Results 201 to 210 of about 405,044 (354)

Multimodal Haptic Perception Through Synergistic Nanocomposite Sensor Arrays

Advanced Materials Technologies, EarlyView.
Multi‐modal fingertip haptics are advanced through a bioinspired &vertical‐via' electronic skin architecture. A confined PDMS/MWCNT/NiNP nanocomposite, sitting at the percolation threshold, enables tactile, thermal, and magnetic sensing. A unique via‐density gradient and dedicated &Un‐Touch' reference nodes provide robust spatial resolution and signal ...
Amos Bardea, Fernando Patolsky
wiley   +1 more source

An Ensemble Learning Artificial Intelligence Model for Alzheimer's Disease Detection Using OCT. [PDF]

Ophthalmol Sci
Ran AR, Hu X, Hui HYH, Dai J, Chan VTT, Ho K, Au LWC, Ng CF, Sham K, Zheng C, Liu X, He Q, Tham CC, Kwok TCK, Hilal S, Cheng CY, Chua J, Schmetterer L, Liu TYA, Tham YC, Li-Hsian Chen C, Wong TY, Mok VCT, Cheung CY.   +23 more
europepmc   +1 more source

COVLIAS 1.0: Lung Segmentation in COVID-19 Computed Tomography Scans Using Hybrid Deep Learning Artificial Intelligence Models. [PDF]

Diagnostics (Basel), 2021
Suri JS, Agarwal S, Pathak R, Ketireddy V, Columbu M, Saba L, Gupta SK, Faa G, Singh IM, Turk M, Chadha PS, Johri AM, Khanna NN, Viskovic K, Mavrogeni S, Laird JR, Pareek G, Miner M, Sobel DW, Balestrieri A, Sfikakis PP, Tsoulfas G, Protogerou A, Misra DP, Agarwal V, Kitas GD, Teji JS, Al-Maini M, Dhanjil SK, Nicolaides A, Sharma A, Rathore V, Fatemi M, Alizad A, Krishnan PR, Frence N, Ruzsa Z, Gupta A, Naidu S, Kalra M.   +39 more
europepmc   +1 more source

At Home Detection of Ovarian Health Biomarker in Menstruation Blood

Advanced Materials Technologies, EarlyView.
A lateral flow assay enables the detection of anti‐Müllerian hormone directly in unprocessed menstrual blood using silica‐gold nanoshells and smartphone‐assisted machine learning analysis. The platform supports decentralized, user‐operated testing in wearable and dipstick formats, highlighting the potential of menstrual blood as a non‐invasive matrix ...
Lucas Dosnon, Thomas Rduch, Salma Sherif Azer, Inge K. Herrmann   +3 more
wiley   +1 more source

Can algorithms come to the rescue of a failing heart? Machine learning, artificial intelligence, and decision-making in cardiogenic shock. [PDF]

J Anesth Analg Crit Care
Bottussi A, Wieruszewski PM, Bignami EG, Swol J, Buda KG, Cheungpasitporn W, Elmadhoun O, D'Andria Ursoleo J.   +7 more
europepmc   +1 more source

Deep Learning Artificial Intelligence Model for Assessment of Hip Dislocation Risk Following Primary Total Hip Arthroplasty From Postoperative Radiographs. [PDF]

J Arthroplasty, 2021
Rouzrokh P, Ramazanian T, Wyles CC, Philbrick KA, Cai JC, Taunton MJ, Maradit Kremers H, Lewallen DG, Erickson BJ.   +8 more
europepmc   +1 more source

Machine Learning Enables Inverse Design of Optically Driven Microscopic Metavehicles

Advanced Optical Materials, EarlyView.
Machine‐learning‐based inverse design is used optimize “metavehicles” — flat microparticles based on metagratings that generate a strong lateral optical force from normally incident light. The optimized design exhibits a force efficiency of ∼88% and a measured propulsion speed in water much higher than previously reported, demonstrating that inverse ...
Vasilii Mylnikov, Mahdi Shanei, Mikael Käll   +2 more
wiley   +1 more source

Tumor cell- and infiltrating immune cell-based supervised learning artificial intelligence multimodal platform for tumor prognosis. [PDF]

NPJ Precis Oncol
Cai XJ, Peng CR, Ding CY, Cui YY, Gao L, Xu ZX, Li L, Zhang JY, Li TJ.   +8 more
europepmc   +1 more source

In-context learning in natural and artificial intelligence.

In-context learning refers to the ability of a neural network to learn from information presented in its context. While traditional learning in neural networks requires adjusting network weights for every new task, in-context learning operates purely by updating internal activations without needing any updates to network weights.
Jagadish, Akshay Kumar, Dasgupta, Ishita, Pesnot Lerousseau, Jacques, Binz, Marcel   +3 more
openaire   +1 more source

Soft Robotic Excretory Care Simulator for Nursing Education: Functional and Perceptual Biomimetics Approach

Advanced Robotics Research, EarlyView.
A soft robotic simulator is developed to replicate the digital removal of feces (DRF), a sensitive yet essential nursing procedure. Integrating soft actuators, sensors, and a realistic rectal model, the simulator balances functional fidelity with perceptual realism. Engineering evaluations and nurse feedback confirm its potential to enhance training in
Shoko Miyagawa, Sota Suzuki, Shizuka Watanabe, Zebing Mao, Young Ah Seong, Toshinori Fujie, Tatsuhiro Horii, Kenjiro Tadakuma, Koichi Suzumori, Hiroyuki Nabae, Shingo Maeda   +10 more
wiley   +1 more source