Results 41 to 50 of about 1,390,290 (339)
Road Extraction by Deep Residual U-Net
, 2017 Road extraction from aerial images has been a hot research topic in the field of remote sensing image analysis. In this letter, a semantic segmentation neural network which combines the strengths of residual learning and U-Net is proposed for road area ...
Liu, Qingjie +2 more
core +1 more source
Cell wall target fragment discovery using a low‐cost, minimal fragment library
FEBS Letters, EarlyView.LoCoFrag100 is a fragment library made up of 100 different compounds. Similarity between the fragments is minimized and 10 different fragments are mixed into a single cocktail, which is soaked to protein crystals. These crystals are analysed by X‐ray crystallography, revealing the binding modes of the bound fragment ligands.
Kaizhou Yan +5 more
wiley +1 more source
Semi-Automatic Method of Extracting Road Networks from High-Resolution Remote-Sensing Images
Applied Sciences, 2022 Road network extraction plays a critical role in data updating, urban development, and decision support. To improve the efficiency of labeling road datasets and addressing the problems of traditional methods of manually extracting road networks from high-
Kaili Yang +5 more
doaj +1 more source
A network mobility indicator using a fuzzy logic approach [PDF]
, 2014 This paper introduces a methodology to assess the mobility of a road transport network from the 3 network perspective. In this research, the mobility of the road transport network is defined as the 4 ability of the road transport network to connect all ...
EL Rashidy, Rawia Ahmed +1 more
core
FEBS Letters, EarlyView.We reconstituted Synechocystis glycogen synthesis in vitro from purified enzymes and showed that two GlgA isoenzymes produce glycogen with different architectures: GlgA1 yields denser, highly branched glycogen, whereas GlgA2 synthesizes longer, less‐branched chains.
Kenric Lee +3 more
wiley +1 more source
Graph Convolutional Networks for Road Networks [PDF]
Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, 2019 Machine learning techniques for road networks hold the potential to facilitate many important transportation applications. Graph Convolutional Networks (GCNs) are neural networks that are capable of leveraging the structure of a road network by utilizing information of, e.g., adjacent road segments.
Jepsen, Tobias Skovgaard +2 more
openaire +4 more sources
Optimization of Road Network Recovery Decisions Considering Road Section Recovery Differences
Shanghai Jiaotong Daxue xuebaoExisting studies on road network recovery decision have ignored the impact of the differences in recovery speed and recovery degree of different road sections on the recovery performance of the road network.
LU Qingchang, LIU Peng, QIN Han, XU Pengcheng
doaj +1 more source
Dynamic Assessment of Road Network Vulnerability Based on Cell Transmission Model
Journal of Advanced Transportation, 2021 The road network maintaining stability is critical for guaranteeing urban traffic function. Therefore, the vulnerable links need to be identified accurately.
Yu Sun +3 more
doaj +1 more source
Structural biology of ferritin nanocages
FEBS Letters, EarlyView.Ferritin is a conserved iron‐storage protein that sequesters iron as a ferric mineral core within a nanocage, protecting cells from oxidative damage and maintaining iron homeostasis. This review discusses ferritin biology, structure, and function, and highlights recent cryo‐EM studies revealing mechanisms of ferritinophagy, cellular iron uptake, and ...
Eloise Mastrangelo, Flavio Di Pisa
wiley +1 more source
Research on road extraction of remote sensing image based on convolutional neural network
EURASIP Journal on Image and Video Processing, 2019 Road is an important kind of basic geographic information. Road information extraction plays an important role in traffic management, urban planning, automatic vehicle navigation, and emergency management.
Yuantao Jiang
doaj +1 more source