ANALYSIS OF MARKOV CHAIN MONTE CARLO METHODS IN MULTI-INDENTURE INVENTORY OPTIMIZATION

U.S. Navy aircraft are required to meet minimum operational availability targets, while minimizing spare parts procurement costs. The current optimization model written by Salmeron and Buss, uses marginal analysis, as described by Sherbrooke, to determine optimal sparing policies for this highly complex multi-indenture model. The literature lacks alternative optimization methodologies for such a problem, so we propose an alternative approach utilizing simulated annealing (SA), a Markov Chain Monte Carlo algorithm. We present three SA approaches tested in three case studies of varying size and complexity. Our initial findings show that in very simple problems, SA is easily capable of outperforming marginal analysis; however, problems with more complexity have large optimality gaps. This is likely because the SA Markov chain is unable to effectively explore the multi-indenture structure of the problem. We implement a method to account for this structure that intelligently builds initial feasible solutions using an epsilon-greedy approach to marginal analysis. This approach produces better results than NAVARM in more than half of the trials on problems of moderate complexity. We also implement a novel method for calculating operational availability that may allow full scale problems to be optimized more efficiently.NAVSUP WSSLieutenant Commander, United States NavyApproved for public release. Distribution is unlimited

Investigating the Effects of a p53 Mutation and Various Tyrosine Kinase Inhibitors on Glioma Progression and Therapy Resistance in Drosophila

Gliomas, which are brain tumors that arise from glial cells, are some of the most aggressive and lethal types of tumors. These brain tumors are difficult to treat because not enough information regarding the mutations present in these tumors exists. This project studies effects of a p53 mutation on Drosophila glioma progression and then will test to see if this results in resistance to current chemotherapy. The main goal of this endeavor is to investigate the numerous defects occurring at the cellular and biochemical level in gliomas, which will give insight into why these types of tumors are so difficult to treat. Additionally, this document also discusses some promising chemotherapeutic agents found through a drug screen project. The effects of five different Tyrosine Kinase inhibitors on glioma development are presented here

Infectious episodes in severely granulocytopenic patients

Contains fulltext : 4314.pdf (publisher's version ) (Open Access

pySODM: Simulating and Optimizing Dynamical Models in Python 3

In this work we present our generic framework to construct, simulate and calibrate dynamical systems in Python 3. Its goal is to reduce the time it takes to implement a dynamical system with n-dimensional states represented by coupled ordinary differential equations (ODEs), simulate the system deterministically or stochastically, and, calibrate the system using n-dimensional data. We demonstrate our code's capabilities by building three models in the context of two case studies. First, we forecast the yields of the enzymatic esterification reaction of D-glucose and Lauric acid, performed in a continuous-flow, packed-bed reactor. The model yields satisfactory predictions under different flow rates and can be applied to design a viable process. Second, we build a stochastic, age-stratified model to make forecasts on the evolution of influenza in Belgium during the 2017-2018 season. By presenting real-world case studies from two scientific disciplines, we demonstrate our code's applicability across domains

The Impact of Temperature on Physical Activity Levels During a 12-Week Walking Intervention

Please view abstract in the attached PDF file

The Mectizan(® )Donation Program – highlights from 2005

Through the Mectizan(® )Donation Program, Merck & Co., Inc. has donated Mectizan (ivermectin, MSD) for the treatment of onchocerciasis worldwide since 1987. Mectizan has also been donated for the elimination of lymphatic filariasis (LF) since 1998 in African countries and in Yemen where onchocerciasis and LF are co-endemic; for LF elimination programs, Mectizan is co-administered with albendazole, which is donated by GlaxoSmithKline. The Mectizan Donation Program works in collaboration with the Mectizan Expert Committee/Albendazole Coordination, its scientific advisory committee. In 2005, a total of 62,201,310 treatments of Mectizan for onchocerciasis were approved for delivery via mass treatment programs in Africa, Latin America, and Yemen. Seventy-seven percent and 20% of these treatments for onchocerciasis were for countries included in the African Programme for Onchocerciasis Control (APOC) and the former-Onchocerciasis Control Programme in West Africa (OCP), respectively. The remaining 3% of treatments approved were for the six onchocerciasis endemic countries in Latin America, where mass treatment is carried out twice-yearly with the goal of completely eliminating morbidity and eventually transmission of infection, and for Yemen. All 33 onchocerciasis endemic countries where mass treatment with Mectizan is indicated have ongoing mass treatment programs. In 2005, 42,052,583 treatments of co-administered albendazole and Mectizan were approved for national Programs to Eliminate LF (PELFs) in Africa and Yemen. There are ongoing PELFs using albendazole and Mectizan in nine African countries and Yemen; these represent 35% of the total number of countries expected to require the co-administration of these two chemotherapeutic agents for LF elimination. In Africa, the expansion of existing PELFs and the initiation of new ones have been hampered by lack of resources, technical difficulties with the mapping of LF endemicity, and the co-endemicity of LF and loiasis. Included in this review are recommendations recently put forward for the co-administration of albendazole and Mectizan in areas endemic for LF, loiasis, and onchocerciasis

Time-to-birth prediction models and the influence of expert opinions

Preterm birth is the leading cause of death among children under five years old. The pathophysiology and etiology of preterm labor are not yet fully understood. This causes a large number of unnecessary hospitalizations due to high--sensitivity clinical policies, which has a significant psychological and economic impact. In this study, we present a predictive model, based on a new dataset containing information of 1,243 admissions, that predicts whether a patient will give birth within a given time after admission. Such a model could provide support in the clinical decision-making process. Predictions for birth within 48 h or 7 days after admission yield an Area Under the Curve of the Receiver Operating Characteristic (AUC) of 0.72 for both tasks. Furthermore, we show that by incorporating predictions made by experts at admission, which introduces a potential bias, the prediction effectiveness increases to an AUC score of 0.83 and 0.81 for these respective tasks