Optical study on characteristics of non-reacting and reacting diesel spray with different strategies of split injection

[EN] Even though studies on split-injection strategies have been published in recent years, there are still many remaining questions about how the first injection affects the mixing and combustion processes of the second one by changing the dwell time between both injection events or by the first injection quantity. In this article, split-injection diesel sprays with different injection strategies are investigated. Visualization of n-dodecane sprays was carried out under both non-reacting and reacting operating conditions in an optically accessible two-stroke engine equipped with a single-hole diesel injector. High-speed Schlieren imaging was applied to visualize the spray geometry development, while diffused backgroundillumination extinction imaging was applied to quantify the instantaneous soot production (net result of soot formation and oxidation). For non-reacting conditions, it was found that the vapor phase of second injection penetrates faster with a shorter dwell time and independently of the duration of the first injection. This could be explained in terms of onedimensional spray model results, which provided information on the local mixing and momentum state within the flow. Under reacting conditions, interaction between the second injection and combustion recession of the first injection is observed, resulting in shorter ignition delay and lift-off compared to the first injection. However, soot production behaves differently with different injection strategies. The maximum instantaneous soot mass produced by the second injection increases with a shorter dwell time and with longer first injection duration.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was partially funded by the Spanish Ministry of Economy and Competitiveness in the frame of the advanced spray combustion models for efficient powertrains (COMEFF) (TRA2014-59483-R) project. Funding for Tiemin Xuan's PhD studies was granted by Universitat Politecnica de Valencia through the Programa de Apoyo para la Investigacion y Desarrollo (PAID) (grant reference FPI-2015-S2-1068)Desantes, J.; García-Oliver, JM.; García Martínez, A.; Xuan, T. (2019). Optical study on characteristics of non-reacting and reacting diesel spray with different strategies of split injection. International Journal of Engine Research. 20(6):606-623. https://doi.org/10.1177/1468087418773012S606623206Arrègle, J., Pastor, J. V., López, J. J., & García, A. (2008). Insights on postinjection-associated soot emissions in direct injection diesel engines. Combustion and Flame, 154(3), 448-461. doi:10.1016/j.combustflame.2008.04.021Mendez, S., & Thirouard, B. (2008). Using Multiple Injection Strategies in Diesel Combustion: Potential to Improve Emissions, Noise and Fuel Economy Trade-Off in Low CR Engines. SAE International Journal of Fuels and Lubricants, 1(1), 662-674. doi:10.4271/2008-01-1329He, Z., Xuan, T., Jiang, Z., & Yan, Y. (2013). Study on effect of fuel injection strategy on combustion noise and exhaust emission of diesel engine. Thermal Science, 17(1), 81-90. doi:10.2298/tsci120603159hKook, S., Pickett, L. M., & Musculus, M. P. B. (2009). Influence of Diesel Injection Parameters on End-of-Injection Liquid Length Recession. SAE International Journal of Engines, 2(1), 1194-1210. doi:10.4271/2009-01-1356Musculus, M. P. B., & Kattke, K. (2009). Entrainment Waves in Diesel Jets. SAE International Journal of Engines, 2(1), 1170-1193. doi:10.4271/2009-01-1355O’Connor, J., Musculus, M. P. B., & Pickett, L. M. (2016). Effect of post injections on mixture preparation and unburned hydrocarbon emissions in a heavy-duty diesel engine. Combustion and Flame, 170, 111-123. doi:10.1016/j.combustflame.2016.03.031O’Connor, J., & Musculus, M. (2013). Post Injections for Soot Reduction in Diesel Engines: A Review of Current Understanding. SAE International Journal of Engines, 6(1), 400-421. doi:10.4271/2013-01-0917O’Connor, J., & Musculus, M. (2014). In-Cylinder Mechanisms of Soot Reduction by Close-Coupled Post-Injections as Revealed by Imaging of Soot Luminosity and Planar Laser-Induced Soot Incandescence in a Heavy-Duty Diesel Engine. SAE International Journal of Engines, 7(2), 673-693. doi:10.4271/2014-01-1255Bruneaux, G., & Maligne, D. (2009). Study of the Mixing and Combustion Processes of Consecutive Short Double Diesel Injections. SAE International Journal of Engines, 2(1), 1151-1169. doi:10.4271/2009-01-1352Pickett, L. M., Kook, S., & Williams, T. C. (2009). Transient Liquid Penetration of Early-Injection Diesel Sprays. SAE International Journal of Engines, 2(1), 785-804. doi:10.4271/2009-01-0839Skeen, S., Manin, J., & Pickett, L. M. (2015). Visualization of Ignition Processes in High-Pressure Sprays with Multiple Injections of n-Dodecane. SAE International Journal of Engines, 8(2), 696-715. doi:10.4271/2015-01-0799Bolla, M., Chishty, M. A., Hawkes, E. R., & Kook, S. (2017). Modeling combustion under engine combustion network Spray A conditions with multiple injections using the transported probability density function method. International Journal of Engine Research, 18(1-2), 6-14. doi:10.1177/1468087416689174Blomberg, C. K., Zeugin, L., Pandurangi, S. S., Bolla, M., Boulouchos, K., & Wright, Y. M. (2016). Modeling Split Injections of ECN «Spray A» Using a Conditional Moment Closure Combustion Model with RANS and LES. SAE International Journal of Engines, 9(4), 2107-2119. doi:10.4271/2016-01-2237Cung, K., Moiz, A., Johnson, J., Lee, S.-Y., Kweon, C.-B., & Montanaro, A. (2015). Spray–combustion interaction mechanism of multiple-injection under diesel engine conditions. Proceedings of the Combustion Institute, 35(3), 3061-3068. doi:10.1016/j.proci.2014.07.054Moiz, A. A., Cung, K. D., & Lee, S.-Y. (2017). Simultaneous Schlieren–PLIF Studies for Ignition and Soot Luminosity Visualization With Close-Coupled High-Pressure Double Injections of n-Dodecane. Journal of Energy Resources Technology, 139(1). doi:10.1115/1.4035071Maes, N., Bakker, P. C., Dam, N., & Somers, B. (2017). Transient Flame Development in a Constant-Volume Vessel Using a Split-Scheme Injection Strategy. SAE International Journal of Fuels and Lubricants, 10(2), 318-327. doi:10.4271/2017-01-0815Moiz, A. A., Ameen, M. M., Lee, S.-Y., & Som, S. (2016). Study of soot production for double injections of n-dodecane in CI engine-like conditions. Combustion and Flame, 173, 123-131. doi:10.1016/j.combustflame.2016.08.005PASTOR, J., JAVIERLOPEZ, J., GARCIA, J., & PASTOR, J. (2008). A 1D model for the description of mixing-controlled inert diesel sprays. Fuel, 87(13-14), 2871-2885. doi:10.1016/j.fuel.2008.04.017Desantes, J. M., Pastor, J. V., García-Oliver, J. M., & Pastor, J. M. (2009). A 1D model for the description of mixing-controlled reacting diesel sprays. Combustion and Flame, 156(1), 234-249. doi:10.1016/j.combustflame.2008.10.008Pastor, J., Garcia-Oliver, J. M., Garcia, A., Zhong, W., Micó, C., & Xuan, T. (2017). An Experimental Study on Diesel Spray Injection into a Non-Quiescent Chamber. SAE International Journal of Fuels and Lubricants, 10(2), 394-406. doi:10.4271/2017-01-0850Settles, G. S. (2001). Schlieren and Shadowgraph Techniques. doi:10.1007/978-3-642-56640-0Pastor, J. V., Payri, R., Garcia-Oliver, J. M., & Briceño, F. J. (2013). Schlieren Methodology for the Analysis of Transient Diesel Flame Evolution. SAE International Journal of Engines, 6(3), 1661-1676. doi:10.4271/2013-24-0041Pastor, J. V., Garcia-Oliver, J. M., Novella, R., & Xuan, T. (2015). Soot Quantification of Single-Hole Diesel Sprays by Means of Extinction Imaging. SAE International Journal of Engines, 8(5), 2068-2077. doi:10.4271/2015-24-2417Pickett, L. M., & Siebers, D. L. (2004). Soot in diesel fuel jets: effects of ambient temperature, ambient density, and injection pressure. Combustion and Flame, 138(1-2), 114-135. doi:10.1016/j.combustflame.2004.04.006Ko¨ylu¨, U. O., & Faeth, G. M. (1994). Optical Properties of Overfire Soot in Buoyant Turbulent Diffusion Flames at Long Residence Times. Journal of Heat Transfer, 116(1), 152-159. doi:10.1115/1.2910849Manin, J., Pickett, L. M., & Skeen, S. A. (2013). Two-Color Diffused Back-Illumination Imaging as a Diagnostic for Time-Resolved Soot Measurements in Reacting Sprays. SAE International Journal of Engines, 6(4), 1908-1921. doi:10.4271/2013-01-2548Choi, M. Y., Mulholland, G. W., Hamins, A., & Kashiwagi, T. (1995). Comparisons of the soot volume fraction using gravimetric and light extinction techniques. Combustion and Flame, 102(1-2), 161-169. doi:10.1016/0010-2180(94)00282-wKnox, B. W., & Genzale, C. L. (2015). Reduced-order numerical model for transient reacting diesel sprays with detailed kinetics. International Journal of Engine Research, 17(3), 261-279. doi:10.1177/1468087415570765Burke, S. P., & Schumann, T. E. W. (1928). Diffusion Flames. Industrial & Engineering Chemistry, 20(10), 998-1004. doi:10.1021/ie50226a005Desantes, J. M., García-Oliver, J. M., Xuan, T., & Vera-Tudela, W. (2017). A study on tip penetration velocity and radial expansion of reacting diesel sprays with different fuels. Fuel, 207, 323-335. doi:10.1016/j.fuel.2017.06.108Nerva, J.-G. (s. f.). An Assessment of fuel physical and chemical properties in the combustion of a Diesel spray. doi:10.4995/thesis/10251/29767Payri, R., Salvador, F. J., Gimeno, J., & Bracho, G. (2008). A NEW METHODOLOGY FOR CORRECTING THE SIGNAL CUMULATIVE PHENOMENON ON INJECTION RATE MEASUREMENTS. Experimental Techniques, 32(1), 46-49. doi:10.1111/j.1747-1567.2007.00188.xPayri, R., Gimeno, J., Novella, R., & Bracho, G. (2016). On the rate of injection modeling applied to direct injection compression ignition engines. International Journal of Engine Research, 17(10), 1015-1030. doi:10.1177/1468087416636281Malbec, L.-M., Eagle, W. E., Musculus, M. P. B., & Schihl, P. (2015). Influence of Injection Duration and Ambient Temperature on the Ignition Delay in a 2.34L Optical Diesel Engine. SAE International Journal of Engines, 9(1), 47-70. doi:10.4271/2015-01-1830Payri, R., García-Oliver, J. M., Xuan, T., & Bardi, M. (2015). A study on diesel spray tip penetration and radial expansion under reacting conditions. Applied Thermal Engineering, 90, 619-629. doi:10.1016/j.applthermaleng.2015.07.042Knox, B. W., & Genzale, C. L. (2017). Scaling combustion recession after end of injection in diesel sprays. Combustion and Flame, 177, 24-36. doi:10.1016/j.combustflame.2016.11.021García-Oliver, J. M., Malbec, L.-M., Toda, H. B., & Bruneaux, G. (2017). A study on the interaction between local flow and flame structure for mixing-controlled Diesel sprays. Combustion and Flame, 179, 157-171. doi:10.1016/j.combustflame.2017.01.02

Solution Approaches for the Management of the Water Resources in Irrigation Water Systems with Fuzzy Costs

[EN] Currently, the management of water networks is key to increase their sustainability. This fact implies that water managers have to develop tools that ease the decision-making process in order to improve the efficiency of irrigation networks, as well as their exploitation costs. The present research proposes a mathematical programming model to optimize the selection of the water sources and the volume over time in water networks, minimizing the operation costs as a function of the water demand and the reservoir capacity. The model, which is based on fuzzy methods, improves the evaluation performed by water managers when they have to decide about the acquisition of the water resources under uncertain costs. Different fuzzy solution approaches have been applied and assessed in terms of model complexity and computational efficiency, showing the solution accomplished for each one. A comparison between different methods was applied in a real water network, reaching a 20% total cost reduction for the best solution.Sanchis, R.; Díaz-Madroñero Boluda, FM.; López Jiménez, PA.; Pérez-Sánchez, M. (2019). Solution Approaches for the Management of the Water Resources in Irrigation Water Systems with Fuzzy Costs. Water. 11(12):1-22. https://doi.org/10.3390/w11122432S1221112Biswas, A. K. (2004). Integrated Water Resources Management: A Reassessment. Water International, 29(2), 248-256. doi:10.1080/02508060408691775Pahl-Wostl, C. (2006). Transitions towards adaptive management of water facing climate and global change. Water Resources Management, 21(1), 49-62. doi:10.1007/s11269-006-9040-4Wu, K., & Zhang, L. (2014). Progress in the Development of Environmental Risk Assessment as a Tool for the Decision-Making Process. Journal of Service Science and Management, 07(02), 131-143. doi:10.4236/jssm.2014.72011Hernández-Bedolla, J., Solera, A., Paredes-Arquiola, J., Pedro-Monzonís, M., Andreu, J., & Sánchez-Quispe, S. (2017). The Assessment of Sustainability Indexes and Climate Change Impacts on Integrated Water Resource Management. Water, 9(3), 213. doi:10.3390/w9030213Hunink, J., Simons, G., Suárez-Almiñana, S., Solera, A., Andreu, J., Giuliani, M., … Bastiaanssen, W. (2019). A Simplified Water Accounting Procedure to Assess Climate Change Impact on Water Resources for Agriculture across Different European River Basins. Water, 11(10), 1976. doi:10.3390/w11101976Pérez-Sánchez, M., Sánchez-Romero, F., Ramos, H., & López-Jiménez, P. (2016). Modeling Irrigation Networks for the Quantification of Potential Energy Recovering: A Case Study. Water, 8(6), 234. doi:10.3390/w8060234Corominas, J. (2010). Agua y energía en el riego, en la época de la sostenibilidad. Ingeniería del agua, 17(3). doi:10.4995/ia.2010.2977Romero, L., Pérez-Sánchez, M., & Amparo López-Jiménez, P. (2017). Improvement of sustainability indicators when traditional water management changes: a case study in Alicante (Spain). AIMS Environmental Science, 4(3), 502-522. doi:10.3934/environsci.2017.3.502Davies, E. G. R., & Simonovic, S. P. (2011). Global water resources modeling with an integrated model of the social–economic–environmental system. Advances in Water Resources, 34(6), 684-700. doi:10.1016/j.advwatres.2011.02.010ALCAMO, J., DÖLL, P., HENRICHS, T., KASPAR, F., LEHNER, B., RÖSCH, T., & SIEBERT, S. (2003). Development and testing of the WaterGAP 2 global model of water use and availability. Hydrological Sciences Journal, 48(3), 317-337. doi:10.1623/hysj.48.3.317.45290Sanchis, R., & Poler, R. (2019). Enterprise Resilience Assessment—A Quantitative Approach. Sustainability, 11(16), 4327. doi:10.3390/su11164327Rahaman, M. M., & Varis, O. (2005). Integrated water resources management: evolution, prospects and future challenges. Sustainability: Science, Practice and Policy, 1(1), 15-21. doi:10.1080/15487733.2005.11907961Markantonis, V., Reynaud, A., Karabulut, A., El Hajj, R., Altinbilek, D., Awad, I. M., … Bidoglio, G. (2019). Can the Implementation of the Water-Energy-Food Nexus Support Economic Growth in the Mediterranean Region? The Current Status and the Way Forward. Frontiers in Environmental Science, 7. doi:10.3389/fenvs.2019.00084Food and Agriculture Organization (FAO)www.fao.orgDirective 2000/60/EC of the European Parliament and of the Councilhttps://eur-lex.europa.eu/eli/dir/2000/60/ojNamany, S., Al-Ansari, T., & Govindan, R. (2019). Sustainable energy, water and food nexus systems: A focused review of decision-making tools for efficient resource management and governance. Journal of Cleaner Production, 225, 610-626. doi:10.1016/j.jclepro.2019.03.304Archibald, T. W., & Marshall, S. E. (2018). Review of Mathematical Programming Applications in Water Resource Management Under Uncertainty. Environmental Modeling & Assessment, 23(6), 753-777. doi:10.1007/s10666-018-9628-0Chen, S., Shao, D., Gu, W., Xu, B., Li, H., & Fang, L. (2017). An interval multistage water allocation model for crop different growth stages under inputs uncertainty. Agricultural Water Management, 186, 86-97. doi:10.1016/j.agwat.2017.03.001Xie, Y. L., Xia, D. H., Huang, G. H., Li, W., & Xu, Y. (2015). A multistage stochastic robust optimization model with fuzzy probability distribution for water supply management under uncertainty. Stochastic Environmental Research and Risk Assessment, 31(1), 125-143. doi:10.1007/s00477-015-1164-8Heumesser, C., Fuss, S., Szolgayová, J., Strauss, F., & Schmid, E. (2012). Investment in Irrigation Systems under Precipitation Uncertainty. Water Resources Management, 26(11), 3113-3137. doi:10.1007/s11269-012-0053-xPereira-Cardenal, S. J., Mo, B., Riegels, N. D., Arnbjerg-Nielsen, K., & Bauer-Gottwein, P. (2015). Optimization of Multipurpose Reservoir Systems Using Power Market Models. Journal of Water Resources Planning and Management, 141(8), 04014100. doi:10.1061/(asce)wr.1943-5452.0000500Kumari, S., & Mujumdar, P. P. (2017). Fuzzy Set–Based System Performance Evaluation of an Irrigation Reservoir System. Journal of Irrigation and Drainage Engineering, 143(5), 04017002. doi:10.1061/(asce)ir.1943-4774.0001155Jairaj, P. G., & Vedula, S. (2000). Water Resources Management, 14(6), 457-472. doi:10.1023/a:1011117918943Li, M., Guo, P., Singh, V. P., & Zhao, J. (2016). Irrigation Water Allocation Using an Inexact Two-Stage Quadratic Programming with Fuzzy Input under Climate Change. JAWRA Journal of the American Water Resources Association, 52(3), 667-684. doi:10.1111/1752-1688.12415Bozorg-Haddad, O., Malmir, M., Mohammad-Azari, S., & Loáiciga, H. A. (2016). Estimation of farmers’ willingness to pay for water in the agricultural sector. Agricultural Water Management, 177, 284-290. doi:10.1016/j.agwat.2016.08.011Raju, K. S., & Duckstein, L. (2003). Multiobjective fuzzy linear programming for sustainable irrigation planning: an Indian case study. Soft Computing - A Fusion of Foundations, Methodologies and Applications, 7(6), 412-418. doi:10.1007/s00500-002-0230-6Regulwar, D. G., & Gurav, J. B. (2012). Sustainable Irrigation Planning with Imprecise Parameters under Fuzzy Environment. Water Resources Management, 26(13), 3871-3892. doi:10.1007/s11269-012-0109-yMula, J., Poler, R., & Garcia-Sabater, J. P. (2008). Capacity and material requirement planning modelling by comparing deterministic and fuzzy models. International Journal of Production Research, 46(20), 5589-5606. doi:10.1080/00207540701413912Díaz-Madroñero, M., Mula, J., Jiménez, M., & Peidro, D. (2016). A rolling horizon approach for material requirement planning under fuzzy lead times. International Journal of Production Research, 55(8), 2197-2211. doi:10.1080/00207543.2016.1223382Mula, J., Poler, R., & Garcia, J. P. (2006). MRP with flexible constraints: A fuzzy mathematical programming approach. Fuzzy Sets and Systems, 157(1), 74-97. doi:10.1016/j.fss.2005.05.045Mula, J., Poler, R., & Garcia-Sabater, J. P. (2007). Material Requirement Planning with fuzzy constraints and fuzzy coefficients. Fuzzy Sets and Systems, 158(7), 783-793. doi:10.1016/j.fss.2006.11.003Díaz-Madroñero, M., Mula, J., & Jiménez, M. (2014). Fuzzy goal programming for material requirements planning under uncertainty and integrity conditions. International Journal of Production Research, 52(23), 6971-6988. doi:10.1080/00207543.2014.920115Pérez-Sánchez, M., Díaz-Madroñero, M., Díaz-Madroñero, D.-M., … Josefa, J. (2017). Mathematical Programming Model for Procurement Selection in Water Irrigation Systems. A Case Study. Journal of Engineering Science and Technology Review, 10(6), 154-162. doi:10.25103/jestr.106.19Herrera, F., & Verdegay, J. L. (1995). Three models of fuzzy integer linear programming. European Journal of Operational Research, 83(3), 581-593. doi:10.1016/0377-2217(93)e0338-xHerrera, F., & Verdegay, J. L. (1996). Fuzzy boolean programming problems with fuzzy costs: A general study. Fuzzy Sets and Systems, 81(1), 57-76. doi:10.1016/0165-0114(94)00324-6Alavidoost, M. H., Babazadeh, H., & Sayyari, S. T. (2016). An interactive fuzzy programming approach for bi-objective straight and U-shaped assembly line balancing problem. Applied Soft Computing, 40, 221-235. doi:10.1016/j.asoc.2015.11.025Torabi, S. A., & Hassini, E. (2008). An interactive possibilistic programming approach for multiple objective supply chain master planning. Fuzzy Sets and Systems, 159(2), 193-214. doi:10.1016/j.fss.2007.08.010Yager, R. R. (1981). A procedure for ordering fuzzy subsets of the unit interval. Information Sciences, 24(2), 143-161. doi:10.1016/0020-0255(81)90017-7Lai, Y.-J., & Hwang, C.-L. (1992). A new approach to some possibilistic linear programming problems. Fuzzy Sets and Systems, 49(2), 121-133. doi:10.1016/0165-0114(92)90318-xZimmermann, H.-J. (1978). Fuzzy programming and linear programming with several objective functions. Fuzzy Sets and Systems, 1(1), 45-55. doi:10.1016/0165-0114(78)90031-3Selim, H., & Ozkarahan, I. (2006). A supply chain distribution network design model: An interactive fuzzy goal programming-based solution approach. The International Journal of Advanced Manufacturing Technology, 36(3-4), 401-418. doi:10.1007/s00170-006-0842-6Bellman, R. E., & Zadeh, L. A. (1970). Decision-Making in a Fuzzy Environment. Management Science, 17(4), B-141-B-164. doi:10.1287/mnsc.17.4.b14

Food Supply without Risk: Multicriteria Analysis of Institutional Conditions of Exporters

[EN] International trade in food knows no borders, hence the need for prevention systems to avoid the consumption of products that are harmful to health. This paper proposes the use of multicriteria risk prevention tools that consider the socioeconomic and institutional conditions of food exporters. We propose the use of three decision-making methods-Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS), Elimination et Choix Traduisant la Realite (ELECTRE), and Cross-Efficiency (CE)-to establish a ranking of countries that export cereals to the European Union, based on structural criteria related to the detection of potential associated risks (notifications, food quality, corruption, environmental sustainability in agriculture, and logistics). In addition, the analysis examines whether the wealth and institutional capacity of supplier countries influence their position in the ranking. The research was carried out biannually over the period from 2012-2016, allowing an assessment to be made of the possible stability of the markets. The results reveal that suppliers' rankings based exclusively on aspects related to food risk differ from importers' actual choices determined by micro/macroeconomic features (price, production volume, and economic growth). The rankings obtained by the three proposed methods are not the same, but present certain similarities, with the ability to discern countries according to their level of food risk. The proposed methodology can be applied to support sourcing strategies. In the future, food safety considerations could have increased influence in importing decisions, which would involve further difficulties for low-income countries.Ministry of Science and Innovation (Spain) and European Commission-ERDF. Project "Strengthening innovation policy in the agri-food sector" (RTI2018-093791-B-C22).Puertas Medina, RM.; Martí Selva, ML.; García Alvarez-Coque, JM. (2020). Food Supply without Risk: Multicriteria Analysis of Institutional Conditions of Exporters. International Journal of Environmental research and Public Health. 17(10):1-21. https://doi.org/10.3390/ijerph17103432S1211710Walker, E., & Jones, N. (2002). An assessment of the value of documenting food safety in small and less developed catering businesses. Food Control, 13(4-5), 307-314. doi:10.1016/s0956-7135(02)00036-1Sun, Y.-M., & Ockerman, H. W. (2005). A review of the needs and current applications of hazard analysis and critical control point (HACCP) system in foodservice areas. Food Control, 16(4), 325-332. doi:10.1016/j.foodcont.2004.03.012Rohr, J. R., Barrett, C. B., Civitello, D. J., Craft, M. E., Delius, B., DeLeo, G. A., … Tilman, D. (2019). Emerging human infectious diseases and the links to global food production. Nature Sustainability, 2(6), 445-456. doi:10.1038/s41893-019-0293-3De Jonge, J., van Trijp, J. C. M., van der Lans, I. A., Renes, R. J., & Frewer, L. J. (2008). How trust in institutions and organizations builds general consumer confidence in the safety of food: A decomposition of effects. Appetite, 51(2), 311-317. doi:10.1016/j.appet.2008.03.008Neill, C. L., & Holcomb, R. B. (2019). Does a food safety label matter? Consumer heterogeneity and fresh produce risk perceptions under the Food Safety Modernization Act. Food Policy, 85, 7-14. doi:10.1016/j.foodpol.2019.04.001Wood, V. R., & Robertson, K. R. (2000). Evaluating international markets. International Marketing Review, 17(1), 34-55. doi:10.1108/02651330010314704Jouanjean, M.-A., Maur, J.-C., & Shepherd, B. (2015). Reputation matters: Spillover effects for developing countries in the enforcement of US food safety measures. Food Policy, 55, 81-91. doi:10.1016/j.foodpol.2015.06.001Van Ruth, S. M., Huisman, W., & Luning, P. A. (2017). Food fraud vulnerability and its key factors. Trends in Food Science & Technology, 67, 70-75. doi:10.1016/j.tifs.2017.06.017Baylis, K., Nogueira, L., & Pace, K. (2010). Food Import Refusals: Evidence from the European Union. American Journal of Agricultural Economics, 93(2), 566-572. doi:10.1093/ajae/aaq149Bouzembrak, Y., & Marvin, H. J. P. (2016). Prediction of food fraud type using data from Rapid Alert System for Food and Feed (RASFF) and Bayesian network modelling. Food Control, 61, 180-187. doi:10.1016/j.foodcont.2015.09.026Tudela-Marco, L., Garcia-Alvarez-Coque, J. M., & Martí-Selva, L. (2016). Do EU Member States Apply Food Standards Uniformly? A Look at Fruit and Vegetable Safety Notifications. JCMS: Journal of Common Market Studies, 55(2), 387-405. doi:10.1111/jcms.12503Verhaelen, K., Bauer, A., Günther, F., Müller, B., Nist, M., Ülker Celik, B., … Wallner, P. (2018). Anticipation of food safety and fraud issues: ISAR - A new screening tool to monitor food prices and commodity flows. Food Control, 94, 93-101. doi:10.1016/j.foodcont.2018.06.029Garcia‐Alvarez‐Coque, J., Taghouti, I., & Martinez‐Gomez, V. (2020). Changes in Aflatoxin Standards: Implications for EU Border Controls of Nut Imports. Applied Economic Perspectives and Policy, 42(3), 524-541. doi:10.1093/aepp/ppy036Fischer, A. R. H., de Jong, A. E. I., de Jonge, R., Frewer, L. J., & Nauta, M. J. (2005). Improving Food Safety in the Domestic Environment: The Need for a Transdisciplinary Approach. Risk Analysis, 25(3), 503-517. doi:10.1111/j.1539-6924.2005.00618.xHoughton, J. R., Rowe, G., Frewer, L. J., Van Kleef, E., Chryssochoidis, G., Kehagia, O., … Strada, A. (2008). The quality of food risk management in Europe: Perspectives and priorities. Food Policy, 33(1), 13-26. doi:10.1016/j.foodpol.2007.05.001Demortain, D. (2012). Enabling global principle-based regulation: The case of risk analysis in the Codex Alimentarius. Regulation & Governance, 6(2), 207-224. doi:10.1111/j.1748-5991.2012.01144.xFAZIL, A., RAJIC, A., SANCHEZ, J., & MCEWEN, S. (2008). Choices, Choices: The Application of Multi-Criteria Decision Analysis to a Food Safety Decision-Making Problem. Journal of Food Protection, 71(11), 2323-2333. doi:10.4315/0362-028x-71.11.2323Ruzante, J. M., Davidson, V. J., Caswell, J., Fazil, A., Cranfield, J. A. L., Henson, S. J., … Farber, J. M. (2010). A Multifactorial Risk Prioritization Framework for Foodborne Pathogens. Risk Analysis, 30(5), 724-742. doi:10.1111/j.1539-6924.2009.01278.xMazzocchi, M., Ragona, M., & Zanoli, A. (2013). A fuzzy multi-criteria approach for the ex-ante impact assessment of food safety policies. Food Policy, 38, 177-189. doi:10.1016/j.foodpol.2012.11.011Govindan, K., Kadziński, M., & Sivakumar, R. (2017). Application of a novel PROMETHEE-based method for construction of a group compromise ranking to prioritization of green suppliers in food supply chain. Omega, 71, 129-145. doi:10.1016/j.omega.2016.10.004Segura, M., Maroto, C., & Segura, B. (2019). Quantifying the Sustainability of Products and Suppliers in Food Distribution Companies. Sustainability, 11(21), 5875. doi:10.3390/su11215875Lau, H., Nakandala, D., & Shum, P. K. (2018). A business process decision model for fresh-food supplier evaluation. Business Process Management Journal, 24(3), 716-744. doi:10.1108/bpmj-01-2016-0015Garcia-Alvarez-Coque, J.-M., Abdullateef, O., Fenollosa, L., Ribal, J., Sanjuan, N., & Soriano, J. M. (2020). Integrating sustainability into the multi-criteria assessment of urban dietary patterns. Renewable Agriculture and Food Systems, 36(1), 69-76. doi:10.1017/s174217051900053xGrant, W. (2012). Economic patriotism in European agriculture. Journal of European Public Policy, 19(3), 420-434. doi:10.1080/13501763.2011.640797Maye, D., & Kirwan, J. (2013). Food security: A fractured consensus. Journal of Rural Studies, 29, 1-6. doi:10.1016/j.jrurstud.2012.12.001Anthony, R. (2011). Taming the Unruly Side of Ethics: Overcoming Challenges of a Bottom-Up Approach to Ethics in the Areas of Food Policy and Climate Change. Journal of Agricultural and Environmental Ethics, 25(6), 813-841. doi:10.1007/s10806-011-9358-7MacMillan, T., & Dowler, E. (2011). Just and Sustainable? Examining the Rhetoric and Potential Realities of UK Food Security. Journal of Agricultural and Environmental Ethics, 25(2), 181-204. doi:10.1007/s10806-011-9304-8Jaud, M., Cadot, O., & Suwa-Eisenmann, A. (2013). Do food scares explain supplier concentration? An analysis of EU agri-food imports. European Review of Agricultural Economics, 40(5), 873-890. doi:10.1093/erae/jbs038Spink, J., Fortin, N. D., Moyer, D. C., Miao, H., & Wu, Y. (2016). Food Fraud Prevention: Policy, Strategy, and Decision-Making – Implementation Steps for a Government Agency or Industry. CHIMIA International Journal for Chemistry, 70(5), 320-328. doi:10.2533/chimia.2016.320Van Ruth, S. M., Luning, P. A., Silvis, I. C. J., Yang, Y., & Huisman, W. (2018). Differences in fraud vulnerability in various food supply chains and their tiers. Food Control, 84, 375-381. doi:10.1016/j.foodcont.2017.08.020Xidonas, P., & Psarras, J. (2009). Equity portfolio management within the MCDM frame: a literature review. International Journal of Banking, Accounting and Finance, 1(3), 285. doi:10.1504/ijbaaf.2009.022717Melo, M. T., Nickel, S., & Saldanha-da-Gama, F. (2009). Facility location and supply chain management – A review. European Journal of Operational Research, 196(2), 401-412. doi:10.1016/j.ejor.2008.05.007Mandic, K., Delibasic, B., Knezevic, S., & Benkovic, S. (2014). Analysis of the financial parameters of Serbian banks through the application of the fuzzy AHP and TOPSIS methods. Economic Modelling, 43, 30-37. doi:10.1016/j.econmod.2014.07.036Uygun, Ö., Kaçamak, H., & Kahraman, Ü. A. (2015). An integrated DEMATEL and Fuzzy ANP techniques for evaluation and selection of outsourcing provider for a telecommunication company. Computers & Industrial Engineering, 86, 137-146. doi:10.1016/j.cie.2014.09.014Wanke, P., Azad, M. D. A. K., & Barros, C. P. (2016). Predicting efficiency in Malaysian Islamic banks: A two-stage TOPSIS and neural networks approach. Research in International Business and Finance, 36, 485-498. doi:10.1016/j.ribaf.2015.10.002Stojčić, M., Zavadskas, E., Pamučar, D., Stević, Ž., & Mardani, A. (2019). Application of MCDM Methods in Sustainability Engineering: A Literature Review 2008–2018. Symmetry, 11(3), 350. doi:10.3390/sym11030350Xu, L., Shah, S. A. A., Zameer, H., & Solangi, Y. A. (2019). Evaluating renewable energy sources for implementing the hydrogen economy in Pakistan: a two-stage fuzzy MCDM approach. Environmental Science and Pollution Research, 26(32), 33202-33215. doi:10.1007/s11356-019-06431-0Huang, I. B., Keisler, J., & Linkov, I. (2011). Multi-criteria decision analysis in environmental sciences: Ten years of applications and trends. Science of The Total Environment, 409(19), 3578-3594. doi:10.1016/j.scitotenv.2011.06.022Pons, O., de la Fuente, A., & Aguado, A. (2016). The Use of MIVES as a Sustainability Assessment MCDM Method for Architecture and Civil Engineering Applications. Sustainability, 8(5), 460. doi:10.3390/su8050460Shishegaran, A., Shishegaran, A., Mazzulla, G., & Forciniti, C. (2020). A Novel Approach for a Sustainability Evaluation of Developing System Interchange: The Case Study of the Sheikhfazolah-Yadegar Interchange, Tehran, Iran. International Journal of Environmental Research and Public Health, 17(2), 435. doi:10.3390/ijerph17020435Wu, H.-Y., Chen, J.-K., Chen, I.-S., & Zhuo, H.-H. (2012). Ranking universities based on performance evaluation by a hybrid MCDM model. Measurement, 45(5), 856-880. doi:10.1016/j.measurement.2012.02.009Shakouri G., H., & Tavassoli N., Y. (2012). Implementation of a hybrid fuzzy system as a decision support process: A FAHP–FMCDM–FIS composition. Expert Systems with Applications, 39(3), 3682-3691. doi:10.1016/j.eswa.2011.09.063Mavi, R. K., Goh, M., & Mavi, N. K. (2016). Supplier Selection with Shannon Entropy and Fuzzy TOPSIS in the Context of Supply Chain Risk Management. Procedia - Social and Behavioral Sciences, 235, 216-225. doi:10.1016/j.sbspro.2016.11.017Montgomery, B., Dragićević, S., Dujmović, J., & Schmidt, M. (2016). A GIS-based Logic Scoring of Preference method for evaluation of land capability and suitability for agriculture. Computers and Electronics in Agriculture, 124, 340-353. doi:10.1016/j.compag.2016.04.013Debnath, A., Roy, J., Kar, S., Zavadskas, E., & Antucheviciene, J. (2017). A Hybrid MCDM Approach for Strategic Project Portfolio Selection of Agro By-Products. Sustainability, 9(8), 1302. doi:10.3390/su9081302Seyedmohammadi, J., Sarmadian, F., Jafarzadeh, A. A., Ghorbani, M. A., & Shahbazi, F. (2018). Application of SAW, TOPSIS and fuzzy TOPSIS models in cultivation priority planning for maize, rapeseed and soybean crops. Geoderma, 310, 178-190. doi:10.1016/j.geoderma.2017.09.012Rostamzadeh, R., Ghorabaee, M. K., Govindan, K., Esmaeili, A., & Nobar, H. B. K. (2018). Evaluation of sustainable supply chain risk management using an integrated fuzzy TOPSIS- CRITIC approach. Journal of Cleaner Production, 175, 651-669. doi:10.1016/j.jclepro.2017.12.071Raut, R. D., Gardas, B. B., Kharat, M., & Narkhede, B. (2018). Modeling the drivers of post-harvest losses – MCDM approach. Computers and Electronics in Agriculture, 154, 426-433. doi:10.1016/j.compag.2018.09.035Qureshi, M. R. N., Singh, R. K., & Hasan, M. A. (2017). Decision support model to select crop pattern for sustainable agricultural practices using fuzzy MCDM. Environment, Development and Sustainability, 20(2), 641-659. doi:10.1007/s10668-016-9903-7Srinivasa Rao, C., Kareemulla, K., Krishnan, P., Murthy, G. R. K., Ramesh, P., Ananthan, P. S., & Joshi, P. K. (2019). Agro-ecosystem based sustainability indicators for climate resilient agriculture in India: A conceptual framework. Ecological Indicators, 105, 621-633. doi:10.1016/j.ecolind.2018.06.038Paul, M., Negahban-Azar, M., Shirmohammadi, A., & Montas, H. (2020). Assessment of agricultural land suitability for irrigation with reclaimed water using geospatial multi-criteria decision analysis. Agricultural Water Management, 231, 105987. doi:10.1016/j.agwat.2019.105987Balezentis, T., Chen, X., Galnaityte, A., & Namiotko, V. (2020). Optimizing crop mix with respect to economic and environmental constraints: An integrated MCDM approach. Science of The Total Environment, 705, 135896. doi:10.1016/j.scitotenv.2019.135896Jahan, A., & Edwards, K. L. (2013). VIKOR method for material selection problems with interval numbers and target-based criteria. Materials & Design, 47, 759-765. doi:10.1016/j.matdes.2012.12.072Pourhejazy, P., Kwon, O., Chang, Y.-T., & Park, H. (2017). Evaluating Resiliency of Supply Chain Network: A Data Envelopment Analysis Approach. Sustainability, 9(2), 255. doi:10.3390/su9020255Stewart, T. J. (1996). Relationships between Data Envelopment Analysis and Multicriteria Decision Analysis. Journal of the Operational Research Society, 47(5), 654-665. doi:10.1057/jors.1996.77Li, X.-B., & Reeves, G. R. (1999). A multiple criteria approach to data envelopment analysis. European Journal of Operational Research, 115(3), 507-517. doi:10.1016/s0377-2217(98)00130-1Zavadskas, E. K., Turskis, Z., & Kildienė, S. (2014). STATE OF ART SURVEYS OF OVERVIEWS ON MCDM/MADM METHODS. Technological and Economic Development of Economy, 20(1), 165-179. doi:10.3846/20294913.2014.892037Mousavi-Nasab, S. H., & Sotoudeh-Anvari, A. (2017). A comprehensive MCDM-based approach using TOPSIS, COPRAS and DEA as an auxiliary tool for material selection problems. Materials & Design, 121, 237-253. doi:10.1016/j.matdes.2017.02.041Bouyssou, D. (1999). Using DEA as a tool for MCDM: some remarks. Journal of the Operational Research Society, 50(9), 974-978. doi:10.1057/palgrave.jors.2600800Özcan, T., Çelebi, N., & Esnaf, Ş. (2011). Comparative analysis of multi-criteria decision making methodologies and implementation of a warehouse location selection problem. Expert Systems with Applications, 38(8), 9773-9779. doi:10.1016/j.eswa.2011.02.022LOKEN, E. (2007). Use of multicriteria decision analysis methods for energy planning problems. Renewable and Sustainable Energy Reviews, 11(7), 1584-1595. doi:10.1016/j.rser.2005.11.005Darji, V. P., & Rao, R. V. (2014). Intelligent Multi Criteria Decision Making Methods for Material Selection in Sugar Industry. Procedia Materials Science, 5, 2585-2594. doi:10.1016/j.mspro.2014.07.519Ceballos, B., Lamata, M. T., & Pelta, D. A. (2016). A comparative analysis of multi-criteria decision-making methods. Progress in Artificial Intelligence, 5(4), 315-322. doi:10.1007/s13748-016-0093-1Sen, B., Bhattacharjee, P., & Mandal, U. K. (2016). A comparative study of some prominent multi criteria decision making methods for connecting rod material selection. Perspectives in Science, 8, 547-549. doi:10.1016/j.pisc.2016.06.016Wu, D. (2006). A note on DEA efficiency assessment using ideal point: An improvement of Wang and Luo’s model. Applied Mathematics and Computation, 183(2), 819-830. doi:10.1016/j.amc.2006.06.030Kou, G., Peng, Y., & Wang, G. (2014). Evaluation of clustering algorithms for financial risk analysis using MCDM methods. Information Sciences, 275, 1-12. doi:10.1016/j.ins.2014.02.137Roy, B. (1991). The outranking approach and the foundations of electre methods. Theory and Decision, 31(1), 49-73. doi:10.1007/bf00134132YOON, K., & HWANG, C.-L. (1985). Manufacturing plant location analysis by multiple attribute decision making: part I—single-plant strategy. International Journal of Production Research, 23(2), 345-359. doi:10.1080/00207548508904712Doyle, J., & Green, R. (1994). Efficiency and Cross-efficiency in DEA: Derivations, Meanings and Uses. Journal of the Operational Research Society, 45(5), 567-578. doi:10.1057/jors.1994.84Martí, L., Martín, J. C., & Puertas, R. (2017). A Dea-Logistics Performance Index. Journal of Applied Economics, 20(1), 169-192. doi:10.1016/s1514-0326(17)30008-9Canadá y la UE: Si Quierohttps://www.Euroganadería.euKARABIYIK, C., & KUTLU KARABIYIK, B. (2018). Benchmarking International Trade Performance of OECD Countries: TOPSIS and AHP Approaches. Gaziantep University Journal of Social Sciences. doi:10.21547/jss.267381Lin, M.-C., Wang, C.-C., Chen, M.-S., & Chang, C. A. (2008). Using AHP and TOPSIS approaches in customer-driven product design process. Computers in Industry, 59(1), 17-31. doi:10.1016/j.compind.2007.05.013Lourenzutti, R., & Krohling, R. A. (2016). A generalized TOPSIS method for group decision making with heterogeneous information in a dynamic environment. Information Sciences, 330, 1-18. doi:10.1016/j.ins.2015.10.005Roy, B. (1968). Classement et choix en présence de points de vue multiples. Revue française d’informatique et de recherche opérationnelle, 2(8), 57-75. doi:10.1051/ro/196802v100571Jaini, N., & Utyuzhnikov, S. (2016). Trade-off ranking method for multi-criteria decision analysis. Journal of Multi-Criteria Decision Analysis, 24(3-4), e1600. doi:10.1002/mcda.1600Farrell, M. J. (1957). The Measurement of Productive Efficiency. Journal of the Royal Statistical Society. Series A (General), 120(3), 253. doi:10.2307/2343100Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429-444. doi:10.1016/0377-2217(78)90138-8Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis. Management Science, 30(9), 1078-1092. doi:10.1287/mnsc.30.9.1078Angulo-Meza, L., & Lins, M. P. E. (2002). Annals of Operations Research, 116(1/4), 225-242. doi:10.1023/a:1021340616758Falagario, M., Sciancalepore, F., Costantino, N., & Pietroforte, R. (2012). Using a DEA-cross efficiency approach in public procurement tenders. European Journal of Operational Research, 218(2), 523-529. doi:10.1016/j.ejor.2011.10.031Puertas, R., & Marti, L. (2019). Sustainability in Universities: DEA-GreenMetric. Sustainability, 11(14), 3766. doi:10.3390/su1114376

Revisión de los métodos computerizados para la reconstrucción de fragmentos arqueológicos de cerámica

[ES] Las cerámicas son los hallazgos más numerosos encontrados en las excavaciones arqueológicas; a menudo se usan para obtener información sobre la historia, la economía y el arte de un sitio. Los arqueólogos rara vez encuentran jarrones completos; en general, están dañados y en fragmentos, a menudo mezclados con otros grupos de cerámica.El análisis y la reconstrucción de fragmentos se realiza por un operador experto mediante el uso del método manual tradicional. Los artículos revisados proporcionaron evidencias de que el método tradicional no es reproducible, no es repetible, consume mucho tiempo y sus resultados generan grandes incertidumbres. Con el objetivo de superar los límites anteriores, en los últimos años, los investigadores han realizado esfuerzos para desarrollar métodos informáticos que permitan el análisis de fragmentos arqueológicos de cerámica, todo ello destinado a su reconstrucción. Para contribuir a este campo de estudio, en este artículo, se presenta un análisis exhaustivo de las publicaciones disponibles más importantes hasta finales de 2019. Este estudio, centrado únicamente en fragmentos de cerámica, se realiza mediante la recopilación de artículos en inglés de la base de datos Scopus, utilizando las siguientes palabras clave: "métodos informáticos en arqueología", "arqueología 3D", "reconstrucción 3D", "reconocimiento y reconstrucción automática de características", "restauración de reliquias en forma de cerámica ". La lista se completa con referencias adicionales que se encuentran a través de la lectura de documentos seleccionados. Los 53 trabajos seleccionados se dividen en tres períodos de tiempo. Según una revisión detallada de los estudios realizados, los elementos clave de cada método analizado se enumeran en función de las herramientas de adquisición de datos, las características extraídas, los procesos de clasificación y las técnicas de correspondencia. Finalmente, para superar las brechas reales, se proponen algunas recomendaciones para futuras investigaciones.[EN] Potteries are the most numerous finds found in archaeological excavations; they are often used to get information about the history, economy, and art of a site. Archaeologists rarely find complete vases but, generally, damaged and in fragments, often mixed with other pottery groups. By using the traditional manual method, the analysis and reconstruction of sherds are performed by a skilled operator. Reviewed papers provided evidence that the traditional method is not reproducible, not repeatable, time-consuming and its results have great uncertainties. To overcome the aforementioned limits, in the last years, researchers have made efforts to develop computer-based methods for archaeological ceramic sherds analysis, aimed at their reconstruction. To contribute to this field of study, in this paper, a comprehensive analysis of the most important available publications until the end of 2019 is presented. This study, focused on pottery fragments only, is performed by collecting papers in English by the Scopus database using the following keywords: “computer methods in archaeology", "3D archaeology", "3D reconstruction", "automatic feature recognition and reconstruction", "restoration of pottery shape relics”. The list is completed by additional references found through the reading of selected papers. The 53 selected papers are divided into three periods of time. According to a detailed review of the performed studies, the key elements of each analyzed method are listed based on data acquisition tools, features extracted, classification processes, and matching techniques. Finally, to overcome the actual gaps some recommendations for future researches are proposed.Highlights:The traditional manual method for reassembling sherds is very time-consuming and costly; it also requires a great deal effort from skilled archaeologists in repetitive and routine activities.Computer-based methods for archaeological ceramic sherds reconstruction can help archaeologists in the above-mentioned repetitive and routine activities.In this paper, the state-of-the-art computer-based methods for archaeological ceramic sherds reconstruction are reviewed, and some recommendations for future researches are proposed.Eslami, D.; Di Angelo, L.; Di Stefano, P.; Pane, C. (2020). Review of computer-based methods for archaeological ceramic sherds reconstruction. Virtual Archaeology Review. 11(23):34-49. https://doi.org/10.4995/var.2020.13134OJS34491123Andrews, S., & Laidlaw, D. H. (2002). Toward a framework for assembling broken pottery vessels. In Proceedings of the National Conference on Artificial Intelligence, (August 2003), (pp. 945-946).Banterle, F., Itkin, B., Dellepiane, M., Wolf, L., Callieri, M., Dershowitz, N., & Scopigno, R. (2017). VASESKETCH: Automatic 3D Representation of Pottery from Paper Catalog Drawings. In Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, 1(693548), (pp. 683-690). https://doi.org/10.1109/ICDAR.2017.117Belenguer, C. S., & Vidal, E. V. (2012). Archaeological fragment characterization and 3D reconstruction based on projective GPU depth maps. In Proceedings of the 2012 18th International Conference on Virtual Systems & Multimedia, VSMM 2012: Virtual Systems in the Information Society, (pp. 275-282). https://doi.org/10.1109/VSMM.2012.6365935Blender. (2018). An open-source 3D graphics and animation software. Retrieved from https://www.blender.orgBrown, B. J., Toler-Franklin, C., Nehab, D., Burns, M., Dobkin, D., Vlachopoulos, A., Weyrich, T. (2008). A system for high-volume acquisition and matching of fresco fragments: Reassembling Theran wall paintings. ACM Transactions on Graphics, 27(3). https://doi.org/10.1145/1360612.1360683Cao, Y., & Mumford, D. (2002). Geometric Structure Estimation of Axially Symmetric Pots from Small Fragments. In Proceedings of the signal processing, pattern recognition and applications, IASTED, Crete, Greece, June 25-28, 2002, (pp. 92-97).Cohen, F., Zhang, Z., & Jeppson, P. (2010). Virtual reconstruction of archaeological vessels using convex hulls of surface markings. 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition-Workshops, (pp. 55-61). http://dx.doi.org/10.1109/CVPRW.2010.5543528Cohen, F., Zhang, Z., & Liu, Z. (2016). Mending broken vessels a fusion between color markings and anchor points on surface breaks. Multimedia Tools and Applications, 75(7), 3709-3732. https://doi.org/10.1007/s11042-014-2190-0Cooper, D. B., Willis, A., Andrews, S., Baker, J., Cao, Y., Han, D., … others. (2001). Assembling virtual pots from 3D measurements of their fragments. In Proceedings of the 2001 Conference on Virtual Reality, Archeology, and Cultural Heritage, (pp. 241-254). https://doi.org/10.1145/584993.585032Di Angelo, L., Di Stefano, P., Morabito, A. E., & Pane, C. (2018). Measurement of constant radius geometric features in archaeological pottery. Measurement: Journal of the International Measurement Confederation, 124 (March), 138-146. https://doi.org/10.1016/j.measurement.2018.04.016Di Angelo, L., Di Stefano, P., & Pane, C. (2018). An automatic method for pottery fragments analysis. Measurement: Journal of the International Measurement Confederation, 128, 138-148. https://doi.org/10.1016/j.measurement.2018.06.008Di Angelo, Luca, Di Stefano, P., & Pane, C. (2017). Automatic dimensional characterization of pottery. Journal of Cultural Heritage, 26, 118-128. https://doi.org/10.1016/j.culher.2017.02.003Fragkos, S., Tzimtzimis, E., Tzetzis, D., Dodun, O., & Kyratsis, P. (2018). 3D laser scanning and digital restoration of an archaeological find. MATEC Web of Conferences, 178. https://doi.org/10.1051/matecconf/201817803013Funkhouser, T., Shin, H., Toler-Franklin, C., Castañeda, A. G., Brown, B., Dobkin, D., Weyrich, T. (2011). Learning how to match fresco fragments. Journal on Computing and Cultural Heritage, 4(2). https://doi.org/10.1145/2037820.2037824Halir, R., & Menard, C. (1996). Diameter estimation for archaeological pottery using active vision. In Proceedings of the 20th Workshop of the Austrian Association for Pattern Recognition (OAGM/AAPR) on Pattern Recognition 1996, (pp. 251-261).Halir, R., & Flusser, J. (1997). Estimation of profiles of sherds of archaeological pottery. In Proceedings of the of the Czech Pattern Recognition Workshop (CPRW'97), Czech Republic, February 1997, 1-5, (pp. 126-130).Halir, R. (1999). An Automatic Estimation Of The Axis Of Rotation Of Fragments Of Archaeological Pottery: A Multi-Step Model-Based Approach. In Proceedings of the 7th International Conference in Central Europe on Computer Graphics, Visualization and Interactive Digital Media (WSCG '99) https://semanticscholar.org/0248/ae5a8dca3d2c6bfff282ce481a5625d32362Hall, N. S., & Laflin, S. (1984). A computer aided design technique for pottery profiles. In Computer applications in Archaeology, (pp. 178-188). Computer Center, University of Birmingham Birmingham. Retrieved from https://www.bcin.ca/bcin/detail.app?id=40524Han, D., & Hahn, H. S. (2014). Axis estimation and grouping of rotationally symmetric object segments. Pattern Recognition, 47(1), 296-312. https://doi.org/10.1016/j.patcog.2013.06.022Hlavackova-Schindler, K., Kampel, M., & Sablatnig, R. (2001). Fitting of a Closed Planar Curve Representing a Profile of an Archaeological Fragment. In Proceedings VAST 2001 Virtual Reality, Archeology, and Cultural Heritage, (pp. 263-269). https://doi.org/10.1145/585031.585034Huang, Q. X., Flöry, S., Gelfand, N., Hofer, M., & Pottmann, H. (2006). Reassembling fractured objects by geometric matching. ACM SIGGRAPH 2006 Papers, SIGGRAPH '06, (May), (pp. 569-578). https://doi.org/10.1145/1179352.1141925Igwe, P. C., & Knopf, G. K. (2006). 3D object reconstruction using geometric computing. Geometric Modeling and Imaging New Trends, 9-14. https://doi.org/10.1109/GMAI.2006.1Kalasarinis, I., & Koutsoudis, A. (2019). Assisting pottery restoration procedures with digital technologies. International Journal of Computational Methods in Heritage Science, 3(1), 20-32. https://doi.org/10.4018/ijcmhs.2019010102Kampel, M., & Sablatnig, R. (2003). Profile-based Pottery Reconstruction. In IEEE Proceeding of Conference on Computer Vision and Pattern Recognition Workshops, Wisconsin, June, (pp. 1-6). https://doi.org/10.1109/CVPRW.2003.10007Kampel, M, & Mara, H. (2005). Robust 3D reconstruction of archaeological pottery based on concentric circular rills. In Proceedings of the Sixth International. Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS'05), Montreux, Switzerland, (pp. 14-20). Retrieved from https://semanticscholar.org/43df/9b3c6fef5aa54964bdc4825a86cc4e9f4531Kampel, M., & Sablatnig, R. (2003). An automated pottery archival and reconstruction system. Journal of Visualization and Computer Animation, 14(3), 111-120. https://doi.org/10.1002/vis.310Kampel, M., & Sablatnig, R. (2004). 3D Puzzling of Archeological Fragments. In Proceedings of 9th Computer Vision Winter Workshop, (February), (pp. 31-40). Retrieved from https://cvl.tuwien.ac.at/wp-content/uploads/2014/12/cvww041Karasik, A., & Smilansky, U. (2011). Computerized morphological classification of ceramics. Journal of Archaeological Science, 38(10), 2644-2657. https://doi.org/10.1016/j.jas.2011.05.023Kashihara, K. (2012). Three-dimensional reconstruction of artifacts based on a hybrid genetic algorithm. In IEEE International Conference on Systems, Man and Cybernetics, (pp. 900-905). https://doi.org/10.1109/ICSMC.2012.6377842Kashihara, K. (2017). An intelligent computer assistance system for artifact restoration based on genetic algorithms with plane image features. International Journal of Computational Intelligence and Applications, 16(3), 1-15. https://doi.org/10.1142/S1469026817500213Kleber, F., & Sablatnig, R. (2009). A survey of techniques for document and archaeology artifact reconstruction. In Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, (March 2014), (pp. 1061-1065). https://doi.org/10.1109/ICDAR.2009.154Kotoula, E. (2016). Semiautomatic fragments matching and virtual reconstruction: a case study on ceramics. International Journal of Conservation Science, 7(1), 71-86. Retrieved from http://eprints.lincoln.ac.uk/id/eprint/31035/Lucena, M., Martínez-Carrillo, A. L., Fuertes, J. M., Javier Carrascosa Malagón, F., & Ruiz Rodríguez, A. (2016). Decision support system for classifying archaeological pottery profiles based on mathematical morphology. Multimedia Tools and Applications, 75(7), 3677-3691. https://doi.org/10.1007/s11042-014-2063-6Maiza, C., & Gaildrat, V. (2005). Automatic classification of archaeological potsherds. In Proceedings of the 8th International Conference on Computer Graphics and Artificial Intelligence, Limoges, France, May 11-12, 2005, (pp. 135-147). https://semanticscholar.org/3c95/82c3e562b44e7d61dc0fd3487ea3dc977ff3Mara, H., Kampel, M., & Sablatnig, R. (2002). Preprocessing of 3D-Data for Classification of Archaeological Fragments in an Automated System. In Proceedings of the 26th Workshop of the Austrian Association for Pattern Recognition, Vision with Non-Traditional Sensors, (ÖAGM/AAPR), Graz, Austria, 10-11 September 2002, (pp. 257-264). https://doi.org/10.1.1.15.748Mara, H., & Sablatnig, R. (2006). The orientation of fragments of rotationally symmetrical 3D-shapes for archaeological documentation. In Proceedings - Third International Symposium on 3D Data Processing, Visualization, and Transmission, 3DPVT 2006, (June), (pp. 1064-1071). https://doi.org/10.1109/3DPVT.2006.105Melero, F. J., Torres, J. C., & Leon, A. (2003). On the interactive 3d reconstruction of Iberian vessels. In 4th International Symposium on Virtual Reality, Archaeology, and Intelligent Cultural Heritage, VAST, 3, (pp. 71-78). http://dx.doi.org/10.2312/VAST/VAST03/071-078Papaioannou, G., Karabassi, E. a., & Theoharis, T. (2000). Automatic Reconstruction of Archaeological Finds-A Graphics Approach. In International Conference on Computer Graphics and Artificial Intelligence, (March), (pp. 117-125). Retrieved from https://semanticscholar.org/6a3c/7ec8f544bbfb83174d868cd406eaaf40f438Papaioannou, G., Karabassi, E. A., & Theoharis, T. (2002). Reconstruction of three-dimensional objects through the matching of their parts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(1), 114-124. https://doi.org/10.1109/34.982888Pulli, K. (1999). Multiview registration for large data sets. In Proceedings of Second International Conference on 3D Digital Imaging and Modeling, Ottawa, ON, Canada, 4-8 December 1999, (pp. 160-168). http://doi.org/10.1109/IM.1999.805346Rasheed, N. A., & Nordin, J. (2015a). A Survey of Computer Methods in Reconstruction of 3D Archaeological Pottery Objects. International Journal of Advanced Research, 3(3), 712-714. Retrieved from https://academia.edu.documents/45540231Rasheed, N. A., & Nordin, M. J. (2014). A polynomial function in the automatic reconstruction of fragmented objects. Journal of Computer Science, 10(11), 2339-2348. https://doi.org/10.3844/jcssp.2014.2339.2348Rasheed, N. A., & Nordin, M. J. (2015b). Archaeological fragments classification based on RGB color and texture features. Journal of Theoretical and Applied Information Technology, 76(3), 358-365. Retrieved from http://repository.uobabylon.edu.iq/papers/publication.aspx?pubid=6746Rasheed, N. A., & Nordin, M. J. (2018). Classification and reconstruction algorithms for the archaeological fragments. Journal of King Saud University-Computer and Information Sciences. https://doi.org/10.1016/j.jksuci.2018.09.019Rasheed, N. A., Nordin, M. J., Dakheel, A. H., Nados, W. L., & Maaroof, M. K. A. (2017). Classification archaeological fragments into groups. Research Journal of Applied Sciences, Engineering, and Technology, 14(9), 324-333. https://doi.org/10.19026/rjaset.14.5072Sablatnig, R., & Menard, C. (1997). 3D Reconstruction of Archaeological Pottery using Profile Primitives. In Proceedings of I International Workshop on Synthetic-Natural Hybrid Coding and Three-Dimensional Imaging, (pp. 93-96).Sablatnig, R., Menard, C., & Kropatseh, W. (1998). Classification of archaeological fragments using a description language. In Proceedings of European Signal Processing Conference, (Eusipco '98), (pp. 1097-1100), 1998.Sakpere, W. (2019). 3D Reconstruction of Archaeological Pottery from Its Point Cloud. In Proceedings of Iberian Conference on Pattern Recognition and Image Analysis, (pp. 125-136). https://doi.org/10.1007/978-3-030-31332-6_11Shin, H., Doumas, C., Funkhouser, T., Rusinkiewicz, S., Steiglitz, K.,Vlachopoulos, & Weyrich, T. (2010). Analyzing Fracture Patterns in Theran Wall Paintings. In Proceedings of the 11th International Symposium on Virtual Reality, Archaeology - VAST, (pp. 71-78). https://doi.org/10.2312/VAST/VAST10/071-078Son, K., Almeida, E. B., & Cooper, D. B. (2013). Axially symmetric 3D pots configuration system using the axis of symmetry and break curve. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, (pp. 257-264). https://doi.org/10.1109/CVPR.2013.40Stamatopoulos, M. I., & Anagnostopoulos, C.-N. (2016). 3D digital reassembling of archaeological ceramic pottery fragments based on their thickness profile. The Computing Research Repository (CoRR). Retrieved from https://arxiv.org/abs/1601.05824Toler-Franklin, C., Funkhouser, T., Rusinkiewicz, S., Brown, B., & Weyrich, T. (2010). Multi-Feature Matching of Fresco Fragments. ACM Transactions on Graphics, 29(6), 1-12. https://doi.org/10.1145/1882261.1866207Üçoluk, G., & Hakki Toroslu, I. (1999). Automatic reconstruction of broken 3-D surface objects. Computers and Graphics, 23(4), 573-582. https://doi.org/10.1016/S0097-8493(99)00075-8Vendrell-Vidal, E., & Sánchez-Belenguer, C. (2014). A Discrete Approach for Pairwise Matching of Archaeological Fragments. Journal on Computing and Cultural Heritage, 7(3), 1-19. https://doi.org/10.1145/2597178Willis, A., Orriols, X., & Cooper, D. B. (2003). Accurately Estimating Sherd 3D Surface Geometry with Application to Pot Reconstruction. In Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, (16-22 June 2003), Madison, Wisconsin, USA (pp. 1-7). https://doi.org/10.1109/CVPRW.2003.10014Willis, A. R., & Cooper, D. B. (2004). Bayesian assembly of 3D axially symmetric shapes from fragments. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1, (pp. 82-89). https://doi.org/10.1109/cvpr.2004.1315017Zhou, Mingquam, Geng, G., Wu, Z., Zheng, X., Shui, W., Lu, K., & Gao, Y. (2007). A system for re-assembly of fragment objects and computer-aided restoration of cultural relics. Virtual Retrospect 2007, 3, 21-27. Retrieved from http://hal.univ-savoie.fr/ENIB/hal-01765241v1Zhou, Mingquan, Geng, G., Wu, Z., & Shui, W. (2010). A Virtual Restoration System for Broken Pottery. In Proceedings of the CAA Conference 37th Computer applications and quantitative methods in archaeology, Williamsburg, VA, USA, 22-26 March 2009; (pp. 391-396). Retrieved from https://semanticscholar.org/87b5/aa5c7710806677abbedb4e43f6134e05304

Cellular automata and artificial brain dynamics

[EN] Brain dynamics, neuron activity, information transfer in brains, etc., are a vast field where a large number of questions remain unsolved. Nowadays, computer simulation is playing a key role in the study of such an immense variety of problems. In this work, we explored the possibility of studying brain dynamics using cellular automata, more precisely the famous Game of Life (GoL). The model has some important features (i.e., pseudo-criticality, 1/f noise, universal computing), which represent good reasons for its use in brain dynamics modelling. We have also considered that the model maintains sufficient flexibility. For instance, the timestep is arbitrary, as are the spatial dimensions. As first steps in our study, we used the GoL to simulate the evolution of several neurons (i.e., a statistically significant set, typically a million neurons) and their interactions with the surrounding ones, as well as signal transfer in some simple scenarios. The way that signals (or life) propagate across the grid was described, along with a discussion on how this model could be compared with brain dynamics. Further work and variations of the model were also examined.This work was partially supported by the European Union's Seventh Framework Programme (FP7-REGPOT-2012-2013-1) under grant agreement no 316165. This work was done with the support of the Czech Science Foundation, project 17-17921S.Fraile, A.; Panagiotakis, E.; Christakis, N.; Acedo Rodríguez, L. (2018). Cellular automata and artificial brain dynamics. Mathematical and Computational Applications (Online). 23(4):1-23. https://doi.org/10.3390/mca23040075S123234TURING, A. M. (1950). I.—COMPUTING MACHINERY AND INTELLIGENCE. Mind, LIX(236), 433-460. doi:10.1093/mind/lix.236.433Sarkar, P. (2000). A brief history of cellular automata. ACM Computing Surveys, 32(1), 80-107. doi:10.1145/349194.349202Ermentrout, G. B., & Edelstein-Keshet, L. (1993). Cellular Automata Approaches to Biological Modeling. Journal of Theoretical Biology, 160(1), 97-133. doi:10.1006/jtbi.1993.1007Boccara, N., Roblin, O., & Roger, M. (1994). Automata network predator-prey model with pursuit and evasion. Physical Review E, 50(6), 4531-4541. doi:10.1103/physreve.50.4531Gerhardt, M., & Schuster, H. (1989). A cellular automaton describing the formation of spatially ordered structures in chemical systems. Physica D: Nonlinear Phenomena, 36(3), 209-221. doi:10.1016/0167-2789(89)90081-xZhu, M. F., Lee, S. Y., & Hong, C. P. (2004). Modified cellular automaton model for the prediction of dendritic growth with melt convection. Physical Review E, 69(6). doi:10.1103/physreve.69.061610KANSAL, A. R., TORQUATO, S., HARSH, G. R., CHIOCCA, E. A., & DEISBOECK, T. S. (2000). Simulated Brain Tumor Growth Dynamics Using a Three-Dimensional Cellular Automaton. Journal of Theoretical Biology, 203(4), 367-382. doi:10.1006/jtbi.2000.2000Hopfield, J. J. (1982). Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences, 79(8), 2554-2558. doi:10.1073/pnas.79.8.2554TSOUTSOURAS, V., SIRAKOULIS, G. C., PAVLOS, G. P., & ILIOPOULOS, A. C. (2012). SIMULATION OF HEALTHY AND EPILEPTIFORM BRAIN ACTIVITY USING CELLULAR AUTOMATA. International Journal of Bifurcation and Chaos, 22(09), 1250229. doi:10.1142/s021812741250229xAcedo, L., Lamprianidou, E., Moraño, J.-A., Villanueva-Oller, J., & Villanueva, R.-J. (2015). Firing patterns in a random network cellular automata model of the brain. Physica A: Statistical Mechanics and its Applications, 435, 111-119. doi:10.1016/j.physa.2015.05.017Chialvo, D. R. (2010). Emergent complex neural dynamics. Nature Physics, 6(10), 744-750. doi:10.1038/nphys1803Priesemann, V. (2014). Spike avalanches in vivo suggest a driven, slightly subcritical brain state. Frontiers in Systems Neuroscience, 8. doi:10.3389/fnsys.2014.00108Langton, C. G. (1990). Computation at the edge of chaos: Phase transitions and emergent computation. Physica D: Nonlinear Phenomena, 42(1-3), 12-37. doi:10.1016/0167-2789(90)90064-vFriedman, N., Ito, S., Brinkman, B. A. W., Shimono, M., DeVille, R. E. L., Dahmen, K. A., … Butler, T. C. (2012). Universal Critical Dynamics in High Resolution Neuronal Avalanche Data. Physical Review Letters, 108(20). doi:10.1103/physrevlett.108.208102Kello, C. T. (2013). Critical branching neural networks. Psychological Review, 120(1), 230-254. doi:10.1037/a0030970Werner, G. (2007). Metastability, criticality and phase transitions in brain and its models. Biosystems, 90(2), 496-508. doi:10.1016/j.biosystems.2006.12.001Bak, P., Chen, K., & Creutz, M. (1989). Self-organized criticality in the ’Game of Life". Nature, 342(6251), 780-782. doi:10.1038/342780a0Hemmingsson, J. (1995). Consistent results on ‘Life’. Physica D: Nonlinear Phenomena, 80(1-2), 151-153. doi:10.1016/0167-2789(95)90071-3Nordfalk, J., & Alstrøm, P. (1996). Phase transitions near the «game of Life». Physical Review E, 54(2), R1025-R1028. doi:10.1103/physreve.54.r1025Ninagawa, S., Yoneda, M., & Hirose, S. (1998). 1ƒ fluctuation in the «Game of Life». Physica D: Nonlinear Phenomena, 118(1-2), 49-52. doi:10.1016/s0167-2789(98)00025-6Allegrini, P., Menicucci, D., Bedini, R., Fronzoni, L., Gemignani, A., Grigolini, P., … Paradisi, P. (2009). Spontaneous brain activity as a source of ideal1/fnoise. Physical Review E, 80(6). doi:10.1103/physreve.80.061914Fox, M. D., & Raichle, M. E. (2007). Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nature Reviews Neuroscience, 8(9), 700-711. doi:10.1038/nrn2201Linkenkaer-Hansen, K., Nikouline, V. V., Palva, J. M., & Ilmoniemi, R. J. (2001). Long-Range Temporal Correlations and Scaling Behavior in Human Brain Oscillations. The Journal of Neuroscience, 21(4), 1370-1377. doi:10.1523/jneurosci.21-04-01370.2001Gilden, D., Thornton, T., & Mallon, M. (1995). 1/f noise in human cognition. Science, 267(5205), 1837-1839. doi:10.1126/science.7892611Bédard, C., Kröger, H., & Destexhe, A. (2006). Does the1/fFrequency Scaling of Brain Signals Reflect Self-Organized Critical States? Physical Review Letters, 97(11). doi:10.1103/physrevlett.97.118102Wolfram, S. (1983). Statistical mechanics of cellular automata. Reviews of Modern Physics, 55(3), 601-644. doi:10.1103/revmodphys.55.601“Life Universal Computer”http://www.igblan.free-online.co.uk/igblan/ca/Bagnoli, F., Rechtman, R., & Ruffo, S. (1991). Some facts of life. Physica A: Statistical Mechanics and its Applications, 171(2), 249-264. doi:10.1016/0378-4371(91)90277-jGarcia, J. B. C., Gomes, M. A. F., Jyh, T. I., Ren, T. I., & Sales, T. R. M. (1993). Nonlinear dynamics of the cellular-automaton ‘‘game of Life’’. Physical Review E, 48(5), 3345-3351. doi:10.1103/physreve.48.3345Huang, S.-Y., Zou, X.-W., Tan, Z.-J., & Jin, Z.-Z. (2003). Network-induced nonequilibrium phase transition in the «game of Life». Physical Review E, 67(2). doi:10.1103/physreve.67.026107Blok, H. J., & Bergersen, B. (1999). Synchronous versus asynchronous updating in the «game of Life». Physical Review E, 59(4), 3876-3879. doi:10.1103/physreve.59.3876Schönfisch, B., & de Roos, A. (1999). Synchronous and asynchronous updating in cellular automata. Biosystems, 51(3), 123-143. doi:10.1016/s0303-2647(99)00025-8Reia, S. M., & Kinouchi, O. (2014). Conway’s game of life is a near-critical metastable state in the multiverse of cellular automata. Physical Review E, 89(5). doi:10.1103/physreve.89.052123De la Torre, A. C., & Mártin, H. O. (1997). A survey of cellular automata like the «game of life». Physica A: Statistical Mechanics and its Applications, 240(3-4), 560-570. doi:10.1016/s0378-4371(97)00046-0Beer, R. D. (2004). Autopoiesis and Cognition in the Game of Life. Artificial Life, 10(3), 309-326. doi:10.1162/1064546041255539Beer, R. D. (2014). The Cognitive Domain of a Glider in the Game of Life. Artificial Life, 20(2), 183-206. doi:10.1162/artl_a_00125Yuste, S. B., & Acedo, L. (2000). Number of distinct sites visited byNrandom walkers on a Euclidean lattice. Physical Review E, 61(3), 2340-2347. doi:10.1103/physreve.61.2340Lachaux, J.-P., Pezard, L., Garnero, L., Pelte, C., Renault, B., Varela, F. J., & Martinerie, J. (1997). Spatial extension of brain activity fools the single-channel reconstruction of EEG dynamics. Human Brain Mapping, 5(1), 26-47. doi:10.1002/(sici)1097-0193(1997)5:13.0.co;2-pMcDowell, J. E., Kissler, J. M., Berg, P., Dyckman, K. A., Gao, Y., Rockstroh, B., & Clementz, B. A. (2005). Electroencephalography/magnetoencephalography study of cortical activities preceding prosaccades and antisaccades. NeuroReport, 16(7), 663-668. doi:10.1097/00001756-200505120-00002Holsheimer, J., & Feenstra, B. W. . (1977). Volume conduction and EEG measurements within the brain: A quantitative approach to the influence of electrical spread on the linear relationship of activity measured at different locations. Electroencephalography and Clinical Neurophysiology, 43(1), 52-58. doi:10.1016/0013-4694(77)90194-8Hodgkin, A. L., & Huxley, A. F. (1952). A quantitative description of membrane current and its application to conduction and excitation in nerve. The Journal of Physiology, 117(4), 500-544. doi:10.1113/jphysiol.1952.sp004764Porooshani, H., Porooshani, A. H., Gannon, L., & Kyle, G. M. (2004). Speed of progression of migrainous visual aura measured by sequential field assessment. Neuro-Ophthalmology, 28(2), 101-105. doi:10.1076/noph.28.2.101.23739Hutsler, J. J. (2003). The specialized structure of human language cortex: Pyramidal cell size asymmetries within auditory and language-associated regions of the temporal lobes. Brain and Language, 86(2), 226-242. doi:10.1016/s0093-934x(02)00531-xWilson, H. R., & Cowan, J. D. (1972). Excitatory and Inhibitory Interactions in Localized Populations of Model Neurons. Biophysical Journal, 12(1), 1-24. doi:10.1016/s0006-3495(72)86068-5Conway’s Game of Life. Examples of patternshttps://en.wikipedia.org/wiki/Conway%27s_Game_of_Life#Examples_of_patternsGardner, M. (1970). Mathematical Games. Scientific American, 223(4), 120-123. doi:10.1038/scientificamerican1070-120Packard, N. H., & Wolfram, S. (1985). Two-dimensional cellular automata. Journal of Statistical Physics, 38(5-6), 901-946. doi:10.1007/bf01010423Nunomura, A., Perry, G., Aliev, G., Hirai, K., Takeda, A., Balraj, E. K., … Smith, M. A. (2001). Oxidative Damage Is the Earliest Event in Alzheimer Disease. Journal of Neuropathology & Experimental Neurology, 60(8), 759-767. doi:10.1093/jnen/60.8.759Kitamura, T., Ogawa, S. K., Roy, D. S., Okuyama, T., Morrissey, M. D., Smith, L. M., … Tonegawa, S. (2017). Engrams and circuits crucial for systems consolidation of a memory. Science, 356(6333), 73-78. doi:10.1126/science.aam6808Anderson, P. W. (1972). More Is Different. Science, 177(4047), 393-396. doi:10.1126/science.177.4047.39