Wei Leng
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Wei Leng has a total of 15 co-authors. Here are the top co-authors by number of shared publications:
- Weiwei Shen
School of Mathematics and Computer Science, Tongling University, Tongling, China
2 shared publications - Chongwei Zheng
Dalian Naval Academy, Dalian 116018, China
1 shared publication - Feng Zhang
Fuzhou Institute of Oceanography, Minjiang University, Fuzhou 350108, China
1 shared publication - Guodong Xia
College of Mathematics and Statistics, Sichuan University of Science & Engineering, Yibin 644000, China
1 shared publication - Huafei Chen
College of Mathematics and Statistics, Sichuan University of Science & Engineering, Yibin 644000, China
1 shared publication Jiaxiang Chen
1 shared publication
Latest Publications
- Open Access
- 6 October 2026
- Published by Elsevier in Results in Engineering
- Vol. 32
Optimization of synthesis parameters for Na-X zeolite using AGA-BP and APSO-BP neural network
- 6 October 2026
- Published by Elsevier in Results in Engineering
- Vol. 32
Open AccessOptimization of synthesis parameters for Na-X zeolite using AGA-BP and APSO-BP neural network
- Open Access
- 11 March 2026
- Published by Elsevier in SSRN Electronic Journal
R-optimal designs for quadratic mixture canonical polynomials with spline
This paper considers R-optimal designs for quadratic mixture canonical polynomials with spline, wherein the effect of one component proportion on the response variable differs from that of the others. We mainly focused on two cases: the sink mixture model and the full mixture model. For the sink mixture model, we derive the minimum support R-optimal design directly. For the full mixture model, which can be reformulated as a second-order Scheffé model, we obtain the corresponding minimum support R-optimal design based on this transformation.
- 11 March 2026
- Published by Elsevier in SSRN Electronic Journal
Open AccessR-optimal designs for quadratic mixture canonical polynomials with spline
This paper considers R-optimal designs for quadratic mixture canonical polynomials with spline, wherein the effect of one component proportion on the response variable differs from that of the others. We mainly focused on two cases: the sink mixture model and the full mixture model. For the sink mixture model, we derive the minimum support R-optimal design directly. For the full mixture model, which can be reformulated as a second-order Scheffé model, we obtain the corresponding minimum support R-optimal design based on this transformation.
- Open Access
- 22 September 2025
- Published by MDPI AG in Sustainability
- Vol. 17 (18) , 8514
A Study on Predicting Natural Gas Prices Utilizing Ensemble Model
Natural gas, a key low-emission energy source with significant strategic value in modern energy systems, necessitates accurate forecasting of its market price to ensure effective policy planning and economic stability. This paper proposes an ensemble framework to enhance natural gas price forecasting accuracy across multiple temporal scales (weekly and monthly) by constructing hybrid models and exploring diverse ensemble strategies, while balancing model complexity and computational efficiency. For weekly data, an Autoregressive Integrated Moving Average (ARIMA) model optimized via 5-fold cross-validation captures linear patterns, while the Long Short-Term Memory (LSTM) network captures nonlinear dependencies in the residual component after seasonal and trend decomposition based on LOESS (STL). For monthly data, the superior-performing model (ARIMA or SARIMA) is integrated with LSTM to address seasonality and trend characteristics. To further improve forecasting performance, three diverse ensemble techniques including stacking, bagging, and weighted averaging are individually implemented to synthesize the predictions of the two baseline models. The bagging ensemble method slightly outperforms other models on both weekly and monthly data, achieving MAPE, MAE, RMSE, and values of 9.60%, 0.3865, 0.5780, and 0.8287 for the weekly data, and 11.43%, 0.5302, 0.6944, and 0.7813 for the monthly data, respectively. The accurate forecasting of natural gas prices is critical for energy market stability and the realization of sustainable development goals.- 22 September 2025
- Published by MDPI AG in Sustainability
- Vol. 17 (18) , 8514
Open AccessA Study on Predicting Natural Gas Prices Utilizing Ensemble Model
Natural gas, a key low-emission energy source with significant strategic value in modern energy systems, necessitates accurate forecasting of its market price to ensure effective policy planning and economic stability. This paper proposes an ensemble framework to enhance natural gas price forecasting accuracy across multiple temporal scales (weekly and monthly) by constructing hybrid models and exploring diverse ensemble strategies, while balancing model complexity and computational efficiency. For weekly data, an Autoregressive Integrated Moving Average (ARIMA) model optimized via 5-fold cross-validation captures linear patterns, while the Long Short-Term Memory (LSTM) network captures nonlinear dependencies in the residual component after seasonal and trend decomposition based on LOESS (STL). For monthly data, the superior-performing model (ARIMA or SARIMA) is integrated with LSTM to address seasonality and trend characteristics. To further improve forecasting performance, three diverse ensemble techniques including stacking, bagging, and weighted averaging are individually implemented to synthesize the predictions of the two baseline models. The bagging ensemble method slightly outperforms other models on both weekly and monthly data, achieving MAPE, MAE, RMSE, and values of 9.60%, 0.3865, 0.5780, and 0.8287 for the weekly data, and 11.43%, 0.5302, 0.6944, and 0.7813 for the monthly data, respectively. The accurate forecasting of natural gas prices is critical for energy market stability and the realization of sustainable development goals. - Open Access
Strong Convergence of the Truncated Euler–Maruyama Method for Nonlinear Stochastic Differential Equations with Jumps
The truncated Euler–Maruyama (EM) method for stochastic differential equations with Poisson jumps (SDEwPJs) has been proposed by Deng et al. in 2019. Although the finite-time -convergence theory has been established, the strong convergence theory remains absent. In this paper, the strong convergence refers to the use of an measure and places the supremum over time inside the expectation operation. Our version can be used to justify the method within Monte Carlo simulations that compute the expected payoff of financial products. Noting that the conditions imposed are too strict, this paper presents an existence and uniqueness theorem for SDEwPJs under general conditions and proves the convergence of the truncated EM method for these equations. Finally, two examples are considered to illustrate the application of the truncated EM method in option price calculation.Open AccessStrong Convergence of the Truncated Euler–Maruyama Method for Nonlinear Stochastic Differential Equations with Jumps
The truncated Euler–Maruyama (EM) method for stochastic differential equations with Poisson jumps (SDEwPJs) has been proposed by Deng et al. in 2019. Although the finite-time -convergence theory has been established, the strong convergence theory remains absent. In this paper, the strong convergence refers to the use of an measure and places the supremum over time inside the expectation operation. Our version can be used to justify the method within Monte Carlo simulations that compute the expected payoff of financial products. Noting that the conditions imposed are too strict, this paper presents an existence and uniqueness theorem for SDEwPJs under general conditions and proves the convergence of the truncated EM method for these equations. Finally, two examples are considered to illustrate the application of the truncated EM method in option price calculation. - Open Access
- 19 September 2024
- Published by MDPI AG in Journal of Marine Science and Engineering
- Vol. 12 (9) , 1675
A Study on the Impact of Vertical Grid Parameter Perturbations in the Regional Ocean Modeling System
In this study, the Regional Ocean Modeling System (ROMS) is employed to construct a three-dimensional barotropic ocean model with a monodirectional upper boundary and homogeneous and steady wind covering the entire computation area. Eight perturbation experiments are designed to determine the vertical grid distribution difference with high resolution at the surface and bottom. Two types are considered in the model, including removing the Coriolis force (type 1) and employing a different Coriolis force (type 2). According to the experiments, the velocity of the current in type 1 yields uncertainty, and wind energy could penetrate the upper ocean and reach the abyss. The surface velocity in type 2 is fundamentally compatible with the empirical relationship constructed by Ekman, and the curved lines of the vertical distribution of horizontal currents nearly match. For type 1, the velocity is very strong from the sea surface to the bottom. When comparing type 1 and type 2 cases, the Coriolis force obstructs the wind energy transfer into the deep ocean. In addition, the European Centre for Medium-Range Weather Forecasts (ECMWF)’s global surface wind distribution indicates that the realistic ocean upper wind boundary is similar to the numerical experiment in the Pacific and Atlantic oceans, where the wind direction is along the latitude line at the equator. In order to make the experimental situation as close as possible to the real ocean, validation experiments are conducted in this study to consider the uncertainty in the current profile at the equator. The simulation results of type 1 differ significantly from the data obtained from the real ocean. This uncertainty may transfer the signal to higher latitudes, causing incorrect simulation results, especially in the critical region. Overall, this research not only makes discoveries in physical ocean theory but also guides predictive and forecasting techniques for ocean modeling.
- 19 September 2024
- Published by MDPI AG in Journal of Marine Science and Engineering
- Vol. 12 (9) , 1675
Open AccessA Study on the Impact of Vertical Grid Parameter Perturbations in the Regional Ocean Modeling System
In this study, the Regional Ocean Modeling System (ROMS) is employed to construct a three-dimensional barotropic ocean model with a monodirectional upper boundary and homogeneous and steady wind covering the entire computation area. Eight perturbation experiments are designed to determine the vertical grid distribution difference with high resolution at the surface and bottom. Two types are considered in the model, including removing the Coriolis force (type 1) and employing a different Coriolis force (type 2). According to the experiments, the velocity of the current in type 1 yields uncertainty, and wind energy could penetrate the upper ocean and reach the abyss. The surface velocity in type 2 is fundamentally compatible with the empirical relationship constructed by Ekman, and the curved lines of the vertical distribution of horizontal currents nearly match. For type 1, the velocity is very strong from the sea surface to the bottom. When comparing type 1 and type 2 cases, the Coriolis force obstructs the wind energy transfer into the deep ocean. In addition, the European Centre for Medium-Range Weather Forecasts (ECMWF)’s global surface wind distribution indicates that the realistic ocean upper wind boundary is similar to the numerical experiment in the Pacific and Atlantic oceans, where the wind direction is along the latitude line at the equator. In order to make the experimental situation as close as possible to the real ocean, validation experiments are conducted in this study to consider the uncertainty in the current profile at the equator. The simulation results of type 1 differ significantly from the data obtained from the real ocean. This uncertainty may transfer the signal to higher latitudes, causing incorrect simulation results, especially in the critical region. Overall, this research not only makes discoveries in physical ocean theory but also guides predictive and forecasting techniques for ocean modeling.