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| 1 | Big Earth data analytics:a survey显示文摘Big Earth data are produced from satellite observations,Internet-ofThings,model simulations,and other sources.The data embed unprecedented insights and spatiotemporal stamps of relevant Earth phenomena for improving our understanding,responding,and addressing challenges of Earth sciences and applications.In the past years,new technologies(such as cloud computing,big data and artificial intelligence)have gained momentum in addressing the challenges of using big Earth data for scientific studies and geospatial applications historically intractable.This paper reviews the big Earth data analytics from several aspects to capture the latest advancements in this fast-growing domain.We first introduce the concepts of big Earth data.The architecture,various functionalities,and supporting modules are then reviewed from a generic methodology aspect.Analytical methods supporting the functionalities are surveyed and analyzed in the context of different tools.The driven questions are exemplified through cutting-edge Earth science researches and applications.A list of challenges and opportunities are proposed for different stakeholders to collaboratively advance big Earth data analytics in the near future. | Chaowei Yang Manzhu Yu Yun Li Fei Hu Yongyao Jiang Qian Liu Dexuan Sha Mengchao Xu Juan Gu | 2019 | Big Earth Data2019，3，2： | 3 |
| 2 | Spatiotemporal event detection: a review显示文摘The advancements of sensing technologies,including remote sensing,in situ sensing,social sensing,and health sensing,have tremendously improved our capability to observe and record natural and social phenomena,such as natural disasters,presidential elections,and infectious diseases.The observations have provided an unprecedented opportunity to better understand and respond to the spatiotemporal dynamics of the environment,urban settings,health and disease propagation,business decisions,and crisis and crime.Spatiotemporal event detection serves as a gateway to enable a better understanding by detecting events that represent the abnormal status of relevant phenomena.This paper reviews the literature for different sensing capabilities,spatiotemporal event extraction methods,and categories of applications for the detected events.The novelty of this review is to revisit the definition and requirements of event detection and to layout the overall workflow(from sensing and event extraction methods to the operations and decision-supporting processes based on the extracted events)as an agenda for future event detection research.Guidance is presented on the current challenges to this research agenda,and future directions are discussed for conducting spatiotemporal event detection in the era of big data,advanced sensing,and artificial intelligence. | Manzhu Yu Myra Bambacus Guido Cervone Keith Clarke Daniel Duffy Qunying Huang Jing Li Wenwen Li Zhenlong Li Qian Liu Bernd Resch Jingchao Yang Chaowei Yang | 2020 | International Journal of Digital Earth2020，13，12： | 2 |
| 3 | Taking the pulse of COVID-19:a spatiotemporal perspective显示文摘The sudden outbreak of the Coronavirus disease(COVID-19)swept across the world in early 2020,triggering the lockdowns of several billion people across many countries,including China,Spain,India,the U.K.,Italy,France,Germany,Brazil,Russia,and the U.S.The transmission of the virus accelerated rapidly with the most confirmed cases in the U.S.,India,Russia,and Brazil.In response to this national and global emergency,the NSF Spatiotemporal Innovation Center brought together a taskforce of international researchers and assembled implementation strategies to rapidly respond to this crisis,for supporting research,saving lives,and protecting the health of global citizens.This perspective paper presents our collective view on the global health emergency and our effort in collecting,analyzing,and sharing relevant data on global policy and government responses,human mobility,environmental impact,socioeconomical impact;in developing research capabilities and mitigation measures with global scientists,promoting collaborative research on outbreak dynamics,and reflecting on the dynamic responses from human societies. | Chaowei Yang Dexuan Sha Qian Liu Yun Li Hai Lan Weihe Wendy Guan Tao Hu Zhenlong Li Zhiran Zhang John Hoot Thompson Zifu Wang David Wong Shiyang Ruan Manzhu Yu Douglas Richardson Luyao Zhang Ruizhi Hou You Zhoua Cheng Zhong Yifei Tian Fayez Beaini Kyla Carte Colin Flynn Wei Liu Dieter Pfoser Shuming Bao Mei Li Haoyuan Zhang Chunbo Liu Jie Jiang Shihong Du Liang Zhao Mingyue Lu Lin Li Huan Zhou Andrew Ding | 2020 | International Journal of Digital Earth2020，13，10： | 2 |
| 4 | Interaction between XRCC1 Polymorphisms and Intake of Long-Term Stored Rice in the Risk of Esophageal Squamous Cell Carcinoma:A Case-Control Study显示文摘Objective This study aimed to explore the roles of three common single nucleotide polymorphisms in the X‐ray repair cross‐complementing group‐1 gene (XRCC1) and of life style factors and their possible interactions in the risk of esophageal squamous cell carcinoma (ESCC) in China.Methods A population‐based case‐control study of 432 cases and 915 controls was conducted in Yangzhong County,Jiangsu Province,China.Subjects were interviewed by trained interviewers using a structured questionnaire that included questions on demographics and life style.XRCC1 genotypes were analyzed using a polymerase chain reaction based restriction fragment length polymorphism (PCR‐RFLP) assay.Unconditional logistic regression analysis was used to calculate adjusted odds ratios (aORs) and 95% confidence intervals (CIs) for associations of ESCC with XRCC1 polymorphisms and lifestyle‐related factors.Results Both the drinking of river water and alcohol intake history were significantly associated with an increased risk of ESCC among men with aORs of 4.20 (95% CI:2.90‐6.07) and 2.03 (95% CI:1.43‐2.89),respectively.For women,the corresponding odds ratios were 8.37 (95% CI:5.09‐13.75) for river water drinking and 12.78 (95% CI:2.69‐60.69) for long‐term stored rice intake.After the XRCC1 G28152A polymorphism was adjusted for potential confounders,subjects with GA and AA genotypes had an increased risk for ESCC (aOR:1.21,95% CI:0.93‐1.56),compared with subjects with a GG genotype,and a positive multiplicative interaction between intake of long‐term stored rice and the XRCC1 G28152A polymorphism was observed (P=0.009).Conclusions Our findings suggest that both lifestyle‐related factors,including drinking river water,long‐term stored rice and alcohol intake,and the XRCC1 G28152A polymorphism were possible risk factors for ESCC,and that the XRCC1 G28152A polymorphism modified the effect of long‐term stored rice intake on the risk of ESCC among Chinese people. | YU HongJie FU ChaoWei WANG JianMing XUE HengChuan XU Biao | 2011 | Biomedical and Environmental Sciences2011，24，3： | 2 |
| 5 | Effects of ensilage on storage and enzymatic degradability of sugar beet pulp显示文摘 | Zheng Yi Yu Chaowei Cheng Yushen | 2011 | Bioresource Technology2011，102，2： | 1 |
| 6 | Evaluation of high solids alkaline pretreatment of rice straw显示文摘 | Cheng Yushen Zheng Yi Yu Chaowei | 2010 | Applied Biochemistry Biotechnology2010，162，6： | 1 |
| 7 | Deep learning for real-time social media text classification for situation awareness-using Hurricanes Sandy,Harvey,and Irma as case studies显示文摘Social media platforms have been contributing to disaster management during the past several years.Text mining solutions using traditional machine learning techniques have been developed to categorize the messages into different themes,such as caution and advice,to better understand the meaning and leverage useful information from the social media text content.However,these methods are mostly event specific and difficult to generalize for cross-event classifications.In other words,traditional classification models trained by historic datasets are not capable of categorizing social media messages from a future event.This research examines the capability of a convolutional neural network(CNN)model in cross-event Twitter topic classification based on three geo-tagged twitter datasets collected during Hurricanes Sandy,Harvey,and Irma.The performance of the CNN model is compared to two traditional machine learning methods:support vector machine(SVM)and logistic regression(LR).Experiment results showed that CNN models achieved a consistently better accuracy for both single event and crossevent evaluation scenarios whereas SVM and LR models had lower accuracy compared to their own single event accuracy results.This indicated that the CNN model has the capability of pre-training Twitter data from past events to classify for an upcoming event for situational awareness. | Manzhu Yu Qunying Huang Han Qin Chris Scheele Chaowei Yang | 2019 | International Journal of Digital Earth2019，12，11： | 1 |
| 8 | Inorganic Composition and Environmental Impact of Biomass Feedstock显示文摘 | THY P YU Chaowei JENKINS B M | 2013 | Energy Fu- els2013，27，7： | 1 |
| 9 | Dilute acid pretreatment and fermentation of sugar beet pulp to ethanol显示文摘 | Yi Zheng Christopher Lee Chaowei Yu Yu-Shen Cheng Ruihong Zhang Bryan M. Jenkins Jean S. VanderGheynst | 2012 | Applied Energy2012，，： | 1 |
| 10 | QoS-Aware Offloading Based on Communication-Computation Resource Coordination for 6G Edge Intelligence显示文摘Driven by the demands of diverse artificial intelligence(AI)-enabled application,Mobile Edge Computing(MEC)is considered one of the key technologies for 6G edge intelligence.In this paper,we consider a serial task model and design a quality of service(QoS)-aware task offloading via communication-computation resource coordination for multi-user MEC systems,which can mitigate the I/O interference brought by resource reuse among virtual machines.Then we construct the system utility measuring QoS based on application latency and user devices’energy consumption.We also propose a heuristic offloading algorithm to maximize the system utility function with the constraints of task priority and I/O interference.Simulation results demonstrate the proposed algorithm’s significant advantages in terms of task completion time,terminal energy consumption and system resource utilization. | Chaowei Wang Xiaofei Yu Lexi Xu Fan Jiang Weidong Wang Xinzhou Cheng | 2023 | China Communications2023，20，3： | 0 |
| 11 | Collaborative Caching in Vehicular Edge Network Assisted by Cell-Free Massive MIMO显示文摘The 6G mobile communications demand lower content delivery latency and higher quality of service for vehicular edge network.With the popularity of content-centric networks,mobile users are paying more and more attention to the delay and reliability of fetching cached content.For reducing communication costs,increasing network capacity and improving the content delivery,we propose a collaborative caching scheme based on deep reinforcement learning for vehicular edge network assisted by cell-free massive multiple-input multipleoutput(MIMO)system,in which the macro base station is considered as the central processor unit,and the roadside units are treated as roadside access points(RSAPs).The proposed scheme can effectively cache contents in edge nodes,i.e.,RSAPs and vehicles with caching capability.We jointly consider the mobility of vehicles and the content request preferences of users,then we use deep Qnetworks algorithm to optimize the caching decisions.Simulation results show that the proposed scheme can significantly reduce the content delivery average latency and increase the content cache hit ratio. | WANG Chaowei WANG Ziye XU Lexi YU Xiaofei ZHANG Zhi WANG Weidong | 2023 | Chinese Journal of Electronics2023，32，6： | 0 |
| 12 | Integrating Optical and Microwave Satellite Observations for High Resolution Soil Moisture Estimate and Applications in CONUS Drought Analyses显示文摘In this study, optical and microwave satellite observations are integrated to estimate soil moisture at the same spatial resolution as the optical sensors (5km here) and applied for drought analysis in the continental United States. A new refined model is proposed to include auxiliary data like soil texture, topography, surface types, accumulated precipitation, in addition to Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) used in the traditional universal triangle method. It is found the new proposed soil moisture model using accumulated precipitation demonstrated close agreements with the U.S. Drought Monitor (USDM) spatial patterns. Currently, the USDM is providing a weekly map. Recently,“flash” drought concept appears. To obtain drought map on daily basis, LST is derived from microwave observations and downscaled to the same resolution as the thermal infrared LST product and used to fill the gaps due to clouds in optical LST data. With the integrated daily LST available under nearly all weather conditions, daily soil moisture can be estimated at relatively higher spatial resolution than those traditionally derived from passive microwave sensors, thus drought maps based on soil moisture anomalies can be obtained on daily basis and made the flash drought analysis and monitoring become possible. | Donglian Sun Yu Li Xiwu Zhan Chaowei Yang Ruixin Yang | 2018 | Remote Sensing2018，7，1： | 0 |
| 13 | Alleviating eutrophication by reducing the abundance of Cyanophyta due to dissolved inorganic carbon fertilization:Insights from Erhai Lake,China显示文摘The eutrophication of lakes is a global environmental problem.Regulating nitrogen(N)and phosphorus(P)on phytoplankton is considered to be the most important basis of lake eutrophication management.Therefore,the effects of dissolved inorganic carbon(DIC)on phytoplankton and its role in mitigating lake eutrophication have often been overlooked.In this study,the relationships between phytoplankton and DIC concentrations,carbon isotopic composition,nutrients(N and P),and hydrochemistry in the Erhai Lake(a karst lake)were investigated.The results showed that when the dissolved carbon dioxide(CO_(2)(aq))concentrations in the water were higher than 15μmol/L,the productivity of phytoplankton was controlled by the concentrations of TP and TN,especially by that of TP.When the N and P were sufficient and the CO_(2)(aq)concentrations were lower than 15μmol/L,the phytoplankton productivity was controlled by the concentrations of TP and DIC,especially by that of DIC.Additionally,DIC significantly affected the composition of the phytoplankton community in the lake(p<0.05).When the CO_(2)(aq)concentrations were higher than 15μmol/L,the relative abundance of Bacillariophyta and Chlorophyta was much higher than those of harmful Cyanophyta.Thus,high concentrations of CO_(2)(aq)can inhibit harmful Cyanophyta blooms. | Chaowei Lai Zhen Ma Zaihua Liu Hailong Sun Qingchun Yu Fan Xia Xuejun He Qian Bao Yongqiang Han Xing Liu Haibo He | 2023 | Journal of Environmental Sciences2023，，9： | 0 |
| 14 | A lightweight data-voting strategy for triple-modular redundant control computers显示文摘Triple-modular redundancy(TMR), a well-known methodology for improving the reliability of computer systems, has been used for onboard control computers in many safety-critical space applications. In this paper, a lightweight data-voting strategy for TMR control computer is proposed, in which an additional straightforward input voting stage is added, so that determination of the maximum admissible deviation for output matching, as required by traditional strategies, can be avoided. The lightweight strategy is a practical data-voting solution for the TMR control computer that exploits the characteristics of the data of the control computer. The addition of input voting, in both the value and time domains, can ensure that the outputs of non-faulty machines are exactly equal, and the bitwise output matching of the TMR control computer can be performed. The new data-voting strategy has been optimized according to the actual application conditions in spacecraft control systems, and has been adopted for the TMR control computer of the Chang’e-5 Return Spacecraft. The results of the ground experiments and successful on-orbit application have demonstrated the advantages of the new data-voting strategy for TMR control computer. | LIU Bo YANG MengFei WANG Yong YUAN Li LIU ChaoWei XU Jian YU Dan HU HongKai FENG Dan | 2022 | Science China(Technological Sciences)2022，65，2： | 0 |
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