Assessment of the “health” of an ecosystem is often of great interest to those interested in monitoring and conservation of ecosystems. Traditionally, scientists have quantified the health of an ecosystem using multimetric indices that are semi-qualitative. Recently, a statistical-based index called the Latent Health Factor Index (LHFI) was devised to address many inadequacies of the conventional indices. Relying on standard modelling procedures, unlike the conventional indices, accords the LHFI many advantages: the LHFI is less arbitrary, and it allows for straightforward model inference and for formal statistical prediction of health for a new site (using only supplementary environmental covariates). In contrast, with conventional indices, formal statistical prediction does not exist, meaning that proper estimation of health for a new site requires benthic data which are expensive and time-consuming to gather. As the LHFI modelling methodology is a relatively new concept, it has so far only been demonstrated (and validated) on freshwater ecosystems. The goal of this thesis is to apply the LHFI modelling methodology to estuarine ecosystems, particularly to the previously unassessed system in Richibucto, New Brunswick. Specifically, the aims of this thesis are threefold: firstly, to investigate whether the LHFI is even applicable to estuarine systems since estuarine and freshwater metrics, or indicators of health, are quite different; secondly, to determine the appropriate form that the LHFI model if the technique is applicable; and thirdly, to assess the health of the Richibucto system. Note that the second objective includes determining which covariates may have a significant impact on estuarine health. As scientists have previously used the AZTI Marine Biotic Index (AMBI) and the Infaunal Trophic Index (ITI) as measurements of estuarine ecosystem health, this thesis investigates LHFI models using metrics from these two indices simultaneously. Two sets of models were considered in a Bayesian framework and implemented using Markov chain Monte Carlo techniques, the first using only metrics from AMBI, and the second using metrics from both AMBI and ITI. Both sets of LHFI models were successful in that they were able to make distinctions between health levels at different sites.

How much information can be transferred over a wireless network and what is the optimal strategy for the operation of such network? This thesis tries to answer some of these questions from an information theoretic approach. A model of wireless network is formulated to capture the main features of the wireless medium as well as topology of the network. The performance metrics are throughput and transport capacity. The throughput is the summation of all reliable communication rates for all source-destination pairs in the network. The transport capacity is a sum rate where each rate is weighted by the distance over which it is transported. Based on the network model, we study the scaling laws for the performance measures as the number of users in the network grows. First, we analyze the performance of multihop wireless network under different criteria for successful reception of packets at the receiver. Then, we consider the problem of information transfer without arbitrary assumptions on the operation of the network. We observe that there is a dichotomy between the cases of relatively high signal attenuation and low attenuation. Moreover, a fundamental relationship between the performance metrics and the total transmitted power of users is discovered. As a result, the optimality of multihop is demonstrated for some scenarios in high attenuation regime, and better strategies than multihop are proposed for the operation in the low attenuation regime. Then, we study the performance of a special class of networks, random networks, where the traffic is uniformly distributed inside the networks. For this special class, the upperbounds on the throughput are presented for both low and high attenuation cases. To achieve the presented upperbounds, a hierarchical cooperation scheme is analyzed and optimized by choosing the number of hierarchical stages and the corresponding cluster sizes that maximize the total throughput. In addition, to apply the hierarchical cooperation scheme to random networks, a clustering algorithm is developed, which divides the whole network into quadrilateral clusters, each with exactly the number of nodes required.

Assessment of the “health” of an ecosystem is often of great interest to those interested in monitoring and conservation of ecosystems. Traditionally, scientists have quantified the health of an ecosystem using multimetric indices that are semi-qualitative. Recently, a statistical-based index called the Latent Health Factor Index (LHFI) was devised to address many inadequacies of the conventional indices. Relying on standard modelling procedures, unlike the conventional indices, accords the LHFI many advantages: the LHFI is less arbitrary, and it allows for straightforward model inference and for formal statistical prediction of health for a new site (using only supplementary environmental covariates). In contrast, with conventional indices, formal statistical prediction does not exist, meaning that proper estimation of health for a new site requires benthic data which are expensive and time-consuming to gather. As the LHFI modelling methodology is a relatively new concept, it has so far only been demonstrated (and validated) on freshwater ecosystems. The goal of this thesis is to apply the LHFI modelling methodology to estuarine ecosystems, particularly to the previously unassessed system in Richibucto, New Brunswick. Specifically, the aims of this thesis are threefold: firstly, to investigate whether the LHFI is even applicable to estuarine systems since estuarine and freshwater metrics, or indicators of health, are quite different; secondly, to determine the appropriate form that the LHFI model if the technique is applicable; and thirdly, to assess the health of the Richibucto system. Note that the second objective includes determining which covariates may have a significant impact on estuarine health. As scientists have previously used the AZTI Marine Biotic Index (AMBI) and the Infaunal Trophic Index (ITI) as measurements of estuarine ecosystem health, this thesis investigates LHFI models using metrics from these two indices simultaneously. Two sets of models were considered in a Bayesian framework and implemented using Markov chain Monte Carlo techniques, the first using only metrics from AMBI, and the second using metrics from both AMBI and ITI. Both sets of LHFI models were successful in that they were able to make distinctions between health levels at different sites.

In multi-agent systems [1], coordinated resource sharing is indispensable for a set of autonomous agents, which are running in the same execution space, to accomplish their computational objectives. This research presents a new approach to network resource control in multi-agent systems, based on the CyberOrgs [2] model. This approach aims to offer a mechanism to reify network resource control in multi-agent systems and to realize this mechanism in a prototype system. <p>In order to achieve these objectives, a uniform abstraction vLink (Virtual Link) is introduced to represent network resource, and based on this abstraction, a coherent mechanism of vLink creation, allocation and consumption is developed. This mechanism is enforced in the network by applying a fine-grained flow-based scheduling scheme. In addition, concerns of computations are separated from those of resources required to complete them, which simplifies engineering of network resource control. Thus, application programmers are enabled to focus on their application development and separately declaring resource request and defining resource control policies for their applications in a simplified way. Furthermore, network resource is bounded to computations and controlled in a hierarchy to coordinate network resource usage. A computation and its sub-computations are not allowed to consume resources beyond their resource boundary. However, resources can be traded between different boundaries. <p> In this thesis, the design and implementation of a prototype system is described as well. The prototype system is a middleware system architecture, which can be used to build systems supporting network resource control. This architecture has a layered structure and aims to achieve three goals: (1) providing an interface for programmers to express resource requests for applications and define their resource control policies; (2) specializing the CyberOrgs model to control network resource; and (3) providing carefully designed mechanisms for routing, link sharing and packet scheduling to enforce required resource allocation in the network.

The main objective of our study is to employ copula methodology to develop Bayesian hierarchical models to study the dependencies exhibited by temporal, spatial and spatio-temporal processes. We develop hierarchical models for both discrete and continuous outcomes. In doing so we expect to address the dearth of copula based Bayesian hierarchical models to study hydro-meteorological events and other physical processes yielding discrete responses. First, we present Bayesian methods of analysis for longitudinal binary outcomes using Generalized Linear Mixed models (GLMM). We allow flexible marginal association among the repeated outcomes from different time-points. An unique property of this copula-based GLMM is that if the marginal link function is integrated over the distribution of the random effects, its form remains same as that of the conditional link function. This unique property enables us to retain the physical interpretation of the fixed effects under conditional and marginal model and yield proper posterior distribution. We illustrate the performance of the posited model using real life AIDS data and demonstrate its superiority over the traditional Gaussian random effects model. We develop a semiparametric extension of our GLMM and re-analyze the data from the AIDS study. Next, we propose a general class of models to handle non-Gaussian spatial data. The proposed model can deal with geostatistical data that can accommodate skewness, tail-heaviness, multimodality. We fix the distribution of the marginal processes and induce dependence via copulas. We illustrate the superior predictive performance of our approach in modeling precipitation data as compared to other kriging variants. Thereafter, we employ mixture kernels as the copula function to accommodate non-stationary data. We demonstrate the adequacy of this non-stationary model by analyzing permeability data. In both cases we perform extensive simulation studies to investigate the performances of the posited models under misspecification. Finally, we take up the important problem of modeling multivariate extreme values with copulas. We describe, in detail, how dependences can be induced in the block maxima approach and peak over threshold approach by an extreme value copula. We prove the ability of the posited model to handle both strong and weak extremal dependence and derive the conditions for posterior propriety. We analyze the extreme precipitation events in the continental United States for the past 98 years and come up with a suite of predictive maps.

Effective routing is critical in mobile ad hoc networks (MANETs). In recent years, many hierarchical routing protocols have been proposed to build a backbone structure for supporting MANET routing, especially for large scalability. The clustering approach is seen as the first step in providing a flat network with a hierarchical architecture. The clusterheads become the backbone nodes (BNs), which use greater power to transmit packets, forming the backbone network. Backbone network routing can reduce the number of data-packet forwarding hops throughout the entire network. However, previous protocols have focused on clustering schemes. Fault tolerance in a backbone structure has not been considered. In this paper, we propose a backup routing scheme that can repair broken links locally without activating a route re-discovery procedure. The backup route is piggybacked in the data packet header to achieve the most durable route. The proposed method can improve the packet delivery ratio and reduce control overhead, compared to general hierarchical routing protocols.

No description available.

A three dimensional (3-D) capacitance extraction algorithm based on a kernel independent hierarchical method and geometric moments is described. Several techniques are incorporated, which leads to a better overall performance for arbitrary interconnect systems. First, the new algorithm hierarchically partitions the bounding box of all interconnect panels to build the partition tree. Then it uses simple shapes to match the low order moments of the geometry of each box in the partition tree. Finally, with the help of a fast matrix-vector product, GMRES is used to solve the linear system. Experimental results show that our algorithm reduces the linear system's size greatly and at the same time maintains a satisfying accuracy. Compared with FastCap, the running time of the new algorithm can be reduced more than a magnitude and the memory usage can be reduced more than thirty times.

This report focuses on introducing two statistical models for dealing with data involving complex social structures. Appropriate handling of data structures is a concern in the context of educational settings. From base single-level data to complex hierarchical with cross-classifications and multiple-memberships, we explain and demonstrate their distinction and establish appropriate regression models. Real data from the National Center for Education Statistics (NECS) is used to demonstrate different way of handling a cross-classified data structure as well as appropriate models. Results will be presented and compared to examine the practical operation for each model. / text

To aid in resolving some of the ambiguity in the literature about the impact of different forms of WOM on brand performance, this dissertation investigates how WOM influences three consumer responses to WOM: purchase, WOM retransmission, and additional information search. The author investigates these questions by analyzing a database comprising more than three years of detailed WOM data from a unique, nationally representative panel merged with other secondary sources that provide various measures of brand strength (the American Consumer Satisfaction Index and Harris Interactive’s Equitrend). Using a series of hierarchical regression models, the results from this study reveal numerous insights into the contextual factors that moderate the impact of a WOM episode. For example, negative WOM about a brand has a larger absolute effect on consumer purchase intentions than positive WOM, but positive WOM has a larger positive effect on WOM retransmission than the positive effect of negative WOM. Offline WOM tends to exacerbate the effect of positive and negative brand sentiment on purchase intentions. WOM between stronger social ties tends to have greater impact on brand-related responses than WOM between weak ties, except in the case of motivating additional information search. The results also indicate that strong brands (those with higher levels of brand equity) tend to reap greater benefits from WOM. For example, negative, mixed, or neutral WOM has greater influence on purchase, and WOM from weak social ties about strong brands motivates higher levels of information search than when WOM from weak ties is about weaker brands.