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There is a growing body of literature exploring the skill content of jobs. This paper contributes to this research by using data on the task content of occupations in developing countries, instead of U.S. data, as most existing studies do. The paper finds that indexes based on U.S. data do not provide a fair approximation of the levels, changes, and drivers of the routine cognitive and nonroutine manual skill content of jobs in developing countries. The paper also uncovers three new stylized facts. First, while developed countries tend to have jobs more intensive in nonroutine cognitive skills than developing countries, income (in growth and levels) is not associated with the skill content of jobs once the analysis accounts for other factors. Second, although adoption of information and communications technology is linked to job de-routinization, international trade is an offsetting force. Last, adoption of information and communications technology is correlated with lower employment growth in countries with a high share of occupations that are intensive in routine tasks.

Education --- Educational Sciences --- Empirical Model --- Female Labor Force --- Global Value Chains --- Labor Market --- Labor Markets --- Labor Skills --- Rural Development --- Rural Labor Markets --- Social Protections and Labor

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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.

Research & information: general --- 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 --- 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

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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.

Research & information: general --- 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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Nowadays, forecast applications are receiving unprecedent attention thanks to their capability to improve the decision-making processes by providing useful indications. A large number of forecast approaches related to different forecast horizons and to the specific problem that have to be predicted have been proposed in recent scientific literature, from physical models to data-driven statistic and machine learning approaches. In this Special Issue, the most recent and high-quality researches about forecast are collected. A total of nine papers have been selected to represent a wide range of applications, from weather and environmental predictions to economic and management forecasts. Finally, some applications related to the forecasting of the different phases of COVID in Spain and the photovoltaic power production have been presented.

Direct Normal Irradiance (DNI) --- IFS/ECMWF --- forecast --- evaluation --- DNI attenuation Index (DAI) --- bias correction --- nowcast --- meteorological radar data --- optical flow --- deep learning --- Bates–Granger weights --- uniform weights --- (REG) ARIMA --- ETS --- Hodrick–Prescott trend --- Google Trends indices --- Himalayan region --- streamflow forecast verification --- persistence --- snow-fed rivers --- intermittent rivers --- costumer relation management --- business to business sales prediction --- machine learning --- predictive modeling --- microsoft azure machine-learning service --- travel time forecasting --- time series --- bus service --- transit systems --- sustainable urban mobility plan --- bus travel time --- learning curve --- forecasting --- production cost --- cost estimating --- semi-empirical model --- logistic map --- COVID-19 --- SARS-CoV-2 --- PV output power estimation --- PV-load decoupling --- behind-the-meter PV --- baseline prediction --- n/a --- Bates-Granger weights --- Hodrick-Prescott trend

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This book captures advancements in the applications of computational intelligence (artificial intelligence, machine learning, etc.) to problems in the mineral and mining industries. The papers present the state of the art in four broad categories: mine operations, mine planning, mine safety, and advances in the sciences, primarily in image processing applications. Authors in the book include both researchers and industry practitioners.

Technology: general issues --- History of engineering & technology --- truck dispatching --- mining equipment uncertainties --- orebody uncertainty --- discrete event simulation --- Q-learning --- grinding circuits --- minerals processing --- random forest --- decision trees --- machine learning --- knowledge discovery --- variable importance --- mineral prospectivity mapping --- random forest algorithm --- epithermal gold --- unstructured data --- blast impact --- empirical model --- mining --- fragmentation --- mine worker fatigue --- random forest model --- health and safety management --- stockpiles --- operational data --- mine-to-mill --- geostatistics --- ore control --- mine optimization --- digital twin --- modes of operation --- geological uncertainty --- multivariate statistics --- partial least squares regression --- oil sands --- bitumen extraction --- bitumen processability --- mine safety and health --- accidents --- narratives --- natural language processing --- random forest classification --- hyperspectral imaging --- multispectral imaging --- dimensionality reduction --- neighbourhood component analysis --- artificial intelligence --- mining exploitation --- masonry buildings --- damage risk analysis --- Bayesian network --- Naive Bayes --- Bayesian Network Structure Learning (BNSL) --- rock type --- mining geology --- bluetooth beacon --- classification and regression tree --- gaussian naïve bayes --- k-nearest neighbors --- support vector machine --- transport route --- transport time --- underground mine --- tactical geometallurgy --- data analytics in mining --- ball mill throughput --- measurement while drilling --- non-additivity --- coal --- petrographic analysis --- macerals --- image analysis --- semantic segmentation --- convolutional neural networks --- point cloud scaling --- fragmentation size analysis --- structure from motion --- truck dispatching --- mining equipment uncertainties --- orebody uncertainty --- discrete event simulation --- Q-learning --- grinding circuits --- minerals processing --- random forest --- decision trees --- machine learning --- knowledge discovery --- variable importance --- mineral prospectivity mapping --- random forest algorithm --- epithermal gold --- unstructured data --- blast impact --- empirical model --- mining --- fragmentation --- mine worker fatigue --- random forest model --- health and safety management --- stockpiles --- operational data --- mine-to-mill --- geostatistics --- ore control --- mine optimization --- digital twin --- modes of operation --- geological uncertainty --- multivariate statistics --- partial least squares regression --- oil sands --- bitumen extraction --- bitumen processability --- mine safety and health --- accidents --- narratives --- natural language processing --- random forest classification --- hyperspectral imaging --- multispectral imaging --- dimensionality reduction --- neighbourhood component analysis --- artificial intelligence --- mining exploitation --- masonry buildings --- damage risk analysis --- Bayesian network --- Naive Bayes --- Bayesian Network Structure Learning (BNSL) --- rock type --- mining geology --- bluetooth beacon --- classification and regression tree --- gaussian naïve bayes --- k-nearest neighbors --- support vector machine --- transport route --- transport time --- underground mine --- tactical geometallurgy --- data analytics in mining --- ball mill throughput --- measurement while drilling --- non-additivity --- coal --- petrographic analysis --- macerals --- image analysis --- semantic segmentation --- convolutional neural networks --- point cloud scaling --- fragmentation size analysis --- structure from motion