Results 61 to 70 of about 60,322 (264)

Extending Service Beyond Active Duty: Generalizability of Rheumatoid Arthritis Research Conducted Among U.S. Veterans

Arthritis Care &Research, Accepted Article.
Biomedical research involving United States Veterans continues to advance healthcare beyond the Veterans Health Administration. This is particularly true in rheumatoid arthritis (RA), where Veteran‐centric research has uncovered novel insights into pathogenesis, risk factors, and disease manifestations, informing clinical care and research across both ...
Austin M. Wheeler, Joshua F. Baker, Jennifer L. Barton, Maria I. Danila, Tate M. Johnson, Kamini Kuchinad, Kaleb Michaud, Amir A. Razmjou, J. Steuart Richards, Namrata Singh, Isaac D. Smith, Katherine D. Wysham, Grant W. Cannon, Bryant R. England, Amy Joseph, Deana Lazaro, Katherine P. Liao, Courtney Loecker, Andreas Reimold, Beth I. Wallace, Ted R. Mikuls   +20 more
wiley   +1 more source

Energy-Efficient Deep Reinforcement Learning with Spiking Transformers

Agent-based Transformers have been widely adopted in recent reinforcement learning advances due to their demonstrated ability to solve complex tasks. However, the high computational complexity of Transformers often results in significant energy consumption, limiting their deployment in real-world autonomous systems. Spiking neural networks (SNNs), with
Mohammad Irfan Uddin, Nishad Tasnim, Md Omor Faruk, Zejian Zhou   +3 more
openaire   +2 more sources

Dynamic Bit-width Reconfiguration for Energy-Efficient Deep Learning Hardware

Proceedings of the International Symposium on Low Power Electronics and Design, 2018
Deep learning models have reached state of the art performance in many machine learning tasks. Benefits in terms of energy, bandwidth, latency, etc., can be obtained by evaluating these models directly within Internet of Things end nodes, rather than in the cloud.
Jahier Pagliari D., Macii E., Poncino M.
openaire   +1 more source

Device-Aware Optimization of Energy-Efficient Deep Learning for Edge Deployment

IEEE Access
Deep neural networks deliver strong accuracy but their energy and latency costs hinder sustainable deployment on resource constrained edge devices. We propose an energy aware framework that couples three complementary efficiency mechanisms: structured ...
Guiling Xiang
doaj   +1 more source

A Workflow to Accelerate Microstructure‐Sensitive Fatigue Life Predictions

Advanced Engineering Materials, EarlyView.
This study introduces a workflow to accelerate predictions of microstructure‐sensitive fatigue life. Results from frameworks with varying levels of simplification are benchmarked against published reference results. The analysis reveals a trade‐off between accuracy and model complexity, offering researchers a practical guide for selecting the optimal ...
Luca Loiodice, Krzysztof S. Stopka, Michael D. Sangid   +2 more
wiley   +1 more source

Energy-Efficient Dynamic Workflow Scheduling in Cloud Environments Using Deep Learning

Sensors
Dynamic workflow scheduling in cloud environments is a challenging task due to task dependencies, fluctuating workloads, resource variability, and the need to balance makespan and energy consumption.
Sunera Chandrasiri, Dulani Meedeniya
doaj   +1 more source

Thermodynamic Pathways of Nonequilibrium Solidification in Wire‐Arc Additive Manufacturing Fe‐Based Multicomponent Alloy Structures

Advanced Engineering Materials, EarlyView.
Geometry‐driven thermal behavior in wire‐arc additive manufacturing (WAAM) influences microstructural evolution during nonequilibrium solidification of a chemically complex Fe–Cr–Nb–W–Mo–C nanocomposite system. By comparing different deposits configurations, distinct entropy–cooling rate correlations, segregation, and carbide evolution are revealed ...
Blanca Palacios, Anthony Gonzalez, Aleksander Aleman, Tyler Dolmetsch, Tanaji Paul, Arvind Agarwal   +5 more
wiley   +1 more source

Renewable-Aware Frequency Scaling Approach for Energy-Efficient Deep Learning Clusters

Applied Sciences
Recently, renewable energy has emerged as an attractive means to reduce energy consumption costs for deep learning (DL) job processing in modern GPU-based clusters.
Hyuk-Gyu Park, Dong-Ki Kang
doaj   +1 more source

Towards Sustainable DC Microgrids: A Comprehensive Review of IoT-Driven Frameworks [PDF]

E3S Web of Conferences
A comprehensive review of cyber-physical architectures for DC microgrids is presented, focusing on the integration of deep learning and LoRa technology for secure, efficient, and scalable communication networks.
Rajitha Morampudi, Ram A. Raghu, Shravani Ch., Lokeshwar Reddy Ch.   +3 more
doaj   +1 more source

Towards Defect Phase Diagrams: From Research Data Management to Automated Workflows

Advanced Engineering Materials, EarlyView.
A research data management infrastructure is presented for the systematic integration of heterogeneous experimental and simulation data required for defect phase diagrams. The approach combines openBIS with a companion application for large‐object storage, automated metadata extraction, provenance tracking and federated data access, thereby supporting ...
Khalil Rejiba, Sang‐Hyeok Lee, Christina Gasper, Martina Freund, Sandra Korte‐Kerzel, Ulrich Kerzel   +5 more
wiley   +1 more source