Seamless and Robust Alginate/Gelatin Coating on Ti-6Al-4V as a Gap Filling Interphase

2022 ◽  
pp. 152393
Author(s):  
Ahmet Engin Pazarçeviren ◽  
Sema Akbaba ◽  
Ayşen Tezcaner ◽  
Dilek Keskin ◽  
Zafer Evis
Keyword(s):  
Gap Filling ◽  
Gelatin Coating

Is L2 vocabulary learned better via context or via translation?

2018 ◽  
Vol 2 (1) ◽  
pp. 39-60
Author(s):  
Michael John Alroe ◽  
Heyo Reinders ◽  
Punchalee Wasanasomsithi
Keyword(s):  
Incidental Learning ◽  
Intentional Learning ◽  
Time On Task ◽  
Translation Group ◽  
Gap Filling ◽  
Freshmen Students ◽  
Context Group ◽  
Comprehensible Input ◽  
Post Test ◽  
L2 Vocabulary

Various studies have shown intentional learning of L2 vocabulary to be more efficient than incidental learning from exposure to comprehensible input. Some have argued that such learning may be further enhanced by recourse to L1 translation, particularly for weaker learners. The present study aims to determine if intentional learning of new vocabulary through L1 does indeed confer an advantage over intentional learning from an L2 context. To this end, 403 Thai freshmen students were pre-tested on thirty vocabulary items set for study on their English course. They were then randomly allocated to either a translation or context group to learn those items. Time on task was controlled. A delayed post-test showed that while the translation group was better at matching the thirty English words with Thai translations, albeit marginally so, there was no benefit conferred on the translation group when it came to using the words in a contextual gap-filling exercise. This finding held for both advanced and weaker learners.

Effect of co-encapsulation using a calcium alginate matrix and fructooligosaccharides with gelatin coating on the survival of Lactobacillus paracasei cells

2021 ◽  
Author(s):  
Rosetânia Correia Neves da Conceição ◽  
Rayssa Dias Batista ◽  
Fernanda Munhoz dos Anjos Leal Zimmer ◽  
Ianna Kelly Martins Trindade ◽  
Alex Fernando de Almeida ◽  
...  
Keyword(s):  
Calcium Alginate ◽  
Lactobacillus Paracasei ◽  
Gelatin Coating ◽  
Alginate Matrix

scholarly journals A Review of Reconstructing Remotely Sensed Land Surface Temperature under Cloudy Conditions

2021 ◽  
Vol 13 (14) ◽  
pp. 2838
Author(s):  
Yaping Mo ◽  
Yongming Xu ◽  
Huijuan Chen ◽  
Shanyou Zhu
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Spatial Scales ◽  
Remotely Sensed ◽  
Urban Heat Islands ◽  
Reconstruction Algorithms ◽  
Gap Filling ◽  
Practical Applications ◽  
Data Gaps

Land surface temperature (LST) is an important environmental parameter in climate change, urban heat islands, drought, public health, and other fields. Thermal infrared (TIR) remote sensing is the main method used to obtain LST information over large spatial scales. However, cloud cover results in many data gaps in remotely sensed LST datasets, greatly limiting their practical applications. Many studies have sought to fill these data gaps and reconstruct cloud-free LST datasets over the last few decades. This paper reviews the progress of LST reconstruction research. A bibliometric analysis is conducted to provide a brief overview of the papers published in this field. The existing reconstruction algorithms can be grouped into five categories: spatial gap-filling methods, temporal gap-filling methods, spatiotemporal gap-filling methods, multi-source fusion-based gap-filling methods, and surface energy balance-based gap-filling methods. The principles, advantages, and limitations of these methods are described and discussed. The applications of these methods are also outlined. In addition, the validation of filled LST values’ cloudy pixels is an important concern in LST reconstruction. The different validation methods applied for reconstructed LST datasets are also reviewed herein. Finally, prospects for future developments in LST reconstruction are provided.

scholarly journals An Active Gelatin Coating Containing Eugenol and Vacuum Delays the Decay of Chinese Seabass (Lateolabrax maculatus) Fillets during Cold Storage: A Microbiome Perspective

Coatings ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 147
Author(s):  
Xuan Ma ◽  
Qianqian Zhou ◽  
Weiqiang Qiu ◽  
Jun Mei ◽  
Jing Xie
Keyword(s):  
Microbial Diversity ◽  
High Throughput Sequencing ◽  
Vacuum Treatment ◽  
C Storage ◽  
Basic Nitrogen ◽  
Spoilage Bacteria ◽  
Lateolabrax Maculatus ◽  
Gelatin Coating ◽  
Microbial Enumeration ◽  
Growth And Reproduction

The purpose of this study was to evaluate the effect of an active gelatin coating containing eugenol and vacuum on the microbial diversity of Chinese seabass (Lateolabrax maculatus) during cold (−0.9 °C) storage. The bacterial sequences in Chinese seabass were observed using a high-throughput sequencing technique targeting the V3–V4 region of the 16S Ribosomal DNA (rDNA) on 0, 12th, and 24th day, which showed a more comprehensive estimate of the microbial diversity in seabass samples compared with microbial enumeration. The results revealed that the species diversity of fresh seabass was rich, mainly including Carnobacterium, Glutamicibacter, and Pseudomonas, with abundance ratios of 0.286, 0.160, and 0.130, respectively. Pseudomonas and Shewanella were the primary contaminants in the spoiled control samples, where the abundance ratios increased from 0.220 and 0.174 on the 12th day to 0.802 and 0.163 on the 24th day, respectively. Vacuum treatment could inhibit the growth of Pseudomonas and Shewanella such that when stored on the 12th day, Brochothrix became the superior genus. However, Pseudomonas and Shewanella dominated the storage until the 24th day, where their abundance ratios were 0.343 and 0.279, respectively. The inhibition of Pseudomonas and Carnobacterium was gradually enhanced with increasing concentrations of eugenol. Furthermore, an active gelatin coating containing eugenol and vacuum treatment was more effective at inhibiting the increase of the total volatile basic nitrogen. This study confirmed that an active gelatin coating containing eugenol and vacuum could reduce the species of bacteria, inhibit the growth and reproduction of the main dominant spoilage bacteria, and delay the spoilage of seabass.

scholarly journals Evaluating Machine Learning and Geostatistical Methods for Spatial Gap-filling of Monthly ESA CCI Soil Moisture in China

2021 ◽  
Vol 13 (14) ◽  
pp. 2848
Author(s):  
Hao Sun ◽  
Qian Xu
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Water Resource Management ◽  
Large Scale ◽  
Gap Filling ◽  
Spatial Continuity ◽  
Study Results ◽  
Data Gaps

Obtaining large-scale, long-term, and spatial continuous soil moisture (SM) data is crucial for climate change, hydrology, and water resource management, etc. ESA CCI SM is such a large-scale and long-term SM (longer than 40 years until now). However, there exist data gaps, especially for the area of China, due to the limitations in remote sensing of SM such as complex topography, human-induced radio frequency interference (RFI), and vegetation disturbances, etc. The data gaps make the CCI SM data cannot achieve spatial continuity, which entails the study of gap-filling methods. In order to develop suitable methods to fill the gaps of CCI SM in the whole area of China, we compared typical Machine Learning (ML) methods, including Random Forest method (RF), Feedforward Neural Network method (FNN), and Generalized Linear Model (GLM) with a geostatistical method, i.e., Ordinary Kriging (OK) in this study. More than 30 years of passive–active combined CCI SM from 1982 to 2018 and other biophysical variables such as Normalized Difference Vegetation Index (NDVI), precipitation, air temperature, Digital Elevation Model (DEM), soil type, and in situ SM from International Soil Moisture Network (ISMN) were utilized in this study. Results indicated that: 1) the data gap of CCI SM is frequent in China, which is found not only in cold seasons and areas but also in warm seasons and areas. The ratio of gap pixel numbers to the whole pixel numbers can be greater than 80%, and its average is around 40%. 2) ML methods can fill the gaps of CCI SM all up. Among the ML methods, RF had the best performance in fitting the relationship between CCI SM and biophysical variables. 3) Over simulated gap areas, RF had a comparable performance with OK, and they outperformed the FNN and GLM methods greatly. 4) Over in situ SM networks, RF achieved better performance than the OK method. 5) We also explored various strategies for gap-filling CCI SM. Results demonstrated that the strategy of constructing a monthly model with one RF for simulating monthly average SM and another RF for simulating monthly SM disturbance achieved the best performance. Such strategy combining with the ML method such as the RF is suggested in this study for filling the gaps of CCI SM in China.

scholarly journals Spatial-Temporal Distribution of the Freeze–Thaw Cycle of the Largest Lake (Qinghai Lake) in China Based on Machine Learning and MODIS from 2000 to 2020

2021 ◽  
Vol 13 (9) ◽  
pp. 1695
Author(s):  
Weixiao Han ◽  
Chunlin Huang ◽  
Juan Gu ◽  
Jinliang Hou ◽  
Ying Zhang
Keyword(s):  
Machine Learning ◽  
Land Surface ◽  
Temporal Dynamics ◽  
Temporal Distribution ◽  
Ice Cover ◽  
Qinghai Lake ◽  
Gap Filling ◽  
Thaw Cycle ◽  
Break Up ◽  
Ice Phenology

The lake ice phenology variations are vital for the land–surface–water cycle. Qinghai Lake is experiencing amplified warming under climate change. Based on the MODIS imagery, the spatio-temporal dynamics of the ice phenology of Qinghai Lake were analyzed using machine learning during the 2000/2001 to 2019/2020 ice season, and cloud gap-filling procedures were applied to reconstruct the result. The results showed that the overall accuracy of the water–ice classification by random forest and cloud gap-filling procedures was 98.36% and 92.56%, respectively. The annual spatial distribution of the freeze-up and break-up dates ranged primarily from DOY 330 to 397 and from DOY 70 to 116. Meanwhile, the decrease rates of freeze-up duration (DFU), full ice cover duration (DFI), and ice cover duration (DI) were 0.37, 0.34, and 0.13 days/yr., respectively, and the duration was shortened by 7.4, 6.8, and 2.6 days over the past 20 years. The increased rate of break-up duration (DBU) was 0.58 days/yr. and the duration was lengthened by 11.6 days. Furthermore, the increase in temperature resulted in an increase in precipitation after two years; the increase in precipitation resulted in the increase in DBU and decrease in DFU in corresponding years, and decreased DI and DFI after one year.

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