Search for associations containing young stars (SACY) VIII. An updated census of spectroscopic binary systems showing hints of non-universal multiplicity among these associations

We seek to update the spectroscopy binary fraction of the SACY (Search for Associations Containing Young stars) sample taking in consideration all possible biases in our identification of binary candidates, such as activity and rotation. Using high-resolution spectroscopic observations we have produced $\sim$1300 cross-correlation functions (CCFs) to disentangle the previously mentioned sources of contamination. The radial velocity values obtained were cross-matched with the literature and were used to revise and update the spectroscopic binary (SB) fraction in each of the SACY association. In order to better describe the CCF profile, we calculated a set of high-order cross-correlation features to determine the origin of the variations in radial velocities. We identified 68 SB candidates from our sample of 410 objects. Our results hint that the youngest associations have a higher SB fraction. Specifically, we found sensitivity-corrected SB fractions of $22 \substack{+15 \\ -11} \%$ for $\epsilon$~Cha , $31 \substack{+16 \\ -14} \%$ for TW Hya and $32 \substack{+9 \\ -8} \%$ for $\beta$~Pictoris, in contrast with the five oldest ($\sim 35-125$ Myr) associations we have sampled which are $\sim 10\%$ or lower. This result seems independent of the methodology used to asses membership to the associations. The new CCF analysis, radial velocity estimates and SB candidates are particularly relevant for membership revision of targets in young stellar associations. These targets would be ideal candidates for follow-up campaigns using high-resolution techniques in order to confirm binarity, resolve the orbits, and ideally calculate dynamical masses. Additionally, if the results on SB fraction in the youngest associations are confirmed, it could hint of non-universal multiplicity among SACY associations.Comment: The paper has been accepted in A&

Fault diagnosis in industrial process by using LSTM and an elastic net

[EN] Fault diagnosis is important for industrial processes because it permits to determine the necessity of emergency stops in a process and/or to propose a maintenance plan. Two strategies for fault diagnosis are compared in this work. On the one hand, the data are preprocessed using the independent components analysis for dimension reduction, then the wavelet transform is used in order to highlight the faulty signals, with this information an artificial neural network was fed. On the other hand, the second strategy, the main contribution of this work, is the implementation of a long short term memory. This memory is fed with the most representative variables selected by an elastic net to use both, the L1 and L2 norms. These strategies are applied in the Tennessee Eastman process, a benchmark widely used for fault diagnosis. The fault isolation had better results than those reported in the literature.[ES] El diagnóstico de fallas es importante en los procesos industriales, ya que permite determinar si es necesario detener el proceso en operación y/o proponer un plan de mantenimiento. En el presente trabajo se comparan dos estrategias para diagnosticar fallas. La primera realiza un preprocesamiento de datos usando el análisis de componentes independientes para reducir la dimensión de los datos, posteriormente, se emplea la transformada wavelet para resaltar las señales de falla, con esta información se alimenta una red neuronal artificial. Por su parte, la segunda estrategia, principal contribución de este trabajo, usa una memoria de corto y largo plazo. Esta memoria es alimentada por las variables más significativas seleccionadas mediante una red elástica para usar tanto la norma $L_1$ como la $L_2$. Como ejemplo de aplicación se utilizó el proceso químico Tennessee Eastman, un proceso ampliamente usado en el diagnóstico de fallas. El aislamiento de fallas mostró mejores resultados con respecto a los reportados en la literatura.Márquez-Vera, MA.; López-Ortega, O.; Ramos-Velasco, LE.; Ortega-Mendoza, RM.; Fernández-Neri, BJ.; Zúñiga-Peña, NS. (2021). Diagnóstico de fallas mediante una LSTM y una red elástica. Revista Iberoamericana de Automática e Informática industrial. 18(2):164-175. https://doi.org/10.4995/riai.2020.13611OJS164175182Adewole, A., Tzoneva, R., Behardien, S., 2016. Distribution network fault section identification and fault location using wavelet entropy and neural networks. Applied Soft Computing 46, 296-306. https://doi.org/10.1016/j.asoc.2016.05.013Alkaya, A., Eker, I., 2011. Variance sensitive adaptive threshold-based PCA method for fault detection with experimental application. 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Fault diagnosis based on deep learning. In: AACC (Ed.), American Control Conference. Boston, USA, pp. 6851-6856. https://doi.org/10.1109/ACC.2016.7526751Lv, F., Wen, C., Liu, M., Bao, Z., 2017. Weighted time series fault diagnosis based on a staked sparce autoencoder. Journal of Chemometrics 31, 16 pages. https://doi.org/10.1002/cem.2912Lv, F., Fan, X., Wen, C., Bao, Z., 2018. Stacked sparse auto encoder network based multimode process monitoring. In: IEEE (Ed.), International Conference on Control Automation & Information Science. Hangzhou, China, pp. 227-232. https://doi.org/10.1109/ICCAIS.2018.8570618Maglaveras, N., Stamkopoulos, T., Diamantaras, K., Pappas, C., Strintzis, M., 1998. ECG pattern recognition and classification using non-linear transfor mations and neural networks: A review. International Journal of Medical Informatics 52, 191-208. https://doi.org/10.1016/S1386-5056(98)00138-5Methnani, S., Lafont, F., Gautier, J., Damak, T., Toumi, A., 2013. Actuator and sensor fault detection, isolation and identification in nonlinear dynamical systems, with applications to a waste water treatment plant. Journal of Computer Engineering and Informatics 1 (4), 112-125. https://doi.org/10.1080/21642583.2014.888525Muñoz-Cobo, J., Mendizábal, R., Miquel, A., Berna, C., Escrivá, A., 2017. Use of the principles of maximum entropy and maximum relative entropy for the determination of uncertain parameter distributions in engineering applications. Entropy 19, 486, 37 pages. https://doi.org/10.3390/e19090486Nguyen, B., Quyen, A., Nguyen, P., Ton, T., July 2017. Wavelet-based neural network for recognition of faults at nhabe power substation of the vietnam power system. In: IEEE (Ed.), International Conference on System Science and Engineering. Ho Chi Minh City, Vietnam, pp. 165-168. https://doi.org/10.1109/ICSSE.2017.8030858Ojeda-González, A., Mendes-Jr., O., Oliveira-Domingues, M., Menconi, V., 2014. Daubechies wavelet coeffcients: a tool to study interplanetary magnetic field fluctuations. Geof'ısica Internacional 53 (2), 101-115. https://doi.org/10.1016/S0016-7169(14)71494-1Oliveira, J., Pontes, K., Santori, I., Embirucu, M., 2017. Fault detection and diagnosis in dynamic systems using weightless neural networks. Expert Systems With Applications 84, 200-219. https://doi.org/10.1016/j.eswa.2017.05.020Patan, K., 2008. Artificial neural networks for the modelling and fault diagnosis of technical process. Lecture Notes in Control and Information Sciences. Springer, India.Rafiee, J., Rafiee, M., Tse, P., 2010. Application of mother wavelet functions for automatic gear and bearing fault diagnosis. Expert Systems with Applications 37, 4568-4579. https://doi.org/10.1016/j.eswa.2009.12.051Ramos-Velasco, L., Ramos-Fernández, J., Islar-Gómez, O., Espejel-Rivera, M., García-Lamont, J., Márquez-Vera, M., 2013. Identificación y control wavenet de un motor de ca. Revista Iberoamericana de Automática e Informática Industrial 10, 269-278. https://doi.org/10.1016/j.riai.2013.05.002Rato, T., Reis, M., 2013. Defining the structure of DPCA models and its impact on process monitoring and prediction ctivities. Chemometrics and Intelligent Laboratory Systems 125, 74-86. https://doi.org/10.1016/j.chemolab.2013.03.009Rockinger, M., Jondeau, E., 2002. Entropy densities with an application to autoregressive conditional skewness and kurtosis. Journal of Econometrics 106, 119-142. https://doi.org/10.1016/S0304-4076(01)00092-6Salahschoor, K., Kiasi, F., July 2008. On-line process monitoring based on wavelet-ICA methodology. In: IFAC (Ed.), Proceedings of the 17th World Congress. Seul- Korea, pp. 6-11. https://doi.org/10.3182/20080706-5-KR-1001.01253Salahshoor, K., Khoshro, M., Kordestani, M., 2011. Fault detection and diagnosis of an industrial steam turbine using a distributed configuration of adaptive neuro-fuzzy inference systems. 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Dynamic neural networkbased fault diagnosis of gas turbine engines. Neurocomputing 125, 153-165. https://doi.org/10.1016/j.neucom.2012.06.050Zvokelj, M., Zupan, S., Prebil, I., 2016. EEMD-based multiscale ICA method for slewing bearing fault detection and diagnosis. Journal of Sound and Vibration 26, 394-423. https://doi.org/10.1016/j.jsv.2016.01.046Wang, X., Qin, Y., Wang, Y., Xiang, S., Chen, H., 2019. ReLTanh: An activation function with vanishing gradient resistance for SAE-based DNNs and its application to rotating machinery fault diagnosis. Neurocomputing 363, 88-98. https://doi.org/10.1016/j.neucom.2019.07.017Wu, F., Tong, F., Yang, Z., 2016. EMGdi signal enhancement based on ICA decomposition and wavelet transform. Applied Soft Computing 43, 561-571. https://doi.org/10.1016/j.asoc.2016.03.002Wu, J., Hsu, C., Wu, G., 2009. Fault gear identification and classification using discrete wavelet transform and adaptive neuro-fuzzy inference. 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In: IEEE (Ed.), International Conference on Machine Learning and Applications. Vol. 14. Miami, USA, pp. 745-748. https://doi.org/10.1109/ICMLA.2015.208Yan, R., Gao, R., Chen, X., 2014. Wavelets for fault diagnosis of rotary machines: A review with applications. Signal Processing 351, 4555-4569. https://doi.org/10.1016/j.sigpro.2013.04.015Yan, Z., Yao, Y., 2015. Variable selection method for fault isolation using least absolute shrinkage and selection operator (LASSO). Chemometrics and Intelligent Laboratory Systems 146, 136-146. https://doi.org/10.1016/j.chemolab.2015.05.019Yao, G., Lei, T., Zhong, J., 2019. A review of convolutional-neural-networkbased action recognition. Pattern Recognition Letters 118, 14-22. https://doi.org/10.1016/j.patrec.2018.05.018Yin, S., Ding, S., Haghani, A., Hao, H., Zhang, P., 2012. A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process. 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N-3 PUFA Supplementation Triggers PPAR-α Activation and PPAR-α/NF-κB Interaction: Anti-Inflammatory Implications in Liver Ischemia-Reperfusion Injury

Dietary supplementation with the n-3 polyunsaturated fatty acids (n-3 PUFA) eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) to rats preconditions the liver against ischemia-reperfusion (IR) injury, with reduction of the enhanced nuclear factor-κB (NF-κB) functionality occurring in the early phase of IR injury, and recovery of IR-induced pro-inflammatory cytokine response. The aim of the present study was to test the hypothesis that liver preconditioning by n-3 PUFA is exerted through peroxisone proliferator-activated receptor α (PPAR-α) activation and interference with NF-κB activation. For this purpose we evaluated the formation of PPAR-α/NF-κBp65 complexes in relation to changes in PPAR-α activation, IκB-α phosphorylation and serum levels and expression of interleukin (IL)-1β and tumor necrosis factor (TNF)-α in a model of hepatic IR-injury (1 h of ischemia and 20 h of reperfusion) or sham laparotomy (controls) in male Sprague Dawley rats. Animals were previously supplemented for 7 days with encapsulated fish oil (General Nutrition Corp., Pittsburg, PA) or isovolumetric amounts of saline (controls). Normalization of IR-altered parameters of liver injury (serum transaminases and liver morphology) was achieved by dietary n-3 PUFA supplementation. EPA and DHA suppression of the early IR-induced NF-κB activation was paralleled by generation of PPAR-α/NF-κBp65 complexes, in concomitance with normalization of the IR-induced IκB-α phosphorylation. PPAR-α activation by n-3 PUFA was evidenced by enhancement in the expression of the PPAR-α-regulated Acyl-CoA oxidase (Acox) and Carnitine-Palmitoyl-CoA transferase I (CPT-I) genes. Consistent with these findings, normalization of IR-induced expression and serum levels of NF-κB-controlled cytokines IL-lβ and TNF-α was observed at 20 h of reperfusion. Taken together, these findings point to an antagonistic effect of PPAR-α on NF-κB-controlled transcription of pro-inflammatory mediators. This effect is associated with the formation of PPAR-α/NF-κBp65 complexes and enhanced cytosolic IκB-α stability, as major preconditioning mechanisms induced by n-3 PUFA supplementation against IR liver injury

The Fourteenth Data Release of the Sloan Digital Sky Survey: First Spectroscopic Data from the extended Baryon Oscillation Spectroscopic Survey and from the second phase of the Apache Point Observatory Galactic Evolution Experiment

The fourth generation of the Sloan Digital Sky Survey (SDSS-IV) has been in operation since July 2014. This paper describes the second data release from this phase, and the fourteenth from SDSS overall (making this, Data Release Fourteen or DR14). This release makes public data taken by SDSS-IV in its first two years of operation (July 2014-2016). Like all previous SDSS releases, DR14 is cumulative, including the most recent reductions and calibrations of all data taken by SDSS since the first phase began operations in 2000. New in DR14 is the first public release of data from the extended Baryon Oscillation Spectroscopic Survey (eBOSS); the first data from the second phase of the Apache Point Observatory (APO) Galactic Evolution Experiment (APOGEE-2), including stellar parameter estimates from an innovative data driven machine learning algorithm known as "The Cannon"; and almost twice as many data cubes from the Mapping Nearby Galaxies at APO (MaNGA) survey as were in the previous release (N = 2812 in total). This paper describes the location and format of the publicly available data from SDSS-IV surveys. We provide references to the important technical papers describing how these data have been taken (both targeting and observation details) and processed for scientific use. The SDSS website (www.sdss.org) has been updated for this release, and provides links to data downloads, as well as tutorials and examples of data use. SDSS-IV is planning to continue to collect astronomical data until 2020, and will be followed by SDSS-V.Comment: SDSS-IV collaboration alphabetical author data release paper. DR14 happened on 31st July 2017. 19 pages, 5 figures. Accepted by ApJS on 28th Nov 2017 (this is the "post-print" and "post-proofs" version; minor corrections only from v1, and most of errors found in proofs corrected

TOI-2084 b and TOI-4184 b: two new sub-Neptunes around M dwarf stars

We present the discovery and validation of two TESS exoplanets orbiting nearby M dwarfs: TOI-2084b, and TOI-4184b. We characterized the host stars by combining spectra from Shane/Kast and Magellan/FIRE, SED (Spectral Energy Distribution) analysis, and stellar evolutionary models. In addition, we used Gemini-South/Zorro & -North/Alopeke high-resolution imaging, archival science images, and statistical validation packages to support the planetary interpretation. We performed a global analysis of multi-colour photometric data from TESS and ground-based facilities in order to derive the stellar and planetary physical parameters for each system. We find that TOI-2084b and TOI-4184b are sub-Neptune-sized planets with radii of Rp = 2.47 +/- 0.13R_Earth and Rp = 2.43 +/- 0.21R_Earth, respectively. TOI-2084b completes an orbit around its host star every 6.08 days, has an equilibrium temperature of T_eq = 527 +/- 8K and an irradiation of S_p = 12.8 +/- 0.8 S_Earth. Its host star is a dwarf of spectral M2.0 +/- 0.5 at a distance of 114pc with an effective temperature of T_eff = 3550 +/- 50 K, and has a wide, co-moving M8 companion at a projected separation of 1400 au. TOI-4184b orbits around an M5.0 +/- 0.5 type dwarf star (Kmag = 11.87) each 4.9 days, and has an equilibrium temperature of T_eq = 412 +/- 8 K and an irradiation of S_p = 4.8 +/- 0.4 S_Earth. TOI-4184 is a metal poor star ([Fe/H] = -0.27 +/- 0.09 dex) at a distance of 69 pc with an effective temperature of T_eff = 3225 +/- 75 K. Both planets are located at the edge of the sub-Jovian desert in the radius-period plane. The combination of the small size and the large infrared brightness of their host stars make these new planets promising targets for future atmospheric exploration with JWST.Comment: Accepted for publication in A&

TOI-2084 b and TOI-4184 b:two new sub-Neptunes around M dwarf stars

Funding: The research leading to these results has received funding from the ARC grant for Concerted Research Actions, financed by the Wallonia-Brussels Federation. This research is in part funded by the European Union’s Horizon 2020 research and innovation program (grants agreements n◦ 803193/BEBOP), and from the Science and Technology Facilities Council (STFC; grant n◦ ST/S00193X/1). U.G.J. gratefully acknowledges support from tthe European Union H2020-MSCA-ITN-2019 under grant No. 860470 (CHAMELEON). We acknowledge funding from the European Research Council under the ERC Grant Agreement n. 337591-ExTrA.We present the discovery and validation of two TESS exoplanets orbiting nearby M dwarfs: TOI-2084 b, and TOI-4184b. We characterized the host stars by combining spectra from Shane/Kast and Magellan/FIRE, spectral energy distribution analysis, and stellar evolutionary models. In addition, we used Gemini-South/Zorro & -North/Alopeke high-resolution imaging, archival science images, and statistical validation packages to support the planetary interpretation. We performed a global analysis of multi-colour photometric data from TESS and ground-based facilities in order to derive the stellar and planetary physical parameters for each system. We find that TOI-2084 band TOI-4184 bare sub-Neptune-sized planets with radii of Rp = 2.47 ± 0.13R⊕ and Rp = 2.43 ± 0.21 R⊕, respectively. TOI-2084 b completes an orbit around its host star every 6.08 days, has an equilibrium temperature of Teq = 527 ± 8 K and an irradiation of Sp = 12.8 ± 0.8 S⊕. Its host star is a dwarf of spectral M2.0 ± 0.5 at a distance of 114 pc with an effective temperature of Teff = 3550 ± 50 K, and has a wide, co-moving M8 companion at a projected separation of 1400 au. TOI-4184 b orbits around an M5.0 ± 0.5 type dwarf star (Kmag = 11.87) each 4.9 days, and has an equilibrium temperature of Teq = 412 ± 8 K and an irradiation of Sp = 4.8 ± 0.4 S⊕. TOI-4184 is a metal poor star ([Fe/H] = −0.27 ± 0.09 dex) at a distance of 69 pc with an effective temperature of Teff = 3225 ± 75 K. Both planets are located at the edge of the sub-Jovian desert in the radius-period plane. The combination of the small size and the large infrared brightness of their host stars make these new planets promising targets for future atmospheric exploration with JWST.Publisher PDFPeer reviewe

NGTS-28Ab:a short period transiting brown dwarf

We report the discovery of a brown dwarf orbiting a M1 host star. We first identified the brown dwarf within the Next Generation Transit Survey data, with supporting observations found in TESS sectors 11 and 38. We confirmed the discovery with follow-up photometry from the South African Astronomical Observatory, SPECULOOS-S, and TRAPPIST-S, and radial velocity measurements from HARPS, which allowed us to characterize the system. We find an orbital period of ∼1.25 d, a mass of 69.0+5.3-4.8 MJ, close to the hydrogen burning limit, and a radius of 0.95 ± 0.05 RJ. We determine the age to be &gt;0.5 Gyr, using model isochrones, which is found to be in agreement with spectral energy distribution fitting within errors. NGTS-28Ab is one of the shortest period systems found within the brown dwarf desert, as well as one of the highest mass brown dwarfs that transits an M dwarf. This makes NGTS-28Ab another important discovery within this scarcely populated region.</div