39,754 research outputs found
The Argos-CLS Kalman filter : error structures and state-space modelling relative to Fastloc GPS data
No full textFunding was provided by the Norwegian Polar Institute centre for Ice, Climate and Ecosystems (ICE).Understanding how an animal utilises its surroundings requires its movements through space to be described accurately. Satellite telemetry is the only means of acquiring movement data for many species however data are prone to varying amounts of spatial error; the recent application of state-space models (SSMs) to the location estimation problem have provided a means to incorporate spatial errors when characterising animal movements. The predominant platform for collecting satellite telemetry data on free-ranging animals, Service Argos, recently provided an alternative Doppler location estimation algorithm that is purported to be more accurate and generate a greater number of locations that its predecessor. We provide a comprehensive assessment of this new estimation process performance on data from free-ranging animals relative to concurrently collected Fastloc GPS data. Additionally, we test the efficacy of three readily-available SSM in predicting the movement of two focal animals. Raw Argos location estimates generated by the new algorithm were greatly improved compared to the old system. Approximately twice as many Argos locations were derived compared to GPS on the devices used. Root Mean Square Errors (RMSE) for each optimal SSM were less than 4.25km with some producing RMSE of less than 2.50km. Differences in the biological plausibility of the tracks between the two focal animals used to investigate the utility of SSM highlights the importance of considering animal behaviour in movement studies. The ability to reprocess Argos data collected since 2008 with the new algorithm should permit questions of animal movement to be revisited at a finer resolution.Peer reviewe
Assessing performance of Bayesian state-space models fit to Argos Satellite telemetry locations processed with Kalman Filtering
No full textArgos recently implemented a new algorithm to calculate locations of satellite-tracked animals that uses a Kalman filter (KF). The KF algorithm is reported to increase the number and accuracy of estimated positions over the traditional Least Squares (LS) algorithm, with potential advantages to the application of state-space methods to model animal movement data. We tested the performance of two Bayesian state-space models (SSMs) fitted to satellite tracking data processed with KF algorithm. Tracks from 7 harbour seals (Phoca vitulina) tagged with ARGOS satellite transmitters equipped with Fastloc GPS loggers were used to calculate the error of locations estimated from SSMs fitted to KF and LS data, by comparing those to “true” GPS locations. Data on 6 fin whales (Balaenoptera physalus) were used to investigate consistency in movement parameters, location and behavioural states estimated by switching state-space models (SSSM) fitted to data derived from KF and LS methods. The model fit to KF locations improved the accuracy of seal trips by 27% over the LS model. 82% of locations predicted from the KF model and 73% of locations from the LS model were <5 km from the corresponding interpolated GPS position. Uncertainty in KF model estimates (5.6±5.6 km) was nearly half that of LS estimates (11.6±8.4 km). Accuracy of KF and LS modelled locations was sensitive to precision but not to observation frequency or temporal resolution of raw Argos data. On average, 88% of whale locations estimated by KF models fell within the 95% probability ellipse of paired locations from LS models. Precision of KF locations for whales was generally higher. Whales’ behavioural mode inferred by KF models matched the classification from LS models in 94% of the cases. State-space models fit to KF data can improve spatial accuracy of location estimates over LS models and produce equally reliable behavioural estimates.Peer reviewe
Prediction of secondary structural content of proteins from their amino acid composition alone. I. New analytic vector decomposition methods
No full textAbstract
The predictive limits of the amino acid composition for the secondary structural content (percentage of residues in the secondary structural states helix, sheet, and coil) in proteins are assessed quantitatively.
For the first time, techniques for prediction of secondary structural content are presented which rely on the amino acid composition as the only information on the query protein. In our first method, the amino acid composition of an unknown protein is represented by the best (in a least square sense) linear combination of the characteristic amino acid compositions of the three secondary structural types computed from a learning set of tertiary structures. The second technique is a generalization of the first one and takes into account also possible compositional couplings between any two sorts of amino acids. Its mathematical formulation results in an eigenvalue/eigenvector problem of the second moment matrix describing the amino acid compositional fluctuations of secondary structural types in various proteins of a learning set. Possible correlations of the principal directions of the eigenspaces with physical properties of the amino acids were also checked. For example, the first two eigenvectors of the helical eigenspace correlate with the size and hydrophobicity of the residue types respectively.
As learning and test sets of tertiary structures, we utilized representative, automatically generated subsets of Protein Data Bank (PDB) consisting of non‐homologous protein structures at the resolution thresholds ≤1.8Å, ≤2.0Å, ≤2.5Å, and ≤3.0Å. We show that the consideration of compositional couplings improves prediction accuracy, albeit not dramatically. Whereas in the self‐consistency test (learning with the protein to be predicted), a clear decrease of prediction accuracy with worsening resolution is observed, the jackknife test (leave the predicted protein out) yielded best results for the largest dataset (≤3.0 Å, almost no difference to the self‐consistency test!), i.e., only this set, with more than 400 proteins, is sufficient for stable computation of the parameters in the prediction function of the second method.
The average absolute error in predicting the fraction of helix, sheet, and coil from amino acid composition of the query protein are 13.7, 12.6, and 11.4%, respectively with r.m.s. deviations in the range of 8.6 ÷ 11.8% for the 3.0 Å dataset in a jackknife test. The absolute precision of the average absolute errors is in the range of 1 ÷ 3% as measured for other representative subsets of the PDB.
Secondary structural content prediction methods found in the literature have been clustered in accordance with their prediction accuracies. To our surprise, much more complex secondary structure prediction methods utilized for the same purpose of secondary structural content prediction achieve prediction accuracies very similar to those of the present analytic techniques, implying that all the information beyond the amino acid composition is, in fact, mainly utilized for positioning the secondary structural state in the sequence but not for determination of the overall number of residues in a secondary structural type. This result implies that higher prediction accuracies cannot be achieved relying solely on the amino acid composition of an unknown query protein as prediction input. Our prediction program SSCP has been made available as a World Wide Web and E‐mail service. © 1996 Wiley‐Liss, Inc
Erratum to: Effect of moderate red wine intake on cardiac prognosis after recent acute myocardial infarction of subjects with Type 2 diabetes mellitus (Diabetic Medicine, (2006), 23, 9, (974-981), 10.1111/j.1464-5491.2006.01886.x)
No full textIn an article by Marfella et al, the author name C. Saron is incorrect and should be listed as C. Sardu. Therefore the correct author list is: R. Marfella, F. Cacciapuoti, M. Siniscalchi, F. C. Sasso, F. Marchese, F. Cinone, E. Musacchio, M. A. Marfella, L. Ruggiero, G. Chiorazzo, D. Liberti, G. Chiorazzo, G. F. Nicoletti, C. Sardu, F. D'Andrea, C. Ammendola, M. Verza and L. Coppola.In an article by Marfella et al, the author name C. Saron is incorrect and should be listed as C. Sardu. Therefore the correct author list is: R. Marfella, F. Cacciapuoti, M. Siniscalchi, F. C. Sasso, F. Marchese, F. Cinone, E. Musacchio, M. A. Marfella, L. Ruggiero, G. Chiorazzo, D. Liberti, G. Chiorazzo, G. F. Nicoletti, C. Sardu, F. D'Andrea, C. Ammendola, M. Verza and L. Coppola
Evaluation of Argos Group shares on the colombian stock exchange 2024-2
No full textEl presente trabajo ofrece una visión general de Cemento Argos, en el que se busca evidenciar los aspectos que sustentan la viabilidad y el beneficio de invertir en la compañía, se incluye la historia de la misma, sus operaciones, su posición en el mercado y su compromiso con la sostenibilidad, además, se realiza un análisis técnico y fundamental, así como un análisis de riesgo, para determinar si este emisor representa una buena o mala inversión. Para estos análisis, compararemos los datos de 2023 con los del 2024, observando de cerca el comportamiento de las acciones.Profesional en Negocios InternacionalesPregradoThis work provides an overview of Cemento Argos, in which we seek to demonstrate the aspects that support the viability and benefit of investing in the company, including its history, operations, market position and commitment to sustainability, as well as a technical and fundamental analysis, and a risk analysis, to determine whether this issuer represents a good or bad investment. For these analyses, we will compare the 2023 data with the 2024 data, closely observing the behavior of the actions
An analysis, design, implementation of the administrative and personnel functional areas of ARGOS
No full textARGOS is a prototype multi-media database system developed by CDR B. B.
Giannotti and Lt Kevin F. Duffy at Naval Postgraduate School for their master's
thesis. They describe how ARGOS can be used by the Battle Group Commander and
shipboard personnel as an efficient and dynamic management and decision support
tool. Their system was developed as a direct outgrowth of the "paperless ship"
philosophy expressed by VADM Metcalf and has had many supporters in DOD. This
thesis furthered their research by analyzing, designing the administrative and
personnel functional areas of ARGOS. The functions and processes identified were
then partially implemented. ARGOS as a prototype system provides an effective and
rapid method for developing and evaluating management tools and decision aids.
This implementation demonstrates both the capabilities and benefits such a system
would have for the Navy.Approved for public release; distribution is unlimited.Lieutenant, United States Navyhttp://archive.org/details/annalysisdesigni109452685
Detailed analysis of the performance and investment suitability of Argos from the arrival of the Covid-19 pandemic until mid-2023
No full textEste proyecto de investigación se centra en examinar minuciosamente la evolución de las acciones del Grupo Argos S.A. desde el año 2020 hasta el 2023. La finalidad primordial es valorar la factibilidad inversionista de este emisor, haciendo hincapié en diversos factores que han logrado afectar positiva y negativamente el mercado para estas acciones. El estudio, enmarcado en el contexto de los Negocios Internacionales, se propone aplicar conocimientos adquiridos en el diplomado de Gobierno Corporativo y Gestión de Inversiones. Para lograr este objetivo, se adoptará un enfoque de análisis fundamental y uno técnico, el cuál permitirá moldear la trayectoria de las acciones de Argos durante el periodo analizado. El análisis de inversión, se orienta a proporcionar una evaluación completa y fundamentada.Profesional en Negocios InternacionalesPregradoThis research project focuses on carefully examining the evolution of the shares of Grupo Argos S.A. from 2020 to 2023. The primary purpose is to assess the investment feasibility of this issuer, emphasizing various factors that have managed to positively and negatively affect the market for these shares. The study, framed in the context of International Business, aims to apply knowledge acquired in the Corporate Governance and Investment Management diploma. To achieve this objective, a fundamental and technical analysis approach will be adopted, which will allow shaping the trajectory of Argos shares during the analyzed period. The investment analysis is aimed at providing a complete and informed evaluation
Trip-averaged error in locations estimated from LS and KF models relative to Argos data quality.
No full text<p>Relationship between mean errors (±SD shown as vertical bars) in locations estimated from state-space models fit to Least Squares (LS) (black) and Kalman filtered (KF) (red) data per harbour seal trip and quality of Argos telemetry data used to fit the models: A–B. Number of locations. C–D. Time step (h) between locations. E–F. Proportion of locations of LC 0-B. Different trips from the same seal have the same symbol.</p
Argos: Design and development of object-oriented, event-driven multimedia data base technology in support of the paperless ship
No full textArgos is a prototype multimedia database developed as both a Battle Group
Commander's assesment tool and a shipboard data management tool. The current
prototype developed by using HyperCard/Macintosh demonstrates an effective
utilization of off-the-shelf technology to solve real world problems commonly faced
by the United States Navy. The ultimate goal of Argos is to provide database
support for the "Paperless Ship".Approved for public release; distribution is unlimited.Lieutenant, United States NavyCommander, United States Navyhttp://archive.org/details/argosdesignnddev109452291
ARGOS at the LBT: Binocular laser guided ground-layer adaptive optics
No full textHaving completed its commissioning phase, the Advanced Rayleigh guided Ground-layer adaptive Optics System (ARGOS) facility is coming online for scientific observations at the Large Binocular Telescope (LBT). With six Rayleigh laser guide stars in two constellations and the corresponding wavefront sensing, ARGOS corrects the ground-layer distortions for both LBT 8.4 m eyes with their adaptive secondary mirrors. Under regular observing conditions, this set-up delivers a point spread function (PSF) size reduction by a factor of 2-3 compared to a seeing-limited operation. With the two LUCI infrared imaging and multi-object spectroscopy instruments receiving the corrected images, observations in the near-infrared can be performed at high spatial and spectral resolution. We discuss the final ARGOS technical set-up and the adaptive optics performance. We show that imaging cases with ground-layer adaptive optics (GLAO) are enhancing several scientific programmes, from cluster colour magnitude diagrams and Milky Way embedded star formation, to nuclei of nearby galaxies or extragalactic lensing fields. In the unique combination of ARGOS with the multi-object near-infrared spectroscopy available in LUCI over a 4â×â4 arcmin field of view, the first scientific observations have been performed on local and high-z objects. Those high spatial and spectral resolution observations demonstrate the capabilities now at hand with ARGOS at the LBT
- …
