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Tungsten-based nanocatalysts with different structures for visible light responsive photocatalytic degradation of bisphenol A

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Journal of Environmental Sciences 2024年第5期139卷 569-588页

作者： Hai Bang Truong Thi Thu Loan Doan nguyen tien Hoang nguyen Van Tam Minh Kim nguyen Le Gia Trung Jin Seog Gwag nguyen tien Tran Optical Materials Research Group Science and Technology Advanced InstituteVan Lang UniversityHo Chi Minh CityViet Nam Faculty of Applied Technology School of TechnologyVan Lang UniversityHo Chi Minh CityViet Nam The University of Da Nang University of Science and Technology54 Nguyen Luong BangDa NangViet Nam The University of Da Nang University of Science and Education459 Ton Duc Thang St.Lien ChieuDa Nang 550000Viet Nam Institute of Veterinary Science and Technology 31ha zoneTrau QuyGia LamHa Noi 12400Viet Nam Department of Physics Yeungnam UniversityGyeongsanGyeongbuk 38541Republic of Korea Center for Advanced Chemistry Institute of Research and DevelopmentDuy Tan University03 Quang TrungDa Nang 550000Viet Nam Faculty of Natural Sciences Duy Tan University03 Quang TrungDa Nang 550000Viet Nam

Environmental pollution,such as water contamination,is a critical issue that must be absolutely ***,three different morphologies of tungsten-based photocatalysts(WO_(3)nanorods,WO_(3)/WS_(2)nanobricks,WO_(3)/WS_(2)nanorods)are made using a simple hydrothermal method by changing the solvents(H_(2)O,DMF,aqueous HCl solution).The as-prepared nanocatalysts have excellent thermal stability,large porosity,and high *** results show all materials have good photocatalytic activity in aqueous media,with WO_(3)/WS_(2)nanorods(NRs)having the best activity in the photodegradation of bisphenol A(BPA)under visible-light *** may originate from increased migration of charge carriers and effective prevention of electron–hole recombination in WO_(3)/WS_(2)NRs,whereby this photocatalyst is able to generate more reactive·OH and·O_(2)^(–)species,leading to greater photocatalytic *** 99.6% of BPA is photodegraded within 60 min when using 1.5 g/L WO_(3)/WS_(2)NRs and 5.0 mg/L BPA at pH ***,the optimal conditions(pH,catalyst dosage,initial BPA concentration)for WO_(3)/WS_(2)NRs are also elaborately *** rod-like heterostructures are expressed as potential catalysts with excellent photostability,efficient reusability,and highly active effectivity in different types of *** particular,the removal efficiency of BPA by WO_(3)/WS_(2)NRs reduces by only 1.5% after five recycling runs and even reaches 89.1%in contaminated lake *** study provides promising insights for the nearly complete removal of BPA from wastewater or different water resources,which is advantageous to various applications in environmental remediation.

关键词： Photodegradation Bisphenol A Tungsten-based nanocatalyst Structure Nanorod

An Enhanced Ensemble-Based Long Short-Term Memory Approach for Traffic Volume Prediction

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Computers, Materials & Continua 2024年第3期78卷 3585-3602页

作者： duy Quang Tran Huy Q.Tran Minh Van nguyen Faculty of Civil Engineering Nha Trang UniversityNha Trang650000Vietnam Robotics and Mechatronics Research Group Faculty of Engineering and TechnologyNguyen Tat Thanh UniversityHo Chi Minh City700000Vietnam Department of Civil Engineering Ho Chi Minh City University of Technology and EducationHo Chi Minh City700000Vietnam

With the advancement of artificial intelligence,traffic forecasting is gaining more and more interest in optimizing route planning and enhancing service *** volume is an influential parameter for planning and operating traffic *** study proposed an improved ensemble-based deep learning method to solve traffic volume prediction problems.A set of optimal hyperparameters is also applied for the suggested approach to improve the performance of the learning *** fusion of these methodologies aims to harness ensemble empirical mode decomposition’s capacity to discern complex traffic patterns and long short-term memory’s proficiency in learning temporal ***,a dataset for automatic vehicle identification is obtained and utilized in the preprocessing stage of the ensemble empirical mode decomposition *** second aspect involves predicting traffic volume using the long short-term memory ***,the study employs a trial-and-error approach to select a set of optimal hyperparameters,including the lookback window,the number of neurons in the hidden layers,and the gradient descent ***,the fusion of the obtained results leads to a final traffic volume *** experimental results show that the proposed method outperforms other benchmarks regarding various evaluation measures,including mean absolute error,root mean squared error,mean absolute percentage error,and *** achieved R-squared value reaches an impressive 98%,while the other evaluation indices surpass the *** findings highlight the accuracy of traffic pattern ***,this offers promising prospects for enhancing transportation management systems and urban infrastructure planning.

关键词： Ensemble empirical mode decomposition traffic volume prediction long short-term memory optimal hyperparameters deep learning

Push forward LC-MS-based therapeutic drug monitoring and pharmacometabolomics for anti-tuberculosis precision dosing and comprehensive clinical management

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Journal of Pharmaceutical Analysis 2024年第1期14卷 16-38页

作者： nguyen Quang Thu nguyen Tran Nam tien nguyen Thi Hai Yen Thuc-Huy Duong nguyen Phuoc Long Huy Truong nguyen Department of Pharmacology and PharmacoGenomics Research Center Inje University College of MedicineBusan47392Republic of Korea Department of Chemistry University of EducationHo Chi Minh City700000Viet Nam Faculty of Pharmacy Ton Duc Thang UniversityHo Chi Minh City700000Viet Nam

The spread of tuberculosis(TB),especially multidrug-resistant TB and extensively drug-resistant TB,has strongly motivated the research and development of new anti-TB *** strategies to facilitate drug combinations,including pharmacokinetics-guided dose optimization and toxicology studies of first-and second-line anti-TB drugs have also been introduced and *** chromatography-mass spectrometry(LC-MS)has arguably become the gold standard in the analysis of both endo-and exo-genous *** technique has been applied successfully not only for therapeutic drug monitoring(TDM)but also for pharmacometabolomics *** improves the effectiveness of treatment,reduces adverse drug reactions,and the likelihood of drug resistance development in TB patients by determining dosage regimens that produce concentrations within the therapeutic target *** on TDM,the dose would be optimized individually to achieve favorable *** is essential in generating and validating hypotheses regarding the metabolism of anti-TB drugs,aiding in the discovery of potential biomarkers for TB diagnostics,treatment monitoring,and outcome *** article highlighted the current progresses in TDM of anti-TB drugs based on LC-MS bioassay in the last two ***,we discussed the advantages and disadvantages of this technique in practical *** pressing need for non-invasive sampling approaches and stability studies of anti-TB drugs was ***,we provided perspectives on the prospects of combining LC-MS-based TDM and pharmacometabolomics with other advanced strategies(pharmacometrics,drug and vaccine developments,machine learning/artificial intelligence,among others)to encapsulate in an all-inclusive approach to improve treatment outcomes of TB patients.

关键词： Tuberculosis Therapeutic drug monitoring LC-MS MIPD Pharmacometabolomics Precision medicine

A deep neural network based surrogate model for damage identification in full-scale structures with incomplete noisy measurements

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Frontiers of Structural and Civil Engineering 2024年第3期18卷 393-410页

作者： Tram BUI-NGOC duy-Khuong LY Tam T TRUONG Chanachai THONGCHOM T.nguyen-THOI Laboratory for Computational Mechanics Institute for Computational Science and Artificial IntelligenceVan Lang UniversityHo Chi Minh City 70000Vietnam Faculty of Mechanical-Electrical and Computer Engineering School of TechnologyVan Lang UniversityHo Chi Minh City 70000Vietnam Faculty of Civil Engineering School of TechnologyVan Lang UniversityHo Chi Minh City 70000Vietnam Department of Computer Science Aarhus UniversityAarhus 8000Denmark Thammasat University research unit in structural and foundation engineering Department of Civil EngineeringThammasat UniversityPathumthani 12120Thailand Laboratory for Applied and Industrial Mathematics Institute for Computational Science and Artificial IntelligenceVan Lang UniversityHo Chi Minh City 70000Vietnam Thammasat School of Engineering Thammasat UniversityPathumthani 12120Thailand

The paper introduces a novel approach for detecting structural damage in full-scale structures using surrogate models generated from incomplete modal data and deep neural networks(DNNs).A significant challenge in this field is the limited availability of measurement data for full-scale structures,which is addressed in this paper by generating data sets using a reduced finite element(FE)model constructed by SAP2000 software and the MATLAB programming *** surrogate models are trained using response data obtained from the monitored structure through a limited number of measurement *** proposed approach involves training a single surrogate model that can quickly predict the location and severity of damage for all potential *** achieve the most generalized surrogate model,the study explores different types of layers and hyperparameters of the training algorithm and employs state-of-the-art techniques to avoid overfitting and to accelerate the training *** approach’s effectiveness,efficiency,and applicability are demonstrated by two numerical *** study also verifies the robustness of the proposed approach on data sets with sparse and noisy measured ***,the proposed approach is a promising alternative to traditional approaches that rely on FE model updating and optimization algorithms,which can be computationally *** approach also shows potential for broader applications in structural damage detection.

关键词： vibration-based damage detection deep neural network full-scale structures finite element model updating noisy incomplete modal data

Machine learning based models for predicting compressive strength of geopolymer concrete

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Frontiers of Structural and Civil Engineering 2024年第7期18卷 1028-1049页

作者： Quang-Huy LE duy-Hung nguyen Thanh SANG-TO Samir KHATIR Hoang LE-MINH Amir H.GANDOMI Thanh CUONG-LE Center for Engineering Application and Technology Solutions Ho Chi Minh City Open UniversityHo Chi Minh 700000Vietnam Institute of Civil Engineering Ho Chi Minh City University of TransportHo Chi Minh 700000Vietnam Faculty of Engineering&Information Technology University of Technology SydneyUltimoNSW 2007Australia

Recently,great attention has been paid to geopolymer concrete due to its advantageous mechanical and environmentally friendly *** effort has been made in experimental studies to advance the understanding of geopolymer concrete,in which compressive strength is one of the most important *** facilitate engineering work on the material,an efficient predicting model is *** this study,three machine learning(ML)-based models,namely deep neural network(DNN),K-nearest neighbors(KNN),and support vector machines(SVM),are developed for forecasting the compressive strength of the geopolymer concrete.A total of 375 experimental samples are collected from the literature to build a database for the development of the predicting models.A careful procedure for data preprocessing is implemented,by which outliers are examined and removed from the database and input variables are standardized before feeding to the fitting *** standard K-fold cross-validation approach is applied for evaluating the performance of the models so that overfitting status is well managed,thus the generalizability of the models is *** effectiveness of the models is assessed via statistical metrics including root mean squared error(RMSE),mean absolute error(MAE),correlation coefficient(R),and the recently proposed performance index(PI).The basic mean square error(MSE)is used as the loss function to be minimized during the model fitting *** three ML-based models are successfully developed for estimating the compressive strength,for which good correlations between the predicted and the true values are obtained for DNN,KNN,and *** numerical results suggest that the DNN model generally outperforms the other two models.

关键词： geopolymer concrete compressive strength prediction machine-learning based model deep neural network K-nearest neighbor support vector machines

Development of an experimental method for well-controlled blast induced traumatic limb fracture in rats

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Defence Technology（防务技术） 2024年第4期34卷 168-176页

作者： Luyang Xu Xiancheng Yu Clement DFavier Ibidumo Igah Thuy-tien nguyen Warren Macdonald Anthony MJ.Bull Centre for Blast Injury Studies Imperial College LondonLondonUnited Kingdom Department of Bioengineering Imperial College LondonLondonUnited Kingdom Dyson School of Design Engineering Imperial College LondonLondonUnited Kingdom Department of Trauma and Orthopaedics Royal Free London National Health Service Foundation TrustLondonUnited Kingdom

Heterotopic ossification(HO)is a consequence of traumatic bone and tissue damage,which occurs in 65%of military casualties with blast-associated ***,the mechanisms behind blast-induced HO remain *** models are used to study blast-induced HO,but developing such models is challenging,particularly in how to use a pure blast wave(primary blast)to induce limb fracture that then requires an *** studies,including our recent study,have developed platforms to induce limb fractures in rats with blast loading or a mixture of blast and impact ***,these models are limited by the survivability of the animal and repeatability of the *** this study,we developed an improved platform,aiming to improve the animal's survivability and injury repeatability as well as focusing on primary blast *** platform exposed only one limb of the rat to a blast wave while providing proper protection to the rest of the rat's *** obtained very consistent fracture outcome in the tibia(location and pattern)in cadaveric rats with a large range of size and ***,the rats did not obviously move during the test,where movement is a potential cause of uncontrolled *** further conducted parametric studies by varying the features of the design of the *** factors,such as how the limb is fixed and how the cavity through which the limb is placed is sealed,significantly affect the resulting *** platform and test setups enable well-controlled limb fracture induced directly by pure blast wave,which is the fundamental step towards a complete in vivo animal model for blast-induced HO induced by primary blast alone,excluding secondary and tertiary blast *** addition,the platform design and the findings presented here,particularly regarding the proper protection of the animal,have implications for future studies investigating localized blast injuries,such as blast induced brain and lung injuries.

关键词： Blast injury Biomechanics Heterotopic ossification Limb fracture Blast wave Animal model

Generating highly active oxide-phosphide heterostructure through interfacial engineering to break the energy scaling relation toward urea-assisted natural seawater electrolysis

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Journal of Energy Chemistry 2024年第10期97卷 687-699页

作者： Ngoc Quang Tran Nam Hoang Vu Jianmin Yu Khanh Vy Pham nguyen Thuy tien nguyen Tran Thuy-Kieu Truong Lishan Peng Thi Anh Le Yoshiyuki Kawazoe Center for Innovative Materials and Architectures Vietnam National University Faculty of Materials Science and Technology University of Science Key Laboratory of Rare Earths Ganjiang Innovation Academy Chinese Academy of Sciences Department of Mechanical Engineering Hanbat National University (HBNU) School of Chemical Engineering-Hanoi University of Science and Technology New Industry Creation Hatchery Center Tohoku University

Urea-assisted natural seawater electrolysis is an emerging technology that is effective for grid-scale carbon-neutral hydrogen mass production yet *** scaling relations is an effective strategy to break through the bottleneck of natural seawater ***,by DFT calculation,we demonstrated that the interface boundaries between Ni2P and MoO2play an essential role in the selfrelaxation of the Ni-O interfacial bond,effectively modulating a coordination number of intermediates to control independently their adsorption-free energy,thus circumventing the adsorption-energy scaling *** this conceptual model,a well-defined 3D F-doped Ni2P-MoO2heterostructure microrod array was rationally designed via an interfacial engineering strategy toward urea-assisted natural seawater *** a result,the F-Ni2P-MoO2exhibits eminently active and durable bifunctional catalysts for both HER and OER in acid,alkaline,and alkaline sea water-based *** in-situ analysis,we found that a thin amorphous layer of NiOOH,which is evolved from the Ni2P during anodic reaction,is real catalytic active sites for the OER and UOR ***,such electrode-assembled urea-assisted natural seawater electrolyzer requires low voltages of 1.29 and 1.75 V to drive 10 and600 mA cm-2and demonstrates superior durability by operating continuously for 100 h at 100 mA cm-2,beyond commercial Pt/C || RuO2and most previous reports.

关键词： Break scaling relationships Doping Interfacial engineering Natural seawater splitting Urea electrolysis

Optimal Scheduling of Multiple Rail Cranes in Rail Stations with Interference Crane Areas

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Intelligent Automation & Soft Computing 2024年第1期39卷 15-31页

作者： nguyen Vu Anh duy nguyen Le Thai nguyen Huu Tho Faculty of Engineering and Technology Nguyen Tat Thanh UniversityHo Chi Minh City700000Vietnam

In this paper,we consider a multi-crane scheduling problem in rail stations because their operations directly influence the throughput of the rail *** particular,the job is not only assigned to cranes but also the job sequencing is implemented for each crane to minimize the makespan of cranes.A dual cycle of cranes is used to minimize the number of working cycles of *** rail crane scheduling problems in this study are based on the movement of *** consider not only the gantry moves,but also the trolley moves as well as the rehandle cases are also included.A mathematical model of multi-crane scheduling is *** traditional and parallel simulated annealing(SA)are adapted to determine the optimal scheduling *** examples are conducted to evaluate the applicability of the proposed *** of the proposed parallel SA is done by comparing it to existing previous *** of numerical computation highlighted that the parallel SA algorithm outperformed the SA and gave better solutions than other considered algorithms.

关键词： Multi-crane scheduling logistics containers makespan rail stations

Building the ARIMA Model for Forecasting the Production of Vietnam’s Coffee Export

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Journal of Applied Mathematics and Physics 2024年第4期12卷 1237-1246页

作者： duy Quang Phung Quoc Thang Trinh Quang Truong Do Ngan Giang nguyen Van Ha nguyen Gia Khiem Ngo Thi Minh Ngoc Tran Faculty of Technology and Data Science Foreign Trade University Hanoi Vietnam School of Economics and International Business Foreign Trade University Hanoi Vietnam

Coffee is a significant industry, accounting for 3% of Vietnam’s GDP, with annual export turnover consistently exceeding USD 3 billion. Despite global economic challenges affecting purchasing power at various times, Vietnam’s coffee exports in December 2023 continued to surge, reaching the highest level in the past 9 months at 190,000 tons, a 59.3% increase compared to November 2023, but still a slight 3.5% decrease from the same period last year. The export turnover reached USD 538 million, a 51% increase from November 2023 and a 26.4% increase from the same period last year. Therefore, forecasting the coffee export volume holds significant importance for coffee producers nationwide. This research employs the Box-Jenkins method to construct an ARIMA model for forecasting Vietnam’s coffee export volume based on annual data published by the General Statistics Office. Results indicate that among the models considered, the ARIMA(1, 1, 2) model is the most suitable. The study also provides short-term forecasts for Vietnam’s coffee export volume. However, the current model is limited to forecasting and is not yet optimized, as the assumed linearity in the model is a simplification.

关键词： ARIMA Forecasting Coffee Export Volume Data Science