Task-based occupational exposure assessment and particle number concentration: two important data resources to perform risk assessment for occupational exposure to particles

Environment monitoring has an important role in occupational exposure assessment. However, due to several factors is done with insufficient frequency and normally don´t give the necessary information to choose the most adequate safety measures to avoid or control exposure. Identifying all the tasks developed in each workplace and conducting a task-based exposure assessment help to refine the exposure characterization and reduce assessment errors. A task-based assessment can provide also a better evaluation of exposure variability, instead of assessing personal exposures using continuous 8-hour time weighted average measurements. Health effects related with exposure to particles have mainly been investigated with mass-measuring instruments or gravimetric analysis. However, more recently, there are some studies that support that size distribution and particle number concentration may have advantages over particle mass concentration for assessing the health effects of airborne particles. Several exposure assessments were performed in different occupational settings (bakery, grill house, cork industry and horse stable) and were applied these two resources: task-based exposure assessment and particle number concentration by size. The results showed interesting results: task-based approach applied permitted to identify the tasks with higher exposure to the smaller particles (0.3 μm) in the different occupational settings. The data obtained allow more concrete and effective risk assessment and the identification of priorities for safety investments.

Occupational exposure assessment to particles with task-based approach

Occupational exposure assessment can be a challenge due to several factors being the most important the costs associate and the result's dependence from the conditions at the time of sampling. Conducting a task-based exposure assessment allow defining better control measures to eliminate or reduce exposure since more easily identifies the task with higher exposure. A research study was developed to show the importance of task-based exposure assessment in four different settings (bakery, horsemanship, waste sorting and cork industry). Measurements were performed using a portable direct-reading hand-held equipment and were conducted near the workers nose during tasks performance. For each task were done measurements of approximately 5 minutes. It was possible to detect the task in each setting that was responsible for higher particles exposure allowing the priority definition regarding investments in preventive and protection measures.

Task-based approach used on surfaces sampling strategy definition: the case of antineoplastic occupational exposure

Task-based approach implicates identifying all the tasks developed in each workplace aiming to refine the exposure characterization. The starting point of this approach is the recognition that only through a more detailed and comprehensive understanding of tasks is possible to understand, in more detail, the exposure scenario. In addition allows also the most suitable risk management measures identification. This approach can be also used when there is a need of identifying the workplace surfaces for sampling chemicals that have the dermal exposure route as the most important. In this case is possible to identify, through detail observation of tasks performance, the surfaces that involves higher contact (frequency) by the workers and can be contaminated. Identify the surfaces to sample when performing occupational exposure assessment to antineoplasic agents. Surfaces selection done based on the task-based approach.

Literacia familiar: contributos para a promoção e desenvolvimento de boas práticas literácitas em contexto socio económico desfavorecido

Dissertação apresentada à Escola Superior de Educação de Lisboa para a obtenção de grau de Mestre em Didática da Língua Portuguesa no 1.º e 2.º Ciclos do Ensino Básico

Lá contribution des technologies a l'apprentissage du français à l'ecole primaire georgienne

Les méthodes modernes d’enseignement exigent de recréer le milieu de la langue étudiée, de faire parler les élèves dans des situations différentes. En Géorgie, l’enseignement de la langue étrangère s’effectue à partir de 6 ans, en même temps que celui de la langue maternelle. Les élèves apprennent à écrire en français après l’apprentissage de l’écriture en géorgien. A l’âge de 7-10 ans, ils connaissent déjà 3 alphabets différents : le géorgien, le latin et le cyrillique. L’objectif de cet article est de proposer une méthode qui pourra faciliter l’apprentissage du français aux non francophones grâce aux moyens audiovisuels qui sont très efficaces surtout au moment quand l’enfant ne sait ni lire, ni écrire en langue étrangère. Cependant, les moyens audiovisuels doivent être utilisés à des doses normales sans empêcher l’activité de l’élève.

Occupational exposure to aflatoxin B1: new (old) occupational risk!

Contrary to fungi, exposure to mycotoxins is not usually identified as a risk factor present in occupational settings. This is probably due to the inexistence of limits regarding concentration of airborne mycotoxins, and also due to the fact that these compounds are rarely monitored in occupational environments. Aflatoxin B1 (AFB1) is the most prevalent aflatoxin and is associated with carcinogenicity, teratogenicity, genotoxicity and immunotoxicity but only a few studies examined exposure in occupational settings. Workers can be exposed to high airborne levels during certain operations in specific occupational settings. Aim of study: The study aimed to assess exposure to AFB1 in three settings: poultry, swine production and waste management.

Importância da avaliação baseada na atividade para o estudo da exposição ocupacional a partículas: o caso da produção de pranchas de surf

Na avaliação da exposição profissional a agentes químicos a monitorização do ar do ambiente de trabalho é a metodologia mais utilizada e o valor-limite mais frequentemente utilizado é a Concentração Média Ponderada. Recentemente alguns estudos têm sido realizados recorrendo a uma avaliação da exposição profissional baseada na atividade desenvolvida pelo trabalhador. O principal objectivo do estudo é demonstrar a importância da avaliação da exposição profissional ser realizada por actividade quando se pretende seleccionar as medidas de eliminação e/ou controlo da exposição mais adequadas e prioritárias. Pretendeu-se igualmente demonstrar a utilidade do conhecimento detalhado da actividade para definir a melhor estratégia de avaliação ambiental.

Importância da avaliação baseada na actividade para o estudo da exposição ocupacional a partículas: o caso da produção de pranchas de surf

Na avaliação da exposição profissional a agentes químicos a monitorização do ar do ambiente de trabalho é a metodologia mais utilizada e o valor-limite mais frequentemente utilizado é a Concentração Média Ponderada. Recentemente alguns estudos têm sido realizados recorrendo a uma avaliação da exposição profissional baseada na actividade desenvolvida pelo trabalhador. O estudo desenvolvido numa empresa de produção de pranchas de surf demonstrou a utilidades desta metodologia na identificação da actividade mais crítica em matéria de exposição a partículas e, ainda, na definição de prioridades de intervenção e de investimento para eliminar e/ou controlar a exposição.

Symbolic Knowledge Extraction from Trained Neural Networks Governed by Lukasiewicz Logics

This work describes a methodology to extract symbolic rules from trained neural networks. In our approach, patterns on the network are codified using formulas on a Lukasiewicz logic. For this we take advantage of the fact that every connective in this multi-valued logic can be evaluated by a neuron in an artificial network having, by activation function the identity truncated to zero and one. This fact simplifies symbolic rule extraction and allows the easy injection of formulas into a network architecture. We trained this type of neural network using a back-propagation algorithm based on Levenderg-Marquardt algorithm, where in each learning iteration, we restricted the knowledge dissemination in the network structure. This makes the descriptive power of produced neural networks similar to the descriptive power of Lukasiewicz logic language, minimizing the information loss on the translation between connectionist and symbolic structures. To avoid redundance on the generated network, the method simplifies them in a pruning phase, using the "Optimal Brain Surgeon" algorithm. We tested this method on the task of finding the formula used on the generation of a given truth table. For real data tests, we selected the Mushrooms data set, available on the UCI Machine Learning Repository.

Parallel GPU architecture for hyperspectral unmixing based on augmented Lagrangian method

Hyperspectral imaging has become one of the main topics in remote sensing applications, which comprise hundreds of spectral bands at different (almost contiguous) wavelength channels over the same area generating large data volumes comprising several GBs per flight. This high spectral resolution can be used for object detection and for discriminate between different objects based on their spectral characteristics. One of the main problems involved in hyperspectral analysis is the presence of mixed pixels, which arise when the spacial resolution of the sensor is not able to separate spectrally distinct materials. Spectral unmixing is one of the most important task for hyperspectral data exploitation. However, the unmixing algorithms can be computationally very expensive, and even high power consuming, which compromises the use in applications under on-board constraints. In recent years, graphics processing units (GPUs) have evolved into highly parallel and programmable systems. Specifically, several hyperspectral imaging algorithms have shown to be able to benefit from this hardware taking advantage of the extremely high floating-point processing performance, compact size, huge memory bandwidth, and relatively low cost of these units, which make them appealing for onboard data processing. In this paper, we propose a parallel implementation of an augmented Lagragian based method for unsupervised hyperspectral linear unmixing on GPUs using CUDA. The method called simplex identification via split augmented Lagrangian (SISAL) aims to identify the endmembers of a scene, i.e., is able to unmix hyperspectral data sets in which the pure pixel assumption is violated. The efficient implementation of SISAL method presented in this work exploits the GPU architecture at low level, using shared memory and coalesced accesses to memory.

Text classification using compression-based dissimilarity measures

Arguably, the most difficult task in text classification is to choose an appropriate set of features that allows machine learning algorithms to provide accurate classification. Most state-of-the-art techniques for this task involve careful feature engineering and a pre-processing stage, which may be too expensive in the emerging context of massive collections of electronic texts. In this paper, we propose efficient methods for text classification based on information-theoretic dissimilarity measures, which are used to define dissimilarity-based representations. These methods dispense with any feature design or engineering, by mapping texts into a feature space using universal dissimilarity measures; in this space, classical classifiers (e.g. nearest neighbor or support vector machines) can then be used. The reported experimental evaluation of the proposed methods, on sentiment polarity analysis and authorship attribution problems, reveals that it approximates, sometimes even outperforms previous state-of-the-art techniques, despite being much simpler, in the sense that they do not require any text pre-processing or feature engineering.