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• A pedestrian-vehicle coupling model with high simulation accuracy is proposed. • Six phase regions were acquired from the heterogeneous traffic phase diagram. • Obtained the difference between the fundamental diagram of heterogeneous and homogeneous traffic. • Tighter speed limits and wider sidewalks can reduce traffic conflicts. In urban streets, the intrusion of pedestrians into the motorway will pose a considerable challenge. However, modelling mixed pedestrian-vehicle traffic scenes is difficult. Pedestrians and vehicles have different motion characteristics and spatial dimensions, so it is hard to carry out unified modelling, and there are few related studies. This paper combines the multi-grid cellular automata model to explore these scenes to connect the vehicle and pedestrian models. An Improved Kerner-Klenov-Wolf (IKKW) model and a pedestrian motion model considering Time-To-Collision (TTC) are proposed, in which the spatial motion of a vehicle and a pedestrian is uniformly updated horizontally and longitudinally. The preliminary analysis of the model shows that it has high simulation accuracy. Applying the model to the simulation of real-life situations, research results reveal the impact of pedestrian intrusion behavior on traffic and the changes in vehicles' speed and flow rate caused by pedestrian intrusion behavior. The fundamental diagram of pedestrian-vehicle heterogeneous traffic shows significant differences, reflecting the impact of pedestrian intrusion on the state of traffic flow, and exposes six phase regions of pedestrian-vehicle mixed traffic flow. Moreover, this paper analyzes the conflicts between pedestrians and vehicles under different speed limits and sidewalk widths, showing that lower speeds and wider sidewalks can effectively reduce the frequency of pedestrian-vehicle conflicts. The peak conflicts' frequency when the vehicles' speed limit is 60.48 km/h exceeds three times that when the speed limit is 30.24 km/h. The cellular automata model proposed in this paper provides a good direction and idea for studying mixed traffic flow and has high scalability. [ABSTRACT FROM AUTHOR]

Emerging Industry 5.0 pushes advanced automation towards resilient solutions with enhanced human role. Resilience as an ability to sustain processes in the face of disruptions and adversarial attacks requires careful modelling and simulation. Cellular automata are efficient mathematical models used to simulate the behavior of complex systems, which change their state based on a set of predefined rules. In this paper, we suggest several updates to cellular automata (particularly Conway's "Game of Life") to address resilience. These include "Life and Creation", "War and Peace", and their hybrid "War and Creation" capable of addressing the important components of resilience, such as controllable creativity and adversarial interactions. Inherited in these updates and known advantages of cellular automata, such as simplicity, emergent behavior, parallelism, and adaptability, makes it a powerful simulation tool for a wide range of Industry 5.0 systems that involve humans, smart infrastructure, their complex and adversarial interactions, safety, and resilience. [ABSTRACT FROM AUTHOR]

Breast cancer is the most common type of cancer found in women. One of the most effiective methods for early identification of breast cancer is the mammogram. Numerous computer-aided systems for detecting breast cancer from mammograms have been introduced. In this paper, we present a new way for combining Cellular Automata with Fuzzy Logic, resulting in a so-called Fuzzy Cellular Automaton. The results obtained by testing our proposed approach on the mini-MIAS dataset are close to the ground truth, which is highly encouraging. The choice of using fuzzy logic provides a more flexible technique for categorizing the pixels of interest. The suggested method produces promising results for segmenting the mass region in mammograms with an accuracy of 98,66%, according to the experimental results. [ABSTRACT FROM AUTHOR]

To present an analysis and application method of ship traffic flow considering navigation rules in narrowing channel, which can make a reasonable judgment for decision makers to improve navigation efficiency and ensure navigation safety. In this paper, the relationship model between narrowing channel and ships is analyzed from the perspective of navigation. Based on the constraint condition of navigation safety domain, the navigation rules are formulated for different positions of channel, and the mutual influence of ships at the narrowed position of channel is analyzed emphatically. The establishment scheme of traffic flow model of narrowing channel with navigation safety distance is put forward by using cellular automata algorithm. According to the measured data, the correctness of the ship traffic flow model of narrowing channel is verified. The experimental results show that the relative error rates are between −2.29% and 10.89%, which indicating that the method can better conform to the actual ship traffic flow characteristics of channel narrowing and has strong application value. [ABSTRACT FROM AUTHOR]

In view of the checkerboard and gray cell optimization problems easily occur in the structural topology optimization under the traditional impact load, In this paper, based on the domain idea of cellular automata algorithm and 0–1 idea of bidirectional asymptotic method, an improved bidirectional asymptotic algorithm is proposed. Taking the most common anti-collision cushion structure in traffic facilities as an example, Topology optimization of multi-objective crashworthiness structure under impact load was carried out. In order to obtain the best structure configuration satisfying multi-objective condition. The simulation results show that the maximum acceleration of the anti-collision cushion configuration obtained by this algorithm is far less than the specification requirements, and the lightweight design of the structure can be achieved on the premise of meeting the functional requirements of the structure. [ABSTRACT FROM AUTHOR]

[Display omitted] • Frontal cellular automata reduce the computational time complexity. • The use of linked lists gives maximal acceleration in sequential calculations. • Frontal cellular automata preserve their advantages in parallel calculations. • FCAs are a very effective tool for the simulation of microstructure evolution. The paper focuses on analysing the computational time complexity of Frontal Cellular Automata (FCAs) and comparing it with Classic Cellular Automata (CCAs). Some variants of CCAs and FCAs are described and presented by showing differences in their structure and transition rules. Several variants of simple two-dimensional (2D) CAs and four variants of three-dimensional (3D) CAs for grain growth have been developed, tested and analysed. These four 3D CA algorithms are described. The calculation time of several simulation cases is measured for 2D and 3D CAs. The computational time complexity is determined on the basis of these measurements. The paper also takes into account the parallelisation of calculations with CAs. Time measurements show the possibility of accelerating the calculations. The paper also presents examples of the application of FCAs to solidification, recrystallisation, phase transformation and grain refinement with giving their simulation time. The computational complexity of different CAs is discussed, and FCAs with cells organised into linked lists show the lowest computational complexity. The paper ends with conclusions highlighting the low computational complexity and time efficiency of FCA. [ABSTRACT FROM AUTHOR]

• The model treats the frost layer as a heterogeneous porous medium. • This model was validated with some experimental data in the literature. • A multistep ascending growth pattern is found for both frost thickness and porosity. • The higher the frost porosity is, the lower the effective frost thermal conductivity is. The frost formation process is involved with many factors which lead to complex morphology. A two-dimensional model to simulate the early-stage frost formation on a cold flat plate is proposed in this paper based on the finite difference and cellular automata method. The concentration diffusion equation is used to constrain the diffusion of water molecules and the mass transfer equation is applied to relate the frost growth to the water vapor concentration and temperature. Furthermore, the temperature distribution during the growth process is obtained by solving the energy conservation equation. With the simulation, a heterogeneous porous medium consisting of ice crystals and air can be obtained and treated as a whole. Then, the physical properties of the frost layer like the frost thickness, the frost porosity and the effective thermal conductivity are calculated and analyzed. Results show that temperature is relatively uniform in the region where ice crystals are concentrated, and the frost surface temperature increases with the frost thickness as the frost grows. A multiple-step ascending growth pattern is observed both for the frost thickness and the frost porosity. The effective thermal conductivity of the frost layer correlates closely with the frost porosity and a higher frost porosity value results in lower thermal conductivity. Besides, the initial crystal nuclei distribution has a great influence on the morphologies of the frost layer, while the initial water vapor supersaturation has more impact on the growth speed of the frost thickness. [ABSTRACT FROM AUTHOR]

This paper presents a very fast numerical approach to simulate microstructures resulting from melt pool solidification including growth competition of columnar dendritic grains, and equiaxed grains nucleated from the melt. To reduce computation time, the key contribution is the development of an upscaling strategy, which instead of considering each dendrite individually consists in defining an average solidification front based on physically-informed dendritic growth velocity. The proposed approach also relies on dendritic preferred growth direction, and favorably oriented grain criterion to determine which grain survives the competition. To significantly reduce the total number of degrees of freedom Voronoi tessellations are used instead of regular grids for numerical implementation. Indeed, 3D regular grids typically leads to N 3 degrees of freedom while Voronoi tessellations lead to only 3 N , which dramatically reduces computation cost. This work is therefore a high-throughput approach enabling large data set generation to explore statistical features of microstructures with respect to melt pool properties. Results have been compared to experimental data, and to phase field and cellular automaton simulations in 2D only. Simulated microstructures are similar as those obtained with cellular automaton. Comparisons in 3D are left for future work. In addition, a convergence analysis is provided for 3D simulations, with thermal conditions corresponding to metal additive manufacturing to demonstrate how the present work can be used in practice. [Display omitted] [ABSTRACT FROM AUTHOR]

Cellular automata (CA) based models are practical tools to simulate the spatiotemporal landscape evolution induced by the land use/-cover change (LUCC). Existing models are struggling to comprehensively handle the spatiotemporal driving relationships amid the nonlinear LUCC process. Besides, the landscape patterns are not considered in most models, making them struggled to support the development strategies. Aiming at overcoming these obstacles, a novel land use/-cover change model concerning spatiotemporal dependency and properties related to landscape evolution (STAPLE) is proposed in this paper. A potential generating module establishing the nonlinear spatiotemporal driving relationship and a spatial allocating module employing a landscape-based CA are integrated for realistic LUCC simulations. As a case study, the proposed model is applied in Zhengzhou, China to assess its performance. It is indicated that the STAPLE model achieves a higher simulation accuracy, and the landscape properties are effectively manipulated. It provides a reproducible tool for policy-makers to explore a low-ecological-risk landscape under different future scenarios and achieve sustainable developments. • Propose an LUCC model concerning spatiotemporal dependency and landscape evolution. • Control the spatial evolution of landscape using a novel CA algorithm. • Promote accuracy by employing ST-CNN to assimilate the latent spatiotemporal dependency. • Provide a reproducible tool for policy-makers to achieve sustainable development. [ABSTRACT FROM AUTHOR]

A significant problem facing cybersecurity is that most client/server identity authentication schemes depend on computational complexity which is recently challenged by the super exponential computational abilities of quantum computers. Quantum cryptography with its quantum mechanics principles solves this problem and provides mathematically un-hackable secure communication for the quantum computers epoch. Biometric authentication has a big challenge related to its protection as it has to be submitted and stored in encrypted form. Unfortunately, most existing biometric cryptosystems rely on classical cryptography, so they are insecure in quantum era. This problem is solved in this paper by introducing a novel three-factor biometric quantum identity authentication scheme combining something you know (password), something you produce (client's voice), and something you are (client's face image) is proposed. The scheme consists of two phases: client enrollment and client login/authentication. The methodology is to use in the enrollment phase, two layers of quantum encryption for the client's voice signal. Each layer consists of two simple quantum circuits of SWAP and C-NOT quantum gates respectively using a different pseudorandom key obtained from a cellular automata (CA) pseudorandom generator. The proposed scheme has many merits. Firstly, it introduces a new design of muti-factors quantum biometric authentication scheme as the biometric voice data is processed, transmitted, and stored at the server database in an encrypted unconditionally secure quantum form which provides parallel processing, real-time communications, inherent level of security against attacks, tamper-proof security and exponential powerful storage capability due to the quantum properties. Secondly, the proposed scheme achieves double-layers security by combing quantum voice encryption and quantum secure direct communication in one process such that the quantum channel eavesdropper cannot obtain any valuable information in case of information leakage. Thirdly , the distribution and management of encryption key problem is solved as there is no need for key exchange prior to communication. Fourthly, it achieves high communication efficiency as although it is a three-factor biometric identity authentication, only one biometric data is sent in quantum encrypted form to the server. Fifthly , the numerical simulations showed that the proposed scheme has large key space, high key sensitivity and more robust performance against different types of attacks compared to other existing classical and quantum voice encryption schemes. Finally, as an identity authentication method, the proposed scheme achieves identification accuracy of 100% with zero False Rejection Rate (FRR) and zero False acceptance rate (FAR) which is perfect. [ABSTRACT FROM AUTHOR]

The emergence of data hiding schemes was to provide multimedia security besides the sharing of information in a confidential manner. Since then, various techniques have cropped up to meet the objective of secured data communication using multimedia documents. In this paper, a novel data hiding approach has been designed to exploit the superpixel of the image to identify the nature of the blocks for embedding data using spatial and transform domain techniques such as Cellular Automata (CA) and Discrete Cosine Transform (DCT) respectively. In addition, an Arnold transform is used to provide a blanket of security through a random selection of blocks via sharable keys. The proposed technique obtains YCbCr color model of an RGB image to use superpixel on only the Y color component. Thereafter, the acquired labeled image of superpixel is partitioned into (8 × 8) blocks which are to be categorised as homogeneous and heterogeneous blocks. Based on the categorization, DCT and CA are applied for the embedding of the secret data in the Cb and Cr color components. The experimental results and its comparison with state-of-the-art schemes uphold the effectiveness of the demonstrated scheme. Various analysis have been conducted to showcase the degree of robustness, imperceptibility, and visual quality attained by the proposed scheme. [ABSTRACT FROM AUTHOR]

Clustering analysis is used in many disciplines and applications. This data mining task is an essential tool that identifies groups of objects based on similarity measures. The ant clustering algorithm is a swarm-intelligent method used for clustering problems, that is inspired by the natural behavior of ants, which collect their corpses and sort their larvae. In recent years, many new metaheuristic algorithms have explored different scientific fields using inspirations or motivations in biological or nature-inspired derivations. The same situation is observed in area of data mining, where plenty of metaheuristics was presented as a new and effective approach. This state causes some difficulties or disappointments to have enough explanations or deep extent analysis to justify such applications. A new version of an ant clustering algorithm, using cellular automata mechanisms of transitions, is proposed to examine the computational efficiency and accuracy of the ant clustering approach. Based on the similarity of two approaches arising from Ant Sleeping Model, computational analysis of difficulties and complexities are presented and results show that the modification of an ant clustering algorithm - ASM MOD produces results that are not only more stable but also more efficiently determined than the prototype of ASM. The aim of this paper is not explicitly criticize these approaches but also to find a way of the repair these algorithms. [ABSTRACT FROM AUTHOR]

The detection of Brain cancer is an essential process, which is based on the clinician's knowledge and experience. An automatic tumor classification model is important to handle radiologists to detect the brain tumors. However, the precision of present model should be enhanced for appropriate treatments. Numerous computer-aided diagnosis (CAD) models are offered in the literary works of medical imaging to help radiologists concerning their patients. This paper proposes an optimization-driven technique, namely Whale Harris Hawks optimization (WHHO) for brain tumor detection using MR images. Here, segmentation is performed using cellular automata and rough set theory. In addition, the features are extracted from the segments, which include tumor size, Local Optical Oriented Pattern (LOOP), Mean, Variance, and Kurtosis. In addition, the brain tumor detection is carried out using deep convolutional neural network (DeepCNN), wherein the training is performed using proposed WHHO. The proposed WHHO is designed by integrating Whale optimization algorithm (WOA) and Harris hawks optimization (HHO) algorithm. The proposed WHHO-based DeepCNN outperformed other methods with maximal accuracy of 0.816, maximal specificity of 0.791, and maximal sensitivity of 0.974, respectively. [ABSTRACT FROM AUTHOR]

Application of cellular automata (CA) to digital image processing has achieved considerable attention in last several years. CA are now employed in noise filtration of digital images; particularly many CA-based impulse noise filters have been proposed. Salt-and-pepper noise is a special kind of impulse noise and is introduced in an image during transmission over transmission media by external noise sources like atmospheric disturbances, or due to corrupted hardware memory locations or fault in camera sensors. In this paper, we present five salt-and-pepper noise filters based on modifications of outer totalistic cellular automata (OTCA) with the adaptive neighborhood. Use of OTCA model makes the proposed filters computationally simple on one hand and the use of adaptive neighborhood help the filters to provide efficient noise filtration at varying noise densities on the other. Comparative analysis of these filters followed by comparison with several standard and CA-based filters in terms of peak signal to noise ratio (PSNR) and structural similarity (SSIM) index is presented. [ABSTRACT FROM AUTHOR]

Glaucoma is the leading cause of blindness worldwide. Glaucoma being a chronic eye disease results in irreversible loss of vision. Everyone is at danger for glaucoma. But, certain groups are greater risk. Hence, accurate Classification of normal and glaucoma images is significant one. In the screening and early detection of glaucoma, Cup to Disc Ratio (CDR) method plays a significant role. The automatic segmentation for early detection from Optimal Coherence Tomography (OCT) images is a fundamental task. Several research works based on OCT were contributed for early detection of glacuoma. However, inherent feature extraction and over fitting issues while performing early glaucoma detection have been researched by the research community for long time. In this paper, we propose Artificial Intelligence architecture, called Phase Quantized Polar Transformed Cellular Automaton (PQPT-CA) method that use the displacement gradients and distance gradients for early glaucoma detection. The proposed PQPT-CA method comprises of inherent feature extraction, robust feature segmentation and early glaucoma detection based on OCT. To start with, First Order Phase Quantization model is applied to the raw input image for extracting inherent features. Next, with the extracted features, Differential Polar Transformation is applied to generate robust segmentation map. Finally, glaucoma detection is performed for three different state employing Cellular Automaton model. The experiments show that our PQPT-CA method achieves state-of-the-art early detection on ACRIMA database. Simultaneously, the proposed method also obtains the satisfactory glaucoma screening performances with calculated diagnosis accuracy on ACRIMA database. The PQPT-CA method increases the Diagnosis Accuracy by 24% and minimizes computational time and false positive rate by 21% and 54% respectively. [ABSTRACT FROM AUTHOR]

Quantum dot cellular automata (QCA) stands as a highly promising nanotechnology, capable of outperforming conventional CMOS technology. Leveraging its advantages, this paper addresses the challenges in adder circuits related to size, delay, and cost by integrating QCA technology. A novel layout for a co-planar full adder in QCA is introduced, featuring a half-adder design with 23 cells in a single layer without any crossover, achieving a delay of 0.25 clock cycles. By utilizing inter-cellular effects inherent in QCA technology, the presented designs offer significant improvements in a variety of design metrics, and a cost efficiency of 64.3%, distinguishing them from traditional logic design approaches. Additionally, in order to ensure an efficient routing channel and a consistent structure of the adder design, a Universal, Scalable, and Efficient (USE) clocking scheme has also been employed. The adder design is further used to build 4-bit and 8-bit Ripple Carry Adders. An extensive study on energy dissipation using QCADesigner-E and QCAPro tools is conducted for three tunneling energies (0.5 E k , 1 E k , and 1.5 E k). The full adder design showcases substantial improvement in energy to delay cost when compared to the closest reported design, achieving improvements of 91.66%, 89.03%, and 88% at 0.5 E k , 1 E k , and 1.5 E k , respectively. The results highlight enhanced circuit efficiency and streamlined layouts, outperforming existing models and offering substantial benefits to high-performance and energy-efficient computing systems. • To demonstrate simple and streamlined layouts, ensuring ease of implementation. • To designs consider the direction of signal flow, enhancing their efficiency. • To take the advantage of the inter-cellular effects' capability of QCA technology, leading to the observed advancements in the adder circuits. • Detailed energy dissipation calculations performed using QCADesigner-E and QCAPro. • Comprehensive Parameter Analysis: The analysis covers a wide range of important parameters, offering a holistic assessment of the adder designs. [ABSTRACT FROM AUTHOR]

[Display omitted] Modeling wildfire dynamics is complex and challenging due to the multiple scales involved in fire propagation, from physical–chemical processes to the interaction with topography and meteorological conditions. To provide reliable indicators of the risk of an ongoing wildfire, models aimed at informing policy-making should quantify the primary sources of uncertainty in their predictions. In this paper, we introduce a novel methodology built on top of Cellular Automata to assess the impact of uncertainty by implementing wildfire ensemble modeling using data from the Spanish National Forestry Data Repositories. Uncertainty is embedded in the model considering the ± 2 σ deviations from the medians of linear regressions of the canopy stratum with LiDAR metrics as explainable variables. The relevance of dynamic meteorological conditions in contrast to static environment conditions is analyzed. Our results suggest that an accurate account of the fuel model, including time-dependent wind and moisture maps, is mandatory to provide reliable predictions. Using a real case study (Concentaina's extreme wildfire), we also illustrate the importance of assessing the impact of the firefighters' mitigation efforts. [ABSTRACT FROM AUTHOR]

With the advent and advancement of connected and automated vehicle technologies, it is now possible to control connected and automated vehicles (CAVs) in mixed traffic flows to reduce traffic oscillations and enhance traffic flow performance. Recently, a control strategy for autonomous vehicles (AVs) named "The Follower Stopper controller" (FS) has been proposed. However, the existing studies for the FS strategy only considered the situation that all CAVs execute the FS strategy individually. And the impact on the mixed traffic flow when CAVs in the mixed traffic flow execute FS strategies as platoons is not investigated. To address this limitation, this paper proposes a control strategy for CAVs that considers the driving behavior of CAVs platoons based on the FS strategy, which allows CAVs to execute the FS strategy as a platoon. Then, a cellular automaton model of mixed traffic flow is developed based on the proposed strategy. Finally, a numerical simulation is conducted to analyze the impacts of the proposed strategy on traffic efficiency, traffic congestion, traffic oscillation, vehicle fuel consumption, and pollutant emissions under different penetration rates (PRs) of CAVs and traffic densities. Moreover, a comparative analysis with the FS strategy was conducted to verify the effectiveness and superiority of the proposed strategy. The result shows that (1) the proposed strategy can effectively mitigate traffic oscillations and reduce congestion. The improvement effect of the proposed control strategy on the mixed traffic flow is significantly affected by the penetration rate of CAVs as well as the traffic density. (2) Compared with the FS strategy, the proposed strategy has better performance in improving traffic efficiency, alleviating traffic oscillations, relieving congestion, and reducing fuel consumption and pollutant emissions under different traffic conditions. (3) The effectiveness of the FS strategy in improving the performance of mixed traffic flow is greatly limited by traffic density. There is a significant marginal utility of the FS strategy in improving traffic flow performance under high traffic density. However, the proposed strategy overcomes the limitations imposed by high traffic densities. The proposed strategy is highly adaptable to diverse traffic densities and can continuously enhance traffic flow performance as the penetration rate of CAVs rises. [ABSTRACT FROM AUTHOR]

Nano-communication networks have critical roles in future worldwide communications, which provide efficient data transmission at ultra-high speeds employing nano-circuits that operate with ultra-low power. The quantum cellular automata (QCA) paradigm enables designing molecular-scale devices operating at terahertz frequencies with negligible power consumption. Moreover, reversible logic aids in reducing thermodynamic power dissipation significantly. In this paper, we design and implement a single-layer Fredkin gate and use it to implement reversible QCA devices, including the crossbar switch, multiplexer, demultiplexer, transmitter, and receiver. These devices are then used to implement a reversible switched network in QCA technology. The proposed circuits employ standard 90-degree QCA cells and coplanar crossovers. The results show that the area-time complexity and power dissipation of the designed reversible switched network have been reduced significantly compared to previous designs. [ABSTRACT FROM AUTHOR]

• Presenting the fault-tolerant majority gate using simple cells. • Verifying recommended gate by physical proof. • Presenting results in a recommended design for significant improvement in power consumption, area, and delay. • Developing an efficient one-bit QCA-based adder and 1:2 decoder based on a suggested gate. Quantum-dot cellular automata (QCA) has recently attracted significant notice thanks to their inherent ability to decrease energy dissipation and decreasing area, which is the primary need of digital circuits. However, the lack of resistance of QCA circuits under defects in previous works is a vital challenge affecting the stability of the circuit and output production. In addition, with the high defect rate in QCA, suggesting resistance and stable structures is critical. Furthermore, the 3-input majority gate is a fundamental component of QCA circuits; therefore, improving this essential gate would enable the development of fault-tolerant circuits. This paper recommends a 3-input majority gate which is 100% fault-tolerant against single-cell omission defects. Moreover, the fundamental gates are introduced based on the proposed gate. In addition, an adder and a 1:2 decoder are also designed. Using QCADesigner 2.0.3 and QCAPro software, simulations of structures and analysis of power consumption are performed. [Display omitted] [ABSTRACT FROM AUTHOR]

The urban morphology is characterised by self-organisation where interactions of multiple agents produce emerging patterns on the urban form. Port-urban relationship added to the complexity of port cities' urban form. Most urban cellular automata (CA) models simulate land-use evolution through transition rules representing multi-factored local interactions. However, calibration of CA-based urban land use and transport interaction (LUTI) models often utilise manual methods due to complexity of the process. This limits insights on urban interactions to a few explored settlements and prevents applications for planning and assessment of transport policies in other contexts. This paper, therefore, addresses three main points. The paper (i) demonstrates an improved method for the calibration of CA-based LUTI models, (ii) contributes to a better understanding of the urban dynamics in port city systems by quantifying generalizable interactions from a wide range of port-urban settlements, and (iii) illustrates how the use of these interactions in a simulation model can allow long-term impact predictions of planning interventions. These were done by formulating a model in a similar structure as a neural network model to enable automatic calibration using an application of the gradient-descent algorithm. The model was then used to quantify the dynamics between land-use, geographic, and transport factors in 46 port-based and 10 non-port settlements across Great Britain, thus enabling cross-sectional analysis. Cluster analysis of the calibrated interactions in the study areas was conducted to examine the variations of these interactions. This produced two main groups. In the first group, consisting larger settlements, connections between ports and other urban activities were weaker than in the second group which consisted of smaller port-settlements. Overall, the findings of the research are consistent with existing evidence in the port-cities literature but go further in quantifying the interaction between urban agents within port-urban systems of various sizes and types. These quantified interactions will enable planners to better predict the longer-term consequences of their interventions. • This paper improved calibration process of urban model using optimization method from machine learning • The improved process allowed examination of wide range of port cities to counter the current trend of case-specific studies • Quantified interactions from wide range of port cities provide insights of the different land use behaviour in different types of port cities • Port-urban interactions in smaller urban areas with port are stronger thn in most other port cities • Interactions quantified in the study can be used in simulation to predict future port city urban form [ABSTRACT FROM AUTHOR]

In this paper, a subdomain hybrid cellular automata method is proposed to carry out the material optimization of thin-walled frame structure under transverse impact with minimum material cost, which is a typical optimal solution searching problem including many discrete variables. A nominal flow stress is introduced to convert the discrete design variables (cell materials) to be continuous for the update of cell materials. The global optimal searching ability of the proposed method is improved by the material update rule based on the step internal energy density target, which is adaptively defined during the material optimization to avoid the optimal solution jumping into a local one. PID control is employed to improve the robustness of the proposed method. The subdomain cellular automata model of a car body frame under transverse impact is constructed to separately perform the material and cost optimization by the proposed method and a parallel efficient global optimization method. The total material cost converges to CNY 1051 within 271 iterations using 32 CPUs by the parallel method and CNY 1101 within 8 iterations using 8 CPUs by the proposed method. The material optimization efficiency is significantly improved by the proposed method with good global searching ability. • New material optimization method of nonlinear dynamic problems with discrete variables. • Significant optimization efficiency improvement with the proposed method. • Global optimization improved by material update rule with step internal energy density. • No gradient required, so especially suitable for nonlinear problems with discrete variables. [ABSTRACT FROM AUTHOR]

[Display omitted] • We coupled a new mixed-cell CA approach and system dynamics model to forecast the ecosystem service value under three alternative scenarios. • The SD-MCCA model was demonstrated to have better performance, higher simulation accuracy and stronger robustness. • This paper employed the coefficient of sensitivity to determine the dependence of ESV on the land use variations. Land use is a crucial factor affecting ecosystem service value (ESV), and forecasting future land use changes and ESV response can guide urban planning and sustainable development decisions. However, the traditional Cellular Automata (CA) model supposes that each cell has only one land use type at each time step, neglects the mixed structure and proportional distribution of land use units, does not take into account its quantitative continuous dynamic change, and lacks the exploration of land use quantity structure and spatial pattern optimization. This study employed a novel mixed-cell cellular automata (MCCA) approach, coupled with the system dynamics (SD) model to predict the spatiotemporal pattern of land use under the natural increase scenario (NIS), economic development scenario (EDS) and ecological protection scenario (EPS) in Xi'an, China, in 2030. The equivalent coefficient method was utilized to investigate the heterogeneity distribution and sensitivity of ESV. The results demonstrated that SD-MCCA exhibited remarkable prediction accuracy and robustness. The main changes in land use in 2000–2015 were due to urban expansion, the conversion of arable land into construction land, and the conversion between grassland and arable land. The total ESV increased from 19554.36×106 CNY in 2000 to 19618.39×106 CNY under the EPS in 2030, and the contribution of climate regulation and hydrological regulation to ESV was the highest. Spatial heterogeneity of ESV revealed a certain regularity, and the high value region was chiefly concentrated in woodland and grassland with favorable ecological conditions. Land use variations under NIS and EPS improved ESV, while the ESV had a negative response to land use transformations under the EDS. This research provides a new way to identify the relationship between future land utilization scenarios and ESV, which is of great significance for the management of land resources and formulation of ecological compensation standards. [ABSTRACT FROM AUTHOR]

• A dynamic model of pavement PV arrays with cellular automata is established. • An STNS model is used to characterize the change of irradiation intensity caused by vehicle shadow. • The irradiance matrix varying with vehicle shadow at different times can be obtained. • The influence of random vehicle shadows on the output characteristics is further explored and analysed. • Three representative scenarios are set to study the randomness of vehicle shading and its effects. This paper proposes a dynamic modelling of two-lane pavement photovoltaic (PV) arrays based on cellular automata theory, and the influence of random vehicle shadows on the output characteristics is explored and analysed. In the proposed model, a mathematical model of two-lane pavement PV array is established with considering the influence of bypass diodes and blocking diodes. In order to characterize the change of irradiation intensity caused by vehicle shadow, an asymmetric two-lane Nagel-Scheckenberg (ATNS) model is introduced in the driving process of vehicles. Moreover, the vehicle position at each time is obtained to further establish the randomly changing vehicle shadow matrix. The actual irradiance matrix can be obtained by combining vehicle shadow matrix with the irradiance matrix related to the external environment, and then a dynamic model of two-lane pavement PV arrays is established. To evaluate the effectiveness of the proposed model, the influence of the slowing probability and shading degree on the proposed model is analysed, where three representative scenarios are set to study the randomness of vehicle shading and its effects. Finally, simulations and experiments are conducted and the results show that the slowing probability and shading degree are the key two factors on the output characteristics of two-lane pavement PV arrays, and the dynamic random vehicle shadows significantly affect the output characteristics, resulting in a changing multi-peak state of the power-voltage curve and the global maximum power point being in a floating fast-changing state. [ABSTRACT FROM AUTHOR]

A broad range of computational models have been proposed to predict microstructure development during solidification processing but they have seldom been compared to each other on a quantitative and systematic basis. In this paper, we compare phase-field (PF) and cellular automaton (CA) simulations of polycrystalline growth in a two-dimensional melt pool under conditions relevant to additive manufacturing (powder-bed fusion). We compare the resulting grain structures using local (point-by-point) measurements, as well as averaged grain orientation distributions over several simulations. We explore the effect of the CA spatial discretization level and that of the melt pool aspect ratio upon the selected grain texture. Our simulations show that detailed microscopic features related to transient growth conditions and solid–liquid interface stability (e.g. the initial planar growth stage prior to its cellular/dendritic destabilization, or the early elimination of unfavorably oriented grains due to neighbor grain sidebranching) can only be captured by PF simulations. The resulting disagreement between PF and CA predictions can only be addressed partially by a refinement of the CA grid. However, overall grain distributions averaged over the entire melt pools of several simulations seem to lead to a notably better agreement between PF and CA, with some variability with the melt pool shape and CA grid. While further research remains required, in particular to identify the appropriate selection of CA spatial discretization and its link to characteristic microstructural length scales, this research provides a useful step forward in this direction by comparing both methods quantitatively at process-relevant length and time scales. [Display omitted] [ABSTRACT FROM AUTHOR]

This paper describes a simplified flood model based on cellular automata in order to be implemented in applications, which require the simulation of a great number of flood scenarios at regional scale. The model is based on a top-down evaluation of the flow process through the cells of the DEM. According to the conditions in a Moore Neighbourhood, the cells are classified in a dynamic process in four possible states with its own rules. To verify the quality of the results, an evaluation is carried out using the benchmarking tests and results shown in Neelz and Pender (2013). Besides, the 2D models HEC-RAS and Iber were used to evaluate quantitatively the efficiency of the model in terms of water depth and flood extent. From this, it is observed that the model produces very acceptable results especially where dynamic effects are less significant. • The model is faster than hydrodynamic models allowing the execution of a large number of scenarios for risk assessment. • It is based on a top-down evaluation of the water flow following the main concepts of CA in a Moore neighbourhood. • The transference of volume and the filling of floodplains use closed-form expressions to keep the conservation of mass. • It shows acceptable results both in high and low complexity areas, especially where dynamic effects are less significant. [ABSTRACT FROM AUTHOR]

Invasive biological species threaten biodiversity and economies worldwide, often resulting in devastating environmental and socio-economic effects. As a result, there has been a growing realisation of the importance of understanding and effectively managing the complex interactions that arise from biological invasions. A prerequisite for launching such a management strategy, however, is an effective means of predicting the expected effects of the strategy over time. To this end, a spatio-temporal model is proposed in this paper for predicting the extent to which a particularly problematic invasive plant species, Prosopis, spreads when confronted with a threshold-triggered control method. In particular, a spatial analysis is advocated during which the study region is discretised and a data set constructed. Thereafter, the power of machine learning algorithms is leveraged to predict habitat suitability for Prosopis. Finally, a cellular automaton is adopted to simulate the spatial spread and temporal growth of Prosopis within the discretised study region. The practicality of our modelling approach is illustrated by means of a real-world case study in the Northern Cape region of South Africa. • A spatio-temporal model is proposed for predicting the spread of the invasive tree species Prosopis. • The model seeks to capture the species response when confronted with a threshold-triggered control strategy. • The practicality of the model is illustrated by means of a case study in the Northern Cape Province of South Africa. • Model outputs may be employed to guide invasive species management. [ABSTRACT FROM AUTHOR]

• An efficient design of QCA-based BCD adder is proposed by applying the USE clocking scheme. • QCADesigner is used to simulate the proposed circuit. • The designed circuit is checked for the power dissipation value using QCA Pro tool. • The performance of the proposed circuit is detailed and compared with the existing designs. Quantum-dot cellular automata (QCA) is a promising nanotechnology that offers an alternative to complementary metal-oxide-semiconductor (CMOS) technology. Nanoscale size, ultra-low power dissipation, and Terahertz range operating frequency make QCA more attractive to circuit designers. Recently, numerous designs of QCA have been proposed but aren't practically implementable due to random clocks in the design. In QCA designs, there is a need for proper clocking scheme to favour placement and routing and to facilitate QCA nano-computing synthesis. In this paper, a novel design of Binary-Coded Decimal (BCD) adder, based on QCA technology, with a realistic clocking scheme is presented. System simulations of the proposed designs are performed using QCA designer software Ver. 2.0.3 while power dissipation is investigated using the QCA Pro tool. Besides, in terms of power dissipation, the suggested QCA BCD adder dissipates 2.788 × 10−5 mW, whereas CMOS- based design consume 1.34 mW of power. [Display omitted] [ABSTRACT FROM AUTHOR]

[Display omitted] • Fluid flow motion significantly affects melt pool boundaries. • Surface grain sealing and surface grain bending phenomena observed. • Planar and dendritic crystal growth implemented via cellular automata approach. • Grain formation of YSZ after laser melting was explained. Laser-processing technologies are often applied to enhance the properties of ceramics, such as laser glazing of Yttria Stabilised Zirconia (YSZ). However, very limited attention was paid to the solidification phenomena and mechanisms of YSZ. In this paper, two coexisting solidification behaviours of laser-melted YSZ have been identified, namely grain bending (crystals with curved grain boundary geometry under the surface) and grain surface sealing (in-plane surficial crystals cover vertical columnar grains). Although these phenomena have been reported, this is the first time the two phenomena coexist in the same material. A new cellular automata (CA) approach has been proposed to explain the formation mechanisms of these two phenomena. This new CA method consists of the separation of growth modes into dendritic and planar growth, whose critical transition value is calculated based on the supercooling theory. Besides, the proposed model for planar growth is far less computationally expensive than the widely used decentred octahedron algorithm. A good agreement with the EBSD data of longitudinal cross-sections and the top surface has been observed which proves that the proposed method can become a more realistic and efficient way to predict the grain microstructure in laser processing, allowing to capture dendritic and planar growth simultaneously. [ABSTRACT FROM AUTHOR]

• Proposed method can concurrently increase the nucleation rate and decrease the grain growth rate. • Refinement mechanism of deformed mixed grain is revealed by cellular automation method. • Refinement effect of the CC-RAT is far more than the isothermal annealing method. The deformed mixed grains of the initial aged GH4169 superalloy need to be refined by annealing treatment. However, for the isothermal annealing treatment, the deformed mixed grains can only be refined to about 10 µm because the increase of nucleation rate needs to increase the temperature while the decrease of grain growth rate requires to decrease temperature. In the paper, an innovative method including an aging treatment and a subsequent continuous-cooling recrystallization annealing treatment (CC-RAT) is proposed. This method can not only increase the nucleation rate but decrease the grain growth rate. The effects of heat treatment parameters on microstructure are investigated. Besides, the grain refinement mechanism is studied using a developed cellular automaton (CA) model. The results show that the recrystallization takes place slowly during aging, while large number of δ precipitates appear. In subsequent CC-RAT, increasing starting temperature (T s), decreasing cooling rate (V) or ending temperature (T e) can remarkably promote recrystallization, thereby refining grain microstructure. This attributes to the increase of recrystallization nucleation rate and the effective control of grain growth rate. In the early stage of CC-RAT, δ precipitates hinder the quick growth of recrystallized grains nucleated firstly. Moreover, the increase of T s accelerates the nucleation rate, and the decrease of V prolongs the time for nucleation. Both of them promote the rapid formation of new recrystallized nuclei, especially at δ phase boundaries. As the annealing temperature decreases, newborn grains grow slowly, and these new grains restrict the growth of grains nucleated first. Therefore, a fine and uniform microstructure with an average grain size of 5.82 µm is obtained. However, when T s is too high or V is too low, some coarse recrystallized grains appear. Hence, the feasible process is concluded as follows: aging treatment (900 °C×12 h), followed by CC-RAT (T s = 990–1010 °C, V =1–2 °C/min, T e =950–970 °C). [ABSTRACT FROM AUTHOR]

Modeling the forest fire phenomena represents one of the most challenging and difficult problems, which need further investigations. Many research works have tackled this issue by developing simulation models in order to investigate the impact of climate change on fire spread behavior. In our previous work, a forest fire spread model has been introduced. The developed model takes into account some parameters that affect this phenomenon, such as topography, land use, nature and density of vegetation, humidity and wind by only considering uniform wind direction and speed. The work presented in this paper provides an improvement of this model for being integrated in real-sitting scenarios. In fact, a model of wind flow has been proposed. It is able to determine accurately the wind direction and speed according to the topography and land use. The proposed model was evaluated using dataset from a region in the north of Morocco, and results are reported to show the impact of the dynamic behavior of the wind on fire propagation. [ABSTRACT FROM AUTHOR]

Quantum-dot cellular automata (QCA) is field-coupled nanotechnology that achieves high device density, high switching speed, and low power dissipation. The flow of information within QCA is fully controlled by the position of the electrons only. A proper cell placement and its respective clock zone for the correct logic operation become the utmost design requirement. Insufficient research in this direction and appropriate use of regular clocking requires rigorous research in this area. An appropriate routing channel to adopt a stable and steady underlying clock scheme plays an essential role in positioning QCA cells. This paper presents a systematic placement of QCA cells targeting sequential logic, which makes efficient use of the underlying regular clocking scheme. The proposed method is verified by implementing the SR and D flip flop incorporating the USE clocking scheme. The effectiveness of the proposed cell positioning algorithm is reported. A significant performance improvement is observed, which advocates the importance of such a scheme in QCA circuit design. [Display omitted] [ABSTRACT FROM AUTHOR]

Along with the increasing prosperity of electric vehicles (EVs), the large-scale penetration of EVs in V2G (Vehicle to Grid) trading market brings difficulties and security issues in the operation of the energy market. The blockchain has the characteristics of security, transparency, decentralization and traceability, which provides an alternative for complex energy market operation in a secure environment. In this paper, a novel hierarchical blockchain architecture is constructed for the V2G trading system including Data Blockchain layer, Operation Blockchain layer and Transaction Blockchain layer. The intelligent contracts of the V2G trading, two-level auction and optimization strategies are designed for scheduling EVs orderly charge and discharge to participate in the grid load regulation so as to minimize the total load variance of the grid and realize reasonable benefit distribution among the involved market entities, i.e., EVs, aggregators and grid. Based on the V2G trading characteristics, a transaction manager with four optimization strategies are designed to further optimize the transaction generation process. The CA (cellular automata) based simulation experiments are performed to verify the effectiveness of the proposed V2G trading system. • A novel V2G trading system based on hierarchical blockchain architecture. • Intelligent contracts are designed to schedule EVs for the grid load regulation. • A transaction manager is designed to improve the transaction execution efficiency. • A Cellular Automata model is applied for the V2G trading simulation. [ABSTRACT FROM AUTHOR]

Directed energy deposition (DED) is an advanced additive manufacturing technology for the fabrication of near-net-shape metal parts with complex geometries and high performance metrics. Studying the grain structure evolution during the process is pivotal to evaluating and tailoring the as-built products' mechanical properties. However, it is time-consuming to simulate the multi-layer deposition process using the physics-based numerical model to optimize the process parameters for achieving the desired microstructure. In this paper, a physics-informed machine learning algorithm to predict the grain structure in the DED process is proposed. To generate training data for the machine learning algorithm, we use an experimentally validated cellular automaton finite volume method (CAFVM) for DED Inconel 718, where CA is applied to model the grain structure and FVM to simulate the heat transfer. We develop a neural network model to identify the correlation between the local thermal features and their corresponding grain structure characteristics. The inputs and outputs of the neural network (NN) model are selected based on the governing physics, and a novel way to extract them is proposed. The NN model can quickly predict the grain structure characteristics with the local thermal data for thin-wall builds, and the predictions are in good agreement with the numerical simulation results. We expect the proposed method can benefit other metal additive manufacturing technologies to formulate efficient and accurate process-structure relationships and in-process feedback control. [Display omitted] • Predict additively manufactured grain structure with a machine learning method. • An 3D cellular automaton finite volume method for "the ground truth" microstructure. • Strategy to represent the grain size and aspect ratio variation in a neural network. • Simulated thin-wall builds of Inconel 718 by directed energy deposition. [ABSTRACT FROM AUTHOR]

Quantum-dot cellular automata (QCA) has gained an exclusive research focus on the current CMOS due to low power dissipation, less circuit area occupancy, and computational speed. The effectiveness of a QCA circuit is affected by the primary logic (majority vote and inverter). Hence, the minimization of these primitives for QCA logic circuit synthesis becomes an extreme necessity. The genetic algorithm (GA) has a more important effect on the synthesis of cost-effective circuits. Besides, the underlying regular clocking scheme is significant in systematic cell layout, scalability, and reliability. This paper emphasizes the design of a cost-effective circuit, applying a genetic algorithm incorporating the regular clock. Multi-output functions are taken into account here to minimize with the help of a genetic algorithm. All the circuits are synthesized by utilizing RES, USE clocking scheme. The analytical study indicates the significance of the genetic algorithm on the design of QCA using the regular clock. [Display omitted] • An elitism-based Genetic Algorithm(GA) is proposed for the minimization of circuits. • Five Multi-Output functions have been considered to investigate the effectiveness. • QCADesigner is used to simulate the pre and post Genetic Algorithm based circuits. • The impact of regular clocking on the circuits designed pre and post GA is explored. • Primary metrics are considered in logic simulation and comparative discussion. [ABSTRACT FROM AUTHOR]

[Display omitted] • The secondary effect between adjacent nanostructures is quantified with the assistance of the Continuous Cellular Automata. • Deposits induced by the secondary effect feature a higher metal content and a faster growth rate. • The paper provides guidance for design parameters choice to avoid/utilize the secondary effect when constructing arrays of nanostructures. • This indirect deposition method can find application in coating, chiral nanostructures and tunability of material properties. Focused Ga ion beam can deposit various materials with high-resolution assisted by elaborately selected precursor gases. During the deposition process, the secondary effect arises due to the generation of secondary particles by primary ions. Prominent morphological changes are caused by the secondary effect between adjacent, densely arranged structures. This study analyzes the morphological changes and structural characterizations from the perspective of secondary particles, including scattered ions, sputtered atoms, and re-emitted precursor molecules, with the assistance of continuous cellular automata. C 14 H 10 and W(CO) 6 were utilized as the precursor gas in the experiments under the same parameters as the numerical model. The experimental and simulation results demonstrated that the deposit morphology on the adjacent structure, and the enhanced growth rate of the processing structure, could be precisely predicted, with a good estimation of secondary particles contribution. Furthermore, the deposition efficiency on the adjacent structure was improved, and the higher metal content in the deposits was validated by Energy Dispersive X-Ray Spectroscopy (EDS). This work, therefore, lays foundation for avoiding the secondary effect when fabricating nanostructure arrays, and utilizing it for coatings, chiral structure construction, and tuning material properties. [ABSTRACT FROM AUTHOR]

Land Use Land Cover (LULC) change assessment and prediction are essential for optimised water resources planning and management. This paper attempts to intercompare the different LULC change modelling techniques (two-hybrid and two traditional models) to predict the future LULC change. These include Multi-Layer Perceptron Markov Chain (MLP_MC), Logistic Regression-Markov Model (LR-MC), and two hybrid models, namely, Multi-Layer Perceptron Markov Chain Cellular Automata (MLP_MC_CA), Logistic Regression Markov Model Cellular Automata (LR_MC_CA) models. These models were tested on Nagavali River Basin (NRB), a river basin in Southern India, which has seen significant land-use changes over the past two decades. Over the past two decades, the study region experienced dominant changes on the order of 17.42% and 15.22% decrease in scrubland and forest, respectively. At the same time, the agricultural land cover is increased by 35.28%. For the LULC prediction, the model was initially trained using the relevant driver variables and LULC maps of 2010 and 2015. The calibrated model was validated using the 2020 LULC map. The statistical results in terms of Kappa values and chi-square results reveal that the hybrid model MLP-MC-CA has a better agreement (Kappa coefficient 0.902) compared to the other models. Further, it is also observed that the CA-based models have a better ability to capture spatial connections. After combining the MLP_MC model with the Cellular Automata, the former model was improved by 10.8% in terms of the overall Kappa coefficient. The best model for LULC prediction over the next decade (LULC map of 2030) showed that the forest area would decrease by 9.02%, and the agricultural land would increase by 8.74%. Further, the results from the study indicate that the hybrid machine learning models provide a promising alternative for land-use change prediction. • Intercomparison of different Machine Learning Methods for Land Use Land Cover Prediction. • Hybrid model based on MLP_MC_CA outperforms other non-hybrid and traditional methods. • LULC 2030 for NRB showed that the forest area would decrease by 9.02%, and the agricultural land would increase by 8.74%. [ABSTRACT FROM AUTHOR]

Many developing countries grapple with the problem of rapid informal settlement emergence and expansion. This exacts considerable costs from neighbouring urban areas, largely as a result of environmental, sustainability and health-related problems associated with such settlements, which can threaten the local economy. Hence, there is a need to understand the nature of, and to be able to predict, future informal settlement emergence locations as well as the rate and extent of such settlement expansion in developing countries. A novel generic framework is proposed in this paper for machine learning-inspired prediction of future spatio-temporal informal settlement population growth. This data-driven framework comprises three functional components which facilitate informal settlement emergence and growth modelling within an area under investigation. The framework outputs are based on a computed set of influential spatial feature predictors pertaining to the area in question. The objective of the framework is ultimately to identify those spatial and other factors that influence the location, formation and growth rate of an informal settlement most significantly, by applying a machine learning modelling approach to multiple data sets related to the households and spatial attributes associated with informal settlements. Based on the aforementioned influential spatial features, a cellular automaton transition rule is developed, enabling the spatio-temporal modelling of the rate and extent of future formations and expansions of informal settlements. • A framework is proposed for modelling informal settlement growth and emergence. • The framework is generic and may be adopted to model informal settlement development in any developing country. • The framework is applied for demonstration purposes within the context of a South African real-world case study. • A novel machine learning-inspired population diffusion model is proposed for inclusion in the framework. • An objective feature selection approach is adopted instead of a subjective feature selection for the transition rule. [ABSTRACT FROM AUTHOR]