Statistical classification of road pavements using near field vehicle rolling noise measurements

Low noise surfaces have been increasingly considered as a viable and cost-effective alternative to acoustical barriers. However, road planners and administrators frequently lack information on the correlation between the type of road surface and the resulting noise emission profile. To address this problem, a method to identify and classify different types of road pavements was developed, whereby near field road noise is analyzed using statistical learning methods. The vehicle rolling sound signal near the tires and close to the road surface was acquired by two microphones in a special arrangement which implements the Close-Proximity method. A set of features, characterizing the properties of the road pavement, was extracted from the corresponding sound profiles. A feature selection method was used to automatically select those that are most relevant in predicting the type of pavement, while reducing the computational cost. A set of different types of road pavement segments were tested and the performance of the classifier was evaluated. Results of pavement classification performed during a road journey are presented on a map, together with geographical data. This procedure leads to a considerable improvement in the quality of road pavement noise data, thereby increasing the accuracy of road traffic noise prediction models.

Occupational exposure to volatile organic compounds in the Portuguese printing industry

In the printing industry, volatile organic compounds main sources are the uses of organic solvents, fountain solutions and cleaning agents. Nowadays, one circumstance which might confuse the exposure reality is that the majority of solvents which are often used have a faint odour. Therefore, the conditions at offset printing in regard to solvent exposure may seem acceptable to workers. Fortunately, general ventilation and local exhaust systems have also become more common, and new printing machines, often with automatic cleaning, have entered the market. The health effects of volatile organic solvents are dependent on the chemicals involved but, normally, are associated with affecting the nervous system, the liver and also the kidneys. The purpose of this study was to document the conditions regarding exposure to volatile organic compounds in an offset printing unit and to permit identify task with higher exposure and with priority for preventive measures application. Exposure assessment was done before and after installation of general ventilation and local exhaust equipments and during printing and cleaning procedure.

Ventilation influence in occupational exposure to fungi and volatile organic compounds: poultry case

Introduction - In poultry houses, large-scale production has led to increased bird densities within buildings. Such high densities of animals kept within confined spaces are a source of human health problems related to occupational organic dust exposure. This organic dust is composed of both non-viable particles and viable particulate matter (also called bioaerosols). Bioaerosols are comprised by airborne bacteria, fungi, viruses and their by-products, endotoxins and mycotoxins. Exposure to fungi in broiler houses may vary depending upon the applied ventilation system. Ventilation can be an important resource in order to reduce air contamination in these type of settings. Nevertheless, some concerns regarding costs, sensitivity of the animal species to temperature differences, and also the type of building used define which type of ventilation is used. Aim of the study - A descriptive study was developed in one poultry unit aiming to assess occupational fungal and volatile organic compounds (VOCs) exposure.

Extraction of hemoglobin with calixarenes and biocatalysis in organic media of the complex with pseudoactivity of peroxidase

The present work involves the use of p-tert-butylcalix[4,6,8]arene carboxylic acid derivatives ((t)Butyl[4,6,8]CH2COOH) for selective extraction of hemoglobin. All three calixarenes extracted hemoglobin into the organic phase, exhibiting extraction parameters higher than 0.90. Evaluation of the solvent accessible positively charged amino acid side chains of hemoglobin (PDB entry 1XZ2) revealed that there are 8 arginine, 44 lysine and 30 histidine residues on the protein surface which may be involved in the interactions with the calixarene molecules. The hemoglobin-(t)Butyl[6]CH2COOH complex had pseudoperoxidase activity which catalysed the oxidation of syringaldazine in the presence of hydrogen peroxide in organic medium containing chloroform. The effect of pH, protein and substrate concentrations on biocatalysis was investigated using the hemoglobin-(t)Butyl[6]CH2COOH complex. This complex exhibited the highest specific activity of 9.92 x 10(-2) U mg protein(-1) at an initial pH of 7.5 in organic medium. Apparent kinetic parameters (V'(max), K'(m), k'(cat) and k'(cat)/K'(m)) for the pseudoperoxidase activity were determined in organic media for different pH values from a Michaelis-Menten plot. Furthermore, the stability of the protein-calixarene complex was investigated for different initial pH values and half-life (t(1/2)) values were obtained in the range of 1.96 and 2.64 days. Hemoglobin-calixarene complex present in organic medium was recovered in fresh aqueous solutions at alkaline pH, with a recovery of pseudoperoxidase activity of over 100%. These results strongly suggest that the use of calixarene derivatives is an alternative technique for protein extraction and solubilisation in organic media for biocatalysis.

Fungal and microbial volatile organic compounds exposure assessment in a waste sorting plant

In the management of solid waste, pollutants over a wide range are released with different routes of exposure for workers. The potential for synergism among the pollutants raises concerns about potential adverse health effects, and there are still many uncertainties involved in exposure assessment. In this study, conventional (culture-based) and molecular real-time polymerase chain reaction (RTPCR) methodologies were used to assess fungal air contamination in a waste-sorting plant which focused on the presence of three potential pathogenic/toxigenic fungal species: Aspergillus flavus, A. fumigatus, and Stachybotrys chartarum. In addition, microbial volatile organic compounds (MVOC) were measured by photoionization detection. For all analysis, samplings were performed at five different workstations inside the facilities and also outdoors as a reference. Penicillium sp. were the most common species found at all plant locations. Pathogenic/toxigenic species (A. fumigatus and S. chartarum) were detected at two different workstations by RTPCR but not by culture-based techniques. MVOC concentration indoors ranged between 0 and 8.9 ppm (average 5.3 ± 3.16 ppm). Our results illustrated the advantage of combining both conventional and molecular methodologies in fungal exposure assessment. Together with MVOC analyses in indoor air, data obtained allow for a more precise evaluation of potential health risks associated with bioaerosol exposure. Consequently, with this knowledge, strategies may be developed for effective protection of the workers.

Exposure to microbial volatile organic compounds in a waste-handling unit

The production of MVOC by fungi has been taken into account especially from the viewpoint of indoor pollution with microorganisms but the relevance of fungal metabolites in working environments has not been sufficiently studied. The purpose of this study was to assess exposure to MVOCs in a waste-handling unit. It was used Multirae equipment (RAE Systems) to measured MVOCs concentration with a 10.6 eV lamps. The measurements were done near workers nose and during the normal activities. All measurements were done continuously and had the duration of 5 minutes at least. It was consider the higher value obtained in each measurement. In addition, for knowing fungi contamination, five air samples of 50 litres were collected through impaction method at 140 L/minute, at one meter tall, on to malt extract agar with the antibiotic chloramphenicol (MEA). MVOCs results range between 4.7 ppm and 8.9 ppm in the 6 locations consider. These results are eight times higher than normally obtained in indoor settings. Considering fungi results, two species were identified in air, being the genera Penicillium found in all the samples in uncountable colonies and Rhizopus only in one sample (40 UFC/m3). These fungi are known as MVOCs producers, namely terpenoids, ketones, alcohols and others. Until now, there has been no evidence that MVOCs are toxicologically relevant, but further epidemiological research is necessary to elucidate their role on human’s health, particularly in occupational settings where microbiological contamination is common. Additionally, further research should concentrate on quantitative analyses of specific MVOCs.

Integrated vehicle classification system

This paper presents an integrated system for vehicle classification. This system aims to classify vehicles using different approaches: 1) based on the height of the first axle and_the number of axles; 2) based on volumetric measurements and; 3) based on features extracted from the captured image of the vehicle. The system uses a laser sensor for measurements and a set of image analysis algorithms to compute some visual features. By combining different classification methods, it is shown that the system improves its accuracy and robustness, enabling its usage in more difficult environments satisfying the proposed requirements established by the Portuguese motorway contractor BRISA.

OC/EC ratio observations in Europe: Re-thinking the approach for apportionment between primary and secondary organic carbon

This study explores a large set of OC and EC measurements in PM(10) and PM(2.5) aerosol samples, undertaken with a long term constant analytical methodology, to evaluate the capability of the OC/EC minimum ratio to represent the ratio between the OC and EC aerosol components resulting from fossil fuel combustion (OC(ff)/EC(ff)). The data set covers a wide geographical area in Europe, but with a particular focus upon Portugal, Spain and the United Kingdom, and includes a great variety of sites: urban (background, kerbside and tunnel), industrial, rural and remote. The highest minimum ratios were found in samples from remote and rural sites. Urban background sites have shown spatially and temporally consistent minimum ratios, of around 1.0 for PM(10) and 0.7 for PM(2.5).The consistency of results has suggested that the method could be used as a tool to derive the ratio between OC and EC from fossil fuel combustion and consequently to differentiate OC from primary and secondary sources. To explore this capability, OC and EC measurements were performed in a busy roadway tunnel in central Lisbon. The OC/EC ratio, which reflected the composition of vehicle combustion emissions, was in the range of 03-0.4. Ratios of OC/EC in roadside increment air (roadside minus urban background) in Birmingham, UK also lie within the range 03-0.4. Additional measurements were performed under heavy traffic conditions at two double kerbside sites located in the centre of Lisbon and Madrid. The OC/EC minimum ratios observed at both sites were found to be between those of the tunnel and those of urban background air, suggesting that minimum values commonly obtained for this parameter in open urban atmospheres over-predict the direct emissions of OC(ff) from road transport. Possible reasons for this discrepancy are explored. (C) 2011 Elsevier Ltd. All rights reserved.

QSAR modeling of antitubercular activity of diverse organic compounds

Tuberculosis (TB) is a worldwide infectious disease that has shown over time extremely high mortality levels. The urgent need to develop new antitubercular drugs is due to the increasing rate of appearance of multi-drug resistant strains to the commonly used drugs, and the longer durations of therapy and recovery, particularly in immuno-compromised patients. The major goal of the present study is the exploration of data from different families of compounds through the use of a variety of machine learning techniques so that robust QSAR-based models can be developed to further guide in the quest for new potent anti-TB compounds. Eight QSAR models were built using various types of descriptors (from ADRIANA.Code and Dragon software) with two publicly available structurally diverse data sets, including recent data deposited in PubChem. QSAR methodologies used Random Forests and Associative Neural Networks. Predictions for the external evaluation sets obtained accuracies in the range of 0.76-0.88 (for active/inactive classifications) and Q(2)=0.66-0.89 for regressions. Models developed in this study can be used to estimate the anti-TB activity of drug candidates at early stages of drug development (C) 2011 Elsevier B.V. All rights reserved.

Short-term Feature Space and Music Genre Classification

In music genre classification, most approaches rely on statistical characteristics of low-level features computed on short audio frames. In these methods, it is implicitly considered that frames carry equally relevant information loads and that either individual frames, or distributions thereof, somehow capture the specificities of each genre. In this paper we study the representation space defined by short-term audio features with respect to class boundaries, and compare different processing techniques to partition this space. These partitions are evaluated in terms of accuracy on two genre classification tasks, with several types of classifiers. Experiments show that a randomized and unsupervised partition of the space, used in conjunction with a Markov Model classifier lead to accuracies comparable to the state of the art. We also show that unsupervised partitions of the space tend to create less hubs.

Automatic vehicle detection and classification

This paper presents a proposal for an automatic vehicle detection and classification (AVDC) system. The proposed AVDC should classify vehicles accordingly to the Portuguese legislation (vehicle height over the first axel and number of axels), and should also support profile based classification. The AVDC should also fulfill the needs of the Portuguese motorway operator, Brisa. For the classification based on the profile we propose:he use of Eigenprofiles, a technique based on Principal Components Analysis. The system should also support multi-lane free flow for future integration in this kind of environments.

Classification and staging of chronic liver disease from multimodal data

Chronic liver disease (CLD) is most of the time an asymptomatic, progressive, and ultimately potentially fatal disease. In this study, an automatic hierarchical procedure to stage CLD using ultrasound images, laboratory tests, and clinical records are described. The first stage of the proposed method, called clinical based classifier (CBC), discriminates healthy from pathologic conditions. When nonhealthy conditions are detected, the method refines the results in three exclusive pathologies in a hierarchical basis: 1) chronic hepatitis; 2) compensated cirrhosis; and 3) decompensated cirrhosis. The features used as well as the classifiers (Bayes, Parzen, support vector machine, and k-nearest neighbor) are optimally selected for each stage. A large multimodal feature database was specifically built for this study containing 30 chronic hepatitis cases, 34 compensated cirrhosis cases, and 36 decompensated cirrhosis cases, all validated after histopathologic analysis by liver biopsy. The CBC classification scheme outperformed the nonhierachical one against all scheme, achieving an overall accuracy of 98.67% for the normal detector, 87.45% for the chronic hepatitis detector, and 95.71% for the cirrhosis detector.

Data mining framework for fatty liver disease classification in ultrasound: a hybrid feature extraction paradigm

PURPOSE: Fatty liver disease (FLD) is an increasing prevalent disease that can be reversed if detected early. Ultrasound is the safest and ubiquitous method for identifying FLD. Since expert sonographers are required to accurately interpret the liver ultrasound images, lack of the same will result in interobserver variability. For more objective interpretation, high accuracy, and quick second opinions, computer aided diagnostic (CAD) techniques may be exploited. The purpose of this work is to develop one such CAD technique for accurate classification of normal livers and abnormal livers affected by FLD. METHODS: In this paper, the authors present a CAD technique (called Symtosis) that uses a novel combination of significant features based on the texture, wavelet transform, and higher order spectra of the liver ultrasound images in various supervised learning-based classifiers in order to determine parameters that classify normal and FLD-affected abnormal livers. RESULTS: On evaluating the proposed technique on a database of 58 abnormal and 42 normal liver ultrasound images, the authors were able to achieve a high classification accuracy of 93.3% using the decision tree classifier. CONCLUSIONS: This high accuracy added to the completely automated classification procedure makes the authors' proposed technique highly suitable for clinical deployment and usage.

The usefulness of ultrasound in the classification of chronic liver disease

Chronic Liver Disease is a progressive, most of the time asymptomatic, and potentially fatal disease. In this paper, a semi-automatic procedure to stage this disease is proposed based on ultrasound liver images, clinical and laboratorial data. In the core of the algorithm two classifiers are used: a k nearest neighbor and a Support Vector Machine, with different kernels. The classifiers were trained with the proposed multi-modal feature set and the results obtained were compared with the laboratorial and clinical feature set. The results showed that using ultrasound based features, in association with laboratorial and clinical features, improve the classification accuracy. The support vector machine, polynomial kernel, outperformed the others classifiers in every class studied. For the Normal class we achieved 100% accuracy, for the chronic hepatitis with cirrhosis 73.08%, for compensated cirrhosis 59.26% and for decompensated cirrhosis 91.67%.

Chronic liver disease staging classification based on ultrasound, clinical and laboratorial data

In this work the identification and diagnosis of various stages of chronic liver disease is addressed. The classification results of a support vector machine, a decision tree and a k-nearest neighbor classifier are compared. Ultrasound image intensity and textural features are jointly used with clinical and laboratorial data in the staging process. The classifiers training is performed by using a population of 97 patients at six different stages of chronic liver disease and a leave-one-out cross-validation strategy. The best results are obtained using the support vector machine with a radial-basis kernel, with 73.20% of overall accuracy. The good performance of the method is a promising indicator that it can be used, in a non invasive way, to provide reliable information about the chronic liver disease staging.