S-Transformer: a new deep learning model enhanced by sequential transformer encoders for drought forecasting

Earth Science Informatics, Journal Year: 2025, Volume and Issue: 18(2)

Published: March 25, 2025

Language: Английский

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Three-dimensional vibrations and instabilities of electron-transporting multi-layered graphene sheets via nonlocal-continuum-based models

Applied Mathematical Modelling, Journal Year: 2025, Volume and Issue: unknown, P. 116103 - 116103

Published: March 1, 2025

Language: Английский

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Enhancing performance prediction of municipal solid waste generation: a strategic management

Frontiers in Environmental Science, Journal Year: 2025, Volume and Issue: 13

Published: April 3, 2025

Municipal Solid Waste Generation (MSWG) presents a significant challenge for sustainable urban development, with waste production escalating at alarming rates worldwide. To address this issue, accurate predictive models are essential optimizing management strategies. This study utilizes comprehensive dataset of 4,343 records from municipal management, incorporating variables such as population density, urbanization indices, and composition. Advanced machine learning algorithms, including Decision Trees (DT), Random Forest (RF), LightGBM, XGBoost, employed, XGBoost being introduced novel approach MSWG prediction. Its ability to model complex nonlinear relationships, handle missing data outliers robustly, prevent overfitting through advanced regularization techniques sets it apart other models. The finds that outperforms the achieving an R 2 value 0.985 RMSE 0.056, making most predictor MSWG. flexibility scalability further enhance its applicability in managing diverse datasets, feature-ranking capability is instrumental identifying key factors influencing generation. results demonstrate into frameworks can significantly improve resource allocation, reduce operational costs, contribute environmental sustainability. not only advances methodologies but also provides actionable insights planners policymakers effectively tackling growing crisis. findings highlight potential learning, particularly transformative tool strategic decision-making management.

Language: Английский

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Inclusive green finance approach to assess energy resilience: Integrating artificial intelligence (AI) utilization in energy strategy perspective

Energy Strategy Reviews, Journal Year: 2025, Volume and Issue: 59, P. 101696 - 101696

Published: April 7, 2025

Language: Английский

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Interface engineering through embedding Co2O3 nanoparticles onto MXenes layered structure for boosting supercapacitor performance

Electrochimica Acta, Journal Year: 2025, Volume and Issue: 525, P. 146121 - 146121

Published: April 8, 2025

Language: Английский

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Development and Characterization of Cr doped NiO/ Fe2O3 Nanomaterials for High-Efficiency Supercapacitor Performance.

Ceramics International, Journal Year: 2025, Volume and Issue: unknown

Published: April 1, 2025

Language: Английский

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Optimising wind-powered hydrogen production: Techno-economic feasibility and GIS-based distribution strategies

Journal of Power Sources, Journal Year: 2025, Volume and Issue: 642, P. 236888 - 236888

Published: April 16, 2025

Language: Английский

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Transition metal-functionalized hexagonal bipyramidal boron clusters as high-performance electrocatalysts for hydrogen evolution reaction: A DFT study

International Journal of Hydrogen Energy, Journal Year: 2025, Volume and Issue: 127, P. 912 - 929

Published: April 19, 2025

Language: Английский

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Enhancing efficiency and sustainability in essential utility services through smart manufacturing: Analysis of bio-electricity expension, agricultural output and renewable energy goals

International Journal of Hydrogen Energy, Journal Year: 2025, Volume and Issue: 128, P. 713 - 724

Published: April 19, 2025

Language: Английский

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Mathematical modeling and analysis of non-Newtonian Rabinowitsch fluid flow in an elliptical duct: Insights into pseudoplastic and dilatant behaviors

Modern Physics Letters B, Journal Year: 2025, Volume and Issue: unknown

Published: April 20, 2025

This research introduces a novel mathematical model for the peristaltic flow of non-Newtonian Rabinowitsch fluid within an elliptical duct, uniquely capturing both pseudoplastic and dilatant behaviors. By employing Cartesian coordinates with boundary conditions, preserves duct’s geometric integrity. The resulting complex partial differential equations, though challenging, were solved exactly using dimensional analysis scaling methods. Additionally, perturbation techniques utilized to thoroughly analyze dynamics. Comprehensive graphical analyses depict key characteristics such as dimensionless velocity, axial pressure gradient, rise, offering fresh insights into behavior in geometries. findings reveal that increase volumetric rate significantly enhances central velocity particularly fluids, while fluids exhibit reduced under similar conditions. Notably, gradient demonstrates distinct patterns, showing oscillatory fluctuations, underscoring limitations Newtonian models accurately representing these

Language: Английский

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