1. Dept. of Epidemiology, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran 2. Dept. of Epidemiology, School of Public Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran 3. Dept. of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran 4. Dept. of Epidemiology, School of Medicine, Kurdistan Uni...

Temperature data from the unsaturated zone (UZ) at Yucca Mountain are analyzed to estimate percolation-flux rates and overall heat flux. A multilayer, one-dimensional analytical solution is presented for determining percolation flux from temperature data. Case studies have shown that the analytical solution agrees very well with results from the numerical code, TOUGH2. The results of the analys...

On the Greenland ice sheet, a significant quantity of surface meltwater refreezes within the firn, creating uncertainty in surface mass balance estimates. This refreezing has the potential to buffer seasonal runoff to future increases in melting, but direct measurement of the process remains difficult. We present a method for quantifying refreezing at point locations using in situ firn temperat...

The 2n-point renormalized coupling constants in the 3d Ising model: estimates by high temperature series to order β 17 Abstract Abstract: We compute the 2n-point renormalized coupling constants in the symmetric phase of the 3d Ising model on the sc lattice in terms of the high temperature expansions O(β 17) of the Fourier transformed 2n-point connected correlation functions at zero momentum. Ou...

We examine the performance of several methods to estimate meteorological 1 inputs for modelling dispersion in urban areas during convective conditions. Sensible heat 2 flux, surface friction velocity and turbulent velocities are estimated from measurements of 3 mean wind speed and the standard deviation of temperature fluctuations at a single level on a 4 tower at two suburban sites and at one ...

Accurate prediction of soil temperature (Ts) is critical for efficient soil, water and field crop management. In this study, hourly Ts variations at 5, 10, 30 cm depth were predicted an arid site (Sirjan) a semi-humid (Sanandaj) in Iran. Existing machine learning models have high performance, but suffer from uncertainty instability prediction. Therefore, GLUE approach was implemented to quantif...