scholarly journals The geoid of China and its surrounding areas determined by shallow layer method

2021 ◽  
Vol 32 (5.2) ◽  
Author(s):  
Youchao Xie ◽  
Wen-Bin Shen ◽  
Jiancheng Han ◽  
Jiancheng Li
Keyword(s):  
Layer Method ◽  
Shallow Layer ◽  
Surrounding Areas

scholarly journals Determination of the height of Mount Everest using the shallow layer method

2021 ◽  
Author(s):  
Youchao Xie ◽  
Wenbin Shen ◽  
Jiancheng Han ◽  
Xiaole Deng
Keyword(s):  
Mount Everest ◽  
Layer Method ◽  
Shallow Layer

An alternative geoid model for Africa using the shallow-layer method

2021 ◽  
Author(s):  
Mostafa Ashry ◽  
Wen-Bin Shen ◽  
Hussein A. Abd-Elmotaal
Keyword(s):  
Geoid Model ◽  
Layer Method ◽  
Shallow Layer

Development of a global geoid model 2020 (GGM2020)

2020 ◽  
Author(s):  
WenBin Shen ◽  
Youchao Xie ◽  
Jiancheng Han ◽  
Jiancheng Li
Keyword(s):  
Gravity Field ◽  
Density Model ◽  
Layer Model ◽  
Geoid Model ◽  
Gravity Field Model ◽  
Layer Method ◽  
Shallow Layer ◽  
Inner Surface ◽  
Gps Leveling ◽  
Better Than

<p>We present an updated 5′ ×5′ global geoid model 2020 (GGM2020), which is determined based on the shallow layer method (or simply Shen method). We choose an inner surface S below the EGM2008 global geoid by 15 m, and the layer bounded by the inner surface S and the Earths geographical surface E is referred to as the shallow layer. We formulate the 3D shallow mass layer model using the refined 5′ ×5′ crust density model, CRUST1.0-5min, which is an improved 5′ ×5′ density model of the CRUST1.0 with taking into account the corrections of the areas covered by ice sheets and the land-ocean crossing regions. Based on the shallow mass layer model and the gravity field EGM2008 that is defined in the region outside the Earth’s geographical surface E, we determine the gravity field model EGM2008S that is defined in the whole region outside the inner surface S. Based on the gravity field EGM2008S and the geoid equation W(P) =W0, where W0 is the geopotential constant on the geoid and P is the point on the geoid G, we established a 5′ ×5′ global geoid model GGM2020. Comparisons show that in average the GGM2020 fits the globally available GPS/leveling points better than the EGM2008 global geoid. This study is supported by NSFCs (grant Nos. 41721003, 41631072, 41874023, 41804012, 41429401, 41574007).</p>

Determining the height of Mount Everest using the shallow layer method

2021 ◽  
Author(s):  
Youchao Xie ◽  
Wenbin Shen ◽  
Jiancheng Han ◽  
Xiaole Deng
Keyword(s):  
Gravity Field ◽  
Field Model ◽  
Space Station ◽  
Geoid Undulation ◽  
Mount Everest ◽  
Gravity Field Model ◽  
Terrain Model ◽  
Earth Gravity ◽  
Layer Method ◽  
Shallow Layer

<p>We proposed an alternative method to determine the height of Mount Everest (HME) based on the shallow layer method (SLM), which was put forward by Shen (2006). We use the precise external global Earth gravity field model (i.e., EGM2008 and EIGEN-6C4 models) as input information, and the digital topographic model (i.e., DTM2006.0) and crust models (i.e., CRUST2.0 and CRUST1.0 models) to construct the shallow layer model. There are four combined strategies：(1) EGM2008 and CRUST1.0 models, (2) EGM2008 and CRUST2.0 models, (3) EIGEN-6C4 and CRUST1.0 models, and (4) EIGEN-6C4 and CRUST2.0 models, respectively. We calculate the HME by two approaches: first approach, the HME is directly calculated by combining the geoid undulation (N) and geodetic height (h); second approach, we calculate the HME by the segment summation approach (SSA) using the gravity field inside the shallow layer determined by the SLM. Numerical results show that for four combined strategies, the differences between our results and the authoritatively released value 8848.86 m by the Chinese and Nepalese governments on December 8, 2020 are 0.448 m, -0.009 m, -0.295 m, and -0.741 m using first approach and 0.539 m, 0.083 m, -0.214 m, and -0.647 m using second approach. The combined calculation of the HME by the terrain model and gravity field model is more accurate than that by the gravity field model alone. This study is supported by the National Natural Science Foundations of China (NSFC) under Grants 42030105, 41721003, 41804012, 41631072, 41874023, Space Station Project (2020)228.</p>

Angular Calibration of Ribosomal Subuntts in Factor Space

1990 ◽  
Vol 48 (1) ◽  
pp. 462-463
Author(s):  
Minakhi Pujari ◽  
Joachim Frank
Keyword(s):  
Three Dimensional ◽  
Carbon Layer ◽  
Uranyl Acetate ◽  
Particle Analysis ◽  
Factor Space ◽  
Multivariate Statistical ◽  
Ribosomal Subunits ◽  
Layer Method ◽  
Dimensional Reconstruction ◽  
Number Of Particles

In single-particle analysis of macromolecule images with the electron microscope, variations of projections are often observed that can be attributed to the changes of the particle’s orientation on the specimen grid (“rocking”). In the multivariate statistical analysis (MSA) of such projections, a single factor is often found that expresses a large portion of these variations. Successful angle calibration of this “rocking factor” would mean that correct angles can be assigned to a large number of particles, thus facilitating three-dimensional reconstruction.In a study to explore angle calibration in factor space, we used 40S ribosomal subunits, which are known to rock around an axis approximately coincident with their long axis. We analyzed micrographs of a field of these particles, taken with 20° tilt and without tilt, using the standard methods of alignment and MSA. The specimen was prepared with the double carbon-layer method, using uranyl acetate for negative staining. In the MSA analysis, the untilted-particle projections were used as active, the tilted-particle projections as inactive objects. Upon tilting, those particles whose rocking axes are parallel to the tilt axis will change their appearance in the same way as under the influence of rocking. Therefore, each vector, in factor space, joining a tilted and untilted projection of the same particle can be regarded as a local 20-degree calibration bar.

Statistical Analsysis to Evaluate Heavy Metal Pollution in the Air Obatained by Moss Technique in Hanoi and its Surrounding Region

2019 ◽  
Vol 29 (3SI) ◽  
pp. 411
Author(s):  
N. H. Quyet ◽  
Le Hong Khiem ◽  
V. D. Quan ◽  
T. T. T. My ◽  
M. V. Frontasieva ◽  
...  
Keyword(s):  
Heavy Metal ◽  
Statistical Analysis ◽  
Metal Pollution ◽  
Heavy Metal Pollution ◽  
Principal Component ◽  
Multivariate Statistical ◽  
Five Factors ◽  
Heavy Metal Elements ◽  
Surrounding Areas ◽  
Potential Pollution

The aim of this paper was the application of statistical analysis including principal component analysis to evaluate heavy metal pollution obtained by moss technique in the air of Ha Noi and its surrounding areas and to evaluate potential pollution sources. The concentrations of 33 heavy metal elements in 27 samples of Barbula Indica moss in the investigated region collected in December of 2016 in the investigated area have been examined using multivariate statistical analysis. Five factors explaining 80% of the total variance were identified and their potential sources have been discussed.

SPECIES, HOST RANGE, AND IDENTIFICATION KEY OF WHITEFLIES OF BOGOR AND SURROUNDING AREA

2019 ◽  
Vol 18 (2) ◽  
pp. 127
Author(s):  
Purnama Hidayat ◽  
Denny Bintoro ◽  
Lia Nurulalia ◽  
Muhammad Basri
Keyword(s):  
Host Range ◽  
Host Plants ◽  
Ornamental Plants ◽  
Identification Key ◽  
Agricultural Crops ◽  
Surrounding Area ◽  
Aleurodicus Dispersus ◽  
Agricultural Plants ◽  
Surrounding Areas ◽  
Forest Plants

Species identification, host range, and identification key of whiteflies of Bogor and surrounding area. Whitefly (Hemiptera: Aleyrodidae) is a group of insects that are small, white, soft-bodied, and easily found on various agricultural crops. Whitefly is a phytophagous insect; some species are important pests in agricultural crops that can cause direct damage and can become vectors of viral diseases. The last few years the damage caused by whitefly in Indonesia has increased. Unfortunately, information about their species and host plants in Indonesia, including in Bogor, is still limited. Kalshoven, in his book entitled Pest of Crops in Indonesia, published in the 1980s reported that there were 9 species of whitefly in Indonesia. The information on the book should be reconfirmed. Therefore, this study was conducted to determine whitefly species and its host plants in Bogor and its surroundings. Whiteflies is identified based on the ‘puparia’ (the last instar of the nymph) collected from various agricultural plants, ornamental plants, weeds, and forest plants. A total of 35 species of whiteflies were collected from 74 species and 29 families of plants. The collwcted whiteflies consist of four species belong to Subfamily Aleurodicinae and 31 species of Subfamily Aleyrodinae. The most often found whitefly species were Aleurodicus dispersus, A. dugesii, and Bemisia tabaci. A dichotomous identification key of whiteflies was completed based on morphological character of 35 collected species. The number of whitefly species in Bogor and surrounding areas were far exceeded the number of species reported previously by Kalshoven from all regions in Indonesia.

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