Results 231 to 240 of about 95,650 (334)

Cost-effective and sustainable operation of microgrids using Improved Whale Optimization Algorithm. [PDF]

Sci Rep
El-Zaher SM, Ahmed AM, Ahmed EM, Sedki YT, Al-Muntaser HK, Sheta AN.   +5 more
europepmc   +1 more source

DASH: Deterministic Attention Scheduling for High-throughput Reproducible LLM Training [PDF]

Xinwei Qiang, Hongmin Chen, Songlin Sun, Jingwen Leng, Xin Liu, Minyi Guo   +5 more
openalex  

Sampling Strategy: An Overlooked Factor Affecting Artificial Intelligence Prediction Accuracy of Peptides’ Physicochemical Properties

Advanced Intelligent Discovery, EarlyView.
This study reveals that sampling strategy (i.e., sampling size and approach) is a foundational prerequisite for building accurate and generalizable AI models in peptide discovery. Reaching a threshold of 7.5% of the total tetrapeptide sequence space was essential to ensure reliable predictions.
Meiru Yan, Ankeer Abuduhebaier, Haojin Zhou, Jiaqi Wang   +3 more
wiley   +1 more source

Reliable Communication in Distributed Photovoltaic Sensor Networks: A Large Language Model-Driven Approach. [PDF]

Sensors (Basel)
Dong W, Liu X, Liu Q, Zhang G, Shi J, Zhao X, Lei Z, Wang W.   +7 more
europepmc   +1 more source

Revealing Protein–Protein Interactions Using a Graph Theory‐Augmented Deep Learning Approach

Advanced Intelligent Discovery, EarlyView.
This study presents a fast, cost‐efficient approach for classifying protein–protein interactions by integrating graph‐theory parametrization with deep learning (DL). Multiscale features extracted from graph‐encoded polarized‐light microscopy (PLM) images enable accurate prediction of binding strengths.
Bahar Dadfar, Gözde Kabay, Matthias Franzreb, Kody Whisnant, Nicholas A. Kotov, Joerg Lahann   +5 more
wiley   +1 more source

Towards AI-based precision rehabilitation via contextual model-based reinforcement learning. [PDF]

J Neuroeng Rehabil
Ye D, Luo H, Winstein C, Schweighofer N.
europepmc   +1 more source

Harnessing Machine Learning to Understand and Design Disordered Solids

Advanced Intelligent Discovery, EarlyView.
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley   +1 more source

Coordinated operation and multi-layered optimization of hybrid photovoltaic-small modular reactor microgrids. [PDF]

Sci Rep
Duan Y, Gao C, Huang Y, Luo Q, Xu Z.
europepmc   +1 more source

Deterministic Frame Stability via DAG Scheduling

Kara Rawson
openalex   +1 more source

Factorization Machine with Iterative Quantum Reverse Annealing: A Python Package for Batch Black‐Box Optimization With Reverse Quantum Annealing

Advanced Intelligent Discovery, EarlyView.
Factorization machine with iterative quantum reverse annealing (FMIRA) leverages quantum reverse annealing to perform batch black‐box optimization. Factorization machine with quantum annealing (FMQA) is a widely used python package for solving black‐box optimization problems using D‐Wave quantum annealers.
Andrejs Tučs, Ryo Tamura, Koji Tsuda
wiley   +1 more source