Two instruments were developed and administered to fifteen female and three male rheumatoid arthritics (mean age 44) previously hospitalized for allergy treatment by ecological methods. The Physical Functioning Ability Scale assessed functional capacity according to common daily activities, and the Rehabilitation Questionnaire surveyed adjustment problems subjects encountered after hospital discharge. ANOVA was performed on the functionality scale comparing dimensions of dependence, pain, and difficulty with categorical activities of mobility, work, and personal care, Significant F values (p(.05) were obtained for interaction and dimensions, but not for categories. Functional independence from human/mechanical assistance was particularly noted. Recommendation for future research concerns replicating this study using a control group of rheumatoid arthritics treated by traditional medical approaches.

This study provides information regarding the validity and reliability of the Wood Prospective Meory Test (WPMT), a newly developed test consisting of three main subscales intended to measure prospective memory. Subjects were 69 college students (50 female, 19 male, age range 18-24), who were administered several memory tasks including the WPMT.The results of this study suggest that the subscales of the WPMT do not have sufficient internal reliability (.50, .60, and .44), and therefore, would be unlikely to correlate highly with any other measures. The usefulness of the WPMT as a clinical instrument is discussed.

The longitudinal beam dynamics in circular accelerators is mainly defined by the interaction of the beam current with the accelerating Radio Frequency (RF) stations. For stable operation, Low Level RF (LLRF) feedback systems are employed to reduce coherent instabilities and regulate the accelerating voltage. The LLRF system design has implications for the dynamics and stability of the closed-loop RF systems as well as for the particle beam, and is very sensitive to the operating range of accelerator currents and energies. Stability of the RF loop and the beam are necessary conditions for reliable machine operation. This dissertation describes theoretical formalisms and models that determine the longitudinal beam dynamics based on the LLRF implementation, time domain simulations that capture the dynamic behavior of the RF station-beam interaction, and measurements from the Positron-Electron Project (PEP-II) and the Large Hadron Collider (LHC) that validate the models and simulations. These models and simulations are structured to capture the technical characteristics of the system (noise contributions, non-linear elements, and more). As such, they provide useful results and insight for the development and design of future LLRF feedback systems. They also provide the opportunity to study diverse longitudinal beam dynamics effects such as coupled-bunch impedance driven …

The Artificial Intelligence field of Integrating Multiple Learned Models (IMLM) explores ways to combine results from sets of trained programs. Aroclor Interpretation is an ill-conditioned problem in which trained programs must operate in scenarios outside their training ranges because it is intractable to train them completely. Consequently, they fail in ways related to the scenarios. We developed a general-purpose IMLM solution, the Combiner, and applied it to Aroclor Interpretation. The Combiner's first step, Scenario Identification (M), learns rules from very sparse, synthetic training data consisting of results from a suite of trained programs called Methods. S1 produces fuzzy belief weights for each scenario by approximately matching the rules. The Combiner's second step, Aroclor Presence Detection (AP), classifies each of three Aroclors as present or absent in a sample. The third step, Aroclor Quantification (AQ), produces quantitative values for the concentration of each Aroclor in a sample. AP and AQ use automatically learned empirical biases for each of the Methods in each scenario. Through fuzzy logic, AP and AQ combine scenario weights, automatically learned biases for each of the Methods in each scenario, and Methods' results to determine results for a sample.