Exposure assessment of one-year-old child to 3G tablet in uplink mode and to 3G femtocell in downlink mode using polynomial chaos decomposition

This paper deals with a new methodology to assess the exposure induced by both uplink and downlink of a cellular network using 3D electromagnetic simulations. It aims to analyze together the exposure induced by a personal device (uplink exposure) and that induced by a base station (downlink exposure). The study involved the major parameters contributing to variability and uncertainty in exposure assessment, such as the user’s posture, the type of wireless device, and the propagation environment. Our approach is relying basically on the modeling of the power radiated by the personal device and the ambient electric field, while taking into account the effects of human body shadowing and the propagation channel fluctuations. The exposure assessment as well as the human-wave interactions has been simulated using the finite difference in time domain method (FDTD). In uplink scenarios, four FDTD simulations were performed with a child model, used in two postures (sitting and standing) and in two usage scenarios (voice and data), which aimed to examine the exposure induced by a mobile phone and a tablet emitting, respectively, at 900 MHz and 1940 MHz. In the downlink scenario, a series of FDTD simulations of an exposure to a single plane wave and multiplane waves have been conducted, and an efficient metamodeling of the exposure using the Polynomial Chaos approach has been developed.

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Exposure assessment of Bisphenol A intake from polymeric baby bottles in formula-fed infants aged less than one year

Toxicology Reports ◽

10.1016/j.toxrep.2015.09.002 ◽

2015 ◽

Vol 2 ◽

pp. 1273-1280 ◽

Cited By ~ 4

Author(s):

Zohreh Abdi Moghadam ◽

Maryam Mirlohi ◽

Hamidreza Pourzamani ◽

Akbar Malekpour ◽

Zohreh Amininoor ◽

...

Keyword(s):

Bisphenol A ◽

Exposure Assessment ◽

Baby Bottles ◽

One Year

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Uncertainty propagation in CFD using polynomial chaos decomposition

Fluid Dynamics Research ◽

10.1016/j.fluiddyn.2005.12.003 ◽

2006 ◽

Vol 38 (9) ◽

pp. 616-640 ◽

Cited By ~ 154

Author(s):

O M Knio ◽

O P Le Maître

Keyword(s):

Polynomial Chaos ◽

Uncertainty Propagation ◽

Chaos Decomposition

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An algorithm to identify residential mobility from electronic health-record data

International Journal of Epidemiology ◽

10.1093/ije/dyab064 ◽

2021 ◽

Author(s):

Jessica R Meeker ◽

Heather Burris ◽

Mary Regina Boland

Keyword(s):

Electronic Health Record ◽

Exposure Assessment ◽

Residential Mobility ◽

Area Deprivation ◽

Health Record ◽

Exposure Misclassification ◽

Electronic Health Record Data ◽

Electronic Health ◽

One Year ◽

Zip Code

Abstract Background Environmental, social and economic exposures can be inferred from address information recorded in an electronic health record. However, these data often contain administrative errors and misspellings. These issues make it challenging to determine whether a patient has moved, which is integral for accurate exposure assessment. We aim to develop an algorithm to identify residential mobility events and avoid exposure misclassification. Methods At Penn Medicine, we obtained a cohort of 12 147 pregnant patients who delivered between 2013 and 2017. From this cohort, we identified 9959 pregnant patients with address information at both time of delivery and one year prior. We developed an algorithm entitled REMAP (Relocation Event Moving Algorithm for Patients) to identify residential mobility during pregnancy and compared it to using ZIP code differences alone. We assigned an area-deprivation exposure score to each address and assessed how residential mobility changed the deprivation scores. Results To assess the accuracy of our REMAP algorithm, we manually reviewed 3362 addresses and found that REMAP was 95.7% accurate. In this large urban cohort, 41% of patients moved during pregnancy. REMAP outperformed the comparison of ZIP codes alone (82.9%). If residential mobility had not been taken into account, absolute area deprivation would have misclassified 39% of the patients. When setting a threshold of one quartile for misclassification, 24.4% of patients would have been misclassified. Conclusions Our study tackles an important characterization problem for exposures that are assigned based upon residential addresses. We demonstrate that methods using ZIP code alone are not adequate. REMAP allows address information from electronic health records to be used for accurate exposure assessment and the determination of residential mobility, giving researchers and policy makers more reliable information.

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