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This manuscript provides information for replicating the Coasting agent-based model presented in "Simulating emerging coastal tourism vulnerabilities an agent-based modelling approach". The model description follows the Overview, Design Concepts, and Details + Human Decision-making (ODD+D) protocol. this website Moreover, this paper includes implementation details on global sensitivity analysis and scenario discovery. Finally, we provide supplementary tables and figures for scenario discovery results not included in the main paper.

 Highlights •Model description for simulating emerging environmental vulnerabilities in a coastal tourism context•Coasting's design facilitates model adaptations to other coastal tourism destinations•Implementation details for applying global sensitivity analysis and scenario discovery to vulnerability assessments.Soil surface roughness controls how water ponds on and flows over soil surfaces. It is a crucial parameter for erosion and runoff studies. Surface roughness has traditionally been measured using manual techniques that are simple but laborious. Newer technologies have been proposed that are less laborious but require expensive equipment and considerable expertise. New depth-camera technologies might provide a useful alternative. We tested the ability of one such camera to measure soil surface roughness. The camera's accuracy was good but decreased with camera-soil distance (0.3% at 750 mm and 0.5% at 1500 mm) however it was very precise ( less then 0.5 mm for elevation and less then 0.05 mm for random roughness). Similarly, the error of the surface area estimation increased with camera-soil distance (0.56% at 750 mm and 2.3% at 1500 mm). We describe the workflow to produce high-resolution digital elevation models from initial images and describe the conditions under which the camera will not work well (e.g. extremes of lighting conditions, inappropriate post-processing options). The camera was reliable, required little in the way of additional technology and was practical to use in the field. We propose that depth cameras are a simple and inexpensive alternative to existing techniques. •We tested a commercially-available 3D depth camera.•The camera gave highly accurate and precise soil surface measurements.•The camera provides an inexpensive alternative to existing techniques.An estimated 3.8 million traumatic brain injuries (TBI) occur each year, the majority classified as mild. Interest in models of mild and repeat mild TBI has grown due to reports of lasting morbidity following sports- or combat-related injury. There remains a paucity of data linking cellular or systems-related mechanisms to behavioral outcomes following repeat mild TBI, particularly in adolescent and adult rats. It is critical, therefore, to develop flexible models to evaluate which parameters of injury are associated with brain vulnerability or poor chronic outcome compared to normal recovery. While there are several existing models of repeat mild TBI in rodents, studying the effects of multiple hits has been complicated by the need for multiple survival surgeries, extensive pre-injury anesthesia time, and limitations due to animal skull thickness.•We developed a chronic "helmet" implant by combining aspects of the Impact Acceleration and Controlled Cortical Impact models.•Implants were performed days before injury, allowing us to decouple surgery from TBI. Critically, by pre-implanting the animals, only minimal anesthesia was required to position them under the impactor.•The implant allows for flexibility in the number and severity of injuries and interval between impacts.Surface topography measurements are vital in industrial quality control. Linear roughness measurements are among the most preferred methods, being quick to perform and easy to interpret. The ISO 16610 standard series prescribes filters that can be used for most cases, but has limitations for restricted measurement lengths. This is because the standard filter type is a Gaussian filter, which like most instances of kernel convolution filters has no output near the edges of the profile, effectively shortening the length of the filtered output profile as compared to the input. In some cases, this leads to a lack of representative data after filtration. Especially in fields such as Additive Manufacturing (AM) this becomes a problem, due to the high "roughness to measurable data length"-ratio that characterizes complex AM parts. This paper describes a method that allows to overcome this limitation•A method for circular padding of short measured tracks is described and validated.•A flexible profile data post-processing tool was developed in MATLAB to grant users more control over the data analysis. Results obtained from roughness profiles long enough for normal ISO procedures are shown to not change significantly when circularly padded. When only a shorter section of the data is available, where the standard protocol would not be able to compute a filtered profile and related parameters anymore, the circular padding method is shown to lead to results that are in good agreement with the ISO standard procedures.Computational methods for machine learning (ML) have shown their meaning for the projection of potential results for informed decisions. Machine learning algorithms have been applied for a long time in many applications requiring the detection of adverse risk factors. This study shows the ability to predict the number of individuals who are affected by the COVID-19[1] as a potential threat to human beings by ML modelling. In this analysis, the risk factors of COVID-19 were exponential smoothing (ES). The Lower Absolute Reductor and Selection Operator, (LASSo), Vector Assistance (SVM), four normal potential forecasts, such as Linear Regression (LR)). [2] Each of these machine-learning models has three distinct kinds of predictions the number of newly infected COVID 19 people, mortality rates and the recovered COVID-19 estimates in the next 10 days. These approaches are better used in the latest COVID-19 situation, as shown by the findings of the analysis. The LR, that is effective in predicting new cases of corona, death numbers and recovery.