Quantitative microbial risk assessment of a drinking water – wastewater cross-connection simulation

2008 ◽  
Vol 7 (5) ◽  
pp. 525-530 ◽  
Author(s):  
Kristina D. Mena ◽  
Linda C. Mota ◽  
Mark C. Meckes ◽  
Christopher F. Green ◽  
William W. Hurd ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Drinking Water ◽  
Quantitative Microbial Risk Assessment ◽  
Microbial Risk Assessment ◽  
Microbial Risk ◽  
Cross Connection

POXN: A New Passive Optical Cross-Connection Network for Low-Cost Power-Efficient Datacenters

2014 ◽  
Vol 32 (8) ◽  
pp. 1482-1500 ◽  
Author(s):  
Wenda Ni ◽  
Changcheng Huang ◽  
Yunqu Leon Liu ◽  
Weiwei Li ◽  
Kin-Wai Leong ◽  
...  
Keyword(s):  
Low Cost ◽  
Power Efficient ◽  
Cross Connection

scholarly journals CostNet: A Concise Overpass Spatiotemporal Network for Predictive Learning

2020 ◽  
Vol 9 (4) ◽  
pp. 209
Author(s):  
Fengzhen Sun ◽  
Shaojie Li ◽  
Shaohua Wang ◽  
Qingjun Liu ◽  
Lixin Zhou
Keyword(s):  
State Of The Art ◽  
Vertical Direction ◽  
Bottom Layer ◽  
Spatiotemporal Data ◽  
Short Term ◽  
The Core ◽  
Predictive Learning ◽  
Fast Gradient ◽  
Unit Output ◽  
Cross Connection

Predicting the futures from previous spatiotemporal data remains a challenging topic. There have been many previous works on predictive learning. However, mainstream models suffer from huge memory usage or the gradient vanishing problem. Enlightened by the idea from the resnet, we propose CostNet, a novel recursive neural network (RNN)-based network, which has a horizontal and vertical cross-connection. The core of this network is a concise unit, named Horizon LSTM with a fast gradient transmission channel, which can extract spatial and temporal representations effectively to alleviate the gradient propagation difficulty. In the vertical direction outside of the unit, we add overpass connections from unit output to the bottom layer, which can capture the short-term dynamics to generate precise predictions. Our model achieves better prediction results on moving-mnist and radar datasets than the state-of-the-art models.

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