Nonparametric Modeling of Dynamic Functional Connectivity in fMRI Data

Dynamic functional connectivity (FC) has in recent years become a topic of interest in the neuroimaging community. Several models and methods exist for both functional magnetic resonance imaging (fMRI) and electroencephalography (EEG), and the results point towards the conclusion that FC exhibits dynamic changes. The existing approaches modeling dynamic connectivity have primarily been based on time-windowing the data and k-means clustering. We propose a non-parametric generative model for dynamic FC in fMRI that does not rely on specifying window lengths and number of dynamic states. Rooted in Bayesian statistical modeling we use the predictive likelihood to investigate if the model can discriminate between a motor task and rest both within and across subjects. We further investigate what drives dynamic states using the model on the entire data collated across subjects and task/rest. We find that the number of states extracted are driven by subject variability and preprocessing differences while the individual states are almost purely defined by either task or rest. This questions how we in general interpret dynamic FC and points to the need for more research on what drives dynamic FC.Comment: 8 pages, 1 figure. Presented at the Machine Learning and Interpretation in Neuroimaging Workshop (MLINI-2015), 2015 (arXiv:1605.04435

Stabilization of metastable tetragonal zirconia nanocrystallites by surface modification

Metastable tetragonal zirconia nanocrystallites were studied in humid air and in water at room temperature (RT). A stabilizing effect of different surfactants on the tetragonal phase was observed. Furthermore, the phase stability of silanized metastable tetragonal zirconia nanocrystallites was tested by prolonged boiling in water. The samples were analyzed with X-ray photoelectron spectroscopy (XPS) and X-ray diffraction (XRD). Changes in the monoclinic volume fraction in the samples were calculated. A number of surfactants were screened for their ability to stabilize the tetragonal phase upon exposure to humidity. Only silanes and phosphate esters of these were able to stabilize the tetragonal phase in water. Even as small amounts of silanes as 0.25 silane molecule per nm2 are able to stabilize the tetragonal phase in water at RT. Aminopropyl trimethoxy silane and γ-methacryloxypropyl trimethoxy silane were even capable of preventing phase transformation during boiling for 48 h in water

Towards Lane-Keeping Electronic Stability Control for Road-Vehicles

The emerging new idea of lane-keeping electronic stability control is investigated. In a critical situation, such as entering a road curve at excessive speed, the optimal behavior may differ from the behavior of traditional ESC, for example, by prioritizing braking over steering response. The important question that naturally arises is if this has a significant effect on safety. The main contribution here is to give a method for some first quantitative measures of this. It is based on optimal control, applied to a double-track chassis model with wheel dynamics and high-fidelity tire-force modeling. The severity of accidents grows with the square of the kinetic energy for high velocities, so using kinetic energy as a measure will at least not overestimate the usefulness of the new safety system principle. The main result is that the safety gain is significant compared to traditional approaches based on yaw rotation, for several situations and different road-condition parameters