Assessment of energy efficiency and sustainability scenarios in the transport system

Background Energy Policy is one of the main drivers of Transport Policy. A number of strategies to reduce current energy consumption trends in the transport sector have been designed over the last decades. They include fuel taxes, more efficient technologies and changing travel behavior through demand regulation. But energy market has a high degree of uncertainty and the effectiveness of those policy options should be assessed. Methods A scenario based assessment methodology has been developed in the frame of the EU project STEPS. It provides an integrated view of Energy efficiency, environment, social and competitiveness impacts of the different strategies. It has been applied at European level and to five specific Regions. Concluding remarks The results are quite site specific dependent. However they show that regulation measures appear to be more effective than new technology investments. Higher energy prices could produce on their turn a deterioration of competitiveness and a threat for social goals.

Degradation in seawater of structural adhesives for hybrid fibre-metal laminated materials

The adhesives used for applications in marine environments are subject to particular chemical conditions, which are mainly characterised by an elevated chlorine ion content and intermittent wetting/drying cycles, among others.These conditions can limit the use of adhesives due to the degradation processes that they experience. In this work, the chemical degradation of two different polymers, polyurethane and vinylester, was studied in natural seawater under immersion for different periods of time.The diffusion coefficients and concentration profiles of water throughout the thickness of the adhesiveswere obtained.Microstructural changes in the polymer due to the action of water were observed by SEM, and the chemical degradation of the polymer was monitored with the Fourier transform infrared spectroscopy (FTIR) and differential scanning calorimetry (DSC). The degradation of the mechanical properties of the adhesive was determined by creep tests withMixed Cantilever Beam (MCB) specimens at different temperatures. After 180 days of immersion of the specimens, it was concluded that the J-integral value (depending on the strain) implies a loss of stiffness of 51% and a decrease in the failure load of 59% for the adhesive tested.

Application of PZT and FBG impedance sensors for structural health monitoring of reinforced concrete beams strengthened with FRP composite materials

Esta Tesis tiene como objetivo principal el desarrollo de métodos de identificación del daño que sean robustos y fiables, enfocados a sistemas estructurales experimentales, fundamentalmente a las estructuras de hormigón armado reforzadas externamente con bandas fibras de polímeros reforzados (FRP). El modo de fallo de este tipo de sistema estructural es crítico, pues generalmente es debido a un despegue repentino y frágil de la banda del refuerzo FRP originado en grietas intermedias causadas por la flexión. La detección de este despegue en su fase inicial es fundamental para prevenir fallos futuros, que pueden ser catastróficos. Inicialmente, se lleva a cabo una revisión del método de la Impedancia Electro-Mecánica (EMI), de cara a exponer sus capacidades para la detección de daño. Una vez la tecnología apropiada es seleccionada, lo que incluye un analizador de impedancias así como novedosos sensores PZT para monitorización inteligente, se ha diseñado un procedimiento automático basado en los registros de impedancias de distintas estructuras de laboratorio. Basándonos en el hecho de que las mediciones de impedancias son posibles gracias a una colocación adecuada de una red de sensores PZT, la estimación de la presencia de daño se realiza analizando los resultados de distintos indicadores de daño obtenidos de la literatura. Para que este proceso sea automático y que no sean necesarios conocimientos previos sobre el método EMI para realizar un experimento, se ha diseñado e implementado un Interfaz Gráfico de Usuario, transformando la medición de impedancias en un proceso fácil e intuitivo. Se evalúa entonces el daño a través de los correspondientes índices de daño, intentando estimar no sólo su severidad, sino también su localización aproximada. El desarrollo de estos experimentos en cualquier estructura genera grandes cantidades de datos que han de ser procesados, y algunas veces los índices de daño no son suficientes para una evaluación completa de la integridad de una estructura. En la mayoría de los casos se pueden encontrar patrones de daño en los datos, pero no se tiene información a priori del estado de la estructura. En este punto, se ha hecho una importante investigación en técnicas de reconocimiento de patrones particularmente en aprendizaje no supervisado, encontrando aplicaciones interesantes en el campo de la medicina. De ahí surge una idea creativa e innovadora: detectar y seguir la evolución del daño en distintas estructuras como si se tratase de un cáncer propagándose por el cuerpo humano. En ese sentido, las lecturas de impedancias se emplean como información intrínseca de la salud de la propia estructura, de forma que se pueden aplicar las mismas técnicas que las empleadas en la investigación del cáncer. En este caso, se ha aplicado un algoritmo de clasificación jerárquica dado que ilustra además la clasificación de los datos de forma gráfica, incluyendo información cualitativa y cuantitativa sobre el daño. Se ha investigado la efectividad de este procedimiento a través de tres estructuras de laboratorio, como son una viga de aluminio, una unión atornillada de aluminio y un bloque de hormigón reforzado con FRP. La primera ayuda a mostrar la efectividad del método en sencillos escenarios de daño simple y múltiple, de forma que las conclusiones extraídas se aplican sobre los otros dos, diseñados para simular condiciones de despegue en distintas estructuras. Demostrada la efectividad del método de clasificación jerárquica de lecturas de impedancias, se aplica el procedimiento sobre las estructuras de hormigón armado reforzadas con bandas de FRP objeto de esta tesis, detectando y clasificando cada estado de daño. Finalmente, y como alternativa al anterior procedimiento, se propone un método para la monitorización continua de la interfase FRP-Hormigón, a través de una red de sensores FBG permanentemente instalados en dicha interfase. De esta forma, se obtienen medidas de deformación de la interfase en condiciones de carga continua, para ser implementadas en un modelo de optimización multiobjetivo, cuya solución se haya por medio de una expansión multiobjetivo del método Particle Swarm Optimization (PSO). La fiabilidad de este último método de detección se investiga a través de sendos ejemplos tanto numéricos como experimentales. ABSTRACT This thesis aims to develop robust and reliable damage identification methods focused on experimental structural systems, in particular Reinforced Concrete (RC) structures externally strengthened with Fiber Reinforced Polymers (FRP) strips. The failure mode of this type of structural system is critical, since it is usually due to sudden and brittle debonding of the FRP reinforcement originating from intermediate flexural cracks. Detection of the debonding in its initial stage is essential thus to prevent future failure, which might be catastrophic. Initially, a revision of the Electro-Mechanical Impedance (EMI) method is carried out, in order to expose its capabilities for local damage detection. Once the appropriate technology is selected, which includes impedance analyzer as well as novel PZT sensors for smart monitoring, an automated procedure has been design based on the impedance signatures of several lab-scale structures. On the basis that capturing impedance measurements is possible thanks to an adequately deployed PZT sensor network, the estimation of damage presence is done by analyzing the results of different damage indices obtained from the literature. In order to make this process automatic so that it is not necessary a priori knowledge of the EMI method to carry out an experimental test, a Graphical User Interface has been designed, turning the impedance measurements into an easy and intuitive procedure. Damage is then assessed through the analysis of the corresponding damage indices, trying to estimate not only the damage severity, but also its approximate location. The development of these tests on any kind of structure generates large amounts of data to be processed, and sometimes the information provided by damage indices is not enough to achieve a complete analysis of the structural health condition. In most of the cases, some damage patterns can be found in the data, but none a priori knowledge of the health condition is given for any structure. At this point, an important research on pattern recognition techniques has been carried out, particularly on unsupervised learning techniques, finding interesting applications in the medicine field. From this investigation, a creative and innovative idea arose: to detect and track the evolution of damage in different structures, as if it were a cancer propagating through a human body. In that sense, the impedance signatures are used to give intrinsic information of the health condition of the structure, so that the same clustering algorithms applied in the cancer research can be applied to the problem addressed in this dissertation. Hierarchical clustering is then applied since it also provides a graphical display of the clustered data, including quantitative and qualitative information about damage. The performance of this approach is firstly investigated using three lab-scale structures, such as a simple aluminium beam, a bolt-jointed aluminium beam and an FRP-strengthened concrete specimen. The first one shows the performance of the method on simple single and multiple damage scenarios, so that the first conclusions can be extracted and applied to the other two experimental tests, which are designed to simulate a debonding condition on different structures. Once the performance of the impedance-based hierarchical clustering method is proven to be successful, it is then applied to the structural system studied in this dissertation, the RC structures externally strengthened with FRP strips, where the debonding failure in the interface between the FRP and the concrete is successfully detected and classified, proving thus the feasibility of this method. Finally, as an alternative to the previous approach, a continuous monitoring procedure of the FRP-Concrete interface is proposed, based on an FBGsensors Network permanently deployed within that interface. In this way, strain measurements can be obtained under controlled loading conditions, and then they are used in order to implement a multi-objective model updating method solved by a multi-objective expansion of the Particle Swarm Optimization (PSO) method. The feasibility of this last proposal is investigated and successfully proven on both numerical and experimental RC beams strengthened with FRP.

Realidad estratégica de la sostenibilidad de los recursos naturales: La explotación del hierro

Es en el campo de los recursos naturales y su aplicación a la industria, el entorno donde se desarrolla esta Tesis. El objetivo de la misma es demostrar cómo la minería del hierro puede resultar una actividad sostenible, logrando continuar de esta manera la estrecha relación de siempre entre las necesidades del hombre y la pervivencia de los recursos naturales. Es en la minería del hierro donde hace mayor énfasis este trabajo, dando lugar a un nuevo Indicador Sostenible que intenta evaluar las explotaciones de mineral de hierro desde una visión sostenible, empleando el consumo energético y las emisiones de CO2 como principales herramientas. Como se observa en el día a día, el tema de la sostenibilidad es de plena actualidad, lográndose en este trabajo implicar, tanto a la eficiencia energética, como al control de emisiones de gases efecto invernadero; ambas herramientas cobran más importancia cada día que pasa. La Tesis se desarrolla en 5 capítulos, aparte de su bibliografía correspondiente. En el primer capítulo se introduce el sentido de la sostenibilidad, desde sus inicios conceptuales, hasta sus actuales clasificaciones y definiciones empleadas; todo ello desde el punto de vista de los recursos naturales, y más habitualmente desde la minería. Resulta llamativo el contraste de opiniones, en lo que se ha dado a llamar la paradoja de la minería sostenible, quedando tras su lectura, la posición de la minería en una situación, si no ventajosa, si de equilibrio en importancia entre las necesidades a cubrir y el agotamiento de recursos. El segundo capítulo nos muestra el entorno donde se va a conducir la Tesis. El marco que engloba este trabajo se extiende desde la extracción del mineral de hierro (minería), su tratamiento y concentración (mineralurgia), su venta a los hornos altos (mercados) hasta su posterior fabricación en acero terminado (siderurgia). En este capítulo se presentan los principales actores que entrarán en el sector de la minería del hierro (productores y fabricantes) incluyendo una serie de datos estadísticos de gran interés para el desarrollo de la Tesis. El tercer capítulo se refiere al proceso completo que precisa la actividad sobre la que se va a evaluar la sostenibilidad. Es donde se definen, paso a paso, y obteniendo todos los datos de consumos energéticos y emisiones de CO2, las diferentes etapas por las que pasa el mineral de hierro, hasta encontrarse laminado en la acería. Es aquí donde se analizan los diversos tipos de yacimientos de hierro dispersos por el mundo y el mineral extraído, de manera que las propiedades aprendidas se puedan emplear más adelante en un indicador, y que así diferencie la sostenibilidad en función de los orígenes motivo de las necesidades energéticas para su transformación. El capítulo 4 consta de dos bloques: el uso de las herramientas de medida de la sostenibilidad, a día de hoy en el mundo industrial, y de una manera pormenorizada, el consumo energético y sus emisiones medioambientales como herramienta de gestión ambiental para la minería del hierro. Esta herramienta resultará básica para el cálculo del indicador buscado para la medida de la sostenibilidad. El capítulo 5 constituye el núcleo de la tesis, y supone el desarrollo del indicador, la metodología de uso y las conclusiones obtenidas. A través de varios ejemplos se logra entender la aplicación del indicador, dando lugar a una clasificación sostenible sencilla y práctica, situando en orden las diferentes explotaciones en función de un nivel de sostenibilidad determinado. Este último capítulo da origen al Indicador Sostenible Energético buscado, mostrándose en todo su esplendor y descubriendo cómo la relación ponderada entre el consumo energético y sus emisiones de CO2 permite, a través de una valoración, mostrar todos los parámetros de relevancia para el mineral de hierro y su posterior transformación en acero. Esa cifra obtenida por el indicador, clasificará la explotación teniendo en cuenta, el tipo de yacimiento, características del mineral (especie mineralógica, tipo de mineral, ley del mineral en hierro, tipo de ganga, características físicas como dureza o tamaño de grano, susceptibilidad magnética, etc.), situación geográfica, infraestructuras, etc. Sin profundizar en la siderurgia, por lo menos sí incluir los principales parámetros (relacionados siempre desde el mineral) que pudieran tener influencia en la disminución de energía requerida (y sus emisiones de CO2 relacionadas): la reducibilidad, el contenido en hierro, y mencionar la influencia del SiO2. Se completa la Tesis con las referencias bibliográficas y documentales, así como con una bibliografía general. ABSTRACT This Thesis is set in a context of natural resources and applied science. The aim of this document is to prove that iron mining is a sustainable activity, so the ancient relationship between men and natural resources will continue. Iron mining is the main subject of this work, so a new sustainable indicator is created in order to evaluate the iron mining from a sustainable point of view. The main tools applied are energy consumption and CO2 emissions. In this research document two relevant issues are involved: energy efficiency and GHGs control; both tools gain significance by the day. This thesis develops along 5 chapters and its bibliography. The first chapter refers to the concept of sustainability, from the beginning to the current definitions and classifications; all this information is focused from the natural resources point of view, especially mining. The contrast of opinion is remarkable, which has been called the “paradox of sustainable mining���; however this chapter concludes that taking into account the less bright side of the mining its activity maintains an important balance between necessities to cover, available resources and environment. The second chapter sets out where this Thesis has been conducted. The frame of this work lies between iron mining, ore processing, the market and the latter steel fabrication (steelmaking). This chapter shows the iron mining key stakeholders, supported with statistical data. The third chapter refers to the whole process definition. From the iron mineral to the rolled steel, all data related with energy consumption and CO2 emissions are considered step by step. Different iron deposits widespread all over the world are analyzed now, as well as the exploited iron mineral in order to apply the lessons learned to create a new sustainability tool. Then, our sustainability studies will consider the influence of this in the energy necessities when iron is transformed. Chapter four is divided in the currently applied sustainability measurement tools, and focusing on energy consumption and CO2 emissions linked to the iron mining process. This tool is essential to calculate the required indicator that reflects the sustainability. Chapter five is the Thesis’ core: it is where the new sustainable indicator is developed, the methodology stated and the final conclusions obtained. Through several examples the indicator application is explained, and a practical and simple sustainable classification will show the ranking of every exploitation. This last chapter develops the sustainable tool and discovers how the weighted relation between energy consumption and CO2 emissions allows understanding all the relevant parameters in the iron mineral transformation. The number calculated will be used to classify the mineral exploitation, taking into account the deposit typology, mineral characteristics (mineralogy, type of mineral, iron percentage, physical properties as hardness or grain size, magnetic susceptibility, etc.), geographic situation, infrastructures, etc. Although steelmaking is not studied in depth, main parameters (from the mineral side) which can operate in the energy decrease (and CO2 emissions in parallel) are referred to: reducibility, iron content and SiO2 influence. The bibliography used is included at the end of this paper.

Efficient management of available technologies. Aplication to mining operations for industry supplies

Nowadays, processing Industry Sector is going through a series of changes, including right management and reduction of environmental affections. Any productive process which looks for sustainable management is incomplete if Cycle of Life of mineral resources sustainability is not taken into account. Raw materials for manufacturing are provided by mineral resources extraction processes, such as copper, aluminum, iron, gold, silver, silicon, titanium? Those elements are necessary for Mankind development and are obtained from the Earth through mineral extractive processes. Mineral extraction processes are operations which must take care about the environmental consequences. Extraction of huge volumes of rock for their transformation into raw materials for industry must be optimized to reduce ecological cost of the final product as l was possible. Reducing the ecological balance on a global scale has no sense to design an efficient manufacturing in secondary industry (transformation), if in first steps of the supply chain (extraction) impact exceeds the savings of resources in successive phases. Mining operations size suggests that it is an environmental aggressive activity, but precisely because of its great impact must be the first element to be considered. That idea implies that a new concept born: Reduce economical and environmental cost This work aims to make a reflection on the parameters that can be modified to reduce the energy cost of the process without an increasing in operational costs and always ensuring the same production capacity. That means minimize economic and environmental cost at same time. An efficient design of mining operation which has taken into account that idea does not implies an increasing of the operating cost. To get this objective is necessary to think in global operation view to make that all departments involved have common guidelines which make you think in the optimization of global energy costs. Sometimes a single operational cost must be increased to reduce global cost. This work makes a review through different design parameters of surface mining setting some key performance indicators (KPIs) which are estimated from an efficient point of view. Those KPIs can be included by HQE Policies as global indicators. The new concept developed is that a new criteria has to be applied in company policies: improve management, improving OPERATIONAL efficiency. That means, that is better to use current resources properly (machinery, equipment,?) than to replace them with new things but not used correctly. As a conclusion, through an efficient management of current technologies in each extractive operation an important reduction of the energy can be achieved looking at downstream in the process. That implies a lower energetic cost in the whole cycle of life in manufactured product.

Metadata Visualization of Cultural Heritage Information within a Collaborative Environment

Cultural content on the Web is available in various domains (cultural objects, datasets, geospatial data, moving images, scholarly texts and visual resources), concerns various topics, is written in different languages, targeted to both laymen and experts, and provided by different communities (libraries, archives museums and information industry) and individuals (Figure 1). The integration of information technologies and cultural heritage content on the Web is expected to have an impact on everyday life from the point of view of institutions, communities and individuals. In particular, collaborative environment scan recreate 3D navigable worlds that can offer new insights into our cultural heritage (Chan 2007). However, the main barrier is to find and relate cultural heritage information by end-users of cultural contents, as well as by organisations and communities managing and producing them. In this paper, we explore several visualisation techniques for supporting cultural interfaces, where the role of metadata is essential for supporting the search and communication among end-users (Figure 2). A conceptual framework was developed to integrate the data, purpose, technology, impact, and form components of a collaborative environment, Our preliminary results show that collaborative environments can help with cultural heritage information sharing and communication tasks because of the way in which they provide a visual context to end-users. They can be regarded as distributed virtual reality systems that offer graphically realised, potentially infinite, digital information landscapes. Moreover, collaborative environments also provide a new way of interaction between an end-user and a cultural heritage data set. Finally, the visualisation of metadata of a dataset plays an important role in helping end-users in their search for heritage contents on the Web.

Data mining applied to the cognitive rehabilitation of patients with acquired brain injury

Acquired brain injury (ABI) is one of the leading causes of death and disability in the world and is associated with high health care costs as a result of the acute treatment and long term rehabilitation involved. Different algorithms and methods have been proposed to predict the effectiveness of rehabilitation programs. In general, research has focused on predicting the overall improvement of patients with ABI. The purpose of this study is the novel application of data mining (DM) techniques to predict the outcomes of cognitive rehabilitation in patients with ABI. We generate three predictive models that allow us to obtain new knowledge to evaluate and improve the effectiveness of the cognitive rehabilitation process. Decision tree (DT), multilayer perceptron (MLP) and general regression neural network (GRNN) have been used to construct the prediction models. 10-fold cross validation was carried out in order to test the algorithms, using the Institut Guttmann Neurorehabilitation Hospital (IG) patients database. Performance of the models was tested through specificity, sensitivity and accuracy analysis and confusion matrix analysis. The experimental results obtained by DT are clearly superior with a prediction average accuracy of 90.38%, while MLP and GRRN obtained a 78.7% and 75.96%, respectively. This study allows to increase the knowledge about the contributing factors of an ABI patient recovery and to estimate treatment efficacy in individual patients.

La Planificación de Áreas Mineras en Declive : Una respuesta multidimensional = Planning Declining Mining Areas. A multidimensional response

La actividad minera tiene un gran impacto sobre el territorio, probablemente más que ninguna otra de las actividades humanas, ya que transforma el espacio en todas sus dimensiones: ecológica, ambiental, social y económica. Cuando la reducción de la rentabilidad de la explotación conduce al cierre de ésta, la repercusión sobre su entorno puede llegar a ser brutal. Pero las explotaciones mineras son muy distintas entre ellas y los efectos que su abandono producen sobre el espacio en la que se enclavan pueden ser diversos, por lo que la decisión sobre el futuro de estas áreas no es simple y evidente. Aquí se propone desarrollar una propuesta de clasificación tipológica de las minas y sus regiones con el objetivo de determinar las estrategias de intervención más adecuadas para el futuro de estos espacios y sus habitantes. En concreto se busca diferenciar los conceptos de Mina, Parque Minero, Espacio Minero y Región Minera, todos ellos fruto de la interacción de la huella de la actividad minera con el medio físico, los enclaves urbanizados, y la estructura socioeconómica de la región en la que se enclavan. Mining activity is having a great impact on the territory, probably more than any other human activity, which transforms the space in all of its dimensions, ecological, environmental, social and economic. When reducing the profitability of the operation leads to the conclusion thereof, the impact on the environment can be brutal. But mining are very different between them and the effects they produce on their abandonment in space that interlock can be diverse, so the decision on the future of these areas is not simple and obvious. This proposal aims to develop a typological classification of mines and their regions in order to determine the most appropriate intervention strategies for the future of these spaces and their inhabitants. Specifically, it seeks to differentiate the concepts of Mine, Mining Park, Space Miner and Mining Region, all the result of the interaction of the mining footprint with the physical environment, the urbanized enclaves, and the socio-economic structure of the region which interlock. El presente libro reúne las ponencias presentadas por los investigadores de la red REUSE dentro del 1er Simposio de Reutilización del Espacio Minero; evento organizado por la Universidad Federal de Minas Gerais (UFMG) en Belo Horizonte, entre el 1 y el 3 de octubre de 2012, en el marco del 1er Seminario Internacional de Reconversión de Territorios. La red REUSE es una red realizada gracias a la financiación del programa CYTED

Survey on student motivation and approval of educational environment at Universidad Politécnica de Madrid within the European Higher Education area implementation

A recent study elaborated by Vicerrectorado de Ordenación Académica y Planificación Estratégica of Technical University of Madrid (UPM) defines the satisfaction of the university student body as "the response that the University offers to the expectations and demands of service of the students, considered in a general way ". Besides an indicator of academic and institutional insertion of the student, the assessment of student engagement allows us to adapt the academic offer and the extension services of the University to the real needs of the students. The process of convergence towards the European Higher Education Area (EHEA) raises the need to form in competitions, that is to say, of developing in our students capacities and knowledge beyond the purely theoretical-practical thing. Therefore, the perception and experience of the educational process and environment by the students is an important issue to be addressed to accomplish their expectations and achieve a curriculum accordingly to EHEA expectations. The present study aims to explore the student motivation and approval of the educational environment at the UPM. To this end a total of 97 students enrolled in the undergraduate program of Civil Engineering, Computer Engineering and Agronomic Engineering at UPM were surveyed. The survey consisted of 40 questions divided in three blocks. The first one of 20 questions of personal character in that they were gathering, besides the sex and the age, the degree of fulfilment, implication and dedication with the institution and the academic tasks. In the second block we identify 10 questions related to the perception of the student on the teaching quality, and finally a block of 10 questions regarding the Bologna Process. The students personal motivation was moderately high, with a score of 3.6 (all scores are provided on a 5-point scale), being the most valuable items obtaining a university degree (4,3) and the friendship between students (4,2). Any significant difference was shown between sexes (P=0.23) since the averages for this block of questions were of 3.7±0.3 and 3.5±0.4 for women and men respectively. The students are moderately satisfied with their graduate studies with an average score of 3,2, being the questions that reflect a minor satisfaction the research profile of the teachers (2,8) and the organization of the Schools (2,9). The best valued questions are related to the usefulness and quality of the degrees, with 3,5 and 3,4 respectively, and to the interest of the courses within the degree (3,4). For sexes, the results of this block of questions are similar (3.1±0.3 and 3.2±0.3 for men and women respectively=0.79). Also, there were no differences (P=0.39) between the students who arrange work and studies or do not work (3.1±0.2 and 3.2±0.3 respectively). In conclusion, students at UPM present an acceptable degree of motivation and satisfaction with regard to the studies and services that offer their respective Schools. Both characteristics receive the same value both for men and for women and so much for students who arrange work and studies as for those who devote themselves only to studying. In a significant way, students who are more engaged and are in-class attendants present the major degree of satisfaction.Overall, there is a great lack of information regarding the Bologna Process. In fact to the majority, they would like to know more on what it is, what it means and what changes will involve its implementation.

Stability analysis of dam failures. Application to aurul tailing pond in Baia Mare (Romania)

La gestión de estériles de una explotación minera es un punto clave en el desarrollo económico de una actividad extractiva, y en especial, del entorno natural y social en el que se emplaza dicho proyecto. La minería de metales preciosos lleva asociada la construcción de balsas de residuos muy peligrosos, fruto de su proceso extractivo, como por ejemplo la cianuración en el caso del oro. Para un correcto funcionamiento de dichos emplazamientos es necesario escoger correctamente el método constructivo a partir de estudios de reconocimiento previos, como estudios de estabilidad geotécnica, contexto geológico de la zona, sismicidad, hidrología, etc. Así mismo, han de llevarse a cabo unas exhaustivas medidas de control y vigilancia para asegurar las condiciones de seguridad exigidas. La ruptura de la balsa de decantación de Aurul S.A. en Baia Mare (Rumania) el 30 de Enero del año 2000 ha sido escogido como caso de estudio de estabilidad de diques. ABSTRACT Tailing's management of a mining exploitation is a key point in the economical development of the extractive activity and, especially, of the natural and social environment of the site. Precious metals mining has high hazardous embankment construction associated, product of its extractive process, i.e. gold cyanidation. A correct operation of those sites makes necessary to choose a suitable construction method, based on previous studies as geotechnical stability studies, geological context of the area, seismicity, hydrology, etc. At the same time, exhaustive control and monitoring must be carried out in order to assure the required safety conditions. Aurul's decantation pond failure in Baia Mare (Romania), on 30th January 2000, has been chosen as a stability analysis case-study.

Management of irrigation frequency and nitrogen fertilization to mitigate GHG and NO emissions from drip-fertigated crops

Drip irrigation combined with split application of fertilizer nitrogen (N) dissolved in the irrigation water (i.e. drip fertigation) is commonly considered best management practice for water and nutrient efficiency. As a consequence, its use is becoming widespread. Some of the main factors (water-filled pore space, NH4+ and NO3−) regulating the emissions of greenhouse gases (i.e. N2O, CO2 and CH4) and NO from agroecosystems 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 and fluxes of N2O, NO, CH4 and CO2 were measured during 85 days. 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. We found no relation between irrigation frequency and N2O emissions. Based on yield-scaled Global Warming Potential as well as NO cumulative emissions, we conclude that weekly fertigation with a NO3−-based fertilizer is the best option to combine agronomic productivity with environmental sustainability. Our study shows that adequate management of drip fertigation, while contributing to the attainment of water and food security, may provide an opportunity for climate change mitigation.

Mining mobile apps reviews to support release planning

The mobile apps market is a tremendous success, with millions of apps downloaded and used every day by users spread all around the world. For apps’ developers, having their apps published on one of the major app stores (e.g. Google Play market) is just the beginning of the apps lifecycle. Indeed, in order to successfully compete with the other apps in the market, an app has to be updated frequently by adding new attractive features and by fixing existing bugs. Clearly, any developer interested in increasing the success of her app should try to implement features desired by the app’s users and to fix bugs affecting the user experience of many of them. A precious source of information to decide how to collect users’ opinions and wishes is represented by the reviews left by users on the store from which they downloaded the app. However, to exploit such information the app’s developer should manually read each user review and verify if it contains useful information (e.g. suggestions for new features). This is something not doable if the app receives hundreds of reviews per day, as happens for the very popular apps on the market. In this work, our aim is to provide support to mobile apps developers by proposing a novel approach exploiting data mining, natural language processing, machine learning, and clustering techniques in order to classify the user reviews on the basis of the information they contain (e.g. useless, suggestion for new features, bugs reporting). Such an approach has been empirically evaluated and made available in a web-­‐based tool publicly available to all apps’ developers. The achieved results showed that the developed tool: (i) is able to correctly categorise user reviews on the basis of their content (e.g. isolating those reporting bugs) with 78% of accuracy, (ii) produces clusters of reviews (e.g. groups together reviews indicating exactly the same bug to be fixed) that are meaningful from a developer’s point-­‐of-­‐view, and (iii) is considered useful by a software company working in the mobile apps’ development market.

Life cycle assessment of concentrated solar power (CSP) and the influence of hybridising with natural gas

Purpose Concentrating Solar Power (CSP) plants based on parabolic troughs utilize auxiliary fuels (usually natural gas) to facilitate start-up operations, avoid freezing of HTF and increase power output. This practice has a significant effect on the environmental performance of the technology. The aim of this paper is to quantify the sustainability of CSP and to analyse how this is affected by hybridisation with different natural gas (NG) inputs. Methods A complete Life Cycle (LC) inventory was gathered for a commercial wet-cooled 50 MWe CSP plant based on parabolic troughs. A sensitivity analysis was conducted to evaluate the environmental performance of the plant operating with different NG inputs (between 0 and 35% of gross electricity generation). ReCiPe Europe (H) was used as LCA methodology. CML 2 baseline 2000 World and ReCiPe Europe E were used for comparative purposes. Cumulative Energy Demands (CED) and Energy Payback Times (EPT) were also determined for each scenario. Results and discussion Operation of CSP using solar energy only produced the following environmental profile: climate change 26.6 kg CO2 eq/KWh, human toxicity 13.1 kg 1,4-DB eq/KWh, marine ecotoxicity 276 g 1,4-DB eq/KWh, natural land transformation 0.005 m2/KWh, eutrophication 10.1 g P eq/KWh, acidification 166 g SO2 eq/KWh. Most of these impacts are associated with extraction of raw materials and manufacturing of plant components. The utilization NG transformed the environmental profile of the technology, placing increasing weight on impacts related to its operation and maintenance. Significantly higher impacts were observed on categories like climate change (311 kg CO2 eq/MWh when using 35 % NG), natural land transformation, terrestrial acidification and fossil depletion. Despite its fossil nature, the use of NG had a beneficial effect on other impact categories (human and marine toxicity, freshwater eutrophication and natural land transformation) due to the higher electricity output achieved. The overall environmental performance of CSP significantly deteriorated with the use of NG (single score 3.52 pt in solar only operation compared to 36.1 pt when using 35 % NG). Other sustainability parameters like EPT and CED also increased substantially as a result of higher NG inputs. Quasilinear second-degree polynomial relationships were calculated between various environmental performance parameters and NG contributions. Conclusions Energy input from auxiliary NG determines the environmental profile of the CSP plant. Aggregated analysis shows a deleterious effect on the overall environmental performance of the technology as a result of NG utilization. This is due primarily to higher impacts on environmental categories like climate change, natural land transformation, fossil fuel depletion and terrestrial acidification. NG may be used in a more sustainable and cost-effective manner in combined cycle power plants, which achieve higher energy conversion efficiencies.

Extracción de conocimientos de la diabetes tipo 1 utilizando la metodología de "Data Mining"

La diabetes mellitus es un trastorno en la metabolización de los carbohidratos, caracterizado por la nula o insuficiente segregación de insulina (hormona producida por el páncreas), como resultado del mal funcionamiento de la parte endocrina del páncreas, o de una creciente resistencia del organismo a esta hormona. Esto implica, que tras el proceso digestivo, los alimentos que ingerimos se transforman en otros compuestos químicos más pequeños mediante los tejidos exocrinos. La ausencia o poca efectividad de esta hormona polipéptida, no permite metabolizar los carbohidratos ingeridos provocando dos consecuencias: Aumento de la concentración de glucosa en sangre, ya que las células no pueden metabolizarla; consumo de ácidos grasos mediante el hígado, liberando cuerpos cetónicos para aportar la energía a las células. Esta situación expone al enfermo crónico, a una concentración de glucosa en sangre muy elevada, denominado hiperglucemia, la cual puede producir a medio o largo múltiples problemas médicos: oftalmológicos, renales, cardiovasculares, cerebrovasculares, neurológicos… La diabetes representa un gran problema de salud pública y es la enfermedad más común en los países desarrollados por varios factores como la obesidad, la vida sedentaria, que facilitan la aparición de esta enfermedad. Mediante el presente proyecto trabajaremos con los datos de experimentación clínica de pacientes con diabetes de tipo 1, enfermedad autoinmune en la que son destruidas las células beta del páncreas (productoras de insulina) resultando necesaria la administración de insulina exógena. Dicho esto, el paciente con diabetes tipo 1 deberá seguir un tratamiento con insulina administrada por la vía subcutánea, adaptado a sus necesidades metabólicas y a sus hábitos de vida. Para abordar esta situación de regulación del control metabólico del enfermo, mediante una terapia de insulina, no serviremos del proyecto “Páncreas Endocrino Artificial” (PEA), el cual consta de una bomba de infusión de insulina, un sensor continuo de glucosa, y un algoritmo de control en lazo cerrado. El objetivo principal del PEA es aportar al paciente precisión, eficacia y seguridad en cuanto a la normalización del control glucémico y reducción del riesgo de hipoglucemias. El PEA se instala mediante vía subcutánea, por lo que, el retardo introducido por la acción de la insulina, el retardo de la medida de glucosa, así como los errores introducidos por los sensores continuos de glucosa cuando, se descalibran dificultando el empleo de un algoritmo de control. Llegados a este punto debemos modelar la glucosa del paciente mediante sistemas predictivos. Un modelo, es todo aquel elemento que nos permita predecir el comportamiento de un sistema mediante la introducción de variables de entrada. De este modo lo que conseguimos, es una predicción de los estados futuros en los que se puede encontrar la glucosa del paciente, sirviéndonos de variables de entrada de insulina, ingesta y glucosa ya conocidas, por ser las sucedidas con anterioridad en el tiempo. Cuando empleamos el predictor de glucosa, utilizando parámetros obtenidos en tiempo real, el controlador es capaz de indicar el nivel futuro de la glucosa para la toma de decisones del controlador CL. Los predictores que se están empleando actualmente en el PEA no están funcionando correctamente por la cantidad de información y variables que debe de manejar. Data Mining, también referenciado como Descubrimiento del Conocimiento en Bases de Datos (Knowledge Discovery in Databases o KDD), ha sido definida como el proceso de extracción no trivial de información implícita, previamente desconocida y potencialmente útil. Todo ello, sirviéndonos las siguientes fases del proceso de extracción del conocimiento: selección de datos, pre-procesado, transformación, minería de datos, interpretación de los resultados, evaluación y obtención del conocimiento. Con todo este proceso buscamos generar un único modelo insulina glucosa que se ajuste de forma individual a cada paciente y sea capaz, al mismo tiempo, de predecir los estados futuros glucosa con cálculos en tiempo real, a través de unos parámetros introducidos. Este trabajo busca extraer la información contenida en una base de datos de pacientes diabéticos tipo 1 obtenidos a partir de la experimentación clínica. Para ello emplearemos técnicas de Data Mining. Para la consecución del objetivo implícito a este proyecto hemos procedido a implementar una interfaz gráfica que nos guía a través del proceso del KDD (con información gráfica y estadística) de cada punto del proceso. En lo que respecta a la parte de la minería de datos, nos hemos servido de la denominada herramienta de WEKA, en la que a través de Java controlamos todas sus funciones, para implementarlas por medio del programa creado. Otorgando finalmente, una mayor potencialidad al proyecto con la posibilidad de implementar el servicio de los dispositivos Android por la potencial capacidad de portar el código. Mediante estos dispositivos y lo expuesto en el proyecto se podrían implementar o incluso crear nuevas aplicaciones novedosas y muy útiles para este campo. Como conclusión del proyecto, y tras un exhaustivo análisis de los resultados obtenidos, podemos apreciar como logramos obtener el modelo insulina-glucosa de cada paciente. ABSTRACT. The diabetes mellitus is a metabolic disorder, characterized by the low or none insulin production (a hormone produced by the pancreas), as a result of the malfunctioning of the endocrine pancreas part or by an increasing resistance of the organism to this hormone. This implies that, after the digestive process, the food we consume is transformed into smaller chemical compounds, through the exocrine tissues. The absence or limited effectiveness of this polypeptide hormone, does not allow to metabolize the ingested carbohydrates provoking two consequences: Increase of the glucose concentration in blood, as the cells are unable to metabolize it; fatty acid intake through the liver, releasing ketone bodies to provide energy to the cells. This situation exposes the chronic patient to high blood glucose levels, named hyperglycemia, which may cause in the medium or long term multiple medical problems: ophthalmological, renal, cardiovascular, cerebrum-vascular, neurological … The diabetes represents a great public health problem and is the most common disease in the developed countries, by several factors such as the obesity or sedentary life, which facilitate the appearance of this disease. Through this project we will work with clinical experimentation data of patients with diabetes of type 1, autoimmune disease in which beta cells of the pancreas (producers of insulin) are destroyed resulting necessary the exogenous insulin administration. That said, the patient with diabetes type 1 will have to follow a treatment with insulin, administered by the subcutaneous route, adapted to his metabolic needs and to his life habits. To deal with this situation of metabolic control regulation of the patient, through an insulin therapy, we shall be using the “Endocrine Artificial Pancreas " (PEA), which consists of a bomb of insulin infusion, a constant glucose sensor, and a control algorithm in closed bow. The principal aim of the PEA is providing the patient precision, efficiency and safety regarding the normalization of the glycemic control and hypoglycemia risk reduction". The PEA establishes through subcutaneous route, consequently, the delay introduced by the insulin action, the delay of the glucose measure, as well as the mistakes introduced by the constant glucose sensors when, decalibrate, impede the employment of an algorithm of control. At this stage we must shape the patient glucose levels through predictive systems. A model is all that element or set of elements which will allow us to predict the behavior of a system by introducing input variables. Thus what we obtain, is a prediction of the future stages in which it is possible to find the patient glucose level, being served of input insulin, ingestion and glucose variables already known, for being the ones happened previously in the time. When we use the glucose predictor, using obtained real time parameters, the controller is capable of indicating the future level of the glucose for the decision capture CL controller. The predictors that are being used nowadays in the PEA are not working correctly for the amount of information and variables that it need to handle. Data Mining, also indexed as Knowledge Discovery in Databases or KDD, has been defined as the not trivial extraction process of implicit information, previously unknown and potentially useful. All this, using the following phases of the knowledge extraction process: selection of information, pre- processing, transformation, data mining, results interpretation, evaluation and knowledge acquisition. With all this process we seek to generate the unique insulin glucose model that adjusts individually and in a personalized way for each patient form and being capable, at the same time, of predicting the future conditions with real time calculations, across few input parameters. This project of end of grade seeks to extract the information contained in a database of type 1 diabetics patients, obtained from clinical experimentation. For it, we will use technologies of Data Mining. For the attainment of the aim implicit to this project we have proceeded to implement a graphical interface that will guide us across the process of the KDD (with graphical and statistical information) of every point of the process. Regarding the data mining part, we have been served by a tool called WEKA's tool called, in which across Java, we control all of its functions to implement them by means of the created program. Finally granting a higher potential to the project with the possibility of implementing the service for Android devices, porting the code. Through these devices and what has been exposed in the project they might help or even create new and very useful applications for this field. As a conclusion of the project, and after an exhaustive analysis of the obtained results, we can show how we achieve to obtain the insulin–glucose model for each patient.

Nuevos Esquemas de Visualizaciones para Mejorar la Comprensibilidad de Modelos de Data Mining

La gran cantidad de datos que se registran diariamente en los sistemas de base de datos de las organizaciones ha generado la necesidad de analizarla. Sin embargo, se enfrentan a la complejidad de procesar enormes volúmenes de datos a través de métodos tradicionales de análisis. Además, dentro de un contexto globalizado y competitivo las organizaciones se mantienen en la búsqueda constante de mejorar sus procesos, para lo cual requieren herramientas que les permitan tomar mejores decisiones. Esto implica estar mejor informado y conocer su historia digital para describir sus procesos y poder anticipar (predecir) eventos no previstos. Estos nuevos requerimientos de análisis de datos ha motivado el desarrollo creciente de proyectos de minería de datos. El proceso de minería de datos busca obtener desde un conjunto masivo de datos, modelos que permitan describir los datos o predecir nuevas instancias en el conjunto. Implica etapas de: preparación de los datos, procesamiento parcial o totalmente automatizado para identificar modelos en los datos, para luego obtener como salida patrones, relaciones o reglas. Esta salida debe significar un nuevo conocimiento para la organización, útil y comprensible para los usuarios finales, y que pueda ser integrado a los procesos para apoyar la toma de decisiones. Sin embargo, la mayor dificultad es justamente lograr que el analista de datos, que interviene en todo este proceso, pueda identificar modelos lo cual es una tarea compleja y muchas veces requiere de la experiencia, no sólo del analista de datos, sino que también del experto en el dominio del problema. Una forma de apoyar el análisis de datos, modelos y patrones es a través de su representación visual, utilizando las capacidades de percepción visual del ser humano, la cual puede detectar patrones con mayor facilidad. Bajo este enfoque, la visualización ha sido utilizada en minería datos, mayormente en el análisis descriptivo de los datos (entrada) y en la presentación de los patrones (salida), dejando limitado este paradigma para el análisis de modelos. El presente documento describe el desarrollo de la Tesis Doctoral denominada “Nuevos Esquemas de Visualizaciones para Mejorar la Comprensibilidad de Modelos de Data Mining���. Esta investigación busca aportar con un enfoque de visualización para apoyar la comprensión de modelos minería de datos, para esto propone la metáfora de modelos visualmente aumentados. ABSTRACT The large amount of data to be recorded daily in the systems database of organizations has generated the need to analyze it. However, faced with the complexity of processing huge volumes of data over traditional methods of analysis. Moreover, in a globalized and competitive environment organizations are kept constantly looking to improve their processes, which require tools that allow them to make better decisions. This involves being bettered informed and knows your digital story to describe its processes and to anticipate (predict) unanticipated events. These new requirements of data analysis, has led to the increasing development of data-mining projects. The data-mining process seeks to obtain from a massive data set, models to describe the data or predict new instances in the set. It involves steps of data preparation, partially or fully automated processing to identify patterns in the data, and then get output patterns, relationships or rules. This output must mean new knowledge for the organization, useful and understandable for end users, and can be integrated into the process to support decision-making. However, the biggest challenge is just getting the data analyst involved in this process, which can identify models is complex and often requires experience not only of the data analyst, but also the expert in the problem domain. One way to support the analysis of the data, models and patterns, is through its visual representation, i.e., using the capabilities of human visual perception, which can detect patterns easily in any context. Under this approach, the visualization has been used in data mining, mostly in exploratory data analysis (input) and the presentation of the patterns (output), leaving limited this paradigm for analyzing models. This document describes the development of the doctoral thesis entitled "New Visualizations Schemes to Improve Understandability of Data-Mining Models". This research aims to provide a visualization approach to support understanding of data mining models for this proposed metaphor visually enhanced models.