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.

On the use of particle swarm optimization to maximize bending stiffness of functionally graded structures

Functionally graded materials are a type of composite materials which are tailored to provide continuously varying properties, according to specific constituent's mixing distributions. These materials are known to provide superior thermal and mechanical performances when compared to the traditional laminated composites, because of this continuous properties variation characteristic, which enables among other advantages, smoother stresses distribution profiles. Therefore the growing trend on the use of these materials brings together the interest and the need for getting optimum configurations concerning to each specific application. In this work it is studied the use of particle swarm optimization technique for the maximization of a functionally graded sandwich beam bending stiffness. For this purpose, a set of case studies is analyzed, in order to enable to understand in a detailed way, how the different optimization parameters tuning can influence the whole process. It is also considered a re-initialization strategy, which is not a common approach in particle swarm optimization as far as it was possible to conclude from the published research works. As it will be shown, this strategy can provide good results and also present some advantages in some conditions. This work was developed and programmed on symbolic computation platform Maple 14. (C) 2013 Elsevier B.V. All rights reserved.

ECG-based biometrics: A real time classification approach

Behavioral biometrics is one of the areas with growing interest within the biosignal research community. A recent trend in the field is ECG-based biometrics, where electrocardiographic (ECG) signals are used as input to the biometric system. Previous work has shown this to be a promising trait, with the potential to serve as a good complement to other existing, and already more established modalities, due to its intrinsic characteristics. In this paper, we propose a system for ECG biometrics centered on signals acquired at the subject's hand. Our work is based on a previously developed custom, non-intrusive sensing apparatus for data acquisition at the hands, and involved the pre-processing of the ECG signals, and evaluation of two classification approaches targeted at real-time or near real-time applications. Preliminary results show that this system leads to competitive results both for authentication and identification, and further validate the potential of ECG signals as a complementary modality in the toolbox of the biometric system designer.

Study of the interactions in a three-way crossed classification model

Crossed classification models are applied in many investigations taking in consideration the existence of interaction between all factors or, in alternative, excluding all interactions, and in this case only the effects and the error term are considered. In this work we use commutative Jordan algebras in the study of the algebraic structure of these designs and we use them to obtain similar designs where only some of the interactions are considered. We finish presenting the expressions of the variance componentes estimators.

Clinical classification versus semiquantification with adapted reference values for 123I-FP-CIT SPECT in a nuclear medicine department

Semi quantification (SQ) in DaTScan® studies is broadly used in clinic daily basis, however there is a suspicious about its discriminative capability, and concordance with the diagnostic classification performed by the physician. Aim: Evaluate the discriminate capability of an adapted database and reference's values of healthy controls for the Dopamine Transporters (DAT) with 123I–FP-IT named DBRV adapted to Nuclear Medicine Department's protocol and population of Infanta Cristina's Hospital, and its concordance with the physician classification.

Partial classification of Lorenz knots: Syllable permutations of torus knots words

We define families of aperiodic words associated to Lorenz knots that arise naturally as syllable permutations of symbolic words corresponding to torus knots. An algorithm to construct symbolic words of satellite Lorenz knots is defined. We prove, subject to the validity of a previous conjecture, that Lorenz knots coded by some of these families of words are hyperbolic, by showing that they are neither satellites nor torus knots and making use of Thurston's theorem. Infinite families of hyperbolic Lorenz knots are generated in this way, to our knowledge, for the first time. The techniques used can be generalized to study other families of Lorenz knots.

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.