Processes controlling atmospheric dispersion through city centres

We develop a process-based model for the dispersion of a passive scalar in the turbulent flow around the buildings of a city centre. The street network model is based on dividing the airspace of the streets and intersections into boxes, within which the turbulence renders the air well mixed. Mean flow advection through the network of street and intersection boxes then mediates further lateral dispersion. At the same time turbulent mixing in the vertical detrains scalar from the streets and intersections into the turbulent boundary layer above the buildings. When the geometry is regular, the street network model has an analytical solution that describes the variation in concentration in a near-field downwind of a single source, where the majority of scalar lies below roof level. The power of the analytical solution is that it demonstrates how the concentration is determined by only three parameters. The plume direction parameter describes the branching of scalar at the street intersections and hence determines the direction of the plume centreline, which may be very different from the above-roof wind direction. The transmission parameter determines the distance travelled before the majority of scalar is detrained into the atmospheric boundary layer above roof level and conventional atmospheric turbulence takes over as the dominant mixing process. Finally, a normalised source strength multiplies this pattern of concentration. This analytical solution converges to a Gaussian plume after a large number of intersections have been traversed, providing theoretical justification for previous studies that have developed empirical fits to Gaussian plume models. The analytical solution is shown to compare well with very high-resolution simulations and with wind tunnel experiments, although re-entrainment of scalar previously detrained into the boundary layer above roofs, which is not accounted for in the analytical solution, is shown to become an important process further downwind from the source.

Nonlinear equalization of Hammerstein OFDM systems

A practical orthogonal frequency-division multiplexing (OFDM) system can generally be modelled by the Hammerstein system that includes the nonlinear distortion effects of the high power amplifier (HPA) at transmitter. In this contribution, we advocate a novel nonlinear equalization scheme for OFDM Hammerstein systems. We model the nonlinear HPA, which represents the static nonlinearity of the OFDM Hammerstein channel, by a B-spline neural network, and we develop a highly effective alternating least squares algorithm for estimating the parameters of the OFDM Hammerstein channel, including channel impulse response coefficients and the parameters of the B-spline model. Moreover, we also use another B-spline neural network to model the inversion of the HPA’s nonlinearity, and the parameters of this inverting B-spline model can easily be estimated using the standard least squares algorithm based on the pseudo training data obtained as a byproduct of the Hammerstein channel identification. Equalization of the OFDM Hammerstein channel can then be accomplished by the usual one-tap linear equalization as well as the inverse B-spline neural network model obtained. The effectiveness of our nonlinear equalization scheme for OFDM Hammerstein channels is demonstrated by simulation results.

Collocating satellite-based radar and radiometer measurements — methodology and usage examples

Collocations between two satellite sensors are occasions where both sensors observe the same place at roughly the same time. We study collocations between the Microwave Humidity Sounder (MHS) on-board NOAA-18 and the Cloud Profiling Radar (CPR) on-board CloudSat. First, a simple method is presented to obtain those collocations and this method is compared with a more complicated approach found in literature. We present the statistical properties of the collocations, with particular attention to the effects of the differences in footprint size. For 2007, we find approximately two and a half million MHS measurements with CPR pixels close to their centrepoints. Most of those collocations contain at least ten CloudSat pixels and image relatively homogeneous scenes. In the second part, we present three possible applications for the collocations. Firstly, we use the collocations to validate an operational Ice Water Path (IWP) product from MHS measurements, produced by the National Environment Satellite, Data and Information System (NESDIS) in the Microwave Surface and Precipitation Products System (MSPPS). IWP values from the CloudSat CPR are found to be significantly larger than those from the MSPPS. Secondly, we compare the relation between IWP and MHS channel 5 (190.311 GHz) brightness temperature for two datasets: the collocated dataset, and an artificial dataset. We find a larger variability in the collocated dataset. Finally, we use the collocations to train an Artificial Neural Network and describe how we can use it to develop a new MHS-based IWP product. We also study the effect of adding measurements from the High Resolution Infrared Radiation Sounder (HIRS), channels 8 (11.11 μm) and 11 (8.33 μm). This shows a small improvement in the retrieval quality. The collocations described in the article are available for public use.

Searching for s-Process-Enhanced Metal-Poor Stars

We present preliminary results for the estimation of barium [Ba/Fe], and strontium [Sr/Fe], abundances ratios using medium-resolution spectra (1-2 angstrom). We established a calibration between the abundance ratios and line indices for Ba and Sr, using multiple regression and artificial neural network techniques. A comparison between the two techniques (showing the advantage of the latter), as well as a discussion of future work, is presented.

MODELING THE EVOLUTION OF COMPLEX NETWORKS THROUGH THE PATH-STAR TRANSFORMATION AND OPTIMAL MULTIVARIATE METHODS

The topology of real-world complex networks, such as in transportation and communication, is always changing with time. Such changes can arise not only as a natural consequence of their growth, but also due to major modi. cations in their intrinsic organization. For instance, the network of transportation routes between cities and towns ( hence locations) of a given country undergo a major change with the progressive implementation of commercial air transportation. While the locations could be originally interconnected through highways ( paths, giving rise to geographical networks), transportation between those sites progressively shifted or was complemented by air transportation, with scale free characteristics. In the present work we introduce the path-star transformation ( in its uniform and preferential versions) as a means to model such network transformations where paths give rise to stars of connectivity. It is also shown, through optimal multivariate statistical methods (i.e. canonical projections and maximum likelihood classification) that while the US highways network adheres closely to a geographical network model, its path-star transformation yields a network whose topological properties closely resembles those of the respective airport transportation network.

Modeling worldwide highway networks

This Letter addresses the problem of modeling the highway systems of different countries by using complex networks formalism. More specifically, we compare two traditional geographical models with a modified geometrical network model where paths, rather than edges, are incorporated at each step between the origin and the destination vertices. Optimal configurations of parameters are obtained for each model and used for the comparison. The highway networks of Australia, Brazil, India, and Romania are considered and shown to be properly modeled by the modified geographical model. (C) 2009 Elsevier B.V. All rights reserved.

On the effects of geographical constraints on task execution in complex

In the present work, the effects of spatial constraints on the efficiency of task execution in systems underlain by geographical complex networks are investigated, where the probability of connection decreases with the distance between the nodes. The investigation considers several configurations of the parameters defining the network connectivity, and the Barabasi-Albert network model is also considered for comparisons. The results show that the effect of connectivity is significant only for shorter tasks, the locality of connection simplied by the spatial constraints reduces efficiency, and the addition of edges can improve the efficiency of the execution, although with increasing locality of the connections the improvement is small.

A WAVELET-BASED SPEAKER VERIFICATION ALGORITHM

This paper presents a study on wavelets and their characteristics for the specific purpose of serving as a feature extraction tool for speaker verification (SV), considering a Radial Basis Function (RBF) classifier, which is a particular type of Artificial Neural Network (ANN). Examining characteristics such as support-size, frequency and phase responses, amongst others, we show how Discrete Wavelet Transforms (DWTs), particularly the ones which derive from Finite Impulse Response (FIR) filters, can be used to extract important features from a speech signal which are useful for SV. Lastly, an SV algorithm based on the concepts presented is described.

Comparison Of Demand Pattern Calibration In Water Distribution Networks With Geographic And Non-Geographic Parameterization

Demands are one of the most uncertain parameters in a water distribution network model. A good calibration of the model demands leads to better solutions when using the model for any purpose. A demand pattern calibration methodology that uses a priori information has been developed for calibrating the behaviour of demand groups. Generally, the behaviours of demands in cities are mixed all over the network, contrary to smaller villages where demands are clearly sectorised in residential neighbourhoods, commercial zones and industrial sectors. Demand pattern calibration has a final use for leakage detection and isolation. Detecting a leakage in a pattern that covers nodes spread all over the network makes the isolation unfeasible. Besides, demands in the same zone may be more similar due to the common pressure of the area rather than for the type of contract. For this reason, the demand pattern calibration methodology is applied to a real network with synthetic non-geographic demands for calibrating geographic demand patterns. The results are compared with a previous work where the calibrated patterns were also non-geographic.

Prediction Of Hydrological Models’ Uncertainty By A Committee Of Machine Learning-Models

This study presents an approach to combine uncertainties of the hydrological model outputs predicted from a number of machine learning models. The machine learning based uncertainty prediction approach is very useful for estimation of hydrological models' uncertainty in particular hydro-metrological situation in real-time application [1]. In this approach the hydrological model realizations from Monte Carlo simulations are used to build different machine learning uncertainty models to predict uncertainty (quantiles of pdf) of the a deterministic output from hydrological model . Uncertainty models are trained using antecedent precipitation and streamflows as inputs. The trained models are then employed to predict the model output uncertainty which is specific for the new input data. We used three machine learning models namely artificial neural networks, model tree, locally weighted regression to predict output uncertainties. These three models produce similar verification results, which can be improved by merging their outputs dynamically. We propose an approach to form a committee of the three models to combine their outputs. The approach is applied to estimate uncertainty of streamflows simulation from a conceptual hydrological model in the Brue catchment in UK and the Bagmati catchment in Nepal. The verification results show that merged output is better than an individual model output. [1] D. L. Shrestha, N. Kayastha, and D. P. Solomatine, and R. Price. Encapsulation of parameteric uncertainty statistics by various predictive machine learning models: MLUE method, Journal of Hydroinformatic, in press, 2013.

Leakage Detection And Localization Method Based On A Hybrid Inverse/Direct Modelling Approach Suitable For Handling Multiple-Leak Scenarios

When an accurate hydraulic network model is available, direct modeling techniques are very straightforward and reliable for on-line leakage detection and localization applied to large class of water distribution networks. In general, this type of techniques based on analytical models can be seen as an application of the well-known fault detection and isolation theory for complex industrial systems. Nonetheless, the assumption of single leak scenarios is usually made considering a certain leak size pattern which may not hold in real applications. Upgrading a leak detection and localization method based on a direct modeling approach to handle multiple-leak scenarios can be, on one hand, quite straightforward but, on the other hand, highly computational demanding for large class of water distribution networks given the huge number of potential water loss hotspots. This paper presents a leakage detection and localization method suitable for multiple-leak scenarios and large class of water distribution networks. This method can be seen as an upgrade of the above mentioned method based on a direct modeling approach in which a global search method based on genetic algorithms has been integrated in order to estimate those network water loss hotspots and the size of the leaks. This is an inverse / direct modeling method which tries to take benefit from both approaches: on one hand, the exploration capability of genetic algorithms to estimate network water loss hotspots and the size of the leaks and on the other hand, the straightforwardness and reliability offered by the availability of an accurate hydraulic model to assess those close network areas around the estimated hotspots. The application of the resulting method in a DMA of the Barcelona water distribution network is provided and discussed. The obtained results show that leakage detection and localization under multiple-leak scenarios may be performed efficiently following an easy procedure.

Aplicação de redes neurais na classificação de rentabilidade futura de empresas

Este trabalho tem por motivação evidenciar a eficiência de redes neurais na classificação de rentabilidade futura de empresas, e desta forma, prover suporte para o desenvolvimento de sistemas de apoio a tomada de decisão de investimentos. Para serem comparados com o modelo de redes neurais, foram escolhidos o modelo clássico de regressão linear múltipla, como referência mínima, e o de regressão logística ordenada, como marca comparativa de desempenho (benchmark). Neste texto, extraímos dados financeiros e contábeis das 1000 melhores empresas listadas, anualmente, entre 1996 e 2006, na publicação Melhores e Maiores – Exame (Editora Abril). Os três modelos foram construídos tendo como base as informações das empresas entre 1996 e 2005. Dadas as informações de 2005 para estimar a classificação das empresas em 2006, os resultados dos três modelos foram comparados com as classificações observadas em 2006, e o modelo de redes neurais gerou o melhor resultado.

A formação de rede para o atendimento de desastres de massa: o caso do acidente aéreo do voo 447 da Air France

Este estudo teve por objetivo verificar como as características do modelo de rede se manifestam em resposta a desastres de massa, por meio de um estudo de caso. O objeto dessa pesquisa foi o acidente aéreo do voo 447 da Air France, que aconteceu em meados de 2009, no Oceano Atlântico em área próxima ao arquipélago de São Pedro e São Paulo, circunscrição do estado de Pernambuco. A rede formada para a resposta a esse acidente foi composta pela Aeronáutica, Marinha, Polícia Federal, Secretaria de Defesa Social de Pernambuco, INTERPOL e Secretaria de Estado da Segurança e da Defesa Social da Paraíba, tendo ainda a participação de outras instituições, que forneceram suporte logístico de pessoas e recursos em geral para a missão. As atividades desenvolvidas durante o evento compreenderam busca e resgate dos corpos, sob a coordenação da Aeronáutica e da Marinha, pré-identificação dos corpos, sob a coordenação da Polícia Federal e da Secretaria de Defesa Social de Pernambuco, necrópsia dos corpos e coleta de material postmortem, sob a coordenação da Secretaria de Defesa Social de Pernambuco e da Secretaria de Estado da Segurança e da Defesa Social da Paraíba, coleta de material antemortem, sob a coordenação da INTERPOL e da Polícia Federal, e identificação dos corpos, que teve como coordenadores a Polícia Federal e a Secretaria de Defesa Social de Pernambuco. A pesquisa realizada compreendeu três momentos distintos: a) montagem da estrutura da rede, b) análise das etapas de gerenciamento e, c) identificação das características da rede. A montagem da estrutura da rede permitiu conhecer em detalhes a rede formada, seus integrantes, objetivos e funcionamento, e subsidiar as etapas seguintes. Com isso foi possível fazer uma análise das etapas de gerenciamento da rede, a ativação, o enquadramento, a mobilização e a síntese, e como cada uma delas aconteceu na rede de atendimento do voo 447 da Air France. Por fim, foi possível identificar as principais características do modelo de rede, a pluralidade, a horizontalidade, a capilaridade, a interdependência, a flexibilidade e a dinâmica do estado, e verificar como elas se manifestaram na missão de resposta ao acidente aéreo do voo 447 da Air France.

Redes nas organizações públicas: o problema de controle

O presente estudo, de natureza exploratória e descritiva, objetiva verificar e analisar de que forma as organizações públicas ao atuar adotando o modelo em rede equacionam o problema do controle. Para alcançar tal objetivo foi realizado levantamento a partir do referencial teórico com a finalidade de identificar um perfil da administração pública burocrática e as mudanças trazidas com a adoção de estratégias propostas pela administração pública gerencial, de forma a cotejar suas premissas e propostas com o modelo em rede interorganizacional, no qual se pressupõe que as organizações se integrem e interajam com vistas à consecução de objetivos e interesses comuns e/ou complementares. A partir da identificação dos diferenciais do modelo em rede, a exemplo da flexibilidade, colaboração, complementaridade e confiança, o foco da análise se concentrou nas características, vantagens e desvantagens que este modelo traz enfocando, de modo especial, as formas e os instrumentos de controle. Enquanto no modelo burocrático a ênfase do controle se dá nos processos e através do sistema racional-legal, a atuação em rede encontra dificuldades para estabelecer mecanismos de controle e monitoramento, problemas esses materializados justamente pelo caráter autônomo das organizações participantes e a ausência de hierarquia formal entre os atores envolvidos. O estudo investigou o funcionamento do sistema de defesa social do Estado da Bahia voltado para a redução da criminalidade e violência naquele Estado. A partir da análise do caso observou-se que para minimizar as dificuldades do modelo em rede quanto ao controle e responsabilização, uma das soluções indicadas é a construção de objetivos e metas de forma pactuada, monitorados de forma compartilhada por um núcleo de gestão formado por representantes de cada uma das organizações envolvidas, de modo a resolver tempestivamente problemas e alinhar a busca pelos objetivos.

Who is the cowboy in Washington?: beating google at their own game with neuroscience and cryptography

Who was the cowboy in Washington? What is the land of sushi? Most people would have answers to these questions readily available,yet, modern search engines, arguably the epitome of technology in finding answers to most questions, are completely unable to do so. It seems that people capture few information items to rapidly converge to a seemingly 'obvious' solution. We will study approaches for this problem, with two additional hard demands that constrain the space of possible theories: the sought model must be both psychologically and neuroscienti cally plausible. Building on top of the mathematical model of memory called Sparse Distributed Memory, we will see how some well-known methods in cryptography can point toward a promising, comprehensive, solution that preserves four crucial properties of human psychology.