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1

Williams, Terry. Modelling complex projects. Chichester: Wiley, 2002.

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Williams, Terry. Modelling complex projects. Chichester: Wiley, 2002.

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Williams, Terry. Modelling complex projects. Chichester: Wiley, 2002.

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4

Modelling complex projects. Chichester: Wiley, 2002.

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5

C, Cacciabue Pietro, ed. Modelling driver behaviour in automotive environments: Critical issues in driver interactions with intelligent transport systems. London: Springer, 2007.

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6

Colloquium of Theoretical and Quantitative Geography (7th 1991 Stockholm). Modelling space and networks: Progress in theoretical and quantitative geography : proceedings from the 7th Colloquium of Theoretical and Quantitative Geography, Stockholm, September 1991. Umeå: Umeå universitet, 1997.

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Nakoinz, Oliver, and Daniel Knitter. Modelling Human Behaviour in Landscapes. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-29538-1.

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8

Bernd, Schmidt. The modelling of human behaviour. Ghent: SCS Europe Publishing House, 2000.

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9

Cacciabue, Pietro Carlo. Modelling and Simulation of Human Behaviour in System Control. London: Springer London, 1998. http://dx.doi.org/10.1007/978-1-4471-1567-0.

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Cacciabue, Pietro Carlo. Modelling and Simulation of Human Behaviour in System Control. London: Springer London, 1998.

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11

Cacciabue, Pietro C. Modelling and simulation of human behaviour in system control. Berlin: Springer, 1998.

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12

Takao, Kumazawa, Kruger Lawrence, and Mizumura Kazue, eds. The polymodal receptor: A gateway to pathological pain. Amsterdam: Elsevier, 1996.

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13

Nakoinz, Oliver, and Daniel Knitter. Modelling Human Behaviour in Landscapes: Basic Concepts and Modelling Elements. Springer London, Limited, 2016.

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14

Nakoinz, Oliver, and Daniel Knitter. Modelling Human Behaviour in Landscapes: Basic Concepts and Modelling Elements. Springer, 2016.

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15

Nakoinz, Oliver, and Daniel Knitter. Modelling Human Behaviour in Landscapes: Basic Concepts and Modelling Elements. Springer, 2018.

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16

Modelling Complex Projects. Wiley & Sons, Incorporated, John, 2003.

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17

Williams, Terry. Modelling Complex Projects. Wiley & Sons, Incorporated, John, 2008.

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18

Cacciabue, Carlo. Modelling Driver Behaviour in Automotive Environments: Critical Issues in Driver Interactions with Intelligent Transport Systems. Springer, 2007.

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19

Cacciabue, Carlo. Modelling Driver Behaviour in Automotive Environments: Critical Issues in Driver Interactions with Intelligent Transport Systems. Springer, 2010.

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20

Hansen, John, Gerhard Schmidt, Huseyin Abut, Kazuya Takeda, and Hanseok Ko. Vehicle Systems and Driver Modelling: DSP, Human-To-vehicle Interfaces, Driver Behavior, and Safety. de Gruyter GmbH, Walter, 2017.

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21

Hansen, John, Gerhard Schmidt, Huseyin Abut, Kazuya Takeda, and Hanseok Ko. Vehicle Systems and Driver Modelling: DSP, Human-To-vehicle Interfaces, Driver Behavior, and Safety. de Gruyter GmbH, Walter, 2017.

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22

1965-, Heinke Dietmar, and Mavritsaki Eirini 1975-, eds. Computational modelling in behavioural neuroscience: Closing the gap between neurophysiology and behaviour. Hove, East Sussex: Psychology Press, 2009.

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23

Heinke, Dietmar, and Eirini Mavritsaki. Computational Modelling in Behavioural Neuroscience: Closing the Gap Between Neurophysiology and Behaviour. Taylor & Francis Group, 2009.

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24

Cacciabue, P. Carlo. Modelling Driver Behaviour in Automotive Environments: Critical Issues in Driver Interactions with Intelligent Transport Systems. Springer, 2007.

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25

Heinke, Dietmar, and Eirini Mavritsaki. Computational Modelling in Behavioural Neuroscience: Closing the Gap Between Neurophysiology and Behaviour. Taylor & Francis Group, 2009.

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26

Heinke, Dietmar, and Eirini Mavritsaki. Computational Modelling in Behavioural Neuroscience: Closing the Gap Between Neurophysiology and Behaviour. Taylor & Francis Group, 2009.

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27

Heinke, Dietmar, and Eirini Mavritsaki. Computational Modelling in Behavioural Neuroscience: Closing the Gap Between Neurophysiology and Behaviour. Taylor & Francis Group, 2009.

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28

Heinke, Dietmar, and Eirini Mavritsaki. Computational Modelling in Behavioural Neuroscience: Closing the Gap Between Neurophysiology and Behaviour. Taylor & Francis Group, 2009.

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29

Mathematical Modelling in Engineering & Human Behaviour 2018. MDPI, 2019. http://dx.doi.org/10.3390/books978-3-03897-805-3.

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30

Modelling and Simulation of Human Behaviour in System Control. Springer, 2011.

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31

Rahimi-Gorji, Mohammad, Tahereh B. Gorji, and Mofid Gorji-Bandpy. CFPD Modelling of Airflow and Particle Behaviour in Human Realistic Airways. Elsevier Science & Technology Books, 2022.

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32

Rahimi-Gorji, Mohammad, Tahereh B. Gorji, and Mofid Gorji-Bandpy. CFPD Modelling of Airflow and Particle Behaviour in Human Realistic Airways. Elsevier Science & Technology, 2022.

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33

Simpson, Stephen J., Carlos Ribeiro, and Daniel González-Tokman. Feeding behavior. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198797500.003.0008.

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Insects need to ingest nutrients at appropriate levels to attain a balanced diet and maximize fitness. They do so by integrated responses that involve physiological mechanisms for sensing current nutritional needs, releasing systemic signals, and producing specific appetites for key required nutrients. Historically, the study of insect feeding behavior was appreciated for its importance in the understanding and control of crop pests and disease vectors. However, current evidence has shown that some mechanisms regulating feeding are highly conserved in animals, from insects to humans, bringing additional interest in insects as models in medicine. The study of insect feeding behavior and nutrition has also given rise to an integrative modelling approach called the geometric framework for nutrition. This approach has proven useful beyond the insects, and allows the understanding of the impact of multiple nutrients on individuals and their interactions in populations, communities, and ecosystems.
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34

Jones, Cheryl Bland. A HUMAN CAPITAL APPROACH TO MODELLING REGISTERED NURSE WAGES AND TURNOVER/RETENTION BEHAVIORS (LABOR MARKET). 1993.

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35

Bi, Xiaojun, Andrew Howes, Per Ola Kristensson, Antti Oulasvirta, and John Williamson. Introduction. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198799603.003.0001.

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This chapter introduces the field of computational interaction, and explains its long tradition of research on human interaction with technology that applies to human factors engineering, cognitive modelling, artificial intelligence and machine learning, design optimization, formal methods, and control theory. It discusses how the book as a whole is part of an argument that, embedded in an iterative design process, computational interaction design has the potential to complement human strengths and provide a means to generate inspiring and elegant designs without refuting the part played by the complicated, and uncertain behaviour of humans. The chapters in this book manifest intellectual progress in the study of computational principles of interaction, demonstrated in diverse and challenging applications areas such as input methods, interaction techniques, graphical user interfaces, information retrieval, information visualization, and graphic design.
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36

Brumby, Duncan P., Christian P. Janssen, Tuomo Kujala, and Dario D. Salvucci. Computational Models of User Multitasking. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198799603.003.0013.

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When users interact with computers and technology ‘in the wild’, multitasking is a practically ubiquitous part of their interactions. Human-computer interaction (HCI) researchers and practitioners have increasingly used computational models to better understand these multitasking behaviours and to build new interactive technologies that facilitate interaction and/or mitigate the problems that arise from multitasking and distraction. This chapter outlines three approaches for modelling: cognitive architectures, cognitive constraint modelling, and uncertainty modelling. These approaches are some of the most common and powerful approaches to computational models of user multitasking, and have complementary strengths. It draws on examples from several domains for which multitasking is a central component, giving a particular focus to in-car multitasking and driver distraction.
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37

Finney, Mark, Sara McAllister, Torben Grumstrup, and Jason Forthofer. Wildland Fire Behaviour. CSIRO Publishing, 2021. http://dx.doi.org/10.1071/9781486309092.

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Wildland fires have an irreplaceable role in sustaining many of our forests, shrublands and grasslands. They can be used as controlled burns or occur as free-burning wildfires, and can sometimes be dangerous and destructive to fauna, human communities and natural resources. Through scientific understanding of their behaviour, we can develop the tools to reliably use and manage fires across landscapes in ways that are compatible with the constraints of modern society while benefiting the ecosystems. The science of wildland fire is incomplete, however. Even the simplest fire behaviours – how fast they spread, how long they burn and how large they get – arise from a dynamical system of physical processes interacting in unexplored ways with heterogeneous biological, ecological and meteorological factors across many scales of time and space. The physics of heat transfer, combustion and ignition, for example, operate in all fires at millimetre and millisecond scales but wildfires can become conflagrations that burn for months and exceed millions of hectares. Wildland Fire Behaviour: Dynamics, Principles and Processes examines what is known and unknown about wildfire behaviours. The authors introduce fire as a dynamical system along with traditional steady-state concepts. They then break down the system into its primary physical components, describe how they depend upon environmental factors, and explore system dynamics by constructing and exercising a nonlinear model. The limits of modelling and knowledge are discussed throughout but emphasised by review of large fire behaviours. Advancing knowledge of fire behaviours will require a multidisciplinary approach and rely on quality measurements from experimental research, as covered in the final chapters.
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38

Oulasvirta, Antti, Per Ola Kristensson, Xiaojun Bi, and Andrew Howes, eds. Computational Interaction. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198799603.001.0001.

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This book presents computational interaction as an approach to explaining and enhancing the interaction between humans and information technology. Computational interaction applies abstraction, automation, and analysis to inform our understanding of the structure of interaction and also to inform the design of the software that drives new and exciting human-computer interfaces. The methods of computational interaction allow, for example, designers to identify user interfaces that are optimal against some objective criteria. They also allow software engineers to build interactive systems that adapt their behaviour to better suit individual capacities and preferences. Embedded in an iterative design process, computational interaction has the potential to complement human strengths and provide methods for generating inspiring and elegant designs. Computational interaction does not exclude the messy and complicated behaviour of humans, rather it embraces it by, for example, using models that are sensitive to uncertainty and that capture subtle variations between individual users. It also promotes the idea that there are many aspects of interaction that can be augmented by algorithms. This book introduces computational interaction design to the reader by exploring a wide range of computational interaction techniques, strategies and methods. It explains how techniques such as optimisation, economic modelling, machine learning, control theory, formal methods, cognitive models and statistical language processing can be used to model interaction and design more expressive, efficient and versatile interaction.
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39

Rosati, Alexandra G. Ecological variation in cognition: Insights from bonobos and chimpanzees. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198728511.003.0011.

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Bonobos and chimpanzees are closely related, yet they exhibit important differences in their wild socio-ecology. Whereas bonobos live in environments with less seasonal variation and more access to fallback foods, chimpanzees face more competition over spatially distributed, variable resources. This chapter argues that bonobo and chimpanzee cognition show psychological signatures of their divergent wild ecology. Current evidence shows that despite strong commonalities in many cognitive domains, apes express targeted differences in specific cognitive skills critical for wild foraging behaviours. In particular, bonobos exhibit less accurate spatial memory, reduced levels of patience and greater risk aversion than do chimpanzees. These results have implications for understanding the evolution of human cognition, as studies of apes are a critical tool for modelling the last common ancestor of humans with nonhuman apes. Linking comparative cognition to species’ natural foraging behaviour can begin to address the ultimate reason for why differences in cognition emerge across species. Les bonobos et les chimpanzés sont prochement liés, pourtant ils montrent d’importantes différences dans leur sociologie naturelle. Alors que les bonobos vivent dans des environnements avec peu de diversité de climat entre saisons et plus d’accès à des ressources de nourriture alternatives, les chimpanzés ménagent une compétition étalée spatialement et des ressources plus variées. Je soutiens que la cognition des chimpanzés et bonobos montre les signatures psychologiques de leur écologie naturelle divergente. Les témoignages courants montrent que, malgré les forts points communs dans en cognition, les grands singes expriment des différences au niveau de compétences cognitives importantes au butinage. En particulier, les bonobos démontrent une mémoire spatial moin précise, moin de patience, et plus d’aversion de risques que les chimpanzés. Ces résultats fournissent des signes dans l’étude de l’évolution de la cognition humaine. Les études des grands singe sont un outil d’importance majeure dans la modélisation du dernier ancêtre commun des humains et grands singes non-humains. Faire des liens cognitives comparatives entre le butinage des différentes espèces peut commencer à dévoiler les raisons pour les différences de cognition entre espèces.
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40

Schmid-Hempel, Paul. Evolutionary Parasitology. 2nd ed. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198832140.001.0001.

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Parasites are ubiquitous and shape almost every aspect of their hosts, including physiology, behaviour, life histories, the structure of the microbiota, and entire communities. Hence, parasitism is one of the most potent forces in nature and, without parasites, the world would look very different. The book gives an overview over the parasite groups and the diversity of defences that hosts have evolved, such as immune systems. Principles of evolutionary biology and ecology analyse major elements of host–parasite interactions, including virulence, infection processes, tolerance, resistance, specificity, memory, polymorphisms, within-host dynamics, diseases spaces, and many other aspects. Genetics is always one of the key elements in these topics. Modelling, furthermore, can predict best strategies for host and parasites. Similarly, the spread of an infectious disease in epidemiology combines with molecular data and genomics. Furthermore, parasites have evolved ways to overcome defences and to manipulate their hosts. Hosts and parasites, therefore, continuously co-evolve, with changes sometimes occurring very rapidly, and sometimes requiring geological times. Many infectious diseases of humans have emerged from a zoonotic origin, in processes governed by the basic principles discussed in the different sections. Hence, this book integrates different fields to study the diversity of host–parasite processes and phenomena. It summarizes the essential topics for the study of evolutionary parasitology and will be useful for a broad audience.
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41

(Editor), T. Kumazawa, L. Kruger (Editor), and K. Mizumura (Editor), eds. The Polymodal Receptor - A Gateway to Pathological Pain (Progress in Brain Research). Elsevier Science, 1996.

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