Why is India the world's stillbirth capital: causes and solutions?

Caicedo, Mauricio Guerrero; Ordóñez, J. L. Gómez

doi:10.15713/ins.mmj.3

Cited by 154 publications

(106 citation statements)

References 3 publications

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“…Indonesia merupakan negara pengekspor terbesar produk pala di Uni Eropa Jika dilihat dari penguasaan pasar, market share pala dan bunga pala utuh sebesar 63,42% dan 77,74% sedangkan market share pala dan bunga pala olahan sebesar 34,87% dan 47,75% [3]. [4]. Luas area tanaman pala di Kabupaten Fakfak mencapai 17.452 Km 2 (58 persen dari total luas area tanaman pala di Provinsi Papua Barat) dengan produksi mencapai 1.884 ton (11 persen dari total produksi pala Indonesia [5].…”

Section: Pendahuluanunclassified

Klasifikasi Mutu Buah Pala (Myristica Fragrans Houtt) Berbasis Pengolahan Citra Menggunakan Metode Deep Learning Arsitektur Faster R-CNN

Subhan¹,

Basri²

2019

Intek

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Fakfak is the number one nutmeg producing area in Indonesia with a land area of around 16,011 Ha. Nutmeg production is projected to continue to increase until 2020, recorded in 2011 the production of nutmeg in particular reached 12,884 tons or 25 percent of the total Indonesian nutmeg production. This increase was not followed by an increase in market share. One solution to get a wider nutmeg market is exports. But currently not all requests for nutmeg can be fulfilled, because the quality of the nutmeg does not meet the requirements requested. One factor is the surface defects in the nutmeg skin that affects the quality of the fruit, especially the appearance of the fruit. The sorting of nutmegs has so far been using conservative methods, namely by observation based on experience (self-taught). This manual method is felt to be less effective because it depends on the conditions and conditions of the sorting staff, different perceptions between each sorter, takes a long time, requires large costs and involves many workers. To deal with these problems, our previous research developed a method for classifying nutmeg seeds, using image processing methods with color and shape parameters combined with a neural network and sigmoid convolution classification algorithm as a validation method, an accuracy of 87%, but this has not been said to be optimal so we try to use the same approach with the latest method improvements using the R-CNN Faster obtained the best accuracy of 95% with a learning rate of 4000 with a processing time of 0.04 minutes per second.

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Section: Pendahuluanunclassified

Klasifikasi Mutu Buah Pala (Myristica Fragrans Houtt) Berbasis Pengolahan Citra Menggunakan Metode Deep Learning Arsitektur Faster R-CNN

Subhan¹,

Basri²

2019

Intek

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“…O petróleo bruto, ou óleo cru, possui em sua composição compostos de tamanhos diferentes e de classes diferentes. Essas diferenças causam misturas com diferentes tamanhos de compostos, onde os menores são mais voláteis e os maiores são não voláteis, mas são mais viscosos e densos (Fingas, 2011).…”

Section: Petróleounclassified

“…A maior parte da constituição do petróleo são ligações de carbonos e hidrogênios, chamados de hidrocarbonetos, porém ainda há milhares de constituintes menores que fazem com que cada óleo tenha características únicas. Esses constituintes incluem variações de enxofre, nitrogênio, oxigênio, sais minerais, níquel, vanádio e cromo (Fingas, 2011). Speight (2006) classifica os hidrocarbonetos encontrados no petróleo como parafinas, hidrocarboneto saturado com cadeias lineares ou ramificadas; cicloparafinas, hidrocarbonetos saturados que contém um ou mais anéis; aromáticos, hidrocarbonetos que contém um ou mais núcleos aromáticos.…”

Section: Petróleounclassified

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Modelagem e simulação de derrames de dois tipos de petróleo baseado na experiência de Tramandaí/RS

Souza

Oleinik²,

Monteiro³

et al. 2019

Rev. Interd. Pesq. Eng.

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Tendo em vista a dependência global do petróleo e os riscos ambientais iminentes do manuseio e transporte de óleo no mar, o presente trabalho tem como objetivo reproduzir o incidente de vazamento de óleo na monoboia de Tramandaí – RS do dia 26 de Janeiro de 2012, e analisar a ação do intemperismo em dois tipos diferentes de óleo sujeitos às condições ambientais no dia do acidente. Este trabalho utilizou de dois diferentes softwares, o modelo de derrame de óleo ECOS acoplado ao modelo hidrodinâmico TELEMAC-3D. Devido ao histórico de vazamentos na região de Tramandaí, delimitou-se essa área como área de estudo a fim de estudar o comportamento do óleo. Com a pesquisa foi possível reproduzir as condições hidrodinâmicas do dia do incidente utilizando o TELEMAC-3D. Os processos de intemperismo implementados não influenciaram no tamanho da mancha de óleo. A influência do processo de emulsificação se mostrou importante na massa específica do óleo. O coeficiente implementado no modelo tende a estabilizar a emulsificação em 70 % para óleos crus e óleos pesados, mas que na realidade deveria variar de acordo com a massa específica de cada óleo. Assim, o resultado de emulsificação foi o mesmo para as duas simulações, não condizendo com o que poderia acontecer em condições reais. A evaporação se mostrou bem implementada no modelo, variando de acordo com cada massa específica do óleo. Os valores obtidos foram de 4 % de evaporação para a simulação A, 19,5 % para a simulação B.

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“…& Oliva, 2012;Long, Moher, Carey, & Konkle, 2018;Long, Konkle, Cohen, & Alvarez, 2016; expected nearby objects and their spatial position:Kaiser, Stein, & Peelen, 2015), and people can…”

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Real-world objects are not stored in holistic representations in visual working memory

Markov¹,

Utochkin²,

Brady³

2019

Preprint

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When storing multiple objects in visual working memory, observers sometimes misattribute perceived features to incorrect locations or objects. These misattributions are called binding errors (or swaps) and have been previously demonstrated mostly in simple objects whose features are easy to encode independently and arbitrarily chosen, like colors and orientations. Here, we tested whether similar swaps can occur with real-world objects, where the connection between features is meaningful rather than arbitrary. In Experiments 1 and 2, observers were simultaneously shown four items from two object categories. Within a category, the two exemplars could be presented in either the same or different states (e.g., open/closed; full/empty). After a delay, both exemplars from one of the categories were probed, and participants had to recognize which exemplar went with which state. We found good memory for state information and exemplar information on their own, but a significant memory decrement for exemplar-state combinations, suggesting that binding was difficult for observers and “swap” errors occurred even for meaningful real-world objects. In Experiment 3, we used the same tasks, but on half of the trials, the locations of the exemplars were swapped at test. We found that participants ascribed incorrect states to exemplars more frequently when the locations of exemplars were swapped. We concluded that the internal features of real-world objects are not perfectly bound in working memory, and location updates impair the object representation. Overall, we provide evidence that even real-world objects are not stored in an entirely unitized format in working memory.

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Why is India the world's stillbirth capital: causes and solutions?

Cited by 154 publications

References 3 publications

Klasifikasi Mutu Buah Pala (Myristica Fragrans Houtt) Berbasis Pengolahan Citra Menggunakan Metode Deep Learning Arsitektur Faster R-CNN

Klasifikasi Mutu Buah Pala (Myristica Fragrans Houtt) Berbasis Pengolahan Citra Menggunakan Metode Deep Learning Arsitektur Faster R-CNN

Modelagem e simulação de derrames de dois tipos de petróleo baseado na experiência de Tramandaí/RS

Real-world objects are not stored in holistic representations in visual working memory

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