To benchmark the dose from paediatric head and chest examinations on CT scanners throughout Scotland, to identify scanners that may require optimisation and to provide optimisation advice based on the protocols from better performing scanners. Anthropomorphic phantoms corresponding to 1, 5 and 10-year olds were sent to 50 CT scanners around Scotland. Head and chest examinations were undertaken by local staff using local techniques on each scanner with each phantom, and details of the protocols used were recorded. CTDIvol and DLP were recorded post-scan. There is a significant variation in performance throughout Scotland. For head examinations, the highest DLP is 13 times the lowest for an equivalent sized phantom. For chest examinations, the highest is 128 times the lowest for an equivalent sized phantom. The wide range of CT dose measurements indicates the potential for variation in image quality across Scotland. Feedback has been provided to all participating sites on their individual results compared to the national data set. Specific feedback was provided where relevant on potential considerations for optimisation. Advances in knowledge Scanners that may be undertaking paediatric CT head and chest examinations in a sub-optimal manner throughout Scotland have been identified along with those aspects of a scan protocol that are most likely to lead to sub-optimal performance. The wide range of CT dose measurements indicates the potential for variation in image quality across Scotland. Feedback has been provided to all participating sites on their individual results compared to the national data set. Specific feedback was provided where relevant on potential considerations for optimisation. Advances in knowledge Scanners that may be undertaking paediatric CT head and chest examinations in a sub-optimal manner throughout Scotland have been identified along with those aspects of a scan protocol that are most likely to lead to sub-optimal performance.Magnetotransport properties of zigzag and armchair graphene nanoribbons that are in contact with superconductors are investigated using a tight-binding model. The cyclotron orbital motion together with the quantum interference under the coexistence of Andreev and normal reflections gives rise to a number of oscillations in characteristic magnetic-field regimes when the superconducting coupling is weak. The oscillations become irregular and/or suppressed as the coupling is made strong. The period of the oscillations differs from that when a nonrelativistic two-dimensional electron gas is employed rather than the graphene sheet. The modifications of the oscillations are attributed to the phase shift associated with the reflection from the graphene-superconductor interface. The presence of a magnetic field suppresses the quantum blocking of Andreev transmission, which occurs for the edge mode of zigzag nanoribbons, in the same way regardless of it being induced by the Andreev retro- or specular reflection.Certain bat species (familyRhinolophidae) dynamically deform their emission baffles (noseleaves) and reception baffles (pinnae) during echolocation. Prior research using numerical models, laboratory characterizations, and experiments with simple targets have suggested that this dynamics may manifest itself in time-variant echo signatures. Since the pronounced random nature of echoes from natural targets such as foliage has not been reflected in these experiments, we have collected a large number (>55 000) of foliage echoes outdoors with a sonar head that mimics the dynamic periphery in bats. The echo data was processed with a custom auditory processing model to create spike-based echo representations. Deep-learning classifiers were able to estimate the dynamic state of the periphery, i.e., static or dynamic, based on single echoes with accuracies of up to 80%. This suggests that the effects of the peripheral dynamics are present in the bat brains and could hence be used by the animals. The best classification performances were obtained for data obtained within a spatially confined area. Hence, if the bat brains suffer from the same generalization issues, they would have to have a way to adapt their neural echo processing to such local fluctuations to exploit the dynamic effects successfully.In this study, a new ATCM phantom was developed to test the performance of the automatic tube current modulation (ATCM) of computed tomography (CT) scanners.. Based on the Chinese reference man and Monte Carlo simulations of x-ray attenuation, a more realistic ATCM phantom made of polymethyl methacrylate was developed. The phantom has a length of 20 cm, and it can be used to measure the dose profile along the central axis using 19 real-time MOSFET detectors. The image noise can be calculated slice by slice in the phantom's center. Test experiments showed that the phantom could initiate tube current modulation under different modulation levels of CT scans, and the actual effects of ATCM could be evaluated with the aid of the dose profile measurements. Using the measured dose profiles and image noise, the preferred dose can easily be identified from a choice of different modulation levels. The new phantom developed in this study can be used to test the ATCM performance of CT scanners, and is useful for further studies of the optimization of CT scan protocols with ATCM.Objective.The electroencephalography (EEG)-based brain-computer interfaces (BCIs) have been used in the control of robotic arms. https://www.selleckchem.com/ The performance of non-invasive BCIs may not be satisfactory due to the poor quality of EEG signals, so the shared control strategies were tried as an alternative solution. However, most of the existing shared control methods set the arbitration rules manually, which highly depended on the specific tasks and developer's experience. In this study, we proposed a novel shared control model that automatically optimized the control commands in a dynamical way based on the context in real-time control. Besides, we employed the hybrid BCI to better allocate commands with multiple functions. The system allowed non-invasive BCI users to manipulate a robotic arm moving in a three-dimensional (3D) space and complete a pick-place task of multiple objects.Approach.Taking the scene information obtained by computer vision as a knowledge base, a machine agent was designed to infer the user's intention and generate automatic commands.