Critérios empiricamente verificáveis para a avaliação do vocabulário na produção oral do professor de língua estrangeira

O EPPLE (Exame de Proficiência para Professores de Línguas Estrangeiras) é um instrumento avaliativo em constante aprimoramento (CONSOLO e TEIXEIRA DA SILVA, 2014; COLOMBO, 2019) que se volta para a avaliação da proficiência do professor de LE no contexto brasileiro. Atualmente, o EPPLE possui apenas uma escala holística para a avaliação do desempenho oral de candidatos, desenvolvida intuitivamente. Dessa forma, é necessário que se construa uma escala de proficiência analítica empiricamente embasada para a produção de um argumento para a validade dos critérios avaliativos que classificam os desempenhos orais no exame. A precisão do vocabulário empregado, entre outros construtos da competência linguística, é de extrema importância para a proficiência oral do professor de LE, uma vez que o professor ensina a LE utilizando a língua-alvo em contextos comunicativos (FREEMAN et al., 2015). O presente estudo apresenta resultados da aplicação dos EBBs (Empirically derived, Binary choice, Boundary definition scales), metodologia empírica para o desenvolvimento de critérios avaliativos e escalas de proficiência linguística desenvolvida por Upshur e Turner (1999), em amostras do banco de dados do EPPLE. A partir da análise, construiu-se de um quadro de critérios empiricamente verificáveis para a avaliação da precisão do vocabulário empregado pelos candidatos do exame, assim como desenvolveu-se uma escala de proficiência analítica que contempla a coerência do vocabulário empregado, o uso preciso de terminologia técnica e o (re)conhecimento de palavras-chave relacionadas à tarefa metalinguística.

Faixas de proficiência empiricamente desenvolvidas para avaliar a fluência do professor de língua estrangeira

A consideração de evidências empíricas para o desenvolvimento de critérios avaliativos em exames de proficiência constitui um argumento para a validade do construto operacionalizado, assim como confere maior confiabilidade aos resultados produzidos por instrumentos avaliativos. O Exame de Proficiência para Professores de Língua Estrangeira (EPPLE), instrumento avaliativo em desenvolvimento para a avaliação de professores de línguas no contexto educacional brasileiro, carece de validação empírica de seus critérios para a avaliação da fluência, assim como não possui uma escala de proficiência linguística analítica. Este trabalho apresenta resultados da aplicação dos Empirically derived, Binary choice, Boundary Definition scales (EBBs), metodologia empírica para a seleção de critérios avaliativos utilizada para a análise de amostras de desempenho oral de futuros professores de língua estrangeira, oriundas do banco de dados do EPPLE. A partir da análise das amostras de desempenho oral, sugere-se que a escala de proficiência analítica e o quadro de critérios avaliativos incorporem fenômenos de hesitação a partir de uma perspectiva global e comunicativa. Além disso, pausas, autocorreções, repetições e reformulações de enunciados foram identificados como aspectos determinantes para definir os desempenhos característicos de cada faixa de proficiência linguística.

Automated liver lesion detection in 68Ga DOTATATE PET/CT using a deep fully convolutional neural network

Background Gastroenteropancreatic neuroendocrine tumors most commonly metastasize to the liver; however, high normal background 68Ga-DOTATATE activity and high image noise make metastatic lesions difficult to detect. The purpose of this study is to develop a rapid, automated and highly specific method to identify 68Ga-DOTATATE PET/CT hepatic lesions using a 2D U-Net convolutional neural network. Methods A retrospective study of 68Ga-DOTATATE PET/CT patient studies (n = 125; 57 with 68Ga-DOTATATE hepatic lesions and 68 without) was evaluated. The dataset was randomly divided into 75 studies for the training set (36 abnormal, 39 normal), 25 for the validation set (11 abnormal, 14 normal) and 25 for the testing set (11 abnormal, 14 normal). Hepatic lesions were physician annotated using a modified PERCIST threshold, and boundary definition by gradient edge detection. The 2D U-Net was trained independently five times for 100,000 iterations using a linear combination of binary cross-entropy and dice losses with a stochastic gradient descent algorithm. Performance metrics included: positive predictive value (PPV), sensitivity, F1 score and area under the precision–recall curve (PR-AUC). Five different pixel area thresholds were used to filter noisy predictions. Results A total of 233 lesions were annotated with each abnormal study containing a mean of 4 ± 2.75 lesions. A pixel filter of 20 produced the highest mean PPV 0.94 ± 0.01. A pixel filter of 5 produced the highest mean sensitivity 0.74 ± 0.02. The highest mean F1 score 0.79 ± 0.01 was produced with a 20 pixel filter. The highest mean PR-AUC 0.73 ± 0.03 was produced with a 15 pixel filter. Conclusion Deep neural networks can automatically detect hepatic lesions in 68Ga-DOTATATE PET. Ongoing improvements in data annotation methods, increasing sample sizes and training methods are anticipated to further improve detection performance.

Automated Liver Lesion Detection in 68Ga DOTATATE PET / CT Using a Deep Fully Convolutional Neural Network

Purpose: Gastroenteropancreatic neuroendocrine tumors most commonly metastasize to the liver, however, high normal background 68Ga-DOTATATE activity, and high image noise make metastatic lesions difficult to detect. The purpose of this study is to develop a rapid, automated, and highly specific method to identify 68Ga-DOTATATE PET/CT hepatic lesions using a 2D U-Net convolutional neural network. Methods: 68Ga-DOTATATE PET/CT patient studies (n=125; 57 with 68Ga-DOTATATE hepatic lesions and 68 without) were physician annotated using a modified PERCIST threshold, and boundary definition by gradient edge detection. The 2D U-Net was trained independently five times for 100,000 iterations using a linear combination of binary cross entropy and dice losses with a stochastic gradient descent algorithm. Performance metrics included: positive predictive value (PPV), sensitivity, F1 score and area under the precision-recall curve (PR-AUC). Five different pixel area thresholds were used to filter noisy predictions.Results: A total of 233 lesions were annotated with each abnormal study containing a mean of 4 ± 2.75 lesions. A pixel filter of 20 produced the highest mean PPV 0.94±0.01. A pixel filter of 5 produced the highest mean sensitivity 0.74±0.02. The highest mean F1 score 0.79±0.01 was produced with a 20 pixel filter. The highest mean PR-AUC 0.73±0.03 was produced with a 15 pixel filter.Conclusion: Deep neural networks can automatically detect hepatic lesions in 68Ga-DOTATATE PET. Ongoing improvements in data annotation methods, increasing sample sizes, and training methods are anticipated to further improve detection performance.

Estimation of Single-Diode Photovoltaic Model Using the Differential Evolution Algorithm with Adaptive Boundaries

This study proposes a calculation methodology that determines the optimal boundary parameters of the single-diode photovoltaic model. It allows the calculation of the single-diode photovoltaic model when no reference parameter boundaries are available. The differential evolution algorithm, integrated with a step-by-step boundary definition module, is used to calculate the optimal parameters of the single-diode photovoltaic model, improving the performance of the classic algorithm compared with other studies. The solution is validated by comparing the results with well-established algorithms described in the state-of-the-art, and by estimating the five important points (cardinal points) of an IV curve, namely short-circuit, maximum power, and open circuit points, using a database composed of 100 solar photovoltaic modules. The results show that an optimal set of parameter boundaries enables the differential evolution algorithm to minimize the error of the estimated cardinal points. Moreover, the proposed calculus methodology is capable of producing high-performance response photovoltaic models for different technologies and rated powers.

A Comprehensive Framework for Standardising System Boundary Definition in Life Cycle Energy Assessments

This paper aims to propose a comprehensive framework for a clear description of system boundary conditions in life cycle energy assessment (LCEA) analysis in order to promote the incorporation of embodied energy impacts into building energy-efficiency regulations (BEERs). The proposed framework was developed based on an extensive review of 66 studies representing 243 case studies in over 15 countries. The framework consists of six distinctive dimensions, i.e., temporal, physical, methodological, hypothetical, spatial, and functional. These dimensions encapsulate 15 components collectively. The proposed framework possesses two key characteristics; first, its application facilitates defining the conditions of a system boundary within a transparent context. This consequently leads to increasing reliability of obtained LCEA results for decision-making purposes since any particular conditions (e.g., truncation or assumption) considered in establishing the boundaries of a system under study can be revealed. Second, the use of a framework can also provide a meaningful basis for cross comparing cases within a global context. This characteristic can further result in identifying best practices for the design of buildings with low life cycle energy use performance. Furthermore, this paper applies the proposed framework to analyse the LCEA performance of a case study in Adelaide, Australia. Thereafter, the framework is utilised to cross compare the achieved LCEA results with a case study retrieved from literature in order to demonstrate the framework’s capacity for cross comparison. The results indicate the capability of the framework for maintaining transparency in establishing a system boundary in an LCEA analysis, as well as a standardised basis for cross comparing cases. This study also offers recommendations for policy makers in the building sector to incorporate embodied energy into BEERs.

Fixing Boundaries: An International Review Of Greenbelt Boundaries

The legislation governing Ontario’s Greater Golden Horseshoe Greenbelt is set to be reviewed in 2015. This will be the first opportunity to review the defined boundaries of the protected area. This paper examines four other greenbelt areas to provide insight into how the province should deal with boundaries at the review. The case study areas are Ottawa’s National Capital Greenbelt, British Columbia’s Agricultural Land Reserve, London’s Metropolitan Greenbelt and Portland’s Urban Growth Boundary. Based on lessons from these case studies, the paper concludes with recommendations to provide a structure to the greenbelt review process, including harmonizing boundary definitions and exploring a more flexible approach to boundary definition.

Fixing boundaries: an international review of greenbelt boundaries.

The legislation governing Ontario’s Greater Golden Horseshoe Greenbelt is set to be reviewed in 2015. This will be the first opportunity to review the defined boundaries of the protected area. This paper examines four other greenbelt areas to provide insight into how the province should deal with boundaries at the review. The case study areas are Ottawa’s National Capital Greenbelt, British Columbia’s Agricultural Land Reserve, London’s Metropolitan Greenbelt and Portland’s Urban Growth Boundary. Based on lessons from these case studies, the paper concludes with recommendations to provide a structure to the greenbelt review process, including harmonizing boundary definitions and exploring a more flexible approach to boundary definition.

Fixing Boundaries: An International Review Of Greenbelt Boundaries

The legislation governing Ontario’s Greater Golden Horseshoe Greenbelt is set to be reviewed in 2015. This will be the first opportunity to review the defined boundaries of the protected area. This paper examines four other greenbelt areas to provide insight into how the province should deal with boundaries at the review. The case study areas are Ottawa’s National Capital Greenbelt, British Columbia’s Agricultural Land Reserve, London’s Metropolitan Greenbelt and Portland’s Urban Growth Boundary. Based on lessons from these case studies, the paper concludes with recommendations to provide a structure to the greenbelt review process, including harmonizing boundary definitions and exploring a more flexible approach to boundary definition.

Fixing boundaries: an international review of greenbelt boundaries.

The legislation governing Ontario’s Greater Golden Horseshoe Greenbelt is set to be reviewed in 2015. This will be the first opportunity to review the defined boundaries of the protected area. This paper examines four other greenbelt areas to provide insight into how the province should deal with boundaries at the review. The case study areas are Ottawa’s National Capital Greenbelt, British Columbia’s Agricultural Land Reserve, London’s Metropolitan Greenbelt and Portland’s Urban Growth Boundary. Based on lessons from these case studies, the paper concludes with recommendations to provide a structure to the greenbelt review process, including harmonizing boundary definitions and exploring a more flexible approach to boundary definition.