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Could wastewater analysis be a useful tool for China?——A review

Journal of Environmental Sciences 2015年第1期27卷 70-79页

作者： Jianfa Gao Jake O'Brien Foon Yin Lai Alexander L.N.van Nuijs Jun He Jochen F.Mueller Jingsha Xu phong k.thai Faculty of Science and Engineering University of Nottingham Ningbo China National Research Centre for Environmental Toxicology The University of Queensland Toxicological Center University of Antwerp

Analysing wastewater samples is an innovative approach that overcomes many limitations of traditional surveys to identify and measure a range of chemicals that were consumed by or exposed to people living in a sewer catchment area. First conceptualised in 2001, much progress has been made to make wastewater analysis（WWA） a reliable and robust tool for measuring chemical consumption and/or exposure. At the moment, the most popular application of WWA, sometimes referred as sewage epidemiology, is to monitor the consumption of illicit drugs in communities around the globe, including China. The approach has been largely adopted by law enforcement agencies as a device to monitor the temporal and geographical patterns of drug consumption. In the future, the methodology can be extended to other chemicals including biomarkers of population health（e.g. environmental or oxidative stress biomarkers, lifestyle indicators or medications that are taken by different demographic groups） and pollutants that people are exposed to（e.g. polycyclic aromatic hydrocarbons,perfluorinated chemicals, and toxic pesticides）. The extension of WWA to a huge range of chemicals may give rise to a field called sewage chemical-information mining（SCIM） with unexplored potentials. China has many densely populated cities with thousands of sewage treatment plants which are favourable for applying WWA/SCIM in order to help relevant authorities gather information about illicit drug consumption and population health status. However, there are some prerequisites and uncertainties of the methodology that should be addressed for SCIM to reach its full potential in China.

关键词： Drug consumption Wastewater analysis Biomarkers Population health

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Temporal trends of per-and polyfluoroalkyl substances(PFAS)in the influent of two of the largest wastewater treatment plants in Australia

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Emerging Contaminants 2019年第1期5卷 211-218页

作者： Hue T.Nguyen Sarit L.kaserzon phong k.thai Soumini Vijayasarathy Jennifer Bräunig Nicholas D.Crosbie Anders Bignert Jochen F.Mueller Queensland Alliance for Environmental Health Sciences(QAEHS) The University of QueenslandBrisbaneQLD4102Australia Melbourne Water Corporation DocklandsVIC3001Australia Swedish Museum of Natural History Box 5000710405StockholmSweden Faculty of Environment University of ScienceVietnam National UniversityHo Chi Minh CityViet nam

Per-and polyfluoroalkyl substances(PFAS)are found ubiquitously in wastewater treatment plants(WWTPs)due to their multiple sources in industry and consumer *** Australia,limited spatial data are available on PFAS levels inWWTPs influent,while no temporal data have been *** aim of this study was to investigate the occurrence and temporal trend of PFAS in the influent of two large WWTPs in Australia(WWTP A and B)over a four-year *** influent samples were collected over one week at different seasons from 2014 to *** perfluoroalkyl acids(PFAA)(*** perfluoroalkyl carboxylic acids(PFCAs)and four perfluoroalkyl sulfonic acids(PFSA))were detected with mean S11PFAA concentrations of 57±3.3e94±17 ng/L at WWTP A,and 31±6.1e142±73 ng/L at WWTP *** highest mean concentrations were observed for perfluorohexanoate(PFHxA)(20±2 ng/L)in WWTP A,and perfluorooctane sulfonate(PFOS)(17±13 ng/L)in WWTP *** precursor 6:2 fluorotelomer sulfonate was detected over five sampling periods from Aug 2016 to Oct 2017,with mean concentrations of 37±18e138±51 ng/L for WWTP A and 8.8±4.5e29±5.1 ng/L for WWTP *** concentration of 6:2 FTS(1.8e11 folds)than those of PFOA and PFOS in WWTP A indicate a likely substitution of C8 PFAA by fluorotelomer-based PFAS in this *** trends(annual and seasonal)in per-capita mass load were observed for some PFAA,increasing for PFPeA,PFHxA,PFHpA,PFNA,and PFHxS,while decreasing for PFBS and PFOS in either ***,elevated levels of PFOS in October 2017 were observed at both WWTPs with the highest per capita mass load of up to 67 mg/day/*** some PFAS release trends,longer sampling periods would be required to achieve acceptable statistical power.

关键词： Per-and polyfluoroalkyl substances(PFAS) 6:2 fluorotelomer sulfonate(6:2 FTS) Temporal trends Wastewater treatment plant(WWTP) Influent Power Effect size

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Monitoring the levels of brominated and organophosphate flame retardants in passenger cars:Utilisation of car air filters as active samplers

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Journal of Environmental Sciences 2020年第5期32卷 142-150页

作者： Long kTran Chang He Dam Hoang Phuc Leisa-Maree LToms Xianyu Wang Meng Xiu Jochen FMueller Adrian Covaci Lidia Morawska phong kthai QAEHS Queensland Alliance for Environmental Health ScienceThe University of QueenslandBrisbaneAustralia International Laboratory for Air Quality and Health Queensland University of TechnologyBrisbaneAustralia Hanoi University of Science and Technology HanoiViet Nam School of Public Health and Social Work Institute of Health and Biomedical InnovationFaculty of HealthQueensland University of TechnologyKelvin GroveQLDAustralia Toxicological Center Department of Pharmaceutical SciencesUniversity of AntwerpUniversiteitsplein 1AntwerpBelgium

Filters in residential and office air conditioning(A/C)systems have been used as sampling devices for monitoring different ***,cabin air filters(CAFs)in the A/C system of passenger cars have not been utilised for this *** this study,we collected22 used CAFs from passenger cars in Hanoi,Vietnam to analyse for 8 polybrominated diphenyl ethers(PBDEs)and 10 organophosphate esters(OPEs).All the analytes were detected in more than 50%of samples with the exception of BDE153 and *** average concentrations of∑10OPEs and∑8 BDEs in the captured dust were 2600 and 40 ng/g,respectively with Tris(1-chloro-2-propyl)phosphate(TCIPP)and BDE209 as the dominant congener in OPE and BDE groups,*** are a potential tool to qualitatively assess the levels of semi-volatile chemicals in suspended dust in cars as a screening step for exposure assessment of those chemicals.

关键词： Flame retardants Vehicle cabin Air quality Human exposure PBDEs OPEs

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PVC和NS复合改性沥青高温流变性能评价

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低温建筑技术 2024年第1期46卷 97-100页

作者：张展铭 Nguyen Hoang phong 江苏高速公路工程养护技术有限公司南京211106 越南林业大学机电与土木工程学院河内13417

为探究聚氯乙烯(PVC)和纳米二氧化硅(NS)对沥青高温流变性能的影响,文中通过基本物理性能试验、温度扫描试验和多重应力蠕变回复试验进行测试。结果表明PVC和NS的加入可提高沥青的粘度和稠度,降低温度敏感性,但PVC对低温性能有明显劣化... 详细信息

为探究聚氯乙烯(PVC)和纳米二氧化硅(NS)对沥青高温流变性能的影响,文中通过基本物理性能试验、温度扫描试验和多重应力蠕变回复试验进行测试。结果表明PVC和NS的加入可提高沥青的粘度和稠度,降低温度敏感性,但PVC对低温性能有明显劣化影响。此外,改性剂的加入可提高沥青的高温流变性能,提升高温抗永久变形能力,延缓车辙病害的发生。

关键词：复合改性沥青 PVC NS 流变性能

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Novel Time Series Bagging Based Hybrid Models for Predicting Historical Water Levels in the Mekong Delta Region, Vietnam

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Computer Modeling in Engineering & Sciences 2022年第6期131卷 1431-1449页

作者： Nguyen Thanh Hoan Nguyen Van Dung Ho Le Thu Hoa Thuy Quynh Nadhir Al-Ansari Tran Van phong Phan Trong Trinh Dam Duc Nguyen Hiep Van Le Hanh Bich Thi Nguyen Mahdis Amiri Indra Prakash Binh thai Pham Institute of Geography Vietnam Academy of Science and TechnologyHanoi10000Viet Nam Department of Civil Environmental and Natural Resources EngineeringLulea University of TechnologyLulea97187Sweden Institute of Geological Sciences Vietnam Academy of Science and Technology(VAST)Dong DaHanoi10000Viet Nam University of Transport Technology Thanh XuanHa Noi10000Viet Nam Department of Watershed&Arid Zone Management Gorgan University of Agricultural Sciences&Natural ResourcesGorgan***Iran DDG(R)Geological Survey of India Gandhinagar382010India

Water level predictions in the river,lake and delta play an important role in flood *** year Mekong River delta of Vietnam is experiencing flood due to heavy monsoon rains and high *** subsidence may also aggravate flooding problems in this ***,accurate predictions of water levels in this region are very important to forewarn the people and authorities for taking timely adequate remedial measures to prevent losses of life and *** are so many methods available to predict the water levels based on historical data but nowadays Machine Learning(ML)methods are considered the best tool for accurate *** this study,we have used surface water level data of 18 water level measurement stations of the Mekong River delta from 2000 to 2018 to build novel time-series Bagging based hybrid ML models namely:Bagging(RF),Bagging(SOM)and Bagging(M5P)to predict historical water levels in the study *** of the Bagging-based hybrid models were compared with Reduced Error Pruning Trees(REPT),which is a benchmark ML *** data of 19 years period was divided into 70:30 ratio for the *** data of the period 1/2000 to 5/2013(which is about 70%of total data)was used for the training and for the period 5/2013 to 12/2018(which is about 30%of total data)was used for testing(validating)the *** of the models was evaluated using standard statistical measures:Coefficient of Determination(R2),Root Mean Square Error(RMSE)and Mean Absolute Error(MAE).Results show that the performance of all the developed models is good(R2>0.9)for the prediction of water levels in the study ***,the Bagging-based hybrid models are slightly better than another model such as ***,these Bagging-based hybrid time series models can be used for predicting water levels at Mekong data.

关键词： Computational techniques bagging water level time series algorithms

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An IoT-Cloud Based Intelligent Computer-Aided Diagnosis of Diabetic Retinopathy Stage Classification Using Deep Learning Approach

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Computers, Materials & Continua 2021年第2期66卷 1665-1680页

作者： k.Shankar Eswaran Perumal Mohamed Elhoseny phong Thanh Nguyen Department of Computer Applications Alagappa UniversityKaraikudi630001India Faculty of Computers and Information Mansoura UniversityMansoura35516Egypt Department of Project Management Ho Chi Minh City Open UniversityHo Chi Minh City7000000Vietnam

Diabetic retinopathy(DR)is a disease with an increasing prevalence and the major reason for blindness among working-age *** possibility of severe vision loss can be extensively reduced by timely diagnosis and *** automated screening for DR has been identified as an effective method for early DR detection,which can decrease the workload associated to manual grading as well as save diagnosis costs and *** studies have been carried out to develop automated detection and classification models for *** paper presents a new IoT and cloud-based deep learning for healthcare diagnosis of Diabetic Retinopathy(DR).The proposed model incorporates different processes namely data collection,preprocessing,segmentation,feature extraction and *** first,the IoT-based data collection process takes place where the patient wears a head mounted camera to capture the retinal fundus image and send to cloud ***,the contrast level of the input DR image gets increased in the preprocessing stage using Contrast Limited Adaptive Histogram Equalization(CLAHE)***,the preprocessed image is segmented using Adaptive Spatial kernel distance measure-based Fuzzy C-Means clustering(ASkFCM)***,deep Convolution Neural Network(CNN)based Inception v4 model is applied as a feature extractor and the resulting feature vectors undergo classification in line with the Gaussian Naive Bayes(GNB)*** proposed model was tested using a benchmark DR MESSIDOR image dataset and the obtained results showcased superior performance of the proposed model over other such models compared in the study.

关键词： Deep learning classification GaussianNaive Bayes feature extraction diabetic retinopathy

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Improved flood susceptibility mapping using a best first decision tree integrated with ensemble learning techniques

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Geoscience Frontiers 2021年第3期12卷 333-342页

作者： Binh thai Pham Abolfazl Jaafari Tran Van phong Hoang Phan Hai Yen Tran Thi Tuyen Vu Van Luong Huu Duy Nguyen Hiep Van Le Loke kok Foong University of Transport Technology Ha Noi 100000Viet Nam Research Institute of Forests and Rangelands Agricultural ResearchEducation and Extension Organization(AREEO)TehranIran Institute of Geological Sciences Vietnam Academy of Sciences and Technology84 Chua Lang StreetDong daHa NoiViet Nam Department of Geography School of Social EducationVinh UniversityViet Nam Department of Resource and Environment Management School of Agriculture and ResourcesVinh UniversityViet Nam Faculty of Geography VNU University of Science334 Nguyen TraiHa NoiViet Nam Institute of Research and Development Duy Tan UniversityDa Nang 550000Viet Nam Department for Management of Science and Technology Development Ton Duc Thang UniversityHo Chi Minh CityViet Nam Faculty of Civil Engineering Ton Duc Thang UniversityHo Chi Minh CityViet Nam

Improving the accuracy of flood prediction and mapping is crucial for reducing damage resulting from flood *** this study,we proposed and validated three ensemble models based on the Best First Decision Tree(BFT)and the Bagging(Bagging-BFT),Decorate(Bagging-BFT),and Random Subspace(RSS-BFT)ensemble learning techniques for an improved prediction of flood susceptibility in a spatially-explicit manner.A total number of 126 historical flood events from the Nghe An Province(Vietnam)were connected to a set of 10 flood influencing factors(slope,elevation,aspect,curvature,river density,distance from rivers,flow direction,geology,soil,and land use)for generating the training and validation *** models were validated via several performance metrics that demonstrated the capability of all three ensemble models in elucidating the underlying pattern of flood occurrences within the research area and predicting the probability of future flood *** on the Area Under the receiver operating characteristic Curve(AUC),the ensemble Decorate-BFT model that achieved an AUC value of 0.989 was identified as the superior model over the RSS-BFT(AUC=0.982)and Bagging-BFT(AUC=0.967)models.A comparison between the performance of the models and the models previously reported in the literature confirmed that our ensemble models provided a reliable estimate of flood susceptibilities and their resulting susceptibility maps are trustful for flood early warning systems as well as development of mitigation plans.

关键词： Machine learning Ensemble learners Hybrid modeling

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Locally weighted learning based hybrid intelligence models for groundwater potential mapping and modeling: A case study at Gia Lai province, Vietnam

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Geoscience Frontiers 2021年第5期12卷 54-68页

作者： Hoang Phan Hai Yen Binh thai Pham Tran Van phong Duong Hai Ha Romulus Costache Hiep Van Le Huu Duy Nguyen Mahdis Amiri Nguyen Van Tao Indra Prakash Department of Geography School of Social Sciences EducationVinh University182 Le DuanVinhNghe AnVietnam University of Transport Technology Hanoi 100000Viet Nam Institute of Geological Sciences Vietnam Academy of Science and Technology(VAST)84 Chua LangDong DaHanoiViet Nam Institute for Water and Environment Hanoi 100000Viet Nam Research Institute of the University of Bucharest 90-92 Sos.Panduri5th DistrictBucharestRomania National Institute of Hydrology and Water Management București-Ploiești Road97E1st District013686 BucharestRomania Faculty of Geography VNU University of ScienceVietnam National University334 Nguyen TraiHanoi 100000Viet Nam Department of Watershed&Arid Zone Management Gorgan University of Agricultural Sciences&Natural ResourcesGorgan ***Iran DDG(R)Geological Survey of India Gandhinagar 382010India

The groundwater potential map is an important tool for a sustainable water management and land use planning,particularly for agricultural countries like *** this article,we proposed new machine learning ensemble techniques namely AdaBoost ensemble(ABLWL),Bagging ensemble(BLWL),Multi Boost ensemble(MBLWL),Rotation Forest ensemble(RFLWL)with Locally Weighted Learning(LWL)algorithm as a base classifier to build the groundwater potential map of Gia Lai province in *** this study,eleven conditioning factors(aspect,altitude,curvature,slope,Stream Transport Index(STI),Topographic Wetness Index(TWI),soil,geology,river density,rainfall,land-use)and 134 wells yield data was used to create training(70%)and testing(30%)datasets for the development and validation of the *** statistical indices were used namely Positive Predictive Value(PPV),Negative Predictive Value(NPV),Sensitivity(SST),Specificity(SPF),Accuracy(ACC),kappa,and Receiver Operating Characteristics(ROC)curve to validate and compare performance of *** show that performance of all the models is good to very good(AUC:0.75 to 0.829)but the ABLWL model with AUC=0.89 is the *** the models applied in this study can support decision-makers to streamline the management of the groundwater and to develop economy not only of specific territories but also in other regions across the world with minor changes of the input parameters.

关键词： Locally weighted learning Hybrid models Groundwater potential GIS Vietnam

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Ensemble machine learning models based on Reduced Error Pruning Tree for prediction of rainfall-induced landslides

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International Journal of Digital Earth 2021年第5期14卷 575-596页

作者： Binh thai Pham Abolfazl Jaafari Trung Nguyen-Thoi Tran Van phong Huu Duy Nguyen Neelima Satyam Md Masroor Sufia Rehman Haroon Sajjad Mehebub Sahana Hiep Van Le Indra Prakash Division of Computational Mathematics and Engineering Institute for Computational ScienceTon Duc Thang UniversityHo Chi Minh CityViet Nam Faculty of Civil Engineering Ton Duc Thang UniversityHo Chi Minh CityViet Nam Research Institute of Forests and Rangelands Agricultural ResearchEducationand Extension Organization(AREEO)TehranIran Institute of Geological Sciences Vietnam Academy of Sciences and TechnologyHanoiViet Nam Faculty of Geography V.N.U.University of ScienceVietnam National UniversityHanoiViet Nam Discipline of Civil Engineering Indian Institute of Technology IndoreMadhya PradheshIndia Department of Geography Jamia Millia IslamiaNew DelhiIndia School of Environment Education and DevelopmentUniversity of ManchesterManchesterUK Institute of Research and Development Duy Tan UniversityDa NangViet Nam DDG(R)Geological Survey of India GujaratIndia

In this paper,we developed highly accurate ensemble machine learning models integrating Reduced Error Pruning Tree(REPT)as a base classifier with the Bagging(B),Decorate(D),and Random Subspace(RSS)ensemble learning techniques for spatial prediction of rainfallinduced landslides in the Uttarkashi district,located in the Himalayan range,*** do so,a total of 103 historical landslide events were linked to twelve conditioning factors for generating training and validation *** Mean Square Error(RMSE)and Area Under the receiver operating characteristic Curve(AUC)were used to evaluate the training and validation performances of the *** results showed that the single REPT model and its derived ensembles provided a satisfactory accuracy for the prediction of *** D-REPT model with RMSE=0.351 and AUC=0.907 was identified as the most accurate model,followed by RSS-REPT(RMSE=0.353 and AUC=0.898),B-REPT(RMSE=0.396 and AUC=0.876),and the single REPT model(RMSE=0.398 and AUC=0.836),*** prominent ensemble models proposed and verified in this study provide engineers and modelers with insights for development of more advanced predictive models for different landslide-susceptible areas around the world.

关键词： Machine learning ensemble modeling Bagging Decorate Random Subspace

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The relationship between chimeric RNAs and gene fusions:Potential implications of reciprocity in cancer

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Journal of Genetics and Genomics 2020年第7期47卷 341-348页

作者： Justin Elfman Lam-phong Pham Hui Li Department of Biochemistry and Molecular Genetics School of MedicineUniversity of VirginiaCharlottesvilleVA22904 USA Department of Pathology School of MedicineUniversity of VirginiaCharlottesvilleVA22904 USA

Gene fusions have long been considered hallmarks of *** into characterization of their prevalence,cause,and function have provided significant progress toward improvements in diagnosis,prognosis assessment,and treatment of numerous *** recently,detection of intergenically spliced chimeric RNAs in cancer has spurred efforts to characterize these transcripts,anticipating similar successes in translation to the *** of chimeric RNAs in normal cells,especially those which precede canonical translocations such as PAX3-F0X01 and JAZF1-JJAZ1,suggests that these physiologically regulated transcripts may function as proto-oncogenic factors,in that their dysregulation can lead to cancer *** findings have given rise to conjecture regarding chimeric RNAguided rearrangements giving rise to fusion genes,termed the cart-before-the-horse ***,we provide context for the relationship between gene fusions,chimeric RNAs,and cancer;assemble evidence in support of the cart-before-the-horse hypothesis;and discuss potential advantages of targeting chimeric RNAs in cancer treatment.

关键词： cancer diagnosis horse

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