Modelling vacuum arcs : from plasma initiation to surface interactions

A better understanding of vacuum arcs is desirable in many of today's 'big science' projects including linear colliders, fusion devices, and satellite systems. For the Compact Linear Collider (CLIC) design, radio-frequency (RF) breakdowns occurring in accelerating cavities influence efficiency optimisation and cost reduction issues. Studying vacuum arcs both theoretically as well as experimentally under well-defined and reproducible direct-current (DC) conditions is the first step towards exploring RF breakdowns. In this thesis, we have studied Cu DC vacuum arcs with a combination of experiments, a particle-in-cell (PIC) model of the arc plasma, and molecular dynamics (MD) simulations of the subsequent surface damaging mechanism. We have also developed the 2D Arc-PIC code and the physics model incorporated in it, especially for the purpose of modelling the plasma initiation in vacuum arcs. Assuming the presence of a field emitter at the cathode initially, we have identified the conditions for plasma formation and have studied the transitions from field emission stage to a fully developed arc. The 'footing' of the plasma is the cathode spot that supplies the arc continuously with particles; the high-density core of the plasma is located above this cathode spot. Our results have shown that once an arc plasma is initiated, and as long as energy is available, the arc is self-maintaining due to the plasma sheath that ensures enhanced field emission and sputtering. The plasma model can already give an estimate on how the time-to-breakdown changes with the neutral evaporation rate, which is yet to be determined by atomistic simulations. Due to the non-linearity of the problem, we have also performed a code-to-code comparison. The reproducibility of plasma behaviour and time-to-breakdown with independent codes increased confidence in the results presented here. Our MD simulations identified high-flux, high-energy ion bombardment as a possible mechanism forming the early-stage surface damage in vacuum arcs. In this mechanism, sputtering occurs mostly in clusters, as a consequence of overlapping heat spikes. Different-sized experimental and simulated craters were found to be self-similar with a crater depth-to-width ratio of about 0.23 (sim) - 0.26 (exp). Experiments, which we carried out to investigate the energy dependence of DC breakdown properties, point at an intrinsic connection between DC and RF scaling laws and suggest the possibility of accumulative effects influencing the field enhancement factor.

Role and function of nonmuscle alpha-actinin-1 and -4 in regulating distinct subcategories of actin stress fibers in mammalian cells

Actin stress fibers are dynamic structures in the cytoskeleton, which respond to mechanical stimuli and affect cell motility, adhesion and invasion of cancer cells. In nonmuscle cells, stress fibers have been subcategorized to three distinct stress fiber types: dorsal and ventral stress fibers and transverse arcs. These stress fibers are dissimilar in their subcellular localization, connection to substratum as well as in their dynamics and assembly mechanisms. Still uncharacterized is how they differ in their function and molecular composition. Here, I have studied involvement of nonmuscle alpha-actinin-1 and -4 in regulating distinct stress fibers as well as their localization and function in human U2OS osteosarcoma cells. Except for the correlation of upregulation of alpha-actinin-4 in invasive cancer types very little is known about whether these two actinins are redundant or have specific roles. The availability of highly specific alpha-actinin-1 antibody generated in the lab, revealed localization of alpha-actinin-1 along all three categories of stress fibers while alphaactinin-4 was detected at cell edge, distal ends of stress fibers as well as perinuclear regions. Strikingly, by utilizing RNAi-mediated gene silencing of alpha-actinin-1 resulted in specific loss of dorsal stress fibers and relocalization of alpha-actinin-4 to remaining transverse arcs and ventral stress fibers. Unexpectedly, aberrant migration was not detected in cells lacking alpha-actinin-1 even though focal adhesions were significantly smaller and fewer. Whereas, silencing of alpha-actinin-4 noticeably affected overall cell migration. In summary, as part of my master thesis study I have been able to demonstrate distinct localization and functional patterns for both alpha-actinin-1 and -4. I have identified alpha-actinin-1 to be a selective dorsal stress fiber crosslinking protein as well as to be required for focal adhesion maturation, while alpha-actinin-4 was demonstrated to be fundamental for cell migration.

Algorithms for Exact Structure Discovery in Bayesian Networks

Bayesian networks are compact, flexible, and interpretable representations of a joint distribution. When the network structure is unknown but there are observational data at hand, one can try to learn the network structure. This is called structure discovery. This thesis contributes to two areas of structure discovery in Bayesian networks: space--time tradeoffs and learning ancestor relations. The fastest exact algorithms for structure discovery in Bayesian networks are based on dynamic programming and use excessive amounts of space. Motivated by the space usage, several schemes for trading space against time are presented. These schemes are presented in a general setting for a class of computational problems called permutation problems; structure discovery in Bayesian networks is seen as a challenging variant of the permutation problems. The main contribution in the area of the space--time tradeoffs is the partial order approach, in which the standard dynamic programming algorithm is extended to run over partial orders. In particular, a certain family of partial orders called parallel bucket orders is considered. A partial order scheme that provably yields an optimal space--time tradeoff within parallel bucket orders is presented. Also practical issues concerning parallel bucket orders are discussed. Learning ancestor relations, that is, directed paths between nodes, is motivated by the need for robust summaries of the network structures when there are unobserved nodes at work. Ancestor relations are nonmodular features and hence learning them is more difficult than modular features. A dynamic programming algorithm is presented for computing posterior probabilities of ancestor relations exactly. Empirical tests suggest that ancestor relations can be learned from observational data almost as accurately as arcs even in the presence of unobserved nodes.