Estado de los suelos y capacidad de uso de la tierra en la finca El Cacao, La Fonseca – Kukra Hill

El presente documento contiene resultados del estudio de suelos en la finca El Cacao, comunidad La Fonseca, municipio de Kukra Hill, referente a los tipos de suelos, sus propiedades físico-químicas, su clasificación taxonómica y las clases de capacidad agrológica, información que permitió generar medidas para el manejo sostenible del recurso suelos. Los suelos encontrados son arcillosos, ácidos a muy ácidos, contenido bajo a medio en materia orgánica, los cuales se desarrollaron a partir de sedimentos, plintita e ignimbrita. Se elaboraron los mapas de Sub grupo Taxonómicos, Clases de Capacidad de Uso de la Tierra y Uso del Suelo. La metodología consistió en tres etapas: planificación de trabajo, fase de campo y procesamiento y análisis de los datos. De acuerdo con el nivel del estudio se realizaron observaciones (barrenadas y calicatas) en un rang o de 1 a 7 observación por km2.Los suelos son muy profundos a pocos profundos, sobre una capa impermeable, en un relieve de plano ha ondulado; presentan saturación de agua y encharcamiento, volviéndose vulnerables ante lixiviación muy prolongada, perdida excesiva de nutrientes y disminución de materia orgánica, una baja capacidad de intercambio catiónica y una satu ración de base baja. Se recomienda que para un aprovechamiento sostenible y amigable con el medio ambiente, se deben manejar los suelos bajo sistemas agroforestales, aplicación de cal y materia orgánica. Estos resultados son un insumo importante para el manejo de la tierra, ya que permite la definición de estrategias y acciones para el aprovechamiento sostenible de los suelos

A fresh look into the interacting dark matter scenario

The elastic scattering between dark matter particles and radiation represents an attractive possibility to solve a number of discrepancies between observations and standard cold dark matter predictions, as the induced collisional damping would imply a suppression of small-scale structures. We consider this scenario and confront it with measurements of the ionization history of the Universe at several redshifts and with recent estimates of the counts of Milky Way satellite galaxies. We derive a conservative upper bound on the dark matter-photon elastic scattering cross section of $\sigma_{\gamma \rm{DM}} < 8 \times 10^{-10} \, \sigma_T \, \left(m_{\rm DM}/{\rm GeV}\right)$ at $95\%$~CL, about one order of magnitude tighter than previous {constraints from satellite number counts}. Due to the strong degeneracies with astrophysical parameters, the bound on the dark matter-photon scattering cross section derived here is driven by the estimate of the number of Milky Way satellite galaxies. Finally, we also argue that future 21~cm probes could help in disentangling among possible non-cold dark matter candidates, such as interacting and warm dark matter scenarios. Let us emphasize that bounds of similar magnitude to the ones obtained here could be also derived for models with dark matter-neutrino interactions and would be as constraining as the tightest limits on such scenarios.Comment: 23 pages, 7 figures. v2: matches the published version. Included discussion on the applicability of constraints derived on dark matter-photon interactions to dark matter-neutrino interactions. References adde

Mass transfer and hydrodynamics in multiphase systems

Tese de doutoramento. Engenharia Química. Faculdade de Engenharia. Universidade do Porto. 200

Computationally efficient analysis of extraordinary optical transmission through infinite and truncated subwavelength hole arrays

The authors present a computationally efficient technique for the analysis of extraordinary transmission through both infinite and truncated periodic arrays of slots in perfect conductor screens of negligible thickness. An integral equation is obtained for the tangential electric field in the slots both in the infinite case and in the truncated case. The unknown functions are expressed as linear combinations of known basis functions, and the unknown weight coefficients are determined by means of Galerkin's method. The coefficients of Galerkin's matrix are obtained in the spatial domain in terms of double finite integrals containing the Green's functions (which, in the infinite case, is efficiently computed by means of Ewald's method) times cross-correlations between both the basis functions and their divergences. The computation in the spatial domain is an efficient alternative to the direct computation in the spectral domain since this latter approach involves the determination of either slowly convergent double infinite summations (infinite case) or slowly convergent double infinite integrals (truncated case). The results obtained are validated by means of commercial software, and it is found that the integral equation technique presented in this paper is at least two orders of magnitude faster than commercial software for a similar accuracy. It is also shown that the phenomena related to periodicity such as extraordinary transmission and Wood's anomaly start to appear in the truncated case for arrays with more than 100 (10×10) slots

Learning how to modify training rates in scene-recognition CNNS

Máster en Image Processing and Computer VisionIn this Master’s Thesis, we pretend to find a measure that allows modifying the value of the learning rate of each individual neuron in a convolutional neural network. Specifically, we aim to handle the effect of the phenomenon known as Catastrophic Forgetting. Starting from a network trained for a source task, this concept refers to the loss in performance that the network undergoes for the source task, when it is trained for a new target task. To this aim, we begin by adapting a neural networks visualization tool to draw conclusions about the behavior and activity of neurons. Using this information, we hypothesize that those neurons with higher activity along the data-set images may be considered useful for the source task and those with lower activity are prone to be treated as free space for the network to learn the target task. To quantitative account for this activity, we leverage on the entropy of the distribution of the neurons’ activities to design weighting functions to dynamically adapt the learning rate of each neuron according to it. In the evaluation section, we compare the results of these functions against a classical fine-tuning strategy focusing on obtaining networks whose joint performance for the source task and the target task is as close as possible to the performance obtained by two different networks fully-trained for each task separately. Obtained results suggest that all of the proposed functions perform better than the fine-tuning strategy in this scope, and some of them perform close to the fullytraining paradigm

Identificación de materiales utilizando el sensor Kinect

En este trabajo de fin de grado se busca conseguir utilizar información frecuencial en una secuencia de imágenes para lograr identificar materiales de forma automática. Además, también se busca estudiar el impacto que puede tener añadir imágenes con información de luz infrarroja y profundidad (aparte de las imágenes RGB) en el desarrollo de esta tarea mencionada. Para ello, se comienza creando un nuevo dataset con ayuda del sensor Kinect, compuesto por imágenes RGB, profundidad e infrarrojo. Se dividen las secuencias grabadas en una parte para entrenamiento y otra parte para evaluación. Aquellas destinadas al entrenamiento del modelo son etiquetadas manualmente para separar los materiales unos de otros. Una vez tengamos los materiales separados, se generan descriptores de esta información mediante la transformada de Fourier y se crean modelos de conocimiento a partir de estos datos, usando para ello el modelo probabilístico Gaussian Mixture Models, de forma que tengamos un modelo por cada material. En la parte de evaluación, se estudia la bondad de nuestros modelos usando para ello las secuencias destinadas a la parte de evaluación. Se realiza la transformada de Fourier de la secuencia completa y se introduce en cada uno de los modelos generados, de forma que devuelve la probabilidad que tiene cada pixel de pertenecer a un objeto. También se estudian posibles modificaciones que se pueden realizar a los descriptores para mejorar la identificación. Por último, se recogen las conclusiones de este trabajo y se menciona el posible trabajo futuro en relación al tema escogido.The objective of this TFG is how to use frequency information in a sequence of images to be able to automatically identify materials. In addition, it also seeks to study the impact of adding images with infrared light and depth information (other than RGB images) in the development of this task. To do this, we start by creating a new dataset with the help of the Kinect sensor, composed of RGB, depth and infrared images. The recorded sequences are divided into two parts: one part for training and another part for test. Those intended for the training of the model are manually labeled to separate the materials from each other. Once we have the materials separated, we generate descriptors of this information using the Fourier transform and create knowledge models from these data, using the probabilistic model Gaussian Mixture Models. In conclusion, we have a model for each material. In the evaluation part, we study the goodness of our models using the sequences assigned to the evaluation part. The Fourier transform of the complete sequence is performed and introduced in each of the generated models, so that it returns the probability that each pixel has to belong to an object. Possible modifications that can be made to descriptors to improve identification are also studied. Finally, we collect the conclusions of this work and mention the possible future work in relation to the chosen task.

Ciudades revisadas: La literatura pos-insular dominicana (1998-2011)

República Dominicana y Cub