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Hydrogen Energy Demand Management in China:A Department Scenario Analysis Method

Energy Engineering 2025年第3期122卷 971-983页

作者： Zhongxun Li Bing Wang Xiaolin Liu Center for Sustainable Development and Energy Policy Research School of Energy and Mining EngineeringChina University of Mining and Technology-BeijingBeijing100083China Research Institute of Decision-Making Science and Big Data School of ManagementChina University of Mining and Technology-BeijingBeijing100083China

The proposal of carbon neutrality target makes decarbonization and hydrogenation typical features of future energy development in *** a wide range of application scenarios,hydrogen energy will experience rapid growth in production and *** formulate an effective hydrogen energy development strategy for the future of China,this study employs the departmental scenario analysis method to calculate and evaluate the future consumption of hydrogen energy in China’s heavy industry,transportation,electricity,and other related *** technical parameters are selected and predicted accurately and reliably in combination with different development *** findings indicate that the period from 2030 to 2050 will enjoy rapid development of hydrogen energy,having an average annual growth rate of 2%to 4%.The technological progress and breakthroughs scenario has the greatest potential for hydrogen demand scale among the four development *** this scenario,the total demand for hydrogen energy is expected to reach 446.37Mt in *** sector will be the sector with the greatest potential for hydrogen deployment growth from 2023 to 2060,which is expected to rise from 0.038Mt to about 163.18Mt,with the ambitious growth in the ***,hydrogen energy has a considerable development potential in the steel sector,and the trend of de-refueling coke by hydrogenation in this sector will be imperative for this energy-intensive industries.

关键词： Hydrogen demand management department scenario analysis carbon neutrality

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The Stock Performance of Green Bond Issuers During COVID-19 Pandemic: The Case of China

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Asia-Pacific Financial Markets 2023年第1期30卷 211-230页

作者： Jin, Jiongye Zhang, Jianing College of Business and Public Management Wenzhou-Kean University Wenzhou 325060 China Center for Big Data and Decision-Making Technologies Wenzhou-Kean University Wenzhou 325060 China

The green bond (GB) is a new financial product in the green finance field that has recently become a corporate social responsibility (CSR) tool for organizations. Previous studies show that high-CSR firms receive more trust from shareholders during a financial crisis. This paper aims to assess the stock performance of publicly listed Chinese companies that issued GBs during the COVID-19 pandemic. The bond sample covers 2016–2019 and consists of 67 listed issuers. The paper uses the event study method based on the market and Fama-French (1993) three-factor models. Our results show that GB issuers exhibited significantly positive cumulative abnormal stock returns on the official announcement dates of the COVID-19 outbreak. The positive cumulative abnormal returns are mainly driven by non-financial GB issuers rather than financial GB issuers. The results reflect the attitudes of investors toward GB-issuing companies primarily in the context of the crisis and contribute to the development of green finance policies. © 2022, The Author(s), under exclusive licence to Springer Japan KK, part of Springer Nature.

关键词： COVID-19 CSR Event study Green bond Stock market

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An efficient multi-attribute combinatorial auction for 4PL transportation service procurement

An efficient multi-attribute combinatorial auction for 4PL t...

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第35届中国控制与决策会议

作者： Na Yuan Haiming Liang Min Huang Yanfeng Zhang College of Information Science and Engineering Northeastern University Center for Network Big Data and Decision-Making Business School Sichuan University College of Computer Science and Engineering Northeastern University

In this paper,we study the mechanism design problem of combinatorial reverse auction for 4 PL transportation service procurement(TSP) considering price and multiple nonprice *** order to achieve efficient winner determination and transportation request allocation,we develop a 0-1 programing model to minimize social cost,where social cost includes real transportation cost of 3 PLs and the costs incurred by non-price attributes.A multi-attribute VCG(MA-VCG) reverse auction mechanism is proposed,which satisfies efficiency,incentive compatibility,individual rationality,and budget ***,a numerical example is designed to demonstrate the effectiveness and applicability of the proposed ***,sensitivity analysis is implemented to illustrate the influences of attribute preference,cost coefficient and other parameters on auction results.

关键词： Transportation service procurement Mechanism design Combinatorial reverse auction Multi-attribute auction

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Time series prediction and application based on multi-kernel support vector regression 2

Time series prediction and application based on multi-kernel...

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2nd International Symposium on Computer Applications and Information Systems, ISCAIS 2023

作者： Yi, Xiaoxing Wen, Xueting Yin, Xiangfeng School of Mathematics and Computational Science Hunan University of Science and Technology Hunan province Xiangtan China School of Mathematics and Computational Science Hunan University of Science and Technology Hunan Engineering Research Center for Big Data and Intelligent Decision-making of Industrial Development Hunan province Xiangtan China

ISBN: (纸本)9781510666665

In this paper, we propose a new MRMRKA-SVR-GARCH model to predict the volatility of time series. This model is a combination of the MRMRKA-SVR model and the GARCH model. Since SVR-GARCH model has been successfully employed to forecast volatility. Empirical results show that the multi-kernel support vector regression based on minimal redundancy maximal correlation criteria and kernel target alignment (MRMRKA-SVR) tend to beat single-kernel SVR models in terms of forecasting accuracy. First,we use the MRMRKA algorithm to select a set of kernels to establish a multi-kernel, and establish the MRMRKA-SVR model. Then we use MRMRKA-SVR to estimate the volatility equation of GARCH model, and establish MRMRKA-SVR-GARCH model. Furthermore, the models’ predictive ability was evaluated using three empirical analysises on China Unicom’s stock price data and stock price data of ICBC. Under different basic kermel numbers m, compare the prediction errors between the proposed model and the SVR-GARCH model based on the mixed Gaussian kernels in the two stock data, the results show that the prediction accuracy of the MRMRKA-SVR-GARCH model fluctuates slightly with the change of m, and the MAE and RMSE of the MRMRKASVR- GARCH model are lower than the mixed-Gaussian-kernel SVR-GARCH model . Description MRMRKA-SVRGARCH model managed to outperform the mixed-Gaussian-kernel SVR-GARCH model. Threrfore, the MRMRKASVR- GARCH model can provide a certain degree of reference in analyzing the trend and prediction of volatility, and has high feasibility and effectiveness. © 2023 SPIE.

关键词： Time series

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Clustering method with axiomatization to support failure mode and effect analysis

A I I E Transactions

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A I I E Transactions 2023年第7期55卷 657-671页

作者： Yucheng Dong Siqi Wu Xiaoping Shi Yao Li Francisco Chiclana a Center for Network Big Data and Decision-Making Business School Sichuan University Chengdu China b Irving K. Barber School of Arts and Sciences University of British Columbia Kelowna Canada a Center for Network Big Data and Decision-Making Business School Sichuan University Chengdu Chinac Andalusian Research Institute in Data Science and Computational Intelligence University of Granada Granada Spain c Andalusian Research Institute in Data Science and Computational Intelligence University of Granada Granada Spaind Institute of Artificial Intelligence School of Computer Science and Informatics De Montfort University Leicester UK

Failure Mode and Effect Analysis (FMEA) is a highly structured risk-prevention management process that improves the reliability and safety of a system. This article investigates one of the most critical issues in FMEA practice: Clustering failure modes based on their risks. In the failure mode clustering problem, all identified failure modes need to be assigned to several predefined and risk-ordered categories to manage their risks. We model the clustering of failure modes through multi-expert multiple criteria decision making with an additive value function, and call it the additiveN-clustering problem. We begin by proposing six axioms that describe an ideal clustering method in the additiveN-clustering problem, and find that the EXogenous Clustering Method (EXCM), where category thresholds can be exogenously provided, is ideal (Exogenous Possibility Theorem), whereas any endogenous clustering method, where the clustering is determined endogenously in the given method, cannot satisfy all six axioms simultaneously (Endogenous Impossibility Theorem). In practice, endogenous clustering methods are important, due to the difficulty in providing accurate and reasonable category thresholds of the EXCM. Therefore, we propose the Consensus-based ENdogenous Clustering Method (CENCM) and discuss its axiomatic properties. We also apply the CENCM to the SARS-CoV-2 prevention case and justify the CENCM through axiomatic comparisons and a detailed simulation experiment.

关键词： Reliability management failure mode and effect analysis clustering axioms consensus

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Volunteer rescue dispatch during the Coronavirus Disease 2019 epidemic:Using the recruitment of volunteers for pneumonia epidemic prevention and control in Chun’an County as an example

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Journal of Safety Science and Resilience 2022年第4期3卷 330-339页

作者： Liting Chen Shengqun Chen Jing Zheng Jianqing Gao College of Electronics and Information Science Fujian Jiangxia UniversityFuzhou 350108China Research Institute for Data Analysis and Intelligent Decision-Making Fujian Jiangxia UniversityFuzhou 350108China School of Information Engineering Fujian Business UniversityFuzhou 350012China Fujian Provincial Universities Engineering Research Center of Big Data Analytics for Business Intelligence Fujian Business UniversityFuzhou 350012China

To solve the problem of volunteer dispatch during the Coronavirus Disease 2019(COVID-19)epidemic,a many-to-many two-sided matching volunteer dispatch method based on an improved predator-search algorithm is ***,different evaluation index sets for volunteers and rescue tasks were developed,and weightings were determined using the analytic hierarchy ***,the actual and expected values of the different indicators of the two parties were determined,and the triangular fuzzy number was used to calculate the satis-faction of the two *** on this number,we used a linear weighting method to calculate the combined satisfaction and build a many-to-many two-sided matching model according to the demands of both ***-sequently,an improved predator-search algorithm was used to solve the ***,taking the recruitment of volunteers for pneumonia epidemic prevention and control in Chun’an County as an example,the method proposed in our study was verified.A comparison and analysis of the results further demonstrated the feasibility and advantages of this method.

关键词： Volunteer Many-to-many Two-sided matching Predator-search algorithm

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Epistasis-aware genome-wide association studies provide insights into the efficient breeding of high-yield and high-quality rice

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Journal of Integrative Agriculture 2024年第8期23卷 2541-2556页

作者： Xiaogang He Zirong Li Sicheng Guo Xingfei Zheng Chunhai Liu Zijie Liu Yongxin Li Zheming Yuan Lanzhi Li Hunan Engineering&Technology Research Center for Agricultural Big Data Analysis&Decision-making/College of Plant Protection Hunan Agricultural UniversityChangsha 410128China Hubei Key Laboratory of Food Crop Germplasm and Genetic Improvement/Food Crop Institute Hubei Academy of Agricultural SciencesWuhan 430064China

Marker-assisted selection(MAS)and genomic selection(GS)breeding have greatly improved the efficiency of rice *** to the influences of epistasis and gene pleiotropy,ensuring the actual breeding effect of MAS and GS is still a difficult challenge to *** this study,113 indica rice varieties(V)and their 565 testcross hybrids(TC)were used as the materials to investigate the genetic basis of 12 quality traits and nine agronomic *** original traits and general combining ability of the parents,as well as the original traits and midparent heterosis of TC,were subjected to genome-wide association *** total,381 primary significantly associated loci(SAL)and 1,759 secondary SALs that had epistatic interactions with these primary SALs were *** these loci,322 candidate genes located within or nearby the SALs were screened,204 of which were cloned genes.A total of 39 MAS molecular modules that are beneficial for trait improvement were identified by pyramiding the superior haplotypes of candidate genes and desirable epistatic alleles of the secondary *** the SALs were used to construct genetic networks,in which 91 pleiotropic loci were ***,we estimated the accuracy of genomic prediction in the parent V and TC by incorporating either no SALs,primary SALs,secondary SALs or epistatic effect SALs as *** the prediction accuracies of the four models were generally not significantly different in the TC dataset,the incorporation of primary SALs,secondary SALs,and epistatic effect SALs significantly improved the prediction accuracies of 5(26%),3(16%),and 11(58%)traits in the V dataset,*** results suggested that SALs and epistatic effect SALs identified based on an additive genotype can provide considerable predictive power for the parental *** also provide insights into the genetic basis of complex traits and valuable information for molecular breeding in rice.

关键词： rice genome-wide association study epistasis gene pleiotropy maker-associated selection genome selection

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An efficient multi-attribute combinatorial auction for 4PL transportation service procurement

An efficient multi-attribute combinatorial auction for 4PL t...

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Chinese Control and decision Conference, CCDC

作者： Na Yuan Haiming Liang Min Huang Yanfeng Zhang College of Information Science and Engineering Northeastern University Shenyang China Center for Network Big Data and Decision-Making Business School Sichuan University Chengdu China College of Computer Science and Engineering Northeastern University Shenyang China

In this paper, we study the mechanism design problem of combinatorial reverse auction for 4PL transportation service procurement (TSP) considering price and multiple non-price attributes. In order to achieve efficient winner determination and transportation request allocation, we develop a 0-1 programing model to minimize social cost, where social cost includes real transportation cost of 3PLs and the costs incurred by non-price attributes. A multi-attribute VCG (MA-VCG) reverse auction mechanism is proposed, which satisfies efficiency, incentive compatibility, individual rationality, and budget balance. Then, a numerical example is designed to demonstrate the effectiveness and applicability of the proposed mechanism. Finally, sensitivity analysis is implemented to illustrate the influences of attribute preference, cost coefficient and other parameters on auction results.

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Scientists’ bounded mobility on the epistemic landscape

arXiv

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arXiv 2023年

作者： Zhang, Shuang Liu, Feifan Xia, Haoxiang Institute of Systems Engineering Dalian University of Technology Dalian116024 China Center for Big Data and Intelligent Decision-Making Dalian University of Technology Dalian116024 China

Despite persistent efforts in revealing the temporal patterns in scientific careers, little attention has been paid to the spatial patterns of scientific activities in the knowledge space. Here, drawing on millions of papers in six disciplines, we consider scientists’ publication sequence as "walks" on the quantifiable epistemic landscape constructed from large-scale bibliometric corpora by combining embedding and manifold learning algorithms, aiming to reveal the individual research topic dynamics and association between research radius with academic performance, along their careers. Intuitively, the visualization shows the localized and bounded nature of mobile trajectories. We further find that the distributions of scientists’ transition radius and transition pace are both left-skewed compared with the results of controlled experiments. Then, we observe the mixed exploration and exploitation pattern and the corresponding strategic trade-off in the research transition, where scientists both deepen their previous research with frequency bias and explore new research with knowledge proximity bias. We further develop a bounded exploration-exploitation (BEE) model to reproduce the observed patterns. Moreover, the association between scientists’ research radius and academic performance shows that extensive exploration will not lead to a sustained increase in academic output but a decrease in impact. In addition, we also note that disruptive findings are more derived from an extensive transition, whereas there is a saturation in this association. Our study contributes to the comprehension of the mobility patterns of scientists in the knowledge space, thereby providing significant implications for the development of scientific policymaking. Copyright © 2023, The Authors. All rights reserved.

关键词： Economic and social effects

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SETAR Model Research Based on Support Vector Regression Parameter Estimation Method and Empirical Analysis 3

SETAR Model Research Based on Support Vector Regression Para...

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2021 3rd International Conference on Robotics, Intelligent Control and Artificial Intelligence, ICRICA 2021

作者： Li, Yun Yin, Xiangfeng Wen, Xueting School of Mathematics and Computational Science Hunan University of Science and Technology Xiangtan China Sch. of Mathematics and Computational Science Hunan University of Science and Technology Hunan Engineering Research Center for Big Data and Intelligent Decision-making of Industrial Development Xiangtan China

In this paper, we use support vector regression (SVR) to estimate the parameters of the SETAR model for predicting the log-return of stock price data. SETAR model is usually estimated using maximum likelihood (ML) and minimize conditional sum-of-squares (CSS), assuming that the data are normal distribution. SVR is a powerful tool for regression estimation. Therefore, this paper proposes to use SVR instead of maximum likelihood and conditional least squares to estimate the parameters of the SETAR model. Empirical analysis on the two stock price data of China Unicom and ICBC, the results show that on the data of China Unicom, the prediction error of the Gaussian kernel SVR-SETAR model is 8% lower than that of the ML-SETAR model and the CSS-SETAR model. And on ICBC data, the prediction error of the Gaussian kernel SVR-SETAR model is 26.32% and 20% lower than that of the ML-SETAR model and CSS-SETAR model, respectively. This suggests that SVR can be used to estimate the parameters of the SESAT model and the SVR-SETAR model has higher predictive power. © Published under licence by IOP Publishing Ltd.

关键词： Normal distribution

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