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Language-Guided Semantic Clustering for Remote Sensing Change Detection.
Sensors (Basel)
December 2024
B-DAT and CICAEET, Nanjing University of Information Science and Technology, Nanjing 210044, China.
Existing learning-based remote sensing change detection (RSCD) commonly uses semantic-agnostic binary masks as supervision, which hinders their ability to distinguish between different semantic types of changes, resulting in a noisy change mask prediction. To address this issue, this paper presents a Language-guided semantic clustering framework that can effectively transfer the rich semantic information from the contrastive language-image pretraining (CLIP) model for RSCD, dubbed LSC-CD. The LSC-CD considers the strong zero-shot generalization of the CLIP, which makes it easy to transfer the semantic knowledge from the CLIP into the CD model under semantic-agnostic binary mask supervision.
View Article and Find Full Text PDFAn Easy-to-Use Tool to Predict SARS-CoV-2 Risk of Infection in Closed Settings: Validation with the Use of an Individual-Based Monte Carlo Simulation.
Microorganisms
November 2024
Physics Department, University of Trieste, 34127 Trieste, Italy.
The dynamics of the SARS-CoV-2 pandemic showed that closed environments, such as hospitals and schools, are more likely to host infection clusters due to environmental variables like humidity, ventilation, and overcrowding. This study aimed to validate our local transmission model by reproducing the data on SARS-CoV-2 diffusion in a hospital ward. We implemented our model in a Monte Carlo procedure that simulates the contacts between patients and healthcare workers in Trieste's geriatric ward and calculates the number of infected individuals.
View Article and Find Full Text PDFEngaging older adults in the process of aging research: a multimethod study evaluating the experience and efficacy of a citizen advisory group for a dementia risk reduction program.
Res Involv Engagem
December 2024
Rotman Research Institute, Baycrest Academy for Research and Education, 3560 Bathurst Street, Toronto, ON, M6A 2E1, Canada.
Background: Collaborative research with end-users is an effective way to generate meaningful research applications and support greater impact on practice and knowledge exchange. To address these needs, a Citizen Advisory Group (CAG) of nine older adults (ages 64-80, 67% women) was formed to advise scientists on the development of Brain Health PRO (BHPro), a web-based platform designed to increase dementia prevention literacy and awareness. The current study evaluated if the CAG met its objectives, how inclusion of the CAG aligned with collaborative research approaches, and the CAG's experience and satisfaction throughout the development process.
View Article and Find Full Text PDFOmnipotent antibacterial cotton fabrics with superhydrophobic and photothermal properties.
Int J Biol Macromol
December 2024
Key Laboratory for Green Chemical Process of Ministry of Education, School of Environmental Ecology and Biological Engineering, Wuhan Institute of Technology, Wuhan 430205, PR China. Electronic address:
Due to the outbreak of global public health emergency, antibacterial fabrics such as face masks are in great demand. However, common antibacterial fabrics cannot kill bacteria in minutes and they are easy to be contaminated and lost biological activity. In this work, omnipotent antibacterial cotton fabrics with superhydrophobic and photothermal properties are developed by the combination of dopamine with copper sulfide (CuS) and silver nanoparticles on cotton fabrics, and post-modification with PDMS.
View Article and Find Full Text PDFFabricSpotDefect: An annotated dataset for identifying spot defects in different fabric types.
Data Brief
December 2024
Center for Computational & Data Sciences, Independent University, Bangladesh, Block B, Bashundhara R/A, Dhaka 1229, Bangladesh.
The FabricSpotDefect dataset is, to the best of our knowledge, the first dataset specifically designed to accurately challenge computer vision in detecting fabric spots. There are a total of 1014 raw images and manually annotated 3288 different categories of spots. This dataset expands to 2300 augmented images after applying six categories of augmentation techniques like flipping, rotating, shearing, saturation adjustment, brightness adjustment, and noise addition.
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