The importance of capturing the early history of computer simulation by producing digital videos of interviews with the pioneers of the field is based on the remarkable degree to which computer simulation?which encompasses discrete event simulation, Monte Carlo, combined discrete-continuous simulation, and hybrid analytic/simulation approaches?has influenced the evolution of computing software, as evidenced by the following: 1. A major incentive for building the Eniac (the first general-purpose electronic computer) and its contemporaries was to enable extension of the Monte Carlo experiments so vital to further advances in the physical and mathematical sciences in the late 1930s and early 1940s?especially nuclear and atmospheric physics as well as military ballistics and statistics. 2. Manufacturing applications of computing over the past six decades have been stimulated far more by applications of simulation modeling and analysis than by purely mathematical techniques. 3. Computer simulation is a multidisciplinary field, thriving on the conjunction of computer science, mathematics, statistics, and operations research and the management sciences (OR/MS). Just as all these disciplines have contributed significantly to the advances in computer simulation, these disciplines have also benefited significantly from all the advances in the simulation field. 4. No other methodology related to OR/MS?including mathematical programming?has enjoyed such widespread use in industry, government, and the military as has computer simulation. 5. With the creation of SIMULA 67, computer simulation produced the dominant programming paradigm of the past two decades?namely, object-oriented programming While the opportunity still exists, science and the history of science must capture and preserve reliable accounts of seminal projects, the related pivotal events, and the distinguished project contributors from the perspectives of, and in the words of, individuals who witnessed the relevant history at firsthand. Documenting the historical development of computer simulation by the pioneers who made that history requires swift action to do the following: (a) identify those pioneers whose health enables them to participate in the project; (b) make the logistical arrangements necessary to produce high-quality digital-video recordings of structured interviews with those pioneers; and (c) carry out the interviews, edit the recordings, and post those recordings in a universally accessible permanent digital repository. Unfortunately, some of the most prominent individuals in the early of history of computer simulation are no longer living. Some of their research colleagues and a number of other simulation pioneers remain, but it is imperative that expeditious action be taken to capture the knowledge and perspectives of the surviving pioneers. Such action is one of the main objectives of the proposed project, and the other key objective is to provide a template that can be used as an effective guide to performing similar projects for other disciplines in the future. The digital videos produced by this project will be made freely available on the Web site of the Simulation Archive hosted by the North Carolina State University (NCSU) Libraries; and the availability of the videos will be widely publicized in the international simulation community and in the broader professional communities that sponsor and participate in the Winter Simulation Conference (WSC). Faculty members who teach simulation will be encouraged to make these videos known to the students in their classes and to use the videos in their courses where appropriate. Furthermore, these videos can be used effectively in courses on the History of Science and Technology. A digital video will be devoted to women who played a prominent role in the early history of computer simulation. Currently we have identified two such women from academia, and both were active in WSC during the period 1970?2000. Further research will be done to identify other women to participate in this

Intellectual Merit. The proposed research is focused on the development, implementation, and evaluation of a comprehensive methodology for steady-state simulation output analysis in a framework called ESPRESS: Effective Sequential Procedures for Risk and Error Estimation in Steady-state Simulation. The developmental aspect encompasses advances in the theory and methodology that underlie the frameworks?s estimation tools. Specifically, we will develop sequential methods for computing point estimators and confidence intervals for the steady-state mean and quantiles of a simulation-generated process that are valid and meet prespecified accuracy criteria. While the mean indicates central tendency, quantiles are used for risk assessment. In the vast majority of simulation applications to problems of risk analysis, interest is centered on the estimation of quantiles such as the Value at Risk of a portfolio. The implementation of ESPRESS relates to the design of computationally efficient algorithms that users can access and apply easily and quickly. Our industry sponsors Imagine That!, SAS Institute, and Simio are very eager to incorporate many components of ESPRESS into their simulation packages. The evaluation of ESPRESS includes the design, execution, and analysis of a comprehensive experimental assessment of its performance. The ultimate objective of this work is to provide practitioners and researchers with new procedures and public-domain software for analysis of steady-state simulation-generated data that are completely automated, robust, and reliable as well as computationally and statistically efficient. The holistic approach in ESPRESS will make the following inroads: (i) The automated sequential procedures for the estimation of the steady-state mean will be based on variance estimators derived from standardized time series. While the PIs have made substantial contributions in related areas, their sequential algorithms have heretofore been based on variants of the classical method of batch means. Whereas the superiority of variance estimators based on standardized time series has been established, the literature is devoid of sequential algorithms based on such estimators. (ii) The sequential procedures for estimating steady-state quantiles will address various challenges reflected in the rather limited literature: (a) lack of an adequate theoretical basis for some methods; (b) lack of effective guidelines for using the methods in practice; (c) poor performance of the estimators in industrial-strength applications; and (d) excessive data processing requirements. (iii) The procedures for estimating steady-state quantiles will be based on conditions that in practice are much more broadly applicable and much easier to check compared with the mixing assumptions typically cited in the literature. Broader Impacts. The steady-state simulation analysis procedures composing ESPRESS will be directly applicable to large-scale simulation studies in the governmental and military sectors as well as in a variety of industries including aerospace, distribution, finance, healthcare, manufacturing, telecommunications, and transportation. The need for the proposed development is evident from the lack of fully automated sequential steady-state mean and quantile estimation procedures in virtually all popular simulation packages. The implementation of ESPRESS will place a powerful tool in the hands of simulation users that will allow them to conduct detailed statistical analyses within a reasonable time frame. Educational outreach will be a key component of the proposed research; all of the investigators share a deep commitment to education at all levels. At Georgia Tech and NC State combined, classes in discrete-event simulation alone average about 350, 50, and 30 students per semester at the undergraduate, masters, and doctoral levels. The incorporation of the proposed automated procedures in leading simulation programs will allow students to conduct sophisticated statis

NCSU through the Research Assistant will provide research and analysis to SAS as set forth in this Agreement. Such research and analysis shall include, but is not limited to, research, generation, testing, and documentation of operations research software. Research Assistant will provide such services for SAS' offices in Cary, North Carolina, at such times as have been mutually agreed upon by the parties. Research Assistant agrees to abide by SAS' policies and procedures regarding security of SAS' facilities and computing resources. Research Assistant further agrees to submit to background verification. If SAS, in its sole discretion, finds Research assistant's background unsuitable, this Agreement shall terminate immediately.

Approximately 23.6 million people in the US have diabetes. Ninety-five percent of these patients have type 2 diabetes. Two out of three diabetes patients will die from either stroke or heart disease (CDC Diabetes Fact Sheet, 2007). These patients often take many medications to control their metabolic factors (HbA1c, blood pressure, and cholesterol), thereby managing their diabetes and risk for adverse events (such as stroke, coronary heart disease (CHD) events, blindness, and kidney failure). While preventative medications can greatly improve long-term outcomes, for a variety of reasons patients often take less than the prescribed dose of their medications. This behavior prevents the patient from fully benefiting from the treatment (i.e., prevention of the adverse events is lessened). Understanding the effects of poor adherence is critical to improving patient treatment in light of poor adherence and helping to motivate patients to improve their adherence to their medications. The proposed work for this dissertation grant consists of three major parts. First, I will study effects that less than perfect adherence to blood pressure and cholesterol medications has on the respective metabolic factors, building on my preliminary research of the effects of poor adherence to statins on cholesterol levels. Second, I will build a multiple medication Markov decision process (MDP) model to determine the optimal timing and sequence of blood pressure and cholesterol medications during the course of a type 2 diabetes patient?s lifetime with a goal of reducing the risk of macrovascular events. Finally, I will combine the MDP model and the adherence effects to determine the changes in optimal treatment when considering the effects of variable adherence to these medications. The specific aims are as follows: Specific Aim 1: Determine the metabolic effects of varying levels of adherence to blood pressure and cholesterol medications as determined by the proxy measure of the percentage of days covered (PDC) by prescription refills. The following two sub aims will be determined separately for males and females, and patients of different ethnicities and health statuses. Sub Aim 1a: Build a Markov model for adherence to medication (e.g., year to year transitions in PDC). Sub Aim 1b: Determine distributions of changes in metabolic state values for each adherence state based on the effectiveness of a given medication. Specific Aim 2: Build an MDP model to determine the optimal treatment plans for managing cholesterol and blood pressure levels in patients with type 2 diabetes, considering perfect adherence. The model will be calibrated separately for males and females and patients of different ethnicities. Specific Aim 3: Combine the MDP model and the adherence Markov models to determine how less than perfect adherence affects the optimal sequence and timing of medications for diabetes patients. Allow for the possibility of interventions to improve adherence. The results and methods produced by this proposed research could potentially be extended to other types of patients and diseases ? both management of hypertension and hyperlipidemia in patients without diabetes and treatment management for complex patients with other chronic conditions.

NCSU through the Research Assistant will provide research and analysis to SAS as set forth in this Agreement. Such research and analysis shall include, but is not limited to, research, generation, testing, and documentation of operations research software. Research Assistant will provide such services for SAS' offices in Cary, North Carolina, at such times as have been mutually agreed upon by the parties. Research Assistant agrees to abide by SAS' policies and procedures regarding security of SAS' facilities and computing resources. Research Assistant further agrees to submit to background verification. If SAS, in its sole discretion, finds Research assistant's background unsuitable, this Agreement shall terminate immediately.

NCSU through the Research Assistant will provide research and analysis to SAS Institute, Inc Cary, NC as set forth in this Agreement. Starting August 23, 2006, one graduate research assistant will be appointed as an industrial trainee and work closely with Trevor Kearney, Analytical Solutions Manager at SAS, until May 15, 2007. Such research and analysis shall include, but is not limited to, research, generation, testing, and documentation of operations research software. Dr. James Wilson will serve as faculty advisor to Ali Tafazoli Yazdi.

NCSU through the Research Assistant will provide reseearch and analysis to SAS Institute, Inc Cary, NC as set forth in this Agreement. Starting June 5, 2006, one graduate research assistant will be appointed as an industrial trainee and work closely with Trevor Kearney, Analytical Solutions Manager at SAS, until August 22, 2006. Such research and analysis shall include, but is not limited to, research, generation, testing, and documentation of operations research software. Dr. James Wilson will serve as faculty advisor to Ali Tafazoli Yazdi.