6,329 research outputs found
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
Towards human control of robot swarms
In this paper we investigate principles of swarm control that enable a human operator to exert influence on and control large swarms of robots. We present two principles, coined selection and beacon control, that differ with respect to their temporal and spatial persistence. The former requires active selection of groups of robots while the latter exerts a passive influence on nearby robots. Both principles are implemented in a testbed in which operators exert influence on a robot swarm by switching between a set of behaviors ranging from trivial behaviors up to distributed autonomous algorithms. Performance is tested in a series of complex foraging tasks in environments with different obstacles ranging from open to cluttered and structured. The robotic swarm has only local communication and sensing capabilities with the number of robots ranging from 50 to 200. Experiments with human operators utilizing either selection or beacon control are compared with each other and to a simple autonomous swarm with regard to performance, adaptation to complex environments, and scalability to larger swarms. Our results show superior performance of autonomous swarms in open environments, of selection control in complex environments, and indicate a potential for scaling beacon control to larger swarms
VIP: A knowledge-based design aid for the engineering of space systems
The Vehicles Implementation Project (VIP), a knowledge-based design aid for the engineering of space systems is described. VIP combines qualitative knowledge in the form of rules, quantitative knowledge in the form of equations, and other mathematical modeling tools. The system allows users rapidly to develop and experiment with models of spacecraft system designs. As information becomes available to the system, appropriate equations are solved symbolically and the results are displayed. Users may browse through the system, observing dependencies and the effects of altering specific parameters. The system can also suggest approaches to the derivation of specific parameter values. In addition to providing a tool for the development of specific designs, VIP aims at increasing the user's understanding of the design process. Users may rapidly examine the sensitivity of a given parameter to others in the system and perform tradeoffs or optimizations of specific parameters. A second major goal of VIP is to integrate the existing corporate knowledge base of models and rules into a central, symbolic form
Bayesian Restricted Likelihood Methods: Conditioning on Insufficient Statistics in Bayesian Regression
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
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