Results 191 to 200 of about 982,398 (277)

Reticular Frameworks for Advanced Polymer Materials

Advanced Materials, EarlyView.
Reticular frameworks (RFs) such as metal–organic frameworks, covalent organic frameworks, and hydrogen‐bonded organic frameworks offer versatile platforms for polymer materials innovation, enabling regulated polymerization, efficient macromolecular purification, and polymer functionalization.
Bohan Cheng, Nobuhiko Hosono, Takashi Uemura   +2 more
wiley   +1 more source

High-Value Patents Recognition with Random Forest and Enhanced Fire Hawk Optimization Algorithm. [PDF]

Biomimetics (Basel)
Yao X, Li H, Wang S.
europepmc   +1 more source

Digital Cognitive Screening and ML‐Enabled Random Forest Modeling for the Detection of Cognitive Impairment

Russell Banks, Karl M. Thompson, Ali Jannati, Sean Tobyne, John Showalter, David W. Bates, Álvaro Pascual‐Leone   +6 more
openalex   +1 more source

Nature‐Inspired Design Strategies for Efficient Atmospheric Water Harvesting

Advanced Materials, EarlyView.
This review presents advances in bioinspired atmospheric water harvesting systems, emphasizing how structural motifs such as wettability gradients, directional transport, and hierarchical porosity have been translated into engineered fog‐collection and vapor‐sorption strategies for enhanced water uptake, accelerated transport, and energy‐efficient ...
Yunchan Lee, Shouhong Fan, Shu Yang
wiley   +1 more source

Exploring determinants of fear of cancer recurrence in postoperative colorectal cancer patients: a random forest model approach. [PDF]

J Cancer Res Clin Oncol
Wu J, Cao Y, Yao L, Chen L, Xu L.
europepmc   +1 more source

Early warning strategies for corporate operational risk: A study by an improved random forest algorithm using FCM clustering

X. Fang
openalex   +2 more sources

Property Optimization of Mn–Ni–Al Oxide Thermistors via an Integrated Framework of Efficient Composition Exploration and Image Analysis

Advanced Materials Interfaces, EarlyView.
In Mn–Ni–Al oxide thermistor materials, we combine efficient composition‐space sampling with convolutional neural networks trained on multimodal Scanning Electron Microscopy – Energy Dispersive X‐ray Spectroscopy (SEM‐EDS) images to construct models predicting resistance and B constant, revealing spinel‐like microstructural regions that control ...
Yo Kato, Keisuke Makino, Shogo Hashimura, Nozomi Arakawa, Hayami Takeda, Naoto Tanibata, Masanobu Nakayama, Naohiro Niizeki, Takayuki Nakaya   +8 more
wiley   +1 more source

Application of generalized linear mixed effects random forest for identifying risk factors of prediabetes in Tehran Lipid and Glucose Study. [PDF]

Sci Rep
Karimi Ghahfarokhi M, Zayeri F, Khalili D, Masci C, Sheidaei A, Mehrali Y, Masaebi F, Mehrabi Y.   +7 more
europepmc   +1 more source

Optimizing diabetes prediction using machine learning: a random forest approach

Aone Maenge, Tshiamo Sigwele, Cliford Bhende, Chandapiwa Mokgethi, Venu Madhav Kuthadi, Blessing Ifeoluwa Omogbehin   +5 more
openalex   +1 more source

Estimating tropical woody species diversity using Sentinel-2A and Random Forest: A case study in Dak Nong, Vietnam [PDF]

Nguyễn Thị Hương, Nguyen Duc Dinh
openalex   +1 more source