15,476 research outputs found
Desarrollo de materiales bioactivos con potencial aplicación odontológica mediante impregnación asistida por CO2 supercrítico
Tesis (DCI)--FCEFN-UNC, 2021En esta tesis se estudió el proceso de incorporación de eugenol en fibras de poliamida 6 (PA6) mediante la impregnación asistida por CO2 supercrítico para desarrollar un material con propiedades antimicrobianas con una potencial aplicación odontológica. Para este propósito, se construyó un equipo de alta presión en el que se llevaron a cabo múltiples ensayos de impregnación de eugenol y de sorción de CO2 en un hilo dental comercial de PA6 en distintas condiciones de presión y temperatura (40 – 60 °C y 8 – 12 MPa). Con el fin de encontrar las mejores condiciones del proceso de impregnación, se evaluó la influencia de diferentes variables operativas (presión, temperatura, tiempo de contacto y velocidad de despresurización) sobre la cantidad de eugenol impregnada en el material. Además, se estudiaron los principales fenómenos difusivos que ocurren en el proceso de impregnación del eugenol en condiciones supercríticas. Para ello, se hicieron ensayos de cinética de sorción del CO2 y del eugenol en PA6 a diferentes condiciones de presión y temperatura y se determinó el coeficiente de difusión aparente para ambas especies en este polímero. Por otra parte, se evaluaron las propiedades finales del material impregnado, analizando las propiedades mecánicas, térmicas y morfológicas del material original, presurizado con CO2 e impregnado con eugenol. Adicionalmente se evaluó la actividad antimicrobiana del material impregnado frente a dos bacterias comunes (Escherichia coli y Staphylococcus aureus). Asimismo, se estudió la migración del compuesto activo impregnado en aire y en saliva artificial, obteniendo datos importantes para el potencial desarrollo de un producto comercial, como la estimación de la vida útil, el tipo de envase, y tipo de aplicación del producto. Finalmente, se hizo un diseño y dimensionamiento de un proceso industrial para la impregnación de eugenol en bobinas de fibras de PA6 en CO2 supercrítico, a partir de los datos de eficiencia de impregnación y parámetros difusivos del hilo impregnado con eugenol previamente obtenidos, realizando el diseño de la bobina, el equipo impregnador y los cálculos de sus principales requerimientos de masa y energía.Fil: Mosquera Ruiz, José Euliser. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales; Argentina.Fil: Mosquera Ruiz, José Euliser. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigación y Desarrollo en Ingeniería de Procesos y Química Aplicada; Argentina
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Reliable Decision-Making with Imprecise Models
The rapid growth in the deployment of autonomous systems across various sectors has generated considerable interest in how these systems can operate reliably in large, stochastic, and unstructured environments. Despite recent advances in artificial intelligence and machine learning, it is challenging to assure that autonomous systems will operate reliably in the open world. One of the causes of unreliable behavior is the impreciseness of the model used for decision-making. Due to the practical challenges in data collection and precise model specification, autonomous systems often operate based on models that do not represent all the details in the environment. Even if the system has access to a comprehensive decision-making model that accounts for all the details in the environment and all possible scenarios the agent may encounter, it may be intractable to solve this complex model optimally. Consequently, this complex, high fidelity model may be simplified to accelerate planning, introducing imprecision. Reasoning with such imprecise models affects the reliability of autonomous systems. A system\u27s actions may sometimes produce unexpected, undesirable consequences, which are often identified after deployment. How can we design autonomous systems that can operate reliably in the presence of uncertainty and model imprecision?
This dissertation presents solutions to address three classes of model imprecision in a Markov decision process, along with an analysis of the conditions under which bounded-performance can be guaranteed. First, an adaptive outcome selection approach is introduced to devise risk-aware reduced models of the environment that efficiently balance the trade-off between model simplicity and fidelity, to accelerate planning in resource-constrained settings. Second, a framework that extends stochastic shortest path framework to problems with imperfect information about the goal state during planning is introduced, along with two solution approaches to solve this problem. Finally, two complementary solution approaches are presented to minimize the negative side effects of agent actions. The techniques presented in this dissertation enable an autonomous system to detect and mitigate undesirable behavior, without redesigning the model entirely
3D printed Microneedles for Transdermal Drug Delivery
3D printing is a revolutionary manufacturing and prototyping technology that has altered the outlooks of numerous industrial and scientific fields since its introduction. Recently, it has attracted attention for its potential as a manufacturing tool for transdermal microneedles for drug delivery. In the present thesis, the 3D printability of solid and hollow microneedles via photopolymerisation-based 3D printing was investigated, aiming at establishing robust manufacturing strategies for reproducible, mechanically strong and versatile microneedles. The developed microneedles were employed as drug delivery systems for the treatment of diabetes via insulin administration.
Solid microneedles featuring different geometries were designed and 3D printed. It was demonstrated that the printing and post-printing parameters affected the printed quality, a finding that was employed to optimise the manufacturing strategy. Microneedle geometry was also found to have an impact on the piercing and fracture behaviour; however all microneedle designs were found to be mechanically safe upon application. The solid microneedles were subsequently coated with insulin-polymer films, using a 2D inkjet printing technology. The coating process achieved spatial control of the drug deposition, with quantitative accuracy. The microneedle geometry was shown to influence the morphology of the coating film, an effect that was pronounced during in the in vitro delivery studies of insulin to porcine skin.
Furthermore, hollow microneedles were designed and 3D printed, featuring different heights. Two photopolymerisation-based technologies were studied, and their performance was compared. The key influential parameters of the printing outcome and microneedle quality were identified to be the printing angle and the size of the microneedle opening. The hollow microneedles were found to be effective in piercing porcine skin without structural damaging. The hollow microneedles were incorporated into complex patches with internal microfluidic structures for the provision and distribution of drug-containing solutions. The developed complex hollow microneedle patches were coupled with a microelectromechanical system to create a novel platform device for controlled, personalised transdermal drug delivery. Advanced imaging techniques revealed that the device achieved distribution of the liquid within porcine skin tissue without the creation of depots that would delay absorption. The device was evaluated for its efficacy to transdermally deliver a model dye and insulin in vitro. In vivo trials were also conducted using diabetic rodents, with the device achieving faster onset of insulin action and sustained glycemic control, in comparison to subcutaneous injections.
Overall, the findings of the present research are anticipated to elucidate key problematic areas associated with the application of 3D printing for microneedle manufacturing and propose feasible solutions. The outermost goal of this work is to contribute to the advancement of knowledge in the field of 3D printed transdermal drug delivery systems, in order to bring them one step closer to their adoption in the clinical setting
Preparación de fibras y otros materiales de carbono para adsorción de CO2 en post-combustión
Finalmente, otro carbón activo, GAL, y una tela de carbón activada, CAD, se sintetizaron por activación química con ácido fosfórico de lignina y tela vaquera. Los distintos materiales se han caracterizado utilizando diversas técnicas y procedimientos, tales como adsorción-desorción de N2 a -196 ºC, adsorción de CO2 a 0 ºC, XPS, DTP y SEM. Para la evaluación de los materiales en la aplicación de interés se han realizado experimentos de adsorción, en equilibrio y en columna lecho fijo, en un rango amplio de condiciones experimentales e incluyendo las temperaturas, presiones y composiciones típicas de los procesos de post-combustión. También se ha estudiado el potencial de regeneración de varios de los materiales mediante ciclos de adsorción-desorción.
Los resultados obtenidos han sido muy prometedores, alcanzándose capacidades de adsorción y selectividades comparables a las de otros materiales complejos incluso a temperaturas elevadas. Cabe destacar, además, que la presencia de H2Ov, o bien no afecta de manera significativa al rendimiento de los materiales analizados; o bien, podría actuar de forma sinérgica y mejorar su capacidad de adsorción. Por otro lado, se ha conseguido profundizar y establecer relaciones muy interesantes entre las características estructurales del material (porosidad, química superficial, morfología, etc.) y la capacidad de adsorción en diferentes condiciones; y se han calculado diversos parámetros termodinámicos y cinéticos importantes para futuras etapas de diseño.La adsorción de CO2 sobre sólidos porosos en sistemas de post-combustión constituye una de las alternativas prioritarias para reducir y estabilizar su concentración a los niveles exigidos. Entre los adsorbentes estudiados, los materiales de carbono resultan especialmente interesantes debido al carácter hidrofóbico (mayor estabilidad en presencia de humedad) y menor calor de adsorción (facilidad de regeneración) que generalmente presentan. En el contexto de desarrollo sostenible, su obtención a partir de residuos biomásicos conllevaría beneficios sinérgicos adicionales, al capturarse CO2 y valorizarse un residuo simultáneamente. Sin embargo, las condiciones típicas de las corrientes de post-combustión suponen un verdadero reto y sus capacidades de adsorción y selectividades aún deben ser mejoradas para su implementación real. Ambos parámetros están intrínsecamente relacionados con las propiedades fisicoquímicas y estructurales del material, por lo que los esfuerzos se están orientando a clarificar su influencia, así como a desarrollar nuevos materiales carbonosos con las características óptimas.
En esta línea, el objetivo principal de esta Tesis Doctoral ha sido caracterizar y evaluar una serie de materiales de carbono diferentes como adsorbentes de CO2 en condiciones de post-combustión. En concreto, se han preparado seis materiales de carbón a partir de cuatro tipos de residuos lignocelulósicos con alto potencial de valorización, abundantes y de bajo coste: fibras de carbón por electrospinning, FCL, y un carbonizado granular, GCL, a partir de lignina Alcell®; dos carbones activos, GAS y GAWBa, por activación física de hueso de aceituna y residuo de aglomerado de madera, respectivamente. GAWBa fue, además, impregnado con acetato de bario en una etapa posterior para dotarlo de un cierto número de grupos básicos superficiales
Deep Learning and Machine Learning for Early Detection of Stroke and Haemorrhage
Stroke and cerebral haemorrhage are the second leading causes of death in the world after ischaemic heart disease. In this work, a dataset containing medical, physiological and environmental tests for stroke was used to evaluate the efficacy of machine learning, deep learning and a hybrid technique between deep learning and machine learning on theMagnetic Resonance Imaging (MRI) dataset for cerebral haemorrhage. In the first dataset (medical records), two features, namely, diabetes and obesity, were created on the basis of the values of the corresponding features. The t-Distributed Stochastic Neighbour Embedding algorithm was applied to represent the high-dimensional dataset in a low-dimensional data space. Meanwhile, the Recursive Feature Elimination algorithm (RFE) was applied to rank the features according to priority and their correlation to the target feature and to remove the unimportant features. The features are fed into the various classification algorithms, namely, Support Vector Machine (SVM), K Nearest Neighbours (KNN), Decision Tree, Random Forest, and Multilayer Perceptron. All algorithms achieved superior results. The Random Forest algorithm achieved the best performance amongst the algorithms; it reached an overall accuracy of 99%. This algorithm classified stroke cases with Precision, Recall and F1 score of 98%, 100% and 99%, respectively. In the second dataset, the MRI image dataset was evaluated by using the AlexNet model and AlexNet+SVM hybrid technique. The hybrid model AlexNet+SVM performed is better than the AlexNet model; it reached accuracy, sensitivity, specificity and Area Under the Curve (AUC) of 99.9%, 100%, 99.80% and 99.86%, respectively
Facial expression recognition and intensity estimation.
Doctoral Degree. University of KwaZulu-Natal, Durban.Facial Expression is one of the profound non-verbal channels through which human emotion state is inferred from the deformation or movement of face components when facial muscles are activated. Facial Expression Recognition (FER) is one of the relevant research fields in Computer Vision (CV) and Human-Computer Interraction (HCI). Its application is not limited to: robotics, game, medical, education, security and marketing. FER consists of a wealth of information. Categorising the information into primary emotion states only limit its performance. This thesis considers investigating an approach that simultaneously predicts the emotional state of facial expression images and the corresponding degree of intensity. The task also extends to resolving FER ambiguous nature and annotation inconsistencies with a label distribution learning method that considers correlation among data. We first proposed a multi-label approach for FER and its intensity estimation using advanced machine learning techniques. According to our findings, this approach has not been considered for emotion and intensity estimation in the field before. The approach used problem transformation to present FER as a multilabel task, such that every facial expression image has unique emotion information alongside the corresponding degree of intensity at which the emotion is displayed. A Convolutional Neural Network (CNN) with a sigmoid function at the final layer is the classifier for the model. The model termed ML-CNN (Multilabel Convolutional Neural Network) successfully achieve concurrent prediction of emotion and intensity estimation. ML-CNN prediction is challenged with overfitting and intraclass and interclass variations. We employ Visual Geometric Graphics-16 (VGG-16) pretrained network to resolve the overfitting challenge and the aggregation of island loss and binary cross-entropy loss to minimise the effect of intraclass and interclass variations. The enhanced ML-CNN model shows promising results and outstanding performance than other standard multilabel algorithms. Finally, we approach data annotation inconsistency and ambiguity in FER data using isomap manifold learning with Graph Convolutional Networks (GCN). The GCN uses the distance along the isomap manifold as the edge weight, which appropriately models the similarity between adjacent nodes for emotion predictions. The proposed method produces a promising result in comparison with the state-of-the-art methods.Author's List of Publication is on page xi of this thesis
Preparación de fibras submicrométricas de base carbonosa para aplicaciones energéticas y medioambientales
En esta Tesis Doctoral se estudia la valorización de la lignina como precursor de fibras de carbón. Para ello, las fibras se han preparado por electrospinning de disoluciones de lignina-etanol y lignina-etanol-H3PO4. Para ello se ha optimizado el proceso de preparación de fibras de carbón prestando atención especial a la etapa de estabilización de las fibras de lignina, ya que esta es la etapa controlante del proceso de preparación. De esta forma se han preparado fibras de carbón con distintas propiedades físico-químicas para su uso en aplicaciones funcionales como en procesos de adsorción en fase líquida, procesos catalíticos en fase gaseosa, y procesos de conversión y almacenamiento de energía.
Gracias a la funcionalización de las fibras de lignina con P se producen unos entrecruzamientos en la estructura de la lignina debido a la formación de grupos éteres fosfatos y polifosfatos, los que mejoran las condiciones de estabilización acortando el proceso en 20 o 50 veces con respecto a las fibras de lignina sin funcionalizar. Además, debido a la funcionalización con P se han obtenido fibras de carbón estructuradas con elevada resistencia a la oxidación, con un carácter ácido y con elevadas superficies específicas (hasta 2000 m2/g).
Las fibras de carbón sin P y funcionalizadas con P se han utilizado como adsorbentes para la adsorción de fenol en fase líquida, presentando elevadas capacidades de adsorción y cinéticas de adsorción muy rápidas, debido al carácter microporoso que estas tiene y que la porosidad está muy accesible desde la superficie de la fibra de carbón. Además, se ha planteado un modelo matemático para simular el comportamiento en columna de adsorción, obteniendo resultados muy satisfactorios. También se ha estudiado la regeneración de los adsorbentes
carbonosos.
Al funcionalizar precursores carbonosos con P, se obtienen materiales carbonosos con grupos de P muy estable térmicamente. De esta forma, se ha estudiado la estabilidad térmica de los grupos de fósforo preparando fibras de carbón con temperaturas de carbonización entre 500 y 1600 ºC. Para ello, se ha realizado la descomposición de isopropanol, una molécula modelo que se utiliza para caracterizar los sitios ácidos-básicos de la superficie. Las fibras de carbón funcionalizadas con P presentan carácter ácido, deshidratando el isopropanol para la producción de etileno. Estos catalizadores carbonosos presentan ácidez comparable a la de un catalizador comercial ácido.
Se han preparado electrocatalizadores carbonosos con muy buena dispersión de partículas de platino por toda la matriz carbonosa. Esta buena dispersión y homogeneidad se debe a que se han preparado fibras de lignina con metales en un solo paso. Estos electrocatalizadores presentan buen comportamiento para la electroxidación de metanol y etanol.
Se propone el uso de fibras de carbón como electrodos carbonosos en supercondensadores sin la adición de promotor de la conductividad y aglomerante. Se han preparado fibras de carbón lineales y con un cierto grado de interconexión a temperaturas de carbonización de 900 ºC, presentando buenas conductividades eléctricas y elevados valores de capacidad. Además, la carga y descarga del supercondensador se realiza en varios segundos
Three-dimensional visualisation and quantitative characterisation of fossil fuel flames using tomography and digital imaging techniques
This thesis describes the design, implementation and experimental evaluation of a prototype instrumentation system for the three-dimensional (3-D) visualisation and quantitative characterisation of fossil fuel flames. A review of methodologies and technologies for the 3-D visualisation and characterisation of combustion flames is given, together with a discussion of main difficulties and technical requirements in their applications. A strategy incorporating optical sensing, digital image processing and tomographic reconstruction techniques is proposed. The strategy was directed towards the reconstruction of 3-D models of a flame and the subsequent quantification of its 3-D geometric, luminous and fluid dynamic parameters. Based on this strategy, a flame imaging system employing three identical synchronised RG B cameras has been developed. The three cameras, placed equidistantly and equiangular on a semicircle around the flame, captured six simultaneous images of the flame from six different directions. Dedicated computing algorithms, based on image processing and tomographic reconstruction techniques have been developed to reconstruct the 3-D models of a flame. A set of geometric, luminous and fluid dynamic parameters, including surface area, volume, length, circularity, luminosity and temperature are determined from the 3-D models generated. Systematic design and experimental evaluation of the system on a gas-fired combustion rig are reported. The accuracy, resolution and validation of the system were also evaluated using purpose-designed templates including a high precision laboratory ruler, a colour flat panel and a tungsten lamp. The results obtained from the experimental evaluation are presented and the relationship between the measured parameters and the corresponding operational conditions are quantified. Preliminary investigations were conducted on a coal-fired industry-scale combustion test facility. The multi-camera system was reconfigured to use only one camera due to the restrictions at the site facility. Therefore the property of rotational symmetry of the flame had to be assumed. Under such limited conditions, the imaging system proved to provide a good reconstruction of the internal structures and luminosity variations inside the This thesis describes the design, implementation and experimental evaluation of a prototype instrumentation system for the three-dimensional (3-D) visualisation and quantitative characterisation of fossil fuel flames. A review of methodologies and technologies for the 3-D visualisation and characterisation of combustion flames is given, together with a discussion of main difficulties and technical requirements in their applications. A strategy incorporating optical sensing, digital image processing and tomographic reconstruction techniques is proposed. The strategy was directed towards the reconstruction of 3-D models of a flame and the subsequent quantification of its 3-D geometric, luminous and fluid dynamic parameters. Based on this strategy, a flame imaging system employing three identical synchronised RG B cameras has been developed. The three cameras, placed equidistantly and equiangular on a semicircle around the flame, captured six simultaneous images of the flame from six different directions. Dedicated computing algorithms, based on image processing and tomographic reconstruction techniques have been developed to reconstruct the 3-D models of a flame. A set of geometric, luminous and fluid dynamic parameters, including surface area, volume, length, circularity, luminosity and temperature are determined from the 3-D models generated. Systematic design and experimental evaluation of the system on a gas-fired combustion rig are reported. The accuracy, resolution and validation of the system were also evaluated using purpose-designed templates including a high precision laboratory ruler, a colour flat panel and a tungsten lamp. The results obtained from the experimental evaluation are presented and the relationship between the measured parameters and the corresponding operational conditions are quantified. Preliminary investigations were conducted on a coal-fired industry-scale combustion test facility. The multi-camera system was reconfigured to use only one camera due to the restrictions at the site facility. Therefore the property of rotational symmetry of the flame had to be assumed. Under such limited conditions, the imaging system proved to provide a good reconstruction of the internal structures and luminosity variations inside the This thesis describes the design, implementation and experimental evaluation of a prototype instrumentation system for the three-dimensional (3-D) visualisation and quantitative characterisation of fossil fuel flames. A review of methodologies and technologies for the 3-D visualisation and characterisation of combustion flames is given, together with a discussion of main difficulties and technical requirements in their applications. A strategy incorporating optical sensing, digital image processing and tomographic reconstruction techniques is proposed. The strategy was directed towards the reconstruction of 3-D models of a flame and the subsequent quantification of its 3-D geometric, luminous and fluid dynamic parameters. Based on this strategy, a flame imaging system employing three identical synchronised RG B cameras has been developed. The three cameras, placed equidistantly and equiangular on a semicircle around the flame, captured six simultaneous images of the flame from six different directions. Dedicated computing algorithms, based on image processing and tomographic reconstruction techniques have been developed to reconstruct the 3-D models of a flame. A set of geometric, luminous and fluid dynamic parameters, including surface area, volume, length, circularity, luminosity and temperature are determined from the 3-D models generated. Systematic design and experimental evaluation of the system on a gas-fired combustion rig are reported. The accuracy, resolution and validation of the system were also evaluated using purpose-designed templates including a high precision laboratory ruler, a colour flat panel and a tungsten lamp. The results obtained from the experimental evaluation are presented and the relationship between the measured parameters and the corresponding operational conditions are quantified. Preliminary investigations were conducted on a coal-fired industry-scale combustion test facility. The multi-camera system was reconfigured to use only one camera due to the restrictions at the site facility. Therefore the property of rotational symmetry of the flame had to be assumed. Under such limited conditions, the imaging system proved to provide a good reconstruction of the internal structures and luminosity variations inside the flame. Suggestions for future development of the technology are also reported
Integrating the HFACS Framework and Fuzzy Cognitive Mapping for In-Flight Startle Causality Analysis
This paper discusses the challenge of modeling in-flight startle causality as a precursor to enabling the development of suitable mitigating flight training paradigms. The article presents an overview of aviation human factors and their depiction in fuzzy cognitive maps (FCMs), based on the Human Factors Analysis and Classification System (HFACS) framework. The approach exemplifies system modeling with agents (causal factors), which showcase the problem space's characteristics as fuzzy cognitive map elements (concepts). The FCM prototype enables four essential functions: explanatory, predictive, reflective, and strategic. This utility of fuzzy cognitive maps is due to their flexibility, objective representation, and effectiveness at capturing a broad understanding of a highly dynamic construct. Such dynamism is true of in-flight startle causality. On the other hand, FCMs can help to highlight potential distortions and limitations of use case representation to enhance future flight training paradigms
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