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Metaheuristics are found to be efficient in different applications where the use of exact algorithms becomes short-handed. In the last decade, many of these algorithms have been introduced and used in a wide range of applications. Nevertheless, most of those approaches share similar components leading to a concern related to their novelty or contribution. Thus, in this paper, a pool template is proposed and used to categorize algorithm components permitting to analyze them in a structured way. We exemplify its use by means of continuous optimization metaheuristics, and provide some measures and methodology to identify their similarities and novelties. Finally, a discussion at a component level is provided in order to point out possible design differences and commonalities.

Autism Spectrum Disorder (ASD) is a deleterious neurodevelopmental disorder affecting 1 in 54 children. The complex interdisciplinary nature of ASD research, however, introduces challenges to the spread and accessibility of new discoveries among researchers in different disciplines; furthermore, this highly complex research environment makes it even harder for practicing physicians and primary care providers to keep up with recent advances, which would profoundly impact the extent toward which recent research impacts standard medical and caretake practices. In order to contribute toward bridging the gaps between researchers in different fields, practicing physicians, and primary caretakers, we have created the most expansive autism ontology up to date (AutimsOnt) through utilizing the Protege ontological framework. With 676 classes and more than 124 properties, AutismOnt can serve as the foundation to support a wide range of applications ranging from decision support systems for practicing physicians to text annotation processes that would allow for the creation of an interdisciplinary research platform where investigators can easily share and retrieve scientific findings. The Ontology is available in the NCBO BioPortal.

When information on a coma patient’s expected outcome is uncertain, a moral dilemma arises in clinical practice: if life-sustaining treatment is continued, the patient may survive with unacceptably poor neurological prospects, but if withdrawn a patient who could have recovered may die. Continuous electroencephalogram-monitoring (cEEG) is expected to substantially improve neuroprognostication for patients in coma after cardiac arrest. This raises expectations that decisions whether or not to withdraw will become easier. This paper investigates that expectation, exploring cEEG’s impacts when it becomes part of a socio-technical network in an Intensive Care Unit (ICU). Based on observations in two ICUs in the Netherlands and one in the USA that had cEEG implemented for research, we interviewed 25 family members, healthcare professionals, and surviving patients. The analysis focuses on (a) the way patient outcomes are constructed, (b) the kind of decision support these outcomes provide, and (c) how cEEG affects communication between professionals and relatives. We argue that cEEG can take away or decrease the intensity of the dilemma in some cases, while increasing uncertainty for others. It also raises new concerns. Since its actual impacts furthermore hinge on how cEEG is designed and implemented, we end with recommendations for ensuring responsible development and implementation.

Background Even when palliative care is an integrated part of the healthcare system, the quality is still substandard for many patients and often initiated too late. There is a lack of structured guidelines for identifying and caring for patients; in particular for those with early palliative care needs. A care guide can act as a compass for best practice and support the care of patients throughout their palliative trajectory. Such a guide should both meet the needs of health care professionals and patients and families, facilitating discussion around end-of-life decision-making and enabling them to plan for the remaining time in life. The aim of this article is to describe the development and pilot testing of a novel Swedish palliative care guide. Methods The Swedish Palliative Care Guide (S-PCG) was developed according to the Medical Research Council framework and based on national and international guidelines for good palliative care. An interdisciplinary national advisory committee of over 90 health care professionals together with patient, family and public representatives were engaged in the process. The feasibility was tested in three pilot studies in different care settings. Results After extensive multi-unit and interprofessional testing and evaluation, the S-PCG contains three parts that can be used independently to identify, assess, address, follow up, and document the individual symptoms and care-needs throughout the whole palliative care trajectory. The S-PCG can provide a comprehensive overview and shared understanding of the patients’ needs and possibilities for ensuring optimal quality of life, the family included. Conclusions Based on broad professional cooperation, patients and family participation and clinical testing, the S-PCG provides unique interprofessional guidance for assessment and holistic care of patients with palliative care needs, promotes support to the family, and when properly used supports high-quality personalised palliative care throughout the palliative trajectory. Future steps for the S-PCG, entails scientific evaluation of the clinical impact and effect of S-PCG in different care settings – including implementation, patient and family outcomes, and experiences of patient, family and personnel.

Considering the coronavirus disease 2019 (COVID‐19) pandemic, the government and health sectors are incapable of making fast and reliable decisions, particularly given the various effects of decisions on different contexts or countries across multiple sectors. Therefore, leaders often seek decision support approaches to assist them in such scenarios. The most common decision support approach used in this regard is multiattribute decision‐making (MADM). MADM can assist in enforcing the most ideal decision in the best way possible when fed with the appropriate evaluation criteria and aspects. MADM also has been of great aid to practitioners during the COVID‐19 pandemic. Moreover, MADM shows resilience in mitigating consequences in health sectors and other fields. Therefore, this study aims to analyse the rise of MADM techniques in combating COVID‐19 by presenting a systematic literature review of the state‐of‐the‐art COVID‐19 applications. Articles on related topics were searched in four major databases, namely, Web of Science, IEEE Xplore, ScienceDirect, and Scopus, from the beginning of the pandemic in 2019 to April 2021. Articles were selected on the basis of the inclusion and exclusion criteria for the identified systematic review protocol, and a total of 51 articles were obtained after screening and filtering. All these articles were formed into a coherent taxonomy to describe the corresponding current standpoints in the literature. This taxonomy was drawn on the basis of four major categories, namely, medical (n = 30), social (n = 4), economic (n = 13) and technological (n = 4). Deep analysis for each category was performed in terms of several aspects, including issues and challenges encountered, contributions, data set, evaluation criteria, MADM techniques, evaluation and validation and bibliography analysis. This study emphasised the current standpoint and opportunities for MADM in the midst of the COVID‐19 pandemic and promoted additional efforts towards understanding and providing new potential future directions to fulfil the needs of this study field.

The use of artificial intelligence (AI) in healthcare comes with opportunities but also numerous challenges. A specific challenge that remains underexplored is the lack of clear and distinct definitions of the concepts used in and/or produced by these algorithms, and how their real world meaning is translated into machine language and vice versa, how their output is understood by the end user. This "semantic" black box adds to the "mathematical" black box present in many AI systems in which the underlying "reasoning" process is often opaque. In this way, whereas it is often claimed that the use of AI in medical applications will deliver "objective" information, the true relevance or meaning to the end-user is frequently obscured. This is highly problematic as AI devices are used not only for diagnostic and decision support by healthcare professionals, but also can be used to deliver information to patients, for example to create visual aids for use in shared decision-making. This paper provides an examination of the range and extent of this problem and its implications, on the basis of cases from the field of intensive care nephrology. We explore how the problematic terminology used in human communication about the detection, diagnosis, treatment, and prognosis of concepts of intensive care nephrology becomes a much more complicated affair when deployed in the form of algorithmic automation, with implications extending throughout clinical care, affecting norms and practices long considered fundamental to good clinical care.

A persistent challenge for the Sustainable Development Goals (SDGs) has been a lack of data for indicators to assess progress towards each goal and varying capacities among nations to conduct these assessments. Rapid developments in big data, however, are facilitating a global approach to the SDGs. Tools and data products are emerging that can be extended to and leveraged by nations that do not yet have the capacity to measure SDG indicators. Big Earth Data, a special class of big data, integrates multi-source data within a geographic context, utilizing the principles and methodologies of the established literature on big data science, applied specifically to Earth system science. This paper discusses the research challenges related to Big Earth Data and the concerted efforts and investments required to make and measure progress towards the SDGs. As an example, the Big Earth Data Science Engineering Program (CASEarth) of the Chinese Academy of Sciences is presented along with other case studies on Big Earth Data in support of the SDGs. Lastly, the paper proposes future priorities for developments in Big Earth Data, such as human resource capacity, digital infrastructure, interoperability, and environmental considerations.

It is challenging to communicate uncertainty for high-impact weather events to the public and decision-makers. As a result, there is an increased emphasis and training within the National Weather Service (NWS) for “impact-based decision support.” A Collaborative Science, Technology, And Research (CSTAR) project led by Stony Brook University (SBU) in collaboration with the Alan Alda Center for Communicating Science, several NWS forecast offices, and NWS operational centers held two workshops at SBU on effective forecast communication of probabilistic information for high-impact weather. Trainers in two 1.5-day workshops helped 15–20 forecasters learn to distill their messages, engage audiences, and more effectively communicate risk and uncertainty to decision-makers, media, and the general public. The novel aspect of the first workshop focused on using improvisational techniques to connect with audiences along with exercises to improve communication skills using short, clear, conversational statements. The same forecasters participated in the second workshop, which focused on matching messages to intended audiences and stakeholder interaction. Using a recent high-impact weather event, representatives in emergency management, TV media, departments of transportation, and emergency services provided feedback on the forecaster oral presentations (2–3 min) and a visual slide. This article describes our innovative workshop approach, illustrates some of the techniques used, and highlights participant feedback.

Health care management science (2021). doi:10.1007/s10729-021-09556-2, Published by Springer Science + Business Media B.V., Dordrecht [u.a.]

This paper has proposed a model of the computational core for the decision support system (DSS) when investing in the projects of information security (IS) of the objects of informatization (OBI). Including those OBI that can be categorized as critically important. Unlike existing solutions, the proposed model deals with decision-making issues in the ongoing process of investing in the projects to ensure the OBI IS by a group of investors. The calculations were based on the bilinear differential quality games with several terminal surfaces. Finding a solution to these games is a big challenge. It is due to the fact that the Cauchy formula for bilinear systems with arbitrary strategies of players, including immeasurable functions, cannot be applied in such games. This gives grounds to continue research on finding solutions in the event of a conflict of multidimensional objects. The result was an analytical solution based on a new class of bilinear differential games. The solution describes the interaction of objects investing in OBI IS in multidimensional spaces. The modular software product "Cybersecurity Invest decision support system " (Ukraine) for the Windows platform is described. Applied aspects of visualization of the results of calculations obtained with the help of DSS have been also considered. The Plotly library for the Python algorithmic language was used to visualize the results. It has been shown that the model reported in this work can be transferred to other tasks related to the development of DSS in the process of investing in high-risk projects, such as information technology, cybersecurity, banking, etc.

PurposeThis paper investigates the suitability of fuzzy-logic-based support tools for initial screening of manufacturing reshoring decisions.Design/methodology/approachTwo fuzzy-logic-based support tools are developed together with experts from a Swedish manufacturing firm. The first uses a complete rule base and the second a reduced rule base. Sixteen inference settings are used in both of the support tools.FindingsThe findings show that fuzzy-logic-based support tools are suitable for initial screening of manufacturing reshoring decisions. The developed support tools are capable of suggesting whether a reshoring decision should be further evaluated or not, based on six primary competitiveness criteria. In contrast to existing literature this research shows that it does not matter whether a complete or reduced rule base is used when it comes to accuracy. The developed support tools perform similarly with no statistically significant differences. However, since the interpretability is much higher when a reduced rule base is used and it require fewer resources to develop, the second tool is more preferable for initial screening purposes.Research limitations/implicationsThe developed support tools are implemented at a primary-criteria level and to make them more applicable, they should also include the sub-criteria level. The support tools should also be expanded to not only consider competitiveness criteria, but also other criteria related to availability of resources and strategic orientation of the firm. This requires further research with regard to multi-stage architecture and automatic generation of fuzzy rules in the manufacturing reshoring domain.Practical implicationsThe support tools help managers to invest their scarce time on the most promising reshoring projects and to make timely and resilient decisions by taking a holistic perspective on competitiveness. Practitioners are advised to choose the type of support tool based on the available data.Originality/valueThere is a general lack of decision support tools in the manufacturing reshoring domain. This paper addresses the gap by developing fuzzy-logic-based support tools for initial screening of manufacturing reshoring decisions.

Sensors and control technologies are being deployed at unprecedented levels in both urban and rural water environments. Because sensor networks and control allow for higher-resolution monitoring and decision making in both time and space, greater discretization of control will allow for an unprecedented precision of impacts, both positive and negative. Likewise, humans will continue to cede direct decision-making powers to decision-support technologies, e.g. data algorithms. Systems will have ever-greater potential to effect human lives, and yet, humans will be distanced from decisions. Combined these trends challenge water resources management decision-support tools to incorporate the concepts of ethical and normative expectations. Toward this aim, we propose the Water Ethics Web Engine (WE)2, an integrated and generalized web framework to incorporate voting-based ethical and normative preferences into water resources decision support. We demonstrate this framework with a ‘proof-of-concept’ use case where decision models are learned and deployed to respond to flooding scenarios. Findings indicate that the framework can capture group ‘wisdom’ within learned models to use in decision making. The methodology and ‘proof-of-concept’ system presented here are a step toward building a framework to engage people with algorithmic decision making in cases where ethical preferences are considered. We share our framework and its cyber components openly with the research community. HIGHLIGHTS Water Ethics Web Engine (WE)2 is a web framework to incorporate voting-based ethical preferences into water resources decision support.; We share (WE)2 openly at our project repository: https://github.com/uihilab/waterethicswebengine.; A proof-of-concept use case is presented where decision models are learned and deployed with flooding scenarios.; Results indicate that the framework can capture group ‘wisdom’ in learned models.

As modern engineered systems become ever more connected and interdependent there is an increasing need to evaluate their complexity. However, evaluating system complexity is challenging due to the complicated conceptual landscape of competing definitions of the term complexity itself, and the range of perspectives that can be taken on what constitutes the System of Interest. This paper attempts to overcome these hurdles through introducing a Complexity Register with which to build and record a shared understanding of system complexity for key stakeholders. In order to overcome current challenges in evaluating the complexity of an engineering system, the Complexity Register encourages personnel to adopt a broad range of perspectives on the potential issues, impacts and mitigations to manage system complexity. The formulation of the Complexity Register is informed by design principles derived from a case study analysis of a system complexity evaluation tool. The Complexity Register should enable more effective shared understanding by encouraging collaboration that makes explicit the multiple viewpoints taken when evaluating system complexity and promoting continued re-evaluation throughout a system or project lifecycle.