Polycyclic aromatic hydrocarbons: levels and phase distributions in preschool microenvironment

This work aims to characterize levels and phase distribution of polycyclic aromatic hydrocarbons (PAHs) in indoor air of preschool environment and to assess the impact of outdoor PAH emissions to indoor environment. Gaseous and particulate (PM1 and PM2.5) PAHs (16 USEPA priority pollutants, plus dibenzo[a,l]pyrene, and benzo[j]fluoranthene) were concurrently sampled indoors and outdoors in one urban preschool located in north of Portugal for 35 days. The total concentration of 18 PAHs (ΣPAHs) in indoor air ranged from 19.5 to 82.0 ng/m3; gaseous compounds (range of 14.1–66.1 ng/m3) accounted for 85% ΣPAHs. Particulate PAHs (range 0.7–15.9 ng/m3) were predominantly associated with PM1 (76% particulate ΣPAHs) with 5-ring PAHs being the most abundant. Mean indoor/outdoor ratios (I/O) of individual PAHs indicated that outdoor emissions significantly contributed to PAH indoors; emissions from motor vehicles and fuel burning were the major sources.

Multi-agent simulation of competitive electricity markets: Autonomous systems cooperation for European market modeling

The electricity market restructuring, and its worldwide evolution into regional and even continental scales, along with the increasing necessity for an adequate integration of renewable energy sources, is resulting in a rising complexity in power systems operation. Several power system simulators have been developed in recent years with the purpose of helping operators, regulators, and involved players to understand and deal with this complex and constantly changing environment. The main contribution of this paper is given by the integration of several electricity market and power system models, respecting to the reality of different countries. This integration is done through the development of an upper ontology which integrates the essential concepts necessary to interpret all the available information. The continuous development of Multi-Agent System for Competitive Electricity Markets platform provides the means for the exemplification of the usefulness of this ontology. A case study using the proposed multi-agent platform is presented, considering a scenario based on real data that simulates the European Electricity Market environment, and comparing its performance using different market mechanisms. The main goal is to demonstrate the advantages that the integration of various market models and simulation platforms have for the study of the electricity markets’ evolution.

Using neural networks and support vector regression to relate marchetti dilatometer test parameters and maximum shear modulus

In the last two decades, small strain shear modulus became one of the most important geotechnical parameters to characterize soil stiffness. Finite element analysis have shown that in-situ stiffness of soils and rocks is much higher than what was previously thought and that stress-strain behaviour of these materials is non-linear in most cases with small strain levels, especially in the ground around retaining walls, foundations and tunnels, typically in the order of 10−2 to 10−4 of strain. Although the best approach to estimate shear modulus seems to be based in measuring seismic wave velocities, deriving the parameter through correlations with in-situ tests is usually considered very useful for design practice.The use of Neural Networks for modeling systems has been widespread, in particular within areas where the great amount of available data and the complexity of the systems keeps the problem very unfriendly to treat following traditional data analysis methodologies. In this work, the use of Neural Networks and Support Vector Regression is proposed to estimate small strain shear modulus for sedimentary soils from the basic or intermediate parameters derived from Marchetti Dilatometer Test. The results are discussed and compared with some of the most common available methodologies for this evaluation.

Using neural networks and support vector regression to relate marchetti dilatometer test parameters and maximum shear modulus

In the last two decades, small strain shear modulus became one of the most important geotechnical parameters to characterize soil stiffness. Finite element analysis have shown that in-situ stiffness of soils and rocks is much higher than what was previously thought and that stress-strain behaviour of these materials is non-linear in most cases with small strain levels, especially in the ground around retaining walls, foundations and tunnels, typically in the order of 10−2 to 10−4 of strain. Although the best approach to estimate shear modulus seems to be based in measuring seismic wave velocities, deriving the parameter through correlations with in-situ tests is usually considered very useful for design practice.The use of Neural Networks for modeling systems has been widespread, in particular within areas where the great amount of available data and the complexity of the systems keeps the problem very unfriendly to treat following traditional data analysis methodologies. In this work, the use of Neural Networks and Support Vector Regression is proposed to estimate small strain shear modulus for sedimentary soils from the basic or intermediate parameters derived from Marchetti Dilatometer Test. The results are discussed and compared with some of the most common available methodologies for this evaluation.

Adaptive modeling and high quality spectral estimation for speech enhancement

In this work an adaptive modeling and spectral estimation scheme based on a dual Discrete Kalman Filtering (DKF) is proposed for speech enhancement. Both speech and noise signals are modeled by an autoregressive structure which provides an underlying time frame dependency and improves time-frequency resolution. The model parameters are arranged to obtain a combined state-space model and are also used to calculate instantaneous power spectral density estimates. The speech enhancement is performed by a dual discrete Kalman filter that simultaneously gives estimates for the models and the signals. This approach is particularly useful as a pre-processing module for parametric based speech recognition systems that rely on spectral time dependent models. The system performance has been evaluated by a set of human listeners and by spectral distances. In both cases the use of this pre-processing module has led to improved results.

Taludes rochosos. Metodologias de inspecção e de avalialição de segurança. Caso de estudo das vertentes rochosas de S. Simão

A instabilização de taludes rochosos, com consequências mais ou menos gravosas, repete-se com frequência no território nacional. Os enquadramentos destes incidentes são diversos, sendo mais comum e mais visível a sua ocorrência em taludes adjacentes a vias de comunicação. No entanto, o fenómeno repete-se também em vertentes naturais, geralmente em alturas de pluviosidade mais prolongada e intensa. No presente trabalho reveem-se conceitos associados a maciços rochosos, nomeadamente as principais classificações geotécnicas e as diferentes tipologias de instabilidade em taludes rochosos. Desenvolve-se um caso de estudo de uma vertente localizada em S. Simão, concelho de Amarante. Percorrem-se as sucessivas fases de estudo, incluindo a realização da fotografia aérea com recurso a um veículo não tripulado, a geração de um modelo 3D de elevada precisão da vertente e a caracterização e a classificação dos diferentes afloramentos rochosos. Desenvolve-se uma metodologia de inspeção com a criação de dois conjuntos de fichas e propõe-se o agravamento da classificação das anomalias perante a simultaneidade de ocorrência de anomalias de idêntica gravidade e a hierarquização dos blocos potencialmente instáveis, de acordo com os respetivos níveis de gravidade (NGB). Recorre-se ao programa de modelação da queda de blocos, “Rocfall (4.0”, da “Rocscience”, a partir de trajetórias definidas no modelo 3D gerado e propõem-se soluções de reforço e de proteção da vertente.