Antibacterial, plasmonic, and toxic properties of engineered nanoparticles

Thesis supervisors: Drs. Mengshi Lin, Azlin Mustapha.Includes vita.There has been increasing application of novel nanomaterials in recent years in the area of agriculture and food science. This dissertation aims to study novel nanomaterials and investigate their applications in food safety, and to develop and use surface-enhanced Raman spectroscopy (SERS) as a rapid, simple, and sensitive analytical method to improve food safety. There have been increasing applications of nanomaterials in various areas, which may cause human exposure and environmental pollution. Therefore, it is important to study the toxicity of different nanomaterials against bacteria and human cells. The objectives of this study were to: (1) develop new types of substrate consisting of monolayer graphene, gold film, and/or gold nanorod structures; (2) detect and measure silver nanoparticles (Ag NPs) in consumer products using SERS and aminothiophenol as an indicator molecule; (3) investigate the effect of graphene oxide (GO) on human intestinal bacteria and human intestinal cells; (4) study the antimicrobial activity of selenium nanoparticles (Se NPs) against foodborne pathogens and the toxicity of Se NPs against Caco-2 cells. A simple, fast, and efficient method was developed to fabricate new SERS substrates by coating a gold nanorod-decorated graphene sheet on silicone substrate. The results demonstrate that GO is biocompatible and has a potential to be used in agriculture and food science, indicating that more studies are needed to exploit its potential applications. The data show that Se NPs can be used as an antimicrobial agent to inhibit the growth of Staphylococcus aureus in foods and can potentially be used as a chemopreventative and chemotherapeutic agent. More studies are needed to elucidate the mechanisms of Se NPs and GO's cytotoxicity and their antibacterial properties. More research is also needed to improve the performance of SERS substrates using different materials and use them in improving food safety.Includes bibliographical references (pages 132-168)

A Study on Environmental Costs in Coal Mining Production in Vietnam

This study is conducted to identify and present environmental costs of mining production in the context of sustainable development (SD) and a lifecycle of coal mining firms. This aim is accompanied by two research questions as (i) What are the environmental costs of mining production in theory and practice in a lifecycle of coal mining firms?; and (ii) What are the key determinants of environmental costs of coal mining production in a lifecycle of coal mining firms? In order to achieve this aim, the process of coal mining production is described from the long-term perspective including projecting, building, operating and liquidation stage of a coal mine. On the basis of process analysis the identification of environmental costs is conducted in a model approach. Environmental costs of mining production are analyzed using international case studies and theoretical and practical assumptions regarding environmental costs management in mining production are formulated. The results show that the environmental costs of mining production are varied in the lifecycle of a coal mine and that they also depend on the geographical location of mining firms. Environmental costs of mining production have to be predicted in a long-term perspective including also post-liquidation costs together with taking into account the sources of their covering and models of financing. Keywords: Coal mining production, mine, lifecycle of a mine, environmental costs. DOI: 10.7176/RJFA/11-18-09 Publication date:September 30th 202

GIS-based modeling of rainfall-induced landslides using data mining-based functional trees classifier with AdaBoost, Bagging, and MultiBoost ensemble frameworks

The main objective of this study is to propose and verify a novel ensemble methodology that could improve prediction performances of landslide susceptibility models. The proposed methodology is based on the functional tree classifier and three current state-of-the art machine learning ensemble frameworks, Bagging, AdaBoost, and MultiBoost. According to current literature, these methods have been rarely used for the modeling of rainfall-induced landslides. The corridor of the National Road 32 (Vietnam) was selected as a case study. In the first stage, the landslide inventory map with 262 landslide polygons that occurred during the last 20 years was constructed and then was randomly partitioned into a ratio of 70/30 for training and validating the models. Second, ten landslide conditioning factors were prepared such as slope, aspect, relief amplitude, topographic wetness index, topographic shape, distance to roads, distance to rivers, distance to faults, lithology, and rainfall. The model performance was assessed and compared using the receiver operating characteristic and statistical evaluation measures. Overall, the FT with Bagging model has the highest prediction capability (AUC = 0.917), followed by the FT with MultiBoost model (AUC = 0.910), the FT model (AUC = 0.898), and the FT with AdaBoost model (AUC = 0.882). Compared with those derived from popular methods such as J48 decision trees and artificial neural networks, the performance of the FT with Bagging model is better. Therefore, it can be concluded that the FT with Bagging is promising and could be used as an alternative in landslide susceptibility assessment. The result in this study is useful for land use planning and decision making in landslide prone areas

Deformation forecasting of a hydropower dam by hybridizing a long short-term memory deep learning network with the coronavirus optimization algorithm

The safety operation and management of hydropower dam play a critical role in social-economic development and ensure people’s safety in many countries; therefore, modeling and forecasting the hydropower dam’s deformations with high accuracy is crucial. This research aims to propose and validate a new model based on deep learning long short-term memory (LSTM) and the coronavirus optimization algorithm (CVOA), named CVOA-LSTM, for forecasting the defor mations of the hydropower dam. The second-largest hydropower dam of Viet nam, located in the Hoa Binh province, is focused. Herein, we used the LSTM to establish the deformation model, whereas the CVOA was utilized to opti mize the three parameters of the LSTM, the number of hidden layers, the learn ing rate, and the dropout. The efficacy of the proposed CVOA-LSTM model is assessed by comparing its forecasting performance with state-of-the-art bench marks, sequential minimal optimization for support vector regression, Gaussian process, M5’ model tree, multilayer perceptron neural network, reduced error pruning tree, random tree, random forest, and radial basis function neural net work. The result shows that the proposed CVOA-LSTM model has high fore casting capability (R2 = 0.874, root mean square error = 0.34, mean absolute error = 0.23) and outperforms the benchmarks. We conclude that CVOA-LSTM is a new tool that can be considered to forecast the hydropower dam’s deforma tions.Ministerio de Ciencia, Innovación y Universidades PID2020-117954RB-C2

Indoor PM₀.₁ and PM₂.₅ in Hanoi: Chemical characterization, source identification, and health risk assessment

This study attempted to provide comprehensive insights into the chemical composition, source identification, and health risk assessment of indoor particulate matter (PM) in urban areas of Vietnam. Three hundred and twenty daily samples of PM₀.₁ and PM₂.₅ were collected at three different types of dwellings in Hanoi in two seasons, namely summer and winter. The samples were analyzed for 10 trace elements (TEs), namely Cr, Mn, Co, Cu, Ni, Zn, As, Cd, Sn, and Pb. The daily average concentrations of indoor PM₀.₁ and PM₂.₅ in the city were in the ranges of 7.0–8.9 μg/m³ and 43.3–106 μg/m³, respectively. The average concentrations of TEs bound to indoor PM ranged from 66.2 ng/m³ to 216 ng/m³ for PM₀.₁ and 391 ng/m³ to 2360 ng/m³ for PM₂.₅. Principle component analysis and enrichment factor were applied to identify the possible sources of indoor PM. Results showed that indoor PM₂.₅ was mainly derived from outdoor sources, whereas indoor PM₀.₁ was derived from indoor and outdoor sources. Domestic coal burning, industrial and traffic emissions were observed as outdoor sources, whereas household dust and indoor combustion were found as indoor sources. 80% of PM₂.₅ was deposited in the head airways, whereas 75% of PM₀.₁ was deposited in alveolar region. Monte Carlo simulation indicated that the intake of TEs in PM₂.₅ can lead to high carcinogenic risk for people over 60 years old and unacceptable non-carcinogenic risks for all ages at the roadside house in winter

NeCo@ALQAC 2023: Legal Domain Knowledge Acquisition for Low-Resource Languages through Data Enrichment

In recent years, natural language processing has gained significant popularity in various sectors, including the legal domain. This paper presents NeCo Team's solutions to the Vietnamese text processing tasks provided in the Automated Legal Question Answering Competition 2023 (ALQAC 2023), focusing on legal domain knowledge acquisition for low-resource languages through data enrichment. Our methods for the legal document retrieval task employ a combination of similarity ranking and deep learning models, while for the second task, which requires extracting an answer from a relevant legal article in response to a question, we propose a range of adaptive techniques to handle different question types. Our approaches achieve outstanding results on both tasks of the competition, demonstrating the potential benefits and effectiveness of question answering systems in the legal field, particularly for low-resource languages.Comment: ISAILD@KSE 202

A novel hybrid evidential belief function-based fuzzy logic model in spatial prediction of rainfall-induced shallow landslides in the Lang Son city area (Vietnam)

The main objective of this study is to investigate potential application of an integrated evidential belief function (EBF)-based fuzzy logic model for spatial prediction of rainfall-induced shallow landslides in the Lang Son city area (Vietnam). First, a landslide inventory map was constructed from various sources. Then the landslide inventory map was randomly partitioned as a ratio of 70/30 for training and validation of the models, respectively. Second, six landslide conditioning factors (slope angle, slope aspect, lithology, distance to faults, soil type, land use) were prepared and fuzzy membership values for these factors classes were estimated using the EBF. Subsequently, fuzzy operators were used to generate landslide susceptibility maps. Finally, the susceptibility maps were validated and compared using the validation dataset. The results show that the lowest prediction capability is the fuzzy SUM (76.6%). The prediction capability is almost the same for the fuzzy PRODUCT and fuzzy GAMMA models (79.6%). Compared to the frequency-ratio based fuzzy logic models, the EBF-based fuzzy logic models showed better result in both the success rate and prediction rate. The results from this study may be useful for local planner in areas prone to landslides. The modelling approach can be applied for other areas

Antibiotic Resistance Profile and Methicillin-Resistant Encoding Genes of Staphylococcus aureus Strains Isolated from Bloodstream Infection Patients in Northern Vietnam

Background:  Evaluating the antibiotic susceptibility and resistance genes is essential in the clinical management of bloodstream infections (BSIs). Nevertheless, there are still limited studies in Northern Vietnam. AIM: This study aimed to determine the antibiotic resistance profile and methicillin-resistant encoding genes of Staphylococcus aureus (S. aureus) causing BSIs in Northern Vietnam. METHODS: The cross-sectional study was done from December 2012 to June 2014 in two tertiary hospitals in Northern Vietnam. Tests performed at the lab of the hospital. RESULTS:  In 43 S. aureus strains isolating, 53.5 % were MRSA. Distribution of gene for overall, MRSA, and MSSA strains were following: mecA gene (58.1 %; 95.7%, and 15%), femA gene (48.8%, 47.8%, and 50%), femB gene (88.4%, 82.6%, and 95%). Antibiotic resistance was highest in penicillin (100%), followed by erythromycin (65.1%) and clindamycin (60.5%). Several antibiotics were susceptible (100%), including vancomycin, tigecycline, linezolid, quinupristin/dalfopristin. Quinolone group was highly sensitive, include ciprofloxacin (83.7%), levofloxacin (86%) and moxifloxacin (86%). CONCLUSION:  In S. aureus causing BSIs, antibiotic resistance was higher in penicillin, erythromycin, and clindamycin. All strains were utterly susceptible to vancomycin, tigecycline, linezolid, quinupristin/dalfopristin

Antibiotic Resistance Profile and Diversity of Subtypes Genes in Escherichia coli Causing Bloodstream Infection in Northern Vietnam

BACKGROUND: Evaluating the antibiotic susceptibility and resistance genes is essential in the clinical management of bloodstream infections (BSIs). But there are still limited studies in Northern Vietnam. AIM: The aim of the study was to determine the antibiotic resistance profile and characteristics of subtypes genes in Escherichia coli causing BSIs in Northern Vietnam. METHODS: The cross-sectional study was done in the period from December 2012 to June 2014 in two tertiary hospitals in Northern Vietnam. Tests were performed at the lab of the hospital. RESULTS: In 56 E. coli strains isolating 39.29 % produced ESBL. 100% of the isolates harbored blaTEM gene, but none of them had the blaPER gene. The prevalence of ESBL producers and ESBL non-producers in blaCTX-M gene was 81.82%, and 73.53%, in blaSHV gene was 18.18% and 35.29%. Sequencing results showed three blaTEM subtypes (blaTEM 1, 79, 82), four blaCTX-M subtypes (blaCTX-M-15, 73, 98, 161), and eight blaSHV subtypes (blaSHV 5, 7, 12, 15, 24, 33, 57, 77). Antibiotic resistance was higher in ampicillin (85.71%), trimethoprim/sulfamethoxazole (64.29%) and cephazolin (50%). Antibiotics were still highly susceptible including doripenem (96.43%), ertapenem (94.64%), amikacin (96.43%), and cefepime (89.29%). CONCLUSION: In Escherichia coli causing BSIs, antibiotic resistance was higher in ampicillin, trimethoprim/sulfamethoxazole and cephazolin. Antibiotics was highly susceptible including doripenem, ertapenem, amikacin, and cefepime