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[Objective] To evaluate the ecological sensitivity of the Weihe River Basin, reveal the impact of human activities on its ecosystem, and predict future changes in ecological sensitivity. [Method]A comprehensive ecological sensitivity analysis was conducted in the Weihe River Basin, selecting landscape ecological risk sensitivity index, biodiversity sensitivity index and soil erosion sensitivity index as research indicators, a comprehensive ecological sensitivity index (CESI) was constructed to evaluate the ecological status of the Weihe River Basin over the past 20 years, and GeoSoS-FLUS were used for simulation and prediction. [Result]The ecological sensitivity of the Weihe River Basin had undergone significant changes from 2000 to 2020, with the total area of extremely sensitive areas was decreased. The areas with high ecological sensitivity in the Weihe River Basin were mostly concentrated in the west, north and small areas in east. The simulation and prediction of cellular automaton showed that the changes in low sensitivity areas and lower sensitivity areas will be relatively small by 2030, and most of these areas belong to nature reserves, which are far away from human activity areas and have not undergone significant changes;The area of moderately and severely sensitive areas may decrease compared to 2020, while the area of extremely sensitive areas may increase. [Conclusion] The ecological situation in the Weihe River Basin has greatly improved over the past 20 years. [ABSTRACT FROM AUTHOR]

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]

Cellular automaton (CA) is a computing model which is emerging rapidly. It is largely used in different types of scientific applications and simulations due to its ability to solve complex problems using simple rule(s). Cellular automata (CAs) are used in different types of applications like cryptography, VLSI systems, fault detection, etc. Typically, most of these applications utilize one-dimensional, 2-state, 3-neighborhood CAs. This paper explores the concept of Next State RMT Transition Diagram (NSRTD) for characterization of all the Elementary Cellular Automata (ECA) rules in periodic boundary condition leading to the identification of all ECA rules forming more than two fixed points (referred to as Single Length Cycle Multi-Attractor CA (MACA)) for an arbitrary CA length (n). For this, the 88 Wolfram classification rules and their equivalent rules have been utilized to reduce the search complexity by avoiding exhaustive searching on all the 256 ECA rules. [ABSTRACT FROM AUTHOR]

Elementary Cellular Automata (ECA) are well-studied computational universes capable of impressive computational variety, but harnessing their potential has been challenging. When combined with Reservoir Computing (RC), harnessing this computation becomes feasible, and furthermore enables energy efficient AI. This study compares ECA reservoirs to topological heterogeneous and more biological plausible counterparts of Partially-Local CA (PLCA) and Homogeneous Homogeneous Random Boolean Networks (HHRBN). Using the 5-bit memory benchmark, Temporal Derrida plots and collapse rate, finding are that more disordered topology does not equate to more disordered computation and moreover the evidence suggest this heterogeneity shrinks the critical range. [ABSTRACT FROM AUTHOR]

The extraction and propagation of caving are complex phenomena involving the breaking of the rock mass, the formation of a column of broken material, and the extraction from the column base. Geomechanical modeling in cave mining commonly uses approaches to model the rock mass as a continuous material, while discontinuous modeling is frequently used for the column of broken material. However, it remains complex to include all mechanisms in a single model. Therefore, to achieve a better representation of ore breakage and extraction in caving mining, this work couples FLAC3D, a continuous finite volume tool, with FlowSim, a discrete tool based on cellular automata, to determine the air gap volume. The methodology first defines the height of caving propagation and the cave back with a tool that models solid rock mass in a continuous manner, which are used to constrain the cellular automata tool that simulates the flow of broken material. The results show that unidirectional FLAC3D‐FlowSim coupling reproduces the generation of cave backs and air gaps in the propagation of caving, rendering the methodology valuable for preliminary estimation of air volumes over fragmented material and the generation of supportive data to control the caving process. [ABSTRACT FROM AUTHOR]

Projecting global urban expansion is crucial for environmental assessment under climate change scenarios. However, existing global future urban land products are typically provided at coarse resolutions (1 km) due to data and computing limitations. This hinders the accurate assessment of the impacts of global urban development at finer scales. Thus, we develop the first Cellular Automata (CA) online tool for simulating future global urban expansion in Google Earth Engine (GEE-CA), which can simulate future urban land change at a 30 m resolution under different SSP scenarios. GEE-CA enables seamless simulations of future urban land at high resolution through a partitioned parallel strategy. Seven large urban agglomerations are simulated under shared socioeconomic pathways as representative regions to present our fine-scale results. Comparatively, our datasets preserve significantly more spatial details than existing global urban land products. The improvement in resolution from 1 km to 30 m reduces errors by a range from 7.11% to 21.27% in the estimation of future urban area. So far, the proposed GEE-CA tool allows users to generate urban land projection maps for any defined region with a resolution as high as 30 m. [ABSTRACT FROM AUTHOR]

Mobile Ad-hoc Networks (MANETs) have emerging applications in real-time with lots of research challenges. Specifically, the dynamic nature of the mobile nodes limits the performance of routing in MANET. The existing routing algorithms, such as AODV, DSR, and DSDV, lack performance due to an ineffectual route discovery procedure. When it comes to large-scale applications such as air pollution monitoring, routing becomes more complex and consumes more energy for route selection. This research work aims to increase data delivery while minimizing energy consumption for air pollution monitoring applications. To achieve this, we have proposed a novel Hybrid Optimization methodology for MANETs. First, we partitioned the network into multiple dynamic clusters by using Dual Constraint Clustering (DCC) approach that works upon Mobility Metric (MoM) and Hop Count (HC). In each cluster, the Cluster Head (CH) is selected by Type-II fuzzy approach. Then, routing is performed by Hybrid Cellular Automata and African Buffalo Optimization (HCA2BO) algorithm. The proposed optimization algorithm considers multiple metrics to select an optimum route. The extensive analysis in the ns-3 simulation tool shows enhanced performance in network lifetime, energy consumption, and delay. Also, an air pollution monitoring application is demonstrated in the proposed work. [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]

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.

Acute myeloid leukaemia (AML) is a haematologic malignancy with high relapse rates in both adults and children. Leukaemic stem cells (LSCs) are central to leukaemopoiesis, treatment response and relapse and frequently associated with measurable residual disease (MRD). However, the dynamics of LSCs within the AML microenvironment is not fully understood. This study utilized three-dimensional cellular automata (CA) modelling to simulate LSC behaviour and treatment response under induction chemotherapy. Our study revealed: (i) a correlation between LSC persistence post-induction chemotherapy and risk of AML relapse; (ii) MRD negativity based on LSC count may not reliably predict outcomes, supporting clinical evidence that patients with MRD-negative status can still be at risk of relapse; (iii) prolonged persistence of LSCs post-chemotherapy without disruption of normal haematopoiesis, aligning with clinical observations of dormant AML clones; (iv) early LSC dynamics post-induction chemotherapy, characterized by stochastic behaviours and movement velocities, are insufficient predictors of long-term prognosis; and (v) a distinct spatiotemporal organization of LSCs in later phases post-induction chemotherapy is correlated with long-term outcomes. Our modelling results provide a theoretical and clinical framework for AML research, and future clinical data validation could refine the utility of CA modelling for oncological studies.

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).

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.

Although the blended wing body aircraft has been long speculated to be the next-generation passenger aircraft, its boarding processes remains relatively unexplored. In this study, we investigate possible boarding strategies for the blended wing body aircraft. The uncertainty in the design of the blended wing body aircraft and future civil aviation industry requires that boarding strategies must be evaluated from multiple perspectives, including adaptability, practicality, boarding rate, occupancy rate, passenger compliance, etc. We propose a novel boarding strategy specifically designed for the blended wing body aircraft, which can be used for different blended wing body aircraft. Performances of boarding strategies adapted from those commonly used today are also evaluated. As a result, while the performance of the novel boarding strategy is limited by operational constraints, it demonstrates better efficiency and robustness compared to conventional boarding strategies. [ABSTRACT FROM AUTHOR]

Analysis of trucks' impacts on the mixed two-lane highway traffic flow with a long tunnel would be a challenge. It needs to figure out the driving behavior of vehicles passing through different positions of a tunnel, i.e., approach section, entrance section, basic section, exit section, and general section. Also, it requires depicting the different driving patterns of trucks and cars, and the interference between them. To deal with these problems, we propose a modified two-lane cellular automaton traffic model with a long tunnel based on a model previously established by our team. The comparison between the calculated and measured results about flows and speeds shows a good agreement. The numerical result shows that the capacity will be reduced when trucks are mixed in the traffic flow, and the more trucks are, the lower the traffic flow will be. Then the strategy of lane restriction for trucks is recommended from the perspective of traffic optimization. The corresponding traffic properties are investigated, and the results are compared with those obtained from the situation without adopting lane restrictions for trucks. When the truck restriction strategy is applied, there is a notable enhancement in traffic capacity, particularly in the scenario with high traffic volume and large proportion of trucks. We investigate the underlying mechanics by analyzing the change in velocities along the road and vehicles' spatiotemporal evolution. It reveals that implementing lane restrictions for trucks, especially in scenarios with medium to high traffic volume, can improve lane utilization, increase average speeds, reduce traffic congestion, and enhance traffic flow. Then the strategy proves efficient, and it may offer valuable guidance for traffic management on highways featuring long tunnels. [ABSTRACT FROM AUTHOR]

A multi-scale multi-physics modeling framework has been developed to predict solidification cracking susceptibility (SCS) during welding. The framework integrates a thermo-mechanical finite element model to simulate temperature and strain rate profiles during welding, a cellular automata model to simulate the solidified microstructure in the weld pool, and a granular model to calculate the pressure drop in the mushy zone. Verification was achieved by comparing the model's predictions with welding experiments on two steels, demonstrating its capability to accurately capture the effects of process parameters, grain refinement, and alloy composition on SCS. Results indicate that increasing welding velocity, while maintaining a constant power-to-velocity ratio, extends the size of the mushy zone and increases the maximum pressure drop in the mushy zone, leading to higher SCS. Grain refinement decreases separation velocities and the permeability of liquid channels, which increases SCS, but it also raises the coalescence temperature, resulting in an overall reduction in SCS. Alloy composition impacts SCS through thermal diffusivity and segregation. Lower thermal diffusivity or stronger segregation tends to elongate the mushy zone, resulting in an increase in SCS. This framework provides a robust tool for understanding the mechanisms of solidification cracking, optimizing welding parameters to prevent its occurrence, and comparing SCS of different compositions during alloy design. [Display omitted] [ABSTRACT FROM AUTHOR]

The Q-switching (QS) phenomenon in lasers refers to the production of high intensity pulses by means of a saturable absorber (passive method) or by modifying the reflectivity or losses of the intracavity optics or mirrors (active method). Theoretically, the QS is studied through the laser rate equations which are useful to predict, at least qualitatively and roughly, the fundamental aspects of laser dynamics. However, specific details such as the spatial distribution of the intensity of the laser emission escape the simplicity of the rate equations. In this work we present a two dimensional cellular automata model (CA) to study the QS phenomenology for both the passive and the active method. To simulate the passive method we consider a spatial distribution of cells whose physical properties emulate those of the saturable adsorbers. And for the active method we introduce a periodic modulation of the lifetime of the photons inside the cavity. We have done numerous numerical simulations that show that despite the simplicity of the evolution rules, the AC model is capable of obtaining the main dynamics of operation of the laser by modifying the system parameters such as the pumping probability and the properties of the absorber. [ABSTRACT FROM AUTHOR]

[Display omitted] • An improved CA model was proposed to simulate the spherical and spherical-to-dendritic transition during solidification, achieving an accurate description of grain size. • The effects of latent heat, solute suppressed nucleation effect, agglomeration of inoculant particles, and cooling rate on the grain size of the Al-8Si-0.2 Mg (wt.%) alloy inoculated with Al-Nb-B were quantitatively revealed. • The prediction results of grain size were consistent with the experimental results. In this paper, an improved cellular automaton (CA) model with low grid anisotropy has been implemented using the zigzag capture rule and growth anisotropy reduction with diffusion method. The improved CA model can describe the evolution of the spherical growth, dendritic growth, and undercooling field, thus achieving a more accurate estimation of grain size than previous models. The model was used to simulate nucleation behavior and grain size of Al-8Si-0.2 Mg (wt.%) alloy inoculated with Al-Nb-B refiner, quantitatively revealing the factors that suppressed nucleation. Results show that when the inoculant particles were uniformly distributed, latent heat was the main factor restricting nucleation. Latent heat inhibited nucleation by reducing the available undercooling and terminating nucleation at the recalescence. When considering the agglomeration of particles, the effects of latent heat and solute suppressed nucleation (SSN) on nucleation inhibition accounted for 37.57 % and 58.58 %, respectively. Agglomeration caused the particle spacing to be smaller than that of a uniform distribution, and the SSN effect significantly increased as the separation distance decreased, resulting in a large portion of particles losing nucleation potency. In addition, it was found that the refinement by high cooling rate was attributed to not only providing more undercooling but also reducing SSN zone thickness. [ABSTRACT FROM AUTHOR]

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. [Display omitted] • Key factors accelerating reaching a green consensus by contractors were identified • A cellular automaton model was used to demonstrate the diffusion of green knowledge • State-transfer paths were based on non-informed/informed/spreader/forgetter classes • Once knowledge diffusion stabilized, only the spreader state remained • Distribution and spreader influence drove knowledge-diffusion efficiency [ABSTRACT FROM AUTHOR]