Results 81 to 90 of about 192,101 (191)

Beyond Pigment Loss: Integrating Clinician‐Reported Outcomes, Patient‐Reported Outcomes, and Digital Biometrics to Quantify Vitiligo Morbidity

Dermatological Reviews, Volume 7, Issue 5, October 2026.
ABSTRACT Background Vitiligo is an autoimmune depigmenting disease with multiple facets to disease severity. Accurate assessment of severity and therapeutic response is essential for both clinical care and research. Methods This narrative review summarizes outcome measures for assessing different aspects of vitiligo severity, including extent, activity,
Shrruthi Velavan, Vishal Gupta
wiley   +1 more source

Multi-scale capsule Swin Transformer-based method for SAR image target recognition

Tongxin xuebao
A multi-scale capsule Swin Transformer network (MSCSTN) was proposed by synergizing the semantic feature encoding of capsule units with the context feature mapping of Swin Transformer. Capsule encoding and the Swin Transformer were jointly applied to SAR
HOU Yuchao, WANG Jie, LI Hongtao, HAO Yan, DUAN Xiaoqi, HUANG Kaiwen, TIAN Youliang   +6 more
doaj  

Automated Lawn Maintenance: An Agronomic and Operational Review of Turf Health, Biodiversity, and Field Performance

Journal of Field Robotics, Volume 43, Issue 7, Page 4603-4623, October 2026.
ABSTRACT Grass mowing is one of the most resource‐consuming activities in green maintenance, whether in private areas such as home gardens or in public spaces like urban parks. In recent years, concerns related to climate change, human health, and sustainability have become increasingly prominent in green maintenance, leading manufacturers and industry
Andrea Palladini, Lorenzo Guerrini, Marco Fontanelli, Lucia Bortolini   +3 more
wiley   +1 more source

Downscaling of Tropical Cyclone Surface Wind Fields With a Hybrid Attention Transformer

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 5, October 2026.
Abstract This study employs a Hybrid Attention Transformer‐based super‐resolution model to reconstruct tropical cyclone (TC) surface wind fields. The model downscales coarse‐resolution ERA5 reanalysis data to high‐resolution HWind analyses (1998–2013), increasing the horizontal resolution by a factor of five.
Chunhua Wang, Dazhi Xi, Na He, Shuai Wang, Kekuan Chu   +4 more
wiley   +1 more source

AI‐Based Regional Emulation for Kilometer‐Scale Dynamical Downscaling

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 5, October 2026.
Abstract An AI‐based Limited‐Area Model (LAM) is developed for dynamical downscaling over the Southern Great Plains and the southeastern United States, with strong generalization abilities under diverse boundary conditions. The model is trained using 0.25° ${}^{\circ}$, 3‐hourly ERA5 as forcings and CONUS404 as targets in 1980–2019, producing 4‐km ...
Yingkai Sha, Tracy Hertneky, Ethan D. Gutmann, Seth McGinnis, Rachel McCrary, Lulin Xue, David John Gagne II, Kathryn Newman, Andrew Newman   +8 more
wiley   +1 more source

KLASIFIKASI PENYAKIT BATU GINJAL MENGGUNAKAN SHIFTED WINDOWS TRANSFORMER (SWIN TRANSFORMER) [PDF]

Batu ginjal adalah masalah urologi umum yang dapat menyebabkan komplikasi serius jika tidak terdeteksi sejak dini. Proses diagnosis tradisional sangat bergantung pada interpretasi manual ultrasonografi, yang rentan terhadap kesalahan dan bisa memakan ...
Prastyo, Alfian Bima
core  

Benchmarking Auto‐Regressive Spatio‐Temporal Predictions of Wildfire Spread From the FireBench Simulation Data Set With a Machine Learning Competition

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 5, October 2026.
Abstract As wildfire severity increases, predicting wildfire spread is important for fire and landscape management. However, wildfire spread is a complex phenomenon that is challenging to capture because it depends on many factors, such as wind speed, slope, landscape topography, and fuel properties. Machine Learning (ML) models are powerful tools that
J. Z. Ho, A. Bazgir, A. Asaithambi, J. Hossain, R. Pawłowski, Z. Li, T. Dubail, Q. Wang, C. Gazen, B. Akoush, W. T. Chung, W. Reade, M. Ihme   +12 more
wiley   +1 more source

Pavement Crack Detection Based on the Improved Swin-Unet Model

Buildings
Accurate pavement surface crack detection is crucial for analyzing pavement survey data and the development of maintenance strategies. On the basis of Swin-Unet, this study develops the improved Swin-Unet (iSwin-Unet) model with the developed skip ...
Song Chen, Zhixuan Feng, Guangqing Xiao, Xilong Chen, Chuxiang Gao, Mingming Zhao, Huayang Yu   +6 more
doaj   +1 more source

Human‐in‐the‐loop eider duck counting in Arctic Canada with an open‐vocabulary multispecies wildlife detector

AI Magazine, Volume 47, Issue 3, Fall 2026.
Abstract Accurate monitoring of eider duck populations in Arctic Canada is essential for understanding ecosystem health and supporting conservation efforts in a rapidly changing climate. Traditional manual counting from aerial imagery is time‐consuming, labor‐intensive, and prone to observer bias.
Jayden Hsiao, Aryan Kalia, Zhonghao Zhang, Hudson Sun, Muhammed Patel, David A. Clausi, Lincoln Linlin Xu, Becky Segal, Joel Heath   +8 more
wiley   +1 more source

A Recognition and Classification Method for Underground Acoustic Emission Signals Based on Improved CELMD and Swin Transformer Neural Networks

Applied Sciences
To address the challenges in processing and identifying mine acoustic emission signals, as well as the inefficiency and inaccuracy issues prevalent in existing methods, an enhanced CELMD approach is adopted for preprocessing the acoustic emission signals.
Xuebin Xie, Yunpeng Yang
doaj   +1 more source