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Articles by GE Research
An uncertainty-aware deep learning framework-based robust design optimization of metamaterial units - Structural and Multidisciplinary Optimization
Abstract Mechanical metamaterials represent an innovative class of artificial structures, distinguished by their extraordinary mechanical characteristics, which are beyond the scope of traditional natural materials. The use of deep generative models has become increasingly popular in the design of metamaterial units.
Accelerating defect predictions in semiconductors using graph neural networks
ALIGNN is a novel approach developed by Choudhary et al.,40 which differs from CGCNN and MEGNET in terms of considering three-body interactions (bond angles) as well in addition to two-body terms (bond lengths). ALIGNN leverages both graph convolutional layers and line graph convolutional63 layers to capture both short-range and long-range correlations in the crystal. The framework [pictured in Fig.
Measurements of stresses in β‐(Y,Yb)2Si2O7 environmental barrier coating layers using Raman spectroscopy
REFERENCES 1, . Residual stress: measurement by diffraction and interpretation. New York: Springer; 1987. 2, , , , , . Measurements of stresses in β-(Y,Yb)2Si2O7 environmental barrier coating layers using Raman spectroscopy. Acta Mater. 2016; 105: 189–198. 3, , , , . Stresses in ytterbium silicate multilayer environmental barrier coatings. Adv X-ray Anal. 2011; 55: 106–115. 4, , , . Nondestructive evaluation of the oxidation stresses through thermal barrier coatings using Cr3+ piezospectroscopy.
Energy Department to Fund 3D Printer Project by GE Research, Voxeljet
Advanced Casting Cell Voxeljet and General Electric’s research business have teamed up under a $14.9 million Department of Energy award to develop and market the Advanced Casting Cell, a 3D printer for large sand-casting molds used by the wind and hydro energy sectors. The ACC program is expected to support the United States’ transition to clean power, 3D Printing Industry reported Tuesday. Voxeljet is part of a consortium supporting the program.
Abstract No. 49 Motion Compensation in 3D MRI-US Fusion Using Fast Deformable Registration: A Feasibility Study for Real-Time Intervention
The heterogeneity of hepatocellular carcinoma may be better seen with magnetic resonance imaging (MRI) compared with CT due to its high soft tissue contrast, providing precise tumor targets during therapy, while ultrasound (US) remains as the imaging modality for real-time guidance. Image fusion methods existing in clinical workflows involve rigid registration only and fail to compensate for liver motion in US.
Accelerating cutaneous healing in a rodent model of type II diabetes utilizing non-invasive focused ultrasound targeted at the spleen
Introduction The cost of care for non-healing wounds in Medicare recipients is over 28 billion as of 2018, with the number of individuals affected being above 8 million and increasing as the population ages (Nussbaum et al., 2018). There is also significant mortality associated with non-healing wounds, with 5-year mortality rates being higher than many common cancers such as prostate and breast (Armstrong et al., 2007).
High resolution DLP stereolithography to fabricate biocompatible hydroxyapatite structures that support osteogenesis
Loading metrics Open Access Peer-reviewed Research Article Abstract Lithography based additive manufacturing techniques, specifically digital light processing (DLP), are considered innovative manufacturing techniques for orthopaedic implants because of their potential for construction of complex geometries using polymers, metals, and ceramics.
Spatial mapping of protein composition and tissue organization: a primer for multiplexed antibody-based imaging - Nature Methods
Abstract Tissues and organs are composed of distinct cell types that must operate in concert to perform physiological functions. Efforts to create high-dimensional biomarker catalogs of these cells have been largely based on single-cell sequencing approaches, which lack the spatial context required to understand critical cellular communication and correlated structural organization.
Virtual Monoenergetic CT Imaging via Deep Learning
Highlights • A ResNet model is modified to map polyenergetic to virtual monoenergetic CT images • The modified ResNet is trained on DECT-derived virtual monoenergetic (VM) images • The trained model produces high-quality approximations of DECT-derived VM images • Learned VM images are used to generate multi-material decomposition images The Bigger Picture The physical process of X-ray CT imaging is described by an energy-dependent non-linear integral equation.
New SEMI-NBMC, GE Research Project to Advance Vital Signs Monitoring of Patients During Emergency Air Transport
MILPITAS, Calif., June 22, 2020 /PRNewswire/ -- Nano-Bio Materials Consortium (NBMC), a SEMI Strategic Association Partner, today launched a project with GE Research to develop a medical-grade wireless patch that monitors the vital signs of medical patients during emergency air transport. The device promises to advance the state-of-the-art in medical care coordination, records management and patient outcomes in aeromedical evacuation but will also help drive advances in consumer-based telehealth.
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