Records |
Author |
Fasciani, I.; Petragnano, F.; Aloisi, G.; Marampon, F.; Rossi, M.; Francesca Coppolino, M.; Rossi, R.; Longoni, B.; Scarselli, M.; Maggio, R. |
Title  |
A new threat to dopamine neurons: the downside of artificial light |
Type |
Journal Article |
Year |
2020 |
Publication |
Neuroscience |
Abbreviated Journal |
Neuroscience |
Volume |
in press |
Issue |
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Pages |
in press |
Keywords |
Review; Human Health; Parkinson's disease; artificial light; dopamine neurons; melatonin; opsins; photoactivation |
Abstract |
Growing awareness of adverse impacts of artificial light on human health has led to recognize light pollution as a significant global environmental issue. Despite, a large number of studies in rodent and monkey models of Parkinson's disease have reported that near infrared light has neuroprotective effects on dopaminergic neurons, recent findings have shown that prolonged exposure of rodents and birds to fluorescent artificial light results in an increase of neuromelanin granules in substantia nigra and loss of dopaminergic neurons. The observed detrimental effect seems to be dependent on a direct effect of light on the substantia nigra rather than a secondary effect of the alterations of circadian rhythms. Moreover, inferences from animal models to human studies have shown a positive correlation between the prevalence of Parkinson's disease and light pollution. The present article discusses experimental evidence supporting a potentially deleterious impact of light on dopaminergic neurons and highlights the mechanisms whereby light might damage neuronal tissue. Moreover, it analyses epidemiological evidence that suggests light pollution to be an environmental risk factor for Parkinson's disease. |
Address |
Department of Biotechnological and Applied Clinical Sciences, University of L'Aquila, L'Aquila, Italy. Electronic address: roberto.maggio@univaq.it |
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0306-4522 |
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PMID:32142863 |
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Call Number |
GFZ @ kyba @ |
Serial |
2839 |
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Author |
Chen, X.; Jia, X.; Pickering, M. |
Title  |
A Nighttime Lights Adjusted Impervious Surface Index (NAISI) with Integration of Landsat Imagery and Nighttime Lights Data from International Space Station |
Type |
Journal Article |
Year |
2019 |
Publication |
International Journal of Applied Earth Observation and Geoinformation |
Abbreviated Journal |
International Journal of Applied Earth Observation and Geoinformation |
Volume |
83 |
Issue |
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Pages |
101889 |
Keywords |
Remote Sensing |
Abstract |
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 @ |
Serial |
2658 |
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Author |
Rea, M.; Skinner, N.; Bullough, J. |
Title  |
A Novel Barricade Warning Light System Using Wireless Communications |
Type |
Journal Article |
Year |
2018 |
Publication |
SAE Technical Paper 2018-01-5036 |
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Volume |
In press |
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Pages |
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Keywords |
Lighting; Safety |
Abstract |
Workers in construction and transportation sectors are at increased risk for work-related injuries and fatalities by nearby traffic. Barricade-mounted warning lights meeting current specifications do not always provide consistent and adequate visual guidance to drivers and can contribute to glare and reduced safety. Through an implementation of sensors and wireless communications, a novel, intelligent set of warning lights and a tablet-based interface were developed. The lights modulate between 100% and 10% of maximum intensity rather than between 100% and off in order to improve visual guidance and adjust their overall intensity based on ambient conditions. The lights can be synchronized or operated in sequential flash patterns at any frequency between 1 and 4 Hz, and sequential patterns automatically update based on global positioning satellite (GPS) locations displayed in the control interface. A successful field demonstration of the system verified that its functions were viewed favorably by transportation safety personnel. |
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NC @ ehyde3 @ |
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2117 |
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Author |
Chen, M.; Cai, H.; Yang, X.; Jin, C. |
Title  |
A novel classification regression method for gridded electric power consumption estimation in China |
Type |
Journal Article |
Year |
2020 |
Publication |
Scientific Reports |
Abbreviated Journal |
Sci Rep |
Volume |
10 |
Issue |
1 |
Pages |
18558 |
Keywords |
Remote Sensing |
Abstract |
Spatially explicit information on electric power consumption (EPC) is crucial for effective electricity allocation and utilization. Many studies have estimated fine-scale spatial EPC based on remotely sensed nighttime light (NTL). However, the spatial non-stationary relationship between EPC and NTL at prefectural level tends to be overlooked in existing literature. In this study, a classification regression method to estimate the gridded EPC in China based on imaging NTL via a Visible Infrared Imaging Radiometer Suite (VIIRS) was described. In addition, owing to some inherent omissions in the VIIRS NTL data, the study has employed the cubic Hermite interpolation to produce a more appropriate NTL dataset for estimation. The proposed method was compared with ordinary least squares (OLS) and geographically weighted regression (GWR) approaches. The results showed that our proposed method outperformed OLS and GWR in relative error (RE) and mean absolute percentage error (MAPE). The desirable results benefited mainly from a reasonable classification scheme that fully considered the spatial non-stationary relationship between EPC and NTL. Thus, the analysis suggested that the proposed classification regression method would enhance the accuracy of the gridded EPC estimation and provide a valuable reference predictive model for electricity consumption. |
Address |
College of Urban and Environment, Liaoning Normal University, Dalian, China |
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2045-2322 |
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PMID:33122690; PMCID:PMC7596565 |
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GFZ @ kyba @ |
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3193 |
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Author |
Hu, T.; Huang, X. |
Title  |
A novel locally adaptive method for modeling the spatiotemporal dynamics of global electric power consumption based on DMSP-OLS nighttime stable light data |
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Journal Article |
Year |
2019 |
Publication |
Applied Energy |
Abbreviated Journal |
Applied Energy |
Volume |
240 |
Issue |
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Pages |
778-792 |
Keywords |
Remote Sensing |
Abstract |
Timely and reliable estimation of electricity power consumption (EPC) is essential to the rational deployment of electricity power resources. Nighttime stable light (NSL) data from the Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) have the potential to model global 1-km gridded EPC. A processing chain to estimate EPC includes: (1) NSL data correction; and (2) regression model between EPC statistics and NSL data. For the global gridded EPC estimation, the current approach is to correct the global NSL image in a uniform manner and establish the linear relationships between NSL and EPC. However, the impacts of local socioeconomic inconsistencies on the NSL correction and model establishment are not fully considered. Therefore, in this paper, we propose a novel locally adaptive method for global EPC estimation. Firstly, we set up two options (with or without the correction) for each local area considering the global NSL image is not saturated everywhere. Secondly, three directions (forward, backward, or average) are alternatives for the inter-annual correction to remove the discontinuity effect of NSL data. Thirdly, four optional models (linear, logarithmic, exponential, or second-order polynomial) are adopted for the EPC estimation of each local area with different socioeconomic dynamic. Finally, the options for each step constitute all candidate processing chains, from which the optimal one is adaptively chosen for each local area based on the coefficient of determination. The results demonstrate that our product outperforms the existing one, at global, continental, and national scales. Particularly, the proportion of countries/districts with a high accuracy (MARE (mean of the absolute relative error) ≤ 10%) increases from 17.8% to 57.8% and the percentage of countries/districts with inaccurate results (MARE > 50%) decreases sharply from 23.0% to 3.7%. This product can enhance the detailed understanding of the spatiotemporal dynamics of global EPC. |
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0306-2619 |
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GFZ @ kyba @ |
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2242 |
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