The economic impact of the event Volta a Portugal em Bicicleta 2015

This thesis is a Field Lab project requested by the organizer of the Volta a Portugal em Bicicleta 2015, Podium Events. Its purpose is to determine the economic impact of the event for the 21 host economies and Portugal as a whole. The Volta started in Viseu and ended in Lisboa, and lasted for two weeks from the 29th July until the 8th August. Results were computed by accessing data provided by the different economic agents involved in the event (spectators, attendees and organizer), through face-to-face surveys and contact via e-mail. The Volta generated a total economic impact of €76.261.876.

Mapping the Pasture Steppe in Bayankhongor, Mongolia: comparison of classification methods, using Landsat-8 and geophysical data

Grasslands in semi-arid regions, like Mongolian steppes, are facing desertification and degradation processes, due to climate change. Mongolia’s main economic activity consists on an extensive livestock production and, therefore, it is a concerning matter for the decision makers. Remote sensing and Geographic Information Systems provide the tools for advanced ecosystem management and have been widely used for monitoring and management of pasture resources. This study investigates which is the higher thematic detail that is possible to achieve through remote sensing, to map the steppe vegetation, using medium resolution earth observation imagery in three districts (soums) of Mongolia: Dzag, Buutsagaan and Khureemaral. After considering different thematic levels of detail for classifying the steppe vegetation, the existent pasture types within the steppe were chosen to be mapped. In order to investigate which combination of data sets yields the best results and which classification algorithm is more suitable for incorporating these data sets, a comparison between different classification methods were tested for the study area. Sixteen classifications were performed using different combinations of estimators, Landsat-8 (spectral bands and Landsat-8 NDVI-derived) and geophysical data (elevation, mean annual precipitation and mean annual temperature) using two classification algorithms, maximum likelihood and decision tree. Results showed that the best performing model was the one that incorporated Landsat-8 bands with mean annual precipitation and mean annual temperature (Model 13), using the decision tree. For maximum likelihood, the model that incorporated Landsat-8 bands with mean annual precipitation (Model 5) and the one that incorporated Landsat-8 bands with mean annual precipitation and mean annual temperature (Model 13), achieved the higher accuracies for this algorithm. The decision tree models consistently outperformed the maximum likelihood ones.

On the influences of the El Niño, La niña and Atlantic Dipole Paterni on the Amazonian Rainfall during 1960-1998

The influence of the large-scale climatic variability dominant modes in the Pacific and in the Atlantic on Amazonian rainfall is investigated. The composite technique of the Amazon precipitation anomalies is used in this work. The basis years for these composites arc those in the period 1960-1998 with occurrences of extremes in the Southern Oscillation (El Niño or La Niña) and the north/south warm (or cold) sea surface temperature (SST) anomalies dipole pattern in the tropical Atlantic. Warm (cold) dipole means positive (negative) anomalies in the tropical North Atlantic and negative (positive) anomalies in the tropical South Atlantic. Austral summer and autumn composites for extremes in the Southern Oscillation (El Niño or La Niña) and independently for north/south dipole pattern (warm or cold) of the SST anomalies in the tropical Atlantic present values (magnitude and sign) consistent with those found in previous works on the relationship between Amazon rainfall variations and the SST anomalies in the tropical Pacific and Atlantic. However, austral summer and autumn composites for the years with simultaneous occurrences of El Niño and warm north/south dipole of the SST anomalies in the tropical Atlantic show negative precipitation anomalies extending eastward over the center-eastern Amazon. This result indicates the important role played by the tropical Atlantic in the Amazon anomalous rainfall distribution.

Safety classification using GIS in decision-making process to define priority road interventions

Given the current economic situation of the Portuguese municipalities, it is necessary to identify the priority investments in order to achieve a more efficient financial management. The classification of the road network of the municipality according to the occurrence of traffic accidents is fundamental to set priorities for road interventions. This paper presents a model for road network classification based on traffic accidents integrated in a geographic information system. Its practical application was developed through a case study in the municipality of Barcelos. An equation was defined to obtain a road safety index through the combination of the following indicators: severity, property damage only and accident costs. In addition to the road network classification, the application of the model allows to analyze the spatial coverage of accidents in order to determine the centrality and dispersion of the locations with the highest incidence of road accidents. This analysis can be further refined according to the nature of the accidents namely in collision, runoff and pedestrian crashes.

Diurnal variability of rainfall in southwest Amazonia during the LBA-TRMM field campaign of the austral summer of 1999

The TRMM-LBA field campaign was held during the austral summer of 1999 in southwestern Amazonia. Among the major objectives, was the identification and description of the diurnal variability of rainfall in the region, associated with the different rain producing weather systems that occurred during the January-February season. By using a network of 40 digital rain gauges implemented in the state of Rondônia, and together with observations and analyses of circulation and convection, it was possible to identify details of the diurnal cycle of rainfall and the associated rainfall mechanisms. Rainfall episodes were characterized by regimes of "low-level easterly" and "westerly" winds in the context of the large-scale circulation. The westerly regime is related to an enhanced South Atlantic Convergence Zone (SACZ) and an intense and/or wide Low Level Jet (LLJ) east of the Andes, which can extend eastward towards Rondônia, even though some westerly regime episodes also show a LLJ that remains close to the foothill of the Andes. The easterly regime is related to easterly propagating systems (e.g. squall-lines) with possible weakened or less frequent LLJs and a suppressed SACZ. Diurnal variability of rainfall during westerly surface wind regime shows a characteristic maximum at late afternoon followed by a relatively weaker second maximum at early evening (2100 Local Standard Time LST). The easterly regime composite shows an early morning maximum followed by an even stronger maximum in the afternoon.

Microphysical evidence of the transition between predominant convective/stratiform rainfall associated with the intraseasonal oscillation in the Southwest Amazon

The distinction between convective and stratiform precipitation profiles around various precipitating systems existent in tropical regions is very important to the global atmospheric circulation, which is extremely sensitive to vertical latent heat distribution. In South America, the convective activity responds to the Intraseasonal Oscillation (IOS). This paper analyzes a disdrometer and a radar profiler data, installed in the Ji-Paraná airport, RO, Brazil, for the field experiment WETAMC/LBA & TRMM/LBA, during January and February of 1999. The microphysical analysis of wind regimes associated with IOS showed a large difference in type, size and microphysical processes of hydrometeor growth in each wind regime: easterly regimes had more turbulence and consequently convective precipitation formation, and westerly regimes had a more stratiform precipitation formation.

The drought of the century in the Amazon Basin: an analysis of the regional variation of rainfall in South America in 1926

The most severe drought in tropical South America during the 20th century occurred in 1926. This extreme El Nino year is further documented anecdotally, in an update of the river stage observations at Manaus, and in annual rainfall records. The annual rainfall anomaly is an east-west dipole over tropical South America, with drought to the west over the Amazon basin whose discharge is documented at Manaus, and with a surplus to the east and including the Nordeste region of Brazil. Speculations about a role for aerosol in aggravating the drought are discussed.

The influence of the conservation status and changes in the rainfall regime on forest-savanna mosaic dynamics in Northern Brazilian Amazonia

Coupled carbon/climate models are predicting changes in Amazon carbon and water cycles for the near future, with conversion of forest into savanna-like vegetation. However, empirical data to support these models are still scarce for Amazon. Facing this scenario, we investigated whether conservation status and changes in rainfall regime have influenced the forest-savanna mosaic over 20 years, from 1986 to 2006, in a transitional area in Northern Amazonia. By applying a spectral linear mixture model to a Landsat-5-TM time series, we identified protected savanna enclaves within a strictly protected nature reserve (Maracá Ecological Station - MES) and non-protected forest islands at its outskirts and compared their areas among 1986/1994/2006. The protected savanna enclaves decreased 26% in the 20-years period at an average rate of 0.131 ha year-1, with a greater reduction rate observed during times of higher precipitation, whereas the non-protected forest islands remained stable throughout the period of study, balancing the encroachment of forests into the savanna during humid periods and savannization during reduced rainfall periods. Thus, keeping favorable climate conditions, the MES conservation status would continue to favor the forest encroachment upon savanna, while the non-protected outskirt areas would remain resilient to disturbance regimes. However, if the increases in the frequency of dry periods predicted by climate models for this region are confirmed, future changes in extension and directions of forest limits will be affected, disrupting ecological services as carbon storage and the maintenance of local biodiversity.

Metabolic profiling and classification of propolis samples from Southern Brazil: an NMR-based platform coupled with machine learning

The chemical composition of propolis is affected by environmental factors and harvest season, making it difficult to standardize its extracts for medicinal usage. By detecting a typical chemical profile associated with propolis from a specific production region or season, certain types of propolis may be used to obtain a specific pharmacological activity. In this study, propolis from three agroecological regions (plain, plateau, and highlands) from southern Brazil, collected over the four seasons of 2010, were investigated through a novel NMR-based metabolomics data analysis workflow. Chemometrics and machine learning algorithms (PLS-DA and RF), including methods to estimate variable importance in classification, were used in this study. The machine learning and feature selection methods permitted construction of models for propolis sample classification with high accuracy (>75%, reaching 90% in the best case), better discriminating samples regarding their collection seasons comparatively to the harvest regions. PLS-DA and RF allowed the identification of biomarkers for sample discrimination, expanding the set of discriminating features and adding relevant information for the identification of the class-determining metabolites. The NMR-based metabolomics analytical platform, coupled to bioinformatic tools, allowed characterization and classification of Brazilian propolis samples regarding the metabolite signature of important compounds, i.e., chemical fingerprint, harvest seasons, and production regions.

Monovarietal extra-virgin olive oil classification: a fusion of human sensory attributes and an electronic tongue

Olive oil quality grading is traditionally assessed by human sensory evaluation of positive and negative attributes (olfactory, gustatory, and final olfactorygustatory sensations). However, it is not guaranteed that trained panelist can correctly classify monovarietal extra-virgin olive oils according to olive cultivar. In this work, the potential application of human (sensory panelists) and artificial (electronic tongue) sensory evaluation of olive oils was studied aiming to discriminate eight single-cultivar extra-virgin olive oils. Linear discriminant, partial least square discriminant, and sparse partial least square discriminant analyses were evaluated. The best predictive classification was obtained using linear discriminant analysis with simulated annealing selection algorithm. A low-level data fusion approach (18 electronic tongue signals and nine sensory attributes) enabled 100 % leave-one-out cross-validation correct classification, improving the discrimination capability of the individual use of sensor profiles or sensory attributes (70 and 57 % leave-one-out correct classifications, respectively). So, human sensory evaluation and electronic tongue analysis may be used as complementary tools allowing successful monovarietal olive oil discrimination.

Land cover classification of Lago Grande de Curuai floodplain (Amazon, Brazil) using multi-sensor and image fusion techniques

Given the limitations of different types of remote sensing images, automated land-cover classifications of the Amazon várzea may yield poor accuracy indexes. One way to improve accuracy is through the combination of images from different sensors, by either image fusion or multi-sensor classifications. Therefore, the objective of this study was to determine which classification method is more efficient in improving land cover classification accuracies for the Amazon várzea and similar wetland environments - (a) synthetically fused optical and SAR images or (b) multi-sensor classification of paired SAR and optical images. Land cover classifications based on images from a single sensor (Landsat TM or Radarsat-2) are compared with multi-sensor and image fusion classifications. Object-based image analyses (OBIA) and the J.48 data-mining algorithm were used for automated classification, and classification accuracies were assessed using the kappa index of agreement and the recently proposed allocation and quantity disagreement measures. Overall, optical-based classifications had better accuracy than SAR-based classifications. Once both datasets were combined using the multi-sensor approach, there was a 2% decrease in allocation disagreement, as the method was able to overcome part of the limitations present in both images. Accuracy decreased when image fusion methods were used, however. We therefore concluded that the multi-sensor classification method is more appropriate for classifying land cover in the Amazon várzea.

O evento levou... Comunicação de marca de uma cidade-sede

Tese de Doutoramento em Ciências da Comunicação - Especialidade em Comunicação Estratégica e Organizacional

Supercritical fluid and pharmaceutical applications. Part I: Process classification

The supercritical fluid technology has been target of many pharmaceuticals investigations in particles production for almost 35 years. This is due to the great advantages it offers over others technologies currently used for the same purpose. A brief history is presented, as well the classification of supercritical technology based on the role that the supercritical fluid (carbon dioxide) performs in the process.

Ontological representation of tumor-node-metastasis classification and an ontology-driven classifier: a study on colorectal cancer

Dissertação de mestrado integrado em Engenharia Biomédica (área de especialização em Informática Médica)