Results 51 to 60 of about 3,246,502 (304)

Spatiotemporal Multivariate Weather Prediction Network Based on CNN-Transformer

Sensors
Weather prediction is of great significance for human daily production activities, global extreme climate prediction, and environmental protection of the Earth.
Ruowu Wu, Yandan Liang, Lianlei Lin, Zongwei Zhang   +3 more
doaj   +1 more source

Spatiotemporal Model Based on Deep Learning for ENSO Forecasts

Atmosphere, 2021
El Niño and Southern Oscillation (ENSO) is closely related to a series of regional extreme climates, so robust long-term forecasting is of great significance for reducing economic losses caused by natural disasters.
Huantong Geng, Tianlei Wang
doaj   +1 more source

Spatial biology in cancer epigenetics

Molecular Oncology, EarlyView.
Spatial epigenomics combines molecular profiling with tissue architecture to reveal how gene regulation is organized within intact tissues. In cancer, these technologies uncover the mechanisms driving tumor heterogeneity and microenvironmental interactions, opening new opportunities for biomarker discovery and precision medicine.
Eva Crespo‐García, Manel Esteller
wiley   +1 more source

Partial Convolutional LSTM for Spatiotemporal Prediction of Incomplete Data

IEEE Access, 2020
Advanced data analysis techniques facilitate data-driven spatiotemporal prediction in various fields. However, in real-world data, missing values are inevitable, which causes the data incomplete and makes predictions more challenging.
Hyesook Son, Yun Jang
doaj   +1 more source

Predicting the Continuous Spatiotemporal State of Ground Fire Based on the Expended LSTM Model with Self-Attention Mechanisms

Fire, 2023
Fire spread prediction is a crucial technology for fighting forest fires. Most existing fire spread models focus on making predictions after a specific time, and their predicted performance decreases rapidly in continuous prediction due to error ...
Xinyu Wang, Xinquan Wang, Mingxian Zhang, Chun Tang, Xingdong Li, Shufa Sun, Yangwei Wang, Dandan Li, Sanping Li   +8 more
doaj   +1 more source

Validity of a Wearable Digital Insole for Assessing Gait ON and OFF in Parkinson's Disease

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Gait impairment is a distinctive symptom of Parkinson's disease that negatively impact mobility. We assessed the validity of wearable digital insoles against a validated reference gait analysis system for measuring select gait characteristics in patients with Parkinson's disease. Methods A comparative analysis between digital insoles
Deborah A. Hall, Kimberly Kwei, Rolando J. Acosta, Bharatkumar Koyani, Erin Robertson, Pratyush Rai, Roland Barge, Emily Timm, Nicollette L. Purcell, Natasha Desai, Dhanesh Patel, Ana‐Maria Visoiu‐Knapp, Samuel Stuart, Rinol Alaj, Matthew F. Wipperman, Oren Levy, Joan A. O'Keefe   +16 more
wiley   +1 more source

Graph convolution networks based on adaptive spatiotemporal attention for traffic flow forecasting

Scientific Reports
Traffic flow is the most direct indicator of traffic conditions, and accurate prediction of traffic flow is a key challenge for scholars in the field of intelligent transportation. However, traffic flow displays significant nonlinearity, dynamic changes,
Hongbo Xiao, Beiji Zou, Jianhua Xiao
doaj   +1 more source

How to control the spatiotemporal spread of Omicron in the region with low vaccination rates

Frontiers in Public Health, 2022
Currently, finding ways to effectively control the spread of Omicron in regions with low vaccination rates is an urgent issue. In this study, we use a district-level model for predicting the COVID-19 symptom onset risk to explore and control the whole ...
Chengzhuo Tong, Zhicheng Shi, Wenzhong Shi, Anshu Zhang   +3 more
doaj   +1 more source

A Fast Lightweight Spatiotemporal Activity Prediction Method [PDF]

IEEE Access, 2019
How to predict spatiotemporal activity from geo-tagged social media is an urgent problem. Existing methods don't make full use of spatiotemporal information and text sequence features. In view of above problem, we design a Fast Lightweight Spatiotemporal Activity Prediction method(FLSAP) based on Gated Recurrent Unit(GRU) neural network.
Changxing Shao, Lei Zhang 0029, Shaojie Zhu, Bailong Liu, Yun Li, Shumin Cui   +5 more
openaire   +2 more sources

REST: Reciprocal Framework for Spatiotemporal-coupled Predictions

Proceedings of the Web Conference 2021, 2021
In recent years, Graph Convolutional Networks (GCNs) have been applied to benefit spatiotemporal predictions. The current shell for spatiotemporal predictions often relies heavily on the quality of handcraft, fixed graphical structures, however, we argue that such a paradigm could be expensive and sub-optimal in many applications.
Haozhe Lin, Yushun Fan, Jia Zhang 0001, Bing Bai   +3 more
openaire   +1 more source