Results on stellar occultations by (307261) 2002 MS4

Transneptunian Objects (TNOs) are the remnants of our planetary system and can retain information about the early stages of the Solar System formation. Stellar occultation is a groundbased method used to study these distant bodies which have been presenting exciting results mainly about their physical properties. The big TNO called 2002 MS4 was discovered by Trujillo, C. A., & Brown, M. E., in 2002 using observations made at the Palomar Observatory (EUA). It is classified as a hot classical TNO, with orbital parameters a = 42 AU, e = 0.139, and i = 17.7º. Using thermal measurements with PACS (Herschel) and MIPS (Spitzer Space Telescope) instruments, Vilenius et al. 2012 obtained a radius of 467 +/- 23.5 km and an albedo of 0.051.Predictions of stellar occultations by this body in 2019 were obtained using the Gaia DR2 catalogue and NIMA ephemeris (Desmars et al. 2015) and made available in the Lucky Star web page (https://lesia.obspm.fr/lucky-star/). Four events were observed in South America and Canada. The first stellar occultation was detected on 09 July 2019, resulting in two positives and four negatives chords, including a close one which proven to be helpful to constrain the body’s size. This detection also allowed us to obtain a precise astrometric position that was used to update its ephemeris and improve the predictions of the following events. Two of them were detected on 26 July 2019, separated by eight hours. The first event was observed from South America and resulted in three positive detections, while the second, observed from Canada, resulted in a single chord. Another double chord event was observed on 19 August 2019 also from Canada.Facultad de Ciencias Astronómicas y Geofísica

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

The benefits of ethanol use for hydrogen production in urban transportation

The purpose of this paper is to describe the benefits of sugar cane ethanol in Brazil, appointing the productivity of this type of fuel based on hectares of plantation, its carbon dioxide cycle and the contribution to reduce the greenhouse effect. In the following step the uses of ethanol for hydrogen production by steam reforming is analyzed and some comparison with natural gas steam reforming is performed. The sugar cane industry in Brazil, in a near future, in the hydrogen era, could be modified according to our purpose, since besides the production of sugar, and ethylic and anhydric alcohol, Brazilian sugar cane industry will also be able to produce biohydrogen. Fuel cells appear like a promising technology for energy generation. Among several technologies in the present, the PEMFC (proton exchange membrane fuel cell) is the most appropriate for vehicles application, because it combines durability, high power density, high efficiency, good response and it works at relatively low temperatures. Besides that it is easy to turn it on and off and it is able to support present vibration in vehicles. A PEMFC's problem is the need of noble catalysts like platinum. Another problem is that CO needs to be in low concentration, requiring a more clean hydrogen to avoid fuel cell deterioration. One part of this paper was developed in Stockholm, where there are some buses within the CUTE (clean urban transport for Europe) project that has been in operation with FC since January 2004. Another part was developed in Guaratinguetá, Brazil. Brazil intends to start up a program of FC buses. As conclusion, this paper shows the economical analysis comparing buses moved by fuel cells using hydrogen by different kinds of production. Electrolyze with wind turbine, natural gas steam reforming and ethanol steam reforming.Ethanol Hydrogen Fuel cell Urban buses Economical analyses