Flash flood is a typical natural hazard that occurs within a short time with high flow velocities and is difficult to predict. In this study, we propose and validate a new soft computing approach ...that is an integration of an Extreme Learning Machine (ELM) and a Particle Swarm Optimization (PSO), named as PSO-ELM, for the spatial prediction of flash floods. The ELM is used to generate the initial flood model, whereas the PSO was employed to optimize the model. A high frequency tropical typhoon area at Northwest of Vietnam was selected as a case study. In this regard, a geospatial database for the study area was constructed with 654 flash flood locations and 12 influencing factors (elevation, slope, aspect, curvature, toposhade, topographic wetness index, stream power index, stream density, NDVI, soil type, lithology, and rainfall). The model performance was validated using several evaluators such as kappa statistics, root-mean-square error (RMSE), mean absolute error (MAE), coefficient of determination (R2), and area under the ROC curve (AUC-ROC) and compared to three state-of-the-art machine learning techniques, including multilayer perceptron neural networks, support vector machine, and C4.5 decision tree. The results revealed that the PSO-ELM model has high prediction performance (kappa statistics = 0.801, RMSE = 0.281; MAE = 0.079, R2 = 0.829, AUC-ROC = 0.954) and successfully outperformed the three machine learning models. We conclude that the proposed model is a new tool for the prediction of flash flood susceptibility at high frequency tropical typhoon areas.
•PSO-ELM is proposed and verified for flash flood susceptibility modeling.•PSO-ELM has high prediction performance.•PSO-ELM performs better than ANN, SVM, and the C4.5 decision tree.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPUK, ZAGLJ, ZRSKP
Display omitted
•The performance of DLNN was assessed for flood susceptibility mapping.•DLNN was compared with the MLP-NN and SVM in terms of their performance.•DLNN with ADAM optimization is robust ...and outperformed other models.•DLNN is a new promising tool for predicting flash flood in prone areas.
This research proposes and evaluates a new approach for flash flood susceptibility mapping based on Deep Learning Neural Network (DLNN)) algorithm, with a case study at a high-frequency tropical storm area in the northwest mountainous region of Vietnam. Accordingly, a DLNN structure with 192 neurons in 3 hidden layers was proposed to construct an inference model that predicts different levels of susceptibility to flash flood. The Rectified Linear Unit (ReLU) and the sigmoid were selected as the activate function and the transfer function, respectively, whereas the Adaptive moment estimation (Adam) was used to update and optimize the weights of the DLNN. A database for the study area, which includes factors of elevation, slope, curvature, aspect, stream density, NDVI, soil type, lithology, and rainfall, was established to train and validate the proposed model. Feature selection was carried out for these factors using the Information gain ratio. The results show that the DLNN attains a good prediction accuracy with Classification Accuracy Rate = 92.05%, Positive Predictive Value = 94.55% and Negative Predictive Value = 89.55%. Compared to benchmarks, Multilayer Perceptron Neural Network and Support Vector Machine, the DLNN performs better; therefore, it could be concluded that the proposed hybridization of GIS and deep learning can be a promising tool to assist the government authorities and involving parties in flash flood mitigation and land-use planning.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPUK, ZAGLJ, ZRSKP
The work aims to study the removal of crystal violet (CV) using laterite soil with surface modification by surfactant (SML). Surface modification of laterite soil was conducted by pre-adsorption of ...sodium dodecyl sulfate (SDS) at pH 4 and low ionic strength to enhance removal of CV. The effective conditions for CV removal through adsorption technique using SML were optimized and found to be contact time 60 min, pH 6, adsorbent dosage 5 mg/mL, and 5 mM NaCl as background electrolyte. The highest removal of CV using SML reached to 86.5% under optimum conditions. We used Fourier transform infrared spectroscopy (FT-IR) to evaluate the change of surface vibrational groups of laterite after SDS pre-adsorption and after CV adsorption while the different charged surface was determined by ζ potential measurements. The CV adsorption onto SML increased when increasing ionic strength from 1 to 10 mM. Nevertheless, at high ionic strength, this trend is reversal due to desorption of SDS from laterite surfaces. Adsorption isotherms of CV onto SML at different NaCl concentrations were tried to fit by Langmuir, Freundlich, and a two-step adsorption models. The adsorption kinetics were in good agreement with pseudo-second-order model. The removal efficiency of CV after four regenerations still reached higher than 85%. On the basis of adsorption isotherms, charged surface change by ζ potential and surface modification by FT-IR, we suggest that CV adsorption onto SML was induced by both non-electrostatic and electrostatic interactions. We also demonstrate that SML is a novel, reusable, and low-cost adsorbent for cationic dye removal from aqueous solution.
To describe the prevalence of antimicrobial resistance among commensal Escherichia coli isolates on household and small-scale chicken farms, common in southern Vietnam, and to investigate the ...association of antimicrobial resistance with farming practices and antimicrobial usage.
We collected data on farming and antimicrobial usage from 208 chicken farms. E. coli was isolated from boot swab samples using MacConkey agar (MA) and MA with ceftazidime, nalidixic acid or gentamicin. Isolates were tested for their susceptibility to 11 antimicrobials and for ESBL production. Risk factor analyses were carried out, using logistic regression, at both the bacterial population and farm levels.
E. coli resistant to gentamicin, ciprofloxacin and third-generation cephalosporins was detected on 201 (96.6%), 191 (91.8%) and 77 (37.0%) of the farms, respectively. Of the 895 E. coli isolates, resistance to gentamicin, ciprofloxacin and third-generation cephalosporins was detected in 178 (19.9%), 291 (32.5%) and 29 (3.2%) of the isolates, respectively. Ciprofloxacin resistance was significantly associated with quinolone usage (OR = 2.26) and tetracycline usage (OR = 1.70). ESBL-producing E. coli were associated with farms containing fish ponds (OR = 4.82).
Household and small farms showed frequent antimicrobial usage associated with a high prevalence of resistance to the most commonly used antimicrobials. Given the weak biocontainment, the high prevalence of resistant E. coli could represent a risk to the environment and to humans.
A highly sensitive non-enzymatic glucose sensor was fabricated by hybridizing 0- dimensional (0D) Ag nanoparticles, 3D flower-like nickel oxide (NiO) nanostructures, and 2D reduced graphene oxide ...(rGO) as the sensing interface by hydrothermal synthesis. The resulting hybrid structures were characterized by Raman spectroscopy, X-ray photoelectron spectroscopy, field-emission scanning electron microscopy, energy dispersive X-ray spectroscopy, and high-resolution transmission electron microscopy. The Ag/NiO/rGO fabricated in this study showed high electrochemical activity towards the oxidation of glucose in a 0.1 M NaOH solution. At an applied potential of +0.6 V, it exhibited a rapid response time (<4 s), a broad linear range of glucose concentrations up 25 mM with an extraordinarily high sensitivity of 1869.4 μA mM−1 cm−2. The detection limit was as low as 2.44 μM. In addition, the response towards common interfering species, such as sucrose, lactose, fructose, ascorbic acid, dopamine, and uric acid were low enough to be avoidable.
Display omitted
•Non-enzymatic glucose sensors were fabricated by hybridizing multi-dimensional Ag/NiO/rGO.•Excellent electrocatalytic activity of the Ag/NiO/rGO toward glucose oxidation was achieved.•The well-dispersed Ag/NiOs on the rGO surface enhanced the charge transfer.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPUK, ZRSKP
Background. Trypanosoma is a genus of unicellular parasitic flagellate protozoa. Trypanosoma brucei species and Trypanosoma cruzi are the major agents of human trypanosomiasis; other Trypanosoma ...species can cause human disease, but are rare. In March 2015, a 38-year-old woman presented to a healthcare facility in southern Vietnam with fever, headache, and arthralgia. Microscopic examination of blood revealed infection with Trypanosoma. Methods. Microscopic observation, polymerase chain reaction (PCR) amplification of blood samples, and serological testing were performed to identify the infecting species. The patient's blood was screened for the trypanocidal protein apolipoprotein L1 (APOL1), and a field investigation was performed to identify the zoonotic source. Results. PCR amplification and serological testing identified the infecting species as Trypanosoma evansi. Despite relapsing 6 weeks after completing amphotericin B therapy, the patient made a complete recovery after 5 weeks of suramin. The patient was found to have 2 wild-type APOL1 alleles and a normal serum APOL1 concentration. After responsive animal sampling in the presumed location of exposure, cattle and/or buffalo were determined to be the most likely source of the infection, with 14 of 30 (47%) animal blood samples testing PCR positive for T. evansi. Conclusions. We report the first laboratory-confirmed case of T. evansi in a previously healthy individual without APOL1 deficiency, potentially contracted via a wound while butchering raw beef, and successfully treated with suramin. A linked epidemiological investigation revealed widespread and previously unidentified burden of T. evansi in local cattle, highlighting the need for surveillance of this infection in animals and the possibility of further human cases.
Full text
Available for:
BFBNIB, NUK, PNG, UL, UM, UPUK
To understand the dynamics behind the worldwide spread of the mcr-1 gene, we determined the population structure of Escherichia coli and of mobile genetic elements (MGEs) carrying the mcr-1 gene. ...After a systematic review of the literature we included 65 E. coli whole genome sequences (WGS), adding 6 recently sequenced travel related isolates, and 312 MLST profiles. We included 219 MGEs described in 7 Enterobacteriaceae species isolated from human, animal and environmental samples. Despite a high overall diversity, 2 lineages were observed in the E. coli population that may function as reservoirs of the mcr-1 gene, the largest of which was linked to ST10, a sequence type known for its ubiquity in human faecal samples and in food samples. No genotypic clustering by geographical origin or isolation source was observed. Amongst a total of 13 plasmid incompatibility types, the IncI2, IncX4 and IncHI2 plasmids accounted for more than 90% of MGEs carrying the mcr-1 gene. We observed significant geographical clustering with regional spread of IncHI2 plasmids in Europe and IncI2 in Asia. These findings point towards promiscuous spread of the mcr-1 gene by efficient horizontal gene transfer dominated by a limited number of plasmid incompatibility types.
Full text
Available for:
IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Abstract
Objectives
To investigate the risk of colonization with ESBL-producing Escherichia coli (ESBL-Ec) in humans in Vietnam associated with non-intensive chicken farming.
Methods
Faecal samples ...from 204 randomly selected farmers and their chickens, and from 306 age- and sex-matched community-based individuals who did not raise poultry were collected. Antimicrobial usage in chickens and humans was assessed by medicine cabinet surveys. WGS was employed to obtain a high-resolution genomic comparison between ESBL-Ec isolated from humans and chickens.
Results
The adjusted prevalence of ESBL-Ec colonization was 20.0% (95% CI 10.8%–29.1%) and 35.2% (95% CI 30.4%–40.1%) in chicken farms and humans in Vietnam, respectively. Colonization with ESBL-Ec in humans was associated with antimicrobial usage (OR = 2.52, 95% CI = 1.08–5.87) but not with involvement in chicken farming. blaCTX-M-55 was the most common ESBL-encoding gene in strains isolated from chickens (74.4%) compared with blaCTX-M-27 in human strains (47.0%). In 3 of 204 (1.5%) of the farms, identical ESBL genes were detected in ESBL-Ec isolated from farmers and their chickens. Genomic similarity indicating recent sharing of ESBL-Ec between chickens and farmers was found in only one of these farms.
Conclusions
The integration of epidemiological and genomic data in this study has demonstrated a limited contribution of non-intensive chicken farming to ESBL-Ec colonization in humans in Vietnam and further emphasizes the importance of reducing antimicrobial usage in both human and animal host reservoirs.
Antimicrobial resistance is a major contemporary public health threat. Strategies to contain antimicrobial resistance have been comprehensively set forth, however in developing countries where the ...need for effective antimicrobials is greatest implementation has proved problematic. A better understanding of patterns and determinants of antibiotic use and resistance in emerging economies may permit more appropriately targeted interventions.Viet Nam, with a large population, high burden of infectious disease and relatively unrestricted access to medication, is an excellent case study of the difficulties faced by emerging economies in controlling antimicrobial resistance.
Our working group conducted a situation analysis of the current patterns and determinants of antibiotic use and resistance in Viet Nam. International publications and local reports published between 1-1-1990 and 31-8-2012 were reviewed. All stakeholders analyzed the findings at a policy workshop and feasible recommendations were suggested to improve antibiotic use in Viet Nam.Here we report the results of our situation analysis focusing on: the healthcare system, drug regulation and supply; antibiotic resistance and infection control; and agricultural antibiotic use.
Market reforms have improved healthcare access in Viet Nam and contributed to better health outcomes. However, increased accessibility has been accompanied by injudicious antibiotic use in hospitals and the community, with predictable escalation in bacterial resistance. Prescribing practices are poor and self-medication is common - often being the most affordable way to access healthcare. Many policies exist to regulate antibiotic use but enforcement is insufficient or lacking.Pneumococcal penicillin-resistance rates are the highest in Asia and carbapenem-resistant bacteria (notably NDM-1) have recently emerged. Hospital acquired infections, predominantly with multi-drug resistant Gram-negative organisms, place additional strain on limited resources. Widespread agricultural antibiotic use further propagates antimicrobial resistance.
Future legislation regarding antibiotic access must alter incentives for purchasers and providers and ensure effective enforcement. The Ministry of Health recently initiated a national action plan and approved a multicenter health improvement project to strengthen national capacity for antimicrobial stewardship in Viet Nam. This analysis provided important input to these initiatives. Our methodologies and findings may be of use to others across the world tackling the growing threat of antibiotic resistance.
Full text
Available for:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
•PSO-MARS is proposed for flash-flood modeling and prediction.•PSO-MARS has high performance on the training and validation datasets.•PSO-MARS outperforms benchmark models i.e. BPANN, SVM, and ...decision tree.
The main objective of this research work was to propose and verify a new soft computing approach based on Multivariate Adaptive Regression Splines (MARS) and Particle Swarm Optimization (PSO) for spatial prediction of flash flood susceptible areas. A high frequency tropical typhoon area located on Northwest of Vietnam was selected as a case study. For this purpose, a GIS database for the study areas was prepared, including 654 flash-flood inundations and 12 influencing variables (elevation, slope, curvature, toposhade, aspect, topographic wetness index, stream power index, stream density, Normalized Difference Vegetation Index, soil type, lithology, and rainfall), which were compiled from various sources. The database was used to build and verify the prediction model. We assessed the model’s performance through various indices including Classification Accuracy Rate, Area under the Curve (AUC), Precision, and Recall. We also compared the model’s usability with five state-of-the-art machine learning techniques including the Backpropagation Neural Network, Support Vector Machine, and Classification Tree. The results revealed that the hybrid PSO-MARS model outperformed other benchmark models in all the employed statistical measures. We conclude that the proposed model can be particularly suited for flash flood forecasting problems at high frequency tropical typhoon area.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPUK, ZAGLJ, ZRSKP
PDF
