Present-day Pattern Variations in the Central and Eastern Pacific El Nino

El Niño and the Southern Oscillation (ENSO) is a cycle that is initiated in the equatorial Pacific Ocean and is recognized on interannual timescales by oscillating patterns in tropical Pacific sea surface temperatures (SST) and atmospheric circulations. Using correlation and regression analysis of datasets that include SST’s and other interdependent variables including precipitation, surface winds, sea level pressure, this research seeks to quantify recent changes in ENSO behavior. Specifically, the amplitude, frequency of occurrence, and spatial characteristics (i.e. events with maximum amplitude in the Central Pacific versus the Eastern Pacific) are investigated. The research is based on the question; “Are the statistics of ENSO changing due to increasing greenhouse gas concentrations?” Our hypothesis is that the present-day changes in amplitude, frequency, and spatial characteristics of ENSO are determined by the natural variability of the ocean-atmosphere climate system, not the observed changes in the radiative forcing due to change in the concentrations of greenhouse gases. Statistical analysis, including correlation and regression analysis, is performed on observational ocean and atmospheric datasets available from the National Oceanographic and Atmospheric Administration (NOAA), National Center for Atmospheric Research (NCAR) and coupled model simulations from the Coupled Model Inter-comparison Project (phase 5, CMIP5). Datasets are analyzed with a particular focus on ENSO over the last thirty years. Understanding the observed changes in the ENSO phenomenon over recent decades has a worldwide significance. ENSO is the largest climate signal on timescales of 2 - 7 years and affects billions of people via atmospheric teleconnections that originate in the tropical Pacific. These teleconnections explain why changes in ENSO can lead to climate variations in areas including North and South America, Asia, and Australia. For the United States, El Niño events are linked to decreased number of hurricanes in the Atlantic basin, reduction in precipitation in the Pacific Northwest, and increased precipitation throughout the southern United Stated during winter months. Understanding variability in the amplitude, frequency, and spatial characteristics of ENSO is crucial for decision makers who must adapt where regional ecology and agriculture are affected by ENSO.

Present-day Pattern Variations in the Central and Eastern Pacific El Nino

El Niño and the Southern Oscillation (ENSO) is a cycle that is initiated in the equatorial Pacific Ocean and is recognized on interannual timescales by oscillating patterns in tropical Pacific sea surface temperatures (SST) and atmospheric circulations. Using correlation and regression analysis of datasets that include SST’s and other interdependent variables including precipitation, surface winds, sea level pressure, this research seeks to quantify recent changes in ENSO behavior. Specifically, the amplitude, frequency of occurrence, and spatial characteristics (i.e. events with maximum amplitude in the Central Pacific versus the Eastern Pacific) are investigated. The research is based on the question; “Are the statistics of ENSO changing due to increasing greenhouse gas concentrations?” Our hypothesis is that the present-day changes in amplitude, frequency, and spatial characteristics of ENSO are determined by the natural variability of the ocean-atmosphere climate system, not the observed changes in the radiative forcing due to change in the concentrations of greenhouse gases. Statistical analysis, including correlation and regression analysis, is performed on observational ocean and atmospheric datasets available from the National Oceanographic and Atmospheric Administration (NOAA), National Center for Atmospheric Research (NCAR) and coupled model simulations from the Coupled Model Inter-comparison Project (phase 5, CMIP5). Datasets are analyzed with a particular focus on ENSO over the last thirty years. Understanding the observed changes in the ENSO phenomenon over recent decades has a worldwide significance. ENSO is the largest climate signal on timescales of 2 - 7 years and affects billions of people via atmospheric teleconnections that originate in the tropical Pacific. These teleconnections explain why changes in ENSO can lead to climate variations in areas including North and South America, Asia, and Australia. For the United States, El Niño events are linked to decreased number of hurricanes in the Atlantic basin, reduction in precipitation in the Pacific Northwest, and increased precipitation throughout the southern United Stated during winter months. Understanding variability in the amplitude, frequency, and spatial characteristics of ENSO is crucial for decision makers who must adapt where regional ecology and agriculture are affected by ENSO.

Predicting delay reductions from freeway traffic diversion for incident management

Traffic incidents are a major source of traffic congestion on freeways. Freeway traffic diversion using pre-planned alternate routes has been used as a strategy to reduce traffic delays due to major traffic incidents. However, it is not always beneficial to divert traffic when an incident occurs. Route diversion may adversely impact traffic on the alternate routes and may not result in an overall benefit. This dissertation research attempts to apply Artificial Neural Network (ANN) and Support Vector Regression (SVR) techniques to predict the percent of delay reduction from route diversion to help determine whether traffic should be diverted under given conditions. The DYNASMART-P mesoscopic traffic simulation model was applied to generate simulated data that were used to develop the ANN and SVR models. A sample network that comes with the DYNASMART-P package was used as the base simulation network. A combination of different levels of incident duration, capacity lost, percent of drivers diverted, VMS (variable message sign) messaging duration, and network congestion was simulated to represent different incident scenarios. The resulting percent of delay reduction, average speed, and queue length from each scenario were extracted from the simulation output. The ANN and SVR models were then calibrated for percent of delay reduction as a function of all of the simulated input and output variables. The results show that both the calibrated ANN and SVR models, when applied to the same location used to generate the calibration data, were able to predict delay reduction with a relatively high accuracy in terms of mean square error (MSE) and regression correlation. It was also found that the performance of the ANN model was superior to that of the SVR model. Likewise, when the models were applied to a new location, only the ANN model could produce comparatively good delay reduction predictions under high network congestion level.

Development of methods for spatial modeling of sediments quality in Lake Okeechobee, Florida

The major objectives of this dissertation were to develop optimal spatial techniques to model the spatial-temporal changes of the lake sediments and their nutrients from 1988 to 2006, and evaluate the impacts of the hurricanes occurred during 1998–2006. Mud zone reduced about 10.5% from 1988 to 1998, and increased about 6.2% from 1998 to 2006. Mud areas, volumes and weight were calculated using validated Kriging models. From 1988 to 1998, mud thicknesses increased up to 26 cm in the central lake area. The mud area and volume decreased about 13.78% and 10.26%, respectively. From 1998 to 2006, mud depths declined by up to 41 cm in the central lake area, mud volume reduced about 27%. Mud weight increased up to 29.32% from 1988 to 1998, but reduced over 20% from 1998 to 2006. The reduction of mud sediments is likely due to re-suspension and redistribution by waves and currents produced by large storm events, particularly Hurricanes Frances and Jeanne in 2004 and Wilma in 2005. Regression, kriging, geographically weighted regression (GWR) and regression-kriging models have been calibrated and validated for the spatial analysis of the sediments TP and TN of the lake. GWR models provide the most accurate predictions for TP and TN based on model performance and error analysis. TP values declined from an average of 651 to 593 mg/kg from 1998 to 2006, especially in the lake’s western and southern regions. From 1988 to 1998, TP declined in the northern and southern areas, and increased in the central-western part of the lake. The TP weights increased about 37.99%–43.68% from 1988 to 1998 and decreased about 29.72%–34.42% from 1998 to 2006. From 1988 to 1998, TN decreased in most areas, especially in the northern and southern lake regions; western littoral zone had the biggest increase, up to 40,000 mg/kg. From 1998 to 2006, TN declined from an average of 9,363 to 8,926 mg/kg, especially in the central and southern regions. The biggest increases occurred in the northern lake and southern edge areas. TN weights increased about 15%–16.2% from 1988 to 1998, and decreased about 7%–11% from 1998 to 2006.

Maintaining your loyal customers during hard times: An observation from the gaming industry

The Las Vegas gaming arena was one of the most severely affected consumeroriented industries to be impacted by the recent economic recession. The purpose of this study was to investigate the impact of relational benefits on customers’ behavioral loyalty in the Las Vegas gaming industry. This study particularly took a comparative approach and examined the relational impact during the economic recession and after the economic recession. Secondary data was obtained and regression analysis was performed to test the study hypothesis. The findings of this study revealed the economic recession impact on the Las Vegas gaming industry, as well as valuable insights for effective utilization of relational benefits to increase customer loyalty.

The influence of HOPE VI public housing on public schools

In the United States, public school enrollment is typically organized by neighborhood boundaries. This dissertation examines whether the federally funded HOPE VI program influenced performance in neighborhood public schools. In effect since 1992, HOPE VI has sought to revitalize distressed public housing using the New Urbanism model of mixed income communities. There are 165 such HOPE VI projects nationwide. Despite nearly two decades of the program's implementation, the literature on its connection to public school performance is thin. My dissertation aims to narrow this research gap. There are three principal research questions: (1) Following HOPE VI, was there a change in socioeconomic status (SES) in the neighborhood public school? The hypothesis is that low SES (measured as the proportion of students qualifying for the Free and Reduced Lunch Program) would reduce. (2) Following HOPE VI, did the performance of neighborhood public schools change? The hypothesis is that the school performance, measured by the proportion of 5th grade students proficient in state wide math and reading tests, would increase. (3) What factors relate to the performance of public schools in HOPE VI communities? The focus is on non-school, neighborhood factors that influence the public school performance. For answering the first two questions, I used t-tests and regression models to test the hypotheses. The analysis shows that there is no statistically significant change in SES following HOPE VI. However, there are statistically significant increases in performance for reading and math proficiency. The results are interesting in indicating that HOPE VI neighborhood improvement may have some relationship with improving school performance. To answer the third question, I conducted a case study analysis of two HOPE VI neighborhood public schools, one which improved significantly (in Philadelphia) and one which declined the most (in Washington DC). The analysis revealed three insights into neighborhood factors for improved school performance: (i) a strong local community organization; (ii) local community's commitment (including the middle income families) to send children to the public school; and (iii) ties between housing and education officials to implement the federal housing program. In essence, the study reveals how housing policy is de facto education policy.

Occupational therapists' attitudes toward family -centered care

The purpose of this study was to examine pediatric occupational therapists attitudes towards family-centered care. Specific attributes identified by the literature (professional characteristics, educational experiences and organizational culture) were investigated to determine their influence on these attitudes. Study participants were 250 pediatric occupational therapists who were randomly selected from the American Occupational Therapy Association special interest sections. ^ Participants received a mail packet with three instruments to complete and mail back within 2 weeks. The instruments were (a) the Professional Attitude Scale, (b) the Professional Characteristics Questionnaire, and (c) the Family-Centered Program Rating Scale. There was a 50% return rate. Data analysis was conducted in SPSS using descriptive statistics, correlations and regression analysis. ^ The analysis showed that pediatric occupational therapists working in various practice settings demonstrate favorable attitudes toward family-centered care as measured by the Professional Attitude Scale. There was no correlation between professional characteristics and educational experiences to therapists' attitudes. A moderate correlation (r = .368, p < .05) was found between the occupational therapists attitudes and the organizational culture of their workplaces. A factor analysis was conducted on the organizational culture instrument (FamPRS) as this sample was exclusively pediatric occupational therapists and the original sample was interdisciplinary professionals. Two factors were extracted using a principal components extraction and varimax rotation, in addition to examination of the scree plot. These two factors accounted for 50% of the total variance of the scores on the instrument. Factor 1, called empowerment accounted for 45.6% of the variance, and Factor 2, responsiveness accounted for 4.3% of the variance of the entire instrument. Stepwise regression analysis demonstrated that these two factors accounted for 16% of the variance toward attitudes clinicians hold toward family-centered care. These factors support the tenets of family-centered care; empowering parents to be leaders in their child's health care and helping organizations become more responsive to family needs. ^ These study findings suggest that organizational culture has some influence on occupational therapists attitudes toward family-centered care (R 2 = .16). These findings suggest educators should consider families as valuable resources when considering program planning in family-centered care at preservice and workplace settings. ^

An Evaluation of Three Nutrition Labeling Formats for Restaurant Menus

This study evaluated three menu nutrition labeling formats: calorie only information, a healthy symbol, and a nutrient list. Daily sales data for a table-service restaurant located on a university campus were recorded during a four-week period from January to February 2013 to examine changes in average nutritional content of the entrees purchased by customers when different nutrition labels were provided. A survey was conducted to assess the customers’ use of nutrition labels, their preferences among the three labeling formats, their entree selections, their cognitive beliefs with regard to healthy eating, and their demographic characteristics. A total of 173 questionnaires were returned and included in data analysis. Analysis of Variance (ANOVA) and regression analyses were performed using SAS. The results showed that favorable attitudes toward healthy eating and the use of nutrition labels were both significantly associated with healthier entrée selections. Age and diet status had some effects on the respondent’s use of nutrition labels. The calorie only information format was the most effective in reducing calories contained in the entrees sold, and the nutrient list was most effective in reducing fat and saturated fat content of the entrees sold. The healthy symbol was the least effective format, but interestingly enough, was most preferred by respondents. The findings provide support for future research and offer implications for policy makers, public health professionals, and foodservice operations.

Predicting delay reductions from freeway traffic diversion for incident management

Traffic incidents are a major source of traffic congestion on freeways. Freeway traffic diversion using pre-planned alternate routes has been used as a strategy to reduce traffic delays due to major traffic incidents. However, it is not always beneficial to divert traffic when an incident occurs. Route diversion may adversely impact traffic on the alternate routes and may not result in an overall benefit. This dissertation research attempts to apply Artificial Neural Network (ANN) and Support Vector Regression (SVR) techniques to predict the percent of delay reduction from route diversion to help determine whether traffic should be diverted under given conditions. The DYNASMART-P mesoscopic traffic simulation model was applied to generate simulated data that were used to develop the ANN and SVR models. A sample network that comes with the DYNASMART-P package was used as the base simulation network. A combination of different levels of incident duration, capacity lost, percent of drivers diverted, VMS (variable message sign) messaging duration, and network congestion was simulated to represent different incident scenarios. The resulting percent of delay reduction, average speed, and queue length from each scenario were extracted from the simulation output. The ANN and SVR models were then calibrated for percent of delay reduction as a function of all of the simulated input and output variables. The results show that both the calibrated ANN and SVR models, when applied to the same location used to generate the calibration data, were able to predict delay reduction with a relatively high accuracy in terms of mean square error (MSE) and regression correlation. It was also found that the performance of the ANN model was superior to that of the SVR model. Likewise, when the models were applied to a new location, only the ANN model could produce comparatively good delay reduction predictions under high network congestion level.