Background: Risk-based breast cancer (BC) screening raises new questions regarding information provision and risk communication. This study aimed to: 1) investigate women’s beliefs and knowledge (i.e., mental models) regarding BC risk and (risk-based) BC screening in view of implications for information development; 2) develop novel informational materials to communicate the screening result in risk-based BC screening, including risk visualizations of both quantitative and qualitative information, from a Human-Centered Design perspective. Methods: Phase 1: Interviews were conducted (n = 15, 40–50 years, 5 lower health literate) on women’s beliefs about BC risk and (risk-based) BC screening. Phase 2: In three participatory design sessions, women (n = 4–6 across sessions, 40–50 years, 2–3 lower health literate) made assignments and created and evaluated visualizations of risk information central to the screening result. Prototypes were evaluated in two additional sessions (n = 2, 54–62 years, 0–1 lower health literate). Phase 3: Experts (n = 5) and women (n = 9, 40–74 years) evaluated the resulting materials. Two other experts were consulted throughout the development process to ensure that the content of the information materials was accurate. Interviews were transcribed literally and analysed using qualitative thematic analysis, focusing on implications for information development. Notes, assignments and materials from the participatory design sessions were summarized and main themes were identified. Results: Women in both interviews and design sessions were positive about risk-based BC screening, especially because personal risk factors would be taken into account. However, they emphasized that the rationale of risk-based screening and classification into a risk category should be clearly stated and visualized, especially for higher- and lower-risk categories (which may cause anxiety or feelings of unfairness due to a lower screening frequency). Women wanted to know their personal risk, preferably visualized in an icon array, and wanted advice on risk reduction and breast self-examination. However, most risk factors were considered modifiable by women, and the risk factor breast density was not known, implying that information should emphasize that BC risk depends on multiple factors, including breast density. Conclusions: The information materials, including risk visualizations of both quantitative and qualitative information, developed from a Human-Centered Design perspective and a mental model approach, were positively evaluated by the target group.

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In design optimization of bulk handling equipment (BHE) we generally focus on the mean performance of the equipment. However, granular materials behave stochastic due to irregularities in particle shape and size which leads to stochastic performance of the equipment. To include the stochastic performance we propose robust metamodel-based design optimization (MBDO). The used metamodels are trained with stochastic performance data from randomly repeated discrete element method (DEM) simulations and predict mean and variance of the equipment performance. This method is compared to the conventional deterministic optimization method by means of a case study of a discharging hopper including verification and validation. The robust MBDO shows more distinctive optimal designs compared to the deterministic approach. In addition, the DEM-based metamodel is a relatively accurate method to predict DEM-model simulation results. However, the validation indicates that differences between DEM-model and experimental results highly affect the reliability of the found optima.

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In calibration of model parameters for discrete element method (DEM) based models the focus lies on matching the mean key performance indicator (KPI) values from laboratory experiments to those from simulation results. However, due to the stochastic nature of granular processes experimental results can show large variances. To include stochastic behaviour, interpolation-based and regression-based metamodels are trained with stochastic data. These metamodels are used in the standard mean calibration approach and newly introduced mean-variance calibration approach to predict the KPIs mean and variance. In addition, the effect of enriching data on the calibration is investigated up to 50 repetitions of experiments and simulations. Based on a hopper case study, use of regression-based metamodels trained with KPI data repeated at least 20 times is recommended. While differences between mean and mean-variance-based metamodels were minor in the considered case study, regression-based metamodeling clearly showed improved accuracy and stability over interpolation-based metamodels.

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Developments in discrete element modelling (DEM) enable detailed modelling of granular flows in bulk handling equipment (BHE) but due to the computational expense of DEM, wide use in analysing equipment performance is not yet feasible. Metamodels are a viable option to effectively use DEM in analysing BHE performance. Metamodels are able to approximate the behaviour of BHE efficiently for a wide range of design parameter values. We present a methodology to construct and validate DEM-based metamodels as well as a discharging hopper case study illustrating the use and benefits of metamodels in combination with DEM. For three different metamodels trained on a DEM data set, the results show that the metamodel quality highly depends on the number of samples and finding proper hyper-parameter values. The constructed metamodels are found capable of adequately representing the relation between performance and design parameters. It is concluded that methodically constructed metamodels are a valuable addition in describing BHE behaviour.

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The work presented in this paper demonstrates the use of metamodels to capture the relations between the discharge flow and the design parameters of a silo. Based on simulation data for a discharging silo a metamodel is developed that relates the size of the discharge opening and the hopper angle to the performance of the silo. The created metamodel is a response surface model which is trained with numerical data from DEM simulations. The results show that with a full factorial DoS a reliable response surface can be created that predicts the simulation results in the points in between the training data. It can be concluded that metamodelling techniques are a promising tool for the prediction of behaviour of granular matter in silo designs and in designs of other types of bulk handling equipment.@en