In this thesis, the Twitter social media is data mined for information about the covid-19 outbreak during the month of March, starting from the 3’rd and ending on the 31’st. 100,000 tweets were collected from Harvard’s opensource data and recreated using Hydrate. This data is analyzed further using different Natural Language Processing (NLP) methodologies, such as termfrequency inverse document frequency (TF-IDF), lemmatizing, tokenizing, Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA). Furthermore, the results of the LSA and LDA algorithms is reduced dimensional data that will be clustered using clustering algorithms HDBSCAN and K-Means for later comparison. Different methodologies are used to determine the optimal parameters for the algorithms. This is all done in the python programing language, as there are libraries for supporting this research, the most important being scikit-learn. The frequent words of each cluster will then be displayed and compared with factual data regarding the outbreak to discover if there are any correlations. The factual data is collected by World Health Organization (WHO) and is then visualized in graphs in ourworldindata.org. Correlations with the results are also looked for in news articles to find any significant moments to see if that affected the top words in the clustered data. The news articles with good timelines used for correlating incidents are that of NBC News and New York Times. The results show no direct correlations with the data reported by WHO, however looking into the timelines reported by news sources some correlation can be seen with the clustered data. Also, the combination of LDA and HDBSCAN yielded the most desireable results in comparison to the other combinations of the dimnension reductions and clustering. This was much due to the use of GridSearchCV on LDA to determine the ideal parameters for the LDA models on each dataset as well as how well HDBSCAN clusters its data in comparison to K-Means.

[pt] Os sistemas de transmissão cumprem uma função vital para o bom desempenho dos mercados de energia elétrica. A precificação do seu uso afeta diretamente a remuneração das empresas concessionárias e os custos dos participantes do mercado. No Brasil, os usuários do sistema interligado nacional (SIN) devem pagar pela disponibilização dos equipamentos que compõem a rede para as transmissoras detentoras destes ativos de forma proporcional ao seu uso. Assim, a agência reguladora brasileira (ANEEL) estabeleceu as tarifas de uso do sistema de transmissão (TUST), que são calculadas anualmente por barra via metodologia nodal. Tais tarifas são compostas por duas parcelas: locacional e selo. A parcela locacional reflete o uso efetivo da rede por cada agente participante, medindo o impacto da injeção de potência marginal de uma barra nos equipamentos do sistema. A parcela selo consiste num valor constante que garantirá a remuneração da porção não utilizada da rede. Em geral, a proximidade elétrica das barras do sistema implica valores tarifários similares. Esta Dissertação de Mestrado propõe uma nova metodologia a ser incorporada no cálculo da TUST, considerando a divisão do SIN em zonas tarifárias de transmissão (ZTT). Desta forma, cada ZTT apresentará uma única tarifa a ser aplicada aos seus participantes, que corresponderá à média ponderada das tarifas finais calculadas via metodologia nodal. Para a identificação das ZTT, são aplicadas técnicas de agrupamento k-Means e espectral nos sistemas IEEE-RTS e SIN. Nesta dissertação, avalia-se também o uso de modelos matemáticos para definir o número ideal de ZTT a ser considerado. São realizadas diversas análises de sensibilidade relativas a mudanças de despacho, alterações de topologia e evolução do sistema ao longo dos anos. Os resultados correspondentes são então extensivamente discutidos. / [en] Transmission systems play a vital role in the good performance of the electrical energy markets. The pricing of its use directly affects the budget of concessionary companies and the costs of market participants. In Brazil, users of the national interconnected system (NIS) must pay for the equipment availability that makes up the network to the transmission companies that own these assets in proportion to their use. Thus, the Brazilian regulatory agency (ANEEL) established the tariffs for transmission system usage (TTSU), which are calculated annually by bus using the nodal methodology. Such tariffs are made up of two installments: locational and postage stamp. The locational portion reflects the effective use of the grid by each participating agent, measuring the impact of the marginal power injection at a bus on the system equipment. The stamp portion consists of a constant amount that will guarantee the remuneration of the unused portion of the network. In general, the electrical proximity of the system buses leads to similar tariff values. This dissertation proposes a new methodology to be incorporated into the TTSU calculation, considering the division of the NIS into transmission tariff zones (TTZ). In this way, each TTZ will present a single tariff to be applied to its participants, which will correspond to the weighted average of the final tariffs calculated via the nodal methodology. For the identification of the TTZ, k-Means and Spectral clustering techniques are applied to the IEEE-RTS and SIN systems. In this dissertation, the use of mathematical models is also assessed to define the ideal number of TTZ to be considered. Various sensitivity analyses are carried out regarding changes in dispatch, grid topology and expansion of the system over the years. The corresponding results are deeply discussed.

Submitted by Johnny Rodrigues (johnnyrodrigues@ufcg.edu.br) on 2018-05-02T13:28:09Z No. of bitstreams: 1 DANILO ABREU SANTOS - DISSERTAÇÃO PPGCC 2015..pdf: 2955839 bytes, checksum: 45290d85cdffbae0320f29fc5e633cb6 (MD5) / Made available in DSpace on 2018-05-02T13:28:09Z (GMT). No. of bitstreams: 1 DANILO ABREU SANTOS - DISSERTAÇÃO PPGCC 2015..pdf: 2955839 bytes, checksum: 45290d85cdffbae0320f29fc5e633cb6 (MD5) Previous issue date: 2015-08-24 / A modalidade de educação online tem crescido significativamente nas últimas décadas em todo o mundo, transformando-se em uma opção viável tanto àqueles que não dispõem de tempo para trabalhar a sua formação acadêmica na forma presencial quanto àqueles que desejam complementá-la. Há também os que buscam ingressar no ensino superior por meio do Exame Nacional do Ensino Médio (ENEM) e utilizam esta modalidade de ensino para complementar os estudos, objetivando sanar lacunas deixadas pela formação escolar. O ENEM é composto por questões objetivas (subdivididas em 4 grandes áreas: Linguagens e Códigos; Matemática; Ciências Humanas; e Ciências Naturais) e a questão subjetiva (redação). Segundo dados do Ministério da Educação (MEC), mais de 50% dos candidatos que fizeram a prova do ENEM em 2014 obtiveram desempenho abaixo de 500 pontos na redação. Esta pesquisa utilizará recomendações pedagógicas baseadas no gênero textual utilizado pelo ENEM, visando prover uma melhoria na escrita da redação dissertativa. Para tanto, foi utilizado, como ferramenta experimental, o ambiente online de aprendizagem MeuTutor. O ambiente possui um módulo de escrita de redação, no qual é utilizada para correção dos textos elaborados pelos alunos, a metodologia de avaliação por pares, cujo pesquisas mostram que os resultados avaliativos são significativos e bastante similares aos obtidos por professores especialistas. Entretanto, apenas apresentar a pontuação da redação por si só, não garante a melhora da produção textual do aluno avaliado. Desta forma, visando um ganho em performance na produção da redação, foi adicionado ao MeuTutor um módulo de recomendação pedagógica baseado em 19 perfis resultados do uso de algoritmos de mineração de dados (DBScan e Kmeans) nos microdados do ENEM 2012 disponibilizado pelo MEC. Estes perfis foram agrupados em 6 blocos que possuíam um conjunto de tarefas nas áreas de escrita, gramática e coerências e concordância textual. A validação destas recomendações foi feita em um experimento de 3 ciclos, onde em cada ciclo o aluno: escreve a redação; avalia os seus pares; realiza a recomendação pedagógica que foi recebida. A partir da análise estatística destes dados, foi possível constatar que o modelo estratégico de recomendação utilizado nesta pesquisa, possibilitou um ganho mensurável na qualidade da produção textual. / Online education has grown significantly in recent years throughout the world, becoming a viable option for those who don’t have the time to pursuit traditional technical training or academic degree. In Brazil, people seek to enter higher education through the National Secondary Education Examination (ENEM) and use online education to complement their studies, aiming to remedy gaps in their school formation. The ENEM consists of objective questions (divided into 4 main areas: languages and codes; Mathematics; Social Sciences, and Natural Sciences), and the subjective questions (the essay). According to the Brazilian Department of Education (MEC), more than 50% of the candidates who took the test (ENEM) in 2014, obtained performance below 500 points (out of a 1000 maximum points) for their essays. This research uses educational recommendations based on the five official correction criteria for the ENEM essays, to improve writing. Thus, this research used an experimental tool in an online learning environment called MeuTutor. The mentioned learning environment has an essay writing/correction module. The correction module uses peer evaluation techniques, for which researches show that the results are, significantly, similar to those obtained by specialists’ correction. However, to simply display the scores for the criteria does not guarantee an improvement in students’ writing. Thus, to promote that, an educational recommendation module was added to MeuTutor. It is based on 19 profiles obtained mining data from the 2012 ENEM. It uses the algorithms DBSCAN and K-Means, and grouped the profiles into six blocks, to which a set of tasks were associated to the areas of writing, grammar and coherence, and textual agreement. The validation of these recommendations was made in an experiment with three cycles, where students should: (1) write the essay; (2) evaluate their peers; (3) perform the pedagogical recommendations received. From the analysis of these data, it was found that the strategic model of recommendation used in this study, enabled a measurable gain in quality of textual production.

The mental health concerns in Sweden have been increasing since the beginning of the 2000’s, where Skåne County in the southern parts of Sweden has shown to be slightly higher in a proportion of reported cases in comparison to other regions. To address the growing need for psychiatric healthcare, the health services of the region of Skåne (Region Skåne) have introduced a psychiatric ambulance unit as a part of first responders. The Prehospital Acute Psychiatry (PAP) ambulance is manned by health care professionals trained in mental health issues. The goal of Region Skåne is to reach 90% of the population within 20 minutes and 99% of the population within 35 minutes. This case study aims to provide valuable and useful information to the decision-makers in Region Skåne when placing additional PAP ambulance units. A PAP ambulance placed in an ambulance station uses an optimisation model previously created and is based on data and travel time analysis. The data analysis consists of K-Means clustering and Linear regression, to find similarities in the data as well as trends in the number of cases. The travel time analysis and the area and population coverage is based on 20, 35, and 60 minutes travel time from a station. The travel time is dependent on the road conditions as well as population density when considering ambulance stations for additional PAP ambulances. Malmö, Helsingborg, and Kristianstad/Hässleholm PAP stations are shown to be optimal choices, due to favourable road conditions and densely populated regions. Ambulances placed in these stations can cover much ground while also being able to attend to a major portion of the population. The data analysis also shows that it is beneficial to place ambulances in these stations, due to an increasing trend of mental illness cases in these areas symbolising a medium to a high number of cases in relation to the rest of Skåne.