This is the first time in Vietnam that people have undergone “social distancing” to minimize the spreading of infectious disease, COVID-19. These deliberate preemptive strategies may have profound ...impacts on the mental health of the population. Therefore, this study aimed to identify the psychological impacts of COVID-19 on Vietnamese people and associated factors. We conducted a cross-sectional study during a one-week social distancing and isolation from April 7 to 14, 2020, in Vietnam. A snowball sampling technique was carried out to recruit participants. Impact of Event Scale-Revised (IES-R) was utilized to assess the psychological impacts of the COVID-19. Of all participants, 233 (16.4%) reported low level of PTSS; 76 (5.3%) rated as moderate, and 77 (5.4%) reported extreme psychological conditions. Being female, above 44 years old, or having a higher number of children in the family were positively associated with a higher level of psychological distress. Being self-employed/unemployed/retired was associated with a higher score of intrusion and hyperarousal subscale. Individuals who have a history of touching objects with the possibility of spreading coronavirus (utensils) were related to a higher level of avoidance. There were relatively high rates of participants suffering from PTSS during the first national lockdown related to COVID-19. Comprehensive strategies for the screen of psychological problems and to support high-risk groups are critical, especially females, middle-aged adults and the elderly, affected laborers, and health care professionals.
PurposeThis study examines the role of anxiety in kaizen behaviour and performance by empirically testing the influence of personal anxiety (state and trait) on individual kaizen behaviours (rule ...adherence, initiative and perseverance of effort), which, in turn, affect individual kaizen performance.Design/methodology/approachThe data were obtained from a survey of 552 employees of four companies in Japan and analysed using structural equation modelling.FindingsThe results show that state anxiety has a significantly positive effect on rule adherence and kaizen performance. Trait anxiety positively influences employees' initiative and perseverance but has a significant negative effect on kaizen performance.Originality/valueThis study contributes to kaizen and continuous improvement theory by focussing on individual kaizen, which is considered to be as important as organisation-level kaizen and investigating the relevance of personal anxiety in individual kaizen behaviours and kaizen performance.
In this study, the AdaBoost, MultiBoost and RealAdaBoost methods were combined with the Quadratic Discriminant Analysis method to develop three new GIS-based Machine Learning ensemble models, i.e., ...ABQDA, MBQDA, and RABQDA for groundwater potential mapping in the Dak Nong Province, Vietnam. In total, 227 groundwater wells and 12 conditioning factors (infiltration, rainfall, river density, topographic wetness index, sediment transport index, stream power index, elevation, aspect, curvature, slope, soil, and land use) were used for this study. Performance of the models was evaluated using the Area Under the Receiver Operating Characteristics Curve AUC (AUC) and several other performance metrics. The results showed that the ABQDA model that achieved AUC = 0.741 was superior to the other models in producing an accurate map of groundwater potential for the Dak Nong Province. The models and potential maps produced here can help policymakers and water resources managers to preserve an optimal exploit from these vital resources.
In the machine learning models, it is desirable to remove most redundant features from the data set to reduce the data processing time and to improve accuracy of the models. In this paper, chi-square ...(CS) and backward elimination (BE), which are well-known feature selection methods, were used for the optimum selection of input features/factors for training artificial neural network (ANN) for landslide susceptibility modeling. Initially, seventeen landslide affecting factors were considered for the ANN model which were reduced to twelve and eleven based on the ANN optimized by CS (CSANN) and BE (BEANN), respectively. Accuracy (ACC), Kappa Index, root mean square error (RMSE), and area under the receiver operating characteristic (AUROC) curve were used to evaluate and validate performance of the models. Results show that both the feature selection methods (CS and BE) improved significantly performance of the hybrid BEANN and CSANN models in comparison to single ANN model. Results indicated that performance of the BEANN model (AUROC 0.963; ACC 91.31) is the best in comparison to CSANN (AUROC 0.950; ACC 89.80) and ANN (AUROC 0.949; ACC 76.40) models in the accurate prediction of landslide susceptible areas/zones. Therefore, it is reasonable to state that the BE is more effective feature selection method than the CS in improving performance of the ANN model and thus, it can be used for better landslide susceptibility analysis for the landslide management of the area.
Obstructive sleep apnea (OSA) is the most common form of respiratory disorders during sleep in children, especially those with severe asthma. However, optimal treatment of asthma might significantly ...improve OSA severity.
It was a cohort study including children aged >5 years old and diagnosed with asthma according to GINA (Global Initiative for Asthma). The data related to age, gender, height, weight, body mass index (BMI), clinical symptoms and medical history of asthma, spirometry (FEV
: forced expiratory in 1 s), and exhaled nitric oxide (F
NO) were recorded for analysis. Respiratory polygraphy (RPG) was done for each study subject to diagnose OSA and its severity.
Among 139 asthmatic children, 99 patients with OSA (71.2%) were included in the present study (9.3 ± 0.2 years): 58.6% with uncontrolled asthma and 32.3% with partial controlled asthma. The mean ACT (asthma control testing) score was 19.0 ± 3.4. The most frequent night-time symptoms were restless sleep (76.8%), snoring (61.6%), sweating (52.5%), and trouble breathing during sleep (48.5%). The common daytime symptoms were irritable status (46.5%) and abnormal behavior (30.3%). The mean AHI (apnea-hypopnea index) was 3.5 ± 4.0 events/h. There was a significant correlation between BMI and snoring index (
= 0.189 and
= 0.027), bronchial and nasal F
NO with AHI (
= 0.046 and
< 0.001;
= 0.037 and
< 0.001; respectively). There was no significant correlation between asthma level, FEV
and AHI. The severity of asthma and respiratory function were improved significantly after 3 months and 6 months of asthma treatment in combination with leukotriene receptor antagonist (LRA) treatment. The symptoms related to OSA were significantly improved after treatment with LRA. The severity of OSA was decreased significantly after 3 months and 6 months of treatment.
The treatment of asthmatic children with comorbid OSA by LRA in combination with standard therapy for asthma could improve the control of asthma and the symptoms and severity of OSA.
Worldwide landslide occurrences imply the need for intelligence tools to identify the most susceptible areas toward adopting efficient mitigation strategies and reaction plans. In this study, we ...developed three spatially explicit ensemble predictive models for the prediction of landslide susceptibility in the Muong Nhe district of the Dien Bien Province, Vietnam. The Multiclass Alternating Decision Trees (MADT) method was used as the base classifier with the Dagging, MultiboostAB, and Random Subspace (RSS) as the ensemble learners. The location of past landslides was identified through field surveys and the interpretation of Google Earth images, aerial photographs, and historical archives of the Muong Nhe district. The landslide locations were liked to twelve landslide conditioning factors (slope, aspect, elevation, curvature, topographic wetness index (TWI), stream power index (SPI), geology, flow accumulation, normalized difference vegetation index (NDVI), and distance to rivers, roads, and faults) to investigate the spatial patterns of landslide susceptibility across the study area. The results showed that the RSS-MADT model achieved the highest performance in terms of predicting future landslides (AUC = 0.878), followed by DG-MADT (AUC = 0.857), MAB-MADT (AUC = 0.854), and the single MADT model (AUC = 0.828), respectively. Approximately 13% and 10% of the Muong Nhe district were identified as having moderate and severe (high/very high) susceptibility to landslide occurrences. These areas that extend along the rivers, primarily in the central parts of the Muong Nhe district, should be treated with the highest priority to mitigate the negative impacts of future landslides.
•Developing three ensemble models for landslide susceptibility mapping.•Performance test of Dagging, MultiboostAB, and Random Subspace ensemble techniques.•Highest reliability of mapping coupling Alternating Decision Trees with Random Subspace.•Landslide prevention activities primarily needed on 13% of the land area.
In this study, a catalyst derived from iron slag (Fe-S) was used for heterogeneous Fenton oxidation (H2O2/Fe-S) of paracetamol in aqueous solution; the conventional homogeneous Fenton reaction ...(H2O2/Fe2＋) was run in parallel for comparison. Degradation of paracetamol, in terms of chemical oxygen demand (COD) removal, was found strongly dependent on the solution pH, with the maximum efficiency obtained at pH 3 for both H2O2/Fe-S and H2O2/Fe2＋ systems. The efficacy of paracetamol degradation was also affected by the ratio (w/w) of hydrogen peroxide to iron, as the maxima degradation was observed at ratios of 1:2 and 2:1 for the heterogeneous and homogeneous Fenton, respectively. In addition, the degradation efficiency decreased when the initial paracetamol concentration increased from 100 mg/L to 500 mg/L. Kinetic experiments showed that degradation of paracetamol fitted a pseudo-first-order kinetic model well, as evidenced by the Kd values of the pseudo-first-order kinetic model that followed the same sequence as the degradation efficiency of paracetamol. Processes involved in the degradation of paracetamol by H2O2/Fe-S mainly included adsorption and oxidation; for latter, the presence of FeO, ZnO, and SiO2 in Fe-S constituent might enhance the decomposition of H2O2 and generate more *OH radicals. The *OH radical-mediated oxidation was confirmed by significant declines in the elimination of paracetamol when the system was subject to various radical-scavengers including t-butanol, chloride, and carbonate species.
Display omitted
•Fe2＋ and Fe-S were used as catalysts for homogeneous and heterogeneous Fenton.•The paracetamol degradation by H2O2/Fe2＋ and H2O2/Fe-S reached the highest at pH 3.•The Fe2＋ ions released from Fe-S enhanced the formation of *OH radicals.•The paracetamol degradation followed pseudo-first order kinetic model.•The paracetamol degradation mechanism in H2O2/Fe-S system involved both adsorption and oxidation.
In this study, we experimentally demonstrated a flexible random laser fabricated on a polyethylene terephthalate (PET) substrate with a high degree of tunability in lasing emissions. Random lasing ...oscillation arises mainly from the resonance coupling between the emitted photons of gain medium (Rhodamine 6G, R6G) and the localized surface plasmon (LSP) of silver nanoprisms (Ag NPRs), which increases the effective cross-section for multiple light scattering, thus stimulating the lasing emissions. More importantly, it was found that the random lasing wavelength is blue-shifted monolithically with the increase in bending strains exerted on the PET substrate, and a maximum shift of ∼15 nm was achieved in the lasing wavelength, when a 50% bending strain was exerted on the PET substrate. Such observation is highly repeatable and reversible, and this validates that we can control the lasing wavelength by simply bending the flexible substrate decorated with the Ag NPRs. The scattering spectrum of the Ag NPRs was obtained using a dark-field microscope to understand the mechanism for the dependence of the wavelength shift on the exerted bending strains. As a result, we believe that the experimental demonstration of tunable lasing emissions based on the revealed structure is expected to open up a new application field of random lasers.
is a difficult respiratory pathogen to treat, when compared to other nontuberculus mycobacteria (NTM), due to its drug resistance. In this study, we aimed to find a new clarithromycin partner that ...potentiated strong, positive, synergy against
among current anti-
drugs, including omadacycline, amikacin, rifabutin, bedaquiline, and cefoxitine. First, we determined the minimum inhibitory concentrations required of all the drugs tested for
subsp.
CIP104536
treatment using a resazurin microplate assay. Next, the best synergistic partner for clarithromycin against
was determined using an
checkerboard combination assay. Among the drug combinations evaluated, omadacycline showed the best synergistic effect with clarithromycin, with a fractional inhibitory concentration index of 0.4. This positive effect was also observed against
clinical isolates and anti-
drug resistant strains. Lastly, this combination was further validated using a
infected zebrafish model. In this model, the clarithromycin-omadacyline regimen was found to inhibit the dissemination of
and it significantly extended the lifespan of the
infected zebrafish. In summation, the synergy between two anti-
compounds, clarithromycin and omadacycline, provides an attractive foundation for a new
treatment regimen.
Water level predictions in the river, lake and delta play an important role in flood management. Every year Mekong River delta of Vietnam is experiencing flood due to heavy monsoon rains and high ...tides. Land subsidence may also aggravate flooding problems in this area. Therefore, accurate predictions of water levels in this region are very important to forewarn the people and authorities for taking timely adequate remedial measures to prevent losses of life and property. There are so many methods available to predict the water levels based on historical data but nowadays Machine Learning (ML) methods are considered the best tool for accurate prediction. In this study, we have used surface water level data of 18 water level measurement stations of the Mekong River delta from 2000 to 2018 to build novel time-series Bagging based hybrid ML models namely: Bagging (RF), Bagging (SOM) and Bagging (M5P) to predict historical water levels in the study area. Performances of the Bagging-based hybrid models were compared with Reduced Error Pruning Trees (REPT), which is a benchmark ML model. The data of 19 years period was divided into 70:30 ratio for the modeling. The data of the period 1/2000 to 5/2013 (which is about 70% of total data) was used for the training and for the period 5/2013 to 12/2018 (which is about 30% of total data) was used for testing (validating) the models. Performance of the models was evaluated using standard statistical measures: Coefficient of Determination (R2), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Results show that the performance of all the developed models is good (R2 > 0.9) for the prediction of water levels in the study area. However, the Bagging-based hybrid models are slightly better than another model such as REPT. Thus, these Bagging-based hybrid time series models can be used for predicting water levels at Mekong data.
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