Thèse (M.Sc.) -- Université Laval, 1997. / Bibliogr.: f. 60-69. Publié aussi en version électronique.

Thesis (Ph. D.)--Ohio State University, 2005. / Title from first page of PDF file. Includes bibliographical references (p. 196-235).

This work uses high-speed video imaging to study the formation of filaments, during impact and rebounding of drops with polymer additives. We use PEO of different concentrations from 10 to 1000 ppm and study how drops rebound from various different surfaces: superhydrophilic, hydrophilic, hydrophobic, and superhydrophobic. Bouncing occurs for all surfaces at low impact velocities. We specifically focus on the phenomenon of the generation of polymer filaments, which are pulled out of the free surface of the drop during its rebounding from micro-pillared or rough substrates. We map the parameter regime, in terms of polymer concentration and impact Weber number, where the filaments are generated in the most repeatable manner. This occurs for regularly pillared surfaces and drops of 100 ppm PEO concentrations, where numerous separated filaments are observed. In contrast, for superhydrophobic coatings with random roughness the filaments tend to merge forming a branching structure. Impacts on inclined surfaces are used to deposit the filaments on top of the pillars for detailed study.

No description available.

Based on a study of existing solar filament and tracking methods, a fully automated solar filament detection and tracking method is presented. An adaptive thresholding technique is used in a segmentation phase to identify candidate filament pixels. This phase is followed by retrieving the actual filament area from a region grown filament by using statistical parameters and morphological operations. This detection technique gives the opportunity to develop an accurate spine extraction algorithm. Features including separation distance, orientation and average intensities are extracted and fed to a Neural Network (NN) classifier to merge broken filament components. Finally, the results for two consecutive images are compared to detect filament disappearance events, taking advantage of the maps resulting from converting solar images to Heliographic Carrington co-ordinates. The study has demonstrated the novelty of the algorithms developed in terms of them now all being fully automated; significantly the algorithms do not require any empirical values to be used whatsoever unlike previous techniques. This combination of features gives the opportunity for these methods to work in real-time. Comparisons with other researchers shows that the present algorithms represent the filaments more accurately and evaluate computationally faster - which could lead to a more precise tracking practice in real-time. An additional development phase developed in this dissertation in the process of detecting solar filaments is the detection of filament disappearances. Some filaments and prominences end their life with eruptions. When this occurs, they disappear from the surface of the Sun within a few hours. Such events are known as disappearing filaments and it is thought that they are associated with coronal mass ejections (CMEs). Filament disappearances are generally monitored by observing and analysing successive solar H-alpha images. After filament regions are obtained from individual H-alpha images, a NN classifier is used to categorize the detected filaments as Disappeared Filaments (DFs) or Miss-Detected Filaments (MDFs). Features such as Area, Length, Mean, Standard Deviation, Skewness and Kurtosis are extracted and fed to this neural network which achieves a confidence level of at least 80%. Comparing the results with other researchers shows high divergence between the results. The NN method shows better convergence with the results of the National Geophysical Data Centre (NGDC) than the results of the others researchers.

Yes / Solar imaging is currently an active area of research. A fast hybrid system for the automated detection of filaments in solar images is presented in this paper. The system includes three major stages. The central solar region is detected in the first stage using integral projections. Intensity filtering and image enhancement techniques are implemented in the second stage to enhance the quality of detection in the central region. Local detection windows are implemented in the third stage to detect the positions of filaments and to define various sized arrays to contain them. The extracted arrays are fed later to a neural network for verification purposes.

This dissertation talks about the dynamics of the drop impact in two parts, the impact of the drop on the deep liquid pool with singular jet and sound emission, and the bouncing drop with filaments on the superhydrophoic solid surface. First, we use experiments and simulations to study drop impacts on a deep liquid pool, with a focus on fine vertical jetting and underwater sound emission from entrapped bubbles, during the rebounding of the hemispherical crater. The much larger parametric complexity introduced by the use of two immiscible liquids, compared to that for the same liquid, leads to an extended variety of compound-dimple shapes. The fastest jet occurs from the rebounding of a telescope dimple shape without bubble pinch-off, at around 45 m/s, which leaves a toroidal micro-bubbles from the air-cusp at the base of the dimple. The finest jets have diameter of only 12 µm. A new focusing mechanism for singular jetting from collapsing drop-impact craters is then proposed based on high-resolution numerical simulations. The fastest jet is confined in a converging conical channel with the entrained air sheet providing a free-slip outer boundary condition. Sound can be emitted from the oscillation of the entrapped dimple-bubble, while the tiny bubble from the initial impact is induced to oscillate with the entrapped bubble, triggering the double crest of the acoustic signal. We track the compression of the bubble volume from the high-speed imaging and relate it to the hydrophone signal. In the second part, we investigate the impact of a polymeric drop on a superhydrophobic solid substrate with micropillar structure. The drop spreads on the substrate, wets the tops of the pillars, and rebounds out of the superhydrophobic soild surface. Numerous liquid filaments are stretched from the liquid drop to the attached adjacent pillars, and minuscule threads would be left on the top of the pillars using the inclined superhydrophobic solid surface. The well-organized exposed polymer threads are left on the top of the pillars after solvent evaporation. The thickness of the deposition of filament bundles using the bouncing method are thinner than those formed by drop evaporation or drop rolling from SEM (scanning electron microscope) observation.

No description available.

This research examines the role of actin cytoskeleton filaments in chondroinduction by cyclic hydrostatic pressurization. A chondroinductive hydrostatic pressurization system was developed and characterized. A pressure of 5 MPa at 1 Hz frequency, applied for 7200 cycles (4 hours intermittent) per day, induced chondrogenic differentiation in C3H10T1/2 cells while 1800 cycles (1 hour intermittent) did not induce chondrogenesis. Quantitative analysis of chondrogenesis was determined as sulfated glycosaminoglycan synthesis and rate of collagen synthesis while qualitative analysis was obtained as Alcian Blue staining and collagen type II immunostaining. Actin disruption using 2 uM Cytochalasin D inhibited the enhanced sGAG synthesis in the chondroinductive hydrostatic pressurization environment and significantly inhibited rate of collagen synthesis to the mean level lower than that of the non-pressurized group. These results suggest an involvement of actin cytoskeleton filaments in mechanotransduction of cyclic hydrostatic pressure.

No description available.