On the Hamilton-Waterloo problem

The Hamilton-Waterloo problem asks for a 2-factorisation of K-v in which r of the 2-factors consist of cycles of lengths a(1), a(2),..., a(1) and the remaining s 2-factors consist of cycles of lengths b(1), b(2),..., b(u) (where necessarily Sigma(i)(=1)(t) a(i) = Sigma(j)(=1)(u) b(j) = v). In thus paper we consider the Hamilton-Waterloo problem in the case a(i) = m, 1 less than or equal to i less than or equal to t and b(j) = n, 1 less than or equal to j less than or equal to u. We obtain some general constructions, and apply these to obtain results for (m, n) is an element of {(4, 6)1(4, 8), (4, 16), (8, 16), (3, 5), (3, 15), (5, 15)}.

“Confessionários or Manuals of Confession: Missionary tools and their colonial uses – The case study of Goa

Existem referências aos manuais de bem confessar que os Jesuitas utilizaram na Índia desde os inícios da sua actividade missionária, mas até agora não se tinha publicado nenhum para os séculos XVI-XVII. Encontrei alguns na British Library em Londres em 1994, e estão aqui analisados, dando a conhecer como a nova religião ajudava a criar cidadãos responsáveis do império colonial e a cumprir as suas leis. Para além de ajudar-nos a compreender o vocabulário e o estilo da língua vernácula destes tempos, alguém que evitasse pagar impostos ao Estado ou manipulasse os livros de contas da aldeia encorria em pecados a confessar.

ANN-Based LMP forecasting in a distribution network with large penetration of DG

In recent years, power systems have experienced many changes in their paradigm. The introduction of new players in the management of distributed generation leads to the decentralization of control and decision-making, so that each player is able to play in the market environment. In the new context, it will be very relevant that aggregator players allow midsize, small and micro players to act in a competitive environment. In order to achieve their objectives, virtual power players and single players are required to optimize their energy resource management process. To achieve this, it is essential to have financial resources capable of providing access to appropriate decision support tools. As small players have difficulties in having access to such tools, it is necessary that these players can benefit from alternative methodologies to support their decisions. This paper presents a methodology, based on Artificial Neural Networks (ANN), and intended to support smaller players. In this case the present methodology uses a training set that is created using energy resource scheduling solutions obtained using a mixed-integer linear programming (MIP) approach as the reference optimization methodology. The trained network is used to obtain locational marginal prices in a distribution network. The main goal of the paper is to verify the accuracy of the ANN based approach. Moreover, the use of a single ANN is compared with the use of two or more ANN to forecast the locational marginal price.

ANN based support for distributed energy resources scheduling in smart grids

The future scenarios for operation of smart grids are likely to include a large diversity of players, of different types and sizes. With control and decision making being decentralized over the network, intelligence should also be decentralized so that every player is able to play in the market environment. In the new context, aggregator players, enabling medium, small, and even micro size players to act in a competitive environment, will be very relevant. Virtual Power Players (VPP) and single players must optimize their energy resource management in order to accomplish their goals. This is relatively easy to larger players, with financial means to have access to adequate decision support tools, to support decision making concerning their optimal resource schedule. However, the smaller players have difficulties in accessing this kind of tools. So, it is required that these smaller players can be offered alternative methods to support their decisions. This paper presents a methodology, based on Artificial Neural Networks (ANN), intended to support smaller players’ resource scheduling. The used methodology uses a training set that is built using the energy resource scheduling solutions obtained with a reference optimization methodology, a mixed-integer non-linear programming (MINLP) in this case. The trained network is able to achieve good schedule results requiring modest computational means.

Contextual intelligent load management with ANN adaptive learning module

With the current increase of energy resources prices and environmental concerns intelligent load management systems are gaining more and more importance. This paper concerns a SCADA House Intelligent Management (SHIM) system that includes an optimization module using deterministic and genetic algorithm approaches. SHIM undertakes contextual load management based on the characterization of each situation. SHIM considers available generation resources, load demand, supplier/market electricity price, and consumers’ constraints and preferences. The paper focus on the recently developed learning module which is based on artificial neural networks (ANN). The learning module allows the adjustment of users’ profiles along SHIM lifetime. A case study considering a system with fourteen discrete and four variable loads managed by a SHIM system during five consecutive similar weekends is presented.

ANN based day-ahead spinning reserve forecast for electricity market simulation

Electricity market players operating in a liberalized environment require adequate decision support tools, allowing them to consider all the business opportunities and take strategic decisions. Ancillary services represent a good negotiation opportunity that must be considered by market players. This paper deals with short-term predication of day-ahead spinning reserve (SR) requirement that helps the ISO to make effective and timely decisions. Based on these forecasted information, market participants can use strategic bidding for day-ahead SR market. The proposed concepts and methodologies are implemented in MASCEM, a multi-agent based electricity market simulator. A case study based on California ISO (CAISO) data is included; the forecasted results are presented and compared with CAISO published forecast.

ANN based day-ahead ancillary services forecast for electricity market simulation

Adequate decision support tools are required by electricity market players operating in a liberalized environment, allowing them to consider all the business opportunities and take strategic decisions. Ancillary services (AS) represent a good negotiation opportunity that must be considered by market players. Based on the ancillary services forecasting, market participants can use strategic bidding for day-ahead ancillary services markets. For this reason, ancillary services market simulation is being included in MASCEM, a multi-agent based electricity market simulator that can be used by market players to test and enhance their bidding strategies. The paper presents the methodology used to undertake ancillary services forecasting, based on an Artificial Neural Network (ANN) approach. ANNs are used to day-ahead prediction of non-spinning reserve (NS), regulation-up (RU), and regulation down (RD). Spinning reserve (SR) is mentioned as past work for comparative analysis. A case study based on California ISO (CAISO) data is included; the forecasted results are presented and compared with CAISO published forecast.

Inventário de Depressão de Beck (BDI) e Escala de Avaliação de Depressão de Hamilton (HAM-D) em pacientes com epilepsia

OBJETIVO: Determinar os pontos de melhor sensibilidade e especificidade do Inventário de Depressão de Beck (BDI) e da Escala de Avaliação de Depressão de Hamilton (HAM-D) no diagnóstico de depressão associada à epilepsia. MÉTODOS: Setenta e três pacientes de um centro de referência no tratamento da epilepsia foram submetidos à avaliação neuropsiquiátrica. Foram colhidos dados clínicos e sociodemográficos, sendo utilizados os seguintes instrumentos: entrevista clínica estruturada (MINI-PLUS) para diagnóstico psiquiátrico conforme o DSM-IV, HAM-D e BDI. RESULTADOS: No momento da entrevista, 27,4% dos pacientes estavam deprimidos e 37% preenchiam critérios para diagnóstico de depressão maior ao longo da vida. A análise da curva ROC indicou que o ponto de corte em 16 (> 16) para o BDI (sensibilidade de 94,4%, especificidade de 90,6%) e em 16 (> 16) para a HAM-D (sensibilidade de 95%, especificidade de 75,5%) representou dicotomização ótima entre deprimidos e não deprimidos. Ambos os instrumentos apresentaram um valor preditivo negativo superior a 95%. CONCLUSÃO: A frequência de depressão maior é elevada em pacientes com epilepsia. BDI e a HAM-D podem auxiliar o clínico na identificação da depressão associada à epilepsia, diminuindo seu subdiagnóstico.

Escala Hamilton: estudo das características psicométricas em uma amostra do sul do Brasil

Objetivo Investigar as características psicométricas de uma versão traduzida da escala, propondo uma Versão Revisada que atenda aos critérios de adaptação transcultural para o contexto brasileiro. Métodos Este estudo incluiu 231 sujeitos – deprimidos (45,5%), bipolares (7,8%) e saudáveis (46,7%) – que participaram de uma pesquisa epidemiológica no sul do Brasil. A avaliação de transtornos mentais foi realizada por meio da Clinical Interview for DSM-IV (SCID) e uma versão traduzida da Escala de Avaliação de Depressão de Hamilton (HAM-D), que habitualmente vem sendo utilizada no país sem estudos de adaptação. Resultados Identificou-se o ponto de corte (9 pontos) para discriminar a presença ou não de sintomas de depressão pela análise da curva ROC, resultando em uma sensibilidade e especificidade de 90 e 91%, respectivamente. A validade interna foi investigada pela análise fatorial e consistência dos itens. Dos 17 itens originais, apenas o item que avalia a “consciência do transtorno” não apresentou carga fatorial satisfatória para avaliar depressão geral e foi eliminado; os 16 restantes agruparam-se em cinco dimensões, denominadas: Humor deprimido, Anorexia, Insônia, Somatização e Ansiedade, as quais, com exceção da última, mostraram homogeneidade nos seus construtos (coeficientes alfa entre 0,66 e 0,78). Na análise de conteúdo dos itens, cinco especialistas sugeriram alterações redacionais em sete itens. Conclusão O estudo determina um ponto de corte diferente do original e evidencia características psicométricas favoráveis para a utilização da escala no Brasil.

The Hamilton fauna; a late Pliocene mammalian fauna from the Grange Burn, Victoria, Australia / [by] William D. Turnbull -- and Ernest L. Lundelius, Jr. --

v.19(1970)

The Macropodoidea (Marsupialia) of the early Pliocene Hamilton local fauna, Victoria, Australia / Timothy F. Flannery --, et al.

n.s. no.25(1992)