Taxi time analysis and prediction with ADS-B data. A case study in Barcelona-El Prat airport.

This document contains a study about taxitime analysis in Barcelona-El Prat airport using ADS-B data. Section 1 shows how to decode ADS-B data and what useful information can be recovered to perform the taxitime analysis. Section 2 shows how to model the airport, including runways, taxiways and stands based on AIP data, satellite images and official maps. Section 3 shows how the positions obtained from ADS-B and the modeled airport can be related to be able to unequivocally define the trajectory that an airplane has followed through this modeled airport. Section 4 shows from all the information compiled in the previous sections, how can be determined the factors that affect taxitime and how to create a model that allows estimating them. In previous studies taxitime calculation had been restricted to very specific situations or locations, but in this document, will be tried to relative all parameters (working with speeds, relative queues and differentiation for operations) to be able to extend this calculation to all airport operations. The results show that this goal has been achieved with an accuracy of 2 minutes (A-CDM requirement) of 73$\%$ in departures and 97$\%$ in arrivals. The proposed model is not only characterized by high accuracy in static conditions but also shows a good adaptation to changing conditions. Although it's true that the model doesn't work well when the training and evaluating data sets have different conditions, the model has proven to be valid under new conditions with a very small set of training data in the new conditions. Traditional models are based on calculation of point-to-point histories, which need a very large period of data to extract conclusions. With this methods it's difficult to calculate taxitime just after a condition change. With the proposed model, thanks to calculations in velocities and relativities, model is able to extract information from all the available data and create predictions with good accuracy even if the conditions have recently changed. This document also presents a real case of calculation in extraordinary conditions where adaptation capacity of the model can be seen in Barcelona-El Prat airport during a one-month runway closure due to maintenance

Identification of Giardia lamblia DHHC Proteins and the Role of Protein S-palmitoylation in the Encystation Process

Protein S-palmitoylation, a hydrophobic post-translational modification, is performed by protein acyltransferases that have a common DHHC Cys-rich domain (DHHC proteins), and provides a regulatory switch for protein membrane association. In this work, we analyzed the presence of DHHC proteins in the protozoa parasite Giardia lamblia and the function of the reversible S-palmitoylation of proteins during parasite differentiation into cyst. Two specific events were observed: encysting cells displayed a larger amount of palmitoylated proteins, and parasites treated with palmitoylation inhibitors produced a reduced number of mature cysts. With bioinformatics tools, we found nine DHHC proteins, potential protein acyltransferases, in the Giardia proteome. These proteins displayed a conserved structure when compared to different organisms and are distributed in different monophyletic clades. Although all Giardia DHHC proteins were found to be present in trophozoites and encysting cells, these proteins showed a different intracellular localization in trophozoites and seemed to be differently involved in the encystation process when they were overexpressed. dhhc transgenic parasites showed a different pattern of cyst wall protein expression and yielded different amounts of mature cysts when they were induced to encyst. Our findings disclosed some important issues regarding the role of DHHC proteins and palmitoylation during Giardia encystation.Fil: Merino, Maria Cecilia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigación Médica Mercedes y Martín Ferreyra. Universidad Nacional de Córdoba. Instituto de Investigación Médica Mercedes y Martín Ferreyra; ArgentinaFil: Zamponi, Nahuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigación Médica Mercedes y Martín Ferreyra. Universidad Nacional de Córdoba. Instituto de Investigación Médica Mercedes y Martín Ferreyra; ArgentinaFil: Vranych, Cecilia Verónica. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigación Médica Mercedes y Martín Ferreyra. Universidad Nacional de Córdoba. Instituto de Investigación Médica Mercedes y Martín Ferreyra; ArgentinaFil: Touz, Maria Carolina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigación Médica Mercedes y Martín Ferreyra. Universidad Nacional de Córdoba. Instituto de Investigación Médica Mercedes y Martín Ferreyra; ArgentinaFil: Ropolo, Andrea Silvana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigación Médica Mercedes y Martín Ferreyra. Universidad Nacional de Córdoba. Instituto de Investigación Médica Mercedes y Martín Ferreyra; Argentin