Academic literature on the topic 'Control reliability'

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Journal articles on the topic "Control reliability"

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Turner, Timothy E. "Wafer level reliability: Process control for reliability." Microelectronics Reliability 36, no. 11-12 (November 1996): 1839–46. http://dx.doi.org/10.1016/0026-2714(96)00211-9.

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Eva Wu, N., and Ron J. Patton. "Reliability and supervisory control." IFAC Proceedings Volumes 36, no. 5 (June 2003): 137–42. http://dx.doi.org/10.1016/s1474-6670(17)36483-2.

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Ziegel, Eric. "Reliability and Quality Control." Technometrics 30, no. 4 (November 1988): 455. http://dx.doi.org/10.1080/00401706.1988.10488445.

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Singh, Ashok K. "Quality Control and Reliability." Technometrics 32, no. 3 (August 1990): 344. http://dx.doi.org/10.1080/00401706.1990.10484692.

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Sherrod, Brad, and Andrea McQuaigue. "Building infection control reliability." Nursing Critical Care 8, no. 4 (July 2013): 37–39. http://dx.doi.org/10.1097/01.ccn.0000429386.10473.a4.

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Veevers, A., P. R. Krishnaiah, and C. R. Rao. "Quality Control and Reliability." Journal of the Royal Statistical Society. Series A (Statistics in Society) 152, no. 3 (1989): 423. http://dx.doi.org/10.2307/2983141.

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Stuart, Michael, and A. P. Basu. "Reliability and Quality Control." Statistician 36, no. 4 (1987): 424. http://dx.doi.org/10.2307/2348855.

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Distefano, Salvatore. "Reliability Assessment of Control Systems." Key Engineering Materials 467-469 (February 2011): 174–80. http://dx.doi.org/10.4028/www.scientific.net/kem.467-469.174.

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Actually modern systems have to ensure higher and higher operating standards, thus including monitoring and control subsystems for their achievement. In safety critical systems control is a crucial task in order to satisfy strict reliability requirements. But it is also necessary that the control system is itself reliable. As a consequence, adequate techniques are necessary in order to perform reliability evaluation of both the controlled and the control systems. Techniques that therefore should avoid over-simplistic assumptions and/or approximations that, for example, are usually introduced when dependencies, interferences and other dynamic reliability aspects are not taken into the right consideration. In this paper, a technique for carefully evaluating the reliability of such systems, also considering dynamic aspects and behaviors, is proposed. Firstly the technique is detailed through the specification of the dynamic reliability block diagrams notation, and therefore in order to demonstrate its effectiveness, it is applied to an example of a computing-based control system taken from literature, thus providing guidelines for the reliability representation and evaluation through DRBD.
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Shpiganovich, A., V. Zatsepina, A. Shpiganovich, and V. Stepanov. "Power-Supply Systems Reliability Control." EAI Endorsed Transactions on Energy Web 5, no. 19 (July 10, 2018): 155083. http://dx.doi.org/10.4108/eai.10-7-2018.155083.

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Udrişte, Constantin, Ionel Ţevy, and Saad Abbas Abed. "OPTIMAL CONTROL ON RELIABILITY PROBLEMS." Far East Journal of Dynamical Systems 29, no. 2-3 (January 25, 2018): 97–115. http://dx.doi.org/10.17654/ds029230097.

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Dissertations / Theses on the topic "Control reliability"

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Steel, Donald. "Software reliability prediction." Thesis, Abertay University, 1990. https://rke.abertay.ac.uk/en/studentTheses/4613ff72-9650-4fa1-95d1-1a9b7b772ee4.

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The aim of the work described in this thesis was to improve NCR's decision making process for progressing software products through the development cycle. The first chapter briefly describes the software development process at NCR, detailing documentation review and software testing techniques. The objectives and reasons for investigating software reliability models as a tool in the decision making process are outlined. There follows a short review of software reliability models, with the Littlewood and Verrall Bayesian model considered in detail. The difficulties in using this model to obtain estimates for model parameters and time to next failure are described. These estimation difficulties exist using the model on good datasets, in this case simulated failure data, and the difficulties are compounded when used with real failure data. The problems of collecting and recording failure data are outlined, highlighting the inadequacies of these collected data, and real failure data are analysed. Software reliability models are used in an attempt to quantify the reliability of real software products. The thesis concludes by summarising the problems encountered when using reliability models to measure software products and suggests future research into metrics that are required in this area of software engineering.
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Wickstrom, Larry E. "Reliability of Electronics." Thesis, University of North Texas, 2014. https://digital.library.unt.edu/ark:/67531/metadc700024/.

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The purpose of this research is not to research new technology but how to improve existing technology and understand how the manufacturing process works. Reliability Engineering fall under the category of Quality Control and uses predictions through statistical measurements and life testing to figure out if a specific manufacturing technique will meet customer satisfaction. The research also answers choice of materials and choice of manufacturing process to provide a device that will not only meet but exceed customer demand. Reliability Engineering is one of the final testing phases of any new product development or redesign.
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Shi, Yuan Feng. "Reliability-based semi-active control using MR damper." Thesis, University of Macau, 2004. http://umaclib3.umac.mo/record=b1445126.

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Olsen, James Jonathan. "Control and reliability of optical networks in multiprocessors." Thesis, Massachusetts Institute of Technology, 1993. http://hdl.handle.net/1721.1/37180.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1993.
Includes bibliographical references (leaves 138-142).
by James Jonathan Olsen.
Ph.D.
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Olteanu, Denisa Anca. "Cumulative Sum Control Charts for Censored Reliability Data." Diss., Virginia Tech, 2010. http://hdl.handle.net/10919/26665.

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Companies routinely perform life tests for their products. Typically, these tests involve running a set of products until the units fail. Most often, the data are censored according to different censoring schemes, depending on the particulars of the test. On occasion, tests are stopped at a predetermined time and the units that are yet to fail are suspended. In other instances, the data are collected through periodic inspection and only upper and lower bounds on the lifetimes are recorded. Reliability professionals use a number of non-normal distributions to model the resulting lifetime data with the Weibull distribution being the most frequently used. If one is interested in monitoring the quality and reliability characteristics of such processes, one needs to account for the challenges imposed by the nature of the data. We propose likelihood ratio based cumulative sum (CUSUM) control charts for censored lifetime data with non-normal distributions. We illustrate the development and implementation of the charts, and we evaluate their properties through simulation studies. We address the problem of interval censoring, and we construct a CUSUM chart for censored ordered categorical data, which we illustrate by a case study at Becton Dickinson (BD). We also address the problem of monitoring both of the parameters of the Weibull distribution for processes with right-censored data.
Ph. D.
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KALEEM, ULLAH MUHAMMAD. "INVENTORY CONTROL SYSTEM : Optimization of production system and reliability." Thesis, Högskolan Dalarna, Datateknik, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:du-4781.

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The main idea of this research to solve the problem of inventory management for the paper industry SPM PVT limited. The aim of this research was to find a methodology by which the inventory of raw material could be kept at minimum level by means of buffer stock level.The main objective then lies in finding the minimum level of buffer stock according to daily consumption of raw material, finding the Economic Order Quantity (EOQ) reorders point and how much order will be placed in a year to control the shortage of raw material.In this project, we discuss continuous review model (Deterministic EOQ models) that includes the probabilistic demand directly in the formulation. According to the formula, we see the reorder point and the order up to model. The problem was tackled mathematically as well as simulation modeling was used where mathematically tractable solution was not possible.The simulation modeling was done by Awesim software for developing the simulation network. This simulation network has the ability to predict the buffer stock level based on variable consumption of raw material and lead-time. The data collection for this simulation network is taken from the industrial engineering personnel and the departmental studies of the concerned factory. At the end, we find the optimum level of order quantity, reorder point and order days.
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Meyer, Tobias [Verfasser]. "Optimization-based reliability control of mechatronic systems / Tobias Meyer." Paderborn : Universitätsbibliothek, 2016. http://d-nb.info/1124894802/34.

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Yang, Luo. "RELIABILITY-BASED DESIGN AND QUALITY CONTROL OF DRIVEN PILES." University of Akron / OhioLINK, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=akron1153755606.

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Wikman, Thomas Stig. "Reflex control for robot system preservation, reliability, and autonomy." Case Western Reserve University School of Graduate Studies / OhioLINK, 1994. http://rave.ohiolink.edu/etdc/view?acc_num=case1057936323.

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Raghu, Anand. "Prediction of workpiece location as a function of fixture-induced errors." Thesis, Georgia Institute of Technology, 2003. http://hdl.handle.net/1853/18840.

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Books on the topic "Control reliability"

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P, Basu Asit, and Proschan Frank 1921-, eds. Reliability and quality control. Amsterdam: North-Holland, 1986.

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Cluley, J. C. Reliability in instrumentationand control. Oxford: Butterworth-Heinemann in association with the Institute of Measurement and Control, 1993.

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Tobias, Paul A. Applied reliability. 2nd ed. New York: Van Nostrand Reinhold, 1995.

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Tobias, Paul A. Applied reliability. New York: Van Nostrand Reinhold, 1986.

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Goble, William M. Control systems safety and reliability. 2nd ed. Research Triangle Park, N.C: ISA, 1998.

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Reliability in instrumentation and control. Oxford: Butterworth-Heinemann, 1992.

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Tobias, Paul A. Applied reliability. 3rd ed. Boca Raton: Chapman and Hall/CRC, 2011.

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Goble, William M. Control systems safety evaluation and reliability. 3rd ed. Research Triangle Park, NC: International Society of Automation, 2010.

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Hollnagel, Erik. Human reliability analysis: Context and control. London: Academic Press, 1993.

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Hiromitsu, Kumamoto, ed. Designing for reliability and safety control. Englewood Cliffs, N.J: Prentice-Hall, 1985.

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Book chapters on the topic "Control reliability"

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Stout, Ken. "Reliability." In Quality Control in Automation, 157–69. Boston, MA: Springer US, 1985. http://dx.doi.org/10.1007/978-1-4684-7499-2_12.

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Amasaka, Kakuro. "Reliability Design." In Science SQC, New Quality Control Principle, 197–216. Tokyo: Springer Japan, 2004. http://dx.doi.org/10.1007/978-4-431-53969-8_12.

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Birolini*, Alessandro. "Statistical Quality Control & Reliability Tests." In Reliability Engineering, 291–348. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14952-8_7.

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Birolini, Alessandro. "Statistical Quality Control and Reliability Tests." In Reliability Engineering, 299–356. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39535-2_7.

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Birolini, Alessandro. "Statistical Quality Control & Reliability Tests." In Reliability Engineering, 311–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2017. http://dx.doi.org/10.1007/978-3-662-54209-5_7.

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Gertsbakh, Ilya. "Preventive Maintenance Based on Parameter Control." In Reliability Theory, 107–37. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-662-04236-6_5.

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Birolini, Alessandro. "Statistical Quality Control and Reliability Tests." In Reliability Engineering, 239–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-662-03792-8_7.

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Birolini, Alessandro. "Statistical Quality Control and Reliability Tests." In Reliability Engineering, 259–301. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-05409-3_7.

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Prendergast, James, and Eamonn Murphy. "Building In Reliability. Is Statistical Process Control Statistical Reliability Control ?" In Frontiers in Statistical Quality Control 7, 319–34. Heidelberg: Physica-Verlag HD, 2004. http://dx.doi.org/10.1007/978-3-7908-2674-6_21.

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Qamber, Isa S. "Power Systems Reliability." In Power Systems Control and Reliability, 65–124. Includes bibliographical references and index.: Apple Academic Press, 2020. http://dx.doi.org/10.1201/9780429287015-5.

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Conference papers on the topic "Control reliability"

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Komoto, Takafumi, Kenji Taguchi, Haralambos Mouratidis, Nobukazu Yoshioka, and Kokichi Futatsugi. "A Modelling Framework to Support Internal Control." In Reliability Improvement Companion. IEEE, 2011. http://dx.doi.org/10.1109/ssiri-c.2011.33.

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Field, R. V., and L. A. Bergman. "Reliability-based covariance control design." In Proceedings of 16th American CONTROL Conference. IEEE, 1997. http://dx.doi.org/10.1109/acc.1997.611745.

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Liu, Yong, Qiangfu Zhao, and Neil Yen. "Variation aware control for reliability." In 2012 IEEE International Conference on Systems, Man and Cybernetics - SMC. IEEE, 2012. http://dx.doi.org/10.1109/icsmc.2012.6378245.

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Yastrebenetsky, Mikhail A., Alexander A. Siora, and Volodymyr I. Tokarev. "Reliability of Reactor Control Digital Systems." In 17th International Conference on Nuclear Engineering. ASMEDC, 2009. http://dx.doi.org/10.1115/icone17-75157.

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In spite of wide expansion of digital systems for NPP control, information about realistic operating reliability measures of these systems is still lacking. The paper is a continuation of report [1] and contains results of analysis of operating reliability of digital control systems used in Ukrainian nuclear power plants. This paper contains: - reliability measures of digital instrumentation and control systems of first generation (designed in 1979–1983); - reliability measures of digital reactor control systems of second generation (designed after 2000) including reasons of failures; - analysis of the reasons of NPP violations due to digital instrumentation and control systems.
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Herbst, Scott A. "Maximizing Electronic Control Unit (ECU) Reliability." In 1989 SAE International Off-Highway and Powerplant Congress and Exposition. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 1989. http://dx.doi.org/10.4271/891889.

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Radevski, V., and Y. Bennani. "Reliability control in committee classifier environment." In Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium. IEEE, 2000. http://dx.doi.org/10.1109/ijcnn.2000.861369.

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Hwang, Woongi, and Woon-sung Lee. "Reliability Improvement of Global Chassis Control." In 2006 SICE-ICASE International Joint Conference. IEEE, 2006. http://dx.doi.org/10.1109/sice.2006.315580.

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Soltan, Hassan. "Advances in Control Charts for Reliability." In 2019 Industrial & Systems Engineering Conference (ISEC). IEEE, 2019. http://dx.doi.org/10.1109/iasec.2019.8686608.

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Yin, Xiandong, Yongbo Li, Zhenzhu Liu, Xinmei Wang, and Zijia Hu. "On Engine Reliability Test Control System." In 2018 37th Chinese Control Conference (CCC). IEEE, 2018. http://dx.doi.org/10.23919/chicc.2018.8483690.

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Boring, Ronald L., and David I. Gertman. "A resilience-reliability crosswalk." In 2010 3rd International Symposium on Resilient Control Systems (ISRCS). IEEE, 2010. http://dx.doi.org/10.1109/isrcs.2010.5602486.

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Reports on the topic "Control reliability"

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Trudnowski, Daniel. Load Control System Reliability. Office of Scientific and Technical Information (OSTI), April 2015. http://dx.doi.org/10.2172/1261634.

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Bertoni, Mariana, and David Fenning. Defect Kinetics and Control for Module Reliability. Office of Scientific and Technical Information (OSTI), December 2020. http://dx.doi.org/10.2172/1844430.

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Stewart, R., M. Ramalho, Q. Xie, M. Tuexen, and P. Conrad. Stream Control Transmission Protocol (SCTP) Partial Reliability Extension. RFC Editor, May 2004. http://dx.doi.org/10.17487/rfc3758.

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Somayaji, Anil B., Wendy A. Amai, and Eleanor A. Walther. An immunological basis for high-reliability systems control. Office of Scientific and Technical Information (OSTI), March 2005. http://dx.doi.org/10.2172/922089.

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Cinlar, E. Markov Processes Applied to Control, Reliability and Replacement. Fort Belvoir, VA: Defense Technical Information Center, April 1989. http://dx.doi.org/10.21236/ada208634.

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Ong, Ghim Ping (Raymond), Samy Noureldin, and Kumares Sinha. Automated Pavement Condition Data Collection Quality Control, Quality Assurance, and Reliability. West Lafayette, Indiana: Purdue University, 2011. http://dx.doi.org/10.5703/1288284314288.

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Susanna P. Gordon, Richard M. Wheeler, Timothy J. Sa, and Donald A. Sheaffer. Optimization of Automatic Train Control for Energy Management and Service Reliability. Office of Scientific and Technical Information (OSTI), November 1999. http://dx.doi.org/10.2172/750893.

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Rosenkrantz, W. A. Mathematical Problems in Stability, Control and Reliability of Random Access Communication Systems. Fort Belvoir, VA: Defense Technical Information Center, June 1985. http://dx.doi.org/10.21236/ada159402.

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Melin, Alexander M., Roger A. Kisner, and David L. Fugate. Embedded Sensors and Controls to Improve Component Performance and Reliability - System Dynamics Modeling and Control System Design. Office of Scientific and Technical Information (OSTI), October 2013. http://dx.doi.org/10.2172/1185367.

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Wang, Wensheng. Advanced SOFC quality control and the role of manufacturing defects on stack reliability. Office of Scientific and Technical Information (OSTI), March 2018. http://dx.doi.org/10.2172/1430240.

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