Non-linear effects on Turing patterns: time oscillations and chaos.

We show that a model reaction-diffusion system with two species in a monostable regime and over a large region of parameter space, produces Turing patterns coexisting with a limit cycle which cannot be discerned from the linear analysis. As a consequence, Turing patterns oscillate in time, a phenomenon which is expected to occur only in a three morphogen system. When varying a single parameter, a series of bifurcations lead to period doubling, quasi-periodic and chaotic oscillations without modifying the underlying Turing pattern. A Ruelle-Takens-Newhouse route to chaos is identified. We also examined the Turing conditions for obtaining a diffusion driven instability and discovered that the patterns obtained are not necessarily stationary for certain values of the diffusion coefficients. All this results demonstrates the limitations of the linear analysis for reaction-diffusion systems

Exploring Introductory Communication Course Administrators\u27 Relationship Management During COVID-19

The COVID-19 pandemic rapidly changed the context of higher education during the Spring 2020 semester. As the virus began to spread across the United States, colleges and universities canceled in-person classes and activities, closed campus, and moved all operations online. Within the communication discipline, introductory communication course (ICC) administrators and instructors were not only dealing with these challenges, but they were also navigating the transition of large multi-section, often standardized, courses online at large institutions. This research project used semi-structured, in-depth interviews with 18 ICC administrators from institutions located in 14 states across the Midwest, mid-Atlantic, Southeastern, and West Coast regions of the U.S. to explore how they engaged in relationship management with their instructors and how their approach to relationship management informed their transition to remote learning due to COVID-19. The analysis results in four emerging themes: (1) rhetorical approaches to relationship management, (2) relational approaches to relationship management, (3) relationship management → positive outcomes, and (4) relationship management as central to navigating COVID-19. Based on these findings we suggest a rhetorical/relational goals approach to course administration and offer practical implications ICC administrators can implement to engage in successful relationship management during times of crisis

La gestión en la instrumentación de programas de formación y actualización de profesores por la coordinación de formación docente, Facultad de Química, UNAM

En este trabajo se presentan las acciones de gestión que la Coordinación de Formación Docente de la Secretaría de Extensión Académica de la Facultad de Química de la Universidad Nacional Autónoma de México realiza antes, durante y después de la instrumentación de programas de formación y actualización de profesores de ciencias educación básica, en servicio

The Boosted DC Algorithm for Linearly Constrained DC Programming

The Boosted Difference of Convex functions Algorithm (BDCA) has been recently introduced to accelerate the performance of the classical Difference of Convex functions Algorithm (DCA). This acceleration is achieved thanks to an extrapolation step from the point computed by DCA via a line search procedure. In this work, we propose an extension of BDCA that can be applied to difference of convex functions programs with linear constraints, and prove that every cluster point of the sequence generated by this algorithm is a Karush–Kuhn–Tucker point of the problem if the feasible set has a Slater point. When the objective function is quadratic, we prove that any sequence generated by the algorithm is bounded and R-linearly (geometrically) convergent. Finally, we present some numerical experiments where we compare the performance of DCA and BDCA on some challenging problems: to test the copositivity of a given matrix, to solve one-norm and infinity-norm trust-region subproblems, and to solve piecewise quadratic problems with box constraints. Our numerical results demonstrate that this new extension of BDCA outperforms DCA.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. FJAA and RC were partially supported by the Ministry of Science, Innovation and Universities of Spain and the European Regional Development Fund (ERDF) of the European Commission (PGC2018-097960-B-C22), and by the Generalitat Valenciana (AICO/2021/165). PTV was supported by Vietnam Ministry of Education and Training Project hosting by the University of Technology and Education, Ho Chi Minh City Vietnam (2023-2024)

Accelerating the DC algorithm for smooth functions

We introduce two new algorithms to minimise smooth difference of convex (DC) functions that accelerate the convergence of the classical DC algorithm (DCA). We prove that the point computed by DCA can be used to define a descent direction for the objective function evaluated at this point. Our algorithms are based on a combination of DCA together with a line search step that uses this descent direction. Convergence of the algorithms is proved and the rate of convergence is analysed under the Łojasiewicz property of the objective function. We apply our algorithms to a class of smooth DC programs arising in the study of biochemical reaction networks, where the objective function is real analytic and thus satisfies the Łojasiewicz property. Numerical tests on various biochemical models clearly show that our algorithms outperform DCA, being on average more than four times faster in both computational time and the number of iterations. Numerical experiments show that the algorithms are globally convergent to a non-equilibrium steady state of various biochemical networks, with only chemically consistent restrictions on the network topology.F. J. Aragón Artacho was supported by MINECO of Spain and ERDF of EU, as part of the Ramón y Cajal program (RYC-2013-13327) and the Grant MTM2014-59179-C2-1-P. R. M. Fleming and P. T. Vuong were supported by the U.S. Department of Energy, Offices of Advanced Scientific Computing Research and the Biological and Environmental Research as part of the Scientific Discovery Through Advanced Computing program, Grant #DE-SC0010429

The persistent cosmic web and its filamentary structure I: Theory and implementation

We present DisPerSE, a novel approach to the coherent multi-scale identification of all types of astrophysical structures, and in particular the filaments, in the large scale distribution of matter in the Universe. This method and corresponding piece of software allows a genuinely scale free and parameter free identification of the voids, walls, filaments, clusters and their configuration within the cosmic web, directly from the discrete distribution of particles in N-body simulations or galaxies in sparse observational catalogues. To achieve that goal, the method works directly over the Delaunay tessellation of the discrete sample and uses the DTFE density computed at each tracer particle; no further sampling, smoothing or processing of the density field is required. The idea is based on recent advances in distinct sub-domains of computational topology, which allows a rigorous application of topological principles to astrophysical data sets, taking into account uncertainties and Poisson noise. Practically, the user can define a given persistence level in terms of robustness with respect to noise (defined as a "number of sigmas") and the algorithm returns the structures with the corresponding significance as sets of critical points, lines, surfaces and volumes corresponding to the clusters, filaments, walls and voids; filaments, connected at cluster nodes, crawling along the edges of walls bounding the voids. The method is also interesting as it allows for a robust quantification of the topological properties of a discrete distribution in terms of Betti numbers or Euler characteristics, without having to resort to smoothing or having to define a particular scale. In this paper, we introduce the necessary mathematical background and describe the method and implementation, while we address the application to 3D simulated and observed data sets to the companion paper.Comment: A higher resolution version is available at http://www.iap.fr/users/sousbie together with complementary material. Submitted to MNRA

The First VLT FORS1 spectra of Lyman-break candidates in the HDF-S and AXAF Deep Field

We report on low-resolution multi-object spectroscopy of 30 faint targets (R \~ 24-25) in the HDF-S and AXAF deep field obtained with the VLT Focal Reducer/low dispersion Spectrograph (FORS1). Eight high-redshift galaxies with 2.75< z < 4 have been identified. The spectroscopic redshifts are in good agreement with the photometric ones with a dispersion $\sigma_z = 0.07$ at z<2 and $\sigma_z = 0.16$ at z>2. The inferred star formation rates of the individual objects are moderate, ranging from a few to a few tens solar masses per year. Five out of the eight high-z objects do not show prominent emission lines. One object has a spectrum typical of an AGN. In the AXAF field two relatively close pairs of galaxies have been identified, with separations of 8.7 and 3.1 proper Mpc and mean redshifts of 3.11 and 3.93, respectively.Comment: 5 pages Latex, with 2 PostScript figures. Astronomy and Astrophysics, in pres