In recent decades, the number of cases of knee arthroplasty among people of working age has increased. The integrated clinical pathway ‘back at work after surgery’ is an initiative to reduce the possible cost of sick leave. The evaluation of this pathway, like many clinical studies, faces the challenge of small data sets with a relatively high number of features. In this study, we investigate the possibility of identifying features that are important in determining the duration of rehabilitation, expressed in the return-to-work period, by using feature selection tools. Several models are used to classify the patient’s data into two classes, and the results are evaluated based on the accuracy and the quality of the ordering of the features, for which we introduce a ranking score. A selection of estimators are used in an optimization step, reorganizing the feature ranking. The results show that for some models, the proposed optimization results in a better ordering of the features. The ordering of the features is evaluated visually and identified by the ranking score. Furthermore, for all models, higher accuracy, with a maximum of 91%, is achieved by applying the optimization process. The features that are identified as relevant for the duration of the return-to-work period are discussed and provide input for further research.
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Reducing the use of pesticides by early visual detection of diseases in precision agriculture is important. Because of the color similarity between potato-plant diseases, narrow band hyper-spectral imaging is required. Payload constraints on unmanned aerial vehicles require reduc- tion of spectral bands. Therefore, we present a methodology for per-patch classification combined with hyper-spectral band selection. In controlled experiments performed on a set of individual leaves, we measure the performance of five classifiers and three dimensionality-reduction methods with three patch sizes. With the best-performing classifier an error rate of 1.5% is achieved for distinguishing two important potato-plant diseases.
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The “Age-Friendly Cities & Communities: States of the Art and Future Perspectives”publication presents contemporary, innovative, and insightful narratives, debates, and frameworks based on an international collection of papers from scholars spanning the fields of gerontology, social sciences, architecture, computer science, and gerontechnology. This extensive collection of papers aims to move the narrative and debates forward in this interdisciplinary field of age-friendly cities and communities. CC BY-NC-ND Book CC BY Chapters © 2021 by the authors Original book at: https://doi.org/10.3390/books978-3-0365-1226-6 (This book is a printed edition of the Special Issue Feature Papers "Age-Friendly Cities & Communities: State of the Art and Future Perspectives" that was published in IJERPH)
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While several governmental and research efforts are set upon mobility-as-a-service (MaaS), most of them are driven by individual travel behavior and potential usage. Scholars argue that this is a too narrow perspective when evaluating government projects because choices individuals make in a private setting might not accurately reflect their preferences towards public policy. Participatory Value Evaluation (PVE) is a novel evaluation framework specifically designed to alleviate this issue by analyzing preferences on the allocation of public budgets. Thus, based on PVE, this project aims at assessing different features of MaaS-services (e.g. enhancing mobility of the elderly and the poor, complementing public transport, etc.) from a social desirability perspective and compare them with investments in alternative social projects. Specifically, it aims at establishing the citizen value of MaaS as compared to social investments in green/recreational areas or transport infrastructure (e.g. bike or bus lanes), and eliciting trade-offs between different features of them. The project includes the selection of different investment projects (and their features) that are politically relevant in Rotterdam. It also includes a qualitative assessment on the way individuals evaluate different social projects and their features and a quantitative assessment based on choice models that allow eliciting trade-offs between different attributes and projects. Finally, policy recommendations are provided based on these results. They allow conceiving investments projects to maximize the societal benefits as well as to construct optimal investment portfolios. This information is to be used as a complement of the evaluation of projects on the basis of individual preferences.
