Effect of the hand on cellular phone radiation

2001 ◽  
Vol 148 (4) ◽  
pp. 247 ◽  
Author(s):  
M. Francavilla ◽  
A. Schiavoni ◽  
P. Bertotto ◽  
G. Richiardi
Keyword(s):  
Cellular Phone ◽  
Cellular Phone Radiation

935 MHz cellular phone radiation. Anin vitrostudy of genotoxicity in human lymphocytes

2006 ◽  
Vol 82 (5) ◽  
pp. 339-346 ◽  
Author(s):  
L. Stronati ◽  
A. Testa ◽  
J. Moquet ◽  
A. Edwards ◽  
E. Cordelli ◽  
...  
Keyword(s):  
Human Lymphocytes ◽  
Cellular Phone ◽  
Cellular Phone Radiation

GSM 900 MHz cellular phone radiation can either stimulate or depress early embryogenesis in Japanese quails depending on the duration of exposure

2013 ◽  
Vol 89 (9) ◽  
pp. 756-763 ◽  
Author(s):  
Olexandr Tsybulin ◽  
Evgeniy Sidorik ◽  
Olga Brieieva ◽  
Lyubov Buchynska ◽  
Sergiy Kyrylenko ◽  
...  
Keyword(s):  
Cellular Phone ◽  
Early Embryogenesis ◽  
Japanese Quails ◽  
Cellular Phone Radiation

scholarly journals Evaluating the Effect of the Financial Status to the Mobility Customs

2021 ◽  
Vol 10 (5) ◽  
pp. 328
Author(s):  
Gergo Pintér ◽  
Imre Felde
Keyword(s):  
Socioeconomic Status ◽  
Economic Status ◽  
Housing Prices ◽  
Principal Component ◽  
Cellular Phone ◽  
Radius Of Gyration ◽  
Financial Status ◽  
Property Price ◽  
Statistical Relationships ◽  
Commuting Patterns

In this article, we explore the relationship between cellular phone data and housing prices in Budapest, Hungary. We determine mobility indicators from one months of Call Detail Records (CDR) data, while the property price data are used to characterize the socioeconomic status at the Capital of Hungary. First, we validated the proposed methodology by comparing the Home and Work locations estimation and the commuting patterns derived from the cellular network dataset with reports of the national mini census. We investigated the statistical relationships between mobile phone indicators, such as Radius of Gyration, the distance between Home and Work locations or the Entropy of visited cells, and measures of economic status based on housing prices. Our findings show that the mobility correlates significantly with the socioeconomic status. We performed Principal Component Analysis (PCA) on combined vectors of mobility indicators in order to characterize the dependence of mobility habits on socioeconomic status. The results of the PCA investigation showed remarkable correlation of housing prices and mobility customs.

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