Elementi nastali raspadom iz prirodnih radioaktivnih nizova u pitkoj vodi i cigaretama u Iranu

The uranium (238U) decay series provides the most important isotopes of elements radium (226Ra), radon (222Rn), and polonium (210Po) with half-lives of about 1600 years, 3.8 days, and 140 days, respectively. Although the chemical structure of radium is very similar to calcium, the fact that it produces a radioactive gas (radon) complicates its handling in the laboratory and natural environment. In this study, we used the average concentrations of naturally occurring radionuclide 226Ra in drinking water at different parts of Iran to estimate the annual effective dose. In the other part of the study, we measured the concentrations of 210Po in Iranian cigarettes to estimate the internal intake of this radionuclide and its concentration in the lung tissues of smokers. The results indicate that the average concentration of 226Ra in Iranian drinking water was below the 100 mBq L-1 recommended by the World Health Organization while the average concentration of 210Po and 210Pb in Iranian cigarettes was relatively high in comparison with other cigarettes found on the market.Uranijev (238U) radioaktivni niz obuhvaća najvažnije izotope radija (226Ra), radona (222Rn) i polonija (210Po), a poluvijek raspada im je 1600 godina za prvi element, 3,8 dana za drugi te 140 dana za treći. Premda je radijeva kemijska struktura vrlo slična kalcijevoj, rukovanje njime u laboratoriju i prirodnome okolišu otežano je činjenicom da proizvodi radioaktivni plin radon. S pomoću prosječnih koncentracija prirodnoga radionuklida 226Ra u pitkoj vodi izračunali smo njegovu godišnju efektivnu dozu u različitim dijelovima Irana. U drugome smo istraživanju izmjerili koncentracije 210Po u iranskim cigaretama i izračunali njegov unos i koncentracije u plućnome tkivu. Naši rezultati upućuju na to da je prosječna koncentracija 226Ra u pitkoj vodi niža od 100 mBq L-1 koju preporučuje Svjetska zdravstvena organizacija, dok je prosječna koncentracija 210Po i 210Pb u iranskim cigaretama relativno visoka u odnosu na druge cigarete dostupne na tržištu

Use of passive alpha detectors to screen for uranium contamination in a field at Fernald, Ohio

This paper reports the results from a field test of newly developed techniques for inexpensive, in situ screening of soil for alpha contamination. Passive alpha detectors that are commercially available for the detection indoor airborne alpha activity (i.e., {sup 222}Rn) have been modified so they can be applied to the detection of alpha contamination on surfaces or in soils. Results reported here are from an intercomparison involving several different techniques with all measurements being made at the same sites in a field near the formerly used uranium processing facility at Fernald, Ohio, during the summer of 1994. The results for two types of passive alpha detector show that the quality of calibration is improved if soils samples are milled to increase homogeneity within the soil matrices. The correlation between laboratory based radiochemical analyses and quick, field-based screening measurements is acceptable and can be improved if the passive devices are left for longer exposure times in the field. The total cost per measurement for either type of passive alpha detector is probably less than $25 and should provide a cost-effective means for site managers to develop the information needed to find areas with remaining alpha contamination so resources can be allocated efficiently

Electret ion chamber-based passive radon-thoron discriminative monitors

Electret ion chambers (EICs), commercially available under brand name E-PERM®, are widely used for measuring indoor and outdoor 222Rn concentrations in air. These are designed to respond only to 222Rn and not to 220Rn by restricting diffusional entry area. Such radon EIC (R EIC) monitors are modified by increasing the entry area to allow 220Rn, in addition to 222Rn. Such modified units are called RT EIC. When a set of R and RT EICs are collocated, it is possible to discriminate and measure both radon and thoron concentrations, using appropriate calibration factors (CFs) and algorithms. The EICs come in different volumes, providing different sensitivities. The thoron CFs for 58-, 210- and 960-ml volume R and RT pairs are, respectively, 2.8-, 18.7- and 89-V drop per (kBq m-3 d), respectively. These provide much wider sensitivities and ranges compared to alpha track-based passive radon-thoron discriminative monitors

Electret ion chamber-based passive radon-thoron discriminative monitor

Electret ion chambers (EICs), commercially available under brand name E-PERM®, are widely used for measuring indoor and outdoor 222Rn concentrations in air. These are designed to respond only to 222Rn and not to 220Rn by restricting diffusional entry area. Such radon EIC (R EIC) monitors are modified by increasing the entry area to allow 220Rn, in addition to 222Rn. Such modified units are called RT EIC. When a set of R and RT EICs are collocated, it is possible to discriminate and measure both radon and thoron concentrations, using appropriate calibration factors (CFs) and algorithms. The EICs come in different volumes, providing different sensitivities. The thoron CFs for 58-, 210- and 960-ml volume R and RT pairs are, respectively, 2.8-, 18.7- and 89-V drop per (kBq m-3 d), respectively. These provide much wider sensitivities and ranges compared to alpha track-based passive radon-thoron discriminative monitors

Sentiment Aware Stock Price Forecasting using an SA-RNN-LBL Learning Model

Stock market historical information is often utilized in technical analyses for identifying and evaluating patterns that could be utilized to achieve profits in trading. Although technical analysis utilizing various measures has been proven to be helpful for forecasting and predicting price trends, its utilization in formulating trading orders and rules in an automated system is complex due to the indeterminate nature of the rules. Moreover, it is hard to define a specific combination of technical measures that identify better trading rules and points, since stocks might be affected by different external factors. Thus, it is important to incorporate investors’ sentiments in forecasting operations, considering dynamically the varying stock behavior. This paper presents a sentiment aware stock forecasting model using a Log BiLinear (LBL) model for learning short term stock market sentiment patterns, and a Recurrent Neural Network (RNN) for learning long-term stock market sentiment patterns. The Sentiment Aware Stock Price Forecasting (SASPF) model achieves a much superior performance compared to standard deep learning based stock price forecasting models.</jats:p