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Customized lipid-coated magnetic mesoporous silica nanoparticle doped with ceria nanoparticles for theragnosis of intracerebral hemorrhage

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Nano Research 2018年第7期11卷 3582-3592页

作者： Bong Geun Cha Han-Gil Jeong Dong-Wan Kang Myong-Joo nam chi Kyung Kim Do Yeon Kim In-Young Choi Seul Ki Ki Song I Kim Ju hee Han Jaeyun Kim Seung-Hoon Lee School of Chemical Engineering Sungkyunkwan University Suwon 16419 Republic of Korea Department of Laboratory of Innovative Nanobiotechnology Biomedical Research Institute Seoul National University Hospital 101 Daehak-ra Jongno-gu Seou103080 Republic of Korea Department of Neurology Seoul National University Hospital 101 Daehak-ro Jongno-gu Seou103080 Republic of Korea Department of Neurology Korea University Guro Hospital and Korea University College of Medicine Seoul 08308 Republic of Korea Department of Health Sdences and Technology Samsung Advanced Institute for Health Science & Technology (SAIHST) Sungkyunkwan University Suwon 16419 Republic of Korea Biomedical Institute for Convergence at SKKU (BICS) Sungkyunkwan University Suwon 16419 Republic of Korea

Intracerebral hemorrhage （ICH）, caused by the sudden rupture of an artery within the brain, is a devastating subtype of stroke, which currently has no effective treatment. Intense inflammatory reactions that occur in the peri-hematomal area after ICH are more deleterious than the hematoma itself, resulting in subsequent brain edema and neurologic deterioration. Thus, we developed lipid-coated magnetic mesoporous silica nanoparticles doped with ceria nanoparticles （CeNPs）, abbreviated as LMCs, which have both potent anti-inflammatory therapeutic effects via scavenging reactive oxygen species and help in increasing the efficacy of magnetic resonance imaging enhancement in the peri-hematomal area. LMCs consist of mesoporous silica nanopartide-supported lipid bilayers, which are loaded with large amounts of CeNPs for scavenging of reactive oxygen species, and iron oxide nanoparticles for magnetic resonance imaging contrast. LMCs loaded with CeNPs exhibited strong anti-oxidative and anti-inflammatory activities in vitro. In the rodent ICH model, intracerebraUy injected LMCs reached the peri-hematomal area and were engulfed by macrophages, which were clearly visualized by magnetic resonance imaging of the brain. Moreover, LMCs reduced inflammatory macrophage infiltration, and thus significantly reduced brain edema. Finally, LMC treatment markedly improved neurologic outcomes of the animals with ICH. Thus, LMC is the first nanobiomaterial that successfully showed theragnostic effects in ICH.

关键词： magnetic mesoporous silica nanoparticle ceria nanoparticles intracerebral hemorrhage reactive oxygen species therapeutics imaging

Velocity equation for grenades while impacting on dry sand media

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Defence Technology（防务技术） 2024年第5期35卷 168-173页

作者： Martin Macko Xuan Son bui Kongsathit Phanthavong Duc Hung Pham Van Gion Do Van Minh Do Jiri Skala University of Defence BrnoCzech Republic Le Quy Don Technical University Ha NoiViet Nam

This paper deals with the collision of sphere shape grenades with sand *** central issue of the article is the establishing of an empirical velocity equation of the grenade while impacting on sand that is used to solve motion equations of the mechanical mechanism inside the impact grenade *** paper focuses on impact velocities that are lower than 5 m s^(-1).An experiment was conducted to study the velocity of the grenade while impacting on dry sand.A high-speed camera video was used to capture the grenade *** grenade velocity in the impact process was generated from these video *** types of fitting curves are used to regress the velocity equation of the grenade while interacting with the sand media and the best-fitting model is *** result shows the regression curve has a high correlation with the experiment data for grenade velocities below 5 m s^(-1).The received regression equation is useful for analyzing the working ability of the inertial mechanism inside the impact grenade or analyzing and choosing the appropriate parameters of each part in the inertial mechanism to meet the required characteristics of the mechanism.

关键词： Dynamic response Impact velocity Impact grenade Low-velocity impact

Performance evaluation of a two-way relay network with energy harvesting^and hardware noises

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Digital Communications and Networks 2021年第1期7卷 45-54页

作者： Nguyen Tien Tung Pham Minh nam Phu Tran Tin Faculty of Electronics Technology Industrial University of Ho Chi Minh CityHo Chi Minh CityVietnam Wireless Communcations Research Group Faculy of Electrical and Electronics EngineeringTon Duc Thang UniversityHo Chi Minh CityVietnam

In this paper,we propose a Two-Way Cognitive Relay Network(TWCRN)where the secondary users operate on an underlay mode to access the licensed *** the proposed protocols,two secondary sources transmit their data to a relay in the first time slot,and then the relay would forward the received information to both sources in the remaining ***,the relay is self-powered by harvesting energy from ambient Radio Frequency(RF)signals,using the Time Switching(TS)and the Power Switching(PS)*** paper concentrates on eval-uating the performance of the secondary networks under the impact of hardware impairments and co-channel interference from the primary *** particular,based on the secondary transmitters'constraint power,we derive the closed-form expressions of the outage probability and the throughput over Rayleigh fading channels in two cases:TS and *** also investigate the energy efficiency issue and the locally optimal position of the relay to maximize the system throughput,which provides much information to install the relay ***,our derivations are verified by Monte Carlo simulation.

关键词： Energy harvesting Two-way relay Energy efficiency Hardware impairments Cognitive network

Prediction of flyrock distance induced by mine blasting using a novel Harris Hawks optimization-based multi-layer perceptron neural network

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Journal of Rock Mechanics and Geotechnical Engineering 2021年第6期13卷 1413-1427页

作者： Bhatawdekar Ramesh Murlidhar Hoang Nguyen Jamal Rostami Xuannam bui Danial Jahed Armaghani Prashanth Ragam Edy Tonnizam Mohamad Centre of Tropical Geoengineering(Geotropik) School of Civil EngineeringFaculty of EngineeringUniversiti Teknologi MalaysiaSkudaiJohor81310Malaysia Department of Mining Engineering Indian Institute of TechnologyKharagpur721302India Department of Surface Mining Mining FacultyHanoi University of Mining and GeologyHanoi100000Viet Nam Innovations for Sustainable and Responsible Mining(ISRM)Group Hanoi University of Mining and GeologyHanoi100000Viet Nam Department of Mining Engineering Earth Mechanics InstituteColorado School of MinesGoldenCO80401USA Department of Urban Planning Engineering Networks and SystemsInstitute of Architecture and ConstructionSouth Ural State UniversityChelyabinsk454080Russia Department of ECE Kakatiya Institute of Technology and ScienceWarangal506015India

In mining or construction projects,for exploitation of hard rock with high strength properties,blasting is frequently applied to breaking or moving them using high explosive ***,use of explosives may lead to the flyrock *** can damage structures or nearby equipment in the surrounding areas and inflict harm to humans,especially workers in the working ***,prediction of flyrock is of high *** this investigation,examination and estimation/forecast of flyrock distance induced by blasting through the application of five artificial intelligent algorithms were carried *** hundred and fifty-two blasting events in three open-pit granite mines in Johor,Malaysia,were monitored to collect field *** collected data include blasting parameters and rock mass ***-specific weathering index(WI),geological strength index(GSI) and rock quality designation(RQD)are rock mass ***-layer perceptron(MLP),random forest(RF),support vector machine(SVM),and hybrid models including Harris Hawks optimization-based MLP(known as HHO-MLP) and whale optimization algorithm-based MLP(known as WOA-MLP) were *** performance of various models was assessed through various performance indices,including a10-index,coefficient of determination(R^(2)),root mean squared error(RMSE),mean absolute percentage error(MAPE),variance accounted for(VAF),and root squared error(RSE).The a10-index values for MLP,RF,SVM,HHO-MLP and WOA-MLP are 0.953,0.933,0.937,0.991 and 0.972,respectively.R^(2) of HHO-MLP is 0.998,which achieved the best performance among all five machine learning(ML) models.

关键词： Flyrock Harris hawks optimization(HHO) Multi-layer perceptron(MLP) Random forest(RF) Support vector machine(SVM) Whale optimization algorithm(WOA)

A Study on the Selection of the Logistics Centre Location—Vietnam-Based Logistics Sector

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Management Studies 2020年第2期8卷 121-127页

作者： JSC Sai Gon Hiep Phuoc Port Ho chi Minh City Viet nam Ho Thi Thu Hoa Nguyen Van Khoang bui Thi Bich Lien Tran Quang Dao To Thi Hang Le Van Thanh To Thi Hang Ho chi Minh City University of Transport Ho Chi Minh CityViet Nam JSC Sai Gon Hiep Phuoc Port Ho Chi Minh CityViet Nam

Logistics can be considered as the major drive of the *** centres play a significant role in the entire logistics networks from the point of materials sourcing to the point of last mile *** efficiency of a logistics network depends on the existence of logistics centres to connect and integrate entire logistics *** article discusses the importance of selection of the logistics centre location and recommends the procedure to specify the appropriate logistics centre location as a case of Vietnam-based logistics sector.

关键词： logistics centre Vietnam Ho chi Minh City location

Deep learning neural networks for spatially explicit prediction of flash flood probability

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Geoscience Frontiers 2021年第3期12卷 370-383页

作者： Mahdi Panahi Abolfazl Jaafari Ataollah Shirzadi Himan Shahabi Omid Rahmati Ebrahim Omidvar Saro Lee Dieu Tien bui Geoscience Platform Research Division Korea Institute of Geoscience and Mineral Resources(KIGAM)124Gwahak-ro Yuseong-guDaejeon 34132Republic of Korea Division of Science Education Kangwon National UniversityChuncheon-siGangwon-do 24341Republic of Korea Research Institute of Forests and Rangelands Agricultural ResearchEducationand Extension Organization(AREEO)Tehran 13185-116Iran Department of Rangeland and Watershed Management Faculty of Natural ResourcesUniversity of KurdistanSanandaj 66177-15175Iran Department of Geomorphology Faculty of Natural ResourcesUniversity of KurdistanSanandajIran Kordestan Agricultural and Natural Resources Research and Education Center Agricultural ResearchEducationand Extension Organization(AREEO)SanandajIran Department of Rangeland and Watershed Management Faculty of Natural Resources and Earth SciencesUniversity of KashanKashan 87317-53153Iran Department of Geophysical Exploration Korea University of Science and Technology217 Gajeong-roYuseong-guDaejeon 34113Republic of Korea Institute of Research and Development Duy Tan UniversityDa Nang 550000Viet Nam Department of Zrebar Lake Environmental Research Kurdistan Studies InstituteUniversity of KurdistanSanandajIran

Flood probability maps are essential for a range of applications,including land use planning and developing mitigation strategies and early warning *** study describes the potential application of two architectures of deep learning neural networks,namely convolutional neural networks(CNN)and recurrent neural networks(RNN),for spatially explicit prediction and mapping of flash flood *** develop and validate the predictive models,a geospatial database that contained records for the historical flood events and geo-environmental characteristics of the Golestan Province in northern Iran was *** step-wise weight assessment ratio analysis(SWARA)was employed to investigate the spatial interplay between floods and different influencing *** CNN and RNN models were trained using the SWARA weights and validated using the receiver operating characteristics *** results showed that the CNN model(AUC=0.832,RMSE=0.144)performed slightly better than the RNN model(AUC=0.814,RMSE=0.181)in predicting future ***,these models demonstrated an improved prediction of floods compared to previous studies that used different models in the same study *** study showed that the spatially explicit deep learning neural network models are successful in capturing the heterogeneity of spatial patterns of flood probability in the Golestan Province,and the resulting probability maps can be used for the development of mitigation plans in response to the future *** general policy implication of our study suggests that design,implementation,and verification of flood early warning systems should be directed to approximately 40%of the land area characterized by high and very susceptibility to flooding.

关键词： Spatial modeling Machine learning Convolutional neural networks Recurrent neural networks GIS Iran

A hybrid machine learning ensemble approach based on a Radial Basis Function neural network and Rotation Forest for landslide susceptibility modeling:A case study in the Himalayan area, India

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International Journal of Sediment Research 2018年第2期33卷 157-170页

作者： Binh Thai Pham Ataollah Shirzadi Dieu Tien bui Indra Prakash M.B.Dholakia Department of Civil Engineering Gujarat Technological University Geographic Information System Group Department of Business and IT University College of Southeast Norway 5. Department of Science ε Technology Bhaskarcharya Institute for Space Applications and Geo-Informatics (BISAG) 6. Department of Civil Engineering LDCE Gujarat Technological University Department of Geotechnical Engineering University of Transport Technology Department of Rangeland and Watershed Management College of Natural Resources University of Kurdistan

In this paper, a hybrid machine learning ensemble approach namely the Rotation Forest based Radial Basis Function(RFRBF) neural network is proposed for spatial prediction of landslides in part of the Himalayan area(India). The proposed approach is an integration of the Radial Basis Function(RBF) neural network classifier and Rotation Forest ensemble, which are state-of-the art machine learning algorithms for classification problems. For this purpose, a spatial database of the study area was established that consists of 930 landslide locations and fifteen influencing parameters(slope angle, road density, curvature, land use, distance to road, plan curvature, lineament density, distance to lineaments, rainfall,distance to river, profile curvature, elevation, slope aspect, river density, and soil type). Using the database, training and validation datasets were generated for constructing and validating the model. Performance of the model was assessed using the Receiver Operating Characteristic(ROC) curve, area under the ROC curve(AUC), statistical analysis methods, and the chi square test. In addition, Logistic Regression(LR), Multi-layer Perceptron Neural Networks(MLP Neural Nets), Na(?)ve Bayes(NB), and the hybrid model of Rotation Forest and Decision Trees(RFDT) were selected for comparison. The results show that the proposed RFRBF model has the highest prediction capability in comparison to the other models(LR, MLP Neural Nets, NB, and RFDT); therefore, the proposed RFRBF model is promising and should be used as an alternative technique for landslide susceptibility modeling.

关键词： Landslide GIS Rotation Forest Radial Base Function Neural Network India

Comparison of machine learning models for gully erosion susceptibility mapping

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Geoscience Frontiers 2020年第5期11卷 1609-1620页

作者： Alireza Arabameri Wei Chen Marco Loche Xia Zhao Yang Li Luigi Lombardo Artemi Cerda Biswajeet Pradhan Dieu Tien bui Department of Geomorphology Tarbiat Modares UniversityTehranIran College of Geology&Environment Xi’an University of Science and TechnologyXi’an 710054China Key Laboratory of Coal Resources Exploration and Comprehensive Utilization Ministry of Land and ResourcesXi’an 710021China Shaanxi Provincial Key Laboratory of Geological Support for Coal Green Exploitation Xi’an 710054China Universita Degli Studi di Cagliari Dipartimento di Scienze Chimiche e Geologiche09042MonserratoCagliariItaly University of Twente Faculty of Geo-Information Science and Earth Observation(ITC)PO Box 217EnschedeAE 7500Netherlands Soil Erosion and Degradation Research Group Departament de GeografiaUniversitat de ValenciaBlasco Iba~nez2846010ValenciaSpain Centre for Advanced Modelling and Geospatial Information Systems(CAMGIS) Faculty of Engineering and Information TechnologyUniversity of Technology SydneyNew South WalesAustralia Department of Energy and Mineral Resources Engineering Sejong UniversityChoongmu-gwan209 Neungdong-ro Gwangjin-guSeoul05006Republic of Korea Institute of Research and Development Duy Tan UniversityDa Nang550000Viet Nam

Gully erosion is a disruptive phenomenon which extensively affects the Iranian territory,especially in the Northern provinces.A number of studies have been recently undertaken to study this process and to predict it over space and ultimately,in a broader national effort,to limit its negative effects on local *** focused on the Bastam watershed where 9.3%of its surface is currently affected by *** learning algorithms are currently under the magnifying glass across the geomorphological community for their high predictive ***,unlike the bivariate statistical models,their structure does not provide intuitive and quantifiable measures of environmental preconditioning *** cope with such weakness,we interpret preconditioning causes on the basis of a bivariate approach namely,Index of ***,we performed the susceptibility mapping procedure by testing three extensions of a decision tree model namely,Alternating Decision Tree(ADTree),Naive-Bayes tree(NBTree),and Logistic Model Tree(LMT).We dichotomized the gully information over space into gully presence/absence conditions,which we further explored in their calibration and validation *** the presence/absence information and associated factors identical,the resulting differences are only due to the algorithmic structures of the three models we *** differences are not significant in terms of performances;in fact,the three models produce outstanding predictive AUC measures(ADTree=0.922;NBTree=0.939;LMT=0.944).However,the associated mapping results depict very different patterns where only the LMT is associated with reasonable susceptibility *** is a strong indication of what model combines best performance and mapping for any natural hazard-oriented application.

关键词： Oil erosion GIS Alternating decision tree model Logistic model tree model

Teachers＇ Concerns and Solutions Towards The Implementation of Digital Storytelling in Teaching English in ESL Classroom in Asian Countries

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Journalism and Mass Communication 2015年第9期5卷 454-462页

作者： Khanh Nguyen bui Ho chi Minh City University of Education Ho Chi Minh City Vietnam The University of Georgia Georgia USA

We are living in the era of Information Technology where students have a lot exposure to the state-of-the-art devices and softwares. The remarkable revolutions in interactivity and multimedia materials together with countless emerging technologies have also enabled the conception of digital storytelling. However, it has not been widely used in many developing countries such as Vietnam. Therefore, the purpose of this paper is to conduct an investigation into the history of digital storytelling, the necessary facilities and how it is being used educationally in the classroom so that teachers in developing countries can widely apply digital storytelling in teaching. This paper also raises students＇ awareness towards using technology in their study, which will make an important breakthrough in teaching and learning English in Vietnam.

关键词： digital storytelling educationally-used technology Asian countries