A developmental education program model for high -risk minority baccalaureate nursing students

The purpose of this study was to define and describe a Developmental Education Program Model for high-risk minority baccalaureate nursing students based upon perceived needs determined by nursing students and nursing faculty. The research examined differences between Black and Non-Black nursing students in level of importance of concerns and issues related to academic, financial, psycho-social and personal areas of student life; faculty perceptions of the differences between Black and Non-Black nursing students in the level of importance of concerns and issues related to academic, financial, psycho-social and personal areas of student life; and the difference between Black and Non-Black nursing faculty perceptions of level of importance of issues and concerns of academic, financial, psycho-social, and personal areas for Black nursing students. In this study two data collection methods were used, questionnaire and interview. The questionnaire was completed by all students and faculty. Black baccalaureate nursing students and nursing faculty were interviewed. The most significant differences were seen in the category of Personal Issues. Student identified concerns and issues related to both academic and health problems. Faculty identified the greatest differences in Academic Issues. The framework for the model which evolved out of the data uses needs from: (1) a whole person perspective (outcome oriented needs); (2) a programmatic perspective (input oriented needs); and (3) learning domain perspective (process oriented needs). ^

Comparison of Some Improved Estimators for Linear Regression Model under Different Conditions

Multiple linear regression model plays a key role in statistical inference and it has extensive applications in business, environmental, physical and social sciences. Multicollinearity has been a considerable problem in multiple regression analysis. When the regressor variables are multicollinear, it becomes difficult to make precise statistical inferences about the regression coefficients. There are some statistical methods that can be used, which are discussed in this thesis are ridge regression, Liu, two parameter biased and LASSO estimators. Firstly, an analytical comparison on the basis of risk was made among ridge, Liu and LASSO estimators under orthonormal regression model. I found that LASSO dominates least squares, ridge and Liu estimators over a significant portion of the parameter space for large dimension. Secondly, a simulation study was conducted to compare performance of ridge, Liu and two parameter biased estimator by their mean squared error criterion. I found that two parameter biased estimator performs better than its corresponding ridge regression estimator. Overall, Liu estimator performs better than both ridge and two parameter biased estimator.

Arts for Learning/Miami Professional Development Program: Does It Measure Up?

This paper examines the relationship between student achievement, teacher practice, and professional development programs for teachers. A theoretical program model is then created and used to evaluate the Arts for Learning/Miami program model.

Branch and price solution approach for order acceptance and capacity planning in make -to -order operations

The increasing emphasis on mass customization, shortened product lifecycles, synchronized supply chains, when coupled with advances in information system, is driving most firms towards make-to-order (MTO) operations. Increasing global competition, lower profit margins, and higher customer expectations force the MTO firms to plan its capacity by managing the effective demand. The goal of this research was to maximize the operational profits of a make-to-order operation by selectively accepting incoming customer orders and simultaneously allocating capacity for them at the sales stage. ^ For integrating the two decisions, a Mixed-Integer Linear Program (MILP) was formulated which can aid an operations manager in an MTO environment to select a set of potential customer orders such that all the selected orders are fulfilled by their deadline. The proposed model combines order acceptance/rejection decision with detailed scheduling. Experiments with the formulation indicate that for larger problem sizes, the computational time required to determine an optimal solution is prohibitive. This formulation inherits a block diagonal structure, and can be decomposed into one or more sub-problems (i.e. one sub-problem for each customer order) and a master problem by applying Dantzig-Wolfe’s decomposition principles. To efficiently solve the original MILP, an exact Branch-and-Price algorithm was successfully developed. Various approximation algorithms were developed to further improve the runtime. Experiments conducted unequivocally show the efficiency of these algorithms compared to a commercial optimization solver.^ The existing literature addresses the static order acceptance problem for a single machine environment having regular capacity with an objective to maximize profits and a penalty for tardiness. This dissertation has solved the order acceptance and capacity planning problem for a job shop environment with multiple resources. Both regular and overtime resources is considered. ^ The Branch-and-Price algorithms developed in this dissertation are faster and can be incorporated in a decision support system which can be used on a daily basis to help make intelligent decisions in a MTO operation.^

Branch and Price Solution Approach for Order Acceptance and Capacity Planning in Make-to-Order Operations

The increasing emphasis on mass customization, shortened product lifecycles, synchronized supply chains, when coupled with advances in information system, is driving most firms towards make-to-order (MTO) operations. Increasing global competition, lower profit margins, and higher customer expectations force the MTO firms to plan its capacity by managing the effective demand. The goal of this research was to maximize the operational profits of a make-to-order operation by selectively accepting incoming customer orders and simultaneously allocating capacity for them at the sales stage. For integrating the two decisions, a Mixed-Integer Linear Program (MILP) was formulated which can aid an operations manager in an MTO environment to select a set of potential customer orders such that all the selected orders are fulfilled by their deadline. The proposed model combines order acceptance/rejection decision with detailed scheduling. Experiments with the formulation indicate that for larger problem sizes, the computational time required to determine an optimal solution is prohibitive. This formulation inherits a block diagonal structure, and can be decomposed into one or more sub-problems (i.e. one sub-problem for each customer order) and a master problem by applying Dantzig-Wolfe’s decomposition principles. To efficiently solve the original MILP, an exact Branch-and-Price algorithm was successfully developed. Various approximation algorithms were developed to further improve the runtime. Experiments conducted unequivocally show the efficiency of these algorithms compared to a commercial optimization solver. The existing literature addresses the static order acceptance problem for a single machine environment having regular capacity with an objective to maximize profits and a penalty for tardiness. This dissertation has solved the order acceptance and capacity planning problem for a job shop environment with multiple resources. Both regular and overtime resources is considered. The Branch-and-Price algorithms developed in this dissertation are faster and can be incorporated in a decision support system which can be used on a daily basis to help make intelligent decisions in a MTO operation.

Trichloroethylene fate and transport studies and biodegradation kinetics in the saturated zone

Permeable reactive barriers (PRB) are constructed from soil solid amendments to support the growth of bacteria that are capable of degrading organic contaminants. The objective of this study was to identify low-cost soil solid amendments that could retard the movement of trichloroethylene (TCE) while serving as long-lived carbon sources to foster its biodegradation in shallow groundwater through the use of a PRB. The natural amendments high in organic carbon content such as eucalyptus mulch, compost, wetland peat, organic humus were compared based on their geophysical characteristics, such as pHw, porosity and total organic carbon (TOC), and as well as TCE sorption potentials. The pHw values were within neutral range except for pine bark mulch and wetland peat. All other geophysical characteristics of the amendments showed suitability for use in a PRB. While the Freundlich model showed better fit for compost and pine bark mulch, the linear sorption model was adequate for eucalyptus mulch, wetland peat and Everglades muck within the concentration range studied (0.2-0.8 mg/L TCE). According to these results, two composts and eucalyptus mulch were selected for laboratory column experiments to evaluate their effectiveness at creating and maintaining conditions suitable for TCE anaerobic dechlorination. The columns were monitored for pH, ORP, TCE degradation, longevity of nutrients and soluble TOC to support TCE dechlorination. Native bacteria in the columns had the ability to convert TCE to DCEs; however, the inoculation with the TCE-degrading culture greatly increased the rate of biodegradation. This caused a significant increase in by-product concentration, mostly in the form of DCEs and VC followed by a slow degradation to ethylene. Of the tested amendments eucalyptus mulch was the most effective at supporting the TCE dechlorination. The experimental results of TCE sequential dechlorination took place in eucalyptus mulch and commercial compost from Savannah River Site columns were then simulated using the Hydrus-1D model. The simulations showed good fit with the experimental data. The results suggested that sorption and degradation were the dominant fate and transport mechanisms for TCE and DCEs in the column, supporting the use of these amendments in a permeable reactive barrier to remediate the TCE.

The order selection and lot sizing problem in the make-to-order environment

This research is motivated by the need for considering lot sizing while accepting customer orders in a make-to-order (MTO) environment, in which each customer order must be delivered by its due date. Job shop is the typical operation model used in an MTO operation, where the production planner must make three concurrent decisions; they are order selection, lot size, and job schedule. These decisions are usually treated separately in the literature and are mostly led to heuristic solutions. The first phase of the study is focused on a formal definition of the problem. Mathematical programming techniques are applied to modeling this problem in terms of its objective, decision variables, and constraints. A commercial solver, CPLEX is applied to solve the resulting mixed-integer linear programming model with small instances to validate the mathematical formulation. The computational result shows it is not practical for solving problems of industrial size, using a commercial solver. The second phase of this study is focused on development of an effective solution approach to this problem of large scale. The proposed solution approach is an iterative process involving three sequential decision steps of order selection, lot sizing, and lot scheduling. A range of simple sequencing rules are identified for each of the three subproblems. Using computer simulation as the tool, an experiment is designed to evaluate their performance against a set of system parameters. For order selection, the proposed weighted most profit rule performs the best. The shifting bottleneck and the earliest operation finish time both are the best scheduling rules. For lot sizing, the proposed minimum cost increase heuristic, based on the Dixon-Silver method performs the best, when the demand-to-capacity ratio at the bottleneck machine is high. The proposed minimum cost heuristic, based on the Wagner-Whitin algorithm is the best lot-sizing heuristic for shops of a low demand-to-capacity ratio. The proposed heuristic is applied to an industrial case to further evaluate its performance. The result shows it can improve an average of total profit by 16.62%. This research contributes to the production planning research community with a complete mathematical definition of the problem and an effective solution approach to solving the problem of industry scale.

Minimizing makespan for hybrid flowshops with batch, discrete processing machines and arbitrary job sizes

This research aims at a study of the hybrid flow shop problem which has parallel batch-processing machines in one stage and discrete-processing machines in other stages to process jobs of arbitrary sizes. The objective is to minimize the makespan for a set of jobs. The problem is denoted as: FF: batch1,sj:Cmax. The problem is formulated as a mixed-integer linear program. The commercial solver, AMPL/CPLEX, is used to solve problem instances to their optimality. Experimental results show that AMPL/CPLEX requires considerable time to find the optimal solution for even a small size problem, i.e., a 6-job instance requires 2 hours in average. A bottleneck-first-decomposition heuristic (BFD) is proposed in this study to overcome the computational (time) problem encountered while using the commercial solver. The proposed BFD heuristic is inspired by the shifting bottleneck heuristic. It decomposes the entire problem into three sub-problems, and schedules the sub-problems one by one. The proposed BFD heuristic consists of four major steps: formulating sub-problems, prioritizing sub-problems, solving sub-problems and re-scheduling. For solving the sub-problems, two heuristic algorithms are proposed; one for scheduling a hybrid flow shop with discrete processing machines, and the other for scheduling parallel batching machines (single stage). Both consider job arrival and delivery times. An experiment design is conducted to evaluate the effectiveness of the proposed BFD, which is further evaluated against a set of common heuristics including a randomized greedy heuristic and five dispatching rules. The results show that the proposed BFD heuristic outperforms all these algorithms. To evaluate the quality of the heuristic solution, a procedure is developed to calculate a lower bound of makespan for the problem under study. The lower bound obtained is tighter than other bounds developed for related problems in literature. A meta-search approach based on the Genetic Algorithm concept is developed to evaluate the significance of further improving the solution obtained from the proposed BFD heuristic. The experiment indicates that it reduces the makespan by 1.93 % in average within a negligible time when problem size is less than 50 jobs.

Tropical seagrass-associated macroalgae distributions and trends relative to water quality

Tropical coastal marine ecosystems including mangroves, seagrass beds and coral reef communities are undergoing intense degradation in response to natural and human disturbances, therefore, understanding the causes and mechanisms present challenges for scientist and managers. In order to protect our marine resources, determining the effects of nutrient loads on these coastal systems has become a key management goal. Data from monitoring programs were used to detect trends of macroalgae abundances and develop correlations with nutrient availability, as well as forecast potential responses of the communities monitored. Using eight years of data (1996–2003) from complementary but independent monitoring programs in seagrass beds and water quality of the Florida Keys National Marine Sanctuary (FKNMS), we: (1) described the distribution and abundance of macroalgae groups; (2) analyzed the status and spatiotemporal trends of macroalgae groups; and (3) explored the connection between water quality and the macroalgae distribution in the FKNMS. In the seagrass beds of the FKNMS calcareous green algae were the dominant macroalgae group followed by the red group; brown and calcareous red algae were present but in lower abundance. Spatiotemporal patterns of the macroalgae groups were analyzed with a non-linear regression model of the abundance data. For the period of record, all macroalgae groups increased in abundance (Abi) at most sites, with calcareous green algae increasing the most. Calcareous green algae and red algae exhibited seasonal pattern with peak abundances (Φi) mainly in summer for calcareous green and mainly in winter for red. Macroalgae Abi and long-term trend (mi) were correlated in a distinctive way with water quality parameters. Both the Abi and mi of calcareous green algae had positive correlations with NO3−, NO2−, total nitrogen (TN) and total organic carbon (TOC). Red algae Abi had a positive correlation with NO2−, TN, total phosphorus and TOC, and the mi in red algae was positively correlated with N:P. In contrast brown and calcareous red algae Abi had negative correlations with N:P. These results suggest that calcareous green algae and red algae are responding mainly to increases in N availability, a process that is happening in inshore sites. A combination of spatially variable factors such as local current patterns, nutrient sources, and habitat characteristics result in a complex array of the macroalgae community in the seagrass beds of the FKNMS.

Influence of gender and life stressors on longitudinal depression treatment outcomes among older primary care patients

Purpose: Depression in older females is a significant and growing problem. Females who experience life stressors across the life span are at higher risk for developing problems with depression than their male counterparts. The primary aim of this study was (a) to examine gender-specific differences in the correlates of depression in older primary care patients based on baseline and longitudinal analyses; and (b) to examine the longitudinal effect of biopsychosocial risk factors on depression treatment outcomes in different models of behavioral healthcare (i.e., integrated care and enhanced referral). Method: This study used a quantitative secondary data analysis with longitudinal data from the Primary Care Research in Substance Abuse and Mental Health for Elderly (PRISM-E) study. A linear mixed model approach to hierarchical linear modeling was used for analysis using baseline assessment, and follow-up from three-month and six-month. Results: For participants diagnosed with major depressive disorder female gender was associated with increased depression severity at six-month compared to males at six-month. Further, the interaction between gender and life stressors found that females who reported loss of family and friends, family issues, money issues, medical illness was related to higher depression severity compared to males whereas lack of activities was related to lower depression severity among females compared to males. Conclusion: These findings suggest that gender moderated the relationship between specific life stressors and depression severity similar to how a protective factor can impact a person's response to a problem and reduce the negative impact of a risk factor on a problem outcome. Therefore, life stressors may be a reliable predictor of depression for both females and males in either behavioral health treatment model. This study concluded that life stressors influence males basic comfort, stability, and survival whereas life stressors influence females' development, personal growth, and happiness; therefore, life stressors may be a useful component to include in gender-based screening and assessment tools for depression. ^

A non-linear discrete stochastic optimization model for Advanced Access patient appointment scheduling

Access to healthcare is a major problem in which patients are deprived of receiving timely admission to healthcare. Poor access has resulted in significant but avoidable healthcare cost, poor quality of healthcare, and deterioration in the general public health. Advanced Access is a simple and direct approach to appointment scheduling in which the majority of a clinic's appointments slots are kept open in order to provide access for immediate or same day healthcare needs and therefore, alleviate the problem of poor access the healthcare. This research formulates a non-linear discrete stochastic mathematical model of the Advanced Access appointment scheduling policy. The model objective is to maximize the expected profit of the clinic subject to constraints on minimum access to healthcare provided. Patient behavior is characterized with probabilities for no-show, balking, and related patient choices. Structural properties of the model are analyzed to determine whether Advanced Access patient scheduling is feasible. To solve the complex combinatorial optimization problem, a heuristic that combines greedy construction algorithm and neighborhood improvement search was developed. The model and the heuristic were used to evaluate the Advanced Access patient appointment policy compared to existing policies. Trade-off between profit and access to healthcare are established, and parameter analysis of input parameters was performed. The trade-off curve is a characteristic curve and was observed to be concave. This implies that there exists an access level at which at which the clinic can be operated at optimal profit that can be realized. The results also show that, in many scenarios by switching from existing scheduling policy to Advanced Access policy clinics can improve access without any decrease in profit. Further, the success of Advanced Access policy in providing improved access and/or profit depends on the expected value of demand, variation in demand, and the ratio of demand for same day and advanced appointments. The contributions of the dissertation are a model of Advanced Access patient scheduling, a heuristic to solve the model, and the use of the model to understand the scheduling policy trade-offs which healthcare clinic managers must make. ^

Investigating the outcomes of two chronic disease self-management programs and understanding the correlates of completion for each program

Chronic disease affects 80% of adults over the age of 65 and is expected to increase in prevalence. To address the burden of chronic disease, self-management programs have been developed to increase self-efficacy and improve quality of life by reducing or halting disease symptoms. Two programs that have been developed to address chronic disease are the Chronic Disease Self-Management Program (CDSMP) and Tomando Control de su Salud (TCDS). CDSMP and TCDS both focus on improving participant self-efficacy, but use different curricula, as TCDS is culturally tailored for the Hispanic population. Few studies have evaluated the effectiveness of CDSMP and TCDS when translated to community settings. In addition, little is known about the correlation between demographic, baseline health status, and psychosocial factors and completion of either CDSMP or TCDS. This study used secondary data collected by agencies of the Healthy Aging Regional Collaborative from 10/01/2008–12/31/2010. The aims of this study were to examine six week differences in self-efficacy, time spent performing physical activity, and social/role activity limitations, and to identify correlates of program completion using baseline demographic and psychosocial factors. To examine if differences existed a general linear model was used. Additionally, logistic regression was used to examine correlates of program completion. Study findings show that all measures showed improvement at week six. For CDSMP, self-efficacy to manage disease (p = .001), self-efficacy to manage emotions (p = .026), social/role activities limitations (p = .001), and time spent walking (p = .008) were statistically significant. For TCDS, self-efficacy to manage disease (p = .006), social/role activities limitations (p = .001), and time spent walking (p = .016) and performing other aerobic activity (p = .005) were significant. For CDSMP, no correlates predicting program completion were found to be significant. For TCDS, participants who were male (OR=2.3, 95%CI: 1.15–4.66), from Broward County (OR=2.3, 95%CI: 1.27–4.25), or living alone (OR=2.0, 95%CI: 1.29-–3.08) were more likely to complete the program. CDSMP and TCDS, when implemented through a collaborative effort, can result in improvements for participants. Effective chronic disease management can improve health, quality of life, and reduce health care expenditures among older adults.

Application of an Education Research Lab (ERL) Model to a Community Sailing Program

This presentation showcases the application of a university-based education research lab (ERL) model to the evaluation of a community sailing program for individuals with disabilities. Presenters conceptualize the ERL model as a mutually beneficial relationship between universities and community education agencies.