 So, today I'm going to talk about SENSUM, a research project on how to use sensing technology to monitor boulders on landslides or woody debris in river catchment. And the PI of this project is Giorgio Bennett from the University of Exeter and Lico Aya Irene Maranzella, now at the University of Twente, but firmly at the University of Plymouth and Aldina Franco at the University of East Anglia. I'm just a postdoc. And this project was possible thanks to collaboration with a wide number of different academic and non-academic institutions, as you see in this slide. And the recent advances in technologies and microelectronics offered new opportunities to use, to study the stability of boulders on landslides and woody debris in river catchments. And smart sensors are small, lightweight, and cheap devices that allows to do this. So we use the motion smart sensor that were originally conceived to track the movements of birds, and we install these sensors in boulders on landslides or large wood as loose pieces in river catchment or as part of nature-based solutions such as large woody debris dams that are used to mitigate flood hazards. Your slides, over to the next slide now. I have no control. There we go. Next one. Okay. Good. So a sensor has organized in different parts. So we have field experiments. We install these sensors in different landslides sites in the UK, in the south of France, and in Nepal. And we did the same also for some river sites, mainly in the UK, in Dartmoor, in Cambria, and the north of Wales. And then we tested this technology also in laboratory experiments. We wanted to understand how reliable were these sensors and how to use these sensors to study the movements. And the data collected from the lab for laboratory experiments and from the field experiments were used to create a larger data set that was investigated using the machine learning algorithms. Then the last part of Samsung was to design visualizations, physically based visualization and game design visualizations to improve our communications on landslides and flood hazards. And also on this last part, for this last part, we had also to develop a web interface to see the data coming through, the data collected from the sensor coming through and to see if it was possible to use this data as a foundation of some sort of early warning system. So this is just an example of the sensor that were installed in lime ridges. So we had concrete, we have multiple concrete cubes, and we installed the sensor in a borehole, and then the borehole was closed with resins. And then when the movement was detected, the sensor was able to send the data to a gateway that was installed in Charmouth, and then from the gateway data was sent to a cloud and we were able to download the data from the cloud. These are just some of the results that were corrected recently. The sensor were able to detect movements, even large movements of 10 meters, and thanks to the different sensors, we were able to understand when the cube was embedded in the... whether the cube remained embedded or not in the landslides during this movement. Then the same sensor were installed in cobbles in very small streams, and the three sensors, the accelerometer, the gyroscope and the magnetometer, when there was a different mode of movement, so when there was basically the threshold between sliding and rolling, and this was something... this is something important for sediment transport. And then we also tested these sensors, of course, in the lab. We combined the sensors together in the lab, and thanks to that, we were able to give a full representation of the movement and to compute a metric to understand, in this case the total kinetic energy, to understand when the cobble was rolling and when the cobble was simply sliding. In another set of experiments, we designed a laboratory analog of a large wooded evidence, and we used the sensor to study the vibration of the dam, and we tried to characterize this vibration to see if there was any connection with the constraints, with the lateral constraints of the dam. And then, by combining the sensors, the output from the sensor, we were able to give also a full motion representation of a wooded owl travelling along the flume, and this was important to understand the transport of wooded debris in streams. Thanks to the collaboration with the University of Manchester, specifically with Edamite Town, Ben Rogers and George Fortacas, we were able to use a numerical model, an SPH model, World Physics, to validate the experimental results, especially those we got from the lab. So we compared the experimental results and the numerical results and the behaviour, the average behaviour, the average experimental behaviour, was matched by the numerical simulations. On the other hand, we used the numerical simulation also to create nice visualizations, as you see the collapse of a large wooded epiderm, and that wooded epiderm was generated using a 3D photogrammatic rendering, and the pictures are taken from a site in Cumbria, from T-Bay catchment. Physical-based visualizations were also combined to other visualizations developed using game design software. This was possible thanks to a collaboration with the College of Art at the University of Plymouth, and the idea is to try to combine these two visualizations together, the physical-based ones and the fictitious ones, for a final event, a workshop that is going to be at the beginning of December, and we are going to use the immersion vision theatre, so the dome, to use these 3D visualizations and to make the content of the project more accessible to the audience. And this is all the people involved in the project. This is it. Thank you, Alessandro.