Comparison of metaheuristic strategies for peakbin selection in proteomic mass spectrometry data

Mass spectrometry (MS) data provide a promising strategy for biomarker discovery. For this purpose, the detection of relevant peakbins in MS data is currently under intense research. Data from mass spectrometry are challenging to analyze because of their high dimensionality and the generally low number of samples available. To tackle this problem, the scientific community is becoming increasingly interested in applying feature subset selection techniques based on specialized machine learning algorithms. In this paper, we present a performance comparison of some metaheuristics: best first (BF), genetic algorithm (GA), scatter search (SS) and variable neighborhood search (VNS). Up to now, all the algorithms, except for GA, have been first applied to detect relevant peakbins in MS data. All these metaheuristic searches are embedded in two different filter and wrapper schemes coupled with Naive Bayes and SVM classifiers.

Selection of CSI-based precoding techniques in the HF channel

Multi-carrier modulations are widely employed in ionospheric communications to mitigate the adverse effects of the HF channel. In this paper we show how performance achieved by these modulations can be further increased by means of CSIbased precoding techniques in the context of our research on interactive digital voice communications. Depending on communication constraints and channel parameters, we will show which of the studied modulations and precoding techniques to select so that to maximise performance.

Design and Optimization of Power Delivery and Distribution Systems Using Evolutionary Computation Techniques

Nowadays computing platforms consist of a very large number of components that require to be supplied with diferent voltage levels and power requirements. Even a very small platform, like a handheld computer, may contain more than twenty diferent loads and voltage regulators. The power delivery designers of these systems are required to provide, in a very short time, the right power architecture that optimizes the performance, meets electrical specifications plus cost and size targets. The appropriate selection of the architecture and converters directly defines the performance of a given solution. Therefore, the designer needs to be able to evaluate a significant number of options in order to know with good certainty whether the selected solutions meet the size, energy eficiency and cost targets. The design dificulties of selecting the right solution arise due to the wide range of power conversion products provided by diferent manufacturers. These products range from discrete components (to build converters) to complete power conversion modules that employ diferent manufacturing technologies. Consequently, in most cases it is not possible to analyze all the alternatives (combinations of power architectures and converters) that can be built. The designer has to select a limited number of converters in order to simplify the analysis. In this thesis, in order to overcome the mentioned dificulties, a new design methodology for power supply systems is proposed. This methodology integrates evolutionary computation techniques in order to make possible analyzing a large number of possibilities. This exhaustive analysis helps the designer to quickly define a set of feasible solutions and select the best trade-off in performance according to each application. The proposed approach consists of two key steps, one for the automatic generation of architectures and other for the optimized selection of components. In this thesis are detailed the implementation of these two steps. The usefulness of the methodology is corroborated by contrasting the results using real problems and experiments designed to test the limits of the algorithms.

Using ecological niche models to support tree species selection for forest restoration planning in largely deforested regions

Species selection for forest restoration is often supported by expert knowledge on local distribution patterns of native tree species. This approach is not applicable to largely deforested regions unless enough data on pre-human tree species distribution is available. In such regions, ecological niche models may provide essential information to support species selection in the framework of forest restoration planning. In this study we used ecological niche models to predict habitat suitability for native tree species in "Tierra de Campos" region, an almost totally deforested area of the Duero Basin (Spain). Previously available models provide habitat suitability predictions for dominant native tree species, but including non-dominant tree species in the forest restoration planning may be desirable to promote biodiversity, specially in largely deforested areas were near seed sources are not expected. We used the Forest Map of Spain as species occurrence data source to maximize the number of modeled tree species. Penalized logistic regression was used to train models using climate and lithological predictors. Using model predictions a set of tools were developed to support species selection in forest restoration planning. Model predictions were used to build ordered lists of suitable species for each cell of the study area. The suitable species lists were summarized drawing maps that showed the two most suitable species for each cell. Additionally, potential distribution maps of the suitable species for the study area were drawn. For a scenario with two dominant species, the models predicted a mixed forest (Quercus ilex and a coniferous tree species) for almost one half of the study area. According to the models, 22 non-dominant native tree species are suitable for the study area, with up to six suitable species per cell. The model predictions pointed to Crataegus monogyna, Juniperus communis, J.oxycedrus and J.phoenicea as the most suitable non-dominant native tree species in the study area. Our results encourage further use of ecological niche models for forest restoration planning in largely deforested regions.

An Interval-based Multiobjective Approach to Feature Subset Selection Using Joint Modeling of Objectives and Variables

This paper studies feature subset selection in classification using a multiobjective estimation of distribution algorithm. We consider six functions, namely area under ROC curve, sensitivity, specificity, precision, F1 measure and Brier score, for evaluation of feature subsets and as the objectives of the problem. One of the characteristics of these objective functions is the existence of noise in their values that should be appropriately handled during optimization. Our proposed algorithm consists of two major techniques which are specially designed for the feature subset selection problem. The first one is a solution ranking method based on interval values to handle the noise in the objectives of this problem. The second one is a model estimation method for learning a joint probabilistic model of objectives and variables which is used to generate new solutions and advance through the search space. To simplify model estimation, l1 regularized regression is used to select a subset of problem variables before model learning. The proposed algorithm is compared with a well-known ranking method for interval-valued objectives and a standard multiobjective genetic algorithm. Particularly, the effects of the two new techniques are experimentally investigated. The experimental results show that the proposed algorithm is able to obtain comparable or better performance on the tested datasets.

Performance of artificial neural networks and genetical evolved artificial neural networks unfolding techniques

With the Bonner spheres spectrometer neutron spectrum is obtained through an unfolding procedure. Monte Carlo methods, Regularization, Parametrization, Least-squares, and Maximum Entropy are some of the techniques utilized for unfolding. In the last decade methods based on Artificial Intelligence Technology have been used. Approaches based on Genetic Algorithms and Artificial Neural Networks have been developed in order to overcome the drawbacks of previous techniques. Nevertheless the advantages of Artificial Neural Networks still it has some drawbacks mainly in the design process of the network, vg the optimum selection of the architectural and learning ANN parameters. In recent years the use of hybrid technologies, combining Artificial Neural Networks and Genetic Algorithms, has been utilized to. In this work, several ANN topologies were trained and tested using Artificial Neural Networks and Genetically Evolved Artificial Neural Networks in the aim to unfold neutron spectra using the count rates of a Bonner sphere spectrometer. Here, a comparative study of both procedures has been carried out.

Time-Frequency based Feature Selection for Discrimination of non stationary Biosignals.

This research proposes a generic methodology for dimensionality reduction upon time-frequency representations applied to the classification of different types of biosignals. The methodology directly deals with the highly redundant and irrelevant data contained in these representations, combining a first stage of irrelevant data removal by variable selection, with a second stage of redundancy reduction using methods based on linear transformations. The study addresses two techniques that provided a similar performance: the first one is based on the selection of a set of the most relevant time?frequency points, whereas the second one selects the most relevant frequency bands. The first methodology needs a lower quantity of components, leading to a lower feature space; but the second improves the capture of the time-varying dynamics of the signal, and therefore provides a more stable performance. In order to evaluate the generalization capabilities of the methodology proposed it has been applied to two types of biosignals with different kinds of non-stationary behaviors: electroencephalographic and phonocardiographic biosignals. Even when these two databases contain samples with different degrees of complexity and a wide variety of characterizing patterns, the results demonstrate a good accuracy for the detection of pathologies, over 98%.The results open the possibility to extrapolate the methodology to the study of other biosignals.

Application of self-organizing techniques for the distribution of heterogeneous multi-tasks in multi-robot systems

This paper focuses on the general problem of coordinating of multi-robot systems, more specifically, it addresses the self-election of heterogeneous and specialized tasks by autonomous robots. In this regard, it has proposed experimenting with two different techniques based chiefly on selforganization and emergence biologically inspired, by applying response threshold models as well as ant colony optimization. Under this approach it can speak of multi-tasks selection instead of multi-tasks allocation, that means, as the agents or robots select the tasks instead of being assigned a task by a central controller. The key element in these algorithms is the estimation of the stimuli and the adaptive update of the thresholds. This means that each robot performs this estimate locally depending on the load or the number of pending tasks to be performed. It has evaluated the robustness of the algorithms, perturbing the number of pending loads to simulate the robot’s error in estimating the real number of pending tasks and also the dynamic generation of loads through time. The paper ends with a critical discussion of experimental results.

Systematic Approach for the selection of monitoring technologies in CO2 geological storage projects. Application of multicriteria decision making

Geologic storage of carbon dioxide (CO2) has been proposed as a viable means for reducing anthropogenic CO2 emissions. Once injection begins, a program for measurement, monitoring, and verification (MMV) of CO2 distribution is required in order to: a) research key features, effects and processes needed for risk assessment; b) manage the injection process; c) delineate and identify leakage risk and surface escape; d) provide early warnings of failure near the reservoir; and f) verify storage for accounting and crediting. The selection of the methodology of monitoring (characterization of site and control and verification in the post-injection phase) is influenced by economic and technological variables. Multiple Criteria Decision Making (MCDM) refers to a methodology developed for making decisions in the presence of multiple criteria. MCDM as a discipline has only a relatively short history of 40 years, and it has been closely related to advancements on computer technology. Evaluation methods and multicriteria decisions include the selection of a set of feasible alternatives, the simultaneous optimization of several objective functions, and a decision-making process and evaluation procedures that must be rational and consistent. The application of a mathematical model of decision-making will help to find the best solution, establishing the mechanisms to facilitate the management of information generated by number of disciplines of knowledge. Those problems in which decision alternatives are finite are called Discrete Multicriteria Decision problems. Such problems are most common in reality and this case scenario will be applied in solving the problem of site selection for storing CO2. Discrete MCDM is used to assess and decide on issues that by nature or design support a finite number of alternative solutions. Recently, Multicriteria Decision Analysis has been applied to hierarchy policy incentives for CCS, to assess the role of CCS, and to select potential areas which could be suitable to store. For those reasons, MCDM have been considered in the monitoring phase of CO2 storage, in order to select suitable technologies which could be techno-economical viable. In this paper, we identify techniques of gas measurements in subsurface which are currently applying in the phase of characterization (pre-injection); MCDM will help decision-makers to hierarchy the most suitable technique which fit the purpose to monitor the specific physic-chemical parameter.

Criteria for mathematical model selection for satellite vibro-acoustic analysis depending on frequency range

Satellites and space equipment are exposed to diffuse acoustic fields during the launch process. The use of adequate techniques to model the response to the acoustic loads is a fundamental task during the design and verification phases. Considering the modal density of each element is necessary to identify the correct methodology. In this report selection criteria are presented in order to choose the correct modelling technique depending on the frequency ranges. A model satellite’s response to acoustic loads is presented, determining the modal densities of each component in different frequency ranges. The paper proposes to select the mathematical method in each modal density range and the differences in the response estimation due to the different used techniques. In addition, the methodologies to analyse the intermediate range of the system are discussed. The results are compared with experimental testing data obtained in an experimental modal test.

Fertilization Management: Assessing New Techniques and Key Interactions to Increase Crop Yields With Less N2O Emissions

El óxido nitroso (N2O) es un potente gas de efecto invernadero (GHG) proveniente mayoritariamente de la fertilización nitrogenada de los suelos agrícolas. Identificar estrategias de manejo de la fertilización que reduzcan estas emisiones sin suponer un descenso de los rendimientos es vital tanto a nivel económico como medioambiental. Con ese propósito, en esta Tesis se han evaluado: (i) estrategias de manejo directo de la fertilización (inhibidores de la nitrificación/ureasa); y (ii) interacciones de los fertilizantes con (1) el manejo del agua, (2) residuos de cosecha y (3) diferentes especies de plantas. Para conseguirlo se llevaron a cabo meta-análisis, incubaciones de laboratorio, ensayos en invernadero y experimentos de campo. Los inhibidores de la nitrificación y de la actividad ureasa se proponen habitualmente como medidas para reducir las pérdidas de nitrógeno (N), por lo que su aplicación estaría asociada al uso eficiente del N por parte de los cultivos (NUE). Sin embargo, su efecto sobre los rendimientos es variable. Con el objetivo de evaluar en una primera fase su efectividad para incrementar el NUE y la productividad de los cultivos, se llevó a cabo un meta-análisis. Los inhibidores de la nitrificación dicyandiamide (DCD) y 3,4-dimetilepyrazol phosphate (DMPP) y el inhibidor de la ureasa N-(n-butyl) thiophosphoric triamide (NBPT) fueron seleccionados para el análisis ya que generalmente son considerados las mejores opciones disponibles comercialmente. Nuestros resultados mostraron que su uso puede ser recomendado con el fin de incrementar tanto el rendimiento del cultivo como el NUE (incremento medio del 7.5% y 12.9%, respectivamente). Sin embargo, se observó que su efectividad depende en gran medida de los factores medioambientales y de manejo de los estudios evaluados. Una mayor respuesta fue encontrada en suelos de textura gruesa, sistemas irrigados y/o en cultivos que reciben altas tasas de fertilizante nitrogenado. En suelos alcalinos (pH ≥ 8), el inhibidor de la ureasa NBPT produjo el mayor efecto. Dado que su uso representa un coste adicional para los agricultores, entender las mejores prácticas que permitan maximizar su efectividad es necesario para posteriormente realizar comparaciones efectivas con otras prácticas que incrementen la productividad de los cultivos y el NUE. En base a los resultados del meta-análisis, se seleccionó el NBPT como un inhibidor con gran potencial. Inicialmente desarrollado para reducir la volatilización de amoniaco (NH3), en los últimos años algunos investigadores han demostrado en estudios de campo un efecto mitigador de este inhibidor sobre las pérdidas de N2O provenientes de suelos fertilizados bajo condiciones de baja humedad del suelo. Dada la alta variabilidad de los experimentos de campo, donde la humedad del suelo cambia rápidamente, ha sido imposible entender mecanísticamente el potencial de los inhibidores de la ureasa (UIs) para reducir emisiones de N2O y su dependencia con respecto al porcentaje de poros llenos de agua del suelo (WFPS). Por lo tanto se realizó una incubación en laboratorio con el propósito de evaluar cuál es el principal mecanismo biótico tras las emisiones de N2O cuando se aplican UIs bajo diferentes condiciones de humedad del suelo (40, 60 y 80% WFPS), y para analizar hasta qué punto el WFPS regula el efecto del inhibidor sobre las emisiones de N2O. Un segundo UI (i.e. PPDA) fue utilizado para comparar el efecto del NBPT con el de otro inhibidor de la ureasa disponible comercialmente; esto nos permitió comprobar si el efecto de NBPT es específico de ese inhibidor o no. Las emisiones de N2O al 40% WFPS fueron despreciables, siendo significativamente más bajas que las de todos los tratamientos fertilizantes al 60 y 80% WFPS. Comparado con la urea sin inhibidor, NBPT+U redujo las emisiones de N2O al 60% WFPS pero no tuvo efecto al 80% WFPS. La aplicación de PPDA incrementó significativamente las emisiones con respecto a la urea al 80% WFPS mientras que no se encontró un efecto significativo al 60% WFPS. Al 80% WFPS la desnitrificación fue la principal fuente de las emisiones de N2O en todos los tratamientos mientras que al 60% tanto la nitrificación como la desnitrificación tuvieron un papel relevante. Estos resultados muestran que un correcto manejo del NBPT puede suponer una estrategia efectiva para mitigar las emisiones de N2O. Con el objetivo de trasladar nuestros resultados de los estudios previos a condiciones de campo reales, se desarrolló un experimento en el que se evaluó la efectividad del NBPT para reducir pérdidas de N y aumentar la productividad durante un cultivo de cebada (Hordeum vulgare L.) en secano Mediterráneo. Se determinó el rendimiento del cultivo, las concentraciones de N mineral del suelo, el carbono orgánico disuelto (DOC), el potencial de desnitrificación, y los flujos de NH3, N2O y óxido nítrico (NO). La adición del inhibidor redujo las emisiones de NH3 durante los 30 días posteriores a la aplicación de urea en un 58% y las emisiones netas de N2O y NO durante los 95 días posteriores a la aplicación de urea en un 86 y 88%, respectivamente. El uso de NBPT también incrementó el rendimiento en grano en un 5% y el consumo de N en un 6%, aunque ninguno de estos incrementos fue estadísticamente significativo. Bajo las condiciones experimentales dadas, estos resultados demuestran el potencial del inhibidor de la ureasa NBPT para mitigar las emisiones de NH3, N2O y NO provenientes de suelos arables fertilizados con urea, mediante la ralentización de la hidrólisis de la urea y posterior liberación de menores concentraciones de NH4 + a la capa superior del suelo. El riego por goteo combinado con la aplicación dividida de fertilizante nitrogenado disuelto en el agua de riego (i.e. fertirriego por goteo) se considera normalmente una práctica eficiente para el uso del agua y de los nutrientes. Algunos de los principales factores (WFPS, NH4 + y NO3 -) que regulan las emisiones de GHGs (i.e. N2O, CO2 y CH4) y NO pueden ser fácilmente manipulados por medio del fertirriego por goteo sin que se generen disminuciones del rendimiento. Con ese propósito se evaluaron opciones de manejo para reducir estas emisiones en un experimento de campo durante un cultivo de melón (Cucumis melo L.). Los tratamientos incluyeron distintas frecuencias de riego (semanal/diario) y tipos de fertilizantes nitrogenados (urea/nitrato cálcico) aplicados por fertirriego. Fertirrigar con urea en lugar de nitrato cálcico aumentó las emisiones de N2O y NO por un factor de 2.4 y 2.9, respectivamente (P < 0.005). El riego diario redujo las emisiones de NO un 42% (P < 0.005) pero aumentó las emisiones de CO2 un 21% (P < 0.05) comparado con el riego semanal. Analizando el Poder de Calentamiento global en base al rendimiento así como los factores de emisión del NO, concluimos que el fertirriego semanal con un fertilizante de tipo nítrico es la mejor opción para combinar productividad agronómica con sostenibilidad medioambiental en este tipo de agroecosistemas. Los suelos agrícolas en las áreas semiáridas Mediterráneas se caracterizan por su bajo contenido en materia orgánica y bajos niveles de fertilidad. La aplicación de residuos de cosecha y/o abonos es una alternativa sostenible y eficiente desde el punto de vista económico para superar este problema. Sin embargo, estas prácticas podrían inducir cambios importantes en las emisiones de N2O de estos agroecosistemas, con impactos adicionales en las emisiones de CO2. En este contexto se llevó a cabo un experimento de campo durante un cultivo de cebada (Hordeum vulgare L.) bajo condiciones Mediterráneas para evaluar el efecto de combinar residuos de cosecha de maíz con distintos inputs de fertilizantes nitrogenados (purín de cerdo y/o urea) en estas emisiones. La incorporación de rastrojo de maíz incrementó las emisiones de N2O durante el periodo experimental un 105%. Sin embargo, las emisiones de NO se redujeron significativamente en las parcelas enmendadas con rastrojo. La sustitución parcial de urea por purín de cerdo redujo las emisiones netas de N2O un 46 y 39%, con y sin incorporación de residuo de cosecha respectivamente. Las emisiones netas de NO se redujeron un 38 y un 17% para estos mismos tratamientos. El ratio molar DOC:NO3 - demostró predecir consistentemente las emisiones de N2O y NO. El efecto principal de la interacción entre el fertilizante nitrogenado y el rastrojo de maíz se dio a los 4-6 meses de su aplicación, generando un aumento del N2O y una disminución del NO. La sustitución de urea por purín de cerdo puede considerarse una buena estrategia de manejo dado que el uso de este residuo orgánico redujo las emisiones de óxidos de N. Los pastos de todo el mundo proveen numerosos servicios ecosistémicos pero también suponen una importante fuente de emisión de N2O, especialmente en respuesta a la deposición de N proveniente del ganado mientras pasta. Para explorar el papel de las plantas como mediadoras de estas emisiones, se analizó si las emisiones de N2O dependen de la riqueza en especies herbáceas y/o de la composición específica de especies, en ausencia y presencia de una deposición de orina. Las hipótesis fueron: 1) las emisiones de N2O tienen una relación negativa con la productividad de las plantas; 2) mezclas de cuatro especies generan menores emisiones que monocultivos (dado que su productividad será mayor); 3) las emisiones son menores en combinaciones de especies con distinta morfología radicular y alta biomasa de raíz; y 4) la identidad de las especies clave para reducir el N2O depende de si hay orina o no. Se establecieron monocultivos y mezclas de dos y cuatro especies comunes en pastos con rasgos funcionales divergentes: Lolium perenne L. (Lp), Festuca arundinacea Schreb. (Fa), Phleum pratense L. (Php) y Poa trivialis L. (Pt), y se cuantificaron las emisiones de N2O durante 42 días. No se encontró relación entre la riqueza en especies y las emisiones de N2O. Sin embargo, estas emisiones fueron significativamente menores en ciertas combinaciones de especies. En ausencia de orina, las comunidades de plantas Fa+Php actuaron como un sumidero de N2O, mientras que los monocultivos de estas especies constituyeron una fuente de N2O. Con aplicación de orina la comunidad Lp+Pt redujo (P < 0.001) las emisiones de N2O un 44% comparado con los monocultivos de Lp. Las reducciones de N2O encontradas en ciertas combinaciones de especies pudieron explicarse por una productividad total mayor y por una complementariedad en la morfología radicular. Este estudio muestra que la composición de especies herbáceas es un componente clave que define las emisiones de N2O de los ecosistemas de pasto. La selección de combinaciones de plantas específicas en base a la deposición de N esperada puede, por lo tanto, ser clave para la mitigación de las emisiones de N2O. ABSTRACT Nitrous oxide (N2O) is a potent greenhouse gas (GHG) directly linked to applications of nitrogen (N) fertilizers to agricultural soils. Identifying mitigation strategies for these emissions based on fertilizer management without incurring in yield penalties is of economic and environmental concern. With that aim, this Thesis evaluated: (i) the use of nitrification and urease inhibitors; and (ii) interactions of N fertilizers with (1) water management, (2) crop residues and (3) plant species richness/identity. Meta-analysis, laboratory incubations, greenhouse mesocosm and field experiments were carried out in order to understand and develop effective mitigation strategies. Nitrification and urease inhibitors are proposed as means to reduce N losses, thereby increasing crop nitrogen use efficiency (NUE). However, their effect on crop yield is variable. A meta-analysis was initially conducted to evaluate their effectiveness at increasing NUE and crop productivity. Commonly used nitrification inhibitors (dicyandiamide (DCD) and 3,4-dimethylepyrazole phosphate (DMPP)) and the urease inhibitor N-(n-butyl) thiophosphoric triamide (NBPT) were selected for analysis as they are generally considered the best available options. Our results show that their use can be recommended in order to increase both crop yields and NUE (grand mean increase of 7.5% and 12.9%, respectively). However, their effectiveness was dependent on the environmental and management factors of the studies evaluated. Larger responses were found in coarse-textured soils, irrigated systems and/or crops receiving high nitrogen fertilizer rates. In alkaline soils (pH ≥ 8), the urease inhibitor NBPT produced the largest effect size. Given that their use represents an additional cost for farmers, understanding the best management practices to maximize their effectiveness is paramount to allow effective comparison with other practices that increase crop productivity and NUE. Based on the meta-analysis results, NBPT was identified as a mitigation option with large potential. Urease inhibitors (UIs) have shown to promote high N use efficiency by reducing ammonia (NH3) volatilization. In the last few years, however, some field researches have shown an effective mitigation of UIs over N2O losses from fertilized soils under conditions of low soil moisture. Given the inherent high variability of field experiments where soil moisture content changes rapidly, it has been impossible to mechanistically understand the potential of UIs to reduce N2O emissions and its dependency on the soil water-filled pore space (WFPS). An incubation experiment was carried out aiming to assess what is the main biotic mechanism behind N2O emission when UIs are applied under different soil moisture conditions (40, 60 and 80% WFPS), and to analyze to what extent the soil WFPS regulates the effect of the inhibitor over N2O emissions. A second UI (i.e. PPDA) was also used aiming to compare the effect of NBPT with that of another commercially available urease inhibitor; this allowed us to see if the effect of NBPT was inhibitor-specific or not. The N2O emissions at 40% WFPS were almost negligible, being significantly lower from all fertilized treatments than that produced at 60 and 80% WFPS. Compared to urea alone, NBPT+U reduced the N2O emissions at 60% WFPS but had no effect at 80% WFPS. The application of PPDA significantly increased the emissions with respect to U at 80% WFPS whereas no significant effect was found at 60% WFPS. At 80% WFPS denitrification was the main source of N2O emissions for all treatments. Both nitrification and denitrification had a determinant role on these emissions at 60% WFPS. These results suggest that adequate management of the UI NBPT can provide, under certain soil conditions, an opportunity for N2O mitigation. We translated our previous results to realistic field conditions by means of a field experiment with a barley crop (Hordeum vulgare L.) under rainfed Mediterranean conditions in which we evaluated the effectiveness of NBPT to reduce N losses and increase crop yields. Crop yield, soil mineral N concentrations, dissolved organic carbon (DOC), denitrification potential, NH3, N2O and nitric oxide (NO) fluxes were measured during the growing season. The inclusion of the inhibitor reduced NH3 emissions in the 30 d following urea application by 58% and net N2O and NO emissions in the 95 d following urea application by 86 and 88%, respectively. NBPT addition also increased grain yield by 5% and N uptake by 6%, although neither increase was statistically significant. Under the experimental conditions presented here, these results demonstrate the potential of the urease inhibitor NBPT in abating NH3, N2O and NO emissions from arable soils fertilized with urea, slowing urea hydrolysis and releasing lower concentrations of NH4 + to the upper soil layer. Drip irrigation combined with split application of N fertilizer dissolved in the irrigation water (i.e. drip fertigation) is commonly considered best management practice for water and nutrient efficiency. Some of the main factors (WFPS, NH4 + and NO3 -) regulating the emissions of GHGs (i.e. N2O, carbon dioxide (CO2) and methane (CH4)) and NO can easily be manipulated by drip fertigation without yield penalties. In this study, we tested management options to reduce these emissions in a field experiment with a melon (Cucumis melo L.) crop. Treatments included drip irrigation frequency (weekly/daily) and type of N fertilizer (urea/calcium nitrate) applied by fertigation. Crop yield, environmental parameters, soil mineral N concentrations, N2O, NO, CH4, and CO2 fluxes were measured during the growing season. Fertigation with urea instead of calcium nitrate increased N2O and NO emissions by a factor of 2.4 and 2.9, respectively (P < 0.005). Daily irrigation reduced NO emissions by 42% (P < 0.005) but increased CO2 emissions by 21% (P < 0.05) compared with weekly irrigation. Based on yield-scaled Global Warming Potential as well as NO emission factors, we conclude that weekly fertigation with a NO3 --based fertilizer is the best option to combine agronomic productivity with environmental sustainability. Agricultural soils in semiarid Mediterranean areas are characterized by low organic matter contents and low fertility levels. Application of crop residues and/or manures as amendments is a cost-effective and sustainable alternative to overcome this problem. However, these management practices may induce important changes in the nitrogen oxide emissions from these agroecosystems, with additional impacts on CO2 emissions. In this context, a field experiment was carried out with a barley (Hordeum vulgare L.) crop under Mediterranean conditions to evaluate the effect of combining maize (Zea mays L.) residues and N fertilizer inputs (organic and/or mineral) on these emissions. Crop yield and N uptake, soil mineral N concentrations, dissolved organic carbon (DOC), denitrification capacity, N2O, NO and CO2 fluxes were measured during the growing season. The incorporation of maize stover increased N2O emissions during the experimental period by c. 105 %. Conversely, NO emissions were significantly reduced in the plots amended with crop residues. The partial substitution of urea by pig slurry reduced net N2O emissions by 46 and 39 %, with and without the incorporation of crop residues respectively. Net emissions of NO were reduced 38 and 17 % for the same treatments. Molar DOC:NO3 - ratio was found to be a robust predictor of N2O and NO fluxes. The main effect of the interaction between crop residue and N fertilizer application occurred in the medium term (4-6 month after application), enhancing N2O emissions and decreasing NO emissions as consequence of residue incorporation. The substitution of urea by pig slurry can be considered a good management strategy since N2O and NO emissions were reduced by the use of the organic residue. Grassland ecosystems worldwide provide many important ecosystem services but they also function as a major source of N2O, especially in response to N deposition by grazing animals. In order to explore the role of plants as mediators of these emissions, we tested whether and how N2O emissions are dependent on grass species richness and/or specific grass species composition in the absence and presence of urine deposition. We hypothesized that: 1) N2O emissions relate negatively to plant productivity; 2) four-species mixtures have lower emissions than monocultures (as they are expected to be more productive); 3) emissions are lowest in combinations of species with diverging root morphology and high root biomass; and 4) the identity of the key species that reduce N2O emissions is dependent on urine deposition. We established monocultures and two- and four-species mixtures of common grass species with diverging functional traits: Lolium perenne L. (Lp), Festuca arundinacea Schreb. (Fa), Phleum pratense L. (Php) and Poa trivialis L. (Pt), and quantified N2O emissions for 42 days. We found no relation between plant species richness and N2O emissions. However, N2O emissions were significantly reduced in specific plant species combinations. In the absence of urine, plant communities of Fa+Php acted as a sink for N2O, whereas the monocultures of these species constituted a N2O source. With urine application Lp+Pt plant communities reduced (P < 0.001) N2O emissions by 44% compared to monocultures of Lp. Reductions in N2O emissions by species mixtures could be explained by total biomass productivity and by complementarity in root morphology. Our study shows that plant species composition is a key component underlying N2O emissions from grassland ecosystems. Selection of specific grass species combinations in the context of the expected nitrogen deposition regimes may therefore provide a key management practice for mitigation of N2O emissions.

Application of NMR techniques for the evaluation of fruit internal quality and physical properties of food

Los alimentos son sistemas complejos, formados por diversas estructuras a diferentes escalas: macroscópica y microscópica. Muchas propiedades de los alimentos, que son importantes para su procesamiento, calidad y tratamiento postcosecha, están relacionados con su microestructura. La presente tesis doctoral propone una metodología completa para la determinación de la estructura de alimentos desde un punto de vista multi-escala, basándose en métodos de Resonancia Magnética Nuclear (NMR). Las técnicas de NMR son no invasivas y no destructivas y permiten el estudio tanto de macro- como de microestructura. Se han utilizado distintos procedimientos de NMR dependiendo del nivel que se desea estudiar. Para el nivel macroestructural, la Imagen de Resonancia Magnética (MRI) ha resultado ser muy útil para la caracterización de alimentos. Para el estudio microestructural, la MRI requiere altos tiempos de adquisición, lo que hace muy difícil la transferencia de esta técnica a aplicaciones en industria. Por tanto, la optimización de procedimientos de NMR basados en secuencias relaxometría 2D T1/T2 ha resultado ser una estrategia primordial en esta tesis. Estos protocolos de NMR se han implementado satisfactoriamente por primera vez en alto campo magnético. Se ha caracterizado la microestructura de productos alimentarios enteros por primera vez utilizando este tipo de protocolos. Como muestras, se han utilizado dos tipos de productos: modelos de alimentos y alimentos reales (manzanas). Además, como primer paso para su posterior implementación en la industria agroalimentaria, se ha mejorado una línea transportadora, especialmente diseñada para trabajar bajo condiciones de NMR en trabajos anteriores del grupo LPF-TAGRALIA. Se han estudiado y seleccionado las secuencias más rápidas y óptimas para la detección de dos tipos de desórdenes internos en manzanas: vitrescencia y roturas internas. La corrección de las imágenes en movimiento se realiza en tiempo real. Asimismo, se han utilizado protocolos de visión artificial para la clasificación automática de manzanas potencialmente afectadas por vitrescencia. El presente documento está dividido en diferentes capítulos: el Capítulo 2 explica los antecedentes de la presente tesis y el marco del proyecto en el que se ha desarrollado. El Capítulo 3 recoge el estado del arte. El Capítulo 4 establece los objetivos de esta tesis doctoral. Los resultados se dividen en cinco sub-secciones (dentro del Capítulo 5) que corresponden con los trabajos publicados bien en revistas revisadas por pares, bien en congresos internacionales o bien como capítulos de libros revisados por pares. La Sección 5.1. es un estudio del desarrollo de la vitrescencia en manzanas mediante MRI y lo relaciona con la posición de la fruta dentro de la copa del árbol. La Sección 5.2 presenta un trabajo sobre macro- y microestructura en modelos de alimentos. La Sección 5.3 es un artículo en revisión en una revista revisada por pares, en el que se hace un estudio microestrcutural no destructivo mediante relaxometría 2D T1/T2. la Sección 5.4, hace una comparación entre manzanas afectadas por vitrescencia mediante dos técnicas: tomografía de rayos X e MRI, en manzana. Por último, en la Sección 5.5 se muestra un trabajo en el que se hace un estudio de secuencias de MRI en línea para la evaluación de calidad interna en manzanas. Los siguientes capítulos ofrecen una discusión y conclusiones (Capítulo 6 y 7 respectivamente) de todos los capítulos de esta tesis doctoral. Finalmente, se han añadido tres apéndices: el primero con una introducción de los principios básicos de resonancia magnética nuclear (NMR) y en los otros dos, se presentan sendos estudios sobre el efecto de las fibras en la rehidratación de cereales de desayuno extrusionados, mediante diversas técnicas. Ambos trabajos se presentaron en un congreso internacional. Los resultados más relevantes de la presente tesis doctoral, se pueden dividir en tres grandes bloques: resultados sobre macroestructura, resultados sobre microestructura y resultados sobre MRI en línea. Resultados sobre macroestructura: - La imagen de resonancia magnética (MRI) se aplicó satisfactoriamente para la caracterización de macroestructura. En particular, la reconstrucción 3D de imágenes de resonancia magnética permitió identificar y caracterizar dos tipos distintos de vitrescencia en manzanas: central y radial, que se caracterizan por el porcentaje de daño y la conectividad (número de Euler). - La MRI proveía un mejor contraste para manzanas afectadas por vitrescencia que las imágenes de tomografía de rayos X (X-Ray CT), como se pudo verificar en muestras idénticas de manzana. Además, el tiempo de adquisición de la tomografía de rayos X fue alrededor de 12 veces mayor (25 minutos) que la adquisición de las imágenes de resonancia magnética (2 minutos 2 segundos). Resultados sobre microestructura: - Para el estudio de microestructura (nivel subcelular) se utilizaron con éxito secuencias de relaxometría 2D T1/T2. Estas secuencias se usaron por primera vez en alto campo y sobre piezas de alimento completo, convirtiéndose en una forma no destructiva de llevar a cabo estudios de microestructura. - El uso de MRI junto con relaxometría 2D T1/T2 permite realizar estudios multiescala en alimentos de forma no destructiva. Resultados sobre MRI en línea: - El uso de imagen de resonancia magnética en línea fue factible para la identificación de dos tipos de desórdenes internos en manzanas: vitrescencia y podredumbre interna. Las secuencias de imagen tipo FLASH resultaron adecuadas para la identificación en línea de vitrescencia en manzanas. Se realizó sin selección de corte, debido a que la vitrescencia puede desarrollarse en cualquier punto del volumen de la manzana. Se consiguió reducir el tiempo de adquisición, de modo que se llegaron a adquirir 1.3 frutos por segundos (758 ms por fruto). Las secuencias de imagen tipo UFLARE fueron adecuadas para la detección en línea de la podredumbre interna en manzanas. En este caso, se utilizó selección de corte, ya que se trata de un desorden que se suele localizar en la parte central del volumen de la manzana. Se consiguió reducir el tiempo de adquisicón hasta 0.67 frutos por segundo (1475 ms por fruto). En ambos casos (FLASH y UFLARE) fueron necesarios algoritmos para la corrección del movimiento de las imágenes en tiempo real. ABSTRACT Food is a complex system formed by several structures at different scales: macroscopic and microscopic. Many properties of foods that are relevant to process engineering or quality and postharvest treatments are related to their microstructure. This Ph.D Thesis proposes a complete methodology for food structure determination, in a multiscale way, based on the Nuclear Magnetic Resonance (NMR) phenomenon since NMR techniques are non-invasive and non-destructive, and allow both, macro- and micro-structure study. Different NMR procedures are used depending on the structure level under study. For the macrostructure level, Magnetic Resonance Imaging (MRI) revealed its usefulness for food characterization. For microstructure insight, MRI required high acquisition times, which is a hindrance for transference to industry applications. Therefore, optimization of NMR procedures based on T1/T2 relaxometry sequences was a key strategy in this Thesis. These NMR relaxometry protocols, are successfully implemented in high magnetic field. Microstructure of entire food products have been characterized for the first time using these protocols. Two different types of food products have been studied: food models and actual food (apples). Furthermore, as a first step for the food industry implementation, a grading line system, specially designed for working under NMR conditions in previous works of the LPF-TAGRALIA group, is improved. The study and selection of the most suitable rapid sequence to detect two different types of disorders in apples (watercore and internal breakdown) is performed and the real time image motion correction is applied. In addition, artificial vision protocols for the automatic classification of apples potentially affected by watercore are applied. This document is divided into seven different chapters: Chapter 2 explains the thesis background and the framework of the project in which it has been worked. Chapter 3 comprises the state of the art. Chapter 4 establishes de objectives of this Ph.D thesis. The results are divided into five different sections (in Chapter 5) that correspond to published peered reviewed works. Section 5.1 assesses the watercore development in apples with MRI and studies the effect of fruit location in the canopy. Section 5.2 is an MRI and 2D relaxometry study for macro- and microstructure assessment in food models. Section 5.3 is a non-destructive microstructural study using 2D T1/T2 relaxometry on watercore affected apples. Section 5.4 makes a comparison of X-ray CT and MRI on watercore disorder of different apple cultivars. Section 5.5, that is a study of online MRI sequences for the evaluation of apple internal quality. The subsequent chapters offer a general discussion and conclusions (Chapter 6 and Chapter 7 respectively) of all the works performed in the frame of this Ph.D thesis (two peer reviewed journals, one book chapter and one international congress).Finally, three appendices are included in which an introduction to NMR principles is offered and two published proceedings regarding the effect of fiber on the rehydration of extruded breakfast cereal are displayed. The most relevant results can be summarized into three sections: results on macrostructure, results on microstructure and results on on-line MRI. Results on macrostructure: - MRI was successfully used for macrostructure characterization. Indeed, 3D reconstruction of MRI in apples allows to identify two different types of watercore (radial and block), which are characterized by the percentage of damage and the connectivity (Euler number). - MRI provides better contrast for watercore than X-Ray CT as verified on identical samples. Furthermore, X-Ray CT images acquisition time was around 12 times higher (25 minutes) than MRI acquisition time (2 minutes 2 seconds). Results on microstructure: - 2D T1/T2 relaxometry were successfully applied for microstructure (subcellular level) characterization. 2D T1/T2 relaxometry sequences have been applied for the first time on high field for entire food pieces, being a non-destructive way to achieve microstructure study. - The use of MRI together with 2D T1/T2 relaxometry sequences allows a non-destructive multiscale study of food. Results on on-line MRI: - The use of on-line MRI was successful for the identification of two different internal disorders in apples: watercore and internal breakdown. FLASH imaging was a suitable technique for the on-line detection of watercore disorder in apples, with no slice selection, since watercore is a physiological disorder that may be developed anywhere in the apple volume. 1.3 fruits were imaged per second (768 ms per fruit). UFLARE imaging is a suitable sequence for the on-line detection of internal breakdown disorder in apples. Slice selection was used, as internal breakdown is usually located in the central slice of the apple volume. 0.67 fruits were imaged per second (1475 ms per fruit). In both cases (FLASH and UFLARE) motion correction was performed in real time, during the acquisition of the images.

Analysis of pattern recognition and dimensionality reduction techniques for odor biometrics

In this paper, we analyze the performance of several well-known pattern recognition and dimensionality reduction techniques when applied to mass-spectrometry data for odor biometric identification. Motivated by the successful results of previous works capturing the odor from other parts of the body, this work attempts to evaluate the feasibility of identifying people by the odor emanated from the hands. By formulating this task according to a machine learning scheme, the problem is identified with a small-sample-size supervised classification problem in which the input data is formed by mass spectrograms from the hand odor of 13 subjects captured in different sessions. The high dimensionality of the data makes it necessary to apply feature selection and extraction techniques together with a simple classifier in order to improve the generalization capabilities of the model. Our experimental results achieve recognition rates over 85% which reveals that there exists discriminatory information in the hand odor and points at body odor as a promising biometric identifier.

Systematizing requirements elicitation technique selection

Context: This research deals with requirements elicitation technique selection for software product requirements and the overselection of open interviews. Objectives: This paper proposes and validates a framework to help requirements engineers select the most adequate elicitation techniques at any time. Method: We have explored both the existing underlying theory and the results of empirical research to build the framework. Based on this, we have deduced and put together justified proposals about the framework components. We have also had to add information not found in theoretical or empirical sources. In these cases, we drew on our own experience and expertise. Results: A new validated approach for requirements technique selection. This new approach selects tech- niques other than open interview, offers a wider range of possible techniques and captures more require- ments information. Conclusions: The framework is easily extensible and changeable. Whenever any theoretical or empirical evidence for an attribute, technique or adequacy value is unearthed, the information can be easily added to the framework.