STRUCTURAL ANALYSIS, LAYER THICKNESS MEASUREMENTS AND MINERALOGICAL INVESTIGATION OF THE LARGE INTERIOR LAYERED DEPOSIT WITHIN GANGES CHASMA, VALLES MARINERIS, MARS

The interior layered deposit (ILD) in Ganges Chasma, Valles Marineris, is a 4.25 km high mound that extends approximately 110 km from west to east. The deposition, deformation, and erosion history of the Ganges ILD records aids in identifying the processes that formed and shaped the Chasma. To interpret structural and geomorphic processes acting on the ILD, multiple layer attitudes and layer thickness transects were conducted on the Ganges ILD. Mineralogical data was analyzed to determine correlations between materials and landforms. Layer thickness measurements indicate that the majority of layers are between 0.5 m and 4 m throughout the ILD. Three major benches dominate the Ganges ILD. Layer thicknesses increase at the ILD benches, suggesting that the benches are formed from the gradual thickening of layers. This indicates that the benches are depositional features draping over basement topography. Layer attitudes indicate overall shallow dips generally confined to a North-South direction that locally appear to follow bench topography. Layering is disrupted on a scale of 40 m to 150 m in 12 separate locations throughout the ILD. In all locations, underlying layering is disturbed by overlying folded layers in a trough-like geometry. These features are interpreted to have formed as submarine channels in a lacustrine setting, subsequently infilled by sediments. Subsequently, the channels were eroded to the present topography, resulting in the thin, curved layering observed. Data cannot conclusively support one ILD formation hypothesis, but does indicate that the Ganges ILD postdates Chasma formation. The presence of water altered minerals, consistently thin layering, and layer orientations provide strong evidence that the ILD formed in a lacustrine setting.

Photograph - Woman Standing by a Large Tree Trunk

A woman wearing a hat and holding a small purse. She is standing next to a large tree trunk and there are benches behind her.

Safe Handling of Large Animals

The article discusses improving welfare by reducing fear by studying: Animal Sensory Perception, Animal Behavior Patterns, Animal Habituation and Temperament, Effects of Previous Handling, Training Animals, Training Time and Temperament, Genetic Effects on Handling, Handling of escaped Animals, Facilities, Aggression in Grazing Animals, Inherent Danger of Large Animals, Cattle and Car Accidents.

Dynamics in large scale networks

In this thesis we study the properties of two large dynamic networks, the competition network of advertisers on the Google and Bing search engines and the dynamic network of friend relationships among avatars in the massively multiplayer online game (MMOG) Planetside 2. We are particularly interested in removal patterns in these networks. Our main finding is that in both of these networks the nodes which are most commonly removed are minor near isolated nodes. We also investigate the process of merging of two large networks using data captured during the merger of servers of Planetside 2. We found that the original network structures do not really merge but rather they get gradually replaced by newcomers not associated with the original structures. In the final part of the thesis we investigate the concept of motifs in the Barabási-Albert random graph. We establish some bounds on the number of motifs in this graph.

A Scalability Study and New Algorithms for Large-Scale Many-Objective Optimization

Many real-world optimization problems contain multiple (often conflicting) goals to be optimized concurrently, commonly referred to as multi-objective problems (MOPs). Over the past few decades, a plethora of multi-objective algorithms have been proposed, often tested on MOPs possessing two or three objectives. Unfortunately, when tasked with solving MOPs with four or more objectives, referred to as many-objective problems (MaOPs), a large majority of optimizers experience significant performance degradation. The downfall of these optimizers is that simultaneously maintaining a well-spread set of solutions along with appropriate selection pressure to converge becomes difficult as the number of objectives increase. This difficulty is further compounded for large-scale MaOPs, i.e., MaOPs possessing large amounts of decision variables. In this thesis, we explore the challenges of many-objective optimization and propose three new promising algorithms designed to efficiently solve MaOPs. Experimental results demonstrate the proposed optimizers to perform very well, often outperforming state-of-the-art many-objective algorithms.