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Journal articles on the topic 'Simulation tools'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Grafton, Carl, and Anne Permaloff. "Microcomputer Simulations and Simulation Writing Tools." PS: Political Science and Politics 22, no. 2 (1989): 247. http://dx.doi.org/10.2307/419603.

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Grafton, Carl, and Anne Permaloff. "Microcomputer Simulations and Simulation Writing Tools." PS: Political Science & Politics 22, no. 02 (1989): 247–57. http://dx.doi.org/10.1017/s1049096500030560.

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3

Dean, T. D., and M. J. Haney. "FASTBUS simulation tools." IEEE Transactions on Nuclear Science 39, no. 4 (1992): 910–14. http://dx.doi.org/10.1109/23.159731.

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4

Mukherjee, S. S., S. V. Adve, T. Austin, J. Emer, and P. S. Magnusson. "Performance simulation tools." Computer 35, no. 2 (2002): 38–39. http://dx.doi.org/10.1109/2.982914.

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5

Ritchie, Nicholas W. M. "Spectrum Simulation in DTSA-II." Microscopy and Microanalysis 15, no. 5 (2009): 454–68. http://dx.doi.org/10.1017/s1431927609990407.

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AbstractSpectrum simulation is a useful practical and pedagogical tool. Particularly with complex samples or trace constituents, a simulation can help to understand the limits of the technique and the instrument parameters for the optimal measurement. DTSA-II, software for electron probe microanalysis, provides both easy to use and flexible tools for simulating common and less common sample geometries and materials. Analytical models based on ϕ(ρz) curves provide quick simulations of simple samples. Monte Carlo models based on electron and X-ray transport provide more sophisticated models of a

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McClintock, Edwin, and Zhonghong Jiang. "Technology Tips: Spreadsheets: Powerful Tools for Probability Simulations." Mathematics Teacher 90, no. 7 (1997): 572–79. http://dx.doi.org/10.5951/mt.90.7.0572.

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The Curriculum and Evaluation Standards for School Mathematics (NCTM 1989) recommends that high school students extend their K-8 experiences with simulations and experimental probability to continue to improve their intuition and build more formal concepts of theoretical probability based on these experiences. In keeping with this idea, we use spreadsheets frequently in studying and investigating probabilistic situations with both high school students and preservice mathematics teachers. Our approach includes simulations but goes beyond the simulation process as a way of learning important con

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Moshagen, Thilo. "Convergence of explicitly coupled simulation tools (co-simulations)." Journal of Numerical Mathematics 27, no. 1 (2019): 23–36. http://dx.doi.org/10.1515/jnma-2017-0073.

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Abstract In engineering, it is a common desire to couple existing simulation tools together into one big system by passing information from subsystems as parameters into the subsystems under influence. As executed at fixed time points, this data exchange gives the global method a strong explicit component. Globally, such an explicit co-simulation schemes exchange time step can be seen as a step of an one-step method which is explicit in some solution components. Exploiting this structure, we give a convergence proof for such schemes. As flows of conserved quantities are passed across subsystem

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Kyncl, Jiří. "Digital Factory Simulation Tools." Manufacturing Technology 16, no. 2 (2016): 371–75. http://dx.doi.org/10.21062/ujep/x.2016/a/1213-2489/mt/16/2/371.

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Rauh, Jochen, and Alexander Eichberger. "Coupling of Simulation Tools." IFAC Proceedings Volumes 30, no. 8 (1997): 221–26. http://dx.doi.org/10.1016/s1474-6670(17)43827-4.

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10

Volesky, Bohumil. "Biosorption process simulation tools." Hydrometallurgy 71, no. 1-2 (2003): 179–90. http://dx.doi.org/10.1016/s0304-386x(03)00155-5.

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11

Li, Linyang. "Review of common spiking neural network simulation tools." Applied and Computational Engineering 37, no. 1 (2024): 81–85. http://dx.doi.org/10.54254/2755-2721/37/20230474.

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As a recognized third-generation neural network, the spiking neural network has high concurrency and complexity. Although the degree of research is still far from the previous generation of neural networks, spiking neural networks are excellent in performance and energy consumption. In this paper, the common spiking neural network simulation tools are reviewed. The most frequently used and mentioned tools are NEURON, NEST, and BRAIN. NEURON is more suitable for simulation based on biological applications, pays more attention to biological characteristics, and can support large-scale network si

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Vesa, Dumitru Claudiu, and Ilie Gligorea. "Creating Modeling and Simulation Scenarios Using Dedicated Tools." International conference KNOWLEDGE-BASED ORGANIZATION 26, no. 1 (2020): 172–75. http://dx.doi.org/10.2478/kbo-2020-0027.

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AbstractSimulating military actions has been an important element since ancient times, the fighting ability being intricately connected to training and exercises simulating real combat. The paper describes the steps followed to create a scenario used for online teaching activities during the pandemic, using the facilities of the Centre for Modelling and Simulation of Military Actions. The scenarios were created by using two modelling and simulation software - JCATS (Joint Conflict and Tactical Simulation) for constructive simulation and the VBS (Virtual Battlespace) for virtual simulation.

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Salgado-Conrado, Lizbeth, Carlos Álvarez-Macías, and Bernardo Reyes-Durán. "A Review of Simulation Tools for Thin-Film Solar Cells." Materials 17, no. 21 (2024): 5213. http://dx.doi.org/10.3390/ma17215213.

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Unlike current silicon-based photovoltaic technology, the development of last-generation thin-film solar cells has been marked by groundbreaking advancements in new materials and novel structures to increase performance and lower costs. However, physically building each new proposal to evaluate the device’s efficiency can involve unnecessary effort and time. Numerical simulation tools provide a solution by allowing researchers to predict and optimize solar cell performance without physical testing. This paper reviews thirteen of the main numerical simulation tools for thin-film solar cells, in

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14

Ikeda, Makoto, Leonard Barolli, Giuseppe De Marco, Tao Yang, Arjan Durresi, and Fatos Xhafa. "Tools for Performance Assessment of OLSR Protocol." Mobile Information Systems 5, no. 2 (2009): 165–76. http://dx.doi.org/10.1155/2009/782684.

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In this paper, we evaluate the performance of Optimized Link State Routing (OLSR) protocol by experimental and simulation results. The experiments are carried out by using our implemented testbed and the simulations by using ns-2 simulator. We also designed and implemented a new interface for the ad-hoc network testbed in order to make more easier the experiments. The comparison between experimental and simulation results shows that for the same parameters set, in the simulation we did not notice any packet loss. On the other hand, in the experiments we experienced packet loss because of the e

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Binder, M., F. Klocke, and D. Lung. "Tool wear simulation of complex shaped coated cutting tools." Wear 330-331 (May 2015): 600–607. http://dx.doi.org/10.1016/j.wear.2015.01.015.

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EUGUI, Iñigo, Rita CAMINAL BARDERI, Joan SAPENA, and Ainara GUIRAL. "Advancements in railway noise simulation: assessment of tools for virtual validation." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 270, no. 6 (2024): 5436–45. http://dx.doi.org/10.3397/in_2024_3592.

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In pursuit of more efficient and cost-effective measurement campaigns, the railway industry is embracing the prospect of virtual validation based on simulations. The primary obstacle lies in the need for noise simulation tools capable of accurately replicating complex sources, installation effects, noise propagation, ground reflection and, for some cases, the diffraction in the different train surfaces. This paper presents insights derived from European project Shift2Rail S2R-CFM-CCA-01-2019 Energy and Noise & Vibration WP6, concentrating on the validation and enhancement of two exterior n

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Semerikov, Serhiy, Illia Teplytskyi, Yuliia Yechkalo, Oksana Markova, Vladimir Soloviev, and Arnold Kiv. "Using spreadsheets as learning tools for neural network simulation." Ukrainian Journal of Educational Studies and Information Technology 10, no. 3 (2022): 42–68. http://dx.doi.org/10.32919/uesit.2022.03.04.

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The article supports the need for training techniques for neural network computer simulations in a spreadsheet context. Their use in simulating artificial neural networks is systematically reviewed. The authors distinguish between fundamental methods for addressing the issue of network computer simulation training in the spreadsheet environment, joint application of spreadsheets and tools for neural network simulation, application of third-party add-ins to spreadsheets, development of macros using embedded languages of spreadsheets, use of standard spreadsheet add-ins for non-linear optimizati

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18

Marcelino, Maria. "HME: a Handheld Model Editor for Educational Contexts." JUCS - Journal of Universal Computer Science 13, no. (7) (2007): 1012–21. https://doi.org/10.3217/jucs-013-07-1012.

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Handheld devices are becoming more and more popular in education. Educational simulation and modelling are not new soil, but for handhelds they are still much under explored. Due to the difficulties teachers usually face in developing computer models and simulations and the lack of adequate tools for building them, we developed an authoring-tool for handheld educational simulation and modelling, called Sim-H (SIMulation for Handhelds). Sim-H is made by several modules each one relating to a type of simulation application that can be used in an educational context. One of these modules, that we

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19

Jones, J. W. "MODEL INTEGRATION AND SIMULATION TOOLS." Acta Horticulturae, no. 456 (March 1998): 411–18. http://dx.doi.org/10.17660/actahortic.1998.456.50.

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20

Pike, A. W., and M. A. Johnson. "Simulation Tools for the 90s." Measurement and Control 27, no. 6 (1994): 185–94. http://dx.doi.org/10.1177/002029409402700602.

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21

Stanton, Scott. "Electric vehicle Development simulation tools." ATZelektronik worldwide 8, no. 6 (2013): 50–53. http://dx.doi.org/10.1365/s38314-013-0213-7.

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22

Yamamoto, H., M. Barton, B. Bhawal, M. Evans, and S. Yoshida. "Simulation tools for future interferometers." Journal of Physics: Conference Series 32 (March 2, 2006): 398–403. http://dx.doi.org/10.1088/1742-6596/32/1/061.

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23

Neureuther, A. R. "Process modeling and simulation tools." Journal of Vacuum Science & Technology A: Vacuum, Surfaces, and Films 4, no. 3 (1986): 912–15. http://dx.doi.org/10.1116/1.574006.

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24

Thode, Henry C. "Statistical Tools for Simulation Practitioners." Technometrics 30, no. 4 (1988): 464. http://dx.doi.org/10.1080/00401706.1988.10488459.

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25

Rahman, Muhammad Azizur, Algirdas Pakštas, and Frank Zhigang Wang. "Network modelling and simulation tools." Simulation Modelling Practice and Theory 17, no. 6 (2009): 1011–31. http://dx.doi.org/10.1016/j.simpat.2009.02.005.

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26

Razzano, Massimiliano. "Pulsar simulation tools for GLAST." Astrophysics and Space Science 309, no. 1-4 (2007): 235–39. http://dx.doi.org/10.1007/s10509-007-9451-6.

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27

Pfeiffer, A., and M. G. Pia. "Tools for simulation and analysis." Nuclear Physics B - Proceedings Supplements 117 (April 2003): 888–90. http://dx.doi.org/10.1016/s0920-5632(03)90699-2.

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28

Tizzard, Keith, and J. P. C. Kleijnen. "Statistical Tools for Simulation Practitioners." Journal of the Royal Statistical Society. Series A (Statistics in Society) 151, no. 2 (1988): 374. http://dx.doi.org/10.2307/2982781.

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29

Delzendeh, E., R. Alaaeddine, M. Al-Adhami, and S. Wu. "Beyond Building Energy Simulation Tools." IOP Conference Series: Earth and Environmental Science 588 (November 21, 2020): 022044. http://dx.doi.org/10.1088/1755-1315/588/2/022044.

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30

Malygina, Svetlana N., and Elena O. Neupokoeva. "Overview of modern simulation tools." Transactions of the Kоla Science Centre of RAS. Series: Engineering Sciences 13, no. 2/2022 (2022): 134–43. http://dx.doi.org/10.37614/2949-1215.2022.13.2.013.

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The article deals with domestic environments for the development of simulation models. The software products, available to Russian users, are compared with each other, as well as with the most popular Anylogic development tool. The comparison is carried out according to the selection criteria identified in the course of multi-year experience.

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31

Oron, Gideon. "Statistical tools for simulation practitioners." European Journal of Operational Research 34, no. 2 (1988): 240–41. http://dx.doi.org/10.1016/0377-2217(88)90361-x.

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32

Reinhart, William F., and Charles J. C. Lloyd. "A Human Factors Simulation Tool for Stereoscopic Displays." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 38, no. 19 (1994): 1290–94. http://dx.doi.org/10.1177/154193129403801909.

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The increasing complexity of advanced display systems places increased importance on simulation tools. Display simulation tools allow the development of displays with the desired balance of image quality and end cost while reducing the time and cost otherwise associated with iterative testing of physical prototypes. In addition, simulation offers the advantage of permitting demonstration of display systems to targeted users and decision makers early in the display design cycle, allowing for more feedback in the design process. A new display simulation tool is described in this paper with the c

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33

Mosca, Ivan. "From Fiction to Reality and Back." International Journal of Gaming and Computer-Mediated Simulations 5, no. 1 (2013): 13–31. http://dx.doi.org/10.4018/jgcms.2013010102.

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The relation between games and simulations can be profitably investigated by combining ontological tools and recent neurological findings. Neurology shows that simulations are connected to fiction or to reality by a suspension of disbelief or alternatively a suspension of belief, and ontological categories of Mimesis (simulation of an event or an object) and Catharsis (simulation of the experience of an event or object) lead to a classification of ludic simulations, which allow to discover some of their hidden properties. This paper raises some new issues for the field, like Embodied Simulatio

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BIAŁASZ, Sebastian, and Ramon PAMIES. "NUMERICAL SIMULATION OF THE DESIGN OF EXTRUSION PROCESS OF POLYMERIC MINI-TUBES." Applied Computer Science 14, no. 3 (2018): 81–95. http://dx.doi.org/10.35784/acs-2018-23.

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In this paper we represent a study reporting the numerical simulation of small-diameter pipes extrusion process. Polypropylene and low density polyethylene were chosen as plastics and a selected transverse head as a tool in the simulations. The aim of the study is to examine the distribution of temperature in the individual sections of the bagasse and tools, in order to optimize the parameters and process flow extrusion and validate the implementation tools, by simulating the flow of plastic by the head.

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35

Reeve, Samuel Temple, David M. Guzman, Lorena Alzate-Vargas, Benjamin Haley, Peilin Liao, and Alejandro Strachan. "Online simulation powered learning modules for materials science." MRS Advances 4, no. 50 (2019): 2727–42. http://dx.doi.org/10.1557/adv.2019.287.

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Simulation tools are playing an increasingly important role in materials science and engineering and beyond their well established importance in research and development, these tools have a significant pedagogical potential. We describe a set of online simulation tools and learning modules designed to help students explore important concepts in materials science where hands-on activities with high-fidelity simulations can provide insight not easily acquired otherwise. The online tools, which involve density functional theory and molecular dynamics simulations, have been designed with non-exper

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Khan, Muhammad A. A., and Anwar K. Sheikh. "Virtual Casting: State of the Art in Metal Casting Simulation Tools." Journal of Engineering Research [TJER] 15, no. 2 (2018): 142. http://dx.doi.org/10.24200/tjer.vol15iss2pp142-154.

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The demands on the productivity and robustness of metal casting processes for high quality components are continuously increasing. Moreover, the financial considerations necessitate meticulous and reliable planning of the entire casting process before it is actually put into practice. A holistic approach to perform cradle to grave analysis of cast products is simulation-based metal casting. This method allows engineers to model, verify, and validate the process followed by its optimization and performance prediction in virtual reality. This paper provides insights on state of the art in simula

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Khan, Muhammad A. A., and Anwar K. Sheikh. "Virtual Casting: State of the Art in Metal Casting Simulation Tools." Journal of Engineering Research [TJER] 15, no. 2 (2018): 42. http://dx.doi.org/10.24200/tjer.vol15iss2pp42-54.

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The demands on the productivity and robustness of metal casting processes for high quality components are continuously increasing. Moreover, the financial considerations necessitate meticulous and reliable planning of the entire casting process before it is actually put into practice. A holistic approach to perform cradle to grave analysis of cast products is simulation-based metal casting. This method allows engineers to model, verify, and validate the process followed by its optimization and performance prediction in virtual reality. This paper provides insights on state of the art in simula

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JØRGENSEN, KATARINA M., and PAULINE C. HADDOW. "VISUALIZATION IN SIMULATION TOOLS: REQUIREMENTS AND A TOOL SPECIFICATION TO SUPPORT THE TEACHING OF DYNAMIC BIOLOGICAL PROCESSES." Journal of Bioinformatics and Computational Biology 09, no. 04 (2011): 579–95. http://dx.doi.org/10.1142/s0219720011005410.

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Simulation tools are playing an increasingly important role behind advances in the field of systems biology. However, the current generation of biological science students has either little or no experience with such tools. As such, this educational glitch is limiting both the potential use of such tools as well as the potential for tighter cooperation between the designers and users. Although some simulation tool producers encourage their use in teaching, little attempt has hitherto been made to analyze and discuss their suitability as an educational tool for noncomputing science students. In

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Fischer, Andreas, Thomas Semm, Michael F. Zäh, Daniel Spescha, and Nino Ceresa. "Der digitale Zwilling der Werkzeugmaschine/The digital twin of machine tools." wt Werkstattstechnik online 111, no. 03 (2021): 179–83. http://dx.doi.org/10.37544/1436-4980-2021-03-87.

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Der digitale Zwilling als Modell gewinnt sowohl für die Entwicklung neuer Maschinengenerationen als auch für Simulationen parallel zum Betrieb stark an Bedeutung. Zur Erstellung entsprechender Modelle sind moderne flexible Mehrkörpersimulationsprogramme besonders geeignet. Im Rahmen dieses Beitrags wird die Simulationsumgebung MORe präsentiert, die sich unter anderem durch ihre Benutzerfreundlichkeit und ihre Recheneffizienz auszeichnet. Zudem ist die Berücksichtigung von Effekten möglich, die bisher im industriellen Umfeld kaum betrachtet wurden, wie beispielsweise Dämpfung. &nbsp; The di

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Eremin, Alexey A., Vladimir S. Makarov, Igor O. Leushin, and Anatoly D. Ryabtsev. "Application of Software Tools for Simulation of Hot Isostatic Pressing." Key Engineering Materials 910 (February 15, 2022): 284–89. http://dx.doi.org/10.4028/p-1wh231.

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This paper is devoted to hot isostatic pressing (HIP). HIP capsule simulation software has been analysed. Abaqus has been proved the most appropriate simulation tool, which enables finite element analysis and allows users to define their own material properties via a special control program. Specific features of HIP simulation models have been evaluated. Powder densification model has been analysed and implemented. A control program has been developed to factor in elastic and plastic material behaviour. Preliminary simulations have been run for a one-element model and a capsule model.

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Angolia, Mark, and April Helene Reed. "A case for early semester utilization of business simulations." Journal of Applied Research in Higher Education 11, no. 1 (2019): 90–101. http://dx.doi.org/10.1108/jarhe-07-2017-0082.

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PurposeThe purpose of this paper is to encourage the use of simulations early in a semester, rather than as a course capstone activity, in an effort to utilize simulations as a foundational experience. The intent is to support teaching and learning, as opposed to using simulations as a capstone assignment or assessment tool.Design/methodology/approachA comprehensive literature review synthesizing higher education business simulation effectivity and evaluation methods provides support for the analysis of 60 undergraduate supply chain management students and 96 surveys conducted over two years.

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42

Alberto Canesin, Carlos, Flávio Alessandro Serrão Gonçalves, and Leonardo Poltronieri Sampaio. "Simulation Tools For Power Electronics Courses, Based In Java Technologies." Eletrônica de Potência 13, no. 2 (2008): 91–98. http://dx.doi.org/10.18618/rep.2008.2.091098.

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Mori, Kotaro, Daisuke Kono, Iwao Yamaji, and Atsushi Matsubara. "Support Placement for Machine Tools Using Stiffness Model." International Journal of Automation Technology 9, no. 6 (2015): 680–88. http://dx.doi.org/10.20965/ijat.2015.p0680.

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The support stiffness model and the stiffness tuning technique are applied to a practical situation. The support stiffness model is integrated with finite element analysis (FEA) to simulate the rocking vibration mode. The support stiffness of a machining center prototype is calculated based on the support stiffness model. The stiffness tuning technique is used to determine the placement of support structures in the simulation. The calculated support stiffness is integrated into a three dimensional model as springs. Rocking vibration modes are obtained from simulations by using the support stif

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Chávez, José Antonio Manco Chávez, Joel Núñez Mejía, Haydeé Verónica Túllume Huayanay, Daniel Enrique Terrones Rojas, Rolando Juan Borja Torres, and Carlos Héctor Cerna Gonzales. "Simulation of magnetic field produced by induction in toroid and solenoid using GeoGebra software." Journal of Posthumanism 5, no. 2 (2025): 85–104. https://doi.org/10.63332/joph.v5i2.406.

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In today’s era of modernity and the appearance of new knowledge-construction approaches supported by technological tools such as A.I., various instruments contribute to enhancing educational quality like GeoGebra, an open-source software with extensive capabilities for simulations. This research established three objectives, all of which are answered in its conclusions. The study focused on simulating magnetic fields with predefined geometric shapes, analyzed using mathematical principles. Computational simulation was the primary methodology, involving the implementation of Ampere’s law, Biot-

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Wang, Wentao, Wenying Yang, and Venkata Dinavahi. "Co-Simulation Interfacing Capabilities in Device-Level Power Electronic Circuit Simulation Tools: An Overview." International Journal of Power Electronics and Drive Systems (IJPEDS) 6, no. 4 (2015): 665. http://dx.doi.org/10.11591/ijpeds.v6.i4.pp665-682.

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Power electronic circuit simulation today has become increasingly more demanding in both the speed and accuracy. Whilst almost every simulator has its own advantages and disadvantages, co-simulations are becoming more prevalent. This paper provides an overview of the co-simulation capabilities of device-level circuit simulators. More specifically, a listing of device-level simulators with their salient features are compared and contrasted. The co-simulation interfaces between several simulation tools are discussed. A case study is presented to demonstrate the co-simulation between a device-lev

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Antelmi, Alessia, Gennaro Cordasco, Giuseppe D’Ambrosio, Daniele De Vinco, and Carmine Spagnuolo. "Experimenting with Agent-Based Model Simulation Tools." Applied Sciences 13, no. 1 (2022): 13. http://dx.doi.org/10.3390/app13010013.

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Agent-based models (ABMs) are one of the most effective and successful methods for analyzing real-world complex systems by investigating how modeling interactions on the individual level (i.e., micro-level) leads to the understanding of emergent phenomena on the system level (i.e., macro-level). ABMs represent an interdisciplinary approach to examining complex systems, and the heterogeneous background of ABM users demands comprehensive, easy-to-use, and efficient environments to develop ABM simulations. Currently, many tools, frameworks, and libraries exist, each with its characteristics and o

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Gkaintatzi-Masouti, M., J. van Duijnhoven, and M. P. J. Aarts. "Review of spectral lighting simulation tools for non-image- forming effects of light." Journal of Physics: Conference Series 2042, no. 1 (2021): 012122. http://dx.doi.org/10.1088/1742-6596/2042/1/012122.

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Abstract Light via our eyes influences visual performance, visual comfort and visual experience, but also affects several health related, non-image-forming (NIF) responses. New metrics have been developed to quantify the NIF effects of light. In order to incorporate these in lighting design practice, simulation tools are required that are able to process information about the spectral distribution of light sources and materials. However, most of the tools currently used for daylight and electric light simulations simplify the spectrum into RGB (Red, Green, Blue) colour values. This paper prese

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de Azevedo, Júlia Pinto Athanázio, Maurício Bezerra de Souza, and José Carlos Pinto. "Process Hazard Analysis Based on Modeling and Simulation Tools." Processes 10, no. 2 (2022): 386. http://dx.doi.org/10.3390/pr10020386.

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Chemical and oil processes are intrinsically sources of potential hazards. Although traditional qualitative hazard identification methods are simple, systematic, and flexible, such methodologies present limitations related to the inherent subjectivity, dependence on the team’s level of experience, and widespread time consumption of the members involved. In this context, the present work aims to develop a systematic way to use computational modeling and simulation tools for hazard identification. After extensive literature review, the present work proposes a methodology based on the association

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Seydaliev, M., and D. Caswell. "CORBA AND MPI-BASED “BACKBONE” FOR COUPLING ADVANCED SIMULATION TOOLS." AECL Nuclear Review 3, no. 2 (2014): 83–90. http://dx.doi.org/10.12943/anr.2014.00036.

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There is a growing international interest in using coupled, multidisciplinary computer simulations for a variety of purposes, including nuclear reactor safety analysis. Reactor behaviour can be modeled using a suite of computer programs simulating phenomena or predicting parameters that can be categorized into disciplines such as Thermalhydraulics, Neutronics, Fuel, Fuel Channels, Fission Product Release and Transport, Containment and Atmospheric Dispersion, and Severe Accident Analysis. Traditionally, simulations used for safety analysis individually addressed only the behaviour within a sing

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Szufel, Przemysław, Bogumił Kamiński, and Piotr Wojewnik. "Controllling Simulation Experiment Design for Agent-Based Models Using Tree Representation of Parameter Space." Foundations of Computing and Decision Sciences 38, no. 4 (2013): 277–98. http://dx.doi.org/10.2478/fcds-2013-0014.

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Abstract:

Abstract An important aspect of the simulation modelling process is sensitivity analysis. In this process, agent-based simulations often require analysis of structurally different parameter specifications - the parameters can be represented as objects and the object-oriented simulation configuration leads to nesting of simulation parameters. The nested parameters are naturally represented as a tree rather than a flat structure. The standard tools supporting multi-agent simulations only allow only the representation of the parameter space as a Cartesian product of possible parameter values. Con

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