Deployment and validation of a smart system for screening of language disorders in primary care

Neuro-evolutive development from birth until the age of six years is a decisive factor in a child?s quality of life. Early detection of development disorders in early childhood can facilitate necessary diagnosis and/or treatment. Primary-care pediatricians play a key role in its detection as they can undertake the preventive and therapeutic actions requested to promote a child?s optimal development. However, the lack of time and little specific knowledge at primary-care avoid to applying continuous early-detection anomalies procedures. This research paper focuses on the deployment and evaluation of a smart system that enhances the screening of language disorders in primary care. Pediatricians get support to proceed with early referral of language disorders. The proposed model provides them with a decision-support tool for referral actions to trigger essential diagnostic and/or therapeutic actions for a comprehensive individual development. The research was conducted by starting from a sample of 60 cases of children with language disorders. Validation was carried out through two complementary steps: first, by including a team of seven experts from the fields of neonatology, pediatrics, neurology and language therapy, and, second, through the evaluation of 21 more previously diagnosed cases. The results obtained show that therapist positively accepted the system proposal in 18 cases (86%) and suggested system redesign for single referral to a speech therapist in three remaining cases.

Simulating potential growth and yield of oil palm (Elaeis guineensis) with PALMSIM: Model description, evaluation and application.

Reducing the gap between water-limited potential yield and actual yield in oil palm production systems through intensification is seen as an important option for sustainably increasing palm oil production. Simulation models can play an important role in quantifying water-limited potential yield, and therefore the scope for intensification, but no oil palm model exists that is both simple enough and at the same time incorporates sufficient plant physiological knowledge to be generally applicable across sites with different growing conditions. The objectives of this study therefore were to develop a model (PALMSIM) that simulates, on a monthly time step, the potential growth of oil palm as determined by solar radiation and to evaluate model performance against measured oil palm yields under optimal water and nutrient management for a range of sites across Indonesia and Malaysia. The maximum observed yield in the field matches the corresponding simulated yield for dry bunch weight with a RMSE of 1.7 Mg ha?1 year?1 against an observed yield of 18.8 Mg ha?1. Sensitivity analysis showed that PALMSIM is robust: simulated changes in yield caused by modifying the parameters by 10% are comparable to other tree crop model evaluations. While we acknowledge that, depending on the soils and climatic environment, yields may be often water limited, we suggest a relatively simple physiological approach to simulate potential yield, which can be usefully applied to high rainfall environments and is considered as a first step in developing an oil palm model that also simulates water-limited potential yield. To illustrate the application possibil- ities of the model, PALMSIM was used to create a potential yield map for Indonesia and Malaysia by sim- ulating the growth and yield at a resolution of 0.1?. This map of potential yield is considered as a first step towards a decision support tool that can identify potentially productive, but at the moment degraded sites in Indonesia and Malaysia. ?

Prototype of EN 301 549 Decision tree

The new European Standard EN 301 549 “Accessibility requirements suitable for public procurement of ICT products and services in Europe” is the response by CEN, CENELEC and ETSI to the European Commission’s Mandate 376. Today, ICT products and services are converging, and the boundaries between product categories are being constantly blurred. For that reason EN 301 549 has been drafted using a feature-based approach, instead of being based on product categories. The result is a standard that can be applied to any ICT product and service, by identifying applicable requirements depending on the features of the ICT. This demonstration presents ongoing work at the research group CETTICO of the Technical University of Madrid. CETTICO is developing a workgroup-based support tool where teams of people can annotate the result of performing a conformity assessment of a given ICT product or service according to the requirements of the EN. One of the functions of the tool is creating evaluation projects. During that task the user defines the features of the corresponding ICT product or service by answering questions presented by the tool. As a result of this process, the tool will create a list of applicable requirements and recommendations.

MOIRA-PLUS use in decision making on the long-term management of contaminated freshwater bodies and catchments

In the mid-long-term after a nuclear accident, the contamination of drinking water sources, fish and other aquatic foodstuffs, irrigation supplies and people?s exposure during recreational activities may create considerable public concern, even though dose assessment may in certain situations indicate lesser importance than for other sources, as clearly experienced in the aftermath of past accidents. In such circumstances there are a number of available countermeasure options, ranging from specific chemical treatment of lakes to bans on fish ingestion or on the use of water for crop irrigation. The potential actions can be broadly grouped into four main categories, chemical, biological, physical and social. In some cases a combination of actions may be the optimal strategy and a decision support system (DSS) like MOIRA-PLUS can be of great help to optimise a decision. A further option is of course not to take any remedial actions, although this may also have significant socio-economic repercussions which should be adequately evaluated. MOIRA-PLUS is designed to allow for a reliable assessment of the long-term evolution of the radiological situation and of feasible alternative rehabilitation strategies, including an objective evaluation of their social, economic and ecological impacts in a rational and comprehensive manner. MOIRA-PLUS also features a decision analysis methodology, making use of multi-attribute analysis, which can take into account the preferences and needs of different types of stakeholders. The main functions and elements of the system are described summarily. Also the conclusions from end-user?s experiences with the system are discussed, including exercises involving the organizations responsible for emergency management and the affected services, as well as different local and regional stakeholders. MOIRAPLUS has proven to be a mature system, user friendly and relatively easy to set up. It can help to better decisionmaking by enabling a realistic evaluation of the complete impacts of possible recovery strategies. Also, the interaction with stakeholders has allowed identifying improvements of the system that have been recently implemented.

Automatic blood glucose classification for gestational diabetes with feature selection: decision trees vs neural networks

Automatic blood glucose classification may help specialists to provide a better interpretation of blood glucose data, downloaded directly from patients glucose meter and will contribute in the development of decision support systems for gestational diabetes. This paper presents an automatic blood glucose classifier for gestational diabetes that compares 6 different feature selection methods for two machine learning algorithms: neural networks and decision trees. Three searching algorithms, Greedy, Best First and Genetic, were combined with two different evaluators, CSF and Wrapper, for the feature selection. The study has been made with 6080 blood glucose measurements from 25 patients. Decision trees with a feature set selected with the Wrapper evaluator and the Best first search algorithm obtained the best accuracy: 95.92%.

Quantity of CO2 Emissions in the Life Cycle of a Highway Infrastructure.

The complexity of climate change and its evolution during the last few years has a positive impact on new developments and approaches to reduce the emissions of CO2. Looking for a methodology to evaluate the sustainability of a roadway, a tool has been developed. Life Cycle Assessment (LCA) is being accepted by the road industry to measure and evaluate the environmental impacts of an infrastructure, as the energy consumption and carbon footprint. This paper describes the methodology to calculate the CO2 emissions associated with the energy embodied on a roadway along its life cycle, including construction, operations and demolition. It will assist to find solutions to improve the energy footprint and reduce the amount of CO2 emissions. Details are provided of both, the methodology and the data acquisition. This paper is an application of the methodology to the Spanish highways, using a local database. Two case studies and a practical example are studied to show the model as a decision support for sustainable construction in the road industry.

Simulating improved combinations tillage-rotation under dryland conditions.

Crop simulation models allow analyzing various tillage-rotation combinations and exploring management scenarios. This study was conducted to test the DSSAT (Decision Support System for Agrotechnology Transfer) modelling system in rainfed semiarid central Spain. The focus is on the combined effect of tillage system and winter cereal-based rotations (cereal/legume/fallow) on the crop yield and soil quality. The observed data come from a 16-year field experiment. The CERES and CROPGRO models, included in DSSAT v4.5, were used to simulate crop growth and yield, and DSSAT- CENTURY was used in the soil organic carbon (SOC) and soil nitrogen (SN) simulations. Genetic coefficients were calibrated using part of the observed data. Field observations showed that barley grain yield was lower for continuous cereal (BB) than for vetch (VB) and fallow (FB) rotations for both tillage systems. The CERES-Barley model also reflected this trend. The model predicted higher yield in the conventional tillage (CT) than in the no tillage (NT) probably due to the higher nitrogen availability in the CT, shown in the simulations. The SOC and SN in the top layer only, were higher in NT than in CT, and decreased with depth in both simulated and observed values. These results suggest that CT-VB and CT-FB were the best combinations for the dry land conditions studied. However, CT presented lower SN and SOC content than NT. This study shows how models can be a useful tool for assessing and predicting crop growth and yield, under different management systems and under specific edapho-climatic conditions. Additional key words: CENTURY model; CERES-Barley; crop simulation models; DSSAT; sequential simula- tion; soil organic carbon.

Modelling spatial decisión support systems focused on the development of a data warehouse: applying software engineering

Nowadays, organizations have plenty of data stored in DB databases, which contain invaluable information. Decision Support Systems DSS provide the support needed to manage this information and planning médium and long-term ?the modus operandi? of these organizations. Despite the growing importance of these systems, most proposals do not include its total evelopment, mostly limiting itself on the development of isolated parts, which often have serious integration problems. Hence, methodologies that include models and processes that consider every factor are necessary. This paper will try to fill this void as it proposes an approach for developing spatial DSS driven by the development of their associated Data Warehouse DW, without forgetting its other components. To the end of framing the proposal different Engineering Software focus (The Software Engineering Process and Model Driven Architecture) are used, and coupling with the DB development methodology, (and both of them adapted to DW peculiarities). Finally, an example illustrates the proposal.

Introducción de criterios de sostenibilidad en la elección de emplazamientos industriales mediante modelos multicriterio

(SPA) La elección de localizaciones para la implantación de actividades industriales es un problema complejo, donde a los criterios de coste y eficiencia se han ido añadiendo otros nuevos relativos tanto al impacto en el medio ambiente como a la imagen de la empresa reflejada en la Responsabilidad Social Empresarial. Los criterios medioambientales han ido adquiriendo gran relevancia en la decisión final, hasta convertirse, gracias a la obligación de someter los proyectos a evaluación ambiental, en elementos clave en la decisión final. Por ello, resulta relativamente frecuente que los promotores consulten previamente con la Administración sobre la viabilidad de sus proyectos antes de iniciar un dilatado procedimiento administrativo. En este trabajo se plantea la utilización de indicadores de sostenibilidad y su aplicación, a través de un modelo de decisiones multicriterio, para la ordenación de las distintas opciones de ubicación inicialmente consideradas, de tal forma que se conviertan en instrumento de tanteo y ayuda en la toma de estas decisiones. Para mostrar su utilidad se propone la utilización de la herramienta de apoyo basada en la metodología PROMETHEE y su aplicación en la ordenación de cinco emplazamientos alternativos para la instalación de una cementera en la Comunidad de Madrid según criterios de sostenibilidad. (ENG) The choice of locations for the implementation of industrial activities is a complex problem where the cost and efficiency criteria have been adding new ones relating to the environment impact and the company’s corporate image reflected in Corporate Social Responsability. The environmental criteria have been getting big importance in the final decision, to become key elements in the final decision, due to the duty of submit of environmental assessment projects. Therefore, promoters, quite often, ask previously to the Administration about the viability of their projects before starting a lengthy administrative procedure. This paper proposes the use of sustainability indicators and their application through a multi-criteria decision model for managing the establishment options initially considered, so that they become an help instrument of estimation in order to making these decisions. To show its usefulness we propose the use of the support tool for decision making based on the PROMETHEE methodology and its application in the management of 5 alternative sites for the installation of a cement factory in the Community of Madrid under sustainability criteria.

Sistema para integrar automáticamente datos públicos del NCBI en la plataforma de medicina personalizada p-medicine

En los últimos años ha habido un gran aumento de fuentes de datos biomédicos. La aparición de nuevas técnicas de extracción de datos genómicos y generación de bases de datos que contienen esta información ha creado la necesidad de guardarla para poder acceder a ella y trabajar con los datos que esta contiene. La información contenida en las investigaciones del campo biomédico se guarda en bases de datos. Esto se debe a que las bases de datos permiten almacenar y manejar datos de una manera simple y rápida. Dentro de las bases de datos existen una gran variedad de formatos, como pueden ser bases de datos en Excel, CSV o RDF entre otros. Actualmente, estas investigaciones se basan en el análisis de datos, para a partir de ellos, buscar correlaciones que permitan inferir, por ejemplo, tratamientos nuevos o terapias más efectivas para una determinada enfermedad o dolencia. El volumen de datos que se maneja en ellas es muy grande y dispar, lo que hace que sea necesario el desarrollo de métodos automáticos de integración y homogeneización de los datos heterogéneos. El proyecto europeo p-medicine (FP7-ICT-2009-270089) tiene como objetivo asistir a los investigadores médicos, en este caso de investigaciones relacionadas con el cáncer, proveyéndoles con nuevas herramientas para el manejo de datos y generación de nuevo conocimiento a partir del análisis de los datos gestionados. La ingestión de datos en la plataforma de p-medicine, y el procesamiento de los mismos con los métodos proporcionados, buscan generar nuevos modelos para la toma de decisiones clínicas. Dentro de este proyecto existen diversas herramientas para integración de datos heterogéneos, diseño y gestión de ensayos clínicos, simulación y visualización de tumores y análisis estadístico de datos. Precisamente en el ámbito de la integración de datos heterogéneos surge la necesidad de añadir información externa al sistema proveniente de bases de datos públicas, así como relacionarla con la ya existente mediante técnicas de integración semántica. Para resolver esta necesidad se ha creado una herramienta, llamada Term Searcher, que permite hacer este proceso de una manera semiautomática. En el trabajo aquí expuesto se describe el desarrollo y los algoritmos creados para su correcto funcionamiento. Esta herramienta ofrece nuevas funcionalidades que no existían dentro del proyecto para la adición de nuevos datos provenientes de fuentes públicas y su integración semántica con datos privados.---ABSTRACT---Over the last few years, there has been a huge growth of biomedical data sources. The emergence of new techniques of genomic data generation and data base generation that contain this information, has created the need of storing it in order to access and work with its data. The information employed in the biomedical research field is stored in databases. This is due to the capability of databases to allow storing and managing data in a quick and simple way. Within databases there is a variety of formats, such as Excel, CSV or RDF. Currently, these biomedical investigations are based on data analysis, which lead to the discovery of correlations that allow inferring, for example, new treatments or more effective therapies for a specific disease or ailment. The volume of data handled in them is very large and dissimilar, which leads to the need of developing new methods for automatically integrating and homogenizing the heterogeneous data. The p-medicine (FP7-ICT-2009-270089) European project aims to assist medical researchers, in this case related to cancer research, providing them with new tools for managing and creating new knowledge from the analysis of the managed data. The ingestion of data into the platform and its subsequent processing with the provided tools aims to enable the generation of new models to assist in clinical decision support processes. Inside this project, there exist different tools related to areas such as the integration of heterogeneous data, the design and management of clinical trials, simulation and visualization of tumors and statistical data analysis. Particularly in the field of heterogeneous data integration, there is a need to add external information from public databases, and relate it to the existing ones through semantic integration methods. To solve this need a tool has been created: the term Searcher. This tool aims to make this process in a semiautomatic way. This work describes the development of this tool and the algorithms employed in its operation. This new tool provides new functionalities that did not exist inside the p-medicine project for adding new data from public databases and semantically integrate them with private data.

Climate variability effects on field crops in Iberian Peninsula. Predictability

La presente Tesis constituye un avance en el conocimiento de los efectos de la variabilidad climática en los cultivos en la Península Ibérica (PI). Es bien conocido que la temperatura del océano, particularmente de la región tropical, es una de las variables más convenientes para ser utilizado como predictor climático. Los océanos son considerados como la principal fuente de almacenamiento de calor del planeta debido a la alta capacidad calorífica del agua. Cuando se libera esta energía, altera los regímenes globales de circulación atmosférica por mecanismos de teleconexión. Estos cambios en la circulación general de la atmósfera afectan a la temperatura, precipitación, humedad, viento, etc., a escala regional, los cuales afectan al crecimiento, desarrollo y rendimiento de los cultivos. Para el caso de Europa, esto implica que la variabilidad atmosférica en una región específica se asocia con la variabilidad de otras regiones adyacentes y/o remotas, como consecuencia Europa está siendo afectada por los patrones de circulaciones globales, que a su vez, se ven afectados por patrones oceánicos. El objetivo general de esta tesis es analizar la variabilidad del rendimiento de los cultivos y su relación con la variabilidad climática y teleconexiones, así como evaluar su predictibilidad. Además, esta Tesis tiene como objetivo establecer una metodología para estudiar la predictibilidad de las anomalías del rendimiento de los cultivos. El análisis se centra en trigo y maíz como referencia para otros cultivos de la PI, cultivos de invierno en secano y cultivos de verano en regadío respectivamente. Experimentos de simulación de cultivos utilizando una metodología en cadena de modelos (clima + cultivos) son diseñados para evaluar los impactos de los patrones de variabilidad climática en el rendimiento y su predictibilidad. La presente Tesis se estructura en dos partes: La primera se centra en el análisis de la variabilidad del clima y la segunda es una aplicación de predicción cuantitativa de cosechas. La primera parte está dividida en 3 capítulos y la segundo en un capitulo cubriendo los objetivos específicos del presente trabajo de investigación. Parte I. Análisis de variabilidad climática El primer capítulo muestra un análisis de la variabilidad del rendimiento potencial en una localidad como indicador bioclimático de las teleconexiones de El Niño con Europa, mostrando su importancia en la mejora de predictibilidad tanto en clima como en agricultura. Además, se presenta la metodología elegida para relacionar el rendimiento con las variables atmosféricas y oceánicas. El rendimiento de los cultivos es parcialmente determinado por la variabilidad climática atmosférica, que a su vez depende de los cambios en la temperatura de la superficie del mar (TSM). El Niño es el principal modo de variabilidad interanual de la TSM, y sus efectos se extienden en todo el mundo. Sin embargo, la predictibilidad de estos impactos es controversial, especialmente aquellos asociados con la variabilidad climática Europea, que se ha encontrado que es no estacionaria y no lineal. Este estudio mostró cómo el rendimiento potencial de los cultivos obtenidos a partir de datos de reanálisis y modelos de cultivos sirve como un índice alternativo y más eficaz de las teleconexiones de El Niño, ya que integra las no linealidades entre las variables climáticas en una única serie temporal. Las relaciones entre El Niño y las anomalías de rendimiento de los cultivos son más significativas que las contribuciones individuales de cada una de las variables atmosféricas utilizadas como entrada en el modelo de cultivo. Además, la no estacionariedad entre El Niño y la variabilidad climática europea se detectan con mayor claridad cuando se analiza la variabilidad de los rendimiento de los cultivos. La comprensión de esta relación permite una cierta predictibilidad hasta un año antes de la cosecha del cultivo. Esta predictibilidad no es constante, sino que depende tanto la modulación de la alta y baja frecuencia. En el segundo capítulo se identifica los patrones oceánicos y atmosféricos de variabilidad climática que afectan a los cultivos de verano en la PI. Además, se presentan hipótesis acerca del mecanismo eco-fisiológico a través del cual el cultivo responde. Este estudio se centra en el análisis de la variabilidad del rendimiento de maíz en la PI para todo el siglo veinte, usando un modelo de cultivo calibrado en 5 localidades españolas y datos climáticos de reanálisis para obtener series temporales largas de rendimiento potencial. Este estudio evalúa el uso de datos de reanálisis para obtener series de rendimiento de cultivos que dependen solo del clima, y utilizar estos rendimientos para analizar la influencia de los patrones oceánicos y atmosféricos. Los resultados muestran una gran fiabilidad de los datos de reanálisis. La distribución espacial asociada a la primera componente principal de la variabilidad del rendimiento muestra un comportamiento similar en todos los lugares estudiados de la PI. Se observa una alta correlación lineal entre el índice de El Niño y el rendimiento, pero no es estacionaria en el tiempo. Sin embargo, la relación entre la temperatura del aire y el rendimiento se mantiene constante a lo largo del tiempo, siendo los meses de mayor influencia durante el período de llenado del grano. En cuanto a los patrones atmosféricos, el patrón Escandinavia presentó una influencia significativa en el rendimiento en PI. En el tercer capítulo se identifica los patrones oceánicos y atmosféricos de variabilidad climática que afectan a los cultivos de invierno en la PI. Además, se presentan hipótesis acerca del mecanismo eco-fisiológico a través del cual el cultivo responde. Este estudio se centra en el análisis de la variabilidad del rendimiento de trigo en secano del Noreste (NE) de la PI. La variabilidad climática es el principal motor de los cambios en el crecimiento, desarrollo y rendimiento de los cultivos, especialmente en los sistemas de producción en secano. En la PI, los rendimientos de trigo son fuertemente dependientes de la cantidad de precipitación estacional y la distribución temporal de las mismas durante el periodo de crecimiento del cultivo. La principal fuente de variabilidad interanual de la precipitación en la PI es la Oscilación del Atlántico Norte (NAO), que se ha relacionado, en parte, con los cambios en la temperatura de la superficie del mar en el Pacífico Tropical (El Niño) y el Atlántico Tropical (TNA). La existencia de cierta predictibilidad nos ha animado a analizar la posible predicción de los rendimientos de trigo en la PI utilizando anomalías de TSM como predictor. Para ello, se ha utilizado un modelo de cultivo (calibrado en dos localidades del NE de la PI) y datos climáticos de reanálisis para obtener series temporales largas de rendimiento de trigo alcanzable y relacionar su variabilidad con anomalías de la TSM. Los resultados muestran que El Niño y la TNA influyen en el desarrollo y rendimiento del trigo en el NE de la PI, y estos impactos depende del estado concurrente de la NAO. Aunque la relación cultivo-TSM no es igual durante todo el periodo analizado, se puede explicar por un mecanismo eco-fisiológico estacionario. Durante la segunda mitad del siglo veinte, el calentamiento (enfriamiento) en la superficie del Atlántico tropical se asocia a una fase negativa (positiva) de la NAO, que ejerce una influencia positiva (negativa) en la temperatura mínima y precipitación durante el invierno y, por lo tanto, aumenta (disminuye) el rendimiento de trigo en la PI. En relación con El Niño, la correlación más alta se observó en el período 1981 -2001. En estas décadas, los altos (bajos) rendimientos se asocian con una transición El Niño - La Niña (La Niña - El Niño) o con eventos de El Niño (La Niña) que están finalizando. Para estos eventos, el patrón atmosférica asociada se asemeja a la NAO, que también influye directamente en la temperatura máxima y precipitación experimentadas por el cultivo durante la floración y llenado de grano. Los co- efectos de los dos patrones de teleconexión oceánicos ayudan a aumentar (disminuir) la precipitación y a disminuir (aumentar) la temperatura máxima en PI, por lo tanto el rendimiento de trigo aumenta (disminuye). Parte II. Predicción de cultivos. En el último capítulo se analiza los beneficios potenciales del uso de predicciones climáticas estacionales (por ejemplo de precipitación) en las predicciones de rendimientos de trigo y maíz, y explora métodos para aplicar dichos pronósticos climáticos en modelos de cultivo. Las predicciones climáticas estacionales tienen un gran potencial en las predicciones de cultivos, contribuyendo de esta manera a una mayor eficiencia de la gestión agrícola, seguridad alimentaria y de subsistencia. Los pronósticos climáticos se expresan en diferentes formas, sin embargo todos ellos son probabilísticos. Para ello, se evalúan y aplican dos métodos para desagregar las predicciones climáticas estacionales en datos diarios: 1) un generador climático estocástico condicionado (predictWTD) y 2) un simple re-muestreador basado en las probabilidades del pronóstico (FResampler1). Los dos métodos se evaluaron en un caso de estudio en el que se analizaron los impactos de tres escenarios de predicciones de precipitación estacional (predicción seco, medio y lluvioso) en el rendimiento de trigo en secano, sobre las necesidades de riego y rendimiento de maíz en la PI. Además, se estimó el margen bruto y los riesgos de la producción asociada con las predicciones de precipitación estacional extremas (seca y lluviosa). Los métodos predWTD y FResampler1 usados para desagregar los pronósticos de precipitación estacional en datos diarios, que serán usados como inputs en los modelos de cultivos, proporcionan una predicción comparable. Por lo tanto, ambos métodos parecen opciones factibles/viables para la vinculación de los pronósticos estacionales con modelos de simulación de cultivos para establecer predicciones de rendimiento o las necesidades de riego en el caso de maíz. El análisis del impacto en el margen bruto de los precios del grano de los dos cultivos (trigo y maíz) y el coste de riego (maíz) sugieren que la combinación de los precios de mercado previstos y la predicción climática estacional pueden ser una buena herramienta en la toma de decisiones de los agricultores, especialmente en predicciones secas y/o localidades con baja precipitación anual. Estos métodos permiten cuantificar los beneficios y riesgos de los agricultores ante una predicción climática estacional en la PI. Por lo tanto, seríamos capaces de establecer sistemas de alerta temprana y diseñar estrategias de adaptación del manejo del cultivo para aprovechar las condiciones favorables o reducir los efectos de condiciones adversas. La utilidad potencial de esta Tesis es la aplicación de las relaciones encontradas para predicción de cosechas de la próxima campaña agrícola. Una correcta predicción de los rendimientos podría ayudar a los agricultores a planear con antelación sus prácticas agronómicas y todos los demás aspectos relacionados con el manejo de los cultivos. Esta metodología se puede utilizar también para la predicción de las tendencias futuras de la variabilidad del rendimiento en la PI. Tanto los sectores públicos (mejora de la planificación agrícola) como privados (agricultores, compañías de seguros agrarios) pueden beneficiarse de esta mejora en la predicción de cosechas. ABSTRACT The present thesis constitutes a step forward in advancing of knowledge of the effects of climate variability on crops in the Iberian Peninsula (IP). It is well known that ocean temperature, particularly the tropical ocean, is one of the most convenient variables to be used as climate predictor. Oceans are considered as the principal heat storage of the planet due to the high heat capacity of water. When this energy is released, it alters the global atmospheric circulation regimes by teleconnection1 mechanisms. These changes in the general circulation of the atmosphere affect the regional temperature, precipitation, moisture, wind, etc., and those influence crop growth, development and yield. For the case of Europe, this implies that the atmospheric variability in a specific region is associated with the variability of others adjacent and/or remote regions as a consequence of Europe being affected by global circulations patterns which, in turn, are affected by oceanic patterns. The general objective of this Thesis is to analyze the variability of crop yields at climate time scales and its relation to the climate variability and teleconnections, as well as to evaluate their predictability. Moreover, this Thesis aims to establish a methodology to study the predictability of crop yield anomalies. The analysis focuses on wheat and maize as a reference crops for other field crops in the IP, for winter rainfed crops and summer irrigated crops respectively. Crop simulation experiments using a model chain methodology (climate + crop) are designed to evaluate the impacts of climate variability patterns on yield and its predictability. The present Thesis is structured in two parts. The first part is focused on the climate variability analyses, and the second part is an application of the quantitative crop forecasting for years that fulfill specific conditions identified in the first part. This Thesis is divided into 4 chapters, covering the specific objectives of the present research work. Part I. Climate variability analyses The first chapter shows an analysis of potential yield variability in one location, as a bioclimatic indicator of the El Niño teleconnections with Europe, putting forward its importance for improving predictability in both climate and agriculture. It also presents the chosen methodology to relate yield with atmospheric and oceanic variables. Crop yield is partially determined by atmospheric climate variability, which in turn depends on changes in the sea surface temperature (SST). El Niño is the leading mode of SST interannual variability, and its impacts extend worldwide. Nevertheless, the predictability of these impacts is controversial, especially those associated with European climate variability, which have been found to be non-stationary and non-linear. The study showed how potential2 crop yield obtained from reanalysis data and crop models serves as an alternative and more effective index of El Niño teleconnections because it integrates the nonlinearities between the climate variables in a unique time series. The relationships between El Niño and crop yield anomalies are more significant than the individual contributions of each of the atmospheric variables used as input in the crop model. Additionally, the non-stationarities between El Niño and European climate variability are more clearly detected when analyzing crop-yield variability. The understanding of this relationship allows for some predictability up to one year before the crop is harvested. This predictability is not constant, but depends on both high and low frequency modulation. The second chapter identifies the oceanic and atmospheric patterns of climate variability affecting summer cropping systems in the IP. Moreover, hypotheses about the eco-physiological mechanism behind crop response are presented. It is focused on an analysis of maize yield variability in IP for the whole twenty century, using a calibrated crop model at five contrasting Spanish locations and reanalyses climate datasets to obtain long time series of potential yield. The study tests the use of reanalysis data for obtaining only climate dependent time series of simulated crop yield for the whole region, and to use these yield to analyze the influences of oceanic and atmospheric patterns. The results show a good reliability of reanalysis data. The spatial distribution of the leading principal component of yield variability shows a similar behaviour over all the studied locations in the IP. The strong linear correlation between El Niño index and yield is remarkable, being this relation non-stationary on time, although the air temperature-yield relationship remains on time, being the highest influences during grain filling period. Regarding atmospheric patterns, the summer Scandinavian pattern has significant influence on yield in IP. The third chapter identifies the oceanic and atmospheric patterns of climate variability affecting winter cropping systems in the IP. Also, hypotheses about the eco-physiological mechanism behind crop response are presented. It is focused on an analysis of rainfed wheat yield variability in IP. Climate variability is the main driver of changes in crop growth, development and yield, especially for rainfed production systems. In IP, wheat yields are strongly dependent on seasonal rainfall amount and temporal distribution of rainfall during the growing season. The major source of precipitation interannual variability in IP is the North Atlantic Oscillation (NAO) which has been related in part with changes in the Tropical Pacific (El Niño) and Atlantic (TNA) sea surface temperature (SST). The existence of some predictability has encouraged us to analyze the possible predictability of the wheat yield in the IP using SSTs anomalies as predictor. For this purpose, a crop model with a site specific calibration for the Northeast of IP and reanalysis climate datasets have been used to obtain long time series of attainable wheat yield and relate their variability with SST anomalies. The results show that El Niño and TNA influence rainfed wheat development and yield in IP and these impacts depend on the concurrent state of the NAO. Although crop-SST relationships do not equally hold on during the whole analyzed period, they can be explained by an understood and stationary ecophysiological mechanism. During the second half of the twenty century, the positive (negative) TNA index is associated to a negative (positive) phase of NAO, which exerts a positive (negative) influence on minimum temperatures (Tmin) and precipitation (Prec) during winter and, thus, yield increases (decreases) in IP. In relation to El Niño, the highest correlation takes place in the period 1981-2001. For these decades, high (low) yields are associated with an El Niño to La Niña (La Niña to El Niño) transitions or to El Niño events finishing. For these events, the regional associated atmospheric pattern resembles the NAO, which also influences directly on the maximum temperatures (Tmax) and precipitation experienced by the crop during flowering and grain filling. The co-effects of the two teleconnection patterns help to increase (decrease) the rainfall and decrease (increase) Tmax in IP, thus on increase (decrease) wheat yield. Part II. Crop forecasting The last chapter analyses the potential benefits for wheat and maize yields prediction from using seasonal climate forecasts (precipitation), and explores methods to apply such a climate forecast to crop models. Seasonal climate prediction has significant potential to contribute to the efficiency of agricultural management, and to food and livelihood security. Climate forecasts come in different forms, but probabilistic. For this purpose, two methods were evaluated and applied for disaggregating seasonal climate forecast into daily weather realizations: 1) a conditioned stochastic weather generator (predictWTD) and 2) a simple forecast probability resampler (FResampler1). The two methods were evaluated in a case study where the impacts of three scenarios of seasonal rainfall forecasts on rainfed wheat yield, on irrigation requirements and yields of maize in IP were analyzed. In addition, we estimated the economic margins and production risks associated with extreme scenarios of seasonal rainfall forecasts (dry and wet). The predWTD and FResampler1 methods used for disaggregating seasonal rainfall forecast into daily data needed by the crop simulation models provided comparable predictability. Therefore both methods seem feasible options for linking seasonal forecasts with crop simulation models for establishing yield forecasts or irrigation water requirements. The analysis of the impact on gross margin of grain prices for both crops and maize irrigation costs suggests the combination of market prices expected and the seasonal climate forecast can be a good tool in farmer’s decision-making, especially on dry forecast and/or in locations with low annual precipitation. These methodologies would allow quantifying the benefits and risks of a seasonal weather forecast to farmers in IP. Therefore, we would be able to establish early warning systems and to design crop management adaptation strategies that take advantage of favorable conditions or reduce the effect of adverse conditions. The potential usefulness of this Thesis is to apply the relationships found to crop forecasting on the next cropping season, suggesting opportunity time windows for the prediction. The methodology can be used as well for the prediction of future trends of IP yield variability. Both public (improvement of agricultural planning) and private (decision support to farmers, insurance companies) sectors may benefit from such an improvement of crop forecasting.

Could it be possible to replace DERIVE with MAXIMA?

In recent years, a considerable number of teachers in Spain have been using DERIVE to teach math subjects in High Schools and Universities. This software has been used by the authors of this work as a support tool in Mathematics courses for Engineering. Since Texas Instruments does not support DERIVE, we were faced with finding an alternative software product, and considering the possibility of using a public-domain software such as MAXIMA. Here we make a comparative study of DERIVE and MAXIMA as support tools for a Calculus course for first year Engineering students.

Detecting Traffic Problems with ILP

Expert systems for decision support have recently been successfully introduced in road transport management. In this paper, we apply three state-of-the art ILP systems to learn how to detect traffic problems.

Arquitectura de información para medicion de rendimiento en entornos e-manufacturing

La implantación de las tecnologías Internet ha permitido la extensión del uso de estrategias e-manufacturing y el desarrollo de herramientas para la recopilación, transformación y sincronización de datos de fabricación vía web. En este ámbito, un área de potencial desarrollo es la extensión del virtual manufacturing a los procesos de Performance Management (PM), área crítica para la toma de decisiones y ejecución de acciones de mejora en fabricación. Este trabajo doctoral propone un Arquitectura de Información para el desarrollo de herramientas virtuales en el ámbito PM. Su aplicación permite asegurar la interoperabilidad necesaria en los procesos de tratamiento de información de toma de decisión. Está formado por tres sub-sistemas: un modelo conceptual, un modelo de objetos y un marco Web compuesto de una plataforma de información y una arquitectura de servicios Web (WS). El modelo conceptual y el modelo de objetos se basa en el desarrollo de toda la información que se necesita para definir y obtener los diferentes indicadores de medida que requieren los procesos PM. La plataforma de información hace uso de las tecnologías XML y B2MML para estructurar un nuevo conjunto de esquemas de mensajes de intercambio de medición de rendimiento (PMXML). Esta plataforma de información se complementa con una arquitectura de servicios web que hace uso de estos esquemas para integrar los procesos de codificación, decodificación, traducción y evaluación de los performance key indicators (KPI). Estos servicios realizan todas las transacciones que permiten transformar los datos origen en información inteligente usable en los procesos de toma de decisión. Un caso práctico de intercambio de datos en procesos de medición del área de mantenimiento de equipos es mostrado para verificar la utilidad de la arquitectura. ABSTRAC The implementation of Internet technologies has led to e-Manufacturing technologies becoming more widely used and to the development of tools for compiling, transforming and synchronizing manufacturing data through the Web. In this context, a potential area for development is the extension of virtual manufacturing to Performance Measurement (PM) processes, a critical area for decision-making and implementing improvement actions in manufacturing. This thesis proposes a Information Architecture to integrate decision support systems in e-manufacturing. Specifically, the proposed architecture offers a homogeneous PM information exchange model that can be applied trough decision support in emanufacturing environment. Its application improves the necessary interoperability in decision-making data processing tasks. It comprises three sub-systems: a data model, a object model and Web Framework which is composed by a PM information platform and PM-Web services architecture. . The data model and the object model are based on developing all the information required to define and acquire the different indicators required by PM processes. The PM information platform uses XML and B2MML technologies to structure a new set of performance measurement exchange message schemas (PM-XML). This PM information platform is complemented by a PM-Web Services architecture that uses these schemas to integrate the coding, decoding, translation and assessment processes of the key performance indicators (KPIs). These services perform all the transactions that enable the source data to be transformed into smart data that can be used in the decision-making processes. A practical example of data exchange for measurement processes in the area of equipment maintenance is shown to demonstrate the utility of the architecture.

Continuous multi-criteria methods for crop and soil conservation planning on La Colacha (Río Cuarto, Province of Cordoba, Argentina)

Agro-areas of Arroyos Menores (La Colacha) west and south of Rand south of R?o Cuarto (Prov. of Cordoba, Argentina) basins are very fertile but have high soil loses. Extreme rain events, inundations and other severe erosions forming gullies demand urgently actions in this area to avoid soil degradation and erosion supporting good levels of agro production. The authors first improved hydrologic data on La Colacha, evaluated the systems of soil uses and actions that could be recommended considering the relevant aspects of the study area and applied decision support systems (DSS) with mathematic tools for planning of defences and uses of soils in these areas. These were conducted here using multi-criteria models, in multi-criteria decision making (MCDM); first of discrete MCDM to chose among global types of use of soils, and then of continuous MCDM to evaluate and optimize combined actions, including repartition of soil use and the necessary levels of works for soil conservation and for hydraulic management to conserve against erosion these basins. Relatively global solutions for La Colacha area have been defined and were optimised by Linear Programming in Goal Programming forms that are presented as Weighted or Lexicographic Goal Programming and as Compromise Programming. The decision methods used are described, indicating algorithms used, and examples for some representative scenarios on La Colacha area are given.