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Temperature-dependent evolutions of excitonic superfluid plasma frequency in a srong excitonic insulator candidate, Ta2_2NiSe5_5

We investigate an interesting anisotropic van der Waals material, Ta$_{2}$NiSe$_{5}$, using optical spectroscopy. Ta$_{2}$NiSe$_{5}$ has been known as one of the few excitonic insulators proposed over 50 years ago. Ta$_{2}$NiSe$_{5}$ has quasi-one dimensional chains along the $a$-axis. We have obtained anisotropic optical properties of a single crystal Ta$_{2}$NiSe$_{5}$ along the $a$- and $c$-axes. The measured $a$- and $c$-axis optical conductivities exhibit large anisotropic electronic and phononic properties. With regard to the $a$-axis optical conductivity, a sharp peak near 3050 cm$^{-1}$ at 9 K, with a well-defined optical gap ($\Delta^{EI} \simeq$ 1800 cm$^{-1}$) and a strong temperature-dependence, is observed. With an increase in temperature, this peak broadens and the optical energy gap closes around $\sim$325 K($T_c^{EI}$). The spectral weight redistribution with respect to the frequency and temperature indicates that the normalized optical energy gap ($\Delta^{EI}(T)/\Delta^{EI}(0)$) is $1-(T/T_c^{EI})^2$. The temperature-dependent superfluid plasma frequency of the excitonic condensation in Ta$_{2}$NiSe$_{5}$ has been determined from measured optical data. Our findings may be useful for future research on excitonic insulators.Comment: 17 pages, 5 figure

Estimation of hospital-based HIV seroprevalence as a nationwide scale by novel method; 2002-2008 in Korea

<p>Abstract</p> <p>Background</p> <p>In Korea, approximately 70% of HIV-positive individuals are currently diagnosed in hospitals, while most HIV-positive patients were diagnosed at public health centers in 1980 s and 1990 s. However, there are no reporting systems to identify how many HIV tests are performed in the Korean hospitals different from public health centers and Blood centers. We estimated how many HIV tests were performed in hospitals and analyzed the nationwide hospital-based HIV seroprevalence in the present study.</p> <p>Methods</p> <p>Between 2002 and 2008, data included HIV tests on insurance claims in hospitals and the proportion of computerized insurance claims from the Health Insurance Review and Assessment Services. The number of HIV tests from the survey in the External Quality Assurance Scheme for hospital laboratories was collected to calculate the insurance claim proportion. HIV seroprevalence was estimated using data of tested individuals, including infected individuals. Statistical analysis was confirmed with the 95% confidence interval. Statistical significance was defined at p-values < 0.05.</p> <p>Results</p> <p>The number of HIV tests in hospitals increased from 2.7 million in 2002 to 5.0 million in 2008. The trend of HIV seroprevalence was decrease (1.5-1.3 per 10,000 individuals, P < 0.0028), except in 2002. The number of women tested was greater than men, and the proportion increased in older individuals and in small towns. Men had a higher annual HIV seroprevalence than women (P < 0.0001). The annual seroprevalence decreased in men (P = 0.0037), but was stable in women. The seroprevalence in the 30-39 year age group demonstrated higher than other age groups except 2008.</p> <p>Conclusions</p> <p>The nationwide hospital-based number of HIV tests and seroprevalence were estimated using a new method and seroprevalence trends were identified. This information will facilitate improvement in national HIV prevention strategies.</p

Comparative analysis of multiple classification models to improve PM10 prediction performance

With the increasing requirement of high accuracy for particulate matter prediction, various attempts have been made to improve prediction accuracy by applying machine learning algorithms. However, the characteristics of particulate matter and the problem of the occurrence rate by concentration make it difficult to train prediction models, resulting in poor prediction. In order to solve this problem, in this paper, we proposed multiple classification models for predicting particulate matter concentrations required for prediction by dividing them into AQI-based classes. We designed multiple classification models using logistic regression, decision tree, SVM and ensemble among the various machine learning algorithms. The comparison results of the performance of the four classification models through error matrices confirmed the f-score of 0.82 or higher for all the models other than the logistic regression model