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Walbeek, T.J.; Harrison, E.M.; Soler, R.R.; Gorman, M.R. |
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Title |
Enhanced Circadian Entrainment in Mice and Its Utility under Human Shiftwork Schedules |
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Journal Article |
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2019 |
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Clocks & Sleep |
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Clocks & Sleep |
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1 |
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3 |
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394-413 |
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Animals |
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The circadian system is generally considered to be incapable of adjusting to rapid changes in sleep/work demands. In shiftworkers this leads to chronic circadian disruption and sleep loss, which together predict underperformance at work and negative health consequences. Two distinct experimental protocols have been proposed to increase circadian flexibility in rodents using dim light at night: rhythm bifurcation and T-cycle (i.e., day length) entrainment. Successful translation of such protocols to human shiftworkers could facilitate alignment of internal time with external demands. To assess entrainment flexibility following bifurcation and exposure to T-cycles, mice in Study 1 were repeatedly phase-shifted. Mice from experimental conditions rapidly phase-shifted their activity, while control mice showed expected transient misalignment. In Study 2 and 3, mice followed a several weeks-long intervention designed to model a modified DuPont or Continental shiftwork schedule, respectively. For both schedules, bifurcation and nocturnal dim lighting reduced circadian misalignment. Together, these studies demonstrate proof of concept that mammalian circadian systems can be rendered sufficiently flexible to adapt to multiple, rapidly changing shiftwork schedules. Flexible adaptation to exotic light-dark cycles likely relies on entrainment mechanisms that are distinct from traditional entrainment. |
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2624-5175 |
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GFZ @ kyba @ |
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2661 |
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Leibrand, A.; Sadoff, N.; Maslak, T.; Thomas, A. |
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Title |
Using Earth Observations to Help Developing Countries Improve Access to Reliable, Sustainable, and Modern Energy |
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Journal Article |
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2019 |
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Frontiers in Environmental Science |
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Front. Environ. Sci. |
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7 |
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Review; Remote Sensing |
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In this review paper, the authors identify priority areas, and opportunities for electric utilities in developing and emerging economies to incorporate Earth observation (EO) data into rural electrification planning, renewable energy resource assessment, distributed generation, grid operation and reliability, and disaster risk reduction and recovery efforts. Using a methodological framework, the authors conducted a comprehensive literature review of primary and gray literature. This paper reviews the many existing applications for EO data, such as the use of nighttime lights imagery for estimations of rural electrification, EO-derived normalized difference vegetation index (NDVI) products for vegetation monitoring for overhead transmission line management, solar radiance data for renewable energy project planning, and nowcasting for extreme weather events and other disaster monitoring. These and other applications can enhance energy security through improved governance of and access to modern and reliable electricity, renewable energy management, and disaster risk assessment in developing nations, paving the way for more sustainable social and economic development. Real-world examples of EO data use by utilities in developing and emerging economies, as well as barriers and opportunities for EO technology transfer, are discussed. Recommendations for stakeholder engagement, future EO training opportunities, and human capacity building are also presented. |
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2296-665X |
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GFZ @ kyba @ |
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2660 |
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Avtar, R.; Tripathi, S.; Aggarwal, A.K. |
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Assessment of Energy–Population–Urbanization Nexus with Changing Energy Industry Scenario in India |
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2019 |
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Land |
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Land |
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8 |
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8 |
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124 |
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Remote Sensing |
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The demand for energy has been growing worldwide, especially in India partly due to the rapid population growth and urbanization of the country. To meet the ever-increasing energy requirement while maintaining an ecological balance is a challenging task. However, the energy industry-induced effect on population and urbanization has not been addressed before. Therefore, this study investigates the linkages between energy, population, and urbanization. The study also aims to find the quantifiable indicators for the population growth and rate of urbanization due to the expanding energy industry. The integrated framework uses a multi-temporal Landsat data to analyze the urbanization pattern, a census data for changes in population growth, night time light (NTL) data as an indicator for economic development and energy production and consumption data for energy index. Multi-attribute model is used to calculate a unified metric, termed as the energy–population–urbanization (EPU) nexus index. The proposed approach is demonstrated in the National Thermal Power Corporation (NTPC) Dadri power plant located in Uttar Pradesh, India. Landsat and NTL data clearly shows the urbanization pattern, economic development, and electrification in the study area. A comparative analysis based on various multi-attribute decision model assessment techniques suggests that the average value of EPU nexus index is 0.529, which significantly large compared to other studies and require special attention by policymakers because large EPU index indicates stronger correlation among energy, population, and urbanization. The authors believe that it would help the policymakers in planning and development of future energy projects, policies, and long-term strategies as India is expanding its energy industry. |
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2073-445X |
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GFZ @ kyba @ |
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2659 |
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Chen, X.; Jia, X.; Pickering, M. |
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Title |
A Nighttime Lights Adjusted Impervious Surface Index (NAISI) with Integration of Landsat Imagery and Nighttime Lights Data from International Space Station |
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Journal Article |
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2019 |
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International Journal of Applied Earth Observation and Geoinformation |
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International Journal of Applied Earth Observation and Geoinformation |
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83 |
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101889 |
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Remote Sensing |
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Accurate mapping of impervious surface is essential for both urbanization monitoring and micro-ecosystem research. However, the confusion between impervious surface and bare soil is the major concern due to their high spectral similarity in optical imagery. Integration of multi-sensor images is considered to offer a better capacity for distinguishing impervious surface from background. In this paper, a new impervious surface index namely nighttime light adjusted impervious surface index (NAISI), which integrates information from Landsat and nighttime lights (NTL) data from International Space Station (NTL-ISS), is proposed. Parallel to baseline subtraction approaches, NAISI integrate the information from the first component of principal component (PC) transformation of NTL-ISS, the Soil Adjusted Vegetation Index (SAVI) and the third component of tasseled cap transform (TC3) of the Landsat data. Visual interpretation and quantitative indices (SDI, Kappa and overall accuracy) were adopted to elevate the accuracy and separability of NAISI. Comparative analysis with NTL derived light intensity, optical indices, as well as existing optical-NTL indices were conducted to examine the performance of NAISI. Results indicate that NAISI achieves a more promising capability in impervious surface mapping. This demonstrates the superiority of integration of optical and nighttime lights information for imperviousness detection. |
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0303-2434 |
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GFZ @ kyba @ |
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2658 |
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Zhang, W.; Jiang, L.; Cui, Y.; Xu, Y.; Wang, C.; Yu, J.; Streets, D.G.; Lin, B. |
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Title |
Effects of urbanization on airport CO2 emissions: A geographically weighted approach using nighttime light data in China |
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Journal Article |
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Year |
2019 |
Publication |
Resources, Conservation and Recycling |
Abbreviated Journal |
Resources, Conservation and Recycling |
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150 |
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104454 |
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Remote Sensing |
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Regional disparities in carbon dioxide (CO2) emissions from airports at the city level are of increasing importance for low-carbon development of the civil aviation sector. However, CO2 emissions from airport operations have rarely been estimated and discussed. We investigate the main driving forces of airport CO2 emissions by using Ordinary Least Square (OLS) and Geographically Weighted Regression (GWR) models, separately, to investigate whether urbanization drives airport CO2 emissions and to investigate spatial heterogeneity at the city level. Nighttime light (NTL) data from satellite observations are adopted as a proxy for urbanization. We obtained energy consumption data by end-use purpose for 70 airports in China and calculated the CO2 emissions from on-ground airport operations. The median CO2 emissions of the 70 sample airports are estimated to be 15.9 million tonnes for 2015. Results from the GWR model indicate that airport CO2 emissions are affected by five main factors: urbanization, foreign direct investment, the share of tertiary industry in gross domestic output, passenger turnover of civil aviation and passenger turnover of railways. The elasticity of urbanization shows an increasing trend from the east of China to the west. The spatial heterogeneity of the CO2 emissions of the five airport clusters that are located in five urban agglomerations is discussed. In order to achieve effective reductions of CO2 emissions from airports, policy-makers should consider the spatial heterogeneity of the major driving factors of carbon emissions in different regions to avoid carbon lock-in. |
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0921-3449 |
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GFZ @ kyba @ |
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2657 |
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