Automata Model Research Articles

Developing a multi-scale model for the simulation of adsorption phenomena based on MD and CA: a Li-ion battery case study

Abstract Adsorption, an essential surface phenomenon, is involved in many industries, from water purification to energy storage and carbon capture, aiming at negative emission technologies. The need to synthesize new materials for these applications necessitates the development of new, flexible modeling tools to simulate complex conditions. This work introduces a multi-scale model to simulate various adsorption scenarios. It involves simulating the details of interatomic interactions in molecular dynamics simulations and scaling up to a laboratory scale through cellular automaton modeling. To showcase its capabilities, we utilized the simplest form of the model to simulate Li-ion adsorption on the surface of an anatase TiO2 sheet. The probability of adsorption and desorption for a Li-ion is quantitatively determined through molecular dynamics simulations and subsequently incorporated into the cellular automaton model. This secondary model simulates the kinetic process of adsorption and quantifies the equilibrium degree of surface coverage across varying concentrations, facilitating comparison with the Langmuir isotherm. An inverse relationship between surface coverage and temperature is consistent with theoretical predictions. Given the model’s computational efficiency, which complements molecular dynamics simulations, it offers extensive potential for extension across a broad spectrum of applications where adsorption, intercalation, diffusion, and other critical surface phenomena are fundamental.

Parameter estimation for cellular automata

Abstract Self-organizing complex systems can be modeled using cellular automaton models. However, the parametrization of these models is crucial and significantly determines the resulting structural pattern. In this research, we introduce and successfully apply a sound statistical method to estimate these parameters. The decisive difference to earlier applications of such approaches is that, in our case, both the CA rules and the resulting patterns are discrete. The method is based on constructing Gaussian likelihoods using characteristics of the structures, such as the mean particle size. We show that our approach is robust for the method parameters, domain size of patterns, or CA iterations.

Investigation of Spatio-Temporal Simulation of Mining Subsidence and Its Determinants Utilizing the RF-CA Model

It is important to carry out timely scientific assessments of surface subsidence in coal resource cities for ecological environmental protection. Traditional subsidence simulation methods cannot quantitatively describe the driving factors that contribute to or ignore the dynamic connections of subsidence across time and space. Thus, a novel spatio-temporal subsidence simulation model is proposed that couples random forest (RF) and cellular automaton (CA) models, which are used to quantify the contributions of driving factors and simulate the spatio-temporal dynamic changes in subsidence. The RF algorithm is first utilized to clarify the contributions of the driving factors to subsidence and to formulate transformation rules for simulation. Then, a spatio-temporal simulation of subsidence is accomplished by combining it with the CA model. Finally, the method is validated based on the Yongcheng coalfield. The results show that the depth–thickness ratio (0.242), distance to the working face (0.159), distance to buildings (0.150), and lithology (0.147) play main roles in the development of subsidence. Meanwhile, the model can effectively simulate the spatio-temporal changes in mining subsidence. The simulation results were evaluated using 2021 subsidence data as the basis data; the simulation’s overall accuracy (OA) was 0.83, and the Kappa coefficient (KC) was 0.71. This method can obtain a more realistic representation of the spatio-temporal distribution of subsidence while considering the driving factors, which provides technological support for land-use planning and ecological and environmental protection in coal resource cities.

Convolutional neural network (CNN) configuration using a learning automaton model for neonatal brain image segmentation.

CNN is considered an efficient tool in brain image segmentation. However, neonatal brain images require specific methods due to their nature and structural differences from adult brain images. Hence, it is necessary to determine the optimal structure and parameters for these models to achieve the desired results. In this article, an adaptive method for CNN automatic configuration for neonatal brain image segmentation is presented based on the encoder-decoder structure, in which the hyperparameters of this network, i.e., size, length, and width of the filter in each layer along with the type of pooling functions with a reinforcement learning approach and an LA model are determined. These LA models determine the optimal configuration for the CNN model by using DICE and ASD segmentation quality evaluation criteria, so that the segmentation quality can be maximized based on the goal criteria. The effectiveness of the proposed method has been evaluated using a database of infant MRI images and the results have been compared with previous methods. The results show that by using the proposed method, it is possible to segment NBI with higher quality and accuracy.

Simulation of grain refinement of Al-8Si-0.2 Mg alloy inoculated with Al-Nb-B via an improved cellular automaton model

Simulation of grain refinement of Al-8Si-0.2 Mg alloy inoculated with Al-Nb-B via an improved cellular automaton model

A modified microscopic cellular automaton model for the simulation of microstructure evolution during solidification/melting of nickel-based superalloys

A modified microscopic cellular automaton model for the simulation of microstructure evolution during solidification/melting of nickel-based superalloys

The Impact of Dependent Behavior on the Design of Classroom Evacuation Exits for Intellectually Disabled Students

The increasing demand for special education in architectural design highlights the urgent need to ensure the safe evacuation of students with intellectual disabilities. However, current research on classroom evacuation for these students remains limited, particularly concerning critical factors, such as the number, location, and distance of exits. This study investigated the impact of dependent behavior on classroom exit design for students with intellectual disabilities by developing a Cellular Automaton (CA) model based on their behavioral characteristics. Simulated evacuation scenarios, considering and disregarding dependent behaviors, were analyzed to assess their effects on the number and positioning of exits, and a predictive model was implemented to establish the relationship between exit spacing and evacuation time. The results indicated that the dependent behavior significantly reduced evacuation efficiency and substantially affected classroom exit design. Considering the dependent behavior, this study demonstrated that setting two exits reduced the average evacuation time for students with intellectual disabilities by 12.99%, with further reductions achieved by placing the exits at the rear rather than at the sides or front of the classroom. The research also revealed that under the influence of dependent behavior, the average evacuation time initially decreased and then increased as the distance between exits increased. As one of the few studies addressing evacuation issues for students and the first to incorporate dependent behavior into the evacuation model, this study provides valuable recommendations for classroom designs that balance evacuation safety and daily usability. It offers essential data to inform architectural designs for classrooms serving students with intellectual disabilities and serves as a reference for future educational building design standards and regulations.

Analysis of solid-state NMR data facilitated by MagRO_NMRViewJ with Graph_Robot: Application for membrane protein and amyloid.

Solid-state NMR (ssNMR) methods have continued to be developed in recent years for the efficient assignment of signals and 3D structure modeling of biomacromolecules. Consequently, we are approaching an era in which vigorous applications of these methods are more widespread in research, including functional elucidation of biomacromolecules and drug discovery. However, multidimensional ssNMR methods are not as advanced as solution NMR methods, especially for automated data analysis. This article describes how a newly developed Graph_Robot module, implemented in MagRO-NMRViewJ, evolved from integrated tools for NMR data analysis named Kujira (developed by Kobayashi et al. [1]). These packaged tools systematically utilize flexible, sophisticated, yet simple libraries that facilitate only for solution-NMR data analysis, offering an intuitive interface accessible even to novice users. In this study, semi-automated assignments of backbone and side chain signals of ssNMR datasets for uniformly 13C/15N labeled aquaporin Z and 42-residue amyloid-β fibril were examined as examples to demonstrate how Graph_Robot can expedite the visual inspection and handling of multidimensional ssNMR spectral data. In addition, the functionality of the Graph_Robot system enables a computer to interpret the behavior of magnetization transfer based on a finite automaton model.

Sustainable ripples: Unveiling contractor knowledge-state transitions and group consensus based on environmental sanction violations

Sustainable development in the construction industry requires the reduction of ecological disturbances and energy consumption, control of pollution, and adherence to environmental regulations to avoid environmental sanctions. Green knowledge affiliated with environmental compliance spreads from the relationship network to the whole group of contractors, and knowledge gaps are filled via mutual learning and dissemination to promote the implementation of environmentally friendly behavior. This study examined the transfer-diffusion dynamics and evolution of knowledge on environmental violations derived from environmental sanctions in different contractor states. The contributions of different elements to the diffusion process were compared to identify the key factors that reduce the time until contractors reach a green consensus and achieve sustainable construction. Using an improved susceptible–exposed–infectious–recovered model, contractors were characterized as non-informed, informed, spreaders, and forgetters to construct state-transfer paths. In addition, a cellular automaton model was used to demonstrate the diffusion of green knowledge in contractor groups and analyze how state-transfer paths, spreader layouts, and knowledge-reachability neighborhoods affect the diffusion efficiency. Simulations indicated that once knowledge diffusion stabilized, the spreader state was the only remaining state; moreover, differences in the scenario parameters affected the transition rates between each state but not the final proportions of the states. Discrete distribution and spreader influence were positively and significantly correlated to knowledge-diffusion efficiency, whereas the state-transition path had little influence. These findings provide guidance for facilitating the diffusion of green knowledge in the contractor community to reduce environmental damage while avoiding environmental risks to meet the green transformation needs of the construction industry.

Evacuation simulation of heterogeneous passengers in the confined carriage of high-speed train

Passenger evacuation in high-speed train carriages is a critical issue that has not been fully explored and understood in previous research. To investigate the evacuation process of passengers from the confined carriage, a fine-grid cellular automaton model considering passenger heterogeneity is proposed in this paper. The influences of passenger attributes, luggage-carrying behavior, exit width and window evacuation mode on evacuation dynamics are analyzed under various scenarios. The simulation results reveal that the evacuation efficiency decreases with the increase of the proportion of elderly passengers in the carriage. The evacuation time increases with the increment of luggage-carrying proportion and duration time of luggage collection. Passengers carrying luggage near the inner door can easily block the aisle. Therefore, appropriately increasing the width of the inner door can reduce exit congestion and facilitate the evacuation process. Furthermore, the evacuation efficiency is the highest when the escape window is located in the middle of carriage for only one window exit. For the two window exits conditions, the escape windows are uniformly distributed in the middle of carriage is more favorable for evacuation. The study is useful to design evacuation plans for passenger flow and improve the emergency management level of high-speed railway stations.

Comparison of gap-based and flow-based control strategies using a new controlled stochastic cellular automaton model for traffic flow

ABSTRACT Autonomous vehicles are essential to future transportation systems, potentially reducing traffic congestion. This study examines the impact of different vehicle control strategies on traffic flow through simulations. We propose a novel stochastic cellular automaton model, the controlled stochastic optimal velocity (CSOV) model, which incorporates vehicle control effects. Within the CSOV model, two control strategies are implemented: gap-based control (GC), which adjusts vehicle velocity to balance the gaps between adjacent vehicles, and flow-based control (FC), which aims to maintain a consistent local flow between the front and rear vehicles. Results show that both control strategies improve traffic flow. However, under weaker control, the GC sometimes resulted in lower flow compared to no control. In contrast, the FC consistently enhanced flow across control strengths, yielding more robust outcomes. Furthermore, when both strategies achieved comparable flow rates, the FC provided a more stable velocity distribution under varying traffic densities than the GC.

Travel Times of a Descending Melting Probe on Europa.

In this study, we calculated the travel times of a thermal probe that descends through Europa's ice shell. The ice column is simplified to a conductive layer. Using a cellular automaton model, the descent of the probe was simulated by tracking temperature changes, with cell interaction dictated by heat conduction and cell state transition rules determined by cell temperatures. Validation tests, including a soil column simulation, and comparison with experimental data, support the reliability of the model. Simulations were performed with 2 different cell sizes, 19 constant probe temperatures, and 5 ice thermal conductivities. A smaller cell size (mm) produced shorter travel times (between 22 days for a probe temperature and ∼4 years for ) than a larger cell size (m), which produced travel times between 27 years ( 600K) and ∼103 years ( 280K). The ice shell's thermal conductivity has a modest impact on descent times. The results are generally consistent with previous approaches that used more detailed probe engineering considerations. These results suggest that a probe relying solely on heat production may traverse Europa's conductive ice shell within a mission's timeframe.

Asymmetric variable depth learning automaton and its application in defending against selfish mining attacks on bitcoin

Learning Automaton (LA) theory, a branch of reinforcement learning, initially began with the Fixed Structure Learning Automaton (FSLA) family and was later expanded to include the Variable Structure Learning Automaton (VSLA) family. Tuning the depth of FSLA in complex environments has long been a challenging task, significantly limiting the ability to effectively navigate the exploration-exploitation dilemma. No solution has been found for this open problem yet. This study addresses this issue by introducing a novel hybrid learning automaton model called Asymmetric Variable Depth Hybrid LA (AVDHLA). The AVDHLA model intelligently learns the depth of fixed structure LA in an autonomous manner by combining LKN,K from FSLA class and Variable Action Set LA (VASLA) from VSLA class. Computer simulations are conducted to validate the proposed model in diverse environments, including both stationary and non-stationary (Markovian switching and State-dependent) scenarios. Performance evaluation is based on predefined metrics, such as total number of rewards (TNR) and action switching (TNAS). Statistical tests indicate that across both stationary and non-stationary environments, AVDHLA consistently outperforms LKN,K in terms of TNR and TNAS across the majority of experiments. Moreover, the AVDHLA model is applied in two key applications. Firstly, it is used to defend against the selfish mining attack in Bitcoin and is compared with the well-known tie-breaking mechanism. Simulation results consistently demonstrate that our proposed method increases the threshold for successful selfish mining attacks from 25% to 40%. Secondly, the AVDHLA model has been applied to develop a novel learning automaton-based recommendation system. The results demonstrate the superiority of the proposed method in terms of the Click-Through Rate (CTR) and Precision compared to previous approaches.

Cellular automaton model of self-healing

We propose a simple cellular automaton model of a self-healing system and investigate its properties. In the model, the substrate is a two-dimensional checkerboard configuration which can be damaged by changing values of a finite number of sites. The cellular automaton we consider is a checkerboard voting rule, a binary rule with Moore neighborhood which is topologically conjugate to the majority voting rule. For a single-color damage (when only cells in the same state are modified), the rule always fixes the damage. For a general damage, when it is localized inside a 3 × 3 square, the rule also fixes it always. When the damage is inside of a larger n × n square, the efficiency of the rule in fixing the damage becomes smaller than 100 % , but it remains better than 98 % for n ≤ 5 and better than 75 % for n ≤ 7 . We show that in the limit of infinite n the efficiency tends to zero.

Simulation of container discharge dynamics using modified cellular automata

In the field of physical logistics, efficient and precise control of material flow during the emptying of containers such as silos and wagons is crucial. This study introduces a not conventional cellular automaton model to simulate spillage from containers. The model is implemented using Excel macros, providing a user-friendly and accessible platform for logistics professionals. By modifying the traditional cellular automaton framework, the simulation can accurately represents the dynamics of material flow during container emptying processes. The proposed method allows for fine-tuned control over the spillage by integrating specific constraints and parameters, enhancing operational efficiency and reducing material loss. The versatility of the model makes it applicable to a wide range of logistical scenarios, demonstrating its potential as a valuable tool in physical logistics management.

A Driving Warning System for Explosive Transport Vehicles Based on Object Detection Algorithm.

Due to the flammable and explosive nature of explosives, there are significant potential hazards and risks during transportation. During the operation of explosive transport vehicles, there are often situations where the vehicles around them approach or change lanes abnormally, resulting in insufficient avoidance and collision, leading to serious consequences such as explosions and fires. Therefore, in response to the above issues, this article has developed an explosive transport vehicle driving warning system based on object detection algorithms. Consumer-level cameras are flexibly arranged around the vehicle body to monitor surrounding vehicles. Using the YOLOv4 object detection algorithm to identify and distance surrounding vehicles, using a game theory-based cellular automaton model to simulate the actual operation of vehicles, simulating the driver's decision-making behavior when encountering other vehicles approaching or changing lanes abnormally during actual driving. The cellular automaton model was used to simulate two scenarios of explosive transport vehicles equipped with and without warning systems. The results show that when explosive transport vehicles encounter the above-mentioned dangerous situations, the warning system can timely issue warnings, remind drivers to make decisions, avoid risks, ensure the safety of vehicle operation, and verify the effectiveness of the warning system.

Cellular automaton model for the analysis of design and plan of bus station in the mixed traffic environment

With the quick development of Connected Autonomous Vehicles (CAVs), CAVs would gradually become an important part of urban traffic. Hence, the impacts of CAVs on urban traffic should be further explored. This study aims to analyze the mixed traffic comprising CAVs and human-driven vehicles (HDVs) in different urban scenarios in which different bus station types (i.e., roadside and bay bus stations) and capacities are considered. To do this analysis, this study proposes a cellular automaton model which simulates car following behaviours of vehicles using a two-state safe speed model, and designs specific modeling rules for lane-changing behaviours and vehicle behaviours near bus stations with consideration of the differences between CAVs and HDVs. The analysis results indicate that the impacts of various bus station types and capacities on the mixed traffic flow vary with traffic volumes, while increasing the CAV penetration rate can reduce the traffic congestion caused by bus stop-and-go. Furthermore, the study explores the optimal number of bus routes for different types of bus stations in different urban traffic scenarios, and several possible policy implications have been given according to the analysis results.

Predicting Prayagraj's Urbanization Trajectory using CA-ANN Modelling: Population Pressures and Land Use Dynamics

Land Use/Land Cover (LULC) dynamics provide a crucial role in the monitoring, planning, and management of resources. They also offer valuable information for developing strategies to balance conservation efforts, resolve conflicts between different land uses, and address pressures from growth. The present study focuses on the assessment of LULC dynamics, their forecasting, and their changes for Prayagraj city (including its surroundings) of India. Using long-term spatiotemporal Landsat datasets (1988–2018), we have explored the interlinkages between the change dynamics and human population pressure to explore the impact of agriculture and urbanization on the city landscape. Future growth prediction is carried out by incorporating Cellular Automaton (CA) and Artificial Neural Network (ANN) models. Six exploratory layers (viz., roads, educational institutes, railway transition, slope, river, and restricted area) are used in the learning process to determine LULC change (1997–2008) simulation. The validation of real and predicted LULC is carried out for 2018, where the correctness percentage and kappa value are found to be 90.29% and 0.87, respectively. Then, the ANN- Multilayer perceptron (MLP) and CA model are applied to predict LULC–2028 using the same trained transition probabilities. Results show that Built-land has grown highly by 10.03%, whereas Agriculture land and Forest land have significantly decreased by 13.43% and 3.03%, respectively, from 1988 to 2018. The predicted LULC of 2028 reveals that Built-land will keep growing by 2.83% during 2018–2028 at the cost of Agriculture land and Forest land, especially in northern, south–western and southern region, including city's inner sphere. United Nations' human population projection reveals that the city is expected to reach a population of 1.625 million by 2028. This indicates that tremendous pressure will be placed on land resources, particularly on agricultural, barren, and forested areas. To address this alarming scenario, it is imperative to delineate future development areas, ensuring better urban planning for the environmental sustainability and economic prosperity of Prayagraj city.

A polycrystal plasticity-cellular automaton integrated modeling method for continuous dynamic recrystallization and its application to AA2196 alloy

Continuous dynamic recrystallization usually dominates the microstructural evolution in hot working of aluminum alloys, in which the high-angle grain boundaries of new grains mainly originate from the gradual increase in subgrain misorientation angles. In this work, an integrated computational method is proposed to simulate continuous dynamic recrystallization process of aluminum alloys by coupling three-dimensional cellular automaton and visco-plastic self-consistent models. The stress response, dislocation accumulation and recovery, and evolution of crystal orientations are computed in the context of polycrystal plasticity; the formation and rotation of subgrains, followed by stored energy and curvature-driven boundary migration, are captured and visualized by cellular automaton. The non-octahedral slip mode {110}<110> is additionally introduced to capture the 〈001〉 texture during hot compression. A universal cell topology deformation method is adopted to achieve an effective track of grain morphology evolution during plastic deformation. The proposed simulation framework is validated through simulating the isothermal uniaxial compression process of AA2196 alloy under different temperatures and strain rates. The orientation dependence of CDRX during compression is numerically reproduced by correlating the subgrain formation and rotation process with the activation state of slip systems. The simulated macroscopic flow stress, 3D microstructure and inherent microstructural characteristics such as subgrain size, subgrain boundaries and textures are in good agreement with the experimental results. The proposed method provides an effective and efficient tool for multi-scale simulation of hot forming process of aluminum alloys.

Law of earthquake productivity in the Olami‒Feder‒Christensen‒Zhurkov model

The generalized cellular model based on the of Olami‒Feder‒Christensen cellular automaton model and modified by the allowance for the lifetime of the material on the basis of the kinetic concept for strength of solids by Academician S.N. Zhurkov is used to model and clarify the nature of the statistical law of earthquake productivity. The modified model is named Olami‒Feder‒Christensen‒Zhurkov model (OFCZ). The OFCZ model implements the main statistical regularities of seismicity: the Gutenberg‒Richter and Omori‒Utsu laws, the Bath’s law, fractal geometry of seismicity, and the law of earthquake productivity. It is shown that the clustering of model events (analogs of earthquakes), corresponding to the law of earthquake productivity, is due to the kinetic component of the OFCZ model. The productivity dependences on material strength and medium temperature are obtained. The influence of the Zhurkov parameter and the cell coupling parameter in the cellular model (the dissipativity of the model) on the productivity is considered. It is shown that the revealed dependences of productivity on strength and temperature are consistent with the empirical data.