Tópicos de física moderna no ensino médio

Proposta de ensino de Física para o 2º grau, que analisa aspectos da Física Moderna, incluindo alguns tópicos de Mecânica Quântica. Destina-se aos alunos de licenciatura em Física, bem como aos professores de Física do 2 O grau, com a intenção de levar os alunos a compreender melhor os conceitos relativísticos e sua relevância na vida moderna. Estas sugestões de aulas são descritas através de teorias, experiências, exemplos, perguntas e respostas que estabelecem vínculos entre cada teoria e o cotidiano. A primeira parte deste trabalho dedica-se analisa a Física Moderna, definições de alguns conceitos básicos relacionados com a Relatividade Restrita e com a Relatividade Geral. A segunda parte do trabalho refere ao estudo de alguns Tópicos de Mecânica Quântica: o efeito fotoelétrico e a dualidade onda partícula

Construção de um reator de plasma frio à pressão atmosférica com materiais de baixo custo / Construction of a cold plasma reactor at atmospheric pressure with low cost materials

Este trabalho visa a construção de um dispositivo gerador de plasma frio à pressão atmosférica. Este dispositivo além de compacto, com facilidade de transporte, possui uma boa relação custo benefício devido ao uso de materiais de baixo custo. O objetivo deste projeto foi construir um gerador de plasma utilizando materiais de baixo custo. Constituído basicamente por uma fonte de alta tensão com frequência e tensão ajustáveis para a ionização do gás utilizado, um braço suporte que pode ser ajustado à diferentes alturas e um dispositivo para a geração do plasma frio à pressão atmosférica. Utilizando gás argônio foi possível gerar plasma frio com um fluxo intenso e direcionável

Segmentation of Lung Tomographic Images Using U-Net Deep Neural Networks

Deep Neural Networks (DNNs) are among the best methods of Artificial Intelligence, especially in computer vision, where convolutional neural networks play an important role. There are numerous architectures of DNNs, but for image processing, U-Net offers great performance in digital processing tasks such as segmentation of organs, tumors, and cells for supporting medical diagnoses. In the present work, an assessment of U-Net models is proposed, for the segmentation of computed tomography of the lung, aiming at comparing networks with different parameters. In this study, the models scored 96% Dice Similarity Coefficient on average, corroborating the high accuracy of the U-Net for segmentation of tomographic images

Segmentation of Lung Tomographic Images Using U-Net Deep Neural Networks

Deep Neural Networks (DNNs) are among the best methods of Artificial Intelligence, especially in computer vision, where convolutional neural networks play an important role. There are numerous architectures of DNNs, but for image processing, U-Net offers great performance in digital processing tasks such as segmentation of organs, tumors, and cells for supporting medical diagnoses. In the present work, an assessment of U-Net models is proposed, for the segmentation of computed tomography of the lung, aiming at comparing networks with different parameters. In this study, the models scored 96% Dice Similarity Coefficient on average, corroborating the high accuracy of the U-Net for segmentation of tomographic images

U-NET aplicada a segmentação de ossos em microtomografias computadorizadas obtidas por radiação síncrotron para análises histomorfométricas

Actually, artificial intelligence (AI) participates increasingly in the elaboration of biomedical diagnoses. Clinical applications have used deep learning (DP) methods in the segmentation process, helping in the early treatment of diseases. Based on this principle, this work proposes, via Deep Neural Network (DNN), U-Net, to segment images of rat tibia, the main idea was to use AI architectures added to the image quantification technique, bone histomorphometry. To obtain the images, it was used the non-destructive technique of Computerized Microtomography obtained by X-rays from Synchrotron Radiation (µTC-RS). The initial objective was to enable models to eliminate marrow and other artifacts, leaving only bone; the final objective was to contribute to the state of the art in the use of PA-based methods in contrast to traditional segmentation methods, seeking to apply them to biomedical images. In this study, the developed models resulted in an average of approximately 90% for the Sørensen-Dice coefficient metric, demonstrating a high replicability rate.Atualmente, a inteligência artificial (IA) participa cada vez mais na elaboração de diagnósticos biomédicos. Aplicações clínicas têm utilizado de métodos de aprendizagem profunda (AP) no processo de segmentação, auxiliando no tratamento antecipado de doenças. Partindo desse pressuposto, este trabalho propõe, via Rede Neural Profunda (RNP), U-Net, segmentar imagens de tíbia de rato, tendo como ideia central utilizar arquiteturas de IA somada a técnica de quantificação de imagem, histomorfometria óssea. Para obtenção das imagens foi utilizado a técnica não destrutiva de Microtomografia Computadorizada obtida por raio-x oriundos de Radiação Síncrotron (µTC-RS). O objetivo inicial foi capacitar modelos para eliminar medula e outros artefatos, permanecendo somente osso; tendo como objetivo final buscar contribuir com o estado da arte no que dita o uso de métodos baseados em AP em contrapartida com métodos tradicionais de segmentação, na busca de aplicá-las em imagens biomédicas. Nesse estudo, os modelos desenvolvidos resultaram em uma média aproximada de 90% para a métrica do coeficiente do Sørensen-Dice, demonstrando uma alta taxa de replicabilidade

Assessment of neural networks training strategies for histomorphometric analysis of synchrotron radiation medical images

Abstract Micro-computed tomography (μCT) obtained by synchrotron radiation (SR) enables magnified images with a high space resolution that might be used as a non-invasive and non-destructive technique for the quantitative analysis of medical images, in particular the histomorphometry (HMM) of bony mass. In the preprocessing of such images, conventional operations such as binarization and morphological filtering are used before calculating the stereological parameters related, for example, to the trabecular bone microarchitecture. However, there is no standardization of methods for HMM based on μCT images, especially the ones obtained with SR X-ray. Notwithstanding the several uses of artificial neural networks (ANNs) in medical imaging, their application to the HMM of SR-μCT medical images is still incipient, despite the potential of both techniques. The contribution of this paper is the assessment and comparison of well-known training algorithms as well as the proposal of training strategies (combinations of training algorithms, sub-image kernel and symmetry information) for feed-forward ANNs in the task of bone pixels recognition in SR-μCT medical images. For a quantitative comparison, the results of a cross validation and a statistical analysis of the results for 36 training strategies are presented. The ANNs demonstrated both very low mean square errors in the validation, and good quality segmentation of the image of interest for application to HMM in SR-μCT medical images

Análise de Imagens Médicas através de Sistemas Computacionais Inteligentes para Apoio ao Diagnóstico Clínico

Histomorfometria óssea é uma importante análise na prevenção e tratamento de câncer e osteoporose,fornecendo informação quantitativa para diagnóstico clínico. A MicrotomografiaComputadorizada por Raios X é uma técnica de imagens não-destrutiva e não-invasiva comuma alta resolução que permite imagens ampliadas. Na análise histomorfométrica de tais imagens,é possível usar técnicas de tratamento tais como filtros morfológicos e binarização. Taistécnicas, no entanto, podem causar perda de informação relevante para a quantificação damassa óssea. Neste trabalho é descrita a aplicação de Redes Neurais Artificiais (RNA) parareconhecimento de tecido ósseo como parte de uma pesquisa sobre análise histomorfométricaem imagens cuja aquisição foi feita no Laboratório ELETTRA, em Trieste, Itália, na linha depesquisa SYRMEP (Synchrotron Radiation for Medical Physics – Radiação Síncrotron paraFísica Médica). Nestes testes iniciais, uma RNA Perceptron Multi-Camadas (PMC) Feed-Forward (FF) com algoritmo de aprendizagem de Retro-Propagação de Erro foi utilizada natarefa de reconhecimento. A qualidade dos resultados na tarefa da classificação de subimagensfoi verificada através de Curvas ROC (Receiver Operating Characteristic). Para este tipo deRNA obtivemos uma area sob a curva de 1,000, o que significa que a arquitetura e o treinamentoda RNA se mostraram adequados para a tarefa de reconhecimento de tecido ósseo. As imagensobtidas também são mostradas neste trabalho. Os resultados dos testes demonstraram aviabilidade de aplicação metodológica de Redes Neurais Artificiais e sua adequação às característicasdas imagens obtidas por Microtomografia Computadorizada por Raios X, para evitar perdas ocasionadas por outras técnicas de manipulação e tratamento de imagens. Tambémapresentamos uma breve descrição das principais tecnologias de implementação do projeto

Health-status outcomes with invasive or conservative care in coronary disease

BACKGROUND In the ISCHEMIA trial, an invasive strategy with angiographic assessment and revascularization did not reduce clinical events among patients with stable ischemic heart disease and moderate or severe ischemia. A secondary objective of the trial was to assess angina-related health status among these patients. METHODS We assessed angina-related symptoms, function, and quality of life with the Seattle Angina Questionnaire (SAQ) at randomization, at months 1.5, 3, and 6, and every 6 months thereafter in participants who had been randomly assigned to an invasive treatment strategy (2295 participants) or a conservative strategy (2322). Mixed-effects cumulative probability models within a Bayesian framework were used to estimate differences between the treatment groups. The primary outcome of this health-status analysis was the SAQ summary score (scores range from 0 to 100, with higher scores indicating better health status). All analyses were performed in the overall population and according to baseline angina frequency. RESULTS At baseline, 35% of patients reported having no angina in the previous month. SAQ summary scores increased in both treatment groups, with increases at 3, 12, and 36 months that were 4.1 points (95% credible interval, 3.2 to 5.0), 4.2 points (95% credible interval, 3.3 to 5.1), and 2.9 points (95% credible interval, 2.2 to 3.7) higher with the invasive strategy than with the conservative strategy. Differences were larger among participants who had more frequent angina at baseline (8.5 vs. 0.1 points at 3 months and 5.3 vs. 1.2 points at 36 months among participants with daily or weekly angina as compared with no angina). CONCLUSIONS In the overall trial population with moderate or severe ischemia, which included 35% of participants without angina at baseline, patients randomly assigned to the invasive strategy had greater improvement in angina-related health status than those assigned to the conservative strategy. The modest mean differences favoring the invasive strategy in the overall group reflected minimal differences among asymptomatic patients and larger differences among patients who had had angina at baseline

Initial invasive or conservative strategy for stable coronary disease

BACKGROUND Among patients with stable coronary disease and moderate or severe ischemia, whether clinical outcomes are better in those who receive an invasive intervention plus medical therapy than in those who receive medical therapy alone is uncertain. METHODS We randomly assigned 5179 patients with moderate or severe ischemia to an initial invasive strategy (angiography and revascularization when feasible) and medical therapy or to an initial conservative strategy of medical therapy alone and angiography if medical therapy failed. The primary outcome was a composite of death from cardiovascular causes, myocardial infarction, or hospitalization for unstable angina, heart failure, or resuscitated cardiac arrest. A key secondary outcome was death from cardiovascular causes or myocardial infarction. RESULTS Over a median of 3.2 years, 318 primary outcome events occurred in the invasive-strategy group and 352 occurred in the conservative-strategy group. At 6 months, the cumulative event rate was 5.3% in the invasive-strategy group and 3.4% in the conservative-strategy group (difference, 1.9 percentage points; 95% confidence interval [CI], 0.8 to 3.0); at 5 years, the cumulative event rate was 16.4% and 18.2%, respectively (difference, 121.8 percentage points; 95% CI, 124.7 to 1.0). Results were similar with respect to the key secondary outcome. The incidence of the primary outcome was sensitive to the definition of myocardial infarction; a secondary analysis yielded more procedural myocardial infarctions of uncertain clinical importance. There were 145 deaths in the invasive-strategy group and 144 deaths in the conservative-strategy group (hazard ratio, 1.05; 95% CI, 0.83 to 1.32). CONCLUSIONS Among patients with stable coronary disease and moderate or severe ischemia, we did not find evidence that an initial invasive strategy, as compared with an initial conservative strategy, reduced the risk of ischemic cardiovascular events or death from any cause over a median of 3.2 years. The trial findings were sensitive to the definition of myocardial infarction that was used