Desarrollo de sensores de deformación basados en antenas con resonadores sobre materiales flexibles

El avance de las Tecnologías de la Información y las Comunicaciones (TIC) en los últimos tiempos ha despertado el interés de crear sistemas más exactos, sensibles y ubicuos, con el fin de establecer comunicaciones sin interrupciones, efectuar medidas de alguna variable física o transmitir/recibir cualquier tipo de información. En consecuencia, la presente tesis evidencia la creación de antenas de microcinta como sensores de deformación usando como sustrato flexible la lámina “Rogers RO4350B”; razón por la cual se efectuaron investigaciones sobre trabajos en los cuales se mejoraba el desempeño de las antenas a partir de la introducción de anillos resonadores regidos bajo el principio de metamateriales, así como trabajos en los cuales se realizaban pruebas de deformación con sustratos dieléctricos flexibles. Seguidamente, se procedió a diseñar las antenas a partir de la teoría recopilada y una buena parametrización hecha a través del software CST Studio, a partir de lo cual se llevó a cabo la fabricación de las antenas para después realizar su caracterización a través del Analizador de Redes Vectoriales. Finalmente, se efectuaron las respectivas pruebas de deformación experimentales, comparando esos resultados con los obtenidos en la simulación de dichas pruebas. Después de cumplir con lo propuesto anteriormente, se pudo evidenciar que la antena funciona como sensor y que dependiendo de la dirección en que sea aplicada la deformación existe un corrimiento en su frecuencia de resonancia, el cual puede ser a la derecha o la izquierda, es decir, aumentar o disminuir en frecuencia, respectivamenteIngeniero de Telecomunicacionespregrad

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost