Flexible Memory: Progress, Challenges, and Opportunities
Flexible memory technology is crucial for flexible electronics integration. This review covers its historical evolution, evaluates rigid systems, proposes a flexible memory framework based on multiple mechanisms, stresses material design's role, presents a coupling model for performance optimization, and points out future directions.
Ruizhi Yuan +5 more
wiley +1 more source
Comparison of Dairy Cow Behavior in Sand and Mattress Freestall Barns in Relation to Lameness
Nigel B. Cook +2 more
openalex +2 more sources
Recherches Sur les Brise-Lames Flottants Destinés à Réfléchir la Houle en Eau Peu Profonde [PDF]
A. K. Johnston
openalex +1 more source
Toward Environmentally Friendly Hydrogel‐Based Flexible Intelligent Sensor Systems
This review summarizes environmentally and biologically friendly hydrogel‐based flexible sensor systems focusing on physical, chemical, and physiological sensors. Furthermore, device concepts moving forward for the practical application are discussed about wireless integration, the interface between hydrogel and dry electronics, automatic data analysis
Sudipta Kumar Sarkar, Kuniharu Takei
wiley +1 more source
Ovine lameness in Ireland: a survey-based investigation of farmer reported prevalence, recognition, and treatment of lameness conditions. [PDF]
Delaney JW +3 more
europepmc +1 more source
Retrospective analysis of prevalence and risk factors of bovine lameness in dairy farms in South Africa [PDF]
openalex +1 more source
A novel machine learning approach classifies macrophage phenotypes with up to 98% accuracy using only nuclear morphology from DAPI‐stained images. Bypassing traditional surface markers, the method proves robust even on complex textured biomaterial surfaces. It offers a simpler, faster alternative for studying macrophage behavior in various experimental
Oleh Mezhenskyi +5 more
wiley +1 more source
Preliminary Investigation of Cecal Microbiota in Experimental Broilers Reared Under the Aerosol Transmission Lameness Induction Model. [PDF]
Do ADT +4 more
europepmc +1 more source
A Machine Learning Model for Interpretable PECVD Deposition Rate Prediction
This study develops six machine learning models (k‐nearest neighbors, support vector regression, decision tree, random forest, CatBoost, and backpropagation neural network) to predict SiNx deposition rates in plasma‐enhanced chemical vapor deposition using hybrid production and simulation data.
Yuxuan Zhai +8 more
wiley +1 more source

