Efficiency assessment and scenario simulation of the water-energy-food system in the Yellow river basin, China

Energy, Journal Year: 2024, Volume and Issue: 305, P. 132279 - 132279

Published: July 2, 2024

Language: Английский

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Group dynamic game under bounded rationality in agreed transfer of China’s carbon trading secondary market

Computers & Industrial Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 110857 - 110857

Published: Jan. 1, 2025

Language: Английский

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Assessment and enhancement pathways of the Water-Energy-Food-Economy-Ecosystem Nexus in China's Yellow River Basin

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 134492 - 134492

Published: Jan. 1, 2025

Language: Английский

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A Decomposition-Integration Framework of Carbon Price Forecasting Based on Econometrics and Machine Learning Methods

Mathematics, Journal Year: 2025, Volume and Issue: 13(3), P. 464 - 464

Published: Jan. 30, 2025

Carbon price forecasting and pricing are critical for stabilizing carbon markets, mitigating investment risks, fostering economic development. This paper presents an advanced decomposition-integration framework which seamlessly integrates econometric models with machine learning techniques to enhance forecasting. First, the complete ensemble empirical mode decomposition adaptive noise (CEEMDAN) method is employed decompose data into distinct modal components, each defined by specific frequency characteristics. Then, Lempel–Ziv complexity dispersion entropy algorithms applied analyze these facilitating identification of their unique attributes. The subsequently employs GARCH predicting high-frequency components a gated recurrent unit (GRU) neural network optimized grey wolf algorithm low-frequency components. Finally, GRU model utilized integrate predictive outcomes nonlinearly, ensuring comprehensive precise forecast. Empirical evidence demonstrates that this not only accurately captures diverse characteristics different but also significantly outperforms traditional benchmark in accuracy. By optimizing optimizer (GWO) algorithm, enhances both prediction stability adaptability, while nonlinear integration approach effectively mitigates error accumulation. innovative offers scientifically rigorous efficient tool forecasting, providing valuable insights policymakers market participants trading.

Language: Английский

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Using the TSA-LSTM two-stage model to predict cancer incidence and mortality

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(2), P. e0317148 - e0317148

Published: Feb. 20, 2025

Cancer, the second-leading cause of mortality, kills 16% people worldwide. Unhealthy lifestyles, smoking, alcohol abuse, obesity, and a lack exercise have been linked to cancer incidence mortality. However, it is hard. Cancer lifestyle correlation analysis mortality prediction in next several years are used guide people's healthy lives target medical financial resources. Two key research areas this paper Data preprocessing sample expansion design Using experimental comparison, study chooses best cubic spline interpolation technology on original data from 32 entry points 420 converts annual into monthly solve problem insufficient prediction. Factor possible because sources indicate changing factors. TSA-LSTM Two-stage attention popular tool with advanced visualization functions, Tableau, simplifies paper's study. Tableau's testing findings cannot analyze predict time series data. LSTM utilized by optimization model. By commencing input feature attention, model technique guarantees that encoder converges subset sequence features during output features. As result, model's natural learning trend quality enhanced. The second step, performance maintains We can choose network improve forecasts based real-time performance. Validating source factor using Most cancers overlapping risk factors, excessive drinking, exercise, obesity breast, colorectal, colon cancer. A poor directly promotes lung, laryngeal, oral cancers, according visual tests. expected climb 18-21% between 2020 2025, 2021. Long-term projection accuracy 98.96 percent, smoking may be main causes.

Language: Английский

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SPPformer: A transformer-based model with a sparse attention mechanism for comprehensive and interpretable ship price analysis

Transportation Research Part E Logistics and Transportation Review, Journal Year: 2025, Volume and Issue: 199, P. 104136 - 104136

Published: April 25, 2025

Language: Английский

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The new fashion for sustainable consumption: would you buy carbon label textiles?: Innovative conceptual model based on the theory of planned behavior and signaling theory

Environment Development and Sustainability, Journal Year: 2025, Volume and Issue: unknown

Published: May 3, 2025

Language: Английский

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Spatio-temporal prediction of total energy consumption in multiple regions using explainable deep neural network

Energy, Journal Year: 2024, Volume and Issue: 301, P. 131526 - 131526

Published: May 3, 2024

Language: Английский

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Incorporating key features from structured and unstructured data for enhanced carbon trading price forecasting with interpretability analysis

Applied Energy, Journal Year: 2025, Volume and Issue: 382, P. 125301 - 125301

Published: Jan. 8, 2025

Language: Английский

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A hybrid model for carbon price forecasting based on SSA-NSTransformer: Considering the role of multi-stage carbon reduction targets

Journal of Environmental Management, Journal Year: 2025, Volume and Issue: 375, P. 124237 - 124237

Published: Jan. 29, 2025

Language: Английский

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