Atmospheric testing of nuclear weapons began in 1945 and largely ceased in 1963. Monitoring of the resulting global fallout was carried out globally by the Environmental Measurements Laboratory and the UK Atomic Energy Research Establishment as well as at national level by some countries. A correlation was identified between fallout deposition and precipitation and an uneven distribution with latitude. In this study, the available data from 1954 to 1976 for (90)Sr and (137)Cs were reanalysed using analysis of covariance (ANCOVA) and logarithmically transformed values of the monthly deposition density as the response variable. Generalized additive models (GAM) were used to explore the relationship of different variables to the response variable and quantify the explanatory power that could be achieved. The explanatory variables which consistently explained most of the variability were precipitation at each site, latitude and change with time and a simple linear model was produced with similar explanatory power as the GAM. The estimates improved as the temporal resolution of the precipitation data increased. A good log-log fit could be obtained if a bias of about 1-6 mm precipitation per month was added, this could be interpreted as dry deposition which is not otherwise accounted for in the model. The deposition rate could then be explained as a simple non-linear power function of the precipitation rate (r(0.2-0.6) depending on latitude band). A similar non-linear power function relationship has been the outcome of some studies linking wash-out and rain-out coefficients with rain intensity. Our results showed that the precipitation rate was an important parameter, not just the total amount. The simple model presented here allows the recreation of the deposition history at a site, allowing comparison with time series of activity concentrations for different environmental compartments, which is important for model validation.
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http://dx.doi.org/10.1016/j.jenvrad.2012.03.006 | DOI Listing |
Langmuir
December 2024
Department of Civil Engineering, Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh 208016, India.
Widespread geogenic uranium (U) contamination of Indian groundwaters is of serious concern; yet little is known of the dominant forms and release mechanisms of U in these aquifers. Interestingly, manganese (Mn)-rich aquifers, highly buffered by dissolved inorganic carbon (DIC) and saturated with rhodochrosite [MnCO], have shown low U (
Nanomaterials (Basel)
December 2024
Institute of Photonics and Nanotechnology, Faculty of Physics, Vilnius University, Saulėtekio Ave. 3, 10257 Vilnius, Lithuania.
We elaborate a method for determining the 0D-1D nanostructure size by photoluminescence (PL) emission spectrum dependence on the nanostructure dimensions. As observed, the high number of diamond-like carbon nanocones shows a strongly blue-shifted PL spectrum compared to the bulk material, allowing for the calculation of their top dimensions of 2.0 nm.
View Article and Find Full Text PDFNanomaterials (Basel)
December 2024
Institute of Physics, Faculty of Physics, Astronomy and Informatics, Nicolaus Copernicus University in Toruń, ul. Grudziądzka, 5, 87-100 Toruń, Poland.
In this work, we present an experimental approach for monitoring the temperature of submicrometric, real-time operating electrical circuits using luminescence thermometry. For this purpose, we utilized lanthanide-doped up-converting nanocrystals as nanoscale temperature probes, which, combined with a highly sensitive confocal photoluminescence microscope, enabled temperature monitoring with spatial resolution limited only by the diffraction of light. To validate our concept, we constructed a simple model of an electrical microcircuit based on a single silver nanowire with a diameter of approximately 100 nm and a length of about 50 µm, whose temperature increase was induced by electric current flow.
View Article and Find Full Text PDFJ Cardiovasc Dev Dis
December 2024
Cardiovascular Section, Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC 27101, USA.
: Fatal coronary heart disease (FCHD) affects ~650,000 people yearly in the US. Electrocardiographic artificial intelligence (ECG-AI) models can predict adverse coronary events, yet their application to FCHD is understudied. : The study aimed to develop ECG-AI models predicting FCHD risk from ECGs.
View Article and Find Full Text PDFJ Imaging
December 2024
National Electronic and Computer Technology Center, National Science and Technology Development Agency, Khlong Luang, Pathum Thani 12120, Thailand.
Accurate human action recognition is becoming increasingly important across various fields, including healthcare and self-driving cars. A simple approach to enhance model performance is incorporating additional data modalities, such as depth frames, point clouds, and skeleton information, while previous studies have predominantly used late fusion techniques to combine these modalities, our research introduces a multi-level fusion approach that combines information at early, intermediate, and late stages together. Furthermore, recognizing the challenges of collecting multiple data types in real-world applications, our approach seeks to exploit multimodal techniques while relying solely on RGB frames as the single data source.
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