A REVIEW OF THE NEOTROPICAL GENUS \u3ci\u3eNEOCORVICOANA\u3c/i\u3e RATCLIFFE AND MICÓ, NEW GENUS (COLEOPTERA: SCARABAEIDAE: CETONIINAE: GYMNETINI)

The southern Neotropical cetoniine genus Neocorvicoana Ratcliffe and Micó new genus (Coleoptera: Scarabaeidae: Cetoniinae: Gymnetini) is established for three species previously placed in Corvicoana Strand, 1934 (nomen nudum) and Gymnetis MacLeay, 1819: N. chalybea (Kirby), N. reticulata (Schürhoff), and N. tricolor (Schürhoff), new combinations. The type species of the new genus is N. reticulata (Kirby). Corvicoana rudolfi (Frölich) is transferred to the genus Gymnetis. Corvicoana suavis (Schürhoff) and C. castanea (Schürhoff) are junior synonyms of N. chalybea. We include a taxonomic key to the species, nomenclatural history, descriptions, illustrations, and commentary. Se establece el género Neocorvicoana Ratcliffe y Micó genero nuevo (Coleoptera: Scarabaeidae: Cetoniinae: Gymnetini) para tres especies de Cetoninos del sur de la región Neotropical, anteriormente incluidos en Corvicoana Strand, 1934 (nomen nudum) y Gymnetis MacLeay, 1819: N. chalybea (Kirby), N. reticulata (Schürhoff), y N. tricolor (Schürhoff), combinaciones nuevas. La especie tipo del nuevo género es N. reticulata (Kirby). Se transfiere Corvicoana rudolfi (Frölich) al género Gymnetis. Corvicoana suavis (Schürhoff) y C. castanea (Schürhoff) son sinónimos de N. chalybea. Se aporta una clave taxonómica de las especies, revisión histórica de la nomenclatura del grupo, ilustraciones y comentarios

Invariant energy in short-term personality dynamics

Caselles, A.; Amigó, S.; Micó, JC. (2020). Invariant energy in short-term personality dynamics. 36-41. http://hdl.handle.net/10251/178203S364

A proposal for quantum short time personality dynamics

Micó, JC.; Amigó, S.; Caselles, A. (2020). A proposal for quantum short time personality dynamics. 102-108. http://hdl.handle.net/10251/178215S10210

Lowest thermal transmittance of an external wall under budget,material and thickness restrictions: An integer linear programming approach

[EN] This paper deals with the minimization of a building¿s external wall thermal transmittance, with theaim of improving the energy efficiency of the building. The wall¿s thermal transmittance must abide bythe current legislation, but also suit the limitations of other construction parameters, mainly budget andthickness, but also time limit, workforce, number and thickness of the layers and availability of materialsdepending on the approach.The optimization is achieved formulating an Integer Linear Programming (ILP) problem involving theparameters mentioned above. Therefore, any available ILP solver can be run to obtain the best combinationof the different materials and thicknesses for the layers, in order to minimize the thermal transmittance.This paper presents a case study of a common but representative external wall consisting of 6 layers,with more than 670,000 possible combinations of materials and their thicknesses. The study concludeswith a comparison of the lowest thermal transmittance obtained for a selection of budget and thicknesscombinations for the mentioned wall.This work was partially supported by the "Ministerio de Economia y Competitividad, Programa Estatal de Investigacion, Desarrollo e Innovacion 582 Orientada a los Retos de la Sociedad, Proyectos I+D+I 2014", Spain, under Grant TEC2014-52690-R.Soler Fernández, D.; Salandin, A.; Micó, JC. (2018). Lowest thermal transmittance of an external wall under budget,material and thickness restrictions: An integer linear programming approach. Energy and Buildings. 158:222-233. https://doi.org/10.1016/j.enbuild.2017.09.078S22223315

A System Dynamics model to predict the impact of COVID-19 in Spain

Sanz, MT.; Caselles, A.; Micó, JC.; Soler, C. (2020). A System Dynamics model to predict the impact of COVID-19 in Spain. 146-151. http://hdl.handle.net/10251/178213S14615

Extending the deterministic Riemann-Liouville and Caputo operators to the random framework: A mean square approach with applications to solve random fractional differential equations

[EN] This paper extends both the deterministic fractional Riemann¿Liouville integral and the Caputo fractional derivative to the random framework using the mean square random calculus. Characterizations and sufficient conditions to guarantee the existence of both fractional random operators are given. Assuming mild conditions on the random input parameters (initial condition, forcing term and diffusion coefficient), the solution of the general random fractional linear differential equation, whose fractional order of the derivative is ¿ ¿ [0, 1], is constructed. The approach is based on a mean square chain rule, recently established, together with the random Fröbenius method. Closed formulae to construct reliable approximations for the mean and the covariance of the solution stochastic process are also given. Several examples illustrating the theoretical results are included.This work has been partially supported by the Ministerio de Economia y Competitividad grant MTM2013-41765-P. The co-author Prof. L. Villafuerte acknowledges the support by Mexican Conacyt.Burgos, C.; Cortés, J.; Villafuerte, L.; Villanueva Micó, RJ. (2017). Extending the deterministic Riemann-Liouville and Caputo operators to the random framework: A mean square approach with applications to solve random fractional differential equations. Chaos, Solitons and Fractals. 102:305-318. https://doi.org/10.1016/j.chaos.2017.02.008S30531810

Biology and personality: a mathematical approach to the body-mind problem

[EN] Purpose ¿ The purpose of this paper is to investigate the body-mind problem from a mathematical invariance principle in relation to personality dynamics in the psychological and the biological levels of description. Design/methodology/approach ¿ The relationship between the two mentioned levels of description is provided by two mathematical models as follows: the response model and the bridge model. The response model (an integro-differential equation) is capable to reproduce the personality dynamics as a consequence of a determined stimulus. The invariance principle asserts that the response model can reproduce personality dynamics at the two levels of description. The bridge model (a second-order partial differential equation) can be deduced as a consequence of this principle: it provides the co-evolution of the general factor of personality (GFP) (mind), the it is an immediate early gene (c-fos) and D3 dopamine receptor gene (DRD3) gens and the glutamate neurotransmitter (body). Findings ¿ An application case is presented by setting up two experimental designs: a previous pilot AB pseudo-experimental design (AB) pseudo-experimental design with one subject and a subsequent ABC experimental design (ABC) experimental design with another subject. The stimulus used is the stimulant drug methylphenidate. The response and bridge models are validated with the outcomes of these experiments. Originality/value ¿ The mathematical approach here presented is based on a holistic personality model developed in the past few years: the unique trait personality theory, which claims for a single personality trait to understand the overall human personality: the GFP. Keywords Integro-differential equation, Body-mind problem, Bridge model, General factor of personality, Response model, Second-order partial differential equation, c-fos, DRD3, Glutamate, Methylphenidate Paper type Research paperMicó, JC.; Amigó, S.; Caselles, A.; Romero, PD. (2021). Biology and personality: a mathematical approach to the body-mind problem. Kybernetes. 50(5):1566-1587. https://doi.org/10.1108/K-03-2020-0138S1566158750

A Methodology for Modeling and Optimizing Social Systems

[EN] A system methodology for modeling and optimizing social systems is presented. It allows constructing dynamical models formulated stochastically, i.e., their results are given by confidence intervals. The models provide optimal intervention ways to reach the stated objectives. Two optimization methods are used: (1) to test strategies and scenarios and (2) to optimize with a genetic algorithm. The application case presented is a small nonformal education Spanish business. First, the model is validated in the 2008-2012 period, and subsequently, the optimal way to obtain a maximum profit in the 2013-2025 period is obtained using the two methods.Caselles, A.; Soler Fernández, D.; Sanz, MT.; Micó, JC. (2020). A Methodology for Modeling and Optimizing Social Systems. Cybernetics & Systems. 51(3):265-314. https://doi.org/10.1080/01969722.2019.1684042S265314513Caselles, A. 1993. System Decomposition and Coupling. Cybernetics and Systems: An International Journal 24 (4):305–323. doi:10.1080/01969729308961712.CASELLES, A. (1994). IMPROVEMENTS IN THE SYSTEMS-BASED MODELS GENERATOR SIGEM. Cybernetics and Systems, 25(1), 81-103. doi:10.1080/01969729408902317Caselles, A., Soler, D., Sanz, M. T., & Micó, J. C. (2014). SIMULATING DEMOGRAPHY AND HUMAN DEVELOPMENT DYNAMICS. Cybernetics and Systems, 45(6), 465-485. doi:10.1080/01969722.2014.929347Djidjeli, K., Price, W. G., Temarel, P., & Twizell, E. H. (1998). Partially implicit schemes for the numerical solutions of some non-linear differential equations. Applied Mathematics and Computation, 96(2-3), 177-207. doi:10.1016/s0096-3003(97)10133-3Gutiérrez, M. M. and H. P. Leone. 2012. DE2M: An environment for developing distributed and executable enterprise models. Advances in Engineering Software 47:80–103. doi:10.1016/j.advengsoft.2011.12.002.SANZ, M. T., MICÓ, J. C., CASELLES, A., & SOLER, D. (2014). A Stochastic Model for Population and Well-Being Dynamics. The Journal of Mathematical Sociology, 38(2), 75-94. doi:10.1080/0022250x.2011.629064Sanz, M. T., Caselles, A., Micó, J. C., & Soler, D. (2016). Including an environmental quality index in a demographic model. International Journal of Global Warming, 9(3), 362. doi:10.1504/ijgw.2016.075448Shannon, R., & Johannes, J. D. (1976). Systems Simulation: The Art and Science. IEEE Transactions on Systems, Man, and Cybernetics, SMC-6(10), 723-724. doi:10.1109/tsmc.1976.430943

Uncertainty quantification analysis of the biological Gompertz model subject to random fluctuations in all its parameters

[EN] In spite of its simple formulation via a nonlinear differential equation, the Gompertz model has been widely applied to describe the dynamics of biological and biophysical parts of complex systems (growth of living organisms, number of bacteria, volume of infected cells, etc.). Its parameters or coefficients and the initial condition represent biological quantities (usually, rates and number of individual/particles, respectively) whose nature is random rather than deterministic. In this paper, we present a complete uncertainty quantification analysis of the randomized Gomperz model via the computation of an explicit expression to the first probability density function of its solution stochastic process taking advantage of the Liouville-Gibbs theorem for dynamical systems. The stochastic analysis is completed by computing other important probabilistic information of the model like the distribution of the time until the solution reaches an arbitrary value of specific interest and the stationary distribution of the solution. Finally, we apply all our theoretical findings to two examples, the first of numerical nature and the second to model the dynamics of weight of a species using real data.This work has been supported by the Spanish Ministerio de Economia, Industria y Competitividad (MINECO), the Agencia Estatal de Investigacion (AEI) and Fondo Europeo de Desarrollo Regional (FEDER UE) grant MTM2017-89664-P.Bevia, V.; Burgos, C.; Cortés, J.; Navarro-Quiles, A.; Villanueva Micó, RJ. (2020). Uncertainty quantification analysis of the biological Gompertz model subject to random fluctuations in all its parameters. Chaos, Solitons and Fractals. 138:1-12. https://doi.org/10.1016/j.chaos.2020.109908S112138Golec, J., & Sathananthan, S. (2003). Stability analysis of a stochastic logistic model. Mathematical and Computer Modelling, 38(5-6), 585-593. doi:10.1016/s0895-7177(03)90029-xCortés, J. C., Jódar, L., & Villafuerte, L. (2009). Random linear-quadratic mathematical models: Computing explicit solutions and applications. Mathematics and Computers in Simulation, 79(7), 2076-2090. doi:10.1016/j.matcom.2008.11.008Dorini, F. A., Cecconello, M. S., & Dorini, L. B. (2016). On the logistic equation subject to uncertainties in the environmental carrying capacity and initial population density. Communications in Nonlinear Science and Numerical Simulation, 33, 160-173. doi:10.1016/j.cnsns.2015.09.009Dorini, F. A., Bobko, N., & Dorini, L. B. (2016). A note on the logistic equation subject to uncertainties in parameters. Computational and Applied Mathematics, 37(2), 1496-1506. doi:10.1007/s40314-016-0409-6Cortés, J.-C., Navarro-Quiles, A., Romero, J.-V., & Roselló, M.-D. (2019). Analysis of random non-autonomous logistic-type differential equations via the Karhunen–Loève expansion and the Random Variable Transformation technique. Communications in Nonlinear Science and Numerical Simulation, 72, 121-138. doi:10.1016/j.cnsns.2018.12.013Calatayud, J., Cortés, J. C., & Jornet, M. (2019). Improving the approximation of the probability density function of random nonautonomous logistic‐type differential equations. Mathematical Methods in the Applied Sciences, 42(18), 7259-7267. doi:10.1002/mma.5834Casabán, M.-C., Cortés, J.-C., Navarro-Quiles, A., Romero, J.-V., Roselló, M.-D., & Villanueva, R.-J. (2016). Probabilistic solution of the homogeneous Riccati differential equation: A case-study by using linearization and transformation techniques. Journal of Computational and Applied Mathematics, 291, 20-35. doi:10.1016/j.cam.2014.11.028Hesam, S., Nazemi, A. R., & Haghbin, A. (2012). Analytical solution for the Fokker–Planck equation by differential transform method. Scientia Iranica, 19(4), 1140-1145. doi:10.1016/j.scient.2012.06.018Lakestani, M., & Dehghan, M. (2009). Numerical solution of Fokker-Planck equation using the cubic B-spline scaling functions. Numerical Methods for Partial Differential Equations, 25(2), 418-429. doi:10.1002/num.20352Mao, X., Yuan, C., & Yin, G. (2005). Numerical method for stationary distribution of stochastic differential equations with Markovian switching. Journal of Computational and Applied Mathematics, 174(1), 1-27. doi:10.1016/j.cam.2004.03.016Casabán, M.-C., Cortés, J.-C., Navarro-Quiles, A., Romero, J.-V., Roselló, M.-D., & Villanueva, R.-J. (2017). Computing probabilistic solutions of the Bernoulli random differential equation. Journal of Computational and Applied Mathematics, 309, 396-407. doi:10.1016/j.cam.2016.02.034Kegan, B., & West, R. W. (2005). Modeling the simple epidemic with deterministic differential equations and random initial conditions. Mathematical Biosciences, 195(2), 179-193. doi:10.1016/j.mbs.2005.02.004Cortés, J.-C., Navarro-Quiles, A., Romero, J.-V., & Roselló, M.-D. (2017). Full solution of random autonomous first-order linear systems of difference equations. Application to construct random phase portrait for planar systems. Applied Mathematics Letters, 68, 150-156. doi:10.1016/j.aml.2016.12.015Cortés, J. C., Navarro‐Quiles, A., Romero, J., & Roselló, M. (2019). (CMMSE2018 paper) Solving the random Pielou logistic equation with the random variable transformation technique: Theory and applications. Mathematical Methods in the Applied Sciences, 42(17), 5708-5717. doi:10.1002/mma.5440Dorini, F. A., & Cunha, M. C. C. (2011). On the linear advection equation subject to random velocity fields. Mathematics and Computers in Simulation, 82(4), 679-690. doi:10.1016/j.matcom.2011.10.008Slama, H., El-Bedwhey, N. A., El-Depsy, A., & Selim, M. M. (2017). Solution of the finite Milne problem in stochastic media with RVT Technique. The European Physical Journal Plus, 132(12). doi:10.1140/epjp/i2017-11763-6Hussein, A., & Selim, M. M. (2013). A general analytical solution for the stochastic Milne problem using Karhunen–Loeve (K–L) expansion. Journal of Quantitative Spectroscopy and Radiative Transfer, 125, 84-92. doi:10.1016/j.jqsrt.2013.03.018Hussein, A., & Selim, M. M. (2019). A complete probabilistic solution for a stochastic Milne problem of radiative transfer using KLE-RVT technique. Journal of Quantitative Spectroscopy and Radiative Transfer, 232, 54-65. doi:10.1016/j.jqsrt.2019.04.034Cortés, J.-C., Jódar, L., Camacho, F., & Villafuerte, L. (2010). Random Airy type differential equations: Mean square exact and numerical solutions. Computers & Mathematics with Applications, 60(5), 1237-1244. doi:10.1016/j.camwa.2010.05.046Bekiryazici, Z., Merdan, M., & Kesemen, T. (2020). Modification of the random differential transformation method and its applications to compartmental models. Communications in Statistics - Theory and Methods, 50(18), 4271-4292. doi:10.1080/03610926.2020.1713372Calatayud, J., Cortés, J.-C., Díaz, J. A., & Jornet, M. (2020). Constructing reliable approximations of the probability density function to the random heat PDE via a finite difference scheme. Applied Numerical Mathematics, 151, 413-424. doi:10.1016/j.apnum.2020.01.012Laird, A. K. (1965). Dynamics of Tumour Growth: Comparison of Growth Rates and Extrapolation of Growth Curve to One Cell. British Journal of Cancer, 19(2), 278-291. doi:10.1038/bjc.1965.32Nahashon, S. N., Aggrey, S. E., Adefope, N. A., Amenyenu, A., & Wright, D. (2006). Growth Characteristics of Pearl Gray Guinea Fowl as Predicted by the Richards, Gompertz, and Logistic Models. Poultry Science, 85(2), 359-363. doi:10.1093/ps/85.2.35