Forever Young: The Gap-Filling Mechanism of the CISG As a Factor of Its Modernization

2021 ◽  
Author(s):  
Marko Jovanović
Keyword(s):  
Gap Filling ◽  
Filling Mechanism

scholarly journals Dynamics of Histone H3 Deposition In Vivo Reveal a Nucleosome Gap-Filling Mechanism for H3.3 to Maintain Chromatin Integrity

2011 ◽  
Vol 44 (6) ◽  
pp. 928-941 ◽  
Author(s):  
Dominique Ray-Gallet ◽  
Adam Woolfe ◽  
Isabelle Vassias ◽  
Céline Pellentz ◽  
Nicolas Lacoste ◽  
...  
Keyword(s):  
Histone H3 ◽  
Gap Filling ◽  
Chromatin Integrity ◽  
Filling Mechanism

Preparation of Nano-CaCO3/Fast-Growing Poplar Composite by the Diffusion Method

2012 ◽  
Vol 184-185 ◽  
pp. 1129-1133
Author(s):  
Zhen Jia ◽  
Guo Liang Li ◽  
Jian Li ◽  
Cheng Yu Wang ◽  
Zhi Jun Zhang
Keyword(s):  
Double Diffusion ◽  
Wood Properties ◽  
Diffusion Method ◽  
Vacuum Impregnation ◽  
Gap Filling ◽  
Characterization Methods ◽  
Performance Indexes ◽  
Sample Characterization ◽  
And Performance ◽  
Filling Mechanism

This paper introduced gap filling mechanism of wood and inorganic composite and used the mechanism to optimize the wood properties of poplar. Through double diffusion method of the vacuum impregnation, CaCO3 and poplar composite was prepared. Using Sample characterization methods and performance indexes, prepared wood inorganic nanometer composite (WINC) was with hydrophobic and higher hardness.

Interpretation and Gap Filling in International Commercial Contracts

2019 ◽  
Author(s):  
Ayse Nihan Karadayi Yalim
Keyword(s):  
Gap Filling

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.

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 Halloysite nanotubes filled with salicylic acid and sodium diclofenac: effects of vacuum pumping on loading and release properties

2021 ◽  
Author(s):  
Lorenzo Lisuzzo ◽  
Giuseppe Cavallaro ◽  
Stefana Milioto ◽  
Giuseppe Lazzara
Keyword(s):  
Salicylic Acid ◽  
Release Kinetics ◽  
Halloysite Nanotubes ◽  
Drug Molecules ◽  
Sodium Diclofenac ◽  
Tunable Release ◽  
Feature Based ◽  
Kinetics Of ◽  
Filling Mechanism

AbstractIn this work, we investigated the effects of the vacuum pumping on both the loading efficiencies and the release kinetics of halloysite nanotubes filled with drug molecules dissolved in ethanol. As model drugs, salicylic acid and sodium diclofenac were selected. For comparison, the loading of the drug molecules was conducted on platy kaolinite to explore the key role of the hollow tubular morphology on the filling mechanism of halloysite. The effects of the pressure conditions used in the loading protocol were interpreted and discussed on the basis of the thermodynamic results provided by Knudsen thermogravimetry, which demonstrated the ethanol confinement inside the halloysite cavity. Several techniques (TEM, FTIR spectroscopy, DLS and $$\zeta$$ ζ -potential experiments) were employed to characterize the drug filled nanoclays. Besides, release kinetics of the drugs were studied and interpreted according to the loading mechanism. This work represents a further step for the development of nanotubular carriers with tunable release feature based on the loading protocol and drug localization into the carrier. Graphic abstract The filling efficiency of halloysite nanotubes is enhanced by the reduction of the pressure conditions used in the loading protocol.

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.

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