Simulation-based assessment is an effective method for evaluating the competence of individuals in emergency situations. Certain skills are challenging to assess since they cannot be simulated. Consequently, participants need to engage in discursive work to facilitate practice and assessment of unsimulable medical information. This study employs an interactional approach to examine how residents and assessors shape the simulated assessments discursively and what effect this has on the assessment. Simulation-based assessments performed by general practitioners were videotaped. The iterative inductive process of conversation analysis was used to analyze and categorize each verbal initiative to discursively introduce unsimulable medical information. The participants used a range of practices to discursively introduce unsimulable medical information in the scenario. In most cases, residents introduced the medical information with a request for information, thus displaying comprehension of the situation. Residents also asked for confirmation of a specific assessment, thus displaying comprehension of the situation and the expectations. Conversely, assessors can introduce the medical information on behalf of the resident, which restricted residents to display their comprehension. The process of bringing unsimulable medical information discursively into play can be seen as a joint construction. This has implications for understanding assessment and learning in simulation contexts.
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The viability of novel network-level circular business models (CBMs) is debated heavily. Many companies are hesitant to implement CBMs in their daily practice, because of the various roles, stakes and opinions and the resulting uncertainties. Testing novel CBMs prior to implementation is needed. Some scholars have used digital simulation models to test elements of business models, but this this has not yet been done systematically for CBMs. To address this knowledge gap, this paper presents a systematic iterative method to explore and improve CBMs prior to actual implementation by means of agent-based modelling and simulation. An agent-based model (ABM) was co-created with case study participants in three Industrial Symbiosis networks. The ABM was used to simulate and explore the viability effects of two CBMs in different scenarios. The simulation results show which CBM in combination with which scenario led to the highest network survival rate and highest value captured. In addition, we were able to explore the influence of design options and establish a design that is correlated to the highest CBM viability. Based on these findings, concrete proposals were made to further improve the CBM design, from company level to network level. This study thus contributes to the development of systematic CBM experimentation methods. The novel approach provided in this work shows that agent-based modelling and simulation is a powerful method to study and improve circular business models prior to implementation.
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The following paper presents a methodology we developed for addressing the case of a multi-modal network to be implemented in the future. The methodology is based on a simulation approach and presents some characteristics that make a challenge to be verified and validated. To overcome this limitation, we proposed a novel methodology that implies interaction with subjectmatter experts, revision of current data, collection and assessment of future performance and educated assumptions. With that methodology we could construct the complete passenger trajectory Door to door in Europe. The results indicate that the approach allows to approach infrastructure analysis at an early stage to have an initial estimation of the upper boundary of performance indicators. To exemplify this, we present the results for a case study in Europe.
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Background: Shared decision-making is one key element of interprofessional collaboration. Communication is often considered to be the main reason for inefficient or ineffective collaboration. Little is known about group dynamics in the process of shared decision-making in a team with professionals, including the patient or their parent. This study aimed to evaluate just that. Methods: Simulation-based training was provided for groups of medical and allied health profession students from universities across the globe. In an overt ethnographic research design, passive observations were made to ensure careful observations and accurate reporting. The training offered the context to directly experience the behaviors and interactions of a group of people. Results: Overall, 39 different goals were defined in different orders of prioritizing and with different time frames or intervention ideas. Shared decision-making was lacking, and groups chose to convince the parents when a conflict arose. Group dynamics made parents verbally agree with professionals, although their non-verbal communication was not in congruence with that. Conclusions: The outcome and goalsetting of an interprofessional meeting are highly influenced by group dynamics. The vision, structure, process, and results of the meeting are affected by multiple inter- or intrapersonal factors.
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On the eve of the large-scale introduction of electric vehicles, policy makers have to decide on how to organise a significant growth in charging infrastructure to meet demand. There is uncertainty about which charging deployment tactic to follow. The main issue is how many of charging stations, of which type, should be installed and where. Early roll-out has been successful in many places, but knowledge on how to plan a large-scale charging network in urban areas is missing. Little is known about return to scale effects, reciprocal effects of charger availability on sales, and the impact of fast charging or more clustered charging hubs on charging preferences of EV owners. This paper explores the effects of various roll-out strategies for charging infrastructure that facilitate the large-scale introduction of EVs, using agent-based simulation. In contrast to previously proposed models, our model is rooted in empirically observed charging patterns from EVs instead of travel patterns of fossil fuelled cars. In addition, the simulation incorporates different user types (inhabitants, visitors, taxis and shared vehicles) to model the diversity of charging behaviours in an urban environment. Different scenarios are explored along the lines of the type of charging infrastructure (level 2, clustered level 2, fast charging) and the intensity of rollout (EV to charging point ratio). The simulation predicts both the success rate of charging attempts and the additional discomfort when searching for a charging station. Results suggest that return to scale and reciprocal effects in charging infrastructure are considerable, resulting in a lower EV to charging station ratio on the longer term.
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The real-time simulation of human crowds has many applications. In a typical crowd simulation, each person ('agent') in the crowd moves towards a goal while adhering to local constraints. Many algorithms exist for specific local ‘steering’ tasks such as collision avoidance or group behavior. However, these do not easily extend to completely new types of behavior, such as circling around another agent or hiding behind an obstacle. They also tend to focus purely on an agent's velocity without explicitly controlling its orientation. This paper presents a novel sketch-based method for modelling and simulating many steering behaviors for agents in a crowd. Central to this is the concept of an interaction field (IF): a vector field that describes the velocities or orientations that agents should use around a given ‘source’ agent or obstacle. An IF can also change dynamically according to parameters, such as the walking speed of the source agent. IFs can be easily combined with other aspects of crowd simulation, such as collision avoidance. Using an implementation of IFs in a real-time crowd simulation framework, we demonstrate the capabilities of IFs in various scenarios. This includes game-like scenarios where the crowd responds to a user-controlled avatar. We also present an interactive tool that computes an IF based on input sketches. This IF editor lets users intuitively and quickly design new types of behavior, without the need for programming extra behavioral rules. We thoroughly evaluate the efficacy of the IF editor through a user study, which demonstrates that our method enables non-expert users to easily enrich any agent-based crowd simulation with new agent interactions.
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Standard mass-production is a well-known manufacturing concept. To make small quantities or even single items of a product according to user specifications at an affordable price, alternative agile production paradigms should be investigated and developed. The system presented in this paper is based on a grid of cheap reconfigurable production units, called equiplets. A grid of these equiplets is capable to produce a variety of different products in parallel at an affordable price. The underlying agent-based software for this system is responsible for the agile manufacturing. An important aspect of this type of manufacturing is the transport of the products along the available equiplets. This transport of the products from equiplet to equiplet is quite different from standard production. Every product can have its own unique path along the equiplets. In this paper several topologies are discussed and investigated. Also, the planning and scheduling in relation to the transport constraints is subject of this study. Some possibilities of realization are discussed and simulations are used to generate results with the focus on efficiency and usability for different topologies and layouts of the grid and its internal transport system.
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To prepare medical students appropriately for the management of toxicological emergencies, we have developed a simulation-based medical education (SBME) training in acute clinical toxicology. Our aim is to report on the feasibility, evaluation and lessons learned of this training. Since 2019, each year approximately 180 fifth-year medical students are invited to participate in the SBME training. The training consists of an interactive lecture and two SBME stations. For each station, a team of students had to perform the primary assessment and management of an intoxicated patient. After the training, the students completed a questionnaire about their experiences and confidence in clinical toxicology. Overall, the vast majority of students agreed that the training provided a fun, interactive and stimulating way to teach about clinical toxicology. Additionally, they felt more confident regarding their skills in this area. Our pilot study shows that SBME training was well-evaluated and feasible over a longer period.
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Agent-based modeling is used for simulating the actions and interactions of autonomous entities aiming to assessing their effects on the system as a whole. At an abstract level, an agent-based model (ABM) is a representation of the many simple agents and interactions among them. The decision making of the agents is based on the rules given to them. In an ABM, the model output is the result of internal decision-making and may differ with alteration in the decision path. On the contrary, with the set of rules embedded in agents, their behavior is modeled to take a ‘certain action’ in a ‘certain situation’. It suggests that the internal decision making behavior of agents is truly responsible for the model output and thus it cannot be ignored while validating ABMs. This research article focuses on the validating agents’ behavior by evaluating decision-making processes of agents. For this purpose, we propose a validation framework based on a participatory simulation game. Using this framework we engage a human player (i.e. a domain stakeholder) to allow us to collect information about choices and validate the behavior of an individual agent. A proof-of-concept game is developed for a city logistics ABM to test the framework.
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The capacity of the newly inaugurated airport terminal in Mexico City, opened in 2022, has sparked debates regarding its adequacy to accommodate future demand. To address this critical question, our study employs simulation-based analysis to assess the terminal's true potential. By simulating various scenarios, we aim to provide insights into its capacity to handle increasing passenger loads over the coming years and decades. Furthermore, our analysis identifies potential challenges and issues that may arise with the terminal's growth. This research seeks to offer valuable perspectives for stakeholders involved in the airport's planning and management, contributing to informed decisionmaking in ensuring efficient and sustainable aviation infrastructure.
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