Development of machine learning models for prediction of current and future dementia. [PDF]
Jeong W, Chung W.
europepmc +1 more source
Modular, Textile‐Based Soft Robotic Grippers for Agricultural Produce Handling
This article introduces textile‐based pneumatic grippers that transform simple textiles into robust bending actuators. Detailed experiments uncover how cut geometry and fabric selection shape performance. Successful handling of fragile agricultural items showcases the potential of textile robotics for safe, scalable automation in food processing and ...
Zeyu Hou +4 more
wiley +1 more source
Explainable machine learning methods for predicting electricity consumption in a long distance crude oil pipeline. [PDF]
Chen H, Gao T, Wang L, Guo P.
europepmc +1 more source
Smart REASSURED Sensors via Machine‐Augmented Printable On‐Paper Arrays
This perspective highlights the emerging role of pattern‐recognition, printable on‐paper sensor arrays for intelligent PoC diagnostics. It discusses how paper's inherent limitations can be overcome through surface modification and scalable printing, and how machine‐learning analysis of cross‐reactive arrays enables multiplexed, low‐cost, and REASSURED ...
Naimeh Naseri, Saba Ranjbar
wiley +1 more source
Machine learning-based identification of determinants of pulse pressure in pregnant women. [PDF]
Aga MA.
europepmc +1 more source
The Magic Curiosity Arousing Tricks (MagicCATs) database in Italian younger and middle-aged adults: Descriptive statistics and rule-based machine learning [PDF]
Caterina Padulo +2 more
openalex +1 more source
Nanosafety data provide a guiding example for establishing best practices in data management, aligning with FAIR principles and quality criteria. This review explores existing quality assessment approaches for reliability, relevance, and completeness, emphasizing the need for harmonization and adaptation to nanomaterials and advanced materials. The aim
Verónica I. Dumit +43 more
wiley +1 more source
Editorial: Breakthroughs in Cryo-EM with machine learning and artificial intelligence. [PDF]
Eng ET, Hanson SM, Sanchez-Garcia R.
europepmc +1 more source
Integrated machine learning framework for phenolic derivatives: classification (toxicity) and regression (logP) models identify top drug‐like compounds. Random Forest outperformed for toxicity, while Linear Regression best predicted logP. A weighted scoring approach prioritized five safe, lipophilicity‐optimized candidates, supporting rational ...
Houria Nacer +7 more
wiley +1 more source
Landslide Susceptibility Evaluation Integrating Machine Learning and SBAS-InSAR-Derived Deformation Characteristics: A Case Study of Yining County, Xinjiang. [PDF]
Ma T, Yi X, Ci H, Wang R, Yang H, Yan Z.
europepmc +1 more source

