algorithms. This method may apply to different and more complex structures to observe the correlation between the parameters, cover an entire range of parameters, and get more accurate results, especially with a high number of inputs requiring less time and computing effort. The proposed method carries great potential to improve the sensing ability and bring a new perspective to the literature.

Fast, Accurate, and System-Specific Variable-Resolution Modeling of Proteins.

In recent years, a few multiple-resolution modeling strategies have been proposed, in which functionally relevant parts of a biomolecule are described with atomistic resolution, with the remainder of the system being concurrently treated using a coarse-grained model. In most cases, the parametrization of the latter requires lengthy reference all-atom simulations and/or the usage of off-shelf coarse-grained force fields, whose interactions have to be refined to fit the specific system under examination. Here, we overcome these limitations through a novel multiresolution modeling scheme for proteins, dubbed coarse-grained anisotropic network model for variable resolution simulations, or CANVAS. This scheme enables a user-defined modulation of the resolution level throughout the system structure; a fast parametrization of the potential without the necessity of reference simulations; and the straightforward usage of the model on the most commonly used molecular dynamics platforms. The method is presented and validated with two case studies, the enzyme adenylate kinase and the therapeutic antibody pembrolizumab, by comparing the results obtained with the CANVAS model against fully atomistic simulations. The modeling software, implemented in Python, is made freely available for the community on a collaborative github repository.

Power Disequilibrium Suppression in Bipolar DC Distribution Grids by Using a Series-Parallel Power Flow Controller

The unbalanced power between positive and negative poles in a bipolar DC distribution network (DC-DN) generates an unbalanced current at the neutral line, which enlarges the power losses of the system and the voltage deviation of DC loads. An unbalanced power suppression strategy based on a series-parallel power flow controller (SP-PFC) is proposed in this paper. The SP-PFC is adopted as the interconnection between two different DC-DNs. The topology and operating modes of SP-PFC are analyzed. Subsequently, SP-PFC output voltage and line current expressions under constant power control are derived. The nonlinear relationship between the output voltage and line current is linearized at the operating point. On this basis, the influences of unbalanced load and receiving-end voltage on the SP-PFC are investigated. A small-signal model of bipolar DC-DN containing an SP-PFC is established, and the system stability is analyzed. A simulation model of the bipolar DC distribution network containing an SP-PFC is built up in MATLAB/Simulink, and the effectiveness of the SP-PFC in the suppression of unbalanced power is verified.

Research on the network handoff strategy based on the best access point name decision.

To improve the network switching performance and efficiency of mobile phone terminals and establish an efficient mobile communication network connection, this paper constructs the SDN+MPTCP+CP (Software Defined Network, Multi-Path TCP, and Mobile Terminal) mobile communication network model and designs a network switching algorithm with a preselected available access point name (APN) based on the potential game method. The constructed network model integrates a 5G mobile communication network, satellite communication, the SDN network, and the MPTCP multi-way communication technology. APN access point is preselected by using Kalman filtering theory, and dual problems are resolved with the Lagrange function. To determine the MPTCP sub-flow transmission path, the differential derivative calculation is introduced. The performance of the network switching strategy is evaluated based on the Jacobian matrix. Then, the game coefficient is designed, and the game function is calculated. A potential game balance point is found, and an updating strategy is formulated to determine the best APN access point. The simulation network model is constructed, and the parameters of the performance evaluation are defined to find the performance comprehensively. The experimental results demonstrated the extreme reliability, stability, and compatibility of the proposed algorithm.

Neural Network Modeling of Microstructure Formation in an AlMg6/10% SiC Metal Matrix Composite and Identification of Its Softening Mechanisms under High-Temperature Deformation

The paper investigates the rheological behavior and microstructuring of an AlMg6/10% SiC metal matrix composite (MMC). The rheological behavior and microstructuring of the AlMg6/10% SiC composite is studied for strain rates ranging between 0.1 and 4 s−1 and temperatures ranging from 300 to 500 °C. The microstructure formation is studied using EBSD analysis, as well as finite element simulation and neural network models. The paper proposes a new method of adding data to a training sample, which allows neural networks to correctly predict the behavior of microstructure parameters, such as the average grain diameter, and the fraction and density of low-angle boundaries with scanty initial experimental data. The use of neural networks has made it possible to relate the thermomechanical parameters of deformation to the microstructure parameters formed under these conditions. These dependences allow us to establish that, at strain rates ranging from 0.1 to 4 s−1 and temperatures between 300 to 500 °C, the main softening processes in the AlMg6/10% SiC MMC are dynamic recovery and continuous dynamic recrystallization accompanied, under certain strain and strain rate conditions at 300 and 350 °C, by geometric recrystallization.

Machine learning-assisted macro simulation for yard arrival prediction

Increasing the modal share of the single wagonload transport in Europe requires improving the reliability and predictability of freight trains running between the yards. In this paper, we propose a novel machine learning-assisted macro simulation framework to increase the predictability of yard departures and arrivals. Machine learning is applied through a random forest algorithm to implement a yard departure prediction model. Our yard departure prediction approach is less complex compared to previous yard simulation approaches, and provides an accuracy level of 92% in predictions. Then, departure predictions assist a macro simulation network model (PROTON) to predict arrivals to the succeeding yards. We tested this framework using data from a stretch between two main yards in Sweden; our experiments show that the current framework performs better than the timetable and a basic machine learning arrival prediction model by R2 of 0.48 and a mean absolute error of 35 minutes. Our current results indicate that combination of approaches, including yard and network interactions, can yield competitive results for complex yard arrival time prediction tasks which can assist yard operators and infrastructure managers in yard re-planning processes and yard-network coordination respectively.

Визначення оптимальної точки приєднання сонячної електростанції до електричної мережі методом комп’ютерного імітаційного моделювання

The purpose of this study is to minimize the negative impact of the solar power plant on the value of the steady voltage deviation and the level of electricity losses by determining the optimal place for its connection to the distribution network. Currently, quite a large number of methods of optimal placement of renewable sources of electricity in electrical networks have been developed. However, most of them either do not fully take into account the multifunctional influence of renewable energy sources on the parameters of the electric network regime, or are quite difficult to use. In order to solve the problem of optimal placement of renewable sources of electricity in electric networks, it is proposed to use the method of computer simulation modeling. The essence of this method is to determine the optimal place for connecting a renewable energy source to the electrical network based on the analysis of the results of computer simulation modeling of network mode parameters. This approach is the most acceptable in the case of connecting a solar power plant of average power to the electrical network of an industrial enterprise. The developed computer simulation model of a distribution electric network with a solar power plant allows for the research of network mode parameters, including the determination of the level of active power loss in network elements and the level of steady voltage deviation. The specified computer simulation models of the distribution electric network with a solar power plant made it possible to determine the optimal place for connecting the solar power plant based on the values of power losses in the elements of the electric network at the level of the established voltage deviation. The results of computer modeling of an electrical network with a solar power plant confirmed the need to take into account not only the values of the established voltage deviation, but also the amount of electrical energy losses in the network elements in the process of choosing a place to install a solar power plant. The results of the research can be used in solving the problems of determining the place of connection of a medium-power solar power plant to the electrical network of an industrial enterprise.

A perceptual image prediction model of professional dress style based on PSO-BP neural network

In order to understand consumers’ cognition of clothing style and design clothing products more in line with people’s emotional needs, a garment style perceptual image prediction model based on PSO-BP neural network was constructed by taking professional dress as an example. Firstly, the professional dress samples were screened and the style design elements were deconstructed and coded. The Kansei engineering theory and factor analysis method were used to determine the representative adjectives, so as to reduce the cognitive dimension of the target users for the style characteristics and perceptual image of the dress. Then, using the sample style design element code as the input layer and the user’s perceptual image evaluation score as the output layer, the PSO-BP neural network’s perceptual image prediction model for professional dress styles is constructed. Finally, the sample data were input into the PSO-BP model, BP neural network and GA-BP model for simulation and calculation, and the error analysis of the results proved that the PSO-BP prediction model is effective and advanced. Designers can use this model to quickly transform customers’ perceptual needs with dress style design elements, so as to improve the scientificity of design decision-making and better meet customer needs.

Accelerated NLRP3 inflammasome-inhibitory peptide design using a recurrent neural network model and molecular dynamics simulations

Anomalous NLRP3 inflammasome responses have been linked to multiple health issues, including but not limited to atherosclerosis, diabetes, metabolic syndrome, cardiovascular disease, and neurodegenerative disease. Thus, targeting NLRP3 and modulating its associated immune response might be a promising strategy for developing new anti-inflammatory drugs. Herein, we report a computational method for de novo peptide design for targeting NLRP3 inflammasomes. The described method leverages a long-short-term memory (LSTM) network based on a recurrent neural network (RNN) to model a valuable latent space of molecules. The resulting classifiers are utilized to guide the selection of molecules generated by the model based on circular dichroism spectra and physicochemical features derived from high-throughput molecular dynamics simulations. Of the experimentally tested sequences, 60% of the peptides showed NLRP3-mediated inhibition of IL-1β and IL-18. One peptide displayed high potency against NLRP3-mediated IL-1β inhibition. However, NLRC4 and AIM2 inflammasome-mediated IL-1β secretion was uninterrupted by this peptide, demonstrating its selectivity toward the NLRP3 inflammasome. Overall, these results indicate that deep learning and molecular dynamics can accelerate the discovery of NLRP3 inhibitors with potent and selective activity.

Global logistics intermodal network simulation modeling by incremental assignment and corridor development simulations in Myanmar

This study develops a more stable model to simulate the global container cargo flows in the intermodal network with link capacities by improving the existing model with incremental assignment and parallel computing. The developed model is validated by comparing it with the observed port cargo throughputs and sensitivity analyses. Subsequently, the model is applied to the ASEAN region and China to examine the impact of logistics corridor development policies. Specifically, the scenarios improving borders between ASEAN and China confirm that improvements in Myanmar borders would shift cargo from other ASEAN countries, while improvements across ASEAN would increase the region-wide land transit cargo. The scenarios improving borders in the East–West Corridor (EWC) of the Greater Mekong Subregion confirm that the EWC is in competitive and complementary relationships with Thilawa port. Finally, the scenarios adding liner services suggest stimulating the demand by improving Myanmar's domestic logistics network than encouraging border crossings.

Resist the type of BWH attack: through introducing discount factor and withdrawal threshold into Bitcoin

Aim: Since the emergence of the Block Withholding (BWH) attack, the prevailing approach in the environment has been to avoid attacking each other as a means of eliminating the threat posed by this attack. However, there is currently no effective and simple scheme to entirely prevent such attacks. To address this issue, this study proposes a novel blockchain model that combines the discount factor and withdrawal threshold to resist the risks associated with these attacks. Methods: This paper provides an overview of blockchain models and the BWH attack. We constructed a network simulation model based on the physical logic of Bitcoin and introduced the discount factor and withdrawal threshold as new parameters to enhance the model's resistance to BWH attacks. We evaluated the effect of these parameters on the model's ability to defend against BWH attacks from the perspective of rewards. Results: This paper presents a novel blockchain model that integrates discount factors and withdrawal thresholds and examines attacker behavior in this model from a rewards-based perspective by comparing different computational environments. Our experimental data indicate that this model has significant advantages in resisting BWH attacks. Conclusion: The experimental results demonstrate that the proposed model is highly effective in resisting profit-driven BWH attacks, with a 98% chance of successfully resisting such attacks at a fixed computational power. This indicates that the proposed model can effectively eliminate the potential threat posed by BWH attacks.

A Three Stage Optimal Scheduling Algorithm for AGV Route Planning Considering Collision Avoidance under Speed Control Strategy

With the trend of terminal automation and the requirement for port operation efficiency to be greatly improved, it is very necessary to optimize the traveling route of automatic guided vehicles (AGV) with reference to the connection of loading and unloading equipment. As a complex multi-equipment system, it is inevitable that AGV will collide when traveling due to various accidents in actual operation, which will lead to AGV locking and reduce the efficiency of terminal operation. Considering the locking problem of AGV, we propose a three-stage integrated scheduling algorithm for AGV route planning. Through joint optimization with quay cranes (QC) and yard blocks, a road network model is established in the front area of the container port to optimize the path of AGV in the road network, and a speed control strategy is proposed to solve the problem of AGV collision avoidance. In the first stage, we establish the AGV optimal route model with the goal of minimizing the AGV path according to the AGV road network situation. In the second stage, on the basis of the determination of AGV route planning, and when the container task is known, the AGV task assignment model is established with the goal of minimizing the maximum completion time, and the model is solved by genetic algorithm (GA). In the third stage, on the basis of AGV task assignment and route determination, the AGV route and AGV task assignment scheme are input into the simulation model by establishing the AGV collision avoidance control model for speed control, and establishing the AGV route network simulation model for automated terminals considering collision avoidance in plant simulation software. The maximum completion time obtained from the simulation model is compared with the completion time obtained from the genetic algorithm. The proposed three-stage joint scheduling algorithm can improve the loading and unloading efficiency of the port, reduce the AGV locking situation, and has a certain contribution to the formulation of the actual operation planning of the port.

A Dynamic Evaluation Method of Urban Ecological Networks Combining Graphab and the FLUS Model

Rapid urbanization has led to landscape fragmentation and habitat loss. As an organic structure integrating green space, an urban ecological network can effectively reduce ecological risks and protect biodiversity if its landscape connectivity is maintained. Chaoyang District in Beijing is facing the challenge of transformational development due to excessive urbanization. Taking this district as the study area, this study assessed the environmental impact of different development scenarios on landscape connectivity indices and explored the most relevant strategies for important green space patches by combining ecological network modeling (Graphab) and scenario simulation techniques (FLUS model). The results show that under the urban expansion scenario, the probability of connectivity (PC) decreases by 59.7%, while under the master plan scenario, it increases by 102.1%. Even under the ideal ecological scenario, the ecological network structure of the region faces structural problems. Patches and corridors with high delta probabilities of connectivity (dPC) are concentrated in the north, with no effective connection between the north and south. Finally, planning strategies and priorities for important patches under different urban development goals are proposed through a strategy matrix. Overall, this study proposes a framework for decision-makers to solve planning conflicts between urban expansion and biodiversity conservation, especially for cities in transition.

COMPUTER SIMULATION MODEL OF A COMPUTER NETWORK WITH FRACTAL TRAFFIC FOR TESTING ROUTING ALGORITHMS


 
 
 The object of research in this article is simulation modeling of a computer network and the process of traffic routing. The relevance of the research is due to the importance of ensuring the quality of service in computer networks, in particular, by necessity reducing the number of lost IP-packets at high traffic intensity values. Determining the routing of traffic packets is a complex process and is based on various indicators or combinations of indicators. If the routing process takes place in a dynamic mode, then the complexity of the route calculation increases, in this case, one of the tools for research and comparison of different routing algorithms can be a computer simulation model of a computer network. The goal of the work is the development and research of a computer simulation model of a computer network for testing traffic routing algorithms. The tasks: to develop computer simulation model of a computer network to generate the network structure and simulate the traffic distribution process with the ability to test different routing algorithms. Research methods: theory of computer networks, theory of fractal analysis, object-oriented programming, theory of algorithms and data structures, theory of complex networks, theory of Markov processes. Conclusions. The paper investigated the basic principles of traffic routing in computer networks. A simulation model of a computer network for testing traffic routing algorithms has been developed. A method based on the theory of complex networks has been developed to generate the structure of a computer network. Theory of fractal analysis and Markov processes are used for traffic generation. A series of experiments was conducted on a developed model to determine how different fractal dimensions of traffic at high traffic intensity values affect the number of lost packets, and therefore the quality of service. Analyzing the results of the experiment, the following conclusions can be drawn: the least number of lost packets occurs when the process is random or has weak trends. The fewest lost packets were at fractal dimension 1.5, i.e., when the process is completely random, there were also few lost packets at fractal dimensions close to this; persistent and anti-persistent processes (those with memory) cause more packet loss for the same traffic intensity and maximum number of packets sent from one device per unit of time. Moreover, anti-persistent processes cause significantly more losses than persistent ones. Thus, when performing traffic routing and finding optimal paths for sending IP-packets, it can be useful to determine and take into account the fractal dimension of traffic at the entrance of each router and use it when calculating metrics to determine the best routes.