An International Comparison of Productivity Change in Agriculture and the Economy as a Whole

A common and longstanding assumption in the economic growth literature has been that total factor productivity growth is lower in the agriculture sector than in the rest of the economy. Using a stochastic production frontier finite mixture model, labor productivity change is decomposed into catch-up, technological change and factor accumulation effects and stochastic shocks. This decomposition is investigated separately in the agriculture sector and the economy as a whole using a balanced panel data set of 45 countries in different development stages during the time period 1967-1992. The impact of labor productivity change components on the evolution of the cross-country counterfactual distribution of labor productivity is also analyzed. For the overall economy, the empirical results indicate that growth and the twin-peak distribution of labor productivity are driven by capital deepening. However, the results for the agriculture sector suggest that labor productivity distribution is brought by total factor productivity changes rather than factor accumulation. Furthermore, the agriculture sector exhibits reductions in capital per worker as well as stronger catch-up and technological change effects. Thus, growth of the rest of the economy appears to owe more to capital deepening and resource reallocation from agriculture than to faster productivity change.Agriculture, Labor Productivity Growth, Catch-Up, Total Factor Productivity, Factor Accumulation, Panel data, Stochastic Production Frontier, Finite Mixture Model.

PhysicTV - Motion-based physical rehabilitation games for the Google-TV

O termo PhysicTV é composto por dois termos distintos. O primeiro (Physic - Físico) refere-se ao aspeto físico associado ao objetivo da plataforma e o segundo (TV - Televisão) refere-se ao aspeto tecnológico uma vez que os jogos a desenvolver vão ser jogados numa televisão.Enquanto plataforma, o PhysicTV contém duas tecnologias principais: XtionPRO e GoogleTV. Os principais objetivos deste projeto são a integração entre essas tecnologias e o desenvolvimento de jogos eletrónicos que possam ser utilizados para efeitos de reabilitação. O primeiro desses objetivos constituirá uma vertente inovadora e o segundo vai permitir ao fisioterapeuta uma ligeira mudança nas sessões com os seus pacientes, tornando-as mais dinâmicas e mantendo os pacientes mais motivados aquando da realização de exercícios. Com o XtionPRO e os seus sensores de movimento, é possível ao utilizador interagir com o sistema através dos movimentos dos braços, permitindo-lhe realizar exercícios e ir melhorando durante as sessões.PhysicTV as a concept is composed of two distinct terms. The first (Physic) relates to the physical aspect associated with the platform and the second (TV) relates to the technological component of the project since the games to developed will be played on a television screen.As a platform, PhysicTV contains two main technologies: XtionPRO and GoogleTV. The main goals of this project are the successful integration between those two technologies and the development of electronic games that can be used for rehabilitation purposes. The first goal will guarantee an inovative side and the second will allow the physioterapist to change a bit the sessions with his patients, making those sessions more dynamic and keeping the patients more motivated when doing the exercises. With the XtionPRO, that has motion sensors, it is possible for the user to interact with the system with arm movements, allowing him/her to do physical exercises and consequently keep improving during the sessions

Os Espelhos da Vanitas: a dinâmica reflexiva e a crítica da representação na filosofia e na pintura do século XVII

Tese apresentada para cumprimento dos requisitos necessários à obtenção do grau de Doutor em Filosofia, especialidade de Filosofia do conhecimento e EpistemologiaEsta tese ocupa-se da noção de representação, no âmbito do pensamento do século XVII. Partiu de duas hipóteses cruzadas: 1) que a noção de representação tem uma dinâmica reflexiva que é imanente ao acto de representar, a qual implica, por isso, uma representação desse acto, ou seja, uma auto-representação; 2) que é possível estabelecer uma analogia profícua entre a representação mental e a representação pictórica, como elas foram entendidas no discurso filosófico e na prática pictórica do século XVII, e que essa analogia ilumina a natureza e as propriedades da noção de representação. Numa primeira parte, a tese procura caracterizar os elementos principais daquilo a que pode chamar-se de paradigma representativo na filosofia e na pintura do século XVII. Essa caracterização faz-se a partir da «Lógica» de Port-Royal, cuja primeira parte se identifica com uma lógica ou, em rigor, com uma epistemologia da ideia, noção ambígua mas que é a sede conceptual de uma teoria do conhecimento representacionista. Os traços do sistema clássico da representação revelados por uma “teoria do signo representativo”, na «Lógica», podem ser complementados pela interpretação arnaldiana de uma teoria da percepção, explicitada na polémica das ideias entre Arnauld e Malebranche, que confirma, ao mesmo tempo, o sentido e o alcance da representatividade do pensamento na epistemologia cartesiana. Por outro lado, a troca epistolar entre Arnauld e Leibniz sobre a noção de “expressão” permite uma interpretação alternativa dessa representatividade do pensamento, reiterando, no entanto, a dinâmica reflexiva do acto de representar, que é próprio da natureza da substância individual. Num segundo momento dessa caracterização, é possível fazer uma aproximação entre o discurso filosófico e a prática artística no que respeita às suas teorias da percepção – a teoria da visão de Kepler, a Dióptrica de Descartes e as catóptricas de Mersenne e Nicéron – e às práticas da representação – a representação em perspectiva linear frontal e anamorfótica

Effect of technical parameters on dose and image quality in a computed radiography system

The discovery of X-rays was undoubtedly one of the greatest stimulus for improving the efficiency in the provision of healthcare services. The ability to view, non-invasively, inside the human body has greatly facilitated the work of professionals in diagnosis of diseases. The exclusive focus on image quality (IQ), without understanding how they are obtained, affect negatively the efficiency in diagnostic radiology. The equilibrium between the benefits and the risks are often forgotten. It is necessary to adopt optimization strategies to maximize the benefits (image quality) and minimize risk (dose to the patient) in radiological facilities. In radiology, the implementation of optimization strategies involves an understanding of images acquisition process. When a radiographer adopts a certain value of a parameter (tube potential [kVp], tube current-exposure time product [mAs] or additional filtration), it is essential to know its meaning and impact of their variation in dose and image quality. Without this, any optimization strategy will be a failure. Worldwide, data show that use of x-rays has been increasingly frequent. In Cabo Verde, we note an effort by healthcare institutions (e.g. Ministry of Health) in equipping radiological facilities and the recent installation of a telemedicine system requires purchase of new radiological equipment. In addition, the transition from screen-films to digital systems is characterized by a raise in patient exposure. Given that this transition is slower in less developed countries, as is the case of Cabo Verde, the need to adopt optimization strategies becomes increasingly necessary. This study was conducted as an attempt to answer that need. Although this work is about objective evaluation of image quality, and in medical practice the evaluation is usually subjective (visual evaluation of images by radiographer / radiologist), studies reported a correlation between these two types of evaluation (objective and subjective) [5-7] which accredits for conducting such studies. The purpose of this study is to evaluate the effect of exposure parameters (kVp and mAs) when using additional Cooper (Cu) filtration in dose and image quality in a Computed Radiography system

Segmentation and detection of Woody Trunks using Deep Learning for Agricultural Robotics

This project aims to help the implementation of image processing algorithms in agriculture robots so that they are robust to different aspects like weather conditions, vineyard terrain irregularities and efficient to operate in small robots with low energy consumption. Along with this, Deep Learning models became more complex. Thus, not all processors can handle such models. So, to develop a system with real-time detection for low-power processors becomes demanding because there is a lack of real datasets annotated for vine trunks and expedite tools to support this work. To support the deployment of deep-learning technology in agricultural robots, this dissertation presents the first public dataset of vine trunk images, called VineSet, with respective annotations for each trunk. This dataset was built from scratch, having a total of 9481 images of 5 different Douro vineyards, resulting from the images initially collected by AgRob V16 and various augmentation operations. Then, this dataset was used to train different state-of-the-art Deep Learning object detection models, together with Google Tensor Processing Unit. In parallel with this, this work presents an assisted labelling procedure that uses our trained models to reduce the time spent on labelling in the creation of new datasets. Also, this dissertation proposes the segmentation of vine trunks, using object detection models and semantic segmentation models. In this way, all the work done will allow the integration of edge-AI algorithms in SLAM, like Vine-SLAM, which will serve for the localisation and mapping of the robot, through natural markers in the vineyards.Agricultural robots need image processing algorithms, which should be reliable under all weather conditions and be computationally efficient. Furthermore, several limitations may arise, such as the characteristic vineyard terrain irregularities or overfitting in the training of neural networks that may affect the performance. In parallel with this, the evolution of Deep Learning models became more complex, demanding an increased computational complexity. Thus, not all processors can handle such models efficiently. So, developing a system with a real-time performance for low-power processors becomes demanding and is nowadays a research and development challenge because there is a lack of real data sets annotated and expedite tools to support this work. To support the deployment of deep-learning technology in agricultural robots, this dissertation presents a public VineSet dataset, the first public large collection of vine trunk images. The dataset was built from scratch, having a total of 9481 real image frames and providing the vine trunks annotations in each one of them. VineSet is composed of RGB and thermal images of 5 different Douro vineyards, with 952 initially collected by AgRob V16 robot, and others 8529 image frames resulting from a vast number of augmentation operations. To check the validity and usefulness of this VineSet dataset, in this work is presented an experimental baseline study, using state-of-the-art Deep Learning models together with Google Tensor Processing Unit. To simplify the task of augmentation in the creation of future datasets, we propose an assisted labelling procedure - by using our trained models - to reduce the labelling time, in some cases ten times faster per frame. This dissertation presents preliminary results to support future research in this topic, for example with VineSet leads possible to train (by transfer learning procedure) existing deep neural networks with Average Precision (AP) higher than 80% for vineyards trunks detection. For example, an AP of 84.16% was achieved for SSD MobileNet-V1. Also, the models trained with VineSet present good results in other environments such as orchards or forests. Our automatic labelling tool proves this, reducing annotation time by more than 30% in various areas of agriculture and more than 70% on vineyards. In this dissertation, we also propose the segmentation of the vine trunks. Firstly, object detection models were used together with VineSet to perform the trunk segmentation. To evaluate the performance of the different models, a script that implements some metrics of semantic segmentation was built. The results showed that the object detection models trained with VineSet were not only suitable for trunk detection but also trunk segmentation. For example, a DICE Similarity Index (DSI) of 70.78% was achieved for SSD MobileNet-V1. Finally, semantic segmentation was also briefly approached. A subset of the images of VineSet was used to train several models. Results show that semantic segmentation can substitute DL-based object detection models for pixel-based classification if a proper training set is provided. In this way, all the work done will allow the integration of edge-AI algorithms in SLAM, like Vine-SLAM, which will serve for the localisation and mapping of the robot, through natural markers in the vineyards