Propuesta de uso de last planner como sistema para la enseñanza de gestión del proceso constructivo en el grado en Arquitectura Técnica en la Universidad de Alicante

La enseñanza de Gestión del Proceso Constructivo en el grado de Arquitectura Técnica se realiza actualmente siguiendo un esquema teórico-práctico. A través de clases magistrales se define el proceso constructivo de una edificación y la relación e interdependencia entre los oficios. Estos contenidos se refuerzan en las sesiones prácticas a través de la herramienta gráfica diagrama de Gantt, donde el alumno se enfrenta de manera individual a la planificación de la obra siguiendo los criterios estipulados en la asignatura. Esta metodología no permite a los estudiantes enfrentarse a los problemas frecuentes de re-planificación y gestión de imprevistos en el entorno de la construcción. Como consecuencia, se plantea una propuesta para la implementación de la herramienta Last Planner en la asignatura como sistema de planificación colaborativa basado en la filosofía Lean Construction. A través del uso de Last Planner como técnica de Gamificación se pretende dotar de dinamismo a las sesiones teórico-prácticas. En cada sesión se simularán diferentes escenarios que requieran procesos constructivos variados, favoreciendo la motivación del alumnado, su capacidad para aprender y proponer soluciones justificadas huyendo de soluciones rígidas y estándar, y el trabajo en equipo de forma colaborativa

Resumen de la tarea Rest-Mex en IberLEF 2022: Sistema de Recomendación, Análisis de Sentimiento y Predicción de Semáforo Covid para Textos Turísticos Mexicanos

This paper presents the framework and results from the Rest-Mex task at IberLEF 2022. This task considered three tracks: Recommendation System, Sentiment Analysis and Covid Semaphore Prediction, using texts from Mexican touristic places. The Recommendation System task consists in predicting the degree of satisfaction that a tourist may have when recommending a destination of Nayarit, Mexico, based on places visited by the tourists and their opinions. On the other hand, the Sentiment Analysis task predicts the polarity of an opinion issued and the attraction by a tourist who traveled to the most representative places in Mexico. We have built corpora for both tasks considering Spanish opinions from the TripAdvisor website. As a novelty, the Covid Semaphore Prediction task aims to predict the color of the Mexican Semaphore for each state, according to the Covid news in the state, using data from the Mexican Ministry of Health. This paper compares and discusses the participants’ results for all three tacks.Este artículo presenta el marco y los resultados de la tarea Rest-Mex en IberLEF 2022. Esta tarea consideró tres sub tareas: Sistema de recomendación, Análisis de sentimiento y Predicción de semáforo Covid, utilizando textos de lugares turísticos mexicanos. La tarea del Sistema de Recomendación consiste en predecir el grado de satisfacción que puede tener un turista al recomendar un destino de Nayarit, México, con base en los lugares visitados por los turistas y sus opiniones. Por otro lado, la tarea de Análisis de Sentimiento predice la polaridad de una opinión emitida y la atracción por parte de un turista que viajó a los lugares más representativos de México. Hemos construido corpus para ambas tareas teniendo en cuenta las opiniones en español de TripAdvisor. Como novedad, la tarea de Predicción de Semáforo Covid tiene como objetivo predecir el color del Semáforo Mexicano para cada estado, de acuerdo a las noticias Covid en el estado, utilizando datos de la Secretaría de Salud de México. Este documento compara y discute los resultados de los participantes para las tres sub tareas

The NWCSAF/GEO software package for the MSG/IODC satellite service

Póster presentado en: EUMETSAT Meteorological Satellite Conference celebrada del 2 al 6 de Octubre de 2017 en Roma

Autonomous cortisol secretion in patients with primary aldosteronism: prevalence and implications on cardiometabolic profile and on surgical outcomes

Purpose: The aim of this study was to evaluate the prevalence of autonomous cortisol secretion (ACS) in patients with primary aldosteronism (PA) and its implications on cardiometabolic and surgical outcomes. Methods: This is a retrospective multicenter study of PA patients who underwent 1 mg dexamethasone-suppression test (DST) during diagnostic workup in 21 Spanish tertiary hospitals. ACS was defined as a cortisol post-DST >1.8 μg/dL (confirmed ACS if >5 μg/dL and possible ACS if 1.8–5 μg/dL) in the absence of spe cific clinical features of hypercortisolism. The cardiometabolic profile was compared with a control group with ACS without PA (ACS group) matched for age and DST levels. Results: The prevalence of ACS in the global cohort of patients with PA (n = 176) was 29% (ACS–PA; n = 51). Ten patients had confirmed ACS and 41 possible ACS. The cardiometabolic profile of ACS–PA and PA-only patients was simil ar, except for older age and larger tumor size of the adrenal lesion in the ACS–PA group. When comparing the ACS–PA group (n = 51) and the ACS group (n = 78), the prevalence of hypertension (OR 7.7 (2.64–22.32)) and cardiovascular events (OR 5.0 (2.29–11.07)) was higher in ACS–PA patients than in ACS patients. The coexistence of ACS in patien ts with PA did not affect the surgical outcomes, the proportion of biochemical cure and clinical cure being similar between ACS–PA and PA-only groups. Conclusion: Co-secretion of cortisol and aldosterone affects almost one-thi rd of patients with PA. Its occurrence is more frequent in patients with larger tumors and advanced age. However, the cardiometabolic and surgical outcomes of patients with ACS–PA and PA-only are similar

The evolution of the ventilatory ratio is a prognostic factor in mechanically ventilated COVID-19 ARDS patients

Background: Mortality due to COVID-19 is high, especially in patients requiring mechanical ventilation. The purpose of the study is to investigate associations between mortality and variables measured during the first three days of mechanical ventilation in patients with COVID-19 intubated at ICU admission. Methods: Multicenter, observational, cohort study includes consecutive patients with COVID-19 admitted to 44 Spanish ICUs between February 25 and July 31, 2020, who required intubation at ICU admission and mechanical ventilation for more than three days. We collected demographic and clinical data prior to admission; information about clinical evolution at days 1 and 3 of mechanical ventilation; and outcomes. Results: Of the 2,095 patients with COVID-19 admitted to the ICU, 1,118 (53.3%) were intubated at day 1 and remained under mechanical ventilation at day three. From days 1 to 3, PaO2/FiO2 increased from 115.6 [80.0-171.2] to 180.0 [135.4-227.9] mmHg and the ventilatory ratio from 1.73 [1.33-2.25] to 1.96 [1.61-2.40]. In-hospital mortality was 38.7%. A higher increase between ICU admission and day 3 in the ventilatory ratio (OR 1.04 [CI 1.01-1.07], p = 0.030) and creatinine levels (OR 1.05 [CI 1.01-1.09], p = 0.005) and a lower increase in platelet counts (OR 0.96 [CI 0.93-1.00], p = 0.037) were independently associated with a higher risk of death. No association between mortality and the PaO2/FiO2 variation was observed (OR 0.99 [CI 0.95 to 1.02], p = 0.47). Conclusions: Higher ventilatory ratio and its increase at day 3 is associated with mortality in patients with COVID-19 receiving mechanical ventilation at ICU admission. No association was found in the PaO2/FiO2 variation

VIII Encuentro de Docentes e Investigadores en Historia del Diseño, la Arquitectura y la Ciudad

Acta de congresoLa conmemoración de los cien años de la Reforma Universitaria de 1918 se presentó como una ocasión propicia para debatir el rol de la historia, la teoría y la crítica en la formación y en la práctica profesional de diseñadores, arquitectos y urbanistas. En ese marco el VIII Encuentro de Docentes e Investigadores en Historia del Diseño, la Arquitectura y la Ciudad constituyó un espacio de intercambio y reflexión cuya realización ha sido posible gracias a la colaboración entre Facultades de Arquitectura, Urbanismo y Diseño de la Universidad Nacional y la Facultad de Arquitectura de la Universidad Católica de Córdoba, contando además con la activa participación de mayoría de las Facultades, Centros e Institutos de Historia de la Arquitectura del país y la región. Orientado en su convocatoria tanto a docentes como a estudiantes de Arquitectura y Diseño Industrial de todos los niveles de la FAUD-UNC promovió el debate de ideas a partir de experiencias concretas en instancias tales como mesas temáticas de carácter interdisciplinario, que adoptaron la modalidad de presentación de ponencias, entre otras actividades. En el ámbito de VIII Encuentro, desarrollado en la sede Ciudad Universitaria de Córdoba, se desplegaron numerosas posiciones sobre la enseñanza, la investigación y la formación en historia, teoría y crítica del diseño, la arquitectura y la ciudad; sumándose el aporte realizado a través de sus respectivas conferencias de Ana Clarisa Agüero, Bibiana Cicutti, Fernando Aliata y Alberto Petrina. El conjunto de ponencias que se publican en este Repositorio de la UNC son el resultado de dos intensas jornadas de exposiciones, cuyos contenidos han posibilitado actualizar viejos dilemas y promover nuevos debates. El evento recibió el apoyo de las autoridades de la FAUD-UNC, en especial de la Secretaría de Investigación y de la Biblioteca de nuestra casa, como así también de la Facultad de Arquitectura de la UCC; va para todos ellos un especial agradecimiento

Modelling building construction speed by using linear regression analysis, artificial neural networks and n-dimensional finite elements

The estimation of the time required to construct building projects has been a topic of great interest to many researchers and practitioners. Delays are a common problem in the construction industry and may be motivated by different factors. In this context, prediction of the construction time of building projects at early project phases has been considered a key element for project success. Initially, the construction time of a building is affected by several factors related to project features, although some factors are more crucial than others. Based on these factors, for the purpose of providing proper tools to estimate construction time and minimise the subjectivity in such estimation, to date, most research works have presented parametric models which were built using linear regression analysis (LRA). Nevertheless, there is an increasing trend for using artificial neural networks (ANNs) to develop better predictive models. In order to produce the best possible predictive models and provide a clearer explanation regarding the relationships that exist between different project scope factors and the construction time of new builds, the research work presented in this thesis used two data sets and three different modelling techniques: LRA, ANNs and a new numerical methodology based on the finite element method (FEM). In particular, this thesis addressed the general assumption that nonlinear modelling techniques are likely to better represent the previously mentioned relationships than LRA. According to available data, predictor variables related to construction costs, gross floor area (GFA), number of floors, and the type of facility were selected to analyse their influence on the duration of the construction process. Additionally, and since that there is no general agreement in the literature regarding which is the most appropriate dependent variable for predicting construction time, both time and speed of construction were analysed to determine which of these offer better predictive models. In this regard, construction speed can be used as a useful and robust benchmark for comparison of contractor performance. In the case of ANNs, two different types of network architectures were tested: the multilayer perceptron (MLP) and the radial basis function (RFB). The modelling process of MLP networks was divided into five stages: (i) selection of the training methodology, (ii) data division, (iii) design of the initial network structure, (iv) network optimisation, and (v) validation of the optimised models. MLP networks were used in conjunction with two different training algorithms and five options for calibration data division. In addition, a methodology was defined to obtain optimised MLP networks with an adequate predictive performance. This methodology develops a stepwise trial and error procedure in which a basic MLP network structure, with enough consistency, is first established and subsequently this initial structure is modified at each step of the proposed optimisation process in order to achieve the best possible network configuration. This thesis also proposes a framework to evaluate the performance of predictive models which includes five different assessment criteria: (i) verification of compliance with the underlying assumptions regarding the statistical procedure used to obtain the models, (ii) checking the goodness of fit of the models to the data set used for generating them, (iii) validation of models in terms of ability to generalise, (iv) assessment of the balance existing between the ability of a model to generalise and the accuracy obtained with the calibration data, and (v) development of a sensitivity analysis to verify model stability. Finally, a sensitivity analysis was also proposed to evaluate the impact of the construction cost variability, caused by the uncertainty in its estimation, on the performance of predictive models. The results obtained with this thesis showed that construction speed is a more appropriate dependent variable than construction time to develop predictive models to estimate the construction process duration of building projects, and that such construction speed is affected more by GFA than by construction cost. Furthermore, the FEM-based numerical methodology provided better predictive models than those generated by MLP networks and LRA. In this regard, the findings of this research work support the idea that linear regression models can provide a good starting point from which to search for better predictive models using nonlinear modelling techniques. The knowledge gained from this thesis will allow for new approaches to be explored in order to better determine the relationships existing between project scope factors and the construction speed of new builds, but always taking into account that the results provided by the models proposed herein are only initial construction speed estimates at early stages of project development, when only basic information is available, and are not intended to replace detailed schedules undertaken by builders

Modeling construction time in Spanish building projects

The literature states that project duration is affected by various scope factors. Using 168 building projects carried out in Spain, this paper uses the multiple regression analysis to develop a forecast model that allows estimating project duration of new builds. The proposed model uses project type, gross floor area (GFA), the cost/GFA relationship and number of floors as predictor variables. The research identified the logarithmic form of construction speed as the most appropriate response variable. GFA has greater influence than cost on project duration but both factors are necessary to achieve a forecast model with the highest accuracy. We developed an analysis to verify the stability of forecasted values and showed how a model with high values of fit and accuracy may display an anomalous behavior in the forecasted values. The sensitivity of the proposed forecast model was also analyzed versus the variability of construction costs.The study presented in this paper was mainly funded by a research grant awarded by the Office of the Vice President for Research, Development and Innovation of the University of Alicante (resolution 22 December 2011). It has also been partially funded by the Spanish Government through the project TEXTMESS 2.0 (TIN2009-13391-C04) and by the Valencian Government through the project PROMETEO (PROMETEO/2009/199)

El aprendizaje cooperativo y el desarrollo de competencias con la experiencia de la multiculturalidad

El aprendizaje cooperativo y las metodologías aplicadas en las actividades de estudio grupales se muestran como un elemento esencial en el desarrollo de competencias del alumno en el marco del Espacio Europeo de Educación Superior. A través de la presente comunicación, se aportan determinados métodos complementarios del trabajo grupal, poniendo especial énfasis en el aprovechamiento de la diversidad cultural. Para ello, se toma como referencia la experiencia en asignaturas como Economía Agraria, impartida actualmente en la titulación de LADE de la Universidad de Almería, donde la creciente incorporación de alumnos procedentes de familias inmigrantes y la consiguiente multiculturalidad implican un valor añadido para la consecución de diversas competencias. A partir de la aplicación de estos métodos, se observan actitudes para contextualizar los hechos económicos con una visión internacional más real, así como la mejora de la capacidad analítica del estudiante para abordar problemas económicos desde diferentes perspectivas.SIN FINANCIACIÓNNo data 200