Results 111 to 120 of about 165,960 (199)

Application of an artificial neural network (ANN) simulator to increase the operational efficiency of a roadheader. [PDF]

Sci Rep
Cheluszka P, Głuszek G, Rostami J.
europepmc   +1 more source

Transparent Inorganic–Organic Bilayer Neural Electrode Array and Integration to Miniscope System for In Vivo Calcium Imaging and Electrophysiology

Advanced Functional Materials, EarlyView.
This study presents the BioCLEAR system, a highly transparent and conductive neural electrode array composed of silver nanowires (AgNWs) and doped PEDOT:PSS, enabling neural recordings with minimal optical artifacts. When integrated with a GRIN lens, this cost‐effective neural implant allows simultaneous electrophysiological recording and GCaMP6‐based ...
Dongjun Han, Kevin Cheng, Swathy Sampath Kumar, Anil Bollimunta, Elizabeth E. Torr, Juhwan Kim, Kyoungmin Kim, Hyungjun Choi, Seongkwon Hwang, Sarah Brodnick, David Cheng, Lindsay Gray, Yei Hwan Jung, William L. Murphy, Aaron J. Suminski, Jonathan J. Nassi, Justin C. Williams, Dong‐Wook Park   +17 more
wiley   +1 more source

Text-to-image generation with enhanced GANs: Bridging semantic gaps using RNN and CNN. [PDF]

PLoS One
Ramzan S, Ramzan HA, Kalsum T, Adnan M, Chaudhary MA, Ali MM.   +5 more
europepmc   +1 more source

Dynamic Control of Synaptic Plasticity by Competing Ferroelectric and Trap‐Assisted Switching in IGZO Transistors with Al2O3/HfO2 Dielectrics

Advanced Functional Materials, EarlyView.
A frequency‐tunable ferroelectric synaptic transistor based on a buried‐gate InGaZnO channel and Al2O3/HfO2 dielectric stack exhibits linear and reversible weight updates using single‐polarity pulses. By switching between ferroelectric and trap‐assisted modes depending on input frequency, the device simplifies neuromorphic circuit design and achieves ...
Ojun Kwon, Dong Ho Lee, Seyoung Oh, Jongwon Yoon, Hyo‐Bae Kim, Ji‐Hoon Ahn, Woojin Park, Han Seul Kim, Byungjin Cho   +8 more
wiley   +1 more source

Lightweight deep learning model with spatial attention for accurate and efficient breast cancer prediction. [PDF]

Sci Rep
Jaafari J, Ezzine H, Douzi K, Douzi S.
europepmc   +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

Ensemble deep learning approach for traffic video analytics in edge computing. [PDF]

Sci Rep
Sathyamoorthy M, Rajasekar V, Krishnamoorthi S, Pamucar D.   +3 more
europepmc   +1 more source

Multicolor Optoelectronic Synapse Enabled by Photon‐Modulated Remote Doping in Solution‐Processed Van Der Waals Heterostructures

Advanced Functional Materials, EarlyView.
Multicolor optoelectronic synapses are realized by vertically integrating solution‐processed MoS2 thin‐film and SWCNT. The electronically disconnected but interactive MoS2 enables photon‐modulated remote doping, producing a bi‐directional photoresponse.
Jihyun Kim, Hyunbin Kim, Gang Jin Cheon, Dohyun Lee, In Soo Kim, Uiyeon Won, Jongseok Lee, Sang Min Won, Joohoon Kang   +8 more
wiley   +1 more source

Brain-Computer Interface Training Enhances Attention Function via Modulating Frontoparietal Connectivity: Evidence From Functional Near-Infrared Spectroscopy. [PDF]

Neural Plast
Huang Y, Ding Q, Chen Z, Chen J, Li Y, Chen L, Yao S, Lan Y, Xu G.   +8 more
europepmc   +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