Results 161 to 170 of about 27,491 (265)

Materials and Music: Selective Imperfection as a Generative Framework for Analysis, Creativity and Discovery

Interdisciplinary Materials, EarlyView.
Vibrations in matter, from spider webs to molecules, water to flames, share a grammar with music. We propose that creativity emerges when constraints force expansion beyond existing possibilities. Selective imperfection restores balance, enabling invention.
Markus J. Buehler
wiley   +1 more source

Progressive Upsampling Generative Adversarial Network with Collaborative Attention for Single-Image Super-Resolution. [PDF]

J Imaging
Lu H, Zhang J, Jing M, Wang Z, Wang W.
europepmc   +1 more source

Markov Chain Wave Generative Adversarial Network for Bee Bioacoustic Signal Synthesis. [PDF]

Sensors (Basel)
Samarappuli K, Ardekani I, Mohaghegh M, Sarrafzadeh A.   +3 more
europepmc   +1 more source

Mapping the Landscape of Over‐Scanning in CT Imaging: A Scoping Review

Journal of Medical Radiation Sciences, EarlyView.
Over‐scanning in CT is highly prevalent and contributes to unnecessary radiation exposure, with notable impact on radiosensitive organs. Standardised protocols and AI‐assisted planning show strong potential to optimise scan range and reduce excess dose.
Mo'men Bani‐Ahmad, Aoife O Sullivan, Yasser H. Hadi, Laura McLaughlin, Andrew England, Mark McEntee   +5 more
wiley   +1 more source

MGANSL: multi-network representation generating with generative adversarial network for synthetic lethality prediction. [PDF]

BMC Bioinformatics
Li J, Lu X, Li Z, Liu X, Liu H, Ruan J.
europepmc   +1 more source

Preparing for Tomorrow's Teamwork: Insights From eSports on How Human Expertise Shapes Training Needs for AI‐Integrated Work

Journal of Organizational Behavior, EarlyView.
ABSTRACT As organizations increasingly adopt human‐AI teams (HATs), understanding how to enhance team performance is paramount. A crucially underexplored area for supporting HATs is training, particularly helping human teammates to work with these inorganic counterparts.
Caitlin M. Lancaster, Christopher Flathmann, Jennifer Hsu, Nathan J. McNeese, Thomas A. O'Neill, Eduardo Salas   +5 more
wiley   +1 more source

Dynamic background motion object semantic segmentation algorithm based on generative adversarial network and transformer collaboration. [PDF]

Sci Rep
Li Y, Luo Z, Chen T, Huang X, Zhong C, Zhu G, Jin D, Cheng C, Zhang Y, Zhao J, Gao P.   +10 more
europepmc   +1 more source

Unlocking Value Co‐Creation in IoT Platforms: Insights From a Collaborative Manufacturing Case

Strategic Change, EarlyView.
ABSTRACT This study examines value co‐creation in IoT platform ecosystems through a case study of the collaborative manufacturing platform E‐LINK. We explore the evolution of platform data capabilities, user interactions, value creation, and the mechanisms that connect them.
Huayao Zhang, Junjie Wu, George Lodorfos, Mingyao Sun, Mickaël Naulleau   +4 more
wiley   +1 more source

Novel molecule design with POWGAN, a policy-optimized Wasserstein generative adversarial network. [PDF]

J Cheminform
Macedo B, Vaz IR, Gomes TT.
europepmc   +1 more source

Feasibility and Reproducibility of a Structure‐Guided Deep Learning Model for Automatic Detection of the Standard Sagittal Plane in First‐Trimester Nuchal Translucency Assessment Using 3D Ultrasound

Journal of Ultrasound in Medicine, EarlyView.
Objectives Accurate nuchal translucency (NT) measurement for assessing the risk of fetal genetic abnormalities requires precise acquisition of the mid‐sagittal plane (MSP). However, achieving an appropriate MSP is technically challenging due to anatomical variability and operator dependence inherent in conventional 2‐dimensional (2D) ultrasound.
Hayan Kwon, Hyewon Hur, Hyun Cheol Cho, Yun ji Jung, Suhra Kim, Ja‐Young Kwon   +5 more
wiley   +1 more source