Relevant bibliographies by topics / Goal-oriented

Academic literature on the topic 'Goal-oriented'

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Published: 4 June 2021
Last updated: 3 February 2022

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Journal articles on the topic "Goal-oriented"

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Horita, Hiroki, and Junji Noguchi. "Fairness Analysis in Goal-Oriented Requirements Selection." International Journal of Trade, Economics and Finance 11, no. 4 (August 2020): 77–82. http://dx.doi.org/10.18178/ijtef.2020.11.4.670.

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Sedvalde, Inara, and Marite Kirikova. "Goal-Oriented Workflows." IFAC Proceedings Volumes 33, no. 12 (June 2000): 83–85. http://dx.doi.org/10.1016/s1474-6670(17)37282-8.

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Gannon, Frank. "Goal‐oriented research." EMBO reports 4, no. 12 (December 2003): 1103. http://dx.doi.org/10.1038/sj.embor.7400039.

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Palanca, Javier, Martí Navarro, Vicente Julian, and Ana García-Fornes. "Distributed goal-oriented computing." Journal of Systems and Software 85, no. 7 (July 2012): 1540–57. http://dx.doi.org/10.1016/j.jss.2012.01.045.

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Bower, Bruce. "Goal-Oriented Brain Cells." Science News 167, no. 18 (April 30, 2005): 278. http://dx.doi.org/10.2307/4016181.

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Singer, Gregory M., Munavvar Izhar, and Henry R. Black. "Goal-Oriented Hypertension Management." Hypertension 40, no. 4 (October 2002): 464–69. http://dx.doi.org/10.1161/01.hyp.0000035858.04434.03.

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Illathuparampil, Mathew. "Goal-Oriented Ethics: Framing the Goal-Setting Concretely." Religions 8, no. 10 (October 17, 2017): 228. http://dx.doi.org/10.3390/rel8100228.

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Mylopoulos, John, Lawrence Chung, and Eric Yu. "From object-oriented to goal-oriented requirements analysis." Communications of the ACM 42, no. 1 (January 1999): 31–37. http://dx.doi.org/10.1145/291469.293165.

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Igusa, Go. "Goal Oriented Rational Behavior on Paid Holidays for Female Workers." Annual review of sociology 2011, no. 24 (2011): 50–61. http://dx.doi.org/10.5690/kantoh.2011.50.

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Brown, Kurt P., Michael J. Carey, and Miron Livny. "Goal-oriented buffer management revisited." ACM SIGMOD Record 25, no. 2 (June 1996): 353–64. http://dx.doi.org/10.1145/235968.233351.

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Dissertations / Theses on the topic "Goal-oriented"

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Chau, Man Ping Grace. "Goal-oriented hardware design." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/45853.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.
Includes bibliographical references (p. 145-146).
This thesis presents Fide, a hardware design system that uses Goal-oriented programming. Goal-oriented programming is a programming framework to specify open-ended decision logic. This approach relies on two fundamental concepts-Goals and Techniques. Goals encode decision points and Techniques are scripts that describe how to satisfy Goals. In Fide, Goals represent the functional requirements (e.g., addition of two 32-bit binary integers) of the target circuit. Techniques represent hardware implementation alternatives that fulfill the functions. Techniques may declare their own subgoals, allowing a hierarchical decomposition of the functions. A Planner selects among Techniques based on the Goals declared to generate an implementation of the target circuit automatically. Users' preferences can be added to generate circuits for different scenarios: for different hardware environments, under different circuit constraints, or different implementation criteria etc. A Beta processor is implemented using Fide. The quality of the implementation is comparable to those optimized manually.
by Man Ping Grace Chau.
S.M.
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Williamson, Victor Lamont. "Goal-oriented Web search." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/61247.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 57-58).
We have designed and implemented a Goal-oriented Web application to search videos, images, and news by querying YouTube, Truveo, Google and Yahoo search services. The Planner module decomposes functionality in Goals and Techniques. Goals declare searches for specific types of content and Techniques query the various Web services. We choose which Web service has the best rating at runtime and return the winning results. Users weight their preferred Web services and declare a repository of their own Techniques to upload and execute.
by Victor Lamont Williamson.
M.Eng.
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Berg, Emanuel. "Goal-Oriented Collision-Free Schedule." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-198097.

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The education of to-be physicians at Akademiska sjukhuset, Uppsala, includes practical services. The students are divided into groups that each has its own goals. The goals specify (1) what services that group's students should perform, and (2) for each service, a minimum number of times each student should attend that service. It is only possible to perform any service at certain occasions: each occasion offers slots, to be filled by students. The occasions make up a calendar. The challenge is to distribute the students over the calendar, so that the goal isa chieved for each student and service. No occasion is overpopulated, and no student is due to attend two (or more) occasions that collide in time. The algorithm to solve this sets up a table with occasions (expanded horizontally by their number of slots) as the x-axis, and dates (expanded vertically by two: the day parts) as the y-axis. Then, distribution of students is done top-down, left-right. Collision is avoided by having students only appear once per row. Overpopulation won't happen as the allocation of students is done explicitly to slots, not to occasionsin general. MS Access forms make up the UI. My thoughts when I set them up was that each form should boil down to a single purpose, but include everything to fulfill that purpose (and nothing else). Also, I setup an intuitive flow of movements between forms, and I made an effort to setup mnemonic shortcuts (and tab chains) as to minimize mouse use
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Baumslag, David J. H. "A goal-oriented theory of science." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq24524.pdf.

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Materna, Daniel. "Goal-oriented recovery bei nichtlinearen Scheibenproblemen." Kassel Univ.-Bibliothek, 2006. https://kobra.bibliothek.uni-kassel.de/handle/urn:nbn:de:hebis:34-2006111515696.

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Heidrich, Jens. "Goal-oriented quantitative software project control." Stuttgart Fraunhofer-IRB-Verl, 2008. http://d-nb.info/993138713/04.

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Li, Harriet. "Model adaptivity for goal-oriented inference." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/101440.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2015.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 47-49).
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
In scientific and engineering contexts, physical systems are represented by mathematical models, characterized by a set of parameters. The inverse problem arises when the parameters are unknown and one tries to infer these parameters based on observations. Solving the inverse problem can require many model simulations, which may be expensive for complex models; multiple models of varying fidelity and complexity may be available to describe the physical system. However, inferring the parameters may only be an intermediate step, and what is ultimately desired may be a low-dimensional Quantity of Interest (QoI); we refer to this as the goal-oriented inverse problem. We present a novel algorithm for solving the goal-oriented inverse problem, which allows one to manage the fidelity of modeling choices while solving the inverse problem. We formulate a hierarchy of models, and assume that the QoI obtained by inferring the parameters with the highest-fidelity model is the most accurate QoI. We derive an estimate for the error in the QoI from inferring the parameters using a lower-fidelity model instead of the highest-fidelity model. This estimate can be localized to individual elements of a discretized domain, and this element-wise decomposition can then be used to adaptively form mixed-fidelity models. These mixed-fidelity models can be used to infer the parameters, while controlling the error in the QoI. We demonstrate the method with two pairs of steady-state models in 2D. In one pair, the models differ in the physics included; in the other pair, the models differ in the space to which the parameters belong. In both cases, we are able to obtain a QoI estimate with a small relative error without having to solve the inverse problem with the high-fidelity model. We also demonstrate a case where solving the inverse problem with the high-fidelity model requires a more complex algorithm, but where our method gives a mixed-fidelity model with which we can infer parameters using a simple Newton solver, while achieving a low error in the QoI.
by Harriet Li.
S.M.
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Nilsson, Björn, and John Linder. "Sport Structure and Goal Oriented Motivation." Thesis, Umeå universitet, Institutionen för psykologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-167443.

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The aim of this study was to assess differences in goal orientation between athletes practicing two different sports. These sports differ structurally in how they are performed and in regard to factors influencing goal orientation. The achievement goal theory (AGT) states that individuals’ motivational orientation is made up of two aspects, task oriented motivation and ego oriented motivation. These two goal orientations are independent of one another and are affected by several factors. The effect on goal orientation by factors influenced by coaches, parents and peers is well documented. The two hypotheses were that climbers would be more task-oriented than squash players and secondly that squash players would be more ego-oriented than climbers. The study was conducted as a quasi-experimental between group comparison. 81 sport practitioners, 43 rock climbers and 38 squash players, took the Task and Ego in Sport Questionnaire (TEOSQ) in connection with performing their sport. Independent t-tests of both sub scales showed a difference in ego-motivation between groups, but not in task-motivation.
Syftet med den här studien var fastställa skillnader i målorientering mellan idrottare som utför olika sporter. Dessa sporter skiljer sig strukturellt i utförande och i avseende av faktorer som påverkar målorientering. Målorienteringsteorin (AGT) säger att individers målorientering består av två aspekter, uppgiftsorienterad motivation och egoorienterad motivation. Dessa två orienteringar är oberoende av varandra och påverkas av flera olika faktorer. Effekten på målorientering av faktorer som påverkas av tränare, föräldrar och kamrater är väldokumenterad. Den första hypotesen var att klättrare är mer uppgifts-orienterade än vad squashspelare är och den andra att squashspelare är mer egoorienterade än vad klättrare är. Studien utfördes som en kvasiexperimentell mellangruppsjämförelse. 81 idrottare, 43 klättrare och 38 squashspelare fyllde i formuläret Task and Ego in Sport Questionnaire (TEOSQ) i samband med sin träning. Oberoende t-test visade en skillnad i ego-motivation, men inte i uppgifts-motivation mellan grupperna.
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Abid, Muhammad Rizwan. "UML profile for goal-oriented modelling." Thesis, University of Ottawa (Canada), 2008. http://hdl.handle.net/10393/27616.

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The Unified Modeling Language (UML) is a standard for visual modelling. We can design abstract models by using its elements. Although the semantic scope of UML elements is very broad, it does not fully address the needs of some modelling domains, including the modelling of goals and non-functional requirements (NFR). To address this problem, UML allows the customization of its metamodel with an extension mechanism called UML profile. Some work has already been done in the area of UML profiles for NFR and goals. In some cases, the proposed solutions were incorrectly or only partially integrated with UML. Sometimes, the profiles were based on metamodels whose nature and accuracy for this domain were unclear. In other cases, the profiling approaches taken were not well supported by tools, which have led to unsatisfactory solutions. In this thesis, we propose a UML profile for the Goal-oriented Requirement Language (GRL), a goal/NFR notation undergoing standardization at the International Telecommunication Union. Our profile is based on an abstract metamodel of GRL, which has already been successfully tested and implemented in non-UML tools. This profile is also implemented in a UML 2 tool, namely Telelogic G2 4.0, and is well integrated with the rest of UML. Challenges and design decisions for the concrete support of this profile with tools are discussed along the way. The profiling approach used in this thesis is one that has been recommended by modellers and standards developers. Our profile for goal-oriented modelling is also illustrated and validated with several examples.
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Nagel, Benjamin [Verfasser]. "Goal-oriented business process engineering / Benjamin Nagel." Paderborn : Universitätsbibliothek, 2015. http://d-nb.info/1072463091/34.

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Books on the topic "Goal-oriented"

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Darlington, Jared L. Search direction by goal failure in goal-oriented programming. Sankt Augustin: Gesellschaft fur Mathematik und Datenverarbeitung, 1987.

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Uniting diverse organizations: Managing goal-oriented advocacy networks. New York: Routledge, 2012.

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Stanturf, John, Palle Madsen, and David Lamb, eds. A Goal-Oriented Approach to Forest Landscape Restoration. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-5338-9.

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Illanko, Kandasamy. Extension of goal-oriented synthesis to functions of many variables. Ottawa: National Library of Canada, 1992.

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Swami, S. A. Self-excellence: Key to preventive stress management & goal-oriented living. Montgomery, WV: Minibook Pub. Co., 1987.

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R, Lankton Stephen, ed. Tales of enchantment: Goal-oriented metaphors for adults and children in therapy. New York: Brunner/Mazel, 1989.

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Kim, Jinwon. A cube-based recursive goal-oriented method for the synthesis of Boolean logic functions. Ottawa: National Library of Canada = Bibliothèque nationale du Canada, 1993.

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Spilker, Imke. Empowering horses to learn their way: Through independence, self-confidence and with goal-oriented play. North Pomfret, Vt: Trafalgar Square Books, 2009.

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al-Suwaida, Kerrayem. Developing a study to evaluate the impact of a goal-oriented, protocol-driven program with home telemonitoring on the blood pressure of type 2 diabetic patients with uncontrolled hypertension. Ottawa: National Library of Canada, 2003.

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J, Stumbo Norma, ed. Leisure education III: More goal-oriented activities. State College, Pa: Venture Pub., 1997.

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Book chapters on the topic "Goal-oriented"

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Klir, George J. "Goal-Oriented Systems." In Facets of Systems Science, 171–90. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1331-5_10.

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Gabbay, Dov, and Nicola Olivetti. "Goal-Oriented Deductions." In Handbook of Philosophical Logic, 199–285. Dordrecht: Springer Netherlands, 2002. http://dx.doi.org/10.1007/978-94-017-0464-9_4.

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Klir, George J. "Goal-Oriented Systems." In Facets of Systems Science, 143–61. Boston, MA: Springer US, 1991. http://dx.doi.org/10.1007/978-1-4899-0718-9_10.

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Klir, George J. "Goal-Oriented Systems." In Architecture of Systems Problem Solving, 355–82. Boston, MA: Springer US, 1985. http://dx.doi.org/10.1007/978-1-4757-1168-4_7.

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Sun, Ne-Zheng, and Alexander Sun. "Goal-Oriented Modeling." In Model Calibration and Parameter Estimation, 509–51. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-2323-6_12.

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Freeman, Chris T., Eric Rogers, Jane H. Burridge, Ann-Marie Hughes, and Katie L. Meadmore. "Goal-Oriented Stroke Rehabilitation." In Iterative Learning Control for Electrical Stimulation and Stroke Rehabilitation, 93–116. London: Springer London, 2015. http://dx.doi.org/10.1007/978-1-4471-6726-6_6.

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Conte, Rosaria. "Rational, Goal-Oriented Agents." In Computational Complexity, 2578–93. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-1800-9_158.

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Conte, Rosaria. "Rational, Goal-Oriented Agents." In Encyclopedia of Complexity and Systems Science, 7533–48. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-30440-3_445.

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Nikolaou, Christos. "Goal Oriented Resource Management." In Real Time Computing, 530–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-642-88049-0_37.

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Vianu, Victor, and Gottfried Vossen. "Goal-oriented concurrency control." In MFDBS 89, 398–414. Berlin, Heidelberg: Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/3-540-51251-9_26.

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Conference papers on the topic "Goal-oriented"

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Mylopoulos, J. "Goal-oriented requirements engineering." In 12th Asia-Pacific Software Engineering Conference (APSEC'05). IEEE, 2005. http://dx.doi.org/10.1109/apsec.2005.68.

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Kurt, Binnur, and Muhittin Gokmen. "Goal oriented edge detection." In 2008 23rd International Symposium on Computer and Information Sciences. IEEE, 2008. http://dx.doi.org/10.1109/iscis.2008.4717965.

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Weiss, David M., David Bennett, John Y. Payseur, Pat Tendick, and Ping Zhang. "Goal-oriented software assessment." In the 24th international conference. New York, New York, USA: ACM Press, 2002. http://dx.doi.org/10.1145/581368.581369.

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Kurt, Binnur, and Muhittin Gokmen. "Goal oriented edge detection." In 2008 IEEE 16th Signal Processing, Communication and Applications Conference (SIU). IEEE, 2008. http://dx.doi.org/10.1109/siu.2008.4632679.

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Salhofer, Peter, Gerald Tretter, Bernd Stadlhofer, and FH Joanneum. "Goal-oriented service selection." In the 2nd International Conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1509096.1509110.

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Khaparde, A. R., A. Mokhade, and P. D. Dahiwale. "Goal oriented ranking scheme." In ICWET '10: International Conference and Workshop on Emerging Trends in Technology. New York, NY, USA: ACM, 2010. http://dx.doi.org/10.1145/1741906.1742019.

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Zhang, Huiliang, Zhiqi Shen, and Chunyan Miao. "Enabling Goal Oriented Action Planning with Goal Net." In 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology. IEEE, 2009. http://dx.doi.org/10.1109/wi-iat.2009.162.

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Mavin, Alistair, Philip Wilkinson, Sabine Teufl, Henning Femmer, Jonas Eckhardt, and Jakob Mund. "Does Goal-Oriented Requirements Engineering Achieve Its Goal?" In 2017 IEEE 25th International Requirements Engineering Conference (RE). IEEE, 2017. http://dx.doi.org/10.1109/re.2017.40.

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Lee, Jun H., and Young B. Park. "Goal-Oriented Requirements Analysis Using Goal Accumulation RDF Graph." In 2014 International Conference on Information Science and Applications (ICISA). IEEE, 2014. http://dx.doi.org/10.1109/icisa.2014.6847407.

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Inoue, Wataru, Shinpei Hayashi, Haruhiko Kaiya, and Motoshi Saeki. "Multi-dimensional Goal Refinement in Goal-Oriented Requirements Engineering." In 10th International Conference on Software Engineering and Applications. SCITEPRESS - Science and and Technology Publications, 2015. http://dx.doi.org/10.5220/0005499301850195.

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Reports on the topic "Goal-oriented"

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Paszynski, M., L. Demkowicz, and D. Pardo. Verification of Goal-Oriented HP-Adaptivity. Fort Belvoir, VA: Defense Technical Information Center, January 2005. http://dx.doi.org/10.21236/ada437840.

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Dede, Luca, and Hugo A. Santos. B-spline goal-oriented error estimators for geometrically nonlinear rods. Fort Belvoir, VA: Defense Technical Information Center, April 2011. http://dx.doi.org/10.21236/ada555331.

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Oden, J. R., S. Prudhomme, and P. Bauman. On the Extension of Goal-Oriented Error Estimation and Hierarchical Modeling to Discrete Lattice Models. Fort Belvoir, VA: Defense Technical Information Center, August 2004. http://dx.doi.org/10.21236/ada438255.

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Ginting, Victor. Enabling Predictive Simulation and UQ of Complex Multiphysics PDE Systems by the Development of Goal-Oriented Variational Sensitivity Analysis and A Posteriori Error Estimation Methods. Office of Scientific and Technical Information (OSTI), March 2014. http://dx.doi.org/10.2172/1123410.

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Estep, Donald. Enabling Predictive Simulation and UQ of Complex Multiphysics PDE Systems by the Development of Goal-Oriented Variational Sensitivity Analysis and a-Posteriori Error Estimation Methods. Office of Scientific and Technical Information (OSTI), November 2015. http://dx.doi.org/10.2172/1227282.

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Keith, B., A. Apostolatos, A. Kodakkal, R. Rossi, R. Tosi, B. Wohlmuth, and C. Soriano. D2.3. Adjoint-based error estimation routines. Scipedia, 2021. http://dx.doi.org/10.23967/exaqute.2021.2.022.

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This document presents a simple and ecient strategy for adaptive mesh renement (AMR) and a posteriori error estimation for the transient incompressible Navier{Stokes equations. This strategy is informed by the work of Prudhomme and Oden [22, 23] as well as modern goal-oriented methods such as [5]. The methods described in this document have been implemented in the Kratos Multiphysics software and uploaded to https://zenodo.org [27].1 This document includes: A review of the state-of-the-art in solution-oriented and goal-oriented AMR. The description of a 2D benchmark model problem of immediate relevance to the objectives of the ExaQUte project. The denition and a brief mathematical summary of the error estimator(s). The results obtained. A description of the API.
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Dorr, Andrea, Eva Heckl, and Joachim Kaufmann. Evaluierung des Förderschwerpunkts Talente. KMU Forschung Austria, December 2020. http://dx.doi.org/10.22163/fteval.2020.495.

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With the funding programme Talents, the Federal Ministry for Climate Action, Environment, Energy, Mobility, Innovation and Technology (BMK) supports people in applied research throughout their entire career. The overarching goal is to increase the utilisation of human potential in the application-oriented, scientific and technical RTI sector. The programme objectives are 1) to inspire young people for research and development, 2) to connect researchers with the economic sector, 3)to guarantee equal opportunities for all. Within the framework of three fields of intervention, there are various programme lines: 1) Intervention field Young Talents with the programme lines Internships for Students and Talents Regional, 2) Intervention field Female Talents with the programme lines FEMtech Internships for Female Students, FEMtech Career and FEMtech Career Check for SMEs (2015 and 2016), as well as FEMtech Research Projects; and 3) Intervention field Professional Talents with the programme lines The Austrian Job Exchange for Research, Development and Innovation as well as Career Grants for Interviews, Relocation and Dual Careers in Applied Research. After an interim evaluation in 2014, a final evaluation took place at the end of the programme period (end of 2020). The programme was analysed with regard to its conception, implementation, achievement of objectives and impact. Furthermore, conclusions and recommendations for the further development of the Talents programme have been drawn. The methodological basis of the evaluation is a document analysis, secondary data analysis (FFG monitoring data), interviews with experts, online surveys of funding recipients (FEMtech Career / FEMtech Career Check for SMEs and Career Grants), case studies (FEMtech Career projects) and workshops.
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