Relevance of lactate level detection in migrane and fibromyalgia

The aim of this study was to determine the blood lactate levels in healthy and pathological subjects, particularly with migraine and fibromyalgia. Moreover we investigated the possible correlation between lactate concentration, postural stability and balance disorders; the composition of the groups were: migraine (n = 25; age 49.7 +/- 12.5), fibromyalgia (n = 10; age 43.7 +/- 21.2), control group (n = 16 age 28.52 +/- 2.4). The results showed that patients with fibromyalgia (FG) had higher lactate levels compared to migraine (MG) and control group (CG) (mean +/- sd: FG = 1.78 +/- 0.9 mmol/L; MG = 1.45 +/- 1 mmol/L; CG = 0,85 +/- 0,07 mmol/L). The same situation was highlighted about the sway path length with eyes closed (FG = 518 +/- 195 mm; MG = 465 +/- 165 mm; CG = 405 +/- 94,72 mm) and with eyes open (FG = 430 +/- 220 mm; MG = 411 +/- 143 mm; CG = 389 +/- 107 mm). This can be explained by the fact that energy-intensive postural strategies must be used to optimize both static and dynamic coordination, in particular with repeated contractions of tonic oxidative muscle cells responsible for postural control

The clustering properties of radio-selected AGN and star-forming galaxies up to redshifts z~3

We present the clustering properties of a complete sample of 968 radio sources detected at 1.4 GHz by the VLA-COSMOS survey with radio fluxes brighter than 0.15 mJy. 92% have redshift determinations from the Laigle et al. (2016) catalogue. Based on their radio-luminosity, these objects have been divided into two populations of 644 AGN and 247 star-forming galaxies. By fixing the slope of the auto-correlation function to gamma=2, we find r_0=11.7^{+1.0}_{-1.1} Mpc for the clustering length of the whole sample, while r_0=11.2^{+2.5}_{-3.3} Mpc and r_0=7.8^{+1.6}_{-2.1} Mpc (r_0=6.8^{+1.4}_{-1.8} Mpc if we restrict our analysis to z<0.9) are respectively obtained for AGN and star-forming galaxies. These values correspond to minimum masses for dark matter haloes of M_min=10^[13.6^{+0.3}_{-0.6}] M_sun for radio-selected AGN and M_min=10^[13.1^{+0.4}_{-1.6}] M_sun for radio-emitting star-forming galaxies (M_min=10^[12.7^{+0.7}_{-2.2}] M_sun for z<0.9). Comparisons with previous works imply an independence of the clustering properties of the AGN population with respect to both radio luminosity and redshift. We also investigate the relationship between dark and luminous matter in both populations. We obtain /M_halo/M_halo<~10^{-2.4} in the case of star-forming galaxies. Furthermore, if we restrict to z<~0.9 star-forming galaxies, we derive /M_halo<~10^{-2.1}, result which clearly indicates the cosmic process of stellar build-up as one moves towards the more local universe. Comparisons between the observed space density of radio-selected AGN and that of dark matter haloes shows that about one in two haloes is associated with a black hole in its radio-active phase. This suggests that the radio-active phase is a recurrent phenomenon.Comment: 11 pages, 7 figures, minor changes to match published version on MNRA

Magellan Spectroscopy of AGN Candidates in the COSMOS Field

We present spectroscopic redshifts for the first 466 X-ray and radio-selected AGN targets in the 2 deg^2 COSMOS field. Spectra were obtained with the IMACS instrument on the Magellan (Baade) telescope, using the nod-and-shuffle technique. We identify a variety of Type 1 and Type 2 AGN, as well as red galaxies with no emission lines. Our redshift yield is 72% down to i_AB=24, although the yield is >90% for i_AB<22. We expect the completeness to increase as the survey continues. When our survey is complete and additional redshifts from the zCOSMOS project are included, we anticipate ~1100 AGN with redshifts over the entire COSMOS field. Our redshift survey is consistent with an obscured AGN population that peaks at z~0.7, although further work is necessary to disentangle the selection effects.Comment: 19 pages, 14 figures. Accepted to ApJS special COSMOS issue. The full electronic version of Table 2 can be found at http://shaihulud.as.arizona.edu/~jtrump/tab2.tx

Finding counterparts for All-sky X-ray surveys with Nway: a Bayesian algorithm for cross-matching multiple catalogues

We release the AllWISE counterparts and Gaia matches to 106,573 and 17,665 X-ray sources detected in the ROSAT 2RXS and XMMSL2 surveys with |b|>15. These are the brightest X-ray sources in the sky, but their position uncertainties and the sparse multi-wavelength coverage until now rendered the identification of their counterparts a demanding task with uncertain results. New all-sky multi-wavelength surveys of sufficient depth, like AllWISE and Gaia, and a new Bayesian statistics based algorithm, NWAY, allow us, for the first time, to provide reliable counterpart associations. NWAY extends previous distance and sky density based association methods and, using one or more priors (e.g., colors, magnitudes), weights the probability that sources from two or more catalogues are simultaneously associated on the basis of their observable characteristics. Here, counterparts have been determined using a WISE color-magnitude prior. A reference sample of 4524 XMM/Chandra and Swift X-ray sources demonstrates a reliability of ~ 94.7% (2RXS) and 97.4% (XMMSL2). Combining our results with Chandra-COSMOS data, we propose a new separation between stars and AGN in the X-ray/WISE flux-magnitude plane, valid over six orders of magnitude. We also release the NWAY code and its user manual. NWAY was extensively tested with XMM-COSMOS data. Using two different sets of priors, we find an agreement of 96% and 99% with published Likelihood Ratio methods. Our results were achieved faster and without any follow-up visual inspection. With the advent of deep and wide area surveys in X-rays (e.g. SRG/eROSITA, Athena/WFI) and radio (ASKAP/EMU, LOFAR, APERTIF, etc.) NWAY will provide a powerful and reliable counterpart identification tool.Comment: MNRAS, Paper accepted for publication. Updated catalogs are available at www.mpe.mpg.de/XraySurveys/2RXS_XMMSL2 . NWAY available at https://github.com/JohannesBuchner/nwa

AGN feedback at z~2 and the mutual evolution of active and inactive galaxies

The relationships between galaxies of intermediate stellar mass and moderate luminosity active galactic nuclei (AGNs) at 1<z<3 are investigated with the Galaxy Mass Assembly ultra-deep Spectroscopic Survey (GMASS) sample complemented with public data in the GOODS-South field. Using X-ray data, hidden AGNs are identified in unsuspected star-forming galaxies with no apparent signs of non-stellar activity. In the color-mass plane, two parallel trends emerge during the ~2 Gyr between the average redshifts z~2.2 and z~1.3: while the red sequence becomes significantly more populated by ellipticals, the majority of AGNs with L(2-10 keV)>10^42.3 erg s^-1 disappear from the blue cloud/green valley where they were hosted predominantly by star-forming systems with disk and irregular morphologies. These results are even clearer when the rest-frame colors are corrected for dust reddening. At z~2.2, the ultraviolet spectra of active galaxies (including two Type 1 AGNs) show possible gas outflows with velocities up to about -500 km s^-1 that are not observed neither in inactive systems at the same redshift, nor at lower redshifts. Such outflows indicate the presence of gas that can move faster than the escape velocities of active galaxies. These results suggest that feedback from moderately luminous AGNs (logL_X~2 by contributing to outflows capable of ejecting part of the interstellar medium and leading to a rapid decrease in the star formation in host galaxies with stellar masses 10<logM<11 M_Sun.Comment: Astrophysical Journal Letters, in press (6 pages, 4 figures

Genetic algorithms for positron lifetime data

Recently, genetic algorithms have been applied for ultrafast optical spectrometry in systems with several convoluted lifetimes. We apply these algorithms and compare the results with POSFIT (by Kirkegaard and Eldrup) and LT programme (by Kansy). The analysis was applied to three types of samples: molybdenum monocrystals, Czochralski-grown silicon with oxygen precipitates, Si with under-surface cavities obtained by He + H ion co- implantation. In all three tests, the genetic algorithm performs very well, in particular for short lifetimes. Further developments to model the resolution function in genetic algorithms are needed

Model-Based Combustion Control to Reduce the Brake Specific Fuel Consumption and Pollutant Emissions under Real Driving Maneuvers

A previously developed piston damage and exhaust gas temperature models are coupled to manage the combustion process and thereby increasing the overall energy conversion efficiency. The proposed model-based control algorithm is developed and validated in a software-in-the-loop simulation environment, and then the controller is deployed in a rapid control prototyping device and tested online at the test bench. In the first part of the article, the exhaust gas temperature model is reversed and converted into a control function, which is then implemented in a piston damage-based spark advance controller. In this way, more aggressive calibrations are actuated to target a certain piston damage speed and exhaust gas temperature at the turbine inlet. A more anticipated spark advance results in a lower exhaust gas temperature, and such decrease is converted into lowering the fuel enrichment with respect to the production calibrations. Moreover, the pollutant emissions associated with production calibrations and the implementation of the developed controller are compared through a GT-Power combustion model. Finally, the complete controller is validated for both the transient and steady-state conditions, reproducing a real vehicle maneuver at the engine test bench. The results demonstrate that the combination of an accurate estimation of the damage induced by knock and the value of the exhaust gas temperature allows to reduce the brake specific fuel consumption by up to 20%. Moreover, the stoichiometric area of the engine operating field is extended by 20%, and the GT-Power simulations show a maximum CO reduction of about 50%

Advanced, Guided Procedure for the Calibration and Generalization of Neural Network-Based Models of Combustion and Knock Indexes

In the last few years, the artificial neural networks have been widely used in the field of engine modeling. Some of the main reasons for this are, their compatibility with the real-time systems, higher accuracy, and flexibility if compared to other data-driven approaches. One of the main difficulties of using this approach is the calibration of the network itself. It is very difficult to find in the literature procedures that guide the user to completely define a network. Typically, the very last steps (like the choice of the number of neurons) must be selected by the user on the base of his sensitivity to the problem. This work proposes an automatic calibration procedure for the artificial neural networks, considering all the main hyper-parameters of the network such as the training algorithms, the activation functions, the number of the neurons, the number of epochs, and the number of hidden layers, for modeling various combustion indexes in a modern internal combustion engine. However, the proposed procedure can be applied to the training of any neural network-based model. The automatic calibration procedure outputs a configuration of the network, giving the optimal combination in terms of hyper-parameters. The decision of the optimal configuration of the neural network is based on a self-developed formula, which gives a rank of all the possible hyper-parameter combinations using some statistical parameters obtained comparing the simulated and the experimental values. In the end, the lowest rank is selected as the optimal one as it represents the combination having the lowest error. Following the definition of this rank, high accuracy on the results has been achieved in terms of the root mean square error index, for example, on the combustion phase model, the error is 0.139°CA under steady-state conditions. On the maximum in-cylinder pressure model, the error is 1.682 bar, while the knock model has an error of 0.457 bar for the same test that covers the whole engine operating field