Abstract: Analyses of vitamin supplements A and E in food samples are performed mostly with high performance liquid chromatography (HPLC). In majority of cases, sample preparation preceding HPLC implies saponification, a step critical to heat sensitivity of analytes. The method of saponification is clearly defined by ISO standards, however, two important factors, temperature and time of saponification are only given in value ranges instead of exact settings. Resolving this deficiency with the promise of eliminating time and cost consuming experimental probes, statistical experimental design (SED) is introduced to find optimum settings of temperature and time for the best recovery of vitamin supplements in food samples. Finding the optimum settings in SED was supported with Statsoft Statistica 7.0. For illustrating SED, margarine samples supplemented with vitamin A and E were applied.

Abstract: We present a discrete optimisation model that chooses an optimal fleet of vessels to support maintenance operations at Offshore Wind Farms (OFWs). The model is presented as a bi-level problem. On the first (tactical) level, decisions are made on the fleet composition for a certain time horizon. On the second (operational) level, the fleet is used to optimise the schedule of operations needed at the OWF, given events of failures and weather conditions.

Abstract: Low-risk prostate cancer can be treated by focal brachytherapy, wherein small radioactive seeds are implanted directly into the prostate. This clinical procedure has reduced side effects compared to conventional radiotherapy treatments that target the entire gland. The planning of such treatment is complicated by post-operative displacement of the seeds from their intended location. This reduces the planned treatment dose and increases the dose to surrounding tissue such as the urethra and rectum, potentially causing harmful side-effects. Current treatment planning methods do not explicitly incorporate the effect of post-operative seed displacement. To address this, we modify the radiation dose rate function used during planning to reflect this displacement using convolution. This new dose rate model enables plans to be produced automatically and efficiently. Simulation experiments show that treatment plans made using the convolved dose rate function are more robust to seed displacement than those using the original unconvolved dose, preserving treatment efficacy but giving increased protection to surrounding tissue.

Abstract: This paper examines the coarse-grained approach to parallelization of the branch-and-bound (\BNB) algorithm in a distributed computing environment. This approach is based on preliminary decomposition of a feasible domain of mixed-integer programming problem into a set of subproblems. The produced subproblems are solved in parallel by a distributed pool of standalone \BNB solvers. The incumbent values found by individual solvers are intercepted and propagated to other solvers to speed up the traversal of \BNB search tree. Presented implementation of the approach is based on SCIP, a non-commercial MINLP solver, and Everest, a web-based distributed computing platform. The implementation was tested on several mixed-integer programming problems and a noticeable speedup has been achieved. In the paper, results of a number of experiments with the Traveling Salesman Problem are presented.

Abstract: Designing a partial differential equations solver is a complex task which involves making choices about the solution algorithm and its parameters. Such choices are usually done on the basis of personal preference or numerical experiments, which can introduce significant bias on the selection process. In this work we develop a methodology to drive this selection process towards the optimal choices by modelling the accuracy and the performance of the solution algorithm. We show how this methodology can be successfully applied on the linear advection problem. As a result, the selection can be optimally performed with a much lower investment on the development of high-performance versions of the solvers and without using the target architecture for numerical experiments.

Abstract: In this paper, 3D fabric drape was reconstructed by using video recorded from a smartphone. Elliptical Fourier Analysis (EFA) and Principle Component Analysis (PCA) were used to parameterize the 3D drape to reveal shape parameters. A cluster analysis of various 3D drapes was implemented to verify the proposed method. Experiment results demonstrated that the 3D drape can be reconstructed and parameterized with a mean error of 0.52 mm when the harmonic number of EFA equals to 25. The cluster result indicated that the new features detected by our method were useful to classify different drapes, which provided a novel idea for 3D drape analysis.