Results 181 to 190 of about 102,109 (257)
Dual contextual learning for semi-supervised medical image classification. [PDF]
Liu J +5 more
europepmc +1 more source
ABSTRACT Using a lab‐in‐the‐field experiment, we investigate how providing information about food miles and pesticide residue influences willingness to pay (WTP) for potatoes among 407 shoppers in Taiwan, split between a supermarket and a farmers market.
Chiu‐Lin Huang +3 more
wiley +1 more source
Bias Calibration for Semi-Supervised Continual Learning. [PDF]
Ji Z, Jiao Z, Miao D, Tang C.
europepmc +1 more source
Exploring the Impact of Meat Alternative Labeling Regulations on the U.S. Meat Consumption Patterns
ABSTRACT The global demand for conventional meat continues to rise, but it is also associated with substantial environmental and health challenges. In response, meat alternatives have gained popularity, sparking debates over meat alternative labeling regulations. This study investigates the effects of meat alternative labeling regulations in the United
Jeong Hun Ji, Sang Hyeon Lee
wiley +1 more source
Abstract This study presents a coupled population balance model (PBM) for describing the degree‐of‐agglomeration (DoA) in crystallization by independently tracking total particle and agglomerate number densities. Applied to an industrial active pharmaceutical ingredient, the model outperformed bridge‐counting methods and accurately captured DoA trends ...
Yung‐Shun Kang +6 more
wiley +1 more source
Reducing annotation burden in medical imaging with ADGNET: A semi-supervised deep learning strategy. [PDF]
Yang X.
europepmc +1 more source
This study applies QSAR‐based new approach methodologies to 90 synthetic tattoo and permanent makeup pigments, revealing systemic links between their physicochemical properties and absorption, distribution, metabolism, and elimination profiles. The correlation‐driven analysis using SwissADME, ChemBCPP, and principal component analysis uncovers insights
Girija Bansod +10 more
wiley +1 more source
MM-WAE: Multimodal Wasserstein Autoencoders for Semi-Supervised Wafer Map Defect Recognition. [PDF]
Zhang Y, Sun Q, Liu Z, Zhang DW.
europepmc +1 more source
This perspective highlights how knowledge‐guided artificial intelligence can address key challenges in manufacturing inverse design, including high‐dimensional search spaces, limited data, and process constraints. It focused on three complementary pillars—expert‐guided problem definition, physics‐informed machine learning, and large language model ...
Hugon Lee +3 more
wiley +1 more source
Semi-supervised disentangled representation learning for single-cell RNA sequencing data. [PDF]
Liu H, Zou Y, Wei Z.
europepmc +1 more source

