Results 191 to 200 of about 7,291,916 (317)

Research on gear shaving error analysis based on knowledge graph. [PDF]

Sci Rep
Men L, Cai A.
europepmc   +1 more source

A Patent Infringement Analysis Approach Based on Patent Knowledge Graph Driven Fusion of Graph and Image Similarity

Liting Jing, C. Zhou, Di Feng, Yubo Dou, Xiaoyan Fan, Shaofei Jiang   +5 more
openalex   +1 more source

KNowNEt:Guided Health Information Seeking from LLMs via Knowledge Graph Integration [PDF]

Youfu Yan, Yu Hou, Yongkang Xiao, Rui Zhang, Qianwen Wang   +4 more
openalex   +1 more source

Functional Materials for Environmental Energy Harvesting in Smart Agriculture via Triboelectric Nanogenerators

Advanced Functional Materials, EarlyView.
This review explores functional and responsive materials for triboelectric nanogenerators (TENGs) in sustainable smart agriculture. It examines how particulate contamination and dirt affect charge transfer and efficiency. Environmental challenges and strategies to enhance durability and responsiveness are outlined, including active functional layers ...
Rafael R. A. Silva, Giulio Fatti, Emanuel Carlos, Guilherme Ferreira, Sumita Goswami, Suman Nandy, Luiz H. C. Mattoso, Elvira Fortunato, Caio G. Otoni, Rodrigo Martins   +9 more
wiley   +1 more source

KSMoFinder-knowledge graph embedding of proteins and motifs for predicting kinases of human phosphosites. [PDF]

Bioinform Adv
Anandakrishnan M, Ross KE, Chen C, Vijay-Shanker K, Wu CH.   +4 more
europepmc   +1 more source

KG-Predict: A knowledge graph computational framework for drug repurposing

, 2022
Zhenxiang Gao, Pingjian Ding, Rong Xu
openalex   +2 more sources

MOFs and COFs in Electronics: Bridging the Gap between Intrinsic Properties and Measured Performance

Advanced Functional Materials, EarlyView.
Metal‐organic frameworks (MOFs) and covalent organic frameworks (COFs) hold promise for advanced electronics. However, discrepancies in reported electrical conductivities highlight the importance of measurement methodologies. This review explores intrinsic charge transport mechanisms and extrinsic factors influencing performance, and critically ...
Jonas F. Pöhls, R. Thomas Weitz
wiley   +1 more source

Unleashing the Power of Machine Learning in Nanomedicine Formulation Development

Advanced Functional Materials, EarlyView.
A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore, Cristiano Pesce, Antonietta Greco, Claudia Pisante, Greta Avancini, Valentina Di Francesco, Yosi Shamay, Paolo Decuzzi   +7 more
wiley   +1 more source

Bootstrapping Knowledge Graphs From Images and Text

, 2019
Jiayuan Mao, Yuan Yao, Stefan Heinrich, Tobias Hinz, Cornelius Weber, Stefan Wermter, Zhiyuan Liu, Maosong Sun   +7 more
openalex   +1 more source

A 3D Biofabricated Disease Model Mimicking the Brain Extracellular Matrix Suitable to Characterize Intrinsic Neuronal Network Alterations in the Presence of a Breast Tumor Disseminated to the Brain

Advanced Functional Materials, EarlyView.
A 3D disease model is developed using customized hyaluronic‐acid‐based hydrogels supplemented with extracellular matrix (ECM) proteins resembling brain ECM properties. Neurons, astrocytes, and tumor cells are used to mimic the native brain surrounding.
Esra Türker, Mateo S. Andrade Mier, Jessica Faber, Mike Friedrich, Zan Lamberger, Jeannette Weigelt, Christian Stigloher, Nicoletta Murenu, Natascha Schaefer, Jörg Tessmar, Gregor Lang, Silvia Budday, Katrin G. Heinze, Antje Appelt‐Menzel, Pamela L. Strissel, Reiner Strick, Carmen Villmann   +16 more
wiley   +1 more source