藉由提供多樣式委託提供交易人直接反應各月份合約價格關係的管道，促進市場活絡, 滿足本國選擇權市場交易人的策略交易需求，增加交易誘因,有關選擇權當日有效策略交易就系統之影響及相關配套措施提出建議以利系統設計之參考及進一步研究跨市場或更複雜之策略交易研究之參考。

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Thesis (MSc.(Anatomy)--Faculty of Health Sciences)-University of Pretoria, 2008.] / Includes bibliographical references.

Thesis (M.S.P.H.)--University of South Florida, 2009. / Title from PDF of title page. Document formatted into pages; contains 57 pages. Includes bibliographical references.

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This thesis analyses the successful of shooting in the highest Czech handball competition - Extraleague Men, in season 2010/2011. The analysis was realized by watching video recordings which were taken during the basic part of the competition. Besides the monitoring of the total success of these teams, we focused on components of the attack on the overall success and, for example, on shooting from the perspective of post player or shooting methods. The data were processed into graphs and commented.

La thèse porte sur la proposition de méthodes d’optimisation (modèles mathématiques et métaheuristique) et leur couplage avec un modèle de simulation pour la résolution de problèmes de livraison collecte incluant la planification des horaires des chauffeurs. L’originalité de ces travaux porte sur la diversité des ressources (véhicule, chauffeur, quai de chargement, de déchargement, contenant, ligne de production, aire de nettoyage) et des contraintes (incompatibilité véhicule/contenant, date de début au plus tôt, date de fin souhaitée, planning…) à prendre en compte. L’objectif est de proposer une organisation permettant de réaliser l’ensemble des transports tout en minimisant les retards et les heures supplémentaires. La première partie s’intéresse au transport d’un seul type de produit. Le problème est modélisé comme un RCPSP avec profil de demande en ressources variable. Les transports à vide sont modélisés comme des temps de montage dépendant de la séquence. Deux programmes linéaires en nombres entiers sont proposés. La seconde partie concerne le transport de plusieurs types de produit. Le problème présente une double complexité qui est résolue par le couplage d’une recherche locale itérée avec un modèle de simulation. Le modèle de simulation permet de répondre à la complexité structurelle et fonctionnelle, notamment en raison de la diversité des ressources. La troisième partie intègre la définition des horaires de travail des chauffeurs. Une approche itérative incluant un modèle de simulation, un programme linéaire en nombres entiers et le couplage précédemment présenté est proposée. Ce problème est traité dans un contexte hospitalier pour le transport de contenants propres ou sales (repas, linge, médicaments) entre sites de consommation et sites de production. Chaque partie fait l’objet d’une expérimentation avec des données réelles. / The thesis focuses on the proposed optimization methods (metaheuristics and mathematical models) and their coupling with a simulation model to solve problems of collection including delivery scheduling drivers. The originality of this work focuses on the diversity of resources (vehicle, driver, dock loading, unloading, container production line, cleaning area) and constraints (incompatibility vehicle / container, start date earlier desired end date, planning ...) to take into account. The objective is to provide an organization to achieve all transport while minimizing delays and overtime. The first part focuses on the transport of a single type of product. The problem is modeled as a demand profile RCPSP with variable resources. Empty transports are modeled as time-dependent assembly sequence. Two integer linear programs are proposed. The second part concerns the transport of several types of product. The problem has a double complexity that is determined by the coupling of an iterated local search with a simulation model. The simulation model allows to meet the structural and functional complexity, mainly because of the diversity of resources. The third part includes the definition of working hours of drivers. An iterative approach including a simulation model, an integer linear program and previously presented coupling is proposed. This problem is treated in a hospital setting for transporting containers clean or dirty (food, clothes, medicine) between sites of consumption and production sites. Each party is subject to an experiment with real data.

Two-arm group sequential designs have been widely used for over forty years, especially for studies with mortality endpoints. The natural generalization of such designs to trials with multiple treatment arms and a common control (MAMS designs) has, however, been implemented rarely. While the statistical methodology for this extension is clear, the main limitation has been an efficient way to perform the computations. Past efforts were hampered by algorithms that were computationally explosive. With the increasing interest in adaptive designs, platform designs, and other innovative designs that involve multiple comparisons over multiple stages, the importance of MAMS designs is growing rapidly. This dissertation proposes a group sequential approach to design MAMS trial where the test statistic is the maximum of the cumulative score statistics for each pair-wise comparison, and is evaluated at each analysis time point with respect to efficacy and futility stopping boundaries while maintaining strong control of the family wise error rate (FWER). In this dissertation we start with a break-through algorithm that will enable us to compute MAMS boundaries rapidly. This algorithm will make MAMS design a practical reality. For designs with efficacy-only boundaries, the computational effort increases linearly with number of arms and number of stages. For designs with both efficacy and futility boundaries the computational effort doubles with successive increases in number of stages. Previous attempts to obtain MAMS boundaries were confined to smaller problems because their computational effort grew exponentially with number of arms and number of stages. We will next extend our proposed group sequential MAMS design to permit adaptive changes such as dropping treatment arms and increasing the sample size at each interim analysis time point. In order to control the FWER in the presence of these adaptations the early stopping boundaries must be re-computed by invoking the conditional error rate principle and the closed testing principle. This adaptive MAMS design is immensely useful in phase~2 and phase~3 settings. An alternative to the group sequential approach for MAMS design is the p-value combination approach. This approach has been in place for the last fifteen years.This alternative MAMS approach is based on combining independent p-values from the incremental data of each stage. Strong control of the FWER for this alternative approach is achieved by closed testing. We will compare the operating characteristics of the two approaches both analytically and empirically via simulation. In this dissertation we will demonstrate that the MAMS group sequential approach dominates the traditional p-value combination approach in terms of statistical power.

This dissertation comprises of three original contributions to empirical forecasting research. Chapter 1 introduces the dissertation. Chapter 2 contributes to the literature on hierarchical time series (HTS) modelling by proposing a disaggregated forecasting system for both inflation rate and its volatility. Using monthly data that underlies the Retail Prices Index for the UK, we analyse the dynamics of the inflation process. We examine patterns in the time-varying covariation among product-level inflation rates that aggregate up to industry-level inflation rates that in turn aggregate up to the overall inflation rate. The aggregate inflation volatility closely tracks the time path of this covariation, which is seen to be driven primarily by the variances of common shocks shared by all products, and by the covariances between idiosyncratic product-level shocks. We formulate a forecasting system that comprises of models for mean inflation rate and its variance, and exploit the index structure of the aggregate inflation rate using the HTS framework. Using a dynamic model selection approach to forecasting, we obtain forecasts that are between 9 and 155 % more accurate than a SARIMA-GARCH(1,1) for the aggregate inflation volatility. Chapter 3 is on improving forecasts using forecast combinations. The paper documents the software implementation of the open source R package for forecast combination that we coded and published on the official R package depository, CRAN. The GeomComb package is the only R package that covers a wide range of different popular forecast combination methods. We implement techniques from 3 broad categories: (a) simple non-parametric methods, (b) regression-based methods, and (c) geometric (eigenvector) methods, allowing for static or dynamic estimation of each approach. Using S3 classes/methods in R, the package provides a user-friendly environment for applied forecasting, implementing solutions for typical issues related to forecast combination (multicollinearity, missing values, etc.), criterion-based optimisation for several parametric methods, and post-fit functions to rationalise and visualise estimation results. The package has been listed in the official R Task Views for Time Series Analysis and for Official Statistics. The brief empirical application in the paper illustrates the package’s functionality by estimating forecast combination techniques for monthly UK electricity supply. Chapter 4 introduces HTS forecasting and forecast combination to a healthcare staffing context. A slowdown of healthcare budget growth in the UK that does not keep pace with growth of demand for hospital services made efficient cost planning increasingly crucial for hospitals, in particular for staff which accounts for more than half of hospitals’ expenses. This is facilitated by accurate forecasts of patient census and churn. Using a dataset of more than 3 million observations from a large UK hospital, we show how HTS forecasting can improve forecast accuracy by using information at different levels of the hospital hierarchy (aggregate, emergency/electives, divisions, specialties), compared to the naïve benchmark: the seasonal random walk model applied to the aggregate. We show that forecast combination can improve accuracy even more in some cases, and leads to lower forecast error variance (decreasing forecasting risk). We propose a comprehensive parametric approach to use forecasts in a nurse staffing model that has the aim of minimising cost while satisfying that the care requirements (e.g. nurse hours per patient day thresholds) are met.

abstract: Gold nanoparticles as potential diagnostic, therapeutic and sensing systems have a long history of use in medicine, and have expanded to a variety of applications. Gold nanoparticles are attractive in biological applications due to their unique optical, chemical and biological properties. Particularly, gold nanorods (GNRs) are increasingly used due to superior optical property in the near infrared (NIR) window. Light absorbed by the nanorod can be dissipated as heat efficiently or re-emitted by the particle. However, the limitations for clinical translation of gold nanorods include low yields, poor stability, depth-restricted imaging, and resistance of cancer cells to hyperthermia, are severe. A novel high-throughput synthesis method was employed to significantly increase in yields of solid and porous gold nanorods/wires. Stable functional nanoassemblies and nanomaterials were generated by interfacing gold nanorods with a variety of polymeric and polypeptide-based coatings, resulting in unique properties of polymer-gold nanorod assemblies and composites. Here the use of these modified gold nanorods in a variety of applications including optical sensors, cancer therapeutics, and nanobiomaterials were described. / Dissertation/Thesis / Ph.D. Chemical Engineering 2012