Study on the Performance of an Alpha Energy Spectrum Radon Measuring Instrument Based on CdZnTe Detector.

ABSTRACT: To realize a fast and reliable approach for continuous radon measurements, a CdZnTe (CZT) detector based on the electrostatic collection method was developed. The experimental results show that when the external temperature varies from 5 °C to 30 °C, the maximum drifts of the characteristic peak positions of 218Po and 214Po are only 8 and 6. Furthermore, the measurement error associated with the constant radon concentration is less than 5.5%. As the temperature increases or decreases, the measurement error becomes larger, with the maximum error being 43.1%. The response of the proposed instrument for constant radon concentration is better than that of the RAD7 radon measurement instrument. At 25 °C, the value measured using the NRL-II radon meter with the PIPS detector is in good agreement with the actual value. The new radon measurement instrument exhibits a good compliance from the third measurement cycle (measurement deviation 0.53-3.95%), while RAD7 has good compliance from the fourth measurement cycle (measurement deviation of 1.17-4.88%). The theoretical and practical values of the iterative correction factor (influence of the previous measurement on the current measurement) of the radon measurement instrument are in good agreement. The iterative correction factor can be used for performing continuous radon measurements independently, with the aim of achieving a rapid response to environmental radon concentrations.

Cross-language opinion lexicon extraction using mutual-reinforcement label propagation.

There is a growing interest in automatically building opinion lexicon from sources such as product reviews. Most of these methods depend on abundant external resources such as WordNet, which limits the applicability of these methods. Unsupervised or semi-supervised learning provides an optional solution to multilingual opinion lexicon extraction. However, the datasets are imbalanced in different languages. For some languages, the high-quality corpora are scarce or hard to obtain, which limits the research progress. To solve the above problems, we explore a mutual-reinforcement label propagation framework. First, for each language, a label propagation algorithm is applied to a word relation graph, and then a bilingual dictionary is used as a bridge to transfer information between two languages. A key advantage of this model is its ability to make two languages learn from each other and boost each other. The experimental results show that the proposed approach outperforms baseline significantly.