18 resultados para Lambert, David M.: Speciation and recognition concept.Theory and application

em Universidad Politécnica de Madrid


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Se ha estudiado la determinación de especies de arsénico y de contenidos totales de arsénico y metales pesados, específicamente cadmio, cromo, cobre, níquel, plomo y cinc, en muestras de interés medioambiental por su elevada capacidad acumuladora de metales, concretamente algas marinas comestibles y plantas terrestres procedentes de suelos contaminados por la actividad minera. La determinación de contenidos totales se ha llevado a cabo mediante espectrometría de emisión atómica con plasma de acoplamiento inductivo (ICP‐AES), así como por espectrometría de fluorescencia atómica con generación de hidruros (HG‐AFS), para bajos contenidos de arsénico. Las muestras fueron mineralizadas en medio ácido y calentamiento en horno de microondas. Los métodos fueron validados a través de su aplicación a materiales de referencia de matriz similar a la de las muestras, certificados en contenidos totales de los elementos seleccionados. Los resultados obtenidos mostraron su elevada capacidad de bioabsorción, especialmente en relación a los elevados contenidos de arsénico encontrados en algunas especies de algas pardas (Phaeophytas). En las plantas, se calcularon los factores de translocación, acumulación y biodisponibilidad de los elementos estudiados, permitiendo identificar a la especie Corrigiola telephiifolia como posible acumuladora de plomo e hiperacumuladora de arsénico. La determinación de especies de arsénico hidrosolubles en las muestras objeto de estudio, se llevó a cabo por cromatografía líquida de alta eficacia (HPLC) acoplado a ICP‐AES, HG‐ICP‐AES y HG‐AFS, incluyendo una etapa previa de foto‐oxidación. Los métodos desarrollados, mediante intercambio aniónico y catiónico, permitieron la diferenciación de hasta once especies de arsénico. Para el análisis de las muestras, fue necesaria la optimización de métodos de extracción, seleccionándose la extracción asistida por microondas (MAE) con agua desionizada. Asimismo, se realizaron estudios de estabilidad de arsénico total y de las especies hidrosolubles presentes en las algas, tanto sobre la muestra sólida como en sus extractos acuosos, evaluando las condiciones de almacenamiento adecuadas. En el caso de las plantas, la aplicación del diseño factorial de experimentos permitió optimizar el método de extracción y diferenciar entre las especies de arsénico presentes en forma de iones sencillos de mayor movilidad y el arsénico más fuertemente enlazado a componentes estructurales. Los resultados obtenidos permitieron identificar la presencia de arseniato (As(V)) y arsenito (As(III)) en las plantas, así como de ácido monometilarsónico (MMA) y óxido de trimetilarsina (TMAO) en algunas especies. En la mayoría de las algas se encontraron especies tóxicas, tanto mayoritarias (arseniato) como minoritarias (ácido dimetilarsínico (DMA)), así como hasta cuatro arsenoazúcares. Los resultados obtenidos y su estudio a través de la legislación vigente, mostraron la necesidad de desarrollar una reglamentación específica para el control de este tipo de alimentos. La determinación de especies de arsénico liposolubles en las muestras de algas se llevó a cabo mediante HPLC, en modo fase inversa, acoplado a espectrometría de masas con plasma de acoplamiento inductivo (ICP‐MS) y con ionización por electrospray (ESI‐MS), permitiendo la elucidación estructural de estos compuestos a través de la determinación de sus masas moleculares. Para ello, fue necesaria la puesta a punto de métodos extracción y purificación de los extractos. La metodología desarrollada permitió identificar hasta catorce especies de arsénico liposolubles en las algas, tres de ellas correspondientes a hidrocarburos que contienen arsénico, y once a arsenofosfolípidos, además de dos especies desconocidas. Las masas moleculares de las especies identificadas fueron confirmadas mediante cromatografía de gases acoplada a espectrometría de masas (GC‐MS) y espectrometría de masas de alta resolución (HR‐MS). ABSTRACT The determination of arsenic species and total arsenic and heavy metal contents (cadmium, chromium, cooper, nickel, lead and zinc) in environmental samples, with high metal accumulator capacity, has been studied. The samples studied were edible marine algae and terrestrial plants from soils polluted by mining activities. The determination of total element contents was performed by inductively coupled plasma atomic emission spectrometry (ICP‐AES), as well as by hydride generation atomic fluorescence spectrometry (HG‐AFS) for low arsenic contents. The samples studied were digested in an acidic medium by heating in a microwave oven. The digestion methods were validated against reference materials, with matrix similar to sample matrix and certified in total contents of the elements studied. The results showed the high biosorption capacity of the samples studied, especially regarding the high arsenic contents in some species of brown algae (Phaeophyta division). In terrestrial plants, the translocation, accumulation and bioavailability factors of the elements studied were calculated. Thus, the plant species Corrigiola telephiifolia was identified as possible lead accumulator and arsenic hyperaccumulator. The determination of water‐soluble arsenic species in the samples studied was carried out by high performance liquid chromatography (HPLC) coupled to ICP‐AES, HG‐ICP‐AES and HG‐AFS, including a prior photo‐oxidation step. The chromatographic methods developed, by anion and cation exchange, allowed us to differentiate up to eleven arsenic species. The sample analysis required the optimization of extraction methods, choosing the microwave assisted extraction (MAE) with deionized water. On the other hand, the stability of total arsenic and water‐soluble arsenic species in algae, both in the solid samples and in the water extracts, was studied, assessing the suitable storage conditions. In the case of plant samples, the application of a multivariate experimental design allowed us to optimize the extraction method and differentiate between the arsenic species present as simple ions of higher mobility and the arsenic more strongly bound to structural components. The presence of arsenite (As(III)) and arsenate (As(V)) was identified in plant samples, as well as monomethylarsonic acid (MMA) and trimethylarsine oxide (TMAO) in some cases. Regarding algae, toxic arsenic species were found in most of them, both As(V) and dimethylarsinic acid (DMA), as well as up to four arsenosugars. These results were discussed according to the current legislation, showing the need to develop specific regulations to control this kind of food products. The determination of lipid‐soluble arsenic species in alga samples was performed by reversed‐phase HPLC coupled to inductively coupled plasma and electrospray mass spectrometry (ICP‐MS and ESI‐MS), in order to establish the structure of these compounds by determining the corresponding molecular masses. For this purpose, it was necessary to develop an extraction method, as well as a clean‐up method of the extracts. The method developed permitted the identification of fourteen lipid‐soluble arsenic compounds in algae, corresponding to three arsenic‐hydrocarbons and eleven arsenosugarphospholipids, as well as two unknown compounds. Accurate mass measurements of the identified compounds were performed by gas chromatography coupled to mass spectrometry (GC‐MS) and high resolution mass spectrometry (HR‐MS).

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A one-step extraction procedure and a leaching column experiment were performed to assess the effects of citric and tartaric acids on Cu and Zn mobilization in naturally contaminated mine soils to facilitate assisted phytoextraction. A speciation modeling of the soil solution and the metal fractionation of soils were performed to elucidate the chemical processes that affected metal desorption by organic acids. Different extracting solutions were prepared, all of which contained 0.01 M KNO3 and different concentrations of organic acids: control without organic acids, 0.5 mM citric, 0.5 mM tartaric, 10 mM citric, 10 mM tartaric, and 5 mM citric +5 mM tartaric. The results of the extraction procedure showed that higher concentrations of organic acids increased metal desorption, and citric acid was more effective at facilitating metal desorption than tartaric acid. Metal desorption was mainly influenced by the decreasing pH and the dissolution of Fe and Mn oxides, not by the formation of soluble metal–organic complexes as was predicted by the speciation modeling. The results of the column study reported that low concentrations of organic acids did not significantly increase metal mobilization and that higher doses were also not able to mobilize Zn. However, 5–10 mM citric acid significantly promoted Cu mobilization (from 1 mg kg−1 in the control to 42 mg kg−1 with 10 mM citric acid) and reduced the exchangeable (from 21 to 3 mg kg−1) and the Fe and Mn oxides (from 443 to 277 mg kg−1) fractions. Citric acid could efficiently facilitate assisted phytoextraction techniques.

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The combination of minimum time control and multiphase converter is a favorable option for dc-dc converters in applications where output voltage variation is required, such as RF amplifiers and dynamic voltage scaling in microprocessors, due to their advantage of fast dynamic response. In this paper, an improved minimum time control approach for multiphase buck converter that is based on charge balance technique, aiming at fast output voltage transition is presented. Compared with the traditional method, the proposed control takes into account the phase delay and current ripple in each phase. Therefore, by investigating the behavior of multiphase converter during voltage transition, it resolves the problem of current unbalance after the transient, which can lead to long settling time of the output voltage. The restriction of this control is that the output voltage that the converter can provide is related to the number of the phases, because only the duty cycles at which the multiphase converter has total ripple cancellation are used in this approach. The model of the proposed control is introduced, and the design constraints of the buck converters filter for this control are discussed. In order to prove the concept, a four-phase buck converter is implemented and the experimental results that validate the proposed control method are presented. The application of this control to RF envelope tracking is also presented in this paper.

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In the present study (i) the impact of plant Boron (B) status on foliar B absorption and (ii) the effect of B complexation with polyols (sorbitol or mannitol) on B absorption and translocation was investigated. Soybean (Glycine max (L.) Meer.) plants grown in nutrient solution containing 0 μM, 10 μM, 30 μM or 100 μM 11B labelled boric acid (BA) were treated with 50 mM 10B labelled BA applied to the basal parts of two leaflets of one leaf, either pure or in combination with 500 mM sorbitol or mannitol. After one week, 10B concentrations in different plant parts were determined. In B deficient leaves (0 μM 11B), 10B absorption was significantly lower than in all other treatments (9.7% of the applied dose vs. 26%–32%). The application of BA in combination with polyols increased absorption by 18–25% as compared to pure BA. The absolute amount of applied 10B moving out of the application zone was lowest in plants with 0 μM 11B supply (1.1% of the applied dose) and highest in those grown in 100 μM 11B (2.8%). The presence of sorbitol significantly decreased the share of mobile 10B in relation to the amount absorbed. The results suggest that 11B deficiency reduces the permeability of the leaf surface for BA. The addition of polyols may increase 10B absorption, but did not improve 10B distribution within the plant, which was even hindered when applied a sorbitol complex.

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Se revisan los métodas de valoración de arbolado

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CO2 capture and storage (CCS) projects are presently developed to reduce the emission of anthropogenic CO2 into the atmosphere. CCS technologies are expected to account for the 20% of the CO2 reduction by 2050. Geophysical, ground deformation and geochemical monitoring have been carried out to detect potential leakage, and, in the event that this occurs, identify and quantify it. This monitoring needs to be developed prior, during and after the injection stage. For a correct interpretation and quantification of the leakage, it is essential to establish a pre-injection characterization (baseline) of the area affected by the CO2 storage at reservoir level as well as at shallow depth, surface and atmosphere, via soil gas measurements. Therefore, the methodological approach is important because it can affect the spatial and temporal variability of this flux and even jeopardize the total value of CO2 in a given area. In this sense, measurements of CO2 flux were done using portable infrared analyzers (i.e., accumulation chambers) adapted to monitoring the geological storage of CO2, and other measurements of trace gases, e.g. radon isotopes and remote sensing imagery were tested in the natural analogue of Campo de Calatrava (Ciudad Real, Spain) with the aim to apply in CO2 leakage detection; thus, observing a high correlation between CO2 and radon (r=0,858) and detecting some vegetation indices that may be successfully applied for the leakage detection.

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Generation of Fission Yield covariance data and application to Fission Pulse Decay Heat calculations

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Caracterización de los procesos de disipación mecánica basándose en la microestructura de los tejidos blandos. We present a continuous damage model with regularized softening (smeared crack models) for fiber reinforced soft tissues. Material parameters of the continuous model derive from the mesoscopic scale. In the mesoscopic scale continuum is considered as a collagenous fibrilreinforced composite. We want to study the continnumlevel response as a function of the nanoscale properties of the collagen and the adherent forces between the tropocollagen molecules.

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At present, all methods in Evolutionary Computation are bioinspired by the fundamental principles of neo-Darwinism, as well as by a vertical gene transfer. Virus transduction is one of the key mechanisms of horizontal gene propagation in microorganisms (e.g. bacteria). In the present paper, we model and simulate a transduction operator, exploring the possible role and usefulness of transduction in a genetic algorithm. The genetic algorithm including transduction has been named PETRI (abbreviation of Promoting Evolution Through Reiterated Infection). Our results showed how PETRI approaches higher fitness values as transduction probability comes close to 100%. The conclusion is that transduction improves the performance of a genetic algorithm, assuming a population divided among several sub-populations or ?bacterial colonies?.

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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. ?

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New technologies such as, the new Information and Communication Technology ICT, break new paths and redefines the way we understand business, the Cloud Computing is one of them. The on demand resource gathering and the per usage payment scheme are now commonplace, and allows companies to save on their ICT investments. Despite the importance of this issue, we still lack methodologies that help companies, to develop applications oriented for its exploitation in the Cloud. In this study we aim to fill this gap and propose a methodology for the development of ICT applications, which are directed towards a business model, and further outsourcing in the Cloud. In the former the Development of SOA applications, we take, as a baseline scenario, a business model from which to obtain a business process model. To this end, we use software engineering tools; and in the latter The Outsourcing we propose a guide that would facilitate uploading business models into the Cloud; to this end we describe a SOA governance model, which controls the SOA. Additionally we propose a Cloud government that integrates Service Level Agreements SLAs, plus SOA governance, and Cloud architecture. Finally we apply our methodology in an example illustrating our proposal. We believe that our proposal can be used as a guide/pattern for the development of business applications.

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In this article an experimental campaign aimed at validating a previously published simplified serviceability design method of the columns of long jointless structures is presented. The proposed method is also extended to include tension stiffening effects which proved to be significant in structures with small amount of reinforcement subjected to small axial loading. This extension allows significant improvement of predictions for this type of element. The campaign involved columns with different reinforcement and squashing load ratios, given that these parameters had been identified as crucial when designing columns subjected to imposed displacements. Experimental results are presented and discussed, with particular regard to cracking behaviour and structural stiffness. Considerations on tension stiffening effects are also made. Finally, the application of the method to typical bridge and building cases is presented, showing the feasibility of jointless construction, and the limits which should be respected.

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Recientemente, el paradigma de la computación en la nube ha recibido mucho interés por parte tanto de la industria como del mundo académico. Las infraestructuras cloud públicas están posibilitando nuevos modelos de negocio y ayudando a reducir costes. Sin embargo, una compañía podría desear ubicar sus datos y servicios en sus propias instalaciones, o tener que atenerse a leyes de protección de datos. Estas circunstancias hacen a las infraestructuras cloud privadas ciertamente deseables, ya sea para complementar a las públicas o para sustituirlas por completo. Por desgracia, las carencias en materia de estándares han impedido que las soluciones para la gestión de infraestructuras privadas se hayan desarrollado adecuadamente. Además, la multitud de opciones disponibles ha creado en los clientes el miedo a depender de una tecnología concreta (technology lock-in). Una de las causas de este problema es la falta de alineación entre la investigación académica y los productos comerciales, ya que aquella está centrada en el estudio de escenarios idealizados sin correspondencia con el mundo real, mientras que éstos consisten en soluciones desarrolladas sin tener en cuenta cómo van a encajar con los estándares más comunes o sin preocuparse de hacer públicos sus resultados. Con objeto de resolver este problema, propongo un sistema de gestión modular para infraestructuras cloud privadas enfocado en tratar con las aplicaciones en lugar de centrarse únicamente en los recursos hardware. Este sistema de gestión sigue el paradigma de la computación autónoma y está diseñado en torno a un modelo de información sencillo, desarrollado para ser compatible con los estándares más comunes. Este modelo divide el entorno en dos vistas, que sirven para separar aquello que debe preocupar a cada actor involucrado del resto de información, pero al mismo tiempo permitiendo relacionar el entorno físico con las máquinas virtuales que se despliegan encima de él. En dicho modelo, las aplicaciones cloud están divididas en tres tipos genéricos (Servicios, Trabajos de Big Data y Reservas de Instancias), para que así el sistema de gestión pueda sacar partido de las características propias de cada tipo. El modelo de información está complementado por un conjunto de acciones de gestión atómicas, reversibles e independientes, que determinan las operaciones que se pueden llevar a cabo sobre el entorno y que es usado para hacer posible la escalabilidad en el entorno. También describo un motor de gestión encargado de, a partir del estado del entorno y usando el ya mencionado conjunto de acciones, la colocación de recursos. Está dividido en dos niveles: la capa de Gestores de Aplicación, encargada de tratar sólo con las aplicaciones; y la capa del Gestor de Infraestructura, responsable de los recursos físicos. Dicho motor de gestión obedece un ciclo de vida con dos fases, para así modelar mejor el comportamiento de una infraestructura real. El problema de la colocación de recursos es atacado durante una de las fases (la de consolidación) por un resolutor de programación entera, y durante la otra (la online) por un heurístico hecho ex-profeso. Varias pruebas han demostrado que este acercamiento combinado es superior a otras estrategias. Para terminar, el sistema de gestión está acoplado a arquitecturas de monitorización y de actuadores. Aquella estando encargada de recolectar información del entorno, y ésta siendo modular en su diseño y capaz de conectarse con varias tecnologías y ofrecer varios modos de acceso. ABSTRACT The cloud computing paradigm has raised in popularity within the industry and the academia. Public cloud infrastructures are enabling new business models and helping to reduce costs. However, the desire to host company’s data and services on premises, and the need to abide to data protection laws, make private cloud infrastructures desirable, either to complement or even fully substitute public oferings. Unfortunately, a lack of standardization has precluded private infrastructure management solutions to be developed to a certain level, and a myriad of diferent options have induced the fear of lock-in in customers. One of the causes of this problem is the misalignment between academic research and industry ofering, with the former focusing in studying idealized scenarios dissimilar from real-world situations, and the latter developing solutions without taking care about how they f t with common standards, or even not disseminating their results. With the aim to solve this problem I propose a modular management system for private cloud infrastructures that is focused on the applications instead of just the hardware resources. This management system follows the autonomic system paradigm, and is designed around a simple information model developed to be compatible with common standards. This model splits the environment in two views that serve to separate the concerns of the stakeholders while at the same time enabling the traceability between the physical environment and the virtual machines deployed onto it. In it, cloud applications are classifed in three broad types (Services, Big Data Jobs and Instance Reservations), in order for the management system to take advantage of each type’s features. The information model is paired with a set of atomic, reversible and independent management actions which determine the operations that can be performed over the environment and is used to realize the cloud environment’s scalability. From the environment’s state and using the aforementioned set of actions, I also describe a management engine tasked with the resource placement. It is divided in two tiers: the Application Managers layer, concerned just with applications; and the Infrastructure Manager layer, responsible of the actual physical resources. This management engine follows a lifecycle with two phases, to better model the behavior of a real infrastructure. The placement problem is tackled during one phase (consolidation) by using an integer programming solver, and during the other (online) with a custom heuristic. Tests have demonstrated that this combined approach is superior to other strategies. Finally, the management system is paired with monitoring and actuators architectures. The former able to collect the necessary information from the environment, and the later modular in design and capable of interfacing with several technologies and ofering several access interfaces.

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Performing activity recognition using the information provided by the different sensors embedded in a smartphone face limitations due to the capabilities of those devices when the computations are carried out in the terminal. In this work a fuzzy inference module is implemented in order to decide which classifier is the most appropriate to be used at a specific moment regarding the application requirements and the device context characterized by its battery level, available memory and CPU load. The set of classifiers that is considered is composed of Decision Tables and Trees that have been trained using different number of sensors and features. In addition, some classifiers perform activity recognition regardless of the on-body device position and others rely on the previous recognition of that position to use a classifier that is trained with measurements gathered with the mobile placed on that specific position. The modules implemented show that an evaluation of the classifiers allows sorting them so the fuzzy inference module can choose periodically the one that best suits the device context and application requirements.

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The development of (static and dynamics)programs with constant and linear elements has shown good behaviour. It seems so natural to combine both advantages so that the results will not be affected by local distortions. This paper will be dedicated to presenting the reserch of mixed elements and the way to solve the over-determination that appears in some cases. Although all the study has been done with the potential theory, its application to elastic problems is straightforward.