Uma abordagem multiespectral espacial para a cartografia de terrenos abandonados

Trabalho de projeto de mestrado, Sistemas de Informação Geográfica (Tecnologias e Aplicações), Universidade de Lisboa, Faculdade de Ciências, 2019A produção mundial de alimentos enfrenta graves limitações com as mudanças climáticas e as restrições de água e de solos aráveis. A previsão do aumento acelerado da população mundial pode ameaçar gravemente a disponibilização de alimentos a nível mundial. Segundo um relatório do Eurostat de 2016, os territórios rurais representam mais de 77% do território da União Europeia (EU), os quais representam uma fonte de alimentos e de postos de trabalho. Ainda numa publicação da Eurostat de 2017, relativamente às medidas a adotar para a redução da fome a nível europeu, diz-se que uma em cada nove pessoas a nível mundial nos dias de hoje, se encontra malnutrida. Para combater estes números, a produtividade agrícola e os lucros dos produtores de alimentos em pequena escala, e a nível local deverão aumentar para o dobro segundo essa publicação. Neste projeto pretende-se localizar e classificar de forma automatizada, territórios que estando desocupados, possam vir a ser alvo de práticas agrícolas caso a sua localização e condicionantes locais o possibilitem. Será testada uma abordagem baseada em objetos espectais homogéneos individualizados por segmentação espetral da imagem. A segmentação da imagem será executada em imagens dos meses de verão e primavera, onde serão feitos cálculos para a média (MD) e para o desvio padrão (DP) do índice de vegetação de diferença normalizada (NDVI) ou Normalized Difference Vegetation Index na literatura anglo-saxónica. Os cálculos foram executados em imagens que foram adquiridas pelos satélite Landsat 5 e Landsat 8 ao longo de um período total de 15 anos não consecutivos. Foram conseguidos, dois produtos em formato “raster”, que revelaram a probabilidade de ocupação do território e que foram denominados de “máscara para regiões ocupadas: ZO” e “máscara para regiões livres: ZL” respetivamente. Foi possível concluir que as técnicas e métodos utilizados servem os objetivos propostos.World food production faces serious constraints with climate change and with water and arable land restrictions. The predicted rapid growth of the world's population can pose serious limitations in the availability of food worldwide. According to a Eurostat report from 2016, the rural territories represent more than 77% of the territory of the European Union (EU), which represent a source of food and jobs. Another Eurostat publication dated from 2017 in relation to measures to reduce hunger at a European level, alleged one in nine people worldwide is malnourished nowadays. To decrease these numbers, agricultural productivity and profits of small-scale food producers are expected to double according to the report. The current project aims to locate and classify in an automated way, vacant territories that may be targeted as agricultural land in case their positioning, local conditions and certain features used to classify the areas, make it feasible. This project will be testing an approach based on homogeneous specular objects individualized by spectral segmentation of the image. Segmentation will be performed on summer months images which were calculated for the mean (MD) and for standard deviation (DP) of the Normalized Difference Vegetation Index (NDVI). The images were acquired by Landsat 5 and Landsat 8 satellites for a total of 15 non-consecutive years. Two products in a raster format were automatically produced which revealed the probability of a certain territory to be occupied named “máscara para regiões ocupadas” and the other product being the probability of the territory to be available and named “máscara para regiões livres”. The product with the best results was the one that representing the non-occupied regions: “máscara para regiões livres”. In case there is a follow up work from this project, this product is here recommended to be used as one of the variables to include in a linear model for farming aptitude. It was possible to conclude that the techniques and methods used serve the proposed goals

Does clinical examination aid in the diagnosis of urinary tract infections in women? A systematic review and meta-analysis

<p>Abstract</p> <p>Background</p> <p>Clinicians should be aware of the diagnostic values of various symptoms, signs and antecedents. This information is particularly important in primary care settings, where sophisticated diagnostic approaches are not always feasible. The aim of the study is to determine the probability that various symptoms, signs, antecedents and tests predict urinary tract infection (UTI) in women.</p> <p>Methods</p> <p>We conducted a systematic search of the MEDLINE and EMBASE databases to identify articles published in all languages through until December 2008. We particularly focused on studies that examined the diagnostic accuracy of at least one symptom, sign or patient antecedent related to the urinary tract. We included studies where urine culture, a gold standard, was preformed by primary care providers on female subjects aged at least 14 years. A meta-analysis of the likelihood ratio was performed to assess variables related to the urinary tract symptoms.</p> <p>Results</p> <p>Of the 1, 212 articles identified, 11 met the selection criteria. Dysuria, urgency, nocturia, sexual activity and urgency with dysuria were weak predictors of urinary tract infection, whereas increases in vaginal discharge and suprapubic pain were weak predictors of the absence of infection. Nitrites or leukocytes in the dipstick test are the only findings that clearly favored a diagnosis of UTI.</p> <p>Conclusions</p> <p>Clinical findings do not aid in the diagnosis of UTI among women who present with urinary symptoms. Vaginal discharge is a weak indicator of the absence of infection. The urine dipstick test was the most reliable tool for detecting UTI.</p

DOCREP: Document Representation for Natural Language Processing

The field of natural language processing (NLP) revolves around the computational interpretation and generation of natural language. The language typically processed in NLP occurs in paragraphs or documents rather than in single isolated sentences. Despite this, most NLP tools operate over one sentence at a time, not utilising the context outside of the sentence nor any of the metadata associated with the underlying document. One pragmatic reason for this disparity is that representing documents and their annotations through an NLP pipeline is difficult with existing infrastructure. Representing linguistic annotations for a text document using a plain text markupbased format is not sufficient to capture arbitrarily nested and overlapping annotations. Despite this, most linguistic text corpora and NLP tools still operate in this fashion. A document representation framework (DRF) supports the creation of linguistic annotations stored separately to the original document, overcoming this nesting and overlapping annotations problem. Despite the prevalence of pipelines in NLP, there is little published work on, or implementations of, DRFs. The main DRFs, GATE and UIMA, exhibit usability issues which have limited their uptake by the NLP community. This thesis aims to solve this problem through a novel, modern DRF, DOCREP; a portmanteau of document representation. DOCREP is designed to be efficient, programming language and environment agnostic, and most importantly, easy to use. We want DOCREP to be powerful and simple enough to use that NLP researchers and language technology application developers would even use it in their own small projects instead of developing their own ad hoc solution. This thesis begins by presenting the design criteria for our new DRF, extending upon existing requirements from the literature with additional usability and efficiency requirements that should lead to greater use of DRFs. We outline how our new DRF, DOCREP, differs from existing DRFs in terms of the data model, serialisation strategy, developer interactions, support for rapid prototyping, and the expected runtime and environment requirements. We then describe our provided implementations of DOCREP in Python, C++, and Java, the most common languages in NLP; outlining their efficiency, idiomaticity, and the ways in which these implementations satisfy our design requirements. We then present two different evaluations of DOCREP. First, we evaluate its ability to model complex linguistic corpora through the conversion of the OntoNotes 5 corpus to DOCREP and UIMA, outlining the differences in modelling approaches required and efficiency when using these two DRFs. Second, we evaluate DOCREP against our usability requirements from the perspective of a computational linguist who is new to DOCREP. We walk through a number of common use cases for working with text corpora and contrast traditional approaches again their DOCREP counterpart. These two evaluations conclude that DOCREP satisfies our outlined design requirements and outperforms existing DRFs in terms of efficiency, and most importantly, usability. With DOCREP designed and evaluated, we then show how NLP applications can harness document structure. We present a novel document structureaware tokenization framework for the first stage of fullstack NLP systems. We then present a new structureaware NER system which achieves stateoftheart results on multiple standard NER evaluations. The tokenization framework produces its tokenization, sentence boundary, and document structure annotations as native DOCREP annotations. The NER system consumes DOCREP annotations and utilises many components of the DOCREP runtime. We believe that the adoption of DOCREP throughout the NLP community will assist in the reproducibility of results, substitutability of components, and overall quality assurance of NLP systems and corpora, all of which are problematic areas within NLP research and applications. This adoption will make developing and combining NLP components into applications faster, more efficient, and more reliable

The experience of transition and adjustment for mature-age, undergraduate students in their first year of university

© 2015 Dr. Lesleigh Merryn Dawborn-GundlachThe increasingly diverse enrolment of first year undergraduate students at tertiary institutions in Australia, and overseas, raises issues about the transition of different groups of students and the support and services required to make a positive adjustment to university. Using a pragmatic approach, this study examined the experiences of transition of mature-age students to university and has added to understandings of the transition experiences of mature-age students in their first undergraduate university courses. The study adopted a sequential mixed methods approach using qualitative and quantitative research methods. The study addressed four research questions: 1. How well do mature-age, undergraduate students adjust to university? 2. What are the experiences of mature-age, undergraduate students in relation to their academic, social and personal adjustment to university? 3. What are the personal and demographic factors which affect mature-age student adjustment to university? 4. What strategies or services support mature-age, undergraduate students through tertiary transition? The findings suggest that the mature-age students in the current study adjusted well, overall, to university; however, they had lower levels of adjustment in the domains of Social and Personal Adjustment than in the domain of Academic Adjustment. Thematic analyses of the qualitative data provide a comprehensive understanding of students’ adjustment experiences; the factors affecting their adjustment, and the strategies and support required for enhancing university adjustment. Difficulties for students in their academic and social adjustment included concerns about their academic skill levels, assessment and group-work, interactions with other students and loneliness and social dislocation. Family and financial obligations, the ability to maintain an appropriate life/university balance, age and identity as a student, were issues that affected students’ personal adjustment. The implication of this study for tertiary institutions is the need to understand the student demographic and provide appropriate programs and support services to ensure the needs of all first year students, including mature-age students are met

Uma abordagem multiespectral espacial para a cartografia de terrenos abandonados

Trabalho de projeto de mestrado, Sistemas de Informação Geográfica (Tecnologias e Aplicações), Universidade de Lisboa, Faculdade de Ciências, 2019A produção mundial de alimentos enfrenta graves limitações com as mudanças climáticas e as restrições de água e de solos aráveis. A previsão do aumento acelerado da população mundial pode ameaçar gravemente a disponibilização de alimentos a nível mundial. Segundo um relatório do Eurostat de 2016, os territórios rurais representam mais de 77% do território da União Europeia (EU), os quais representam uma fonte de alimentos e de postos de trabalho. Ainda numa publicação da Eurostat de 2017, relativamente às medidas a adotar para a redução da fome a nível europeu, diz-se que uma em cada nove pessoas a nível mundial nos dias de hoje, se encontra malnutrida. Para combater estes números, a produtividade agrícola e os lucros dos produtores de alimentos em pequena escala, e a nível local deverão aumentar para o dobro segundo essa publicação. Neste projeto pretende-se localizar e classificar de forma automatizada, territórios que estando desocupados, possam vir a ser alvo de práticas agrícolas caso a sua localização e condicionantes locais o possibilitem. Será testada uma abordagem baseada em objetos espectais homogéneos individualizados por segmentação espetral da imagem. A segmentação da imagem será executada em imagens dos meses de verão e primavera, onde serão feitos cálculos para a média (MD) e para o desvio padrão (DP) do índice de vegetação de diferença normalizada (NDVI) ou Normalized Difference Vegetation Index na literatura anglo-saxónica. Os cálculos foram executados em imagens que foram adquiridas pelos satélite Landsat 5 e Landsat 8 ao longo de um período total de 15 anos não consecutivos. Foram conseguidos, dois produtos em formato “raster”, que revelaram a probabilidade de ocupação do território e que foram denominados de “máscara para regiões ocupadas: ZO” e “máscara para regiões livres: ZL” respetivamente. Foi possível concluir que as técnicas e métodos utilizados servem os objetivos propostos.World food production faces serious constraints with climate change and with water and arable land restrictions. The predicted rapid growth of the world's population can pose serious limitations in the availability of food worldwide. According to a Eurostat report from 2016, the rural territories represent more than 77% of the territory of the European Union (EU), which represent a source of food and jobs. Another Eurostat publication dated from 2017 in relation to measures to reduce hunger at a European level, alleged one in nine people worldwide is malnourished nowadays. To decrease these numbers, agricultural productivity and profits of small-scale food producers are expected to double according to the report. The current project aims to locate and classify in an automated way, vacant territories that may be targeted as agricultural land in case their positioning, local conditions and certain features used to classify the areas, make it feasible. This project will be testing an approach based on homogeneous specular objects individualized by spectral segmentation of the image. Segmentation will be performed on summer months images which were calculated for the mean (MD) and for standard deviation (DP) of the Normalized Difference Vegetation Index (NDVI). The images were acquired by Landsat 5 and Landsat 8 satellites for a total of 15 non-consecutive years. Two products in a raster format were automatically produced which revealed the probability of a certain territory to be occupied named “máscara para regiões ocupadas” and the other product being the probability of the territory to be available and named “máscara para regiões livres”. The product with the best results was the one that representing the non-occupied regions: “máscara para regiões livres”. In case there is a follow up work from this project, this product is here recommended to be used as one of the variables to include in a linear model for farming aptitude. It was possible to conclude that the techniques and methods used serve the proposed goals

Thematic Analysis of Qualitative Data Using Diverse yet Complementary Approaches

This paper explores two different but complementary methods of thematic analysis to code interview data and survey questionnaire responses. The first approach represents the traditional ‘by hand’ identification of common themes. The second approach utilises the software tool QSR NVivo. The strength of this as a process lies in the triangulation of the two methods, providing enhanced identification and validation of the emerging significant themes