Use of structural equation modeling to examine the association between breast cancer risk perception and repeat screening mammography among United States women

Breast cancer is the second leading cause of cancer death in United States women, estimated to be diagnosed in 1 out of 8 women in their lifetime. Screening mammography detects breast cancer in its pre-clinical stages when treatment strategies have the greatest chance of success, and is currently the only population-wide prevention method proven to reduce the morbidity and mortality associated with breast cancer. Research has shown that the majority of women are not screened annually, with estimates ranging front 6% - 30% of eligible women receiving all available annual mammograms over a 5-year or greater time frame. Health behavior theorists believe that perception of risk/susceptibility to a disease influences preventive health behavior, in this case, screening mammography The purpose of this dissertation is to examine the association between breast cancer risk perception and repeat screening mammography using a structural equation modeling (SEM) framework. A series of SEM multivariate regressions were conducted using self-reported, nationally representative data from the 2005 National Health Interview Survey. Interaction contrasts were tested to measure the potential moderating effects of variables which have been shown to be predictive of mammography use (physician recommendation, economic barriers, structural barriers, race/ethnicity) on the association between breast cancer risk perception and repeat mammography, while controlling for the covariates of age, income, region, nativity, and educational level. Of the variables tested for moderation, results of the SEM analyses identify physician recommendation as the only moderator of the relationship between risk perception and repeat mammography, thus the potentially most effective point of intervention to increase mammography screening, and decrease the morbidity and mortality associated with breast cancer. These findings expand the role of the physician from recommendation to one of attenuating the effect of risk perception and increasing repeat screening. The long range application of the research is the use of the SEM methodology to identify specific points of intervention most likely to increase preventive behavior in population-wide research, allowing for the most effective use of intervention funds.^

Modeling seasonal dynamics of small fish cohorts in fluctuating freshwater marsh landscapes

Small-bodied fishes constitute an important assemblage in many wetlands. In wetlands that dry periodically except for small permanent waterbodies, these fishes are quick to respond to change and can undergo large fluctuations in numbers and biomasses. An important aspect of landscapes that are mixtures of marsh and permanent waterbodies is that high rates of biomass production occur in the marshes during flooding phases, while the permanent waterbodies serve as refuges for many biotic components during the dry phases. The temporal and spatial dynamics of the small fishes are ecologically important, as these fishes provide a crucial food base for higher trophic levels, such as wading birds. We develop a simple model that is analytically tractable, describing the main processes of the spatio-temporal dynamics of a population of small-bodied fish in a seasonal wetland environment, consisting of marsh and permanent waterbodies. The population expands into newly flooded areas during the wet season and contracts during declining water levels in the dry season. If the marsh dries completely during these times (a drydown), the fish need refuge in permanent waterbodies. At least three new and general conclusions arise from the model: (1) there is an optimal rate at which fish should expand into a newly flooding area to maximize population production; (2) there is also a fluctuation amplitude of water level that maximizes fish production, and (3) there is an upper limit on the number of fish that can reach a permanent waterbody during a drydown, no matter how large the marsh surface area is that drains into the waterbody. Because water levels can be manipulated in many wetlands, it is useful to have an understanding of the role of these fluctuations.

A tidal creek water budget: Estimation of groundwater discharge and overland flow using hydrologic modeling in the Southern Everglades

Taylor Slough is one of the natural freshwater contributors to Florida Bay through a network of microtidal creeks crossing the Everglades Mangrove Ecotone Region (EMER). The EMER ecological function is critical since it mediates freshwater and nutrient inputs and controls the water quality in Eastern Florida Bay. Furthermore, this region is vulnerable to changing hydrodynamics and nutrient loadings as a result of upstream freshwater management practices proposed by the Comprehensive Everglades Restoration Program (CERP), currently the largest wetland restoration project in the USA. Despite the hydrological importance of Taylor Slough in the water budget of Florida Bay, there are no fine scale (∼1 km2) hydrodynamic models of this system that can be utilized as a tool to evaluate potential changes in water flow, salinity, and water quality. Taylor River is one of the major creeks draining Taylor Slough freshwater into Florida Bay. We performed a water budget analysis for the Taylor River area, based on long-term hydrologic data (1999–2007) and supplemented by hydrodynamic modeling using a MIKE FLOOD (DHI,http://dhigroup.com/) model to evaluate groundwater and overland water discharges. The seasonal hydrologic characteristics are very distinctive (average Taylor River wet vs. dry season outflow was 6 to 1 during 1999–2006) with a pronounced interannual variability of flow. The water budget shows a net dominance of through flow in the tidal mixing zone, while local precipitation and evapotranspiration play only a secondary role, at least in the wet season. During the dry season, the tidal flood reaches the upstream boundary of the study area during approximately 80 days per year on average. The groundwater field measurements indicate a mostly upwards-oriented leakage, which possibly equals the evapotranspiration term. The model results suggest a high importance of groundwater contribution to the water salinity in the EMER. The model performance is satisfactory during the dry season where surface flow in the area is confined to the Taylor River channel. The model also provided guidance on the importance of capturing the overland flow component, which enters the area as sheet flow during the rainy season. Overall, the modeling approach is suitable to reach better understanding of the water budget in the mangrove region. However, more detailed field data is needed to ascertain model predictions by further calibrating overland flow parameters.

An Exploratory Qualitative Study of the Proximal Goal Setting of Two Introductory Modeling Physics Students

A qualitative investigation on the impact of goal-setting strategies on self-efficacy of two students taking Introductory Modeling Physics was conducted. The study found that the problem solving process can be divided into two main themes: goal-setting and self-efficacy. Self-efficacy plays a role in the goal setting process of these two students, and may be linked to the retention of students in physics.

The role of recharge and evapotranspiration as hydraulic drivers of ion concentrations in shallow groundwater on Everglades tree islands, Florida (USA)

Recently, evapotranspiration has been hypothesized to promote the secondary formation of calcium carbonate year-round on tree islands in the Everglades by influencing groundwater ions concentrations. However, the role of recharge and evapotranspiration as drivers of shallow groundwater ion accumulation has not been investigated. The goal of this study is to develop a hydrologic model that predicts the chloride concentrations of shallow tree island groundwater and to determine the influence of overlying biomass and underlying geologic material on these concentrations. Groundwater and surface water levels and chloride concentrations were monitored on eight constructed tree islands at the Loxahatchee Impoundment Landscape Assessment (LILA) from 2007 to 2010. The tree islands at LILA were constructed predominately of peat, or of peat and limestone, and were planted with saplings of native tree species in 2006 and 2007. The model predicted low shallow groundwater chloride concentrations when inputs of regional groundwater and evapotranspiration-to-recharge rates were elevated, while low evapotranspiration-to-recharge rates resulted in a substantial increase of the chloride concentrations of the shallow groundwater. Modeling results indicated that evapotranspiration typically exceeded recharge on the older tree islands and those with a limestone lithology, which resulted in greater inputs of regional groundwater. A sensitivity analysis indicated the shallow groundwater chloride concentrations were most sensitive to alterations in specific yield during the wet season and hydraulic conductivity in the dry season. In conclusion, the inputs of rainfall, underlying hydrologic properties of tree islands sediments and forest structure may explain the variation in ion concentration seen across Everglades tree islands.

The Role of Recharge and Evapotranspiration as Hydraulic Drivers of Ion Concentrations in Shallow Groundwater on Everglades Tree Islands, Florida (USA)

Recently, evapotranspiration has been hypothesized to promote the secondary formation of calcium carbonate year-round on tree islands in the Everglades by influencing groundwater ions concentrations. However, the role of recharge and evapotranspiration as drivers of shallow groundwater ion accumulation has not been investigated. The goal of this study is to develop a hydrologic model that predicts the chloride concentrations of shallow tree island groundwater and to determine the influence of overlying biomass and underlying geologic material on these concentrations. Groundwater and surface water levels and chloride concentrations were monitored on eight constructed tree islands at the Loxahatchee Impoundment Landscape Assessment (LILA) from 2007 to 2010. The tree islands at LILA were constructed predominately of peat, or of peat and limestone, and were planted with saplings of native tree species in 2006 and 2007. The model predicted low shallow groundwater chloride concentrations when inputs of regional groundwater and evapotranspiration-to-recharge rates were elevated, while low evapotranspiration-to-recharge rates resulted in a substantial increase of the chloride concentrations of the shallow groundwater. Modeling results indicated that evapotranspiration typically exceeded recharge on the older tree islands and those with a limestone lithology, which resulted in greater inputs of regional groundwater. A sensitivity analysis indicated the shallow groundwater chloride concentrations were most sensitive to alterations in specific yield during the wet season and hydraulic conductivity in the dry season. In conclusion, the inputs of rainfall, underlying hydrologic properties of tree islands sediments and forest structure may explain the variation in ion concentration seen across Everglades tree islands.

Background traffic modeling for large-scale network simulation

Network simulation is an indispensable tool for studying Internet-scale networks due to the heterogeneous structure, immense size and changing properties. It is crucial for network simulators to generate representative traffic, which is necessary for effectively evaluating next-generation network protocols and applications. With network simulation, we can make a distinction between foreground traffic, which is generated by the target applications the researchers intend to study and therefore must be simulated with high fidelity, and background traffic, which represents the network traffic that is generated by other applications and does not require significant accuracy. The background traffic has a significant impact on the foreground traffic, since it competes with the foreground traffic for network resources and therefore can drastically affect the behavior of the applications that produce the foreground traffic. This dissertation aims to provide a solution to meaningfully generate background traffic in three aspects. First is realism. Realistic traffic characterization plays an important role in determining the correct outcome of the simulation studies. This work starts from enhancing an existing fluid background traffic model by removing its two unrealistic assumptions. The improved model can correctly reflect the network conditions in the reverse direction of the data traffic and can reproduce the traffic burstiness observed from measurements. Second is scalability. The trade-off between accuracy and scalability is a constant theme in background traffic modeling. This work presents a fast rate-based TCP (RTCP) traffic model, which originally used analytical models to represent TCP congestion control behavior. This model outperforms other existing traffic models in that it can correctly capture the overall TCP behavior and achieve a speedup of more than two orders of magnitude over the corresponding packet-oriented simulation. Third is network-wide traffic generation. Regardless of how detailed or scalable the models are, they mainly focus on how to generate traffic on one single link, which cannot be extended easily to studies of more complicated network scenarios. This work presents a cluster-based spatio-temporal background traffic generation model that considers spatial and temporal traffic characteristics as well as their correlations. The resulting model can be used effectively for the evaluation work in network studies.

Integrated construction project delivery system in the U.S. public sector: An information modeling framework

Integrated project delivery (IPD) method has recently emerged as an alternative to traditional delivery methods. It has the potential to overcome inefficiencies of traditional delivery methods by enhancing collaboration among project participants. Information and communication technology (ICT) facilitates IPD by effective management, processing and communication of information within and among organizations. While the benefits of IPD, and the role of ICT in realizing them, have been generally acknowledged, the US public construction sector is very slow in adopting IPD. The reasons are - lack of experience and inadequate understanding of IPD in public owner as confirmed by the results of the questionnaire survey conducted under this research study. The public construction sector should be aware of the value of IPD and should know the essentials for effective implementation of IPD principles - especially, they should be cognizant of the opportunities offered by advancements in ICT to realize this.^ In order to address the need an IPD Readiness Assessment Model (IPD-RAM) was developed in this research study. The model was designed with a goal to determine IPD readiness of a public owner organization considering selected IPD principles, and ICT levels, at which project functions were carried out. Subsequent analysis led to identification of possible improvements in ICTs that have the potential to increase IPD readiness scores. Termed as the gap identification, this process was used to formulate improvement strategies. The model had been applied to six Florida International University (FIU) construction projects (case studies). The results showed that the IPD readiness of the organization was considerably low and several project functions can be improved by using higher and/or advanced level ICT tools and methods. Feedbacks from a focus group comprised of FIU officials and an independent group of experts had been received at various stages of this research and had been utilized during development and implementation of the model. Focus group input was also helpful for validation of the model and its results. It was hoped that the model developed would be useful to construction owner organizations in order to assess their IPD readiness and to identify appropriate ICT improvement strategies.^

Role of carbon nanotube dispersion in fracture toughening of plasma sprayed aluminum oxide - carbon nanotube nanocomposite coating

Aluminum oxide (A1203, or alumina) is a conventional ceramic known for applications such as wear resistant coatings, thermal liners, heaters, crucibles, dielectric systems, etc. However applications of A1203 are limited owing to its inherent brittleness. Due to its excellent mechanical properties and bending strength, carbon nanotubes (CNT) is an ideal reinforcement for A1203 matrix to improve its fracture toughness. The role of CNT dispersion in the fracture toughening of the plasma sprayed A1203-CNT nanocomposite coating is discussed in the current work. Pretreatment of powder feedstock is required for dispersing CNTs in the matrix. Four coatings namely spray dried A1203 (A-SD), A1203 blended with 4wt.% CNT (A4C-B), composite spray dried A1203-4wt.% CNT (A4C-SD) and composite spray dried A1203-8wt.% CNT (A8CSD), are synthesized by plasma spraying. Owing to extreme temperatures and velocities involved in the plasma spraying of ceramics, retention of CNTs in the resulting coatings necessitates optimizing plasma processing parameters using an inflight particle diagnostic sensor. A bimodal microstructure was obtained in the matrix that consists of fully melted and resolidified structure and solid state sintered structure. CNTs are retained both in the fully melted region and solid-state sintered regions of processed coatings. Fracture toughness of A-SD, A4C-B, A4C-SD and A8C-SD coatings was 3.22, 3.86, 4.60 and 5.04 MPa m1/2 respectively. This affirms the improvement of fracture toughness from 20 % (in A4C-B coating) to 43% (in A4C-SD coating) when compared to the A-SD coating because of the CNT dispersion. Fracture toughness improvement from 43 % (in A4C-SD) to 57% (in A8C-SD) coating is evinced because of the CNT content. Reinforcement by CNTs is described by its bridging, anchoring, hook formation, impact alignment, fusion with splat, and mesh formation. The A1203/CNT interface is critical in assisting the stress transfer and utilizing excellent mechanical properties of CNTs. Mathematical and computational modeling using ab-initio principle is applied to understand the wetting behavior at the A1203/CNTinterface. Contrasting storage modulus was obtained by nanoindentation (~ 210, 250, 250-350 and 325-420 GPa in A-SD, A4C-B, A4C-SD, and A8C-SD coatings respectively) depicting the toughening associated with CNT content and dispersion.

Role of Hydrological Process Representation on Erosion, Deposition, and Sediment Yield Estimate

Soil erosion by water is a major driven force causing land degradation. Laboratory experiments, on-site field study, and suspended sediments measurements were major fundamental approaches to study the mechanisms of soil water erosion and to quantify the erosive losses during rain events. The experimental research faces the challenge to extent the result to a wider spatial scale. Soil water erosion modeling provides possible solutions for scaling problems in erosion research, and is of principal importance to better understanding the governing processes of water erosion. However, soil water erosion models were considered to have limited value in practice. Uncertainties in hydrological simulations are among the reasons that hindering the development of water erosion model. Hydrological models gained substantial improvement recently and several water erosion models took advantages of the improvement of hydrological models. It is crucial to know the impact of changes in hydrological processes modeling on soil erosion simulation.

This dissertation work first created an erosion modeling tool (GEOtopSed) that takes advantage of the comprehensive hydrological model (GEOtop). The newly created tool was then tested and evaluated at an experimental watershed. The GEOtopSed model showed its ability to estimate multi-year soil erosion rate with varied hydrological conditions. To investigate the impact of different hydrological representations on soil erosion simulation, a 11-year simulation experiment was conducted for six models with varied configurations. The results were compared at varied temporal and spatial scales to highlight the roles of hydrological feedbacks on erosion. Models with simplified hydrological representations showed agreement with GEOtopSed model on long temporal scale (longer than annual). This result led to an investigation for erosion simulation at different rainfall regimes to check whether models with different hydrological representations have agreement on the soil water erosion responses to the changing climate. Multi-year ensemble simulations with different extreme precipitation scenarios were conducted at seven climate regions. The differences in erosion simulation results showed the influences of hydrological feedbacks which cannot be seen by purely rainfall erosivity method.

Background Traffic Modeling for Large-Scale Network Simulation

Network simulation is an indispensable tool for studying Internet-scale networks due to the heterogeneous structure, immense size and changing properties. It is crucial for network simulators to generate representative traffic, which is necessary for effectively evaluating next-generation network protocols and applications. With network simulation, we can make a distinction between foreground traffic, which is generated by the target applications the researchers intend to study and therefore must be simulated with high fidelity, and background traffic, which represents the network traffic that is generated by other applications and does not require significant accuracy. The background traffic has a significant impact on the foreground traffic, since it competes with the foreground traffic for network resources and therefore can drastically affect the behavior of the applications that produce the foreground traffic. This dissertation aims to provide a solution to meaningfully generate background traffic in three aspects. First is realism. Realistic traffic characterization plays an important role in determining the correct outcome of the simulation studies. This work starts from enhancing an existing fluid background traffic model by removing its two unrealistic assumptions. The improved model can correctly reflect the network conditions in the reverse direction of the data traffic and can reproduce the traffic burstiness observed from measurements. Second is scalability. The trade-off between accuracy and scalability is a constant theme in background traffic modeling. This work presents a fast rate-based TCP (RTCP) traffic model, which originally used analytical models to represent TCP congestion control behavior. This model outperforms other existing traffic models in that it can correctly capture the overall TCP behavior and achieve a speedup of more than two orders of magnitude over the corresponding packet-oriented simulation. Third is network-wide traffic generation. Regardless of how detailed or scalable the models are, they mainly focus on how to generate traffic on one single link, which cannot be extended easily to studies of more complicated network scenarios. This work presents a cluster-based spatio-temporal background traffic generation model that considers spatial and temporal traffic characteristics as well as their correlations. The resulting model can be used effectively for the evaluation work in network studies.

Integrated Construction Project Delivery System in the U.S. Public Sector: An Information Modeling Framework

Integrated project delivery (IPD) method has recently emerged as an alternative to traditional delivery methods. It has the potential to overcome inefficiencies of traditional delivery methods by enhancing collaboration among project participants. Information and communication technology (ICT) facilitates IPD by effective management, processing and communication of information within and among organizations. While the benefits of IPD, and the role of ICT in realizing them, have been generally acknowledged, the US public construction sector is very slow in adopting IPD. The reasons are - lack of experience and inadequate understanding of IPD in public owner as confirmed by the results of the questionnaire survey conducted under this research study. The public construction sector should be aware of the value of IPD and should know the essentials for effective implementation of IPD principles - especially, they should be cognizant of the opportunities offered by advancements in ICT to realize this. In order to address the need an IPD Readiness Assessment Model (IPD-RAM) was developed in this research study. The model was designed with a goal to determine IPD readiness of a public owner organization considering selected IPD principles, and ICT levels, at which project functions were carried out. Subsequent analysis led to identification of possible improvements in ICTs that have the potential to increase IPD readiness scores. Termed as the gap identification, this process was used to formulate improvement strategies. The model had been applied to six Florida International University (FIU) construction projects (case studies). The results showed that the IPD readiness of the organization was considerably low and several project functions can be improved by using higher and/or advanced level ICT tools and methods. Feedbacks from a focus group comprised of FIU officials and an independent group of experts had been received at various stages of this research and had been utilized during development and implementation of the model. Focus group input was also helpful for validation of the model and its results. It was hoped that the model developed would be useful to construction owner organizations in order to assess their IPD readiness and to identify appropriate ICT improvement strategies.

Quantifying and Modeling the Influence of Forest on the Magnitude and Duration of Mountain Snow Storage in the Pacific Northwest, USA

Thesis (Ph.D.)--University of Washington, 2016-08

Systems Modeling of Lung Cancer Screening Programs to Improve Quality

Thesis (Master's)--University of Washington, 2016-08

User Behavioral Modeling of Web-based Systems for Continuous User Authentication

Authentication plays an important role in how we interact with computers, mobile devices, the web, etc. The idea of authentication is to uniquely identify a user before granting access to system privileges. For example, in recent years more corporate information and applications have been accessible via the Internet and Intranet. Many employees are working from remote locations and need access to secure corporate files. During this time, it is possible for malicious or unauthorized users to gain access to the system. For this reason, it is logical to have some mechanism in place to detect whether the logged-in user is the same user in control of the user's session. Therefore, highly secure authentication methods must be used. We posit that each of us is unique in our use of computer systems. It is this uniqueness that is leveraged to "continuously authenticate users" while they use web software. To monitor user behavior, n-gram models are used to capture user interactions with web-based software. This statistical language model essentially captures sequences and sub-sequences of user actions, their orderings, and temporal relationships that make them unique by providing a model of how each user typically behaves. Users are then continuously monitored during software operations. Large deviations from "normal behavior" can possibly indicate malicious or unintended behavior. This approach is implemented in a system called Intruder Detector (ID) that models user actions as embodied in web logs generated in response to a user's actions. User identification through web logs is cost-effective and non-intrusive. We perform experiments on a large fielded system with web logs of approximately 4000 users. For these experiments, we use two classification techniques; binary and multi-class classification. We evaluate model-specific differences of user behavior based on coarse-grain (i.e., role) and fine-grain (i.e., individual) analysis. A specific set of metrics are used to provide valuable insight into how each model performs. Intruder Detector achieves accurate results when identifying legitimate users and user types. This tool is also able to detect outliers in role-based user behavior with optimal performance. In addition to web applications, this continuous monitoring technique can be used with other user-based systems such as mobile devices and the analysis of network traffic.