Study on the Correlation Between GNSS Vertical Time Series and the Space-Time Distribution of Groundwater in California

2020 ◽  
pp. 214-221
Author(s):  
Xiaoguang Pang ◽  
Yong Luo ◽  
Shaodong Jing ◽  
Shuangcheng Zhang ◽  
Kunchao Lei
Keyword(s):  
Time Series ◽  
Time Distribution ◽  
Space Time

Space-Time Distribution of Normal and Pathological Human Skin Dielectric Properties in the Millimeter Wave Range

1994 ◽  
Vol 13 (1) ◽  
pp. 15-25
Author(s):  
Igor Ivanchenko ◽  
Eugeny Andreyev ◽  
Victor Lizogub ◽  
Ludmila Sveshnikova
Keyword(s):  
Dielectric Properties ◽  
Human Skin ◽  
Millimeter Wave ◽  
Time Distribution ◽  
Space Time ◽  
Millimeter Wave Range ◽  
Wave Range

A spectroscopic study of the global space-time distribution of atmospheric CO

Tellus ◽  
1981 ◽  
Vol 33 (3) ◽  
pp. 262-273 ◽  
Author(s):  
V. I. Dianov-KLOKOV ◽  
L. N. Yurganov
Keyword(s):  
Spectroscopic Study ◽  
Time Distribution ◽  
Space Time ◽  
Global Space

The physical basis of application of positron emission tomography and modern radiopharms

2021 ◽  
Vol 4 ◽  
Author(s):  
L.I. , Galchenko ◽  
◽  
A.N. Kalyagin
Keyword(s):  
Positron Emission Tomography ◽  
Time Distribution ◽  
Space Time ◽  
Physical Basis ◽  
Emission Tomography ◽  
Annihilation Radiation ◽  
Review Of The Literature ◽  
Diagnostic Practice ◽  
Positron Emission

This article provides a review of the literature on the history, physical and technical foundations and features of the application of positron emission tomography (PET), which came into practice in the 1970s. PET is a method of visualizing the space-time distribution of a positron-emitting radiopharmaceutical (RP) in the patient‘s body by annihilation radiation. The classification of radiopharmaceuticals that are used in clinical and diagnostic practice is considered.

scholarly journals PEMODELAN GENERALIZED SPACE TIME AUTOREGRESSIVE (GSTAR) SEASONAL PADA DATA CURAH HUJAN EMPAT KABUPATEN DI PROVINSI JAWA TENGAH

2019 ◽  
Vol 8 (4) ◽  
pp. 418-427
Author(s):  
Eko Siswanto ◽  
Hasbi Yasin ◽  
Sudarno Sudarno
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Space Time ◽  
Series Data ◽  
Inverse Distance Weighted ◽  
Modeling And Forecasting ◽  
Distance Weighted ◽  
The Relationship ◽  
Inverse Distance

In many applications, several time series data are recorded simultaneously at a number of locations. Time series data from nearby locations often to be related by spatial and time. This data is called spatial time series data. Generalized Space Time Autoregressive (GSTAR) model is one of space time models used to modeling and forecasting spatial time series data. This study applied GTSAR model to modeling volume of rainfall four locations in Jepara Regency, Kudus Regency, Pati Regency, and Grobogan Regency. Based on the smallest RMSE mean of forecasting result, the best model chosen by this study is GSTAR (11)-I(1)12 with the inverse distance weighted. Based on GSTAR(11)-I(1)12 with the inverse distance weighted, the relationship between the location shown on rainfall Pati Regency influenced by the rainfall in other regencies. Keywords: GSTAR, RMSE, Rainfall

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