This thesis is focused on achieving materials with non-equilibrium structures fabricated by high-energy laser sintering. The chosen precursor materials have rigid and inert structures like high-melting point ceramics or metals. It was necessary to use real-time monitoring of temperature and spectrum profiles for selecting the optimal laser parameters for the laser sintering process. This monitoring was done by an off-axial setup that also controls the surface morphologies during the laser irradiation process. The laser focal spot receives very high temperatures and subsequent extreme cooling rates within a short time period. New non-equilibrium structures will emerge ruled by kinetics, huge temperature gradients or stresses and freeze by quenching in solid state. These material structures were found to form at different length scales from nano- to macro-level, frequently by a hierarchical ordering. This opens a method to engineer materials with both hierarchical and non-equilibrium structures by a single operation in both metal and ceramics by laser sintering. In the Co-Cr-Mo alloy system, structures on three levels of lengths were observed, namely i) nano-level structures dominated by the grain boundary segregation; ii) micron-level structures characterized by the interlocked clusters of columns; and iii) macro-level structures defined by the selected laser scan patterns. The non-equilibrium structures of the Co-Cr-Mo alloy are related to mechanical, corrosion and bio-compatibility properties. In ZrO2 ceramics, the final product had a non-equilibrium nano- and micron-sized structure created by uneven absorption of laser energy and rupture. The structure inside the micron-sized grains is formed through ordered coalescence of nano-crystals. Properties of the laser sintered materials were established and related to the observed structures. The materials properties might be tailored by controlling the structures in different levels and potential applications of the new materials will be given. / <p>At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 2: Accepted. Paper 3: Accepted. Paper 4: Submitted. Paper 5: Manuscript.</p>

This thesis consists of four individual essays and an introduction chapter. The essays are in the field of multilevel analysis of economic data. The first essay estimates capitalisation effects of farm attributes, with a particular focus on single farm payments (SFP), into the price of farms. Using a sample of Swedish farm transactions sold all across the country, the results from a spatial multiple-membership model suggests that the local effect of SFP is negative while there is a positive between-region effect of SFP, on farm prices.   The second essay investigates the extent to which differences in the probability to exit from part-time unemployment to a full-time job can be accounted for by spatial contextual factors and individual characteristics. To correctly incorporate contextual effects, a multilevel analysis was applied to explore whether contextual factors account for differences in the probability of transition to full-time employment between individuals with different characteristics. The results indicate that there is a contextual effect and that there are some spatial spill-over effects from neighbouring municipalities.   The third essay investigates the determinants of educational attainment for third-generation immigrants and natives in Sweden. Using a mixed-effects model that includes unobserved family heterogeneity, for linked register data, the main result is that the effect of parent’s educational attainment is mainly due to the between-parental education effect of family income.   The fourth and last essay presents a new robust strategy for performance evaluation in the case of panel data that is based on routinely collected variables or indicators. The suggested strategy applies a cross-classified, mixed-effect model. The strategy is implemented in two illustrative empirical examples, and the robustness is investigated in a Monte Carlo study.

Companies operating in the wood processing industry need to increase their productivity by implementing automation technologies in their production systems. An increasing global competition and rising raw material prizes challenge their competitiveness. Yet, too extensive automation brings risks such as a deterioration in situation awareness and operator deskilling. The concept of Levels of Automation is generally seen as means to achieve a balanced task allocation between the operators’ skills and competences and the need for automation technology relieving the humans from repetitive or hazardous work activities. The aim of this thesis was to examine to what extent existing methods for assessing Levels of Automation in production processes are applicable in the wood processing industry when focusing on an improved competitiveness of production systems. This was done by answering the following research questions (RQ): RQ1: What method is most appropriate to be applied with measuring Levels of Automation in the wood processing industry? RQ2: How can the measurement of Levels of Automation contribute to an improved competitiveness of the wood processing industry’s production processes? Literature reviews were used to identify the main characteristics of the wood processing industry affecting its automation potential and appropriate assessment methods for Levels of Automation in order to answer RQ1. When selecting the most suitable method, factors like the relevance to the target industry, application complexity or operational level the method is penetrating were important. The DYNAMO++ method, which covers both a rather quantitative technical-physical and a more qualitative social-cognitive dimension, was seen as most appropriate when taking into account these factors. To answer RQ 2, a case study was undertaken at a major Swedish manufacturer of interior wood products to point out paths how the measurement of Levels of Automation contributes to an improved competitiveness of the wood processing industry. The focus was on the task level on shop floor and concrete improvement suggestions were elaborated after applying the measurement method for Levels of Automation. Main aspects considered for generalization were enhancements regarding ergonomics in process design and cognitive support tools for shop-floor personnel through task standardization. Furthermore, difficulties regarding the automation of grading and sorting processes due to the heterogeneous material properties of wood argue for a suitable arrangement of human intervention options in terms of work task allocation.  The application of a modified version of DYNAMO++ reveals its pros and cons during a case study which covers a high operator involvement in the improvement process and the distinct predisposition of DYNAMO++ to be applied in an assembly system.

This thesis studies the use of the Source Reconstruction Method (SRM) to characterize antennas. The SRM calculates equivalent sources/currents on an arbitrarily shaped reconstruction surface to represent the original antenna. This is done by enforcing that the original antenna and equivalent currents radiate the same field at user selected measurement locations. These equivalent currents spatially characterize the original antenna because they can be used in direct radiation problems to obtain field estimates anywhere outside the reconstruction surface, including the far-field. First a spherical SRM algorithm is implemented and the diagnostic capabilities of the SRM are also synthetically shown through an example with an array of elementary dipoles. It is then shown that the SRM compares well to pre-existing commercial antenna software over different frequencies and can also be used successfully with a partial dataset. It is demonstrated that the equivalent currents can also provide meaningful information with experimental data. Next the hierarchical matrix framework is studied in conjunction with the SRM to decrease the algorithm's memory requirement and increase the speed of execution. It is shown that it is beneficial to use the hierarchical matrix framework with the SRM when using Love's condition or with measured data on a surface very close to the reconstruction surface. The SRM is then used to obtain incident field estimates in microwave imaging systems. Using a 2D transverse magnetic framework, we show that even with the limited data available in typical microwave tomography setups the SRM can produce incident field estimates in the imaging domain. These estimates are then used along with an MR-GNI algorithm to image synthetic and experimental objects with uncalibrated measured data. / October 2016

This dissertation project researched sudent mobility-- school changes not due to customary promotion-- and its educational correlates, for students and schools in Vermont. Student mobility research in other states has found that the majority of these students are disadvantaged youth from low-income families, and they lag behind their peers academically. Academic consequences of student mobility affect not only students, but also their schools since NCLBA implementation sanctions influence school enrollments by increasing student transfers. The need for information about rural student mobility during early NCLBA implementation is significant in predominantly rural Vermont. This was the first statewide study of outcomes of mobility for students and schools in a rural state. Three basic research questions were: (a) What is the incidence of mobility among Vermont students and schools? (b) What is the impact of mobility, i.e., how does the incidence of mobility vary according to educational correlates for students and schools? (c) What do multilevel analytical models reveal about variation in mobility from student and school perspectives that may be useful for educational policy and practice? To address these questions, the study analyzed data for Vermont public school students, grades 1 through 12, during school years 1999-2004. Data sources included: (a) the Vermont Department of Education Student Census and Demographic Update; (b) student New Standards Reference Examination English Language Arts and Mathematics tests, grades 4, 8, and 10; (c) Vermont School Report indicators, and (d) NCES-US Census public school location information. In-depth cross-sectional and longitudinal analyses of mobility, performance, sociodemographic, and educational correlates revealed significant and disturbing relationships that merit policy and prevention follow-up programming. School-level mobility incidence indicated that while in-migration was 20% on average, over 30% of the schools experienced much higher rates, mirroring urban-based mobility incidence. Academically, mobile students performed 3-10 percentile ranks lower than their stable counterparts did across grade levels and content areas on standardized tests, for longitudinal cohorts as well as cross-sectional grade groups. Risk factor analyses revealed that mobile students, relative to their stable peers, were (a) more likely to participate in free or reduced lunch programs at school, (b) less likely to have a 504 plan in place, (c) more likely to have kept a writing portfolio for 0-1 years (versus 2-5 years), (d) more likely to have kept a mathematics portfolio 0-1 years (versus 2-5 years), and (e) more likely to not meet the standard on mathematics performance tests. Hierarchical generalized nonlinear modeling analyses indicated that between 8% and 32% of the variation in student mobility was attributable to school-level composition and resources. This project aimed to benefit the Vermont educational community in several ways. Analytical methodology will provide the framework for developing a longitudinal monitoring system with mobility incidence, impact, and relevant educational information. Information from analytical results will inform a case study during spring 2005 to address student mobility by raising public awareness of associated issues that affect not only the students and their families, but also classrooms, schools and communities.

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.

Methods for determining clusters of data under- specified constraints have recently gained popularity. Although general constraints may be used, we focus on clustering methods with the constraint of a minimal cluster size. In this dissertation, we propose two constrained k-means algorithms: Linear Programming Algorithm (LPA) and Genetic Constrained K-means Algorithm (GCKA). Linear Programming Algorithm modifies the k-means algorithm into a linear programming problem with constraints requiring that each cluster have m or more subjects. In order to achieve an acceptable clustering solution, we run the algorithm with a large number of random sets of initial seeds, and choose the solution with minimal Root Mean Squared Error (RMSE) as our final solution for a given data set. We evaluate LPA with both generic data and simulated data and the results indicate that LPA can obtain a reasonable clustering solution. Genetic Constrained K-Means Algorithm (GCKA) hybridizes the Genetic Algorithm with a constrained k-means algorithm. We define Selection Operator, Mutation Operator and Constrained K-means operator. Using finite Markov chain theory, we prove that the GCKA converges in probability to the global optimum. We test the algorithm with several datasets. The analysis shows that we can achieve a good clustering solution by carefully choosing parameters such as population size, mutation probability and generation. We also propose a Bi-Nelder algorithm to search for an appropriate cluster number with minimal RMSE.

This study examined, for the first time, specific links between parents' memory and children's diabetes behaviors and metabolic control. Data revealed that parental memory and responsibility predicted children's percentage of calories from fat and carbohydrates, and metabolic control, accounting for 7.3% of the variance in calories from fat and 18.5% of the variance in metabolic control for the total sample. These effects were stronger when limited to dietary behaviors of younger youth; parental memory accounted for 30.3% and 33.6% of the variance in percentage of calories from fat and carbohydrates, respectively, for younger children. Level of parent responsibility, with memory, moderated younger children's percentage of calories from fat and carbohydrates, and children's metabolic control. Parents with higher memory scores and more responsibility had disease indictors that were closer to ADA recommendations. Results suggest intervention to enhance parent memory may improve diabetes care and health status for youths with IDDM.

Abstract In a two-level hierarchical linear model(HLM2), the outcome as well as covariates may have missing values at any of the levels. One way to analyze all available data in the model is to estimate a multivariate normal joint distribution of variables, including the outcome, subject to missingness conditional on covariates completely observed by maximum likelihood(ML); draw multiple imputation (MI) of missing values given the estimated joint model; and analyze the hierarchical model given the MI [1,2]. The assumption is data missing at random (MAR). While this method yields efficient estimation of the hierarchical model, it often estimates the model given discrete missing data that is handled under multivariate normality. In this thesis, we evaluate how robust it is to estimate a hierarchical linear model given discrete missing data by the method. We simulate incompletely observed data from a series of hierarchical linear models given discrete covariates MAR, estimate the models by the method, and assess the sensitivity of handling discrete missing data under the multivariate normal joint distribution by computing bias, root mean squared error, standard error, and coverage probability in the estimated hierarchical linear models via a series of simulation studies. We want to achieve the following aim: Evaluate the performance of the method handling binary covariates MAR. We let the missing patterns of level-1 and -2 binary covariates depend on completely observed variables and assess how the method handles binary missing data given different values of success probabilities and missing rates. Based on the simulation results, the missing data analysis is robust under certain parameter settings. Efficient analysis performs very well for estimation of level-1 fixed and random effects across varying success probabilities and missing rates. MAR estimation of level-2 binary covariate is not well estimated when the missing rate in level-2 binary covariate is greater than 10%. The rest of the thesis is organized as follows: Section 1 introduces the background information including conventional methods for hierarchical missing data analysis, different missing data mechanisms, and the innovation and significance of this study. Section 2 explains the efficient missing data method. Section 3 represents the sensitivity analysis of the missing data method and explain how we carry out the simulation study using SAS, software package HLM7, and R. Section 4 illustrates the results and useful recommendations for researchers who want to use the missing data method for binary covariates MAR in HLM2. Section 5 presents an illustrative analysis National Growth of Health Study (NGHS) by the missing data method. The thesis ends with a list of useful references that will guide the future study and simulation codes we used.

The focus of this study is aristocratic fathers and sons in the middle and late Roman Republic (264 – 27 B.C.). By considering legal, literary, and material evidence, it addresses the behaviour of elite families throughout this period. Although there is a great deal of important research conducted on family relations in the ancient world more generally, there is no extensive study which analyses the bonds of duty, obligation, and affection between fathers and sons in republican Rome. It is this gap in the scholarship which is addressed in my thesis. The key aspects of this relationship are considered through several interconnected chapters. Each reflects the social nature of this analysis, and demonstrates that traditional values, dynastic considerations, and social ideals promoted a sense of common identity and unity within the household. Although the hierarchical nature of Roman family life also provided opportunities for conflict between father and son, ultimately the relationship between the two was governed by these three concerns, as well as the close correlation between public and private in the lives of the republican elite. The discussion begins by considering the high valuation of fatherhood at Rome, evidenced by the use of terms derived from pater, and argues that the qualities expected of this individual were similar to those associated with the ideal statesman (Ch. I). From there, depictions of the Roman father by Greek and Roman authors are analysed to show that the former often emphasised the morality of the episode in question, while the latter stressed the conflict between the well-being of the family and the safety of the state (Ch. II). The argument then moves on to explore social expectations. Cicero’s Pro Roscio Amerino provides an example in which the ideals for father and son relationships are manipulated in order to persuade an audience (Ch. III). This shows that pietas, duty, companionship, and support towards one another were recognised as norms for these individuals. The discussion of the paterfamilias in the following chapter demonstrates that he was expected to act as a role model for future generations, and to provide education and protection to his dependants (Ch. IV). The reputation and continuity of the family line were also important considerations for the aristocratic head of household. From there, traditional values, dynastic considerations, and social ideals are explored through the family life-cycle (Ch. V). This section establishes that these three areas fostered a sense of common identity and unity within the household, and exerted significant pressure upon fathers and sons to maintain relatively harmonious relationships. The final chapter considers literary portrayals of Rome’s founders in order to reiterate the close correlation between the ideal of the father and the ideal of the statesman (Ch. VI). It concludes that the use of the father-figure by Augustus and later emperors to legitimise their position in the state develops from the ideological significance of fatherhood in the Republic.