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| 1 | Analyzing key influence factors of city logistics development using the fuzzy decision mak- ing trial and evaluation laboratory (DEMATEL) method显示文摘 | HONGMEI HE HAIFANG CHENG | 2012 | African Journal of Business Management2012，14，6： | 1 |
| 2 | Analyzing key influence factors of city logistics development using the fuzzy decision making trial andevaluation laboratory (DEMATEL) method显示文摘 | Hongmei He Haifang Cheng | | 0，，6： | 1 |
| 3 | Analyzing Key Influence Factors of City Logistics Development Using the Fuzzy Decision Making Trial and Evaluation Laboratory (DEMATEL) Method显示文摘 | He Hongmei Cheng Haifang | 2012 | African Journal of Business Management2012，14，11： | 1 |
| 4 | Climate change and Aedes albopictus risks in China:current impact and future projection显示文摘Background Future distribution of dengue risk is usually predicted based on predicted climate changes using general circulation models(GCMs).However,it is difficult to validate the GCM results and assess the uncertainty of the predictions.The observed changes in climate may be very different from the GCM results.We aim to utilize trends in observed climate dynamics to predict future risks of Aedes albopictus in China.Methods We collected Ae.albopictus surveillance data and observed climate records from 80 meteorological stations from 1970 to 2021.We analyzed the trends in climate change in China and made predictions on future climate for the years 2050 and 2080 based on trend analyses.We analyzed the relationship between climatic variables and the prevalence of Ae.albopictus in different months/seasons.We built a classification tree model(based on the average of 999 runs of classification and regression tree analyses)to predict the monthly/seasonal Ae.albopictus distribution based on the average climate from 1970 to 2000 and assessed the contributions of different climatic variables to the Ae.albopictus distribution.Using these models,we projected the future distributions of Ae.albopictus for 2050 and 2080.Results The study included Ae.albopictus surveillance from 259 sites in China found that winter to early spring(November–February)temperatures were strongly correlated with Ae.albopictus prevalence(prediction accuracy ranges 93.0–98.8%)—the higher the temperature the higher the prevalence,while precipitation in summer(June–September)was important predictor for Ae.albopictus prevalence.The machine learning tree models predicted the current prevalence of Ae.albopictus with high levels of agreement(accuracy>90%and Kappa agreement>80%for all 12 months).Overall,winter temperature contributed the most to Ae.albopictus distribution,followed by summer precipitation.An increase in temperature was observed from 1970 to 2021 in most places in China,and annual change rates varied substantially from-0.22℃/year to 0.58℃/year among sites,with the largest increase in temperature occurring from February to April(an annual increase of 1.4–4.7℃ in monthly mean,0.6–4.0℃ in monthly minimum,and 1.3–4.3℃ in monthly maximum temperature)and the smallest in November and December.Temperature increases were lower in the tropics/subtropics(1.5–2.3℃ from February–April)compared to the high-latitude areas(2.6–4.6℃ from February–April).The projected temperatures in 2050 and 2080 by this study were approximately 1–1.5℃ higher than those projected by GCMs.The estimated current Ae.albopictus risk distribution had a northern boundary of north-central China and the southern edge of northeastern China,with a risk period of June–September.The projected future Ae.albopictus risks in 2050 and 2080 cover nearly all of China,with an expanded risk period of April–October.The current at-risk population was estimated to be 960 million and the future at-risk population was projected to be 1.2 billion.Conclusions The magnitude of climate change in China is likely to surpass GCM predictions.Future dengue risks will expand to cover nearly all of China if current climate trends continue. | Hongmei Liu Xiaodan Huang Xiuxia Guo Peng Cheng Haifang Wang Lijuan Liu Chuanhui Zang Chongxing Zhang Xuejun Wang Guofa Zhou Maoqing Gong | 2023 | Infectious Diseases of Poverty2023，12，2： | 1 |
| 5 | Evaluation of the toxicity of food additive silica nanoparticles on gastrointestinal cells显示文摘 | Yi‐Xin Yang Zheng‐Mei Song Bin Cheng Kun Xiang Xin‐Xin Chen Jia‐Hui Liu Aoneng Cao Yanli Wang Yuanfang Liu Haifang Wang | 2014 | J Appl Toxicol2014，，4： | 1 |
| 6 | Method for degree fully fuzzy linear programming problems using deviation measure显示文摘 | Cheng Haifang Huang Weilai Cai Jianhu | 2013 | Journal of Systems Engineering and Electron- cs2013，，5： | 1 |
| 7 | Method for solving fully fuzzy linear programming problems using deviation degree measure显示文摘A new fully fuzzy linear programming(FFLP)problem with fuzzy equality constraints is discussed.Using deviation degree measures,the FFLP problem is transformed into a crispδ-parametric linear programming(LP)problem.Giving the value of deviation degree in each constraint,theδ-fuzzy optimal solution of the FFLP problem can be obtained by solving this LP problem.An algorithm is also proposed to find a balance-fuzzy optimal solution between two goals in conflict:to improve the values of the objective function and to decrease the values of the deviation degrees.A numerical example is solved to illustrate the proposed method. | Haifang Cheng Weilai Huang Jianhu Cai | 2013 | Journal of Systems Engineering and Electronics2013，24，5： | 0 |
| 8 | A Simple yet Effective Framework for Active Learning to Rank显示文摘While China has become the largest online market in the world with approximately 1 billion internet users,Baidu runs the world's largest Chinese search engine serving more than hundreds of millions of daily active users and responding to billions of queries per day.To handle the diverse query requests from users at the web-scale,Baidu has made tremendous efforts in understanding users'queries,retrieving relevant content from a pool of trillions of webpages,and ranking the most relevant webpages on the top of the res-ults.Among the components used in Baidu search,learning to rank(LTR)plays a critical role and we need to timely label an extremely large number of queries together with relevant webpages to train and update the online LTR models.To reduce the costs and time con-sumption of query/webpage labelling,we study the problem of active learning to rank(active LTR)that selects unlabeled queries for an-notation and training in this work.Specifically,we first investigate the criterion-Ranking entropy(RE)characterizing the entropy of relevant webpages under a query produced by a sequence of online LTR models updated by different checkpoints,using a query-by-com-mittee(QBC)method.Then,we explore a new criterion namely prediction variances(PV)that measures the variance of prediction res-ults for all relevant webpages under a query.Our empirical studies find that RE may favor low-frequency queries from the pool for la-belling while PV prioritizes high-frequency queries more.Finally,we combine these two complementary criteria as the sample selection strategies for active learning.Extensive experiments with comparisons to baseline algorithms show that the proposed approach could train LTR models to achieve higher discounted cumulative gain(i.e.,the relative improvement DCG4=1.38%)with the same budgeted labellingefforts. | Qingzhong Wang Haifang Li Haoyi Xiong Wen Wang Jiang Bian Yu Lu Shuaiqiang Wang Zhicong Cheng Dejing Dou Dawei Yin | 2024 | Machine Intelligence Research2024，21，1： | 0 |
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