The aplication of land use and nighttime lights on the development of high-resolution emission inventories for South America

In this paper, nighttime light data are suggested as a proxy for spatial distribution of vehicles running in urban and nearby areas. Nighttime lights focus on human activities, in contrast to traditional Earth observing systems that focus on natural systems. It is the human activity being visible in the form of brightness of nocturnal lights. Two available nighttime lights dataset were used in this work. The first one was provided by the U.S. Air Force Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS), henceforth, DMSO-OLS. The second one is the NASA-NOAA Suomi National Polar-orbiting Polar-orbiting Partnership (NPP) satellite, henceforth, Suomi-NPP. To validate the new proposed methodology, hundreds of urban areas of South America were analyzed in a high degree of resolution. The results of this study showed that night-time lights are very well correlated with vehicle fleet, population, and impervious surfaces but with strong spatial variability. The results of this study suggest a better understanding of the human activities in the context of a vehicular-based city conception.

Identifying clouds over the Pierre Auger Observatory using infrared satellite data

We describe a new method of identifying night-time clouds over the Pierre Auger Observatory using infrared data from the Imager instruments on the GOES-12 and GOES-13 satellites. We compare cloud identifications resulting from our method to those obtained by the Central Laser Facility of the Auger Observatory. Using our new method we can now develop cloud probability maps for the 3000 km2 of the Pierre Auger Observatory twice per hour with a spatial resolution of ∼2.4 km by ∼5.5 km. Our method could also be applied to monitor cloud cover for other ground-based observatories and for space-based observatories.

Weight gain in relation to night work among nurses

Objective: To investigate the relationship between working at night and increased body weight in nursing. In addition, we evaluated the differences in the proportion of variables sociodemographic, work and health, according to the work shift and their association with body mass index. Methods: Based on questionnaires, we obtained data from 446 nursing professionals about aspects of their job, health and lifestyle. We performed linear and logistic regression analysis. Results: Working at night is associated with a weight gain greater than (beta=0.24 kg/m(2)) working during the day (beta=0.15 kg/m(2)), as well as with aging (beta=0.16 kg/m(2)) and duration of working in nursing (beta=0.18 kg/m(2)). Night workers have a higher educational level, have been working for more years in nursing and also in the current shift, do not have diabetes and have reported longer sleep than day workers. There are also a higher number of smokers among the night workers than day workers. Logistic regression analysis also showed the more time to work in nursing and as an assistant was more likely to develop overweight/obesity. Conclusion: Working at the night contributes to more weight gain than the day shift, aging and duration of working in nursing.

Bioenergetics and neuromuscular determinants of the time to exhaustion at velocity corresponding to (V) over dotO(2)max in recreational long-distance runners

Bertuzzi, R, Bueno, S, Pasqua, LA, Acquesta, FM, Batista, MB, Roschel, H, Kiss, MAPDM, Serrao, JC, Tricoli, V, and Ugrinowitsch, C. Bioenergetics and neuromuscular determinants of the time to exhaustion at velocity corresponding to (V) over dotO(2)max in recreational long-distance runners. J Strength Cond Res 26(8): 2096-2102, 2012-The purpose of this study was to investigate the main bioenergetics and neuromuscular determinants of the time to exhaustion (T-lim) at the velocity corresponding to maximal oxygen uptake in recreational long-distance runners. Twenty runners performed the following tests on 5 different days: (a) maximal incremental treadmill test, (b) 2 submaximal tests to determine running economy and vertical stiffness, (c) exhaustive test to measured the T-lim, (d) maximum dynamic strength test, and (e) muscle power production test. Aerobic and anaerobic energy contributions during the T-lim test were also estimated. The stepwise multiple regression method selected 3 independent variables to explain T-lim variance. Total energy production explained 84.1% of the shared variance (p = 0.001), whereas peak oxygen uptake ((V) over dotO(2)peak) measured during T-lim and lower limb muscle power ability accounted for the additional 10% of the shared variance (p = 0.014). These data suggest that the total energy production, (V) over dotO(2)peak, and lower limb muscle power ability are the main physiological and neuromuscular determinants of T-lim in recreational long-distance runners.