Abstract
Purpose In the vast majority of the individual robot installations, the robot arm is just one piece of a complex puzzle of components, such as grippers, jigs or external axis, that together compose an industrial robotic cell. The success of such installations is very dependent not only on the selection of such components but also on the layout and design of the final robotic cell, which are the main tasks of the system integrators. Consequently, successful robot installations are often empirical tasks owing to the high number of experimental combinations that could lead to exhaustive and time-consuming testing approaches. Design/methodology/approach A newly developed optimized technique to deal with automatic planning and design of robotic systems is proposed and tested in this paper. Findings The application of a genetic-based algorithm achieved optimal results in short time frames and improved the design of robotic work cells. Here, the authors show that a multi-layer optimization approach, which can be validated using a robotic tool, is able to help with the design of robotic systems. Originality/value To date, robotic solutions lack flexibility to cope with the demanding industrial environments. The results presented here formalize a new flexible and modular approach, which can provide optimal solutions throughout the different stages of design and execution control of any work cell.

Abstract
Background Decision-making in palliative care can be complex due to the uncertain prognosis and general fear surrounding decisions. Decision-making in palliative care may be influenced by spiritual and cultural beliefs or values. Determinants of the decision-making process are not completely understood, and spirituality is essential for coping with illness. Thus, this study aims to explore the influence of spirituality on the perception of healthcare decision-making in palliative care outpatients. Methods A cross-sectional study was developed. A battery of tests was administered to 95 palliative outpatients, namely: sociodemographic questionnaire (SQ), Decisional Conflict Scale (DCS), Functional Assessment of Chronic Illness Therapy-Spiritual Well-Being scale (FACIT-Sp), and a semi-structured interview (SSI) to study one's perception of spirituality and autonomy in decision-making. Statistical analyses involved descriptive statistics for SQ and SSI. The Mann-Whitney test was used to compare scale scores between groups and correlations were used for all scales and subscales. The analysis of patients' definitions of spirituality was based on the interpretative phenomenological process. Results Spiritual wellbeing significantly correlated with greater levels of physical, emotional and functional wellbeing and a better quality of life. Greater spiritual wellbeing was associated with less decisional conflict, decreased uncertainty, a feeling of being more informed and supported and greater satisfaction with one's decision. Most patients successfully implemented their decision and identified themselves as capable of early decision-making. Patients who were able to implement their decision presented lower decisional conflict and higher levels of spiritual wellbeing and quality of life. Within the 16 themes identified, spirituality was mostly described through family. Patients who had received spiritual care displayed better scores of spiritual wellbeing, quality of life and exhibited less decisional conflict. Patients considered spirituality during illness important and believed that the need to receive spiritual support and specialised care could enable decision-making when taking into consideration ones' values and beliefs. Conclusion The impact of spiritual wellbeing on decision-making is evident. Spirituality is a key component of overall wellbeing and it assumes multidimensional and unique functions. Individualised care that promotes engagement in decision-making and considers patients' spiritual needs is essential for promoting patient empowerment, autonomy and dignity.

Abstract
We propose a pixel color amplification theory and family of enhancement methods to facilitate segmentation tasks on retinal images. Our novel re-interpretation of the image distortion model underlying dehazing theory shows how three existing priors commonly used by the dehazing community and a novel fourth prior are related. We utilize the theory to develop a family of enhancement methods for retinal images, including novel methods for whole image brightening and darkening. We show a novel derivation of the Unsharp Masking algorithm. We evaluate the enhancement methods as a pre-processing step to a challenging multi-task segmentation problem and show large increases in performance on all tasks, with Dice score increases over a no-enhancement baseline by as much as 0.491. We provide evidence that our enhancement preprocessing is useful for unbalanced and difficult data. We show that the enhancements can perform class balancing by composing them together. © Springer Nature Switzerland AG 2020.

Abstract
Purpose To study how supply chain decision makers gather, process and use the available internal and external information when facing supply chain disruptions. Design/methodology/approach The paper reviews relevant supply chain literature to build an information management model for disruption management. Afterwards, three case studies in the vehicle assembly sector, namely cars, trucks and aircraft wings, bring the empirical insights to the information management model. Findings This research characterises the phases of disruption management and identifies the information companies use to recover from a variety of disruptive events. It presents an information management model to enhance supply chain visibility and support disruption management at the operational level. Moreover, it arrives at two design propositions to help companies in the redesign of their disruption discovery and recovery processes. Originality/value This research studies how companies manage operational disruptions. The proposed information management model allows to provide visibility to support the disruption management process. Also, based on the analysis of the disruptions occurring at the operational level we propose a conceptual model to support decision makers in the recovery from daily disruptive events.

Abstract
Investigating strategies that are able to efficiently deal with multi-label classification tasks is a current research topic in machine learning. Many methods have been proposed, making the selection of the most suitable strategy a challenging issue. From this premise, this paper presents an extensive empirical analysis of the binary transformation strategies and base algorithms for multi-label learning. This subset of strategies uses the one-versus-all approach to transform the original data, generating one binary data set per label, upon which any binary base algorithm can be applied. Considering that the influence of the base algorithm on the predictive performance obtained by the strategies has not been considered in depth by many empirical studies, we investigated the influence of distinct base algorithms on the performance of several strategies. Thus, this study covers a family of multi-label strategies using a diversified range of base algorithms, exploring their relationship over different perspectives. This finding has significant implications concerning the methodology of evaluation adopted in multi-label experiments containing binary transformation strategies, given that multiple base algorithms should be considered. Despite these improvements in strategy and base algorithms, for many data sets, a large number of labels, mainly those less frequent, were either never predicted, or always misclassified. We conclude the experimental analysis by recommending strategies and base algorithms in accordance with different performance criteria.

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