Results 171 to 180 of about 318,560 (303)

Automation of Surgical Workflow Recognition: Unveiling the Surgical Instrument Kinematics that Underly Robot‐Assisted Prostatectomy Procedures

Advanced Intelligent Discovery, EarlyView.
Automated procedural analysis is recognized as one of the major game changers for robotic surgery. Meaning digital analysis needs to replace the manual assessments that set todays standard. Mechanical robotic‐instrument tracking enables the derivation of quantitative kinematic metrics that support behavior‐based workflow segmentation into distinct ...
Kateryna Pirkovets, Matthias N. van Oosterom, Vera A. Ottens, Henk G. van der Poel, Fijs W. B. van Leeuwen   +4 more
wiley   +1 more source

Multi-scenario simulation of land use change and landscape ecological vulnerability analysis in Fuzhou City based on GA-PLUS coupled modeling. [PDF]

Sci Rep
Zhou F, Wang J, Li Z, Zhou D, Hu T, Wang F.   +5 more
europepmc   +1 more source

Advanced Experiment Design Strategies for Drug Development

Advanced Intelligent Discovery, EarlyView.
Wang et al. analyze 592 drug development studies published between 2020 and 2024 that applied design of experiments methodologies. The review surveys both classical and emerging approaches—including Bayesian optimization and active learning—and identifies a critical gap between advanced experimental strategies and their practical adoption in ...
Fanjin Wang, Anthony Harker, Mohan Edirisinghe, Maryam Parhizkar   +3 more
wiley   +1 more source

First records of the genus <i>Parazygiella</i> Wunderlich, 2004 (Araneae, Araneidae) from China, with the description of two new species. [PDF]

Zookeys
Mi X, Wang C, Chen H.
europepmc   +1 more source

Factorization Machine‐Based Active Learning for Functional Materials Design with Optimal Initial Data

Advanced Intelligent Discovery, EarlyView.
This work investigates the optimal initial data size for surrogate‐based active learning in functional material optimization. Using factorization machine (FM)‐based quadratic unconstrained binary optimization (QUBO) surrogates and averaged piecewise linear regression, we show that adequate initial data accelerates convergence, enhances efficiency, and ...
Seongmin Kim, In‐Saeng Suh
wiley   +1 more source

Understanding landowner preferences for traditional and nature-based solutions incentive programs in North Carolina, USA. [PDF]

PLoS One
Deviney CB, Parajuli R, Cubbage FW, Sills EO, Bardon RE.   +4 more
europepmc   +1 more source

Bayesian Exploration of Metal‐Organic Framework‐Derived Nanocomposites for High‐Performance Supercapacitors

Advanced Intelligent Discovery, EarlyView.
An AI‐assisted approach is introduced to decode synthesis–performance relationships in metal‐organic framework‐derived supercapacitor materials using Bayesian optimization and predictive modeling, streamlining the search for optimal energy storage properties.
David Gryc, Pritam Banerjee, Felipe Jacques Lima Duarte, Joerg R. Jinschek, Premkumar Murugesan, Babak Rezaei, Stephan Sylvest Keller, Roland A. Fischer, Mian Zahid Hussain   +8 more
wiley   +1 more source

Syndrome of Inappropriate Secretion of Antidiuretic Hormone in a Dog With Meningoencephalitis of Unknown Etiology. [PDF]

Vet Med Sci
Lee Y, Park J, Chae Y, Yun T, Kim H, Kang BT.   +5 more
europepmc   +1 more source

FIRE‐GNN: Force‐Informed, Relaxed Equivariance Graph Neural Network for Rapid and Accurate Prediction of Surface Properties

Advanced Intelligent Discovery, EarlyView.
This study introduces FIRE‐GNN, a force‐informed, relaxed equivariant graph neural network for predicting surface work functions and cleavage energies from slab structures. By incorporating surface‐normal symmetry breaking and machine learning interatomic potential‐derived force information, the approach achieves state‐of‐the‐art accuracy and enables ...
Circe Hsu, Claire Schlesinger, Karan Mudaliar, Jordan Leung, Robin Walters, Peter Schindler   +5 more
wiley   +1 more source

Water use and physiological ecological adaptation strategies of Caragana microphylla of different forest ages in hunshandake sandy. [PDF]

Sci Rep
Gu R, Zhang L, Wan Z, He T, Dong L, Zhang X, Gao X, Li R, An Q.   +8 more
europepmc   +1 more source