scholarly journals Remote Sensing for Monitoring Photovoltaic Solar Plants in Brazil Using Deep Semantic Segmentation

Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2960
Author(s):  
Marcus Vinícius Coelho Vieira da Costa ◽  
Osmar Luiz Ferreira de Carvalho ◽  
Alex Gois Orlandi ◽  
Issao Hirata ◽  
Anesmar Olino de Albuquerque ◽  
...  
Keyword(s):  
Solar Energy ◽  
Operating Characteristic ◽  
Low Cost ◽  
Computational Cost ◽  
Area Under The Curve ◽  
Semantic Segmentation ◽  
Tropical Country ◽  
Feature Pyramid ◽  
Characteristic Area ◽  
The Cost

Brazil is a tropical country with continental dimensions and abundant solar resources that are still underutilized. However, solar energy is one of the most promising renewable sources in the country. The proper inspection of Photovoltaic (PV) solar plants is an issue of great interest for the Brazilian territory’s energy management agency, and advances in computer vision and deep learning allow automatic, periodic, and low-cost monitoring. The present research aims to identify PV solar plants in Brazil using semantic segmentation and a mosaicking approach for large image classification. We compared four architectures (U-net, DeepLabv3+, Pyramid Scene Parsing Network, and Feature Pyramid Network) with four backbones (Efficient-net-b0, Efficient-net-b7, ResNet-50, and ResNet-101). For mosaicking, we evaluated a sliding window with overlapping pixels using different stride values (8, 16, 32, 64, 128, and 256). We found that: (1) the models presented similar results, showing that the most relevant approach is to acquire high-quality labels rather than models in many scenarios; (2) U-net presented slightly better metrics, and the best configuration was U-net with the Efficient-net-b7 encoder (98% overall accuracy, 91% IoU, and 95% F-score); (3) mosaicking progressively increases results (precision-recall and receiver operating characteristic area under the curve) when decreasing the stride value, at the cost of a higher computational cost. The high trends of solar energy growth in Brazil require rapid mapping, and the proposed study provides a promising approach.

Pedometer-Determined Step-Count Guidelines for Afterschool Programs

2012 ◽  
Vol 9 (1) ◽  
pp. 71-77 ◽  
Author(s):  
Michael W. Beets ◽  
Aaron Beighle ◽  
Matteo Bottai ◽  
Laura Rooney ◽  
Fallon Tilley
Keyword(s):  
Operating Characteristic ◽  
Low Cost ◽  
Vigorous Physical Activity ◽  
Area Under The Curve ◽  
High Specificity ◽  
Step Count ◽  
Afterschool Programs ◽  
Policy Goal ◽  
Community Based ◽  
Random Intercept

Background:Policies to require afterschool programs (ASPs, 3 PM to 6 PM) to provide children a minimum of 30 minutes of moderate-to-vigorous physical activity (MVPA) exist. With few low-cost, easy-to-use measures of MVPA available to the general public, ASP providers are limited in their ability to track progress toward achieving this policy-goal. Pedometers may fill this gap, yet there are no step-count guidelines for ASPs linked to 30 minutes of MVPA.Methods:Steps and accelerometer estimates of MVPA were collected concurrently over multiple days on 245 children (8.2 years, 48% boys, BMI-percentile 68.2) attending 3 community-based ASPs. Random intercept logit models and receiver operating characteristic (ROC) analyses were used to identify a threshold of steps that corresponded with attaining 30 minutes of MVPA.Results:Children accumulated an average of 2876 steps (standard error [SE] 79) and 16.1 minutes (SE0.5) of MVPA over 111 minutes (SE1.3) during the ASP. A threshold of 4600 steps provided high specificity (0.967) and adequate sensitivity (0.646) for discriminating children who achieved the 30 minutes of MVPA; 93% of the children were correctly classified. The total area under the curve was 0.919. Children accumulating 4600 steps were 25times more likely to accumulate 30 minutes of MVPA.Conclusions:This step threshold will provide ASP leaders with an objective, low-cost, easy-to-use tool to monitor progress toward policy-related goals.

scholarly journals Diagnostic Performance of Bronchoalveolar Lavage (1,3)-β-d-Glucan Assay for Pneumocystis jirovecii Pneumonia

2020 ◽  
Vol 6 (4) ◽  
pp. 200
Author(s):  
Shiwei Zhou ◽  
Kathleen A. Linder ◽  
Carol A. Kauffman ◽  
Blair J. Richards ◽  
Steve Kleiboeker ◽  
...  
Keyword(s):  
Bronchoalveolar Lavage ◽  
Test Performance ◽  
Predictive Value ◽  
Operating Characteristic ◽  
Area Under The Curve ◽  
Lavage Fluid ◽  
Pneumocystis Pneumonia ◽  
Pneumocystis Jirovecii ◽  
Pneumocystis Jirovecii Pneumonia ◽  
Characteristic Area

We evaluated the performance of the (1,3)-β-d-glucan (BDG) assay on bronchoalveolar lavage fluid (BALF) as a possible aid to the diagnosis of Pneumocystis jirovecii pneumonia. BALF samples from 18 patients with well-characterized proven, probable, and possible Pneumocystis pneumonia and 18 well-matched controls were tested. We found that the best test performance was observed with a cut-off value of 128 pg/mL; receiver operating characteristic/area under the curve (ROC/AUC) was 0.70 (95% CI 0.52–0.87). Sensitivity and specificity were 78% and 56%, respectively; positive predictive value was 64%, and negative predictive value was 71%. The low specificity that we noted limits the utility of BALF BDG as a diagnostic tool for Pneumocystis pneumonia.

scholarly journals Feasibility and visualization of deep learning detection and classification of inferior vena cava filters

2020 ◽  
Author(s):  
Brian J. Park ◽  
Vlasios S. Sotirchos ◽  
Jason Adleberg ◽  
S. William Stavropoulos ◽  
Tessa S. Cook ◽  
...  
Keyword(s):  
Deep Learning ◽  
Inferior Vena Cava ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Inferior Vena ◽  
Vena Cava ◽  
Area Under The Curve ◽  
Receiver Operating Characteristic Area ◽  
Inferior Vena Cava Filters ◽  
Characteristic Area

AbstractPurposeThis study assesses the feasibility of deep learning detection and classification of 3 retrievable inferior vena cava filters with similar radiographic appearances and emphasizes the importance of visualization methods to confirm proper detection and classification.Materials and MethodsThe fast.ai library with ResNet-34 architecture was used to train a deep learning classification model. A total of 442 fluoroscopic images (N=144 patients) from inferior vena cava filter placement or removal were collected. Following image preprocessing, the training set included 382 images (110 Celect, 149 Denali, 123 Günther Tulip), of which 80% were used for training and 20% for validation. Data augmentation was performed for regularization. A random test set of 60 images (20 images of each filter type), not included in the training or validation set, was used for evaluation. Total accuracy and receiver operating characteristic area under the curve were used to evaluate performance. Feature heatmaps were visualized using guided backpropagation and gradient-weighted class activation mapping.ResultsThe overall accuracy was 80.2% with mean receiver operating characteristic area under the curve of 0.96 for the validation set (N=76), and 85.0% with mean receiver operating characteristic area under the curve of 0.94 for the test set (N=60). Two visualization methods were used to assess correct filter detection and classification.ConclusionsA deep learning model can be used to automatically detect and accurately classify inferior vena cava filters on radiographic images. Visualization techniques should be utilized to ensure deep learning models function as intended.

Solar Energy Development in Japan

1989 ◽  
Vol 7 (4) ◽  
pp. 251-261
Author(s):  
Takashi Horigome ◽  
Hiroshi Sugimoto
Keyword(s):  
Solar Cell ◽  
Solar Energy ◽  
High Efficiency ◽  
Technology Development ◽  
Low Cost ◽  
Energy Development ◽  
Photovoltaic System ◽  
Development Organization ◽  
New Energy ◽  
The Cost

Solar energy development at the New Energy and Industrial Technology Development Organization (NEDO) is concerned with reducing the cost of photovoltaic (PV) systems by promoting low cost, high efficiency solar cell manufacturing technology and photovoltaic system demonstations. The first involves reducing the cost of solar cell modules by producing better silicon materials and improving fabrication techniques. A number of demonstration systems are in operation.

scholarly journals OTIMIZAÇÃO DE CÉLULAS FOTOVOLTAICAS ORGÂNICAS

e-xacta ◽  
2013 ◽  
Vol 6 (2) ◽  
pp. 79
Author(s):  
Ana Carolina Silva ◽  
Allan Douglas Martins ◽  
Camila C. S. Braga ◽  
Carolina Cardoso Franco ◽  
Dyeice Amélia Sales ◽  
...  
Keyword(s):  
Titanium Dioxide ◽  
Solar Cells ◽  
Solar Energy ◽  
Organic Dyes ◽  
Low Cost ◽  
Electrical Energy ◽  
Nanocrystalline Titanium ◽  
The Cost ◽  
Very High ◽  
Energy Converting

<p align="justify">Com a previsível escassez dos recursos energéticos, as preocupações com as questões ambientais se tornam cada vez mais evidentes. Com isso, houve um incremento na busca de recursos alternativos para a produção de energia elétrica, principalmente aqueles baseados em fontes limpas e renováveis, como a energia solar. Para a conversão de energia solar em energia elétrica são utilizadas, na maioria das vezes, células solares fotovoltaicas, que se baseiam na propriedade semicondutora de silício. Como o custo dessa tecnologia ainda é muito alto, são propostos novos materiais para a substituição desse cristal, com destaque para a célula solar nanocristalina de dióxido de titânio (TiO2), acrescida de moléculas orgânicas de corantes. Essa célula apresenta vantagens em relação às células convencionais de silício, pois, na sua fabricação, são utilizados materiais disponíveis no mercado e corantes extraídos de plantas, modelo proposto por Gratzël, além de ser preparada através de processos simples e não poluentes. O objetivo deste trabalho é recriar as células solares nanocristalinas de dióxido de titânio, otimizando-a para a utilização de materiais com baixo custo, de modo que se obtenha a maior eficiência energética possível.</p><p align="justify">Abstract</p><p align="justify">With the expected shortage of energy resources, the concerns about environmental issues are becoming increasingly evident. Thus, there was an increase in search for alternative resources for energy production power, especially those based on clean sources and renewable energies such as solar energy. Converting solar energy into electrical energy, in most cases, solar cells photovoltaics, which based on property semiconductor silicon are used. As the cost of this technology is still very high, new materials are proposed for substitution this crystal, with emphasis on the cell nanocrystalline titanium dioxide (TiO2) solar plus molecules organic dyes. This cell has advantages compared to conventional silicon cells, because in his manufacturing, available materials are used in the market and extracted dyes from plants, the model proposed its by Grätzel, besides being prepared through Simple and clean process. The goal of this essay is to recreate the nanocrystalline solar cells titanium dioxide, optimizing it to the use of materials with low cost, so as to obtain the energy efficient as possible.</p>

149 Bovine embryo selection can be improved by the characteristics of secreted extracellular vesicles

2019 ◽  
Vol 31 (1) ◽  
pp. 199
Author(s):  
E. Mellisho ◽  
M. Briones ◽  
F. O. Castro ◽  
L. Rodriguez-Alvarez
Keyword(s):  
Receiver Operating Characteristic ◽  
Predictive Power ◽  
Operating Characteristic ◽  
Culture Media ◽  
Area Under The Curve ◽  
Blastocyst Stage ◽  
Blastocyst Quality ◽  
Characteristic Area ◽  
Receiver Operating

Extracellular vesicles (EV) secreted by blastocysts might be relevant to predict competence of embryos produced in vitro. The aim of this study was to develop a model to select competent embryos that combines blastocyst morphokinetics data and morphological parameters of EV secreted during blastulation (Days 5-7.5). Embryos were cultured in groups up to Day 5; morulae were selected and individually cultured in SOFaa depleted of EV until Day 7.5 after IVF. Embryo competence was determined by in vitro post-hatching development up to Day 11. A retrospective classification of blastocyst and culture media was performed based on blastulation time [early (EB) or late (LB)] and competence at Day 11 [competent (C) or non-competent (NC)]. The EV were isolated from culture media of individual embryos, their properties determined by nanoparticle tracking analysis. The model was based on a binary logistic regression to describe the dichotomous-dependent variable of the blastocyst (C=1 and NC=0). A set of independent variables of blastocyst morphokinetics (blastulation time, blastocyst stage, blastocyst quality and blastocyst diameter at Day 7.5) and EV morphological parameters [mean size (ME), mode size (MO) and particle concentration (CO)] were analysed with multiple regression. The analysis generated the coefficients and their standard errors and significance level of an equation to calculate a probability, where values between 0.5 and 1 predict competent embryos. To verify the predictive power of the algorithm, the following indicators were used: the receiver operating characteristic with the determination of area under the curve, percentage correct predictions, and Omnibus tests. Statistical significance was determined at the P&lt;0.05 level. A rough guide for classifying the accuracy of a predictive model is as follows: 0.9 to 1=excellent, 0.8 to 0.9=good, 0.7 to 0.8=fair, 0.6 to 0.7=poor, 0.5 to 0.6=fail. A total of 254 embryos were used in this study; from them, 73 were classified in C-EB, 68 in NC-EB, 61 in C-LB and 52 in NC-LB. Initially, all independent variables were analysed in model 1; the most significant predictors associated with embryo competence were blastocyst stage, blastocyst quality, blastocyst diameter, ME and CO (P&lt;0.05). In model 2 no significant variables were excluded (blastulation time and MO). The statistical test of predictive power indicates that models 1 and 2 achieved a receiver operating characteristic-area under the curve of 0.853 (95% confidence interval, 0.806-0.9; P&lt;0.001) and correct predictions of 77.2 and 77.6%, respectively. When EV characteristics were excluded and the model considers only variables from the embryo, the receiver operating characteristic-area under the curve value was 0.714 (95% confidence interval, 0.651-0.777; P&lt;0.001) and correct predictions was reduced to 65.4. Model 2 was consider the most appropriate from the practical point of view because it avoids disturbing embryo culture during blastulation. The results indicate that incorporating EV properties increases accuracy of embryo selection, supporting the possibility to improve conventional methods by combining blastocyst morphology and characteristics of EV obtained by nanoparticle tracking analysis. This work was supported by Fondecyt 1170310.

Clinical and Host Biological Factors Predict Colectomy Risk in Children Newly Diagnosed With Ulcerative Colitis

2021 ◽  
Author(s):  
Jeffrey S Hyams ◽  
Michael Brimacombe ◽  
Yael Haberman ◽  
Thomas Walters ◽  
Greg Gibson ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ulcerative Colitis ◽  
Confidence Interval ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Area Under The Curve ◽  
Newly Diagnosed ◽  
Receiver Operating Characteristic Area ◽  
Characteristic Area ◽  
Receiver Operating

Abstract Background Develop a clinical and biological predictive model for colectomy risk in children newly diagnosed with ulcerative colitis (UC). Methods This was a multicenter inception cohort study of children (ages 4-17 years) newly diagnosed with UC treated with standardized initial regimens of mesalamine or corticosteroids (CS) depending upon initial disease severity. Therapy escalation to immunomodulators or infliximab was based on predetermined criteria. Patients were phenotyped by clinical activity per the Pediatric Ulcerative Colitis Activity Index (PUCAI), disease extent, endoscopic/histologic severity, and laboratory markers. In addition, RNA sequencing defined pretreatment rectal gene expression and high density DNA genotyping by the Affymetrix UK Biobank Axiom Array. Coprimary outcomes were colectomy over 3 years and time to colectomy. Generalized linear models, Cox proportional hazards multivariate regression modeling, and Kaplan-Meier plots were used. Results Four hundred twenty-eight patients (mean age 13 years) started initial theapy with mesalamine (n = 136), oral CS (n = 144), or intravenous CS (n = 148). Twenty-five (6%) underwent colectomy at ≤1 year, 33 (9%) at ≤2 years, and 35 (13%) at ≤3 years. Further, 32/35 patients who had colectomy failed infliximab. An initial PUCAI ≥ 65 was highly associated with colectomy (P = 0.0001). A logistic regression model predicting colectomy using the PUCAI, hemoglobin, and erythrocyte sedimentation rate had a receiver operating characteristic area under the curve of 0.78 (95% confidence interval [0.73, 0.84]). Addition of a pretreatment rectal gene expression panel reflecting activation of the innate immune system and response to external stimuli and bacteria to the clinical model improved the receiver operating characteristic area under the curve to 0.87 (95% confidence interval [0.82, 0.91]). Conclusions A small group of children newly diagnosed with severe UC still require colectomy despite current therapies. Our gene signature observations suggest additional targets for management of those patients not responding to current medical therapies.

scholarly journals Validation of a Blood-Based Laboratory Test to Aid in the Confirmation of a Diagnosis of Schizophrenia

2010 ◽  
Vol 5 ◽  
pp. BMI.S4877 ◽  
Author(s):  
Emanuel Schwarz ◽  
Rauf Izmailov ◽  
Michael Spain ◽  
Anthony Barnes ◽  
James P. Mapes ◽  
...  
Keyword(s):  
Operating Characteristic ◽  
Area Under The Curve ◽  
Healthy Controls ◽  
Receiver Operating Characteristic Area ◽  
Specific Prediction ◽  
Characteristic Area ◽  
Disease Signature ◽  
Based Medicine ◽  
And Control ◽  
Independent Cohort

We describe the validation of a serum-based test developed by Rules-Based Medicine which can be used to help confirm the diagnosis of schizophrenia. In preliminary studies using multiplex immunoassay profiling technology, we identified a disease signature comprised of 51 analytes which could distinguish schizophrenia (n = 250) from control (n = 230) subjects. In the next stage, these analytes were developed as a refined 51-plex immunoassay panel for validation using a large independent cohort of schizophrenia (n = 577) and control (n = 229) subjects. The resulting test yielded an overall sensitivity of 83% and specificity of 83% with a receiver operating characteristic area under the curve (ROC-AUC) of 89%. These 51 immunoassays and the associated decision rule delivered a sensitive and specific prediction for the presence of schizophrenia in patients compared to matched healthy controls.

scholarly journals Extração semiautomática de vegetação arbórea em áreas urbanas a partir de imagens aéreas de alta resolução espacial

2020 ◽  
Vol 13 (3) ◽  
pp. 1391
Author(s):  
Jakeline Jesus Silva ◽  
Lucas Prado Osco ◽  
Ana Paula Marques Ramos ◽  
Wesley Barbosa Dourado
Keyword(s):  
Receiver Operating Characteristic ◽  
Spatial Resolution ◽  
Roc Curve ◽  
Urban Areas ◽  
Operating Characteristic ◽  
High Spatial Resolution ◽  
Confusion Matrix ◽  
Computational Cost ◽  
Area Under The Curve ◽  
Receiver Operating

O mapeamento da vegetação arbórea em áreas urbanas pode ser realizado por classificação semiautomática ou automática de imagens orbitais ou aéreas. Contudo, esse tipo de tarefa tem um custo computacional dependente da resolução espacial da imagem. Neste estudo é proposto uma abordagem de extração semiautomática de vegetação arbórea em imagens de alta resolução espacial a baixo custo computacional. Trabalhamos com ortofotos de 1m de resolução, disponibilizadas por órgãos gestores públicos. A abordagem proposta aplica um filtro de médias em recortes de imagens, com 500x500 pixels cada. Ao todo utilizamos 90 recortes. Testamos o algoritmo nas seguintes configurações: separadamente nas bandas (azul, verde e vermelho), em imagem colorida (RGB) e em imagem em tons de cinza. Validamos sua performance usando a matriz de confusão e a curva do Receiver Operating Characteristic (ROC), considerando 3.695 pontos distribuídos homogeneamente em todos os recortes de imagens. Comparamos, ainda, a performance do algoritmo com uma classificação supervisionado por pixel (máxima verossimilhança). Obtivemos uma acurácia global de 90,18%, um índice kappa de 0,80 e uma velocidade de processamento de aproximadamente 1 minuto e 30 segundos para o algoritmo proposto em um computador convencional. A curva ROC obteve uma Area Under the Curve (AUC) equivalente a 0,91 para o algoritmo, considerando o resultado de todas as bandas, e um valor de 0,79 para a classificação supervisionada por pixel. Concluímos que nossa abordagem é computacionalmente eficiente para separar as áreas cobertas por vegetação de áreas não cobertas em ambiente urbano. Semiautomatic extraction of arboreal vegetation in urban areas using aerial imagery of high spatial resolution A B S T R A C TMapping of tree vegetation in urban areas can be performed by semi-automatic or automatic classification of orbital or aerial images. However, this type of task has a computational cost dependent on the spatial resolution of the image. This study proposes an approach of semi-automatic tree vegetation extraction in high spatial resolution images at a low computational cost. We work with 1m resolution orthophotos, made available by public management agencies. The proposed approach applies a medium filter on image clippings of 500x500 pixels each. In all, we use 90 clippings. We tested the algorithm in the following configurations: separately in the bands (blue, green and red), color image (RGB) and grayscale image. We validated its performance using the Confusion Matrix and Receiver Operating Characteristic (ROC) curve, considering 3,695 points evenly distributed across all clippings. We also compared the performance of the algorithm with a pixel supervised classification (maximum likelihood). We obtained an overall accuracy of 90.18%, a kappa index of 0.80 and a processing speed of approximately 1 minute and 30 seconds for the proposed algorithm in a conventional computer. The ROC curve obtained an Area Under the Curve (AUC) equivalent to 0.91 for the algorithm, considering the result of all bands, and a value of 0.79 for the supervised pixel classification. We conclude that our approach is computationally efficient for separating areas covered by vegetation from areas not covered in an urban environment.Keywords: digital image processing; image classification; urban environmental planning.

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