Design optimality for treatment comparison experiments has been intensively studied by numerous researchers, employing a variety of statistically sound criteria. Their general formulation is based on the idea that optimality functions of the treatment information matrix are invariant to treatment permutation. This implies equal interest in all treatments. In practice, however, there are many experiments where not all treatments are equally important. When selecting a design for such an experiment, it would be better to weight the information gathered on different treatments according to their relative importance and/or interest. This dissertation develops a general theory of weighted design optimality, with special attention to the block design problem. Among others, this study develops and justifies weighted versions of the popular A, E and MV optimality criteria. These are based on the weighted information matrix, also introduced here. Sufficient conditions are derived for block designs to be weighted A, E and MV-optimal for situations where treatments fall into two groups according to two distinct levels of interest, these being important special cases of the "2-weight optimality" problem. Particularly, optimal designs are developed for experiments where one of the treatments is a control. The concept of efficiency balance is also studied in this dissertation. One view of efficiency balance and its generalizations is that unequal treatment replications are chosen to reflect unequal treatment interest. It is revealed that efficiency balance is closely related to the weighted-E approach to design selection. Functions of the canonical efficiency factors may be interpreted as weighted optimality criteria for comparison of designs with the same replication numbers. / Ph. D.

Generalized hill climbing (GHC) algorithms provide a framework for using local search algorithms to address intractable discrete optimization problems. Many well-known local search algorithms can be formulated as GHC algorithms, including simulated annealing, threshold accepting, Monte Carlo search, and pure local search (among others). This dissertation develops a mathematical framework for simultaneously addressing a set of related discrete optimization problems using GHC algorithms. The resulting algorithms, termed simultaneous generalized hill climbing (SGHC) algorithms, can be applied to a wide variety of sets of related discrete optimization problems. The SGHC algorithm probabilistically moves between these discrete optimization problems according to a problem generation probability function. This dissertation establishes that the problem generation probability function is a stochastic process that satisfies the Markov property. Therefore, given a SGHC algorithm, movement between these discrete optimization problems can be modeled as a Markov chain. Sufficient conditions that guarantee that this Markov chain has a uniform stationary probability distribution are presented. Moreover, sufficient conditions are obtained that guarantee that a SGHC algorithm will visit the globally optimal solution over all the problems in a set of related discrete optimization problems. Computational results are presented with SGHC algorithms for a set of traveling salesman problems. For comparison purposes, GHC algorithms are also applied individually to each traveling salesman problem. These computational results suggest that optimal/near optimal solutions can often be reached more quickly using a SGHC algorithm. / Ph. D.

Dropping out of school is a major concern as high school graduation credentials have been used as an important measurement tool to define post-secondary success. Numerous researchers presented a multitude of factors that predict dropouts at individual and school levels. Curriculum track choice, or high school course-taking sequence, defines students' schooling career and ultimately the post-secondary path that they choose (Plank, DeLuca, & Estacion, 2008). Scholars have debated on various outcomes related to dropouts influenced by various curriculum choices, namely academic, career and technical education (CTE), dual enrollment, and general curriculum. Several argued students following academic tracks are more likely to graduate. Others claim that CTE benefits students who are at-risk and suppresses dropout likelihood (Rumberger & Sun, 2008). New vocationalism or dual enrollment has proven successful at reducing dropout rates. This study attempted to investigate the influence of curriculum track and CTE program areas on dropout likelihood while controlling for possible individual differences. Analysis was conducted via Hierarchical Generalized Linear Modeling (HGLM) due to the nested data structure of Education Longitudinal Study of 2002 (ELS). Variables included were academic background, academic and career aspiration, school-sponsored activity participation, school minority composition, school average student socio-economic status (SES), school type (private or public), school urbanicity, CTE courses offered at the school, and demographic indicators (gender, race, and SES). Findings reflect higher dropout likelihood among general curriculum participants than academic and occupational concentrators after controlling for all possible individual differences. Dual concentrators had 0% dropout rate, and therefore comparison with other curriculum tracks was not possible via HGLM analysis. Results suggest substantial importance of academic background, post-secondary education plans, and school-sponsored activity participation in predicting dropout likelihood. Comparing CTE program areas, Family and Consumer Sciences, Human Services, Public Services, Health and Education (Human Services area) participants were more likely to drop out than other program areas while Technology Education participants were less likely to drop out than Human Services and 2 or more CTE program area participants. Results suggest 9th grade overall GPA and school-sponsored activity participation as substantial predictors of dropout likelihood among occupational concentrators. Variability across schools was insignificant. / Ph. D.

Graphical summaries are becoming important tools for evaluating designs. The need to compare designs in term of their prediction variance properties advanced this development. A recent graphical tool, the Fraction of Design Space plot, is useful to calculate the fraction of the design space where the scaled prediction variance (SPV) is less than or equal to a given value. In this dissertation we adapt FDS plots, to study three specific design problems: robustness to model assumptions, robustness to measurement error and design properties for generalized linear models (GLM). This dissertation presents a graphical method for examining design robustness related to the SPV values using FDS plots by comparing designs across a number of potential models in a pre-specified model space. Scaling the FDS curves by the G-optimal bounds of each model helps compare designs on the same model scale. FDS plots are also adapted for comparing designs under the GLM framework. Since parameter estimates need to be specified, robustness to parameter misspecification is incorporated into the plots. Binomial and Poisson examples are used to study several scenarios. The third section involves a special type of response surface designs, mixture experiments, and deals with adapting FDS plots for two types of measurement error which can appear due to inaccurate measurements of the individual mixture component amounts. The last part of the dissertation covers mixture experiments for the GLM case and examines prediction properties of mixture designs using the adapted FDS plots. / Ph. D.

Despite recent advances in computer power and the development of better algorithms, theoretical scheduling methodologies developed for batch-chemical production are seldom applied in industry (Musier & Evans 1989 and Grossmann et al. 1992). Scheduling decisions may have significant impact on overall company profitability by defining how capital is utilized, the operating costs required, and the ability to meet due dates. The purpose of this research is to compare different production scheduling methods by applying them to a real-world multi-stage, multi-product, batch-chemical production line. This research addresses the problem that the theoretical algorithms are seldom applied in industry and allows for performance analysis of several theoretical algorithms. The research presented in this thesis focuses on the development and comparison of several scheduling algorithms. The two objectives of this research are to: 1. modify different heuristic production scheduling algorithms to minimize tardiness for a multi-product batch plant involving multiple processing stages with several out-of-phase parallel machines in each stage; and 2. compare the robustness and performance of these production schedules using a stochastic discrete event simulation of a real-world production line. The following three scheduling algorithms are compared: 1. a modified Musier and Evans scheduling algorithm (1989); 2. a modified Ku and Karimi Sequence Building Algorithm (1991); and 3. a greedy heuristic based on an earliest-due-date (EDD) policy. Musier and Evans' heuristic improvement method (1989) is applied to the three algorithms. The computation times to determine the total tardiness of each schedule are compared. Finally, all the schedules are tested for robustness and performance in a stochastic setting with the use of a discrete event simulation (DES) model. Mignon, Honkomp, and Reklaitis' evaluation techniques (1995) and Multiple Comparison of the Best are used to help determine the best algorithm. / Master of Science

In recent years, research on the design and performance evaluation of suboptimal receiver implementations has received considerable attention owing to complexity in the realization of the optimal receiver algorithms over wireless channels. This thesis addresses the effects of using reduced complexity receivers for the Satellite Digital Audio Radio (SDAR), Code Division Multiple Access (CDMA) and UltraWideband (UWB) communications technologies. A graphical-user-interface simulation tool has been developed to predict the link reliability performance of the SDAR services in the continental United States. Feasibility study of receiving both satellite and terrestrial repeater signals using a selection diversity (single antenna) receiver has also been performed. The thesis also develops a general mathematical framework for studying the efficacy of a sub-optimal generalized selection combining (GSC) diversity receiver over generalized fading channel models. The GSC receiver adaptively combines a subset of M diversity paths with the highest instantaneous signal-to-noise ratios (SNR) out of the total L available diversity paths. The analytical framework is applicable for rake receiver designs in CDMA and UWB communications. / Master of Science

To analyze a communication system without the aid of simulation, the channel noise for the simulation must be assumed to be normal. The assumption is often valid, but the normal distribution may not be able to model the channel noise adequately in some environments. This thesis will explore the generalized exponential distribution for better noise modeling and robustness testing in communication system. When using the generalized exponential distribution for the channel noise, the analysis will become analytically intractable, and simulation becomes mandatory. To generate the noise with the distribution, the rejection method can be used. However, since the distribution can take on different shapes, finding the appropriate Upper Bounding Function (UBF) for the method is very difficult. Thus, two modified versions of the rejection method will be examined. They are the Transformed Density Rejection (TDR) and Ratio-of-Uniform (RoU) method; their quality, efficient, trade-offs, etc will be discussed. Choosing TDR, a simulation of a BPSK communication system will be performed. With the simulation, it can further ascertain that the random variates generated by TDR can be used to model the channel noise and to test the robustness of a communication system. / Master of Science

京都大学 / 新制・課程博士 / 博士(農学) / 甲第23527号 / 農博第2474号 / 新制||農||1087(附属図書館) / 学位論文||R3||N5358(農学部図書室) / 京都大学大学院農学研究科地域環境科学専攻 / (主査)教授 藤原 正幸, 教授 中村 公人, 准教授 宇波 耕一 / 学位規則第4条第1項該当 / Doctor of Agricultural Science / Kyoto University / DFAM

The ongoing opioid crisis presents a significant public health challenge particularly for people who use opioids (PWUO). Naloxone is an opioid antagonist crucial to reducing opioid overdose mortality. Inconsistencies exist among PWUO in obtaining, carrying, discussing, and administering naloxone. Using sequential mixed methods, this study was aimed at investigating the use of implementation intentions on naloxone use among PWUO. Semi-structured interviews were conducted with 83 PWUO to gather individual experiences with using naloxone and contextual details regarding its use. An essentialist thematic analysis with inductive coding revealed valuable insights into where, for whom, and when naloxone is implemented. The analysis identified major themes such as caring for others' needs, knowledge gaps, reinforcement through overdose experiences, duality of overdose and compassion, and stigma. Minor themes related to syringe services program implementation and drug use were identified. Building on these qualitative findings a quantitative analysis determined the impact of implementation intentions on naloxone implementation. Participants were randomly assigned to develop implementation intentions or goal intentions for the use of naloxone. Follow-up surveys assessed changes in participants' intentions to obtain, carry, discuss, and administer naloxone and their actual implementation over a 6-month period. At the 3-month follow-up the experimental condition exhibited statistically significant positive intentions to obtain naloxone and engage in discussions about naloxone in social contexts of drug use. Changes in the magnitude of naloxone implementation were observed at the 3- and 6-month timepoints. Specifically, the self-reported discussion of naloxone showed noticeable changes in implementation frequency over time. This suggests that while implementation intentions may not have statistically significant effects on the use of naloxone it had some influence on the frequency of discussing naloxone prior to drug use. This work makes a valuable contribution to the existing literature because of its attempt to apply the Theory of Planned Behavior and implementation intentions in a novel way. Though the experimental hypothesis was not supported statistically significant observations were made for some behaviors at the 3-month follow-up. The pragmatic nature of the setting enhances the relevance of the findings and provides valuable insights for future interventions supporting PWUO. / Doctor of Philosophy / The ongoing crisis of opioid addiction poses a significant public health challenge particularly for individuals who use opioids. Naloxone is a medication that can reverse opioid overdoses and it plays a crucial role in saving lives. People who use opioids often face difficulties in accessing, carrying, discussing, and using naloxone consistently. This study was aimed at investigating the use of naloxone by employing qualitative and quantitative methods. We conducted interviews with 83 individuals who use opioids to explore their experiences and gather insights into naloxone use. These interviews provided valuable information about when, where, and for whom naloxone is used. Several important themes emerged including the significance of helping others, knowledge gaps, the influence of personal experiences, the conflict between the fear of overdose and caring for others, and the stigma associated with drug use. We investigated the impact of a specific approach called "implementation intentions" in improving naloxone use. Participants were randomly assigned to create specific plans or general goals for naloxone use. Through surveys conducted over a 6-month period we examined changes in participants' intentions and actions related to naloxone use. Although the specific approach did not yield significant improvements, we observed changes in how people discussed naloxone over time. This study contributes to the existing research by introducing innovative ideas to support positive behavioral changes among individuals who use opioids. The real-world setting in which the study took place enhances the applicability of the findings and offers valuable insights for future programs supporting individuals who use opioids.

Characterizing streamflow trends is important for water resources management. Streamflow conditions, and trends thereof, are critical drivers of all aspects of stream geomorphology, sediment and nutrient transport, and ecological processes. Using the non-parametric modified Mann-Kendall test, we analyzed streamflow trends from 1996 to 2022 for the Southern Appalachian (SA) region of the U.S. The forested uplands of the SA receive high amounts of rain and act as a "water tower" for the surrounding lowland area, both of which have experienced higher than average population growth and urban development. For the total of 127 USGS gages with continuous streamflow measurements, we also evaluated precipitation and land change rates and patterns within the upstream contributing areas. Statistical methods (i.e., generalized linear models) were then used to assess any linkages between land cover change (LCC) and streamflow trends. Our results show that 42 drainage areas are experiencing increasing trends in their precipitation, and 1 is experiencing a negative trend. A total of 71 drainage areas are experiencing increasing trends in either their annual streamflow minimums, maximums, medians, or variability, with some experiencing changes in multiple. From our models, it is suggested that agricultural expansion is associated with increasing minimum streamflow trends, but increasing precipitation is also positively linked. With this information, water managers would be aware of which areas are experiencing changes in streamflow amounts from LCC or precipitation and could then apply this in planning and predictions. / Master of Science / Water availability is important for resources management. Streamflow is a measure of available surface water and is an important component in the hydrological cycle. Using the non-parametric modified Mann-Kendall test, we analyzed streamflow trends from 1996 to 2022 for the Southern Appalachian (SA) region of the U.S. The forested uplands of the SA receive high amounts of rain and act as a "water tower" for the surrounding lowland area, both of which have experienced higher than average population growth and city expansion. For the total of 127 USGS gages with continuous streamflow measurements, we also evaluated precipitation and land cover change rates within the area upstream of the gage (or drainage/contributing area). Statistical methods (i.e., generalized linear models) were then used to assess any linkages between land cover change (LCC) and streamflow trends. Our results show that 42 drainage areas are experiencing increasing trends in their precipitation, and 1 is experiencing a negative trend. A total of 71 drainage areas are experiencing increasing trends in either their annual streamflow minimums, maximums, medians, or variability, with some experiencing changes in multiple. From our models, it is suggested that agricultural expansion is associated with increasing minimum streamflow trends, but increasing precipitation is also positively linked. With this information, water managers would be aware of which areas are experiencing changes in streamflow amounts from LCC or precipitation and could then apply this in planning and predictions.