Cellular Automata
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2022 ◽  
Vol 155 ◽  
pp. 111784
Michele Mugnaine ◽  
Enrique C. Gabrick ◽  
Paulo R. Protachevicz ◽  
Kelly C. Iarosz ◽  
Silvio L.T. de Souza ◽  

Daniel Varela ◽  
José Santos

AbstractProtein folding is the dynamic process by which a protein folds into its final native structure. This is different to the traditional problem of the prediction of the final protein structure, since it requires a modeling of how protein components interact over time to obtain the final folded structure. In this study we test whether a model of the folding process can be obtained exclusively through machine learning. To this end, protein folding is considered as an emergent process and the cellular automata tool is used to model the folding process. A neural cellular automaton is defined, using a connectionist model that acts as a cellular automaton through the protein chain to define the dynamic folding. Differential evolution is used to automatically obtain the optimized neural cellular automata that provide protein folding. We tested the methods with the Rosetta coarse-grained atomic model of protein representation, using different proteins to analyze the modeling of folding and the structure refinement that the modeling can provide, showing the potential advantages that such methods offer, but also difficulties that arise.

Agniva Datta ◽  
Muktish Acharyya

The results of Kermack–McKendrick SIR model are planned to be reproduced by cellular automata (CA) lattice model. The CA algorithms are proposed to study the model of an epidemic, systematically. The basic goal is to capture the effects of spreading of infection over a scale of length. This CA model can provide the rate of growth of the infection over the space which was lacking in the mean-field like susceptible-infected-removed (SIR) model. The motion of the circular front of an infected cluster shows a linear behavior in time. The correlation of a particular site to be infected with respect to the central site is also studied. The outcomes of the CA model are in good agreement with those obtained from SIR model. The results of vaccination have been also incorporated in the CA algorithm with a satisfactory degree of success. The advantage of the present model is that it can shed a considerable amount of light on the physical properties of the spread of a typical epidemic in a simple, yet robust way.

Felix S. K. Agyemang ◽  
Elisabete Silva ◽  
Sean Fox

The global urban population is expected to grow by 2.5 billion over the next three decades, and 90% of this growth will occur in African and Asian countries. Urban expansion in these regions is often characterised by ‘informal urbanization’ whereby households self-build without planning permission in contexts of ambiguous, insecure or disputed property rights. Despite the scale of informal urbanization, it has received little attention from scholars working in the domains of urban analytics and city science. Towards addressing this gap, we introduce TI-City, an urban growth model designed to predict the locations, legal status and socio-economic status of future residential developments in an African city. In a bottom-up approach, we use agent-based and cellular automata modelling techniques to predict the geospatial behaviour of key urban development actors, including households, real estate developers and government. We apply the model to the city-region of Accra, Ghana, drawing on local data collection, including a household survey, to parameterise the model. Using a multi-spatial-scale validation technique, we compare TI-City’s ability to simulate historically observed built-up patterns with SLEUTH, a highly popular urban growth model. Results show that TI-City outperforms SLEUTH at each scale, suggesting the model could offer a valuable decision support tool in similar city contexts.

2022 ◽  
Vol 15 ◽  
Reinier Xander A. Ramos ◽  
Jacqueline C. Dominguez ◽  
Johnrob Y. Bantang

Realistic single-cell neuronal dynamics are typically obtained by solving models that involve solving a set of differential equations similar to the Hodgkin-Huxley (HH) system. However, realistic simulations of neuronal tissue dynamics —especially at the organ level, the brain— can become intractable due to an explosion in the number of equations to be solved simultaneously. Consequently, such efforts of modeling tissue- or organ-level systems require a lot of computational time and the need for large computational resources. Here, we propose to utilize a cellular automata (CA) model as an efficient way of modeling a large number of neurons reducing both the computational time and memory requirement. First, a first-order approximation of the response function of each HH neuron is obtained and used as the response-curve automaton rule. We then considered a system where an external input is in a few cells. We utilize a Moore neighborhood (both totalistic and outer-totalistic rules) for the CA system used. The resulting steady-state dynamics of a two-dimensional (2D) neuronal patch of size 1, 024 × 1, 024 cells can be classified into three classes: (1) Class 0–inactive, (2) Class 1–spiking, and (3) Class 2–oscillatory. We also present results for different quasi-3D configurations starting from the 2D lattice and show that this classification is robust. The numerical modeling approach can find applications in the analysis of neuronal dynamics in mesoscopic scales in the brain (patch or regional). The method is applied to compare the dynamical properties of the young and aged population of neurons. The resulting dynamics of the aged population shows higher average steady-state activity 〈a(t → ∞)〉 than the younger population. The average steady-state activity 〈a(t → ∞)〉 is significantly simplified when the aged population is subjected to external input. The result conforms to the empirical data with aged neurons exhibiting higher firing rates as well as the presence of firing activity for aged neurons stimulated with lower external current.

Electronics ◽  
2022 ◽  
Vol 11 (2) ◽  
pp. 184
Yongkun Zhou ◽  
Dan Song ◽  
Bowen Ding ◽  
Bin Rao ◽  
Man Su ◽  

In system science, a swarm possesses certain characteristics which the isolated parts and the sum do not have. In order to explore emergence mechanism of a large–scale electromagnetic agents (EAs), a neighborhood selection (NS) strategy–based electromagnetic agent cellular automata (EA–CA) model is proposed in this paper. The model describes the process of agent state transition, in which a neighbor with the smallest state difference in each sector area is selected for state transition. Meanwhile, the evolution rules of the traditional CA are improved, and performance of different evolution strategies are compared. An application scenario in which the emergence of multi–jammers suppresses the radar radiation source is designed to demonstrate the effect of the EA–CA model. Experimental results show that the convergence speed of NS strategy is better than those of the traditional CA evolution rules, and the system achieves effective jamming with the target after emergence. It verifies the effectiveness and prospects of the proposed model in the application of electronic countermeasures (ECM).

Computers ◽  
2022 ◽  
Vol 11 (1) ◽  
pp. 8
Benjamín Luna-Benoso ◽  
José Cruz Martínez-Perales ◽  
Jorge Cortés-Galicia ◽  
Rolando Flores-Carapia ◽  
Víctor Manuel Silva-García

Any cancer type is one of the leading death causes around the world. Skin cancer is a condition where malignant cells are formed in the tissues of the skin, such as melanoma, known as the most aggressive and deadly skin cancer type. The mortality rates of melanoma are associated with its high potential for metastasis in later stages, spreading to other body sites such as the lungs, bones, or the brain. Thus, early detection and diagnosis are closely related to survival rates. Computer Aided Design (CAD) systems carry out a pre-diagnosis of a skin lesion based on clinical criteria or global patterns associated with its structure. A CAD system is essentially composed by three modules: (i) lesion segmentation, (ii) feature extraction, and (iii) classification. In this work, a methodology is proposed for a CAD system development that detects global patterns using texture descriptors based on statistical measurements that allow melanoma detection from dermoscopic images. Image analysis was carried out using spatial domain methods, statistical measurements were used for feature extraction, and a classifier based on cellular automata (ACA) was used for classification. The proposed model was applied to dermoscopic images obtained from the PH2 database, and it was compared with other models using accuracy, sensitivity, and specificity as metrics. With the proposed model, values of 0.978, 0.944, and 0.987 of accuracy, sensitivity and specificity, respectively, were obtained. The results of the evaluated metrics show that the proposed method is more effective than other state-of-the-art methods for melanoma detection in dermoscopic images.

2022 ◽  
Vol 8 (2) ◽  
pp. 115-126
Dramane Issiako ◽  
Ousséni Arouna ◽  
Karimou Soufiyanou ◽  
Ismaila Toko Imorou ◽  
Brice Tente

The dynamics of land cover and land use in the classified forest of the upper Alibori (FCAS) in relation to the disturbance of agro-pastoral activities is a major issue in the rational management of forest resources. The objective of this research is to simulate the evolutionary trend of land cover and land use in the FCAS by 2069 based on satellite images. Landsat images from 2009, 2014 and 2019 obtained from the earthexplorer-usgs archive were used. The methods used are diachronic mapping and spatial forecasting based on senarii. The MOLUSCE module available under QGIS remote sensing 2.18.2 is used to simulate the future evolution of land cover and land use in the FCAS. The land cover and use in the year 2069 is simulated using cellular automata based on the scenarios. The results show that natural land cover units have decreased while anthropogenic formations have increased between 2009 and 2014 and between 2014 and 2019. Under the "absence multi-criteria zoning (MZM)" scenario over a 50-year interval, land cover and use will be dominated by crop-fallow mosaics (88%). On the other hand, the scenario "implementation of a multicriteria zoning (MZE)", was issued with the aim of reversing the regressive trend of vegetation types by making a rational and sustainable management of resources.

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