Superconducting technology applications in electric machines have long been pursued due to their significant advantages of higher efficiency and power density over conventional technology. However, ...in spite of many successful technology demonstrations, commercial adoption has been slow, presumably because the threshold for value versus cost and technology risk has not yet been crossed. One likely path for disruptive superconducting technology in commercial products could be in applications where its advantages become key enablers for systems which are not practical with conventional technology. To help systems engineers assess the viability of such future solutions, we present a technology roadmap for superconducting machines. The timeline considered was ten years to attain a Technology Readiness Level of 6+, with systems demonstrated in a relevant environment. Future projections, by definition, are based on the judgment of specialists, and can be subjective. Attempts have been made to obtain input from a broad set of organizations for an inclusive opinion. This document was generated through a series of teleconferences and in-person meetings, including meetings at the 2015 IEEE PES General meeting in Denver, CO, the 2015 ECCE in Montreal, Canada, and a final workshop in April 2016 at the University of Illinois, Urbana-Champaign that brought together a broad group of technical experts spanning the industry, government and academia.
The concept of sustainable development has gained worldwide attention in recent years which had enhanced its implementation. However, few studies have attempted to map the global research of ...sustainability. This study utilizes scientometric review of global trend and structure of sustainability research in 1991–2016 using techniques such as co-author, co-word, co-citation, clusters, and geospatial analyses. A total of 2094 bibliographic records from the Web of Science database were analyzed to generate the study's research power networks and geospatial map. The findings reveal an evolution of the research field from the definition of its concepts in the Brundtland Commission report to the recent development of models and sustainability indicators. The most significant contributions in sustainability research have originated primarily from the United States, China, United Kingdom and Canada. Also, existing studies in sustainability research focus mainly on subject categories of environmental sciences, green & sustainable science technology, civil engineering, and construction & building technology. Emerging trends in sustainability research were sustainable urban development, sustainability indicators, water management, environmental assessment, public policy, etc.; while the study generated 21 co-citation clusters. This study provides its readers with an extensive understanding of the salient research themes, trends and pattern of sustainability research worldwide.
•Sustainability research is evolving and expanding to several frontiers.•This research reviewed 2096 bibliographic records from the Web of Science database.•Salient and emerging sustainability research trends and clusters were identified.•The findings are applicable to key stakeholders such as the government and organizations, researchers, among others.
•A novel two-stage forecasting architecture is proposed for wind power forecasting.•Considering error factor in wind power forecasting to improve model’s performance.•A novel ensemble method is ...proposed in the proposed forecasting model.•The developed model can also perform better for wind power interval prediction.
With the fast growth of wind power penetration into the electric grid, wind power forecasting plays an increasingly significant role in the secure and economic operation of power systems. Although there have been numerous studies concerning wind power forecasting, most of them have failed to make the best of the information implied in the error value, focused only on simple error correction, adopted a simple ensemble method to aggregate the predictions of each component, and considered improving only forecasting accuracy. Recognizing these issues, a novel two-stage forecasting model based on the error factor, a nonlinear ensemble method and the multi-objective grey wolf optimizer algorithm is proposed for wind power forecasting. More specially, in stage I, the extreme learning machine optimized by the multi-objective grey wolf optimizer is used to forecast the components decomposed by variational mode decomposition, and an error prediction model based on the extreme learning machine optimized by the multi-objective grey wolf optimizer is utilized to predict forecast errors; also, a novel nonlinear ensemble method based on the extreme learning machine optimized by the multi-objective grey wolf optimizer is utilized to integrate all the components and forecast error values in stage II. Three real-world wind power datasets collected from Canada and Spain are introduced to demonstrate the forecasting performance of the developed model. The forecasting results reveal that the proposed model is superior to all the other considered models in terms of both accuracy and stability and thus can be a useful tool for wind power forecasting.
Ubiquitous facial recognition technology can expose individuals' political orientation, as faces of liberals and conservatives consistently differ. A facial recognition algorithm was applied to ...naturalistic images of 1,085,795 individuals to predict their political orientation by comparing their similarity to faces of liberal and conservative others. Political orientation was correctly classified in 72% of liberal-conservative face pairs, remarkably better than chance (50%), human accuracy (55%), or one afforded by a 100-item personality questionnaire (66%). Accuracy was similar across countries (the U.S., Canada, and the UK), environments (Facebook and dating websites), and when comparing faces across samples. Accuracy remained high (69%) even when controlling for age, gender, and ethnicity. Given the widespread use of facial recognition, our findings have critical implications for the protection of privacy and civil liberties.
Gendering the nation Armatage, Kay
Gendering the nation,
c1999, 19990610, 1999, 2000, 1999-01-01
eBook
The definitive collection of essays, both original and previously published, that address the impact and influence of a century of women's film making in Canada.
Climate change and global warming as the main human societies’ threats are fundamentally associated with energy consumption and GHG emissions. The residential sector, representing 27% and 17% of ...global energy consumption and CO2 emissions, respectively, has a considerable role to mitigate global climate change. Ten countries, including China, the US, India, Russia, Japan, Germany, South Korea, Canada, Iran, and the UK, account for two-thirds of global CO2 emissions. Thus, these countries’ residential energy consumption and GHG emissions have direct, significant effects on the world environment. The aim of this paper is to review the status and current trends of energy consumption, CO2 emissions and energy policies in the residential sector, both globally and in those ten countries. It was found that global residential energy consumption grew by 14% from 2000 to 2011. Most of this increase has occurred in developing countries, where population, urbanization and economic growth have been the main driving factors. Among the ten studied countries, all of the developed ones have shown a promising trend of reduction in CO2 emissions, apart from the US and Japan, which showed a 4% rise. Globally, the residential energy market is dominated by traditional biomass (40% of the total) followed by electricity (21%) and natural gas (20%), but the total proportion of fossil fuels has decreased over the past decade. Energy policy plays a significant role in controlling energy consumption. Different energy policies, such as building energy codes, incentives, energy labels have been employed by countries. Those policies can be successful if they are enhanced by making them mandatory, targeting net-zero energy building, and increasing public awareness about new technologies. However, developing countries, such as China, India and Iran, still encounter with considerable growth in GHG emissions and energy consumption, which are mostly related to the absence of strong, efficient policy.
In recent years, the world witnessed many devastating wildfires that resulted in destructive human and environmental impacts across the globe. Emergency response and rapid response for mitigation ...calls for effective approaches for near real-time wildfire monitoring. Capable of penetrating clouds and smoke, and imaging day and night, Synthetic Aperture Radar (SAR) can play a critical role in wildfire monitoring. In this communication, we investigated and demonstrated the potential of Sentinel-1 SAR time series with a deep learning framework for near real-time wildfire progression monitoring. The deep learning framework, based on a Convolutional Neural Network (CNN), is developed to detect burnt areas automatically using every new SAR image acquired during the wildfires and by exploiting all available pre-fire SAR time series to characterize the temporal backscatter variations. The results show that Sentinel-1 SAR backscatter can detect wildfires and capture their temporal progression as demonstrated for three large and impactful wildfires: the 2017 Elephant Hill Fire in British Columbia, Canada, the 2018 Camp Fire in California, USA, and the 2019 Chuckegg Creek Fire in northern Alberta, Canada. Compared to the traditional log-ratio operator, CNN-based deep learning framework can better distinguish burnt areas with higher accuracy. These findings demonstrate that spaceborne SAR time series with deep learning can play a significant role for near real-time wildfire monitoring when the data becomes available at daily and hourly intervals with the launches of RADARSAT Constellation Missions in 2019, and SAR CubeSat constellations.
•The impact of oil price uncertainty on economic growth in OECD countries is studied.•Taking a historic perspective, the sample covers a 144-year period starting in 1870.•The negative impact of ...uncertainty is more severe for oil producing countries.•A smaller impact is recorded in the post-World War II subsample period.•Reasons for declining impact of oil price uncertainty are proposed.
This paper uses a number of different panel data estimators, including fixed effects, bias-corrected least squares dummy variables (LSDVC), generalised methods of moments (GMM), feasible generalised least squares (FGLS), and random coefficients (RC) to analyse the impact of real oil price volatility on the growth in real GDP for 17 member countries of the Organisation for Economic Co-operation and Development (OECD), over a 144-year time period from 1870 to 2013. The main finding of the study is that oil price volatility has a negative and statistically significant impact on economic growth of the OECD countries in the sample. In addition, when allowing for slope heterogeneity, oil-producing countries are significantly negatively impacted by oil price uncertainty, most notably Norway and Canada.
To investigate the nexus among clean energy consumption, economic growth and CO2 emissions, a newly developed bootstrap ARDL bounds test with structural breaks is employed to survey the cointegration ...and causality for G7 countries. We find no cointegration among real GDP per capita, clean energy consumption and CO2 emissions in Canada, France, Italy, the US and the UK. However, cointegration exists in Germany when real GDP per capita and CO2 emissions serve as dependent variables and in Japan when CO2 emissions is the dependent variable. Regarding the results of causality test that we find clean energy consumption causes real GDP per capita for Canada, Germany and the US and CO2 emissions causes clean energy consumption for Germany. Besides, we find feedbacks between clean energy consumption and CO2 emissions for Germany, and unidirectional causality running from clean energy consumption to CO2 emissions for the US. Our study has important policy implications for G7 countries conducting efficient energy-use strategy to reduce CO2 emissions.
Intergenerational income mobility is lower in the United States than in Canada but varies significantly within each country. Our subnational analysis finds that the national border only partially ...distinguishes the approximately 1,000 regions we analyze within these countries. The Canada-US border divides central and eastern Canada from the US Great Lakes and northeastern regions. Simultaneously, some Canadian regions have more in common with the low-mobility southern parts of the United States than with the rest of Canada; that these areas represent a much larger fraction of the US population also explains why mobility is lower in the United States.
PDF
