Results 201 to 210 of about 16,074,262 (296)

LLM‐Based Multimodal Robotic Endoscope Control Framework for Enhancing Human–Robot Interaction in Minimally Invasive Surgery

open access: yesAdvanced Intelligent Systems, EarlyView.
An large language model‐powered multimodal framework is developed for robotic endoscope control. It integrates speech recognition and real‐time instrument tracking, achieving 89.47% command accuracy with ~1s latency for natural human–robot interaction in minimally invasive surgery.
Yisen Huang   +7 more
wiley   +1 more source

Development of an Inchworm‐Like Dual‐Balloon‐Assisted Colonoscopic Robot Based on a Two‐Segment Extensible Continuum Section

open access: yesAdvanced Intelligent Systems, EarlyView.
This work presents an inchworm‐like dual‐balloon‐assisted colonoscopic robot (IDCR) based on a rod‐driven two‐segment continuum section. The IDCR enables the active straightening of the sigmoid colon through an inchworm‐like locomotion strategy, thereby avoiding loop formation.
Dezhi Song   +4 more
wiley   +1 more source

Prediction models for dynamic respiratory muscle strength in COPD and asthma patients based on fixed-pressure inspiratory muscle performance. [PDF]

open access: yesBMC Pulm Med
Yilmaz YA   +9 more
europepmc   +1 more source

Revealing Hidden Raman Signatures Through Attention‐Based Spectral Unmixing

open access: yesAdvanced Intelligent Systems, EarlyView.
Weak Raman signatures are recovered from background‐dominated spectra using a transformer‐based, reference‐free spectral unmixing AI framework. Self‐attention reconstructs substrate contribution directly from mixed data, enabling reliable extraction of previously inaccessible vibrational features.
Dmitriy A. Poteryayev   +9 more
wiley   +1 more source

Integration of Liquid Handling Robotics, Turbidimetry, and Machine Learning Toward Inverse Design of Lower Critical Solution Temperature Polymer Formulations

open access: yesAdvanced Intelligent Systems, EarlyView.
An integrated OT‐2/plate reader platform automates LCST cloud point measurement of NIPAM‐co‐DMAEMA copolymers across high‐dimensional formulation spaces. Machine learning trained on the resulting high‐throughput datasets achieves R2 up to 0.96 and enables inverse design of polymer formulations to target transition temperatures with <1% error.
Lachlan Alexander   +2 more
wiley   +1 more source

Error report for Joel et al. (2017)

open access: yes
Joel, Eastwick, & Finkel (2017) "Is Romantic Desire Predictable? Machine Learning Applied to Initial Romantic Attraction" was determined to contain Minor Errors that do not affect the core conclusions of the manuscript.
ERROR
core  

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