817 resultados para Traffic clustering
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TPM Vol. 21, No. 4, December 2014, 435-447 – Special Issue © 2014 Cises.
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This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Traffic emissions and tobacco smoke are considered two main sources of polycyclic aromatic hydrocarbons (PAHs) in indoor and outdoor air. In this study, the impact of these sources on the level of fine particulate matter (PM2.5) and on the distribution of 15 PAHs regarded as priority pollutants by the US-EPA on PM2.5 were evaluated and compared. Outdoor and indoor PM2.5 samples were collected during winter 2008 in Oporto city in Portugal, for sampling periods of 12 and 24 hours, respectively. The outdoor PM2.5 were sampled at one site directly influenced by traffic emissions and the indoor PM2.5 samples were collected at one home directly influenced by tobacco smoke and another one without smoke. A methodology based on microwave-assisted extraction and liquid chromatography with fluorescence detection was applied for the efficient PAHs determination in indoor and outdoor PM2.5. PAHs in indoor PM2.5 concentrations were significantly influenced by the presence of traffic and tobacco smoking emissions. The mean of ΣPAHs in the outdoor traffic PM2.5 was not significantly different from the value attained in the indoor without smoking site. The tobacco smoke increased significantly PAHs concentrations on average about 1000 times more, when compared with the outdoor profile samples suggesting that tobacco smoking may be the most important source of indoor PAHs pollution.
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Air pollution represents a serious risk not only to environment and human health, but also to historical heritage. In this study, air pollution of the Oporto Metropolitan Area and its main impacts were characterized. The results showed that levels of CO, PM10 and SO2 have been continuously decreasing in the respective metropolitan area while levels of NOx and NO2 have not changed significantly. Traffic emissions were the main source of the determined polycyclic aromatic hydrocarbons (PAHs; 16 PAHs considered by U.S. EPA as priority pollutants, dibenzo[a,l]pyrene and benzo[j]fluoranthene) in air of the respective metropolitan area. The mean concentration of 18 PAHs in air was 69.9±39.7 ng m−3 with 3–4 rings PAHs accounting for 75% of the total ΣPAHs. The health risk analysis of PAHs in air showed that the estimated values of lifetime lung cancer risks considerably exceeded the health-based guideline level. Analytical results also confirm that historical monuments in urban areas act as passive repositories for air pollutants present in the surrounding atmosphere. FTIR and EDX analyses showed that gypsum was the most important constituent of black crusts of the characterized historical monument Monastery of Serra do Pilar classified as “UNESCO World Cultural Heritage”. In black crusts, 4–6 rings compounds accounted approximately for 85% of ΣPAHs. The diagnostic ratios confirmed that traffic emissions were the major source of PAHs in black crusts; PAH composition profiles were very similar for crusts and PM10 and PM2.5.
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Because polycyclic aromatic hydrocarbons (PAHs) have been proven to be toxic, mutagenic, and/or carcinogenic, there is widespread interest in analyzing and evaluating exposure to PAHs in atmospheric environments influenced by different emission sources. Because traffic emissions are one of the biggest sources of fine particles, more information on carcinogenic PAHs associated with fine particles needs to be provided. Aiming to further understand the impact of traffic particulate matter (PM) on human health, this study evaluated the influence of traffic on PM10 (PM with aerodynamic diameter <10 µm) and PM2.5 (PM with aerodynamic diameter <2.5 µm), considering their concentrations and compositions in carcinogenic PAHs. Samples were collected at one site influenced by traffic emissions and at one reference site using lowvolume samplers. Analysis of PAHs was performed by microwave-assisted extraction combined with liquid chromatography (MAE-LC); 17 PAHs, including 9 carcinogenic ones, were quantified. At the site influenced by traffic emissions, PM10 and PM2.5 concentrations were, respectively, 380 and 390% higher than at the background site. When influenced by traffic emissions, the total concentration of nine carcinogenic compounds (naphthalene, chrysene, benzo(a)anthracene, benzo(b) fluoranthene, benzo(k)fluoranthene, benzo(a)pyrene, dibenzo(a,h)anthracene, indeno(1,2,3-cd)pyrene, and dibenzo(a,l)pyrene) was increased by 2400 and 3000% in PM10 and PM2.5, respectively; these nine carcinogenic compounds represented 68 and 74% of total PAHs (ƩPAHs) for PM10 and PM2.5, respectively. All PAHs, including the carcinogenic compounds, were mainly present in fine particles. Considering the strong influence of these fine particles on human health, these conclusions are relevant for the development of strategies to protect public health.
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Clustering analysis is a useful tool to detect and monitor disease patterns and, consequently, to contribute for an effective population disease management. Portugal has the highest incidence of tuberculosis in the European Union (in 2012, 21.6 cases per 100.000 inhabitants), although it has been decreasing consistently. Two critical PTB (Pulmonary Tuberculosis) areas, metropolitan Oporto and metropolitan Lisbon regions, were previously identified through spatial and space-time clustering for PTB incidence rate and risk factors. Identifying clusters of temporal trends can further elucidate policy makers about municipalities showing a faster or a slower TB control improvement.
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Research on the problem of feature selection for clustering continues to develop. This is a challenging task, mainly due to the absence of class labels to guide the search for relevant features. Categorical feature selection for clustering has rarely been addressed in the literature, with most of the proposed approaches having focused on numerical data. In this work, we propose an approach to simultaneously cluster categorical data and select a subset of relevant features. Our approach is based on a modification of a finite mixture model (of multinomial distributions), where a set of latent variables indicate the relevance of each feature. To estimate the model parameters, we implement a variant of the expectation-maximization algorithm that simultaneously selects the subset of relevant features, using a minimum message length criterion. The proposed approach compares favourably with two baseline methods: a filter based on an entropy measure and a wrapper based on mutual information. The results obtained on synthetic data illustrate the ability of the proposed expectation-maximization method to recover ground truth. An application to real data, referred to official statistics, shows its usefulness.
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Research on cluster analysis for categorical data continues to develop, new clustering algorithms being proposed. However, in this context, the determination of the number of clusters is rarely addressed. We propose a new approach in which clustering and the estimation of the number of clusters is done simultaneously for categorical data. We assume that the data originate from a finite mixture of multinomial distributions and use a minimum message length criterion (MML) to select the number of clusters (Wallace and Bolton, 1986). For this purpose, we implement an EM-type algorithm (Silvestre et al., 2008) based on the (Figueiredo and Jain, 2002) approach. The novelty of the approach rests on the integration of the model estimation and selection of the number of clusters in a single algorithm, rather than selecting this number based on a set of pre-estimated candidate models. The performance of our approach is compared with the use of Bayesian Information Criterion (BIC) (Schwarz, 1978) and Integrated Completed Likelihood (ICL) (Biernacki et al., 2000) using synthetic data. The obtained results illustrate the capacity of the proposed algorithm to attain the true number of cluster while outperforming BIC and ICL since it is faster, which is especially relevant when dealing with large data sets.
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In data clustering, the problem of selecting the subset of most relevant features from the data has been an active research topic. Feature selection for clustering is a challenging task due to the absence of class labels for guiding the search for relevant features. Most methods proposed for this goal are focused on numerical data. In this work, we propose an approach for clustering and selecting categorical features simultaneously. We assume that the data originate from a finite mixture of multinomial distributions and implement an integrated expectation-maximization (EM) algorithm that estimates all the parameters of the model and selects the subset of relevant features simultaneously. The results obtained on synthetic data illustrate the performance of the proposed approach. An application to real data, referred to official statistics, shows its usefulness.
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Considering vehicular transport as one of the most health‐relevant emission sources of urban air, and with aim to further understand its negative impact on human health, the objective of this work was to study its influence on levels of particulate‐bound PAHs and to evaluate associated health risks. The 16 PAHs considered by USEPA as priority pollutants, and dibenzo[a, l]pyrene associated with fine (PM2.5) and coarse (PM2.5–10) particles were determined. The samples were collected at one urban site, as well as at a reference place for comparison. The results showed that the air of the urban site was more seriously polluted than at the reference one, with total concentrations of 17 PAHs being 2240% and 640% higher for PM2.5 and PM2.5–10, respectively; vehicular traffic was the major emission source at the urban site. PAHs were predominantly associated with PM2.5 (83% to 94% of ΣPAHs at urban and reference site, respectively) with 5 rings PAHs being the most abundant groups of compounds at both sites. The risks associated with exposure to particulate PAHs were evaluated using the TEF approach. The estimated value of lifetime lung cancer risks exceeded the health‐based guideline levels, thus demonstrating that exposure to PM2.5‐bound PAHs at levels found at urban site might cause potential health risks. Furthermore, the results showed that evaluation of benzo[a] pyrene (regarded as a marker of the genotoxic and carcinogenic PAHs) alone would probably underestimate the carcinogenic potential of the studied PAH mixtures.
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Technological developments are pulling fieldbus networks to support a new wide class of applications, such as industrial multimedia applications. To enable its use in this kind of applications the TCP/IP suite of protocols can be integrated within a fieldbus stack, leading to a dual-stack approach that is briefly outlined in the paper. One important requirement that must be fulfilled by this approach is that the hard real-time guarantees provided to the control-related traffic ("native" fieldbus traffic) are kept. At the same time it must also provide the desired quality of service (QoS) to IP applications. The focus of the paper is on how, in such a dual-stack approach, QoS can be efficiently provided to IP applications requiring quasi-constant bandwidth.
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In this paper we address the ability of WorldFIP to cope with the real-time requirements of distributed computer-controlled systems (DCCS). Typical DCCS include process variables that must be transferred between network devices both in a periodic and sporadic (aperiodic) basis. The WorldFIP protocol is designed to support both types of traffic. WorldFIP can easily guarantee the timing requirements for the periodic traffic. However, for the aperiodic traffic more complex analysis must be made in order to guarantee its timing requirements. This paper describes work that is being carried out to extend previous relevant work, in order to include the actual schedule for the periodic traffic in the worst-case response time analysis of sporadic traffic in WorldFIP networks
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This paper focus on a demand response model analysis in a smart grid context considering a contingency scenario. A fuzzy clustering technique is applied on the developed demand response model and an analysis is performed for the contingency scenario. Model considerations and architecture are described. The demand response developed model aims to support consumers decisions regarding their consumption needs and possible economic benefits.
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The IEEE 802.15.4 is the most widespread used protocol for Wireless Sensor Networks (WSNs) and it is being used as a baseline for several higher layer protocols such as ZigBee, 6LoWPAN or WirelessHART. Its MAC (Medium Access Control) supports both contention-free (CFP, based on the reservation of guaranteed time-slots GTS) and contention based (CAP, ruled by CSMA/CA) access, when operating in beacon-enabled mode. Thus, it enables the differentiation between real-time and best-effort traffic. However, some WSN applications and higher layer protocols may strongly benefit from the possibility of supporting more traffic classes. This happens, for instance, for dense WSNs used in time-sensitive industrial applications. In this context, we propose to differentiate traffic classes within the CAP, enabling lower transmission delays and higher success probability to timecritical messages, such as for event detection, GTS reservation and network management. Building upon a previously proposed methodology (TRADIF), in this paper we outline its implementation and experimental validation over a real-time operating system. Importantly, TRADIF is fully backward compatible with the IEEE 802.15.4 standard, enabling to create different traffic classes just by tuning some MAC parameters.