Industrial Symbiosis Networks (ISNs) consist of firms that exchange residual materials and energy locally, in order to gain economic, environmental and/or social advantages. In practice, ISNs regularly fail when partners leave and the recovery of residual streams ends. Regarding the current societal need for a shift towards sustainability, it is undesirable that ISNs should fail. Failures of ISNs may be caused by actor behaviour that leads to unanticipated economic losses. In this paper, we explore the effect of these behaviours on ISN robustness by using an agent-based model (ABM). The constructed model is based on insights from both literature and participatory modelling in three real-world cases. It simulates the implementation of synergies for local waste exchange and compost production. The Theory of Planned Behaviour (TPB) was used to model agent behaviour in time-dependent bilateral negotiations and synergy evaluation processes. We explored model behaviour with and without TPB logic across a range of possible TPB input variables. The simulation results show how the modelled planned behaviour affects the cash flow outcomes of the social agents and the robustness of the network. The study contributes to the theoretical development of industrial symbiosis research by providing a quantitative model of all ISN implementation stages, in which various behavioural patterns of entrepreneurs are included. It also contributes to practice by offering insights on how network dynamics and robustness outcomes are not only related to context and ISN design, but also to actor behaviour.
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Equestrianism is currently facing a range of pressing challenges. These challenges, which are largely based on evolving attitudes to ethics and equine wellbeing, have consequences for the sport’s social licence to operate. The factors that may have contributed to the current situation include overarching societal trends, specific aspects of the equestrian sector, and factors rooted in human nature. If equestrianism is to flourish, it is evident that much needs to change, not the least,human behaviour. To this end, using established behaviour change frameworks that have been scientifically validated and are rooted in practice — most notably, Michie et al.’s COM-B model and Behaviour Change Wheel — could be of practical value for developing and implementing equine welfare strategies. This review summarises the theoretical underpinnings of some behaviour change frameworks and provides a practical, step-by-step approach to designing an effective behaviour change intervention. A real-world example is provided through the retrospective analysis of an intervention strategy that aimed to increase the use of learning theory in (educational) veterinary practice. We contend that the incorporation of effective behaviour change interventions into any equine welfare improvement strategy may help to safeguard the future of equestrianism.
MULTIFILE
Player behavioural modelling has grown from a means to improve the playing strength of computer programs that play classic games (e.g., chess), to a means for impacting the player experience and satisfaction in video games, as well as in cross-domain applications such as interactive storytelling. In this context, player behavioural modelling is concerned with two goals, namely (1) providing an interesting or effective game AI on the basis of player models and (2) creating a basis for game developers to personalise gameplay as a whole, and creating new user-driven game mechanics. In this article, we provide an overview of player behavioural modelling for video games by detailing four distinct approaches, namely (1) modelling player actions, (2) modelling player tactics, (3) modelling player strategies, and (4) player profiling. We conclude the article with an analysis on the applicability of the approaches for the domain of video games.
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Receiving the first “Rijbewijs” is always an exciting moment for any teenager, but, this also comes with considerable risks. In the Netherlands, the fatality rate of young novice drivers is five times higher than that of drivers between the ages of 30 and 59 years. These risks are mainly because of age-related factors and lack of experience which manifests in inadequate higher-order skills required for hazard perception and successful interventions to react to risks on the road. Although risk assessment and driving attitude is included in the drivers’ training and examination process, the accident statistics show that it only has limited influence on the development factors such as attitudes, motivations, lifestyles, self-assessment and risk acceptance that play a significant role in post-licensing driving. This negatively impacts traffic safety. “How could novice drivers receive critical feedback on their driving behaviour and traffic safety? ” is, therefore, an important question. Due to major advancements in domains such as ICT, sensors, big data, and Artificial Intelligence (AI), in-vehicle data is being extensively used for monitoring driver behaviour, driving style identification and driver modelling. However, use of such techniques in pre-license driver training and assessment has not been extensively explored. EIDETIC aims at developing a novel approach by fusing multiple data sources such as in-vehicle sensors/data (to trace the vehicle trajectory), eye-tracking glasses (to monitor viewing behaviour) and cameras (to monitor the surroundings) for providing quantifiable and understandable feedback to novice drivers. Furthermore, this new knowledge could also support driving instructors and examiners in ensuring safe drivers. This project will also generate necessary knowledge that would serve as a foundation for facilitating the transition to the training and assessment for drivers of automated vehicles.
The Ph.D. candidate will investigate the seismic response of connection details frequently used in traditional Dutch construction practice, specifically in the Groningen area. The research will focus on the experimental and numerical definition of the complete load-deflection behaviour of each considered connection; specifically, the tests will aim at identifying stiffness, strength, ductility, and dissipative behaviour of the connections. The experiments will be conducted on scaled or full-scale components that properly resemble the as-built and retrofitted as well connection details. The tests will involve monotonic and cyclic loading protocols to be able to define the load and displacement response of the connection to reversal loads, such as earthquakes, as well as the development of failure mechanisms under such loading cases. Possibly, also dynamic tests will be performed. Numerical models will be created and calibrated versus the experimental findings. Characteristic hysteretic behaviours of the examined connection types will be provided for the use of engineers and researchers.
