<p>Natural steroidal estrogen hormones in swine wastes generated from Concentrated Animal Feeding Operations (CAFOs) have become a potential pollutant to many aquatic environments due to their adverse impacts on the reproductive biology of aquatic organisms. In North Carolina, the swine CAFO industry is a major agricultural economic enterprise that is responsible for the generation of large volumes of waste. However, there is limited scientific understanding regarding the concentration, fate, and transport of the estrogenic compounds from these swine facilities into terrestrial and aquatic environments. To address this issue, my research involved the development and application of integrated Bayesian networks (BNs) models that can be used to better characterize and assess the generation, fate, and transport of site-specific swine CAFO-derived estrogen compounds. The developed model can be used as decision support tool towards estrogen risk assessment. Modularized and melded BN approaches were used to capture the predictive and casual relationships of the estrogen budget and its movement within and between the three major systems of a swine farrowing CAFO. These systems include the animal barns, the anaerobic waste lagoon, and the spray fields. For the animal barn system, a facility-wide estrogen budget was developed to assess the operation-specific estrogen excretion, using an object-oriented BN (OOBN) approach. The developed OOBN model provides a means to estimate and predict estrogen fluxes from the whole swine facility in the context of both estrogen type and animal operating unit. It also accounts for the uncertainties in the data and in our understanding of the system. Next, mass balance melding BN models were developed to predict the natural estrogen fates and budgets in two lagoon compartments, the slurry and the sludge storage. This involved utilizing mass balance equations to account for the mechanisms of flushing, sorption, transformation, settling, and burial reactions of estrogen compounds in the slurry and sludge storages. As an alternative approach, a regression based BN melding approach was developed to both characterize estrogen fate and budgets as a result of the sequential transformation processes between natural estrogen compounds and to assess the seasonal effects on the estrogen budgets in the two different lagoon compartments. Finally, a dynamic BN model was developed to characterize rainfall-driven estrogen runoff processes from the spray fields. The dynamic BN models were used to assess the potential risk of estrogen runoff to adjacent waterways. In addition, the dynamic model was used to quantify the effects of manure application rates, rainfall frequency, the time of rainfall and irrigation, crop types, on-farm best management practices, seasonal variability, and successive rainfall and manure application events on estrogen runoff. </p><p>The model results indicate that the farrowing barn is the biggest contributor of total estrogen as compared to the breeding and gestation operating barns. Once the estrogen reaches the anaerobic lagoon, settling and burial reactions were shown to be the most significant factors influencing estrogen levels in the slurry and sludge, respectively. The estrogen budgets in the lagoon were also found to vary by season, with higher slurry and sludge estrogen levels in the spring as compared to the summer. The risk of estrogen runoff was predicted to be lower in the summer as compared to the spring, primarily due to the spray field crop management plans adopted. The results also indicated that Bermuda grass performed more favorably when compared to soybean, when it came to retaining surface water runoff in the field. Model predictions indicated that there is a low risk of estrogen runoff losses from the spray fields under multiple irrigation and rainfall events, unless the time interval between irrigation was less than 10 days and/or in the event of a prolonged high magnitude rainstorm event. Overall, the estrone was the most persistent form of natural estrogens in the three major systems of the swine farrowing CAFO.</p> / Dissertation

Risk-based authentication is an increasingly popular component in the security architecture deployed by many organizations in mitigating online identity threat. Risk-based authentication uses contextual and historical information extracted from online communications to build a risk profile for the user that can be used to make accordingly authentication and authorization decisions. Existing risk-based authentication systems rely on basic web communication information such as the source IP address or the velocity of transactions performed by a specific account, or originating from a certain IP address. Such information can easily be spoofed and as such put in question the robustness and reliability of the proposed systems. In this thesis, we propose in this work an online risk-based authentication system which provides more robust user identity information by combining mouse dynamics, keystroke dynamics biometrics, and user site actions in a multimodal framework. We propose a Bayesian network model for analyzing free keystrokes and mouse movements involved in web sessions. Experimental evaluation of our proposed model with 24 participants yields an Equal Error Rate of 6.91%. This is encouraging considering that we are dealing with free text and mouse movements and the fact that many web sessions tend to be short. / Graduate

Background: Several previous studies have reported that dyslipidemia is associated with the risk of hypertension, but these studies are mainly conducted in European and US populations, with a very few studies in the Asian population. Moreover, the effects of atherosclerotic indices, including atherogenic coefficient (AC) and atherogenic risk of plasma (AIP), on hypertension in Asians have not been well described so far. Methods: From 2010 to 2016, altogether 211,833 Chinese adults were ultimately recruited at the health centers in 11 Chinese cities (including Shanghai, Beijing, Nanjing, Suzhou, Shenzhen, Changzhou, Chengdu, Guangzhou, Hefei, Wuhan, and Nantong). Differences in continuous variables between the two groups were analyzed by the Mann–Whitney test, while those in categorical variables were examined by the Chi-squared test. Logistic regression was applied to evaluate the association between lipid profiles and the risk of hypertension. The predictive values of AC and AIP for the incidence of hypertension were analyzed using the area under the receiver operating characteristic (ROC) curve. Meanwhile, Bayesian network (BN) models were performed to further analyze the associations between the different covariates and the incidence of hypertension. Results: A total of 117,056 participants were included in the final analysis. There were significant differences in baseline characteristics between normotension and hypertension groups (p < 0.001). In multivariate logistic regression, the risk of hypertension increased by 0.2% (1.002 [1.001–1.003]), 0.2% (1.002 [1.001–1.003]), and 0.2% (1.002 [1.001–1.003]) per 1 mg/dl increase in total cholesterol (TC), low-density lipoprotein (LDL), and non-high-density lipoprotein cholesterol (non-HDL-c), respectively. However, after adjusting for body mass index (BMI), an increase in HDL level was associated with a higher risk of hypertension (p for a trend < 0.001), and the risk of hypertension increased by 0.6% per 1 mg/dl increase in HDL-c (1.006 [1.003–1.008]). In women, AC had the highest predictive value for the incidence of hypertension with an area under the curve (AUC) of 0.667 [95% confidence interval (CI): 0.659–0.674]. BN models suggested that TC and LDL were more closely related to the incidence of hypertension. Conclusions: Overall, lipid profiles were significantly abnormal in the hypertensive population than in the normotensive population. TC and LDL were strongly associated with the incidence of hypertension. TC, LDL, and non-HDL-c levels show a positive association, HDL-c shows a negative association, while TG is not significantly associated with the risk of hypertension. After adjusting for BMI, HDL-c turns out to be positively associated with the risk of hypertension. In addition, AC has a good predictive value for the incidence of hypertension in women.