888 resultados para Plant architecture model


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La gestión del conocimiento (KM) se basa en la captación, filtración, procesamiento y análisis de unos datos en bruto que con dicho refinamiento podrán llegar a convertirse en conocimiento o Sabiduría. Estas prácticas tendrán lugar en este PFC en una WSN (Wireless Sensor Network) compuesta de unos sofisticados dispositivos comúnmente conocidos como “motas” y cuya principal característica son sus bajas capacidades en cuanto a memoria, batería o autonomía. Ha sido objetivo primordial de este Proyecto de fin de Carrera aunar una WSN con la Gestión del Conocimiento así como demostrar que es posible llevar a cabo grandes procesamientos de información, con tan bajas capacidades, si se distribuyen correctamente los procesos. En primera instancia, se introducen conceptos básicos acerca de las WSN (Wireless Sensor Networks) así como de los elementos principales en dichas redes. Tras conocer el modelo de arquitectura de comunicaciones se procede a presentar la Gestión del Conocimiento de forma teórica y a continuación la interpretación que se ha hecho a partir de diversas referencias bibliográficas para llevar a cabo la implementación del proyecto. El siguiente paso es describir punto por punto todos los componentes del Simulador; librerías, funcionamiento y demás cuestiones sobre configuración y puesta a punto. Como escenario de aplicación se plantea una red de sensores inalámbricos básica cuya topología y ubicación es completamente configurable. Se lleva a cabo una configuración a nivel de red basada en el protocolo 6LowPAN pero con posibilidad de simplificarlo. Los datos se procesan de acuerdo a un modelo piramidal de Gestión de Conocimiento adaptable a las necesidades del usuario. Mediante la utilización de las diversas opciones que proporciona la interfaz gráfica implementada y los documentos de resultados que se van generando, se puede llevar a cabo un detallado estudio posterior de la simulación y comprobar si se cumplen las expectativas planteadas. Knowledge management (KM) is based on the collection, filtering, processing and analysis of some raw data which such refinement it can be turned into knowledge or wisdom. These practices will take place in a WSN (Wireless Sensor Network) consists of sophisticated devices commonly known as "dots" and whose main characteristics are its low capacity for memory, battery or autonomy. A primary objective of this Project will be to join a WSN with Knowledge Management and show that it is possible make largo information processing, with such low capacity if the processes are properly distributed. First, we introduce basic concepts about the WSN (Wireless Sensor Networks) and major elements of these networks. After meeting the communications architecture model, we proceed to show the Knowledge Management theory and then the interpretation of several bibliographic references to carry out the project implementation. The next step is discovering point by point all over the Simulator components; libraries, operation and the rest of points about configuration and tuning. As application scenario we propose a basic wireless sensor network whose topology and location is completely customizable. It will perform a network level configuration based in W6LowPAN Protocol. Data is processed according to a pyramidal pattern Knowledge Management adaptable to the user´s needs. The hardware elements will suffer more or less energy dependence depending on their role and activity in the network. Through the various options that provide the graphical interface has been implemented and results documents that are generated, can be carried out after a detailed study of the simulation and verify compliance with the expectations raised.

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Se inicia un análisis de los procesos de trabajo de stop-motion porque ayudan a comprender las diferentes escalas en arquitectura donde las maquetas se convierten en futuros prototipos de infraestructuras de edificios o de paisaje. Stop motion es una técnica de animación fotograma a fotograma de objetos estáticos mediante la manipulación de figuras de plastilina en entornos fijos con cambios de luz, color y sonido. Igual que dicha técnica reúne lo mejor del rodaje tradicional -story board, escenografía, fotografía, personajes, iluminación- la animación de maquetas de interiores sintetiza micro-procesos de mayor repercusión -habitaciones con cambios de humedad, de temperatura, de ventilación y de iluminación- incorporando efectos especiales que son procesados digitalmente en post-producción. Se construyen varios prototipos de habitación con parámetros fijos como el tamaño y la posición de la cámara y otros variables como los materiales, los personajes y la iluminación. Representan un mundo en miniatura que intenta aportar un acercamiento sensorial y atmosférico analizando la magia y la fantasía que Junichirô Tanizaki describe en la penumbra de las construcciones tradicionales japonesas y estudiando las imperfecciones de los escenarios que Tim Burton manipula en su películas de animación con una textura que las tecnologías digitales no pueden igualar. El objetivo es utilizar una escala micro para realizar unos modelos interiores donde las condiciones atmosféricas están controladas y reducidas, y tomar datos que se podrían aplicar a un proceso de modelado a escala intermedia para testar prototipos de edificios como el túnel de viento; o, finalmente, a una escala macro con maquetas de un sector de la costa o de un río donde los fenómenos meteorológicos son los protagonistas para simular inundaciones y diseñar futuras medidas de prevención y seguridad.

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In today's internet world, web browsers are an integral part of our day-to-day activities. Therefore, web browser security is a serious concern for all of us. Browsers can be breached in different ways. Because of the over privileged access, extensions are responsible for many security issues. Browser vendors try to keep safe extensions in their official extension galleries. However, their security control measures are not always effective and adequate. The distribution of unsafe extensions through different social engineering techniques is also a very common practice. Therefore, before installation, users should thoroughly analyze the security of browser extensions. Extensions are not only available for desktop browsers, but many mobile browsers, for example, Firefox for Android and UC browser for Android, are also furnished with extension features. Mobile devices have various resource constraints in terms of computational capabilities, power, network bandwidth, etc. Hence, conventional extension security analysis techniques cannot be efficiently used by end users to examine mobile browser extension security issues. To overcome the inadequacies of the existing approaches, we propose CLOUBEX, a CLOUd-based security analysis framework for both desktop and mobile Browser EXtensions. This framework uses a client-server architecture model. In this framework, compute-intensive security analysis tasks are generally executed in a high-speed computing server hosted in a cloud environment. CLOUBEX is also enriched with a number of essential features, such as client-side analysis, requirements-driven analysis, high performance, and dynamic decision making. At present, the Firefox extension ecosystem is most susceptible to different security attacks. Hence, the framework is implemented for the security analysis of the Firefox desktop and Firefox for Android mobile browser extensions. A static taint analysis is used to identify malicious information flows in the Firefox extensions. In CLOUBEX, there are three analysis modes. A dynamic decision making algorithm assists us to select the best option based on some important parameters, such as the processing speed of a client device and network connection speed. Using the best analysis mode, performance and power consumption are improved significantly. In the future, this framework can be leveraged for the security analysis of other desktop and mobile browser extensions, too.

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An economic analysis has been performed to establish when it is advantageous to use structured packing in air separation plant. A model of a low pressure cycle was developed to calculate the power saved when packing is used, and cost models were developed for the columns and cold box. The rate of return was calculated on the extra investment required for a packed plant based on the annual power saving. Structured packing was found to be economic only in larger plants, where economies of scale mean that the increased capital cost becomes less significant compared with the power saved. It was also found that different sized plants favour different packings. The analysis identified that the packing variable with the biggest impact on the economic balance was the efficiency and that increasing the efficiency of current packings could enhance their balance in air distillation. A new packing was therefore developed to have a higher efficiency than conventional ones. The vapour phase resistance was targeted for reduction, since most packing models predict this to be dominant. The final shape was chosen as the easiest and most economic to make. It has converging and diverging channels and was manufactured in two specific areas and with two block heights by Tianjin University Packing Factory. A 0.3 m diameter distillation column test rig was designed, built and commissioned with the standard Sulzer Mellapak 500YW. It was then used to test the new packing alongside some standard ones. Because the packings had different specific areas, correlations of published results were developed to allow a true comparison to be made. The test results show that, unexpectedly, the packings with 0.1 m high blocks have an efficiency about 8% greater than the standard 0.2 m blocks. The new shape as implemented in the 350Y packing shows an additional 7% greater efficiency, so it is 15% better than a standard packing with the same area. It has a better efficiency than the Mellapak 500YW and the higher capacity associated with its lower area. The new 500Y did not show a significant advantage.

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Software development is an extremely complex process, during which human errors are introduced and result in faulty software systems. It is highly desirable and important that these errors can be prevented and detected as early as possible. Software architecture design is a high-level system description, which embodies many system features and properties that are eventually implemented in the final operational system. Therefore, methods for modeling and analyzing software architecture descriptions can help prevent and reveal human errors and thus improve software quality. Furthermore, if an analyzed software architecture description can be used to derive a partial software implementation, especially when the derivation can be automated, significant benefits can be gained with regard to both the system quality and productivity. This dissertation proposes a framework for an integrated analysis on both of the design and implementation. To ensure the desirable properties of the architecture model, we apply formal verification by using the model checking technique. To ensure the desirable properties of the implementation, we develop a methodology and the associated tool to translate an architecture specification into an implementation written in the combination of Arch-Java/Java/AspectJ programming languages. The translation is semi-automatic so that many manual programming errors can be prevented. Furthermore, the translation inserting monitoring code into the implementation such that runtime verification can be performed, this provides additional assurance for the quality of the implementation. Moreover, validations for the translations from architecture model to program are provided. Finally, several case studies are experimented and presented.

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Ensuring the correctness of software has been the major motivation in software research, constituting a Grand Challenge. Due to its impact in the final implementation, one critical aspect of software is its architectural design. By guaranteeing a correct architectural design, major and costly flaws can be caught early on in the development cycle. Software architecture design has received a lot of attention in the past years, with several methods, techniques and tools developed. However, there is still more to be done, such as providing adequate formal analysis of software architectures. On these regards, a framework to ensure system dependability from design to implementation has been developed at FIU (Florida International University). This framework is based on SAM (Software Architecture Model), an ADL (Architecture Description Language), that allows hierarchical compositions of components and connectors, defines an architectural modeling language for the behavior of components and connectors, and provides a specification language for the behavioral properties. The behavioral model of a SAM model is expressed in the form of Petri nets and the properties in first order linear temporal logic.^ This dissertation presents a formal verification and testing approach to guarantee the correctness of Software Architectures. The Software Architectures studied are expressed in SAM. For the formal verification approach, the technique applied was model checking and the model checker of choice was Spin. As part of the approach, a SAM model is formally translated to a model in the input language of Spin and verified for its correctness with respect to temporal properties. In terms of testing, a testing approach for SAM architectures was defined which includes the evaluation of test cases based on Petri net testing theory to be used in the testing process at the design level. Additionally, the information at the design level is used to derive test cases for the implementation level. Finally, a modeling and analysis tool (SAM tool) was implemented to help support the design and analysis of SAM models. The results show the applicability of the approach to testing and verification of SAM models with the aid of the SAM tool.^

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Ensuring the correctness of software has been the major motivation in software research, constituting a Grand Challenge. Due to its impact in the final implementation, one critical aspect of software is its architectural design. By guaranteeing a correct architectural design, major and costly flaws can be caught early on in the development cycle. Software architecture design has received a lot of attention in the past years, with several methods, techniques and tools developed. However, there is still more to be done, such as providing adequate formal analysis of software architectures. On these regards, a framework to ensure system dependability from design to implementation has been developed at FIU (Florida International University). This framework is based on SAM (Software Architecture Model), an ADL (Architecture Description Language), that allows hierarchical compositions of components and connectors, defines an architectural modeling language for the behavior of components and connectors, and provides a specification language for the behavioral properties. The behavioral model of a SAM model is expressed in the form of Petri nets and the properties in first order linear temporal logic. This dissertation presents a formal verification and testing approach to guarantee the correctness of Software Architectures. The Software Architectures studied are expressed in SAM. For the formal verification approach, the technique applied was model checking and the model checker of choice was Spin. As part of the approach, a SAM model is formally translated to a model in the input language of Spin and verified for its correctness with respect to temporal properties. In terms of testing, a testing approach for SAM architectures was defined which includes the evaluation of test cases based on Petri net testing theory to be used in the testing process at the design level. Additionally, the information at the design level is used to derive test cases for the implementation level. Finally, a modeling and analysis tool (SAM tool) was implemented to help support the design and analysis of SAM models. The results show the applicability of the approach to testing and verification of SAM models with the aid of the SAM tool.

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Natural selection mediated by pollinators has influenced the evolution of floral diversity of the flowering plants (angiosperms). The scope of this thesis was to study: 1) phenotypic selection, 2) mating systems, and 3) floral shifts involved in plant speciation. Model plant species were Platanthera bifolia and P. chlorantha (Orchidaceae). These orchids are moth-pollinated, strictly co-sexual (bisexual flowers), and produce a spike that displays 10-20 white flowers. I explored the influence of characters on plant fitness by using multiple linear regressions. Pollen removal (male fitness) and fruit set (female fitness) increased with more flowers per plant in three P. bifolia populations. There was selection towards longer spurs in a dry year when average spur length was shorter than in normal-wet years. Female function was sensitive to drought, which enabled an application of the male function hypothesis of floral evolution (Bateman's principle). The results show that selection may vary between populations, years, and sex-functions. I examined inbreeding by estimating levels of geitonogamy (self-pollination between flowers of an individual) with an emasculation method in two P. bifolia populations. Geitonogamy did not vary with inflorescence size. Levels of geitonogamy was 20-40% in the smaller, but non-significant in the larger population. This may relate to lower number of possible mates and pollinator activity in the smaller population. Platanthera bifolia exhibits the ancestral character state of tongue-attachment of pollinia on the pollinator. Its close relative P. chlorantha attaches its pollinia onto the pollinator's eyes. To explore the mechanism of a floral shift, pollination efficiency and speed was compared between the two species. The results showed no differences in pollination efficiency, but P. chlorantha had faster pollen export and import. Efficiency of pollination in terms of speed may cause floral shifts, and thus speciation.

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Tese submetida à Universidade de Lisboa, Instituto Superior Técnico e aprovada em provas públicas para a obtenção do Grau de Doutor em Sistemas Sustentáveis de Energia.

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Mechanistic models used for prediction should be parsimonious, as models which are over-parameterised may have poor predictive performance. Determining whether a model is parsimonious requires comparisons with alternative model formulations with differing levels of complexity. However, creating alternative formulations for large mechanistic models is often problematic, and usually time-consuming. Consequently, few are ever investigated. In this paper, we present an approach which rapidly generates reduced model formulations by replacing a model’s variables with constants. These reduced alternatives can be compared to the original model, using data based model selection criteria, to assist in the identification of potentially unnecessary model complexity, and thereby inform reformulation of the model. To illustrate the approach, we present its application to a published radiocaesium plant-uptake model, which predicts uptake on the basis of soil characteristics (e.g. pH, organic matter content, clay content). A total of 1024 reduced model formulations were generated, and ranked according to five model selection criteria: Residual Sum of Squares (RSS), AICc, BIC, MDL and ICOMP. The lowest scores for RSS and AICc occurred for the same reduced model in which pH dependent model components were replaced. The lowest scores for BIC, MDL and ICOMP occurred for a further reduced model in which model components related to the distinction between adsorption on clay and organic surfaces were replaced. Both these reduced models had a lower RSS for the parameterisation dataset than the original model. As a test of their predictive performance, the original model and the two reduced models outlined above were used to predict an independent dataset. The reduced models have lower prediction sums of squares than the original model, suggesting that the latter may be overfitted. The approach presented has the potential to inform model development by rapidly creating a class of alternative model formulations, which can be compared.

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Cell wall deposition is a key process in the formation, growth, and differentiation of plant cells. The most important structural components of the wall are long cellulose microfibrils, which are synthesized by synthases embedded in the plasma membrane. A fundamental question is how the microfibrils become oriented during deposition at the plasma membrane. The current textbook explanation for the orientation mechanism is a guidance system mediated by cortical microtubules. However, too many contraindications are known in secondary cell walls for this to be a universal mechanism, particularly in the case of helicoidal arrangements, which occur in many situations. An additional construction mechanism involves liquid crystalline self-assembly [A. C. Neville (1993) Biology of Fibrous Composites: Development Beyond the Cell Membrane (Cambridge Univ. Press, Cambridge, U.K.)], but the required amount of bulk material that is able to equilibrate thermally is not normally present at any stage of the wall deposition process. Therefore, we have asked whether the complex ordered texture of helicoidal cell walls can be formed in the absence of direct cellular guidance mechanisms. We propose that they can be formed by a mechanism that is based on geometrical considerations. It explains the genesis of the complicated helicoidal texture and shows that the cell has intrinsic, versatile tools for creating a variety of textures. A compelling feature of the model is that local rules generate global order, a typical phenomenon of life.

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This paper presents a new method for producing a functional-structural plant model that simulates response to different growth conditions, yet does not require detailed knowledge of underlying physiology. The example used to present this method is the modelling of the mountain birch tree. This new functional-structural modelling approach is based on linking an L-system representation of the dynamic structure of the plant with a canonical mathematical model of plant function. Growth indicated by the canonical model is allocated to the structural model according to probabilistic growth rules, such as rules for the placement and length of new shoots, which were derived from an analysis of architectural data. The main advantage of the approach is that it is relatively simple compared to the prevalent process-based functional-structural plant models and does not require a detailed understanding of underlying physiological processes, yet it is able to capture important aspects of plant function and adaptability, unlike simple empirical models. This approach, combining canonical modelling, architectural analysis and L-systems, thus fills the important role of providing an intermediate level of abstraction between the two extremes of deeply mechanistic process-based modelling and purely empirical modelling. We also investigated the relative importance of various aspects of this integrated modelling approach by analysing the sensitivity of the standard birch model to a number of variations in its parameters, functions and algorithms. The results show that using light as the sole factor determining the structural location of new growth gives satisfactory results. Including the influence of additional regulating factors made little difference to global characteristics of the emergent architecture. Changing the form of the probability functions and using alternative methods for choosing the sites of new growth also had little effect. (c) 2004 Elsevier B.V. All rights reserved.

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John Frazer's architectural work is inspired by living and generative processes. Both evolutionary and revolutionary, it explores informatin ecologies and the dynamics of the spaces between objects. Fuelled by an interest in the cybernetic work of Gordon Pask and Norbert Wiener, and the possibilities of the computer and the "new science" it has facilitated, Frazer and his team of collaborators have conducted a series of experiments that utilize genetic algorithms, cellular automata, emergent behaviour, complexity and feedback loops to create a truly dynamic architecture. Frazer studied at the Architectural Association (AA) in London from 1963 to 1969, and later became unit master of Diploma Unit 11 there. He was subsequently Director of Computer-Aided Design at the University of Ulter - a post he held while writing An Evolutionary Architecture in 1995 - and a lecturer at the University of Cambridge. In 1983 he co-founded Autographics Software Ltd, which pioneered microprocessor graphics. Frazer was awarded a person chair at the University of Ulster in 1984. In Frazer's hands, architecture becomes machine-readable, formally open-ended and responsive. His work as computer consultant to Cedric Price's Generator Project of 1976 (see P84)led to the development of a series of tools and processes; these have resulted in projects such as the Calbuild Kit (1985) and the Universal Constructor (1990). These subsequent computer-orientated architectural machines are makers of architectural form beyond the full control of the architect-programmer. Frazer makes much reference to the multi-celled relationships found in nature, and their ongoing morphosis in response to continually changing contextual criteria. He defines the elements that describe his evolutionary architectural model thus: "A genetic code script, rules for the development of the code, mapping of the code to a virtual model, the nature of the environment for the development of the model and, most importantly, the criteria for selection. In setting out these parameters for designing evolutionary architectures, Frazer goes beyond the usual notions of architectural beauty and aesthetics. Nevertheless his work is not without an aesthetic: some pieces are a frenzy of mad wire, while others have a modularity that is reminiscent of biological form. Algorithms form the basis of Frazer's designs. These algorithms determine a variety of formal results dependent on the nature of the information they are given. His work, therefore, is always dynamic, always evolving and always different. Designing with algorithms is also critical to other architects featured in this book, such as Marcos Novak (see p150). Frazer has made an unparalleled contribution to defining architectural possibilities for the twenty-first century, and remains an inspiration to architects seeking to create responsive environments. Architects were initially slow to pick up on the opportunities that the computer provides. These opportunities are both representational and spatial: computers can help architects draw buildings and, more importantly, they can help architects create varied spaces, both virtual and actual. Frazer's work was groundbreaking in this respect, and well before its time.

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This paper argues a model of open systems evolution based on evolutionary thermodynamics and complex system science, as a design paradigm for sustainable architecture. The mechanism of open system evolution is specified in mathematical simulations and theoretical discourses. According to the mechanism, the authors propose an intelligent building model of sustainable design by a holistic information system of the end-users, the building and nature. This information system is used to control the consumption of energy and material resources in building system at microscopic scale, to adapt the environmental performance of the building system to the natural environment at macroscopic scale, for an evolutionary emergence of sustainable performance of buildings.

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Plant biosecurity requires statistical tools to interpret field surveillance data in order to manage pest incursions that threaten crop production and trade. Ultimately, management decisions need to be based on the probability that an area is infested or free of a pest. Current informal approaches to delimiting pest extent rely upon expert ecological interpretation of presence / absence data over space and time. Hierarchical Bayesian models provide a cohesive statistical framework that can formally integrate the available information on both pest ecology and data. The overarching method involves constructing an observation model for the surveillance data, conditional on the hidden extent of the pest and uncertain detection sensitivity. The extent of the pest is then modelled as a dynamic invasion process that includes uncertainty in ecological parameters. Modelling approaches to assimilate this information are explored through case studies on spiralling whitefly, Aleurodicus dispersus and red banded mango caterpillar, Deanolis sublimbalis. Markov chain Monte Carlo simulation is used to estimate the probable extent of pests, given the observation and process model conditioned by surveillance data. Statistical methods, based on time-to-event models, are developed to apply hierarchical Bayesian models to early detection programs and to demonstrate area freedom from pests. The value of early detection surveillance programs is demonstrated through an application to interpret surveillance data for exotic plant pests with uncertain spread rates. The model suggests that typical early detection programs provide a moderate reduction in the probability of an area being infested but a dramatic reduction in the expected area of incursions at a given time. Estimates of spiralling whitefly extent are examined at local, district and state-wide scales. The local model estimates the rate of natural spread and the influence of host architecture, host suitability and inspector efficiency. These parameter estimates can support the development of robust surveillance programs. Hierarchical Bayesian models for the human-mediated spread of spiralling whitefly are developed for the colonisation of discrete cells connected by a modified gravity model. By estimating dispersal parameters, the model can be used to predict the extent of the pest over time. An extended model predicts the climate restricted distribution of the pest in Queensland. These novel human-mediated movement models are well suited to demonstrating area freedom at coarse spatio-temporal scales. At finer scales, and in the presence of ecological complexity, exploratory models are developed to investigate the capacity for surveillance information to estimate the extent of red banded mango caterpillar. It is apparent that excessive uncertainty about observation and ecological parameters can impose limits on inference at the scales required for effective management of response programs. The thesis contributes novel statistical approaches to estimating the extent of pests and develops applications to assist decision-making across a range of plant biosecurity surveillance activities. Hierarchical Bayesian modelling is demonstrated as both a useful analytical tool for estimating pest extent and a natural investigative paradigm for developing and focussing biosecurity programs.