Browsing by Issue Date, starting with "2016-08"
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- Voice health of teachers in the north of Portugal: epidemiological indicatorsPublication . Santos, Marisa; Araújo, André; Andrade, Ana; Amaro, JoanaEffective communication is a fundamental requisite for teachers and other professionals in the education field. Teachers are considered professional voice users, as voice quality is central to their speech and communicative profile. In the last decades, teachers have been shown to be a risk group concerning voice disorders. Several studies have already identified specific risk factors within this population, and proposed voice health promotion measures, including prevention actions, and labour policy changes. The intervention of teachers as active advocates of their own interests is crucial to ensure improvements in their working conditions and in their labour rights. This study is the first conducted in Portugal by the initiative of a teachers’ syndicate and was included in a Voice Prevention Program implemented in northern Portugal. Thus, the aims of this study were to determine a) the prevalence of voice disorders self-reported by teachers of this region, b) the most frequent voice symptoms, and c) the risk and protection factors associated with voice disorders in this group. An observational analytic case-control study was conducted for epidemiologic and correlation analysis purposes. The population of teachers in northern Portugal is estimated to be more than 60.000 professionals. Our sample was composed by 405 teachers which participated in a voice health promotion initiative. A self-directed questionnaire, previously developed and validated in other similar Portuguese studies, was used. This instrument included questions exploring demographic information, teaching experience and specialty, individual and environmental factors, health information, voice problems, and vocal symptoms, among others. The discrete variables were compared with chi-square test and a logistic regression model was used in order to calculate the adjusted odds ratios and their confidence intervals at 95%. Significance level was determined at the 5% level. The prevalence of self-reported voice disorders in the last year was 57%. Most relevant symptoms were: loss of voice control, dry throat, tired and weak voice, roughness, tightness or pressure, and voice breaks in speech. Several risk factors were identified, namely: being a female teacher, teaching in the 1st cycle, teaching non-specific disciplines, working in stuffy classrooms, having difficult access to water, and anxious personal profile, suffering of depressive disorder, sinusitis and gastro-oesophageal reflux disease, and yelling. Protection factors were: teaching in secondary level (high school), teaching physicochemical sciences, and working in a classroom with natural ventilation. Voice disorders’ prevalence was high, although in line with other regions and countries previously described. Considering existing literature, symptoms were also similar, but some risk factors where different. As this was a pilot study, future work will expand the number and geographical distribution of the sample, reinforcing its statistical relevance, in order to contribute to better labour policies in Portugal.
- Down syndrome: rapid maxillary expansion and ENT evolutionPublication . Moura, Carla Pinto; José, David; Andrade, Casimiro; Cunha, Luís Miguel; Cunha, Maria João; Clemente, Manuel António Caldeira Pais; Pueshel, Sigfried M.; Gonçalves, Maria João MoreiraDown syndrome is the most common aneu-ploid disorder among live born infants. Phenotypic character-istics include hypotonia, pharyngeal and maxillary hypoplasiawith relative macroglossia, and frequently constricted maxil-lary arch with nasal obstruction. This prospective study as-sesses the effects of rapid maxillary expansion (RME) onotolaryngologic disorders in children with Down syndrome.
- Lessons learned in building a middleware for smart gridsPublication . Macarulla, Marcel; Albano, Michele; Ferreira, Luís Lino; Teixeira, CésarSmart grids play an important role in the modernization and optimization of the existing electrical grid, to accomplish the current European Union Energy and Climate targets. Smart grids require distributed applications to manage the grid more efficiently. The performance of the distributed applications impacts on the communications delay time and on the timely interaction with the devices located in the users’ Home Area Networks. This paper presents the results of the ENCOURAGE project related to the development of a software platform to support smart grids. The work presented in this paper assesses four different middleware configurations and analyses the results on the delay performance tests. The results show that the mean end-to-end delay is between 310 ms and 453 ms in proper conditions. In terms of operational costs, the optimal configuration enables managing houses with less than 0.25 Euros per month per house. This paper justifies the maturity of the technology to support smart grids, and the possibility to transfer the ENCOURAGE project results to the industry.
- Applying Data Mining Techniques to Improve Breast Cancer DiagnosisPublication . Diz, Joana; Marreiros, Goreti; Freitas, AlbertoIn the field of breast cancer research, and more than ever, new computer aided diagnosis based systems have been developed aiming to reduce diagnostic tests false-positives. Within this work, we present a data mining based approach which might support oncologists in the process of breast cancer classification and diagnosis. The present study aims to compare two breast cancer datasets and find the best methods in predicting benign/malignant lesions, breast density classification, and even for finding identification (mass / microcalcification distinction). To carry out these tasks, two matrices of texture features extraction were implemented using Matlab, and classified using data mining algorithms, on WEKA. Results revealed good percentages of accuracy for each class: 89.3 to 64.7 % - benign/malignant; 75.8 to 78.3 % - dense/fatty tissue; 71.0 to 83.1 % - finding identification. Among the different tests classifiers, Naive Bayes was the best to identify masses texture, and Random Forests was the first or second best classifier for the majority of tested groups.