Search Results (320)

Understanding asymmetric interactions among heterogeneous traffic participants is essential for managing congestion and enhancing safety at urban signalized intersections. This study proposes a cellular automaton modeling framework that captures the spatial and behavioral asymmetries among vehicles, bicycles, and pedestrians, with a particular focus on right-of-way hierarchies and conflict anticipation. Beyond simulation, the framework integrates a behavior pattern mining module that applies unsupervised trajectory clustering to identify recurrent interaction patterns emerging from mixed traffic flows. Simulation experiments are conducted under varying demand levels to investigate the propagation of congestion and the structural distribution of conflicts. The results reveal distinct asymmetric behavior patterns, such as right-turn vehicle blockage, non-lane-based bicycle overtaking, and pedestrian-induced disruptions. These patterns provide interpretable insights into the spatiotemporal dynamics of intersection performance and offer a data-driven foundation for optimizing signal control and multimodal traffic flow separation. The proposed framework demonstrates the value of combining microscopic modeling with data mining techniques to uncover latent structures in complex urban traffic systems. Full article

Investigating land cover patterns, changes in carbon stocks, and forecasting future conditions are essential for formulating regional sustainable development strategies and enhancing ecological and environmental quality. This study centers on Guiyang, a mountainous urban area in southwestern China, to analyze the dynamic changes in land cover and their effects on carbon stocks from 2000 to 2035. A carbon stocks assessment framework was developed using a cellular automaton-based artificial neural network model (CA-ANN), the InVEST model, and the geographical detector model to predict future land cover changes and identify the primary drivers of variations in carbon stocks. The results indicate that (1) from 2000 to 2020, impervious surfaces expanded significantly, increasing by 199.73 km². Compared to 2020, impervious surfaces are projected to increase by 1.06 km², 13.54 km², and 34.97 km² in 2025, 2030, and 2035, respectively, leading to further reductions in grassland and forest areas. (2) Over time, carbon stocks in Guiyang exhibited a general decreasing trend; spatially, carbon stocks were higher in the western and northern regions and lower in the central and southern regions. (3) The level of greenness, measured by the normalized vegetation index (NDVI), significantly influenced the spatial variation of carbon stocks in Guiyang. Changes in carbon stocks resulted from the combined effects of multiple factors, with the annual average temperature and NDVI being the most influential. These findings provide a scientific basis for advancing low-carbon development and constructing an ecological civilization in Guiyang. Full article

In ecosystems, spatial structure plays a fundamental role in shaping the observed dynamics. In particular, the availability and distribution of unoccupied sites—potential habitats—can strongly affect species persistence. However, mathematical models of ecosystems based on ordinary differential equations (ODEs) often neglect the explicit representation of these unoccupied sites. Here, probabilistic cellular automata (PCA) are used to reproduce two basic ecological scenarios: competition between two species and a predator–prey relationship. In these PCA-based models, unoccupied sites are taken into account. Subsequently, a mean field approximation of the PCA behavior is formulated in terms of ODEs. The variables of these ODEs are the numbers of individuals of both species and the number of empty cells in the PCA lattice. Including the empty cells in the ODEs leads to a modified version of the Lotka–Volterra system. The long-term behavior of the solutions of the ODE-based models is examined analytically. In addition, numerical simulations are carried out to compare the time evolutions generated by these two modeling approaches. The impact of explicitly considering unoccupied sites is discussed from a modeling perspective. Full article

This study proposes a new method to improve the accuracy of car-following models in predicting the mobility of mixed traffic flow involving trucks and automated vehicles (AVs). A classification is developed to categorize car-following behaviors into eight distinct modes based on vehicle type (passenger car/truck) and autonomy level (human-driven vehicle [HDV]/AV) for parameter calibration and simulation. The car-following model parameters are calibrated based on the HighD dataset, and the models are selected through minimizing statistical error. A cellular-automaton-based simulation platform is implemented in MATLAB (R2023b), and a decision framework is developed for the simulation. Key findings demonstrate that mode-specific parameter calibration improves model accuracy, achieving an average error reduction of 80% compared to empirical methods. The simulation results reveal a positive correlation between the AV penetration rate and traffic flow stability, which consequently enhances capacity. Specifically, a full transition from 0% to 100% AV penetration increases traffic capacity by 50%. Conversely, elevated truck penetration rates degrade traffic flow stability, reducing the average speed by 75.37% under full truck penetration scenarios. Additionally, higher AV penetration helps stabilize traffic flow, leading to reduced speed fluctuations and lower emissions, while higher truck proportions contribute to higher emissions due to increased traffic instability. Full article

Various approaches have been explored to address the path planning problem for mobile robots. However, it remains a significant challenge, particularly in environments where a multi-tasking mobile robot operates alongside stochastically moving humans. This paper focuses on path planning for a mobile robot executing multiple pickup and delivery tasks in an environment shared with humans. To plan a safe path and achieve high task success rate, a Reinforcement Learning (RL)-based double layer controller is proposed in which a double-layer learning algorithm is developed. The high-level layer integrates a Finite-State Automaton (FSA) with RL to perform global strategy learning and task-level decision-making. The low-level layer handles local path planning by incorporating a Markov Decision Process (MDP) that accounts for environmental uncertainties. We verify the proposed double layer algorithm under different configurations and evaluate its performance based on several metrics, including task success rate, reward, etc. The proposed method outperforms conventional RL in terms of reward (+63.1%) and task success rate (+113.0%). The simulation results demonstrate the effectiveness of the proposed algorithm in solving path planning problem with stochastic human uncertainties. Full article

Additive manufacturing techniques, such as laser-based powder bed fusion of metals (PBF-LB/M), have now gained high industrial and academic interest. Despite its design flexibility and the ability to fabricate intricate components, LPBF has not yet reached its full potential, partly due to the challenges associated with microstructure control. The precise manipulation of the microstructure in LPBF is a formidable yet highly rewarding endeavor, offering the capability to engineer components at a local level. This work introduces an innovative parallelized Cellular Automaton (CA) framework for modeling the evolution of the microstructure during the LPBF process. LPBF involves remelting and subsequent nucleation followed by crystal growth during solidification, which complicates and burdens microstructure simulations. In this research, a novel approach to nucleation seeding and crystal growth is implemented, focusing exclusively on the final stages of melting and solidification, enhancing the computational efficiency by 30%. This approach streamlines the simulation process, making it more efficient and effective. The developed model was employed to simulate the microstructure of an austenitic advanced high-strength steel (AHSS). The model was validated by comparing the simulation results qualitatively and quantitatively with the experimental data obtained under the same process parameters. The predicted microstructure closely aligned with the experimental findings. Simulations were also conducted at varying resolutions of CA cells, enabling a comprehensive study of their impact on microstructure evolution. Furthermore, the computational efficiency was critically evaluated. Full article

This work explores the morphogenesis of the skeletal mineral component, with a specific emphasis on hydroxyapatite (HAp) crystal assembly. Bone is fundamentally a triphasic biomaterial, consisting of an inorganic mineral phase, an organic matrix, and an aqueous component. The inorganic phase (hydroxyapatite), is characterized by its hexagonal prismatic nanocrystalline structure. We leverage a cellular automata (CA) paradigm to computationally simulate the mineralization process, leading to the formation of the bone’s hydroxyapatite framework. This model exclusively considers the physicochemical aspects of bone formation, intentionally excluding the biological interactions that govern in vivo skeletal development. To optimize computational efficiency, a simplified anatomical segment of a long bone (e.g., the femur) is modeled. This geometric simplification encompasses an outer ellipsoidal cylindrical boundary (periosteal envelope), an inner ellipsoidal surface defining the interface between cortical and cancellous bone, and a central circular cylindrical lumen representing the medullary cavity, which accommodates the bone marrow and primary vasculature. The CA methodology is applied to generate the internal bone microarchitecture, while deliberately omitting the design of smaller, secondary vascular channels. Full article

This article discusses and compares approaches to the visualization of the danger zone formed as a result of spreading toxic substances during a fire at an industrial enterprise, to create predictive models and scenarios for evacuation and environmental protection measures. The purpose of this study is to analyze the features and conditions for the application of algorithms for predicting the spread of a danger zone, based on the Gauss equation and the probabilistic algorithm of a cellular automaton. The research is also aimed at the analysis of the consequences of a fire at an industrial enterprise, taking into account natural and climatic conditions, the development of the area, and the scale of the fire. The subject of this study is the development of software and algorithmic support for the visualization of the danger zone and analysis of the consequences of a fire, which can be confirmed by comparing a computational experiment and actual measurements of toxic substance concentrations. The main research methods include a Gaussian model and probabilistic, frontal, and empirical cellular automation. The results of the study represent the development of algorithms for a cellular automation model for the visual forecasting of a dangerous zone. They are characterized by taking into consideration the rules for filling the dispersion ellipse, as well as determining the effects of interaction with obstacles, which allows for a more accurate mathematical description of the spread of a cloud of toxic combustion products in densely built-up areas. Since the main problems of the cellular automation approach to modeling the dispersion of pollutants are the problems of speed and numerical diffusion, in this article the frontal cellular automation algorithm with a 16-point neighborhood pattern is used, which takes into account the features of the calculation scheme for finding the shortest path. Software and algorithmic support for an integrated system for the visualization and analysis of fire consequences at an industrial enterprise has been developed; the efficiency of the system has been confirmed by computational analysis and actual measurement. It has been shown that the future development of the visualization of dangerous zones during fires is associated with the integration of the Bayesian approach and stochastic forecasting algorithms based on Markov chains into the simulation model of a dangerous zone for the efficient assessment of uncertainties associated with complex atmospheric processes. Full article

Thorough investigation into dune morphology is pivotal for grasping the intricacies of constructing and operating power transmission lines in desert terrains. However, there remains a notable gap in the quantitative analysis and assessment of how dune dynamics evolve under the influence of transmission infrastructure. In this study, the Real-Space Cellular Automaton Laboratory is deployed to explore how transverse dunes evolve around transmission towers under diverse wind velocities and varying dune dimensions. The results reveal that, beyond the immediate vicinity of the transmission tower, the height of the transverse dune remains largely stable across broad spatial scales, unaffected by the transmission line. As wind velocities wane, the structural integrity of the transverse dunes is compromised, leading to an expansion in the size of the trail structures. Initially, the height of the dune surges, only to decline progressively over time, with the maximum fluctuation reaching nearly $1\mathrm{m}$. The height of larger dunes escalates gradually at first, peaks, and then subsides, with the pinnacle height nearing $6.5\mathrm{m}$. As a critical metric for safety evaluation, the height of the transmission line above ground initially plummets, then gradually rebounds, and shifts backward over time after hitting its nadir. Full article

Topology optimization has been present in modern engineering for several decades, becoming an important tool for solving design problems. Today, it is difficult to imagine progress in engineering design without the search for new approaches to the generation of optimal structural topologies and the development of efficient topological optimization algorithms. The generation of topologies for structures under random loads is one of many research problems where topology optimization is present. It is important to predict the topologies of structures in the case of load uncertainty, since random load changes can significantly affect resulting topologies. This paper proposes an easy-to-implement numerical approach that allows the prediction of the resulting topologies of structures. The basic idea is to transform a random loads case into the deterministic problem of multiple loads. The concept of equivalent load scheme (ELS) is introduced. Instead of generating hundreds of loads applied at random, the selection of a few representative load cases allows the reduction of the numerical effort of the computations. The numerical implementation of proposed concepts is based on the cellular automaton mimicking colliding bodies, which has been recently introduced as an efficient structural topology generator. The examples of topology optimization under randomly applied loads, performed for both plane and spatial structures, have been selected to illustrate the proposed concepts. Confirmed by results of numerical simulations, the efficiency, versatility and ease of implementation of the proposed concept can make an original contribution to research in topological optimization under loads applied in a random manner. Full article

Automation technologies play a pivotal role in optimizing logistics operations within warehouse facilities. Retail companies invest in these technologies to maintain pace with the customers demands by increasing their production capacity while reducing their financial expenses. In this paper, we conduct a study on warehouse automation in the European e-commerce sector by analyzing historical data from three fulfillment centers. Accordingly, we explore diverse data science approaches applied to trained machine learning models to determine the automatons that have the greatest impact on financial costs. The purpose is to support supply chain managers in identifying the most profitable logistics automatons that merit consideration in future automation projects. The study offers a comprehensive analysis that encourages e-commerce companies to invest in tailored automation for future warehouse installations. Full article

Silicon–lithium clusters are promising candidates for hydrogen storage due to their lightweight composition, high gravimetric capacities, and favorable non-covalent binding characteristics. In this study, we employ density functional theory (DFT), global optimization (AUTOMATON and Kick–MEP), and Born–Oppenheimer molecular dynamics (BOMD) simulations to evaluate the structural stability and hydrogen storage performance of key Li–Si systems. The exploration of their potential energy surface (PES) reveals that the true global minima of Li₆Si₆ and Li₁₀Si₁₀ differ markedly from those of the earlier Si–Li structures proposed as structural analogs of aromatic hydrocarbons such as benzene and naphthalene. Instead, these clusters adopt compact geometries composed of one or two Si₄ (T_d) units and a Si₂ dimer, all stabilized by surrounding Li atoms. Motivated by the recurrence of the Si₄–T_d motif, we explore oligomers of Li₄Si₄, which can be viewed as electronically transmuted analogues of P₄, confirming the additive H₂ uptake across dimer, trimer, and tetramer assemblies. Within the series of Si–Li clusters evaluated, the Li₁₂Si₅ sandwich complex, featuring a σ-aromatic Si₅¹⁰⁻ ring encapsulated by two Li₆⁵⁺ moieties, achieves the highest hydrogen capacity, adsorbing 34 H₂ molecules with a gravimetric density of 23.45 wt%. Its enhanced performance arises from the high density of accessible Li⁺ adsorption sites and the electronic stabilization afforded by delocalized σ-bonding. BOMD simulations at 300 and 400 K confirm their dynamic stability and reversible storage behavior, while analysis of the interaction regions confirms that hydrogen adsorption proceeds via weak, dispersion-driven physisorption. These findings clarify the structure–property relationships in Si–Li clusters and provide a basis for designing modular, lightweight, and thermally stable hydrogen storage materials. Full article

Obstacles “upstream” of the exit significantly impact evacuation efficiency and deserve attention. Based on the discrete cellular automaton model, this paper studies the impact of different obstacle settings on evacuation efficiency in different emergency levels under different exit loads. Through simulation, we found that at low emergency levels, the appearance of obstacles has little impact on evacuation efficiency, while at high emergency levels, the changes in evacuation efficiency vary greatly under different obstacle settings: when the exit is relatively wide (evacuation pressure is low) and has the “faster is faster” effect, obstacles upstream of the exit reduce the evacuation efficiency, and setting obstacles directly opposite of the safety exit has the most obvious impact on the evacuation efficiency; while when the exit is narrow (evacuation pressure is high) and has the “faster is slower” effect, appropriately setting obstacles can slightly improve the evacuation efficiency. Our findings help to understand the impact of obstacles on evacuation efficiency under different exit loads to set upstream obstacles reasonably. Full article

The elastic modulus is one of the fundamental parameters controlling the mechanical behaviour of concrete. In this study, the Movable Cellular Automata (MCA) method is applied to predict the Young’s modulus of concrete based on the properties of its components. Each automaton represents one component: cement paste, fine aggregate, or coarse aggregate. A parametric sensitivity analysis was performed using Grey System Theory (GST) on hypothetical concrete modeled with the MCA method. The analysis showed that the coarse aggregate type, coarse aggregate-to-total aggregate ratio, and water-to-cement ratio have the greatest impact on the Young’s modulus. To test the effectiveness of the MCA method in modelling concrete, results of numerical simulations were compared with experimental data available in the literature. The first numerical simulations were conducted for mortars containing cement paste and sand as well as for concretes produced by adding granite to them. Two approaches were used to perform the simulations; in the first approach, a sample contained automata representing cement paste, sand, and granite, while in the second the automata represented mortar and granite. High consistency was achieved, with results from both approaches differing by only 0.6%. Subsequent simulations focused on concretes with different water-to-cement ratios (0.45, 0.55, and 0.65), the origin of the basaltic aggregate, and various aggregate contents (60%, 54%, 48%, and 42%). Results showed high agreement between simulations and experimental data, confirmed by a high coefficient of determination R² of 0.84 and mean squared error of 2.43 GPa². Finally, simulations were performed for lightweight expanded clay aggregate concrete, resulting in an R² of 0.86 and mean squared error of 0.81 GPa², which demonstrates the effectiveness of the MCA method in predicting the static elastic modulus of concrete. Full article

A system is said to be critically observable if the operator can always determine whether the current state belongs to a set of critical states. Due to the communication failures, systems may suffer from intermittent loss of observations, which makes the system not critically observable. In this sense, to characterize critical observability in a quantitative way, this paper extends the notion of critical observability to stochastic discrete event systems modeled as partially observable probabilistic finite automata. Two new notions, called step-based almost critical observability and almost critical observability are proposed, which describe a measure of critical observability for a given system against intermittent loss of observations. We introduce a new language operation to obtain a probabilistic finite automaton describing the behavior of the plant system under intermittent loss of observations. Based on this structure, we also present verification methodologies to check the aforementioned two notions and analyze the complexity. Finally, the results are applied to a raw coal processing system, which shows the effectiveness of the proposed methods. Full article