Atmospheric conditions affect the development of cascades of secondary particles produced by primary cosmic rays. Global Data Assimilation System, implementing atmospheric models based on meteorological measurements and numerical weather predictions, could significantly improve the outcomes of the simulations for extensive air shower.In this work, we present a methodology to simulate the effect of the atmospheric models in secondary particle flux at the Earth's surface. The method was implemented for Bucaramanga-Colombia, using ARTI: a complete computational framework developed by the Latin American Giant Observatory Collaboration to estimate the particle spectra on Water Cherenkov Detectors depending on the geographical coordinates. As preliminary results, we observe differences in the total flux that varies from month to month with respect to the subtropical summer atmospheric profile.
Social networks have become the main media for information dissemination in the so-called Web 2.0. The core of these networks is social tagging, the act of annotating what users see in their social space. In the education domain, social tagging is potentially a useful resource to improve the organization (cataloguing) of large repositories of learning objects. To the present moment, however, many questions are open about social tagging in e-learning. In this work, hence, we proceed to answer three questions: (1) Can social tagging successfully catalog elearning objects? (2) How do students behave according to Körner's classification: categorizers or describers? and (3) Does social tagging converge to a well-defined descriptive vocabulary of tags? We performed a large experiment with 336 technician students that marked 218 electronic learning objects for about 4,985 times. Our results show that social tagging is a promising practice for e-learning; however some issues must be addressed to prevent an excessive number of categorizer students and, also, a premature convergence of the vocabulary of tags. Our conclusions are specific for the setting of our experiment, but we generalize them as much as possible suggesting guidelines of how to use social tagging in e-learning.
We present the design, and preliminary results of the LIDRAE water-Cherenkov air shower array installed at UFABC (23.6 • S, 46.5 • W, 750 m a.s.l.). LIDRAE detects the particles of extensive air showers with energies exceeding 100 TeV and is able to measure the arrival direction and energy of the primary cosmic rays. The array is composed of three tanks each filled with one thousand liters of water with a large aperture photomultiplier on the top cover of each tank overlooking the water volume. The photomultipliers detect the Cherenkov light generated by the passage of ultrarelativistic charged particles through the water. The produced signals are then sent to the data acquisition electronics where they are amplified, formatted, digitized and stored. The data are recorded in single and triple coincidence modes.
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