Water is the most common solvent for most biological reactions, therefore it is vital that we fully understand water and all its properties. The complex hydrogen bonding network that water forms can influence protein-protein and protein-substrate interactions and can slow protein conformational shifts. Here, I examine an important property of water known as energetic roughness. The network of interactions between individual water molecules affect the energy landscape of proteins by altering the underlying energetic roughness. I have attributed this roughness to the making and breaking of hydrogen bonds as the network of hydrogen bonds constantly adopts new conformations. Through a novel computational approach I have analyzed five prominent water models and have determined their inherent roughness to be between 0.43 and 0.62 kcal/mol.

The ability to recognize emotions is an important part of building intelligent computers. Extracting the emotional aspects of a situation could provide computers with a rich context to make appropriate decisions about how to interact with the user or adapt the system response. The problem that we address in this thesis is that the current methods of determining user emotion have two issues: the equipment that is required is expensive, and the majority of these sensors are invasive to the user. These problems limit the real-world applicability of existing emotion-sensing methods because the equipment costs limit the availability of the technology, and the obtrusive nature of the sensors are not realistic in typical home or office settings. Our solution is to determine user emotions by analyzing the rhythm of an individuals typing patterns on a standard keyboard. Our keystroke dynamics approach would allow for the uninfluenced determination of emotion using technology that is in widespread use today. We conducted a field study where participants keystrokes were collected in situ and their emotional states were recorded via self reports. Using various data mining techniques, we created models based on 15 different emotional states. With the results from our cross-validation, we identify our best-performing emotional state models as well as other emotional states that can be explored in future studies. We also provide a set of recommendations for future analysis on the existing data set as well as suggestions for future data collection and experimentation.

This thesis presents the results of an experimental and analytical study of single-port buoyant turbulent jets discharged into shallow water. The experimental results include the measured downstream dilution, centerline concentration and trajectory. Independent parameters considered were Froude number, submerged depth, discharge angle and velocity ratio. Results indicate that decreasing the discharge depth provides earlier occurrence of surface effect and greatly decreases dilution. Dilution increases with decreasing Froude number. Increasing the discharge angle from the horizontal into cross current increases the dilution ratio. The effect of ambient current on dilution depends on the angle of discharge. For cross-flow discharges, the dilution rate decreases with increasing ambient current, while for co-flow discharge the reverse trend was observed. As plumes reach the water surface, the dilution rate increases with increasing ambient velocity. The jets bend over rapidly for cross-flow discharges when large ambient currents are present. The analytical portion of this report presents an integral method proposed by Davis (1975) for merging multiple buoyant jets. This merging model was used to simulate the single-port buoyant jet in shallow water. This was done by using an image method where the submerged depth was simulated by the spacing between images. The entrainment function as presented by Kannberg and Davis (1978) was used except for a modification within the zone of merged plumes. Comparisons of the model prediction were made with experimental data. Results indicate that good predictions are obtained for buoyant jets discharging at 0 and 45 degrees into shallow water by using the image method as long as the Froude number is above 13.5. For lower Froude number and vertical discharges, model predictions are only fair. / Graduation date: 1991

The number of allosteric sites and active sites in phosphofructokinase from Bacillus stearothermophilus create an intricate network of communication within the enzyme. With thermodynamic linkage analysis, the overall allosteric communication can be quantified. This value, however, represents an average contribution for all the interactions involved. The recent development of a hybrid strategy has allowed for the quantification of single interactions, both heterotropic and homotropic. Focusing on the heterotropic interactions whose inhibition is entropy-driven, residues and regions within the enzyme can now be identified to further characterize each specific interaction using the hybrid strategy. Among the many components of entropy, the hybrid strategy has now allowed for the strategic placement of a reporter of side chain dynamics to identify conformational differences between the four ligand bound enzyme species of a single heterotropic interaction. In this study, a combination of these approaches was used in the methodology including constructing hybrids to isolate a single heterotropic interaction along with single tryptophan reporter. Site directed mutagenesis combined with the hybrid strategy was also implemented to directly assess the role of a single residue in the communication path of a single interaction. The region surrounding the allosteric site with the nearest active site has been implicated to be significant in transmitting the allosteric signal. In addition two single residues, T158 and D59, within this region have been identified to potentially contribute to the inhibition of this same interaction. An additional residue, G184, located outside this local region has also been identified as possibly having a significant role in the transmission of the inhibitory signal of a unique heterotropic interaction. The implications of this study have led to the initial identification of residues involved in the 22A route of allosteric communication of a single active site and allosteric site. This allosteric communication occurs to allow the enzyme to compensate for the binding of both ligands. With the location of these residues implicated to be involved in the communication of this isolated interaction, this compensation is not contained within a confined region but is however felt throughout the single subunit.

In this thesis, we prove two results. The first concerns the dynamics of typical maps in families of higher degree unimodal maps, and the second concerns the Hausdorff dimension of the Julia sets of certain quadratic maps. In the first part, we construct a lamination of the space of unimodal maps whose critical points have fixed degree d greater than or equal to 2 by the hybrid classes. As in [ALM], we show that the hybrid classes laminate neighbourhoods of all but countably many maps in the families under consideration. The structure of the lamination yields a partition of the parameter space for one-parameter real analytic families of unimodal maps of degree d and allows us to transfer a priori bounds from the phase space to the parameter space. This result implies that the statistical description of typical unimodal maps obtained in [ALM], [AM3] and [AM4] also holds in families of higher degree unimodal maps, in particular, almost every map in such a family is either regular or stochastic. In the second part, we prove the Poincare exponent for the Fibonacci map is less than two, which implies that the Hausdor ff dimension of its Julia set is less than two.

Abstract A stochastic model for orderbook dynamics is proposed in Cont et al.(2010) and empirically evaluated in thisthesis. Arrival rates of limit, marketand cancellation orders are described interms of a Markov chain where thearrival rates are exponentiallydistributed. The model not onlyconsiders the best bid and ask queuesbut also additional price levels of theorder book. Methods for computingseveral quantities important to highfrequency trading are proposed usingLaplace transforms and continuedfractions. These quantities includeconditional probabilities such as theprobability of a price increasedepending on the profile of the orderbook. Computing these probabilities aresupposed to be easy enough to computeanalytically. However this was not thecase. We failed in the inversion of theLaplace transform methods and the mainreason is that the instructions in Contet al. (2010) are not adequate when itcomes to perform the inversion. Hence wedraw the conclusion that the method isno good for predicting short termbehavior of limit order books. For longterm applications the model can be usedto simulate the order book with goodresults.

I have used an analytic model of tidal interactions to predict the evolution of a substructure in a static dark matter halo. Given the initial conditions of the satellite and background halo, the model predicts with high accuracy the mass loss of the satellite and also its density evolution. The main phenomena taken into account in the model are tidal truncation at the tidal radius of the satellite and heating due to tidal shocks at the pericenter of its orbit. To calibrate and test the model, it has been compared with numerical simulations of a satellite orbiting in a static dark matter halo. The model predicts a set of tidal radii for the satellite in different stages of its evolution. The mass of the satellite is accurately calculated at each stage by truncating an NFW (Navarro, Frenk and White) profile at the tidal radius. The mass lost beyond the tidal limit is scaled by half the instantaneous orbital period of the satellite. The model can also be used to predict analytically the new density profile of the satellite. This new profile is given by a modification of the NFW density profile as a function of radius. The tidal radius is the only parameter going into this modification. The effect of numerical relaxation has been studied and quantified by performing the same simulations in lower resolutions. I find that substructures with less than 1000 particles are artificially relaxed and this process affects their mass loss and results in their premature disruptions. This underlines the utility of an analytic model predicting the evolution of substructures in minor mergers.

The ability to recognize emotions is an important part of building intelligent computers. Extracting the emotional aspects of a situation could provide computers with a rich context to make appropriate decisions about how to interact with the user or adapt the system response. The problem that we address in this thesis is that the current methods of determining user emotion have two issues: the equipment that is required is expensive, and the majority of these sensors are invasive to the user. These problems limit the real-world applicability of existing emotion-sensing methods because the equipment costs limit the availability of the technology, and the obtrusive nature of the sensors are not realistic in typical home or office settings. Our solution is to determine user emotions by analyzing the rhythm of an individuals typing patterns on a standard keyboard. Our keystroke dynamics approach would allow for the uninfluenced determination of emotion using technology that is in widespread use today. We conducted a field study where participants keystrokes were collected in situ and their emotional states were recorded via self reports. Using various data mining techniques, we created models based on 15 different emotional states. With the results from our cross-validation, we identify our best-performing emotional state models as well as other emotional states that can be explored in future studies. We also provide a set of recommendations for future analysis on the existing data set as well as suggestions for future data collection and experimentation.

Understanding the dynamical relationships between low frequency forcings and the interannual variability of the Earths atmosphere is critical for accurate extended-range forecasts and climate prediction. This thesis investigates possible dynamical couplings between the stratosphere and troposphere by implementing lagged multivariate linear regressions. These regressions were chosen to untangle the separate responses of distinct atmospheric forcings upon zonal mean climate variability. The regressions incorporate monthly meteorological data with indices of four dominant forcings of low frequency atmospheric variability: the El Nino Southern Oscillation, the Quasi-Biennial Oscillation, the 11-year solar cycle, and volcanic activity. The analysis uses data from both the NCAR/NCEP and ECMWF reanalyses for two distinct time periods to expose possible satellite measurement influences. One period consists of all data since 1958, while the other period includes only data since 1979, a period of extensive satellite observations. Diagnostic tools include piecewise potential vorticity inversions, an assessment of anomalous Eliassen-Palm fluxes, stream function analyses, and general circulation model diagnoses. The examination reveals robust patterns associated with each forcing, consistent with existing theories in climate dynamics of the coupling mechanisms between the stratosphere and the troposphere. To better predict climate variability, however, the next step is to investigate the nonlinearities known to play an important role in this system.

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