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Spatio-temporal deep learning has drawn a lot of attention since many downstream real-world applications can benefit from accurate predictions. For example, accurate prediction of heavy rainfall events is essential for effective urban water usage, flooding warning, and mitigation. In this paper, we propose a strategy to leverage spatially connected real-world features to enhance prediction accuracy. Specifically, we leverage spatially connected real-world climate data to predict heavy rainfall risks in a broad range in our case study. We experimentally ascertain that our Trans-Graph Convolutional Network (TGCN) accurately predicts heavy rainfall risks and real estate trends, demonstrating the advantage of incorporating external spatially-connected real-world data to improve model performance, and it shows that this proposed study has a significant potential to enhance spatio-temporal prediction accuracy, aiding in efficient urban water usage, flooding risk warning, and fair housing in real estate.
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Ultrasonic Bubble Cleaner as a Sustainable Solution
We aim to develop a floor-cleaning design by exploiting oscillating bubbles combined with ambient pressure waves to clean various surfaces. Previous studies of this method in lab settings have proven its efficacy, but practical applications, especially concerning real-world conditions like dirt surfaces, remain largely unprobed. Our findings indicate that, excluding a configuration with a heavy mass bottom transducer, all tested configurations achieved approximately 60–70% cleaning performance. A slight improvement in cleaning performance was observed with the introduction of microbubbles, although it was within the error margin. Particularly noteworthy is the substantial reduction in water consumption in configurations with a water pocket, decreasing from 280 mL to a mere 3 mL, marking a significant step toward more environmentally sustainable cleaning practices, such as reduced water usage. This research provides implications for real-world cleaning applications, promising an eco-friendly and efficient cleaning alternative that reduces water usage and handles a variety of materials without causing damage.
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- Award ID(s):
- 2002714
- PAR ID:
- 10520035
- Publisher / Repository:
- MDPI
- Date Published:
- Journal Name:
- Fluids
- Volume:
- 8
- Issue:
- 11
- ISSN:
- 2311-5521
- Page Range / eLocation ID:
- 291
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/fluids8110291
