A GIS extension model to calculate urban heat island intensity based on urban geometry

This paper presents a simulation model, which was incorporated into a Geographic Information System (GIS), in order to calculate the maximum intensity of urban heat islands based on urban geometry data. The method-ology of this study stands on a theoretical-numerical basis (Okeâ s model), followed by the study and selection of existing GIS tools, the design of the calculation model, the incorporation of the resulting algorithm into the GIS platform and the application of the tool, developed as exemplification. The developed tool will help researchers to simulate UHI in different urban scenarios.

Predictors of dropout in a controlled clinical trial of psychotherapy for moderate depression

A significant number of psychotherapy clients remain untreated, and dropping out is one of the main reasons. Still, the literature around this subject is incoherent. The present study explores potential pre-treatment predictors of dropout in a sample of clients who took part in a clinical trial designed to test the efficacy of narrative therapy for major depressive disorder compared to cognitive-behavioral therapy. Logistic regression analysis showed that: (1) treatment assignment did not predict dropout, (2) clients taking psychiatric medication at intake were 80% less likely to drop out from therapy, compared to clients who were not taking medication, and (3) clients presenting anxious comorbidity at intake were 82% less likely to dropout compared to those clients not presenting anxious comorbidity. Results suggest that clinicians should pay attention to depressed clients who are not taking psychiatric medication or have no comorbid anxiety. More research is needed in order to understand this relationship.

Sensory intensity assessment of olive oils using an electronic tongue

Olive oils may be commercialized as intense, medium or light, according to the intensity perception of fruitiness, bitterness and pungency attributes, assessed by a sensory panel. In this work, the capability of an electronic tongue to correctly classify olive oils according to the sensory intensity perception levels was evaluated. Cross-sensitivity and non-specific lipid polymeric membranes were used as sensors. The sensor device was firstly tested using quinine monohydrochloride standard solutions. Mean sensitivities of 14±2 to 25±6 mV/decade, depending on the type of plasticizer used in the lipid membranes, were obtained showing the device capability for evaluating bitterness. Then, linear discriminant models based on sub-sets of sensors, selected by a meta-heuristic simulated annealing algorithm, were established enabling to correctly classify 91% of olive oils according to their intensity sensory grade (leave-one-out cross-validation procedure). This capability was further evaluated using a repeated K-fold cross-validation procedure, showing that the electronic tongue allowed an average correct classification of 80% of the olive oils used for internal-validation. So, the electronic tongue can be seen as a taste sensor, allowing differentiating olive oils with different sensory intensities, and could be used as a preliminary, complementary and practical tool for panelists during olive oil sensory analysis.

RNA-seq analysis of the Quercus suber root response to drought

Dissertação de mestrado em Plant Molecular Biology, Biotechnology and Bioentrepreneurship

Differential predictors of acute post-surgical pain intensity after abdominal hysterectomy and major joint arthroplasty

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