Associations between energetic expenditure, physical activity and body composition: exploratory study with industrial workers

This study, which involved a target population comprised by 292 workers of different industrial areas (metalmechanics, foundry, chemical, wood, food), aimed to verify the association between energy expenditure-EE, physical activity level-PAL and body composition (Body Mass Index-BMI, Waist-Hip Ratio-WHR and Waist To Height Ratio, WTHR) of participants. The work was completed with the description of the variables relating to the gender of the individuals (male and female) and the activities carried out in the two sectors of industrial work (administrative sector and productive sector). In this research, the statistical technique of principal components analysis (PCA) and the hierarchical analysis of clusters (HCA) were used. Sociodemographic and anthropometric data were collected as well as the level of physical activity and energy expenditure were assessed. The vast majority of individuals who spend greater energy expenditure and has more intense physical activity were male. Most of these workers are in the production sector. We can confirm that that both, gender and labor activity, are factors that have influence on the EE and the PAL.

Exploring TM image texture and its relationships with biomass estimation in Rondônia, Brazilian Amazon

Many texture measures have been developed and used for improving land-cover classification accuracy, but rarely has research examined the role of textures in improving the performance of aboveground biomass estimations. The relationship between texture and biomass is poorly understood. This paper used Landsat Thematic Mapper (TM) data to explore relationships between TM image textures and aboveground biomass in Rondônia, Brazilian Amazon. Eight grey level co-occurrence matrix (GLCM) based texture measures (i.e., mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment, and correlation), associated with seven different window sizes (5x5, 7x7, 9x9, 11x11, 15x15, 19x19, and 25x25), and five TM bands (TM 2, 3, 4, 5, and 7) were analyzed. Pearson's correlation coefficient was used to analyze texture and biomass relationships. This research indicates that most textures are weakly correlated with successional vegetation biomass, but some textures are significantly correlated with mature forest biomass. In contrast, TM spectral signatures are significantly correlated with successional vegetation biomass, but weakly correlated with mature forest biomass. Our findings imply that textures may be critical in improving mature forest biomass estimation, but relatively less important for successional vegetation biomass estimation.

Resolução da ambivalência em terapia narrativa

Dissertação de mestrado integrado em Psicologia

Discrimination of Brazilian propolis according to the seasoning using chemometrics and machine learning based on UV-Vis scanning data

Propolis is a chemically complex biomass produced by honeybees (Apis mellifera) from plant resins added of salivary enzymes, beeswax, and pollen. The biological activities described for propolis were also identified for donor plants resin, but a big challenge for the standardization of the chemical composition and biological effects of propolis remains on a better understanding of the influence of seasonality on the chemical constituents of that raw material. Since propolis quality depends, among other variables, on the local flora which is strongly influenced by (a)biotic factors over the seasons, to unravel the harvest season effect on the propolis chemical profile is an issue of recognized importance. For that, fast, cheap, and robust analytical techniques seem to be the best choice for large scale quality control processes in the most demanding markets, e.g., human health applications. For that, UV-Visible (UV-Vis) scanning spectrophotometry of hydroalcoholic extracts (HE) of seventy-three propolis samples, collected over the seasons in 2014 (summer, spring, autumn, and winter) and 2015 (summer and autumn) in Southern Brazil was adopted. Further machine learning and chemometrics techniques were applied to the UV-Vis dataset aiming to gain insights as to the seasonality effect on the claimed chemical heterogeneity of propolis samples determined by changes in the flora of the geographic region under study. Descriptive and classification models were built following a chemometric approach, i.e. principal component analysis (PCA) and hierarchical clustering analysis (HCA) supported by scripts written in the R language. The UV-Vis profiles associated with chemometric analysis allowed identifying a typical pattern in propolis samples collected in the summer. Importantly, the discrimination based on PCA could be improved by using the dataset of the fingerprint region of phenolic compounds ( = 280-400m), suggesting that besides the biological activities of those secondary metabolites, they also play a relevant role for the discrimination and classification of that complex matrix through bioinformatics tools. Finally, a series of machine learning approaches, e.g., partial least square-discriminant analysis (PLS-DA), k-Nearest Neighbors (kNN), and Decision Trees showed to be complementary to PCA and HCA, allowing to obtain relevant information as to the sample discrimination.

Narrative innovations predict symptom improvement: Studying innovative moments in narrative therapy of depression

Objective:Innovative moments (IMs) are moments in the therapeutic dialog that constitute exceptions toward the client's problems. These narrative markers of meaning transformation are associated with change in different models of therapy and diverse diagnoses. Our goal is to test if IMs precede symptoms change, or, on the contrary, are a mere consequence of symptomatic 15 change. Method: For this purpose, IMs and symptomatology (Outcome Questionnaire-10.2) were assessed at every session in a sample of 10 cases of narrative therapy for depression. Hierarchical linear modeling was conducted to explore whether (i) IMs in a given session predict patients' symptoms in the following session and/or (ii) symptoms in a given session predict IMs in the next session. Results: Results suggested that IMs are better predictors of symptoms than the reverse. Conclusions: These results are discussed considering the contribution of meanings and narrative processes' changes to symptomatic improvement.

Balanceamento e redefinição de layouts de células de costura de uma empresa da indústria automóvel

Dissertação de mestrado integrado em Engenharia e Gestão Industrial

Redes de inovação em pequenas e médias empresas: um estudo de caso no setor têxtil

Dissertação de mestrado em Engenharia Industrial

Predicting the distribution of forest tree species using topographic variables and vegetation index in eastern Acre, Brazil

Species distribution modeling has relevant implications for the studies of biodiversity, decision making about conservation and knowledge about ecological requirements of the species. The aim of this study was to evaluate if the use of forest inventories can improve the estimation of occurrence probability, identify the limits of the potential distribution and habitat preference of a group of timber tree species. The environmental predictor variables were: elevation, slope, aspect, normalized difference vegetation index (NDVI) and height above the nearest drainage (HAND). To estimate the distribution of species we used the maximum entropy method (Maxent). In comparison with a random distribution, using topographic variables and vegetation index as features, the Maxent method predicted with an average accuracy of 86% the geographical distribution of studied species. The altitude and NDVI were the most important variables. There were limitations to the interpolation of the models for non-sampled locations and that are outside of the elevation gradient associated with the occurrence data in approximately 7% of the basin area. Ceiba pentandra (samaúma), Castilla ulei (caucho) and Hura crepitans (assacu) is more likely to occur in nearby water course areas. Clarisia racemosa (guariúba), Amburana acreana (cerejeira), Aspidosperma macrocarpon (pereiro), Apuleia leiocarpa (cumaru cetim), Aspidosperma parvifolium (amarelão) and Astronium lecointei (aroeira) can also occur in upland forest and well drained soils. This modeling approach has potential for application on other tropical species still less studied, especially those that are under pressure from logging.

A peaceful pyramid? Hierarchy and anonymity in newspaper comment sections

Several studies have linked deindividuation to an increase in aggression and incivility. This paper seeks to ascertain the influence of anonymity and hierarchy in online aggression by comparing two different newspaper comment sections: one with a hierarchical system and the other with an equalitarian setting. This study distinguishes itself form previous works by analyzing systems where identification is optional and where identified and anonymous users coexist. The hierarchical solution might be relevant to dissuade aggression when optional identifiability is seen as an essential asset. Results show that a hierarchical system provides some improvements in terms of civility and comment moderation, but that poor implementation of the hierarchy causes perversions in the system and affects its effectiveness.

Biomimetic strategies to engineer mineralised human tissues

In the last few years, many reports have been describing promising biocompatible and biodegradable materials that can mimic in a certain extent the multidimensional hierarchical structure of bone, while are also capable of releasing bioactive agents or drugs in a controlled manner. Despite these great advances, new developments in the design and fabrication technologies are required to address the need to engineer suitable biomimetic materials in order tune cells functions, i.e. enhance cell-biomaterial interactions, and promote cell adhesion, proliferation, and differentiation ability. Scaffolds, hydrogels, fibres and composite materials are the most commonly used as biomimetics for bone tissue engineering. Dynamic systems such as bioreactors have also been attracting great deal of attention as it allows developing a wide range of novel in vitro strategies for the homogeneous coating of scaffolds and prosthesis with ceramics, and production of biomimetic constructs, prior its implantation in the body. Herein, it is overviewed the biomimetic strategies for bone tissue engineering, recent developments and future trends. Conventional and more recent processing methodologies are also described.

Avaliação de desempenho docente: Contributos para as práticas de avaliação na escola

Tese de Doutoramento em Ciências da Educação (Especialidade em Desenvolvimento Curricular)

Concise server-wide causality management for eventually consistent data stores

Large scale distributed data stores rely on optimistic replication to scale and remain highly available in the face of net work partitions. Managing data without coordination results in eventually consistent data stores that allow for concurrent data updates. These systems often use anti-entropy mechanisms (like Merkle Trees) to detect and repair divergent data versions across nodes. However, in practice hash-based data structures are too expensive for large amounts of data and create too many false conflicts. Another aspect of eventual consistency is detecting write conflicts. Logical clocks are often used to track data causality, necessary to detect causally concurrent writes on the same key. However, there is a nonnegligible metadata overhead per key, which also keeps growing with time, proportional with the node churn rate. Another challenge is deleting keys while respecting causality: while the values can be deleted, perkey metadata cannot be permanently removed without coordination. Weintroduceanewcausalitymanagementframeworkforeventuallyconsistentdatastores,thatleveragesnodelogicalclocks(BitmappedVersion Vectors) and a new key logical clock (Dotted Causal Container) to provides advantages on multiple fronts: 1) a new efficient and lightweight anti-entropy mechanism; 2) greatly reduced per-key causality metadata size; 3) accurate key deletes without permanent metadata.

Foreign acquisition and internal organization

NIPE WP 05/2016

Extremely strong and tough hydrogels as prospective candidates for tissue repair – A review

Ideal candidates for the repair of robust biological tissues should exhibit diverse features such as biocompatibility, strength, toughness, self-healing ability and a well-defined structure. Among the available biomaterials, hydrogels, as highly hydrated 3D-crosslinked polymeric networks, are promising for Tissue Engineering purposes as result of their high resemblance with native extracellular matrix. However, these polymeric structures often exhibit a poor mechanical behavior, hampering their use in load-bearing applications. During the last years, several efforts have been made to create new strategies and concepts to fabricate strong and tough hydrogels. Although it is already possible to shape the mechanical properties of artificial hydrogels to mimic biotissues, critical issues regarding, for instance, their biocompatibility and hierarchical structure are often neglected. Therefore, this review covers the structural and mechanical characteristics of the developed methodologies to toughen hydrogels, highlighting some pioneering efforts employed to combine the aforementioned properties in natural-based hydrogels.

Modelos de regressão multinível no estudo do desempenho escolar

Dissertação de mestrado em Estatística