Results 181 to 190 of about 255,719 (262)

Computational and experimental investigation of nanoparticle effects on tissue optical properties and optical coherence tomography imaging. [PDF]

Biomed Opt Express
Khatami SS, Ansari MA, Shariati B K B, Tuchin VV.   +3 more
europepmc   +1 more source

Why Physics Still Matters: Improving Machine Learning Prediction of Material Properties With Phonon‐Informed Datasets

Advanced Intelligent Discovery, EarlyView.
Phonons‐informed machine‐learning predictive models are propitious for reproducing thermal effects in computational materials science studies. Machine learning (ML) methods have become powerful tools for predicting material properties with near first‐principles accuracy and vastly reduced computational cost.
Pol Benítez, Cibrán López, Edgardo Saucedo, Teruyasu Mizoguchi, Claudio Cazorla   +4 more
wiley   +1 more source

Improved patient models for simulation of clinically realistic ⁶⁸Ga-SSTR PET and ¹⁷⁷Lu-PRRT SPECT studies. [PDF]

EJNMMI Phys
Gustafsson J, Kalaitzidis P, Curkic Kapidzic S, Ljungberg M, Sjögreen Gleisner K.   +4 more
europepmc   +1 more source

Factorization Machine with Iterative Quantum Reverse Annealing: A Python Package for Batch Black‐Box Optimization With Reverse Quantum Annealing

Advanced Intelligent Discovery, EarlyView.
Factorization machine with iterative quantum reverse annealing (FMIRA) leverages quantum reverse annealing to perform batch black‐box optimization. Factorization machine with quantum annealing (FMQA) is a widely used python package for solving black‐box optimization problems using D‐Wave quantum annealers.
Andrejs Tučs, Ryo Tamura, Koji Tsuda
wiley   +1 more source

Correction: Dal Monte et al. A Topical Formulation of Melatoninergic Compounds Exerts Strong Hypotensive and Neuroprotective Effects in a Rat Model of Hypertensive Glaucoma. <i>Int. J. Mol. Sci.</i> 2020, <i>21</i>, 9267. [PDF]

Int J Mol Sci
Dal Monte M, Cammalleri M, Amato R, Pezzino S, Corsaro R, Bagnoli P, Rusciano D.   +6 more
europepmc   +1 more source

AI‐Guided Co‐Optimization of Advanced Field‐Effect Transistors: Bridging Material, Device, and Fabrication Design

Advanced Intelligent Discovery, EarlyView.
This article outlines how artificial intelligence could reshape the design of next‐generation transistors as traditional scaling reaches its limits. It discusses emerging roles of machine learning across materials selection, device modeling, and fabrication processes, and highlights hierarchical reinforcement learning as a promising framework for ...
Shoubhanik Nath, Shauryaraj K. Jilkar, Aditya Udgaonkar, Yu (Joe) Lu, Ali Mesbah   +4 more
wiley   +1 more source

Modelling Skin Pigmentation Using the Monte Carlo Technique: A Review. [PDF]

Sensors (Basel)
Al-Halawani R, Qassem M, Kyriacou PA.
europepmc   +1 more source

Parametric Analysis of Spiking Neurons in 16 nm Fin Field‐Effect Transistor Technology

Advanced Intelligent Discovery, EarlyView.
Energy efficient computing has driven a shift toward brain‐inspired neuromorphic hardware. This study explores the design of three distinct silicon neuron topologies implemented in 16 nm fin field‐Effect transistor technology. While the Axon‐Hillock design achieves gigahertz throughput, its functional fragility persists. The Morris–Lecar model captures
Logan Larsh, Raiyan Siddique, Sarah S. Sharif, Yaser Mike Banad   +3 more
wiley   +1 more source

Individualized Dose-volume Reconstruction Workflow for the Normal Tissues of Pediatric Patients Treated with Passive Scattering Proton Therapy. [PDF]

Radiat Res
Griffin KT, Schuemann J, McNamara AL, Morató S, Joseph K, Esposito CE, Shin J, Mille MM, Jung JW, Lee C, Kitahara CM, Gibson TM, Berrington de González A, Yock TI, Paganetti H, Lee C.   +15 more
europepmc   +1 more source