Blood-brain barrier alterations in multiple sclerosis

Dijkstra, C.D. [Promotor]Vries, H.E. de [Copromotor

Effect of quantum confinement on the dielectric function of PbSe

Monolayers of lead selenide nanocrystals of a few nanometers in height have been made by electrodeposition on a Au(111) substrate. These layers show a thickness-dependent dielectric function, which was determined using spectroscopic ellipsometry. The experimental results are compared with electronic structure calculations of the imaginary part of the dielectric function of PbSe nanocrystals. We demonstrate that the size-dependent variation of the dielectric function is affected by quantum confinement at well-identifiable points in the Brillouin zone, different from the position of the band-gap transition

Use of induced acceleration to quantify the (de)stabilization effect of external and internal forces on postural responses

Due to the mechanical coupling between the body segments, it is impossible to see with the naked eye the causes of body movements and understand the interaction between movements of different body parts. The goal of this paper is to investigate the use of induced acceleration analysis to reveal the causes of body movements. We derive the analytical equations to calculate induced accelerations and evaluate its potential to study human postural responses to support-surface translations. We measured the kinematic and kinetic responses of a subject to sudden forward and backward translations of a moving platform. The kinematic and kinetics served as input to the induced acceleration analyses. The induced accelerations showed explicitly that the platform acceleration and deceleration contributed to the destabilization and restabilization of standing balance, respectively. Furthermore, the joint torques, coriolis and centrifugal forces caused by swinging of the arms, contributed positively to stabilization of the center of mass. It is concluded that induced acceleration analyses is a valuable tool in understanding balance responses to different kinds of perturbations and may help to identify the causes of movement in different pathologies

VIENA2: A Driving Anticipation Dataset

Action anticipation is critical in scenarios where one needs to react before the action is finalized. This is, for instance, the case in automated driving, where a car needs to, e.g., avoid hitting pedestrians and respect traffic lights. While solutions have been proposed to tackle subsets of the driving anticipation tasks, by making use of diverse, task-specific sensors, there is no single dataset or framework that addresses them all in a consistent manner. In this paper, we therefore introduce a new, large-scale dataset, called VIENA2, covering 5 generic driving scenarios, with a total of 25 distinct action classes. It contains more than 15K full HD, 5s long videos acquired in various driving conditions, weathers, daytimes and environments, complemented with a common and realistic set of sensor measurements. This amounts to more than 2.25M frames, each annotated with an action label, corresponding to 600 samples per action class. We discuss our data acquisition strategy and the statistics of our dataset, and benchmark state-of-the-art action anticipation techniques, including a new multi-modal LSTM architecture with an effective loss function for action anticipation in driving scenarios.Comment: Accepted in ACCV 201

Responding to Major Burn Disasters in Resource-Limited Settings: Lessons Learned From an Oil Tanker Explosion in Nakuru, Kenya.

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