Objective: To evaluate the risk of root resorption, individual finite element models (FEMs) of extracted human maxillary first premolars were created, and the distribution of the hydrostatic pressure in the periodontal ligament (PDL) of these models was simulated. Materials and Methods: A continuous lingual torque of 3 Nmm and 6 Nmm respectively was applied in vivo to the aforementioned teeth. After extraction, FEMs of these double-rooted teeth were created based on high-resolution microcomputed tomographics (micro CT, voxel size: 35 microns). This high volumetric resolution made the recognition of very small resorption lacunae possible. Scanning electron micrographs of the root surfaces were created as well. This enabled the investigation of advantages and disadvantages of the different imaging techniques from the viewpoint of the examination of root resorption. Using the FEMs, the same loading conditions as applied in vivo were simulated. Results:The results of clinical examination and simulations were compared using the identical roots of the teeth. The regions that showed increased hydrostatic pressure (Ͼ0.0047 MPa) correlated well with the locations of root resorption for each tooth. Increased torque resulted in increased high-pressure areas and increased magnitudes of hydrostatic pressure, correlating with the experiments. Conclusion: If hydrostatic pressure exceeds typical human capillary blood pressure in the PDL, the risk of root resorption increases.

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The periodontal ligament (PDL), as other soft biological tissues, shows a strongly non-linear and time-dependent mechanical response and can undergo large strains under physiological loads. Therefore, the characterization of the mechanical behavior of soft tissues entails the definition of constitutive models capable of accounting for geometric and material non-linearity. The microstructural arrangement determines specific anisotropic properties. A hyperelastic anisotropic formulation is adopted as the basis for the development of constitutive models for the PDL and properly arranged for investigating the viscous and damage phenomena as well to interpret significant aspects pertaining to ordinary and degenerative conditions. Visco-hyperelastic models are used to analyze the time-dependent mechanical response, while elasto-damage models account for the stiffness and strength decrease that can develop under significant loading or degenerative conditions. Experimental testing points out that damage response is affected by the strain rate associated with loading, showing a decrease in the damage limits as the strain rate increases. These phenomena can be investigated by means of a model capable of accounting for damage phenomena in relation to viscous effects. The visco-hyperelastic-damage model developed is defined on the basis of a Helmholtz free energy function depending on the strain-damage history. In particular, a specific damage criterion is formulated in order to evaluate the influence of the strain rate on damage. The model can be implemented in a general purpose finite element code. The accuracy of the formulation is evaluated by using results of experimental tests performed on animal model, accounting for different strain rates and for strain states capable of inducing damage phenomena. The comparison shows a good agreement between numerical results and experimental data.

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Modern consumer and industrial unmanned aerial vehicles (UAVs) are easy to use flying sensor platforms. They offer stable flight, good maneuverability, hovering, and even waypoint flights in autopilot mode. For stabilization and localization sensors such as internal measurement units (IMUs) including gyroscope and accelerometer, barometric sensor, and global navigation satellite system (GNSS) are used. To sense the direct environment of the UAV, for instance for collision avoidance or fully automated flights, additional sensors are needed. State-of-the-art combinations of infrared sensors, ultrasonic sensors as well as vision based sensors (monocular and/or stereo vision) capture the close vicinity. Using radar sensors is advantageous, as they are able to directly sense range and velocity and are not prone to lighting conditions and contrast. With the help of a multi-channel radar, the angular information can also be extracted. UAVs can lift a considerable payload with respect to their size. All these characteristics combined with radar sensors make them a promising tool for a large variety of applications.