Cellular Automata Research Articles

Development necessitates evolutionarily conserved factors

Early-stage generalised transcription factors in biological development are often evolutionarily conserved across species. Here, we find for the first time that similar factors functionally emerge in an alternative medium of development. Through comprehensively analysing a Neural Cellular Automata (NCA) model of morphogenesis, we find multiple properties of the hidden units that are functionally analogous to early factors in biological development. We test the generalisation abilities of our model through transfer learning of other morphologies and find that developmental strategies learnt by the model are reused to grow new body forms by conserving its early generalised factors. Our paper therefore provides evidence that nature did not become locked into one arbitrary method of developing multicellular organisms: the use of early generalised factors as fundamental control mechanisms and the resulting necessity for evolutionary conservation of those factors may be fundamental to development, regardless of the details of how development is implemented.

A Local Moran’s I guided transformer cellular automata for simulating heterogeneous urban growth

The rapid advancement of urbanization in recent decades has attracted extensive application of cellular automata (CA)-based models to simulate urban growth for planning and decision-making. However, the inaccurate representation of heterogeneous spatial interactions between urban units and the neglect of autocorrelated growth patterns in urbanization lead to unreliable simulation results of CA-based models. To address these two limitations, this study proposes a novel CA-based model integrated with Transformer network and Local Moran’s I, namely TL-CA. The Transformer network is built to quantify heterogeneous interaction between neighbors using the self-attention mechanism. Subsequently, Local Moran’s I is employed to implicitly guide the network in learning spatially autocorrelated patterns of urban growth through auxiliary learning. Finally, the development potential estimated from driving factors, i.e. the network output, is incorporated into CA to simulate urban growth. Land use data from Wuhan (2000–2020) are selected to verify TL-CA’s performance. The results demonstrate that TL-CA achieves the highest simulation accuracy, with an average increase in the figure of merit (FoM) of 9.97%. Attention visualization and residual analysis explain the model’s effectiveness in modeling heterogeneous interactions and autocorrelated growth. Additionally, TL-CA exhibits high computational efficiency and low resource consumption, with sufficient potential to support larger-scale research.

Active lane-changing model based on cellular automata to promote CAV platoon

Active lane-changing model based on cellular automata to promote CAV platoon

Impacts of tourism on LULC and LST dynamics in district Buner and Shangla, Pakistan

Tourism activities are changing the global landscape pattern. This study attempted to estimate changes in Land Use Land Cover (LULC) and Land Surface Temperature (LST) in District Buner and Shangla, Khyber Pakhtunkhwa (KPK), Pakistan, and specifically its tourist spots. Using remote sensing data from satellites (1990–2020) and future projections (2035–2050), we applied Artificial Neural Network (ANN) and Cellular Automata Markov (CA-Markov) models to examine past and future LULC and LST dynamics across two districts including four major tourist spots (Shangla Top as tourist spot one (TS1), Bar Puran (TS2), Shahida Sar (TS3), and Daggar (TS4). The LULC classification for the whole study area (1990–2020) indicates that built-up and agricultural areas increased with a net change of +0.8% and +3.2% for the Shangla and Buner districts, respectively. The highest mean LST was found in the built-up areas. The simulation results indicate an expansion of 4.5% and 5.8% of the total built-up areas, and the LST above 31 °C will cover 76% and 88% of the total areas in 2035 and 2050, respectively. This conversion is driven by tourism activities, causing urban heat island effects (UHIs), and environmental degradation. The analysis of tourist spots (1990–2020) shows the highest change in built-up areas at Shangla Top (TS1), while the highest LST (28 °C) for the Daggar (TS4). The future simulation (2035–2050) results for tourist spots show that TS4 would have the highest LULC change in built-up areas (5.67%), and TS4 would have the highest LST (31 °C) from 65.23 to 82.20%. These findings provide an essential understandings for developing long-term tourism policies meant to moderate the environmental impact of tourism in the region.

Modelling bush encroachment dynamics using Intensity Analysis and the Cellular Automata model

Bush encroachment is a globally recognized phenomenon linked to adverse effects, including the degradation of grasslands and loss in biodiversity, thereby challenging the conservation of keystone and flagship species, the recreational value of landscapes and local livelihoods. Therefore, a comprehensive analysis of bush encroachment is essential to gain insights into its past, present and future encroachment, as well as the severity of transitions. Using RapidEye and PlanetScope satellite imagery, this study adopted Intensity Analysis to examine past and current bush encroachment trends for the periods 2009–2014, 2014–2019 and 2019–2023, while the Cellular Automata (CA) model was used to project future encroachment trends for 2028 and 2033 within a protected area. The results indicated a continuous increase in bush encroachment within the study area. Analysis of land cover intensities shows an intensive change in the research area’s land cover in the first period (2009–2014) compared to subsequent periods. In the first two periods (i.e. 2009–2014 and 2014–2019), woody vegetation gains were more pronounced at the expense of grasslands. However, during the 2019–2023 period, woody vegetation gains were less intensive to grasslands. Moreover, throughout the study period, most grassland gains occurred in bare areas, whilst the primary cause of grassland losses was bush encroachment. The projection of future encroachment trends indicates a continued increase in woody vegetation over the next decade. The results also indicate that bush encroachment is projected to expand by 5.50 and 6.67% in 2028 and 2033, respectively. These findings highlight the urgent need to assess and enhance management schemes within the study area. Gaining critical insights into bush encroachment progression trends and transition intensities can help prioritise landscape management efforts and support decision-making for the restoration of grasslands.

The Effects of Artificial Intelligence Applications in Natural Resource Management

Artificial intelligence methods have been increasingly used in natural resource management as an alternative to classical methods. Three computational challenges in natural resource management are data management and communication, data analysis, and optimization and control. Artificial intelligence methods can be a solution to these problems due to their ability to manage dynamic activities in natural resources. There are several artificial intelligence algorithms that have found various applications in various fields. In this article, some artificial intelligence methods, including artificial neural networks, fuzzy models, genetic algorithms, cellular automata, multi-agent systems, collective intelligence, and hybrid systems, are introduced, and some of their applications in natural resource management are listed.

Forecasting deforestation and carbon loss across New Guinea using machine learning and cellular automata.

Forecasting deforestation and carbon loss across New Guinea using machine learning and cellular automata.

Discrete Prediction of Grain Evolution in Solid-State Welding of Ti<sub>6</sub>Al<sub>4</sub>- V Alloy

Titanium (Ti) alloys, known for their high strength and low weight, are essential in aircraft and aerospace applications. This study focuses on friction spot welding as an innovative, efficient process that creates robust Ti alloys joints while minimizing the carbon footprint. The research aims to develop a cellular automata (CA) model to analyze grain evolution during the friction stir spot welding of Ti6Al4V alloy. The methodology involved simulating the welding process at rotational speeds ranging from 800 to 1,200 rpm using a complex curved-thread shoulder. The CA model incorporated both deterministic and probabilistic approaches to capture the dynamic recrystallization (DRX) behavior. Grain nucleation and growth were modeled based on dislocation density, allowing for an in-depth assessment of the alloy's microstructural changes driven by hardening and softening mechanisms. Validation was performed by comparing the model's predictions with experimental measurements of temperature and grain size. The findings indicate that heat and deformation during welding significantly influence grain size evolution, enhancing the understanding of microstructural behavior in high-strength titanium joints. This work contributes valuable insights into optimizing welding techniques for titanium alloys in aerospace engineering.

Design of compact and high-performance SRAM cell in QCA technology for next-generation memories

Abstract The most significant high-speed memory used in random access memories (RAMs) is static random-access memory (SRAM). SRAM is fast and robust but confronts design issues in sub-nanoscale complementary metal oxide semiconductors (CMOS), including leakage current, area, and stability. A different technology that can be utilized as an alternative to overcome the limitations of traditional SRAM is Quantum-dot Cellular Automata (QCA) technology. In this work, a QCA-based SRAM cell is implemented using three 3-input majority gates and an inverter which significantly improves the minimal waste area, layout area, overall cell counts, QCA cost, and the clock delay (latency). The layout has 25 cell counts with an SRAM cell size of 0.02 μm2 and a clock latency of 0.5 phases. Compared to a standard existing design, our proposed circuit achieves an improvement of 76% in cell counts, 84.6% in SRAM cell area, and 98.7% in QCA cost, demonstrating its potential for cost-efficient applications in modern electronic devices.

Achieving Fine‐Grained Microstructure in Low‐Alloy Steel: A Study on Static Recrystallization Using Experimental and Simulation Approaches

Recrystallization kinetics and microstructure evolution of low‐alloy steel with the capability to take part in the transformation‐induced plasticity phenomenon are investigated through experimentations and cellular automata (CA) simulations. The study primarily focuses on static recrystallization mechanisms, employing scanning electron microscopy and X‐ray diffraction for microstructural and phase analysis, alongside Vickers hardness and tensile testing for mechanical characterization. The results indicate that subjecting the steel to annealing at a temperature of 570 °C for a duration corresponding to complete recrystallization leads to a refined microstructure and a good balance of strength and ductility. This duration is determined through detailed microstructural observations and recrystallization kinetics analysis, ensuring that the steel achieves desired mechanical properties. To find out more about the ferrite recrystallization during annealing at 570 °C, a two‐dimensional CA model is created, and the outcomes of simulations are compared with experimental results in terms of recrystallized grain size and recrystallization kinetics. A satisfactory agreement is observed between the experimental results and predictions made using the CA model, confirming the applicability of the presented approach for controlling the microstructure and mechanical properties of advanced high‐strength steels.

Dynamic Load Balancing Based on Hypergraph Partitioning for Parallel Geospatial Cellular Automata Models

Parallel computing techniques have been adopted in geospatial cellular automata (CA) models to improve computational efficiency, enabling large-scale complex simulations of land use and land cover (LULC) changes at fine scales. However, the spatial distribution of computational intensity often changes along with the spatiotemporal dynamics of LULC during the simulation, leading to an increase in load imbalance among computing units and degradation of the computational performance of a parallel CA. This paper presents a dynamic load balancing method based on hypergraph partitioning for multi-process parallel geospatial CA models. During the simulation, the sub-domains are dynamically reassigned to computing processes through hypergraph partitioning according to the spatial variation in computational workloads to restore load balance. In addition, a novel mechanism called Migrated-SubCellspaces-First (MSCF) is proposed to reduce the cost of workload migration by employing a non-blocking communication technique to further improve computational performance. To demonstrate and evaluate the effectiveness of our method, a parallel geospatial CA model with hypergraph-based dynamic load balancing is developed. Experiments using a dataset from California showed that the proposed dynamic load balancing method achieved a computational performance enhancement of 62.59% by using 16 processes compared with a parallel CA with static load balancing.

The "Distributed Ghost" with independence - a study on computational ability of skewed asynchronous cellular automata

This paper explores the computational ability of ``distributed ghost" cellular automata (CA) \cite{10.1162/artl_e_00450} after introducing independence in the updating scheme. Traditionally, the CA system dictates all cells to update together following the concept of the global clock. To introduce independence in the system, CA researchers have introduced the notion of fully asynchronous updating scheme with atomicity property where, again, the CA system dictates two neighbouring cells not to update together. In this study, we explore the skewed asynchronous system after breaking the atomicity property. Specifically, we study the computational ability of the proposed skewed asynchronous system in the context of the density classification problem. In this direction, the first theoretical study includes identification of two attractor (all $0$ and all $1$) skewed asynchronous system (here, $13$ candidate ECA rules) after considering the problem statement of density classification. Next, following the basins of attraction dynamics of the two attractor systems, we consider ECA rules $170$,~$178$ and $184$ as candidates for this distributed consensus problem. Finally, we demonstrate the computational ability (i.e., efficiency) of the proposed cellular system with independence during density classification.

Framework for predicting the spatial distribution of green algae blooms utilizing historical GOCI and MODIS imagery.

Framework for predicting the spatial distribution of green algae blooms utilizing historical GOCI and MODIS imagery.

Multi-Agent Modeling for Indoor Fire Risk Prediction during Evacuation Based on Cellular Automata and Artificial Neural Network

Multi-Agent Modeling for Indoor Fire Risk Prediction during Evacuation Based on Cellular Automata and Artificial Neural Network

An improved multi-velocity cellular automaton model that considers psychological impatience

An improved multi-velocity cellular automaton model that considers psychological impatience

Two graphs: Resolving the periodic reversibility of one-dimensional finite cellular automata

Two graphs: Resolving the periodic reversibility of one-dimensional finite cellular automata

Numerical simulation of Sn grain growth in composite solder joint using a modified cellular automaton model

Numerical simulation of Sn grain growth in composite solder joint using a modified cellular automaton model

Finite element analysis of wave propagation in austenitic stainless steel welds with three-dimensional solidification structures predicted by cellular automaton method

Finite element analysis of wave propagation in austenitic stainless steel welds with three-dimensional solidification structures predicted by cellular automaton method

Recent Progress of Digital Reconstruction in Polycrystalline Materials

Abstract This study comprehensively reviews recent advances in the digital reconstruction of polycrystalline materials. Digital reconstruction serves as both a representative volume element for multiscale modelling and a source of quantitative data for microstructure characterisation. Three main types of digital reconstruction in polycrystalline materials exist: (i) experimental reconstruction, which links processing-structure-properties-performance by reconstructing actual polycrystalline microstructures using destructive or non-destructive methods; (ii) physics-based models, which replicate evolutionary processes to establish processing-structure linkages, including cellular automata, Monte Carlo, vertex/front tracking, level set, machine learning, and phase field methods; and (iii) geometry-based models, which create ensembles of statistically equivalent polycrystalline microstructures for structure-properties-performance linkages, using simplistic morphology, Voronoi tessellation, ellipsoid packing, texture synthesis, high-order, reduced-order, and machine learning methods. This work reviews the key features, procedures, advantages, and limitations of these methods, with a particular focus on their application in constructing processing-structure-properties-performance linkages. Finally, it summarises the conclusions, challenges, and future directions for digital reconstruction in polycrystalline materials within the framework of computational materials engineering.

ExaCA v2.0: A versatile, scalable, and performance portable cellular automata application for additive manufacturing solidification

ExaCA v2.0: A versatile, scalable, and performance portable cellular automata application for additive manufacturing solidification