Background and purpose Multiple sclerosis (MS) patients frequently report cognitive difficulties which impact daily functioning. The objective was to investigate the relationship between patient‐reported cognitive impairment and depression, demographic and MS‐related variables, and to clarify its impact on self‐reported health measures and employment. Method A large two‐centre survey included the MS Neuropsychological Screening Questionnaire (MSNQ), the two‐question screening tool for depression, vitality, health‐related quality of life, the Health‐Promoting Lifestyle Profile II and questions assessing social network satisfaction and employment status. Results Of the 751 respondents (median age 54 years, median Expanded Disability Status Scale 5, 66.2% female), two‐thirds reported perceived neuropsychological impairment or depressive symptoms. Whilst depressive symptoms were related to higher MSNQ scores, the MSNQ poorly predicted depression. After correcting for confounders, higher MSNQ scores and depressive symptoms decreased vitality, health‐related quality of life and health‐promoting behaviours and increased the probability of being socially dissatisfied. In participants below retirement age, higher MSNQ and Expanded Disability Status Scale scores increased the probability of unemployment, whilst depression did not. Conclusion The contribution of the MSNQ to self‐reported health measures and its unique explanatory power regarding unemployment suggest that subjective cognitive complaints are connected to subtle, yet meaningful, neuropsychological dysfunction.

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In this paper, an initial study is made on the optimal input design for Wiener systems that consists of a FIR filter, followed by a polynomial nonlinearity. A design method, based on the dispersion function, is introduced, in order to find an optimal set of elementary designs. For the considered class of Wiener systems, it is shown that these elementary designs are couples of successive input values. However, concatenation of these elementary designs is not straight forward. By imposing symmetry conditions on the total design, a solution is obtained that can be realized as a time sequence and that is optimal in the subspace of symmetric designs.

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Optimal input design is an important step of the identification process in order to reduce the model variance. In this work a D-optimal input design method for finite-impulse-response-type nonlinear systems is presented. The optimization of the determinant of the Fisher information matrix is expressed as a convex optimization problem. This problem is then solved using a dispersion-based optimization scheme, which is easy to implement and converges monotonically to the optimal solution. Without constraints, the optimal design cannot be realized as a time sequence. By imposing that the design should lie in the subspace described by a symmetric and non-overlapping set, a realizable design is found. A graph-based method is used in order to find a time sequence that realizes this optimal constrained design. These methods are illustrated on a numerical example of which the results are thoroughly discussed. Additionally the computational speed of the algorithm is compared with the general convex optimizer cvx.

Background and purpose: Data from neuro-imaging techniques allow us to estimate a brain's age. Brain age is easily interpretable as 'how old the brain looks' and could therefore be an attractive communication tool for brain health in clinical practice. This study aimed to investigate its clinical utility by investigating the relationship between brain age and cognitive performance in multiple sclerosis (MS). Methods: A linear regression model was trained to predict age from brain magnetic resonance imaging volumetric features and sex in a healthy control dataset (HC_train, n = 1673). This model was used to predict brain age in two test sets: HC_test (n = 50) and MS_test (n = 201). Brain-predicted age difference (BPAD) was calculated as BPAD = brain age minus chronological age. Cognitive performance was assessed by the Symbol Digit Modalities Test (SDMT).

In this paper, techniques for optimal input design are used to optimize the waveforms of perturbative experiments in modern fusion devices. The main focus of this paper is to find the modulation frequency for which the accuracy of the estimated diffusion coefficient is maximal. Mathematically, this problem can be formulated as an optimization problem in which the Fisher information matrix is maximized. First, this optimization problem is solved for a simplified diffusion model, while assuming a slab geometry and a semi-infinite domain. Later, the optimization is repeated under more general conditions such as a cylindrical geometry, finite domain, and simultaneous estimation of multiple transport coefficients. Based on the results of these optimizations, guidelines are offered to select the modulation frequency and to determine the optimality of the corresponding experiment.

In the Industry 4.0 area, there is an increasing demand for highly customized products in small batch sizes. Final assembly operations are frequently targeted to embed flexibility and compensate for the growing manufacturing uncertainties. Therefore, an adequately designed and operated flexible assembly workstation is crucial. Converting the flexibility needs into design and operational decisions requires versatile formal models delivering generic descriptions of needs and capacities. Skills form the central connector between products, processes and resources. Here, a skill-centered model for describing resource activities, the related production needs and flexibility impacts is introduced. The model fits both plug and produce and design optimization settings and goes beyond current skill-based modelling by offering a framework which, by design, does not limit the applications and easily adapts to the desired level of detail. One key strength is its ability to combine abstract and executable skills. Next to the product-action skills, also assistive skills related to operator support, parts storing, ergonomics etc. can be easily modelled. The use of the model is illustrated by an example based on an industrial use case from Flemish industry.