A morning workshop addressing the topic of "What You Always Wanted to Know About Science but Were Afraid to Ask" was held at the Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) 2024 annual meeting. In brief, Profs. Ste... read more
PURPOSE: Systematic study of the effects of averaging and other relevant training strategies in deep learning (DL)-based denoising is required to optimize such processing pipelines for improving the quality of arterial spin labeling (ASL) images. read more
The respiratory virus known as human metapneumovirus (hMPV) is linked to seasonal outbreaks and primarily affects elderly people and young children. Infodemiology, which uses digital data sources, including social media, online news, and search trend... read more
This research proposes an integrated optimization framework combining artificial neural networks (ANN) and Taguchi robust design for an eco-friendly deep eutectic solvent-based electrolyte. Five key process parameters-applied voltage, processing time... read more
To enhance the motion flexibility and environmental adaptability of underwater robots, this study proposes a novel design, Seeker-M, inspired by the locomotion mechanism of the mantis shrimp. The robot imitates the mantis shrimp's multi-pleopod swimm... read more
The practice of allogeneic hematopoietic stem cell transplantation (alloHCT) has evolved from an experimental therapy with high mortality rates to a routine treatment that is increasingly performed worldwide. Parallel to this expansion, transplantati... read more
The integration of Automated Delivery Robots (ADRs) into pedestrian-heavy
urban spaces introduces unique challenges in terms of safe, efficient, and
socially acceptable navigation. We develop the complete pipeline for a single
vision sensor based m... read more
Accurate prediction of placental diseases via whole slide images (WSIs) is
critical for preventing severe maternal and fetal complications. However, WSI
analysis presents significant computational challenges due to the massive data
volume. Existing... read more
We developed a global carbon flux dataset, GloFlux, using a machine learning model that integrates in situ observations from FLUXNET, AmeriFlux, ICOS, JapanFlux2024, and HBRFlux with satellite remote sensing and meteorological data. The dataset cover... read more
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