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Examination of n-T_(9) conditions required by N=50,82,126 waiting points in r-process

Communications in Theoretical Physics 2021年第10期73卷 147-157页

作者： nguyen duy ly nguyen Ngoc duy nguyen Kim Uyen Vinh N T Pham Faculty of Fundamental Sciences Vanlang UniversityHo Chi Minh City 700000Vietnam Department of Physics Sungkyunkwan UniversitySuwon 16419Republic of Korea Department of Physics Ho Chi Minh City University of EducationHo Chi Minh City 700000Vietnam

We examined the conditions of neutron density(n) and temperature(T9) required for the N = 50, 82,and 126 isotopes to be waiting points(WP) in the r-process. The nuclear mass based on experimental data presented in the AME2020 database(AME and AME ± Δ) and that predicted using FRDM,WS4, DZ10, and KTUY models were employed in our estimations. We found that the conditions required by the N = 50 WP significantly overlap with those required by the N = 82 ones, except for the WS4 model. In addition, the upper(or lower) bounds of the n-T9 conditions based on the models are different from each other due to the deviations in the two-neutron separation *** standard deviations in the nuclear mass of 108 isotopes in the three N = 50, 82, and 126 groups are about rms = 0.192 and 0.434 Me V for the pairs of KTUY-AME and WS4-KTUY models,respectively. We found that these mass uncertainties result in a large discrepancy in the nn-T_(9) conditions, leading to significant differences in the conditions for simultaneously appearing all the three peaks in the r-process abundance. The newly updated FRDM and WS4 calculations can give the overall conditions for the appearance of all the peaks but vice versa for their old versions in a previous study. The change in the final r-process isotopic abundance due to the mass uncertainty is from a few factors to three orders of magnitude. Therefore, accurate nuclear masses of the r-process key nuclei, especially for 76 Fe,81Cu,127Rh,132Cd,192Dy, and 197Tm, are highly recommended to be measured in radioactive-ion beam facilities for a better understanding of the r-process evolution.

关键词： neutron capture r-process nuclear mass isotopic abundance precise mass measurement

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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

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Discovering peptides and computational investigations of a multiepitope vaccine target Mycobacterium tuberculosis

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Synthetic and Systems Biotechnology 2024年第3期9卷 391-405页

作者： Truc ly nguyen Heebal Kim Department of Agricultural Biotechnology and Research Institute of Agriculture and Life Sciences Seoul National UniversitySeoul08826Republic of Korea Interdisciplinary Program in Bioinformatics Seoul National UniversitySeoul08826Republic of Korea eGnome Inc.Seoul05836Republic of Korea

Mycobacterium tuberculosis(MTB)is the causative agent of tuberculosis(TB),a prevalent airborne infectious *** the availability of the Bacille Calmette-Guerin vaccine,its global efficacy remains modest,and tuberculosis persists as a significant global public health *** this challenge and advancing towards the End MTB Strategy,we developed a multiepitope vaccine(MEV)based on immunoinformatics and compu-tational *** screening of MBT protein identified immune-dominant epitopes based on Major Histocompatibility Complex(MHC)allele binding,immunogenicity,antigenicity,allergenicity,toxicity,and cytokine *** epitopes were integrated into an MEV construct with adjuvant and linkers,forming a fully immunogenic vaccine *** analyses encompassed the evaluation of immunological and physicochemical properties,determination of tertiary structure,molecular docking with Toll-Like Receptors(TLR),molecular dynamics(MD)simulations for all atoms,and immune *** MEV comprises 534 amino acids,featuring 6 cytotoxic T lymphocyte,8 helper T lymphocyte,and 7 linear B lymphocyte epitopes,demonstrating high antigenicity and ***,molecular docking studies and triplicate MD simulations revealed enhanced interactions and stability of MEV with the TLR4 complex compared to *** addition,the immune simulation indicated the capacity to effectively induce elevated levels of an-tibodies and cytokines,emphasizing the vaccine’s robust immunogenic *** study presents a promising MEV against TB,exhibiting favorable immunological and physicochemical *** findings provide theoretical support for TB vaccine *** study aligns with the global initiative of the End MTB Strategy,emphasizing its potential impact on addressing persistent challenges in TB control.

关键词： Mycobacterium tuberculosis Tuberculosis Multiepitope vaccine Docking molecular Molecular dynamics simulation Immune simulation

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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

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Integrating immunoinformatics and computational epitope prediction for a vaccine candidate against respiratory syncytial virus

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Infectious Disease Modelling 2024年第3期9卷 763-774页

Respiratory syncytial virus(RSV)poses a significant global health threat,especially affecting infants and the *** this,the present study proposes an innovative approach to vaccine design,utilizing immunoinformatics and computational *** analyzed RSV's structural proteins across both subtypes A and B,identifying potential helper T lymphocyte,cytotoxic T lymphocyte,and linear B lymphocyte *** such as antigenicity,allergenicity,toxicity,and cytokine-inducing potential were rigorously ***,we evaluated the conservancy of these epitopes and their population coverage across various RSV *** comprehensive analysis identified six major histocompatibility complex class I(MHC-I)binding,five MHC-II binding,and three B-cell *** were integrated with suitable linkers and adjuvants to form the ***,molecular docking and molecular dynamics simulations demonstrated stable interactions between the vaccine candidate and human Toll-like receptors(TLR4 and TLR5),with a notable preference for *** simulation analysis underscored the vaccine's potential to elicit a strong immune *** study presents a promising RSV vaccine candidate and offers theoretical support,marking a significant advancement in vaccine development ***,the promising in silico findings need to be further validated through additional in vivo studies.

关键词： Respiratory syncytial virus Multiepitope vaccine Immunoinformatics Docking molecular Molecular dynamics simulation Immune simulation

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Application of extreme gradient boosting in predicting the viscoelastic characteristics of graphene oxide modified asphalt at medium and high temperatures

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Frontiers of Structural and Civil Engineering 2024年第6期18卷 899-917页

作者： Huong-Giang Thi HOANG Hai-Van Thi MAI Hoang Long nguyen Hai-Bang ly University of Transport Technology Hanoi 100000Vietnam

Complex modulus(G^(*))is one of the important criteria for asphalt classification according to AASHTO M320-10,and is often used to predict the linear viscoelastic behavior of asphalt *** addition,phase angle(φ)characterizes the deformation resilience of asphalt and is used to assess the ratio between the viscous and elastic *** is thus important to quickly and accurately estimate these two *** purpose of this investigation is to construct an extreme gradient boosting(XGB)model to predict G^(*)andφof graphene oxide(GO)modified asphaltat medium and high *** data sets are gathered from previously published experiments,consisting of 357 samples for G^(*)and 339 samples forφ,and the se are used to develop the XGB model using nine inputs representing theasphalt binder *** findings show that XGB is an excellent predictor of G^(*)andφof GO-modified asphalt,evaluated by the coefficient of determination R^(2)(R^(2)=0.990 and 0.9903 for G^(*)andφ,respectively)and root mean square error(RMSE=31.499 and 1.08 for G^(*)andφ,respectively).In addition,the model’s performance is compared with experimental results and five other machine learning(ML)models to highlight its *** the final step,the Shapley additive explanations(SHAP)value analysis is conducted to assess the impact of each input and the correlation between pairs of important features on asphalt’s two physical properties.

关键词： complex modulus phase angle graphene oxide asphalt extreme gradient boosting machine learning

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Prevalence and outcomes of neonates with severe COVID-19:An observational study at Children’s Hospital 1 in Ho Chi Minh City,Vietnam

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Asian Pacific Journal of Tropical Medicine 2024年第9期17卷 392-399,I0001页

作者： Phung nguyen The nguyen Tran Thanh Thuc nguyen Thanh Hung Ngo Ngoc Quang Minh Dang Quoc duy Tran Minh Nhut nguyen Hoang Dung Tran nguyen Ai Nuong Dinh nguyen Cam Tu Department of Pediatrics University of Medicine and Pharmacy at Ho Chi Minh cityHo Chi Minh CityVietnam Children’s Hospital 1 Ho Chi Minh CityVietnam Pediatric Department School of MedicineVietnam National University Ho Chi Minh CityVietnam

Objective:To evaluate the impact of the severe COVID-19 pandemic on neonates and develop strategies to improve their ***:We conducted an observational cross-sectional study at Children's Hospital 1(CH1)from July 25,2021,to May 31,*** neonates who had fever or respiratory symptoms or were born from mothers with COVID-19 and had a positive RT-PCR SARS-CoV-2 result would be *** classified neonates with COVID-19 into 2 groups:mild/moderate and severe for *** between groups were analyzed using Fisher's exact test/Chi-square test for categorical variables and Student's t-test/Wilcoxon Rank Sum test for continuous ***:This study included 88 newborns who had positive RT-PCR SARS-CoV-2 *** severity COVID-19 rate among neonatal cases was found to be 13.6%(12/88),with a corresponding mortality rate of 1.1%(1/88).All severe cases showed lung abnormalities as evident on chest X-ray *** addition to respiratory symptoms,a higher incidence of gastrointestinal manifestations,such as vomiting and diarrhea,was observed in the severe group,indicating a compelling *** administration of anticoagulant and anti-inflammatory drugs in the study group resulted in a satisfactory outcome with no significant ***:The COVID-19 pandemic has had a substantial impact on the well-being of *** management of COVID-19 in this population presents significant challenges.

关键词： COVID-19 Neonate Anti-inflammatory Anticoagulant

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Photocatalytic degradation of antibiotic sulfamethizole by visible light activated perovskite LaZnO_(3)

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Journal of Environmental Sciences 2024年第10期144卷 212-224页

作者： Bui The Huy XCuong nguyen Vu Khac Hoang Bui nguyen Ngoc Tri Iqra Rabani Nhu Hoa Thi Tran Quang Viet ly Hai Bang Truong Major of Biomedical Engineering Division of Smart HealthcareCollege of Information Technology and ConvergencePukyong National UniversityBusan 48513Korea Center for Advanced Chemistry Institute of Research and DevelopmentDuy Tan UniversityDa Nang 550000Viet Nam Faculty of Environmental and Chemical Engineering Duy Tan UniversityDa Nang 550000Viet Nam Department of Environment and Energy Sejong UniversitySeoul 05006Korea Lab of Computational Chemistry and Modelling Department of ChemistryFaculty of Natural SciencesQuy Nhon UniversityQuy NhonViet Nam Department of Nanotechnology and Advanced Materials Engineering Sejong UniversitySeoul 05006Korea Faculty of Materials Science and Technology University of ScienceHo Chi Minh City 700000Viet Nam Vietnam National University Ho Chi Minh City 700000Viet Nam Department of Environmental Engineering Seoul National University of Science and TechnologySeoul 01811Korea 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

In thiswork,the perovskite LaZnO_(3) was synthesized via sol-gel method and applied for photocatalytic treatment of sulfamethizole(SMZ)antibiotics under visible light *** was almost completely degraded(99.2%±0.3%)within 4 hr by photocatalyst LaZnO3 at the optimal dosage of 1.1 g/L,with amineralization proportion of 58.7%±0.4%.The efficient performance of LaZnO_(3) can be attributed to itswide-range light absorption and the appropriate energy band edge levels,which facilitate the formation of active agents such as·O_(2)^(−),h^(+),and·*** integration of RP-HPLC/Q-TOF-MS and DFT-based computational techniques revealed three degradation pathways of SMZ,which were initiated by the deamination reaction at the aniline ring,the breakdown of the sulfonamidemoieties,and a process known as Smile-type rearrangement and SO2 *** toxicity of SMZ and the intermediateswere analyzed by quantitative structure activity relationship(QSAR),indicating the effectiveness of LaZnO_(3)-based photocatalysis in preventing secondary pollution of the intermediates to the ecosystem during the degradation *** visible-light-activated photocatalyst LaZnO_(3) exhibited efficient performance in the occurrence of inorganic anions and maintained high durability across multiple recycling tests,making it a promising candidate for practical antibiotic treatment.

关键词： Perovskite Antibiotics Photocatalysis Density functional theory Toxicity

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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

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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

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