Characterisation of MS phenotypes across the age span using a novel data set integrating 34 clinical trials (NO.MS cohort): age is a key contributor to presentation

Background: The Oxford Big Data Institute, multiple sclerosis (MS) physicians and Novartis aim to address unresolved questions in MS with a novel comprehensive clinical trial data set. Objective: The objective of this study is to describe the Novartis–Oxford MS (NO.MS) data set and to explore the relationships between age, disease activity and disease worsening across MS phenotypes. Methods: We report key characteristics of NO.MS. We modelled MS lesion formation, relapse frequency, brain volume change and disability worsening cross-sectionally, as a function of patients’ baseline age, using phase III study data (≈8000 patients). Results: NO.MS contains data of ≈35,000 patients (>200,000 brain images from ≈10,000 patients), with >10 years follow-up. (1) Focal disease activity is highest in paediatric patients and decreases with age, (2) brain volume loss is similar across age and phenotypes and (3) the youngest patients have the lowest likelihood (<25%) of disability worsening over 2 years while risk is higher (25%–75%) in older, disabled or progressive MS patients. Young patients benefit most from treatment. Conclusion: NO.MS will illuminate questions related to MS characterisation, progression and prognosis. Age modulates relapse frequency and, thus, the phenotypic presentation of MS. Disease worsening across all phenotypes is mediated by age and appears to some extent be independent from new focal inflammatory activity

System Identification of the 2B6 Wind Turbine: A regularised PBSIDopt approach

Making offshore wind energy more cost competitive in comparison to fossil-fuel based production, is vital to maintain the direction the European Union has taken in renewable energy. Increasing the lifetime of a turbine can play a big role in driving down the overall costs of energy. The more energy the turbine is able to produce in its lifetime, the lower the costs per MWh will be. Fatigue damage is one of the limiting factors in a turbine’s lifetime. These damages are inversely proportional to the damping ratios and as such, estimating these ratios accurately, allows for optimisation of the structural design as well for improving control algorithms.Modelling software is used to estimate the modal properties, such as damping ratios, in thedesign phase of a turbine. However, these modelled properties often have a mismatch withreality due to differences in material properties, soil characteristic and others. Design basedon these mismatched properties can lead to suboptimal control and a decrease in lifetime ofthe turbine. In order to eliminate this mismatch, it is of importance to accurately obtain themodal properties from the real turbine.System identification can play an important role in this. Using measurement data obtainedduring idling and operation of the turbine, the modal properties can be identified. Whenusing measurement data, the danger of over-fitting is always present however. Often, a tradeoffneeds to be made between the variance and bias of the estimation. To protect againstill-conditioned data matrices, as well to better deal with the variance-bias trade-off, regularisationcan be added to the identification algorithm.The first goal of this thesis is to successfully identify the modal properties of the first towermodes and first coupled drive-train mode of a real turbine. Secondly, the effect of addingregularisation will be examined on the estimation of these modal properties. The optimisedPredictor Based Subspace Identification algorithm will be used for identification. This willbe extended to include Tikhonov regularisation, truncated SVD regularisation and nuclearnorm regularisation. The performance of these techniques are compared in two case studies,after which one is selected to be used on the measurement data from the turbine.What follows is the estimation of modal properties from the turbine and evaluation of theresults for both with and without added regularisation