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| 1 | Characterization of Air Pollution in Urban Areas of Yangtze River Delta,China显示文摘The hallmark of development in the Yangtze River Delta(YRD) of East China has been sprawling urbanization. However, air pollution is a significant problem in these urban areas. In this paper, we investigated and analyzed the air pollution index(API) in four cities(Shanghai, Nanjing, Hangzhou and Ningbo) in the YRD from 2001 to 2012. We attempted to empirically examine the relationship between meteorological factors and air quality in the urban areas of the YRD. According to the monitoring data, the API in Shanghai, Nanjing, Hangzhou slightly declined and that in Ningbo increased over the study period. We analyzed the inter-annual, seasonal, and monthly variations of API, from which we found that the air quality had different temporal changes in the four cities. It was indicated that air quality was poor in winter and spring and best in summer. Furthermore, different weather conditions affected air quality level. The wind direction was considered as an important and influential factor to air pollution, which has an impact on the accumulating or cleaning processes of pollutants. The air quality was influenced by the different wind directions that varied with seasons and cities. | CHEN Tan DENG Shulin GAO Yu QU Lean LI Manchun CHEN Dong | 2017 | Chinese Geographical Science2017，27，5： | 7 |
| 2 | FORECASTING CHINA'S FOREIGN TRADE VOLUME WITH A KERNEL-BASED HYBRID ECONOMETRIC-AI ENSEMBLE LEARNING APPROACH显示文摘Due to the complexity of economic system and the interactive effects between all kinds of economic variables and foreign trade, it is not easy to predict foreign trade volume. However, the difficulty in predicting foreign trade volume is usually attributed to the limitation of many conventional forecasting models. To improve the prediction performance, the study proposes a novel kernel-based ensemble learning approach hybridizing econometric models and artificial intelligence (AI) models to predict China's foreign trade volume. In the proposed approach, an important econometric model, the co-integration-based error correction vector auto-regression (EC-VAR) model is first used to capture the impacts of all kinds of economic variables on Chinese foreign trade from a multivariate linear anal- ysis perspective. Then an artificial neural network (ANN) based EC-VAR model is used to capture the nonlinear effects of economic variables on foreign trade from the nonlinear viewpoint. Subsequently, for incorporating the effects of irregular events on foreign trade, the text mining and expert's judgmental adjustments are also integrated into the nonlinear ANN-based EC-VAR model. Finally, all kinds of economic variables, the outputs of linear and nonlinear EC-VAR models and judgmental adjustment model are used as input variables of a typical kernel-based support vector regression (SVR) for en- semble prediction purpose. For illustration, the proposed kernel-based ensemble learning methodology hybridizing econometric techniques and AI methods is applied to China's foreign trade volume predic- tion problem. Experimental results reveal that the hybrid econometric-AI ensemble learning approach can significantly improve the prediction performance over other linear and nonlinear models listed in this study. | Lean YU Shouyang WANG Kin Keung LAI | 2008 | Journal of Systems Science & Complexity2008，21，1： | 5 |
| 3 | Blockchain-driven supply chain finance solution for small and medium enterprises显示文摘Blockchain has attracted much attention in recent years with the development of cryptocurrency and digital assets.As the underlying technology of cryptocurrency,blockchain has numerous benefits,such as decentralization,collective maintenance,tamper-resistance,traceability,and anonymity.The potential of the blockchain technology(BT)is widely recognized in the financial field.Although some scholars have proposed the combination of blockchain and supply chain finance(SCF),the details of this combination is rarely mentioned.This study first analyzes the coupling between SCF and blockchain technology.Second,the conceptual framework of blockchain-driven SCF platform(BcSCFP)is presented.Third,the operation process of three SCF models on the BcSCFP is proposed.Finally,a case study combined with actual events is conducted.This paper has a positive practical significance in the operation and management of banks and loan enterprises. | Jian LI Shichao ZHU Wen ZHANG Lean YU | 2020 | Frontiers of Engineering Management2020，7，4： | 3 |
| 4 | Human resource allocation for multiple scientific research projects via improved pigeon-inspired optimization algorithm显示文摘Aiming at the complex and restrictive characteristics of human resource allocation in multiple scientific university research projects, an improved pigeon-inspired optimization(IPIO) algorithm is proposed wherein loss minimization and the shortest project delay time are considered as optimization goals. Firstly, mathematical modelling of the problem is carried out, and the multi-objective optimization problem is transformed into a single-objective optimization problem by means of a weighted solution. In the second step, the traditional pigeon-inspired optimization(PIO) algorithm is discretized, and an adaptive parameter strategy is adopted to improve the shortcomings of the algorithm itself. Finally, by comparing the simulation results with the original algorithm and the genetic algorithm in the optimization of human resource allocation in multiple projects, the feasibility and superiority of the proposed algorithm in the optimization of human resource allocation in multi-scientific research projects is verified. | LIU ChuanBin MA YongHong YIN Hang YU LeAn | 2021 | Science China(Technological Sciences)2021，64，1： | 3 |
| 5 | A Novel Hybrid FA-Based LSSVR Learning Paradigm for Hydropower Consumption Forecasting显示文摘Due to the nonlinearity and nonstationary of hydropower market data, a novel hybrid learning paradigm is proposed to predict hydropower consumption, by incorporating firefly algorithm(FA) into least square support vector regression(LSSVR), i.e., FA-based LSSVR model. In the novel model, the powerful and effective artificial intelligence(AI) technique, i.e., LSSVR, is employed to forecast hydropower consumption. Furthermore, a promising AI optimization tool, i.e., FA, is especially introduced to address the crucial but difficult task of parameters determination in LSSVR(e.g.,hyper and kernel function parameters). With the Chinese hydropower consumption as sample data,the empirical study has statistically confirmed the superiority of the novel FA-based LSSVR model to other benchmark models(including existing popular traditional econometric models, AI models and similar hybrid LSSVRs with other popular parameter searching tools), in terms of level and directional accuracy. The empirical results also imply that the hybrid FA-based LSSVR learning paradigm with powerful forecasting tool and parameters optimization method can be employed as an effective forecasting tool for not only hydropower consumption but also other complex data. | TANG Ling WANG Zishu LI Xinxie YU Lean ZHANG Guoxing | 2015 | Journal of Systems Engineering and Electronics2015，26，5： | 3 |
| 6 | Option Pricing under the Double Exponential Jump-Diffusion Model with Stochastic Volatility and Interest Rate显示文摘This paper proposes an efficient option pricing model that incorporates stochastic interest rate(SIR),stochastic volatility(SV),and double exponential jump into the jump-diffusion settings.The model comprehensively considers the leptokurtosis and heteroscedasticity of the underlying asset’s returns,rare events,and an SIR.Using the model,we deduce the pricing characteristic function and pricing formula of a European option.Then,we develop the Markov chain Monte Carlo method with latent variable to solve the problem of parameter estimation under the double exponential jump-diffusion model with SIR and SV.For verification purposes,we conduct time efficiency analysis,goodness of fit analysis,and jump/drift term analysis of the proposed model.In addition,we compare the pricing accuracy of the proposed model with those of the Black-Scholes and the Kou(2002)models.The empirical results show that the proposed option pricing model has high time efficiency,and the goodness of fit and pricing accuracy are significantly higher than those of the other two models. | Rongda Chen Zexi Li Liyuan Zeng Lean Yu Qi Lin Jia Liu | 2017 | Journal of Management Science and Engineering2017，2，4： | 2 |
| 7 | Forecasting crude oil price with an EMD-based neural network ensemble learningparadigm 显示文摘 | Yu Lean Wang Shouyang Lai K K | 2008 | Eneregy Economics2008，30，5： | 1 |
| 8 | Variable precision rough set for group decision-making: An application显示文摘 | Gang Xie Jinlong Zhang K.K. Lai Lean Yu | 2007 | International Journal of Approximate Reasoning2007，，2： | 1 |
| 9 | Neural network-based mean-variance-skewness model for portfolio selection显示文摘 | WANG SHOUYANG LAI KINKEUNG | 2008 | Computers & Operations Research2008，35，： | 1 |
| 10 | A neural-network- based nonlinear recta-modeling approach to financial time series forecasting显示文摘 | YU Lean WANG Shouyang LAI K K | 2009 | Applied Soft Computing2009，，： | 1 |
| 11 | Evolving least squares support vector machines for stock market trend mining 显示文摘 | YU LEAN CHEN HUANHUAN WANG SHOUYANG | 2009 | IEEE Transactions on Evolutionary Computation2009，13，1： | 1 |
| 12 | Least squares support vector machines ensemble models for credit scoring 显示文摘 | Zhou H-gang Lai N-Keung Yu Lean | 2010 | Expert Systems with Applications2010，37，1： | 1 |
| 13 | Mean-variance-skewness-kurtosis based portfolio optimization 显示文摘 | Kin Keung Lai Lean Yu Wang Shouyang | 2006 | Computer and computational Sciences2006，32，10： | 1 |
| 14 | Support vector machine based multi-agent ensemble learning for credit risk evaluation显示文摘 | Yu Lean Yue Wuyi Wang Shouyang | 2010 | Expert System with Applica- tions2010，37，2： | 1 |
| 15 | Neural network-based mean-variance-skewness mode] for portfolio selectionv 显示文摘 | Lean Yu Wang Sbouyang Kin Keung Lai | 2008 | Computers & Operations Research2008，35，1： | 1 |
| 16 | A distance-based group decision-making methodology for multi-person multi-criteria emergency decision support 显示文摘 | Lean Yu Kin Keung Lai | 2011 | Decision Support Systems2011，51，2： | 1 |
| 17 | Estimating the impact of extreme events on crude oil price: An EMD-based event analysis method显示文摘 | Xun Zhang Lean Yu Shouyang Wang Kin Keung Lai | 2009 | Energy Economics2009，31，5： | 1 |
| 18 | A novel nonlinear ensemble forecasting model incorporating GLAR and ANN for foreign exchange rates 显示文摘 | YU Lean WANG Shou-yang LAI K K | 2005 | Computers&Operations Research2005，32，： | 1 |
| 19 | A novel nonlinear ensemble forecasting model incorporating GLAR and ANN for foreign exchange rates显示文摘 | Yu Lean Wang Shouyang Lai Kin Keung | 2005 | Computers & Operations Research2005，32，10： | 1 |
| 20 | Carbon emission trading scheme exploration in China : A multi - agent - based model显示文摘 | LING TANG JIAQIAN WU LEAN YU | 2015 | Energy Policy2015，81，： | 1 |
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