Results 161 to 170 of about 248,546 (297)

Comparison of DeePMD, MTP, GAP, ACE and MACE Machine‐Learned Potentials for Radiation‐Damage Simulations: A User Perspective

Advanced Intelligent Discovery, EarlyView.
The authors evaluated six machine‐learned interatomic potentials for simulating threshold displacement energies and tritium diffusion in LiAlO2 essential for tritium production. Trained on the same density functional theory data and benchmarked against traditional models for accuracy, stability, displacement energies, and cost, Moment Tensor Potential ...
Ankit Roy, Ram Devanathan, Sarah Allec, Giridhar Nandipati, Andrew M. Casella, David J. Senor, Duane D. Johnson, Ganesh Balasubramanian, Ayoub Soulami   +8 more
wiley   +1 more source

Low Temperature-ROS-Hormones Co-Regulation of Seed Dormancy Release and Germination in <i>Xanthoceras sorbifolium</i>. [PDF]

Plants (Basel)
Wang Y, An N, Chen Y, Wu Q, Yang Z, Cai H, Di J.   +6 more
europepmc   +1 more source

Cell Segmentation Beyond 2D—A Review of the State‐of‐the‐Art

Advanced Intelligent Discovery, EarlyView.
Cell segmentation underpins many biological image analysis tasks, yet most deep learning methods remain limited to 2D despite the inherently 3D nature of cellular processes. This review surveys segmentation approaches beyond 2D, comparing 2.5D and fully 3D methods, analyzing 31 models and 32 volumetric datasets, and introducing a unified reference ...
Fabian Schmeisser, Haseeb Muhammad, Junaid Younas, Gillian Lovell, Bianca Migliori, Andreas Dengel, Sheraz Ahmed   +6 more
wiley   +1 more source

Multi‐Property Machine Learning Models to Accelerate the Transition Toward Bio‐Based Emulsion Polymers

Advanced Intelligent Discovery, EarlyView.
A machine learning framework simultaneously predicts four critical properties of monomers for emulsion polymerization: propagation rate constant, reactivity ratios, glass transition temperature, and water solubility. These tools can be used to systematically identify viable bio‐based monomer pairs as replacements for conventional formulations, with ...
Kiarash Farajzadehahary, Nicholas Ballard   +1 more
wiley   +1 more source

A joint pilot optimization and channel estimation algorithm based on CBAM-CNN for multipath fading environments in underground coal mines. [PDF]

Sci Rep
Han F, Liu C, Lei Y, Sun Z.
europepmc   +1 more source

Determinants of Long-term Economic Development: An Empirical Cross-country Study Involving Rough Sets Theory and Rule Induction [PDF]

Empirical findings on determinants of long-term economic growth are numerous, sometimes inconsistent, highly exciting and still incomplete. The empirical analysis was almost exclusively carried out by standard econometrics.
Wilk, Szymon, Obersteiner, Michael
core  

Accelerating Discovery of Organic Molecular Crystals via Materials Informatics and Autonomous Experiments

Advanced Intelligent Discovery, EarlyView.
Materials informatics and autonomous experimentation are transforming the discovery of organic molecular crystals. This review presents an integrated molecule–crystal–function–optimization workflow combining machine learning, crystal structure prediction, and Bayesian optimization with robotic platforms.
Takuya Taniguchi, Kazuki Ishizaki, Ryo Fukasawa   +2 more
wiley   +1 more source

Deciphering the Complexity of Smoke Point in Virgin Olive Oils to Develop Simple Predictive Models. [PDF]

Foods
Díez-Betriu A, Quintanilla-Casas B, Masdemont JJ, Tres A, Vichi S, Guardiola F.   +5 more
europepmc   +1 more source

A High‐Precision Dynamic Movement Recognition Algorithm Using Multimodal Biological Signals for Human–Machine Interaction

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
This article describes a multimodal fusion data acquisition and processing system about electromyography for dynamic movement recognition and bioelectrical impedance for key posture recognition. In addition, a new dynamic–static fusion algorithm strategy is designed.
Chenhao Cao, Gang Ma, Zelin Chen, Yiming Ouyang, Hu Jin, Shiwu Zhang   +5 more
wiley   +1 more source

Tumor-SAM: Segment Anything Model for Semi-automatic Lung Tumor Segmentation in CT. [PDF]

Proc SPIE Int Soc Opt Eng
Xie L, Tong Y, Wu C, Torigian DA, Udupa JK, Wan Y.   +5 more
europepmc   +1 more source