Results 151 to 160 of about 835 (216)

Machine Learning Unveils Isolated‐Surrounded Pt Motifs in High‐Entropy Alloys for Superior Low‐Temperature Ammonia Oxidation

ENERGY &ENVIRONMENTAL MATERIALS, EarlyView.
A physics‐informed machine learning approach successfully decodes the complex catalytic activity of high‐entropy alloys for ammonia oxidation. By revealing a synergistic mechanism involving lattice and electronic couplings, the study identifies a superior “isolated‐surrounded” platinum motif.
Shangfeng Jiang, Ting Tao, Kexiang Guo, Xinyu Fan, Xu Zhang, Zhen Zhou   +5 more
wiley   +1 more source

Spurious alignment between large language models and brains can emerge from non-robust methods and overlooked confounds. [PDF]

Nat Commun
Hadidi N, Feghhi E, Song BH, Blank IA, Kao JC.   +4 more
europepmc   +1 more source

Bridging computational and clinical strategies for presurgical identification of epileptogenic networks

Epilepsia Open, EarlyView.
Abstract Objective About one third of epilepsy patients are drug‐resistant. Resective epilepsy surgery remains a key treatment option but depends critically on accurate identification of the seizure onset zone (SOZ), which is still guided mainly by subjective visual inspection of electrophysiological signals.
Tena Dubcek, Debora Ledergerber, Kristina Koenig, Adham Elshahabi, Rafael Polania, Lukas Imbach   +5 more
wiley   +1 more source

Uncertainty aware and explainable construction cost prediction using a hybrid probabilistic learning model. [PDF]

Sci Rep
Chen L, Khalid OW, Tiang JJ, Tiang SS, Shin DY, Choo YH, Sharma A, Lim WH.   +7 more
europepmc   +1 more source

Tree‐Boost–Guided CNN–BiLSTM–Transformer for Solar Irradiance Forecasting: Cross‐Regional Evidence for Sustainable Energy Planning

Energy Science &Engineering, EarlyView.
This graphical abstract illustrates a reproducible pipeline that combines gradient‐boosting‐based feature selection with a CNN–BiLSTM–Transformer model to forecast solar irradiance across multi‐site satellite and ground datasets, delivering robust, high‐accuracy predictions that support sustainable grid planning and reliable PV integration.
Muhammad Farhan Hanif, Usama Hassan, Javid Hussain, Irfan Ali Khan, Yuldasheva Gulora, Syed Masood Arif Bukhari   +5 more
wiley   +1 more source

Understanding Peelle's Pertinent Puzzle bias in generalized least squares regression through eigenspectrum analysis. [PDF]

Sci Rep
Walton NAW, Fritsch WN, Lewis AM, Forbes J, Neudecker D, Sobes V.   +5 more
europepmc   +1 more source

Predicting Solar Photovoltaic Power Output in Saudi Arabia's Jazan Region: Performance Comparison of Machine Learning Models

Energy Science &Engineering, EarlyView.
Workflow of the PV power estimation and ML forecasting methodology. ABSTRACT Accurate prediction of solar panel energy output is vital for managing power systems effectively and maintaining a stable electrical grid. This is especially important in regions that rely heavily on renewable sources. This research provides a direct comparison of five machine
Abdoalateef Alzhrani, Abdullahi Abubakar Mas'ud, Mohd Khairul Amri Kamarudin, Firdaus Muhammad‐Sukki, Abdullahi Mohammad   +4 more
wiley   +1 more source

Generative Principal Component Regression via Variational Inference. [PDF]

IEEE Trans Signal Process
Talbot A, Keller CJ, Trevino C, Carlson DE, Dammer EB, Johnson ECB, Wingo TS, Kotlar AV.   +7 more
europepmc   +1 more source

Triggered Calcium Lightning Programs Cochlear Development

Exploration, EarlyView.
Summary: Before the onset of hearing, the developing inner ear generates spontaneous calcium signals that are thought to guide maturation. In this study, we discovered a rapid and widespread calcium flash—dubbed “Ca2+ lightning”—originating from supporting cells beneath the sensory hair cells, which triggers coordinated calcium waves across the entire ...
Qiang Ma, Hai‐Peng Wang, Yu‐Meng Jiang, Ke Lai, Ming‐Xian Li, Zhen Zhang, Li‐Na Gong, Jing‐Wei Lai, Kai‐Ming Su, Lu‐Yang Wang, Hui Wang, Shan‐Kai Yin, Hao He, Hai‐Bo Shi   +13 more
wiley   +1 more source

Predicting EU Emissions Allowance Prices Using Macroeconomic Indicators and Hybrid AI Models

Journal of Forecasting, EarlyView.
ABSTRACT Predicting carbon allowance prices has grown more crucial in relation to carbon market regulation, financial strategy, and environmental policy development. This study examines a hybrid forecasting system that combines deep learning with ensemble machine learning models to forecast the price fluctuations of EU Emissions Allowance (EUAs) within
Saptarshi Ganguly, Pooja Sengupta, Divya Aggarwal   +2 more
wiley   +1 more source