This paper is exploring return migration in Sweden by implementing logistic regression technics on the cohort born 1978. In order to evaluate how socio-economic and geographical characteristics influence individuals propensities to re-circulate to the municipality of origin. Previous studies have indicated that socio-economic status is a selective trait that can either push or pull return migrants, depending on the setting. The theory of urban hierarchies was also applied to investigate if people were more likely to move back to certain region types. The calculations showed that 22 % of the movers had returned to their place of origin, with regional variations ranging from 18-30 %. The regression result revealed that a high socio-economic status decreased the likelihood of returning, while growing up in metropolitan city and having strong social capital in the place of origin increased the propensity. The findings were further supporting that movers have higher incomes than stayers, while return migrants gained less on their re-location in relation to all movers. I argue that these varying likelihoods depend on structural socio-economic divisions, which are pulling human capital to the metropolitan regions and causing a brain drain in the periphery. These population trends are replicating themselves over time and it is assumed that these processes are to enforce the regional disparities in the future.

This dissertation is about 'transtructures', a term coined to describe new kinds of infrastructures that are more attentive and responsive to the needs of contemporary society, its emerging economies and technological capabilities. The purpose of this inquiry is to begin to explore the character and possibilities of a design practice that could guide responsibly and ethically the transition of existing industrial infrastructures towards these new configurations: what processes it could follow, and what materials it could include. Through a series of design experiments in the areas of logistics and telecommunications, I started to prototype and develop a programmatic framework for a 'redirective' design practice, which is aimed at engaging publics with infrastructural issues. Design probes and speculative mockups have been employed to express and materialize present and future infrastructural configurations, opening them up to public scrutiny and participation. The premise of this work is fairly simple: if we want to provide more citizen-centered solutions to emerging social demands, we need to explore what changes are possible, and even required, within the industrial systems that currently frame our possibilities for implementing such innovations. Thus, certain design interventions will be necessary to allow people outside these systems to understand and relate to these networks and to identify possibilities for their transformation. The result of this inquiry is the early 'prototype' of what a practice for redirecting and transitioning towards the design of such postindustrial infrastructures could be like. In particular, it exemplifies how design may inquire into the artificial space of industrial infrastructures and explore opportunities for their reconfiguration toward more contextually adaptive forms and functions.

Approved for public release, distribution is unlimited / This thesis analyzes the effects of commissioning sources on the retention and promotion of U S Army officers The data in this thesis were taken from the Active Duty Military Master File, with separate data sets for cohorts commissioned from 1981 through 2001 We describe three logistic regression models: Retention to the Grade of O-4, Promotion to O-4, and Promotion to O-5 We conclude that Academy graduates have the lowest retention rates, whereas OCS graduates have the highest retention rates Among male officers, retention rates are higher for ROTC graduates than for those with Direct Appointments; among female officers retention rates are higher for Direct Appointments than ROTC graduates The Promotion to O-4 Model indicates that the effect of commissioning source is different within gender, race and marital status groups The results of the promotion to O-5 model contrasts with those of the O-4 models Academy graduates are more likely to be promoted to Lieutenant Colonel than those from other sources, followed by ROTC graduates and then Direct Appointments The effects of the Army's reduction in force ("drawdown") between 1989 and 1996 are not accounted for in this thesis as they cannot be modeled with the data at hand. / First Lieutenant, Turkish Army

How does body armor and posture affect Soldier marksmanship? The Interceptor Body Armor (IBA) has significantly improved Soldier combat survivability, but in what ways does it change rifleman lethality? Moreover, can we model these effects so as to develop better tactics and operational plans? This study quantifies the effects of Soldier equipment on lethality through multi-factor logistic regression using data from range experiments with the 1st Brigade, 1st Infantry Division (Mechanized), at Fort Riley, Kansas. The designed experiment of this study estimates the probability of a qualified US rifleman hitting a human target. It uses the rifleman's equipment, posture, Military Occupational Specialty (MOS), and experience along with the target's distance, time exposure and silhouette presentation as input factors. The resulting family of mathematical models provides a Probability of Hit prediction tailored to a shooter-target scenario. The study shows that for targets closer than 150 meters, Soldiers shot better while wearing body armor than they did without. Body armor had a negative effect for targets farther than 200 meters, and this could significantly impact the employment of the Squad Designated Marksman. The study also shows that the kneeling posture is an effective technique and recommends standardized training on this method of firing.

Human activity and other events can cause environmental changes to the habitat of organisms. The environmental changes effect the vital rates for a population. In order to predict the impact of these environmental changes on populations, we use two different models for population dynamics. One simpler linear model that ignores environmental competition between individuals and another model that does not. Our population models take into consideration the age distribution of the population and thus takes into consideration the impact of demographics. This thesis generalize two theorems, one for each model, developed by Sonja Radosavljevic regarding long term upper and lower bounds of a population with periodic birth rate ; see [6] and [5]. The generalisation consist in including the case where the periodic part of the birth rate can be expressed with a finite Fourier series and also infinite Fourier series under some constraints. The old theorems only considers the case when the periodic part of the birth rate can be expressed with one cosine term. From the theorems we discover a connection between the frequency of oscillation and the effect on population growth. From this derived connection we conclude that periodical changing environments can have both positive and negative effects on the population.

Students transitioning into college from public school require more than just academic readiness; they also need the personal attributes that allow them to successfully transition into a new community (Braxton, Doyle, Hartley III, Hirschy, Jones, & McLendon, 2014; Nora, 2002; Nora, 2004; Tinto, 1975). Rural students have a different educational experience than their peers at schools in suburban and urban locations (DeYoung & Howley, 1990; Gjelten, 1982). Additionally, the resources, culture, and educational opportunities at rural schools also vary among different types of rural communities. Although some studies have examined the influence of rural students' academic achievement on college access and success, little research has analyzed the relationship between students of different types of rural communities and their persistence in post-secondary education. This study examined the likelihood for college-going students from three different types of rural communities to successfully transition into and persist at a four-year residential college. Multilevel logistic modeling was used to analyze the likelihood for students to persist in college for up to two academic years based on whether they were from rural tourist communities, college communities, and other rural communities. The analysis controlled for a variety of student and high school factors. Findings revealed that student factors related to poverty and academic readiness have the greatest effects, while the type of rural community has no significant influence on college persistence.

The postsecondary enrollment of student veterans has increased with the troop draw down in Iraq and Afghanistan as well as the generous amendments made to the Post 9/11 GI Bill. Acquired disabilities remain a reality for this population as they transition into the civilian world; consequently, previous literature cites the role of disabilities amongst student veterans. Also, prior research often aggregates these two groups without a thorough understanding of the ways in which they differ. This study compared student veterans with disabilities to student veterans without disabilities in order to understand the enrollment and demographic factors on which they differed, if any. Using a secondary data analysis of the 2007-2008 National Postsecondary Student Aid Survey, univariate tests of significance, a logistic regression, and a discriminant function analysis examined the relationship between disability status and seven predictor variables: age, gender, GPA, major, risk index, degree program type, and whether or not a student was exclusively a distance learner. These seven variables as a whole were not significant predictors of disability status; however, the models provided valuable insight into the similarities and characteristics shared within this population. Univariate tests of significance revealed that students with disabilities had a significantly lower mean GPA, were more often male, tended to favor certain academic majors over others, more often enrolled in bachelor’s degree versus associate and certificate programs, and had a lower risk of attrition based on their index of risk. Major, degree program type, and risk index proved to be the most significant predictors of disability status in LR and DFA. A student veteran’s age and whether they were a distance learner had no significant bearing on disability status indicating that student veterans enroll in distance learning or campus-based programs without influence from an orthopedic or mobility impairment, the most common type of disability amongst student veterans. This study offers a full description of student veterans with disabilities including the specific types of disabilities with which this population enters higher education.

Variable selection is an important topic in regression analysis and is intended to select the best subset of predictors. Least absolute shrinkage and selection operator (Lasso) was introduced by Tibshirani in 1996. This method can serve as a tool for variable selection because it shrinks some coefficients to exact zero by a constraint on the sum of absolute values of regression coefficients. For logistic regression, Lasso modifies the traditional parameter estimation method, maximum log likelihood, by adding the L1 norm of the parameters to the negative log likelihood function, so it turns a maximization problem into a minimization one. To solve this problem, we first need to give the value for the parameter of the L1 norm, called tuning parameter. Since the tuning parameter affects the coefficients estimation and variable selection, we want to find the optimal value for the tuning parameter to get the most accurate coefficient estimation and best subset of predictors in the L1 regularized regression model. There are two popular methods to select the optimal value of the tuning parameter that results in a best subset of predictors, Bayesian information criterion (BIC) and cross validation (CV). The objective of this paper is to evaluate and compare these two methods for selecting the optimal value of tuning parameter in terms of coefficients estimation accuracy and variable selection through simulation studies.

Drinking groundwater is the primary way humans accumulate arsenic. Chronic exposure to inorganic arsenic (iAs) (over decades) has been shown to be associated with multiple health effects at low levels (5-10 ppb) including: cancer, elevated blood pressure and cardiovascular disease, skin lesions, renal failure, and peripheral neuropathy. Using hypertension (or high blood pressure) as a surrogate marker for cardiovascular disease, we examined the effect of iAs alone and in a mixture with other metals using a cross-sectional study of adults in United States (National Health and Examination Survey, NHANES, 2005-2010) adjusting for covariates: urinary creatinine level (mg/dL), poverty index ratio (PIR, measure of socioeconomic status, 1 to 5), age, smoking (yes/no), alcohol usage, gender, non-Hispanic Black, and overweight (BMI>=25). A logistic regression model suggests that a one-unit increase in log of inorganic arsenic increases the odds of hypertension by a factor of 1.093 (95% Confidence Interval=0.935, 1.277) adjusted for these covariates , which indicates that there was not significant evidence to claim that inorganic arsenic is a risk factor for hypertension. Biomonitoring data provides evidence that humans are not only exposed to inorganic arsenic but also to mixtures of chemicals including inorganic arsenic, total mercury, cadmium, and lead. We tested for a mixture effect of these four environmental chemicals using weighted quantile sum (WQS) regression, which takes into account the correlation among the chemicals and with the outcome. For one-unit increase in the weighted sum, the adjusted odds of developing hypertension increases by a factor of 1.027 (95% CI=0.882,1.196), which is also not significant after taking into account the same covariates. The insignificant finding may be due to the low inorganic arsenic concentration (8-620 μg /L) in US drinking water, compared to those in countries like Bangladesh where the concentrations are much higher. Literature provides conflicting evidence of the association of inorganic arsenic and hypertension in low/moderate regions; future studies, especially a large cohort study, are needed to confirm if inorganic arsenic alone or with other metals is associated with hypertension in the United States.

The findings of this research indicate that volunteering is influenced by a number of factors, one of which is gender. The data used in this study reveal a different profile of the volunteer than is presented in much of the research on volunteering, which tends to profile the "most likely" volunteer as female, employed by the public sector, possessing a higher education and having children. The questions addressed in this research are: 1) What are the contextual effects of volunteering and 2) Is there a relationship of one or more of these effects to gender? The findings indicate men in this sample were not only more likely to volunteer, but were more likely to engage in volunteer activities that included political and civic roles. In addition, men were able to volunteer more hours as their family ties increased. The hours women volunteered were found to decrease as family ties increased. Women were less likely to volunteer for political and civic activities and more likely to volunteer for roles that included the care of children, elderly and family-oriented activities. These findings have implications for how volunteer activities contribute to the building of social and political resources for both men and women and bring to light how gendered definitions dominate patterns of civic engagement.