Prevention and healing of diabetes-related foot ulcers:Motivational interviewing, objective adherence and offloading

With this thesis the knowledge and understanding of the prevention and healing of foot ulcers in people with diabetes in clinical practice are expanded. First, it was shown that adherence to wearing orthopedic shoes is suboptimal in most people at moderate-to-high risk of foot ulceration. People with a consistent wearing pattern showed higher daily wearing times than those with an inconsistent pattern. Besides, orthopedic shoes were worn less during weekend days compared to weekdays. Secondly, following the triangulation of the qualitative and quantitative results of the application of MI it can be concluded that after a basic MI-training, podiatrists can effectively apply MI in daily clinical practice at a solid beginner level. Furthermore, the findings support implementation of MI in practice and encourage MI training in the primary podiatrist training and maintenance training for daily clinical practice. Thirdly, one podiatrist-led MI-consultation did not contribute to improving adherence to wearing orthopedic shoes in people with diabetes at low-to-high risk of foot ulceration. Finally, if a wound occurs due to low adherence or due to another reason, a knee-high and non-removable device ensures the best plantar pressure reduction. Overall, it can be concluded that there seems to be no simple standalone solution to prevent and heal diabetes-related foot ulcers and that improved communication of the whole multidisciplinary team with the patient is necessary to help patients at risk as good as possible. We hope that the findings of this thesis support researchers and clinicians in further investigating strategies to prevent and heal foot ulcers in people with diabetes

Hierarchical learning of sparse image representations using steered mixture-of-experts

Previous research showed highly efficient compression results for low bit-rates using Steered Mixture-of-Experts (SMoE), higher rates still pose a challenge due to the non-convex optimization problem that becomes more difficult when increasing the number of components. Therefore, a novel estimation method based on Hidden Markov Random Fields is introduced taking spatial dependencies of neighboring pixels into account combined with a tree-structured splitting strategy. Experimental evaluations for images show that our approach outperforms state-of-the-art techniques using only one robust parameter set. For video and light field modeling even more gain can be expected

A Micro-Raman Spectroscopic Study of Hydrazine-Treated Human Dental Calculus

Hydrazine has been used to remove organic components and to isolate the mineral(s) from human calculus. Micro-Raman measurements were performed on the mineral phase. After the hydrazine-treatment, not only a large reduction in fluorescence but also an increase in Raman signal was observed. The treatment was essential in minimizing thermally-induced chemical changes which could otherwise occur to the original calculus mineral due to the intense laser light. The Raman spectral features of the mineral were nearly all identical among the Raman spectra obtained at many randomly-selected sites by the micro-Raman microbe with a lateral resolution of approximately 1 μm, and were consistent with those of impure hydroxyapatite containing CO32- and HPO42-. The spectra contained typical hydroxyapatite bands including PO43- bands of the v1, v2, v3 and v4 modes and one OH- stretch band. Other minor bands due to the CO32- v1 and v3 modes and bands possibly due to the HPO42- v1, v2 and v4 modes were observable by the technique despite the hydrazine-treatment that could in principle remove the HPO4 and CO3 ions from the mineral. In comparison with pure synthetic hydroxyapatite, the intensity of the OH- stretch band relative to that of the PO43- v1 band was approximately 70% weaker, and the bandwidth of the phosphate v1 band was 200% broader, reflecting various crystal imperfections presumably present in the calculus mineral

Detection of Permanent Magnet DC Motor Failure Due to Brush Wear Using Parameter Estimation and Statistical Analysis

Failure detection of DC motors is a common study, and could be extremely useful in real world applications. Undiagnosed eminent motor failure could cause a range of effects, and without maintenance will inevitably occur. Motor faults can be classified as electrical or mechanical, both with wide ranges of causes. Electrical failure includes stator or rotor winding faults, inverter faults, position of sensor faults in brushless motors, bearing faults, and brush faults. Mechanical faults include bearing faults, broken rotor bar, rotor eccentricity faults, end ring faults, and load faults. The aim of this study was to observe the effect of brush fault within a permanent magnet DC (PMDC) motor. Carbon contact brushes are used in PMDC motors to transmit electrical current from the stator of the motor to the rotor of the motor, ensuring the rotation of the commutators. Over time, the carbon contact becomes worn down from commutators continually moving across them. As the contacts length is decreased, the spring holding it in place becomes more stretched out, putting in more effort to hold the brush in place. This introduces a resistance, referred to as a contact resistance, that can affect the motor speed and performance. Changes in speed and resistance can be measured and observed, and curves can be fitted to their relationship with statistical significance. We can also create a simulation method using basic differential equations that describe the motor and introducing random noise to the simulation with generation of random numbers for the motor parameters. Finally, a confidence interval is generated, and eminent motor failure can be predicted when values measured values stray from the simulated path. Erratic motor behavior can also be observed at the point of eminent motor failure