Condutas e cuidados no fim da vida: avaliação dos óbitos ocorridos no Hospital Universitário/UFSC

Trabalho de Conclusão de Curso - Universidade Federal de Santa Catarina. Curso de Medicina. Departamento de Clínica Médica

Lessons Learned in Qatar: The Role of the Netherlands and Its Businesses in Addressing Human Rights Abuses in Mega-Sporting Events

Mega-sporting events (MSEs) can have a negative impact on human rights throughout their lifecycle, from the bidding stage, over to the planning and preparation stage, the delivery of the event, and also as part of their legacy after the event has concluded. They can be linked to land grabbing, forced evictions, forced labour and many other human rights abuses. The problem is that only a very few of these cases are actually addressed in the sense that rights-holders receive an effective remedy and those responsible for the abuse are held to account. MSEs are jointly organized and staged by public, private, national, and international actors, which each contribute in different ways to the associated human rights impact. Rather than looking at the responsibility of those actors directly involved in organizing and staging the event, this article looks at the responsibility of the participating actors of states that are represented at the event, namely businesses and sports bodies, using the Netherlands and the 2022 FIFA World Cup in Qatar as the guiding example. The central questions it tries to explore based on lessons learned and opportunities missed in Qatar are how such actors are connected to adverse human rights impacts associated with MSEs, which responsibilities under the human rights framework flow from those connections, and how participating states should then ensure that businesses live up to their responsibilities

CALF: Categorical Automata Learning Framework

Automata learning is a popular technique used to automatically construct an automaton model from queries, and much research has gone into devising specific adaptations of such algorithms for different types of automata. This thesis presents a unifying approach to many existing algorithms using category theory, which eases correctness proofs and guides the design of new automata learning algorithms. We provide a categorical automata learning framework---CALF---that at its core includes an abstract version of the popular L* algorithm. Using this abstract algorithm we derive several concrete ones. We instantiate the framework to a large class of Set functors, by which we recover for the first time a tree automata learning algorithm from an abstract framework, which moreover is the first to cover also algebras of quotiented polynomial functors. We further develop a general algorithm to learn weighted automata over a semiring. On the one hand, we identify a class of semirings, principal ideal domains, for which this algorithm terminates and for which no learning algorithm previously existed; on the other hand, we show that it does not terminate over the natural numbers. Finally, we develop an algorithm to learn automata with side-effects determined by a monad and provide several optimisations, as well as an implementation with experimental evaluation. This allows us to improve existing algorithms and opens the door to learning a wide range of automata

Educação científica : as relações de gênero na ciência

A perspectiva de gênero está presente no desenvolvimento científico, pois valores sociais e culturais desempenham um papel fundamental na Ciência. As epistemologias feministas corroboram com um conhecimento científico situado. A Educação Científica numa perspectiva de práxis feminista situada pode propiciar um ensino de caráter inclusivo e consciente. Nesta pesquisa buscamos analisar as noções de docentes em relação à construção do conhecimento científico elaborado por mulheres e homens e discutir como essas noções podem influenciar na Educação Científica. Foram aplicados questionários antes e após uma intervenção pedagógica. As respostas foram analisadas por meio da analise de conteúdo temático categorial. Após a análise percebe-se a necessidade de aprofundamento das discussões de gênero na Ciência na formação docent

Optimizing Automata Learning via Monads

Automata learning has been successfully applied in the verification of hardware and software. The size of the automaton model learned is a bottleneck for scalability, and hence optimizations that enable learning of compact representations are important. This paper exploits monads, both as a mathematical structure and a programming construct, to design, prove correct, and implement a wide class of such optimizations. The former perspective on monads allows us to develop a new algorithm and accompanying correctness proofs, building upon a general framework for automata learning based on category theory. The new algorithm is parametric on a monad, which provides a rich algebraic structure to capture non-determinism and other side-effects. We show that our approach allows us to uniformly capture existing algorithms, develop new ones, and add optimizations. The latter perspective allows us to effortlessly translate the theory into practice: we provide a Haskell library implementing our general framework, and we show experimental results for two specific instances: non-deterministic and weighted automata

Learning automata with side-effects

Automata learning has been successfully applied in the verification of hardware and software. The size of the automaton model learned is a bottleneck for scalability, and hence optimizations that enable learning of compact representations are important. This paper exploits monads, both as a mathematical structure and a programming construct, to design and prove correct a wide class of such optimizations. Monads enable the development of a new learning algorithm and correctness proofs, building upon a general framework for automata learning based on category theory. The new algorithm is parametric on a monad, which provides a rich algebraic structure to capture non-determinism and other side-effects. We show that this allows us to uniformly capture existing algorithms, develop new ones, and add optimizations