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A newer nanoscale technology called quantum-dot cellular automata (QCA) has been used by researchers to design digital circuits in place of the more traditional complementary metal–oxide semiconductor (CMOS) technology. This recent development in the technology change is due to the problems faced by CMOS technology in terms of power consumption and physical limitations. The advantages of QCA technology over CMOS technology are high density, low power consumption, high-speed operation, and less footprint area. This research provides a novel circuit for D-latch and static random access memory (SRAM) cells based on QCA technology. Initially, a D-latch circuit is proposed with a layout area of 0.01 μm2, a 0.5 clock cycle delay (latency), and a cell count of 18 QCA cells. Furthermore, an SRAM cell is proposed using the same D-latch circuit, which uses cell counts of 26 QCA cells and contributes to a layout area of 0.02 μm2 with a 0.75 clock cycle delay (latency). It is observed that our proposed circuits have a smaller layout area, fewer QCA cell counts, and a lower clock cycle delay (latency) than existing circuits. [ABSTRACT FROM AUTHOR]

Molecular quantum-dot cellular automata (QCA) may provide high speed, low-power, classical information processing in the post-CMOS era. The readout of molecular QCA bits is challenging because the molecules may be much smaller than transistors and even single-electron transistors. This paper builds on a recent proposal for the spectroscopic readout of bits from asymmetric QCA molecules. Here, we propose circuits for fanning out a bit onto a large QCA circuit to increase the spectroscopic signal-to-noise ratio. As the number of molecules in a fanout circuit grows, the internal bias of each asymmetric cell accumulates, and the circuit may become stuck, tolerating only a very small internal bias. We also propose the use of an applied electric field to compensate for a candidate molecule's internal bias, thereby restoring switchability, even when the internal bias is significant. [ABSTRACT FROM AUTHOR]

Quantum-dot Cellular Automata (QCA) has become one of the promising studies for nano-scale computing. QCA is one of the candidate technologies to be replaced with CMOS technology. QCA technology not only reduces power consumption and delay but also increases operating frequency and speed. The arithmetic logic unit is the essential component in a processor that performs arithmetic and logical operations. This paper presents a novel 5 × 5 reversible logic gate called the NRRG (Norouzi_Rasouli Reversible Gate) which can be used as the basic building block of 4:1 and 8:1 reversible multiplexers. Then, we have designed a RALU (reversible arithmetic and logic unit) using this gate. Our design can perform 20 operations such as AND, NAND, OR, XOR, XNOR, COPY, addition, and increment. The proposed QCA RALU requires 0.44 μm2 area, 480 QCA cells, and 10 clock phases. The proposed design needs less cell count, delay, and cost of QCA compared to previous works. The structure is implemented without any rotated cells and only uses one layer which improves the manufacturability of the design. The architectures are designed and simulated using QCA Designer 2.0.3. [ABSTRACT FROM AUTHOR]

Quantum Dot Cellular Automata (QCA) is an emerging trend in the field of nanoelectronics, and computing can be regarded as an alternative to the traditional complementary metal–oxide–semiconductor technology. The paper is devoted to the study of the key functional properties of the cells for molecular QCA based on mixed valence molecules. The theoretical results for the heat dissipation under the conditions of the fast nonadiabatic switching event and cell–cell response function are obtained in the framework of the quantum-mechanical vibronic approach. These results are parameterized using the previous reliable ab initio calculations performed for oxidized norbornadiene and its polycyclic derivatives with variable lengths of the bridge. The comparative analysis of the dimeric and bidimeric molecular cells composed of these compounds is given. It is underlined that the conditions of a strong non-linear response and a low heat release are contradictory. However, despite this problem, a parametric regime is proposed, which provides a low heat release in combination with a strong nonlinear response of the working cell to the electric field induced by the polarized driver cell. [ABSTRACT FROM AUTHOR]

In this article, we develop a vibronic theory of clocking in molecular quantum cellular automata (QCA). The clocking mechanism is considered for a trigonal trimeric mixed-valence (MV) system with one mobile electron, which is shown to act as the dimeric unit encoding binary information (Boolean states 0 or 1) coupled to a third redox center (Null state). The model includes the electron transfer between the three centers; vibronic coupling of the mobile charge with the "breathing" modes, forming a double degenerate Jahn–Teller vibration of the molecular triangle; and two electric fields, one collinear to the dimeric unit, which controls the binary states, and the other perpendicular to this unit, performing clocking. In the framework of the adiabatic approximation, the potential surface of the trimeric system has been studied and the condition determining switching and clocking has been analyzed in terms of the two controlling fields and the vibronic and transfer parameters. A thorough understanding of the site populations is achieved through the quantum-mechanical solution of the vibronic problem, maintaining the adiabatic condition for the controlling fields. It is shown that a MV trimer can act as a molecular clocked QCA cell, with favorable conditions being a positive electron transfer parameter and sufficiently strong vibronic coupling. [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]

ABSTRACT 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]

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]

Due to the complex nature of Alzheimer’s disease (AD), it begins insidiously in the brain many years before the appearance of the first symptoms and is usually diagnosed, when the disease is progressive and symptoms have appeared. It can be said that AD’s emergence is almost untrackable and the straightforward mechanism of AD is still unclear for the most part. Therefore, any kind of research on the emergence of AD can lead to valuable results for predicting and treating AD. Previously, we proposed a novel mathematical model based on cellular automata for the study of Alzheimer’s disease progress. In this paper, cellular automata (CA) mathematical modeling is used as a tool for simulation and experimenting with drugs’ effects in this far-fetched situation. For this goal, immune system interactions and neuron-to-neuron communications are assumed to have prominent roles. We have used the CA network, pathophysiological process, and physiological realities to get a clear insight into neuron-to-neuron communications. Our simple model investigates the influence of three Acetylcholinesterase Inhibitor injections, including Donepezil, Rivastigmine, and Galantamine on the reduction of defined factors such as Alzheimer’s Rate (AR), Critical Rate (CR), and Warning Rate (WR). It is shown that using drugs has different effects on the betterment of neurons and can decrease AR and CR factors and enhance WR, many years before the appearance of symptoms. [ABSTRACT FROM AUTHOR]

Using agents to simulate human wayfinding processes helps to understand human decision-making and spatial cognition. The performance of large language models (LLMs) in agent-based wayfinding is still unclear. To address this, this study harnessed the thinking and reasoning capabilities of LLMs by designing an agent wayfinding framework based on cellular automata. This framework provides environmental descriptions in text form to the LLMs, enabling them to perceive the wayfinding environment and navigate within it. We simulated different spatial memory conditions for the LLM, including three types of spatial memories and one condition with no spatial memory, to observe its wayfinding behavior in a real-world setting. Multiple experiments were designed to compare the wayfinding performance of the LLM under different spatial memories, and 32 human participants conducted the same wayfinding experiments for reference. The results indicate that the LLM autonomously explores the environment and displays spatial reasoning abilities similar to humans. Furthermore, the LLM can develop reasonable wayfinding strategies to complete the tasks. This framework allows agents with different spatial memories to accurately reach their destinations. This research provides new ideas for utilizing large language models to construct more intelligent, efficient, and flexible wayfinding agents, which can help advance the application of agent technology in fields such as navigation and robotics. [ABSTRACT FROM AUTHOR]

When dealing with complex models in real situations, many optimization problems require the use of more than one objective function to adequately represent the relevant characteristics of the system under consideration. Multi-objective optimization algorithms that can deal with several objective functions are necessary in order to obtain reasonable results within an adequate processing time. This paper presents the multi-objective version of a recent metaheuristic algorithm that optimizes a single objective function, known as the Majority–minority Cellular Automata Algorithm (MmCAA), inspired by cellular automata operations. The algorithm presented here is known as the Multi-objective Majority–minority Cellular Automata Algorithm (MOMmCAA). The MOMmCAA adds repository management and multi-objective search space density control to complement the performance of the MmCAA and make it capable of optimizing multi-objective problems. To evaluate the performance of the MOMmCAA, results on benchmark test sets (DTLZ, quadratic, and CEC-2020) and real-world engineering design problems were compared against other multi-objective algorithms recognized for their performance (MOLAPO, GS, MOPSO, NSGA-II, and MNMA). The results obtained in this work show that the MOMmCA achieves comparable performance with the other metaheuristic methods, demonstrating its competitiveness for use in multi-objective problems. The MOMmCAA was implemented in MATLAB and its source code can be consulted in GitHub. [ABSTRACT FROM AUTHOR]

This paper proposes an innovative cellular automata model based on the Harris Hawk Optimization (HHO) algorithm. HHO is an intelligent optimization algorithm inspired by the cooperative hunting behavior of Harris's hawks, demonstrating excellent optimization efficiency in spatial searches. Combining the HHO algorithm with the CA model, we establish the HHO-CA model for simulating urban growth in Guangzhou, China. The simulation achieves a total accuracy of 91.95%, an accuracy of urban cells of 82.43%, and a Kappa coefficient of 0.7441, all superior to the Null model. Furthermore, comparing the HHO-CA model with other representative CA models, the HHO-CA model outperforms in total accuracy, accuracy of urban cells, and Kappa coefficient, showcasing significant advantages in using the HHO algorithm to mine transition rules during the simulation of urban growth processes. [ABSTRACT FROM AUTHOR]

Due to the flammable and explosive nature of explosives, there are significant potential hazards and risks during transportation. During the operation of explosive transport vehicles, there are often situations where the vehicles around them approach or change lanes abnormally, resulting in insufficient avoidance and collision, leading to serious consequences such as explosions and fires. Therefore, in response to the above issues, this article has developed an explosive transport vehicle driving warning system based on object detection algorithms. Consumer-level cameras are flexibly arranged around the vehicle body to monitor surrounding vehicles. Using the YOLOv4 object detection algorithm to identify and distance surrounding vehicles, using a game theory-based cellular automaton model to simulate the actual operation of vehicles, simulating the driver's decision-making behavior when encountering other vehicles approaching or changing lanes abnormally during actual driving. The cellular automaton model was used to simulate two scenarios of explosive transport vehicles equipped with and without warning systems. The results show that when explosive transport vehicles encounter the above-mentioned dangerous situations, the warning system can timely issue warnings, remind drivers to make decisions, avoid risks, ensure the safety of vehicle operation, and verify the effectiveness of the warning system. [ABSTRACT FROM AUTHOR]

Purpose: Using computer-aided design (CAD) systems, this research endeavors to enhance breast cancer segmentation by addressing data insufficiency and data complexity during model training. As perceived by computer vision models, the inherent symmetry and complexity of mammography images make segmentation difficult. The objective is to optimize the precision and effectiveness of medical imaging. Methods: The study introduces a hybrid strategy combining shape-guided segmentation (SGS) and M3D-neural cellular automata (M3D-NCA), resulting in improved computational efficiency and performance. The implementation of Shape-guided segmentation (SGS) during the initialization phase, coupled with the elimination of convolutional layers, enables the model to effectively reduce computation time. The research proposes a novel loss function that combines segmentation losses from both components for effective training. Results: The robust technique provided aims to improve the accuracy and consistency of breast tumor segmentation, leading to significant improvements in medical imaging and breast cancer detection and treatment. Conclusion: This study enhances breast cancer segmentation in medical imaging using CAD systems. Combining shape-guided segmentation (SGS) and M3D-neural cellular automata (M3D-NCA) is a hybrid approach that improves performance and computational efficiency by dealing with complex data and not having enough training data. The approach also reduces computing time and improves training efficiency. The study aims to improve breast cancer detection and treatment methods in medical imaging technology. [ABSTRACT FROM AUTHOR]

In case of fire, effective evacuation guidance can reduce evacuees' hesitation time and improve evacuation efficiency. In this paper, an improved model of fire evacuation (guided fire cellular automata, GF-CA) is provided. First, the parameters of the improved model are quantified and analyzed to determine the parameter calculation method and variation law. Second, a typical subway interchange station in Shenyang is used as an example for evacuation simulation. Then, a lightweight building information modeling (BIM) is constructed and applied to the example combining the evacuation simulation software of Pathfinder and virtual reality technology for personnel evacuation simulation. Three types of simulation scenarios are examined, including simulation without guiding factors, simulation with guiding factors based on the improved model, and simulation based on the virtual reality experiment with guiding factors. The results show that the evacuation efficiency is increased by 75.63% after effective guidelines through personnel diversion and reasonable exit allocation, noting that the path results of the model are highly similar to those of the virtual reality experiment, which has good validity and rationality. Meanwhile, we analyze the guiding scheme and propose that we should pay attention to the position of the sign and the configuration of the conductor, and flexibly use the composite scheme, which provides effective technical support for improving evacuation safety and evacuation efficiency. [ABSTRACT FROM AUTHOR]

While the tool-chip contact behavior plays a dominant role in determining tool-chip friction, the intermittent cutting condition applied by ultrasonic vibration assistance significantly alters the thermomechanical properties of the tool-chip interface, which subsequently affects the microstructure evolution in the chip formation process. In the present work, we elucidate the mechanisms of grain refinement, as well as their dependence on vibration amplitude, in ultrasonic elliptical vibration-assisted cutting (UEVC) of polycrystalline copper by multi-scale numerical simulations. Specifically, two simulation approaches of finite element simulation and cellular automaton are bridged based on the thermomechanical condition of the tool-chip interface, which is subsequently utilized to reveal the tool-chip friction-induced microstructural evolution in the formed chip. Finite element simulation results indicate that the temperature and strain in the tool-chip interface during UEVC are both significantly higher than that during ordinary cutting, due to the high frequent impact of tool on chip, as well as the dominant shear frictional force between tool and chip. Consequently, corresponding cellular automaton simulations demonstrate the promoted grain refinement through dynamic recrystallization in chip during UEVC from ordinary cutting, due to the intensified thermomechanical condition in the tool-chip interface. Subsequent evaluation on the influence of vibration amplitude on grain refinement in chip during UEVC is further conducted, which shows the more pronounced grain refinement for higher amplitude, accompanied with lowered tool-chip contact length and cutting force. At the highest amplitude of 4 µm, the proportion of DRX reaches the highest value of 89.5% accompanied with the lowest average grain size of 81 nm. [ABSTRACT FROM AUTHOR]

Interpreting the GNVW index for 1D quantum cellular automata (QCA) in terms of the Jones index for subfactors leads to a generalization of the index defined for QCA on more general abstract spin chains. These include fusion spin chains, which arise as the local operators invariant under a global (categorical/MPO) symmetry, and as the boundary operators of 2D topological codes. We show that for the fusion spin chains built from the fusion category Fib , the index is a complete invariant for the group of QCA modulo finite depth circuits. [ABSTRACT FROM AUTHOR]

Soil moisture movement reveals the hydrological environment and irrigation characteristics of plants, which is very important and basic hydrological problem. A soil moisture movement algorithm based on cellular automata suitable for greenhouse was proposed to simulate the lateral flow and vertical penetration of soil water in different soil layers. Artemisia annua was used as an experimental plant, and two numerical simulations were set up to determine the minimum water replenishment and the degree of upward soil modification. To verify the performance of the model, corn was used as an experimental plant for application simulation. The numerical simulation results show that the effect of only water for plant cells is superior to soil cells, and the disturbance behavior of the underlying soil layer should be minimized during the cultivation process. The average MAE/RMSE of all soil layers is 0.86/1.06 and in deep layer (50 cm and 60 cm) is 0.53/0.64. It shows the model has a certain prediction and simulation ability, especially in deep soil layers. The proposed algorithm can simply calculate soil flow, set the minimum water replenishment, and evaluate the water replenishment efficiency, which can provide a theoretical reference for the water replenishment and soil replacement scheme. [ABSTRACT FROM AUTHOR]

Affected by human activities, the naturally occurring river network in the southeastern plain of Yinzhou has gradually evolved into a natural–artificial composite water system, and changes in river connectivity due to changes in river network systems have caused water security problems, including urban flooding. To clarify the river connectivity change and its relationship with the urbanization process, this paper discusses an evaluation method for river connectivity based on complex networks and cellular automata (CA) from the perspective of complex systems, quantitatively analyzes the spatial–temporal characteristics of the structural and functional river connectivity in the study area during the 1990s–2020s, and reveals the impact of river nodes and chains on the connectivity level under the disturbance of natural or human factors. The results contained the following revelations: ① River connectivity showed a decreasing trend in the initial and rapid development stages of urbanization from the 1990s to the 2010s and a limited increasing trend in the optimization and upgrading stages from the 2010s to the 2020s. ② River network degradation and ongoing connectivity decline are found in the northeastern part of the study area. The highest river connectivity exists in Dongqianhukaifaqu. ③ The number of river nodes and chains should be maintained at approximately 80% for normal river connectivity. The nodes of high degree in the inflow area are listed in the key protection areas. ④ Changes in river connectivity are significantly correlated with the urbanization process. Changes in the functional connectivity level affect the magnitude of a flood. This study provides a theoretical basis for river network connectivity improvement and flood prevention in plain areas. [ABSTRACT FROM AUTHOR]

This paper presents discontinuous Riemann integrable functions on the unit interval $ [0, 1] $ derived from the dynamics of two-dimensional elementary cellular automata. Based on the self-similarities of their orbits, we write down the numbers of nonzero states in the spatial and spatio-temporal patterns and obtain discontinuous Riemann integrable functions by normalizing the values. We calculate the integrals of the two obtained functions over $ [0, 1] $ and demonstrate the relationship between them. [ABSTRACT FROM AUTHOR]

This paper proposes a novel Switching Chaotic Coupled Map Lattices (SCCML) system based on Elementary Cellular Automata (ECA) to address issues found in Coupled Map Lattices (CML) systems such as period windows, poor lattice correlation, and limited chaotic behavior parameters. The system uses a bidirectional coupling method and introduces two ECA with different rules to continuously alter the indexes of the coupled lattices and the underlying chaotic mapping. This approach effectively mitigates correlation issues in CML systems. At the same time, the iterative results of ECA are cascaded with the chaotic mapping as a perturbation of the SCCML system to extend the parameter range of chaotic behavior. The Kolmogorov–Sinai entropy density and universality, bifurcation diagrams, uniformity and correlation are analyzed to investigate the dynamical properties of the proposed system. Theoretical analysis and numerical simulations demonstrate that, compared with other novel spatiotemporal chaotic systems, the proposed system eliminates periodic windows, expands the chaotic behavior parameter range, and decreases the correlation between different lattices. The above experimental results demonstrate the proposed system's excellent potential for cryptosystems and secure communication. Furthermore, an Encrypted Code Index Modulation (E-CIM) scheme based on reversible ECA encryption is proposed to address the problems of limited pseudo-random sequence resources and low spectrum utilization in direct sequence spread spectrum systems. The proposed scheme uses a chaotic sequence with good correlation and randomness generated by the SCCML spatiotemporal chaos system as the spreading code sequence. In the scheme, the information bits are divided into two parts, namely modulation bits and mapping bits. The modulation bits are mapped to points on the modulation constellation diagram. The mapping bits are encrypted using reversible ECA, and the mapped bits before and after encryption are mapped as spreading sequences with different indexes. Simulation and analysis show that under the same spectrum efficiency conditions, the BER performance of E-CIM is superior to that of the Code Index Modulation (CIM) and Generalized Code Index Modulation (GCIM) schemes by about 2–4 dB and superior to that of the Nonorthogonal Code Shift Keying and Code Index Modulation (N-CSK-CIM) scheme by about 0.5 dB in an additive Gaussian white noise channel when the BER is 10 − 5 . [ABSTRACT FROM AUTHOR]

AbstractProjecting 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]

We consider the problem of finding the largest clique of a graph. This is an NP-hard problem and no exact algorithm to solve it exactly in polynomial time is known to exist. Several heuristic approaches have been proposed to find approximate solutions. Markov Chain Monte Carlo is one of these. In the context of Markov Chain Monte Carlo, we present a class of "parallel dynamics", known as Probabilistic Cellular Automata, which can be used in place of the more standard choice of sequential "single spin flip" to sample from a probability distribution concentrated on the largest cliques of the graph. We perform a numerical comparison between the two classes of chains both in terms of the quality of the solution and in terms of computational time. We show that the parallel dynamics are considerably faster than the sequential ones while providing solutions of comparable quality. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

To provide accurate and efficient forecasting of fluvial flooding assessment in the river basin, the present study links the well-known CA-based urban inundation modeling (2D-OFM-CA) with a one-dimensional river flow model (1D-RFM) as a coupled 1D-2D river–overland modeling. Rules to delineate the geometric linking between the 1D-RFM and 2D-OFM-CA along embankments are developed. The corresponding exchanged water volume across an embankment is then computed by using the free and submerged weir flow formulas. The applicability of the proposed coupled model on fluvial flooding assessment is then assessed and compared with a well-recognized commercial software (HEC-RAS model) through an idealized fluvial case and an extensively studied real-scale fluvial case in the Severn River Basin. Based on the simulated results concerning the numerical accuracy, the coupled model is found to give similar results in the aspects of the river flow and overland flow modeling in both two study cases, which demonstrates the effectiveness of the linking methodology between the 1D-RFM and 2D-OFM-CA. From the viewpoint of numerical efficiency, the coupled model is 47% and 41% faster than the HEC-RAS model in the two cases, respectively. The above results indicate that the coupled model can reach almost the same accuracy as the HEC-RAS model with an obvious reduction in its computational time. Hence, it is concluded that the coupled model has considerable potential to be an effective alternative for fluvial flooding assessment in the river basin. [ABSTRACT FROM AUTHOR]

The flood-prone city of Zaio in Morocco necessitates a precise, fast, real-time flood depth mapping model due to its recurrent flood history. Whether it's the exclusive prediction of one flood category, relying on hard-to-measure inputs like flood hydrographs, or employing less accurate training methods such as cellular automata models, the existing Convolutional Neural Network (CNN) models face limitations in predicting flood depth in a city whose flood dynamics are influenced by outer watersheds such as Zaio. This study addresses these issues by introducing a CNN tailored for real-time pluvial and fluvial flood depth mapping in Zaio at fine resolution (2 m). Training involved eight rainfall hyetographs, with four used for testing. The model's validation included three "unseen" rainfall distribution patterns. The proposed CNN seamlessly connects rainfall-runoff modeling and hydrodynamic 2D flood depth simulation, with a novelty of predicting both pluvial and fluvial flood depth, and reducing computational time by a significant 99.17%. [ABSTRACT FROM AUTHOR]

Electron beam powder bed fusion (PBF‐EB) is a promising technology for fabricating complex parts with near‐net‐shape precision. Moreover, PBF‐EB offers a unique opportunity to tailor the microstructure, thereby tuning local mechanical properties. Numerical simulation has emerged as a powerful tool for predicting the evolution of texture and grain structure during PBF‐EB. Herein, the in‐house developed and experimentally validated simulation software, SAMPLE3D$\text{S} A M \left(\text{PLE}\right)^{3 \text{D}}$, is employed to investigate the impact of scanning strategy on the texture and grain structure of CMSX‐4 in PBF‐EB‐processed thin tilted lattice struts, commonly found in cellular structures. The core of SAMPLE3D$\text{S} A M \left(\text{PLE}\right)^{3 \text{D}}$ consists of a finite difference solver for temperature field computation and a cellular automaton model for simulating grain structure evolution. Nine distinct scanning strategies are systematically explored. The resulting texture and grain structures are meticulously compared and comprehensively discussed. Notably, the contour scanning strategy yields distinctive texture and grain structures compared to other explored scanning strategies. This study highlights the capability of SAMPLE3D$\text{S} A M \left(\text{PLE}\right)^{3 \text{D}}$ in assisting microstructure customization in the PBF‐EB process, and advances the understanding of the relationship between PBF‐EB scanning strategy and resulting microstructure in tilted lattice struts. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

Recently, there has been a lot of research in Quantum Cellular Automata (QCA) technology because it promises low power consumption, low complexity, low latency, and compact space. Simultaneously, approximate arithmetic, a new paradigm in computing, streamlines the computational process and emerges as a low-power, high-performance design approach for arithmetic circuits. Furthermore, the XOR gate has been widely used in digital design and is a basic building block that can be used in many upcoming technologies. The full adder (FA) circuit is a key component of QCA technology and is utilized in arithmetic logic operations including subtraction, multiplication, and division. A great deal of research has been done on the design of approximate FA, full subtractor (FS), full adder/subtractor (FA/S), and 4-bit ripple carry adder (RCA) based on XOR logic, establishing them as essential components in the creation of QCA-based arithmetic circuits. This study presents three new and effective QCA-based circuits, based on XOR logic: an approximate FA, an approximate FS, an approximate FA/S, and an approximate 4-bit ripple carry adder (RCA). Interestingly, some designs have inputs on one side and outputs on the other, making it easier to reach the components without being encircled by other cells and leading to a more effective circuit design. In particular, a delay of 0.5 clock phases, an area of 0.01 μm2, and implementation utilizing just 11 cells was accomplished in the approximate FA and subtractor designs. In a similar vein, the estimated FA/S designs showed 0.5 clock phase delay, 0.01 μm2 area, and 12 cells used for implementation. An approximate 4-bit RCA is proposed using 64 QCA cells. The effectiveness of these designs is evaluated through functional verification with the QCADesigner program. According to simulation results, these proposed solutions not only function well but significantly outperform previous ideas in terms of speed and space. The proposed FA, FS, and RCA designs surpassed the previous best designs by 21%, 21%, and 43%, respectively, in terms of cell count. [ABSTRACT FROM AUTHOR]

Campylobacter jejuni plays a significant role in human health, food production, and veterinary practice. Biofilm formation is a likely mechanism explaining the survival of C. jejuni in seemingly unfavourable environments, but the underlying mechanisms are poorly understood. We propose a mathematical model to unify various observations regarding C. jejuni biofilm formation. Specifically, we present a cellular automaton with stochastic dynamics that describes both the probability of biofilm initiation and its subsequent growth. Our model incorporates fundamental processes such as cell rearrangement, diffusion of chemical compounds, accumulation of extracellular material, cell growth, lysis, and deactivation due to nutrient scarcity. The model predicts an optimal nutrient concentration that enhances population survival, revealing a trade-off where higher nutrient levels may harm individual cells but benefit the overall population. Our results suggest that the lower biofilm accumulation observed experimentally in aerobic conditions compared to microaerobic conditions may be due to a reduced surface invasion probability of individual cells. However, cells that do manage to invade can generate microcolonies of a similar size under both aerobic and microaerobic conditions. These findings provide new insights into the survival probability and size of C. jejuni biofilms, suggesting potential targets for controlling its biofilm formation in various environments. [ABSTRACT FROM AUTHOR]

African savannah elephants (Loxodonta africana) are key ecosystem engineers that migrate over large spatiotemporal scales foraging as they require copious amounts of food and water across habitable landscapes. Therefore a need to understand movement patterns arises in relation to vegetation type and landscape variability, moreso in forage depauparate arid areas such as Gonarezhou National Park (GNP) in Zimbabwe. The objectives of this study were to: (i) assess the performance of vegetation indices in modelling the distribution of African savannah elephants, and (ii) model future landscape variability in Gonarezhou National Park (GNP) in Zimbabwe. Maximum entropy (MaxEnt) algorithm was used to explore the relationship between vegetation indices and distribution of African savannah elephants in the GNP. The Soil Adjusted Vegetation Index (SAVI) performs better relative to other indices in modelling the distribution of African savannah elephants across all habitat types in the GNP. Cellular automata‐Artificial Neural Network (CA‐ANN) showed a significant future decrease (Kruskal Anova; p < 0.05) in landscape suitable to sustain large populations of African savannah elephants in the GNP by the year 2083. Future remote sensing reveals directional insights into the future consequences of current landscape management for African savannah elephant conservation which is a crucial in the sustainability of climate threatened arid protected areas such as the GNP. [ABSTRACT FROM AUTHOR]

To enhance land use/cover change (LUCC) simulation accuracy, we introduced ViViT-ANN-CA, blending video vision transformer's spatio-temporal features extraction ability, artificial neural network's (ANN) non-linearity computing ability, and CA's spatial computing. Compared to 3DCNN-ANN-CA, ViViT-ANN-CA showed higher accuracy in simulating water bodies and vegetation, with overall improvements in Hailing District and Wuxi City. ViViT demonstrates comparable spatio-temporal feature extraction ability to three-dimensional convolutional neural network (3DCNN), promising for future ynamic LUCC simulations. [ABSTRACT FROM AUTHOR]

Dynamic recrystallisation (DRX) is one of the fundamental phenomena in materials science, significantly impacting the microstructure and mechanical properties of components subjected to large plastic deformations. Experimental research on that topic carried out for a wide range of new metallic materials is often supported by computational materials science. A direct consideration and detailed understanding of this phenomenon are possible with a class of full-field numerical models based on the cellular automata (CA) method. However, the classical CA approach is based on a regular, fixed computational space and has limitations in capturing large deformations of the computational domain. Therefore, the main goal of the work is to develop and implement an alternative solution to overcome this limitation. The proposed solution is based on coupling the finite element (FE) method with the random cellular automata (RCA) approach. Such a model can directly consider the influence of geometrical changes in microstructure during large plastic deformation on recrystallisation progress. Details of the developed RCA DRX model assumptions and coupling issues with FE mesh are discussed. Particular attention is also paid to increasing model efficiency and robustness studies. [ABSTRACT FROM AUTHOR]

As the relationship between energy and information loss and reversible gates was revealed, much interest in reversible gate design arose, and as quantum-dot cellular automata (QCA) gained attention as a next-generation nano circuit design technology, various reversible gates based on QCA emerged. The proposed study optimizes the performance and design costs of existing QCA-based reversible gates including TR, RUG, PQR, and URG. According to most indicators, the proposed circuits showed significant improvement rates and outperformed existing studies. In particular, the proposed optimal TR, RUG, PQR, and URG showed performance improvements of 266%, 265%, 300%, and 144% in CostAD, respectively, compared with the best existing circuit. This shows outstanding improvement and superiority in terms of area and delay, which are the most important factors in the performance of nano-scale circuits that are becoming extremely miniaturized. Additionally, the exceptionally high-output polarization of the proposed circuits is an important indicator of the circuit's expansion and connection and increases the circuit's reliability. [ABSTRACT FROM AUTHOR]

• Proposed a method for multi-axis random vibration topology optimization. • Enhancing vibration resistance and extending life during the topology phase. • Prompting the structure to add load-bearing paths to resist random vibration loads. • Guiding the topology towards forming a vibration-resistant structure. • Expanding HCA into the field of random vibration performance topology optimization. Considering the lack of effective solutions for multi-axis random vibration topology optimization problems, and recognizing that multi-axis random vibration excitation is the most common loading conditions experienced by structures during their operational life, this paper proposes a multi-objective topology optimization method for multi-axis random vibration. By combining Hybrid Cellular Automata (HCA) and Analytic Hierarchy Process (AHP), this method aims to enhance the vibration resistance of structures and extend their fatigue life during the topology optimization phase. To validate the effectiveness and engineering practicality of this method, two engineering cases were designed: a cantilever beam case and an automotive steering knuckle case. Multi-objective topology optimization were conducted with objectives including mass, stiffness, and vibration intensity metric (root-mean-square stress). The two cases demonstrate that the control group's topology model exhibits weaker resistance to random vibrations and shorter fatigue life. In contrast, the validation group's topology model introduces an additional load path near the excitation point, effectively dissipating the impact of vibration excitation. This enhances the structure's vibration resistance and significantly extends its fatigue life. [ABSTRACT FROM AUTHOR]

In this paper, we examine the impact of vehicles executing a full turn or U-turn in a single-lane roundabout system using a cellular automaton model. We investigate how increasing the number of these U-turning vehicles affects traffic flow characteristics and energy dissipation. Our findings reveal that as the prevalence of U-turning vehicles rises, the phase diagram undergoes significant changes; the maximum current phase expands, detrimentally impacting the free flow and congestion phases, while giving rise to a jamming phase. This shift results in a gradual increase in capacity at circulating lanes and a steady decline at entry/exit lanes. We also explore the role of aimless vehicles — those circulating without a fixed destination. As the proportion of such vehicles augments, it fosters an enlargement of the maximum current phase at the expense of the free flow and congestion phases. Furthermore, these vehicles influence energy dissipation across the three lanes of a roundabout. Significantly, we elucidate that variations in the percentage of these specific vehicular groups critically affect CO2 emissions. Our research unravels vital insights into optimizing roundabout management to enhance traffic flow and reduce environmental impact. [ABSTRACT FROM AUTHOR]

We aim to enhance the parking efficiency for a group of autonomous vehicles in a large parking lot during peak hours. Two parking principles, i.e. the near-priority (NP) and distant-priority (DP) principles, are proposed and quantitatively examined. The NP principle characterizes individual parking behavior, where autonomous vehicles tend to select available parking spaces that are closer in proximity. Conversely, the DP principle is proposed from the perspective of the entire parking system, prioritizing the allocation of more distant parking spaces within a certain range around each entrance. Two time indicators, including the overall parking time and the average parking time, are adopted to evaluate the performance of the two principles. A potential-based cellular automata (CA) model is proposed to formulate the dynamic parking process of vehicles in a two-dimensional space, where vehicle navigation is driven by a so-called potential field. Then, two dynamic navigation algorithms are developed for parking navigation under the NP and DP principles. Furthermore, by conducting a set of comparative simulation experiments, we have obtained some management insights into peak parking management in the era of autonomous driving. • Offers a fresh perspective on parking space management for autonomous vehicles. • Examine two parking principles: near-priority (NP) and distant-priority (DP). • Provide accurate vehicle navigation paths by proposing a potential-based microscopic model. • Address several operation schemes for improving parking efficiency. [ABSTRACT FROM AUTHOR]

Tailings dams generally face problems such as a large number of hazardous reservoirs and difficulties in supervision, and the current numerical models related to tailings dam failure have shortcomings such as weak targeting and reliance on empirical formulas. In order to solve the above problems, based on the CTGAN data enhancement algorithm of 132 dam break cases database, a tailings dam break numerical model (hereinafter referred to as TDB_S) is established and applied to the analysis of 18 tailings dam break cases. By comparing the simulation results of the TDB-S model with three other common dam failure models (DAMBRK model, BREACH model, and Cellular Automaton model), it was found that the TDB-S model can effectively predict the expansion of the dam breach and the peak flow rate under most operating conditions. The comparison results with other models show that the average flow error of the TDB-S model in predicting the collapse process of 18 tailings dams worldwide is 12.36%; the average error of the breach is 13.28%. The above conclusion proves that the TDB-S model has better simulation effect and higher accuracy compared to other models, and can provide a certain reference basis for the study of tailings dam breach safety issues. [ABSTRACT FROM AUTHOR]

Rapid urbanisation, especially in developing countries like India, has resulted in unplanned and haphazard urban expansion. With saturated urban cores, growth is observed in the peri-urban areas, resulting in severe challenges for urban planners. The present study aims to study the urban growth patterns of the fast-growing Mumbai Metropolitan Region (MMR) using the Landsat data from 1999 to 2019 and to evaluate the neighbourhood configurations' effect on urban growth prediction. The urban area maps are classified using a maximum likelihood algorithm and are used along with the potential drivers to test three levels of neighbourhood considerations. The first model assumes no neighbourhood effect, the second incorporates the built-up pixels in the neighbourhood as an additional potential driver variable, and the third uses a Cellular Automata (CA). The CA model explores variations in neighbourhood types and sizes, distance decay and iterations to identify the optimal configuration. The results show an 89.44% increase in built-up areas over two decades (1999-2019). The urban growth prediction model testing reveals the importance of neighbourhood, with the first model without neighbourhood consideration giving the least accuracy (67%) while the inbuilt neighbourhood model gives better results (71%). However, the CA-based model with a 9 × 9 Moore neighbourhood, distance exponent β = 2 and two iterations give the highest accuracy (76%). The growth prediction shows a new wave of peri-urban growth in MMR, with overall urban areas increasing by 25% between 2019 and 2029 and 20% between 2029 and 2039. The results provide urban planners with a valuable tool for informed decision-making and promoting sustainable development. [ABSTRACT FROM AUTHOR]

The menace of sedimentation to reservoirs has a significant implication for water quality, storage capacity and reservoir lifetime. Rainfall patterns and other anthropogenic and environmental impacts alter the erosion rate and, by extension, directly affect sedimentation rates if left unchecked. This research focused on using the integration of Markov Chains and Cellular Automata (MC – CA) models to estimate and forecast the future bathymetric surface of the Kainji reservoir in Nigeria for the year 2050. The bathymetric datasets used for this research comprise two different epochs (1990 and 2020). The datasets were acquired using a Single Beam Echosounder at Low and High frequencies of 20 kHz and 200 kHz. The preliminary investigation revealed that sedimentation is exacerbating a greater danger to the reservoir functionality. The results show that the maximum observed depth is 71.2 m, indicating a 7.53% loss in depth from the 1990 archived data and a 16.24% depth loss to sedimentation from 1968 to 2020 and 22.35% depth loss in the year 2050 as shown on the projected surface. Consequently, the integrated model (MC and CA) efficiently predicted the future bathymetric surface of the Kainji reservoir for the year 2050 based on the data characteristics. However, the proven techniques for analysing spatial data, such as the Markov Chain and Cellular Automata, best suited for analysing categorical transition data, show some artefacts (black spots) on the projected generated map which is subject to further investigation. [ABSTRACT FROM AUTHOR]

The extraordinary success of convolutional neural networks (CNNs) in various computer vision tasks has revitalized the field of artificial intelligence. The out-sized expectations created by this extraordinary success have, however, been tempered by a recognition of CNNs' fragility. Importantly, the magnitude of the problem is unclear due to a lack of rigorous benchmark datasets. Here, we propose a solution to the benchmarking problem that reveals the extent of the vulnerabilities of CNNs and of the methods used to provide interpretability to their predictions. We employ cellular automata (CA) to generate images with rigorously controllable characteristics. CA allow for the definition of both extraordinarily simple and highly complex discrete functions and allow for the generation of boundless datasets of images without repeats. In this work, we systematically investigate the fragility and interpretability of the three popular CNN architectures using CA-generated datasets. We find a sharp transition from a learnable phase to an unlearnable phase as the latent space entropy of the discrete CA functions increases. Furthermore, we demonstrate that shortcut learning is an inherent trait of CNNs. Given a dataset with an easy-to-learn and strongly predictive pattern, CNN will consistently learn the shortcut even if the pattern occurs only on a small fraction of the image. Finally, we show that widely used attribution methods aiming to add interpretability to CNN outputs are strongly CNN-architecture specific and vary widely in their ability to identify input regions of high importance to the model. Our results provide significant insight into the limitations of both CNNs and the approaches developed to add interpretability to their predictions and raise concerns about the types of tasks that should be entrusted to them. [ABSTRACT FROM AUTHOR]

Within scenario-based research of social-ecological systems, there has been a growing recognition of the importance of normative scenarios that define positive outcomes for both nature and society. While several frameworks exist to guide the co-creation of normative scenario narratives, examples of operationalizing these narratives in quantitative simulation modelling are still limited. To address this gap, this paper presents an example of how aspects of normative scenarios can be realized within a spatial model of land use and land cover change. This is achieved through a combination of data-driven approaches to encapsulate scenario-specific differences in local and global scale phenomena, as well as iterative expert elicitation to quantify descriptive trends from narratives. This approach is demonstrated with a case study simulating five scenarios of landscape change (three normative and two exploratory) in Switzerland between 2020 and 2060. The resulting maps of future land use and land cover exhibited distinct variations between the scenarios, notably with regard to the prevalence of areas of heterogeneous semi-natural land, such as alpine pastures and grassland, often considered culturally emblematic of Switzerland. While the simulation results were generally consistent with the outcomes expressed in the scenario narratives, following a process of expert feedback, we reflect that there are clear challenges in leveraging such results to elicit further discussions as to the desirability and plausibility of future scenarios. Specifically, the need to summarize spatial simulations in a manner that is easily interpretable and encourages consideration of the broader patterns of change rather than focusing on fine-scale details. [ABSTRACT FROM AUTHOR]

A cellular automata model was developed and parameterized to test the effectiveness of application of biological control insects to water hyacinth (Pontederia crassipes), which is an invasive floating plant species in many parts of the world and outcompetes many submersed native aquatic species in southern Florida. In the model, P. crassipes was allowed to compete with Nuttall's waterweed (Elodea nuttallii). In the absence of biocontrol acting on the P. crassipes, E. nuttallii excluded P. crassipes at low concentrations of the limiting nutrient (nitrogen), and the reverse occurred at high nutrient concentrations. At intermediate values, alternative stable states could occur; either P. crassipes alone or a mixture of the two species. When the biocontrol agent, the weevil Neochetina eichhorniae, was applied in the model, there was initially a rapid reduction of the P. crassipes, however, over time a regular striped pattern of moving spatially alternating stripes of P. crassipes and E. nuttallii emerged. -This pattern of moving stripes emerged and persisted over thousands of days but could quickly transform into an irregular pattern at some apparently random time, when either external stochasticity (added adult weevils) or only the weak internal stochasticity of weevil movements occurred. The cause of the end of the long transient can be traced to a single slightly irregular pixel within the striped pattern. Model parameters were varied to study effects of plant growth rate, nutrient concentration and nutrient diffusion rate on the dynamics of the system. [ABSTRACT FROM AUTHOR]

The aim of this research was to simulate the future water balance of the Silwani watershed, Jharkhand, India, under the combined effect of land use and climate change based on the Soil and Water Assessment Tool (SWAT) and Cellular Automata (CA)-Markov Chain model. The future climate prediction was done based on daily bias-corrected datasets of the INMCM5 climate model with Shared Socioeconomic Pathway 585 (SSP585), which represent the fossil fuel development of the world. After a successful model run, water balance components like surface runoff, groundwater contribution to stream flow, and ET were simulated. The anticipated change in land use/land cover (LULC) between 2020 and 2030 reflects a slight increase (3.9 mm) in groundwater contribution to stream flow while slight decrease in surface runoff (4.8 mm). The result of this research work helps the planners to plan any similar watershed for future conservation. [ABSTRACT FROM AUTHOR]

Floods have become increasingly frequent and devastating in recent decades, posing unignorable risks as highly destructive natural hazards. To effectively manage and mitigate these risks, accurate flood hazard mapping is crucial. Machine learning models have emerged as valuable approaches for flood hazard assessment. In this study, six machine learning (ML) models, including Maximum Entropy, Support Vector Machine, Extreme Gradient Boosting (XGB), Random Forest (RF), multi-layer perceptron, and Naive Bayes, were utilized to evaluate urban flood hazard in Zaio, NE Morocco, and estimate the flood presence extent. Nine flood conditioning factors were used as input variables. Historical flood presence and absence data were employed for models training and testing, incorporating 663 flood presence and absence locations dating back to past flood events. Performance evaluation metrics such as Kappa statistic, accuracy, sensitivity, specificity, and area under the curve (AUC) were calculated for each model. RF (AUC = 0.92) and XGB (AUC = 0.9) models showed excellent classification capabilities, surpassing the performance of the other models, while the other models exhibited lower but recognizable performances. Additionally, the hazard presence extent maps generated by the ML models exhibited a decent alignment with a historical flood event maps created by the hydrodynamic and the cellular automata models. The results imply that ML models offer effective solutions for mapping urban flood hazards. The innovative integration of various ensemble and single ML models demonstrates their potential in urban flood hazard susceptibility and extent mapping, effectively surpassing the limitations associated with limited availability of hydrologic/hydraulic data and computational burden. These mapped results can be instrumental for local authorities in shaping mitigation strategies in the city of Zaio. [ABSTRACT FROM AUTHOR]

Nanogrooves with high aspect ratios possess small size effects and high-precision optical control capabilities, as well as high specific surface area and catalytic performance, demonstrating significant application value in the fields of optics, semiconductor processes, and biosensing. However, existing manufacturing methods face issues such as complexity, high costs, low efficiency, and low precision, especially in the difficulty of fabricating nanogrooves with high resolution on the nanoscale. This study proposes a method based on focused ion beam technology and a layer-by-layer etching process, successfully preparing V-shaped and rectangular nanogrooves on a silicon dioxide substrate. Combining with cellular automaton algorithm, the ion sputtering flux and redeposition model was simulated. By converting three-dimensional grooves to discrete rectangular slices through a continuous etching process and utilizing the sputtering and redeposition effects of gallium ion beams, high-aspect-ratio V-shaped grooves with up to 9.6:1 and rectangular grooves with nearly vertical sidewalls were achieved. In addition, the morphology and composition of the V-shaped groove sidewall were analyzed in detail using transmission electron microscopy (TEM) and tomography techniques. The influence of the etching process parameters (ion current, dwell time, scan times, and pixel overlap ratio) on groove size was analyzed, and the optimized process parameters were obtained. [ABSTRACT FROM AUTHOR]

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.