瑞士

The number of natural disasters has risen significantly in recent years, and with climate change there is no end in sight. Consequently, the demands on rescue forces around the world are increasing. For this reason, I asked myself what I can do to improve the work of rescue teams. Advances in artificial intelligence and drone technology enable new possibilities for problem solving. Based on the technological advances mentioned above, an autonomous Search and Rescue drone was developed as part of this project. The system assists rescue workers in searching for survivors of natural disasters or missing people. This paper also suggests a method for prioritizing survivors based on their vitality. The system was implemented using a commercial Parrot ANAFI drone and Python. The software was tested on a simulated drone. To simplify the development, the whole system was divided into the following subsystems: Navigation System, Search System and Mission Abort System. These subsystems were tested independently. The testing of solutions and new concepts were performed using smaller test programs on the simulated drone and finally on the physical drone. The Search and Rescue system was successfully developed. The person detection system can detect humans and distinguish them from the environment. Furthermore, based on the movements of a person, the system can distinguish whether the person is a rescuer or a victim. In addition, an area to be flown over can be defined. If something goes wrong during the mission, the mission can be aborted by the Mission Abort System. In the simulation, the predefined area can successfully be flown over. Unfortunately, controlling the physical drone does not work. It stops in the air after takeoff due to the firmware of the drone. It does not change the flight state of the drone, which results in all subsequent commands from the system being ignored. This paper shows that artificial intelligence and drone technologies can be combined to deliver better rescue services. The same system can be applied to other applications.

The number of natural disasters has risen significantly in recent years, and with climate change there is no end in sight. Consequently, the demands on rescue forces around the world are increasing. For this reason, I asked myself what I can do to improve the work of rescue teams. Advances in artificial intelligence and drone technology enable new possibilities for problem solving. Based on the technological advances mentioned above, an autonomous Search and Rescue drone was developed as part of this project. The system assists rescue workers in searching for survivors of natural disasters or missing people. This paper also suggests a method for prioritizing survivors based on their vitality. The system was implemented using a commercial Parrot ANAFI drone and Python. The software was tested on a simulated drone. To simplify the development, the whole system was divided into the following subsystems: Navigation System, Search System and Mission Abort System. These subsystems were tested independently. The testing of solutions and new concepts were performed using smaller test programs on the simulated drone and finally on the physical drone. The Search and Rescue system was successfully developed. The person detection system can detect humans and distinguish them from the environment. Furthermore, based on the movements of a person, the system can distinguish whether the person is a rescuer or a victim. In addition, an area to be flown over can be defined. If something goes wrong during the mission, the mission can be aborted by the Mission Abort System. In the simulation, the predefined area can successfully be flown over. Unfortunately, controlling the physical drone does not work. It stops in the air after takeoff due to the firmware of the drone. It does not change the flight state of the drone, which results in all subsequent commands from the system being ignored. This paper shows that artificial intelligence and drone technologies can be combined to deliver better rescue services. The same system can be applied to other applications.

As part of the European project GEOTHERMICA - ERA NET and in order to assess the capacity of heat storage in Switzerland, the Centre d'Hydrogéologie et de Géothermie de l'Université de Neuchâtel (CHYN) is taking part in the HEATSTORE project, launched in 2018. The latter is expected to lead to commercial heat storage projects in the near future in Geneva and Bern, in fractured aquifers. The evaluation of the geological characteristics of these aquifers is essential to understand the thermal energy transport processes of fractured aquifers. For this, it is necessary to be able to measure the temperature distribution along boreholes. Thus, the study focuses on the evaluation of a distributed temperature system (DTS) and its optical fiber in order to determine its operation, limits and potential for use in geothermal energy. Laboratory and field tests have been carried out that the water temperature deviation measurements, with a scan time of 30 s, are reliable at less than 0.5°C at +/-5 m over 500 m of fiber. For absolute temperature values, however, a bath and a reference probe must be used to recalculate the absolute temperature to within 0.5°C. The acquired data are essential for a broader understanding of the locations of fractured and karstified aquifers at Concise, allowing the system to be used to better understand the potential for water storage at a depth of 45°C at 35 m.

The surgical mask has been our daily companion since the outbreak of the Corona pandemic. The nonwovens (outer layers, not the filter membrane) from which the surgical mask is constructed consist of very long and thin polypropylene fibers. This leads to the question of whether microplastics are released during breathing through the surgical mask, which could enter the respiratory tract or the lungs. This would have a negative impact on our health, depending on the size of the detached fiber fragments - the smaller the worse because they can enter much deeper in our respiratory tract. In order to investigate the question of whether fiber fragments are released during breathing through a surgical mask, a filtration device was built. The filters were examined under an optical microscope after filtration. If fiber fragments would detach from the surgical mask, they would be found on the filter. Different surgical masks were tested, those that were not worn at all to surgical masks that were worn all day. It was found that fiber fragments were coming off the surgical masks. There were different fiber fragment types. Some fiber fragments were still undamaged (exhibited nice fractures), while others were frayed. Clump-like fragments occurred, but also smaller fine fiber fragments. All these different fiber fragments had a certain size, so that they could be called microplastics. The remarkable result of the whole study is that there is a direct correlation between the wearing time of the surgical mask and the number of detaching fiber fragments. In the case of the unworn surgical masks, 10 times fewer fiber fragments occurred during filtration than in the case of the surgical masks that were worn all day.