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Recent developments in the natural gas industry warrant new analysis of related issues. Environmental, social, and governance (ESG) investments have accelerated the shift away from coal as the dominant source of electricity. Its low environmental impact, reduced volume, and broad availability make liquefied natural gas (LNG) a popular alternative, during this time of transition between traditional fuels and newer options. In the United States, the shale gas revolution has made natural gas a game changer. In this book, we focus on empirical analyses of the natural gas market and its growing relevance worldwide.

spillover effect --- market integration --- natural gas market --- time frequency dynamics --- BRICS --- exchange rates --- connectedness --- time domain --- frequency domain --- natural gas --- crude oil --- electricity utilities sector index --- time–frequency dynamics --- ESG --- renewable energy --- copula --- value-at-risk --- electricity --- spot --- futures --- transmission --- pipelines --- external cost --- health --- property damage --- bodily injury --- uncertainty --- insurance --- coal --- spillover effects --- dynamic approaches --- forecasting --- logistic regression --- random forests --- support vector machines --- US natural gas crises --- XGboost --- neural networks --- oil futures prices crashes --- foresting --- logistical regression --- extreme gradient boosting --- moving window --- SVAR --- oil price --- gas price --- US macroeconomic aggregates --- GDP --- CPI

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Remote sensing data and techniques have been widely used for disaster monitoring and assessment. In particular, recent advances in sensor technologies and artificial intelligence-based modeling are very promising for disaster monitoring and readying responses aimed at reducing the damage caused by disasters. This book contains eleven scientific papers that have studied novel approaches applied to a range of natural disasters such as forest fire, urban land subsidence, flood, and tropical cyclones.

wildfire --- satellite vegetation indices --- live fuel moisture --- empirical model function --- Southern California --- chaparral ecosystem --- forest fire --- forest recovery --- satellite remote sensing --- vegetation index --- burn index --- gross primary production --- South Korea --- land subsidence --- PS-InSAR --- uneven settlement --- building construction --- Beijing urban area --- floodplain delineation --- inaccessible region --- machine learning --- flash flood --- risk --- LSSVM --- China --- Himawari-8 --- threshold-based algorithm --- remote sensing --- dryness monitoring --- soil moisture --- NIR–Red spectral space --- Landsat-8 --- MODIS --- Xinjiang province of China --- SDE --- PE --- groundwater level --- compressible sediment layer --- tropical cyclone formation --- WindSat --- disaster monitoring --- wireless sensor network --- debris flow --- anomaly detection --- deep learning --- accelerometer sensor --- total precipitable water --- Himawari-8 AHI --- random forest --- deep neural network --- XGBoost --- n/a --- NIR-Red spectral space

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Building around innovative services related to different modes of transport and traffic management, intelligent transport systems (ITS) are being widely adopted worldwide to improve the efficiency and safety of the transportation system. They enable users to be better informed and make safer, more coordinated, and smarter decisions on the use of transport networks. Current ITSs are complex systems, made up of several components/sub-systems characterized by time-dependent interactions among themselves. Some examples of these transportation-related complex systems include: road traffic sensors, autonomous/automated cars, smart cities, smart sensors, virtual sensors, traffic control systems, smart roads, logistics systems, smart mobility systems, and many others that are emerging from niche areas. The efficient operation of these complex systems requires: i) efficient solutions to the issues of sensors/actuators used to capture and control the physical parameters of these systems, as well as the quality of data collected from these systems; ii) tackling complexities using simulations and analytical modelling techniques; and iii) applying optimization techniques to improve the performance of these systems. It includes twenty-four papers, which cover scientific concepts, frameworks, architectures and various other ideas on analytics, trends and applications of transportation-related data.

image dehazing --- traffic video dehazing --- dark channel prior --- spatial-temporal correlation --- contrast enhancement --- traffic signal control --- game theory --- decentralized control --- large-scale network control --- railway intrusion detection --- scene segmentation --- scene recognition --- adaptive feature extractor --- convolutional neural networks --- in-cylinder pressure identification --- speed iteration model --- EKF --- frequency modulation --- amplitude modulation --- sensor synchronization --- microscopic traffic data --- trajectory reconstruction --- expectation maximization --- vehicle matching --- artificial neural networks --- metro --- transportation --- user flow forecast --- matrix inversion --- time-varying matrix --- noise problem in time-varying matrix inversion --- recurrent neural network (RNN) --- RNN-based solver --- real-time fast computing --- real-time estimation --- probe vehicle --- traffic density --- neural network --- level of market penetration rate --- deep neural network --- neural artistic extraction --- objectification --- ride comfort --- subjective evaluation --- road surface recognition --- Gaussian background model --- abnormal road surface --- acceleration sensor --- traffic state prediction --- spatio-temporal traffic modeling --- simulation --- machine learning --- hyper parameter optimization --- ITS --- crash risk modeling --- hazardous materials --- highway safety --- operations research --- prescriptive analytics --- shortest path problem --- trucking --- vehicle routing problem --- data visualization --- descriptive analytics --- predictive analytics --- urban rail transit interior noise --- smartphone sensing --- XGBoost classifier --- railway maintenance --- vehicle trajectory prediction --- license plate data --- trip chain --- turning state transit --- route choice behavior --- real world experiment --- Intelligent Transportation Systems (ITS) --- advanced traveler information systems (ATIS) --- connected vehicles --- particle filter --- Kalman filter --- road safety --- travel time information system --- safety performance function --- bicycle sharing systems --- public transport systems --- data-driven classification of trips --- BSS underlying network --- trip index --- automatic rail-surface-scratch recognition and computation --- triangulation algorithm --- complete closed mesh model --- online rail-repair --- autonomous vehicle --- obstacle avoidance --- artificial potential field --- model predictive control --- human-like --- variable speed limits --- intelligent transportation systems --- ITS services --- driving simulator studies --- traffic modelling --- surrogate safety measures --- driving safety --- driving emotions --- driving stress --- lifestyle --- sensors --- heart rate --- plate scanning --- low-cost sensor --- sensor location problem --- traffic flow estimation --- n/a