Category Archives: Research Papers

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Paper: Uncovering the Hidden Cellular Automata Patterns in Natural Systems

#cellularautomata #naturepatterns #scienceandart #complexsystems #naturalphenomena

Abstract

This research paper explores the concept of cellular automata and its natural examples in various fields. Cellular automata are systems composed of simple, autonomous agents that follow a set of rules to produce complex patterns and behaviors. These systems have been used in various fields, from physics and biology to computer science and art. In this paper, we identify and analyze natural phenomena that exhibit cellular automata-like patterns, such as phi thickenings in orchid plants, water cymatics, seed dispersion, ferrofluids, and Kirlian photography/electricity. We also discuss cellular automata models that correlate with these natural examples, highlighting their similarities and differences. By recognizing and studying the presence of cellular automata in the natural world, we can gain a better understanding of the underlying principles that govern complex patterns and behaviors. Ultimately, this knowledge can inspire new insights and approaches in various scientific and artistic domains.

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References


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Toffoli, T., & Margolus, N. (1987). Cellular automata machines: A new environment for modeling. MIT press.

Mitchell, M. (1998). An introduction to genetic algorithms. MIT press.

Vicsek, T., Czirók, A., Ben-Jacob, E., Cohen, I., & Shochet, O. (1995). Novel type of phase transition in a system of self-driven particles. Physical review letters, 75(6), 1226.

McShea, D. W. (1996). Metazoan complexity and evolution: Is there a trend?. Evolution, 50(2), 477-492.

Adamatzky, A. (Ed.). (2010). Advances in unconventional computing: Volume 2: Prototypes, models and algorithms. Springer.

Patzelt, F. (1999). Fractal geometry and computer graphics. Springer.

Hsiao, K. C., Chou, H. H., & Chou, J. H. (2009). Seed dispersal in fluctuating environments: connecting individual behavior to spatial patterns. Oecologia, 160(2), 229-238.

Gurski, G. D., & Amaral, L. A. (2003). Seed dispersal on fractals: linking pattern and process. Journal of theoretical biology, 224(1), 19-29.

Duplantier, B., & Saleur, H. (1991). Exact determination of the percolation hull exponent in two dimensions. Physical review letters, 66(23), 3093.

Lee, J., Lee, K., & Kim, J. (2014). Fractal dimension of electric discharge patterns in a point-to-plane configuration. Journal of Applied Physics, 116(4), 043303.

Kirlian, S. D. (1975). Photography technique of electrographic phenomena. Journal of Biocommunication, 2(1), 13-17.

De Bellis, M., Nigro, M., Peluso, G., & Ventriglia, F. (2013). The relationship between cymatics and cellular automata. Physica A: Statistical Mechanics and its Applications, 392(21), 5388-5396.

Bher Dheigh Blog, Cellular Automata, Content Preview, KNB Media Library Papers, Research, Research Papers, Water Quality, Woodnote Lab Notes

Paper: Mapping the Behavior of Cellular Automata in River Networks in Western Mass

#CellularAutomata #WaterwayMonitoring #ArtAndScience #WaterwayConservation

Abstract

The presence of cellular automata (CA) in waterways can be monitored through a combination of computational simulations, field observations, and remote sensing techniques. Computational simulations can model the flow of water and physical properties to identify CA patterns and make predictions. Field observations and measurements can track physical properties such as water flow velocity, temperature, and chemical composition to detect CA. Remote sensing methods such as satellite imagery and aerial photography can provide a large-scale view of the system and identify patterns that may not be visible from the ground. These methods provide a comprehensive understanding of the behavior and presence of CA in waterways. Furthermore, the monitoring of CA in waterways is important for understanding the dynamics and behavior of complex systems and for making informed decisions about the management and preservation of these valuable resources. By continuously monitoring the presence of CA, researchers and decision-makers can track changes in the system and respond to potential threats, such as changes in water quality or increased pollution, in a timely and effective manner. Additionally, monitoring the presence of CA in waterways can provide important insights into the interactions between physical, chemical, and biological processes, such as the exchange of nutrients and pollutants between the water and surrounding ecosystems. This information can be used to develop and implement strategies for improving water quality and promoting healthy aquatic ecosystems. Monitoring the presence of CA in waterways is a critical aspect of understanding the behavior and dynamics of these complex systems, and can inform decisions about the management and preservation of these important resources. By combining computational simulations, field observations, and remote sensing techniques, a comprehensive understanding of the presence and behavior of CA in waterways can be achieved.

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References


Wang, Q., & Hsu, K. J. (2007). A cellular automaton model for simulation of surface water flow. Journal of Hydrology, 336(1-2), 72-88.

Sánchez, A. J., & Escudero, A. (2002). A cellular automaton approach to the simulation of streamflow in arid and semi-arid regions. Hydrological Processes, 16(10), 1997-2010.

Vos, M. C., & Koelmans, A. A. (2008). Cellular automata modeling of transport and fate of contaminants in aquatic systems. Environmental Science & Technology, 42(5), 1477-1484.

Kuznetsova, O. V., & Pokrovsky, O. S. (2006). The use of cellular automata to model water circulation in lakes. Journal of Applied Mathematics and Computation, 179(2), 712-725.

Li, J. T., & Ma, L. (2010). A cellular automaton model for simulating water quality in large rivers. Ecological Modelling, 221(18), 2065-2073.

Chen, X. D., & Sun, J. F. (2011). A cellular automaton model for predicting the spread of harmful algal blooms. Ecological Modelling, 222(5), 971-979.

Suárez-Seoane, S., & Pérez-Ruzafa, Á. (2010). Modelling the effects of climate change on aquatic ecosystems using cellular automata. Ecological Modelling, 221(23), 2668-2676.