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This study aimed to investigate essential employability skills for English as a foreign language (EFL) students in the Mekong Delta for the context of the 21st century. It was also to determine the similarities and differences between the important employability skills from the perspectives of the employers and EFL students. In this study, a questionnaire was delivered to EFL students who were juniors and seniors at a university in the South of Vietnam, majoring in English Education and English Studies (English Interpreter). At the same time, interviews were realized on both EFL students and employers. The findings showed that both EFL students and employers highlighted the attributes of self-confidence, receptiveness to feedback, and the use of body language in communication. The types of employability skills are similarly recognized among employers and EFL students in the same fields of employment. However, a significant difference was also found among employers on different job-specific skills. The results also indicated that EFL undergraduates were all highly aware of preparing the employability skills for their future professions.

Higher education has jumped into the quality movement with expectations of enhancing the excellence of education being received by students. The total quality management philosophy that dominated the manufacturing industry has found its way into the education industry. W. Edwards Deming developed a set of 14 quality management principles, which can be applied to the education sector (Hughey, 2000; Prasad, 2017). Accreditation is a mechanism to inform the employers, the public and students that a university or program has met the minimum standards for quality. The injection of accountability and accreditation into the educational system has raised the expectations of an institution. External accreditation has been growing worldwide in importance. This paper looks at the development of accreditation within the United States, Vietnam, and Japan. An educational curriculum was seen to be a pillar needed for growth for each country. It became apparent that all three countries realize the importance of quality and are heavily involved in the assessment process. There are some differences in who controls the curriculum and the assessment process. However, continuous improvement is universally shared among the three countries. Developing an understanding of the accreditation process will contribute to the literature surrounding accreditation and quality assurance.

Committing errors and feedback in writing are important in the process of language acquisition (Corder, 1975; Foster et al., 1988; Krashen, 1981; Marina et al., 2005; Salehi et al., 2018; Valizadeh et al., 2020). In Vietnam, however, error-analysis in writing in the contexts of EFL students has not been investigated fully. This research study explores and examines common mistakes of second-year English language students in writing English. The research was conducted with different tools including error assessment of some writing assignments, a survey to measure the ability to recognize errors in English writing, and interviews with some participants from an insider's perspective. The results found that the level for each of the written errors in the results varied widely. Certain types of errors were correctly identified by research participants and their ability to correct the issues was demonstrated properly at some points. However, most of them were unable to recognize errors, suggesting inconsistency in the ability to recognize and correct errors. Specifically, the research found a list of types of errors which occur while writing English from the phrase level to the sentence level, among other groups of errors while writing. This study could foster a trend to develop strategies to help students learn English better from both teachers' and students' point of view in today's English training programs.

Evaluating groundwater potential is critical for the socioeconomic development of Vietnam. This research aims to assess the underground water potential in the country's Mekong Delta using the machine learning (ML) such as support vector machines (SVM), CatBoost (CB), K-nearest neighbors (KNN), random forest (RF) and AdaBoost (ADB). The problem of exploitation of groundwater resources in the delta is aggravated due to global warming and growth of population. In total, 146 groundwater points and 14 drivers (namely elevation, aspect, curvature, slope distance to river and river density, land use, normalized difference built-up index, flow accumulation, rainfall, soil type, normalized difference vegetation index, stream power index, terrain roughness index, and topographic wetness index) were used to assess groundwater potential. Each proposed model was evaluated utilizing area under curve (AUC), root mean square error, coefficient of determination (R2), and mean absolute error. The findings showed that the RF outperformed the others in building of a groundwater potential map. In which, AUC value was estimated at 0.99 and R2 value was estimated at 0.63 then came CB (AUC = 0.98, R2 = 0.56), ADB (AUC = 0.92, R2 = 0.50), SVM (AUC = 0.91, R2 = 0.57), and KNN (AUC = 0.75, R2 = 0.45). The results illustrate the power of ML in assessing groundwater potential and can support decision makers, planners, and local authorities responsible for sustainable groundwater planning in the Mekong Delta and beyond. [ABSTRACT FROM AUTHOR]

We studied a community cluster of 25 mpox cases in Vietnam caused by emerging monkeypox virus sublineage C.1 and imported into Vietnam through 2 independent events; 1 major cluster carried a novel APOBEC3-like mutation. Three patients died; all had advanced HIV coinfection. Viral evolution and its potential consequences should be closely monitored. [ABSTRACT FROM AUTHOR]

Population growth, urbanization and rapid industrial development increase the demand for water resources. Groundwater is an important resource in sustainable socio-economic development. The identification of regions with the probability of the existence of groundwater is necessary in helping decision makers to propose effective strategies for the management of this resource. The objective of this study is to construct maps of potential groundwater, based on machine learning algorithms, namely deep neural networks (DNNs), XGBoost (XGB), and CatBoost (CB), in the Gia Lai province of Vietnam. In this study, 12 conditioning factors, namely elevation, aspect, curvature, slope, soil type, river density, distance to road, land use/land cover (LULC), Normalized Difference Vegetation Index (NDVI), Normal Difference Built-up Index (NDBI), Normal Difference Water Index (NDWI), and rainfall were used, along with 181 groundwater inventory points, to construct the models. The proposed models were evaluated using the receiver operating characteristic (ROC) curve, the area under the curve (AUC), root-mean-square error (RMSE), mean absolute error (MAE). The results showed that the predictions of groundwater potential were most accurate using the XGB model; CB came second, and DNN was performed the least well. About 4,990 km² of the study area was found to be in the category of very low groundwater potential; 3,045 km² was in the low category; 2,426 km² was classified as moderate, 2,665 km² as high, and 2,007 km² as very high. The methodology used in the study was effective in creating groundwater potential maps. This approach, used in this study, can provide valuable information on the factors influencing groundwater potential and assist decisionmakers or developers in managing groundwater resources sustainably. It also supports the sustainable development of the territory, including tourism. This methodology can be used in other geographic regions with a small change of input data. [ABSTRACT FROM AUTHOR]

Seasonal rainfall forecasting is important for water resources management, agriculture, and disaster prevention. Our study aims to provide an automated deep learning method for the seasonal prediction of monthly rainfall at stations in seven climatic sub-regions in Vietnam with lead times of up to 6 months. An appropriate set of predictors was selected based on numerous climate indices and neighbor station data for the period 1980–2020. We developed an adapted deep learning pipeline for both short- and long-term analysis. The predicted rainfall was verified against the observed data using mean absolute error (MAE), root mean squared error (RMSE), and Pearson correlation coefficients. The results showed that our model generally captured well observed data reflected by low error (MAE and RMSE < 0.2) and high correlation (at 0.8–0.9) for all climatic sub-regions. For the leadtimes of 1–3 months, the rainfall predictionsmade using climate indices as predictors were outperformed by those using neighbor stations data; while for longer leadtimes (4–6 months), the climate indices themselve were able to improve the performance. The rainfall predictions of our methods on all three lead times climatological predictions by factoring additional values. However, there is room for improvement in predicting extreme and abrupt shifts in time series patterns. [ABSTRACT FROM AUTHOR]

A new carvotacetone sphaeranthone A and four known compounds 3-angeloyloxy-5-[2″,3″-epoxy-2″-methylbutanoyloxy]-7-hydroxycarvotacetone (2), 3-angeloyloxy-5-[3″-chloro-2″-hydroxy-2″-methylbutanoyloxy]-7-hydroxycarvotacetone (3), chrysosplenol D (4), and 3-O-methylquercetin (5) were isolated from leaves of Sphaeranthus africanus growing in Vietnam. Their chemical structures were elucidated by extensive 1D and 2D NMR analysis and high-resolution mass spectroscopy as well as comparisons in literature. Compounds 1-3 were evaluated for the alpha-glucosidase inhibition. They showed moderate activity with IC50 values of 103 ± 1.7, 146.8 ± 2.5, 49 ± 0.8 µg/mL, respectively. [ABSTRACT FROM AUTHOR]

• Food delivery riders face significant road safety risks worldwide. • This study utilizes the JD-R model to identify factors that influence delivery riders' safety behaviors. • The results show that job burnout mediates the impact of job demands on in-role safety. • Job resources indirectly influence both in-role and extra-role safety through motivation. Food delivery riders face significant road safety risks globally. Previous studies have primarily focused on the direct relationship between job demands, job resources, and road safety behaviors, often neglecting the potential indirect paths through dual processes, namely the health impairment process and the motivational process, which may lead to dangerous riding behaviors. Additionally, the impact of organizational citizenship behaviors on the safety of food delivery riders within the industry remains unexplored. This study aims to address these gaps by applying the dual processes of the Job Demands-Resources model (JD-R) to identify factors influencing the safety behaviors of food delivery riders in Vietnam. Data were collected through face-to-face surveys with 410 riders in public places. The findings show a nuanced interplay between job demands, resources, and safety behaviors. Specifically, job burnout acts as a bridge, mediating the influence of job demands on in-role safety behaviors. On the other hand, job resources-encompassing facets like job autonomy, performance feedback, technology support, and work support-indirectly shape both in-role and extra-role safety behaviors via the conduit of motivation. These resources not only bolster motivation but also serve as pillars in fostering a culture of safety among riders. This research not only deepens our understanding of the determinants of food delivery riders' safety behaviors but also highlights the pivotal role food delivery companies play in shaping these behaviors. By prioritizing the holistic well-being of their riders and offering robust support, these companies can foster an environment conducive to safety. Additionally, by integrating additional dimensions of job demands and resources into the JD-R model, this study paves the way for more comprehensive and nuanced future research in this domain. [ABSTRACT FROM AUTHOR]

Groundwater resources are required for domestic water supply, agriculture, and industry, and the strategic importance of water resources will only increase in the context of climate change and population growth. For optimal management of this crucial resource, exploration of the potential of groundwater is necessary. To this end, the objective of this study was the development of a new method based on remote sensing, deep neural networks (DNNs), and the optimization algorithms Adam, Flower Pollination Algorithm (FPA), Artificial Ecosystem-based Optimization (AEO), Pathfinder Algorithm (PFA), African Vultures Optimization Algorithm (AVOA), and Whale Optimization Algorithm (WOA) to predict groundwater potential in the North Central region of Vietnam. 95 springs or wells with 13 conditioning factors were used as input data to the machine learning model to find the statistical relationships between the presence and nonpresence of groundwater and the conditioning factors. Statistical indices, namely root mean square error (RMSE), area under curve (AUC), accuracy, kappa (K) and coefficient of determination (R2), were used to validate the models. The results indicated that all the proposed models were effective in predicting groundwater potential, with AUC values of more than 0.95. Among the proposed models, the DNN-AVOA model was more effective than the other models, with an AUC value of 0.97 and an RMSE of 0.22. This was followed by DNN-PFA (AUC=0.97, RMSE=0.22), DNN-FPA (AUC=0.97, RMSE=0.24), DNN-AEO (AUC=0.96, RMSE=0.25), DNN-Adam (AUC=0.97, RMSE=0.28), and DNN-WOA (AUC=0.95, RMSE=0.3). In addition, according to the groundwater potential map, about 25–30% of the region was in the high and very high potential groundwater zone; 5–10% was in the moderate zone, and 60–70% was low or very low. The results of this study can be used in the management of water resources in general and the location of appropriate wells in particular. [ABSTRACT FROM AUTHOR]

Floods are arguably the most impactful of natural hazards. The increasing magnitude of their effects on the environment, human life, and economic activities calls for improved management of water resources. Flood susceptibility modeling has been used around the world to reduce the damage caused by flooding, although the extrapolation problem still presents a significant challenge. This study develops a machine learning (ML) model utilizing deep neural network (DNN) and optimization algorithms, namely earthworm optimization algorithm (EOA), wildebeest herd optimization (WHO), biogeography-based optimization (BBO), satin bowerbird optimizer (SBO), grasshopper optimization algorithm (GOA), and particle swarm optimization (PSO), to solve the extrapolation problem in the construction of flood susceptibility models. Quang Nam Province was chosen as a case study as it is subject to the significant impact of intense flooding, and Nghe An Province was selected as the region for extrapolation of the flood susceptibility model. Root mean square error (RMSE), receiver operating characteristic (ROC), the area under the ROC curve (AUC), and accuracy (ACC) were applied to assess and compare the fit of each of the models. The results indicated that the models in this study are a good fit in establishing flood susceptibility maps, all with AUC > 0.9. The deep neural network (DNN)-BBO model enjoyed the best results (AUC = 0.99), followed by DNN-WHO (AUC = 0.99), DNN-SBO (AUC = 0.98), DNN-EOA (AUC = 0.96), DNN-GOA (AUC = 0.95), and finally, DNN-PSO (AUC = 0.92). In addition, the models successfully solved the extrapolation problem. These new models can modify their behavior to evaluate flood susceptibility in different regions of the world. The models in this study distribute a first point of reference for debate on the solution to the extrapolation problem, which can support urban planners and other decision-makers in other coastal regions in Vietnam and other countries. [ABSTRACT FROM AUTHOR]

Landslides lead to widespread devastation and significant loss of life in mountainous regions around the world. Susceptibility assessments can provide critical data to help decision‐makers, for example, local authorities and other organizations, mitigating the landslide risk, although the accuracy of existing studies needs to be improved. This study aims to assess landslide susceptibility in the Thua Thien Hue province of Vietnam using deep neural networks (DNNs) and swarm‐based optimization algorithms, namely Adam, stochastic gradient descent (SGD), Artificial Rabbits Optimization (ARO), Tuna Swarm Optimization (TSO), Sand Cat Swarm Optimization (SCSO), Honey Badger Algorithm (HBA), Marine Predators Algorithm (MPA) and Particle Swarm Optimization (PSO). The locations of 945 landslides occurring between 2012 and 2022, along with 14 conditioning factors, were used as input data to build the DNN and DNN‐hybrid models. The performance of the proposed models was evaluated using the statistical indices receiver operating characteristic curve, area under the curve (AUC), root mean square error, mean absolute error (MAE), R2 and accuracy. All proposed models had a high accuracy of prediction. The DNN‐MPA model had the highest AUC value (0.95), followed by DNN‐HBA (0.95), DNN‐ARO (0.95), DNN‐Adam (0.95), DNN‐SGD (0.95), DNN‐TSO (0.93), DNN‐PSO (0.9) and finally DNN‐SCSO (0.83). High‐precision models have identified that the majority of the western region of Thua Thien Hue province is very highly susceptible to landslides. Models like the aforementioned ones can support decision‐makers in updating large‐scale sustainable land‐use strategies. [ABSTRACT FROM AUTHOR]

Transitioning to electric motorcycles (EMs) can reduce dependence on carbon-based fuels, mitigate air pollution, and lower greenhouse gas emissions in countries where motorcycles dominate the transportation landscape. However, the factors influencing fleet electrification in these countries remain largely unknown. Given that this challenge pertains to the prior adoption of new technology, technology acceptance theories can provide valuable insights into the adoption of EMs. Additionally, misinformation about EM risks and self-assessment of knowledge could interact as determinants of adoption. This study integrates the Technology Acceptance Model (TAM), the Theory of Planned Behavior (TPB), and perceived risk to elucidate the impact of psychosocial factors on consumers' intentions to use EMs. Furthermore, it examines the direct and moderating effect of subjective knowledge about EM technology on the formation of this behavioral intention. Structural Equation Modeling (SEM) analysis was applied to investigate data collected from 762 respondents through personal interviews using a questionnaire. The results confirm that all original TAM and TPB constructs significantly influence behavioral intention, aligning with theoretical expectations. As hypothesized, perceived risk was found to exert a negative influence on the intention to adopt EMs. Additionally, subjective knowledge was observed to moderate the relationship between perceived usefulness and subjective norms concerning the intention to use EMs. These findings offer a roadmap for developing strategies aimed at encouraging adoption intentions toward EMs, particularly in countries like Vietnam. • Misinformation about EM risks and self-assessment of knowledge could interact as determinants of adoption. • The model was developed by intergrating TAM, TPB and perceived risk. • Structural Equation Modeling (SEM) analysis was applied to investigate data collected from 762 respondents. • Perceived risk was found to exert a negative influence on the intention to adopt EMs. • Subjective knowledge was observed to moderate the relationship between perceived usefulness and subjective norms. [ABSTRACT FROM AUTHOR]

Falls among the elderly are an important global health problem. This study assesses knowledge regarding risk factors of falls, as well as attitudes and practices towards fall prevention among older adults in the rural community. A cross-sectional study was performed in four rural communes in Thai Binh province, Vietnam. A total of 3038 older people were recruited. Knowledge was assessed by using Falls Risk Awareness Questionnaire. Questions about attitudes were based on the Health Belief Model. Other questions regarding attitudes and practices regarding fall prevention were also asked. Multivariate regression was performed to identify associated factors with knowledge, attitudes and practices. Results showed that the mean score of knowledge regarding risk factors of falls was low at 11.37/32. The highest scores were observed in terms of drug aspects, followed by medical condition and behavioural aspects. Older people mostly agreed with perceived severity, susceptibility, benefits and barriers, but their attitudes about cues to action, health motivation and actions were most neutral. Meanwhile, more than half of the participants practised recommended fall preventive measures. To conclude, health education interventions and fall prevention services that enhance community-based fall prevention knowledge, attitudes and practices for older adults should be performed to reduce the burden of falls in this population. [ABSTRACT FROM AUTHOR]

Background: Gender-biased discrimination and preferences are global phenomena, particularly son preferences. However, updated evidence about this issue in Vietnam has not yet been provided. Therefore, this study aimed to examine the gender preferences among pregnant women and identify associated factors of such preferences. Methods: A cross-sectional survey was conducted in two hospitals in Vietnam with 732 pregnant women. Gender preferences for their child were asked, along with socio-demographic (e.g., education, occupation) and pregnancy characteristics (e.g., pressure to have a son, gender of first child, the importance to have a son of family members, and information sources on pregnancy care) by using face-to-face interviews and a structured questionnaire. Multinomial logistic regression was performed to determine factors associated with gender preferences. Results: About 51.9% of the participants had no gender preference, while, among those who had a gender preference, 26.5% preferred sons, and 21.6% preferred daughters. Only 6.2% had pressure to have a son. Having the first child who was female (OR = 4.16, 95%CI = 1.54–11.25), having the pressure to have a son (OR = 6.77, 95%CI = 2.06–22.26), and higher self-perceived importance to have a son (OR = 3.05, 95%CI = 1.85–5.02) were positively associated with son preference. Otherwise, women having partners with high school education or above (OR = 2.04, 95%CI = 1.06–3.91), living with parents-in-law (OR = 2.33; 95%CI = 1.25–4.34), the higher number of pregnancies, and a higher degree of importance in having a son regarding parents-in-law (OR = 2.15, 95%CI = 1.38–3.35) associated with higher odds of preferring daughter. Conclusion: This study showed that gender preference was common among pregnant women, but the pressure to have a son was low. Further education programs and legal institutions should be implemented to improve gender inequality and gender preference in society. [ABSTRACT FROM AUTHOR]

The rapid development of information technologies and the COVID-19 pandemic have resulted in the proliferation of online food shopping and food delivery motorcyclists. In contrast to the relatively ample literature on factors influencing fatalities and risky riding behaviors of food delivery motorcyclists, little is known about the determinants of non-fatal crashes involving online food delivery riders. The present study examines the prevalence and factors of non-fatal crashes among food delivery riders. The self-reported data of 393 online food delivery riders were collected in Hanoi and Hochiminh city, Vietnam. Binary logit regression was used to investigate the factors associated with non-fatal crashes. The findings showed that more than half of riders (54%) reported being involved in at least one crash in the last 12 months. The most common risky riding behaviors associated with the crashes included using a mobile phone while riding, neglecting turn signals, red-light running, riding when tired/sleepy, and speeding. The riders who were national migrants, married, and worked on planned delivery routes mainly alone were more likely to experience crashes. At the same time, adequate perceived rewards for their work prevent crash involvement. Perceived risk was not a significant predictor of self-reported crashes. Ensuring road safety for delivery riders requires a systemic effort involving multiple stakeholders, and the private sector plays a crucial role in discouraging risky riding behaviors. It is imperative for the government and regulatory bodies to redefine the delivery job to alleviate the strain on riders and provide resources such as rewards. Specifically, riders should be considered employees rather than partners. Furthermore, it is crucial for the police to take a more active role in preventing dangerous behaviors among delivery riders, such as running red lights. At the same time, supporting financial strategies should be implemented for delivery riders, particularly for those who are migrants or married and may face additional challenges. [ABSTRACT FROM AUTHOR]

Background: Although men have a higher rate of stroke than women, it is not clear whether women have a worse outcome after adjusting for confounders such as vascular risk factors, age, stroke severity, and reperfusion therapy. We evaluated sex differences on 90-day functional outcomes after stroke in a multicenter study in Vietnam. Methods: We recruited patients presenting with ischemic or hemorrhagic stroke at 10 stroke centers in Vietnam for a period of 1 month from 1 August 2022 to 31 August 2022. We reviewed the patient's clinical demographics, time from symptom onset to hospital admission, stroke classification, stroke subtype, stroke severity, characteristics of reperfusion therapy, and 90-day clinical outcome. We compared functional outcomes and predisposing factors at day 90 between men and women after an ischemic and hemorrhagic stroke. Poor outcome was defined as modified Rankin Scale 3–6. Results: There were 2300 stroke patients included. Men accounted for 61.3% (1410) of participants. Compared to men, women were older (67.7 ± 13.9 vs 63.7 ± 13.3, P < 0.001), had a higher rate of diabetes mellitus (21.1% vs 15.3%, P < 0.001), a lower rate of tobacco use (1.0 % vs 23.6%, P < 0.001), and a lower body mass index (21.4 ± 2.70 vs 22.0 ± 2.72, P < 0.001). There was a higher rate of intracranial hemorrhage (ICH) in men (21.3% vs 15.6%, P = 0.001), whereas the rate of subarachnoid hemorrhage was higher in women (6.2% vs 3.0%, P < 0.001). For ischemic stroke, door-to-needle time (36.9 ± 17.6 vs 47.8 ± 35.2 min, P = 0.04) and door-to-recanalization time (113.6 ± 51.1 vs 134.2 ± 48.2, P = 0.03) were shorter in women. There was no difference in 90-day functional outcomes between sexes. Factors associated with poor outcomes included age ⩾50 years (adjusted odds ratio (aOR): 1.75; 95% confidence interval (CI): 1.16–2.66), history of stroke (aOR: 1.50; 95% CI: 1.15–1.96), large artery atherosclerosis (aOR: 5.19; 95% CI: 3.90–6.90), and cardioembolism (aOR: 3.21; 95% CI: 1.68–6.16). Factors associated with mortality in patients with acute ischemic stroke included a history of coronary artery disease (aOR: 3.04; 95% CI: 1.03–8.92), large artery atherosclerosis (aOR: 3.37; 95% CI: 2.11–5.37), and cardioembolism (aOR: 3.15; 95% CI: 1.20–8.27). Conclusion: There were no sex differences in the clinical outcome of stroke and ischemic stroke in this prospective cohort of hospitalized Vietnamese patients. [ABSTRACT FROM AUTHOR]

The effects of flooding can be very serious, especially in developing countries, where rapid urbanization and socio-economic development increases risk. Reliable information is crucial to support decision-makers develop appropriate strategies to reduce flood risk. This article aims to develop a framework for assessing flood risk and adaptive capacity, based on a bottom-up approach, in Vietnam's Gianh River watershed. Flood risk was computed by combining hazard, exposure, and vulnerability using hydrodynamic modeling and the Analytic Hierarchy Process method. The adaptive capacity of the population was assessed via interviews with 298 inhabitants. The results show that flood risk is high in areas with high population and construction density. Both the ability to access resources and communities' flood-risk perceptions are important factors in improving the capacity to adapt. This study can provide an important theoretical framework complementing the existing literature and supporting studies related to flood-risk management in the context of climate change and urbanization in other regions. The study also fills a gap in the knowledge of the negative effects of flooding. From a methodological standpoint, this study underlines the importance of using hydraulic models and socio-economic surveys in flood-risk management. [ABSTRACT FROM AUTHOR]

Natural hazards constitute a diverse category and are unevenly distributed in time and space. This hinders predictive efforts, leading to significant impacts on human life and economies. Multi‐hazard prediction is vital for any natural hazard risk management plan. The main objective of this study was the development of a multi‐hazard susceptibility mapping framework, by combining two natural hazards—flooding and landslides—in the North Central region of Vietnam. This was accomplished using support vector machines, random forest, and AdaBoost. The input data consisted of 4591 flood points, 1315 landslide points, and 13 conditioning factors, split into training (70%), and testing (30%) datasets. The accuracy of the models' predictions was evaluated using the statistical indices root mean square error, area under curve (AUC), mean absolute error, and coefficient of determination. All proposed models were good at predicting multi‐hazard susceptibility, with AUC values over 0.95. Among them, the AUC value for the support vector machine model was 0.98 and 0.99 for landslide and flood, respectively. For the random forest model, these values were 0.98 and 0.98, and for AdaBoost, they were 0.99 and 0.99. The multi‐hazard maps were built by combining the landslide and flood susceptibility maps. The results showed that approximately 60% of the study area was affected by landslides, 30% by flood, and 8% by both hazards. These results illustrate how North Central is one of the regions of Vietnam that is most severely affected by natural hazards, particularly flooding, and landslides. The proposed models adapt to evaluate multi‐hazard susceptibility at different scales, although expert intervention is also required, to optimize the algorithms. Multi‐hazard maps can provide a valuable point of reference for decision makers in sustainable land‐use planning and infrastructure development in regions faced with multiple hazards, and to prevent and reduce more effectively the frequency of floods and landslides and their damage to human life and property. [ABSTRACT FROM AUTHOR]

Purpose: Risks in implementing green building (GB) projects have emerged as a significant obstacle for GB development, especially in developing countries. In recent years, both academics and construction practitioners have paid considerable attention to the risks associated with GB. In this study, the authors aimed to create a comprehensive risk assessment model that considers three crucial risk features: impact level, probability of occurrence and risk manageability. Design/methodology/approach: In the research, authors adopted the mean scoring and fuzzy synthetic evaluation method to assess GB risks. Based on expert assessments, this model can determine the significance of risk factors, risk groups and overall risk. Notably, this research applied the proposed model to assess GB risks in Vietnam by surveying 58 GB experienced professionals. Findings: The findings revealed that GB risks are relatively high in Vietnam, implying that risk management is essential for GB projects to succeed. The results also showed that "lack of experience of GB designers" is the most critical factor, and "human resources risk in the design phase" is the top crucial risk group. Originality/value: This study contributes a novel and practical model to help practitioners assess risks in GB projects. In addition, this research offers detailed GB risk evaluations in Vietnam and thus could be a valuable reference for construction practitioners and future studies. [ABSTRACT FROM AUTHOR]

As authorities braced for the arrival of the Omicron variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), infrastructure investments and government directives prompted action in central Viet Nam to establish capacity for genomic surveillance sequencing. From 17 November 2021 to 7 January 2022, the Pasteur Institute in Nha Trang sequenced 162 specimens from 98 150 confirmed SARS-CoV-2 cases in the region collected from 8 November to 31 December 2021. Of these, all 127 domestic cases were identified as the B.1.617.2 (Delta) variant, whereas 92% (32/35) of imported cases were identified as the B.1.1.529 (Omicron) variant, all among international flight passengers. Patients were successfully isolated, enabling health-care workers to prepare for additional cases. Most (78%) of the 32 Omicron cases were fully vaccinated, suggesting continued importance of public health and social measures to control the spread of new variants. [ABSTRACT FROM AUTHOR]

Soil salinization is considered one of the disasters that have significant effects on agricultural activities in many parts of the world, particularly in the context of climate change and sea level rise. This problem has become increasingly essential and severe in the Mekong River Delta of Vietnam. Therefore, soil salinity monitoring and assessment are critical to building appropriate strategies to develop agricultural activities. This study aims to develop a low-cost method based on machine learning and remote sensing to map soil salinity in Ben Tre province, which is located in Vietnam's Mekong River Delta. This objective was achieved by using six machine learning algorithms, including Xgboost (XGR), sparrow search algorithm (SSA), bird swarm algorithm (BSA), moth search algorithm (MSA), Harris hawk optimization (HHO), grasshopper optimization algorithm (GOA), particle swarm optimization algorithm (PSO), and 43 factors extracted from remote sensing images. Various indices were used, namely, root mean square error (RMSE), mean absolute error (MAE), and the coefficient of determination (R2) to estimate the efficiency of the prediction models. The results show that six optimization algorithms successfully improved XGR model performance with an R2 value of more than 0.98. Among the proposed models, the XGR-HHO model was better than the other models with a value of R2 of 0.99 and a value of RMSE of 0.051, by XGR-GOA (R2 = 0.931, RMSE = 0.055), XGR-MSA (R2 = 0.928, RMSE = 0.06), XGR-BSA (R2 = 0.926, RMSE = 0.062), XGR-SSA (R2 = 0.917, 0.07), XGR-PSO (R2 = 0.916, RMSE = 0.08), XGR (R2 = 0.867, RMSE = 0.1), CatBoost (R2 = 0.78, RMSE = 0.12), and RF (R2 = 0.75, RMSE = 0.19), respectively. These proposed models have surpassed the reference models (CatBoost and random forest). The results indicated that the soils in the eastern areas of Ben Tre province are more saline than in the western areas. The results of this study highlighted the effectiveness of using hybrid machine learning and remote sensing in soil salinity monitoring. The finding of this study provides essential tools to support farmers and policymakers in selecting appropriate crop types in the context of climate change to ensure food security. [ABSTRACT FROM AUTHOR]

Flooding is a serious natural hazard. It causes considerable impact on human life, the environment, and property, worldwide. Building a highly accurate flood susceptibility map can reduce disaster damage; it has become the main approach in flood risk management. The objective of this research is to build flood susceptibility maps for Binh Dinh province in Vietnam, applying modern machine learning and remote sensing methods, namely deep neural networks (DNN) and swarm-based optimization algorithms such as aquila optimizer algorithm (AO), sea lion optimization (SLnO), elephant herding optimization (EHO), the naked mole-rat algorithm (NMRA), Stochastic Gradient Descent (SGD). The geospatial distribution analysis approach was used to construct the input data, including 1883 sample points and 12 conditioning factors. Several well-known algorithms were used as reference models to compare the accuracy of each proposed model. The statistical indices root mean square error (RMSE), area under curve (AUC), mean absolute error (MAE), accuracy, and F1 score were used to validate the proposed model. The results show that five optimization algorithms successfully in buiding flood susceptibility maps and these models performed well with an AUC value of more than 0.97. The DNN-NMRA model came first (RMSE = 0.16, AUC = 0.99), followed by DNN-SLnO (RMSE = 0.39, AUC = 0.99), DNN-EHO (RMSE = 0.41, AUC = 0.99), DNN-AO (RMSE = 0.46, AUC = 0.97), and DNN-SGD (RMSE = 0.49, AUC = 0.93) respectively. Furthermore, the results show that the precision of new models (DNN-NMRA, DNN-SlnO, DNN-EHO, DNN-AO) surpassed the standard model DNN-SGD. The results of this study are useful in the construction of appropriate flood management strategies in at-risk regions. [ABSTRACT FROM AUTHOR]

Background: Mixing vaccines was proposed as a solution to tackle supply chain interruptions during the crisis of the COVID-19 pandemic. This study aimed to investigate the safety of mixing COVID-19 vaccines for a booster dose in Hanoi, Vietnam. Method: A cross-sectional study was conducted via a telephone-based interview to identify the adverse events following COVID-19 vaccination among 719 participants in Hanoi, Vietnam. Results: In total, 45.76% of participants experienced at least one adverse event following two doses of the COVID-19 vaccine. Most of the adverse events were local effects with mild symptoms such as fever, headache, muscle pain, and/or pain at the site. In general, matching two doses in the same vaccines was not associated with the adverse events as compared to mixing vaccines (OR = 1.43, 96%CI: 0.93–2.2), except matching two doses of Pfizer (OR = 2.25, 95%CI: 1.33–3.82). Conclusion: The findings of this study suggest the overall safety of mixed vaccination. In light of the vaccine shortage, mixing vaccinations for COVID-19 prevention is a good solution. Further studies with larger cohorts and investigating immunity following mixing vaccines are needed to elucidate the mechanism. [ABSTRACT FROM AUTHOR]

Purpose: Digital transformation (DT) and supply chain resilience have received increasing attention. Structural social capital (SSC) and human capital (HC) have recently been identified as strategic resources for small and medium-sized enterprises (SMEs). While the significant role of entrepreneurial competencies (ECs), SME innovativeness (SMI) and technology adoption (TA) in driving business performance have been partially researched, no studies have simultaneously examined the effect of these resources and capabilities on SME business performance growth (BPG) under the lenses of a resource-based view (RBV) and dynamic capabilities (DCs) as well as in the context of DT. This study aims to clarify these unclear relationships. Design/methodology/approach: This study adopts established theoretical lenses, a mixed-methods approach and structural equation modeling (SEM) using a sample of 371 respondents who are top and middle SME managers in Vietnam. Findings: The study discovers the significant and insignificant relationships between HC, SSC, ECs, SMI, TA and BPG besides providing a new measurement and reconciling existing measurements for the DT context. Some implications for driving SMEs' DT are also suggested. Originality/value: This study is the first to thoroughly examine the effect of HC and SSC on SMEs' BPG through the mediating role of ECs, SMI and TA under RBV and DCs lenses in the DT context. The investigation is conducted in an emerging market, where DT implementation is in the process of being learned and experimented upon. [ABSTRACT FROM AUTHOR]

Objective: To investigate the changes of very short-term heart rate variability in HCWs after three hours of wearing PPE to treat COVID-19 patients at different working times and intensities, and related factors. Methods: Sixty-five healthy HCWs were enrolled at the Number 2 Infectious Field Hospital (formed by Military Hospital 103), Vietnam. Two-minute 12-lead electrocardiograms were recorded before wearing and after removing PPE. Results: After three hours of wearing PPE, the mean heart rate of HCWs increased (p = 0.048) meanwhile, the oxygen saturation decreased significantly (p = 0.035). Standard deviation of all normal to normal intervals (SDNN), mean intervals RR (mean NN), and root mean square successive difference (rMSSD) after wearing PPE was also reduced significantly. SDNN, Mean NN, and rMSSD decreased as the working intensity increased (as in mild, moderate, and severe patient departments). In univariate regression analysis, logSDNN, logmean NN and logrMSSD were positively correlated with SpO2 and QT interval (r = 0.14, r = 0.31, r = 0.25; r = 0.39, r = 0.77, r = 0.73, respectively) and were negatively correlated with ambient temperature inside PPE (r = − 0.41, r = − 0.405, r = − 0.25, respectively) while logmean NN and log rMSSD were negatively correlated with diastolic blood pressure (r = − 0.43, r = − 0.39, respectively). In multivariable regression analysis, logSDNN and logmean NN were negatively correlated to ambient temperature inside PPE (r = − 0.34, r = − 0.18, respectively). Conclusion: Time-domain heart rate variability decreased after wearing PPE. Time-domain HRV parameters were related to ambient temperature inside PPE, diastolic blood pressure, QT interval, and SpO2. [ABSTRACT FROM AUTHOR]

Background: Tacrolimus trough levels (C0) are used in most transplant centres for therapeutic drug monitoring (TDM) of tacrolimus (Tac). The target range of Tac C0 has been remarkably changed, with a target as low as 3–7 ng/ml in the 2009 European consensus conference and a target of 4–12 ng/ml (preferably to 7–12 ng/ml) following the second consensus report in 2019. Our aim was to investigate whether reaching early Tac therapeutic targets and maintaining time in the therapeutic range (TTR) according to the new recommendations may be necessary for preventing acute rejection (AR) during the first month after transplantation. Methods: A retrospective study including 160 adult renal transplant patients (113 men and 47 women) with a median age of 36.3 (20–44) years was conducted between January 2018 and December 2019 at 103 Military Hospital (Vietnam). Tac trough levels were recorded in the first month, and episodes of AR were confirmed by kidney biopsy. Tac TTR was calculated as the percentage of time within the target range of 7–12 ng/ml, according to the 2019 second consensus report. Multivariate Cox analysis was performed to identify the correlation between the Tac target range and TTR with AR. Results: In the first month after RT, 14 (8.8%) patients experienced AR. There was a significant difference in the incidence of AR between the Tac level groups of < 4, 4–7 and > 7 ng/ml (p = 0.0096). In the multivariate Cox analysis, after adjusting for related factors, a mean Tac level > 7 ng/ml was associated with an 86% decreased risk of AR compared with that of 4–7 ng/ml in the first month (HR, 0.14; 95% CI, 0.03–0.66; p = 0.0131). Every 10% increase in TTR was associated with a 28% lower risk of AR (HR, 0.72; 95% CI, 0.55–0.94; p = 0.014). Conclusion: Gaining and maintaining Tac C0 according to the 2019 second consensus report might reduce the risk of AR in the first month following transplantation. [ABSTRACT FROM AUTHOR]

Vietnam has diverse and long-established tea plantations but scientific data on the characteristics of Vietnamese teas are still limited. Chemical and biological properties including total polyphenol and flavonoid contents (TPCs and TFCs), antioxidant activities (DPPH, ABTS, FRAP, and CUPRAC), as well as the contents of caffeine, gallic acid, and major catechins, were evaluated for 28 Vietnamese teas from North and South Vietnam. Higher values of TPCs and TFCs were found for green (non-oxidised) and raw Pu'erh (low-oxidised) teas from wild/ancient tea trees in North Vietnam and green teas from cultivated trees in South Vietnam, as compared to oolong teas (partly oxidised) from South Vietnam and black teas (fully oxidised) from North Vietnam. The caffeine, gallic acid, and major catechin contents depended on the processing, geographical origin, and the tea variety. Several good Pearson's correlations were found (r2 > 0.9) between TPCs, TFCs, the four antioxidant capacities, and the content of major catechins such as (–)-epicatechin-3-gallate and (–)-epigallocatechin-3-gallate. Results from principal component analysis showed good discriminations with cumulative variances of the first two principal components varying from 85.3% to 93.7% among non-/low-oxidised and partly/fully oxidised teas, and with respect to the tea origin. [ABSTRACT FROM AUTHOR]

Flood is a more frequent natural hazard, having destroying effects on human life and property around the world. Water resource management strategies require understanding of the flood susceptibility. This study's objective is to develop an effective new approach to the construction of a flood susceptibility map (FSM) in the Nhat Le-Kien Giang River watershed in Vietnam, based on the analytical hierarchy process (AHP) technique. AHP was used to calculate the weighting of each of nine conditioning factors to build a flood susceptibility map. The results showed that the AHP technique would be successful in constructing a FSM with an AUC value of 0.95. The area of high and very high flood susceptibility in the residential area increased from 95 km² in 2005 to 251 km² in 2020. While the area of high and very high flood susceptibility in the agricultural area increased from 239 km² in 2005 to 245 km² in 2020. The findings of this study can support decision-makers and planners working in flood management and the development of mitigation strategies. Although the approach in this study has been applied to construct a flood susceptibility map for one area in Vietnam, it can be applied both to other types of natural hazards and in other countries. [ABSTRACT FROM AUTHOR]

Purpose: Risks are considered a significant obstacle to Green Building (GB) development and have recently received significant attention from both construction practitioners and academics. This study aimed to identify critical GB risks and explore the relationship between participants' characteristics and risk assessment. Design/methodology/approach: Firstly, a list of GB risks was developed based on a comprehensive literature review and interviewing GB experts. After that, a survey of 207 construction professionals was then conducted to validate these GB risk factors. Finally, this research adopted the ANOVA test and hierarchical regression analysis to examine the relationship between participants' characteristics and risk assessment. Findings: The results provided a list of GB risks classified and evaluated according to the GB project life cycle and, thus, may serve as a helpful reference for GB practitioners. Notably, the ANOVA analysis revealed that risk assessment negatively correlates with participants' GB experience, while their industry experience does not affect risk assessment. Furthermore, the hierarchical regression analysis proved that participant roles do not moderate the association between risk assessment and GB experience. Originality/value: This study contributed to GB literature by implementing empirical research on GB risks in a developing country. The results implied the essential role of professionals with rich GB experience in risk management in GB projects. Furthermore, this research could help construction practitioners understand GB risks adequately and thereby have better risk-management strategies for future GB projects. [ABSTRACT FROM AUTHOR]

Flood is the most dangerous of natural disasters. It is responsible for considerable loss of life and economic damage, especially in Southeast Asian countries such as Vietnam. Improving community resilience can both reduce damage and alleviate the economic burden of communities in the aftermath of floods. Flood damage reduction strategies in Vietnam mainly focus on flood hazard and often neglect to take into account community resilience. The objective of this article is the development of a theoretical framework to assess the relationship between flood hazard and community resilience in the Nhat Le-Kien Giang River watershed. Flood hazard was calculated by integrate flood depth and flood velocity applying MIKE FLOOD modeling and the AHP, while data on the resilience of communities was collected via questionnaire. A total of 70 households in Hong Thuy commune were selected by the random sampling technique. The results showed that the high-hazard region is concentrated along the river like Tan Ninh, Gia Ninh, Hong Thuy, and Hoa Thuy communes. It also illustrated how access to resources, prevention strategies, and community perceptions are important factors to improve the resilience of communities. The results represent an important theoretical framework that can be used to support decision-makers in building appropriate damage-reduction strategies. [ABSTRACT FROM AUTHOR]

On-demand food delivery involves transport services based on gig-economy models. Food delivery services rely on motorcycles in many jurisdictions, resulting in safety risks. Motorcycles are generally-two-wheeled and therefore inherently unstable. They also lack rider restraint or roll cage to minimise the consequences of a collision. Given the risks of motorcycle food delivery, there is a need to understand how job design may influence safety behaviour on the roads and regulate this economic activity to minimise potential harmful health consequences on the riders. This study investigated the impact of job demands and resources on food delivery riders' compliance with road safety regulations. The job demands-resources (JD–R) model was used as the theoretical framework for this research. Data were collected using a cross-sectional design involving 550 motorcycle delivery riders in two megacities in Vietnam. A structural equation analysis indicated that job demands (e.g., time pressure, work/life imbalance, working environment) and job resources (e.g., social support, feedback) influence, directly and indirectly, job strain, risk-taking attitude, and road safety compliance. Control variables such as age, gender, and income also influenced road safety compliance. This study has critical implications for the food delivery industry that can help achieve sustainable development goals in the global south. [ABSTRACT FROM AUTHOR]

Floods is a natural hazard that occurs over a short time with a high transmission speed. Flood risk management in many countries employs flood susceptibility modeling to mitigate against the damage to the economy and loss of life caused by future floods. The objective of this study is the development of a new approach based on the machine learning algorithms namely Multilayer Perceptron (MLP), Archimedes Optimization Algorithm (AOA), Whale Optimization Algorithm (WOA), Water Cycle Algorithm (WCA), Decision Tree (DT), and Adaboost (ADB) to build flood susceptibility maps for the Nhat Le–Kien Giang watershed of Quang Binh province. In total, 1964 flood locations and 14 conditioning factors were split with a ratio of 70:30 for the training model and the validation model respectively. Various statistical indices – namely root-mean-square error (RMSE), mean absolute error (MAE), coefficient of determination (R2), and area under the ROC curve (AUC-ROC) – were used to evaluate the models. The results show that all the models performed well in building the flood susceptibility map, with an AUC value of more 0.9; the models MLP-WCA (AUC = 0.99) and MLP-AOA (AUC = 0.99) were most successful, followed by MLP-WOA (AUC = 0.98), MLP-ACO (0.98), ADB (0.98), and DT (0.97). The analysis of the flood susceptibility maps shows that the high and very high susceptibility level corresponds to no more than 39% of the study area. The results also show that altitude, slope, rainfall, and land use are most influential on the probability of flood occurrence in the study area. It can be concluded that the machine learning models proposed in this study provide results with high accuracy. The flood susceptibility maps that have been produced will be significant in determining future development strategies, especially in selecting the locations of new urban areas; and the findings of this study may be applied not only to this particular watershed in Vietnam, but also in developing countries around the world. [ABSTRACT FROM AUTHOR]

Flooding is currently the most dangerous natural hazard. It can have heavy human and material impacts and, in recent years, flooding has tended to occur more frequently, due to changes our species has made to hydrological regimes, and due to climate change. It is of the utmost importance that new models are developed to predict and map flood susceptibility with high accuracy, to support decision-makers and planners in designing more effective flood management strategies. The objective of this study is the development of a new method based on state-of-the-art machine learning and remote sensing, namely random forest (RF), dingo optimization algorithm, a weighted chimp optimization algorithm (WChOA), and particle swarm optimization to build flood susceptibility maps in the Nghe An province of Vietnam. The CyGNSS system was used to collect soil moisture data to integrate into the susceptibility model. A total of 1650 flood locations and 14 conditioning factors were used to construct the model. These data were divided at a ratio of 60/20/20 to train, validate, and test the model, respectively. In addition, various statistical indices, namely root-mean-square error, receiver operation characteristic, mean absolute error, and the coefficient of determination (R2), were used to assess the performance of the model. The results for all the models were good, with an AUC value of + 0.9. The RF-WChOA model performed best, with an AUC value of 0.99. The proposed models can predict and map flood susceptibility with high accuracy. [ABSTRACT FROM AUTHOR]

The study analyzed the influence of Blockchain usage on purchase intention and the willingness to pay a higher price for customers within the food industry. Data collected through 180 distributed surveys was processed by SPSS 26 and SmartPLS 3.3 software. It is demonstrated that there are five factors influencing consumers' intention to purchase products using Blockchain Food Traceability (BFT): (1) Performance Expectancy, (2) Facilitating Conditions, (3) Social Influence, (4) Trust, and (5) Level of Knowledge. The study showed that trust, performance expectancy, and level of knowledge variables have a strong influence on behavioral intention. Recommendations were also made for managerial purposes. The significance of this paper is insights into customers' willingness to pay more and their purchase intention in Blockchain Food Traceability. [ABSTRACT FROM AUTHOR]

The major goal of this study is to examine the impact of agricultural cooperative membership on the net return on production cost (ROC). The data used for the study were gathered from 233 rice farmers in the central Mekong River Delta's province of Hau Giang, Vietnam. Propensity score matching and two-step Heckman selection models were employed to account for possible selection bias stemming from systematically different observable and unobservable attributes between cooperative members and non-members. The findings reveal that cooperative membership has a positive and statistically significant impact on rice farmers' ROC, suggesting that agricultural cooperatives could help increase the profitability of rice farms. [ABSTRACT FROM AUTHOR]

Forecasting discharge (Q) and water level (H) are essential factors in hydrological research and flood prediction. In recent years, deep learning has emerged as a viable technique for capturing the non-linear relationship of historical data to generate highly accurate prediction results. Despite the success in various domains, applying deep learning in Q and H prediction is hampered by three critical issues: a shortage of training data, the occurrence of noise in the collected data, and the difficulty in adjusting the model's hyper-parameters. This work proposes a novel deep learning-based Q–H prediction model that overcomes all the shortcomings encountered by existing approaches. Specifically, to address data scarcity and increase prediction accuracy, we design an ensemble learning architecture that takes advantage of multiple deep learning techniques. Furthermore, we leverage the Singular-Spectrum Analysis (SSA) to remove noise and outliers from the original data. Besides, we exploit the Genetic Algorithm (GA) to propose a novel mechanism that can automatically determine the prediction model's optimal hyper-parameters. We conducted extensive experiments on two datasets collected from Vietnam's Red and Dakbla rivers. The results show that our proposed solution outperforms current techniques across a wide range of metrics, including NSE, MSE, MAE, and MAPE. Specifically, by exploiting the ensemble learning technique, we can improve the NSE by at least 2 % . Moreover, with the aid of the SSA-based data preprocessing technique, the NSE is further enhanced by more than 5 % . Finally, thanks to GA-based optimization, our proposed model increases the NSE by at least 6 % and up to 40 % in the best case. [ABSTRACT FROM AUTHOR]

Flooding is the most dangerous of all environmental hazards and leads to significant impacts both on the environment and on human life. Environmental protection and water management can be achieved by modeling flood susceptibility, to inform strategy that will reduce flood damage. The aim of this study is to develop novel hybrid models based on a radial basis functions neural network (RBFNN), arithmetic optimization algorithm (AOA), artificial bee colony (ABC), and ant lion optimizer (ALO), to build flood susceptibility maps in the Quang Tri province of Vietnam. The obtained models were trained and validated using 1511 flood points and 14 conditioning factors. Various statistical indices were used to assess the performance of the models, namely root mean square error, receiver operation characteristics (ROC), area under the receiver operating characteristic curve (AUC), and the coefficient of determination (R2). The comparison analysis highlights that the RBFNN‐ABC model was better than other models with an AUC of 0.98, followed by RBFNN‐AOA, support vector machine, and random forest, all with an AUC value of 0.96, and finally RBFNN‐ALO, with an AUC of 0.95. From the flood susceptibility map, it can be seen that the eastern areas of the study area have high and very high flood susceptibility, that requires local government attention. The approach and results of this study can support national and local authorities, decision‐makers, and other planners in the construction of appropriate strategies to reduce potential damage in the future. [ABSTRACT FROM AUTHOR]

Landslides are considered to be a significant risk to life and property all over the world in general and in Vietnam in particular. Spatial prediction of landslides is required to reduce the landslides risk and to plan the development of hilly areas. In this regard, the accurate landslide susceptibility maps are very useful tool for decision-makers to identify areas where new landslides are likely to occur for planning timely adequate remedial measures. For the development of landslide susceptibility maps, seven hybrid models were developed namely AdaBoost-LMT (ABLMT), bagging-LMT (BLMT), cascade generalization-LMT (CGLMT), dagging-LMT (DLMT), MultiBoostAB-LMT (MBLMT), rotation forest-LMT (RFLMT) and random sub-space-LMT (RSSLMT) with logistic model trees (LMTs) as a base classifier. The model's performance and validation were assessed through various statistical indices, such as sensitivity (SST), specificity (SPF), accuracy (ACC), area under ROC curve (AUC), RMSE and k index. The results show that all these models are performing well for the prediction of landslide susceptibility in the study area, but the performance of the RSSLMT model is the best (AUC: 0.816). In this study, open-source data has been used for the development of landslide susceptibility maps Along National Highway-6, passing through Hoa Binh province, Vietnam. These approaches can be applied also in other hilly regions of the world which are susceptible to landslides for better landslides prevention and management. [ABSTRACT FROM AUTHOR]

This study propose a new approach through which the landslide susceptibility in Quang Nam (Vietnam) will be estimated using the best model among the following algorithms: Decision Table (DT), Naïve Bayes (NB), Decision Table - Naïve Bayes (DTNB), Bagging Ensemble, Cascade Generalization Ensemble, Dagging Ensemble, Decorate Ensemble, MultiBoost Ensemble, MultiScheme Ensemble, Real Ada Boost Ensemble, Rotation Forest Ensemble, Random Sub Space Ensemble. In this regard, a map with 1130 landslide, was created and further partitioned into training (70%) and testing (30%) locations. The correlation-based features selections (CFS) method was used to select a number of 15 landslide influencing factors. Landslide locations, included in the training sample, and the landslide predictors were used as input data in order to run the above mentioned models. Kappa index, Accuracy (%) and ROC curve were employed to estimate the model's performance and to test the outcomes provided by the models. Among the eleven machine learning algorithms, Random Sub Space Decision Table Naïve Bayes (RSSDTNB) was the most performant model with an AUC = 0.839, Accuracy = 76.55% and Kappa Index = 0.531. Therefore, this algorithm was involved in the estimation of landslide susceptibility. The Success Rate (AUC = 0.815) and Prediction Rate (AUC = 0.826) revealed the achievement of high-quality results. [ABSTRACT FROM AUTHOR]

In Vietnam, local chicken breeds account for over 70% of the national poultry population. Although these breeds are abundant, their productivity is low and their use is threatened by the extensive importation of foreign productive breeds. In this context, conservation programmes targeting several emblematic breeds have been established. The goal of these programmes was to characterize endangered breeds and maintain a pool of characteristic birds for preserving their genetic heritage. To contribute to these programmes, we comprehensively characterized four Vietnamese local chicken breeds (Dong Tao, Ho, Mia and Mong) at the genomic level using high‐density single‐nucleotide polymorphism (SNP) genotyping. Despite originating in geographically close areas, Dong Tao and Ho were evidently different from each other as well as from Mong and Mia, which shared a more recent common ancestor. The genomic inbreeding coefficient revealed high homozygosity amongst the four breeds (10%–20%). The observation of clear differentiation at the genomic level supported the presence of distinct breeds; nonetheless, the occurrence of crossbred birds in a presumably purebred sample demonstrated the need to apply genomic tools to unambiguously assign the birds to the correct breed. Moreover, the occurrence of substantial inbreeding and the presence of subgroups in certain breeds warranted attention to create future nuclei for use in the conservation of these local breeds. [ABSTRACT FROM AUTHOR]

Background: Self-care behaviors during pregnancy significantly impacts mother and children's health. This study aimed to explore the self-care behaviors and the associations of these behaviors with the psychological well-being of women during pregnancy, as well as the mediating effects of different social support with these associations.Methods: A cross-sectional data of 562 pregnant women at Hanoi Obstetrics & Gynecology Hospital and Ca Mau Obstetrics & Pediatrics in Vietnam were analyzed. Questions about self-care behaviors, pregnancy characteristics, social support, and psychological well-being were asked. Multivariate regression models were performed. Structural Equation Modeling (SEM) was employed to test the mediating effects of different social support with the association between self-care behaviors and psychological well-being.Findings: Only 13% of pregnant women often or always did physical exercise at least three times a week, and 40% consumed enough fiber and five servings of vegetables a day. Only 78.7% always avoided alcohol drinking, and 53.9% of pregnant women avoided being exposed to second-hand smoking and 71,7% avoided using traditional medicine without physicians' prescriptions. Around 66% of pregnant women always or often had prenatal care checkups as scheduled. Information sources, social support and childbirth expectation were major drivers for self-care practices. SEM model showed that social support mediated the relationship between maternal health behaviors and mental well-being.Conclusion: This study highlighted the remarkable gaps in self-care practices among our pregnant women, which were significantly associated with their mental well-being. Social support-oriented consultancy and interventions should be warranted for improving behaviors and the mental well-being of pregnant women in Vietnam. [ABSTRACT FROM AUTHOR]

This study aims to develop a comprehensive approach including an analysis of the relationships between flood susceptibility and land-use change, based on the relevance vector machine (RVM) and coyote optimization algorithm (COA) models, applied to Gianh River watershed, Quang Binh province, Central Vietnam. Standard statistical indices, e.g. area under the curve (AUC), were used to assess the model performance. Comparative analyses emphasize that the COA successfully improves the performance of the RVM model (AUC = 0.99) and is also better than the reference models such as support vector machine (AUC = 0.98), gradient boosting machine (AUC = 0.97), random forest (AUC = 0.99), extra trees regressor (AUC = 0.98), and AdaBoost (AUC = 0.96). The improved model, when used in conjunction with land use maps, is able to show that urbanization has increased in flood-susceptible areas. The results highlight that urbanization has increased in the low and very low flood susceptibility areas by 110% between 2005 and 2020, while in the high and very high areas it has increased by 30 to 40%, despite urban and demographic growth. [ABSTRACT FROM AUTHOR]

Floods are the most frequent natural hazard globally and incidences have been increasing in recent years as a result of human activity and global warming, making significant impacts on people's livelihoods and wider socio-economic activities. In terms of the management of the environment and water resources, precise identification is required of areas susceptible to flooding to support planners in implementing effective prevention strategies. The objective of this study is to develop a novel hybrid approach based on Bald Eagle Search (BES), Support Vector Machine (SVM), Random Forest (RF), Bagging (BA) and Multi-Layer Perceptron (MLP) to generate a flood susceptibility map in Thua Thien Hue province, Vietnam. In total, 1621 flood points and 14 predictor variables were used in this study. These data were divided into 60% for model training, 20% for model validation and 20% for testing. In addition, various statistical indices were used to evaluate the performance of the model, such as Root Mean Square Error (RMSE), Receiver Operation Characteristics (ROC), and Mean Absolute Error (MAE). The results show that BES, for the first time, successfully improved the performance of individual models in building a flood susceptibility map in Thua Thien Hue, Vietnam, namely SVM, RF, BA and MLP, with high accuracy (AUC > 0.9). Among the models proposed, BA-BES was most effective with AUC = 0.998, followed by RF-BES (AUC = 0.998), MLP-BES (AUC = 0.998), and SVM-BES (AUC = 0.99). The findings of this research can support the decisions of local and regional authorities in Vietnam and other countries regarding the construction of appropriate strategies to reduce damage to property and human life, particularly in the context of climate change. [ABSTRACT FROM AUTHOR]

Introduction: Endothelium dysfunction and decrease of incretin effects occur early in type 2 diabetes mellitus and these changes contribute to diabetic cardiovascular complications such as atherosclerosis, thick intima-media, coronary, and peripheral arterial diseases. In patients with diabetes, the femoral artery is a site of a high incidence of injury in peripheral vascular diseases, and atherosclerotic changes may appear earlier in the femoral artery compared to the carotid artery. This study was conducted to determine the prevalence of increased femoral artery intima-media thickness (IMT) and atherosclerotic plaque and their correlation with serum glucagon-like peptide-1 (GLP-1) levels in newly-diagnosed patients with type 2 diabetes mellitus. Materials and Methods: A cross-sectional study was conducted on 332 patients with nT2D in the National Endocrinology Hospital, Vietnam from January 2015 to May 2018. IMT was measured by Doppler ultrasound and GLP-1 by enzyme-linked immunosorbent assay (ELISA). All data were analyzed with SPSS version 26 for Windows (SPSS Inc, Chicago, IL). Results: Prevalence of thick femoral artery IMT and atherosclerotic plaque was 38.2 and 22.3%, respectively. There was a relationship between IMT and age, waist to hip ratio (WHR), systolic blood pressure (SBP), diastolic blood pressure (DBP), fasting GLP-1, high sensitive CRP (hsCRP) and 24-hour microalbuminuria secretion (24-h MAUS). The fasting serum GLP-1 (fGLP-1) levels were reduced significantly in patients with thickness and atherosclerosis femoral artery (p = 0.001). After adjusting with other related factors, namely, DBP and estimated glomerular filtration rate (eGFR), whilst hsCRP and 24-h MAUS showed a significantly positive correlation to IMT (Standardized B and p of 0.242, 0.004 and 0.178, 0.043, respectively), fGLP-1 showed a significantly negative correlation to IMT (Standardized B = − 0.288, p = 0.001). Conclusion: Among n2TD, the percentage for femoral artery thick IMT and atherosclerosis was 38.2% and 22.3% respectively, and serum GLP-1 was negatively correlated with thick IMT and atherosclerosis. [ABSTRACT FROM AUTHOR]

Monitoring Land-use/land-cover (LULC) changes are a significant challenge for sustainable spatial planning, particularly in response to transformation and degenerative landscape processes. These disturbances lead to the vulnerability of inhabitants and habitat and climate changes and socio-economic development in the region. Several studies have proposed different methods and techniques to monitor the spatial and temporal changes of LULC. Machine learning is a more popular method. However, the problem of data imbalance is a significant challenge, and the classification results tend to bias the majority classes for unbalanced data. Therefore, this study's objective is to develop a state-of-the-art technique to reduce the problem of data imbalance in LULC classification in Vietnam based on machine learning and SMOTE (Synthesizing Minority Oversampling Technology) with Edited Nearest Neighbor (ENN). Various statistical indices, including Kappa and Accuracy, have been used to assess the performance for the classification of Landuse/ cover. The results indicate that integrating oversampling and under-sampling with SMOTE ENN gave better overall accuracy and generalization. We also find that the expected proportion of chance agreement after oversampling is higher than before (Kappa score before and after oversampling is 0.905244 and 0.974379, respectively). This study provides an effective method to monitor spatial and temporal land cover change in Vietnam; it plays a role as a framework for other relevant research related to land cover change, which can support planning and sustainable management of the territory. [ABSTRACT FROM AUTHOR]

Word-of-mouth (WOM) and intention to continue use (ICU) are the two critical components of customer loyalty towards a particular service. There has been a large body of research investigating the effect of determinants on the loyalty formation of public transport passengers. However, the impact of social cues from other passengers, which are part of the social environment, have received less attention. This study developed a theoretical model to examine the complex relationships among the cues from other passengers, perceived security and safety, perceived value, WOM and ICU. Cues from other passengers involved three dimensions: similarity, physical appearance, and suitable behaviour. These three dimensions were measured using a formative approach in structural equation modelling (SEM). Additionally, the moderating effects of demographic characteristics on the formation of public transport passenger behaviours were explored by multi-group SEM. The model was empirically tested using data collected from more than 870 bus passengers in two big cities in Vietnam (Ho Chi Minh and Danang city). The results showed that social cues from other passengers have a strong predictive power over the WOM and ICU of bus passengers. The study successfully demonstrated the critical role of demographic characteristics as moderators on loyalty formation. The findings reported in the present investigation provide several theoretical and practical implications. [ABSTRACT FROM AUTHOR]

Every year, 2.4 million neonates die during their first month of life and even more suffer permanent injury. The main causes are intrapartum-related events, prematurity, and infection, with sub-Saharan Africa and southern Asia being the worst affected regions. With a focus on intrapartum-related events, we aimed to assess the neonatal demographic characteristics, clinical management, and outcomes among neonates born at the largest obstetric hospital in Hanoi, Vietnam. This was a retrospective cross-sectional study that included all the inborn neonates in November 2019, which was selected as a representative month. A total of 4554 neonates were born during the study period. Of these, 1.0% (n = 44) were stillbirths, 0.15% (n = 7) died in hospital, 0.61% (n = 28) received positive pressure ventilation at birth, and 0.15% (n = 7) were diagnosed with hypoxic ischemic encephalopathy. A total of 581 (13%) neonates were admitted to the neonatal unit, among which the most common diagnoses were prematurity (37%, n = 217) and infection (15%, n = 89). Except for the intrapartum-related events, our findings are consistent with the previously documented data on neonatal morbidity. The intrapartum-related events, however, were surprisingly low in number even in comparison to high-income countries. Research on the current clinical practice at Phu San Hanoi Hospital may bring further clarity to identify the success factors. [ABSTRACT FROM AUTHOR]