In this paper,the spatio-temporal variation and propagation direction of coal fire were studied in the Jharia Coalfield(JCF),India during 2006–2015 through satellite-based night-time land surface temperature(LST)imag...In this paper,the spatio-temporal variation and propagation direction of coal fire were studied in the Jharia Coalfield(JCF),India during 2006–2015 through satellite-based night-time land surface temperature(LST)imaging.The LST was retrieved from Advanced Spaceborne Thermal Emission and Reflection Radiometer(ASTER)night-time thermal-infrared data by a robust split-window algorithm based on scene-specific regression coefficients,band-specific hybrid emissivity,and night-time atmospheric transmittance.The LST-profile-based coal fire detection algorithm was formulated through statistical analysis of the LST values along multiple transects across diverse coal fire locations in the JCF in order to compute date-specific threshold temperatures for separating thermally-anomalous and background pixels.This algorithm efficiently separates surface fire,subsurface fire,and thermally-anomalous transitional pixels.During the observation period,it was noticed that the coal fire area increased significantly,which resulted from new coal fire at many places owing to extensive opencast-mining operations.It was observed that the fire propagation occurred primarily along the dip direction of the coal seams.At places,lateral-propagation of limited spatial extent was also observed along the strike direction possibly due to spatial continuity of the coal seams along strike.Moreover,the opencast-mining activities carried out during 2009–2015 and the structurally weak planes facilitated the fire propagation.展开更多
Electron-electron correlations play central role in condensed matter physics,governing phenomena from superconductivity to magnetism and numerous technological applications.Two-dimensional(2D)materials with flat elect...Electron-electron correlations play central role in condensed matter physics,governing phenomena from superconductivity to magnetism and numerous technological applications.Two-dimensional(2D)materials with flat electronic bands provide natural playground to explore interaction-driven physics,thanks to their highly localized electrons.The search for 2D flat band materials has attracted intensive efforts,especially now with open science databases encompassing thousands of materials with computed electronic bands.Here we automate the otherwise daunting task of materials search and classification by combining supervised and unsupervised machine learning algorithms.To this end,convolutional neural network was employed to identify 2D flat band materials,which were then subjected to symmetry-based analysis using a bilayer unsupervised learning algorithm.Such hybrid approach of exploring materials databases allowed us to construct a genome of 2D materials hosting flat bands and to reveal material classes outside the known flat band paradigms.展开更多
文摘In this paper,the spatio-temporal variation and propagation direction of coal fire were studied in the Jharia Coalfield(JCF),India during 2006–2015 through satellite-based night-time land surface temperature(LST)imaging.The LST was retrieved from Advanced Spaceborne Thermal Emission and Reflection Radiometer(ASTER)night-time thermal-infrared data by a robust split-window algorithm based on scene-specific regression coefficients,band-specific hybrid emissivity,and night-time atmospheric transmittance.The LST-profile-based coal fire detection algorithm was formulated through statistical analysis of the LST values along multiple transects across diverse coal fire locations in the JCF in order to compute date-specific threshold temperatures for separating thermally-anomalous and background pixels.This algorithm efficiently separates surface fire,subsurface fire,and thermally-anomalous transitional pixels.During the observation period,it was noticed that the coal fire area increased significantly,which resulted from new coal fire at many places owing to extensive opencast-mining operations.It was observed that the fire propagation occurred primarily along the dip direction of the coal seams.At places,lateral-propagation of limited spatial extent was also observed along the strike direction possibly due to spatial continuity of the coal seams along strike.Moreover,the opencast-mining activities carried out during 2009–2015 and the structurally weak planes facilitated the fire propagation.
基金This research was supported by the European Research Council(ERC)under the European Union’s Horizon 2020 research and innovation program(Grant Agreement No.865590)the Royal Society International Exchanges 2019 Cost Share Program(IEC\R2\192001)+1 种基金A.B.acknowledges the Commonwealth Scholarship Commission in the UK for financial assistance.Q.Y.acknowledges the funding from Leverhulme Early Career Fellowship ECF-2019-612Dame Kathleen Ollerenshaw Fellowship from the University of Manchester,and Royal Society University Research Fellowship URF\R1\221096.
文摘Electron-electron correlations play central role in condensed matter physics,governing phenomena from superconductivity to magnetism and numerous technological applications.Two-dimensional(2D)materials with flat electronic bands provide natural playground to explore interaction-driven physics,thanks to their highly localized electrons.The search for 2D flat band materials has attracted intensive efforts,especially now with open science databases encompassing thousands of materials with computed electronic bands.Here we automate the otherwise daunting task of materials search and classification by combining supervised and unsupervised machine learning algorithms.To this end,convolutional neural network was employed to identify 2D flat band materials,which were then subjected to symmetry-based analysis using a bilayer unsupervised learning algorithm.Such hybrid approach of exploring materials databases allowed us to construct a genome of 2D materials hosting flat bands and to reveal material classes outside the known flat band paradigms.
