<p>This dissertation asks whether frequency misspecification of a New Keynesian model</p><p>results in temporal aggregation bias of the Calvo parameter. First, when a</p><p>New Keynesian model is estimated at a quarterly frequency while the true</p><p>data generating process is the same but at a monthly frequency, the Calvo</p><p>parameter is upward biased and hence implies longer average price duration.</p><p>This suggests estimating a New Keynesian model at a monthly frequency may</p><p>yield different results. However, due to mixed frequency datasets in macro</p><p>time series recorded at quarterly and monthly intervals, an estimation</p><p>methodology is not straightforward. To accommodate mixed frequency datasets,</p><p>this paper proposes a data augmentation method borrowed from Bayesian</p><p>estimation literature by extending MCMC algorithm with</p><p>"Rao-Blackwellization" of the posterior density. Compared to two alternative</p><p>estimation methods in context of Bayesian estimation of DSGE models, this</p><p>augmentation method delivers lower root mean squared errors for parameters</p><p>of interest in New Keynesian model. Lastly, a medium scale New Keynesian</p><p>model is brought to the actual data, and the benchmark estimation, i.e. the</p><p>data augmentation method, finds that the average price duration implied by</p><p>the monthly model is 5 months while that by the quarterly model is 20.7</p><p>months.</p> / Dissertation

The objective of this thesis is to address the problem of energy conservation in wireless sensor networks by tackling two fundamental problems: topology control and data delivery. We first address energy-aware topology control taking into account throughput per unit energy as the primary metric of interest. Through both experimental observations and analysis, we show that the optimal topology is a function of traffic load in the network. We then propose a new topology control scheme, Adaptive Topology Control (ATC), which increases throughput per unit energy. Based on different coordinations among nodes, we proposed three ATC schemes: ATC-CP, ATC-IP, and ATC-MS. Through simulations, we show that three ATC schemes outperform static topology control schemes, and particularly the ATC-MS has the best performance under all environments. Secondly, we explore an energy-aware data delivery problem consisting of two sub-problems: downstream (from a sink to sensors) and upstream (from sensors to a sink) data delivery. Although we address the problems as two independent ones, we eventually solve those problems with two approaches: GARUDA-DN and GARUDA-UP which share a common structure, the minimum dominating set. For the downstream data delivery, we consider reliability as well as energy conservation since unreliable data delivery can increase energy consumption under high data loss rates. To reduce energy consumption and achieve robustness, we propose GARUDA-DN which is scalable to the network size, message characteristics, loss rate and the reliable delivery semantics. From ns2-based simulations, we show that GARUDA-DN performs significantly better than the basic schemes proposed thus far in terms of latency and energy consumption. For the upstream data delivery, we address an energy efficient aggregation scheme to gather correlated data with theoretical solutions: the shortest path tree (SPT), the minimum spanning tree (MST) and the Steiner minimum tree (SMT). To approximate the optimal solution in case of perfect correlation among data, we propose GARUDA-UP which combines the minimum dominating set (MDS) with SPT in order to aggregate correlated data. From discrete event simulations, we show that GARUDA-UP outperforms the SPT and closely approximates the centralized optimal solution, SMT, with less amount of overhead and in a decentralized fashion.

In the current study, behavioral responses of several species of calanoid copepods to mimics of oceanographic structure were observed and evaluated in the context of foraging and aggregation. Zooplankton distributions in oceanic habitats are often attributed to physical forcing; however, physical factors only act to drive ecological patterns at large scales (m to km). At fine to intermediate scales (cm to m) zooplankton behavior is believed to govern observed distributions, but the mechanisms and ecological significance of these behaviors are not well understood. In a water column, biological activity is often concentrated into one or a few regions, called thin layers, on the order of a meter thick, and zooplankton, such as copepods, must be able to reliably locate and exploit these patches for survival. Thin layers commonly are associated with oceanic structure such as flow gradients, fluid density jumps, or chemical composition gradients. Utilization of mechanosensory or chemosensory cues associated with thin layers may increase foraging success, thus translating into a significant ecological advantage. A laboratory apparatus was developed to create isolated and combined thin layer properties. Copepods then were exposed to laboratory mimics of thin layers. All of the tested species of copepods exhibited behavioral responses associated with area-restricted search behavior to one of the physical gradients (flow velocity or fluid density), but not both. Similar responses were observed for chemical exudate layer experiments and included increased proportional residence times, swimming speeds, and turn frequency. Food layers induced feeding responses from all tested species (increased proportional residence time, decreased swimming speed). Responses to various combinations of gradients were not fully synergistic, but suggested that some copepods employ a cue hierarchy to locate food-rich areas. Velocity or density gradients acted as initial cues for narrowing search regions, while chemical exudates induced responses that strengthened or removed the initial reactions. A simple foraging model was used to illustrate how such behavioral changes can lead to observed aggregations at larger temporal and spatial scales. Consequently, these results suggest that individual responses to oceanographic structure may have far reaching influence on population dynamics, succession, and biodiversity in coastal and pelagic ecosystems.

The fate and transformation of laboratory-prepared nano-ZnO, nano-Ag and nano-CeO2 in three aqueous solutions under different environmental conditions were investigated in this work. Over the past decades nanomaterials have been widely used in different technical fields and consumer goods. As a result, nanomaterials might enter the environmental media via different routes and then posed potential hazards to the environment and human health. Researches in this regard have received much attention worldwide. In this work it was found that the solubility of each nanomaterial was highly influenced by the solution pH, but not by the solution temperature. The maximal solubility for the tested nanomaterials was obtained at pH 3, namely about 100% for nano-ZnO and lower than 2% for both nano-Ag and nano-CeO2. The solution pH and ionic strength were found to affect the stability of nanoparticles in different aquatic environments. For the solution pH of higher than the isoelectric point of the concerned nanomaterial, the higher the solution pH is, the greater the degree of stabilization of nanoparticles would be. On the contrary, nanoparticles aggregated as the ionic strength of the solution exceeded its critical aggregation concentration (CAC). CAC for each concerned nanomaterial could also be graphically determined as the attachment efficiency (£\) of nanoparticles increased with increasing ionic strength of the solution and then leveled off after reaching CAC. Experimental results also showed that Zn(OH)2(s) would form when nano-ZnO was in the solution of pH 10. The crystalline structure of the said precipitates was confirmed by X-ray diffraction. Likewise, Ce4+ dissolved from nano-CeO2 reacted with SO42- in aqueous solution yielding Ce(SO4)2(s). Clearly, transformation of nanomaterials might take place when they are in contact with various species in different aquatic environments. Humic acid in aqueous solution was found to be beneficial to the stability of nanomaterial of concern. Efforts have also been made to study the reaction behaviors among di(2-ethylhexyl)phthalate, erythromycin, and selected nanomaterials when they co-existed in the same solution. Their interactions, however, seemed to be unobvious. In this work it was found that under sunlight irradiation nano-ZnO did show its antibiotic effect due to photocatalysis. Nano-Ag was proven to have a strong antibacterial ability even in natural aquatic environments. It yielded the total bacteria survival ratio of less than 2% within one hour of reaction. In summary, the findings of this study showed that the behaviors of nano-ZnO, nano-Ag, and nano-CeO2 in aqueous solutions could be greatly influenced by different factors in different reaction systems.

This study proposes one plausible procedure to address two methodological issues, which are common in micro- and macro- econometric analyses, for the full realization of research potential brought by recently available high dimensional data. To address the issue of how to infer the causal structure from empirical regularities, graphical causal models are proposed to inductively infer causal structure from non-temporal and non-experimental data. However, the (probabilistic) stability condition for the graphical causal models can be violated for high dimensional data, given that close co-movements and thus near deterministic relations are oftentimes observed among variables in high dimensional data. Aggregation methods are proposed as one possible way to address this matter, allowing one to infer causal relationships among disaggregated variables based on aggregated variables. Aggregation methods also are helpful to address the issue of how to incorporate a large information set into an empirical model, given that econometric considerations, such as degrees-of-freedom and multicollinearity, require an economy of parameters in empirical models. However, actual aggregation requires legitimate classifications for interpretable and consistent aggregation. Based on the generalized condition for the consistent and interpretable aggregation derived from aggregation theory and statistical dimensional methods, we propose plausible methodological procedure to consistently address the two related issues of causal inference and actual aggregation procedures. Additional issues for empirical studies of micro-economics and macro-economics are also discussed. The proposed procedure provides an inductive guidance for the specification issues among the direct, inverse, and mixed demand systems and an inverse demand system, which is statistically supported, is identified for the consumer behavior of soft drink consumption. The proposed procedure also provides ways to incorporate large information set into an empirical model with allowing structural understanding of U.S. macro-economy, which was difficult to obtain based on the previously used factor augmented vector autoregressive (FAVAR) framework. The empirical results suggest the plausibility of the proposed method to incorporate large information sets into empirical studies by inductively addressing multicollinearity problem in high dimensional data.

Overfishing of stock and decreasing biodiversity are grave concerns for the U.S. and the rest of the world. Understanding and applying spatial and temporal information of marine species’ reproductive ecology and critical life habitat is vital to the development of effective strategies for marine resource management. In the Caribbean, one of the critical science gaps hindering effective management is the lack of information on how environmental factors may make fish spawning aggregation (FSA) sites optimal for spawning. Understanding the patterns of seafloor characteristics of spawning aggregation sites is of great interest to managers who need a means to efficiently design marine protected areas to help rebuild regional fish stocks. The specific goals of the study were: (1) to map the seafloor at historically known grouper and snapper spawning aggregation sites in three different countries, and (2) to characterize quantitatively the geomorphology of the sites including horizontal and vertical curvature profiles of the reefs, bottom depth at spawning sites, distance between spawning sites and shelf-edges/reef promontory tips, and the shortest distance between the spawning sites and 100 m water depth. These data were field-collected with a GPS and single-beam eco-sounder that provided latitude/longitude and depth. The point data were interpolated to surfaces in GIS to determine slope, aspect, curvature, and distance from spawning sites and three-dimensional reef structures. This study revealed that all 12 known Nassau grouper spawning aggregation sites in Belize and 5 known sites in the Cayman Islands were located at convex-shaped seaward extending reefs (reef promontories) jutting into deep water, within 1 km of reef promontory tips. However, spawning aggregations did not always occur at the tips of reef promontories, though all were found along the shelf edges within 1 km of promontory tips. Sixteen sites were multi-species spawning sites. These general characteristics were used to predict an undiscovered multi-species spawning aggregation in Belize. A successful prediction in Belize, together with the compiled data from multiple sites indicate: 1) reef promontories are vital locations for transient reef fish spawning aggregations, and 2) this study provides a potential tool for prediction of unknown spawning sites in the Caribbean.

Most of the time series are constructed by some kind of aggregation and temporal aggregation that can be defined as aggregation over consecutive time periods. Temporal aggregation takes an important role in time series analysis since the choice of time unit clearly influences the type of model and forecast results. A totally different time series model can be fitted on the same variable over different time periods. In this thesis, the effect of temporal aggregation on univariate time series models is studied by considering modeling and forecasting procedure via a simulation study and an application based on a southern oscillation data set. Simulation study shows how the model, mean square forecast error and estimated parameters change when temporally aggregated data is used for different orders of aggregation and sample sizes. Furthermore, the effect of temporal aggregation is also demonstrated through southern oscillation data set for different orders of aggregation. It is observed that the effect of temporal aggregation should be taken into account for data analysis since temporal aggregation can give rise to misleading results and inferences.

Quality of Service (QoS) schedulers are one of the most important components for the end-to-end QoS support in the Internet. The focus of this thesis is the hardware design and implementation of the QoS schedulers, that is scalable for high line speeds and large number of traffic flows. FPGA is the selected hardware platform. Previous work on the hardware design and implementation of QoS schedulers are mostly algorithm specific. In this thesis, a general architecture for the design of the class of Packet Fair Queuing (PFQ) schedulers is proposed. Worst Case Fair Weighted Fair Queuing Plus (WF2Q+) scheduler is implemented and tested in hardware to demonstrate the proposed architecture and design enhancements. The maximum line speed that PFQ algorithms can operate decreases as the number of scheduled flows increases. For this reason, this thesis proposes to aggregate the flows to scale the PFQ architecture to high line speeds. The Window Based Fair Aggregator (WBFA) algorithm that this thesis suggests for flow aggregation provides a tunable trade-off between the efficient use of the available bandwidth and the fairness among the constituent flows. WBFA is also integrated to the hardware PFQ architecture. The QoS support provided by the proposed PFQ architecture and WBFA is measured by conducting hardware experiments on a custom built high speed network testbed which consists of three data processing cards and a backplane. In these experiments, the input traffic is provided by the hardware traffic generator which is designed in the scope of this thesis.

Verbal information plays a pivot role in human daily communication. Recent research has pointed out that the performance of human cognition in processing verbal information has no significant difference from that in processing numerical information. However, no proper model is available to describe human cognition in processing of verbal information. Therefore, this dissertation explores the difference between human cognition and normative models in processing verbal terms, and further analyzes the decision rules employed by decision-makers to illustrate the proper form of a descriptive model. The explored verbal operations include the following statistics: representation, mean, and variance. In the study of verbal representation, the differences among numerical representation, fuzzy representation, and cognitive representation of Likert verbal evaluations are revealed. This cognitive representation is obtained by the proposed interval estimation method. The proposed method can simultaneously construct the verbal categories in a Likert scale. The result shows that the cognitive representation is inconsistent with the assumption of equal interval in numerical representation, and those of symmetry and equal space in fuzzy representation. In the study of verbal mean operation, the research first investigated the differences among numerical, fuzzy, and cognitive methods in aggregating verbal terms by conducting three experiments. The results reveal that the numerical operation deviates much from actually decision making. The performances of fuzzy aggregations are also poor. This fact shows that fuzzy aggregations are still not qualified as descriptive operators. However, using cognitive representation to conduct fuzzy number operations can obtain a higher match-rate with the human decision (from 0.62 to 0.77). To understand the decision rules underlying human cognition, the research conduct a Multi-Dimensional Scaling (MDS) analysis. The results show that, other than numerical mean, subjects use two intuitive rules to aggregate opinions, namely, extreme-value and polarity. In the study of verbal variance operation, the research obtained the subjective judgments by a paired-comparison procedure. Furthermore, a factorial experiment is conducted to investigate the factors that might influence subjects¡¦ verbal consensus judgment. The results show that subjects¡¦ verbal consensus judgment is related to numerical variance, entropy, polarity, the interaction between numerical variance and polarity, the interaction between entropy and polarity, and the interaction among numerical variance, entropy, and polarity. Above all, entropy is a more significant descriptive operator than numerical variance. The results of the dissertation could complement the current numerical methods in processing qualitative data. Possible applications of the research findings are also discussed. Keywords: verbal information, cognitive operation, verbal representation, aggregation of verbal opinions, and consensus judgment of verbal opinions.

The functional group composition of acid polysaccharides was determined after isolation using cross-flow ultrafiltration, radiolabeling with 234Th(IV) and other isotopes, and separation using isoelectric focusing (IEF) and polyacrylamide gel electrophoresis (PAGE). Phosphate and sulphate concentrations were determined from cultured bacterial and phytoplankton colloid, particulate and colloidal samples collected from the Gulf of M??xico (GOM). Characterization of the 234Th(IV)-binding biomolecule was performed using ion chromatography (IC), and gas chromatography-mass spectrometry (GC-MS). Radiotracer experiments and culture experiments were conducted in determining the binding environment of the 234Th(IV)-binding ligand (i.e., sorption onto suspended particles), as well as the origin of the ligand in seawater systems. In all samples, 234Th(IV) isoelectric focusing profiles indicated that 49% to 65% of the 234Th(IV) labeled EPS from Roseobacter gallaeciensis, Sagittula stellata, Emiliania huxleyi, Synechococcus elongatus and GOM Station 4-72m was found at a pHIEF of 2 in the IEF spectrum. The carboxylic acid group appeared at the same pHIEF as 234Th(IV) for EPS from Roseobacter gallaeciensis, Emiliania huxleyi, Synechococcus elongatus and GOM colloidal organic matter sample. The phosphate group appeared at the same pHIEF as 234Th(IV) for EPS from Roseobacter gallaeciensis, and Synechococcus elongatus sample. The sulphate group was found at the same pHIEF as 234Th(IV) for EPS from S. elongatus and GOM colloidal organic matter sample. The total polysaccharide content was only 14% and 8%, uronic acids were approximately 5.4% and 87.1%, and total protein content was 2.6% and 6.2% of total carbon content of Sagittula stellata and Synechococcus elongatus, respectively. Monosaccharides identified in both Sagittula stellata and Synechococcus elongatus were galactose, glucose, and xylose in common. In addition, Sagittula stellata contained mannose and Synechococcus elongatus had galactoglucuronic acid. Thus, depending on the species, the size, structural composition, and functional groups of the 234Th(IV)-binding, acidic polysaccharides will vary. From these observations, it is concluded that the steric environment and not necessarily the exact functional group might actually be responsible for thorium-234 complexation to macromolecular organic matter. This research helped to improve our understanding of the observed variability in POC/234Th ratios in the ocean and provided insights into factors that regulate organic carbon export fluxes.