Disentangling the role of environmental processes in galaxy clusters

In this work we present the results of a novel approach devoted to disentangle the role of the environmental processes affecting galaxies in clusters. This is based on the analysis of the NUV-r' distributions of a large sample of star-forming galaxies in clusters spanning more than four absolute magnitudes. The galaxies inhabit three distinct environmental regions: virial regions, cluster infall regions and field environment. We have applied rigorous statistical tests in order to analyze both, the complete NUV-r' distributions and their averages for three different bins of r'-band galaxy luminosity down to M_r' ~ -18, throughout the three environmental regions considered. We have identified the environmental processes that significantly affect the star-forming galaxies in a given luminosity bin by using criteria based on the characteristics of these processes: their typical time-scales, the regions where they operate and the galaxy luminosity range for which their effects are more intense. We have found that the high-luminosity (M_r'<=-20) star-forming galaxies do not show significant signs in their star formation activity neither of being affected by the environment in the last ~10^8 yr nor of a sudden quenching in the last 1.5 Gyr. The intermediate-luminosity (-20<M_r'<=-19) star-forming galaxies appear to be affected by starvation in the virial regions and by the harassment both, in the virial and infall regions. Low-luminosity (-19<M_r'<=-18.2) star-forming galaxies seem to be affected by the same environmental processes as intermediate-luminosity star-forming galaxies in a stronger way, as it would be expected for their lower luminosities.Comment: 42 pages, 7 figures, 5 tables; accepted for publication in Ap

Recent star formation in clusters of galaxies: extreme compact starbursts in A539 and A634

We report on the detection of two Halpha-emitting extreme compact objects from deep images of the Abell 634 and Abell 539 clusters of galaxies at z ~ 0.03. Follow up long slit spectroscopy of these two unresolved sources revealed that they are members of their respective clusters showing HII type spectra. The luminosity and the extreme equivalent width of Halpha+[NII] measured for these sources, together with their very compact appearance, has raised the question about the origin of these intense starbursts in the cluster environment. We propose the compact starburst in Abell 539 resulted from the compression of the interstellar gas of a dwarf galaxy when entering the cluster core; while the starburst galaxy in Abell 634 is likely to be the result of a galaxy-galaxy interaction, illustrating the preprocessing of galaxies during their infall towards the central regions of clusters. The contribution of these compact star-forming dwarf galaxies to the star formation history of galaxy clusters is discussed, as well as a possible link with the recently discovered early-type ultra-compact dwarf galaxies. We note that these extreme objects will be rarely detected in normal magnitude-limited optical or NIR surveys, mainly due to their low stellar masses (of the order of 10^6 solar masses), whereas they will easily show up in dedicated Halpha surveys given the high equivalent width of their emission lines.Comment: Accepted for publication in the Astronomical Journal. 31 pages, 10 fig

Cell death in hepatocellular carcinoma: Pathogenesis and therapeutic opportunities

Hepatocellular carcinoma (HCC) is the most prevalent primary liver cancer and the third leading cause of cancer death worldwide. Closely associated with liver inflammation and fibrosis, hepatocyte cell death is a common trigger for acute and chronic liver disease arising from different etiologies, including viral hepatitis, alcohol abuse, and fatty liver. In this review, we discuss the con-tribution of different types of cell death, including apoptosis, necroptosis, pyroptosis, or autophagy, to the progression of liver disease and the development of HCC. Interestingly, inflammasomes have recently emerged as pivotal innate sensors with a highly pathogenic role in various liver diseases. In this regard, an increased inflammatory response would act as a key element promoting a pro-oncogenic microenvironment that may result not only in tumor growth, but also in the formation of a premetastatic niche. Importantly, nonparenchymal hepatic cells, such as liver sinusoidal endothelial cells, hepatic stellate cells, and hepatic macrophages, play an important role in establishing the tumor microenvironment, stimulating tumorigenesis by paracrine communication through cytokines and/or angiocrine factors. Finally, we update the potential therapeutic options to inhibit tumorige-nesis, and we propose different mechanisms to consider in the tumor microenvironment field for HCC resolution. © 2021 by the authors. Licensee MDPI, Basel, Switzerland

Recreational water skiing in people with paraplegia: A study of three cases

Objetivos: se analizaron la condición cardiorrespiratoria y la intensidad de esfuerzo durante la práctica recreativa del esquí náutico de slalom. Metodología: participaron tres esquiadores náuticos con paraplejia moderadamente activos. Realizaron un test incremental en un ergómetro de brazos para determinar su VO2pico y los umbrales ventilatorios y completaron 3 sesiones de práctica de esquí náutico, separadas por 48h, registrándose la FC cada 5 s. Resultados: obtuvieron un VO2pico de 22,3 ± 0,6 mL·kg-1·min-1 y los umbrales ventilatorios se analizaron al ~80 y ~50% del VO2pico. La FC media en las sesiones de esquí náutico fue de 111 ppm, lo que representó una intensidad de ~45% de la FC de reserva (FCR), permaneciendo por encima del 40% de la FCR ~12 min. Conclusión: la intensidad moderada de la práctica recreativa de esquí náutico de slalom podría servir para mantener o mejorar la condición cardiorrespiratoria en estas tres personas con paraplejiaObjectives: the cardiorespiratory fitness and the intensity of effort were analyzed during the recreational practice of slalom water skiing. Methodology: three moderately active water skiers with paraplegia participated. They performed an incremental test on an arm ergometer to determine their VO2peak and ventilatory thresholds and completed 3 sessions of water skiing, separated by 48h, where the HR was recorded every 5 s. Results: they obtained a VO2peak of 22.3 ± 0.6 mL·kg-1·min-1 and the ventilatory thresholds were analyzed at ~80 and ~50% of the VO2peak. The average heart rate in the water ski sessions was 111 bpm, which represented an intensity of ~45% of the heart rate reserve (HRR), remaining above 40% of the HRR ~12 min. Conclusion: the moderate intensity of recreational slalom skiing could serve to maintain or improve the cardiorespiratory fitness in these three people with paraplegi

Rotor fault detection in induction motors based on time-frequency analysis using the bispectrum and the autocovariance of stray flux signals

[EN] The aim of this work is to find out, through the analysis of the time and frequency domains, significant differences that lead us to obtain one or several variables that may result in an indicator that allows diagnosing the condition of the rotor in an induction motor from the processing of the stray flux signals. For this, the calculation of two indicators is proposed: the first is based on the frequency domain and it relies on the calculation of the sum of the mean value of the bispectrum of the flux signal. The use of high order spectral analysis is justified in that with the one-dimensional analysis resulting from the Fourier Transform, there may not always be solid differences at the spectral level that enable us to distinguish between healthy and faulty conditions. Also, based on the high-order spectral analysis, differences may arise that, with the classical analysis with the Fourier Transform, are not evident, since the high order spectra from the Bispectrum are immune to Gaussian noise, but not the results that can be obtained using the one-dimensional Fourier transform. On the other hand, a second indicator based on the temporal domain that is based on the calculation of the square value of the median of the autocovariance function of the signal is evaluated. The obtained results are satisfactory and let us conclude the affirmative hypothesis of using flux signals for determining the condition of the rotor of an induction motor.This research was funded by MEC, grant number MTM 2016-7963-P.Iglesias-Martínez, ME.; Antonino Daviu, JA.; Fernández De Córdoba, P.; Conejero, JA. (2019). Rotor fault detection in induction motors based on time-frequency analysis using the bispectrum and the autocovariance of stray flux signals. Energies. 12(4):1-16. https://doi.org/10.3390/en12040597S116124Henao, H., Capolino, G.-A., Fernandez-Cabanas, M., Filippetti, F., Bruzzese, C., Strangas, E., … Hedayati-Kia, S. (2014). Trends in Fault Diagnosis for Electrical Machines: A Review of Diagnostic Techniques. IEEE Industrial Electronics Magazine, 8(2), 31-42. doi:10.1109/mie.2013.2287651Riera-Guasp, M., Antonino-Daviu, J. A., & Capolino, G.-A. (2015). Advances in Electrical Machine, Power Electronic, and Drive Condition Monitoring and Fault Detection: State of the Art. IEEE Transactions on Industrial Electronics, 62(3), 1746-1759. doi:10.1109/tie.2014.2375853Jiang, C., Li, S., & Habetler, T. G. (2017). A review of condition monitoring of induction motors based on stray flux. 2017 IEEE Energy Conversion Congress and Exposition (ECCE). doi:10.1109/ecce.2017.8096907Ramirez-Nunez, J. A., Antonino-Daviu, J. A., Climente-Alarcon, V., Quijano-Lopez, A., Razik, H., Osornio-Rios, R. A., & Romero-Troncoso, R. de J. (2018). Evaluation of the Detectability of Electromechanical Faults in Induction Motors Via Transient Analysis of the Stray Flux. IEEE Transactions on Industry Applications, 54(5), 4324-4332. doi:10.1109/tia.2018.2843371Park, Y., Yang, C., Kim, J., Kim, H., Lee, S. B., Gyftakis, K. N., … Capolino, G.-A. (2019). Stray Flux Monitoring for Reliable Detection of Rotor Faults Under the Influence of Rotor Axial Air Ducts. IEEE Transactions on Industrial Electronics, 66(10), 7561-7570. doi:10.1109/tie.2018.2880670Iglesias-Martinez, M. E., de Cordoba, P. F., Antonino-Daviu, J. A., & Conejero, J. A. (2018). Detection of Bar Breakages in Induction Motor via Spectral Subtraction of Stray Flux Signals. 2018 XIII International Conference on Electrical Machines (ICEM). doi:10.1109/icelmach.2018.8507078Mendel, J. M. (1991). Tutorial on higher-order statistics (spectra) in signal processing and system theory: theoretical results and some applications. Proceedings of the IEEE, 79(3), 278-305. doi:10.1109/5.75086Nikias, C. L., & Mendel, J. M. (1993). Signal processing with higher-order spectra. IEEE Signal Processing Magazine, 10(3), 10-37. doi:10.1109/79.22132

An alternative mechanistic paradigm for the β-Z hydrosilylation of terminal alkynes: The role of acetone as a silane shuttle

The β-Z selectivity in the hydrosilylation of terminal alkynes has been hitherto explained by introduction of isomerisation steps in classical mechanisms. DFT calculations and experimental observations on the system [M(I)2{κ-C,C,O,O-(bis-NHC)}]BF4 (M=Ir (3 a), Rh (3 b); bis-NHC=methylenebis(N-2-methoxyethyl)imidazole-2-ylidene) support a new mechanism, alternative to classical postulations, based on an outer-sphere model. Heterolytic splitting of the silane molecule by the metal centre and acetone (solvent) affords a metal hydride and the oxocarbenium ion [R 3Si - O(CH3)2]+, which reacts with the corresponding alkyne in solution to give the silylation product [R 3Si - CHï£C - R]+. Thus, acetone acts as a silane shuttle by transferring the silyl moiety from the silane to the alkyne. Finally, nucleophilic attack of the hydrido ligand over [R3Si - CHï£C - R]+ affords selectively the β-(Z)- vinylsilane. The β-Z selectivity is explained on the grounds of the steric interaction between the silyl moiety and the ligand system resulting from the geometry of the approach that leads to β-(E)-vinylsilanes. Silanes catch the shuttle: An outer-sphere mechanism that explains the β-Z hydrosilylation of terminal alkynes based on the role of acetone as a silane shuttle is disclosed. Heterolytic splitting of the silane molecule by the metal centre and acetone affords a metal hydride and the oxocarbenium ion [R 3Si - O(CH3)2]+, which reacts with the alkyne in solution to give the silylation product [R3Si - CHï£C - R]+ (see figure). © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.This work was supported by the Spanish Ministry of Economy and Competitiveness (MINECO/FEDER) (CONSOLIDER INGENIO-2010, CTQ2011-27593 projects, and “Ramón y Cajal” (P.J.S.M.) and “Juan de la Cierva” (M.I.) programmes) and the DGA/FSE (E07).Peer Reviewe

Antiangiogenic Therapy in Epithelial Ovarian Cancer

Approximately 75% of women with ovarian cancer are diagnosed at advanced stages (FIGO stage III/IV), with 15-23 months median global survival and 20% 5-year survival. Angiogenesis plays an important role in tumour development and proliferation. Increased angiogenesis is associated with worse clinical outcome in ovarian cancer. Here we review the play of bevacizumab in the treatment of ovarian cancer and also other antiangiogenic drugs. In total, to date there are no promising results for most of the reviewed antiangiogenic agents, except those already known for bevacizumab, trebananib, pazopanib, cediranib and nintedanib. Ongoing research will shed more light on this fascinating tumour process and its control

Higher-order spectral analysis of stray flux signals for faults detection in induction motors

[EN] This work is a review of current trends in the stray flux signal processing techniques applied to the diagnosis of electrical machines. Initially, a review of the most commonly used standard methods is performed in the diagnosis of failures in induction machines and using stray flux; and then specifically it is treated and performed the algorithms based on statistical analysis using cumulants and polyspectra. In addition, the theoretical foundations of the analyzed algorithms and examples applications are shown from the practical point of view where the benefits that processing can have using HOSA and its relationship with stray flux signal analysis, are illustrated.This work has been supported by Generalitat Valenciana, Conselleria d'Educació, Cultura i Esport in the framework of the "Programa para la promoción de la investigación científica, el desarrollo tecnológico y la innovación en la Comunitat Valenciana", Subvenciones para grupos de investigación consolidables (ref: AICO/2019/224). J. Alberto Conejero is also partially supported by MEC Project MTM2016-75963-P.Iglesias Martínez, ME.; Antonino Daviu, JA.; Fernández De Córdoba, P.; Conejero, JA. (2020). Higher-order spectral analysis of stray flux signals for faults detection in induction motors. Applied Mathematics and Nonlinear Sciences. 5(2):1-14. https://doi.org/10.2478/amns.2020.1.00032S11452H. Akçay and E. Germen. Subspace-based identification of acoustic noise spectra in induction motors. IEEE Transactions on Energy Conversion, 30(1):32–40, 2015.J. Antonino-Daviu, M. Riera-Guasp, J. Roger-Folch, F. Martínez-Giménez, and A. Peris. Application and optimization of the discrete wavelet transform for the detection of broken rotor bars in induction machines. Applied and Computational Harmonic Analysis, 21(2):268–279, 2006.N. Arthur and J. Penman. Induction machine condition monitoring with higher order spectra. IEEE Transactions on Industrial Electronics, 47(5):1031–1041, 2000.T. P. Banerjee and S. Das. Multi-sensor data fusion using support vector machine for motor fault detection. Information Sciences, 217:96–107, 2012.G. Bin, J. Gao, X. Li, and B. Dhillon. Early fault diagnosis of rotating machinery based on wavelet packets—empirical mode decomposition feature extraction and neural network. Mechanical Systems and Signal Processing, 27:696–711, 2012.B. Boashash, E. J. Powers, and A. M. Zoubir. Higher-order statistical signal processing. Longman Cheshire, 1995.A. Ceban, R. Pusca, and R. Romary. Eccentricity and broken rotor bars faults-effects on the external axial field. In The XIX International Conference on Electrical Machines-ICEM 2010, pages 1–6. IEEE, 2010.I. Chernyavska and O. Vítek. Analysis of broken rotor bar fault in a squirrel-cage induction motor by means of stator current and stray flux measurement. In 2016 IEEE International Power Electronics and Motion Control Conference (PEMC), pages 532–537. IEEE, 2016.T. Chow and G. Fei. Three phase induction machines asymmetrical faults identification using bispectrum. IEEE Transactions on Energy Conversion, 10(4):688–693, 1995.X. Dai and Z. Gao. From model, signal to knowledge: A data-driven perspective of fault detection and diagnosis. IEEE Transactions on Industrial Informatics, 9(4):2226–2238, 2013.J. de Jesus Rangel-Magdaleno, H. Peregrina-Barreto, J. M. Ramirez-Cortes, P. Gomez-Gil, and R. Morales-Caporal. Fpga-based broken bars detection on induction motors under different load using motor current signature analysis and mathematical morphology. IEEE Transactions on Instrumentation and Measurement, 63(5):1032–1040, 2013.P. A. Delgado-Arredondo, D. Morinigo-Sotelo, R. A. Osornio-Rios, J. G. Avina-Cervantes, H. Rostro-Gonzalez, and R. de Jesus Romero-Troncoso. Methodology for fault detection in induction motors via sound and vibration signals. Mechanical Systems and Signal Processing, 83:568–589, 2017.M. Drif and A. J. M. Cardoso. Stator fault diagnostics in squirrel cage three-phase induction motor drives using the instantaneous active and reactive power signature analyses. IEEE Transactions on Industrial Informatics, 10(2):1348–1360, 2014.L. Frosini, C. Harlişca, and L. Szabó. Induction machine bearing fault detection by means of statistical processing of the stray flux measurement. IEEE Transactions on Industrial Electronics, 62(3):1846–1854, 2014.Z. Gao, C. Cecati, and S. X. Ding. A survey of fault diagnosis and fault-tolerant techniques—part i: Fault diagnosis with model-based and signal-based approaches. IEEE Transactions on Industrial Electronics, 62(6):3757–3767, 2015.M. Geethanjali and H. Ramadoss. Fault diagnosis of induction motors using motor current signature analysis: A review. In Advanced Condition Monitoring and Fault Diagnosis of Electric Machines, pages 1–37. IGI Global, 2019.T. Ghanbari and A. Farjah. A magnetic leakage flux-based approach for fault diagnosis in electrical machines. IEEE Sensors Journal, 14(9):2981–2988, 2014.A. Glowacz. Acoustic based fault diagnosis of three-phase induction motor. Applied Acoustics, 137:82–89, 2018.A. Glowacz, W. Glowacz, Z. Glowacz, and J. Kozik. Early fault diagnosis of bearing and stator faults of the single-phase induction motor using acoustic signals. Measurement, 113:1–9, 2018.T. Goktas, M. Zafarani, K. W. Lee, B. Akin, and T. Sculley. Comprehensive analysis of magnet defect fault monitoring through leakage flux. IEEE Transactions on Magnetics, 53(4):1–10, 2016.K. C. Gryllias and I. A. Antoniadis. A support vector machine approach based on physical model training for rolling element bearing fault detection in industrial environments. Engineering Applications of Artificial Intelligence, 25(2):326–344, 2012.F. Gu, Y. Shao, N. Hu, A. Naid, and A. Ball. Electrical motor current signal analysis using a modified bispectrum for fault diagnosis of downstream mechanical equipment. Mechanical Systems and Signal Processing, 25(1):360–372, 2011.C. Harlişca, L. Szabó, L. Frosini, and A. Albini. Diagnosis of rolling bearings faults in electric machines through stray magnetic flux monitoring. In 2013 8TH International Symposium on Advanced Topics in Electrical Engineering (Atee), pages 1–6. IEEE, 2013.R. Hoppler and R. A. Errath. Motor bearings, not must a piece of metal. In 2007 IEEE Cement Industry Technical Conference Record, pages 214–233. IEEE, 2007.R. M. Howard. Principles of random signal analysis and low noise design: The power spectral density and its applications. John Wiley & Sons, 2004.J.-N. Hwang and Y. H. Hu. Handbook of neural network signal processing. CRC press, 2001.M. E. Iglesias-Martínez, J. A. Antonino-Daviu, P. Fernández de Córdoba, and J. A. Conejero. Rotor fault detection in induction motors based on time-frequency analysis using the bispectrum and the autocovariance of stray flux signals. Energies, 12(4):597, 2019.M. E. Iglesias-Martinez, P. F. de Cordoba, J. Antonino-Daviu, and J. A. Conejero. Detection of nonadjacent rotor faults in induction motors via spectral subtraction and autocorrelation of stray flux signals. IEEE Transactions on Industry Applications, 55(5):4585–4594, 2019.M. E. Iglesias-Martínez, P. F. de Córdoba, J. A. Antonino-Daviu, and J. A. Conejero. Detection of bar breakages in induction motor via spectral subtraction of stray flux signals. In 2018 XIII International Conference on Electrical Machines (ICEM), pages 1796–1802. IEEE, 2018.M. E. Iglesias-Martínez, P. F. de Córdoba, J. A. Antonino-Daviu, and J. A. Conejero. Detection of adjacent and non-adjacent bar breakages in induction motors via convolutional analysis of sound signals. Preprint, 2020.F. Immovilli, A. Bellini, R. Rubini, and C. Tassoni. Diagnosis of bearing faults in induction machines by vibration or current signals: A critical comparison. IEEE Transactions on Industry Applications, 46(4):1350–1359, 2010.C. Jiang, S. Li, and T. G. Habetler. A review of condition monitoring of induction motors based on stray flux. In 2017 IEEE Energy Conversion Congress and Exposition (ECCE), pages 5424–5430. IEEE, 2017.L. Jiang, Y. Liu, X. Li, and S. Tang. Using bispectral distribution as a feature for rotating machinery fault diagnosis. Measurement, 44(7):1284–1292, 2011.Q. Jiang and F. Chang. A novel rolling-element bearing faults classification method combines lower-order moment spectra and support vector machine. Journal of Mechanical Science and Technology, 33(4):1535–1543, 2019.X. Jin and T. W. Chow. Anomaly detection of cooling fan and fault classification of induction motor using mahalanobis–taguchi system. Expert Systems with Applications, 40(15):5787–5795, 2013.J. Józwik. Identification and monitoring of noise sources of CNC machine tools by acoustic holography methods. Advances in Science and Technology Research Journal, 10(30), 2016.S. M. Kay. Fundamentals of statistical signal processing. Prentice Hall PTR, 1993.R. Liu, B. Yang, E. Zio, and X. Chen. Artificial intelligence for fault diagnosis of rotating machinery: A review. Mechanical Systems and Signal Processing, 108:33–47, 2018.Z. Liu, H. Cao, X. Chen, Z. He, and Z. Shen. Multi-fault classification based on wavelet svm with pso algorithm to analyze vibration signals from rolling element bearings. Neurocomputing, 99:399–410, 2013.J. M. Mendel. Tutorial on higher-order statistics (spectra) in signal processing and system theory: Theoretical results and some applications. Proceedings of the IEEE, 79(3):278–305, 1991.M. Mrugalski, M. Witczak, and J. Korbicz. Confidence estimation of the multi-layer perceptron and its application in fault detection systems. Engineering Applications of Artificial Intelligence, 21(6):895–906, 2008.V. Muralidharan and V. Sugumaran. A comparative study of naïve bayes classifier and bayes net classifier for fault diagnosis of monoblock centrifugal pump using wavelet analysis. Applied Soft Computing, 12(8):2023–2029, 2012.Y. Ono, Y. Onishi, T. Koshinaka, S. Takata, and O. Hoshuyama. Anomaly detection of motors with feature emphasis using only normal sounds. In Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on, pages 2800–2804. IEEE, 2013.R. H. C. Palácios, I. N. da Silva, A. Goedtel, and W. F. Godoy. A comprehensive evaluation of intelligent classifiers for fault identification in three-phase induction motors. Electric Power Systems Research, 127:249–258, 2015.P. Panagiotou, I. Arvanitakis, N. Lophitis, J. A. Antonino-Daviu, and K. N. Gyftakis. Analysis of stray flux spectral components in induction machines under rotor bar breakages at various locations. In 2018 XIII International Conference on Electrical Machines (ICEM), pages 2345–2351. IEEE, 2018.P. A. Panagiotou, I. Arvanitakis, N. Lophitis, J. Antonino-Daviu, and K. N. Gyftakis. A new approach for broken rotor bar detection in induction motors using frequency extraction in stray flux signals. IEEE Transactions on Industry Applications, 2019.K. Pandey, P. Zope, and S. Suralkar. Review on fault diagnosis in three-phase induction motor. MEDHA–2012, Proceedings published by International Journal of Computer Applications (IJCA), 2012.J. Rafiee, F. Arvani, A. Harifi, and M. Sadeghi. Intelligent condition monitoring of a gearbox using artificial neural network. Mechanical systems and signal processing, 21(4):1746–1754, 2007.A. Sadeghian, Z. Ye, and B. Wu. Online detection of broken rotor bars in induction motors by wavelet packet decomposition and artificial neural networks. IEEE Transactions on Instrumentation and Measurement, 58(7):2253–2263, 2009.L. Saidi, J. B. Ali, and F. Fnaiech. Application of higher order spectral features and support vector machines for bearing faults classification. ISA transactions, 54:193–206, 2015.L. Saidi, F. Fnaiech, G. Capolino, and H. Henao. Stator current bi-spectrum patterns for induction machines multiple-faults detection. In IECON 2012-38th Annual Conference on IEEE Industrial Electronics Society, pages 5132–5137. IEEE, 2012.L. Saidi, F. Fnaiech, H. Henao, G. Capolino, and G. Cirrincione. Diagnosis of broken-bars fault in induction machines using higher order spectral analysis. ISA Transactions, 52(1):140–148, 2013.M. Salah, K. Bacha, and A. Chaari. An improved spectral analysis of the stray flux component for the detection of air-gap irregularities in squirrel cage motors. ISA transactions, 53(3):816–826, 2014.B. Samanta. Gear fault detection using artificial neural networks and support vector machines with genetic algorithms. Mechanical systems and signal processing, 18(3):625–644, 2004.P. Sangeetha and S. Hemamalini. Dyadic wavelet transform-based acoustic signal analysis for torque prediction of a three-phase induction motor. IET Signal Processing, 11(5):604–612, 2017.J. Sanz, R. Perera, and C. Huerta. Gear dynamics monitoring using discrete wavelet transformation and multi-layer perceptron neural networks. Applied Soft Computing, 12(9):2867–2878, 2012.Z. Shen, X. Chen, X. Zhang, and Z. He. A novel intelligent gear fault diagnosis model based on emd and multi-class tsvm. Measurement, 45(1):30–40, 2012.A. Singhal and M. A. Khandekar. Bearing fault detection in induction motor using fast fourier transform. In IEEE Int. Conf. on Advanced Research in Engineering & Technology, 2013.A. Soualhi, K. Medjaher, and N. Zerhouni. Bearing health monitoring based on hilbert–huang transform, support vector machine, and regression. IEEE Transactions on Instrumentation and Measurement, 64(1):52–62, 2014.A. Swami, G. B. Giannakis, and G. Zhou. Bibliography on higher-order statistics. Signal processing, 60(1):65–126, 1997.O. Vitek, M. Janda, and V. Hajek. Effects of eccentricity on external magnetic field of induction machine. In Melecon 2010–2010 15th IEEE Mediterranean Electrotechnical Conference, pages 939–943. IEEE, 2010.H. Wang, X. Bao, C. Di, and Z. Cheng. Detection of eccentricity fault using slot leakage flux monitoring. In 2015 9th International Conference on Power Electronics and ECCE Asia (ICPE-ECCE Asia), pages 2188–2193. IEEE, 2015.Y. Wang, J. Xiang, R. Markert, and M. Liang. Spectral kurtosis for fault detection, diagnosis and prognostics of rotating machines: A review with applications. Mechanical Systems and Signal Processing, 66:679–698, 2016.Z. Wang and C. Chang. Online fault detection of induction motors using frequency domain independent components analysis. In 2011 IEEE International Symposium on Industrial Electronics, pages 2132–2137. IEEE, 2011.Z. Wang, C. Chang, and Y. Zhang. A feature based frequency domain analysis algorithm for fault detection of induction motors. In 2011 6th IEEE Conference on Industrial Electronics and Applications, pages 27–32. IEEE, 2011.W. Wenbing and X. Jinquan. The application of coupled three order cumulants’ differential feature in fault diagnosis. In 2017 International Conference on Virtual Reality and Visualization (ICVRV), pages 374–375. IEEE, 2017.I. Zamudio-Ramirez, R. A. Osornio-Rios, M. Trejo-Hernandez, R. d. J. Romero-Troncoso, and J. A. Antonino-Daviu. Smart-sensors to estimate insulation health in induction motors via analysis of stray flux. Energies, 12(9):1658, 2019.X. Zhang and J. Zhou. Multi-fault diagnosis for rolling element bearings based on ensemble empirical mode decomposition and optimized support vector machines. Mechanical Systems and Signal Processing, 41(1–2):127–140, 2013.W. Zhao, T. Tao, and E. Zio. System reliability prediction by support vector regression with analytic selection and genetic algorithm parameters selection. Applied Soft Computing, 30:792–802, 2015.W. Zhao, Y. Zhang, and Y. Zhu. Diagnosis for transformer faults based on combinatorial Bayes Network. In 2009 2nd International Congress on Image and Signal Processing, pages 1–3. IEEE, 2009.F. Zidat, J.-P. Lecointe, F. Morganti, J.-F. Brudny, T. Jacq, and F. Streiff. Non invasive sensors for monitoring the efficiency of ac electrical rotating machines. Sensors, 10(8):7874–7895, 2010

Detection of nonadjacent rotor faults in induction motors via spectral subtraction and autocorrelation of stray flux signals

(c) 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.[EN] In this paper, statistical signal processing techniques are applied to electromotive force signals captured in external coil sensors for adjacent and nonadjacent broken bars detection in induction motors. An algorithm based on spectral subtraction analysis is applied for broken bar identification, independent of the relative position of the bar breakages. Moreover, power spectrum analyses enable the discrimination between healthy and faulty conditions. The results obtained with experimental data prove that the proposed approach provides good results for fault detectability. Moreover, the identification of the faults, and the signal correlation indicator to prove the results are also presented for different positions of the flux sensor.This work was supported in part by MEC under Project MTM 2016-7963-P and in part by the Spanish 'Ministerio de Ciencia Innovacion y Universidades' and FEDER program in the framework of the 'Proyectos de I+D de Generacion de Conocimiento del Programa Estatal de Generacion de Conocimiento y Fortalecimiento Cientifico y Tecnologico del Sistema de I+D+i, Subprograma Estatal de Generacion de Conocimiento' (ref: PGC2018-095747-B-I00).Iglesias-Martínez, ME.; Fernández De Córdoba, P.; Antonino Daviu, JA.; Conejero, JA. (2019). Detection of nonadjacent rotor faults in induction motors via spectral subtraction and autocorrelation of stray flux signals. IEEE Transactions on Industry Applications. 55(5):4585-4594. https://doi.org/10.1109/TIA.2019.2917861S4585459455

Analysis of the Genetic Parameters for Dairy Linear Appraisal and Zoometric Traits: A Tool to Enhance the Applicability of Murciano-Granadina Goats Major Areas Evaluation System

Selection for zoometrics defines individuals’ productive longevity, endurance, enhanced productive abilities and consequently, their long-term profitability. When zoometric analysis is aimed at large highly selected populations or in those at different levels of selection, linear appraisal systems (LAS) provide a timely response. This study estimates genetic and phenotypic parameters for zoometric/LAS traits in Murciano-Granadina goats, estimating genetic and phenotypic correlations among all traits, and determining whether major area selection would be appropriate or if adaptability strategies may need to be followed. Heritability estimates for the zoometric/LAS traits were low to high, ranging from 0.09 to 0.43, and the accuracy of estimation has improved after decades, rendering standard errors negligible. Scale inversion of specific traits may need to be performed before major areas selection strategies are implemented. Genetic and phenotypic correlations suggests that negative selection against thicker bones and higher rear insertion heights indirectly results in the optimization of selection practices in the rest of the traits, especially those in the structure, capacity and mammary system major areas. The integration and implementation of the strategies proposed within the Murciano-Granadina breeding program maximizes selection opportunities and the sustainable international competitiveness of the Murciano-Granadina goat in the dairy goat breed panorama