Publications

Ocular current stimulation (oCS) with weak current intensities (a few mA) has shown positive effects on retinal nerve cells, which indicates that neurodegenerative ocular diseases could be treated with current stimulation of the eye. During oCS, a significant polarity-independent reduction in the characteristic P50 amplitude of a pattern-reversal electroretinogram was found, while no current stimulation effect was found for a full field electroretinogram (ffERG). The ffERG data indicated a trend for a polarity-dependent influence during oCS on the photopic negative response (PhNR) wave, which represents the sum activity of the retinal ganglion cells. Therefore, an ffERG with adjusted parameters for the standardized measurement of the PhNR wave was combined with simultaneous oCS to study the potential effects of direct oCS on cumulative ganglion cell activity. Compared with that measured before oCS, the PhNR amplitude in the cathodal group increased significantly during current stimulation, while in the anodal and sham groups, no effect was visible (α = 0.05, pcathodal = 0.006*). Furthermore, repeated-measures ANOVA revealed a significant difference in PhNR amplitude between the anodal and cathodal groups as well as between the cathodal and sham groups (p* ≤ 0.0167, pcathodal - anodal = 0.002*, pcathodal - sham = 0.011*).

In this work, Sm3+ and Zn2+ co-substituted magnetite Zn0.1SmxFe2.9-xO4 (x = 0.0, 0.01, 0.02, 0.03, 0.04 and 0.05) nanoparticles, have been prepared via co-precipitation method and were electrostatically and sterically stabilized by citric acid and pluronic F127 coatings. The coated nanoparticles were well dispersed in an aqueous solution (pH 5.5). Magnetic and structural properties of the nanoparticles and their ferrofluids were studied by different methods. XRD studies illustrated that all as-prepared nanoparticles have a single phase spinel structure, with lattice constants affected by samarium cations substitution. The temperature dependence of the magnetization showed that Curie temperatures of the uncoated samples monotonically increased from 430 to 480 ˚C as Sm3+ content increased, due to increase in A-B super-exchange interactions. Room temperature magnetic measurements exhibited a decrease in saturation magnetization of the uncoated samples from 98.8 to 71.9 emu/g as the Sm3+ content increased, which is attributed to substitution of Sm3+ (1.5 µB) ions for Fe3+ (5 µB) ones in B sublattices. FTIR spectra confirmed that Sm3+ substituted Zn0.1SmxFe2.9-xO4 nanoparticles were coated with both citric acid and pluronic F127 properly. The mean particle size of the coated nanoparticles was 40 nm. Calorimetric measurements showed that the maximum SLP and ILP values obtained for Sm3+ substituted nanoparticles were 259 W/g and 3.49 nHm2/kg (1.08 mg/ml, measured at f = 290 kHz and H = 16kA/m), respectively, that are related to the sample with x = 0.01. Magnetic measurements revealed coercivity, which indicated that hysteresis loss may represent a substantial portion in heat generation. Our results show that these ferrofluids are potential candidates for magnetic hyperthermia applications.

With the increase in available data and the stricter control requirements for mineral processes, the development of automated methods for data processing and model creation are becoming increasingly important. In this paper, the application of data quality assessment methods for the development of semirigorous and empirical models of a primary milling circuit in a platinum concentrator plant is investigated to determine their validity and how best to handle multivariate input data. The data set used consists of both routine operating data and planned step tests. Applying the data quality assessment method to this data set, it was seen that selecting the appropriate subset of variables for multivariate assessment was difficult. However, it was shown that it was possible to identify regions of sufficient value for modeling. Using the identified data, it was possible to fit empirical linear models and a semirigorous nonlinear model. As expected, models obtained from the routine operating data were, in general, worse than those obtained from the planned step tests. However, using the models obtained from routine operating data as the initial seed models for the automated advanced process control methods would be extremely helpful. Therefore, it can be concluded that the data quality assessment method was able to extract and identify regions sufficient and acceptable for modeling.

Weighted automata are classical finite automata in which the transitions carry weights. These weights may model quantitative properties like the amount of resources needed for executing a transition or the probability or reliability of its successful execution. Using weighted automata, we may also count the number of successful paths labeled by a given word. As an introduction into this field, we present selected classical and recent results concentrating on the expressive power of weighted automata.

Optically pumped magnetometers (OPMs) are quickly widening the scopes of noninvasive neurophysiological imaging. The possibility of placing these magnetic field sensors on the scalp allows not only to acquire signals from people in movement, but also to reduce the distance between the sensors and the brain, with a consequent gain in the signal-to-noise ratio. These advantages make the technique particularly attractive to characterise sources of brain activity in demanding populations, such as children and patients with epilepsy. However, the technology is currently in an early stage, presenting new design challenges around the optimal sensor arrangement and their complementarity with other techniques as electroencephalography (EEG). In this article, we present an optimal array design strategy focussed on minimising the brain source localisation error. The methodology is based on the Cramér-Rao bound, which provides lower error bounds on the estimation of source parameters regardless of the algorithm used. We utilise this framework to compare whole head OPM arrays with commercially available electro/magnetoencephalography (E/MEG) systems for localising brain signal generators. In addition, we study the complementarity between EEG and OPM-based MEG, and design optimal whole head systems based on OPMs only and a combination of OPMs and EEG electrodes for characterising deep and superficial sources alike. Finally, we show the usefulness of the approach to find the nearly optimal sensor positions minimising the estimation error bound in a given cortical region when a limited number of OPMs are available. This is of special interest for maximising the performance of small scale systems to ad hoc neurophysiological experiments, a common situation arising in most OPM labs.