Land Use Change and Socio-Economic Evaluation in São Jorge Island (Between 15th and 20th Century)

The agenda explain the historical evolution of land uses in São Jorge Island (Azores- Portugal) between 15th-20th century.The economic exploitation of the island space prosecuted itself in simultaneous with his colony, one form to guarantee the auto supplying of the populations. First we assess the capacity of the island territory for different uses based on agronomic analysis and transform these capacities in attractiveness coefficients.Then we design a spatial interaction model with five different sectors which employment can be closely related with surface area, first to five zones in the island and within those zones to small plots of 1 hectare each.Finally we use historical data on population and main export crops in order to calibrate the model for each century. Therefore, based on data on the export crop and on the population it is possible to estimate the different land use of the island for all the sectors and to assess the carrying capacity of the island.

Dynamic analysis and active control of lattice structures

This thesis presents an investigation of the factors controlling the performance of two forms of active vibration control applied to lattice structures, such as those used for space applications. The structure considered is based on a lattice structure assembled by NASA in 1984. It consists of a satellite boom with 93 aluminium members connected rigidly through 33 spherical joints. The structure has two distinct forms of motion which are categorized in terms of short and long wavelength modes. The short wavelength modes occurs when the length of the individual members is a multiple of half wavelength of bending waves. The second category, named long wavelength modes occur when the length of the whole structure is a multiple of half wavelength of waves propagating by longitudinal motion in the structure. Simple expressions are derived to identify the factors that control the frequency bands where short and long wavelength modes occur. It is possible to alter the dynamic behaviour of the system by changing some of the factors in these expressions and thus study the active and passive control of vibration in a variety of such structures. The two strategies of active control considered in the thesis are feedforward control and integral force feedback control. Feedforward control usually requires deterministic forms of disturbance sources while feedback control can be applied to random disturbances. It has been found that short wavelength modes can reduce the performance in the feedback control strategy, while the results of feedforward control are not affected so much. To support this analysis, the energy dissipation and power flow mechanisms in the structure are studied. The results in this thesis are based on numerical simulations and experimental tests which have been used to validate the mathematical model of the structure

Dynamics Of Drop Impact Against Surfaces Covered With Langmuir-blodgett Layers

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)The dynamics of sucessive impacts of water droplets against flat glass surfaces covered by Langmuir-Blodgett films of zinc stearate with 1, 3, 5 and 7 layers was investigated. The structure and resistance of monolayers to the impact was evaluated by using fast images of the drop deformation, Brewster angle microscopy (BAM) and contact angle measurements. Eventual disruption (erosion) of the layers was investigated by using sum-frequency vibrational spectroscopy (SFG).272314320CNPqFapespConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP

A deep learning classifier for sentence classification in biomedical and computer science abstracts

The automatic classification of abstract sentences into its main elements (background, objectives, methods, results, conclusions) is a key tool to support scientific database querying, to summarize relevant literature works and to assist in the writing of new abstracts. In this paper, we propose a novel deep learning approach based on a convolutional layer and a bidirectional gated recurrent unit to classify sentences of abstracts. First, the proposed neural network was tested on a publicly available repository containing 20 thousand abstracts from the biomedical domain. Competitive results were achieved, with weight-averaged Precision, Recall and F1-score values around 91%, and an area under the ROC curve (AUC) of 99%, which are higher when compared to a state-of-the-art neural network. Then, a crowdsourcing approach using gamification was adopted to create a new comprehensive set of 4111 classified sentences from the computer science domain, focused on social media abstracts. The results of applying the same deep learning modeling technique trained with 3287 (80%) of the available sentences were below the ones obtained for the larger biomedical dataset, with weight-averaged Precision, Recall and F1-score values between 73 and 76%, and an AUC of 91%. Considering the dataset dimension as a likely important factor for such performance decrease, a data augmentation approach was further applied. This involved the use of text mining to translate sentences of the computer science abstract corpus while retaining the same meaning. Such approach resulted in slight improvements (around 2 percentage points) for the weight-averaged Recall and F1-score values.This work was supported by Fundação para a Ciência e Tecnologia (FCT) within the Project Scope: UID/CEC/00319/2019