Smale flows on S2×S1\mathbb{S}^2\times\mathbb{S}^1

In this paper, we use abstract Lyapunov graphs as a combinatorial tool to obtain a complete classification of Smale flows on $\mathbb{S}^2\times\mathbb{S}^1$. This classification gives necessary and sufficient conditions that must be satisfied by an abstract Lyapunov graph in order for it to be associated to a Smale flow on $\mathbb{S}^2\times\mathbb{S}^1$

Atividades lúdicas como elementos mediadores da aprendizagem no ensino de ciências da natureza

Este relato focaliza os resultados obtidos na aplicação de um jogo de tabuleiro, o Autódromo Alquímico. O estuo foi realizado com alunos do 9o ano do Ensino Fundamental em escolas da Rede Privada do Município de Itabuna (Ba, Brasil), com o objetivo de verificar o papel dessas dinâmicas como recurso mediador/facilitador da construção do conhecimento pelos sujeitos envolvidos no processo. Observamos que cerca de 90% dos sujeitos do processo aprenderam os conteúdos abordados. Além disso, foram desenvolvidas habilidades importantes para a constituição de um sujeito capaz do exercício ativo de sua cidadania, tais como respeito a identidades e diferenças; inter-relação de pensamentos, idéias e conceitos; aumento da criatividade e da capacidade de argumentação

Uso de óleos essenciais de cravo-da-índia, melaleuca, eucalipto e menta na sedação de peixe.

Com objetivo de avaliar o efeito no comportamento, tempo para sedação e tempo para recuperação de soluções preparadas com óleos essenciais extraídos de quatro tipos vegetais, com a finalidade de reduzir estresse dos peixes.Organizado por: Sílvio Ricardo Maurano; AQUACIÊNCIA 2012

Doses e épocas de aplicação de fosfito comercial na cultura da soja.

Nesse trabalho o objetivo foi avaliar o efeito de diferentes doses de fosfito comercial em diferentes estádios do ciclo cultural da soja

Contribution of biomass fires to black carbon supply in a tropical river basin assessed using a Lagrangian atmospheric transport model and MODIS burned area product

Black carbon (BC) is known to be a potential sink of carbon for the global carbon cycle, particularly if long-term ocean stores are reached. Fluvial transport to the oceans can occur through the dissolution of BC in river water. Evidence from the Paraiba do Sul river basin, Brazil suggests that river DBC concentration is related to charcoal formed during the deforestation of the Brazilian Atlantic Forest. However, we highlight several key potential sources of BC to the basin that are yet to be considered. We hypothesize that external biomass fires are a source of BC to the basin on the basis that BC released from them can be transported over large distances before being deposited. This hypothesis is tested by quantifying the number of biomass fires intercepted by trajectories en route to the basin using the HYSPLIT model and a MODIS burned area dataset. We then create a Black Carbon Fallout Index (BCFI) which is rationalized by our assumption that atmospheric BC delivery to the basin is proportional to the number of interceptions of air masses en route to the basin. Our results suggest that the BC fallout from air masses reaching the basin in the dry season can explain 50% of the variance in DBC measured in the PSR channel during a subsequent collection campaign (p<.001). Spatial and temporal variations in the supply of BC to the basin throughout the dry season may in part be linked to the fires associated with the cultivation of sugarcane in southeast Brazil

The SpatialCIM methodology for spatial document coverage disambiguation and the entity recognition process aided by linguistic techniques.

Abstract. Nowadays it is becoming more usual for users to take into account the geographical localization of the documents in the retrieval information process. However, the conventional retrieval information systems based on key-word matching do not consider which words can represent geographical entities that are spatially related to other entities in the document. This paper presents the SpatialCIM methodology, which is based on three steps: pre-processing, data expansion and disambiguation. In the pre-processing step, the entity recognition process is carried out with the support of the Rembrandt tool. Additionally, a comparison between the performances regarding the discovery of the location entities in the texts of the Rembrandt tool against the use of a controlled vocabulary corresponding to the Brazilian geographic locations are presented. For the comparison a set of geographic labeled news covering the sugar cane culture in the Portuguese language is used. The results showed a F-measure value increase for the Rembrandt tool from 45% in the non-disambiguated process to 0.50 after disambiguation and from 35% to 38% using the controlled vocabulary. Additionally, the results showed the Rembrandt tool has a minimal amplitude difference between precision and recall, although the controlled vocabulary has always the biggest recall values.GeoDoc 2012, PAKDD 2012

Empirical methods for estimating reference surface net radiation from solar radiation.

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Advances in machine-learning-based sampling motivated by lattice quantum chromodynamics

Sampling from known probability distributions is a ubiquitous task in computational science, underlying calculations in domains from linguistics to biology and physics. Generative machine-learning (ML) models have emerged as a promising tool in this space, building on the success of this approach in applications such as image, text, and audio generation. Often, however, generative tasks in scientific domains have unique structures and features -- such as complex symmetries and the requirement of exactness guarantees -- that present both challenges and opportunities for ML. This Perspective outlines the advances in ML-based sampling motivated by lattice quantum field theory, in particular for the theory of quantum chromodynamics. Enabling calculations of the structure and interactions of matter from our most fundamental understanding of particle physics, lattice quantum chromodynamics is one of the main consumers of open-science supercomputing worldwide. The design of ML algorithms for this application faces profound challenges, including the necessity of scaling custom ML architectures to the largest supercomputers, but also promises immense benefits, and is spurring a wave of development in ML-based sampling more broadly. In lattice field theory, if this approach can realize its early promise it will be a transformative step towards first-principles physics calculations in particle, nuclear and condensed matter physics that are intractable with traditional approaches.Comment: 11 pages, 5 figure