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This paper presents a mathematical model for the propagation of errors in body segment kinematics to the location of the center of rotation. Three functional calibration techniques, usually employed for the gleno-humeral joint, are studied: the methods based on the pivot of the instantaneous helical axis (PIHA) or the finite helical axis (PFHA), and the ¿symmetrical center of rotation estimation¿ (SCoRE). A procedure for correcting the effect of soft tissue artifacts is also proposed, based on the equations of those techniques and a model of the artifact, like the one that can be obtained by double calibration. An experiment with a mechanical analog was performed to validate the procedure and compare the performance of each technique. The raw error (between 57 and 68 mm) was reduced by a proportion of between 1:6 and less than 1:15, depending on the artifact model and the mathematical method. The best corrections were obtained by the SCoRE method. Some recommendations about the experimental setup for functional calibration techniques and the choice of a mathematical method are derived from theoretical considerations about the formulas and the results of the experiment. ; This work has been funded by the Spanish Government (Grants DPI2009-13830-C02-01, DPI2009-13830-C02-02, DPI2010-20814-C02-01, and DPI2010-20814-C02-02). ; De Rosario Martínez, H.; Page Del Pozo, AF.; Besa Gonzálvez, AJ.; Valera Fernández, Á. (2013). Propagation of soft tissue artifacts to the center of rotation: A model for the correction of functional calibration techniques. Journal of Biomechanics. 46(15):2619-2625. doi:10.1016/j.jbiomech.2013.08.006 ; S ; 2619 ; 2625 ; 46 ; 15

[EN] The accurate location of the main axes of rotation (AoR) is a crucial step in many applications of human movement analysis. There are different formal methods to determine the direction and position of the AoR, whose performance varies across studies, depending on the pose and the source of errors. Most methods are based on minimizing squared differences between observed and modelled marker positions or rigid motion parameters, implicitly assuming independent and uncorrelated errors, but the largest error usually results from soft tissue artefacts (STA), which do not have such statistical properties and are not effectively cancelled out by such methods. However, with adequate methods it is possible to assume that STA only account for a small fraction of the observed motion and to obtain explicit formulas through differential analysis that relate STA components to the resulting errors in AoR parameters. In this paper such formulas are derived for three different functional calibration techniques (Geometric Fitting, mean Finite Helical Axis, and SARA), to explain why each technique behaves differently from the others, and to propose strategies to compensate for those errors. These techniques were tested with published data from a sit-to-stand activity, where the true axis was defined using bi-planar fluoroscopy. All the methods were able to estimate the direction of the AoR with an error of less than 5 degrees whereas there were errors in the location of the axis of 30-40 mm. Such location errors could be reduced to less than 17 mm by the methods based on equations that use rigid motion parameters (mean Finite Helical Axis, SARA) when the translation component was calculated using the three markers nearest to the axis. (C) 2017 Elsevier Ltd. All rights reserved. ; This work was funded by the Spanish Government and co-financed by EU FEDER funds (Grant DPI2013-44227-R). We would like to thank Prof. Tung-Wu Lu, Tsung-Yuan Tsai, Mei-Ying Kuo and Horn-Chaung Hsu from National Taiwan University for making the data ...

Financial risk control has always been challenging and becomes now an even harder problem as joint extreme events occur more frequently. For decision makers and government regulators, it is therefore important to obtain accurate information on the interdependency of risk factors. Given a stressful situation for one market participant, one likes to measure how this stress affects other factors. The CoVaR (Conditional VaR) framework has been developed for this purpose. The basic technical elements of CoVaR estimation are two levels of quantile regression: one on market risk factors; another on individual risk factor. Tests on the functional form of the two-level quantile regression reject the linearity. A flexible semiparametric modeling framework for CoVaR is proposed. A partial linear model (PLM) is analyzed. In applying the technology to stock data covering the crisis period, the PLM outperforms in the crisis time, with the justification of the backtesting procedures. Moreover, using the data on global stock markets indices, the analysis on marginal contribution of risk (MCR) defined as the local first order derivative of the quantile curve sheds some light on the source of the global market risk.

Financial risk control has always been challenging and becomes now an even harder problem as joint extreme events occur more frequently. For decision makers and government regulators, it is therefore important to obtain accurate information on the interdependency of risk factors. Given a stressful situation for one market participant, one likes to measure how this stress affects other factors. The CoVaR (Conditional VaR) framework has been developed for this purpose. The basic technical elements of CoVaR estimation are two levels of quantile regression: one on market risk factors; another on individual risk factor. Tests on the functional form of the two-level quantile regression reject the linearity. A flexible semiparametric modeling framework for CoVaR is proposed. A partial linear model (PLM) is analyzed. In applying the technology to stock data covering the crisis period, the PLM outperforms in the crisis time, with the justification of the backtesting procedures. Moreover, using the data on global stock markets indices, the analysis on marginal contribution of risk (MCR) defined as the local first order derivative of the quantile curve sheds some light on the source of the global market risk.

[EN] This paper proposes a variation of the instantaneous helical pivot technique for locating centers of rotation. The point of optimal kinematic error (POKE), which minimizes the velocity at the center of rotation, may be obtained by just adding a weighting factor equal to the square of angular velocity in Woltring's equation of the pivot of instantaneous helical axes (PIHA). Calculations are simplified with respect to the original method, since it is not necessary to make explicit calculations of the helical axis, and the effect of accidental errors is reduced. The improved performance of this method was validated by simulations based on a functional calibration task for the gleno-humeral joint center. Noisy data caused a systematic dislocation of the calculated center of rotation towards the center of the arm marker cluster. This error in PIHA could even exceed the effect of soft tissue artifacts associated to small and medium deformations, but it was successfully reduced by the POKE estimation. ; This work has been funded by the Spanish Government (Grants DPI2009-13830-C02-01, DPI2009-13830-CO2-02, DPI2010-20814-CO2-01, DPI2010-20814-CO2-02). ; De Rosario Martínez, H.; Page Del Pozo, AF.; Mata Amela, V. (2014). Point of optimal kinematic error: Improvement of the instantaneous helical pivot method for locating centers of rotation. Journal of Biomechanics. 47(7):1742-1747. doi:10.1016/j.jbiomech.2014.02.003 ; S ; 1742 ; 1747 ; 47 ; 7

SuperCam is a highly integrated remote-sensing instrumental suite for NASA¿s Mars 2020 mission. It consists of a co-aligned combination of Laser-Induced Breakdown Spectroscopy (LIBS), Time-Resolved Raman and Luminescence (TRR/L), Visible and Infrared Spectroscopy (VISIR), together with sound recording (MIC) and high-magnification imaging techniques (RMI). They provide information on the mineralogy, geochemistry and mineral context around the Perseverance Rover. The calibration of this complex suite is a major challenge. Not only does each technique require its own standards or references, their combination also introduces new requirements to obtain optimal scientific output. Elemental composition, molecular vibrational features, fluorescence, morphology and texture provide a full picture of the sample with spectral information that needs to be co-aligned, correlated, and individually calibrated. The resulting hardware includes different kinds of targets, each one covering different needs of the instrument. Standards for imaging calibration, geological samples for mineral identification and chemometric calculations or spectral references to calibrate and evaluate the health of the instrument, are all included in the SuperCam Calibration Target (SCCT). The system also includes a specifically designed assembly in which the samples are mounted. This hardware allows the targets to survive the harsh environmental conditions of the launch, cruise, landing and operation on Mars during the whole mission. Here we summarize the design, development, integration, verification and functional testing of the SCCT. This work includes some key results obtained to verify the scientific outcome of the SuperCam system. ; Funding was provided by MINECO, projects ESP2014-56138-C3-1-R; ESP2055-71965-REDT, ESP2017-87690-C3-1-R and RED2018-102600-T ; With funding from the Spanish government through the "María de Maeztu Unit of Excellence" accreditation (MDM-2017-0737)

La Functional Urban Area (FUA) di Milano è un'area densamente popolata (2.254.263 abitanti) dove l'approvvigionamento idrico è garantito esclusivamente mediante prelievi idrici sotterranei. Per questa ragione la protezione della qualità delle falde rientra tra le priorità delle politiche ambientali di Regione Lombardia. Recentemente è stato avviato un programma di studi ed interventi aventi lo scopo d'individuare i principali plumes di contaminazione da solventi clorurati distinguendone l'impatto da quello legato all'inquinamento diffuso. In questo articolo si presenta il modello di flusso sviluppato per il settore NE della FUA di Milano, settore utilizzato quale area pilota per sviluppare e testare una nuova metodologia che combina statistica e modellistica al fine di distinguere il contributo delle fonti di contaminazione puntuale rispetto a quelle diffuse.

"Given a randomly drawn sample, calibration weighting can provide double protection against the selection bias resulting from unit nonresponse. This means that if either an assumed linear prediction model or an implied unit selection model holds, the resulting estimator will be asymptotically unbiased in some sense. The functional form of the selection model when using linear alibration adjustment is dubious. The authors discuss an alternative, nonlinear calibration-weighting procedure and software that can, among other things, implicitly estimate a logistic-response model." (author's abstract)

60 67 62 ; S ; [EN] The accurate location of the main axes of rotation (AoR) is a crucial step in many applications of human movement analysis. There are different formal methods to determine the direction and position of the AoR, whose performance varies across studies, depending on the pose and the source of errors. Most methods are based on minimizing squared differences between observed and modelled marker positions or rigid motion parameters, implicitly assuming independent and uncorrelated errors, but the largest error usually results from soft tissue artefacts (STA), which do not have such statistical properties and are not effectively cancelled out by such methods. However, with adequate methods it is possible to assume that STA only account for a small fraction of the observed motion and to obtain explicit formulas through differential analysis that relate STA components to the resulting errors in AoR parameters. In this paper such formulas are derived for three different functional calibration techniques (Geometric Fitting, mean Finite Helical Axis, and SARA), to explain why each technique behaves differently from the others, and to propose strategies to compensate for those errors. These techniques were tested with published data from a sit-to-stand activity, where the true axis was defined using bi-planar fluoroscopy. All the methods were able to estimate the direction of the AoR with an error of less than 5 degrees whereas there were errors in the location of the axis of 30-40 mm. Such location errors could be reduced to less than 17 mm by the methods based on equations that use rigid motion parameters (mean Finite Helical Axis, SARA) when the translation component was calculated using the three markers nearest to the axis. (C) 2017 Elsevier Ltd. All rights reserved. This work was funded by the Spanish Government and co-financed by EU FEDER funds (Grant DPI2013-44227-R). We would like to thank Prof. Tung-Wu Lu, Tsung-Yuan Tsai, Mei-Ying Kuo and Horn-Chaung Hsu from National Taiwan University for ...

Brain-computer interfaces (BCIs) are systems that allow users to control devices using brain activity alone. However, the ability of participants to command BCIs varies from subject to subject. About 20% of potential users of sensorimotor BCIs do not gain reliable control of the system. The inefficiency to decode user's intentions requires the identification of neurophysiological factors determining 'good' and 'poor' BCI performers. One of the important neurophysiological aspects in BCI research is that the neuronal oscillations, used to control these systems, show a rich repertoire of spatial sensorimotor interactions. Considering this, we hypothesized that neuronal connectivity in sensorimotor areas would define BCI performance. Analyses for this study were performed on a large dataset of 80 inexperienced participants. They took part in a calibration and an online feedback session recorded on the same day. Undirected functional connectivity was computed over sensorimotor areas by means of the imaginary part of coherency. The results show that post- as well as pre-stimulus connectivity in the calibration recording is significantly correlated to online feedback performance in μ and feedback frequency bands. Importantly, the significance of the correlation between connectivity and BCI feedback accuracy was not due to the signal-to-noise ratio of the oscillations in the corresponding post and pre-stimulus intervals. Thus, this study demonstrates that BCI performance is not only dependent on the amplitude of sensorimotor oscillations as shown previously, but that it also relates to sensorimotor connectivity measured during the preceding training session. The presence of such connectivity between motor and somatosensory systems is likely to facilitate motor imagery, which in turn is associated with the generation of a more pronounced modulation of sensorimotor oscillations (manifested in ERD/ERS) required for the adequate BCI performance. We also discuss strategies for the up-regulation of such connectivity in order to enhance BCI performance. ; CV was supported by MINECO-RyC-2014-15671. SH was supported by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (Grant agreement No. 758985). K-RM was supported in part by the Institute for Information & Communications Technology Promotion and funded by the Korea government (MSIT) (No. 2017-0-00451), and was partly supported by the German Ministry for Education and Research (BMBF) under Grants 01IS14013A-E, 01GQ1115, 01GQ0850, 01IS18025A, and 01IS18037A; the German ResearchFoundation (DFG) under Grant Math+, EXC 2046/1, Project ID 390685689. VVN was partly supported by the HSE Basic Research Program and the Russian Academic Excellence Project 5-100.