Centrum Wiskunde & Informatica

Occupational health professionals (OHPs) collaborate to support return to work (RTW) for employees on long-term sick leave, but differences in roles and perspectives can result in inconsistent work ability assessments. To promote a shared understanding of work ability, the ICF(International Classification of Functioning, Disability, and Health)-based Work Ability & Reintegration Description (WARD) instrument was developed. This study evaluated its psychometric properties, in particular criterion validity and reliability. A mixed-methods study was conducted in three phases. Firstly, a reference standard set for assessing work ability in 10 case vignettes was established employing the Modified Delphi method with nine OHPs. Secondly, 22 OHPs independently assessed work ability with the WARD-instrument, for which overall agreement with the reference standard, and inter-rater reliability were calculated. Thirdly, a focus group with eight OHPs explored sources of variation for assessing work ability with WARD. Agreement with the reference standard ranged from 58.6% to 99.5%. High agreement (> 70%) was found for 23 out of 29 items. Specificity (59.7%-99.5%) generally exceeded sensitivity (18.2%-95.5%), indicating greater consistency in ruling out non-relevant items than in identifying relevant ones. Focus group discussions emphasized that assessments should be context-dependent to support rehabilitation purposes. The WARD-instrument shows promising criterion validity and reliability, especially for objectifiable and physical aspects of functioning. More complex and context-dependent domains, such as cognitive aspects of functioning and personal factors, remain challenging to consistently assess among different OHPs. Its focus on identifying context-sensitive RTW barriers may support more person-centered sick leave guidance. Further research in real-life practice is needed to strengthen validation and guide implementation.

Community-based energy initiatives, such as local energy markets (LEMs), are gaining attention as a means to foster a more equitable and participatory energy transition. To fulfill this potential, local communities require robust energy-sharing mechanisms that ensure the fair distribution of financial benefits among participants. This paper presents a novel algorithm for virtual energy distribution in a Peer-to-Community (P2C) local energy market. The algorithm first determines the optimal number of participants in each time period of the LEM, based on their current energy supplier tariffs, to ensure benefits for all participants. It then applies an egalitarian energy-distribution method, called the Glass-Filling algorithm, to allocate locally produced energy within the community. Using real-world data from a UK trial involving 200 households, half of which are prosumers with solar PV and residential batteries, we demonstrate that the proposed mechanism improves equity in energy sharing compared with a benchmark double-auction mechanism. The proposed market mechanism yields an average annual bill reduction of 9.9% for producers and 5% for consumers.

We introduce the concept of cyclic covers, which generalizes the classical notion of covers in strings. Given any string X , a factor W of X is called a cyclic cover if each position of X belongs to an occurrence of a cyclic shift of W in X . Two cyclic covers are distinct if one is not a cyclic shift of the other. The cyclic covers problem asks for all distinct cyclic covers of an input string X . We present an algorithm that solves the cyclic covers problem in O ( n log ⁡ n ) time, where n is the length of X . It is based on finding a well-structured set of standard occurrences of a constant number of factors of a cyclic cover candidate W , computing the regions of X covered by cyclic shifts of W , extending those factors, and taking the union of the results. • We introduce the cyclic cover problem. • Two cyclic covers are distinct if one is not a cyclic shift of the other. • The cyclic cover problem requires finding all distinct cyclic covers of X . • We show that for a string of length n, the cyclic cover problem can be solved in O ( n log ⁡ n ) time.

Hadamard-based single-pixel microscopy (HSPM) is a versatile non-conventional imaging technique where a binary function base is projected over the sample in a microscope setup to recover its information.One HSPM's main challenge is the need to project numerous patterns to retrieve the image of the object under study.This leads to potential phototoxicity damage and a reduction in temporal resolution.Aiming to reduce the total pattern projection time, this study explores the use of statistical compressive sensing (CS) using the kernel density estimator (KDE) approach to learn the probability distribution of the most relevant Hadamard spectrum (HS) sampling coefficients, based on a large-scale dataset of 50,000 histopathology images.The probability distribution can then be sampled to generate the set of Hadamard patterns to be projected.The proposed KDE-guided CS method is implemented and tested on biological and nonbiological samples.An image resolution of 550 lp/mm was recovered at a 25% sampling ratio (SR) using the proposed method, a level not reached by the well-established TV-based approach.Moreover, compared to TV-based sampling, the Michelson contrast increased from 0.09 to 0.17 at a 25% SR and from 0.12 to 0.29 at a 30% SR.These results demonstrate the feasibility of the proposed method for HSPM CS applications.

Abstract In this chapter we consider polynomial optimization problems, asking to minimize a polynomial function over a compact semialgebraic set, defined by polynomial inequalities. This models a great variety of (in general, nonlinear nonconvex) optimization problems. Various hierarchies of (lower and upper) bounds have been introduced, having the remarkable property that they converge asymptotically to the global minimum. These bounds exploit algebraic representations of positive polynomials in terms of sums of squares and can be computed using semidefinite optimization. Our focus in this chapter lies in the performance analysis of these hierarchies of bounds, namely, in how far the bounds are from the global minimum as the degrees of the sums of squares they involve tend to infinity. We present the main state-of-the-art results and offer a gentle introductory overview over the various techniques that have been recently developed to establish them, stemming from the theory of orthogonal polynomials, approximation theory, Fourier analysis, and more.

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In location analysis, the effects of demand aggregation have been the subject of many studies. This body of literature is mainly focused on p -median and p -center problems. Relatively few papers in the literature on aggregation explicitly concern the Capacitated Facility Location Problem (CFLP) . Our work examines the beneficial use of aggregation in the context of the CFLP. We focus on problems where there are significantly more demand points than potential facility locations, since this is where aggregation is most applicable in reducing complexity. We examine ways to obtain an aggregation at a fixed resolution, that is likely to perform well for a given instance of the problem. These aggregation techniques will form the core of a broader algorithmic framework, which contributes to the literature concerning heuristics for CFLPs. Our core aggregation method is based on applying k -means clustering in R m , where m is the number of potential facilities. The space in which we apply the clustering is constructed by applying a transformation to the normalized distance matrix corresponding to the original CFLP problem. The aim of applying the transformation is to magnify differences in distance where relevant, and to compress irrelevant differences in distance. We evaluate our heuristic method on larger instances based on a real-world problem in reverse logistics. The results are encouraging and indicate that our method is capable of outperforming an intuitive benchmark aggregation method. We find that choosing the right hyperparameters and starting with a good initialization help our method perform better.