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In this paper, a novel method was presented for forecasting mixed grains within large-scale forgings of 12%Cr heat-resistant steel during the single-pass hot deformation process. The proposed approach integrates a dynamic recrystallization cellular automaton (DRX-CA) model with finite element (FE) simulations and is complemented by evaluation criteria for mixed grains microstructure. Firstly, through the single-pass hot compression experiment, the necessary data for constructing the DRX-CA model were obtained, leading to its establishment. Based on the DRX-CA model, the stress–strain curves, grain microstructure, and DRX percentage observed in the experiment were simulated and reproduced, which verified the model's effectiveness in describing 12%Cr heat-resistant steel's DRX behavior; Secondly, grain microstructure evolution in different deformation zones during 12%Cr heat-resistant steel's single-pass hot deformation (upsetting process) was studied through the integration of DRX-CA model with FE simulation. The mixed grains degree of the resulting microstructure was assessed based on evaluation criteria for mixed grains microstructure; Finally, the grain structure obtained by the above method was validated through upsetting experiments. The results revealed that the average grain size and mixed grain degree obtained from both the experiments and simulations were not significantly different, indicating that the combination of the DRX-CA model and FE simulation can effectively predict the mixed grains during the upsetting process. This novel method for predicting mixed grains shifts the evaluation point of mixed grains forward, integrating the characteristics of defect prediction and process optimization, and provides a reference for controlling mixed grains during the hot forging process of large forgings. [ABSTRACT FROM AUTHOR]

The density classification (DC) task, a computation which maps global density information to local density, is studied using one-dimensional non-unitary quantum cellular automata (QCAs). Two approaches are considered: one that preserves the number density and one that performs majority voting. For number-preserving DC, two QCAs are introduced that reach the fixed-point solution in a time scaling quadratically with the system size. One of the QCAs is based on a known classical probabilistic cellular automaton which has been studied in the context of DC. The second is a new quantum model that is designed to demonstrate additional quantum features and is restricted to only two-body interactions. Both can be generated by continuous-time Lindblad dynamics. A third QCA is a hybrid rule defined by both discrete-time and continuous-time three-body interactions that is shown to solve the majority voting problem within a time that scales linearly with the system size. [ABSTRACT FROM AUTHOR]

Powder nickel-based superalloy is the key material for hot-end components such as turbine disks and gas engine disks in aeroengines, and its microstructure uniformity has an important influence on the disks' service performance. However, thermomechanical treatments make it easy to produce abnormally large grains (ALGs) in powder superalloy disks. In order to investigate the relationship between the hot deformation conditions and ALGs of powder superalloys, isothermal compression experiments under various deformation conditions were carried out and a FE-CA method was constructed to investigate the ALGs formed by the limited recrystallization mechanism. The results indicate a close relationship between the ALGs formed after the supersolvus treatment of this alloy and the equivalent stress after thermal deformation, and the local dissolution of the γ′ phase in supersolvus heat treatment does not produce ALGs. [ABSTRACT FROM AUTHOR]

The crystalline state of matter serves as a reference point in the context of studies of properties of a variety of chemical compounds. This is due to the fact that prepared crystalline solids of practically useful materials (inorganic or organic) may be utilized for the thorough characterization of important properties such as (among others) energy bandgap, light absorption, thermal and electric conductivity, and magnetic properties. For that reason it is important to develop mathematical descriptions (models) of properties and structures of crystals. They may be used for the interpretation of experimental data and, as well, for predictions of properties of novel, unknown compounds (i.e., the design of novel compounds for practical applications such as photovoltaics, catalysis, electronic devices, etc.). The aim of this article is to review the most important mathematical models of crystal structures and properties that vary, among others, from quantum models (e.g., density functional theory, DFT), through models of discrete mathematics (e.g., cellular automata, CA), to machine learning (e.g., artificial neural networks, ANNs). [ABSTRACT FROM AUTHOR]

Chronic wounds are a syndrome that affects around 4% of the world population due to several pathologies. The COV-19 pandemic has enforced the need of developing new techniques and technologies that can help clinicians to monitor the affected patients easily and reliably. In this prospective observational study a new device, the Wound Viewer, that works through a memristor-based Discrete-Time Cellular Neural Network (DT-CNN) has been developed and tested through a clinical trial of 150 patients. The WV has been developed to serve as the state-of-art tool, capable to return the actual clinical information that is most needed by the caregivers: through the WBP scale, it classifies four classes of wounds by the type of tissue: A-only granular tissue; B-<50% slough; C->50% slough; D-necrosis. This work aims to describe in depth the technology and the computational techniques that have been implemented, and to demonstrate reliability in automatically identifying, classifying through internationally accepted clinical scales and measuring such wounds, that peaked to over a 90% of accuracy. [ABSTRACT FROM AUTHOR]

Landslides are among the most serious and frequent environmental disasters, involving the fall of large masses of rock and soil that can significantly impact human structures and inhabited areas. Anticipating these events is crucial to reduce risks through real-time monitoring of areas at risk during extreme weather events, such as heavy rains, allowing for early warnings. This study aims to develop a methodology to enhance the prediction of landslide susceptibility, creating a more reliable system for early identification of risk areas. Our project involves creating a model capable of quickly predicting the susceptibility index of specific areas in response to extreme weather events. We represent the terrain using cellular automata and implement a random forest model to analyze and learn from weather patterns. Providing data with high spatial accuracy is vital to identify vulnerable areas and implement preventive measures. The proposed method offers an early warning mechanism by comparing the predicted susceptibility index with the current one, allowing for the issuance of alarms for the entire observed area. This early warning mechanism can be integrated into existing emergency protocols to improve the response to natural disasters. We applied this method to the area of Prunella, a small village in the municipality of Melito di Porto Salvo, known for numerous historical landslides. This approach provides an early warning mechanism, allowing for alarms to be issued for the entire observed area, and it can be integrated into existing emergency protocols to enhance disaster response. [ABSTRACT FROM AUTHOR]

The surge in lithium-ion battery (LIB) use, essential for mass-scale renewable energy storage, raises concerns about fire hazards. However, to date, there is a lack of industry-wide understanding of large-scale LIB fire propagation. This paper suggests a translational forensic approach to promote fire safety awareness and introduces the cellular automata (CA) model coupled with the Monte Carlo (MC) approach to address the complex fire propagation simulation within an energy storage system (ESS). The objective is to demonstrate that the CA-MC model can provide a flexible and scalable connection for all levels of battery fire studies. The numerical model is coupled with experimental tests which have been performed to establish the actual timing of fire propagation from a single source. Cellular automata simulation, conducted through hybrid modeling and an applied risk analysis approach to evaluate fire hazards associated with LIBs, offers crucial insights into potential risks. The results demonstrate that, with fire incident initiation at a probability of 0.1 (10%), 33% of batteries will burn, and at a probability of 0.6 (60%) and beyond, the entire battery module will face complete burndown. Achieving full combustion of the entire module will take only approximately 42 timesteps on average, indicating rapid fire propagation. The actual time for a complete fire to occur in the battery module has been estimated to be 304 s per timestep, or 3.5 h total. Using this example, it is shown that the CA-MC approach can be extended to many other aspects of battery fire studies and is ideal as a translational tool, spanning all domains of the LIB industry. [ABSTRACT FROM AUTHOR]

Objectives: Electroencephalograms (EEGs) or multi-unit activities (MUAs) of tonic–clonic seizures typically exhibit a distinct structure. After a preliminary phase (DC shift, spikes), the tonic phase is characterized by synchronized activity of numerous neurons, followed by the clonic phase, marked by a periodic sequence of spikes. However, the mechanisms underlying the transition from tonic to clonic phases remain poorly understood. Methods: We employ a simple two-dimensional cellular automaton model to simulate seizure activity, specifically focusing on replicating the tonic–clonic transition. This model effectively illustrates the physical processes during the ictal phase and, more importantly, differentiates the roles of neurons' activity, identifying their origin as either synaptic or ephaptic. Results: Our model reveals an intriguing interaction between the synaptic and ephaptic modes of action potential wave conduction. By replicating the EEG and multi-unit activity (MUA) structure of a tonic–clonic seizure and comparing it with real MUA data, we validate the model's underlying assumption: the transition from tonic to clonic phases is driven by a shift in dominance from synaptic to ephaptic conduction. During synaptic-mode control, neural conduction occurs through synaptic transmission involving chemical substances, while in the ephaptic mode, information transfer occurs through direct Ohmic conduction. Significance: Gaining a deeper understanding of the neuronal electrical conduction transitions during tonic–clonic seizures is crucial for improving the treatment of this debilitating condition. [ABSTRACT FROM AUTHOR]

With the development of the economy, the demand for 3D model animation in fields such as games, film and television, and education is constantly increasing. At present, many 3D model animations are built based on genetic algorithm. However, due to the slow convergence speed and poor local search ability of genetic algorithm, this model still has many shortcomings. Therefore, a cellular automaton is used to optimize the genetic algorithm, and a cellular genetic algorithm is proposed. It is integrated with WebAR technology, which has the characteristics of lightweight and low cost, aiming to improve the separation and dynamic binding mechanism of 3D model animation. Experimental analysis showed that when the number of chromosome genes was 90, the vertex gene value of the research algorithm was the highest, ranging from 281 to 501, and the convergence was the highest. The file size of the model dataset based on the research method was reduced by 50% compared with before. The FPDT based on the animation separation and dynamic binding mechanism was decreased by 15.2 s on average. To sum up, it can be seen that the designed 3D model animation is more detailed and the animation dynamic is also the best. This research method can significantly improve the separation and dynamic binding mechanism of 3D model animation. This research will improve the integration of genetic algorithm and WebAR technology, which can not only effectively deal with complex 3D models and animations, but also has broad application prospects. [ABSTRACT FROM AUTHOR]

An effective safety evacuation program is an important basis for safeguarding the lives of people, and reasonable planning of evacuation routes is of great significance for formulating personnel evacuation plans. This article considers the global search ability of the ant colony algorithm and the local search ability of the artificial potential field. The artificial potential field is integrated into the ant colony algorithm, and combined with the extended Moore type cellular automata, an extended cellular automata model based on the potential field ant colony algorithm is proposed to optimize the calculation of personnel evacuation and path planning in the evacuation area. Analyze the performance of different algorithms in planning path smoothness, total path length, and calculation time from the same location in single exit and multi exit evacuation areas. And to verify the effectiveness of the algorithm, we use part of a teaching building as an evacuation scenario. The results show that combining the potential field ant colony algorithm with the extended Moore type cellular automata for path planning can reduce the number of invalid nodes and redundant turning points in the shortest path, improve the smoothness of the path, improve planning efficiency, and provide a design basis for emergency evacuation of buildings. [ABSTRACT FROM AUTHOR]

Enhancing model interpretability remains an ongoing challenge in predictive modelling, especially when applied to simulation data from complex systems. Investigating the influence and effects of design factors within computer simulations of complex systems requires assessing variable importance through statistical models. These models are crucial for capturing the relationships between factors and response variables. This study focuses on understanding functional patterns and their magnitudes of influence regarding designed factors affecting cognitive agent decision-making in a cellular automaton-based highway crossing simulation. We aim to identify the most influential design factors in the complex system simulation model to better understand the relationship between the decision outcomes and the designed factors. We apply Generalized Linear Mixed-Effects Models to explain the significant functional connections between designed factors and response variables, specifically quantifying variable importance. Our analysis demonstrates the practicality and effectiveness of the proposed models and methodologies for analyzing data from complex systems. The findings offer a deeper understanding of the connections between design factors and their resulting responses, facilitating a greater understanding of the underlying dynamics and contributing to the fields of applied mathematics, simulation modelling, and computation. [ABSTRACT FROM AUTHOR]

The continuous growth of air transportation demand and the tightness of airspace resources in the terminal area of a hub are becoming more and more prominent. A dynamic programming approach to limit optimization (DPALO) is proposed to solve the arrived landing problem (ALP). First, a discrete mathematical model of flight landing scheduling with time window constraints is established, and a recursive formula for solving ALP with a fixed order is derived. The flight time window is optimized by combining the ALP problem characteristics with the constraints, and it is proved that the proposed method does not affect the solution of the optimal value of the model. Second, elite genetic algorithms, particle swarm algorithms, the linear loop swapping and the linear loop interpolation are applied to adjust the flight sequences and then to finding an optimal solution. Finally, validation is performed on the OR-Library dataset. The experimental results show that using the elite genetic algorithms to adjust the flight landing sequence, DPALO outperforms the best known values (BKV), the bionic algorithm (BA) and the displacement decision algorithm (DDA) and obtains similar results to those of the cellular automata optimization approach (CAO), the tight subsequence algorithm (CSA), and the rolling horizon framework of hybrid particle swarm optimization local search algorithm (RH-HPSO-LS). The time efficiency of DPALO achieves milliseconds in time on the small sample dataset, and it is improved by 76.88%, 89.11%, and 78.28% on the large sample dataset in comparison to CSA, CAO, and RH-HPSO-LS, respectively. [ABSTRACT FROM AUTHOR]

Airport surface operations involve multiple operational entities and complex operating environments. Given its relatively fixed structure, the airport surface has gradually become a bottleneck area for improving the efficiency of air traffic network operations. This paper proposes an airport surface operations model (ASOM) based on the Nagel-Schreckenberg (NS) cellular automaton. ASOM considers four aircraft operational characteristics and the cooperative operational mechanism between vehicles and aircraft. It accurately characterizes the aircraft taxiways and vehicle operation path structures while providing high-precision "aircraft-vehicle-surface" coordination simulation. Case studies are conducted using real airport structural data and surface monitoring data from Shenzhen Bao'an International Airport, and the average magnitude of error (AME) between the simulation results and the real operational data is less than 6%, demonstrating the accuracy and applicability of ASOM in depicting airport surface operations. [ABSTRACT FROM AUTHOR]

Studying and modeling the process of the disease and spread of blast can guide fertilization and reduce the amount of pesticide application. Since most of the existing research focuses on the pathogenesis of the rice blast fungus and the interaction between the plant structure and the fungus, this paper presents a method to simulate the spread of rice blast among rice populations. The method is divided into the following processes. First, based on the cellular automata grid dynamics model, the cell state corresponds to the degree of infection of the rice blast fungus, and its ability to simulate the spatiotemporal evolution of complex systems is used to simulate the spread of disease spots on the surface of rice leaves. Second, according to the biological mechanism of the rice blast fungus and the position of the first infected leaf in a single rice plant, the normal distribution function is used to calculate the infection probability of the remaining leaves, and then the transmission mode of the disease inside a single rice plant is simulated. Then, in view of the natural phenomenon that the blast fungus spores spread with the wind, the wind factor is added to propose a method for the spread of the rice blast fungus among the population of rice plants, and thus a model for the spread of the pathogen among the entire plant population was established. Finally, we set up a simulation system for the spread of rice blasts in rice fields and visualized the spread and spread trend of rice blasts in rice populations. In the simulation experiment, the infection process of rice blast on the surface of the rice leaf was simulated, and the change process of the shape of the diseased spot can be visually seen. This paper not only provides a new idea for the research on pathogen propagation and diffusion between plants in agricultural production but also provides a reference for how to realistically visualize the pathogen propagation model in the virtual agricultural scene. [ABSTRACT FROM AUTHOR]

As one of the unconventional intersection designs, the median U-turn (MUT) intersection design has been confirmed to have the potential to improve intersection efficiency and produce lower vehicle delays, and it has been widely applied to many urban networks. Most existing studies have successfully evaluated the operational performance of MUT intersections by analyzing the overall traffic flow, particularly for intersection delay and capacity. However, the impact of the critical parameters and behavior of individual vehicles in MUT intersection operations has not been adequately studied. These limitations cannot provide a complete and objective evaluation of MUT intersections. Therefore, a comprehensive cellular automata model is proposed to simulate vehicle dynamic variations, considering road channelization information and vehicle random movements. This model will be compared to the VISSIM model to validate rationality by using field data, and it shows a good agreement between the simulation results of both models. In addition, sensitive experiments reveal that properly adjusting the elements, including the separation distance, the proportion of left-turn vehicles, and green phase percentage, can improve the operation of MUT intersections to varying degrees. By appropriately improving intersection features and conducting reasonable evaluations, the overall performance and sustainability of the MUT intersections in Xi'an city can be enhanced. Finally, this paper offers a valuable guideline for the evaluation and modeling strategies of MUT intersections. [ABSTRACT FROM AUTHOR]

Laser scanning data are increasingly used to segment forests into homogeneous units, called segments or stand compartments. Region growing and region merging have been traditionally employed for this purpose. Recently, alternative methods such as cellular automata (CA), self-organizing maps (SOM), and combinatorial optimization have emerged, promising more homogeneous stand delineations. However, these newer methods often require fine-tuning due to rugged stand boundaries. To address this, we propose and assess a novel hybrid approach that combines two segmentation algorithms, resulting in smoother boundaries and homogeneous stands. Our hybrid method outperforms traditional fine-tuning techniques, particularly using CA or SOM for initial segmentation and then refining it with another cellular automaton (CA'), adjusted to produce smooth boundaries. The new two-step hybrid method resulted in better segmentation results than the one-step algorithm combined with previously suggested fine-tuning methods. SOM-CA' showed the highest degree of explained variance (R2) of the LiDAR metrics used in the segmentation. CA-CA' reached almost the same R2 values with a larger average segment area. When the R2 of the LiDAR metrics was used as the criterion, the segments produced by the hybrid method outperformed the stand delineations of the current Spanish forest map with a clear margin. [ABSTRACT FROM AUTHOR]

Large‐scale wildfires can significantly reduce the air gap insulation resistance of high‐voltage transmission lines and cause chain tripping incidents. To assess the resilience of the power transmission system during wildfire, this paper proposes a resilience assessment framework for transmission system that considers the entire process of wildfire disaster. Firstly, a wildfire spread model, considering multiple influencing factors, is developed based on the cellular automaton. Based on the air gap breakdown mechanism during wildfires, the trip‐out probability of transmission lines is calculated, and various failure scenarios are obtained by using the Monte Carlo sampling. Secondly, considering the geographical location of failures, maintenance personnel schedules and restoration time, a power transmission system restoration model is established. Thus, a resilience assessment method for power transmission system during wildfire disasters is proposed. Finally, IEEE RTS‐79 transmission system is taken as an example to demonstrate the effectiveness of the proposed resilience assessment method. The results show that the proposed method can effectively calculate the wildfire spread tendency and transmission line's trip‐out probability. Furthermore, three typical resilience improvement measures are quantitatively analysed, which provides a quantifiable reference for the power sector to formulate prevention and recovery strategies for extreme wildfire disasters. [ABSTRACT FROM AUTHOR]

In contemporary wildlife conservation, drones have become essential for the non-invasive monitoring of animal populations and habitats. However, the sensitive data captured by drones, including images and videos, require robust encryption to prevent unauthorized access and exploitation. This paper presents a novel encryption algorithm designed specifically for safeguarding wildlife data. The proposed approach integrates one-dimensional and two-dimensional memory cellular automata (1D MCA and 2D MCA) with a bitwise XOR operation as an intermediate confusion layer. The 2D MCA, guided by chaotic rules from the sine-exponential (SE) map, utilizes varying neighbor configurations to enhance both diffusion and confusion, making the encryption more resilient to attacks. A final layer of 1D MCA, controlled by pseudo-random number generators, ensures comprehensive diffusion and confusion across the image. The SHA-256 hash of the input image is used to derive encryption parameters, providing resistance against plaintext attacks. Extensive performance evaluations demonstrate the effectiveness of the proposed scheme, which balances security and complexity while outperforming existing algorithms. [ABSTRACT FROM AUTHOR]

This paper deals with the actual and challenging process of the optimal design of topologies of periodic structures taking into account the design-dependent loads. The topology formulation used in this paper minimizes the compliance value of the structure and is subject to a total volume constraint while maintaining a periodic pattern and self-weight load. This combination represents a promising and original contribution to the field of ongoing research, although it is not yet widely recognized. This paper aims to fill this gap by presenting the first results of numerical optimization tests. The redistribution of material within a design domain is governed by the rules of Cellular Automata, a locally oriented optimization tool that can be applied to all types of structural optimization, including topology optimization. The technique has been demonstrated by numerical tests on two- and three-dimensional examples. The calculations were performed for different types of periodic schemes. The optimized structures did not show the checkerboard effect or the presence of residual gray elements in the final topologies. The strategy used in this paper ensures connectivity between periodic subdomains without imposing additional conditions on the algorithm. [ABSTRACT FROM AUTHOR]

Researchers have long been studying the effects of the modification of friction material compositions on their tribological properties. Predictive models have also been developed, but they are of limited use in the design of new compositions. Therefore, this research aims to investigate the tribological behaviour of single ingredients in friction materials to develop a tribological dataset. This dataset could then be used as a foundation for a cellular automaton (CA) predictive model, intended to be a tool for designing friction materials. Tribological samples were almost entirely composed of four distinct friction material ingredients, and one sample composed of their mixture was successfully produced. Pin-on-disc (PoD) tribometer testing and scanning electron microscopy/energy-dispersive X-ray spectroscopy (SEM/EDXS) analysis were used for the tribological characterization. Each material showed distinct tribological properties and evolution of the contact surface features, and the synergistic effect of their mutual interaction was also demonstrated by their mixture. [ABSTRACT FROM AUTHOR]

With societal progress, urban brownfields have become restrictive, and redevelopment studies have become an important part of urban renewal. In this work, we developed a two-step model for urban brownfield redevelopment, while considering the Wangping brownfield as the study area. Site suitability evaluation models for brownfield parks, agricultural picking gardens, and creative industrial centers were developed based on the elevation, slope, and surface runoff, and the evaluation results were categorized into five levels. The redevelopment plan was formulated based on these evaluation results. To study the effect of the plan, a transition matrix of land use was assessed using satellite images and the cellular automata (CA)–Markov model; based on the analysis, we predicted the land use situation of the Wangping brownfield, with respect to natural development, for 2030. A comparison of the redevelopment planning with the forecasted results revealed that the proportions of grassland, construction, and unused land decreased by 25.68, 3.12, and 2.38% and those of plowland and forest land increased by 6.61 and 24.57%. This confirms the advantages of redevelopment planning for restoring plowland and increasing biological carbon sinks. Notably, our two-step urban brownfield redevelopment model can enrich the current research on urban brownfields and guide similar urban renewal projects. [ABSTRACT FROM AUTHOR]

Special vehicles (SVs) are vehicles which conduct tasks such as the maintenance of urban roads and are typically characterized by travelling at a lower speed at a constant rate of speed within the same lane. In order to reduce the influence of SVs, guidance zone is designed and provides traffic guidance suggestions (TGS) for human‐driven vehicles (HVs) helping drivers for better decision between car‐following (CF) and lane‐changing (LC). To verify the effectiveness of TGS, an improved Dogit‐agent‐based model is established to simulate the captive and not captive choice of CF and LC for different driver types under TGS, and build the rules for mixed traffic flow of SV and HVs. Finally, a numerical simulation with a three‐lane system is conducted to analyze the traffic efficiency through a set of indicators, and the results show that the TGS can reduce the influence of SVs on traffic flow in a specific occupancy rates range, increase the cross‐section traffic volume by about 5%. The TGS also can increase the average speed of HVs in the lane behind SV by about 5% to 30%, and increase traffic density to 200% on the underutilized lane in the raw space in front of the SV. [ABSTRACT FROM AUTHOR]

The elementary cellular automata (ECAs) under the chaotic rule possess long periodicity and are widely used in pseudo-random number generators. However, their period is limited, related to the rule and the number of cells. Meanwhile, the Boolean functions of some ECAs are linear and vulnerable to linear analysis. Thus, the ECA cannot be directly implemented in the stream cipher. In this paper, a hybrid ECA (HECA) with dynamic mask (HECA-M) is designed. The HECA-M consists of two parts: the driving and mask parts. The driving part based on a HECA is used in generating the keystream, and the mask part based on a chaotic ECA is utilized to determine the iterative rule of the driving part. Subsequently, a stream cipher based on the HECA-M and SHA-512 is proposed. The statistic and secure analyses indicate that the proposed stream cipher possesses good randomness and can resist stream cipher analyses, such as exhaustive search, Berlekamp–Massey synthesis, guess and determine attack, time–memory–data tradeoff attack, etc. Hence, the proposed scheme can meet security requirements. Moreover, the time and space consumption of the proposed stream cipher is qualified. [ABSTRACT FROM AUTHOR]

Lightning-induced forest fires frequently inflict substantial damage on forest ecosystems, with the Daxing'anling region in northern China recognized as a high-incidence region for such phenomena. To elucidate the occurrence patterns of forest fires caused by lightning and to prevent such fires, this study employs a multifaceted approach, including statistical analysis, kernel density estimation, and spatial autocorrelation analysis, to conduct a comprehensive examination of the spatiotemporal distribution patterns of lightning-induced forest fires in the Greater Khingan Mountains region from 2016–2020. Additionally, the geographical detector method is utilized to assess the explanatory power of three main factors: climate, topography, and fuel characteristics associated with these fires, encompassing both univariate and interaction detections. Furthermore, a mixed-methods approach is adopted, integrating the Zhengfei Wang model with a three-dimensional cellular automaton to simulate the spread of lightning-induced forest fire events, which is further validated through rigorous quantitative verification. The principal findings are as follows: (1) Spatiotemporal Distribution of Lightning-Induced Forest Fires: Interannual variability reveals pronounced fluctuations in the incidence of lightning-induced forest fires. The monthly concentration of incidents is most significant in May, July, and August, demonstrating an upward trajectory. In terms of temporal distribution, fire occurrences are predominantly concentrated between 1:00 PM and 5:00 PM, conforming to a normal distribution pattern. Spatially, higher incidences of fires are observed in the western and northwestern regions, while the eastern and southeastern areas exhibit reduced rates. At the township level, significant spatial autocorrelation indicates that Xing'an Town represents a prominent hotspot (p = 0.001), whereas Oupu Town is identified as a significant cold spot (p = 0.05). (2) Determinants of the Spatiotemporal Distribution of Lightning-Induced Forest Fires: The spatiotemporal distribution of lightning-induced forest fires is influenced by a multitude of factors. Univariate analysis reveals that the explanatory power of these factors varies significantly, with climatic factors exerting the most substantial influence, followed by topographic and fuel characteristics. Interaction factor analysis indicates that the interactive effects of climatic variables are notably more pronounced than those of fuel and topographical factors. (3) Three-Dimensional Cellular Automaton Fire Simulation Based on the Zhengfei Wang Model: This investigation integrates the fire spread principles from the Zhengfei Wang model into a three-dimensional cellular automaton framework to simulate the dynamic behavior of lightning-induced forest fires. Through quantitative validation against empirical fire events, the model demonstrates an accuracy rate of 83.54% in forecasting the affected fire zones. [ABSTRACT FROM AUTHOR]

Wetland vegetation is the most crucial primary producer in wetland ecosystems and serves as an indicator of ecosystem health. After 2003, significant changes occurred in the “river-lake relationship” between Poyang Lake and the Yangtze River, with intensified dry conditions in autumn and winter leading to a shortened inundation period for sandbanks and significant changes in the community structure and spatial distribution of wetland vegetation. By coupling a Gaussian model with a population dynamics model, this study simulated the response of wetland vegetation to inundation duration under different hydrological year types, revealing the relationship between vegetation distribution and inundation duration. Key parameters, such as growth and mortality rates, were identified, and the vegetation succession and spatial distribution of Poyang Lake wetlands were simulated for five representative years: a wet year (2010), a normal year (2001), a dry year (2006), an extreme wet year (1998), and an extreme dry year (2022). The results showed that the response of wetland vegetation to inundation duration followed a Gaussian curve, with each species exhibiting an optimal range of inundation durations. Mortality rates increased when the duration fell outside this range. In 2010, the total area of wetland vegetation was similar to that in a normal year, but the proportions of different plant species varied significantly. Compared to 2001, the areas of Phalaris arundinacea and Polygonum criopolitanum increased by 50.92 % and 24.86 %, respectively, while the areas of Phragmites australis and Triarrhena lutarioriparia decreased by 71.66 % and 83.49 %. In 1998, the total vegetation area shrank considerably, with a 54.74 % reduction compared to 2001 and a 64.32 % reduction compared to 2006. In contrast, the total wetland vegetation area reached its maximum in the extreme dry year of 2022, increasing by 41.35 % compared to 2001. Among dominant species, Carex spp., Cynodon dactylon, and Phragmites australis expanded significantly in dry years, while Phalaris arundinacea and Polygonum criopolitanum were better adapted to flood conditions.

The risk of forest fires is substantial due to uneven precipitation distributions and abnormal climate change. This study employs cellular automata principles to analyze forest fire behavior, taking into account meteorological elements, combustible material types, and terrain slopes. The Wang Zhengfei model is utilized to compute fire spread speed, and a multifactor coupled forest fire model is developed. Comparisons with experimental data show a mean calculated fire spread speed of 0.69 m/min, which is consistent with the experimental results. Using the forest fire in Anning city, Yunnan Province, as a case study with a mean burned area of 2281 ha, the burned area, rate of change in burned area, and burning area demonstrated an increasing trend, with fluctuating states in the rate of change of the burning area. Employing the controlled variable method to examine forest fire spreading patterns under varying factors such as wind speed, vegetation type, and maximum slope reveals that under wind influence, the fire site adopts an elliptical shape with the downwind direction as the major axis. Quantitatively, when the wind speed increases from 2 m/s to 10 m/s, the burned area expands by a factor of 1.37. The ratio of the combustible material configuration coefficient to the burned area remains consistent across the different vegetation types, and the burned area increases by a factor of 1.92 when the maximum slope increases from 5° to 25°.

Abstract Special vehicles (SVs) are vehicles which conduct tasks such as the maintenance of urban roads and are typically characterized by travelling at a lower speed at a constant rate of speed within the same lane. In order to reduce the influence of SVs, guidance zone is designed and provides traffic guidance suggestions (TGS) for human‐driven vehicles (HVs) helping drivers for better decision between car‐following (CF) and lane‐changing (LC). To verify the effectiveness of TGS, an improved Dogit‐agent‐based model is established to simulate the captive and not captive choice of CF and LC for different driver types under TGS, and build the rules for mixed traffic flow of SV and HVs. Finally, a numerical simulation with a three‐lane system is conducted to analyze the traffic efficiency through a set of indicators, and the results show that the TGS can reduce the influence of SVs on traffic flow in a specific occupancy rates range, increase the cross‐section traffic volume by about 5%. The TGS also can increase the average speed of HVs in the lane behind SV by about 5% to 30%, and increase traffic density to 200% on the underutilized lane in the raw space in front of the SV.

In recent years Quantum Cellular Automata (QCA) technology has emerged as an ideal option to substitute the current CMOS technology. QCA offers operation in the terahertz range, small area, and low power in nano-scale circuit design. This paper explores the application of quantum dot cellular automata(QCA) technology in efficient floorplanning of digital code converters using the tile based architecture of QCA XOR gate. The proposed code converter circuits exhibits the benefits of low cell count, area, cost and low energy dissipation. The suggested layouts have achieved the 11.42% reduction in cell count, 29.53% reduction in total occupational area,30.93% reduction in cost and 11.52% increase in area utilization factor in comparison to similar counterparts. The functional validity of the suggested designs were validated using QCADesigner 2.0.3 tool. In addition, the energy dissipation analysis were calculated using the QCAPro tool at standard tunelling energy levels o 0.5EK, 1.0EK, 1.5EK. [ABSTRACT FROM AUTHOR]

Abstract Arid regions tend to form compact urban patterns that have significant implications on urban growth and future urban patterns. Spatial simulation and projection using cellular automata (CA)-based models are important for achieving sustainable urban development in arid regions. In response to this need, we developed a new CA model (GSA-CA) using the gravitational search algorithm (GSA) to capture and project urban growth patterns in arid regions. We calibrated the GSA-CA model for the arid city of Urumqi in Northwest China from 2000 to 2010, and validated the model from 2010 to 2020, and then applied to project urban growth in 2040. The results indicated that the optimal performance of the model was achieved when the fraction of the population was 0.5. GSA-CA achieved an overall accuracy of 98.42% and a figure of merit (FOM) of 43.03% for the year 2010, and an overall accuracy of 98.52% with FOM of 37.64% for 2020. The results of the study help to adjust urban planning and development policies. The developed model has the potential to be employed in simulating urban growth and future scenarios in arid regions globally, including Northwest China and Africa.

Abstract The colloid cellular automata do not imitate the physical structure of colloids but are governed by logical functions derived from them. We analyze the space-time complexity of Boolean circuits derived from the electrical responses of colloids-specifically ZnO (zinc oxide, an inorganic compound also known as calamine or zinc white, which naturally occurs as the mineral zincite), proteinoids (microspheres and crystals of thermal abiotic proteins), and their combinations in response to electrical stimulation. To extract Boolean circuits from colloids, we send all possible configurations of two-, four-, and eight-bit binary strings, encoded as electrical potential values, to the colloids, record their responses, and infer the Boolean functions they implement. We map the discovered functions onto the cell-state transition rules of cellular automata-arrays of binary state machines that update their states synchronously according to the same rule-creating the colloid cellular automata. We then analyze the phenomenology of the space-time configurations of the automata and evaluate their complexity using measures such as compressibility, Shannon entropy, Simpson diversity, and expressivity. A hierarchy of phenomenological and measurable space-time complexity is constructed.

Abstract Water plays a significant role in every sector of the ecosystem. The fluctuation of the water levels in lakes is influenced by natural and man-made factors within the water catchment. Lake Baringo, which has no visible outlet, has been rising drastically, causing panic among native communities and businesses on the shores of the lake. Using GIS and Remote Sensing, this study intends to analyze the changes in water lakes using the Automatic water extraction index (AWEI), determine the causes of the fluctuation using Land use Land cover, land surface temperature, soil erosion, and siltation in the lake basin and the lake respectively, precipitation, and later predict of the water level for the year 2030 using the MOLUSCE tool. The tool utilizes an artificial neural network and cellular automata to analyze land use and land cover conveniently. It was found that the lake’s water level has been increasing drastically over the years, and the leading causes of the fluctuations were increased rainfall and human activities within the water basin. There are visible increased human activities within the water basin, such as agriculture, deforestation, settlement, and urbanization. It was also found that there will be a further increase in water level in 2030. With all the above results, it is recommended that better policies be made to conserve the water basin effectively, and a plan should be drawn to re-delineate the new riparian buffer.

Due to rapid growth in the integrated circuit (IC) industry, the demand for compact digital system design is high. However, the continued technology reductions made the feasibility of further scaling down transistor size more challenging. In response to the growing demand for ultra-compact IC designs, the revolutionary quantum-dot cellular automata (QCA) technology has emerged as a promising solution. In a digital era, the counters are widely adopted in the peer-to-peer process flow to establish a mechanism for generating unique values for each identifier/number. In this work, a unique synchronous and asynchronous counters architecture is proposed with a reliable D and T flip-flop design. The proposed QCA architecture is implemented and validated with the QCA designer tool. Furthermore, in QCA technology, unreliable QCA designs can lead to frequent errors and malfunctions in the implemented logic. To overcome this challenge, the proposed design prioritizes cell placement (the relative positions of QCA cells) to make the circuit more robust. As a result, the circuit can still produce the expected functionality even if some QCA cells malfunction. Hence, to ensure the reliability of the proposed QCA architecture, the missing cell defect analysis is carried out in comparison with existing state-of-the-art designs. Based on comparison results, the unique designs like the proposed multiplexer, D flip-flop and T flip-flop design exhibit success rates of 67.28, 77.04 and 85.15%, respectively. The experimental results demonstrate that the proposed counter-architecture outperforms existing architectures. [ABSTRACT FROM AUTHOR]

Quantum-dot cellular automata (QCA) technology is one of the nanotechnologies which attracted a great deal of consideration because of features like the speed of the circuit, low-power utilization and small size. In digital electronics, the comparator plays a vital role, due to its vast area of applications like state machine design, digital testing and fault tolerance, digital control systems, etc. This paper proposes a 4-bit/8-bit/16-bit comparator in QCA technology using the XOR Gate. The proposed method is an efficient method, which has two stages: finding whether the inputs are equal or not in stage 1 and less than or greater than in stage 2 using an XOR gate. The proposed comparator circuits have less area and cost with less delay as compared to the previous works. The proposed energy calculations are carried out with the QCA DesignerE tool, and it is observed that the proposed structures are energy efficient. In addition, the proposed comparators are simulated and synthesized with Vivado design suite 2021.1 and implemented on the Virtex UltraScale VCU129-PP FPGA board. [ABSTRACT FROM AUTHOR]

Universities play a crucial role in urban economic and structural development. The government of Bangladesh has undertaken the initiative to establish a public university in each of the 64 districts. These newly founded universities have the potential to impact urban growth significantly. We aimed to project university-induced urban expansion to address this knowledge gap and identify the mechanisms driving urban growth. The classification of supervised and unsupervised methods was employed to analyze urban development for the years 2000, 2010, 2016, and 2022. We used the Cellular Automata and Markov Chain approach to forecast future urban growth and land transition capacity. Additionally, the driving factors and selection of the study area were derived from Focus Group Discussions (FGD), Key Informant Interviews (KII), Probit Model, and Perception Index (PI). The findings of this study reveal a 1.6% urban growth rate within ten years of the establishment of the university, while urban expansion accelerated to 29.78% after ten years. The predictions also indicate a sustained urban growth rate of 4.7% by 2042. Furthermore, the PI index demonstrates that the establishment of the university has resulted in high demand for rental housing, serving as one of the primary drivers of urban expansion. Moreover, the Probit model highlights strong economic capability, proximity to the town, railway station, hospital, and easy access to credit as vital facilitators behind the drivers of urban expansion. Policymakers, the scientific community, and urban planners can benefit from this study in pursuing sustainable city development through university establishment. [ABSTRACT FROM AUTHOR]