13 results on '"Yeung, T."'
Search Results
2. A condition-based deterioration model for the crack propagation in a submerged concrete structure
- Author
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El Hajj, B., Schoefs, F., Bruno Castanier, and Yeung, T.
- Published
- 2014
3. Magnetic field imaging with NV ensembles
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Pham, L. M., Sage, D. Le, Stanwix, P. L., Yeung, T. K., Glenn, D., Trifonov, A., Cappellaro, P., Hemmer, P. R., Lukin, M. D., Park, H., Yacoby, A., and Walsworth, R. L.
- Subjects
Quantum Physics ,Condensed Matter - Mesoscale and Nanoscale Physics ,Mesoscale and Nanoscale Physics (cond-mat.mes-hall) ,FOS: Physical sciences ,Quantum Physics (quant-ph) - Abstract
We demonstrate a method of imaging spatially varying magnetic fields using a thin layer of nitrogen-vacancy (NV) centers at the surface of a diamond chip. Fluorescence emitted by the two-dimensional NV ensemble is detected by a CCD array, from which a vector magnetic field pattern is reconstructed. As a demonstration, AC current is passed through wires placed on the diamond chip surface, and the resulting AC magnetic field patterns are imaged using an echo-based technique with sub-micron resolution over a 140 \mu m x 140 \mu m field of view, giving single-pixel sensitivity ~100 nT/\sqrt{Hz}. We discuss ongoing efforts to further improve sensitivity and potential bioimaging applications such as real-time imaging of activity in functional, cultured networks of neurons., Comment: 15 pages, 5 figures
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- 2012
- Full Text
- View/download PDF
4. Impact of maintenance on the replacement investment under technological improvement
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Khanh Nguyen, Yeung, T. G., and Castanier, B.
- Published
- 2011
5. The Effect of the Disorder on the Longitudinal Resistance of a Graphene p-n Junction in Quantum Hall Regime
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Chen, Jiang-chai, Yeung, T. C. Au, and Sun, Qing-feng
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Condensed Matter - Mesoscale and Nanoscale Physics ,Mesoscale and Nanoscale Physics (cond-mat.mes-hall) ,FOS: Physical sciences - Abstract
The longitudinal resistances of a six-terminal graphene p-n junction under a perpendicular magnetic field are investigated. Because of the chirality of the Hall edge states, the longitudinal resistances on top and bottom edges of the graphene ribbon are not equal. In the presence of suitable disorder, the top-edge and bottom-edge resistances well show the plateau structures in the both unipolar and bipolar regimes and the plateau values are determined by the Landau filling factors only. These plateau structures are in excellent agreement with the recent experiment. For the unipolar junction, the resistance plateaus emerge in the absence of impurity and they are destroyed by strong disorder. But for the bipolar junction, the resistances are very large without the plateau structures in the clean junction. The disorder can strongly reduce the resistances and leads the formation of the resistance plateaus, due to the mixture of the Hall edge states in virtue of the disorder. In addition, the size effect of the junction on the resistances is studied and some extra resistance plateaus are found in the long graphene junction case. This is explained by the fact that only part of the edge states participate in the full mixing., Comment: 7 pages, 5 figures
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- 2010
- Full Text
- View/download PDF
6. The role of S_11 resonance in pi N Scattering
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Surya, Yohanes, Hin, Kam Chan, Yeung, T. C. Au, Shangguan, W. Z., Purwanto, Wirawan, Yusuf, Eddy, Kusno, Darmadi, and Gunawan, Oki
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Nuclear Theory (nucl-th) ,Nuclear Theory ,FOS: Physical sciences ,Nuclear Experiment - Abstract
We analyze Pion Nucleon Scattering up to 700 MeV using a simple, relativistic, unitary model\cite{1}. The kernel of the integral equation includes nucleon, roper, delta, D_13 as well as S_11 poles with their corresponding crossed pole terms approximated by contact interactions. The s- and p- wave phase shifts are calculated from the model and shown to agree very well with the values derived from pi N scattering data \cite {2}. All parameters which involve S_{11} are presented., Comment: 8 pages, 5 figures, to reproduce the figures, please refer to the txt file called "readme"
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- 1999
- Full Text
- View/download PDF
7. Bayesian updating for road maintenance optimization
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Zouch, M., Yeung, T., Bruno Castanier, Institut de Recherche en Communications et en Cybernétique de Nantes (IRCCyN), Mines Nantes (Mines Nantes)-École Centrale de Nantes (ECN)-Ecole Polytechnique de l'Université de Nantes (EPUN), Université de Nantes (UN)-Université de Nantes (UN)-PRES Université Nantes Angers Le Mans (UNAM)-Centre National de la Recherche Scientifique (CNRS), Mines Nantes (Mines Nantes), and R. Virolainen & T. Aven
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Road maintenance ,[SPI]Engineering Sciences [physics] ,Imperfect maintenance ,[INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO] ,Markov decision process - Abstract
International audience; Pavement structures are subject to several deterioration patterns classified in surface or structural failure modes. Structural deteriorations are frequent and lead to heavy and costly maintenance. We restrict our study to the fatigue longitudinal cracks which arise in the underlying layers and growth up to the surface due to traffic repetitive tensile stresses. The characterization of the complete cracking process is very complex because of a large number of covariates and the strong randomness of the environment (climate and traffic loads). Moreover, the current maintenance indicator is a cracking percentage of the road section surface. It gives only partial information onto the underlying racking. In such a context, a condition-based maintenance model on a single variable does not allow to guarantee an optimal maintenance decision. In [10], we have proposed a new model for the longitudinal cracking based on a bivariate stochastic process where the joint probability is a function of the current system state. It allows first to propose a new modeling of imperfect maintenance and then to differentiate maintenance according to their own cracking speed. Nevertheless, one of the main limits is the difficulty of its implementation in operation. The objective of this work is to deepen the model in [10] for improving its applicability for road maintenance while keeping their theoretical properties and advantages. Two directions are developed. First, a new definition of the bivariate deterioration process and the construction of the respective joint probability law based on classical results in Bayesian theory are presented. Then the derivation of the statistical framework for estimating the associated parameters will be proposed. The second direction is in the modeling of the uncertainty in the maintenance impact onto the cracking process.
8. A condition-based Maintenance policy based on a probabilistic meta-model in the case of chloride-induced corrosion
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El Hajj, B., Bruno Castanier, Schoefs, F., Bastidas-Arteaga, E., Yeung, T., Institut de Recherche en Communications et en Cybernétique de Nantes (IRCCyN), Mines Nantes (Mines Nantes)-École Centrale de Nantes (ECN)-Ecole Polytechnique de l'Université de Nantes (EPUN), Université de Nantes (UN)-Université de Nantes (UN)-PRES Université Nantes Angers Le Mans (UNAM)-Centre National de la Recherche Scientifique (CNRS), Contrôle de santé fiabilité et calcul des structures (TRUST), Institut de Recherche en Génie Civil et Mécanique (GeM), Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Université de Nantes (UN)-École Centrale de Nantes (ECN)-Centre National de la Recherche Scientifique (CNRS)-Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Université de Nantes (UN)-École Centrale de Nantes (ECN)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Angevin de Recherche en Ingénierie des Systèmes (LARIS), Université d'Angers (UA), and Projet SI3M
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Degradation models ,Maintenance ,[SDE.MCG]Environmental Sciences/Global Changes ,0211 other engineering and technologies ,020101 civil engineering ,02 engineering and technology ,stochastic process ,Gamma Process ,0201 civil engineering ,[SPI.MAT]Engineering Sciences [physics]/Materials ,Corrosion ,[SPI.GCIV]Engineering Sciences [physics]/Civil Engineering ,Chloride ingress ,021105 building & construction ,data-driven approach ,[SPI.GCIV.STRUCT]Engineering Sciences [physics]/Civil Engineering/Structures ,Concrete ,Meta-model - Abstract
International audience; Maintenance and management policies are usually focused on minimizing the life-cycle cost only. Therefore the optimal solution in this context does not necessarily result in a satisfactory long-term structural performance. In this paper, we will present an approach for modeling the degradation of structures and infrastructures for maintenance purposes. The degradation is modeled using probabilistic data-driven state dependent stochastic processes, hereafter called meta-model. This work implements this degradation model into a maintenance framework and carries out two numerical examples in order to show the applicability of our meta-model in a maintenance and management optimization context. This paves the road for future work on meta-model updating and maintenance optimization by considering a multi-objective optimization policies.
9. Bayesian network-based model for bridge network management
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Kosgodagan, A., Oswaldo Morales Nápoles, Maljaars, J., Castanier, B., and Yeung, T. G.
10. Bayesian networks to quantify transition rates in degradation modeling: Application to a set of steel bridges in The Netherlands
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Kosgodagan, A., Morales-Napoles, O., johan maljaars, Yeung, T. G., Castanier, B., Aluminium Structures, Institut de Recherche en Communications et en Cybernétique de Nantes (IRCCyN), Mines Nantes (Mines Nantes)-École Centrale de Nantes (ECN)-Ecole Polytechnique de l'Université de Nantes (EPUN), Université de Nantes (UN)-Université de Nantes (UN)-PRES Université Nantes Angers Le Mans (UNAM)-Centre National de la Recherche Scientifique (CNRS), Mines Nantes (Mines Nantes), The Netherlands Organisation for Applied Scientific Research (TNO), Eindhoven University of Technology [Eindhoven] (TU/e), Laboratoire Angevin de Recherche en Ingénierie des Systèmes (LARIS), and Université d'Angers (UA)
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[MATH.MATH-PR]Mathematics [math]/Probability [math.PR] ,[SPI.GCIV]Engineering Sciences [physics]/Civil Engineering ,TS - Technical Sciences ,SR - Structural Reliability ,2015 Urbanisation ,Buildings and Infrastructures ,[SPI.GCIV.RISQ]Engineering Sciences [physics]/Civil Engineering/Risques ,2015 Fluid & Solid Mechanics ,[SPI.GCIV.STRUCT]Engineering Sciences [physics]/Civil Engineering/Structures ,Architecture and Building - Abstract
International audience; Bridge lifetime pose an important challenge in terms of maintenance for decision makers or asset managers. In this regard Markov chains have been used successfully in practice as models for bridge deterioration. However, one limitation of Markov chains can be the assessment of the transition probabilities. In this paper, we propose an approach based on Bayesian networks (BNs) to quantify the transition probabilities of the system state. One of the advantages of doing so is that the BN may be quantified through physical variables linked to the underlying degradation process in an intuitive way through expert judgment combined with field measurements. In addition, the possibility of using Bayesian inference allows updating the probabilities when observations become available that could provide different relevant views of the long-term degradation. An application to a hypothetical stock of steel bridges in the Netherlands is presented and illustrates the method.
11. Open data from the third observing run of LIGO, Virgo, KAGRA and GEO
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The LIGO Scientific Collaboration, The Virgo Collaboration, The KAGRA Collaboration, Abbott, R., Abe, H., Acernese, F., Ackley, K., Adhicary, S., Adhikari, N., Adhikari, R. X., Adkins, V. K., Adya, V. B., Affeldt, C., Agarwal, D., Agathos, M., Aguiar, O. D., Aiello, L., Ain, A., Ajith, P., Akutsu, T., Albanesi, S., Alfaidi, R. A., Al-Jodah, A., Alléné, C., Allocca, A., Almualla, M., Altin, P. A., Amato, A., Amez-Droz, L., Amorosi, A., Anand, S., Ananyeva, A., Andersen, R., Anderson, S. B., Anderson, W. G., Andia, M., Ando, M., Andrade, T., Andres, N., Andrés-Carcasona, M., Andrić, T., Ansoldi, S., Antelis, J. M., Antier, S., Aoumi, M., Apostolatos, T., Appavuravther, E. Z., Appert, S., Apple, S. K., Arai, K., Araya, A., Araya, M. C., Areeda, J. S., Arène, M., Aritomi, N., Arnaud, N., Arogeti, M., Aronson, S. M., Arun, K. G., Asada, H., Ashton, G., Aso, Y., Assiduo, M., Melo, S. Assis de Souza, Aston, S. M., Astone, P., Aubin, F., AultONeal, K., Babak, S., Badalyan, A., Badaracco, F., Badger, C., Bae, S., Bagnasco, S., Bai, Y., Baier, J. G., Baiotti, L., Baird, J., Bajpai, R., Baka, T., Ball, M., Ballardin, G., Ballmer, S. W., Baltus, G., Banagiri, S., Banerjee, B., Bankar, D., Baral, P., Barayoga, J. C., Barber, J., Barish, B. C., Barker, D., Barneo, P., Barone, F., Barr, B., Barsotti, L., Barsuglia, M., Barta, D., Barthelmy, S. D., Barton, M. A., Bartos, I., Basak, S., Basalaev, A., Bassiri, R., Basti, A., Bawaj, M., Bayley, J. C., Baylor, A. C., Bazzan, M., Bécsy, B., Bedakihale, V. M., Beirnaert, F., Bejger, M., Bell, A. S., Benedetto, V., Beniwal, D., Benoit, W., Bentley, J. D., Yaala, M. Ben, Bera, S., Berbel, M., Bergamin, F., Berger, B. K., Bernuzzi, S., Beroiz, M., Berry, C. P. L., Bersanetti, D., Bertolini, A., Betzwieser, J., Beveridge, D., Bevins, N., Bhandare, R., Bhandari, A. V., Bhardwaj, U., Bhatt, R., Bhattacharjee, D., Bhaumik, S., Bianchi, A., Bilenko, I. A., Bilicki, M., Billingsley, G., Bini, S., Birnholtz, O., Biscans, S., Bischi, M., Biscoveanu, S., Bisht, A., Biswas, B., Bitossi, M., Bizouard, M. -A., Blackburn, J. K., Blair, C. D., Blair, D. G., Blair, R. M., Bobba, F., Bode, N., Boër, M., Bogaert, G., Boileau, G., Boldrini, M., Bolingbroke, G. N., Bonavena, L. D., Bondarescu, R., Bondu, F., Bonilla, E., Bonilla, G. S., Bonnand, R., Booker, P., Bork, R., Boschi, V., Bose, N., Bose, S., Bossilkov, V., Boudart, V., Bouffanais, Y., Bozzi, A., Bradaschia, C., Brady, P. R., Braglia, M., Branch, A., Branchesi, M., Brau, J. E., Breschi, M., Briant, T., Brillet, A., Brinkmann, M., Brockill, P., Brooks, A. F., Brooks, J., Brown, D. D., Brunett, S., Bruno, G., Bruntz, R., Bryant, J., Bucci, F., Buchanan, J., Bulashenko, O., Bulik, T., Bulten, H. J., Buonanno, A., Burtnyk, K., Buscicchio, R., Buskulic, D., Buy, C., Byer, R. L., Davies, G. S. Cabourn, Cabras, G., Cabrita, R., Cadonati, L., Caesar, S., Cagnoli, G., Cahillane, C., Bustillo, J. Calderón, Callaghan, J. D., Callister, T. A., Calloni, E., Camp, J. B., Canepa, M., Santoro, G. Caneva, Cannavacciuolo, M., Cannon, K. C., Cao, H., Cao, Z., Capistran, L. A., Capocasa, E., Capote, E., Carapella, G., Carbognani, F., Carlassara, M., Carlin, J. B., Carpinelli, M., Carter, J. J., Carullo, G., Diaz, J. Casanueva, Casentini, C., Castaldi, G., Castro-Lucas, S. Y., Caudill, S., Cavaglià, M., Cavalieri, R., Cella, G., Cerdá-Durán, P., Cesarini, E., Chaibi, W., Chakalis, W., Subrahmanya, S. Chalathadka, Champion, E., Chan, C., Chan, C. L., Chandra, K., Chang, I. P., Chang, W., Chanial, P., Chao, S., Chapman-Bird, C., Charlton, E. L., Charlton, P., Chassande-Mottin, E., Chastain, L., Chatterjee, C., Chatterjee, Debarati, Chatterjee, Deep, Chaturvedi, M., Chaty, S., Chatziioannou, K., Chen, D., Chen, H., Chen, H. Y., Chen, J., Chen, K. H., Chen, X., Chen, Y. -R., Chen, Y., Cheng, H., Chessa, P., Cheung, H. Y., Chia, H. Y., Chiadini, F., Chiang, C-I., Chiang, C., Chiarini, G., Chiba, A., Chiba, R., Chierici, R., Chincarini, A., Chiofalo, M. L., Chiummo, A., Choudhary, S., Christensen, N., Chua, S. S. Y., Chung, K. W., Ciani, G., Ciecielag, P., Cieślar, M., Cifaldi, M., Ciobanu, A. A., Ciolfi, R., Clara, F., Clark, J. A., Clarke, T. A., Clearwater, P., Clesse, S., Cleva, F., Coccia, E., Codazzo, E., Cohadon, P. -F., Colleoni, M., Collette, C. G., Colombo, A., Colpi, M., Compton, C. M., Conti, L., Cooper, S. J., Corban, P., Corbitt, T. R., Cordero-Carrión, I., Corezzi, S., Cornish, N. J., Corsi, A., Cortese, S., Coschizza, A. C., Cottingham, R., Coughlin, M. W., Coulon, J. -P., Countryman, S. T., Coupechoux, J. -F., Cousins, B., Couvares, P., Coward, D. M., Cowart, M. J., Cowburn, B. D., Coyne, D. C., Coyne, R., Craig, K., Creighton, J. D. E., Creighton, T. D., Criswell, A. W., Crockett-Gray, J. C. G., Croquette, M., Crowder, S. G., Cudell, J. R., Cullen, T. J., Cumming, A., Cummings, R., Cuoco, E., Curyło, M., Dabadie, P., Canton, T. Dal, Dall'Osso, S., Dálya, G., D'Angelo, B., Danilishin, S., D'Antonio, S., Danzmann, K., Darroch, K. E., Darsow-Fromm, C., Dasgupta, A., Datrier, L. E. H., Datta, Sayantani, Dattilo, V., Dave, I., Davenport, A., Davier, M., Davis, D., Davis, M. C., Daw, E. J., Dax, M., DeBra, D., Deenadayalan, M., Degallaix, J., De Laurentis, M., Deléglise, S., Del Favero, V., De Lillo, F., De Lillo, N., Dell'Aquila, D., Del Pozzo, W., De Matteis, F., D'Emilio, V., Demos, N., Dent, T., Depasse, A., De Pietri, R., De Rosa, R., De Rossi, C., DeSalvo, R., De Simone, R., Dhurandhar, S., Diab, R., Diamond, P. Z., Díaz, M. C., Didio, N. A., Dietrich, T., Di Fiore, L., Di Fronzo, C., Di Giorgio, C., Di Giovanni, F., Di Giovanni, M., Di Girolamo, T., Diksha, D., Di Lieto, A., Di Michele, A., Di Pace, S., Di Palma, I., Di Renzo, F., Divyajyoti, Dmitriev, A., Doctor, Z., Dohmen, E., Doleva, P. P., Donahue, L., D'Onofrio, L., Donovan, F., Dooley, K. L., Dooney, T., Doravari, S., Dorosh, O., Drago, M., Driggers, J. C., Drori, Y., Ducoin, J. -G., Dunn, L., Dupletsa, U., Durante, O., D'Urso, D., Duverne, P. -A., Dwyer, S. E., Eassa, C., Easter, P. J., Ebersold, M., Eckhardt, T., Eddolls, G., Edelman, B., Edo, T. B., Edy, O., Effler, A., Eichholz, J., Eisenmann, M., Eisenstein, R. A., Ejlli, A., Engelby, E., Engl, A. J., Errico, L., Essick, R. C., Estellés, H., Estevez, D., Etzel, T., Evans, C., Evans, M., Evans, T. M., Evstafyeva, T., Ewing, B. E., Fabrizi, F., Faedi, F., Fafone, V., Fair, H., Fairhurst, S., Fan, P. C., Fan, X., Farah, A. M., Farr, B., Farr, W. M., Fauchon-Jones, E. J., Favaro, G., Favata, M., Fays, M., Feicht, J., Fejer, M. M., Fenyvesi, E., Ferguson, D. L., Fernandez-Galiana, A., Ferrante, I., Ferreira, T. A., Fidecaro, F., Figura, P., Fiori, A., Fiori, I., Fishbach, M., Fisher, R. P., Fittipaldi, R., Fiumara, V., Flaminio, R., Fleischer, S. M., Fleming, L. S., Floden, E., Fong, H. K., Font, J. A., Fornal, B., Forsyth, P. W. F., Franke, A., Frasca, S., Frasconi, F., Freed, J. P., Frei, Z., Freise, A., Freitas, O., Frey, R., Fritschel, P., Frolov, V. V., Fronzé, G. G., Fujimoto, Y., Fukunaga, I., Fulda, P., Fyffe, M., Gabbard, H. A., Gabella, W. E., Gadre, B. U., Gaglani, K., Gair, J. R., Gais, J., Galaudage, S., Gallardo, S., Gamba, R., Ganapathy, D., Ganguly, A., Gao, D., Gaonkar, S. G., Garaventa, B., Garcia-Bellido, J., García-Núñez, C., García-Quirós, C., Gardner, K. A., Gargiulo, J., Garufi, F., Gasbarra, C., Gateley, B., Gayathri, V., Gemme, G., Gennai, A., George, J., Gerberding, O., Gergely, L., Ghonge, S., Ghosh, Abhirup, Ghosh, Archisman, Ghosh, Shaon, Ghosh, Shrobana, Ghosh, Tathagata, Giacoppo, L., Giaime, J. A., Giardina, K. D., Gibson, D. R., Gier, C., Giri, P., Gissi, F., Gkaitatzis, S., Glanzer, J., Gleckl, A. E., Glotin, F., Godfrey, J., Godwin, P., Goetz, E., Goetz, R., Golomb, J., Goncharov, B., González, G., Gosselin, M., Gouaty, R., Gould, D. W., Goyal, S., Grace, B., Grado, A., Graham, V., Granata, M., Granata, V., Gras, S., Grassia, P., Gray, C., Gray, R., Greco, G., Green, A. C., Green, R., Green, S., Green, S. R., Gretarsson, A. M., Gretarsson, E. M., Griffith, D., Griffiths, W. L., Griggs, H. L., Grignani, G., Grimaldi, A., Grote, H., Gruson, A. S., Guerra, D., Guetta, D., Guidi, G. M., Guimaraes, A. R., Gulati, H. K., Gulminelli, F., Gunny, A. M., Guo, H., Guo, Y., Gupta, Anchal, Gupta, Anuradha, Gupta, Ish, Gupta, N. C., Gupta, P., Gupta, S. K., Gurs, J., Gushima, Y., Gustafson, E. K., Gutierrez, N., Guzman, F., Haegel, L., Hain, G., Haino, S., Halim, O., Hall, E. D., Hamilton, E. Z., Hammond, G., Han, W. -B., Haney, M., Hanks, J., Hanna, C., Hannam, M. D., Hannuksela, O. A., Hansen, H., Hanson, J., Harada, R., Harder, T., Haris, K., Harmark, T., Harms, J., Harry, G. M., Harry, I. W., Hartwig, D., Haskell, B., Haster, C. -J., Hathaway, J. S., Haughian, K., Hayakawa, H., Hayama, K., Hayes, F. J., Healy, J., Heffernan, A., Heidmann, A., Heintze, M. C., Heinze, J., Heinzel, J., Heitmann, H., Hellman, F., Hello, P., Helmling-Cornell, A. F., Hemming, G., Hendry, M., Heng, I. S., Hennes, E., Hennig, J. -S., Hennig, M., Henshaw, C., Vivanco, F. Hernandez, Heurs, M., Hewitt, A. L., Higginbotham, S., Hild, S., Hill, P., Himemoto, Y., Hines, A. S., Hirata, N., Hirose, C., Ho, J., Hochheim, S., Hofman, D., Hohmann, J. N., Holcomb, D. G., Holland, N. A., Holley-Bockelmann, K., Hollows, I. J., Holmes, Z. J., Holt, K., Holz, D. E., Hong, Q., Hornung, J., Hoshino, S., Hough, J., Hourihane, S., Howell, D., Howell, E. J., Hoy, C. G., Hoyland, D., Hsieh, B. -H., Hsieh, H. -F., Hsiung, C., Hsu, H., Hu, P., Hu, Q., Huang, H. -Y., Huang, Y. -J., Huang, Y., Huang, Y. T., Hübner, M. T., Huddart, A. D., Hughey, B., Hui, D. C. Y., Hui, V., Husa, S., Huttner, S. H., Huxford, R., Huynh-Dinh, T., Hyland, J., Iakovlev, A., Iandolo, G. A., Idzkowski, B., Iess, A., Inayoshi, K., Inoue, Y., Iorio, G., Iosif, P., Irwin, J., Isi, M., Ismail, M. A., Itoh, Y., Iyer, B. R., JaberianHamedan, V., Jacqmin, T., Jacquet, P. -E., Jadhav, S. J., Jadhav, S. P., Jain, D., Jain, T., James, A. L., Jan, A. Z., Jani, K., Janiurek, L., Janquart, J., Janssens, K., Janthalur, N. N., Jaraba, S., Jaranowski, P., Jarov, S., Jasal, P., Jaume, R., Javed, W., Jenkins, A. C., Jenner, K., Jennings, A., Jia, W., Jiang, J., Liu, Jian, Jin, H. -B., Johansmeyer, K., Johns, G. R., Johnson, N. A., Johnston, R., Johny, N., Jones, A. W., Jones, D. H., Jones, D. I., Jones, P., Jones, R., Joshi, P., Ju, L., Jung, K., Junker, J., Juste, V., Kajita, T., Kalaghatgi, C., Kalogera, V., Kamai, B., Kamiizumi, M., Kanda, N., Kandhasamy, S., Kang, G., Kanner, J. B., Kapadia, S. J., Kapasi, D. P., Karat, S., Karathanasis, C., Karki, S., Kasamatsu, D., Kas-danouche, Y. A., Kashyap, R., Kasprzack, M., Kastaun, W., Kato, J., Katsanevas, S., Katsavounidis, E., Katsuren, J. K., Katzman, W., Kaur, T., Kawabe, K., Kawazoe, K., Kéfélian, F., Keitel, D., Kellard, I., Kelley-Derzon, J., Kennington, J., Key, J. S., Khadka, S., Khalili, F. Y., Khan, S., Khanam, T., Khazanov, E. A., Khursheed, M., Kijbunchoo, N., Kim, C., Kim, J. C., Kim, K., Kim, M. H., Kim, P., Kim, S., Kim, W. S., Kim, Y. -M., Kimball, C., Kimura, N., Kinley-Hanlon, M., Kirchhoff, R., Kissel, J. S., Kiyota, T., Klimenko, S., Klinger, T., Knee, A. M., Knust, N., Kobayashi, Y., Koch, P., Koehlenbeck, S. M., Koekoek, G., Kohri, K., Kokeyama, K., Koley, S., Koliadko, N. D., Kolitsidou, P., Kolstein, M., Kondrashov, V., Kong, A. K. H., Kontos, A., Korobko, M., Kossak, R. V., Kouvatsos, N., Kovalam, M., Koyama, N., Kozak, D. B., Kranzhoff, L., Kranzhoff, S. L., Kringel, V., Krishnendu, N. 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FOS: Physical sciences ,General Relativity and Quantum Cosmology (gr-qc) ,General Relativity and Quantum Cosmology - Abstract
The global network of gravitational-wave observatories now includes five detectors, namely LIGO Hanford, LIGO Livingston, Virgo, KAGRA, and GEO 600. These detectors collected data during their third observing run, O3, composed of three phases: O3a starting in April of 2019 and lasting six months, O3b starting in November of 2019 and lasting five months, and O3GK starting in April of 2020 and lasting 2 weeks. In this paper we describe these data and various other science products that can be freely accessed through the Gravitational Wave Open Science Center at https://gwosc.org. The main dataset, consisting of the gravitational-wave strain time series that contains the astrophysical signals, is released together with supporting data useful for their analysis and documentation, tutorials, as well as analysis software packages., 27 pages, 3 figures
12. Genotype analyses using SNP (using Maldi-Tof mass spectrometry) and STR (microsatellite) markers in the determination of zygosity status of Chinese Twins
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Lim, C. K. B., Yeung, V. S. Y., Yeung, T. Z., Tam, A. C. Y., Ho, C. S. H., Wong, S. W. L., Chow, B. W. Y., Ho, Y. M., Chou, C. H. N., Fung, C. -H F., Wong, C. F., and Mary Miu Yee Waye
13. Gleditsia sinensis fruit extract-induced apoptosis involves changes of reactive oxygen species level, mitochondrial membrane depolarization and caspase 3 activation
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Chui, C. H., Lau, F. Y., Chan, A. S., Cheng, G. Y., Wong, R. S., Lai, K. B., Kok, S. H., Yeung, T. T., Teo, I. T., Yau, M. Y., Cheung, F., Cheng, C. H., and Johnny Tang
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