Optimal filter design for power converters regulated by FCS-MPC in the MEA

For the DC electrical power distribution system onboard more electric aircraft, the voltage quality of DC bus is of a great concern since there could be significant harmonics distortions when feeding different power electronics loads. This problem can be potentially addressed by introducing a dc filter to the point-of-load converters regulated by the finite control set model predictive control (FCS-MPC). To optimize this filter, Genetic Algorithm (GA) is utilized for searching the optimal design which guarantees a low mass and low power losses. Different from the conventional filter design methods, the proposed method treats LC as design variables which need to be optimised while ensuring the output power quality. First, relations among variables, operation conditions and constraints are built based on commercial data and circuit simulations. Then, the design and optimization are developed with these relations and a Pareto-front is finally given by GA. After that, the best design is obtained by an index integrating two objectives. Lastly, the design approach is verified by experiment where an FCS-MPC regulated converter was used as a particular example fed by three different LC filters

Machine learning methods for detecting structure in metabolic flow networks

Metabolic flow networks are large scale, mechanistic biological models with good predictive power. However, even when they provide good predictions, interpreting the meaning of their structure can be very difficult, especially for large networks which model entire organisms. This is an underaddressed problem in general, and the analytic techniques that exist currently are difficult to combine with experimental data. The central hypothesis of this thesis is that statistical analysis of large datasets of simulated metabolic fluxes is an effective way to gain insight into the structure of metabolic networks. These datasets can be either simulated or experimental, allowing insight on real world data while retaining the large sample sizes only easily possible via simulation. This work demonstrates that this approach can yield results in detecting structure in both a population of solutions and in the network itself. This work begins with a taxonomy of sampling methods over metabolic networks, before introducing three case studies, of different sampling strategies. Two of these case studies represent, to my knowledge, the largest datasets of their kind, at around half a million points each. This required the creation of custom software to achieve this in a reasonable time frame, and is necessary due to the high dimensionality of the sample space. Next, a number of techniques are described which operate on smaller datasets. These techniques, focused on pairwise comparison, show what can be achieved with these smaller datasets, and how in these cases, visualisation techniques are applicable which do not have simple analogues with larger datasets. In the next chapter, Similarity Network Fusion is used for the first time to cluster organisms across several levels of biological organisation, resulting in the detection of discrete, quantised biological states in the underlying datasets. This quantisation effect was maintained across both real biological data and Monte-Carlo simulated data, with related underlying biological correlates, implying that this behaviour stems from the network structure itself, rather than from the genetic or regulatory mechanisms that would normally be assumed. Finally, Hierarchical Block Matrices are used as a model of multi-level network structure, by clustering reactions using a variety of distance metrics: first standard network distance measures, then by Local Network Learning, a novel approach of measuring connection strength via the gain in predictive power of each node on its neighbourhood. The clusters uncovered using this approach are validated against pre-existing subsystem labels and found to outperform alternative techniques. Overall this thesis represents a significant new approach to metabolic network structure detection, as both a theoretical framework and as technological tools, which can readily be expanded to cover other classes of multilayer network, an under explored datatype across a wide variety of contexts. In addition to the new techniques for metabolic network structure detection introduced, this research has proved fruitful both in its use in applied biological research and in terms of the software developed, which is experiencing substantial usage.EPSR

Network Analysis and Modeling in Systems Biology

This thesis is dedicated to the study and comprehension of biological networks at the molecular level. The objectives were to analyse their topology, integrate it in a genotype-phenotype analysis, develop richer mathematical descriptions for them, study their community structure and compare different methodologies for estimating their internal fluxes. The work presented in this document moves around three main axes. The first one is the biological. Which organisms were studied in this thesis? They range from the simplest biological agents, the viruses, in this case the Potyvirus genus to prokariotes such as Escherichia coli and complex eukariotes (Arabidopsis thaliana, Nicotiana benthamiana). The second axis refers to which biological networks were studied. Those are protein-protein interaction (PPIN) and metabolic networks (MN). The final axis relates to the mathematical and modelling tools used to generate knowledge from those networks. These tools can be classify in three main branches: graph theory, constraint-based modelling and multivariate statistics. The document is structured in six parts. The first part states the justification for the thesis, exposes a general thesis roadmap and enumerates its main contributions. In the second part important literature is reviewed, summarized and integrated. From the birth and development of Systems Biology to one of its most popular branches: biological network analysis. Particular focus is put on PPIN and MN and their structure, representations and features. Finally a general overview of the mathematical tools used is presented. The third, fourth and fifth parts represent the central work of this thesis. They deal respectively with genotypephenotype interaction and classical network analysis, constraint-based modelling methods comparison and modelling metabolic networks and community structure. Finally, in the sixth part the main conclusions of the thesis are summarized and enumerated. This thesis highlights the vital importance of studying biological entities as systems and how powerful and promising this integrated analysis is. Particularly, network analysis becomes a fundamental avenue of research to gain insight into those biological systems and to extract, integrate and display this new information. It generates knowledge from just data.Esta tesis está dedicada al estudio y comprensión de redes biológicas a nivel molecular. Los objetivos fueron analizar su topología, integrar esta en un análisis de genotipo-fenotipo, desarrollar descripciones matemáticas más completas para ellas, estudiar su estructura de comunidades y comparar diferentes metodologías para estimar sus flujos internos. El trabajo presentado en este documento gira entorno a tres ejes principales. El primero es el biológico. ¿Qué organismos han sido estudiados en esta tesis? Estos van desde los agentes biológicos mas simples, los virus, en este caso el género Potyvirus, hasta procariotas como Escherichia coli y eucariotas complejos (Arabidopsis thaliana, Nicotiana benthamiana). El segundo eje hace referencia a las redes biológicas estudiadas, que fueron redes de interacción de proteínas (PPIN) y redes metabólicas (MN). El eje final es el de las herramientas matemáticas y de modelización empleadas para interrogar esas redes. Estas herramientas pueden clasificarse en tres grandes grupos: teoría de grafos, modelización basada en restricciones y estadística multivariante. Este documento está estructurado en seis partes. La primera expone la justificación para la tesis, muestra un mapa visual de la misma y enumera sus contribuciones principales. En la segunda parte, la bibliografía relevante es revisada y resumida. Desde el nacimiento y desarrollo de la Biología de Sistemas hasta una de sus ramas más populares: el análisis de redes biomoleculares. Especial interés es puesto en PPIN y MN: su estructura, representación y características. Finalmente, un resumen general de las herramientas matemáticas usadas es presentado. Los capítulos tercero, cuarto y quinto representan el cuerpo central de esta tesis. Estos tratan respectivamente sobre la interacción de genotipo-fenotipo y análisis topolólogico clásico de redes, modelos basados en restricciones y modelización de redes metabólicas y su estructura de comunidades. Finalmente, en la sexta parte las principales conclusiones de la tesis son resumidas y expuestas. Esta tesis pone énfasis en la vital importancia de estudiar los fenómenos biológicos como sistemas y en la potencia y prometedor futuro de este análisis integrativo. En concreto el análisis de redes supone un camino de investigación fundamental para obtener conocimiento sobre estos sistemas biológicos y para extraer y mostrar información sobre los mismos. Este análisis genera conocimiento partiendo únicamente desde datos.Aquesta tesi està dedicada a l'estudi i comprensió de xarxes biològiques a nivell molecular. Els objectius van ser analitzar la seva topologia, integrar aquesta en una anàlisi de genotip-fenotip, desenvolupar descripcions matemàtiques més completes per a elles, estudiar la seva estructura de comunitats o modularitat i comparar diferents metodologies per estimar els fluxos interns. El treball presentat en aquest document gira entorn de tres eixos principals. El primer és el biològic. ¿Què organismes han estat estudiats en aquesta tesi? Aquests van des dels agents biològics mes simples, els virus, en aquest cas el gènere Potyvirus, fins procariotes com Escherichia coli i eucariotes complexos (Arabidopsis thaliana, Nicotiana benthamiana). El segon eix fa referència a les xarxes biològiques estudiades, que van ser les xarxes d'interacció de proteïnes (PPIN) i les xarxes metabòliques (MN). L'eix final és el de les eines matemàtiques i de modelització emprades per interrogar aquestes xarxes. Aquestes eines poden classificarse en tres grans grups: teoria de grafs, modelització basada en restriccions i estadística multivariant. Aquest document està estructurat en sis parts. La primera exposa la justificació per a la tesi, mostra un mapa visual de la mateixa i enumera les seves contribucions principals. A la segona part, la bibliografia rellevant és revisada i resumida. Des del naixement i desenvolupament de la Biologia de Sistemes fins a una de les seves branques més populars: l'anàlisi de xarxes moleculars. Especial interès és posat en PPIN i MN: la seva estructura, representació i característiques. Finalment, un resum general de les eines matemàtiques utilitzades és presentat. Els capítols tercer, quart i cinquè representen el cos central d'aquesta tesi. Aquests tracten respectivament sobre la interacció de genotip-fenotip i anàlisi topolólogico clàssic de xarxes, models basats en restriccions i modelització de xarxes metabòliques i la seva estructura de comunitats. Finalment, en la sisena part les principals conclusions de la tesi són resumides i exposades. Aquesta tesi posa èmfasi en la vital importància d'estudiar els fenòmens biològics com sistemes i en la potència i prometedor futur d'aquesta anàlisi integratiu. En concret l'anàlisi de xarxes suposa un camí d'investigació fonamental per obtenir coneixement sobre aquests sistemes biològics i per extreure i mostrar informació sobre els mateixos. Aquest anàlisi genera coneixement partint únicament des de dades.Bosque Chacón, G. (2017). Network Analysis and Modeling in Systems Biology [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/79082TESI

Performance on Well and Ill-Structured Problems in University Physics: The role of Instruction in Cooperative Learning

In this study, I explore the learning opportunities that emerge from ill-structured activities in a university physics course and how collaboration enables performance under different teaching conditions. The study was conducted over two months on three sections of an introductory physics course in a University in Northern Chile. Each section utilized a different variety of teaching strategies and combinations of problems (well and ill-structured) for assessing content on the day of data collection, students were asked to work in groups and ill-structured activity in physics. I gathered audio of students' discussion in four groups for a total of 26 participants, and I collected the generated problems from the whole sample. From the audio, I explored the emergent processes students engaged while solving the problems. Student generated activities were coded to investigate combinations of concepts and problem characteristics, which were later combined into a measure of problem elaboration. Later, I explored students’ social networks to determine how different instructional strategies led to different social configurations and their differences in academic performance. For this, I collected data on students' performance on a physics test designed with well-structured problems and problem elaboration, and I asked students to respond to an on-line peer-nomination survey related to their social interactions engaged for information seeking to solve problems. I tested the effect of different network structures over academic performance on both types of activities by setting statistical linear models. Generating problems is an opportunity for students to propose ideas and make decisions regarding the content and the contextual details to introduce into their problems, as well as to engage in problem solving strategies. The combination of concepts and attributes for problem elaboration showed students' familiarity with particular portions of the content and characteristics, with differences across sections. Finally, students who actively sought out information from multiple peers were less likely to achieve good performance on well-structured problems, whereas for ill-structured problems, this effect depended on the features of the learning environment enacted in each section. These results suggest that teaching and instructional strategies have a key role in the way cooperation lead to good performance