Abstract

Abstract
Este estudio plantea una alternativa de consecución de los parámetros eléctricos en modelos de transformadores trifásicos mediante registros obtenidos con lecturas de aparatos de medición en sistemas de protección y monitoreo asociados al transformador eléctrico. El posible uso de este procedimiento se focaliza en la estimación del modelo eléctrico en transformadores trifásicos durante su operación y funcionamiento en un sistema eléctrico y su respuesta frente a eventos transitorios. Esta propuesta permitirá aminorar recursos y uso de tiempo en comparación con metodologías existentes; puesto que, éstas usualmente necesitan que el transformador este fuera de servicio para la conexión de equipo especializado de alto costo, siendo muchas veces necesario trasladar el transformador a un laboratorio especializado.  Los trabajos existentes proporcionan modelos completos del transformador para simulación de eventos transitorios, estos se usaron como datos de partida para sintonizar los parámetros en MATLAB-SIMULINK. Evaluada la efectividad y confiabilidad del estudio, éste será de gran ayuda en la determinación de parámetros eléctricos transitorios en transformadores nuevos o con un tiempo considerable de funcionamiento, pues debido al uso del equipo y las condiciones de operación, los parámetros eléctricos pueden variar con respecto a sus registros de fábrica.

Abstract
Predawn leaf water potential (Psi(pd)) is widely used to assess plant water status. Also, pigments concentration work as proxy of canopy's water status. Spectral data methods have been applied to monitor and assess crop's biophysical variables. This work developed two models to estimate Psi(pd) using a hand-held spectroradiometer (400-1010 nm) to obtain canopy and foliar reflectance in four dates of 2018 and a pressure chamber to measure Psi(pd). Two modelling approaches, combining spectral data and several machine learning algorithms (MLA), were used to estimate Psi(pd) in a commercial vineyard in the Douro Wine Region. The first approach estimated Psi(pd) through vine's canopy reflectance; several vegetation indices (VIs) were computed and selected, namely the SPVi(opt)(1_)(950;596;521;) SPVIopt2_896;880;901; PRI_CI2(opt_539;560,573;716 )and NPCIopt_983;972, as well as a time-dynamic variable based on Psi(pd) (Psi(pd)_(0)). The second modelling approach is based on pigments' concentrations; several VIs were optimized for non-correlated pigments of vine's leaves, assessed by its hyperspectral reflectance. The following variables for Psi(pd) estimation were selected through stepwise forward method: Psi(pd)_(0); NRIgreen_LUT520;532; NRIgreen_LWC540;551. The B-MARS algorithm performed the best results for both modelling approaches, presenting a RRMSE in both validation modelling approaches between 13-14%.

Abstract
In the past decade, we have witnessed the widespread adoption of Deep Neural Networks (DNNs) in several Machine Learning tasks. However, in many critical domains, such as healthcare, finance, or law enforcement, transparency is crucial. In particular, the lack of ability to conform with prior knowledge greatly affects the trustworthiness of predictive models. This paper contributes to the trustworthiness of DNNs by promoting monotonicity. We develop a multi-layer learning architecture that handles a subset of features in a dataset that, according to prior knowledge, have a monotonic relation with the response variable. We use two alternative approaches: (i) imposing constraints on the model's parameters, and (ii) applying an additional component to the loss function that penalises non-monotonic gradients. Our method is evaluated on classification and regression tasks using two datasets. Our model is able to conform to known monotonic relations, improving trustworthiness in decision making, while simultaneously maintaining small and controllable degradation in predictive ability.

Abstract
In the past decades, the resource on the resource-constrained project scheduling problem (RCPSP) has grown rapidly, resulting in an overwhelming amount of solution procedures that provide (near)-optimal solutions in a reasonable time. Despite the rapid progress, little is still known what makes a project instance hard to solve. Inspired by a previous research study that has shown that even small instances with only up to 30 activities is sometimes hard to solve, the current study provides an analysis of the project data used in the academic literature. More precisely, it investigates the ability of four well-known resource indicators to predict the hardness of an RCPSP instance. The study introduces a new instance equivalence concept to show that instances might have very different values for their resource indicators without changing any possible solution for this instance. The concept is based on four theorems and a search algorithm that transforms existing instances into new equivalent instances with more compact resources. This algorithm illustrates that the use of resource indicators to predict the hardness of an instance is sometimes misleading. In a set of computational experiment on more than 10,000 instances, it is shown that the newly constructed equivalent instances have values for the resource indicators that are not only different than the values of the original instances, but also often are better in predicting the hardness the project instances. It is suggested that the new equivalent instances are used for further research to compare results on the new instances with results obtained from the original dataset.

Abstract