Determination of airborne nanoparticles in elderly care centres

According to numerous studies, airborne nanoparticles have a potential to produce serious adverse human health effects when deposited into the respiratory tract. The most important parts of the lung are the alveolar regions with their enormous surface areas and potential to transfer nanoparticles into the blood stream. These effects may be potentiated in case of the elderly, since this population is more susceptible to air pollutants in general and more to nanoparticles than larger particles. The main goal of this investigation was to determine the exposure of institutionalized elders to nanoparticles using Nanoparticle Surface Area Monitor (NSAM) equipment to calculate the deposited surface area (DSA) of nanoparticles into elderly lungs. In total, 193 institutionalized individuals over 65 yr of age were examined in four elderly care centers (ECC). The occupancy daily pattern was achieved by applying a questionnaire, and it was concluded that these subjects spent most of their time indoors, including the bedroom and living room, the indoor microenvironments with higher prevalence of elderly occupancy. The deposited surface area ranged from 10 to 46 μm2/cm3. The living rooms presented significantly higher levels compared with bedrooms. Comparing PM10 concentrations with nanoparticles deposited surface area in elderly lungs, it is conceivable that living rooms presented the highest concentration of PM10 and were similar to the highest average DSA. The temporal distribution of DSA was also assessed. While data showed a quantitative fluctuation in values in bedrooms, high peaks were detected in living rooms.

Contributo para a estimativa da idade fetal à data da morte na população portuguesa

A estimativa da idade gestacional (IG) em restos cadavéricos fetais é importante em contextos forenses. Para esse efeito, os especialistas forenses recorrem à avaliação do padrão de calcificação dentária e/ou ao estudo do esqueleto. Neste último, o comprimento das diáfises de ossos longos é um dos métodos mais utilizados, sendo utilizadas equações de regressão de obras pouco atuais ou baseadas em dados ecográficos, cujas medições diferem das efetuadas diretamente no osso. Este trabalho tem como objetivo principal a obtenção de equações de regressão para a população Portuguesa, com base na medição das diáfises de fémur, tíbia e úmero, utilizando radiografias postmortem. A amostra é constituída por 80 fetos de IG conhecida. Tratando-se de um estudo retrospectivo, os casos foram selecionados com base nas informações clínicas e anatomopatológicas, excluindo-se aqueles cujo normal crescimento se encontrava efetiva ou potencialmente comprometido. Os resultados confirmaram uma forte correlação entre o comprimento das diáfises estudadas e a IG, apresentando o fémur a correlação mais forte (r=0.967; p <0,01). Assim, foi possível obter uma equação de regressão para cada um dos ossos estudados. Concluindo, os objetivos do estudo foram atingidos com a obtenção das equações de regressão para os ossos estudados. Pretende-se, futuramente, alargar a amostra para validar e consolidar os resultados obtidos neste estudo.

Assessment of data-driven modeling strategies for water delivery canals

The aim of this paper is to develop models for experimental open-channel water delivery systems and assess the use of three data-driven modeling tools toward that end. Water delivery canals are nonlinear dynamical systems and thus should be modeled to meet given operational requirements while capturing all relevant dynamics, including transport delays. Typically, the derivation of first principle models for open-channel systems is based on the use of Saint-Venant equations for shallow water, which is a time-consuming task and demands for specific expertise. The present paper proposes and assesses the use of three data-driven modeling tools: artificial neural networks, composite local linear models and fuzzy systems. The canal from Hydraulics and Canal Control Nucleus (A parts per thousand vora University, Portugal) will be used as a benchmark: The models are identified using data collected from the experimental facility, and then their performances are assessed based on suitable validation criterion. The performance of all models is compared among each other and against the experimental data to show the effectiveness of such tools to capture all significant dynamics within the canal system and, therefore, provide accurate nonlinear models that can be used for simulation or control. The models are available upon request to the authors.

Analysis and visualization of seismic data using mutual information

Seismic data is difficult to analyze and classical mathematical tools reveal strong limitations in exposing hidden relationships between earthquakes. In this paper, we study earthquake phenomena in the perspective of complex systems. Global seismic data, covering the period from 1962 up to 2011 is analyzed. The events, characterized by their magnitude, geographic location and time of occurrence, are divided into groups, either according to the Flinn-Engdahl (F-E) seismic regions of Earth or using a rectangular grid based in latitude and longitude coordinates. Two methods of analysis are considered and compared in this study. In a first method, the distributions of magnitudes are approximated by Gutenberg-Richter (G-R) distributions and the parameters used to reveal the relationships among regions. In the second method, the mutual information is calculated and adopted as a measure of similarity between regions. In both cases, using clustering analysis, visualization maps are generated, providing an intuitive and useful representation of the complex relationships that are present among seismic data. Such relationships might not be perceived on classical geographic maps. Therefore, the generated charts are a valid alternative to other visualization tools, for understanding the global behavior of earthquakes.

Feature selection for clustering categorical data with an embedded modelling approach

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.

Categorical data clustering using a minimum message length criterion

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.

Determining the number of clusters in categorical data

Cluster analysis for categorical data has been an active area of research. A well-known problem in this area is the determination of the number of clusters, which is unknown and must be inferred from the data. In order to estimate the number of clusters, one often resorts to information criteria, such as BIC (Bayesian information criterion), MML (minimum message length, proposed by Wallace and Boulton, 1968), and ICL (integrated classification likelihood). In this work, we adopt the approach developed by Figueiredo and Jain (2002) for clustering continuous data. They use an MML criterion to select the number of clusters and a variant of the EM algorithm to estimate the model parameters. This EM variant seamlessly integrates model estimation and selection in a single algorithm. For clustering categorical data, we assume a finite mixture of multinomial distributions and implement a new EM algorithm, following a previous version (Silvestre et al., 2008). Results obtained with synthetic datasets are encouraging. The main advantage of the proposed approach, when compared to the above referred criteria, is the speed of execution, which is especially relevant when dealing with large data sets.

Melhoria do planeamento programação e controlo de produção (ppcp) de uma empresa de calçado

Atualmente o sistema produtivo do tipo job shop é muito comum nas PMEs (Pequenas e Médias Empresas). Estas empresas trabalham por encomenda. Produzem grande variedade de modelos, e em pequenas quantidades. Os prazos de entrega são um fator de elevada importância, pois os clientes exigem um produto de qualidade no tempo certo. O presente trabalho, pretende criar uma ferramenta de programação da produção para a secção da costura, usando dados reais da empresa, que tem uma implantação do tipo job shop com máquinas multi-operação (Multi-Purpose -Machines Job Shop). No final, são reunidas as principais conclusões e perspetivados futuros desenvolvimentos. Os resultados obtidos comprovam que o algoritmo desenvolvido, com base no algoritmo de Giffler & Thompson, consegue obter com grande precisão e de forma rápida o escalonamento / balanceamento da secção da costura. Com a ferramenta criada, a empresa otimiza a programação da secção da costura e fornece informação importante á gestão da produção, possibilitando uma melhoria do planeamento da empresa.

Disseminating data using broadcast when topology is unknown

Consider the problem of disseminating data from an arbitrary source node to all other nodes in a distributed computer system, like Wireless Sensor Networks (WSNs). We assume that wireless broadcast is used and nodes do not know the topology. We propose new protocols which disseminate data faster and use fewer broadcasts than the simple broadcast protocol.

The influence of inter-domain mobility on message stream response time in wired/wireless PROFIBUS-based networks

In previous works we have proposed a hybrid wired/wireless PROFIBUS solution where the interconnection between the heterogeneous media was accomplished through bridge-like devices with wireless stations being able to move between different wireless cells. Additionally, we had also proposed a worst-case timing analysis assuming that stations were stationary. In this paper we advance these previous works by proposing a worst-case timing analysis for the system’s message streams considering the effect of inter-cell mobility.

Transfering data from a server to an Android Mobile application: a case study

Nowadays, due to the incredible grow of the mobile devices market, when we want to implement a client-server applications we must consider mobile devices limitations. In this paper we discuss which can be the more reliable and fast way to exchange information between a server and an Android mobile application. This is an important issue because with a responsive application the user experience is more enjoyable. In this paper we present a study that test and evaluate two data transfer protocols, socket and HTTP, and three data serialization formats (XML, JSON and Protocol Buffers) using different environments and mobile devices to realize which is the most practical and fast to use.

Workload balancing in distributed virtual reality environments

Virtual Reality (VR) has grown to become state-of-theart technology in many business- and consumer oriented E-Commerce applications. One of the major design challenges of VR environments is the placement of the rendering process. The rendering process converts the abstract description of a scene as contained in an object database to an image. This process is usually done at the client side like in VRML [1] a technology that requires the client’s computational power for smooth rendering. The vision of VR is also strongly connected to the issue of Quality of Service (QoS) as the perceived realism is subject to an interactive frame rate ranging from 10 to 30 frames-per-second (fps), real-time feedback mechanisms and realistic image quality. These requirements overwhelm traditional home computers or even high sophisticated graphical workstations over their limits. Our work therefore introduces an approach for a distributed rendering architecture that gracefully balances the workload between the client and a clusterbased server. We believe that a distributed rendering approach as described in this paper has three major benefits: It reduces the clients workload, it decreases the network traffic and it allows to re-use already rendered scenes.

Critérios de projecto de um data centre

Dissertação de Natureza Científica para obtenção do grau de Mestre em Engenharia Civil na Área de Especialização de Edificações

Evaluation of a real time DGPS data server

The goal of the this paper is to show that the DGPS data Internet service we designed and developed provides campus-wide real time access to Differential GPS (DGPS) data and, thus, supports precise outdoor navigation. First we describe the developed distributed system in terms of architecture (a three tier client/server application), services provided (real time DGPS data transportation from remote DGPS sources and campus wide data dissemination) and transmission modes implemented (raw and frame mode over TCP and UDP). Then we present and discuss the results obtained and, finally, we draw some conclusions.

Mining protein structure data

The principal topic of this work is the application of data mining techniques, in particular of machine learning, to the discovery of knowledge in a protein database. In the first chapter a general background is presented. Namely, in section 1.1 we overview the methodology of a Data Mining project and its main algorithms. In section 1.2 an introduction to the proteins and its supporting file formats is outlined. This chapter is concluded with section 1.3 which defines that main problem we pretend to address with this work: determine if an amino acid is exposed or buried in a protein, in a discrete way (i.e.: not continuous), for five exposition levels: 2%, 10%, 20%, 25% and 30%. In the second chapter, following closely the CRISP-DM methodology, whole the process of construction the database that supported this work is presented. Namely, it is described the process of loading data from the Protein Data Bank, DSSP and SCOP. Then an initial data exploration is performed and a simple prediction model (baseline) of the relative solvent accessibility of an amino acid is introduced. It is also introduced the Data Mining Table Creator, a program developed to produce the data mining tables required for this problem. In the third chapter the results obtained are analyzed with statistical significance tests. Initially the several used classifiers (Neural Networks, C5.0, CART and Chaid) are compared and it is concluded that C5.0 is the most suitable for the problem at stake. It is also compared the influence of parameters like the amino acid information level, the amino acid window size and the SCOP class type in the accuracy of the predictive models. The fourth chapter starts with a brief revision of the literature about amino acid relative solvent accessibility. Then, we overview the main results achieved and finally discuss about possible future work. The fifth and last chapter consists of appendices. Appendix A has the schema of the database that supported this thesis. Appendix B has a set of tables with additional information. Appendix C describes the software provided in the DVD accompanying this thesis that allows the reconstruction of the present work.