27 research outputs found

    Optimisation of Microfluidic Flow Systems

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    This project investigates the fluid flow and heat transfer in microfluidic flow systems using Computational Fluid Dynamics (CFD) and experiments. The first part of this work focuses on developing a CFD - enabled optimisation methodology of the geometrical features of i) a microfluidic heatsink design and ii) a single-phase (SP) continuous-flow (CF) Polymerase Chain Reaction (PCR) Device. This is achieved using COMSOL Multiphysics 5.4 ®to simulate the fluid flow, heat transfer (and PCR kinetics for the case of the microfluidic PCR device). Optimisation problems are then formed, selecting objective functions related to the performance of the devices. Design of Experiments is then used together with COMSOL Multiphysics 5.4 ® to collect the values of the objective functions over the design domain. Matlab© is then used to generate the response surfaces of the objective functions, using different techniques, locate the optimum design solutions (genetic algorithm, multi-level coordinate search method) and obtain the Pareto front for the cases of multi-objective optimisation problems. Results of this work indicate the possibility of significantly enhancing the performance of SP-CF-PCR devices in terms of the DNA amplification, device volume, total operating time and total pressure drop by up to 16.4%, 43.2%, 17.8% and 80.5% respectively, after applying the appropriate design modifications for each objective. The increase in the DNA amplification is achieved by increasing the channel width and residence times while minimising the channel height. The reduction in the device volume, total operating time and total pressure drop are achieved when using the smallest residence times and higher channel width. According to this investigation, the DNA amplification appears to be linked to the temperature uniformity and to the residence time in the extension zone. The second part of this work focuses on i) obtaining a better understanding of the role that the concentration and presence of droplets play in conjugate heat transfer phenomena in droplet-laden flows, ii) creating and optimising a reusable, cheap and easy-to-fabricate device that can perform Melting Curve Analysis (MCA), in order to facilitate the work of a group of biologists at the University of Leeds. More specifically, this device aims to check for the presence of rare DNA species and possible contaminations in their collected samples in a fast, robust and cheap way, by testing if the DNA product has a unique melting temperature. The experimental setup is designed after performing a series of simulations using COMSOL Multiphysics 5.4 ®, considering different potential designs while at the same time simulating the energy requirements of the system. After finalising the design, a PID temperature controller is implemented on the Arduino Platform, achieving the required temperature difference between the two ends of the device. The results obtained during the experiments demonstrate a successful temperature control that is robust and does not require the adjustment of the PID parameters for the performance of similar experiments in the different temperature ranges tested

    Integrated Chemical Processes in Liquid Multiphase Systems

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    The essential principles of green chemistry are the use of renewable raw materials, highly efficient catalysts and green solvents linked with energy efficiency and process optimization in real-time. Experts from different fields show, how to examine all levels from the molecular elementary steps up to the design and operation of an entire plant for developing novel and efficient production processes

    Academic Year 2019-2020 Faculty Excellence Showcase, AFIT Graduate School of Engineering & Management

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    An excerpt from the Dean\u27s Message: There is no place like the Air Force Institute of Technology (AFIT). There is no academic group like AFIT’s Graduate School of Engineering and Management. Although we run an educational institution similar to many other institutions of higher learning, we are different and unique because of our defense-focused graduate-research-based academic programs. Our programs are designed to be relevant and responsive to national defense needs. Our programs are aligned with the prevailing priorities of the US Air Force and the US Department of Defense. Our faculty team has the requisite critical mass of service-tested faculty members. The unique composition of pure civilian faculty, military faculty, and service-retired civilian faculty makes AFIT truly unique, unlike any other academic institution anywhere

    Automatic machine learning:methods, systems, challenges

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    This open access book presents the first comprehensive overview of general methods in Automatic Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first international challenge of AutoML systems. The book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. Many of the recent machine learning successes crucially rely on human experts, who select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters; however the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself

    Automatic machine learning:methods, systems, challenges

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    Pertanika Journal of Science & Technology

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    Pertanika Journal of Science & Technology

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    Metaheuristic design of feedforward neural networks: a review of two decades of research

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    Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era

    Validación de las redes neuronales artificiales como metodología para la asignación donante-receptor en el trasplante hepático

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    1. Introducción o motivación de la tesis. El trasplante hepático constituye la mejor opción terapéutica para un gran número de patologías hepáticas en fase terminal. Desafortunadamente, existe un disbalance entre el número de candidatos y el número de donantes disponibles, lo que conlleva a muertes y exclusiones en lista de espera. En los últimos años se han realizado numerosos esfuerzos para incrementar el pool de donantes, así como para optimizar la priorización en lista de los posibles receptores. Entre ellos, destacan la utilización de los denominados “donantes con criterios extendidos” (ECD, extended criteria donors) y la adopción de un sistema de priorización mediante un score basado en la gravedad del candidato (MELD, Mayo Model for End Stage Liver Disease). La asignación donante-receptor es un factor determinante en los resultados del trasplante hepático, para lo cual se han propuesto múltiples “scores” en la literatura. Sin embargo, ninguno de ellos se considera óptimo para realizar este emparejamiento. En 2014, nuestro grupo publicó la utilidad de las redes neuronales artificiales (RNA) como una herramienta óptima para el matching donante-receptor en el trasplante hepático. Para ello se realizó un estudio multicéntrico a nivel nacional, en el que se demostró la superioridad de este modelo para predecir la supervivencia post-trasplante. El objetivo de nuestro estudio es analizar si las redes neuronales tienen un comportamiento similar al demostrado en España en un sistema de salud diferente, y si son una herramienta superior a los modelos actuales utilizados para el matching donante-receptor. 2. Contenido de la investigación. Se recogieron 822 pares donante-receptor (D-R) de trasplantes hepáticos realizados de forma consecutiva en el hospital King’s College de Londres durante los años 2002 a 2010, teniendo en cuenta variables del donante, del receptor y del trasplante. Para cada par, se calcularon dos probabilidades: la probabilidad de supervivencia (CCR) y la probabilidad de pérdida del injerto (MS) a los 3 meses del trasplante. Para ello se construyeron dos modelos de redes neuronales artificiales diferentes y no complementarios: el modelo de aceptación y el modelo de rechazo. Se construyeron varios modelos: 1) Entrenamiento y generalización con los pares D-R del hospital británico (a 3 y a 12 meses post-trasplante) , 2) Entrenamiento con pares D-R españoles y generalización con los británicos y 3) Modelo combinado: entrena y generaliza con pares españoles y británicos. Además, para ayudar en la toma de decisiones según los resultados obtenidos por la red neuronal, se construyó un sistema basado en reglas. Los modelos diseñados para el hospital King’s College demostraron una excelente capacidad de predicción para ambos: 3 meses (CCR-AUC=0,9375; MS-AUC=0,9374) y 12 meses (CCR-AUC=0,7833; MS-AUC=0,8153), casi un 15% superior a la mejor capacidad de predicción obtenida por otros scores como MELD o BAR (Balance of Risk). Además, estos resultados mejoran los publicados previamente en el modelo multicéntrico español. Sin embargo, esta capacidad de predicción no es tan buena cuando el modelo entrena y generaliza con pares D-R procedentes de sistemas de salud diferentes, ni tampoco en el modelo combinado. 3.Conclusiones. 1. El empleo de Redes Neuronales Artificiales para la Asignación Donante-Receptor en el Trasplante Hepático ha demostrado excelentes capacidades de predicción de Supervivencia y No Supervivencia del injerto, al ser validadas en un sistema de salud distinto de otro país, por lo tanto la metodología de la Inteligencia Artificial ha quedado claramente validada como herramienta óptima para el “matching D-R”. 2. Nuestros resultados apoyan que los distintos equipos de Trasplante Hepático consideren las Redes Neuronales Artificiales como el método más exhaustivo y objetivo descrito hasta la fecha para el manejo de la lista de espera del Trasplante Hepático, evitando criterios subjetivos y arbitrarios y maximizando los principios de equidad, utilidad y eficiencia. 3. Nuestro modelo de validación, es decir, la RNA generada con pares D-R del Hospital King’s College de Londres ha logrado la máxima capacidad de predicción, superando el resto de modelos y apoyando el hecho de que cada RNA debe ser entrenada, testada y optimizada para un propósito específico, en una única población. Así, cada programa de TH debería disponer de su propio modelo construido con sus propios datos, para apoyar la decisión del “matching D-R”. 4. El modelo de Asignación D-R generado por las RNAs combina lo mejor del sistema MELD con el Beneficio de Supervivencia Global, usando para ello un sistema basado en reglas, maximizando la utilidad de los injertos disponibles. Esto los convierte en sistemas complementarios para un mismo fin, en lugar de considerarlos competitivos
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