O natural e o farmacológico: padrões de consumo terapêutico na população portuguesa

Neste artigo identificam-se os padrões de consumo terapêutico na população portuguesa, visando dar conta de um novo padrão emergente nas sociedades modernas, aqui designado de Pluralismo Terapêutico, noção com a qual se categoriza o uso conjugado ou alternado de recursos farmacológicos e naturais nas trajetórias terapêuticas dos indivíduos. O respetivo suporte empírico decorre de uma investigação, já concluída, que teve por base uma amostra nacional representativa. Os resultados mostram uma dualização dos consumos terapêuticos que é constituída por um padrão dominante de Farmacologismo – i.e., uso exclusivo de fármacos – coexistente com uma tendência crescente de pluralismo terapêutico. O efeito das fontes de informação terapêutica e dos seus usos leigos, bem como das perceções sociais de risco sobre o natural e o farmacológico, constitui neste estudo uma referência analítica central para a interpretação dos padrões encontrados. - ABSTRACT: In this article we identify patterns of therapeutic consumption, with the purpose of assessing an emerging pattern in modern societies, here designated as Therapeutic Pluralism, referring to the conjugated or alternated use of pharmacological and natural resources in the therapeutic trajectories of individuals. The empirical basis for this analysis stems from a concluded research on the topic, and is focused on a questionnaire administered to a representative sample of the Portuguese population. The results show a duality in therapeutic consumptions, expressed in the coexistence of a dominant pattern of Pharmacologism – that is, the exclusive therapeutic consumption of pharmaceuticals – and a growing trend towards therapeutic pluralism. The effects of information sources on health and its lay uses, as well as of the social perceptions of risk concerning the natural and the pharmacological, constitute key analytical references for this study’s interpretation of the identified patterns.

Direct multisearch for multiobjective optimization

In practical applications of optimization it is common to have several conflicting objective functions to optimize. Frequently, these functions are subject to noise or can be of black-box type, preventing the use of derivative-based techniques. We propose a novel multiobjective derivative-free methodology, calling it direct multisearch (DMS), which does not aggregate any of the objective functions. Our framework is inspired by the search/poll paradigm of direct-search methods of directional type and uses the concept of Pareto dominance to maintain a list of nondominated points (from which the new iterates or poll centers are chosen). The aim of our method is to generate as many points in the Pareto front as possible from the polling procedure itself, while keeping the whole framework general enough to accommodate other disseminating strategies, in particular, when using the (here also) optional search step. DMS generalizes to multiobjective optimization (MOO) all direct-search methods of directional type. We prove under the common assumptions used in direct search for single objective optimization that at least one limit point of the sequence of iterates generated by DMS lies in (a stationary form of) the Pareto front. However, extensive computational experience has shown that our methodology has an impressive capability of generating the whole Pareto front, even without using a search step. Two by-products of this paper are (i) the development of a collection of test problems for MOO and (ii) the extension of performance and data profiles to MOO, allowing a comparison of several solvers on a large set of test problems, in terms of their efficiency and robustness to determine Pareto fronts.

O papel da interactividade na construção da narrativa em role-playing games digitais

Dissertação apresentada à Escola Superior de Comunicação Social como parte dos requisitos para obtenção de grau de mestre em Audiovisual e Multimédia.

A experiência de marca na fidelização do consumidor: o caso Starbucks Portugal

Dissertação apresentada à Escola Superior de Comunicação Social como parte dos requisitos para obtenção de grau de mestre em Publicidade e Marketing.

Entre o palco e o ecrã: feras amestradas: comédia musical negra à portuguesa: intermédia

Trabalho de Projeto submetido à Escola Superior de Teatro e Cinema para cumprimento dos requisitos necessários à obtenção do grau de Mestre em Teatro - especialização em Encenação

Finge: relatório de estágio

Relatório de Estágio submetido à Escola Superior de Teatro e Cinema para cumprimento dos requisitos necessários à obtenção do grau de Mestre em Teatro, especialização em Artes Performativas - interpretação.

GLODS: Global and Local Optimization using Direct Search

Locating and identifying points as global minimizers is, in general, a hard and time-consuming task. Difficulties increase in the impossibility of using the derivatives of the functions defining the problem. In this work, we propose a new class of methods suited for global derivative-free constrained optimization. Using direct search of directional type, the algorithm alternates between a search step, where potentially good regions are located, and a poll step where the previously located promising regions are explored. This exploitation is made through the launching of several instances of directional direct searches, one in each of the regions of interest. Differently from a simple multistart strategy, direct searches will merge when sufficiently close. The goal is to end with as many direct searches as the number of local minimizers, which would easily allow locating the global extreme value. We describe the algorithmic structure considered, present the corresponding convergence analysis and report numerical results, showing that the proposed method is competitive with currently commonly used global derivative-free optimization solvers.

GPU implementation of the simplex identification via split augmented Lagrangian

Hyperspectral imaging can be used for object detection and for discriminating between different objects based on their spectral characteristics. One of the main problems of hyperspectral data analysis is the presence of mixed pixels, due to the low spatial resolution of such images. This means that several spectrally pure signatures (endmembers) are combined into the same mixed pixel. Linear spectral unmixing follows an unsupervised approach which aims at inferring pure spectral signatures and their material fractions at each pixel of the scene. The huge data volumes acquired by such sensors put stringent requirements on processing and unmixing methods. This paper proposes an efficient implementation of a unsupervised linear unmixing method on GPUs using CUDA. The method finds the smallest simplex by solving a sequence of nonsmooth convex subproblems using variable splitting to obtain a constraint formulation, and then applying an augmented Lagrangian technique. The parallel implementation of SISAL presented in this work exploits the GPU architecture at low level, using shared memory and coalesced accesses to memory. The results herein presented indicate that the GPU implementation can significantly accelerate the method's execution over big datasets while maintaining the methods accuracy.