A quantitative study of spin-flip co-tunneling transport in a quantum dot

We report detailed transport measurements in a quantum dot in a spin-flip co-tunneling regime, and a quantitative comparison of the data to microscopic theory. The quantum dot is fabricated by lateral gating of a GaAs/AlGaAs heterostructure, and the conductance is measured in the presence of an in-plane Zeeman field. We focus on the ratio of the nonlinear conductance values at bias voltages exceeding the Zeeman threshold, a regime that permits a spin flip on the dot, to those below the Zeeman threshold, when the spin flip on the dot is energetically forbidden. The data obtained in three different odd-occupation dot states show good quantitative agreement with the theory with no adjustable parameters. We also compare the theoretical results to the predictions of a phenomenological form used previously for the analysis of non-linear co-tunneling conductance, specifically the determination of the heterostructure g-factor, and find good agreement between the two.Comment: 5 pages, 5 figure

Mycobacterium tuberculosis Beijing genotype emerging in Vietnam.

To assess whether the Mycobacterium tuberculosis Beijing genotype is emerging in Vietnam, we analyzed 563 isolates from new cases by spoligotyping and examined the association between the genotype and age, resistance, and BCG vaccination status. Three hundred one (54%) patients were infected with Beijing genotype strains. The genotype was associated with younger age (and hence with active transmission) and with isoniazid and streptomycin resistance, but not with BCG vaccination

Relativistic dynamical polarizability of hydrogen-like atoms

Using the operator representation of the Dirac Coulomb Green function the analytical method in perturbation theory is employed in obtaining solutions of the Dirac equation for a hydrogen-like atom in a time-dependent electric field. The relativistic dynamical polarizability of hydrogen-like atoms is calculated and analysed.Comment: 15 pages, 3 figures (not included, but hard copies are available upon request

Indoor PM₀.₁ and PM₂.₅ in Hanoi: Chemical characterization, source identification, and health risk assessment

This study attempted to provide comprehensive insights into the chemical composition, source identification, and health risk assessment of indoor particulate matter (PM) in urban areas of Vietnam. Three hundred and twenty daily samples of PM₀.₁ and PM₂.₅ were collected at three different types of dwellings in Hanoi in two seasons, namely summer and winter. The samples were analyzed for 10 trace elements (TEs), namely Cr, Mn, Co, Cu, Ni, Zn, As, Cd, Sn, and Pb. The daily average concentrations of indoor PM₀.₁ and PM₂.₅ in the city were in the ranges of 7.0–8.9 μg/m³ and 43.3–106 μg/m³, respectively. The average concentrations of TEs bound to indoor PM ranged from 66.2 ng/m³ to 216 ng/m³ for PM₀.₁ and 391 ng/m³ to 2360 ng/m³ for PM₂.₅. Principle component analysis and enrichment factor were applied to identify the possible sources of indoor PM. Results showed that indoor PM₂.₅ was mainly derived from outdoor sources, whereas indoor PM₀.₁ was derived from indoor and outdoor sources. Domestic coal burning, industrial and traffic emissions were observed as outdoor sources, whereas household dust and indoor combustion were found as indoor sources. 80% of PM₂.₅ was deposited in the head airways, whereas 75% of PM₀.₁ was deposited in alveolar region. Monte Carlo simulation indicated that the intake of TEs in PM₂.₅ can lead to high carcinogenic risk for people over 60 years old and unacceptable non-carcinogenic risks for all ages at the roadside house in winter

VlogQA: Task, Dataset, and Baseline Models for Vietnamese Spoken-Based Machine Reading Comprehension

This paper presents the development process of a Vietnamese spoken language corpus for machine reading comprehension (MRC) tasks and provides insights into the challenges and opportunities associated with using real-world data for machine reading comprehension tasks. The existing MRC corpora in Vietnamese mainly focus on formal written documents such as Wikipedia articles, online newspapers, or textbooks. In contrast, the VlogQA consists of 10,076 question-answer pairs based on 1,230 transcript documents sourced from YouTube -- an extensive source of user-uploaded content, covering the topics of food and travel. By capturing the spoken language of native Vietnamese speakers in natural settings, an obscure corner overlooked in Vietnamese research, the corpus provides a valuable resource for future research in reading comprehension tasks for the Vietnamese language. Regarding performance evaluation, our deep-learning models achieved the highest F1 score of 75.34% on the test set, indicating significant progress in machine reading comprehension for Vietnamese spoken language data. In terms of EM, the highest score we accomplished is 53.97%, which reflects the challenge in processing spoken-based content and highlights the need for further improvement.Comment: To appear as the main conference paper at EACL 202

Assessment of seasonal winter temperature forecast errors in the regcm model over northern Vietnam

This study verified the seasonal six-month forecasts for winter temperatures for northern Vietnam in 1998–2018 using a regional climate model (RegCM4) with the boundary conditions of the climate forecast system Version 2 (CFSv2) from the National Centers for Environmental Prediction (NCEP). First, different physical schemes (land-surface process, cumulus, and radiation parameterizations) in RegCM4 were applied to generate 12 single forecasts. Second, the simple ensemble forecasts were generated through the combinations of those different physical formulations. Three subclimate regions (R1, R2, R3) of northern Vietnam were separately tested with surface observations and a reanalysis dataset (Japanese 55-year reanalysis (JRA55)). The highest sensitivity to the mean monthly temperature forecasts was shown by the land-surface parameterizations (the biosphere−atmosphere transfer scheme (BATS) and community land model version 4.5 (CLM)). The BATS forecast groups tended to provide forecasts with lower temperatures than the actual observations, while the CLM forecast groups tended to overestimate the temperatures. The forecast errors from single forecasts could be clearly reduced with ensemble mean forecasts, but ensemble spreads were less than those root-mean-square errors (RMSEs). This indicated that the ensemble forecast was underdispersed and that the direct forecast from RegCM4 needed more postprocessing