Results 181 to 190 of about 16,300 (265)

Reinforcement Learning With a Low‐Cost Parallel Linkage Gripper for Static and Dynamic Planar In‐Hand Manipulation

Advanced Intelligent Systems, EarlyView.
Hardware‐learning co‐design combines volar‐plate‐inspired passive‐joint constraints with mechanism‐aware reinforcement learning for a low‐cost parallel‐linkage gripper. The resulting system performs quasi‐static object adjustment and dynamic pinch grasping, demonstrating reliable planar in‐hand manipulation and successful sim‐to‐real transfer. Parallel
Yanzhou Jin, Adam J. Spiers, Yusuke Iwasawa, Yutaka Matsuo, Yaonan Zhu   +4 more
wiley   +1 more source

Enhanced CT-based deep learning radiomics for bladder cancer prognosis. [PDF]

Mil Med Res
Cho WC.
europepmc   +1 more source

A Multidimensional Perspective on Network Conditions in Telesurgery

Advanced Intelligent Systems, EarlyView.
A multidimensional framework systematically evaluates how combined network impairments influence telesurgical performance. By jointly analyzing latency, jitter, bandwidth, and packet loss, the proposed methodology identifies relevant network condition parameters and their interactions, supporting the design of reliable and safe telesurgical systems ...
Florian Heemeyer, Marco Cina, Ege Cem Kirci, Laurent Vanbever, Quentin Boehler   +4 more
wiley   +1 more source

bHLH Family Transcription Factors: Molecular Switches in Plant Specialized Metabolism. [PDF]

Cells
Han X, Chen G, Peng J, Cao N, Li F, Wan S.   +5 more
europepmc   +1 more source

Engineering Intelligent iPSC Biomanufacturing: Integrating Robotic Automation, AI, and Process Control

Advanced Intelligent Systems, EarlyView.
This review highlights recent advances in automated iPSC culture systems integrating robotics, advanced imaging, artificial intelligence, and process control. These technologies improve reproducibility, scalability, and quality in cell expansion, differentiation, and real‐time monitoring, while emerging AI‐enabled and closed‐loop approaches offer new ...
Hyoryong Lee, Roma Desai, Byunggik Kim, Deok‐Ho Kim   +3 more
wiley   +1 more source

Exploring self-directed learning practices through ChatGPT among non-native Arabic learners at a Saudi university. [PDF]

Front Psychol
Alsamiri YA, Alshehri AA.
europepmc   +1 more source

Objectives and Key Results–Driven Multiagent Framework for Mechanical Design and Simulation

Advanced Intelligent Systems, EarlyView.
Objectives and key results (OKR)‐agent, an OKR‐driven multiagent framework, turns a plain‐language engineering goal into completed mechanical simulations. Three peer agents—supervisor, modeler, and simulator—share a compact board of OKR and coordinate external molecular dynamics (MD) and finite‐element solvers through a standardized tool interface. The
Jie Tian, Lin Pang, Jixin Hou, Xiao Sun, James Jaraczewski, Xianyan Chen, Kenan Song, Xiaoming Zhai, Tianming Liu, Keke Tang, Xianqiao Wang   +10 more
wiley   +1 more source

Acrothoracican bioerosion as evidence of Early Cretaceous barnacles (Cirripedia) in northwestern Gondwana. [PDF]

PeerJ
Cadena EA.
europepmc   +1 more source

How Organizations Learn and Innovate to Institutionalize Generative AI: Trust, Feedback Loops, and Cultural Friction

AI &Innovation, EarlyView.
ABSTRACT This study examines how organizations advance in their use of Generative AI (GenAI) tools after initial adoption with particular attention to the dynamics of trust, cultural resistance, and feedback mechanisms. Grounded in organizational learning theory and institutional theory, we propose a framework that captures the factors influencing how ...
Surajit Bag, Gautam Srivastava, Santosh Kumar Shrivastav, Daniel Rees   +3 more
wiley   +1 more source

When Do Undergraduate Students Prefer AI? Insights into AI Scoring and Feedback. [PDF]

Behav Sci (Basel)
Yildirim-Erbasli SN, Ilgun Dibek M, Thomas ML, Lesoway N.   +3 more
europepmc   +1 more source