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En el siguiente trabajo se presentan las diferentes alternativas tecnológicas que brindarán soporte para las comunicaciones en el Proyecto Campo Conectado, priorizando el bajo consumo y mantenimiento. Esta plataforma de transmisión proveerá la conectividad necesaria para la recolección y envío de los datos requeridos por los productores agropecuarios, con el fin de mejorar la gestión de los procesos productivos y comerciales. Se comenta además los orígenes del proyecto y los actores involucrados en el mismo, destacándose el trabajo interinstitucional e interdisciplinar que se lleva a cabo.
Biomass in general, wood and grasses in particular represent attractive renewable sources for the fabrication of so-called building block chemicals (1). Thus, environmentally benign antimicrobial nanoparticles based on a silver-infused lignin core were recently reported underlying the high potential for valorization of lignin (2). The contribution presents specific correlations regarding the structural differences of lignins depending on both: source (wood vs. grass) and isolation procedure (Kraft vs. Organosolv). Special focus will be drawn on detailed structure deviations caused by Miscanthus genotypes (M. gigantheus, M. robustus, M. sisnensis).
Recent approaches in scaffold engineering for bone defects feature hybrid hydrogels made of a polymeric network (retains water and provides light and porous structures) and inorganic ceramics (add mechanical strength and improve cell-adhesion). Innovative scaffold materials should also induce bone tissue formation and incorporation of stem cells (osteogenic differentiation) and/or growth factors (inducing/supporting differentiation). Recently, purinergic P2X and P2Y receptors have been found to significantly influence the osteogenic differentiation process of human mesenchymal stem cells (hMSC). (1) Aim of this work is to develop polysaccharide (PS) composites to be used as scaffolds containing complementary receptor ligands to enable guided stem cell differentiation towards bone formation.
Impact of atmospheric aerosols on photovoltaic energy production - Scenario for the Sahel zone
(2017)
Photovoltaic (PV) energy is one option to serve the rising global energy need with low environmental impact. PV is of particular interest for local energy solutions in developing countries prone to high solar insolation. In order to assess the PV potential of prospective sites, combining knowledge of the atmospheric state modulating solar radiation and the PV performance is necessary. The present study discusses the PV power as function of atmospheric aerosols in the Sahel zone for clear-sky-days. Daily yields for a polycrystalline silicon PV module are reduced by up to 48 % depending on the climatologically-relevant aerosol abundances.
The MAP-Elites algorithm produces a set of high-performing solutions that vary according to features defined by the user. This technique to 'illuminate' the problem space through the lens of chosen features has the potential to be a powerful tool for exploring design spaces, but is limited by the need for numerous evaluations. The Surrogate-Assisted Illumination (SAIL) algorithm, introduced here, integrates approximative models and intelligent sampling of the objective function to minimize the number of evaluations required by MAP-Elites.
The ability of SAIL to efficiently produce both accurate models and diverse high-performing solutions is illustrated on a 2D airfoil design problem. The search space is divided into bins, each holding a design with a different combination of features. In each bin SAIL produces a better performing solution than MAP-Elites, and requires several orders of magnitude fewer evaluations. The CMA-ES algorithm was used to produce an optimal design in each bin: with the same number of evaluations required by CMA-ES to find a near-optimal solution in a single bin, SAIL finds solutions of similar quality in every bin.
A new method for design space exploration and optimization, Surrogate-Assisted Illumination (SAIL), is presented. Inspired by robotics techniques designed to produce diverse repertoires of behaviors for use in damage recovery, SAIL produces diverse designs that vary according to features specified by the designer. By producing high-performing designs with varied combinations of user-defined features a map of the design space is created. This map illuminates the relationship between the chosen features and performance, and can aid designers in identifying promising design concepts. SAIL is designed for use with compu-tationally expensive design problems, such as fluid or structural dynamics, and integrates approximative models and intelligent sampling of the objective function to minimize the number of function evaluations required. On a 2D airfoil optimization problem SAIL is shown to produce hundreds of diverse designs which perform competitively with those found by state-of-the-art black box optimization. Its capabilities are further illustrated in a more expensive 3D aerodynamic optimization task.
Die allgemeine Konnotation von Technik mit Männlichkeit hat Auswirkungen auf die Berufswahlentscheidungen und das Technikverständnis von jungen Frauen. Nur gut 22 Prozent aller Studierenden in den Ingenieurswissenschaften waren 2014 in Deutschland weiblich (vgl. MonitorING)1. Seit Jahren wird versucht, diese Zahlen nach oben zu korrigieren, indem man Programme für Mädchen und junge Frauen anbietet, die erste Kontakte zu technischen Arbeitsfeldern her stellen. Auch für bereits berufstätige Ingenieurinnen gibt es zahlreiche Förderprogramme, um den Drop-out hochqualiizierter Frauen auf der Karriere leiter zu verhindern. Dennoch verändern sich die prozentualen Anteile von Frauen in ingenieurswissenschaftlichen Studiengängen und Berufen kaum. Aktuelle Studien belegen, dass vor allem kulturell bedingte Erwartungen und Einstellungen hierfür verantwortlich sind (vgl. Paulitz 2012).
Evolutionary illumination is a recent technique that allows producing many diverse, optimal solutions in a map of manually defined features. To support the large amount of objective function evaluations, surrogate model assistance was recently introduced. Illumination models need to represent many more, diverse optimal regions than classical surrogate models. In this PhD thesis, we propose to decompose the sample set, decreasing model complexity, by hierarchically segmenting the training set according to their coordinates in feature space. An ensemble of diverse models can then be trained to serve as a surrogate to illumination.
Ressourceneffiziente Optimierung von Hohlkörpern aus Kunststoff mittels Multiskalensimulation
(2017)
Neuroevolution methods evolve the weights of a neural network, and in some cases the topology, but little work has been done to analyze the effect of evolving the activation functions of individual nodes on network size, an important factor when training networks with a small number of samples. In this work we extend the neuroevolution algorithm NEAT to evolve the activation function of neurons in addition to the topology and weights of the network. The size and performance of networks produced using NEAT with uniform activation in all nodes, or homogenous networks, is compared to networks which contain a mixture of activation functions, or heterogenous networks. For a number of regression and classification benchmarks it is shown that, (1) qualitatively different activation functions lead to different results in homogeneous networks, (2) the heterogeneous version of NEAT is able to select well performing activation functions, (3) the produced heterogeneous networks are significantly smaller than homogeneous networks.
The MAP-Elites algorithm produces a set of high-performing solutions that vary according to features defined by the user. This technique has the potential to be a powerful tool for design space exploration, but is limited by the need for numerous evaluations. The Surrogate-Assisted Illumination algorithm (SAIL), introduced here, integrates approximative models and intelligent sampling of the objective function to minimize the number of evaluations required by MAP-Elites.
The ability of SAIL to efficiently produce both accurate models and diverse high performing solutions is illustrated on a 2D airfoil design problem. The search space is divided into bins, each holding a design with a different combination of features. In each bin SAIL produces a better performing solution than MAP-Elites, and requires several orders of magnitude fewer evaluations. The CMA-ES algorithm was used to produce an optimal design in each bin: with the same number of evaluations required by CMA-ES to find a near-optimal solution in a single bin, SAIL finds solutions of similar quality in every bin.