Results 211 to 220 of about 34,476 (259)

Using art history to explore society's changing connections with agriculture

PLANTS, PEOPLE, PLANET, EarlyView.
Food insecurity is a looming challenge that especially affects those least fortunate. Consumer food choices have a substantial impact on the sustainability of current food systems. Here, we use art as a lens through which to consider our contemporary and historical relationship to one of the world's most crucial crops, the potato, in the context of the
Edward F. Hill‐King, Martin R. McAinsh, Michael R. Roberts   +2 more
wiley   +1 more source

Image Translation by Domain-Adversarial Training. [PDF]

Comput Intell Neurosci, 2018
Li Z, Wang W, Zhao Y.
europepmc   +1 more source

Stop Using Limiting Stimuli as a Measure of Sensitivities of Energetic Materials

Propellants, Explosives, Pyrotechnics, EarlyView.
ABSTRACT Accurately estimating the sensitivity of explosive materials is a potentially life‐saving task that requires standardised protocols across nations. One of the most widely applied procedures worldwide is the so‐called ‘1‐In‐6’ test from the United Nations (UN) Manual of Tests in Criteria, which estimates a ‘limiting stimulus’ for a material. In
Dennis Christensen, Geir Petter Novik
wiley   +1 more source

Adversarial training with cycle consistency for unsupervised super-resolution in endomicroscopy. [PDF]

Med Image Anal, 2019
Ravì D, Szczotka AB, Pereira SP, Vercauteren T.   +3 more
europepmc   +1 more source

Statistical post‐processing of operational dual‐resolution wind‐speed ensemble forecasts

Quarterly Journal of the Royal Meteorological Society, EarlyView.
The performance of raw and post‐processed 50‐member medium‐ and 100‐member extended‐range 10‐m wind‐speed forecasts of the European Centre for Medium‐Range Weather Forecasts and their various dual‐resolution combinations is investigated. Results show that post‐processing improves skill and reduces the differences between the various configurations ...
Sándor Baran, Mária Lakatos
wiley   +1 more source

Deep Reinforcement Learning‐Based Control for Real‐Time Hybrid Simulation of Civil Structures

International Journal of Robust and Nonlinear Control, EarlyView.
ABSTRACT Real‐time Hybrid Simulation (RTHS) is a cyber‐physical technique that studies the dynamic behavior of a system by combining physical and numerical components that are coupled through a boundary condition enforcer. In structural engineering, the numerical components are subjected to environmental loads that become dynamic displacements of the ...
Andrés Felipe Niño, Alejandro Palacio‐Betancur, Piedad Miranda‐Chiquito, Juan David Amaya, Christian E. Silva, Mariantonieta Gutierrez Soto, Luis Felipe Giraldo   +6 more
wiley   +1 more source

Machine learning‐driven advances in carbon‐based quantum dots: Opportunities accompanied by challenges

Responsive Materials, EarlyView.
Machine learning provides a unifying framework to connect structure, fluorescence properties, and applications of carbon‐based quantum dots. This review highlights how data‐driven strategies enable fluorescence regulation, reveal underlying mechanisms, and accelerate the rational design of functional carbon dots.
Liangfeng Chen, Siwei Yang, Xiangqian Cui, Ziheng Liu, Hang Wang, Yongqiang Li, Peng He, Miao Wang, Guqiao Ding   +8 more
wiley   +1 more source

Lacking data? No worries! How synthetic images can alleviate image scarcity in wildlife surveys: A case study with muskox (Ovibos moschatus)

Remote Sensing in Ecology and Conservation, EarlyView.
This study investigates the integration of synthetic imagery, created with diffusion‐based models, to supplement limited training data and improve muskox (Ovibos moschatus) detection in zero‐shot (ZS) and few‐shot (FS) settings. ZS models detected more than 80% of muskoxen in real images, confirming the potential of synthetic data as a substitute for ...
Simon Durand, Samuel Foucher, Alexandre Delplanque, Joëlle Taillon, Jérôme Théau   +4 more
wiley   +1 more source

Deep learning‐based super‐resolution reconstruction and improved YOLOv9 for efficient benthos detection: a case study at Lake Hamana, Japan

Remote Sensing in Ecology and Conservation, EarlyView.
This study presents a UAV‐based framework that integrates deep learning‐based super‐resolution reconstruction and an enhanced YOLO detector to improve centimetre‐scale benthic organism monitoring. Using hermit crabs in Lake Hamana, a coastal lagoon in Japan, as a case study, the method substantially enhanced small‐object detection performance ...
Fan Zhao, Bangzhang Ma, Dianhan Xi, Jiaqi Wang, Yijia Chen, Yongying Liu, Xinlei Shao, Mowen Zhang, Guocheng Zhang, Jundong Chen, Katsunori Mizuno   +10 more
wiley   +1 more source

Integrating Image Segmentation and Deep Learning to Improve Radio Frequency Propagation Models

International Journal of Satellite Communications and Networking, EarlyView.
ABSTRACT This paper proposes a multi‐sensor approach to improve radio frequency (RF) propagation models, which play a key role in the rapidly expanding field of connected vehicle technology. Focusing on the 1‐ to 20‐GHz frequency range, which is critical for both satellite‐to‐vehicle and base station‐to‐vehicle communications, our study introduces a ...
Jonathan Israel, Xavier Boulanger, Vegard Arneson   +2 more
wiley   +1 more source