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  • 1
    ISSN: 0197-8462
    Keywords: exposure chamber ; psychophysics ; detection ; environmental guidelines ; Life and Medical Sciences ; Occupational Health and Environmental Toxicology
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Biology , Physics
    Notes: The objective of this study was to assess the ability of humans to detect the presence of DC electric fields and ion currents. An exposure chamber simulating conditions present in the vicinity of high-voltage DC (HVDC) lines was designed and built for this purpose. In these experiments, the facility was used to expose observers to DC electric fields up to 50 kV/m and ion current densities up to 120 nA/m2. Forty-eight volunteers (25 women and 23 men) between the ages of 18 and 57 years served as observers. Perception of DC fields was examined by using two psychophysical methods: an adaptive staircase procedure and a rating method derived from signal-detection theory. Subjects completed three different series of observations by using each of these methods: one was conducted without ion currents, and the other two involved various combinations of electric fields and ion currents. Overall, subjects were significantly more likely to detect DC fields as the intensity increased. Observers were able to detect the presence of DC fields alone, but only at high intensities; the average threshold was 45 kV/m. Except in the most sensitive individuals, ion current densities up to 60 nA/m2 did not significantly facilitate the detection of DC fields. However, higher ion current densities were associated with a substantial lowering of sensory thresholds in a large majority of observers. Data analysis also revealed large variations in perceptual thresholds among observers. Normative data indicating DC field and ion current intensities that can be detected by 50% of all observers are provided. In addition, for the most sensitive observers, several other detection proportions were derived from the distribution of individual detection capabilities. These data can form the basis for environmental guidelines relating to the design of HVDC lines. © 1996 Wiley-Liss, Inc.
    Additional Material: 5 Ill.
    Type of Medium: Electronic Resource
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  • 2
    Publication Date: 2020-05-21
    Description: Background: Little is known about risk factors for sick building symptoms (SBS) among health care workers (HCWs) who often face the workload, exposure to chemicals, and biological contaminants in the workplace. This study aims to evaluate the correlation between SBS and the symptoms among HCWs. Methods: A total of 207 HCWs were recruited in a large hospital-based cross-sectional survey between March and June 2017, southern Vietnam. Face-to-face interviews were conducted for collecting data on demographics, SBS-related symptoms, working environments, and conditions. Indoor environmental conditions were measured. SBS scores, ranging from 0 to 24, were determined by a sum of the scores of general symptoms, mucosal irritation, and skin symptoms; multivariate regression analyses and the Lindeman, Merenda, and Gold (LMG) test were used to investigate the predictors and its impact on the SBS. Results: A mean SBS score was 9.7 (range: 1–21). Compared with males, females were more likely to report higher SBS scores (10.2 vs. 7.9, p 〈 0.001). Being female, atopy, varying temperature room, stuffy “bad” air dust, and dirt had higher SBS scores of 2.0; 1.8; 1.7; 1.9; 3.8, respectively. LMG test showed that dust and dirt, and stuffy “bad” air were the predominant risk factors for SBS. Conclusions: Our study reveals that working conditions are important and significantly associated with SBS. Taken together with our findings, the working condition criteria approach trained for architects, builders, owners, and maintenance of the building is highly recommended for indoor air quality improvement. Furthermore, larger-sample studies about working condition are urgently needed to better manage SBS.
    Print ISSN: 1661-7827
    Electronic ISSN: 1660-4601
    Topics: Energy, Environment Protection, Nuclear Power Engineering , Medicine
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  • 3
    Publication Date: 2019-09-12
    Description: Background: Acinetobacter baumannii (Ab) is an opportunistic bacterial pathogen found in hospital-acquired infections including nosocomial pneumonia, especially multidrug-resistant Ab. This study aims to survey the drug resistance profiles of Ab isolated from patients in Thong Nhat Dong Nai General Hospital and assess the relationship between genotypes and antibiotic resistance; Methods: Ninety-seven Ab strains isolated from 340 lower respiratory tract specimens among pneumonia patients were used to screen the most common local carbapenemase genes. Antimicrobial susceptibility testing results and demographic data were collected and minimum inhibitory concentrations (MIC) of colistin were also determined; Results: Over 80% and 90% of Ab strains were determined as carbapenem-resistant and multidrug-resistant (MDR), respectively. Most of the strains carried carbapenemase genes, including blaOXA-51, blaOXA-23-like, blaOXA-58-like, and blaNDM-1, with proportions of 97 (100%), 76 (78.4%), 10 (10.3%), 6 (6.2%), respectively. Amongst these genes, blaOXA-23-like was the only gene which significantly influenced the resistance (p 〈 0.0001); and Conclusions: The severity of Ab antibiotic resistance is urgent and specifically related to carbapenemase encoding genes. Therefore, screening of MDR Ab and carbapenemase for better treatment options is necessary.
    Electronic ISSN: 2079-6382
    Topics: Chemistry and Pharmacology , Medicine
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  • 4
    Publication Date: 2020-05-01
    Print ISSN: 0022-1694
    Electronic ISSN: 1879-2707
    Topics: Architecture, Civil Engineering, Surveying , Geography , Geosciences
    Published by Elsevier
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  • 5
    Publication Date: 2021-03-12
    Description: Radar-based rainfall forecasts are widely used extrapolation algorithms that are popular in systems of precipitation for predicting up to six hours in lead time. Nevertheless, the reliability of rainfall forecasts gradually declines for heavy rain events with lead time due to the lack of predictability. Recently, data-driven approaches were commonly implemented in hydrological problems. In this research, the data-driven models were developed based on the data obtained from a radar forecasting system named McGill Algorithm for Precipitation nowcasting by Lagrangian Extrapolation (MAPLE) and ground rain gauges. The data included thirteen urban stations in the five metropolitan cities located in South Korea. The twenty-five data points of MAPLE surrounding each rain station were utilized as the model input, and the observed rainfall at the corresponding gauges were used as the model output. The results showed superior capabilities of long short-term memory (LSTM) network in improving 180-min rainfall forecasts at the stations based on a comparison of five different data-driven models, including multiple linear regression (MLR), multivariate adaptive regression splines (MARS), multi-layer perceptron (MLP), basic recurrent neural network (RNN), and LSTM. Although the model still produced an underestimation of extreme rainfall values at some examined stations, this study proved that the LSTM could provide reliable performance. This model can be an optional method for improving rainfall forecasts at the stations for urban basins.
    Electronic ISSN: 2073-4441
    Topics: Energy, Environment Protection, Nuclear Power Engineering
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