67 resultados para Hidden markov models


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Composite materials have a complex behavior, which is difficult to predict under different types of loads. In the course of this dissertation a methodology was developed to predict failure and damage propagation of composite material specimens. This methodology uses finite element numerical models created with Ansys and Matlab softwares. The methodology is able to perform an incremental-iterative analysis, which increases, gradually, the load applied to the specimen. Several structural failure phenomena are considered, such as fiber and/or matrix failure, delamination or shear plasticity. Failure criteria based on element stresses were implemented and a procedure to reduce the stiffness of the failed elements was prepared. The material used in this dissertation consist of a spread tow carbon fabric with a 0°/90° arrangement and the main numerical model analyzed is a 26-plies specimen under compression loads. Numerical results were compared with the results of specimens tested experimentally, whose mechanical properties are unknown, knowing only the geometry of the specimen. The material properties of the numerical model were adjusted in the course of this dissertation, in order to find the lowest difference between the numerical and experimental results with an error lower than 5% (it was performed the numerical model identification based on the experimental results).

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Field lab: Business project

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We intend to study the algebraic structure of the simple orthogonal models to use them, through binary operations as building blocks in the construction of more complex orthogonal models. We start by presenting some matrix results considering Commutative Jordan Algebras of symmetric matrices, CJAs. Next, we use these results to study the algebraic structure of orthogonal models, obtained by crossing and nesting simpler ones. Then, we study the normal models with OBS, which can also be orthogonal models. We intend to study normal models with OBS (Orthogonal Block Structure), NOBS (Normal Orthogonal Block Structure), obtaining condition for having complete and suffcient statistics, having UMVUE, is unbiased estimators with minimal covariance matrices whatever the variance components. Lastly, see ([Pereira et al. (2014)]), we study the algebraic structure of orthogonal models, mixed models whose variance covariance matrices are all positive semi definite, linear combinations of known orthogonal pairwise orthogonal projection matrices, OPOPM, and whose least square estimators, LSE, of estimable vectors are best linear unbiased estimator, BLUE, whatever the variance components, so they are uniformly BLUE, UBLUE. From the results of the algebraic structure we will get explicit expressions for the LSE of these models.

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Both culture coverage and digital journalism are contemporary phenomena that have undergone several transformations within a short period of time. Whenever the media enters a period of uncertainty such as the present one, there is an attempt to innovate in order to seek sustainability, skip the crisis or find a new public. This indicates that there are new trends to be understood and explored, i.e., how are media innovating in a digital environment? Not only does the professional debate about the future of journalism justify the need to explore the issue, but so do the academic approaches to cultural journalism. However, none of the studies so far have considered innovation as a motto or driver and tried to explain how the media are covering culture, achieving sustainability and engaging with the readers in a digital environment. This research examines how European media which specialize in culture or have an important cultural section are innovating in a digital environment. Specifically, we see how these innovation strategies are being taken in relation to the approach to culture and dominant cultural areas, editorial models, the use of digital tools for telling stories, overall brand positioning and extensions, engagement with the public and business models. We conducted a mixed methods study combining case studies of four media projects, which integrates qualitative web features and content analysis, with quantitative web content analysis. Two major general-interest journalistic brands which started as physical newspapers – The Guardian (London, UK) and Público (Lisbon, Portugal) – a magazine specialized in international affairs, culture and design – Monocle (London, UK) – and a native digital media project that was launched by a cultural organization – Notodo, by La Fábrica – were the four case studies chosen. Findings suggest, on one hand, that we are witnessing a paradigm shift in culture coverage in a digital environment, challenging traditional boundaries related to cultural themes and scope, angles, genres, content format and delivery, engagement and business models. Innovation in the four case studies lies especially along the product dimensions (format and content), brand positioning and process (business model and ways to engage with users). On the other hand, there are still perennial values that are crucial to innovation and sustainability, such as commitment to journalism, consistency (to the reader, to brand extensions and to the advertiser), intelligent differentiation and the capability of knowing what innovation means and how it can be applied, since this thesis also confirms that one formula doesn´t suit all. Changing minds, exceeding cultural inertia and optimizing the memory of the websites, looking at them as living, organic bodies, which continuously interact with the readers in many different ways, and not as a closed collection of articles, are still the main challenges for some media.

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Neurological disorders are a major concern in modern societies, with increasing prevalence mainly related with the higher life expectancy. Most of the current available therapeutic options can only control and ameliorate the patients’ symptoms, often be-coming refractory over time. Therapeutic breakthroughs and advances have been hampered by the lack of accurate central nervous system (CNS) models. The develop-ment of these models allows the study of the disease onset/progression mechanisms and the preclinical evaluation of novel therapeutics. This has traditionally relied on genetically engineered animal models that often diverge considerably from the human phenotype (developmentally, anatomically and physiologically) and 2D in vitro cell models, which fail to recapitulate the characteristics of the target tissue (cell-cell and cell-matrix interactions, cell polarity). The in vitro recapitulation of CNS phenotypic and functional features requires the implementation of advanced culture strategies that enable to mimic the in vivo struc-tural and molecular complexity. Models based on differentiation of human neural stem cells (hNSC) in 3D cultures have great potential as complementary tools in preclinical research, bridging the gap between human clinical studies and animal models. This thesis aimed at the development of novel human 3D in vitro CNS models by integrat-ing agitation-based culture systems and a wide array of characterization tools. Neural differentiation of hNSC as 3D neurospheres was explored in Chapter 2. Here, it was demonstrated that human midbrain-derived neural progenitor cells from fetal origin (hmNPC) can generate complex tissue-like structures containing functional dopaminergic neurons, as well as astrocytes and oligodendrocytes. Chapter 3 focused on the development of cellular characterization assays for cell aggregates based on light-sheet fluorescence imaging systems, which resulted in increased spatial resolu-tion both for fixed samples or live imaging. The applicability of the developed human 3D cell model for preclinical research was explored in Chapter 4, evaluating the poten-tial of a viral vector candidate for gene therapy. The efficacy and safety of helper-dependent CAV-2 (hd-CAV-2) for gene delivery in human neurons was evaluated, demonstrating increased neuronal tropism, efficient transgene expression and minimal toxicity. The potential of human 3D in vitro CNS models to mimic brain functions was further addressed in Chapter 5. Exploring the use of 13C-labeled substrates and Nucle-ar Magnetic Resonance (NMR) spectroscopy tools, neural metabolic signatures were evaluated showing lineage-specific metabolic specialization and establishment of neu-ron-astrocytic shuttles upon differentiation. Chapter 6 focused on transferring the knowledge and strategies described in the previous chapters for the implementation of a scalable and robust process for the 3D differentiation of hNSC derived from human induced pluripotent stem cells (hiPSC). Here, software-controlled perfusion stirred-tank bioreactors were used as technological system to sustain cell aggregation and dif-ferentiation. The work developed in this thesis provides practical and versatile new in vitro ap-proaches to model the human brain. Furthermore, the culture strategies described herein can be further extended to other sources of neural phenotypes, including pa-tient-derived hiPSC. The combination of this 3D culture strategy with the implemented characterization methods represents a powerful complementary tool applicable in the drug discovery, toxicology and disease modeling.

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This work project is based on the MIES (Map of Innovation and Social Entrepreneurship in Portugal) database and it aims to understand the characteristics of social business models in the context of the portuguese market, by determining whether they follow the proposed characteristics by John Elkington and Pamela Hartigan, and then adding to their matrix. Furthermore, it tries to determine success patterns by comparing a group of successful social ventures with a group of less successful ones, with the objective of increasing the knowledge of social entrepreneurship as it applies to Portugal and provide a framework for future study.

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This thesis is a first step in the search for the characteristics of funders, and the underlying motivation that drives them to participate in crowdfunding. The purpose of the study is to identify demographics and psychographics that influence a funder’s willingness to financially support a crowdfunding project (WFS). Crowdfunding, crowdsourcing and donation literature are combined to create a conceptual model in which age, gender, altruism and income, together with several control variables, are expected to have an influence on a funder’s WFS. Primary data collection was conducted using a survey, and a dataset of 175 potential crowdfunders was created. The data is analysed using a multiple regression and provided several interesting results. First of all, age and gender have a significant effect on WFS, males and young adults until the age of 30 have a higher intention to give money to crowdfunding projects. Second, altruism is significantly positively related to WFS, meaning that the funders do not just care about the potential rewards they could receive, but also about the benefits that they create for the entrepreneur and the people affected by the crowdfunding project. Third, the moderation effect of income was found to be insignificant in this model. It shows that income does not affect the strength of the relationship between the age, gender and altruism, and WFS. This study provides important theoretical contributions by, to the best of my knowledge, being the first study to quantitatively investigate the characteristics of funders and using the funder as the unit of analysis. Moreover, the study provides important insights for entrepreneurs who wish to target the crowd better in order to attract and retain more funders, thereby increasing the chance of success of their project.