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EP Aquarii:A New Picture of the Circumstellar Envelope

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Research in Astronomy and Astrophysics 2024年第3期24卷 83-105页

作者： Pham T.Nhung Do T.Hoai Pierre Darriulat Pham tuan-Anh Pham N.Diep Nguyen B.Ngoc tran T.Thai Department of Astrophysics Vietnam National Space CenterVietnam Academy of Science and Technology18Hoang Quoc VietNghia DoCau GiayHa Noivietnam

New analyses of earlier ALMA observations of oxygen-rich AGB star EP Aquarii are presented,which complete a previously published analysis and offer a different interpretation of the morpho-kinematics of the circumstellar envelope.The birth of the equatorial density enhancement(EDE)is shown to occur very close to the star where evidence for rotation has been obtained.Close to the star and where outflows have been observed:their interaction with the gas of the nascent EDE is seen to play an important role in the development of the wind and the evolution of its radial velocity from 8 to 10 km s^(-1) on the polar symmetry axis to~2 km s^(-1) at the equator.It implies complex morpho-kinematics:making reliable interpretations with reasonable confidence is difficult.In particular,it questions an earlier interpretation implying the presence of a white dwarf companion orbiting the star at an angular distance of -0″4 from its center.It proposes instead an interpretation in terms of a standard mass ejection associated with a shock wave leaving a void of emission in its wake.High Doppler velocity wings are seen to consist of two components,the upper velocity end of the global wind,reaching above±12 km s^(-1),and an effective line broadening,confined within 200 mas from the center of the star,reaching above±20 km s^(-1) and interpreted as caused by the pattern of shock waves resulting from the interaction between stellar pulsation and convective cell partition.

关键词： stars AGB and post-AGB-(stars) circumstellar matter-radio lines stars

Object Detection in Remote Sensing Images Using Picture Fuzzy Clustering and MapReduce

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Computer Systems Science & Engineering 2022年第12期43卷 1241-1253页

作者： tran Manh tuan tran Thi Ngan Nguyen tu Trung Faculty of Computer Science and Engineering Thuyloi University175 Tay SonDong DaHanoi010000Vietnam

In image processing, one of the most important steps is image segmentation. The objects in remote sensing images often have to be detected in order toperform next steps in image processing. Remote sensing images usually havelarge size and various spatial resolutions. Thus, detecting objects in remote sensing images is very complicated. In this paper, we develop a model to detectobjects in remote sensing images based on the combination of picture fuzzy clustering and MapReduce method (denoted as MPFC). Firstly, picture fuzzy clustering is applied to segment the input images. Then, MapReduce is used to reducethe runtime with the guarantee of quality. To convert data for MapReduce processing, two new procedures are introduced, including Map_PFC and Reduce_PFC.The formal representation and details of two these procedures are presented in thispaper. The experiments on satellite image and remote sensing image datasets aregiven to evaluate proposed model. Validity indices and time consuming are usedto compare proposed model to picture fuzzy clustering model. The values ofvalidity indices show that picture fuzzy clustering integrated to MapReduce getsbetter quality of segmentation than using picture fuzzy clustering only. Moreover,on two selected image datasets, the run time of MPFC model is much less thanthat of picture fuzzy clustering.

关键词： Remote sensing images picture fuzzy clustering image segmentation object detection MapReduce

rAL Effectors Drive transcription Bidirectionally in Plants

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Molecular Plant 2017年第2期10卷 285-296页

作者： Li Wang Fabio C. Rinaldi Pallavi Singh Erin L. Doyle Zoe E. Dubrow tuan tu tran Alvaro L. Perez-Quintero Boris Szurek Adam J. Bogdanove Plant Pathology and Plant-Microbe Biology Section School of Integrative Plant Science Cornell University 334 Plant Science Building Ithaca NY 14853 USA Department of Biology Doane University 1014 Boswell Avenue Crete NE 68333 USA UMR Interactions-Plantes-Microorganismes-Environnement IRD-Cirad-Universite Montpellier Montpellier France

TAL effectors delivered by phytopathogenic Xanthomonas species are DNA-sequence-specific transcrip- tional activators of host susceptibility genes and sometimes resistance genes. The modularity of DNA recognition by TAL effectors makes them important also as tools for gene targeting and genome editing. Effector binding elements （EBEs） recognized by native TAL effectors in plants have been identified only on the forward strand of target promoters. Here, we demonstrate that TAL effectors can drive plant tran- scription from EBEs on either strand and in both directions. Furthermore, we show that a native TAL effector from Xanthomonas oryzae pv. oryzicola drives expression of a target with an EBE on each strand of its promoter. By inserting that promoter and derivatives between two reporter genes oriented head to head, we show that the TAL effector drives expression from either EBE in the respective orientations, and that activity at the reverse-strand EBE also potentiates forward transcription driven by activity at the forward-strand EBE. Our results reveal new modes of action for TAL effectors, suggesting the possibility of yet unrecognized targets important in plant disease, expanding the search space for off-targets of custom TAL effectors, and highlighting the potential of TAL effectors for probing fundamental aspects of plant transcription.

关键词： xanthomonas transcription activator-like effector bidirectional reporter enhancer element off-target

Combining Entropy Optimization and Sobel Operator for Medical Image Fusion

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Computer Systems Science & Engineering 2023年第1期44卷 535-544页

作者： Nguyen tu Trung tran Thi Ngan tran Manh tuan To Huu Nguyen Faculty of Computer Science and Engineering Thuyloi University175 Tay SonDong DaHanoi010000Vietnam University of Information and Communication Technology Thai Nguyen UniversityThai Nguyen240000Vietnam

Fusing medical images is a topic of interest in processing medical images.This is achieved to through fusing information from multimodality images for the purpose of increasing the clinical diagnosis accuracy.This fusion aims to improve the image quality and preserve the specific features.The methods of medical image fusion generally use knowledge in many differentfields such as clinical medicine,computer vision,digital imaging,machine learning,pattern recognition to fuse different medical images.There are two main approaches in fusing image,including spatial domain approach and transform domain approachs.This paper proposes a new algorithm to fusion multimodal images.This algorithm is based on Entropy optimization and the Sobel operator.Wavelet transform is used to split the input images into components over the low and high frequency domains.Then,two fusion rules are used for obtaining the fusing images.Thefirst rule,based on the Sobel operator,is used for high frequency components.The second rule,based on Entropy optimization by using Particle Swarm Optimization(PSO)algorithm,is used for low frequency components.Proposed algorithm is implemented on the images related to central nervous system diseases.The experimental results of the paper show that the proposed algorithm is better than some recent methods in term of brightness level,the contrast,the entropy,the gradient and visual informationfidelity for fusion(VIFF),Feature Mutual Information(FMI)indices.

关键词： Medical image fusion wavelet entropy optimization PSO Sobel operator

An Efficient Differential Evolution for Truss Sizing Optimization Using AdaBoost Classifier

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Computer Modeling in Engineering & Sciences 2023年第1期134卷 429-458页

作者： tran-Hieu Nguyen Anh-tuan Vu Hanoi University of Civil Engineering Hanoi100000Vietnam

Design constraints verification is the most computationally expensive task in evolutionary structural optimization due to a large number of structural analyses thatmust be conducted.Building a surrogatemodel to approximate the behavior of structures instead of the exact structural analyses is a possible solution to tackle this problem.However,most existing surrogate models have been designed based on regression techniques.This paper proposes a novel method,called CaDE,which adopts a machine learning classification technique for enhancing the performance of the Differential Evolution(DE)optimization.The proposed method is separated into two stages.During the first optimization stage,the original DE is implemented as usual,but all individuals produced in this phase are stored as inputs of the training data.Based on design constraints verification,these individuals are labeled as“safe”or“unsafe”and their labels are saved as outputs of the training data.When collecting enough data,an AdaBoost model is trained to evaluate the safety state of structures.This model is then used in the second stage to preliminarily assess new individuals,and unpromising ones are rejected without checking design constraints.This method reduces unnecessary structural analyses,thereby shortens the optimization process.Five benchmark truss sizing optimization problems are solved using the proposed method to demonstrate its effectiveness.The obtained results show that the CaDE finds good optimal designs with less structural analyses in comparison with the original DE and four other DE variants.The reduction rate of five examples ranges from 18 to over 50%.Moreover,the proposed method is applied to a real-size transmission tower design problem to exhibit its applicability in practice.

关键词： Structural optimization machine learning surrogate model differential evolution AdaBoost classifier

籼型三系不育系'福农A'的选育及其综合农艺性状分析

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农业生物技术学报 2024年第3期32卷 701-711页

作者：涂诗航连玲洪永河周鹏郑菲艳郑家团张建福福建省农业科学院水稻研究所

杂交水稻(Oryza sativa)不育系是选育优良品种的重要前提之一，因此培育优异的不育系对杂交稻育种具有重要意义。本研究以高抗稻瘟病且优质的水稻品种’华航丝苗’作为供体亲本，与综合农艺性状优良但米质和稻瘟病抗性不佳的籼稻三系保... 详细信息

杂交水稻(Oryza sativa)不育系是选育优良品种的重要前提之一，因此培育优异的不育系对杂交稻育种具有重要意义。本研究以高抗稻瘟病且优质的水稻品种’华航丝苗’作为供体亲本，与综合农艺性状优良但米质和稻瘟病抗性不佳的籼稻三系保持系’福稻B’进行杂交，在F3代中，选择优异单株做父本，用不育系’谷丰A’做母本进行测交；测交后代不断在三亚和沙县基地进行穿梭育种选择。经过多个世代的淘汰和选择，最终选育出了育性稳定的三系不育系’福农A’，其对应的保持系定名为’福农B’。综合农艺性状分析发现，’福农A’株叶形态好，茎秆粗壮，剑叶挺直，其午前开花率达85%，茎秆的直径和厚度明显大于亲本。田间测试及显微观察结果显示，其不育株率为100%，花粉不育度为99.99%，柱头外露率为57.12%。’福农A’的茎秆倒三节直径明显较大，茎秆壁也明显较厚，且茎秆中半纤维素和硅含量较高。高密度芯片分析表明，’福农A’含有半矮秆基因Sd-1 (semi dwarfing gene 1)和Sdt97 (semi dwarfing mutant gene 97)、直穗基因qNGR9及粒重基因OsSPL16 (squamosa promoter-binding-like gene 16)。同时，’福农A’成功聚合了3个稻瘟病菌抗性基因Pi5 (Pyricularia oryzae resistance-5)、Pid2和Pid3，室内鉴定其抗病株率达81.82%。此外，利用’福农A’所育成的7个水稻品种均表现出产量高、米质好、中抗稻瘟病等特征。由此可见，’福农A’具有较强的开发潜力，能为配组和选育抗性好、产量高、米质优的杂交稻提供种质资源。

关键词：籼型三系不育系 ’福农A’ 稻瘟病抗性综合农艺性状

Fusing Satellite Images Using ABC Optimizing Algorithm

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Computer Systems Science & Engineering 2023年第9期46卷 3901-3909页

作者： Nguyen Hai Minh Nguyen tu Trung tran Thi Ngan tran Manh tuan University of Information and Communication Technology Thai Nguyen UniversityThai Nguyen240000Vietnam Faculty of Computer Science and Engineering Thuyloi University175 Tay SonDong DaHanoi010000Vietnam

Fusing satellite(remote sensing)images is an interesting topic in processing satellite images.The result image is achieved through fusing information from spectral and panchromatic images for sharpening.In this paper,a new algorithm based on based the Artificial bee colony(ABC)algorithm with peak signalto-noise ratio(PSNR)index optimization is proposed to fusing remote sensing images in this paper.Firstly,Wavelet transform is used to split the input images into components over the high and low frequency domains.Then,two fusing rules are used for obtaining the fused images.The first rule is“the high frequency components are fused by using the average values”.The second rule is“the low frequency components are fused by using the combining rule with parameter”.The parameter for fusing the low frequency components is defined by using ABC algorithm,an algorithm based on PSNR index optimization.The experimental results on different input images show that the proposed algorithm is better than some recent methods.

关键词： Remote sensing image satellite images image fusion wavelet PSNR optimization ABC

A Deep Learning Model of Traffic Signs in Panoramic Images Detection

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Intelligent Automation & Soft Computing 2023年第7期37卷 401-418页

作者： Kha tu Huynh Thi Phuong Linh Le Muhammad Arif Thien Khai tran International University Ho Chi Minh CityVietnam-Vietnam National UniversityHo Chi Minh City700000Vietnam Department of Computer Science The Superior UniversityLahorePakistan Ho Chi Minh City University of Foreign Languages and Information Technology Ho Chi Minh City700000Vietnam

To pursue the ideal of a safe high-tech society in a time when traffic accidents are frequent,the traffic signs detection system has become one of the necessary topics in recent years and in the future.The ultimate goal of this research is to identify and classify the types of traffic signs in a panoramic image.To accomplish this goal,the paper proposes a new model for traffic sign detection based on the Convolutional Neural Network for com-prehensive traffic sign classification and Mask Region-based Convolutional Neural Networks(R-CNN)implementation for identifying and extracting signs in panoramic images.Data augmentation and normalization of the images are also applied to assist in classifying better even if old traffic signs are degraded,and considerably minimize the rates of discovering the extra boxes.The proposed model is tested on both the testing dataset and the actual images and gets 94.5%of the correct signs recognition rate,the classification rate of those signs discovered was 99.41%and the rate of false signs was only around 0.11.

关键词： Deep learning convolutional neural network Mask R-CNN traffic signs detection

Conflict management styles of nurse managers and influencing factors in children’s hospital, Vietnam

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Journal of Integrative Nursing 2023年第4期5卷 266-272页

作者： Ha Manh tuan tran Thi Hong HUONG Le Viet tuNG Postgraduate Department University of Medicine and Pharmacy at Ho Chi Minh CityHo Chi Minh CityVietnam Head of Branch 2 University Medical Center Ho Chi Minh CityHo Chi Minh CityVietnam Department of Ophthalmology Children’s Hospital 1Ho Chi Minh CityVietnam Faculty of Public Health University of Medicine and Pharmacy at Ho Chi Minh CityHo Chi Minh CityVietnam Training and Scientific Research Department University Medical Center Ho Chi Minh CityHo Chi Minh CityVietnam

Objective:This study aimed to describe the conflict management styles and identify factors affecting the conflict management styles of nurse managers.Materials and Methods:A cross-sectional study was conducted by interviewing nurse managers with a questionnaire,including the Rahim Organizational Conflict Inventory-II Form B,used to assess an individual’s approach to conflict management across five styles:cooperating,competing,compromising,obliging,and avoiding.Results:A total of 30 nurse managers participated in this study.The mean scores of the cooperating style were the highest at 4.3±0.4,followed by a compromising style at 3.4±0.5,an avoiding style at 3.1±0.6,an accommodating style at 2.9±0.7,and a competing style at 2.8±0.8(χ^(2)=66.624,P<0.001).The mean scores for avoiding style among bachelor’s degree nurse managers(3.2±0.6)were higher than those of master’s degree nurse managers(2.7±0.6)(P=0.036).Nurse managers over 40 years old,holding a managerial position for≥10 years,and frequent conflict handling rated higher mean scores for the cooperating style than their counterparts(P<0.05).Conclusion:The cooperating style was the most commonly used conflict management style among nurse managers.Factors affecting the conflict management style were age,education level,and experience in management and conflict handling.These findings are useful for developing interventions that enhance the competencies of nurse managers in conflict management.

关键词： Conflict conflict management nurse nurse manager