A qualitative assessment of multi-level nutrition strategies for reducing racial and ethnic disparities in diabetes

The purpose of this study is to examine the stages of program realization of the interventions that the Bronx Health REACH program initiated at various levels to improve nutrition as a means for reducing racial and ethnic disparities in diabetes. This study was based on secondary analyses of qualitative data collected through the Bronx Health REACH Nutrition Project, a project conducted under the auspices of the Institute on Urban Family Health, with support from the Centers for Disease Control and Prevention (CDC). Local human subjects' review and approval through the Institute on Urban Family Health was required and obtained in order to conduct the Bronx Health REACH Nutrition Project. ^ The study drew from two theoretical models—Glanz and colleagues' nutrition environments model and Shediac-Rizkallah and Bone's sustainability model. The specific study objectives were two-fold: (1) to categorize each nutrition activity to a specific dimension (i.e. consumer, organizational or community nutrition environment); and (2) to evaluate the stage at which the program has been realized (i.e. development, implementation or sustainability). ^ A case study approach was applied and a constant comparative method was used to analyze the data. Triangulation of data based was also conducted. Qualitative data from this study revealed the following principal findings: (1) communities of color are disproportionately experiencing numerous individual and environmental factors contributing to the disparities in diabetes; (2) multi-level strategies that targeted the individual, organizational and community nutrition environments can appropriately address these contributing factors; (3) the nutrition strategies greatly varied in their ability to appropriately meet criteria for the three program stages; and (4) those nutrition strategies most likely to succeed (a) conveyed consistent and culturally relevant messages, (b) had continued involvement from program staff and partners, (c) were able to adapt over time or setting, (d) had a program champion and a training component, (e) were integrated into partnering organizations, and (f) were perceived to be successful by program staff and partners in their efforts to create individual, organizational and community/policy change. As a result of the criteria-based assessment and qualitative findings, an ecological framework elaborating on Glanz and colleagues model was developed. The qualitative findings and the resulting ecological framework developed from this study will help public health professionals and community leaders to develop and implement sustainable multi-level nutrition strategies for addressing racial and ethnic disparities in diabetes. ^

High-resolution multi-proxy record of the last glacial period from Lake Van, eastern Anatolian high plateau, Turkey

Detailed analyses of the Lake Van pollen, Ca/K ratio and stable oxygen isotope record allow the identification of millennial-scale vegetation and environmental changes in eastern Anatolia throughout the last glacial (~75-15 ka BP). The climate within the last glacial was cold and dry, with low arboreal pollen (AP) levels. The driest and coldest period corresponds to Marine Isotope Stage (MIS) 2 (~28-14.5 ka BP) dominated by the highest values of xerophytic steppe vegetation. Our high-resolution multi proxy record shows rapid expansions and contractions of tree populations that reflects variability in temperature and moisture availability. This rapid vegetation and environmental changes can be linked to the stadial-interstadial pattern of the Dansgaard-Oeschger (DO) events as recorded in the Greenland ice cores. Periods of reduced moisture availability were characterized by enhanced xerophytic species and high terrigenous input from the Lake Van catchment area. Furthermore, comparison with the marine realm reveals that the complex atmosphere-ocean interaction can be explained by the strength and position of the westerlies, which is responsible for the supply of humidity in eastern Anatolia. Influenced by diverse topography of the Lake Van catchment, larger DO interstadials (e.g. DO 19, 17-16, 14, 12 and 8) show the highest expansion of temperate species within the last glacial. However, Heinrich events (HE), characterized by highest concentrations of ice-rafted debris (IRD) in marine sediments, are identified in eastern Anatolia by AP values not lower and high steppe components not more abundant than during DO stadials. In addition, this work is a first attempt to establish a continuous microscopic charcoal record over the last glacial in the Near East, which documents an initial immediate response to millennial-scale climate and environmental variability and enables us to shed light on the history of fire activity during the last glacial.

High-resolution multi-proxy record of the last interglacial period from Lake Van, eastern Anatolian high plateau, Turkey

A high-resolution multi-proxy record from Lake Van, eastern Anatolia, derived from a lacustrine sequence cored at the 357 m deep Ahlat Ridge (AR), allows a comprehensive view of paleoclimate and environmental history in the continental Near East during the last interglacial (LI). We combined paleovegetation (pollen), stable oxygen isotope (d18Obulk) and XRF data from the same sedimentary sequence, showing distinct variations during the period from 135 to 110 ka ago leading into and out of full interglacial conditions. The last interglacial plateau, as defined by the presence of thermophilous steppe-forest communities, lasted ca. 13.5 ka, from ~129.1-115.6 ka BP. The detailed palynological sequence at Lake Van documents a vegetation succession with several climatic phases: (I) the Pistacia zone (ca. 131.2-129.1 ka BP) indicates summer dryness and mild winter conditions during the initial warming, (II) the Quercus-Ulmus zone (ca. 129.1-127.2 ka BP) occurred during warm and humid climate conditions with enhanced evaporation, (III) the Carpinus zone (ca. 127.2-124.1 ka BP) suggest increasingly cooler and wetter conditions, and (IV) the expansion of Pinus at ~124.1 ka BP marks the onset of a colder/drier environment that extended into the interval of global ice growth. Pollen data suggest migration of thermophilous trees from refugial areas at the beginning of the last interglacial. Analogous to the current interglacial, the migration documents a time lag between the onset of climatic amelioration and the establishment of an oak steppe-forest, spanning 2.1 ka. Hence, the major difference between the last interglacial compared to the current interglacial (Holocene) is the abundance of Pinus as well as the decrease of deciduous broad-leaved trees, indicating higher continentality during the last interglacial. Finally, our results demonstrate intra-interglacial variability in the low mid-latitudes and suggest a close connection with the high-frequency climate variability recorded in Greenland ice cores.

SADA and DESYRE DSSs descriptive classification criteria (main issues related to DSS's)

Brownfield rehabilitation is an essential step for sustainable land-use planning and management in the European Union. In brownfield regeneration processes, the legacy contamination plays a significant role, firstly because of the persistent contaminants in soil or groundwater which extends the existing hazards and risks well into the future; and secondly, problems from historical contamination are often more difficult to manage than contamination caused by new activities. Due to the complexity associated with the management of brownfield site rehabilitation, Decision Support Systems (DSSs) have been developed to support problem holders and stakeholders in the decision-making process encompassing all phases of the rehabilitation. This paper presents a comparative study between two DSSs, namely SADA (Spatial Analysis and Decision Assistance) and DESYRE (Decision Support System for the Requalification of Contaminated Sites), with the main objective of showing the benefits of using DSSs to introduce and process data and then to disseminate results to different stakeholders involved in the decision-making process. For this purpose, a former car manufacturing plant located in the Brasov area, Central Romania, contaminated chiefly by heavy metals and total petroleum hydrocarbons, has been selected as a case study to apply the two examined DSSs. Major results presented here concern the analysis of the functionalities of the two DSSs in order to identify similarities, differences and complementarities and, thus, to provide an indication of the most suitable integration options.

Multi-objective Estimation of Distribution Algorithm Based on Joint Modeling of Objectives and Variables

This paper proposes a new multi-objective estimation of distribution algorithm (EDA) based on joint modeling of objectives and variables. This EDA uses the multi-dimensional Bayesian network as its probabilistic model. In this way it can capture the dependencies between objectives, variables and objectives, as well as the dependencies learnt between variables in other Bayesian network-based EDAs. This model leads to a problem decomposition that helps the proposed algorithm to find better trade-off solutions to the multi-objective problem. In addition to Pareto set approximation, the algorithm is also able to estimate the structure of the multi-objective problem. To apply the algorithm to many-objective problems, the algorithm includes four different ranking methods proposed in the literature for this purpose. The algorithm is applied to the set of walking fish group (WFG) problems, and its optimization performance is compared with an evolutionary algorithm and another multi-objective EDA. The experimental results show that the proposed algorithm performs significantly better on many of the problems and for different objective space dimensions, and achieves comparable results on some compared with the other algorithms.

Some techniques for automated, resource-aware distributed and mobile computing in a multi-paradigm programming system

Distributed parallel execution systems speed up applications by splitting tasks into processes whose execution is assigned to different receiving nodes in a high-bandwidth network. On the distributing side, a fundamental problem is grouping and scheduling such tasks such that each one involves sufñcient computational cost when compared to the task creation and communication costs and other such practical overheads. On the receiving side, an important issue is to have some assurance of the correctness and characteristics of the code received and also of the kind of load the particular task is going to pose, which can be specified by means of certificates. In this paper we present in a tutorial way a number of general solutions to these problems, and illustrate them through their implementation in the Ciao multi-paradigm language and program development environment. This system includes facilities for parallel and distributed execution, an assertion language for specifying complex programs properties (including safety and resource-related properties), and compile-time and run-time tools for performing automated parallelization and resource control, as well as certification of programs with resource consumption assurances and efñcient checking of such certificates.

Markov blanket-based approach for learning multi-dimensional Bayesian network classifiers: An application to predict the European Quality of Life-5 Dimensions (EQ-5D) from the 39-item Parkinson's Disease Questionnaire (PDQ-39)

Multi-dimensional Bayesian network classifiers (MBCs) are probabilistic graphical models recently proposed to deal with multi-dimensional classification problems, where each instance in the data set has to be assigned to more than one class variable. In this paper, we propose a Markov blanket-based approach for learning MBCs from data. Basically, it consists of determining the Markov blanket around each class variable using the HITON algorithm, then specifying the directionality over the MBC subgraphs. Our approach is applied to the prediction problem of the European Quality of Life-5 Dimensions (EQ-5D) from the 39-item Parkinson’s Disease Questionnaire (PDQ-39) in order to estimate the health-related quality of life of Parkinson’s patients. Fivefold cross-validation experiments were carried out on randomly generated synthetic data sets, Yeast data set, as well as on a real-world Parkinson’s disease data set containing 488 patients. The experimental study, including comparison with additional Bayesian network-based approaches, back propagation for multi-label learning, multi-label k-nearest neighbor, multinomial logistic regression, ordinary least squares, and censored least absolute deviations, shows encouraging results in terms of predictive accuracy as well as the identification of dependence relationships among class and feature variables.

Regularized model learning in EDAs for continuous and multi-objective optimization

Probabilistic modeling is the de�ning characteristic of estimation of distribution algorithms (EDAs) which determines their behavior and performance in optimization. Regularization is a well-known statistical technique used for obtaining an improved model by reducing the generalization error of estimation, especially in high-dimensional problems. `1-regularization is a type of this technique with the appealing variable selection property which results in sparse model estimations. In this thesis, we study the use of regularization techniques for model learning in EDAs. Several methods for regularized model estimation in continuous domains based on a Gaussian distribution assumption are presented, and analyzed from di�erent aspects when used for optimization in a high-dimensional setting, where the population size of EDA has a logarithmic scale with respect to the number of variables. The optimization results obtained for a number of continuous problems with an increasing number of variables show that the proposed EDA based on regularized model estimation performs a more robust optimization, and is able to achieve signi�cantly better results for larger dimensions than other Gaussian-based EDAs. We also propose a method for learning a marginally factorized Gaussian Markov random �eld model using regularization techniques and a clustering algorithm. The experimental results show notable optimization performance on continuous additively decomposable problems when using this model estimation method. Our study also covers multi-objective optimization and we propose joint probabilistic modeling of variables and objectives in EDAs based on Bayesian networks, speci�cally models inspired from multi-dimensional Bayesian network classi�ers. It is shown that with this approach to modeling, two new types of relationships are encoded in the estimated models in addition to the variable relationships captured in other EDAs: objectivevariable and objective-objective relationships. An extensive experimental study shows the e�ectiveness of this approach for multi- and many-objective optimization. With the proposed joint variable-objective modeling, in addition to the Pareto set approximation, the algorithm is also able to obtain an estimation of the multi-objective problem structure. Finally, the study of multi-objective optimization based on joint probabilistic modeling is extended to noisy domains, where the noise in objective values is represented by intervals. A new version of the Pareto dominance relation for ordering the solutions in these problems, namely �-degree Pareto dominance, is introduced and its properties are analyzed. We show that the ranking methods based on this dominance relation can result in competitive performance of EDAs with respect to the quality of the approximated Pareto sets. This dominance relation is then used together with a method for joint probabilistic modeling based on `1-regularization for multi-objective feature subset selection in classi�cation, where six di�erent measures of accuracy are considered as objectives with interval values. The individual assessment of the proposed joint probabilistic modeling and solution ranking methods on datasets with small-medium dimensionality, when using two di�erent Bayesian classi�ers, shows that comparable or better Pareto sets of feature subsets are approximated in comparison to standard methods.

Identifying the semantics of eligibility criteria of clinical trials based on relevant medical ontologies

An important objective of the INTEGRATE project1 is to build tools that support the efficient execution of post-genomic multi-centric clinical trials in breast cancer, which includes the automatic assessment of the eligibility of patients for available trials. The population suited to be enrolled in a trial is described by a set of free-text eligibility criteria that are both syntactically and semantically complex. At the same time, the assessment of the eligibility of a patient for a trial requires the (machineprocessable) understanding of the semantics of the eligibility criteria in order to further evaluate if the patient data available for example in the hospital EHR satisfies these criteria. This paper presents an analysis of the semantics of the clinical trial eligibility criteria based on relevant medical ontologies in the clinical research domain: SNOMED-CT, LOINC, MedDRA. We detect subsets of these widely-adopted ontologies that characterize the semantics of the eligibility criteria of trials in various clinical domains and compare these sets. Next, we evaluate the occurrence frequency of the concepts in the concrete case of breast cancer (which is our first application domain) in order to provide meaningful priorities for the task of binding/mapping these ontology concepts to the actual patient data. We further assess the effort required to extend our approach to new domains in terms of additional semantic mappings that need to be developed.

Metodología para el desarrollo de un sistema de visualización multi-proyector con características cromáticas uniformes

Los sistemas de proyección multi-proyector han adquirido gran popularidad en los últimos años para su uso en un amplio rango de aplicaciones como sistemas de realidad virtual, simuladores y visualización de datos. Esto es debido a que normalmente estas aplicaciones necesitan representar sus datos a muy alta resolución y a lo largo de una gran superficie. Este tipo de sistemas de proyección son baratos en comparación con las resoluciones que pueden conseguir, se pueden configurar para proyectar sobre prácticamente cualquier tipo de superficie, sea cual sea su forma, y son fácilmente escalables. Sin embargo, para hacer que este tipo de sistemas generen una imagen sin discontinuidades geométricas o colorimétricas requieren de un ajuste preciso. En la presente tesis se analizan en detalle todos los problemas a los que hay que enfrentarse a la hora de diseñar y calibrar un sistema de proyección de este tipo y se propone una metodología con una serie de optimizaciones para hacer el ajuste de estos sistemas más sencillo y rápido. Los resultados de esta metodología se muestran aplicados a la salida gráfica de un simulador de entrenamiento. Multi-projector display systems have gained high popularity over the past years for its use in a wide range of applications such as virtual reality systems, simulators or data visualization where a high resolution image over a large projection surface is required. Such systems are cheap for the resolutions they can provide, can be configured to project images on almost any kind of screen shapes and are easily scalable, but in order to provide a seamless image with no photometric discontinuities they require a precise geometric and colour correction. In this thesis, we analyze all the problems that have to be faced in order to design and calibrate a multi-projector display. We propose a calibration methodology with some optimizations that make the adjustment of this kind of displays easier and faster. The results of the implementation of this methodology on a training simulator are presented and discussed

Multi-dimensional classification using Bayesian networks for stationary and evolving streaming data

Hoy en día, con la evolución continua y rápida de las tecnologías de la información y los dispositivos de computación, se recogen y almacenan continuamente grandes volúmenes de datos en distintos dominios y a través de diversas aplicaciones del mundo real. La extracción de conocimiento útil de una cantidad tan enorme de datos no se puede realizar habitualmente de forma manual, y requiere el uso de técnicas adecuadas de aprendizaje automático y de minería de datos. La clasificación es una de las técnicas más importantes que ha sido aplicada con éxito a varias áreas. En general, la clasificación se compone de dos pasos principales: en primer lugar, aprender un modelo de clasificación o clasificador a partir de un conjunto de datos de entrenamiento, y en segundo lugar, clasificar las nuevas instancias de datos utilizando el clasificador aprendido. La clasificación es supervisada cuando todas las etiquetas están presentes en los datos de entrenamiento (es decir, datos completamente etiquetados), semi-supervisada cuando sólo algunas etiquetas son conocidas (es decir, datos parcialmente etiquetados), y no supervisada cuando todas las etiquetas están ausentes en los datos de entrenamiento (es decir, datos no etiquetados). Además, aparte de esta taxonomía, el problema de clasificación se puede categorizar en unidimensional o multidimensional en función del número de variables clase, una o más, respectivamente; o también puede ser categorizado en estacionario o cambiante con el tiempo en función de las características de los datos y de la tasa de cambio subyacente. A lo largo de esta tesis, tratamos el problema de clasificación desde tres perspectivas diferentes, a saber, clasificación supervisada multidimensional estacionaria, clasificación semisupervisada unidimensional cambiante con el tiempo, y clasificación supervisada multidimensional cambiante con el tiempo. Para llevar a cabo esta tarea, hemos usado básicamente los clasificadores Bayesianos como modelos. La primera contribución, dirigiéndose al problema de clasificación supervisada multidimensional estacionaria, se compone de dos nuevos métodos de aprendizaje de clasificadores Bayesianos multidimensionales a partir de datos estacionarios. Los métodos se proponen desde dos puntos de vista diferentes. El primer método, denominado CB-MBC, se basa en una estrategia de envoltura de selección de variables que es voraz y hacia delante, mientras que el segundo, denominado MB-MBC, es una estrategia de filtrado de variables con una aproximación basada en restricciones y en el manto de Markov. Ambos métodos han sido aplicados a dos problemas reales importantes, a saber, la predicción de los inhibidores de la transcriptasa inversa y de la proteasa para el problema de infección por el virus de la inmunodeficiencia humana tipo 1 (HIV-1), y la predicción del European Quality of Life-5 Dimensions (EQ-5D) a partir de los cuestionarios de la enfermedad de Parkinson con 39 ítems (PDQ-39). El estudio experimental incluye comparaciones de CB-MBC y MB-MBC con los métodos del estado del arte de la clasificación multidimensional, así como con métodos comúnmente utilizados para resolver el problema de predicción de la enfermedad de Parkinson, a saber, la regresión logística multinomial, mínimos cuadrados ordinarios, y mínimas desviaciones absolutas censuradas. En ambas aplicaciones, los resultados han sido prometedores con respecto a la precisión de la clasificación, así como en relación al análisis de las estructuras gráficas que identifican interacciones conocidas y novedosas entre las variables. La segunda contribución, referida al problema de clasificación semi-supervisada unidimensional cambiante con el tiempo, consiste en un método nuevo (CPL-DS) para clasificar flujos de datos parcialmente etiquetados. Los flujos de datos difieren de los conjuntos de datos estacionarios en su proceso de generación muy rápido y en su aspecto de cambio de concepto. Es decir, los conceptos aprendidos y/o la distribución subyacente están probablemente cambiando y evolucionando en el tiempo, lo que hace que el modelo de clasificación actual sea obsoleto y deba ser actualizado. CPL-DS utiliza la divergencia de Kullback-Leibler y el método de bootstrapping para cuantificar y detectar tres tipos posibles de cambio: en las predictoras, en la a posteriori de la clase o en ambas. Después, si se detecta cualquier cambio, un nuevo modelo de clasificación se aprende usando el algoritmo EM; si no, el modelo de clasificación actual se mantiene sin modificaciones. CPL-DS es general, ya que puede ser aplicado a varios modelos de clasificación. Usando dos modelos diferentes, el clasificador naive Bayes y la regresión logística, CPL-DS se ha probado con flujos de datos sintéticos y también se ha aplicado al problema real de la detección de código malware, en el cual los nuevos ficheros recibidos deben ser continuamente clasificados en malware o goodware. Los resultados experimentales muestran que nuestro método es efectivo para la detección de diferentes tipos de cambio a partir de los flujos de datos parcialmente etiquetados y también tiene una buena precisión de la clasificación. Finalmente, la tercera contribución, sobre el problema de clasificación supervisada multidimensional cambiante con el tiempo, consiste en dos métodos adaptativos, a saber, Locally Adpative-MB-MBC (LA-MB-MBC) y Globally Adpative-MB-MBC (GA-MB-MBC). Ambos métodos monitorizan el cambio de concepto a lo largo del tiempo utilizando la log-verosimilitud media como métrica y el test de Page-Hinkley. Luego, si se detecta un cambio de concepto, LA-MB-MBC adapta el actual clasificador Bayesiano multidimensional localmente alrededor de cada nodo cambiado, mientras que GA-MB-MBC aprende un nuevo clasificador Bayesiano multidimensional. El estudio experimental realizado usando flujos de datos sintéticos multidimensionales indica los méritos de los métodos adaptativos propuestos. ABSTRACT Nowadays, with the ongoing and rapid evolution of information technology and computing devices, large volumes of data are continuously collected and stored in different domains and through various real-world applications. Extracting useful knowledge from such a huge amount of data usually cannot be performed manually, and requires the use of adequate machine learning and data mining techniques. Classification is one of the most important techniques that has been successfully applied to several areas. Roughly speaking, classification consists of two main steps: first, learn a classification model or classifier from an available training data, and secondly, classify the new incoming unseen data instances using the learned classifier. Classification is supervised when the whole class values are present in the training data (i.e., fully labeled data), semi-supervised when only some class values are known (i.e., partially labeled data), and unsupervised when the whole class values are missing in the training data (i.e., unlabeled data). In addition, besides this taxonomy, the classification problem can be categorized into uni-dimensional or multi-dimensional depending on the number of class variables, one or more, respectively; or can be also categorized into stationary or streaming depending on the characteristics of the data and the rate of change underlying it. Through this thesis, we deal with the classification problem under three different settings, namely, supervised multi-dimensional stationary classification, semi-supervised unidimensional streaming classification, and supervised multi-dimensional streaming classification. To accomplish this task, we basically used Bayesian network classifiers as models. The first contribution, addressing the supervised multi-dimensional stationary classification problem, consists of two new methods for learning multi-dimensional Bayesian network classifiers from stationary data. They are proposed from two different points of view. The first method, named CB-MBC, is based on a wrapper greedy forward selection approach, while the second one, named MB-MBC, is a filter constraint-based approach based on Markov blankets. Both methods are applied to two important real-world problems, namely, the prediction of the human immunodeficiency virus type 1 (HIV-1) reverse transcriptase and protease inhibitors, and the prediction of the European Quality of Life-5 Dimensions (EQ-5D) from 39-item Parkinson’s Disease Questionnaire (PDQ-39). The experimental study includes comparisons of CB-MBC and MB-MBC against state-of-the-art multi-dimensional classification methods, as well as against commonly used methods for solving the Parkinson’s disease prediction problem, namely, multinomial logistic regression, ordinary least squares, and censored least absolute deviations. For both considered case studies, results are promising in terms of classification accuracy as well as regarding the analysis of the learned MBC graphical structures identifying known and novel interactions among variables. The second contribution, addressing the semi-supervised uni-dimensional streaming classification problem, consists of a novel method (CPL-DS) for classifying partially labeled data streams. Data streams differ from the stationary data sets by their highly rapid generation process and their concept-drifting aspect. That is, the learned concepts and/or the underlying distribution are likely changing and evolving over time, which makes the current classification model out-of-date requiring to be updated. CPL-DS uses the Kullback-Leibler divergence and bootstrapping method to quantify and detect three possible kinds of drift: feature, conditional or dual. Then, if any occurs, a new classification model is learned using the expectation-maximization algorithm; otherwise, the current classification model is kept unchanged. CPL-DS is general as it can be applied to several classification models. Using two different models, namely, naive Bayes classifier and logistic regression, CPL-DS is tested with synthetic data streams and applied to the real-world problem of malware detection, where the new received files should be continuously classified into malware or goodware. Experimental results show that our approach is effective for detecting different kinds of drift from partially labeled data streams, as well as having a good classification performance. Finally, the third contribution, addressing the supervised multi-dimensional streaming classification problem, consists of two adaptive methods, namely, Locally Adaptive-MB-MBC (LA-MB-MBC) and Globally Adaptive-MB-MBC (GA-MB-MBC). Both methods monitor the concept drift over time using the average log-likelihood score and the Page-Hinkley test. Then, if a drift is detected, LA-MB-MBC adapts the current multi-dimensional Bayesian network classifier locally around each changed node, whereas GA-MB-MBC learns a new multi-dimensional Bayesian network classifier from scratch. Experimental study carried out using synthetic multi-dimensional data streams shows the merits of both proposed adaptive methods.

Multi-objective Aerodynamic Optimization of High-speed Trains in Tunnels

A genetic algorithm (GA) is employed for the multi-objective shape optimization of the nose of a high-speed train. Aerodynamic problems observed at high speeds become still more relevant when traveling along a tunnel. The objective is to minimize both the aerodynamic drag and the amplitude of the pressure gradient of the compression wave when a train enters a tunnel. The main drawback of GA is the large number of evaluations need in the optimization process. Metamodels-based optimization is considered to overcome such problem. As a result, an explicit relationship between pressure gradient and geometrical parameters is obtained.

Bounded non-deterministic planning for multimedia adaptation

This paper proposes a novel combination of artificial intelligence planning and other techniques for improving decision-making in the context of multi-step multimedia content adaptation. In particular, it describes a method that allows decision-making (selecting the adaptation to perform) in situations where third-party pluggable multimedia conversion modules are involved and the multimedia adaptation planner does not know their exact adaptation capabilities. In this approach, the multimedia adaptation planner module is only responsible for a part of the required decisions; the pluggable modules make additional decisions based on different criteria. We demonstrate that partial decision-making is not only attainable, but also introduces advantages with respect to a system in which these conversion modules are not capable of providing additional decisions. This means that transferring decisions from the multi-step multimedia adaptation planner to the pluggable conversion modules increases the flexibility of the adaptation. Moreover, by allowing conversion modules to be only partially described, the range of problems that these modules can address increases, while significantly decreasing both the description length of the adaptation capabilities and the planning decision time. Finally, we specify the conditions under which knowing the partial adaptation capabilities of a set of conversion modules will be enough to compute a proper adaptation plan.

A 3D multi-objective optimization planning algorithm for wireless sensor networks

The complexity of planning a wireless sensor network is dependent on the aspects of optimization and on the application requirements. Even though Murphy's Law is applied everywhere in reality, a good planning algorithm will assist the designers to be aware of the short plates of their design and to improve them before the problems being exposed at the real deployment. A 3D multi-objective planning algorithm is proposed in this paper to provide solutions on the locations of nodes and their properties. It employs a developed ray-tracing scheme for sensing signal and radio propagation modelling. Therefore it is sensitive to the obstacles and makes the models of sensing coverage and link quality more practical compared with other heuristics that use ideal unit-disk models. The proposed algorithm aims at reaching an overall optimization on hardware cost, coverage, link quality and lifetime. Thus each of those metrics are modelled and normalized to compose a desirability function. Evolutionary algorithm is designed to efficiently tackle this NP-hard multi-objective optimization problem. The proposed algorithm is applicable for both indoor and outdoor 3D scenarios. Different parameters that affect the performance are analyzed through extensive experiments; two state-of-the-art algorithms are rebuilt and tested with the same configuration as that of the proposed algorithm. The results indicate that the proposed algorithm converges efficiently within 600 iterations and performs better than the compared heuristics.

Naturaleza, ética, y arquitectura: Autenticidad y criterios éticos que integran el desarrollo de una arquitectura más sostenible = Nature, ethics and architecture: Authenticity and ethics criteria that integrate the development of a more sustainable architecture

La ecología no solamente ha puesto de manifiesto problemas ambientales, sino que ha confirmado la necesidad de una nueva armonía entre los propios seres humanos y de éstos con la naturaleza y con todos los seres que la habitan. Es necesario un nuevo contrato que determine nuestras relaciones con la Naturaleza (Serrs1), y una nueva Ética para nuestras vidas (Guattari2). La ética medioambiental nos ha dado una visión universal y supra-generacional de la gestión de la naturaleza y como consecuencia, una nueva forma de construir nuestra ‘segunda’ Naturaleza, que es la arquitectura. ¿Qué es lo esencial que esta nueva ética exige para la arquitectura? Este es un momento crucial para reconsiderar los objetivos de la arquitectura, porque lo ‘eco’ está produciendo grandes cambios. ¿Implica esta era post-ecológica una particular ética, es decir, referida a sus fines y medios? ¿Porqué, para qué, para quién, cómo debemos hacer la arquitectura de nuestro tiempo? Es momento de afrontar críticamente el discurso de la eco-arquitectura, e incluso de repensar los propios límites de la arquitectura. El desarrollo actual del conocimiento medioambiental es esencialmente técnico y utilitario, pero ¿es el reto solamente técnico?¿Es suficiente la suma de lo medioambiental-social-económico-cultural para definirla? ¿Hay claves que nos puedan dar la dimensión ética de esta aproximación técnica-empírica? ¿Sabemos lo que estamos haciendo cuando aplicamos este conocimiento? Y, sobre todo, ¿cuál es el sentido de lo que estamos haciendo? La tesis que se propone puede resumirse: De acuerdo con el actual conocimiento que tenemos de la Naturaleza, la Arquitectura de nuestro tiempo deber reconsiderar sus fines y medios, puesto que la ética medioambiental está definiendo nuevos objetivos. Para fundamentar y profundizar en esta afirmación la tesis analiza cómo son hoy día las relaciones entre Ética-Naturaleza-Arquitectura (Fig.1), lo que facilitará las claves de cuáles son los criterios éticos (en cuanto a fines y medios) que deben definir la arquitectura del tiempo de la ecología. ABSTRACT Ecology shows us not only environmental problems; it shows that we need a new balance and harmony between individuals, beings, communities and Nature. We need a new contract with Nature according to Serres576, and a new Ethics for our lives according to Guattari577. Environmental ethics have given us a universal and supra-generational vision of the management of our Nature and, as a consequence, a new way to construct our ‘second’ nature, which is architecture. What is essential for this new architecture that the new ethics demand? This is a critical moment to reconsider the object of architecture, because the ‘eco’ is making significant changes in it. Are there any specifically ethical concerns (ends and means) in the post-ecological era? Why, for what, for whom, how should we make architecture in our times? This is the time to approach the eco-architectural discourse critically and to question the current boundaries of architecture itself: Where is eco-architecture going? The current development of environmental knowledge is essentially technical and utilitarian, but it is its technical aspect the only challenge? Is the sum of environmental-social-economic aspects enough to define it? Are there any clues which can give an ethical sense to this technical-empirical approach? Do we know what we are doing when we apply this knowledge? And overall, what is the meaning of what we are doing? Exploring this subject, this thesis makes a statement: In accordance with the actual knowledge of Nature, Architecture of our time must reconsider its ends and means, since the environmental ethics is defining new objectives. To support that, the thesis analyzes what the relationships between Ethics –Nature- Architecture (Fig. 53) are like nowadays, this will provide the clues of which ethical criteria (ends and means) must architecture of an ecological era define.