Results 41 to 50 of about 3,246,502 (304)

Spatiotemporal prediction of air quality based on LSTM neural network

Alexandria Engineering Journal, 2021
Accurate monitoring of air quality is of great importance to our daily life. By predicting the air quality in advance, we can make timely warnings and defenses to minimize the threat to life.
D. Seng, Qiyan Zhang, Xuefeng Zhang, Guangsen Chen, Xiyuan Chen   +4 more
semanticscholar   +1 more source

A Survey on Spatial and Spatiotemporal Prediction Methods

CoRR, 2020
With the advancement of GPS and remote sensing technologies, large amounts of geospatial and spatiotemporal data are being collected from various domains, driving the need for effective and efficient prediction methods. Given spatial data samples with explanatory features and targeted responses (categorical or continuous) at a set of locations, the ...
openaire   +2 more sources

The planar cell polarity protein Vangl2 interacts with the PDZ‐domains of Scribble but not with a unique PDZ‐like domain in Inturned

FEBS Letters, EarlyView.
Structural and biochemical characterisations show that the planar cell polarity (PCP) protein Inturned harbours a unique PDZ‐like domain that does not bind canonical PDZ‐binding motifs (PBMs) like that of another PCP protein Vangl2. In contrast, the apical‐basal polarity protein Scribble contains four PDZ domains that bind Vangl2, but one PDZ domain ...
Stephan Wilmes, Jan Brysch, Carmen Gelze, Lilli Meier, Daniel Kümmel   +4 more
wiley   +1 more source

Spatiotemporal Sequence Prediction Based on Spatiotemporal Self-Attention Mechanism

INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL
This paper introduces the GCN-Transformer model, an innovative approach that combines Graph Convolutional Networks (GCNs) and Transformer architectures to enhance spatiotemporal sequence prediction. Targeted at applications requiring precise analysis of complex spatial and temporal data, the model was tested on two distinct datasets: PeMSD8 for traffic
Yuan Zhao, Junlin Lu
openaire   +1 more source

Unsupervised Transfer Learning for Spatiotemporal Predictive Networks

CoRR, 2020
This paper explores a new research problem of unsupervised transfer learning across multiple spatiotemporal prediction tasks. Unlike most existing transfer learning methods that focus on fixing the discrepancy between supervised tasks, we study how to transfer knowledge from a zoo of unsupervisedly learned models towards another predictive network. Our
Zhiyu Yao, Yunbo Wang, Mingsheng Long, Jianmin Wang 0001   +3 more
openaire   +3 more sources

Modelling stem cell differentiation related processes—A practical overview for biologists

FEBS Letters, EarlyView.
Stem cell differentiation is complex and difficult to control experimentally. This review introduces suitable computational modelling approaches that can support stem cell research, from mechanistic ODE and abstract models to multiscale and deep learning methods.
Ricco Zeegelaar, Xinqi Yan, Lucas F. Jansen Klomp, Stefano Schivo, Janine N. Post   +4 more
wiley   +1 more source

Spatiotemporal Soil Moisture Prediction Using a Causal-Guided Deep Learning Model

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
The spatiotemporal prediction of RZSM refers to the process of estimating its future spatial distribution and temporal variations using predictive models.
Tingtao Wu, Lei Xu, Ziwei Pan, Ruinan Cai, Jin Dai, Shuang Yang, Xihao Zhang, Xi Zhang, Nengcheng Chen   +8 more
doaj   +1 more source

CGConvLSTM: A Spatial‐Aware Ionospheric Total Electron Content Spatiotemporal Prediction Model

Space Weather
The current deep learning models used in ionospheric total electron content (TEC) spatiotemporal prediction models rely on standard convolutions to extract spatial features.
Yan Ma, Jing Yuan, Haijun Liu, Zhihong Zhang, Rui Zhou, Jian Xiao, Guoming Yuan   +6 more
semanticscholar   +1 more source

Spatiotemporal modeling and prediction of soil heavy metals based on spatiotemporal cokriging

Scientific Reports, 2017
AbstractSoil heavy metals exhibit significant spatiotemporal variability and are strongly correlated with other soil heavy metals. Thus, other heavy metals can be used to improve the accuracy of predictions when performing spatiotemporal predictions of soil heavy metals within a given area.
Zhang, Bei, Yang, Yong
openaire   +2 more sources

Investigating transcription factor dynamics in health and disease using FRAP

FEBS Letters, EarlyView.
FRAP analysis of GFP‐tagged transcription factors reveals how molecular mobility and target engagement change in response to drug treatment. By combining live‐cell imaging, quantitative model fitting, and statistical analysis, this approach uncovers transcription factor dynamics linked to disease mechanisms, providing a powerful framework for ...
Kannan Govindaraj, Carolina Vazquez Garzon, Jose Joaquin Velasco, Janine N. Post   +3 more
wiley   +1 more source