901 resultados para System Compositional Approach
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The potential of type-2 fuzzy sets for managing high levels of uncertainty in the subjective knowledge of experts or of numerical information has focused on control and pattern classification systems in recent years. One of the main challenges in designing a type-2 fuzzy logic system is how to estimate the parameters of type-2 fuzzy membership function (T2MF) and the Footprint of Uncertainty (FOU) from imperfect and noisy datasets. This paper presents an automatic approach for learning and tuning Gaussian interval type-2 membership functions (IT2MFs) with application to multi-dimensional pattern classification problems. T2MFs and their FOUs are tuned according to the uncertainties in the training dataset by a combination of genetic algorithm (GA) and crossvalidation techniques. In our GA-based approach, the structure of the chromosome has fewer genes than other GA methods and chromosome initialization is more precise. The proposed approach addresses the application of the interval type-2 fuzzy logic system (IT2FLS) for the problem of nodule classification in a lung Computer Aided Detection (CAD) system. The designed IT2FLS is compared with its type-1 fuzzy logic system (T1FLS) counterpart. The results demonstrate that the IT2FLS outperforms the T1FLS by more than 30% in terms of classification accuracy.
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This article analyses the impact of the reference pricesystem on the price-setting strategies of thepharmaceutical firms and on the level of generic usage.This model is the first to take explicitly into accountthe impact of the reference price mechanism on the levelof competition between brand-name and generic drugs andnational pharmaceutical spending. We consider aduopolistic model with one firm producing the brand-namedrug, whose patent has already expired, and the otherproducing the corresponding generic version. We work ina partial equilibrium framework where firms set pricessequentially and consumers face heterogeneous switchingcosts.We show that brand producers compensate thedecline of profits by selling greater quantities insteadof charging higher prices, thus fostering pricecompetition in the pharmaceutical market. This result isa consequence of both the assumption of a verticallydifferentiated model and the introduction of thereference price system.
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A mobile ad hoc network (MANET) is a decentralized and infrastructure-less network. This thesis aims to provide support at the system-level for developers of applications or protocols in such networks. To do this, we propose contributions in both the algorithmic realm and in the practical realm. In the algorithmic realm, we contribute to the field by proposing different context-aware broadcast and multicast algorithms in MANETs, namely six-shot broadcast, six-shot multicast, PLAN-B and ageneric algorithmic approach to optimize the power consumption of existing algorithms. For each algorithm we propose, we compare it to existing algorithms that are either probabilistic or context-aware, and then we evaluate their performance based on simulations. We demonstrate that in some cases, context-aware information, such as location or signal-strength, can improve the effciency. In the practical realm, we propose a testbed framework, namely ManetLab, to implement and to deploy MANET-specific protocols, and to evaluate their performance. This testbed framework aims to increase the accuracy of performance evaluation compared to simulations, while keeping the ease of use offered by the simulators to reproduce a performance evaluation. By evaluating the performance of different probabilistic algorithms with ManetLab, we observe that both simulations and testbeds should be used in a complementary way. In addition to the above original contributions, we also provide two surveys about system-level support for ad hoc communications in order to establish a state of the art. The first is about existing broadcast algorithms and the second is about existing middleware solutions and the way they deal with privacy and especially with location privacy. - Un réseau mobile ad hoc (MANET) est un réseau avec une architecture décentralisée et sans infrastructure. Cette thèse vise à fournir un support adéquat, au niveau système, aux développeurs d'applications ou de protocoles dans de tels réseaux. Dans ce but, nous proposons des contributions à la fois dans le domaine de l'algorithmique et dans celui de la pratique. Nous contribuons au domaine algorithmique en proposant différents algorithmes de diffusion dans les MANETs, algorithmes qui sont sensibles au contexte, à savoir six-shot broadcast,six-shot multicast, PLAN-B ainsi qu'une approche générique permettant d'optimiser la consommation d'énergie de ces algorithmes. Pour chaque algorithme que nous proposons, nous le comparons à des algorithmes existants qui sont soit probabilistes, soit sensibles au contexte, puis nous évaluons leurs performances sur la base de simulations. Nous montrons que, dans certains cas, des informations liées au contexte, telles que la localisation ou l'intensité du signal, peuvent améliorer l'efficience de ces algorithmes. Sur le plan pratique, nous proposons une plateforme logicielle pour la création de bancs d'essai, intitulé ManetLab, permettant d'implémenter, et de déployer des protocoles spécifiques aux MANETs, de sorte à évaluer leur performance. Cet outil logiciel vise à accroître la précision desévaluations de performance comparativement à celles fournies par des simulations, tout en conservant la facilité d'utilisation offerte par les simulateurs pour reproduire uneévaluation de performance. En évaluant les performances de différents algorithmes probabilistes avec ManetLab, nous observons que simulateurs et bancs d'essai doivent être utilisés de manière complémentaire. En plus de ces contributions principales, nous fournissons également deux états de l'art au sujet du support nécessaire pour les communications ad hoc. Le premier porte sur les algorithmes de diffusion existants et le second sur les solutions de type middleware existantes et la façon dont elles traitent de la confidentialité, en particulier celle de la localisation.
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In this discussion, after a few general comments, I will propose a systems reading of the intervention so elegantly described by Kaija Puura. I will draw parallels between the therapeutic and the family groups as framing-developing systems and formalize the steps taken by the family toward healing under the influence of the therapist's team. En esta discusión, después de algunos comentarios generales, propongo una lectura sistemática de la intervención tan elegantemente descrita por Kaija Puura. Buscaré paralelos entre los grupos terapéuticos y de familia como sistemas de desarrollo enmarcado y formalizaré los pasos tomados por la familia hacia la cicatrización bajo la influencia del equipo del terapista. Après quelques commentaires généraux, je proposerai dans cette discussion une lecture systémique de l'intervention si élégamment décrite par Kaija Puura. J'établirai des parallèles entre les groupes thérapeutiques et familiaux en tant que systèmes d'encadrement-développement et je formaliserai les étapes de guérison franchies par la famille grâce à l'influence de l'équipe thérapeutique. In dieser Diskussion, werde ich nach einigen allgemeineren Aussagen, eine systemische Lesart der von Kaija Puura so eingängig beschriebenen Intervention vorschlagen. Ich werde darin Parallelen zwischen der therapeutischen und Rahmengebenden Familiengruppen ziehen, und die Schritte der Familien hin zu einer Heilung unter dem Einfluss des Therapeutenteams formalisieren.
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Clinical practice guidelines have become an important source of information to support clinicians in the management of individual patients. However, current guideline methods have limitations that include the lack of separating the quality of evidence from the strength of recommendations. The Grading of Recommendations, Assessment, Development and Evaluation (GRADE) working group, an international collaboration of guideline developers, methodologists, and clinicians have developed a system that addresses these shortcomings. Core elements include transparent methodology for grading the quality of evidence, the distinction between quality of the evidence and strength of a recommendation, an explicit balancing of benefits and harms of health care interventions, an explicit recognition of the values and preferences that underlie recommendations. The GRADE system has been piloted in various practice settings to ensure that it captures the complexity involved in evidence assessment and grading recommendations while maintaining simplicity and practicality. Many guideline organizations and medical societies have endorsed the system and adopted it for their guideline processes.
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In this paper, a new algorithm for blind inversion of Wiener systems is presented. The algorithm is based on minimization of mutual information of the output samples. This minimization is done through a Minimization-Projection (MP) approach, using a nonparametric “gradient” of mutual information.
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Finding out whether Plasmodium spp. are coevolving with their vertebrate hosts is of both theoretical and applied interest and can influence our understanding of the effects and dynamics of malaria infection. In this study, we tested for local adaptation as a signature of coevolution between malaria blood parasites, Plasmodium spp. and its host, the great tit, Parus major. We conducted a reciprocal transplant experiment of birds in the field, where we exposed birds from two populations to Plasmodium parasites. This experimental set-up also provided a unique opportunity to study the natural history of malaria infection in the wild and to assess the effects of primary malaria infection on juvenile birds. We present three main findings: i) there was no support for local adaptation; ii) there was a male-biased infection rate; iii) infection occurred towards the end of the summer and differed between sites. There were also site-specific effects of malaria infection on the hosts. Taken together, we present one of the few experimental studies of parasite-host local adaptation in a natural malaria system, and our results shed light on the effects of avian malaria infection in the wild.
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Numerous studies assess the correlation between genetic and species diversities, but the processes underlying the observed patterns have only received limited attention. For instance, varying levels of habitat disturbance across a region may locally reduce both diversities due to extinctions, and increased genetic drift during population bottlenecks and founder events. We investigated the regional distribution of genetic and species diversities of a coastal sand dune plant community along 240 kilometers of coastline with the aim to test for a correlation between the two diversity levels. We further quantify and tease apart the respective contributions of natural and anthropogenic disturbance factors to the observed patterns. We detected significant positive correlation between both variables. We further revealed a negative impact of urbanization: Sites with a high amount of recreational infrastructure within 10 km coastline had significantly lowered genetic and species diversities. On the other hand, a measure of natural habitat disturbance had no effect. This study shows that parallel variation of genetic and species diversities across a region can be traced back to human landscape alteration, provides arguments for a more resolute dune protection, and may help to design priority conservation areas.
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Peer-reviewed
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The model of Questions Answering (Q&A) for eLearning is based on collaborative learning through questions that are posed by students and their answers to that questions which are given by peers, in contrast with the classical model in which students ask questions to the teacher only. In this proposal we extend the Q&A model including the social presence concept and a quantitative measure of it is proposed; besides it is considered the evolution of the resulting Q&A social network after the inclusion of the social presence and taking into account the feedback on questions posed by students and answered by peers. The social network behaviorwas simulated using a Multi-Agent System to compare the proposed social presence model with the classical and the Q&A models
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Family businesses are among the longest-lived most prevalent institutions in the world and they are an important source of economic development and growth. Ownership is a key to the business life of the firm and also one main key in family business definition. There is only a little portfolio entrepreneurship or portfolio business research within family business context. The absence of empirical evidence on the long-term relationship between family ownership and portfolio development presents an important gap in the family business literature. This study deals with the family business ownership changes and the development of portfolios in the family business and it is positioned in to the conversation of family business, growth, ownership, management and strategy. This study contributes and expands the existing body of theory on family business and ownership. From the theoretical point of view this study combines insights from the fields of portfolio entrepreneurship, ownership, and family business and integrate them. This crossfertilization produces interesting empirical and theoretical findings that can constitute a basis for solid contributions to the understanding of ownership dynamics and portfolio entrepreneurship in family firms. The research strategy chosen for this study represents longitudinal, qualitative, hermeneutic, and deductive approaches.The empirical part of study is using a case study approach with embedded design, that is, multiple levels of analysis within a single study. The study consists of two cases and it begins with a pilot case which will form a preunderstanding on the phenomenon. Pilot case develops the methodology approach to build in the main case and the main case will deepen the understanding of the phenomenon. This study develops and tests a research method of family business portfolio development focusing on investigating how ownership changes are influencing to the family business structures over time. This study reveals the linkages between dimensions of ownership and how they give rise to portfolio business development within the context of the family business. The empirical results of the study suggest that family business ownership is dynamic and owners are using ownership as a tool for creating business portfolios.
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In this paper, a Petri Net approach is introduced for modelling and simulation of control strategies in Intelligent Building. In this context, it is claimed that integration with other building systems can be achieved in a more systematic way considering a mechatronic approach (i.e. multidisciplinary concepts applied to the development of systems). The case study is the Ambulatory Building of Medical School Hospital of University of São Paulo. Particularly, the developed methodology is applied to the elevator system and to the HVAC (Heating, Ventilation and Air Conditioning) system. It is shown that using this approach, the control systems could be integrated, improving performance.
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Apoptosis, a form of programmed cell death (PCD), has been described as essential for normal organogenesis and tissue development, as well as for the proper function of cell-renewal systems in adult organisms. Apoptosis is also pivotal in the pathogenesis of several different diseases. In this paper we discuss, from two different points of view, the role of apoptosis in parasitic diseases. The description of apoptotic death in three different species of heteroxenic trypanosomatids is reviewed, and considerations on the phylogenesis of apoptosis and on the eventual role of PCD on their mechanism of pathogenesis are made. From a different perspective, an increasing body of evidence is making clear that regulation of host cell apoptosis is an important factor on the definition of a host-pathogen interaction. As an example, the molecular mechanisms by which Trypanosoma cruzi is able to induce apoptosis in immunocompetent cells, in a murine model of Chagas' disease, and the consequences of this phenomenon on the outcome of the experimental disease are discussed.
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The growing population in cities increases the energy demand and affects the environment by increasing carbon emissions. Information and communications technology solutions which enable energy optimization are needed to address this growing energy demand in cities and to reduce carbon emissions. District heating systems optimize the energy production by reusing waste energy with combined heat and power plants. Forecasting the heat load demand in residential buildings assists in optimizing energy production and consumption in a district heating system. However, the presence of a large number of factors such as weather forecast, district heating operational parameters and user behavioural parameters, make heat load forecasting a challenging task. This thesis proposes a probabilistic machine learning model using a Naive Bayes classifier, to forecast the hourly heat load demand for three residential buildings in the city of Skellefteå, Sweden over a period of winter and spring seasons. The district heating data collected from the sensors equipped at the residential buildings in Skellefteå, is utilized to build the Bayesian network to forecast the heat load demand for horizons of 1, 2, 3, 6 and 24 hours. The proposed model is validated by using four cases to study the influence of various parameters on the heat load forecast by carrying out trace driven analysis in Weka and GeNIe. Results show that current heat load consumption and outdoor temperature forecast are the two parameters with most influence on the heat load forecast. The proposed model achieves average accuracies of 81.23 % and 76.74 % for a forecast horizon of 1 hour in the three buildings for winter and spring seasons respectively. The model also achieves an average accuracy of 77.97 % for three buildings across both seasons for the forecast horizon of 1 hour by utilizing only 10 % of the training data. The results indicate that even a simple model like Naive Bayes classifier can forecast the heat load demand by utilizing less training data.