Aligned electric and magnetic Weyl fields

We analyze the spacetimes admitting a direction for which the relative electric and magnetic Weyl fields are aligned. We give an invariant characterization of these metrics and study the properties of its Debever null vectors. The directions 'observing' aligned electric and magnetic Weyl fields are obtained for every Petrov type. The results on the no existence of purely magnetic solutions are extended to the wider class having homothetic electric and magnetic Weyl fields.Comment: 14 page

O que Dinâmica Não Linear pode nos ensinar sobre Comunidades Planctônicas?

Toda a nossa ciência moderna se desenvolveu basicamente sobre um paradigma de mundo que pode ser denominado de paradigma linear de acordo com a definição de paradigma de Thomas Kuhn (1962). Dentro deste paradigma os efeitos são sempre proporcionais às causas, de maneira tal que causas de intensidade pequena geram efeitos finais pequenos. A grande maioria das teorias e modelos construídos em toda a biologia foi estruturada sob esta ótica. A partir do surgimento e crescimento do poder computacional nos últimos 50 anos surgiu um novo paradigma, denominado paradigma não linear ou ciência não linear. Dentro desta nova ótica, efeitos não são proporcionais à intensidade das causas, e prevalece na maioria dos casos uma dinâmica de não equilíbrio regendo os fenômenos. O presente artigo tem por foco questões de dinâmica de comunidades planctônicas que ganharam novas formas de entendimento a partir deste paradigma, sem fazer uso de uma matemática mais densa e elaborada na exposição das questões, buscando torná-las assim mais acessíveis em sua essência. Questões como o paradoxo do plâncton de Hutchinson (1961), que pareciam insolúveis dentro de um paradigma de mundo linear, tornam-se melhor tratáveis a partir de métodos não lineares, que apontam também novas formas de manejar e se entender comunidades planctônicas de elevada riqueza e o fenômeno de blooms algais. Em decorrência das questões referidas, aborda-se no presente artigo três “lições” oriundas deste novo paradigma, possivelmente bastante úteis para o entendimento e manejo de comunidades planctônicas.Palavras Chave: Caos; Manejo; Comunidades Planctônicas; Não Linearidade; Blooms Algais.ABSTRACTWhat Nonlinear Dynamics can teach us about plankton communities?All our modern science has developed largely on world paradigm that can be called linear paradigm, according to the definition of Thomas Kuhn (1962). Within this paradigm the effects are always proportional to the causes, in a manner that small intensity causes generate small end effects. The vast majority of theories and models built in biology has been structured under this point of view. From the emergence and growth of computing power in the last fifty years has emerged a new paradigm called nonlinear paradigm or nonlinear science. Within this new perspective, effects are not proportional to the intensity of the causes, and prevails in most cases a non-equilibrium dynamics governing the phenomena. This article focuses on questions about planktonic communities that have gained new ways of understanding from this paradigm, without using a dense and elaborated mathematics for the exposition of the issues, trying to make them more accessible in their essence. Questions such as the plankton paradox of Hutchinson (1961), which seemed insoluble within a linear world paradigm, ones become better treatable from nonlinear methods that also suggest new ways to manage and to understand high species richness planktonic communities and the phenomenon of algal blooms. As a result of the above issues, three experiences arising out this new paradigm will be explored, which can be quite useful for the understanding and management of real planktonic communities.Key Words: Chaos; Management; Planktonic communities; Non Linearity; Algal Blooms

Advances of Robust Subspace Face Recognition

Face recognition has been widely applied in fast video surveillance and security systems and smart home services in our daily lives. Over past years, subspace projection methods, such as principal component analysis (PCA), linear discriminant analysis (LDA), are the well-known algorithms for face recognition. Recently, linear regression classification (LRC) is one of the most popular approaches through subspace projection optimizations. However, there are still many problems unsolved in severe conditions with different environments and various applications. In this chapter, the practical problems including partial occlusion, illumination variation, different expression, pose variation, and low resolution are addressed and solved by several improved subspace projection methods including robust linear regression classification (RLRC), ridge regression (RR), improved principal component regression (IPCR), unitary regression classification (URC), linear discriminant regression classification (LDRC), generalized linear regression classification (GLRC) and trimmed linear regression (TLR). Experimental results show that these methods can perform well and possess high robustness against problems of partial occlusion, illumination variation, different expression, pose variation and low resolution

Soil–plant interactions in a pasture of the Italian Alps

A detailed assessment of a pasture\u2019s functioning based on soil properties characterization, floristic composition, and \u2018functional summary\u2019 by evaluating competitor\u2013stress tolerator\u2013ruderal (CSR) strategies is provided for a doline in Central Italian Alps. A floristic survey was carried out at 35 sampling points, representative of the main topographic features, soil and vegetation types; the functional profile at the community level was evaluated by assessing for each species its Grime\u2019s CSR strategy; each point was characterized through soil profiles and topsoil (0\u201310 cm) sampling; pH, soil organic carbon and total nitrogen, available P, soil humus fraction, root density, bulk density, water content, and available water capacity were determined. Our study showed i) a strong relationship between vegetation, soil properties, topography, and grazing; ii) a prevalence of stress-tolerant strategies; iii) the ability of plant strategy variation to reflect the ecological parameters; and iv) the vegetation potentiality to be an indicator of environmental spatial variability

Prediction and statistics of pseudoknots in RNA structures using exactly clustered stochastic simulations

Ab initio RNA secondary structure predictions have long dismissed helices interior to loops, so-called pseudoknots, despite their structural importance. Here, we report that many pseudoknots can be predicted through long time scales RNA folding simulations, which follow the stochastic closing and opening of individual RNA helices. The numerical efficacy of these stochastic simulations relies on an O(n^2) clustering algorithm which computes time averages over a continously updated set of n reference structures. Applying this exact stochastic clustering approach, we typically obtain a 5- to 100-fold simulation speed-up for RNA sequences up to 400 bases, while the effective acceleration can be as high as 100,000-fold for short multistable molecules (<150 bases). We performed extensive folding statistics on random and natural RNA sequences, and found that pseudoknots are unevenly distributed amongst RNAstructures and account for up to 30% of base pairs in G+C rich RNA sequences (Online RNA folding kinetics server including pseudoknots : http://kinefold.u-strasbg.fr/ ).Comment: 6 pages, 5 figure