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Prevalence and risk factors associated with long COVID symptoms in children and adolescents in a southern province of Vietnam

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Asian Pacific Journal of Tropical Medicine 2024年第3期17卷 119-128,I0001,I0002页

作者： Huynh Ngoc Linh nguyen The Tan Le thi Minh Thu nguyen Tu Loan nguyen thi To Uyen Le Thanh Thao trang Truong Thanh Nam Doan Hoang Phu Faculty of Medicine Ca Mau Medical CollegeCa Mau 98000Vietnam Ca Mau Obstetrics and Pediatrics Hospital Ca Mau 98000Vietnam Faculty of Basic Science and Public Health Dong Thap Medical CollegeDong Thap 81000Vietnam Graduate University of Sciences and Technology Vietnam Academy of Science and TechnologyHa Noi 10000Vietnam Community Public Health Research Program School of Public HealthWalailak UniversityNakhon Si Thammarat 80160Thailand Faculty Public Health Can Tho University of Medicine and PharmacyCan Tho City 94000Vietnam Doctoral Program in Health Sciences College of Graduate StudiesWalailak UniversityNakhon Si Thammarat 80160Thailand

Objective:To investigate the prevalence and risk factors associated with long COVID symptoms among children and adolescents who have recovered from ***:this study applied a cross-sectional approach within community settings in a southern province of Vietnam.A structured questionnaire featuring socio-demographic information and common long COVID symptoms was *** correlation coefficients assessed associations among pairs of long COVID ***,multivariable logistic regression models were performed to investigate the risk factors of long COVID in recovered COVID-19 children and ***:Among 422 participants,39.3%reported long COVID symptoms,with a prevalence of 45.2%(SD=0.5)in children and 22.2%(SD=0.4)in *** symptoms reported were cough 34.6%(SD=0.5),fatigue 20.6%(SD=0.4),shortness of breath 10.9%(SD=0.3),and lack of appetite 6.6%(SD=0.3).Concerning risk factors of long COVID,a higher risk was observed among demographic groups,including girls(OR 1.25,95%CI 1.15-1.37;Pthis study provide crucial guidance for predicting the factors associated with the occurrence of long COVID in pediatric patients,contributing to strategic interventions aimed at mitigating the long COVID risks among children and adolescents in Vietnam.

关键词： Long COVID Prevalence Risk factors Children Adolescent Vietnam

Unknown DDoS Attack Detection with Fuzzy C-Means Clustering and Spatial Location Constraint Prototype Loss

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Computers, Materials & Continua 2024年第2期78卷 2181-2205页

作者： Thanh-Lam nguyen HaoKao Thanh-Tuan nguyen Mong-Fong Horng Chin-Shiuh Shieh Department of Electronic Engineering National Kaohsiung University of Science and TechnologyKaohsiung807618Taiwan Department of Electronic and Automation Engineering Nha Trang UniversityNha Trang650000Vietnam

Since its inception,the Internet has been rapidly *** the advancement of science and technology and the explosive growth of the population,the demand for the Internet has been on the *** applications in education,healthcare,entertainment,science,and more are being increasingly deployed based on the ***,malicious threats on the internet are on the rise as *** Denial of Service(DDoS)attacks are among the most common and dangerous threats on the internet *** scale and complexity of DDoS attacks are constantly *** Detection Systems(IDS)have been deployed and have demonstrated their effectiveness in defense against those *** addition,the research of Machine Learning(ML)and Deep Learning(DL)in IDS has gained effective results and significant ***,one of the challenges when applying ML and DL techniques in intrusion detection is the identification of unknown *** attacks,which are not encountered during the system’s training,can lead to misclassification with significant *** this research,we focused on addressing the issue of Unknown Attack Detection,combining two methods:Spatial Location Constraint Prototype Loss(SLCPL)and Fuzzy C-Means(FCM).With the proposed method,we achieved promising results compared to traditional *** proposed method demonstrates a very high accuracy of up to 99.8%with a low false positive rate for known attacks on the Intrusion Detection Evaluation Dataset(CICIDS2017)***,the accuracy is also very high,reaching 99.7%,and the precision goes up to 99.9%for unknown DDoS attacks on the DDoS Evaluation Dataset(CICDDoS2019)*** success of the proposed method is due to the combination of SLCPL,an advanced Open-Set Recognition(OSR)technique,and FCM,a traditional yet highly applicable clustering *** has yielded a novel method in the field of unknown attack *** further expands the trend of applying DL and M

关键词： Cybersecurity DDoS unknown attack detection machine learning deep learning incremental learning convolutional neural networks(CNN) open-set recognition(OSR) spatial location constraint prototype loss fuzzy c-means CICIDS2017 CICDDoS2019

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

A comparative analysis of weight-based machine learning methods for landslide susceptibility mapping in Ha Giang area

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Big Earth Data 2023年第4期7卷 1005-1034页

作者： Thanh Trinh Binh Thanh Luu trang Ha thi Le Duong Huy nguyen Trong Van Tran thi Hai Van nguyen Khanh Quoc nguyen Lien thi nguyen Faculty of Computer Science Phenikaa UniversityYen NghiaHa DongHanoi 12116Vietnam Phenikaa Research and Technology Institute(PRATI) A&A Green Phoenix Group JSCNo.167 Hoang NganTrung HoaCau GiayHanoi 11313Vietnam Northwestern Geological Division General Department of Geology and Minerals of VietnamHanoiVietnam Youth union office Thuyloi University175 Tay SonDong DaHanoiVietnam Center of Remote Sensing and Geohazards Vietnam Institute of Geosciences and Mineral ResourcesHanoiVietnam

Landslide susceptibility maps (LSMs) are very crucial for planningpolicies in hazardous areas. However, the accuracy and reliability ofLSMs depend on available data and the selection of suitable *** study is conducted to produce LSMs by combinations of machinelearning methods and weighting techniques for Ha Giang province,Vietnam, where has limited data. In study area, we gather 11 landslideconditioning factors and establish a landslide inventory *** the weights of classes (or factors) is very important toprepare data for machine learning methods to generate LSMs. Wefrst use frequency ratio (FR) and analytic hierarchy process (AHP)techniques to generate the weights. Then, random forest (RF), supportvector machine (SVM), logistic regression (LR), and AHP methods arecombined with FR and AHP weights to yield accurate and reliableLSMs. Finally, the performance of these methods is evaluated by fivestatistical metrics, ROC and R-index. The empirical results have shownthat RF is the best method in terms of R-index and the five metrics, *** rate (0.9661), FP rate (0.0), ACC (0.9835), MAE (0.0046), and RMSE(0.0350) for this study area. this study opens the perspective of weightbasedmachine learning methods for landslide susceptibility mapping.

关键词： Landslide logistic regression random forest SvM Vietnam

Reviewing Stability Criteria for Positive Homogeneous Systems and Adding One for Discrete-Time Cases with Degree Less Than One

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Circuits and Systems 2024年第1期15卷 1-11页

作者： nguyen Quang Tuan Faculty of Information Technology Nha Trang University Khanh Hoa Vietnam

The article synthesizes and presents the results regarding the stability of positive homogeneous systems that have been researched and published in recent years. Next, we provide a sufficient condition for global exponential stability in the case of discrete-time positive homogeneous systems with an order less than one with time-varying delays.

关键词： Exponential Stability Homogeneous Systems Positive Systems Positive Homogeneous Systems

OPT-BAG Model for Predicting Student Employability

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Computers, Materials & Continua 2023年第8期76卷 1555-1568页

作者： Minh-Thanh Vo trang nguyen Tuong Le Faculty of Information Technology HUTECH UniversityHo Chi Minh CityVietnam Faculty of Information Technology Ho Chi Minh City Open UniversityHo Chi Minh CityVietnam Laboratory for Artificial Intelligence Institute for Computational Science and Artificial IntelligenceVan Lang UniversityHo Chi Minh CityVietnam Faculty of Information Technology School of TechnologyVan Lang UniversityHo Chi Minh CityVietnam

The use of machine learning to predict student employability is important in order to analyse a student’s capability to get a *** on the results of this type of analysis,university managers can improve the employability of their students,which can help in attracting students in the *** addition,learners can focus on the essential skills identified through this analysis during their studies,to increase their *** effectivemethod calledOPT-BAG(OPTimisation of BAGging classifiers)was therefore developed to model the problem of predicting the employability of *** model can help predict the employability of students based on their competencies and can reveal weaknesses that need to be ***,we analyse the relationships between several variables and the outcome variable using a correlation heatmap for a student employability ***,a standard scaler function is applied in the preprocessing module to normalise the variables in the student employability *** training set is then input to our model to identify the optimal parameters for the bagging classifier using a grid search cross-validation ***,the OPT-BAG model,based on a bagging classifier with optimal parameters found in the previous step,is trained on the training dataset to predict student *** empirical outcomes in terms of accuracy,precision,recall,and F1 indicate that the OPT-BAG approach outperforms other cutting-edge machine learning models in terms of predicting student *** this study,we also analyse the factors affecting the recruitment process of employers,and find that general appearance,mental alertness,and communication skills are the most *** indicates that educational institutions should focus on these factors during the learning process to improve student employability.

关键词： Ensemble classifier grid search cross-validation OPT-BAG student employability

Properties of poly（1-naphthylamine）/Fe3O4 composites and arsenic adsorption capacity in wastewater

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Frontiers of Materials Science 2016年第1期10卷 56-65页

作者： Minh thi TRAN thi Huyen trang nguyen Quoc Trung VU Minh Vuong nguyen Faculty of Physics Hanoi National University of Education 136-Xuanthuy Street Caugiay District Hanoi Vietnam Faculty of Chemistry Hanoi National University of Education 136-Xuanthuy Street Caugiay District Hanoi Vietnam Department of Physics Quynhon University 170 An Duong Vuong Quynhon Vietnam

The research results of poly（1-naphthylamine）lFe3O4 （PNA/Fe3O4） nanocomposites synthesized by a chemical method for As（Ⅲ） wastewater treatment are presented in this paper. XRD patterns and TEM images showed that the Fe3O4 grain size varied from 13 to 20 nm. The results of Raman spectral analysis showed that PNA participated in part of the PNA/Fe3O4 composite samples. The grain size of PNA/Fe3O4 composite samples is about 25-30 nm measured by SEM. The results of vibrating sample magnetometer measurements at room temperature showed that the saturation magnetic moment of PNA/Fe3O4 samples decreased from 63.13 to 43.43 emu/g, while the PNA concentration increased from 5% to 15%. The nitrogen adsorption-desorption isotherm of samples at 77 K at a relative pressure PIPo of about 1 was studied in order to investigate the surface and porous structure of nanoparticles by the BET method. Although the saturation magnetic moments of samples decreased with the polymer concentration increase, the arsenic adsorption capacity of the PNA/Fe3O4 sample with the PNA concentration of 5% is better than that of Fe3O4 in a solution with pH = 7. In the solution with pH 〉 14, the arsenic adsorption of magnetic nanoparticles is insignificant.

关键词： poly(1-naphthylamin)/Fe3O4 nanocomposite magnetization arsenic adsorption

Understanding bike trip patterns leveraging bike sharing system open data

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Frontiers of Computer Science 2017年第1期11卷 38-48页

作者： Longbiao CHEN Xiaojuan MA thi-Mai-trang nguyen Gang PAN Jeremie JAKUBOWICZ Institut Mines-Telecom Telecom SudParis UMR CNRS Samovar Evry 91000 France Laboratoire d'Informatique de Paris 6 (LIP6) University of Paris 6 Paris 75005 France College of Computer Science Zhejiang University Hangzhou 310027 China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong China

Bike sharing systems are booming globally as a green and flexible transportation mode, but the flexibility also brings difficulties in keeping the bike stations balanced with enough bikes and docks. Understanding the spatio-temporal bike trip patterns in a bike sharing system, such as the popular trip origins and destinations during rush hours, is important for researchers to design models for bike scheduling and sta- tion management. However, due to privacy and operational concerns, bike trip data are usually not publicly available in many cities. Instead, the station feeds about real-time bike and dock number in stations are usually public, which we refer to as bike sharing system open data. In this paper, we propose an approach to infer the spatio-temporal bike trip patterns from the public station feeds. Since the number of possible trips （i.e., origin-destination station pairs） is much larger than the number of stations, we define the trip infer- ence as an ill-posed inverse problem. To solve this problem, we identify the sparsity and locality properties of bike trip patterns, and propose a sparse and weighted regularization model to impose both properties in the solution. We evaluate our method using real-world data from Washington, D.C. and New York City. Results show that our method can effectively infer the spatio-temporal bike trip patterns and outperform the baselines in both cities.

关键词： bike sharing system open data ill-posed inverse problems urban computing

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越南学生对汉语网络语言的认知及态度研究

越南学生对汉语网络语言的认知及态度研究

作者： nguyen thi THUY trang（阮紫彤）郑州大学

学位级别：硕士

随着互联网的迅速发展与普及,大量的网络用语应运而出。近年来接地气的网络词语经常出现在中国政府部门文件以及领导人发言当中。为了与时俱进,作为新中国第一部规范性词典——《现代汉语词典》也收录了被广泛使用且稳定性强的网络新词... 详细信息

随着互联网的迅速发展与普及,大量的网络用语应运而出。近年来接地气的网络词语经常出现在中国政府部门文件以及领导人发言当中。为了与时俱进,作为新中国第一部规范性词典——《现代汉语词典》也收录了被广泛使用且稳定性强的网络新词语。这表明了汉语网络语言已经由小众的专利变成了大众共建、共用、共享的语言产品。汉语网络语言已经渐渐步入人们的日常生活中并成为汉语不可或缺的组成部分。随着中国在国际上的影响力不断扩大,汉语的地位也显著提高,“汉语热”席卷全球,学习汉语的人数不断增加。海外汉语学习者以年轻人居多,该群体对新事物的包容性和接受能力也更强,因此汉语网络语言是他们关注的新焦点,同时也对他们的第二语言学习(即汉语学习)带来巨大的影响。中国与越南是山水相连的友好邻邦。越南本地的汉语教学得到了政府部门的高度重视与大力支持,学习汉语的人数不断创新高,赴华留学的越南留学生也越来越多。越南学生是海外汉语学习者的重要组成部分。目前,网络语言本体研究已获得较为丰硕的成果,但是网络语言在国际中文教育中尚未受到足够的重视。那么,在网络语言实际教学中,教师该“教什么”、“怎么教”才能趋利避害?越南学生对汉语网络语言的认知与态度如何?本文主要采用文献研究法、问卷调查法和综合分析法等方法进行研究。笔者对高级阶段的越南学生进行问卷调查,通过分析调查结果了解越南学生对汉语网络语言的认知、使用情况及态度等问题。调查结果显示,高级阶段的越南学生对汉语网络语言的认知程度与使用频率较高,对汉语网络语言及网络语言教学大多持支持态度。此外,研究还表明,学习时间长短、在中国生活情况、使用汉语交流的频率以及使用中国社交软件的频率等四个因素都直接影响到学生对网络语言的认知程度。之后,笔者基于前人的研究成果,结合实际情况与调查问卷结果,从教师、学生以及学校等三个角度提出汉语网络语言教学与学习的建议。希望本论文能够帮助包括越南学生在内的外国学生加深对汉语网络语言的了解,从而正确运用汉语网络语言,也希望能够为这方面的研究提供新的视角和方向,起到抛砖引玉的作用,为以后的研究尽绵薄之力。

关键词：网络语言越南学生认知态度教学策略