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Journal articles on the topic 'Simulation and Modelling'

Author: Grafiati

Published: 4 June 2021

Last updated: 6 September 2023

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1

Troy, Amy-Jane, and Joe Bogue. "Simulation modelling." British Food Journal 117, no. 2 (February 2, 2015): 943–62. http://dx.doi.org/10.1108/bfj-12-2013-0363.

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Purpose – The purpose of this paper is to utilise market-oriented methodologies and simulation analysis to generate information regarding consumers’ attitudes, opinions and preferences towards novel β-glucan-enriched breads. Design/methodology/approach – Focus Groups and In-depth interviews were used to generate consumer information on potentially suitable attributes that would maximise consumer acceptance. The approach involved the creation of a set of realistic product concepts from a combination of attribute levels and the presentation of these product concepts to consumers. A fractional factorial design generated an orthogonal array of 22 hypothetical products that were presented to consumers for rating while K-means cluster analysis was used to segment consumers into four specific consumer segments. The simulation analysis then identified β-glucan-enriched bread concepts targeted specifically at these four market segments. Findings – The simulation analysis identified β-glucan-enriched bread concepts targeted specifically at these four market segments. Originality/value – This research can assist manufacturers who wish to optimally design foods with novel ingredients and aid in the development of product concepts which maximise consumer acceptance and increase the likelihood of product success.

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2

Taylor, Simon J. E., and Stewart Robinson. "Simulation Modelling." OR Insight 14, no. 4 (October 2001): 2. http://dx.doi.org/10.1057/ori.2001.13.

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3

Gohil, Mehul D., Praful G. Patil, and U. V. Shah U.V. Shah. "Modelling and Simulation of Parallel Motion Fender." International Journal of Scientific Research 2, no. 5 (June 1, 2012): 263–64. http://dx.doi.org/10.15373/22778179/may2013/87.

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4

Szántó, András, and Sándor Hajdu. "Vehicle Modelling and Simulation in Simulink." International Journal of Engineering and Management Sciences 4, no. 1 (March 3, 2019): 260–65. http://dx.doi.org/10.21791/ijems.2019.1.33.

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In this paper a vehicle dynamics model is presented, which is an example that contains all the necessary aspects of making a decent vehicle model. Several examples show the use of such a model: basic vehicle dynamics phenomena can be recognized with the simulation of a detailed vehicle model. We are dealing with the connection between downforce and under/oversteer in this paper. In addition, the use of numerical simulations in the field of control systems is pointed out by an example of simulating an ABS control for the vehicle.

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5

Vasileva, Svetlana, and Aleksandar Milev. "Simulation Studies of Distributed Two-phase Locking in Distributed Database Management Systems." Information Technologies and Control 13, no. 1-2 (June 1, 2015): 46–55. http://dx.doi.org/10.1515/itc-2016-0010.

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Abstract This paper considers algorithms simulating the implementation of distributed two-phase locking (2PL) protocols in distributed database systems and simulation results. It describes specifically the simulations of two-version 2PL and 2PL with integrated timestamp ordering mechanism. Integrated modelling algorithms for deadlock avoiding are suggested in the paper: twoversion architecture of database and timestamp ordering strategy “wait-die”. The results of the simulations of these two variants of the 2PL method at different scales of the networks for data transmission and at different intensities of inflow transactions are also presented. Modelling algorithms are developed by means of the system for simulation modelling GPSS World Personal Version.

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6

HAGEN, HANS, SIEGFRIED HEINZ, MICHAEL THESING, and THOMAS SCHREIBER. "SIMULATION BASED MODELLING." International Journal of Shape Modeling 04, no. 03n04 (September 1998): 143–64. http://dx.doi.org/10.1142/s0218654398000118.

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7

Kompare, Boris. "Modelling and simulation." Ecological Modelling 98, no. 2-3 (May 1997): 245–47. http://dx.doi.org/10.1016/s0304-3800(96)01916-3.

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8

Catlow, C. R. A. "Modelling and simulation." Current Opinion in Solid State and Materials Science 7, no. 1 (February 2003): 1–2. http://dx.doi.org/10.1016/s1359-0286(03)00030-5.

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9

Kurin, M. O., O. O. Horbachov, A. V. Onopchenko, and T. V. Loza. "Modelling and Simulation of the Plastic Flows in Metal." METALLOFIZIKA I NOVEISHIE TEKHNOLOGII 44, no. 6 (September 6, 2022): 785–806. http://dx.doi.org/10.15407/mfint.44.06.0785.

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10

Gramatikov, Pavlin. "GALLIUM NITRIDE POWER ELECTRONICS FOR AEROSPACE - MODELLING AND SIMULATION." Journal Scientific and Applied Research 15, no. 1 (March 3, 2019): 11–21. http://dx.doi.org/10.46687/jsar.v15i1.250.

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11

Haigh, John, and Hamdy A. Taha. "Simulation Modelling and SIMNET." Mathematical Gazette 73, no. 463 (March 1989): 62. http://dx.doi.org/10.2307/3618231.

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12

Paul, Ray J., and Francis Neelamkavil. "Computer Simulation and Modelling." Journal of the Operational Research Society 38, no. 11 (November 1987): 1092. http://dx.doi.org/10.2307/2582236.

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13

Žagar, Dušan. "Ecohydraulics Modelling and Simulation." Water 13, no. 16 (August 8, 2021): 2172. http://dx.doi.org/10.3390/w13162172.

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Ecohydraulics, the interdiscipline of ecology and hydraulics, has been rapidly developing and receiving ever-growing attention both in hydraulic research efforts and in environmentally oriented professional and lay communities in recent years [...]

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14

Masic, Zlatan, Konny Lajhner, and Haris Pandza. "Computer Modelling and Simulation." International Journal on Biomedicine and Healthcare 9, no. 3 (2021): 173. http://dx.doi.org/10.5455/ijbh.2021.9.173-182.

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15

Dave, U., and L. Oakshott. "Business Modelling and Simulation." Journal of the Operational Research Society 49, no. 2 (February 1998): 180. http://dx.doi.org/10.2307/3009986.

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16

Paul, Ray J. "Computer Simulation and Modelling." Journal of the Operational Research Society 38, no. 11 (November 1987): 1092–93. http://dx.doi.org/10.1057/jors.1987.181.

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17

DePriest, Douglas J. "Computer Simulation and Modelling." Technometrics 30, no. 4 (November 1988): 463–64. http://dx.doi.org/10.1080/00401706.1988.10488458.

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18

J. Schriber, Thomas. "Business modelling and simulation." Simulation Practice and Theory 5, no. 6 (August 1997): p30. http://dx.doi.org/10.1016/s0928-4869(97)84255-3.

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19

Raffo, David, and Paul Wernick. "Software Process Simulation Modelling." Journal of Systems and Software 59, no. 3 (December 2001): 223–25. http://dx.doi.org/10.1016/s0164-1212(01)00063-2.

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20

Gani, J. "Epidemic modelling and simulation." Mathematics and Computers in Simulation 32, no. 1-2 (April 1990): 3–12. http://dx.doi.org/10.1016/0378-4754(90)90211-z.

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21

Grover, D. J. "Computer simulation and modelling." Computer-Aided Design 20, no. 4 (May 1988): 222. http://dx.doi.org/10.1016/0010-4485(88)90281-3.

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22

Handel, Andreas, Nicole L. La Gruta, and Paul G. Thomas. "Simulation modelling for immunologists." Nature Reviews Immunology 20, no. 3 (December 5, 2019): 186–95. http://dx.doi.org/10.1038/s41577-019-0235-3.

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23

Dave, U. "Business Modelling and Simulation." Journal of the Operational Research Society 49, no. 2 (February 1998): 180–81. http://dx.doi.org/10.1057/palgrave.jors.2600005.

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24

Dave, U. "Business Modelling and Simulation." Journal of the Operational Research Society 49, no. 2 (1998): 180–81. http://dx.doi.org/10.1038/sj.jors.2600005.

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25

Hou, Jennifer C., and P. R. Kumar. "Network modelling and simulation." Computer Networks 50, no. 12 (August 2006): 1885–86. http://dx.doi.org/10.1016/j.comnet.2005.10.025.

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26

Crookes, J. G. "Computer simulation and modelling." European Journal of Operational Research 36, no. 3 (September 1988): 422–23. http://dx.doi.org/10.1016/0377-2217(88)90141-5.

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27

Pidd, M. "Simulation modelling with pascal." European Journal of Operational Research 47, no. 3 (August 1990): 397–98. http://dx.doi.org/10.1016/0377-2217(90)90228-4.

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28

Myrtveit, Magne, and Michael Bean. "Business modelling and simulation." Wirtschaftsinformatik 42, no. 2 (April 2000): 156–60. http://dx.doi.org/10.1007/bf03250730.

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29

Blundell, M. V. "The modelling and simulation of vehicle handling Part 3: Tyre modelling." Proceedings of the Institution of Mechanical Engineers, Part K: Journal of Multi-body Dynamics 214, no. 1 (March 1, 2000): 1–32. http://dx.doi.org/10.1243/1464419001544115.

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This is the third in a series of four papers (Parts 1 to 4) looking at the application of computer-based analysis methods to model vehicles and simulate vehicle handling. The material contained in these papers is based on a study carried out in order to investigate the influence of suspension and tyre modelling on the outputs predicted by vehicle handling simulations. The papers deal with analysis methods, vehicle modelling (both in the previous Issue), tyre modelling and handling simulation. In this paper an overview of the use of tyre models in vehicle dynamics is provided. This is followed by a more detailed description of three tyre modelling approaches that can be used for handling simulations. A description is also provided of a computer-based modelling system where FORTRAN routines represent the various models and a computer model of a tyre test rig is used to interrogate the models and data before integrating these into a full vehicle handling simulation. The use of this system to compare the accuracy of the tyre models under consideration is also presented. The examples used to illustrate the concepts explained throughout this series of papers have been generated using the ADAMS (Automatic Dynamic Analysis of Mechanical Systems) program.

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30

Bazaz, Mohammad Abid, Mashuq un Nabi, and S. Janardhanan. "Modelling and simulation strategy for parametric transient electromagnetic simulations." International Journal of Modelling, Identification and Control 18, no. 3 (2013): 251. http://dx.doi.org/10.1504/ijmic.2013.052819.

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31

Vashisht, Bhavya, Ahmad Belal Anwar, and Gaurav Gautam. "Modelling and Simulation of Solar Panel." International Journal of Advance Research and Innovation 2, no. 2 (2014): 96–102. http://dx.doi.org/10.51976/ijari.221414.

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This paper aims to model a solar photovoltaic system with built in MPPT(Maximum Power Point Tracker) for Photovoltaic (PV) system. It provides theoretical study of PV systems and modelling techniques using equivalent electric circuits. MATLAB simulations verify each individual block as well as combined simulation of model containing solar panel, MPPT and Cuk converter. The results validate that MPPT can significantly increase the efficiency and the performance of PV system.

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32

Ormrod, David, and Benjamin Turnbull. "The Modelling and Simulation of Integrated Battlefield Cyber-Kinetic Effects." International Journal of Cyber Warfare and Terrorism 9, no. 4 (October 2019): 1–26. http://dx.doi.org/10.4018/ijcwt.2019100101.

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Despite the academic and military interest in the use of combined cyber-kinetic effects in future warfare, there is little that seeks to scientifically analyse the impact of combined cyber and kinetic military action. Current approaches to simulation focus on either the physical or cyber, but there are no current simulation approaches that combine these two domains. Military simulations in particular favor physical domains over cyber ones, relegating the uniqueness and nuances of cyber a second order consideration. Future warfare will incorporate a combined multi-domain conflict, and this includes cyber. To effectively simulate this, the uniqueness of each domain must be considered, including the interaction between domains to produce combined effects. This work introduces the Battlespace Integrated Cyber-Kinetic Effects (BICKE) simulation framework. The BICKE framework is designed specifically to combine the cyber and physical domains for the purposes of simulating mission impact. There is a requirement for researchers to measure the synergistic effects across domains.

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33

Pullar, David. "Simulation Modelling Applied to Runoff Modelling Using MapScript." Transactions in GIS 7, no. 2 (March 2003): 267–83. http://dx.doi.org/10.1111/1467-9671.00144.

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34

Miltényi, Máté, and Levente Czégé. "Simulation of production processes with Plant Simulation." International Journal of Engineering and Management Sciences 4, no. 4 (December 12, 2019): 10–16. http://dx.doi.org/10.21791/ijems.2019.4.2.

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This article presents the modelling of interconnected companies’ logistic processes with Tecnomatix Plant Simulation by Siemens. It has two main parts. The first contains the details of recognition including simulation. The second part presents the modelling with Tecnomatix Plant Simulation. My publication is supported by the EFOP3.6.1-16-2016-00022 project. The project is co-financed by the European Union and the European Social Fund.

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35

Rehman, Muhammad Aziz ur, and Yan Yan Huang. "Modelling and Simulation on Traffic Emergency Response Based on Unity3D." International Journal of Future Computer and Communication 8, no. 2 (June 2019): 68–71. http://dx.doi.org/10.18178/ijfcc.2019.8.2.542.

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36

Sugano, Kiyohiko. "Lost in modelling and simulation?" ADMET and DMPK 9, no. 2 (March 20, 2021): 75–109. http://dx.doi.org/10.5599/admet.923.

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Over the past few decades, physiologically-based pharmacokinetic modelling (PBPK) has been anticipated to be a powerful tool to improve the productivity of drug discovery and development. However, recently, multiple systematic evaluation studies independently suggested that the predictive power of current oral absorption (OA) PBPK models needs significant improvement. There is some disagreement between the industry and regulators about the credibility of OA PBPK modelling. Recently, the editorial board of AMDET&DMPK has announced the policy for the articles related to PBPK modelling (Modelling and simulation ethics). In this feature article, the background of this policy is explained: (1) Requirements for scientific writing of PBPK modelling, (2) Scientific literacy for PBPK modelling, and (3) Middle-out approaches. PBPK models are a useful tool if used correctly. This article will hopefully help advance the science of OA PBPK models.

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37

Paul, Ray J., and Sew Tee Chew. "Simulation Modelling Using an Interactive Simulation Program Generator." Journal of the Operational Research Society 38, no. 8 (August 1987): 735. http://dx.doi.org/10.2307/2582845.

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38

Törn, Aimo. "The simulation net approach to modelling and simulation." SIMULATION 57, no. 3 (September 1991): 196–98. http://dx.doi.org/10.1177/003754979105700311.

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39

Paul, Ray J., and Sew Tee Chew. "Simulation Modelling Using an Interactive Simulation Program Generator." Journal of the Operational Research Society 38, no. 8 (August 1987): 735–52. http://dx.doi.org/10.1057/jors.1987.122.

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40

LINDBERG, ERIK. "MODELLING AND SIMULATION OF CHUA'S CIRCUIT." Journal of Circuits, Systems and Computers 03, no. 02 (June 1993): 537–52. http://dx.doi.org/10.1142/s0218126693000332.

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Experiments with modelling and simulation of Chua's circuit are presented in order to transfer a simple explanation of its behaviour from the wonderful fairy-tale world of mathematicians to the marvellous physical world of electronic engineers. It is demonstrated that the behaviour of the circuit is based on the interaction (superposition) of two different kinds of energy balance: (1) Chaotic behaviour based on the interaction of two unstable DC-states and (2) stable limit cycle behaviour based on the balance between the energy lost in the regions with mainly positive losses and the energy gained in the regions with mainly negative losses. Convergence problems observed in connection with simulation of the ideal piecewise-linear model are solved by means of a smooth continuous model of the nonlinear element based on the ideal operational amplifier model instead of a polynomial approximation. The preliminary results are presented. The NAP2 program made by the late Thomas Rübner-Petersen has been used for the simulations.

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41

Ruan, Mingchaun, and Jan B. M. Wiggers. "Modelling sewer emission using simulink." Water Science and Technology 36, no. 5 (September 1, 1997): 185–92. http://dx.doi.org/10.2166/wst.1997.0194.

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A dynamic programming package SIMULINK has been used to develop a conceptual emission model named SEWSIM for both event-based and continuous simulations of urban sewer systems. The model variables are mostly vectorized for simulation efficiency. The impervious catchment and the sewer network are schematised as two linear dynamic reservoirs. The physical processes that are conceptually modelled include solids buildup and washoff (catchment surface) and sediment erosion and deposition (sewer network). The conceptual model calibrated using measurements or simulation results of hydrodynamic models is able to predict the sewer emission using a long series of historical rainfall records.

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42

Xie, Shengkun, Anna T. Lawniczak, and Junlin Hao. "Modelling Autonomous Agents’ Decisions in Learning to Cross a Cellular Automaton-Based Highway via Artificial Neural Networks." Computation 8, no. 3 (July 8, 2020): 64. http://dx.doi.org/10.3390/computation8030064.

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A lot of effort has been devoted to mathematical modelling and simulation of complex systems for a better understanding of their dynamics and control. Modelling and analysis of computer simulations outcomes are also important aspects of studying the behaviour of complex systems. It often involves the use of both traditional and modern statistical approaches, including multiple linear regression, generalized linear model and non-linear regression models such as artificial neural networks. In this work, we first conduct a simulation study of the agents’ decisions learning to cross a cellular automaton based highway and then, we model the simulation data using artificial neural networks. Our research shows that artificial neural networks are capable of capturing the functional relationships between input and output variables of our simulation experiments, and they outperform the classical modelling approaches. The variable importance measure techniques can consistently identify the most dominant factors that affect the response variables, which help us to better understand how the decision-making by the autonomous agents is affected by the input factors. The significance of this work is in extending the investigations of complex systems from mathematical modelling and computer simulations to the analysis and modelling of the data obtained from the simulations using advanced statistical models.

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43

Menter, F. "Turbulence Modelling for Technical Flows." NAFEMS International Journal of CFD Case Studies 5 (April 2006): 41–49. http://dx.doi.org/10.59972/zy8s6eve.

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The paper describes the need for turbulence modelling in industrial flow simulations. It will describe the general problem in the simulation of turbulent flows and the required averaging procedures to arrive at a manageable equation system. The principal concepts behind turbulence models will be described and some current model formulations will be described briefly. Simulations will be shown for generic testcases and industrial applications.

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44

Madear, Gelu, and Camelia Madear. "Environmental modelling - a modern tool towards sustainability." MATEC Web of Conferences 342 (2021): 03013. http://dx.doi.org/10.1051/matecconf/202134203013.

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One way to solve environmental problems is through modelling. Humankind developed a series of models, from mental models, physical models to computer simulation models. Building a model assumes abstraction, simplifying the natural system by considering only the essential details and discarding irrelevant ones. Mapping the real worlds to the world of models is done by choosing an abstraction level and the corresponding modelling tool. The right abstraction level is paramount for any modelling project, depending on the real problem being analysed. In modern simulation modelling, there are three methods, each having a particular range of abstraction levels: system dynamics, discrete event (process-centric modelling) and agent-based models. Ecosystems and generally any environmental problems (real world) are complex dynamics that challenge our comprehension. Understanding the significant environmental challenges is vital to adopt adequate policies for a sustainable environment through modelling and simulation. Since our cognitive abilities are limited, we need a simulation of the environmental systems to see the dynamic patterns and how humans interact with the environment. Environmental modelling helps us understand complex systems by building mathematical models and running simulations using a high abstraction level. The system dynamics method of modelling and simulation is used to clarify the representation of the stocks and flows and the feedback process that control the flows and describe the dynamic behaviour (growth, decay, or oscillations) of complex systems over time. Modelling for prediction, understanding across time and spatial scales, and environmental systems disciplines is key for a sustainable future.

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45

Kluska, Kamila. "Automatic simulation modelling of warehouses." Logforum 17, no. 1 (March 30, 2021): 59–69. http://dx.doi.org/10.17270/j.log.2021.547.

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46

Jönsson, Siv, Anja Henningsson, Monica Edholm, and Tomas Salmonson. "Role of Modelling and Simulation." Clinical Pharmacokinetics 51, no. 2 (February 2012): 69–76. http://dx.doi.org/10.2165/11596650-000000000-00000.

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47

Baskent, E. Z., and G. A. Jordan. "Spatial wood supply simulation modelling." Forestry Chronicle 67, no. 6 (December 1, 1991): 610–21. http://dx.doi.org/10.5558/tfc67610-6.

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Conventional wood supply simulation models have been found inadequate in both calculating true assessments of wood supply and in translating management strategies into on-the-ground management design. These models treat forests as aspatial entities and are unable to include economic and wildlife considerations in management design and calculation of wood supply. This paper presents and discusses the design and construction of a GIS-based (geographic information system) spatial wood supply model. The model uses geographic distribution of stand development types and stages and their change over time to control harvesting and calculate wood supply based on extraction economics ($/m3) and wildlife habitat values (opening size and green-up). The paper points out that: a spatial model is capable of producing harvest schedules and forest performance indicators that reflect geographic context as well as condition of stands; a GIS database is more important in spatial modelling than GIS technology; harvest blocks are the basic geographic element in spatial modelling; a spatial model provides a truer assessment of wood supply; and stand topology makes it relatively easy to integrate wildlife and timber management. Key words: Timber, wildlife, forest management, GIS, simulation model, wood supply

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48

Gujer, Willi, and Mogens Henze. "Activated Sludge Modelling and Simulation." Water Science and Technology 23, no. 4-6 (February 1, 1991): 1011–23. http://dx.doi.org/10.2166/wst.1991.0553.

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The matrix format for the presentation of biokinetic models is explained with a simple model and expanded for the ‘Activated Sludge Model No. 1' of the IAWPRC Task Group for Mathematical Modelling for Design and Operation of Biological Wastewater treatment. With the aid of a simulation program a complex activated sludge model which includes two organic substrates and nitrification is developed stepwise and compared to experimental results.

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49

Barker, A. R., W. J. J. Stut, J. P. J. de Valk, and G. R. Reijns. "PACS costs: modelling and simulation." Medical Informatics 13, no. 4 (January 1988): 307–13. http://dx.doi.org/10.3109/14639238809012095.

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50

Paul, Ray J. "Recent Developments in Simulation Modelling." Journal of the Operational Research Society 42, no. 3 (March 1991): 217. http://dx.doi.org/10.2307/2583310.

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