Cited by Machine learning-based stacked ensemble model for predicting and regulating oxygen-containing compounds in nitrogen-rich pyrolysis bio-oil

Machine learning-based prediction model for the yield of nitrogen-enriched biomass pyrolysis products: Performance evaluation and interpretability analysis DOI

Dongmei Bi, Hui Wang,

Yinjiao Liu

et al.

Journal of Analytical and Applied Pyrolysis, Journal Year: 2024, Volume and Issue: 182, P. 106723 - 106723

Published: Aug. 28, 2024

Language: Английский

Citations

Spatial Distribution Characteristics and Influencing Factors of Neofusicoccum laricinum in China DOI

Hongwei Zhou, Chuanping Yang, Yantao Zhou

et al.

Forests, Journal Year: 2025, Volume and Issue: 16(3), P. 450 - 450

Published: March 2, 2025

The long-term spatial–temporal variation in shoot blight of larch China has not yet been clearly defined, and the mechanisms behind its long-distance spread remain unknown. This study, based on historical occurrence dataset China, used spatial statistical analysis to describe changes disease across five stages since 1973. Subsequently, study utilized Geo Detector Random Forest models investigate relationship between seven influencing factors. results revealed following: (1) exhibits significant directionality, with affected regions distributed along a northeast–southwest axis, epicenter is shifting southwestward; (2) Shandong Jilin provinces served as initial introduction points for larch, most infected counties other experiencing outbreaks 1989 1996, accompanied by noticeable neighboring provinces; (3) demonstrates positive clustering effect, forming monocentric “core–periphery” structure centered Liaoning Province, where kernel density values decrease gradually outward from core. identified “seedling planting area” potential driving factor disease. These findings underscore critical influence combined effects human activities natural factors shaping spatiotemporal distribution patterns larch.

Language: Английский

Citations

Precipitation and soil water amplify the influence of climatic factors on global vegetation reversals DOI

Shunping Ji, Zhaohui Luo,

Shouhai Shi

et al.

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: March 26, 2025

Abstract Satellite observations reveal significant changes in global terrestrial vegetation over the past four decades. However, neglect of interactions among influencing factors has sparked intense debates regarding dynamics at regional and scales. This study systematically evaluated spatiotemporal evolution (1982–2020) their complex driving mechanisms by integrating five remote sensing-derived products with climate socio-economic data. The results showed that approximately 51.98% area experienced change reversals between 1995 2005, “greenness” predominantly transitioning from increase to decrease. percentage differs different zones, ranging 46.41% cold zone 54.99% tropical zone. phenomenon was primarily driven weakening (6% ± 4%) interactive coupling total precipitation (TP), soil water (SW), other (e.g., temperature, vapor pressure deficit (VPD)), rather than being mainly attributed VPD as reported previous studies. findings underscore need more explicitly consider impact water-related on a warming climate.

Language: Английский

Citations

Study on Carbon Emission Accounting Method System and Its Application in the Iron and Steel Industry DOI

Le Ren,

Sihong Cheng,

Yali Tong

et al.

Sustainability, Journal Year: 2025, Volume and Issue: 17(9), P. 3829 - 3829

Published: April 24, 2025

Amid global climate change and the pursuit of carbon neutrality, steel industry, a major source emissions, urgently requires robust scientific emission accounting system to achieve sustainable development. This study systematically examines methods in constructing comprehensive framework that includes method classification, application analysis, future trend projections. The aim is provide theoretical support practical guidance for industry’s low-carbon transition. Through in-depth analysis existing methods, this summarizes their characteristics regarding boundaries, calculation principles, data requirements, explores current applications limitations industry. Looking ahead, research anticipates with advancement new-generation information technologies increasing governance demands, industry will evolve towards digitalization, refinement, standardization, offering more reliable transformation. offers foundation direction precise management enterprises provides basis policymakers develop effective reduction policies strategies, thereby promoting development

Language: Английский

Citations

Carbon emissions and government interventions in urban agglomerations of China: An integrated GWR and neural network approach DOI

Yang XU, Feng Xu, Guangqing Chi

et al.

Applied Geography, Journal Year: 2025, Volume and Issue: 179, P. 103645 - 103645

Published: April 29, 2025

Language: Английский

Citations

Machine Learning-Based Prediction Model for the Yield of Nitrogen-Enriched Biomass Pyrolysis Products: Performance Evaluation and Interpretability Analysis DOI

Dongmei Bi, Hui Wang,

Yinjiao Liu

et al.

Published: Jan. 1, 2024

Language: Английский

Citations

Machine learning-based stacked ensemble model for predicting and regulating oxygen-containing compounds in nitrogen-rich pyrolysis bio-oil DOI

Hui Wang,

Dongmei Bi,

Zhisen He

et al.

Renewable Energy, Journal Year: 2024, Volume and Issue: unknown, P. 122330 - 122330

Published: Dec. 1, 2024

Language: Английский

Citations