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Objective satellite methods including AI algorithms reviewed for the tenth International workshop on tropical cyclones(IWTC-10)

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Tropical Cyclone Research and Review 2023年第4期12卷 259-266页

作者： quoc-phi duong Anthony Wimmers Derrick Herndon Zhe-Min Tan Jing-Yi Zhuo John Knaff Ibrahim Al Abdulsalam Takeshi Horinouchi Ryota Miyata Arthur Avenas Laboratoire de l’Atmosphere et des Cyclones(UMR8105 LACy) Universite de La ReunionCNRSMeteo-FranceSaint-DenisFrance Cooperative Institute for Meteorological Satellite Studies University of Wisconsin–MadisonWisconsinUSA Key Laboratory of Mesoscale Severe Weather/Ministry of Education Nanjing UniversityNanjingChina School of Atmospheric Sciences Nanjing UniversityNanjingChina NOAA Center for Satellite Applications and Research Fort CollinsCOUSA Center of Excellence for Satellite Applications Oman Faculty of Environmental Earth Science Hokkaido UniversityHokkaidoJapan Faculty of Engineering University of the RyukyusOkinawaJapan IFREMER Universite BrestCNRSIRDLaboratoire d'Oceanographie Physique et SpatialeBrestFrance

Here we explore the latest four years(2019–2022)of using satellite data to objectively analyze tropical cyclones(TC)and issue recommendations for improved *** first discuss new methods of direct retrieval from SAR and geostationary ***,we survey some of the most prominent new techniques in AI and discuss their major capabilities(especially accuracy in nonlinear TC behavior,characterization of model uncertainty and creation of synthetic satellite imagery)and limitations(especially lack of transparency and limited amount of training data).We also identify concerns with biases and unlabeled uncertainties in the Best Track records as being a first-order limitation for further progress in objective *** article concludes with recommendations to improve future objective methods,especially in the area of more accurate and reliable training data sets.

关键词： AI Tropical cyclone Deep learning Satellite imagery analysis

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Schistosomal appendicitis:A serious and often misdiagnosed condition

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World Journal of Clinical Cases 2025年第10期13卷 62-64页

作者： phi duong Nguyen Thanh Kien Lam Orthopaedic-Burn-Plastic Surgery City Children’s HospitalHo Chi Minh City 71815Viet Nam The Faculty of Medical Technology Van Lang UniversityHo Chi Minh City 700000Viet Nam

Schistosomal appendicitis(SA)is a rare but serious complication of schistosomiasis,a parasitic disease affecting over 250 million people worldwide.A recent retrospective study by Wang et al provides important insights into the clinicopathological characteristics of *** study compared 136 cases of SA to 5418 cases of non-SA over a ten-year *** findings include a higher average age of SA patients(61.73 years vs 35.8 years for non-SA),a higher proportion of acute on chronic appendicitis(33.1%vs 16%),and a significantly higher incidence of colorectal cancer(11.7%vs 2.2%).Despite these differences,SA remains a diagnostic challenge due to its nonspecific clinical presentation and lack of specific laboratory *** study also highlights the persistent prevalence of SA,accounting for 1.6%-3.4%of all appendicitis cases each year from 2013 to *** findings underscore the need for enhanced awareness,early detection,and prompt treatment of SA in endemic *** the association with colorectal cancer,patients with SA require thorough screening and *** research into the pathogenesis and diagnostic markers of SA is *** the global battle against schistosomiasis continues,targeted efforts to diagnose and manage SA can significantly improve patient outcomes.

关键词： Schistosomal appendicitis Schistosomiasis Colorectal cancer Misdiagnosis Clinicopathological characteristics

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Privacy-preserving explainable AI: a survey

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Science China(Information Sciences) 2025年第1期 23-56页

作者： Thanh Tam NGUYEN Thanh Trung HUYNH Zhao REN Thanh Toan NGUYEN phi Le NGUYEN Hongzhi YIN quoc Viet Hung NGUYEN School of Information and Communication Technology Griffith University School of Computer and Communication Sciences Ecole Polytechnique Federale de Lausanne Faculty of Mathematics and Computer Science University of Bremen Faculty of Information Technology HUTECH University Department of Computer Science Hanoi University of Science and Technology School of Electrical Engineering and Computer Science The University of Queensland

As the adoption of explainable AI(XAI) continues to expand, the urgency to address its privacy implications intensifies. Despite a growing corpus of research in AI privacy and explainability, there is little attention on privacy-preserving model explanations. This article presents the first thorough survey about privacy attacks on model explanations and their countermeasures. Our contribution to this field comprises a thorough analysis of research papers with a connected taxonomy that facilitates the categorization of privacy attacks and countermeasures based on the targeted explanations. This work also includes an initial investigation into the causes of privacy leaks. Finally, we discuss unresolved issues and prospective research directions uncovered in our analysis. This survey aims to be a valuable resource for the research community and offers clear insights for those new to this domain. To support ongoing research, we have established an online resource repository, which will be continuously updated with new and relevant findings.

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On Mixed Multiplicities of Good Filtrations

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Algebra Colloquium 2015年第3期22卷 421-436页

作者： duong quoc Viet Le Van Dinh Department of Mathematics Hanoi National University of Education 136 Xuan Thuy Street Hanoi Vietnam

This paper interprets mixed multiplicities of good filtrations as Hilbert-Samuel multiplicities, and shows that （ε1,..., εm）-superficial sequences in [16] （2007） and superficial sequences in [15] （1973） are weak-（FC）-sequences in [19] （2000）. As consequences, we not only obtain generalized results for mixed multiplicities of ideals in [15, 16, 19] but also get an improvement of [19, Theorem 3.4] that seems to account well for the essence of [16, Theorem 1.4].

关键词： mixed multiplicity filtration (FC)-sequence superficial sequence

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Spatial prediction of rainfall-induced shallow landslides using hybrid integration approach of Least-Squares Support Vector Machines and differential evolution optimization:a case study in Central Vietnam

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International Journal of Digital Earth 2016年第11期9卷 1077-1097页

作者： Dieu Tien Bui Binh Thai Pham quoc phi Nguyen Nhat-Duc Hoang Geographic Information System Group Department of Business Administration and Computer ScienceUniversity College of Southeast NorwayBøi TelemarkNorway Faculty of Geomatics and Land Administration Hanoi University of Mining and GeologyHanoiVietnam Department of Civil Engineering Gujarat Technological UniversityAhmedabadGujaratIndia Department of Geotechnical Engineering University of Transport TechnologyHa NoiVietNam Department of Environmental Sciences Hanoi University of Mining and GeologyHanoiVietnam Faculty of Civil Engineering Institute of Research and DevelopmentDuy Tan UniversityDanangVietnam

This study represents a hybrid intelligence approach based on the differential evolution optimization and Least-Squares Support Vector Machines for shallow landslide prediction,named as *** LSSVM is used to establish a landslide prediction model whereas the DE is adopted to search the optimal tuning parameters of the LSSVM *** this research,a GIS database with 129 historical landslide records in the Quy Hop area(Central Vietnam)has been collected to establish the hybrid *** receiver operating characteristic(ROC)curve and area under the curve(AUC)were used to assess the performance of the newly constructed *** results show that the proposed model has high performances with approximately 82%of AUCs on both training and validating *** model’s results were compared with those obtained from other methods,Support Vector Machines,Multilayer Perceptron Neural Networks,and J48 Decision *** result comparison demonstrates that the DE-LSSVMSLP deems best suited for the dataset at hand;therefore,the proposed model can be a promising tool for spatial prediction of rainfall-induced shallow landslides for the study area.

关键词： Shallow landslide Least-Squares Support Vector Machines differential evolution GIS Vietnam

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Insights and implications from the study on meniscus reconstruction using tendon autograft

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World Journal of Orthopedics 2024年第11期15卷 1109-1111页

作者： phi duong Nguyen Thanh Kien Lam Department of Orthopaedic-Burn-Plastic Surgery City Children's HospitalHo Chi Minh City 71815Viet Nam The Faculty of Medical Technology Van Lang UniversityHo Chi Minh City 700000Viet Nam

This letter addresses the recent study by Zhu et al on the predictive factors for coronal and sagittal graft extrusion length following medial meniscus reconstruction using tendon *** study provides valuable insights into the importance of preoperative joint space width and tunnel positioning as predictors of graft ***,it found strong correlations between preoperative joint space width and medial,posterior,and mean graft extrusion at both 1 week and 8 months ***,tunnel edge distance at 1 week postoperation correlated with anterior and posterior graft *** findings offer critical guidance for improving surgical ***,the letter highlights the need for further research with larger sample sizes and comparative studies involving different graft types to strengthen these findings and broaden their applicability in clinical *** study's contributions to understanding meniscus reconstruction using tendon autografts are acknowledged,along with suggestions for future research directions.

关键词： Meniscus reconstruction Tendon autograft Graft extrusion Joint space width Tunnel positioning Surgical outcomes

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Deep learning long short-term memory combined with discrete element method for porosity prediction in gravel-bed rivers

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International Journal of Sediment Research 2023年第1期38卷 128-140页

作者： duong Tran Anh Ahad Hasan Tanim Daniel Prakash Kushwaha quoc Bao Pham Van Hieu Bui HUTECH University Center for River Harbor and Landslide Research Chittagong University of Engineering & Technology Govind Ballabh Pant University of Agriculture and Technology Institute of Applied Technology Thu Dau Mot University Faculty of Mechanical Engineering Thuyloi University

The porosity of gravel riverbed material often is an essential parameter to estimate the sediment transport rate, groundwater-river flow interaction, river ecosystem, and fluvial geomorphology. Current methods of porosity estimation are time-consuming in simulation. To evaluate the relation between porosity and grain size distribution(GSD), this study proposed a hybrid model of deep learning Long Short-Term Memory(LSTM) combined with the Discrete Element Method(DEM). The DEM is applied to model the packing pattern of gravel-bed structure and fine sediment infiltration processes in threedimensional(3D) space. The combined approaches for porosity calculation enable the porosity to be determined through real time images, fast labeling to be applied, and validation to be done. DEM outputs based on the porosity dataset were utilized to develop the deep learning LSTM model for predicting bed porosity based on the GSD. The simulation results validated with the experimental data then segregated into 800 cross sections along the vertical direction of gravel pack. Two DEM packing cases, i.e., clogging and penetration are tested to predict the porosity. The LSTM model performance measures for porosity estimation along the z-direction are the coefficient of determination(R~2), root mean squared error(RMSE), and mean absolute error(MAE) with values of 0.99, 0.01, and 0.01 respectively, which is better than the values obtained for the Clogging case which are 0.71, 0.14, and 0.03, respectively. The use of the LSTM in combination with the DEM model yields satisfactory results in a less complex gravel pack DEM setup, suggesting that it could be a viable alternative to minimize the simulation time and provide a robust tool for gravel riverbed porosity prediction. The simulated results showed that the hybrid model of the LSTM combined with the DEM is reliable and accurate in porosity prediction in gravel-bed river test samples.

关键词： Numerical modeling Discrete element method LSTM Bed porosity Gravel-bed river

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Predicting soil erosion hazard in Lattakia Governorate(W Syria)

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International Journal of Sediment Research 2021年第2期36卷 207-220页

作者： Mohammed Safwan Khallouf Alaa Alshiehabi Omran Bao Pham quoc Thi Thuy Linh Nguyen Nam Thai Van Tran Anh duong Harsányi Endre Institution of Land Utilization Technology and Regional Planning Faculty of Agricultural and Food Sciences and Environmental Management University of Debrecen Administration of Natural Resources Management General Commission for Scientific Agricultural Research (GCSAR) 3. Department of Soil Sciences and Land Reclamation Faculty of Agriculture Al-Baath University Department of Soil and Environment Swedish University of Agricultural Sciences (SLU) Institute of Research and Development Duy Tan University Faculty of Environmental and Chemical Engineering Duy Tan University Faculty of Water Resource Engineering Thuyloi University Ho Chi Minh City University of Technology (HUTECH) Sustainable Management of Natural Resources and Environment Research Group Faculty of Environment and Labour Safety Ton Duc Thang University

The main objective of this study is to predict soil erosion in the Lattakia Governorate(W Syria) using the Water Erosion Prediction Project model(WEPP) and to compare the result with that of the RUSLE. Field survey and data collection were carried out, and 44 soil samples were analyzed. In addition, all the necessary input files were prepared for use in the WEPP model and RUSLE. Results show that more than of 80% of the locations studied experience slight to moderate erosion(less than 5 t/ha/y), whereas the rest of the locations experience severe soil erosion hazard. Moreover, the volume of runoff estimated by the WEPP model is in the range of 51 e321 mm, and the R~2 between the simulated soil erosion and the predicted runoff reached 0.68. Interestingly, the R~2 between the WEPP model and RUSLE is 0.56, which indicates a good correlation between the two models.

关键词： Soil erosion Run-off WEPP RUSLE Syria

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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

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Drought and conflicts at the local level: Establishing a water sharing mechanism for the summer-autumn rice production in Central Vietnam

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International Soil and Water Conservation Research 2019年第4期7卷 362-375页

作者： Chuong Van Huynh Catharien Terwisscha van Scheltinga Ty Huu Pham Non quoc duong Phuong Thi Tran Linh Hoang Khanh Nguyen Tung Gia Pham Ngoc Bich Nguyen Jos Timmerman Hue University Viet Nam Climate Change and Adaptive Land and Water Management Group Wageningen Environmental ResearchNetherlands University of Agriculture and Forestry Hue UniversityViet Nam

In recent years,water for agricultural production gradually became a significant challenge in the context of climate change in *** solutions are required,which consider the use of resources for both human needs and ecology,and that account for the equitable distribution and the livelihood of the farmers now and in the *** particular,the farmers in the province of Quang Nam faring water shortage in the cultivation of paddy in the summer-autumn *** arise regarding the sharing of the water between the farmers,the drinking water company and the hydropower *** the context of climate change,the water shortage is expected to increase in the *** article presents the results of participatory action research (PAR) approach to develop a local level mechanism for water sharing,in which stakeholders actively *** sharing mechanism was developed,envisioning a sustainable solution for inclusive water *** mechanism was successfully implemented in two cases,one at commune level (Tho stream) and one at the district level (Mo stream).The participatory approach proved to be successful in setting up a broadly acceptable mechanism that will need to be further incorporated in the institutional set-up.

关键词： Adaptation Agriculture Climate change Governance Rice production Water sharing

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