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data science --- artificial intelligence --- data analytics --- predictive modeling --- cybersecurity --- algorithms

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A critical look at how the US military is weaponizing technology and data for new kinds of warfare--and why we must resist. War Virtually is the story of how scientists, programmers, and engineers are racing to develop data-driven technologies for fighting virtual wars, both at home and abroad. In this landmark book, Roberto J. González gives us a lucid and gripping account of what lies behind the autonomous weapons, robotic systems, predictive modeling software, advanced surveillance programs, and psyops techniques that are transforming the nature of military conflict. González, a cultural anthropologist, takes a critical approach to the techno-utopian view of these advancements and their dubious promise of a less deadly and more efficient warfare.   With clear, accessible prose, this book exposes the high-tech underpinnings of contemporary military operations--and the cultural assumptions they're built on. Chapters cover automated battlefield robotics; social scientists' involvement in experimental defense research; the blurred line between political consulting and propaganda in the internet era; and the military's use of big data to craft new counterinsurgency methods based on predicting conflict. González also lays bare the processes by which the Pentagon and US intelligence agencies have quietly joined forces with Big Tech, raising an alarming prospect: that someday Google, Amazon, and other Silicon Valley firms might merge with some of the world's biggest defense contractors. War Virtually takes an unflinching look at an algorithmic future--where new military technologies threaten democratic governance and human survival.

Artificial intelligence --- Military art and science --- Military applications. --- Automation. --- Silicon valley. --- United States military technology. --- algorithms. --- anthropology. --- artificial intelligence. --- contractors. --- department of defense. --- drone warfare. --- palantir. --- predictive modeling. --- robotic weapons. --- tech companies.

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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.

Research & information: general --- 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 --- 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

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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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Wildfire risk can be perceived as the combination of wildfire hazards (often described by likelihood and intensity) with the susceptibility of people, property, or other valued resources to that hazard. Reflecting the seriousness of wildfire risk to communities around the world, substantial resources are devoted to assessing wildfire hazards and risks. Wildfire hazard and risk assessments are conducted at a wide range of scales, from localized to nationwide, and are often intended to communicate and support decision making about risks, including the prioritization of scarce resources. Improvements in the underlying science of wildfire hazard and risk assessment and in the development, communication, and application of these assessments support effective decisions made on all aspects of societal adaptations to wildfire, including decisions about the prevention, mitigation, and suppression of wildfire risks. To support such efforts, this Special Issue of the journal Fire compiles articles on the understanding, modeling, and addressing of wildfire risks to homes, water resources, firefighters, and landscapes.

Research & information: general --- Biology, life sciences --- Forestry & related industries --- wildfire risk --- object-oriented image analysis --- Sentinel-2 --- fire behavior --- flammap --- wildfire management --- water supply --- erosion --- wildfire containment --- Potential fire Operational Delineations --- Monte Carlo simulation --- transmission risk --- WUI --- fire --- defensible space --- prescribed fire --- community vulnerability --- fire suppression costs --- Zillow --- wildfire --- predictive modeling --- fire spread model --- Monte Carlo --- spatial modeling --- area difference index --- statistics --- precision --- recall --- principal components analysis --- risk assessment --- structure loss --- wildland–urban interface --- mitigation --- mapping --- land use --- disaster --- fire spread models --- surrogate modeling --- sensitivity analysis --- global sensitivity analysis --- colour coding --- communication --- forest fire --- ordinal categorization --- palette --- risk --- firefighter safety --- safe separation distance --- safety zones --- LCES --- Google Earth Engine --- lidar --- LANDFIRE --- Landsat --- GEDI --- parcel-level risk --- post-fire analysis --- risk mitigation --- rapid assessment --- natural hazards --- fuels --- fire hazard --- remote sensing --- LiDAR --- Sentinel --- modeling --- simulation --- wildfire risk --- object-oriented image analysis --- Sentinel-2 --- fire behavior --- flammap --- wildfire management --- water supply --- erosion --- wildfire containment --- Potential fire Operational Delineations --- Monte Carlo simulation --- transmission risk --- WUI --- fire --- defensible space --- prescribed fire --- community vulnerability --- fire suppression costs --- Zillow --- wildfire --- predictive modeling --- fire spread model --- Monte Carlo --- spatial modeling --- area difference index --- statistics --- precision --- recall --- principal components analysis --- risk assessment --- structure loss --- wildland–urban interface --- mitigation --- mapping --- land use --- disaster --- fire spread models --- surrogate modeling --- sensitivity analysis --- global sensitivity analysis --- colour coding --- communication --- forest fire --- ordinal categorization --- palette --- risk --- firefighter safety --- safe separation distance --- safety zones --- LCES --- Google Earth Engine --- lidar --- LANDFIRE --- Landsat --- GEDI --- parcel-level risk --- post-fire analysis --- risk mitigation --- rapid assessment --- natural hazards --- fuels --- fire hazard --- remote sensing --- LiDAR --- Sentinel --- modeling --- simulation