Abstract
Medical training in oral health includes interpreting images of the oral cavity. The use of extended datasets, real cases, and the ability to monitor the progress of students is beneficial. In this work, we investigate the feasibility of using a teledentistry solution to support medical training. The rOral integrated teledentistry solution, developed by the research team, allows the decoupling of image acquisition and image review: images of the oral cavity can be recorded using the camera of regular mobile devices, near the subject of care; the images are then uploaded to a secure storage and reviewed by experts, using a web environment. This workflow, primarily designed for remote oral health diagnosis (e.g.: population screening), can be used for dentistry education, allowing students to play the role of experts. A population of dentistry students was involved in the present study and used rOral to access anonymized cases to form a diagnosis. The results show that the existing solution, planned for remote diagnosis, is suitable for education and that smartphone-acquired images can be used in diagnosis assessment activities. © 2020, Springer Nature Switzerland AG.

Abstract
Background: The occurrence of an orofacial trauma can originate health, social, economic and professional problems. A 13-year boy suffered the avulsion of tooth 11 and 21, lost at the scenario. Methods: Three intraoral appliances were manufactured: A Hawley appliance with a central expansion screw and two central incisors (1), trumpet edentulous anterior tooth appliance (2) and a customized splint (3) were designed as part of the rehabilitation procedure. Objectively assessing the sound quality of the trumpet player with these new devices in terms of its spectral, temporal, and spectro-temporal audio properties. A linear frequency response microphone was adopted for precision measurement of pitch, loudness, and timbre descriptors. Results: Pitch deviations may result from the different intra-oral appliances due to the alteration of the mouth cavity, respectively, the area occupied and modification/interaction with the anatomy. This investigation supports the findings that the intra-oral appliance which occupies less volume is the best solution in terms of sound quality. Conclusions: Young wind instrumentalists should have dental impressions of their teeth made, so their dentist has the most reliable anatomy of the natural teeth in case of an orofacial trauma. Likewise, the registration of their sound quality should be done regularly to have standard parameters for comparison.

Abstract
Over the last years, robotics has increased its interest in learning human-like behaviors and activities. One of the most common actions searched, as well as one of the most fun to replicate, is the ability to play sports. This has been made possible with the steady increase of automated learning, encouraged by the tremendous developments in computational power and improved reinforcement learning (RL) algorithms. This paper implements a beginner Robot player for precision ball sports like boccia and bocce. A new simulated environment (PrecisionBall) is created, and a seven degree-of-freedom (DoF) robotic arm, is able to learn from scratch how to win the game and throw different types of balls towards the goal (the jack), using deep reinforcement learning. The environment is compliant with OpenAI Gym, using the MuJoCo realistic physics engine for a realistic simulation. A brief comparison of the convergence of different RL algorithms is performed. Several ball weights and various types of materials correspondent to bocce and boccia are tested, as well as different friction coefficients. Results show that the robot achieves a maximum success rate of 92.7% and mean of 75.7% for the best case. While learning to play these sports with the DDPG+HER algorithm, the robotic agent acquired some relevant skills that allowed it to win.

Abstract
Infrared observations of Sgr A* probe the region close to the event horizon of the black hole at the Galactic center. These observations can constrain the properties of low-luminosity accretion as well as that of the black hole itself. The GRAVITY instrument at the ESO VLTI has recently detected continuous circular relativistic motion during infrared flares which has been interpreted as orbital motion near the event horizon. Here we analyze the astrometric data from these flares, taking into account the effects of out-of-plane motion and orbital shear of material near the event horizon of the black hole. We have developed a new code to predict astrometric motion and flux variability from compact emission regions following particle orbits. Our code combines semi-analytic calculations of timelike geodesics that allow for out-of-plane or elliptical motions with ray tracing of photon trajectories to compute time-dependent images and light curves. We apply our code to the three flares observed with GRAVITY in 2018. We show that all flares are consistent with a hotspot orbiting at R similar to 9 gravitational radii with an inclination of i similar to 140 degrees. The emitting region must be compact and less than similar to 5 gravitational radii in diameter. We place a further limit on the out-of-plane motion during the flare.

Abstract
In this article we discuss how social tagging can be used to improve the methodology used for clustering evaluation. We analyze the impact of the integration of tags in the clustering process and its effectiveness. Following the semiotic theory, the own nature of tags allows the reflection of which ones should be considered depending on the interpretant (community of users, or tag writer). Using a case with the community of users as the interpretant, our novel clustering algorithm (k-C), which is based on community detection on a network of tags, was compared with the standard k-means algorithm. The results indicate that the k-C algorithm created more effective clusters. © 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
The development of wearable health systems has been the focus of many researchers who aim to find solutions in healthcare. Additionally, the large potential of textiles to integrate electronics, together with the comfort and usability they provide, has contributed to the development of smart garments in this area. In the field of neurological disorders with motor impairment, clinicians look for wearable devices that may provide quantification of movement symptoms. Neurological disorders affect different motion abilities thus requiring different needs in movement quantification. With this background we designed and developed an inertial textile-embedded wearable device that is adaptable to different movement-disorders quantification requirements. This adaptative device is composed of a low-power 9-axis inertial unit, a customised textile band and a web and Android cross application used for data collection, debug and calibration. The textile band comprises a snap buttons system that allows the attachment of the inertial unit, as well as its connection with the analog sensors through conductive textile. The resulting system is easily adaptable for quantification of multiple motor symptoms in different parts of the body, such as rigidity, tremor and bradykinesia assessments, gait analysis, among others. In our project, the system was applied for a specific use-case of wrist rigidity quantification during Deep Brain Stimulation surgeries, showing its high versatility and receiving very positive feedback from patients and doctors.