932 resultados para predictive power


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The overarching theme of this thesis is mesoscale optical and optoelectronic design of photovoltaic and photoelectrochemical devices. In a photovoltaic device, light absorption and charge carrier transport are coupled together on the mesoscale, and in a photoelectrochemical device, light absorption, charge carrier transport, catalysis, and solution species transport are all coupled together on the mesoscale. The work discussed herein demonstrates that simulation-based mesoscale optical and optoelectronic modeling can lead to detailed understanding of the operation and performance of these complex mesostructured devices, serve as a powerful tool for device optimization, and efficiently guide device design and experimental fabrication efforts. In-depth studies of two mesoscale wire-based device designs illustrate these principles—(i) an optoelectronic study of a tandem Si|WO3 microwire photoelectrochemical device, and (ii) an optical study of III-V nanowire arrays.

The study of the monolithic, tandem, Si|WO3 microwire photoelectrochemical device begins with development and validation of an optoelectronic model with experiment. This study capitalizes on synergy between experiment and simulation to demonstrate the model’s predictive power for extractable device voltage and light-limited current density. The developed model is then used to understand the limiting factors of the device and optimize its optoelectronic performance. The results of this work reveal that high fidelity modeling can facilitate unequivocal identification of limiting phenomena, such as parasitic absorption via excitation of a surface plasmon-polariton mode, and quick design optimization, achieving over a 300% enhancement in optoelectronic performance over a nominal design for this device architecture, which would be time-consuming and challenging to do via experiment.

The work on III-V nanowire arrays also starts as a collaboration of experiment and simulation aimed at gaining understanding of unprecedented, experimentally observed absorption enhancements in sparse arrays of vertically-oriented GaAs nanowires. To explain this resonant absorption in periodic arrays of high index semiconductor nanowires, a unified framework that combines a leaky waveguide theory perspective and that of photonic crystals supporting Bloch modes is developed in the context of silicon, using both analytic theory and electromagnetic simulations. This detailed theoretical understanding is then applied to a simulation-based optimization of light absorption in sparse arrays of GaAs nanowires. Near-unity absorption in sparse, 5% fill fraction arrays is demonstrated via tapering of nanowires and multiple wire radii in a single array. Finally, experimental efforts are presented towards fabrication of the optimized array geometries. A hybrid self-catalyzed and selective area MOCVD growth method is used to establish morphology control of GaP nanowire arrays. Similarly, morphology and pattern control of nanowires is demonstrated with ICP-RIE of InP. Optical characterization of the InP nanowire arrays gives proof of principle that tapering and multiple wire radii can lead to near-unity absorption in sparse arrays of InP nanowires.

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Dissertação de Mestrado, Psicologia da Educação, Faculdade de Ciências Humanas e Sociais, Universidade do Algarve, 2015

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Conocer los factores intervinientes en el rendimiento académico de los estudiantes es de vital importancia para mejorar el proceso de enseñanza-aprendizaje y la calidad universitaria. Esta investigación busca identificar los posibles factores que intervienen en el rendimiento académico de los estudiantes universitarios del grado en Arquitectura Técnica de la Universidad de Alicante. El estudio pretende determinar el poder explicativo y predictivo de nueve variables para pronosticar el rendimiento académico de los estudiantes mediante regresión lineal múltiple. Se ha identificado que seis de las variables estudiadas son estadísticamente significativas y que dos de ellas tienen una gran influencia sobre el rendimiento académico.

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Dissertação (mestrado)—Universidade de Brasília, Faculdade de Economia, Administração e Contabilidade, Programa de Pós-Graduação em Administração, Mestrado Profissional em Administração, 2015.

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Dissertação de Mestrado apresentada ao Instituto Superior de Psicologia Aplicada para obtenção de grau de Mestre na especialidade de Psicologia Social e das Organizações.

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The phosphatidylinositide 3-kinases (PI3K) and mammalian target of rapamycin-1 (mTOR1) are two key targets for anti-cancer therapy. Predicting the response of the PI3K/AKT/mTOR1 signalling pathway to targeted therapy is made difficult because of network complexities. Systems biology models can help explore those complexities but the value of such models is dependent on accurate parameterisation. Motivated by a need to increase accuracy in kinetic parameter estimation, and therefore the predictive power of the model, we present a framework to integrate kinetic data from enzyme assays into a unified enzyme kinetic model. We present exemplar kinetic models of PI3K and mTOR1, calibrated on in vitro enzyme data and founded on Michaelis-Menten (MM) approximation. We describe the effects of an allosteric mTOR1 inhibitor (Rapamycin) and ATP-competitive inhibitors (BEZ2235 and LY294002) that show dual inhibition of mTOR1 and PI3K. We also model the kinetics of phosphatase and tensin homolog (PTEN), which modulates sensitivity of the PI3K/AKT/mTOR1 pathway to these drugs. Model validation with independent data sets allows investigation of enzyme function and drug dose dependencies in a wide range of experimental conditions. Modelling of the mTOR1 kinetics showed that Rapamycin has an IC50 independent of ATP concentration and that it is a selective inhibitor of mTOR1 substrates S6K1 and 4EBP1: it retains 40% of mTOR1 activity relative to 4EBP1 phosphorylation and inhibits completely S6K1 activity. For the dual ATP-competitive inhibitors of mTOR1 and PI3K, LY294002 and BEZ235, we derived the dependence of the IC50 on ATP concentration that allows prediction of the IC50 at different ATP concentrations in enzyme and cellular assays. Comparison of the drug effectiveness in enzyme and cellular assays showed that some features of these drugs arise from signalling modulation beyond the on-target action and MM approximation and require a systems-level consideration of the whole PI3K/PTEN/AKT/mTOR1 network in order to understand mechanisms of drug sensitivity and resistance in different cancer cell lines. We suggest that using these models in systems biology investigation of the PI3K/AKT/mTOR1 signalling in cancer cells can bridge the gap between direct drug target action and the therapeutic response to these drugs and their combinations.

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Interactions in mobile devices normally happen in an explicit manner, which means that they are initiated by the users. Yet, users are typically unaware that they also interact implicitly with their devices. For instance, our hand pose changes naturally when we type text messages. Whilst the touchscreen captures finger touches, hand movements during this interaction however are unused. If this implicit hand movement is observed, it can be used as additional information to support or to enhance the users’ text entry experience. This thesis investigates how implicit sensing can be used to improve existing, standard interaction technique qualities. In particular, this thesis looks into enhancing front-of-device interaction through back-of-device and hand movement implicit sensing. We propose the investigation through machine learning techniques. We look into problems on how sensor data via implicit sensing can be used to predict a certain aspect of an interaction. For instance, one of the questions that this thesis attempts to answer is whether hand movement during a touch targeting task correlates with the touch position. This is a complex relationship to understand but can be best explained through machine learning. Using machine learning as a tool, such correlation can be measured, quantified, understood and used to make predictions on future touch position. Furthermore, this thesis also evaluates the predictive power of the sensor data. We show this through a number of studies. In Chapter 5 we show that probabilistic modelling of sensor inputs and recorded touch locations can be used to predict the general area of future touches on touchscreen. In Chapter 7, using SVM classifiers, we show that data from implicit sensing from general mobile interactions is user-specific. This can be used to identify users implicitly. In Chapter 6, we also show that touch interaction errors can be detected from sensor data. In our experiment, we show that there are sufficient distinguishable patterns between normal interaction signals and signals that are strongly correlated with interaction error. In all studies, we show that performance gain can be achieved by combining sensor inputs.

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Objetivo: Determinar un modelo predictivo para uso del condón y consumo de alcohol como conductas de riesgo relacionadas el contagio de VIH/Sida en mujeres trabajadoras sexuales de la ciudad de Bogotá en el año 2015. Métodos Estudio de tipo transversal con diseño observacional, se tomaron 255 mujeres trabajadoras sexuales de la ciudad de Bogotá; La información analizada fue tomada del estudio realizado en cinco ciudades de Colombia en el año 2015, las hipótesis planteadas se soportaron en la asociación entre las condiciones sociodemográficas, de conocimiento, practicas, hábitos, apoyo social y de ocupación propia de las mujeres trabajadoras sexuales que podían explicar y predecir la adopción de conductas riesgosas para VIH/sida como son el uso del condón y el consumo de alcohol en ejercicio de su ocupación. Resultados El promedio de edad de inicio en el trabajo sexual fue 22,1±7,1 años, tres cuartas partes son solteras y residen en estrato dos y tres; el 96,5% dijo usar el condón con el último cliente y el 27,8% de ellas consumió alcohol durante su último servicio. En la conducta de riesgo uso del condón, se encontraron asociados entre otras, la edad [OR=1,10(1,03-1,17)], vivir en estrato dos [OR=7,7(1,5-39,5)], el ingreso por trabajo sexual [OR=1,0(1,0-1,0)], la disponibilidad del condón para el servicio [OR=0,03(0,008-0,16)] y contar con otro método de planificación (ligadura de trompas) [OR=4,47(1,0-18,3)]. En la conducta de riesgo consumo de alcohol, se encontró asociado ente otros: estrato socioeconómico dos [OR=5,8(1,54-22,3)], nivel de escolaridad secundaria [OR=0,12(0,16-0,96)], vivir con otros familiares [OR=3,45(1,7-7,02)], ingreso por trabajo sexual [OR=1,0(1,0-1,0)] y el sitio donde se ofrece el servicio [OR=0,07(0,04-0,15)]. Después de ajustar, se encontró que las variables que mejor explican el uso del condón fueron edad [OR=1,1(1,02-1,17)] y disponibilidad del condón [OR=0,04(0,008-0,024)], el modelo tuvo poca sensibilidad 33,3% y buena capacidad predictiva (84,6%). Las variables que mejor explicaron el consumo de alcohol durante el servicio fueron edad [OR= 0,95(0,91-0,98)], Número de clientes por semana [OR=0,9(0,90-0,98)], sitio donde ofrece el servicio [OR=7,1(3,45-14,8)], y estrato socioeconómico [OR=1,8 (0,90-3,83)], resultando un modelo con buena sensibilidad (71,8%) y buena capacidad predictiva (86,4%). Conclusiones Aspectos como la edad, el estrato socioeconómico, escolaridad, estado civil, ingreso económico por trabajo sexual, edad de inicio en el trabajo sexual, número de clientes antiguos en la última semana, disponibilidad del condón para prestar el servicio y ligadura de trompas como método diferente de planificación, se asociaron estadísticamente con el uso del condón. Sin embargo al ajustar las variables solo la edad y la disponibilidad del condón se mantuvieron como variables explicativas. Cabe anotar, que aunque el modelo mostró buena capacidad predictiva (84,6%), la precisión en sus estimaciones fue baja debido a la poca frecuencia del no uso del condón con el ultimo cliente (3,5%), y la sensibilidad del modelo apenas fue del 33,3%. Por otro lado, factores como la edad, el estrato socioeconómico, nivel educativo, ingreso económico, sitio de oferta del servicio, composición familiar, número de hijos, número de clientes atendidos en la última semana y número de clientes antiguos mostraron asociación estadística con el consumo de alcohol. Sin embargo, al ajustar las variables solo edad, estrato socioeconómico, sitio donde se ofrece el servicio y número de clientes por semana mantuvieron asociación estadística; observándose además que el estrato socioeconómico (uno y dos) y sitio donde se ofrece el servicio (establecimiento), son factores de riesgo para el consumo de alcohol en ejercicio de la ocupación y la poca edad y un número reducido de clientes por semana se comportan como factores de protección para el consumo de alcohol. El modelo predictivo que se desarrolló para la conducta de riesgo de consumo de alcohol, con una sensibilidad del 71,8% y un poder predictivo del 86,4%. .

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Electrification of vehicular systems has gained increased momentum in recent years with particular attention to constant power loads (CPLs). Since a CPL potentially threatens system stability, stability analysis of hybrid electric vehicle with CPLs becomes necessary. A new power buffer configuration with battery is introduced to mitigate the effect of instability caused by CPLs. Model predictive control (MPC) is applied to regulate the power buffer to decouple source and load dynamics. Moreover, MPC provides an optimal tradeoff between modification of load impedance, variation of dc-link voltage and battery current ripples. This is particularly important during transients or starting of system faults, since battery response is not very fast. Optimal tradeoff becomes even more significant when considering low-cost power buffer without battery. This paper analyzes system models for both voltage swell and voltage dip faults. Furthermore, a dual mode MPC algorithm is implemented in real time offering improved stability. A comprehensive set of experimental results is included to verify the efficacy of the proposed power buffer.

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Active Voltage Control (AVC) is an implementation of classic Proportional-Derivative (PD) control and multi-loop feedback control to force IGBT to follow a pre-set switching trajectory. The initial objective of AVC was mainly to synchronise the switching of IGBTs connected in series so as to realise voltage balancing between devices. For a single IGBT switching, the AVC reference needs further optimisation. Thus, a predictive manner of AVC reference generation is required to cope with the nonlinear IGBT switching parameters while performing low loss switching. In this paper, an improved AVC structure is adopted along with a revised reference which accommodates the IGBT nonlinearity during switching and is predictive based on current being switched. Experimental and simulation results show that close control of a single IGBT switching is realised. It is concluded that good performance can be obtained, but the proposed method needs careful stability analysis for parameter choice. © 2013 IEEE.

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This paper describes a framework that is being developed for the prediction and analysis of electronics power module reliability both for qualification testing and in-service lifetime prediction. Physics of failure (PoF) reliability methodology using multi-physics high-fidelity and reduced order computer modelling, as well as numerical optimization techniques, are integrated in a dedicated computer modelling environment to meet the needs of the power module designers and manufacturers as well as end-users for both design and maintenance purposes. An example of lifetime prediction for a power module solder interconnect structure is described. Another example is the lifetime prediction of a power module for a railway traction control application. Also in the paper a combined physics of failure and data trending prognostic methodology for the health monitoring of power modules is discussed.

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Extending the work presented in Prasad et al. (IEEE Proceedings on Control Theory and Applications, 147, 523-37, 2000), this paper reports a hierarchical nonlinear physical model-based control strategy to account for the problems arising due to complex dynamics of drum level and governor valve, and demonstrates its effectiveness in plant-wide disturbance handling. The strategy incorporates a two-level control structure consisting of lower-level conventional PI regulators and a higher-level nonlinear physical model predictive controller (NPMPC) for mainly set-point manoeuvring. The lower-level PI loops help stabilise the unstable drum-boiler dynamics and allow faster governor valve action for power and grid-frequency regulation. The higher-level NPMPC provides an optimal load demand (or set-point) transition by effective handling of plant-wide interactions and system disturbances. The strategy has been tested in a simulation of a 200-MW oil-fired power plant at Ballylumford in Northern Ireland. A novel approach is devized to test the disturbance rejection capability in severe operating conditions. Low frequency disturbances were created by making random changes in radiation heat flow on the boiler-side, while condenser vacuum was fluctuating in a random fashion on the turbine side. In order to simulate high-frequency disturbances, pulse-type load disturbances were made to strike at instants which are not an integral multiple of the NPMPC sampling period. Impressive results have been obtained during both types of system disturbances and extremely high rates of load changes, right across the operating range, These results compared favourably with those from a conventional state-space generalized predictive control (GPC) method designed under similar conditions.

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A constrained non-linear, physical model-based, predictive control (NPMPC) strategy is developed for improved plant-wide control of a thermal power plant. The strategy makes use of successive linearisation and recursive state estimation using extended Kalman filtering to obtain a linear state-space model. The linear model and a quadratic programming routine are used to design a constrained long-range predictive controller One special feature is the careful selection of a specific set of plant model parameters for online estimation, to account for time-varying system characteristics resulting from major system disturbances and ageing. These parameters act as nonstationary stochastic states and help to provide sufficient degrees-of-freedom to obtain unbiased estimates of controlled outputs. A 14th order non-linear plant model, simulating the dominant characteristics of a 200 MW oil-fired pou er plant has been used to test the NPMPC algorithm. The control strategy gives impressive simulation results, during large system disturbances and extremely high rate of load changes, right across the operating range. These results compare favourably to those obtained with the state-space GPC method designed under similar conditions.

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