Adsorption kinetic data recorded for α-lactalbumin, β-casein, β-lactoglobulin, bovine serum albumin and lysozyme at silianized silica surfaces of low and high hydrophobicity, along with a simple model for adsorption and surfactant-mediated elution of protein, were used to analyze the removal of each protein by sodium dodecylsulfate (SDS) and dodecyltrimethylammonium bromide (DTAB) at each surface. The model relates resistance to surfactant elution to two rate constants: one governing conversion of removable protein to a nonremovable form (s₁), and one governing removal of protein by the surfactant (k [subscript s]). Elution of each protein from hydrophobic silica with SDS was interpreted as providing information relevant to protein-surface binding strength, or si; i.e., protein-specific differences in removal were a result of SDS adsorption to the surface and displacement of surface-bound protein, as opposed to solubilization driven by SDS binding to the protein. SDS-mediated removal of protein from surfaces of lower hydrophobicity were interpreted as generally proceeding according to a similar, displacement mechanism. The model indicated that data recorded for DTAB-mediated elution at each surface were generally less representative of protein-surface behavior, and more a function of k [subscript s], where differences in surfactant attachment to protein and solubilization appeared to play an important role in protein removal. Under controlled conditions use of the model would allow identification of cases where k [subscript s] in particularly protein specific, and illustrates the point that in such cases surfactant-mediated elution of a protein may reveal little about its surface behavior. / Graduation date: 1996

Active Surveillance (AS) for the clinical management of prostate cancer (PC) is a treatment option for men with low-risk PC. Screening procedures have led to the overdetection of PCs that would have never caused problems. Active treatment (e.g., surgery or radiation) for these non-aggressive tumors may not be necessary given the slow-growing nature of PC. AS provides a way to monitor the disease and delay treatment-related compromises on quality of life until clinically indicated (e.g., rising PSA level). However, the intensive monitoring in AS may be a stressful experience and lead to greater anxiety, an emotional state that has been associated with undergoing active treatment despite physician recommendation for AS. The current study aimed to identify psychosocial correlates of anxiety in men undergoing AS. Using Mishel’s Reconceptualized Uncertainty in Illness Model as a framework, the proposed study aimed to examine the relationships between perceived stress management skills, PC psychosocial concerns, and anxiety/arousal. Hierarchical multiple regression analyses were conducted on a sample of 71 men undergoing AS, who were on average 65.40 years old (SD=7.85) and ethnically diverse (52% non-Hispanic White; 31% Hispanic; 17% African American). Results indicated that greater PSMS were significantly associated with less IES-R anxiety (β=-.28, p<.04). PSMS were not significantly associated with PC concerns (β=-.02, p>.05), but greater PC concerns were significantly associated with greater IES-R anxiety (β=.61, p<.01) and PSA anxiety (β=.42, p<.01). These associations held after controlling for relevant covariates. The results suggest a possible role for stress management skills as perceived ability to manage stress was related to less anxiety in the AS experience. Future studies should examine the relationship among these factors in longitudinal designs and whether greater stress is associated with unnecessary active treatment in low-risk PC.

Thesis (Ed. D.)--Graduate School of Education, Oral Roberts University, 2006. / Includes abstract and vita. Includes bibliographical references (leaves 157-168).

The development of a leading edge propeller blade reconfiguration system using Shape Memory Allow (SMA) muscles is presented. This work describes the design and testing of a leading edge flap, which is used to alter the local camber of a propeller blade. The leading edge flap is deflected by SMA wires housed in the blade and maintained in a fixed position with a shaft locking and releasing mechanism. A locking and releasing mechanism is utilized so that constant actuation of the SMAs is not required to maintain leading edge deflection. The profile at 70% span of the propeller blade was used to create a two-dimensional blade for leading edge flap design implementation and load testing. Deflection of up to five degrees was obtained with the final design of the leading edge flap and locking and releasing mechanism. The SMA muscles used to deflect the leading edge were actuated electronically through resistive heating and were controlled by a proportional/integral gain control algorithm with closed-loop feedback from a linear displacement sensor within the blade. With the final design of the leading edge flap and locking and releasing mechanism, a preliminary design for a three-dimensional propeller was created.

BACKGROUND: Active transportation, such as using a bicycle to get one from one place to another, has definite benefits over driving or some other form of travel that requires less exertion; the most obvious of these is that it helps a person meet the daily recommendations for physical activity. College campuses tend to have higher rates of bicycle-commuters than non-campus environments, although Georgia State University faces unique barriers to bicycling due to its downtown location. In 2009, a cross-sectional study was conducted to assess bicycling attitudes, knowledge, and behaviors. In the two years that followed, a faculty-student collaborative known as GSU Bikes implemented efforts to try to increase bicycling on campus. Campus bicycle count data between the two years showed positive increases. In 2011, the study was repeated to examine if bicycling attitudes, knowledge, and behavior had changed since 2009. METHODS: 211 Georgia State University undergraduate and graduate students were surveyed in Fall 2011. The data they provided were then analyzed and compared to the 2009 bicycle data using independent-samples t­-tests and a chi-square analysis to identify significant differences between the two data sets. RESULTS: Few significant differences between the two sets of data were identified. Participants in 2011 had significantly higher agreement that they could locate information regarding bicycle safety and repairs, as well as reported a significantly greater likelihood of bicycling to campus if educational programs to, from, and around the GSU campus were implemented. Written feedback suggested that fear of collisions was still a major barrier; many students suggested a campus bicycle-share program and more information disseminated to students about bicycling to campus. DISCUSSION: The results from this study demonstrate that efforts aimed at encouraging students to bicycle to campus, between the 2009 and 2011 data collections, may not have been as effective as they were intended. Bicycle promotion that reaches a greater number of Georgia State University students is suggested. Because of the method of data sampling in this study, the data analyzed may not be truly representative of the Georgia State University population. In the future, an improved survey that is disseminated electronically may result in a larger sample size, increasing statistical validity and ability to generalize findings.

Automated human activity recognition has attracted increasing attention in the past decade. However, the application of machine learning and probabilistic methods for activity recognition problems has been studied only in the past couple of years. For the first time, this thesis explores the application of semi-supervised and active learning in activity recognition. We present a new and efficient semi-supervised training method for parameter estimation and feature selection in conditional random fields (CRFs),a probabilistic graphical model. In real-world applications such as activity recognition, unlabeled sensor traces are relatively easy to obtain whereas labeled examples are expensive and tedious to collect. Furthermore, the ability to automatically select a small subset of discriminatory features from a large pool can be advantageous in terms of computational speed as well as accuracy. We introduce the semi-supervised virtual evidence boosting (sVEB)algorithm for training CRFs — a semi-supervised extension to the recently developed virtual evidence boosting (VEB) method for feature selection and parameter learning. sVEB takes advantage of the unlabeled data via mini-mum entropy regularization. The objective function combines the unlabeled conditional entropy with labeled conditional pseudo-likelihood. The sVEB algorithm reduces the overall system cost as well as the human labeling cost required during training, which are both important considerations in building real world inference systems. Moreover, we propose an active learning algorithm for training CRFs is based on virtual evidence boosting and uses entropy measures. Active virtual evidence boosting (aVEB) queries the user for most informative examples, efficiently builds up labeled training examples and incorporates unlabeled data as in sVEB. aVEB not only reduces computational complexity of training CRFs as in sVEB, but also outputs more accurate classification results for the same fraction of labeled data. Ina set of experiments we illustrate that our algorithms, sVEB and aVEB, benefit from both the use of unlabeled data and automatic feature selection, and outperform other semi-supervised and active training approaches. The proposed methods could also be extended and employed for other classification problems in relational data.

The control of nonholonomic wheeled mobile robots (WMRs) has gained a lot of attention in the field of robotics over the past few decades as WMRs provide an increased range of motion resulting in a larger workspace. This research focuses on the application of Model Predictive Control (MPC) for real-time trajectory tracking of a nonholonomic WMR. MPC is a control strategy in which the control law is designed based on optimizing a cost function. The input and output constraints that may arise in practical situations can be directly incorporated into the control system using MPC. Computation time is the biggest hurdle in adapting MPC strategies for trajectory tracking. This research applies a non-feasible active set MPC algorithm developed in [1] which is faster than the traditional active set methods (ASMs). A discrete-time linear model of a general WMR is used for the simulation. MATLAB simulations are performed for tracking circular as well as square trajectories using the discretized WMR model and the non-feasible ASM (NF-ASM). The performance of NF-ASM is compared to two other well-known traditional algorithms, i.e. Fletcher’s ASM and MATLAB’s Quadratic Programming algorithm. It is shown that, although all these algorithms are capable of providing satisfactory trajectory tracking performance, NF-ASM is a better choice in terms of the simulation time and required number of iterations for realtime trajectory tracking of any type as long as the constraints on the inputs stay active for a long period during the simulation. / UOIT

Graduation date: 1991

In a world of war and turmoil in developing nations, land mines are becoming a concern, as millions of them are scattered in countries all over the world. Moreover, land mine prevention programs cannot clear land mine fields as fast as they are detonated each day. As a result, there are thousands that fall victim each year. There is immense demand for newer technologies to replace the aging prostheses used in these war torn nations. The active prosthetic knee (APK) design project is a novel design that utilizes simple, robust one degree of freedom (DOF) mechanics, while providing fully active knee torque control. The APK utilizes a high-speed brushed servomotor, providing the necessary torque and dynamics to provide the necessary gait characteristics of human locomotion. The main research contributions of this thesis are: 1) the mechanics and 2) the intelligence of the APK. This thesis investigates and highlights the prosthetic’s design process. The human biological system is studied and used as the design criteria when designing the device. Anthropometric data was used to determine the sizing and other physical properties. Moreover, Adaptive-Network-based Fuzzy-Interference Systems (ANFIS) and Fuzzy Interference Systems (FIS) are used to provide control to the APK. Finite element analysis (FEA) verifies the structural integrity of the APK. Four simulations are analyzed: equivalent stress, equivalent strain, shear stress and total deformation. These four simulations provide a mathematical interpretation of the physical system. We found that the first prototype, although a sound design, can be further improved to allow greater loading capabilities. Moreover, cyclical loading and total life cycles would also be significantly improved. A modular test stand is also designed and prototyped to allow APK testing. Since the APK prototype cannot be immediately placed on a human test subject, the test stand allows for experimentation in replicating human gait cycles.

The question as to whether the distribution of radio loudness in active galactic nuclei (AGN) is actually bimodal has been discussed extensively in the literature. Furthermore, there have been claims that radio loudness depends on black hole mass and Eddington ratio. We investigate these claims using the low redshift broad line AGN sample of Greene & Ho 2007, which consists of 8434 objects at z < 0.35 from the Sloan Digital Sky Survey (SDSS) Fourth Data Release. We obtained radio fluxes from the Very Large Array Faint Images of the Radio Sky at Twenty-Centimeters (FIRST) survey for the SDSS AGN. Out of the 8434 SDSS AGN, 846 have radio emission within 4" of the optical counterpart and are considered to be core emission. We also perform a systematic search for extended emission in FIRST that can be positively associated with the optical counterparts and find 51 out of the 846 previously detected core sources have extended emission that must be taken into account when calculating the total radio luminosity. Further, we find an additional 12 objects that have extended radio emission but no detectable radio core and have classic FR II type morphologies. Using these data, the question of radio bimodality and the dependence of radio-loudness on physical parameters are investigated for different subsets of the total sample. We find modest trends in the radio-loud fraction as a function of black hole mass and Eddington ratio, where the fraction of RL AGN increases for the largest black hole mass group and decreases with increasing Eddington ratio. With extended emission taken into account, we find strong evidence for a bimodal distribution in radio-loudness, where the lower radio luminosity core-only sources appear as a population separate from the extended sources with a dividing line at log(R) = 1.75. This dividing line is interesting in that it requires the radio luminosity to be 50 times the optical luminosity, ensuring that these are indeed the most RL AGN, which may have different or extreme physical conditions in their central engines when compared to the more numerous radio quiet AGN in this sample.