Tidal Estuary Morphodynamics of the Knik Arm

Presented to the Faculty of the University of Alaska Anchorage in Partial Fulfillment of the Requirements For the Degree of MASTER OF CIVIL ENGINEERINGA three-dimensional unsteady flow numerical model was developed to study sediment transport due to tidal circulation within Knik Arm, a dynamic well mixed macro-tidal sub-estuary of Cook Inlet in Alaska. The model was developed to gain a better understanding of the mechanisms that are creating the Point MacKenzie Shoal, located approximately 4 kilometers south of Port MacKenzie. Hydrodynamic conditions within the estuary are very complex in that ebb-and-flood tides, freshwater mixing, and wetting/drying of tidal mud flats significantly effects sediment transport within the estuary. A Mike 3 numerical model was applied to simulate the sediment transport within the estuary under the action of tidal currents in the vicinity of the shoal. The computational domain of this simulation includes four sediment laden freshwater sources; Matanuska, Knik, Susitna, and Twenty-Mile Rivers as well as an open ocean boundary. The spatial resolution of the triangulated flexible mesh model is 0.00045 degrees2 with a coupled fine resolution model of 0.000045 degrees2. The results of the numerical model are in agreement with previously collected field data. Simulation results indicate the shoal formation is the result of turbid tidal flows and deposition is occurring naturally.Signature Page / Title Page / Abstract / Table of Contents / List of Figures / List of Tables / Background / Introduction / Physical Setting / Sediment / Freshwater Sources / Water Properties / Tidal Datum / Numerical Model / Model Selection / Methodology / Model Domain / Hydrodynamics / Sediment Transport / Summary of Model Input / Results and Discussion / Conclusion / Acknowledgments Reference

Regional Economic Performance in New Zealand: How Does Auckland Compare?

In this study we investigate Auckland’s economic performance relative to other large cities in New Zealand, to medium-sized urban centres and to small towns and rural areas. Measures of regional economic performance are not well developed in New Zealand and there is a relative lack of official data at the regional level. Previous measures developed by two non-governmental organisations have suggested that Auckland is “underperforming” relative to other regions in New Zealand. However, neither of these measures satisfactorily capture productivity performance by areas that are classified according to the density of economic activity that takes place within them. We use data from the annual New Zealand Income Survey to examine hourly earnings and other measures of labour productivity and utilisation for a number of regional areas. Our results tell a fairly consistent story. Auckland and Wellington have the highest levels of productivity performance based on almost all measures of earnings. In particular, both have significantly higher average levels of labour income, and wage rates than the three other comparison areas. Auckland has also experienced stronger growth in wages, in particular for wage/salary workers, than other regions. Our findings cast doubt on the hypothesis that Auckland has been a productivity underperformer within New Zealand. In fact, Auckland appears to be a relatively good performer and this is consistent with agglomeration economies being at work in New Zealand’s largest urban concentration. However, because we limited our investigations to within New Zealand we are not able to say how Auckland’s productivity performance compares to innovative, high-skill cities in other countries. Given New Zealand’s overall poorer performance in labour productivity and the rather modest wage rate growth that we find even for Auckland, it is unlikely to have been as good.regional economic performance, Auckland, productivity, New Zealand

Tap 'N' Shake: Gesture-based Smartwatch-Smartphone Communications System

Smartwatches have recently seen a surge in popularity, and the new technology presents a number of interesting opportunities and challenges, many of which have not been adequately dealt with by existing applications. Current smartwatch messaging systems fail to adequately address the problem of smartwatches requiring two-handed interactions. This paper presents Tap 'n' Shake, a novel gesture-based messaging system for Android smartwatches and smartphones addressing the problem of two-handed interactions by utilising various motion-gestures within the applications. The results of a user evaluation carried out with sixteen subjects demonstrated the usefulness and usability of using gestures over two-handed interactions for smartwatches. Additionally, the study provides insight into the types of gestures that subjects preferred to use for various actions in a smartwatch-smartphone messaging system

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Bayesian methods have proven themselves to be successful across a wide range of scientific problems and have many well-documented advantages over competing methods. However, these methods run into difficulties for two major and prevalent classes of problems: handling data sets with outliers and dealing with model misspecification. We outline the drawbacks of previous solutions to both of these problems and propose a new method as an alternative. When working with the new method, the data is summarized through a set of insufficient statistics, targeting inferential quantities of interest, and the prior distribution is updated with the summary statistics rather than the complete data. By careful choice of conditioning statistics, we retain the main benefits of Bayesian methods while reducing the sensitivity of the analysis to features of the data not captured by the conditioning statistics. For reducing sensitivity to outliers, classical robust estimators (e.g., M-estimators) are natural choices for conditioning statistics. A major contribution of this work is the development of a data augmented Markov chain Monte Carlo (MCMC) algorithm for the linear model and a large class of summary statistics. We demonstrate the method on simulated and real data sets containing outliers and subject to model misspecification. Success is manifested in better predictive performance for data points of interest as compared to competing methods