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Partial Linear Model Averaging Prediction for Longitudinal Data

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Journal of Systems Science & Complexity 2024年第2期37卷 863-885页

作者： LI Na FEI Yu zhang xinyu Faculty of Science Kunming University of Science and TechnologyKunming 650500China School of Statistics and Mathematics Yunnan University of Finance and EconomicsKunming 650221China Academy of Mathematics and Systems Science Chinese Academy of SciencesBeijing 100190China

Prediction plays an important role in data *** averaging method generally provides better prediction than using any of its *** though model averaging has been extensively investigated under independent errors,few authors have considered model averaging for semiparametric models with correlated *** this paper,the authors offer an optimal model averaging method to improve the prediction in partially linear model for longitudinal *** model averaging weights are obtained by minimizing criterion,which is an unbiased estimator of the expected in-sample squared error loss plus a *** properties,including asymptotic optimality and consistency of averaging weights,are established under two scenarios:(i)All candidate models are misspecified;(ii)Correct models are available in the candidate *** studies and an empirical example show that the promise of the proposed procedure over other competitive methods.

关键词： Asymptotic optimality longitudinal data model averaging estimator partially linear model prediction

A Demand Forecasting Method Based on Stochastic Frontier Analysis and Model Average: An Application in Air Travel Demand Forecasting

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Journal of Systems Science & Complexity 2019年第2期32卷 615-633页

作者： zhang xinyu ZHENG Yafei WANG Shouyang Academy of Mathematics and Systems Science Chinese Academy of Sciences Centerfor Forecasting Science Chinese Academy of Sciences Postdoctoral Working Station Shenwan Hongyuan Securities Co. Ltd. Center for Forecasting Science Chinese Academy of Sciences

Demand forecasting is often difficult due to the unobservability of the applicable historical demand series. In this study, the authors propose a demand forecasting method based on stochastic frontier analysis(SFA) models and a model average technique. First, considering model uncertainty,a set of alternative SFA models with various combinations of explanatory variables and distribution assumptions are constructed to estimate demands. Second, an average estimate from the estimated demand values is obtained using a model average technique. Finally, future demand forecasts are achieved, with the average estimates used as historical observations. An empirical application of air travel demand forecasting is implemented. The results of a forecasting performance comparison show that in addition to its ability to estimate demand, the proposed method outperforms other common methods in terms of forecasting passenger traffic.

关键词： Air travel demand demand forecasting model average model uncertainty stochastic frontier analysis

KeEL: knowledge enhanced entity linking in automatic biography construction

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The Journal of China Universities of Posts and Telecommunications 2015年第1期22卷 57-64,71页

作者： zhang Tianlei zhang xinyu Guo Mu Computer Science Department Tsinghua University

Biography is a direct and extensive way to know the representation of well known peoples, however, for common people, there is poor knowledge for them to be recognized. In recent years, information extraction （IE） technologies have been used to automatically generate biography for any people with online information. One of the key challenges is the entity linking （EL） which can link biography sentence to corresponding entities. Currently the used general EL systems usually generate errors originated from entity name variation and ambiguity. Compared with general text, biography sentences possess unique yet rarely studied relational knowledge （RK） and temporal knowledge （TK）, which could sufficiently distinguish entities. This article proposed a new statistical framework called the knowledge enhanced EL （KeEL） system for automated biography construction. It utilizes commonsense knowledge like PK and TK to enhance Entity Linking. The performance of KeEL on Wikipedia data was evaluated. It is shown that, compared with state-of-the-art method, KeEL significantly improves the precision and recall of Entity Linking.

关键词： knowledge enhanced entity linking entity linking biography construction Markov logic network Knowledge

Recent advances in transition-metal catalyzed nitrene transfer reactions with carbamates

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Chinese Chemical Letters 2024年第3期35卷 76-86页

作者： Yanshu Luo xinyu zhang Yuanzhi Xia College of Chemistry and Materials Engineering Wenzhou UniversityWenzhou 325035China

Nitrene transfer reactions are powerful tools in synthetic organic *** recent years,transitionmetal catalyzed nitrene transfer reactions with carbamates as the nitrene precursors have been widely *** species undergoes facile C-H amination,aziridination,and bifunctionalization of alkenes under the catalysis of different transition metals including Rh,Fe,Ru and others,enabling the efficient construction of various nitrogen-containing *** this review,the recent developments in nitrene transfer reactions with carbamates via N-O bond cleavage were introduced based on different types of reaction,and the key mechanistic information and synthetic applications of the methodologies were discussed.

关键词： Transition-metal catalysis Nitrene transfer Carbamate C—H amination Aziridination Bifunctionalization

Effect of methamidophos on soil fungi community in microcosms by plate count,DGGE and clone library analysis

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Journal of Environmental Sciences 2008年第5期20卷 619-625页

作者： LI xinyu zhang Huiwen WU Minna zhang Yan zhang Chenggang Microbial Resources and Ecology Group Institute of Applied Ecology Chinese Academy of Sciences Shenyang 110016 China

Methamidophos was widely used a pesticide in northern China. The potential influences of methamidophos on soil fungal community in black soil were assessed by plate count, 28S rDNA-PCR-DGGE, and clone library analysis. Three methamidophos levels （50, 150, and 250 mg/kg） were tested in soil microcosms. Results from plate count during a 60-d microcosm experiment showed that high concentrations of methamidophos （250 mg/kg） could significantly stimulate fungal populations. DGGE （denaturing gradient gel electrophoresis） fingerprinting patterns showed a significant difference between the responses of culturable and total fungi communities under the stress of methamidophos. Shannon diversity indices calculated from DGGE profiles indicated that culturable fungi in all microcosms with methamidophos treatment increased after 1 week of incubation. However, the diversity indices of total fungi decreased in the first week, as compared to the stimulation of culturable fungi. At the 8th week, however, all the microcosms treated by methamidophos were similar to the control microcosms in community structure as suggested by the Shannon diversity indices for both culturable and total fungi. In contrast, after 1 week the fungal structure of culturable and unculturable both were disturbed to different extent under the stresses of methamidophos by clustering analysis. Clone sequencing analysis indicated the stimulation of pathogenic and unculturable fungal populations by methamidophos treatment, suggetsing potential risks of plant disease outbreak.

关键词： methamidophos clone sequencing DGGE (denaturing gradient gel electrophoresis) fungi diversity

TMN4 complex embedded graphene as bifunctional electrocatalysts for high efficiency OER/ORR

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Journal of Energy Chemistry 2021年第4期30卷 437-443页

作者： Zhe Xue xinyu zhang Jiaqian Qin Riping Liu State Key Laboratory of Metastable Materials Science and Technology Yanshan UniversityQinhuangdao 066004HebeiChina Metallurgy and Materials Science Research Institute Chulalongkorn UniversityBangkok 10330Thailand

Developing highly active bifunctional electrocatalysts for oxygen evolution reaction(OER)and oxygen reduction reaction(ORR)is of great significance in energy conversion and storage *** this study,we systematically investigated the OER/ORR electrocatalytic activity of TMN_(4)@G system by using density functional theory(DFT)***,IrN_(4)@G is a very promising bifunctional catalyst for both OER and ORR with the extremely low overpotentials of 0.30 and 0.26 V,*** outstanding electrocatalytic performance is mainly attributed to the synergistic effect of Ir and *** importantly,by constructing 2D activity volcano plots,we obtained the limiting overpotentials of TMN_(4)@G system with the values of 0.26 V for OER and 0.24 V for *** findings open up new opportunities for further exploring graphene-based materials for highly efficient OER/ORR electrocatalysts.

关键词： N-doped graphene Transition Metal-N4 cluster First-principles calculations Oxygen evolution reaction Oxygen reduction reaction

Kriging Model Averaging Based on Leave-One-Out Cross-Validation Method

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Journal of Systems Science & Complexity 2024年第5期37卷 2132-2156页

作者： FENG Ziheng ZONG Xianpeng XIE Tianfa zhang xinyu School of Mathematics Statistics and MechanicsBeijing University of TechnologyBeijing 100124China Academy of Mathematics and Systems Science Chinese Academy of SciencesBeijing 100190China

In recent years,Kriging model has gained wide popularity in various fields such as space geology,econometrics,and computer *** a result,research on this model has *** this paper,the authors propose a model averaging estimation based on the best linear unbiased prediction of Kriging model and the leave-one-out cross-validation method,with consideration for the model *** authors present a weight selection criterion for the model averaging estimation and provide two theoretical justifications for the proposed ***,the estimated weight based on the proposed criterion is asymptotically optimal in achieving the lowest possible prediction ***,the proposed method asymptotically assigns all weights to the correctly specified models when the candidate model set includes these *** effectiveness of the proposed method is verified through numerical analyses.

关键词： Asymptotic optimality best linear unbiased prediction cross-validation Kriging model model averaging

Data-driven prediction of dimensionless quantities for semi-infinite target penetration by integrating machine-learning and feature selection methods

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Defence Technology（防务技术） 2024年第10期40卷 105-124页

作者： Qingqing Chen xinyu zhang Zhiyong Wang Jie zhang Zhihua Wang Institute of Applied Mechanics College of Mechanical and Vehicle EngineeringTaiyuan University of TechnologyTaiyuan 030024China Shanxi Key Lab.of Material Strength&structural Impact College of Mechanical and Vehicle EngineeringTaiyuan 030024China Department of Civil and Environmental Engineering National University of Singapore1 Engineering Drive 2117576Singapore

This study employs a data-driven methodology that embeds the principle of dimensional invariance into an artificial neural network to automatically identify dominant dimensionless quantities in the penetration of rod projectiles into semi-infinite metal targets from experimental *** derived mathematical expressions of dimensionless quantities are simplified by the examination of the exponent matrix and coupling relationships between feature *** a physics-based dimension reduction methodology,this way reduces high-dimensional parameter spaces to descriptions involving only a few physically interpretable dimensionless quantities in penetrating *** the relative importance of various dimensionless feature variables on the penetration efficiencies for four impacting conditions is evaluated through feature selection *** results indicate that the selected critical dimensionless feature variables by this synergistic method,without referring to the complex theoretical equations and aiding in the detailed knowledge of penetration mechanics,are in accordance with those reported in the ***,the determined dimensionless quantities can be efficiently applied to conduct semi-empirical analysis for the specific penetrating case,and the reliability of regression functions is validated.

关键词： Data-driven dimensional analysis Penetration Semi-infinite metal target Dimensionless numbers Feature selection

Anchoring Mo on C_(9)N_(4) monolayers as an efficient single atom catalyst for nitrogen fixation

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Journal of Energy Chemistry 2021年第6期30卷 443-450,I0011页

Electrochemical nitrogen fixation via a convenient and sustainable manner,exhibits an intriguing prospect for ammonia generation under ambient ***,the design and development of high-efficiency and low-cost electrocatalysts remains the major challenge confronting nitrogen reduction reaction(NRR).Herein,anchoring the single Mo atom on the C_(9)N_(4) substrate(Mo@C_(9)N_(4)) to form an efficient single-atom catalyst(SAC) is proposed for the conversion of N2 to *** employing density functional theory(DFT) calculations,we demonstrated that gas phase N2 can be sufficiently activated and efficiently reduced to NH3 on the surface of Mo@C_(9)N_(4).Meanwhile,we found that the NRR dominantly occurred on the Mo center via a preferred distal pathway with favorable limiting potential of 0.40 ***,the as-established Mo@C_(9)N_(4) catalyst exhibits an outstanding structural stability and good selectivity toward *** findings provide a promising platform for designing Mo-based SACs for electrochemical N2 fixation.

关键词： Carbon nitride covalent compounds Electrochemical nitrogen reduction Single-atom catalyst Ammonia synthesis First-principles calculations