Results 221 to 230 of about 2,145,263 (295)
Root Microbiome and Metabolome Traits Associated with Improved Post-Harvest Root Storage for Sugar Beet Breeding Lines Under Southern Idaho Conditions. [PDF]
Int J Mol SciMajumdar R +5 more
europepmc +1 more source
Arthritis Care &Research, Accepted Article.Objective High body mass index (BMI≥27 kg/m2) and aberrant gait biomechanics are primary risk factors for knee osteoarthritis (KOA), yet the specific loading patterns linked to clinical disease features remain unclear. Unsupervised machine learning (ML) was used to identify vertical ground reaction force (vGRF) waveform modes of variation and ...
Ashley N. Buck +10 more
wiley +1 more source
Generative AI in clinical practice: how should rheumatologists evaluate and monitor emerging tools?
Arthritis Care &Research, Accepted Article.Generative artificial intelligence (AI) is increasingly being used in rheumatology practice, to assist with tasks such as documentation, record summarization, and clinical decision support. Rheumatologists need a practical approach to determining what evidence and oversight each application requires.
Koushan Kouranloo +2 more
wiley +1 more source
A genomic dataset integrating genotyping-by-sequencing, SolCAP array and PCR marker data on tetraploid potato advanced breeding lines. [PDF]
Front Plant SciLeuenberger J +13 more
europepmc +1 more source
Arthritis Care &Research, Accepted Article.Objectives This study synthesised the content and reporting completeness of published physical therapist‐led interventions for glenohumeral osteoarthritis (GHOA). Methods This systematic review undertook electronic database searches in PubMed, EMBASE, Cochrane, CINAHL and PEDro (until June 2025) for any peer‐reviewed articles/studies describing ...
Daha G Muhammad +5 more
wiley +1 more source
dynoGP: Deep Gaussian Processes for Dynamic System Identification
International Journal of Adaptive Control and Signal Processing, EarlyView.This work introduces a novel class of deep models for system identification, dynamical deep Gaussian processes, which combine the strengths of data‐driven methods, such as those based on neural network architectures, with the ability to output a probability distribution for uncertainty representation.
Alessio Benavoli +3 more
wiley +1 more source
Advanced Engineering Materials, EarlyView.Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt +8 more
wiley +1 more source
Advanced Engineering Materials, EarlyView.A simplified thermoplastic pultrusion model is developed to predict thermal fields in glass fiber/polyethylene terephthalate (GF/PET) composites with reduced computational cost. By combining effective material homogenization, validation against literature data, and Gaussian‐process‐based optimization, the study reveals how heating limits, pulling speed,
Elder Soares +3 more
wiley +1 more source