Results 71 to 80 of about 6,914 (235)

Hybrid Simulation–Machine Learning Surrogates for Coordinate‐Based Solar and Wind Energy Yield Assessment in Iraq: A Streamlit Decision‐Support Tool

Energy Science &Engineering, EarlyView.
This study integrates climatic simulations with machine learning to predict solar and wind energy across Iraq. Results show Random Forest excels for solar (R2 = 0.98) and neural networks for wind (R2 = 0.97), enabling a practical web tool for renewable energy planning. ABSTRACT Driven by the global shift away from fossil fuels, solar and wind resources
Bassam Musheer Kareem, Abdolsalam Ebrahimpour, Jafar Ghafouri, Roghayyeh Motallebzadeh   +3 more
wiley   +1 more source

Investigation on piezoelectric output performances of P(VDF-TrFE)-based composite films through component design

Journal of Advanced Dielectrics
In this work, conductor MXene (Ti3C2Tx), ferroelectric Bi[Formula: see text]Na[Formula: see text]Nb5O[Formula: see text] (BNN) and NaNbO3 (NN) modified P(VDF-TrFE) piezoelectric composite films were prepared by electrostatic spinning.
Guangming Su, Yuanyu Wang
doaj   +1 more source

Epistemic and aleatoric uncertainty quantification in weather and climate models

Quarterly Journal of the Royal Meteorological Society, EarlyView.
Aleatoric and epistemic uncertainties over time on weather and climate time‐scales, estimated through ensembles that sample aleatoric and epistemic uncertainty using Bayesian neural networks for parameterisations in the Lorenz 1996 model. The spread shows the 16th and 84th percentiles.
Laura A. Mansfield, Hannah M. Christensen   +1 more
wiley   +1 more source

Integrating Dropout and Kullback-Leibler Regularization in Bayesian Neural Networks for improved uncertainty estimation in Regression

MethodsX
The objective of the study is to enhance uncertainty prediction in regression problems by introducing a revolutionary Bayesian Neural Network (BNN) model.
Raghavendra M. Devadas, Vani Hiremani
doaj   +1 more source

Uncertainty Quantification for Machine Learning‐Based Ionosphere and Space Weather Forecasting: Ensemble, Bayesian Neural Network, and Quantile Gradient Boosting

Space Weather, 2023
Machine learning (ML) has been increasingly applied to space weather and ionosphere problems in recent years, with the goal of improving modeling and forecasting capabilities through a data‐driven modeling approach of nonlinear relationships.
Randa Natras, Benedikt Soja, Michael Schmidt   +2 more
doaj   +1 more source

Responsive nitric oxide‐releasing antibacterial materials for biofilm‐associated infections: Mechanisms, design strategies, and applications

Responsive Materials, EarlyView.
This review summarizes the recent progress in responsive NO‐releasing materials and their applications in biofilm‐associated infectious diseases. The design principles and response mechanisms are given to provide inspiration toward the future development of multi‐responsive NO‐releasing materials.
Wenyue Sun, Meiqi Li, Di Nan, Jiaqi Zhao, Yu Chen, Yao Gao, Shichen Zhang, Kirsten L. Van Landuyt, Manlin Qi   +8 more
wiley   +1 more source

Ecological and Technological Insights to Enhance Rice Production: A Machine Learning Approach for Sustainable Agriculture in Granma, Cuba

Sustainable Development, EarlyView.
ABSTRACT Amid the quest for sustainable agriculture, this study explores key ecological and technological factors influencing crop production under climate change. We conduct a comprehensive assessment of temperature, biomass, farmer education, renewable energy devices, greenhouse gas emissions and their effects on rice yields in Granma, Cuba, from ...
Afzal Ahmed Dar, Zhi Chen, Sergio RodrIguez‐Rodríguez, Fariborz Haghighat, Chunjiang An, Roberto Alejandro García Reyes, Beatriz González‐Rosales   +6 more
wiley   +1 more source

Enhanced probabilistic prediction of pavement deterioration using Bayesian neural networks and cuckoo search optimization

Scientific Reports
The predictive performance of probabilistic pavement condition deterioration is critical for effective maintenance and rehabilitation decisions.
Feng Xiao, Biying Shi, Jie Gao, Huapeng Chen, Di Yang   +4 more
doaj   +1 more source

MXene‐Based Flexible Memory and Neuromorphic Devices

Small, EarlyView.
The unique two‐dimensional structure, excellent electrical conductivity, and diverse surface groups of MXenes have garnered significant attention. Coupled with their exceptional flexibility, MXene‐based devices hold immense potential for flexible memory and neuromorphic systems. This review comprehensively discusses the fundamentals of flexible devices,
Yan Li, Guanglong Ding, Yongbiao Zhai, Ziyu Lv, Yan Yan, Shuangmei Xue, Kui Zhou, Meng Zhang, Yutong Zhang, Qi‐Jun Sun, Yi Liu, Vellaisamy A. L. Roy, Ye Zhou, Su‐Ting Han   +13 more
wiley   +1 more source

Behavioral Study of Bayesian Neural Networks Under a Typical Surrogate Model-Assisted Evolutionary Search Framework

IEEE Access
The machine learning method for surrogate modeling is a keystone in surrogate model-assisted evolutionary algorithms (SAEAs). The current arguably most widely used surrogate modeling methods in SAEAs are Gaussian process and radial basis function.
Yushi Liu, Muhammad Ali Imran, Masood Ur-Rehman, Bo Liu   +3 more
doaj   +1 more source