Results 161 to 170 of about 7,280 (257)

AI‐Powered Framework for Evaluating Drug Efficacy for Three‐Dimensional In Vitro Cancer Models in Robot‐Assisted Production

open access: yesAdvanced Robotics Research, EarlyView.
An AI‐powered, robot‐assisted framework automatically produces, images, and analyzes 3D tumor spheroids to evaluate drug efficacy. Integrated modules handle spheroid formation, live/dead staining, brightfield imaging, and automated image analysis, including spheroid segmentation, viability and metrics to assess the drug treatment efficacy. The workflow
Dalia Mahdy   +13 more
wiley   +1 more source

The convex hull of parking functions of length $n$ [PDF]

open access: yesEnumerative Combinatorics and Applications, 2021
Aruzhan Amanbayeva, Danielle Wang
doaj  

Molecular Appendix Adjusted Architectural Propagations of Red‐Light Controlled Macroscopic Photoactuating Performances

open access: yesAdvanced Robotics Research, EarlyView.
Amphiphilic chloroazobenzenes, bearing different amino acids as end groups alanine, phenylalanine, and valine, exhibit excellent photoisomerizations. Varying structures of three amino acid groups participate in supramolecular formations and therefore photoinduced morphological transformations via adjusting intermolecular interactions. These interaction
Shuangshuang Meng   +3 more
wiley   +1 more source

3D Printing of Soft Robotic Systems: Advances in Fabrication Strategies and Future Trends

open access: yesAdvanced Robotics Research, EarlyView.
Collectively, this review systematically examines 3D‐printed soft robotics, encompassing material selections, function integration, and manufacturing methodologies. Meanwhile, fabrication strategies are analyzed in order of increasing complexity, highlighting persistent challenges with proposed solutions.
Changjiang Liu   +5 more
wiley   +1 more source

Robotic Control for Human–Robot Collaborative Assembly Based on Digital Human Model and Reinforcement Learning

open access: yesAdvanced Robotics Research, EarlyView.
This work presents a robotic control method for human–robot collaborative assembly based on a biomechanics‐constrained digital human model. Reinforcement learning is used to generate physiologically plausible human motion trajectories, which are integrated into a virtual environment for robot control learning.
Bitao Yao   +4 more
wiley   +1 more source

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