21 resultados para Straight-forward method
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Dissertation presented to obtain the Ph.D degree in Chemistry.
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Human Activity Recognition systems require objective and reliable methods that can be used in the daily routine and must offer consistent results according with the performed activities. These systems are under development and offer objective and personalized support for several applications such as the healthcare area. This thesis aims to create a framework for human activities recognition based on accelerometry signals. Some new features and techniques inspired in the audio recognition methodology are introduced in this work, namely Log Scale Power Bandwidth and the Markov Models application. The Forward Feature Selection was adopted as the feature selection algorithm in order to improve the clustering performances and limit the computational demands. This method selects the most suitable set of features for activities recognition in accelerometry from a 423th dimensional feature vector. Several Machine Learning algorithms were applied to the used accelerometry databases – FCHA and PAMAP databases - and these showed promising results in activities recognition. The developed algorithm set constitutes a mighty contribution for the development of reliable evaluation methods of movement disorders for diagnosis and treatment applications.
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This thesis evaluates a start-up company (Jogos Almirante Lda) whose single asset is a board game named Almirante. It aims to conclude whether it makes sense to create a company or just earn copyrights. The thesis analyzes the board game’s market, as part of the general toy’s market, from which some data exists: European countries as well as the USA. In this work it is analyzed the several ways to finance a start-up company and then present an overview of the valuation of the Jogos Almirante based on three different methods: Discounted Cash Flow, Venture Capital Method and Real Options.
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UNL - NSBE
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The present PhD thesis develops the cell functional enviromics (CFE) method to investigate the relationship between environment and cellular physiology. CFE may be defined as the envirome-wide cellular function reconstruction through the collection and systems-level analysis of dynamic envirome data. Throughout the thesis, CFE is illustrated by two main applications to cultures of a constitutive P. pastoris X33 strain expressing a scFv antibody fragment. The first application addresses the challenge of culture media development. A dataset was built from 26 shake flask experiments, with variations in trace elements concentrations and basal medium dilution based on the standard BSM+PTM1. Protein yield showed high sensitivity to culture medium variations, while biomass was essentially determined by BSM dilution. High scFv yield was associated with high overall metabolic fluxes through central carbon pathways concomitantly with a relative shift of carbon flux from biosynthetic towards energy-generating pathways. CFE identified three cellular functions (growth, energy generation and by-product formation) that together described 98.8% of the variance in observed fluxes. Analyses of how medium factors relate to identified cellular functions showed iron and manganese at concentrations close to PTM1 inhibit overall metabolic activity. The second application addresses bioreactor operation. Pilot 50 L fed-batch cultivations, followed by 1H-NMR exometabolite profiling, allowed the acquisition of data for 21 environmental factors over time. CFE identified five major metabolic pathway groups that are frequently activated by the environment. The resulting functional enviromics map may serve as template for future optimization of media composition and feeding strategies for Pichia pastoris. The present PhD thesis is a step forward towards establishing the foundations of CFE that is still at its infancy. The methods developed herein are a contribution for changing the culture media and process development paradigm towards a holistic and systematic discipline in the future.
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Laggards are the last users to adopt a product. Prior literature on user-led innovation ignores laggards’ impact on innovation. In this paper, we develop the Lag-User Method, through which laggards can generate new ideas. Through six studies with 62 teams in three countries, we apply the method to different technologies and services and present our findings to executives to get managerial insights. Findings reveal that laggards who generate new ideas (lag-users) have different perceptions of user-friendly products and different unfulfilled needs. They prefer simple products. We propose that by involving lag-users in NPD, firms can improve the effectiveness of NPD.