Relative Quasiconvexity using Fine Hyperbolic Graphs

We provide a new and elegant approach to relative quasiconvexity for relatively hyperbolic groups in the context of Bowditch's approach to relative hyperbolicity using cocompact actions on fine hyperbolic graphs. Our approach to quasiconvexity generalizes the other definitions in the literature that apply only for countable relatively hyperbolic groups. We also provide an elementary and self-contained proof that relatively quasiconvex subgroups are relatively hyperbolic.Comment: 21 pages, 6 figures. New section on fine graphs. Version to appear in AG

Variational Principles for multisymplectic second-order classical field theories

We state a unified geometrical version of the variational principles for second-order classical field theories. The standard Lagrangian and Hamiltonian variational principles and the corresponding field equations are recovered from this unified framework.Comment: 6 pp. Minor corrections. Clarifications and comments have been added. Two new sections ("Introduction" and "The higher-order case") have been added. Bibliography enlarge

Photonic molecules for improving the optical response of macroporous silicon photonic crystals for gas sensing purposes

In this paper, we report the benefits of working with photonic molecules in macroporous silicon photonic crystals. In particular, we theoretically and experimentally demonstrate that the optical properties of a resonant peak produced by a single photonic atom of 2.6 µm wide can be sequentially improved if a second and a third cavity of the same length are introduced in the structure. As a consequence of that, the base of the peak is reduced from 500 nm to 100 nm, while its amplitude remains constant, increasing its Q-factor from its initial value of 25 up to 175. In addition, the bandgap is enlarged almost twice and the noise within it is mostly eliminated. In this study we also provide a way of reducing the amplitude of one or two peaks, depending whether we are in the two- or three-cavity case, by modifying the length of the involved photonic molecules so that the remainder can be used to measure gas by spectroscopic methods.Postprint (published version

Modeling the live-pig trade network in Georgia: Implications for disease prevention and control.

Live pig trade patterns, drivers and characteristics, particularly in backyard predominant systems, remain largely unexplored despite their important contribution to the spread of infectious diseases in the swine industry. A better understanding of the pig trade dynamics can inform the implementation of risk-based and more cost-effective prevention and control programs for swine diseases. In this study, a semi-structured questionnaire elaborated by FAO and implemented to 487 farmers was used to collect data regarding basic characteristics about pig demographics and live-pig trade among villages in the country of Georgia, where very scarce information is available. Social network analysis and exponential random graph models were used to better understand the structure, contact patterns and main drivers for pig trade in the country. Results indicate relatively infrequent (a total of 599 shipments in one year) and geographically localized (median Euclidean distance between shipments = 6.08 km; IQR = 0-13.88 km) pig movements in the studied regions. The main factors contributing to live-pig trade movements among villages were being from the same region (i.e., local trade), usage of a middleman or a live animal market to trade live pigs by at least one farmer in the village, and having a large number of pig farmers in the village. The identified villages' characteristics and structural network properties could be used to inform the design of more cost-effective surveillance systems in a country which pig industry was recently devastated by African swine fever epidemics and where backyard production systems are predominant

Supporting task creation inside FPGA devices

The most common model to use co-processors/accelerators is the master-slave model where the slaves (coprocessors/ accelerators) are driven by a general purpose cpu. This simplifies the management of the accelerators because they cannot actively interact with the runtime and they are just passive slaves that operate over the memory under demand. However, the master-slave model limits system possibilities and introduces synchronization overheads that could be avoided. To overcome those limitations and increase the possibilities of accelerators, we propose extending task based programming models (like OpenMP [1] or OmpSs) to support some runtime APIs inside the FPGA co-processor. As a proof-of-concept, we implemented our proposal over the OmpSs@FPGA environment [2] adding the needed infrastructure in the FPGA bitstream and modifying the existing tools to support creation of children tasks inside a task offloaded to an FPGA accelerator. In addition, we added support to synchronize the children tasks created by a FPGA task regardless they are executed in a SMP host thread or they also target another FPGA accelerator in the same co-processor

Iterative design of dynamic experiments in modeling for optimization of innovative bioprocesses

Finding optimal operating conditions fast with a scarce budget of experimental runs is a key problem to speed up the development and scaling up of innovative bioprocesses. In this paper, a novel iterative methodology for the model-based design of dynamic experiments in modeling for optimization is developed and successfully applied to the optimization of a fed-batch bioreactor related to the production of r-interleukin-11 (rIL-11) whose DNA sequence has been cloned in an Escherichia coli strain. At each iteration, the proposed methodology resorts to a library of tendency models to increasingly bias bioreactor operating conditions towards an optimum. By selecting the ‘most informative’ tendency model in the sequel, the next dynamic experiment is defined by re-optimizing the input policy and calculating optimal sampling times. Model selection is based on minimizing an error measure which distinguishes between parametric and structural uncertainty to selectively bias data gathering towards improved operating conditions. The parametric uncertainty of tendency models is iteratively reduced using Global Sensitivity Analysis (GSA) to pinpoint which parameters are keys for estimating the objective function. Results obtained after just a few iterations are very promising.Fil: Cristaldi, Mariano Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; ArgentinaFil: Grau, Ricardo José Antonio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; ArgentinaFil: Martínez, Ernesto Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentin