Improving the predictability of time series by chaotic parameter estimation

Limited literature regarding parameter estimation of dynamic systems has been identified as the central-most reason for not having parametric bounds in chaotic time series. However, literature suggests that a chaotic system displays a sensitive dependence on initial conditions, and our study reveals that the behavior of chaotic system: is also sensitive to changes in parameter values. Therefore, parameter estimation technique could make it possible to establish parametric bounds on a nonlinear dynamic system underlying a given time series, which in turn can improve predictability. By extracting the relationship between parametric bounds and predictability, we implemented chaos-based models for improving prediction in time series. ^ This study describes work done to establish bounds on a set of unknown parameters. Our research results reveal that by establishing parametric bounds, it is possible to improve the predictability of any time series, although the dynamics or the mathematical model of that series is not known apriori. In our attempt to improve the predictability of various time series, we have established the bounds for a set of unknown parameters. These are: (i) the embedding dimension to unfold a set of observation in the phase space, (ii) the time delay to use for a series, (iii) the number of neighborhood points to use for avoiding detection of false neighborhood and, (iv) the local polynomial to build numerical interpolation functions from one region to another. Using these bounds, we are able to get better predictability in chaotic time series than previously reported. In addition, the developments of this dissertation can establish a theoretical framework to investigate predictability in time series from the system-dynamics point of view. ^ In closing, our procedure significantly reduces the computer resource usage, as the search method is refined and efficient. Finally, the uniqueness of our method lies in its ability to extract chaotic dynamics inherent in non-linear time series by observing its values. ^

Managing small-medium cities in a time of globalization: Experiences and evidence from Florida's public managers

This study examines how public management practitioners in small and medium-sized Florida cities perceive globalization and its impact on public management practice. Using qualitative analysis, descriptive statistics and factor analysis methods, data obtained from a survey and semi-structured interviews were studied to comprehend how public managers view the management and control of their municipalities in a time of globalization. The study shows that the public managers’ perceptions of globalization and its impact on public management in Florida’s small-medium cities are nuanced. Whereas some public managers feel that globalization has significant impacts on municipalities’ viability, others opine that globalization has no local impact. The study further finds that globalization processes are perceived as altering the public management functions of decision-making, economic development and service delivery in some small-medium cities in Florida as a result of transnational shifts, rapidly changing technologies, and municipalities’ heightened involvement in the global economy. The study concludes that the globalization discourse does not resonate among some public managers in Florida’s small-medium cities in ways implied in extant literature.

Low-income first time mothers: Effects on advanced practice nurse (APN) follow up telephone calls on maternal and infant health and health care charges

The United States has over 4 million births annually. Currently healthy women with non-complicated deliveries receive little to no routine postpartum support when discharged from the hospital. This is especially problematic if mothers are first time mothers, poor, have language barriers and little to no social support after giving birth. The purpose of this randomized clinical trial was to compare maternal and infant health outcomes, and health care charges between 2 groups of mothers and newborns. A control ( n = 69) group received routine posthospital discharge care. An intervention group (n = 70) received routine posthospital discharge care plus follow up telephone calls by advanced practice nurses (APNs) on days 3,7,14,21,28 and week 8. Both groups were followed for the first 8 weeks posthospital discharge following delivery to examine maternal health outcomes (perceived maternal stress, social support and perceived maternal physical health), infant health outcomes (routine medical follow up visits immunizations, weight gain), morbidity (urgent care visits, emergency room visits, rehospitalizations), health care charges (urgent care visits, emergency room visits, rehospitalizations) in both groups and charges for APN follow up in the intervention group only. Data were analyzed using descriptive statistics and two-sample t-tests. Study findings indicated that intervention group had significantly lower perceived maternal stress, significantly higher rating of perceived maternal health and higher levels of social support and by the end of the 2nd month posthospital discharge compared to control group mothers. Infants in the intervention group had: increased number of immunizations; fewer emergency room visits; and 1 infant rehospitalization compared to 3 infant rehospitalizations in the control group. The intervention groups' health care charges were significantly lower compared to the control group $14,333/$497 vs. $70,834/$1,068. These study results indicate that an intervention of APN follow up telephone calls in this sample of first time low-income culturally diverse mothers was an effective, safe, low cost, easy to apply intervention which improved mothers' and infants' health outcomes and reduced healthcare charges.