Self-powered broadband photodetection enabled by facile CVD-grown MoS2/GaN heterostructures.

Achieving self-powered photodetection without biasing is a notable challenge for photodetectors. In this work, we demonstrate the successful fabrication of large-scale van der Waals epitaxial molybdenum disulfide (MoS2) on a p-GaN/sapphire substrate using a straightforward chemical vapor deposition (CVD) technique. Our research primarily centers on the characterization of these photodetectors produced through this method. The MoS2/GaN heterojunction photodetector showcases a broad and extensive photoresponse spanning from ultraviolet A (UVA) to near-infrared (NIR). When illuminated by a 532 nm laser, its self-powered photoresponse is characterized by a rise time (τr) of ∼18.5 ms and a decay time (τd) of ∼123.2 ms. The photodetector achieves a responsivity (R) of ∼0.13 A W-1 and a specific detectivity (D*) of ∼3.8 × 1010 Jones at zero bias. Additionally, while utilizing a 404 nm laser, the photodetector reaches a maximum R and D* of ∼1.7 × 104 A/W and ∼1.6 × 1013 Jones, respectively, at Vb = 5 V. The operational mechanism of the device can be explained by the diode characteristics involving a tunneling current in the presence of reverse bias. The exceptional performance of these photodetectors can be attributed to the pristine interface between the CVD-grown MoS2 and GaN, providing an impeccably clean tunneling surface. Additionally, our investigation has unveiled that MoS2/GaN heterostructure photodetectors, featuring MoS2 coverage percentages spanning from 20% to 50%, exhibit improved responsivity capabilities at an external bias voltage. As a result, this facile CVD growth technique for MoS2 photodetectors holds significant potential for large-scale production in the manufacturing industry.

Identifying and quantifying PM2.5 pollution episodes with a fusion method of moving window technique and constrained Positive Matrix Factorization.

PM2.5 pollution episodes rapidly and significantly deteriorate the air quality and are a critical concern worldwide. This study developed a fusion method based on the moving window dataset technique and constrained Positive Matrix Factorization (PMF) to differentiate and characterize potential factors in a PM2.5 episode case assuming having one new contributor. The hourly PM2.5 compositions of elements, ions and carbonaceous components, were collected from September to December 2020 in Taipei, Taiwan. Constraint targets based on the bootstrap analysis result of a PMF model using a long-term input dataset were imposed on the modeling of each moving window to ensure similar features of the retrieved factors. The constituents of an additionally differentiated factor to the episode, which was identified as regional transport, were stable among each moving window that covered the occurrence of the episode as revealed by the profile matching index. The results showed that the largest contributor to the PM2.5 mass during the episode period of 12/12/2020 was regional transport (61%), whereas that of 12/13 was the regular pollution of industry/ammonium sulfate related (43%). According to our review of the literature, this study is the first to apply both the moving window technique and constrained PMF to characterize the episode. The findings provide valuable information that can be used to explore the causes of PM2.5 episodes and implement air pollution control strategies.

Bovine tuberculosis in Taiwan, 2008-2019.

Bovine tuberculosis (bTB) is a zoonosis caused by Mycobacterium bovis. The impact of bTB on global TB control has been underestimated. We adopted the One Health approach to human bTB surveillance in Taiwan. Of 20,972 human TB cases, 202 (1.0%) were bTB, 78.2% were in males, 85.1% were new cases, 83.2% were pulmonary TB, and most were in Central (52.5%) and Southern (24.8%) Taiwan. Only 18.8% of bTB patients had known animal contact. Of the 202 human M. bovis strains, 100% were resistant to pyrazinamide (PZA), 30.2% were concurrently resistant to isoniazid (INH) and 2.0% were multidrug resistant, defined as being resistant to at least INH and rifampin. Whereas, of the 22 animal M. bovis strains, 100% and 22.7% were resistant to PZA and INH, respectively. Seven spoligotypes and 25 mycobacterial interspersed repetitive unit genotypes were identified. The predominant genotype, SB0265, was also prevalent in livestock. Notably, six animal-specific M. bovis genotypes were identified. bTB differential diagnosis and drug resistance detection are crucial for TB control. Comprehensive surveillance and human-animal interface investigations are needed.

Estimating monthly PM2.5 concentrations from satellite remote sensing data, meteorological variables, and land use data using ensemble statistical modeling and a random forest approach.

Fine particulate matter (PM2.5) is associated with various adverse health outcomes and poses serious concerns for public health. However, ground monitoring stations for PM2.5 measurements are mostly installed in population-dense or urban areas. Thus, satellite retrieved aerosol optical depth (AOD) data, which provide spatial and temporal surrogates of exposure, have become an important tool for PM2.5 estimates in a study area. In this study, we used AOD estimates of surface PM2.5 together with meteorological and land use variables to estimate monthly PM2.5 concentrations at a spatial resolution of 3 km2 over Taiwan Island from 2015 to 2019. An ensemble two-stage estimation procedure was proposed, with a generalized additive model (GAM) for temporal-trend removal in the first stage and a random forest model used to assess residual spatiotemporal variations in the second stage. We obtained a model-fitting R2 of 0.98 with a root mean square error (RMSE) of 1.40 µg/m3. The leave-one-out cross-validation (LOOCV) R2 with seasonal stratification was 0.82, and the RMSE was 3.85 µg/m3, whereas the R2 and RMSE obtained by using the pure random forest approach produced R2 and RMSE values of 0.74 and 4.60 µg/m3, respectively. The results indicated that the ensemble modeling approach had a higher predictive ability than the pure machine learning method and could provide reliable PM2.5 estimates over the entire island, which has complex terrain in terms of land use and topography.

Quantifying spatial heterogeneity of vulnerability to short-term PM2.5 exposure with data fusion framework.

The current estimations of the burden of disease (BD) of PM2.5 exposure is still potentially biased by two factors: ignorance of heterogeneous vulnerabilities at diverse urbanization levels and reliance on the risk estimates from existing literature, usually from different locations. Our objectives are (1) to build up a data fusion framework to estimate the burden of PM2.5 exposure while evaluating local risks simultaneously and (2) to quantify their spatial heterogeneity, relationship to land-use characteristics, and derived uncertainties when calculating the disease burdens. The feature of this study is applying six local databases to extract PM2.5 exposure risk and the BD information, including the risks of death, cardiovascular disease (CVD), and respiratory disease (RD), and their spatial heterogeneities through our data fusion framework. We applied the developed framework to Tainan City in Taiwan as a use case estimated the risks by using 2006-2016 emergency department visit data, air quality monitoring data, and land-use characteristics and further estimated the BD caused by daily PM2.5 exposure in 2013. Our results found that the risks of CVD and RD in highly urbanized areas and death in rural areas could reach 1.20-1.57 times higher than average. Furthermore, we performed a sensitivity analysis to assess the uncertainty of BD estimations from utilizing different data sources, and the results showed that the uncertainty of the BD estimations could be contributed by different PM2.5 exposure data (20-32%) and risk values (0-86%), especially for highly urbanized areas. In conclusion, our approach for estimating BD based on local databases has the potential to be generalized to the developing and overpopulated countries and to support local air quality and health management plans.

Examining the Effects of Art Therapy on Reoccurring Tobacco Use in a Taiwanese Youth Population: A Mixed-Method Study.

BACKGROUND: Cigarette smoking is a primary risk factor affecting mental and physical health worldwide. Many chronic diseases are closely related to smoking. Adolescents in Taiwan are increasingly using tobacco, especially in rural areas. OBJECTIVES: This research project used a mixed-method study to examine the effects of art therapy on smoking cessation in rural Taiwanese youth smokers. METHODS: Participants from years 10-11, were drawn from three senior high schools in Taiwan. The experimental group participated in a six-week smoking cessation intervention using art therapy. The comparison group participated in typical courses on smoking cessation. Quantitative measures included need for smoking, nicotine dependence, self-esteem, self-efficacy, and smoking cessation status. Qualitative analysis was based on phenomenology. RESULTS: A total of 66 students (n = 40 experimental group; n = 26 comparison group) were the subjects of quantitative analysis. No differences were noted in baseline characteristics of groups. Generalized estimating equation analyses suggested significant between-group differences in change from pre- to follow-up test scores in the "social domain" (B = -5.12, p < 0.05). Qualitative data (n = 17 experimental group; n = 10 control group) suggested three domains: effects of art therapy on smoking prevention, benefits of art therapy on other outcome measures, and comparison between art therapy and traditional smoking cessation programs. Conclusions/importance: The findings of this study can potentially contribute significantly to existing knowledge regarding the perceptions of art therapy on reoccurring tobacco use in Taiwanese youth.

Source apportionment of urban air pollutants using constrained receptor models with a priori profile information.

Exposure to air pollutants such as volatile organic compounds (VOCs) and fine particulate matter (PM2.5) are associated with adverse health effects. This study applied multiple time resolution data of hourly VOCs and 24-h PM2.5 to a constrained Positive Matrix Factorization (PMF) model for source apportionment in Taipei, Taiwan. Ninety-two daily PM2.5 samples and 2208 hourly VOC measurements were collected during four seasons in 2014 and 2015. With some a priori information, we used different procedures to constrain retrieved factors toward realistic sources. A total of nine source factors were identified as: natural gas/liquefied petroleum gas (LPG) leakage, solvent use/industrial process, contaminated marine aerosol, secondary aerosol/long-range transport, oil combustion, traffic related, evaporative gasoline emission, gasoline exhaust, and soil dust. Results showed that solvent use/industrial process was the largest contributor (19%) to VOCs while the largest contributor to PM2.5 mass was secondary aerosol/long-range transport (57%). A robust regression analysis showed that secondary aerosol was mostly contributed by regional transport related factor (25%).

Proteinuria can predict short-term prognosis in critically ill cirrhotic patients.

BACKGROUND AND AIM: Increasing evidence supports that proteinuria is a useful tool in several clinical situations. Cirrhotic patients with proteinuria admitted to intensive care units (ICUs) have high mortality rates. This study analyzed the outcomes of critically ill cirrhotic patients and determined the prognostic value of proteinuria. METHODS: A total of 230 cirrhotic patients were admitted to the ICU of a hospital in Taiwan between March 2008 and February 2011. We prospectively collected data, including demographic parameters and clinical characteristics, of patients on day 1 of admission to the ICU and analyzed these variables as predictors of mortality. RESULTS: The overall ICU, hospital, and 90-day mortality rates were 54%, 60%, and 63%, respectively. The patients with proteinuria had higher rates of acute kidney injury (84% vs. 53%, P<0.001), ICU death (60% vs. 25%, P<0.001), and 90-day mortality (79% vs. 40%, P<0.001). Patients with proteinuria had a hazard ratio for 90-day mortality of 2.800 (P<0.001; 95% CI, 1.927-4.069). Multivariate analysis showed that proteinuria and the Sequential Organ Failure Assessment score were predictors of short-term prognosis. CONCLUSIONS: Proteinuria in critically ill cirrhotic patients is associated with increased complications of liver cirrhosis, ICU mortality, and poor short-term prognosis.