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Decision prediction based on neurophysiological signals is of great application value in many real‐life situations, especially in human–AI collaboration or counteraction. Single‐trial analysis of electroencephalogram (EEG) signals is a very valuable step in the development of an online decision‐prediction system. However, previous EEG‐based decision‐prediction methods focused mainly on averaged EEG signals of all decision‐making trials to predict an individual's general decision tendency (e.g., risk seeking or aversion) over a period rather than on a specific decision response in a single trial. In the present study, we used a rock–paper–scissors game, which is a common multichoice decision‐making task, to explore how to predict participants' single‐trial choice with EEG signals. Forty participants, comprising 20 females and 20 males, played the game with a computer player for 330 trials. Considering that the decision‐making process of this game involves multiple brain regions and neural networks, we proposed a new algorithm named common spatial pattern‐attractor metagene (CSP‐AM) to extract CSP features from different frequency bands of EEG signals that occurred during decision making. The results showed that a multilayer perceptron classifier achieved an accuracy significantly exceeding the chance level among 88.57% (31 of 35) of participants, verifying the classification ability of CSP features in multichoice decision‐making prediction. We believe that the CSP‐AM algorithm could be used in the development of proactive AI systems. [ABSTRACT FROM AUTHOR]

Improved global access to novel age‐appropriate formulations for paediatric subsets, either of new chemical entities or existing drugs, is a priority to ensure that medicines meet the needs of these patients. However, despite regulatory incentives, the introduction to the market of paediatric formulations still lags behind adult products. This is mainly caused by additional complexities associated with the development of acceptable age‐appropriate paediatric medicines. This position paper recommends the use of a paediatric Quality Target Product Profile as an efficient tool to facilitate early planning and decision making across all teams involved in paediatric formulation development during the children‐centric formulation design for new chemical entities, or to repurpose/reformulate off‐patent drugs. Essential key attributes of a paediatric formulation are suggested and described. Moreover, greater collaboration between formulation experts and clinical colleagues, including healthcare professionals, is advocated to lead to safe and effective, age‐appropriate medicinal products. Acceptability testing should be a secondary endpoint in paediatric clinical trials to ensure postmarketing adherence is not compromised by a lack of acceptability. Not knowing the indications and the related age groups and potential dosing regimens early enough is still a major hurdle for efficient paediatric formulation development; however, the proposed paediatric Quality Target Product Profile could be a valuable collaborative tool for planning and decision making to expedite paediatric product development, particularly for those with limited experience in developing a paediatric product. [ABSTRACT FROM AUTHOR]

Mouse tracking, a new action-based measure of behavior, has advanced theories of decision making with the notion that cognitive and social decision making is fundamentally dynamic. Implicit in this theory is that people's decision strategies, such as discounting delayed rewards, are stable over task design and that mouse trajectory features correspond to specific segments of decision making. By applying the hierarchical drift diffusion model and the Bayesian delay discounting model, we tested these assumptions. Specifically, we investigated the extent to which the "mouse-tracking" design of decision-making tasks (delay discounting task, DDT and stop-signal task, SST) deviate from the standard "keypress" design of decision making tasks. We found remarkable agreement in delay discounting rates (intertemporal impatience) obtained in the keypress and mouse-tracking versions of DDT (ρ = 0.90) even though these tasks were given about 1 week apart. Rates of evidence accumulation converged well in the two versions (DDT, ρ = .86; SST, ρ = .55). Omission/commission error in SST showed high agreement (ρ = .42, ρ = .53). Mouse-motion features such as maximum velocity and AUC (area under the curve) correlated well with nondecision time (ρ = -.42) and boundary separation (ρ = .44)-the amount of information needed to accumulate prior to making a response. These results indicate that the response time (RT) and motion-based decision tasks converge well at a fundamental level, and that mouse-tracking features such as AUC and maximum velocity do indicate the degree of decision conflict and impulsivity., (© 2021 Cognitive Science Society LLC.)

Introduction: For too many people, their care plans are designed without fully accounting for who they are, the lives they live, what matters to them or what they aspire to achieve. We aimed to summarize instruments capable of measuring dimensions of patient–clinician collaboration to make care fit. Methods: We systematically searched several databases (Medline, Embase, Cochrane, Scopus and Web of Science) from inception to September 2021 for studies using quantitative measures to assess, evaluate or rate the work of making care fit by any participant in real‐life clinical encounters. Eligibility was assessed in duplicate. After extracting all items from relevant instruments, we coded them deductively on dimensions relevant to making care fit (as presented in a recent Making Care Fit Manifesto), and inductively on the main action described. Results: We included 189 papers, mostly from North America (N = 83, 44%) and in the context of primary care (N = 54, 29%). Half of the papers (N = 88, 47%) were published in the last 5 years. We found 1243 relevant items to assess efforts of making care fit, included within 151 instruments. Most items related to the dimensions 'Patient‐clinician collaboration: content' (N = 396, 32%) and 'Patient‐clinician collaboration: manner' (N = 382, 31%) and the least related to 'Ongoing and iterative process' (N = 22, 2%) and in 'Minimally disruptive of patient lives' (N = 29, 2%). The items referred to 27 specific actions. Most items referred to 'Informing' (N = 308, 25%) and 'Exploring' (N = 93, 8%), the fewest items referred to 'Following up', 'Comforting' and 'Praising' (each N = 3, 0.2%). Discussion: Measures of the work that patients and clinicians do together to make care fit focus heavily on the content of their collaborations, particularly on exchanging information. Other dimensions and actions previously identified as crucial to making care fit are assessed infrequently or not at all. The breadth of extant measures of making care fit and the lack of appropriate measures of this key construct limit both the assessment and the successful implementation of efforts to improve patient care. Patient Contribution: Patients and caregivers from the 'Making care fit Collaborative' were involved in drafting the dimensions relevant to patient–clinician collaboration. [ABSTRACT FROM AUTHOR]

Background: Liver disease is an increasing cause of morbidity and mortality in the United Kingdom and can be challenging to live with in the advanced stages. There has been little research exploring the healthcare experiences of UK individuals with decompensated disease when the liver cannot carry out its functions properly. A PhD research project was developed with people who have liver disease to explore care experiences in decompensated advanced liver disease. Public involvement (PI) is an essential aspect of meaningful health research, and this paper reports on the progression of our PI approach in this ongoing study. Objective: To embed PI throughout the research project to ensure that the study is meaningful to individuals with liver disease and the people who support them. Methods: The research adopts a Constructivist Grounded Theory methodology to develop a theory of care experience. Various PI approaches were considered in developing the PI strategy for this qualitative study. Initially, Embedded consultation was the preferred model, which has evolved to include aspects of collaboration and coproduction. A PI group was set up to oversee the project through the national public engagement website VOICE, and reflections on PI from three members of the group are included in this paper to illuminate the PI process. Results: Six individuals with liver disease and three carers from across the United Kingdom are part of an ongoing PI group. Their role includes commenting on the findings of the systematic literature review for this project and contributing to decisions about recruitment, data collection and data analysis. Additionally, they had a direct impact on changing the focus of the research. The PI group will continue involvement until the completion of the project. Conclusion: Successfully embedding PI into doctoral research, as demonstrated in this project, requires commitment, planning and dedication to reciprocal working for the benefit of PI contributors as well as the research. This approach could be adopted by other postgraduate researchers. Patient or Public Contribution: This project is overseen by the PI group, whose contribution is described throughout, including reflections from three PI group members. [ABSTRACT FROM AUTHOR]

With the development of automated vehicles, researches related to automated vehicle platoon (AVP) have received more and more attention. AVP is considered one of the effective means to alleviate traffic congestion and reduce vehicle energy consumption. This paper studies a three‐layer method of avoiding obstacle vehicles in traffic by switching the formation for the automated heavy vehicle platoon. In the decision‐making layer, a decision‐making system based on the finite‐state machine is established for formation switching. In the second layer, the lane‐changing trajectory is optimized based on the quantic polynomial curve fitting for vehicles that need to change lanes. In terms of vehicle control layer, each vehicle has a longitudinal controller based on sliding mode control and a lateral controller based on model predictive control to track the planned trajectory to complete the target formation. Finally, the proposed method is simulated in MATLAB/TruckSim. The simulation results show that the proposed method could effectively avoid the obstacle vehicles by switching the formation and has a small average value of errors in speed tracking and trajectory tracking. [ABSTRACT FROM AUTHOR]

Background: Achieving universal health coverage (UHC) in the context of limited resources will require prioritising the most vulnerable and ensuring health policies and services are responsive to their needs. One way of addressing this is through the engagement of marginalised voices in the priority setting process. Public engagement approaches that enable group level deliberation as well as individual level preference capturing might be valuable in this regard, but there are limited examples of their practical application, and gaps in understanding their outcomes, especially with rural populations. Objective: To address this gap, we implemented a modified priority setting tool (Choosing All Together—CHAT) that enables individuals and groups to make trade‐offs to demonstrate the type of health services packages that may be acceptable to a rural population. The paper presents the findings from the individual choices as compared to the group choices, as well as the differences among the individual choices using this tool. Methods: Participants worked in groups and as individuals to allocate stickers representing the available budget to different health topics and interventions using the CHAT tool. The allocations were recorded at each stage of the study. We calculated the median and interquartile range across study participants for the topic totals. To examine differences in individual choices, we performed Wilcoxon rank sum tests. Results: The results show that individual interests were mostly aligned with societal ones, and there were no statistically significant differences between the individual and group choices. However, there were some statistically significant differences between individual priorities based on demographic characteristics like age. Discussion: The study demonstrates that giving individuals greater control and agency in designing health services packages can increase their participation in the priority setting process, align individual and community priorities, and potentially enhance the legitimacy and acceptability of priority setting. Methods that enable group level deliberation and individual level priority setting may be necessary to reconcile plurality. The paper also highlights the importance of capturing the details of public engagement processes and transparently reporting on these details to ensure valuable outcomes. Public Contribution: The facilitator of the CHAT groups was a member from the community and underwent training from the research team. The fieldworkers were also from the community and were trained and paid to capture the data. The participants were all members of the rural community‐ the study represents their priorities. [ABSTRACT FROM AUTHOR]

In unmanned aerial vehicle ad‐hoc network (UANET), the node speed of unmanned aerial vehicles (UAVs) may reach up to 400 km/h. The fast or slow movement of UAV nodes leads to different speeds of topology change of the nodes. Traditional optimized link state routing (OLSR) protocol cannot adaptively adjust the routing update period when the network topology changes, which may lead to the nodes calculating incorrect routing tables. This increases the average end‐to‐end delay and packet loss rate for packet transmission. To enhance the adaptability of OLSR routing protocol to network topology changes, this paper proposes a multi‐agent independent deep deterministic policy gradient‐OLSR (MA‐IDDPG‐OLSR) routing protocol based on distributed multi‐agent reinforcement learning. The protocol deploys DDPG algorithm on each UAV node, and each UAV node adaptively adjusts the Hello and TC message sending intervals, according to the one‐hop neighbouring nodes as well as its own state. Simulation results show that the proposed protocol is able to improve the throughput and reduce the packet loss rate as compared to traditional AODV, GRP, OLSR, and distributed multiple‐agent independent proximal policy optimization‐OLSR (MA‐IPPO‐OLSR), distributed multiple‐agent independent twin delayed deep deterministic policy gradient‐OLSR (MA‐ITD3‐OLSR) routing protocols. Since MA‐IDDPG‐OLSR relies only on local information, there is a minor performance degradation in MA‐IDDPG‐OLSR compared to centralized single‐agent DQN‐OLSR routing protocol. But it is more suitable to a completely distributed UAV network without a centralized node. [ABSTRACT FROM AUTHOR]

Vehicular caching (VC) in electro‐mobility networks has become promising for supporting the needs of low end‐to‐end service delays and reducing the load of networks (i.e. edge caching [EC]) for content delivery service. However, since VCs are available only when they are in the vicinity of the user, intermittent connectivity should be considered. In this paper, the opportunistic offloading scheme for content delivery service is proposed, and a decision is made regarding whether to use VC or EC for the content delivery service. In the proposed scheme, when VC is not available, the user determines whether to choose VC or EC for the content download. If VC is chosen, the user delays the content download to increase the VC utilization according to the expectation of VC contacts. This results in the delayed service completion but can reduce the EC load (i.e. offloading gain). If EC is chosen, the user can download the content without any delay or service interruption. To find the optimal policy, this paper formulates a Markov decision process (MDP) problem and uses the value iteration algorithm to solve the problem. The evaluation results show that the proposed scheme outperforms the comparison schemes based on the expected reward. [ABSTRACT FROM AUTHOR]

Background: Collaborations between patient organisations (POs) and the pharmaceutical industry can help identify and address the unmet needs of people living with a disease. In Alzheimer's disease (AD), the scale and complexity of the current unmet needs call for a broad and cross‐sectoral collaboration, including people living with Alzheimer's (PLWA), their care partners and the wider research community. Objective: This study aimed to describe learnings from the Finding Alzheimer's Solutions Together (F.A.S.T.) Council, a collaboration between POs and Roche, convened to better understand the unmet needs of PLWA and their care partners. Results: 1. Learnings from the collaboration, including clarifying objectives and members' expectations upfront, and establishing a set of guiding values and engagement principles. 2. Insights and recommendations for improving care in AD, including a wide range of unmet needs and potential solutions, systematically captured throughout the PLWA journey. These have resulted in several published reports and other outcomes, including (1) 'Portraits of care', highlighting the role of care partners, and the impact of coronavirus disease 2019 on care; (2) Clinical trial guidebook, recommending how PLWA and care partner experience can be incorporated into trial design; (3) 'Commitments Catalogue', highlighting progress by governmental organisations in achieving their commitments; and (4) a report to guide policy on improving diversity, equity and inclusion in clinical trials. Conclusions: Close collaboration between POs and the pharmaceutical industry in AD can enable effective research, in which PLWA and care partners are engaged as 'experts through experience' to help identify key unmet needs and co‐create solutions with the wider AD research community. This paper and the work undertaken by the F.A.S.T. Council may act as a blueprint for meaningful collaboration between POs and the pharmaceutical industry. Patient or Public Contribution: The paper reports the collaboration between POs, the F.A.S.T. Council and Roche to progress towards a future in which PLWA can live fulfilling lives with their disease managed well. Clinical Trial Registration: Not applicable. [ABSTRACT FROM AUTHOR]

Introduction: To guide good practices in patient and public involvement (PPI), several calls have been made to share detailed accounts of practical experiences. We describe our collaboration with young people with a chronic condition (YPCC) in the development, testing and implementation of the digital communication tool MyBoT (Map your Burden of Treatment). Methods: MyBoT was developed by a team of academic researchers, some of whom were practising care providers, YPCC and designers. In addition to the two co‐researchers in the research team, various groups of YPCC were involved in decision‐making through participation in a design session, workshops and a dialogue session. The Involvement Matrix was used to reflect on the PPI of all YPCC. Results: Initially, the two co‐researchers were involved in the roles of informer and co‐thinker, but their decision‐making power within the study increased over time. In the final stages of the study, the co‐researchers and researchers became partners. The other YPCC who participated in the different sessions and workshops were co‐thinkers in all stages of the study. Conclusion: The PPI of two YPCCs as co‐researchers within the research team ensured continuous involvement, whereas the PPI of various groups of YPCCs guaranteed a representative and inclusive approach. Researchers play an essential role in bringing all perspectives together, integrating them within the technical and financial constraints and ultimately building a tool that is tailored to its users' needs. Patient or Public Contribution: YPCC played a significant role in the present study. Two YPCC—who are also co‐authors of this paper—were involved in all stages of this project as members of the research team. In addition, various YPCCs were involved in the development, testing and implementation stage of MyBoT by organizing design sessions, workshops and a dialogue session. [ABSTRACT FROM AUTHOR]

Using a rich new data source, we explore the selection of economics lecturers into the last UK Research Excellence Framework (REF) exercise. Only some one‐in‐two (54%) of these lecturers were submitted to REF2014; 57% of men and 46% of women. The decision making of institutions is found to be well approximated by a simplified selection approach; focusing on working papers and higher quality journal publications. Our results also reveal sizeable conditional differences in the probability of selection, especially so in departments with higher research rankings. More than half of the variance in selection probability remains unexplained, revealing considerable idiosyncrasies in the management of submissions and uncertainty across the discipline in this research assessment process. [ABSTRACT FROM AUTHOR]

The article presents the discussion on special issue of Systems Research and Behavioral Science (SRBS) on systems thinking for creative and flexible practice. Topics include prominent projects in sectors such as ecological management, social innovation, sustainable energy, regenerative farming, organizational development and holistic wellbeing; and dependent on nature and nurture drawing from the related disciplines of psychiatry, psychology, neuroscience and sociology.

This is the fourth of a series of papers on the stages of critical systems practice. Critical systems practice is a multimethodology that seeks to employ the ideas of critical systems thinking to intervene in and improve complex real‐world 'messes'. It has four stages—Explore (the problem situation), Produce (an intervention strategy), Intervene (flexibly), and Check (on progress)—called to mind as EPIC. The aim is to set out where thinking has reached on the best way to carry out each of these stages and to invite comment on what more needs doing. This paper discusses the fourth stage, Check. During Check, decision makers, and other stakeholders, evaluate the intervention, reflect on what has happened with a view to improving future interventions, and discuss next steps. [ABSTRACT FROM AUTHOR]

Introduction: Neonatal care is complex, involving multiple people and technologies within a community of care. When preterm babies are cared for far from home and/or transferred between units, the whole community of care (and particularly parent participation) is disrupted. Although previous studies have captured subjective experiences of parents, there has been little research exploring the material practices undertaken by parents as a consequence of place‐of‐care decisions, or the social organisation of those practices. Methods: As part of a wider study exploring optimal place‐of‐care, semistructured interviews were conducted between July 2018 and October 2019 with 48 parents (36 families) with one or more preterm babies (born at 27–31 weeks gestation) cared for in a neonatal unit in the last 12 months. Findings: We highlight parents' labour‐intensive and stressful work to: (1) parent in the neonatal care community (an oversight role that goes beyond contemporary notions of 'involvement'); (2) create continuity amid place‐of‐care disruptions; and (3) adapt to the managerial logics of neonatal care settings. Our analysis focuses on the work generated by managerial systems that organise place‐of‐care decision‐making and other efficiency‐focused practices. Parents are absorbed into negotiating institutional systems and diverted from routine parenting activities. Conclusion: Those involved in the organisation and management of neonatal care should take account of how managerial systems impact parents' workload, ability to participate in their baby's community of care and, ultimately, on the wellbeing and development of babies and their families. Patient or Public Contribution: The OPTI‐PREM study embedded parents' experiences of neonatal care into the research, through a discrete workstream that employed qualitative methodology to capture parents' experiences—as reported in this paper. The OPTI‐PREM project was also supported by a Bliss volunteer parent panel, which was involved in designing and overseeing the research. Bliss 'champion[s] the right for every baby born premature or sick to receive the best care by supporting families, campaigning for change and supporting professionals and enabling life‐changing research' (https://www.bliss.org.uk/about-us/about-bliss). A representative of Bliss is a co‐author of this manuscript, and a parent representative (named in the Acknowledgements) provided feedback during its preparation. [ABSTRACT FROM AUTHOR]

Although the implementation of person‐centred care (PCC) has gained momentum in healthcare systems, it is not always obvious why it is an important objective in clinical settings and educational institutions that train future healthcare professionals. Various conceptions about PCC have flourished, such as that it only works in certain healthcare contexts, it is the same as the patient making all their own healthcare decisions, it is too demanding for most patients, the word 'patient' may no longer be used, the care provided is already person‐centred, and that there is simply not enough time and resources to achieve it. This viewpoint paper aims to problematize these conceptions and may be used for didactical and reflective exercises that support the transition towards more person‐centred healthcare. Patient or Public Contribution: The Person Council for patients and carers at the University of Gothenburg provided focus group input on and validated the occurrence of the conceptions discussed in the present paper. The person council consists of a group of persons with many years of personal experiences of health care, either as patients and/or relatives/carers. One member of the person council who is also a designer and artist created the illustration for the article. [ABSTRACT FROM AUTHOR]

Background: The role of patient participation and representation during crises, such as the COVID‐19 pandemic, has been under‐researched. Existing studies paint a pessimistic picture of patient representation during the pandemic. However, there are indications that patient representatives have adapted to the new situation and can contribute to the resilience of healthcare systems. This paper aims to further explore the potential contribution of patient representatives for healthcare system resilience during the COVID‐19 pandemic. Methods: The study used a qualitative approach. We conducted a thematic analysis on the following data: interviews with client council members (n = 32) and representatives from patient organizations (n = 6) and focus groups (n = 2) to investigate patient representation on both the national policy level and organizational level in the Netherlands. Results: We identified the crisis discourse, the dependent position, the diversity of patient perspectives and the layered decision‐making structure as themes that help to understand what made patient representation in pandemic times a struggle for national and local patient representatives. The analysis of the subjects these representatives put forward during decision‐making shows that their input can play an important role in broadening discussions, challenging decisions, and suggesting alternatives during a crisis. We identified several strategies (e.g., collaborating with other actors, proactively putting subjects on the policy agenda, finding new ways of contacting their 'constituency') used by the patient representatives studied to exert influence despite the difficulties encountered. Conclusions: The struggle for patient representation during pandemic decision‐making is a missed opportunity for resilient healthcare systems as these representatives can play a role in opening up discussions and putting different perspectives to the fore. Moreover, the adaptive strategies used by representatives to influence decision‐making offer lessons for future representation activities. However, adaptations to the crisis decision‐making structure are also needed to enable patient representatives to play their role. Patient Contribution: We conducted interviews with patient representatives and discussed our preliminary findings with patient representatives during the focus groups. Zorgbelang, a patient organization supporting client councils and enabling and organizing patient participation for organizations and municipalities, was partner in this research and contributed to the interview guide, conducting interviews and focus groups. Additionally, the analysis made by the first author was discussed and refined multiple times with the partners of Zorgbelang and one of them co‐authored this paper. [ABSTRACT FROM AUTHOR]

In the cooperative vehicle‐infrastructure system (CVIS), due to its computation limitation, vehicles are difficult to handle computing‐intensive delay‐sensitive tasks, so offload tasks to roadside unit (RSU) become popular. Due to the complexity of vehicles' tasks and tasks generated by different vehicles have different delay constraints, minimize energy consumption of RSUs under task dependence and delay constraints is challenging. This paper defines the task priority queuing criterion for the task priority division problem, proposes a task scheduling strategy for energy‐packet queue length tradeoff (TSET) in CVIS under RSUs distributed task scheduling problem and establishes the vehicle speed state model, task model, data queue model, task computing model and energy consumption model. After Lyapunov optimization theory transformed the optimization model, a knapsack problem was described. The simulation results verify that TSET reduces the average energy consumption of roadside units and ensures the stability of the data queue under task dependence and deadline conditions. [ABSTRACT FROM AUTHOR]

Objectives: Despite high levels of mental ill‐health amongst young people (aged 15–30), this group demonstrates low help‐seeking and high drop‐out from mental health services (MHS). Whilst shared decision‐making can assist people in receiving appropriate and effective health care, young people frequently report that they do not feel involved in treatment decisions. The current study focused on co‐design of a clinical education and participant information programme for the Brain and Mind Centre Youth Model of Care. This model, which articulates a youth‐focused form of highly personalised and measurement‐based care, is designed to promote shared decision‐making between young people and clinical service providers. Methods: We conducted workshops with 24 young people (16–31; MAge = 21.5) who had accessed mental health services. Participants were asked what advice they would give to young people entering services, before giving advice on existing materials. Workshops were conducted and transcripts were coded using thematic analysis by two lived experience researchers and a clinical researcher. Results: Young people found it empowering to be educated on transdiagnostic models of mental illness, namely clinical staging, which gives them a better understanding of why certain treatments may be inappropriate and ineffective, and thus reduce self‐blame. Similarly, young people had limited knowledge of links between mental health and other life domains and found it helpful to be educated on multidisciplinary treatment options. Measurement‐based care was seen as an important method of improving shared decision‐making between young people and health professionals; however, to facilitate shared decision‐making, young people also wanted better information on their rights in care and more support to share their expertise in their own needs, values and treatment preferences. Conclusions: These findings will inform the delivery of the further development and implementation of a youth‐specific clinical education and participant information programme for the BMC Youth Model. Patient or Public Contribution: Workshops were facilitated by researchers with lived expertise in mental ill‐health (A.H. and/or C.G.) and a clinical researcher (who has expertise as an academic and a clinical psychologist). A.H. and C.G. were also involved in conceptualisation, analysis, interpretation, review and editing of this paper. [ABSTRACT FROM AUTHOR]

With the continuous growth of electricity demand, the safe and stable operation of distribution lines is crucial for power transportation. Unmanned aerial vehicle (UAV) inspection has been widely used for the maintenance and repair of distribution lines. Due to the limitations of computational power and endurance, it is difficult for UAVs to independently complete data processing. Combined with mobile edge computing (MEC), this paper proposes a computing offloading strategy based on multi‐agent reinforcement learning and double‐layer offloading mechanism, which can further utilize the computing power of non‐task devices and edge servers. Firstly, three‐layer system architecture, named MEC‐U‐NTDC (MEC‐UAV‐Non‐task Device Cloud), is built. Secondly, double‐layer offloading mechanism is designed to comprehensively utilize the computing power of edge servers and neighbouring non‐task devices. Finally, a multi‐agent algorithm DLMQMIX is proposed to minimize the total cost for UAV inspection. Simulation experiments show that the proposed algorithm can effectively solve the task offloading problem of UAV‐aided distribution line inspection, and compared with algorithms such as PSO, GA, and QMIX, it performs better in terms of average delay, system cost, and load balancing, achieving a smaller total system cost. [ABSTRACT FROM AUTHOR]

The key motivation of this paper lies in the development of a high‐level decision‐making framework for autonomous overtaking maneuvers on two‐lane country roads with dynamic oncoming traffic. To generate an optimal and safe decision sequence for such scenario, an innovative high‐level decision‐making framework that combines model predictive control (MPC) and switching control methodologies is introduced. Specifically, the autonomous vehicle is abstracted and modelled as a switched system. This abstraction allows vehicle to operate in different modes corresponding to different high‐level decisions. It establishes a crucial connection between high‐level decision‐making and low‐level behaviour of the autonomous vehicle. Furthermore, barrier functions and predictive models that account for the relationship between the autonomous vehicle and oncoming traffic are incorporated. This technique enables us to guarantee the satisfaction of constraints, while also assessing performance within a prediction horizon. By repeatedly solving the online constrained optimization problems, we not only generate an optimal decision sequence for overtaking safely and efficiently but also enhance the adaptability and robustness. This adaptability allows the system to respond effectively to potential changes and unexpected events. Finally, the performance of the proposed MPC framework is demonstrated via simulations of four driving scenarios, which shows that it can handle multiple behaviours. [ABSTRACT FROM AUTHOR]

Processing queries in wireless sensor networks causes quality of service challenges such as data latency and validity. In fact, this paper is going to shed light on identifying the right trade‐off between latency and the freshness of the data. For this purpose, a fuzzy logic‐based decision‐making system that assigns incoming queries either to the sensor network, where the queries are waiting to be processed into a queue, or a central database which provides stored and potentially stale data or to a cache server which in turn provides acceptable latency and recent data, is provided. Thus, a new rule‐based system decision is introduced, that minimizes the request latency and returns the most recent data. In addition, a comparison between the approach and other work that is restricted to the database server and sensor network with no focus on the caching server is provided. This paper is going to carry on with the theory of Markov Chain which is more complicated compared to the fuzzy logic system. This approach is confirmed in the basis of an experience is developed with python. [ABSTRACT FROM AUTHOR]

Background: Patients with advanced incurable cancer face difficult decisions about palliative treatment options towards their end of life. However, they are often not provided with the appropriate information and support that is needed to make informed decisions. This review aimed to identify contexts and mechanisms associated with communication tools, patient decision‐aids and shared decision‐making (SDM) approaches that influence patient outcomes. Methods: We used a realist review method to search for published studies of patients (adults > 18) with advanced cancer who were expected to make a decision about palliative treatment and/or supportive care in consultation with healthcare practitioners. We appraised and synthesised literature describing the contexts of (when and how) decision aids and SDM approaches are used, and how these contexts interact with mechanisms (resources and reasoning) which impact patient outcomes. Stakeholders including academics, palliative healthcare professionals (HCPs) and people with lived experience of supporting people with advanced incurable cancer contributed to identifying explanatory accounts. These accounts were documented, analysed and consolidated to contribute to the development of a programme theory. Results: From the 33 included papers, we consolidated findings into 20 explanatory accounts to develop a programme theory that explains key contexts and mechanisms that influence patient and SDM. Contexts include underlying patients' and HCPs' attitudes and approaches. These need to be understood in relation to key mechanisms, including presenting information in multiple formats and providing adequate time and opportunities to prepare for and revisit decisions. Contexts influenced mechanisms which then influence the levels of patient decisional satisfaction, conflict and regret. Conclusions: Our programme theory highlights mechanisms that are important in supporting shared treatment decisions for advanced noncurative cancer. The findings are informative for developing and evaluating interventions to improve understanding and involvement in SDM for patients with advanced incurable cancer. Patient and Public Contribution: We included patient and public involvement (PPI) representatives in four stakeholder meetings. PPI helped to define the scope of the review, identify their unique experiences and perspectives, synthesise their perspectives with our review findings, make decisions about which theories we included in our programme theory and develop recommendations for policy and practice and future research. [ABSTRACT FROM AUTHOR]

Background: Involving men and boys as both users and supporters of Family Planning (FP) is now considered essential for optimising maternal and child health outcomes. Evidence on how to engage men and boys to meet FP needs is therefore important. Objectives: The main objective of this review was to assess the strength of evidence in the area and uncover the effective components and critical process‐ and system‐level characteristics of successful interventions. Search Methods: We searched nine electronic databases, seven grey literature databases, organisational websites, and the reference lists of systematic reviews relating to FP. To identify process evaluations and qualitative papers associated with the included experimental studies, we used Connected Papers and hand searches of reference lists. Selection Criteria: Experimental and quasi‐experimental studies of behavioural and service‐level interventions involving males aged 10 years or over in low‐ and middle‐income countries to increase uptake of FP methods were included in this review. Data Collection and Analysis: Methodology was a causal chain analysis involving the development and testing of a logic model of intervention components based on stakeholder consultation and prior research. Qualitative and quantitative data relating to the evaluation studies and interventions were extracted based on the principles of 'effectiveness‐plus' reviews. Quantitative analysis was undertaken using r with robust variance estimation (RVE), meta‐analysis and meta‐regression. Qualitative analysis involved 'best fit' framework synthesis. Results: We identified 8885 potentially relevant records and included 127 in the review. Fifty‐nine (46%) of these were randomised trials, the remainder were quasi‐experimental studies with a comparison group. Fifty‐four percent of the included studies were assessed as having a high risk of bias. A meta‐analysis of 72 studies (k = 265) showed that the included group of interventions had statistically significantly higher odds of improving contraceptive use when compared to comparison groups (odds ratio = 1.38, confidence interval = 1.21 to 1.57, prediction interval = 0.36 to 5.31, p < 0.0001), but there were substantial variations in the effect sizes of the studies (Q = 40,647, df = 264, p < 0.0001; I2 = 98%) and 73% was within cluster/study. Multi‐variate meta‐regression revealed several significant intervention delivery characteristics that moderate contraceptive use. These included community‐based educational FP interventions, interventions delivered to women as well as men and interventions delivered by trained facilitators, professionals, or peers in community, home and community, or school settings. None of the eight identified intervention components or 33 combinations of components were significant moderators of effects on contraceptive use. Qualitative analysis highlighted some of the barriers and facilitators of effective models of FP that should be considered in future practice and research. Authors' Conclusions: FP interventions that involve men and boys alongside women and girls are effective in improving uptake and use of contraceptives. The evidence suggests that policy should continue to promote the involvement of men and boys in FP in ways that also promote gender equality. Recommendations for research include the need for evaluations during conflict and disease outbreaks, and evaluation of gender transformative interventions which engage men and boys as contraceptive users and supporters in helping to achieve desired family size, fertility promotion, safe conception, as well as promoting equitable family planning decision‐making for women and girls. [ABSTRACT FROM AUTHOR]

Artificially intelligent systems (AISs) are being created by software developing companies (SDCs) to influence clinical decision-making. Historically, clinicians have led healthcare decision-making, and the introduction of AISs makes SDCs novel actors in the clinical decision-making space. Although these AISs are intended to influence a clinician's decision-making, SDCs have been clear that clinicians are in fact the final decision-makers in clinical care, and that AISs can only inform their decisions. As such, the default position is that clinicians should hold responsibility for the outcomes of the use of AISs. This is not the case when an AIS has influenced a clinician's judgement and their subsequent decision. In this paper, we argue that this is an imbalanced and unjust position, and that careful thought needs to go into how personal moral responsibility for the use of AISs in clinical decision-making should be attributed. This paper employs and examines the difference between prospective and retrospective responsibility and considers foreseeability as key in determining how personal moral responsibility can be justly attributed. This leads us to the view that moral responsibility for the outcomes of using AISs in healthcare ought to be shared by the clinical users and SDCs., (© 2023 The Authors. Bioethics published by John Wiley & Sons Ltd.)

Background: Informal care features high on the policy agenda of many countries to deal with workforce shortages. As a consequence, care provision increasingly takes place in the care triad of care recipients, informal caregivers and care professionals. How collaboration between care partners takes shape depends on how the different partners perceive this collaboration. This paper aims to investigate the relative importance of the different aspects of collaboration from the perspectives of care recipients, informal caregivers and care professionals in the context of the care for older persons in The Netherlands. Methods: Using Q‐methodology, 32 participants ranked 28 statements that reflect different aspects of collaboration in the care triad and explained their ranking during a follow‐up interview. Participants comprised 9 older persons, 10 informal caregivers and 13 care professionals. Data were analysed using by‐person factor analysis to identify common patterns in the rankings of the statements. Emerging patterns were interpreted and described as views on collaboration using aggregated rankings and qualitative data from the interviews. Results: Five distinct views on collaboration were found: (1) Emphasizing warm collaboration, (2) trusting care professional's expertise, (3) open and compassionate care professionals, (4) responsive decision‐making by autonomous care professionals and (5) prioritizing care recipient's and informal caregiver's interests. Care recipients and/or informal caregivers were associated with views 1, 3 and, 5, whereas care professionals were associated with all five views. Conclusions: Our study highlights the importance of recognizing the potential diversity of views between and within different partner groups in care triads. Governmental and organizational policy makers, as well as healthcare professionals who aim to increase or support the involvement of informal caregivers, should take this heterogeneity into consideration. Patient or Public Contribution: An advisory board of older persons (care recipients and informal caregivers) was involved in the recruitment of the participants, the formulation of the statements and the reflection on the findings of the study and potential implications. [ABSTRACT FROM AUTHOR]

An important cause of stunting is limited consumption of complementary foods, in terms of both quantities and nutrients. Although existing studies show a positive association between fathers' engagement and children's diet, programmes designed to improve complementary feeding practices often only target mothers. In response to this, maternal behaviour change communication (BCC), paternal BCC and food voucher programmes were designed and implemented in Ethiopia using a clustered randomized controlled trial design. The paternal BCC programme included gender‐equal messages to increase fathers' participation in childcare, household labour and decision making. The research reported in this paper is an examination of the BCC programmes, characterizing the behavioural, normative and control beliefs of both mothers and fathers in BCC households compared to those in control households. In this study, a total of 40 participants were included, with 13 mother–father pairs in the BCC + food voucher group, and seven pairs in the control group. Each participant was interviewed separately. We found that BCC mothers showed more gender‐equal tendencies than the control mothers despite being more rural in location. By contrast, the beliefs of BCC and control fathers were similar overall, suggesting men are more resistant to gender‐equal BCC. More work is needed to develop and test effective methods for changing fathers' beliefs and practices. Key messages: Mothers in behaviour change communication (BCC) group mothers held more gender‐equal beliefs than control mothers, while BCC and control fathers shared similar views, suggesting a male resistance to gender equality.Mothers found fathers' involvement in childcare socially acceptable but often perceived them as inexperienced, which limited their participation.Control mothers' traditional view on household chores maintained the conventional labour division, influencing fathers' involvement.Fathers typically resisted maternal control of resources; however, they agreed that the more knowledgeable should lead decision‐making. BCC mothers showed greater confidence in making household decisions as effectively as fathers. [ABSTRACT FROM AUTHOR]

The vehicle platoon lane changing is significant for alleviating road congestion and diminishing transportation energy consumption. However, the lane changing strategy for a group of vehicles is still a great challenge in this field. This paper investigates the vehicle platoon lane changing problems, in which the safety and efficiency in the lane changing procedure are both taken into consideration. Since the safety of the platoon lane changing would be affected by the lane changing gap and the length of the platoon, a novel platoon lane changing strategy is proposed by using the deep Q‐network. In detail, the proposed platoon lane changing strategy contains two layers, where the first one is a decision layer and the other one is the verification layer. In the decision layer, the deep Q‐network is employed to improve the lane changing efficiency. Then, the verification layer is presented to enhance the platoon lane changing safety. In final, some typical platoon lane changing scenarios are provided in an existing ramp containing a vehicle platoon and some random vehicles. The related numerical simulations are conducted to validate the feasibility and effectiveness of the proposed approaches. [ABSTRACT FROM AUTHOR]

Tuberculosis (TB) is an extremely contagious illness caused by Mycobacterium tuberculosis. Chest tuberculosis classification is conducted based on a deep convolutional neural network architecture. In this research, a pre‐trained network is utilized to demonstrate the advantage of using the oversampling technique on the classification of TB and compare results with recent research that used the same dataset. Therefore, the dataset consists of 3500 uninfected TB cases and 700 infected with TB. This paper circumvents the imbalance by using the oversampling technique in X‐ray TB images to be fed into several pre‐trained networks for TB classification. The oversampling technique is crucial in enhancing the performance of TB classification compared with other pre‐trained models reported here. Inceptionv3 shows a promising result compared to other pre‐trained models; it achieves 99.94% accuracy, 99.88% precision, 100% recall, and 99.94% F1‐Score. [ABSTRACT FROM AUTHOR]

We propose an autoregressive weighted network (ARWNet) time series forecasting model inspired by the idea of ensemble learning. The model adopts the classical autoregressive analysis to optimize weak learners. Meanwhile, the combined weight optimization method is used to construct an efficient, strong learner. With these methodological foundations, the scalability of the framework is greatly enhanced by the possibility of experimenting with other learners to assist in decision‐making. Machine learning can provide great utility and acceptable cost in the prediction process of electricity transformers. Over the years, many research papers on time series prediction have been reported. This work will focus on the analysis using the potential properties in the series: long‐term, continuity, periodicity, and delay. In our experiments, the ETT‐small dataset is used to compare the prediction accuracy of ARWNet and other mainstream models. All results suggest that the proposed ARWNet model demonstrates strong generalization ability and high predicting accuracy with delay characteristics, which outperform current popular time‐series prediction methods. [ABSTRACT FROM AUTHOR]

As a transformative technology, connected and automated driving technology is expected to deal with the issues of traffic congestion and conflicts, and meanwhile to ensure the driving safety in intelligent transportation systems (ITS). Chen et al. presented a finite time disturbance observer (FTDO)-based super-twisting sliding mode control (SMC) for a vehicle platoon system against parameter uncertainties and external disturbances. Cai et al. designed a Platoon Sharing Deep Deterministic Policy Gradient Algorithm for a multi-vehicle network. [Extracted from the article]

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Adaptation decision‐scientists increasingly use real‐option analysis to consider the value of learning about future climate variable development in adaptation decisions. Toward this end learning scenarios are needed, which are scenarios that provide information on future variable values seen not only from today (as static scenarios), but also seen from future moments in time. Decision‐scientists generally develop learning scenarios themselves, mostly through time‐independent (stationary) or highly simplified methods. The climate learning scenarios thus attained generally only poorly represent the uncertainties of state‐of‐the‐art climate science and thus may lead to biased decisions. This paper first motivates the need for learning scenarios by providing a simple example to illustrate characteristics and benefits of learning scenarios. Next, we analyze how well learning scenarios represent climate uncertainties in the context of sea level rise and present a novel method called direct fit to generate climate learning scenarios that outperforms existing methods. This is illustrated by quantifying the difference of the sea level rise learning scenarios created with both methods to the original underlying scenario. The direct fit method is based on pointwise probability distributions, for example, boxplots, and hence can be applied to static scenarios as well as ensemble trajectories. Furthermore, the direct fit method offers a much simpler process for generating learning scenarios from static or "ordinary" climate scenarios. Plain Language Summary: Many climate change adaptation decisions require large investments in infrastructure (e.g., dikes), while at the same time future projections about critical variables (e.g., sea level rise) are highly uncertain. Decision‐scientists address these challenges with methods based on flexibility and staged decision‐making. For example, a coastal decision‐maker could implement a dike with a wider foundation, and, if necessary, upgrade the dike height in the future. The decision‐maker will learn by observing future sea level rise if higher dike protection levels are actually necessary in the future. In order to assess whether it is economically beneficial to wait for future learning through observations, and thus to justify additional expenses for flexible infrastructure investments, learning scenarios are required. Learning scenarios provide projections of critical variables seen from today and from future moments in time. For example, learning scenarios of sea level rise contain sea level rise projections seen from 2050 onward, depending on a certain amount of sea level rise observed until 2050. In this paper, we provide a simple example to illustrate coastal decision‐making with a learning scenario, propose a new method to generate learning scenarios, and apply this method to generate sea level rise learning scenarios. Key Points: We show how climate learning scenarios can be applied for improving and justifying investments in flexible long‐lasting infrastructureWe develop sea level rise learning scenarios based on Intergovernmental Panel on Climate Change sixth Assessment Report using a novel method termed direct fitOur new method reduces the average deviation of learning scenarios from the original data by 83% compared to standard methods [ABSTRACT FROM AUTHOR]

Nowadays, nonlinear loads are widely employed within the industrial applications that cause harmonic distortions in the network. Passive harmonic filters stand out as the preeminent and cost‐efficient remedy for mitigating the impact of harmonic distortions. Any changes in power system conditions such as load variability and photovoltaic resources uncertainty have a significant impact on the harmonic conditions which can affect the quantity and positioning of passive harmonic filters. Hence, in this paper, a method including an innovative optimization problem is proposed for probabilistic planning of passive harmonic filters considering system conditions variability, which is solved by heuristic methods and multi‐criteria decision‐making techniques. By doing this, a solution is obtained where grid losses and the passive harmonic filter costs are minimized and the power factor and frequency response are improved on selected buses. Also, in this paper, the index that gauges the severity of total harmonic distortion is proposed to determine the worst case of the network in terms of harmonic distortions which is so vital for system operators, especially the systematic approach to designing passive harmonic filters. To indicate the efficiency of the proposed approach, the electrical system of an actual Iranian copper mine of significant scale is employed, featuring the integration of non‐linear loads. [ABSTRACT FROM AUTHOR]

This paper realizes the evaluation and optimal decision of the spoofing effect on the global navigation satellite system (GNSS) under navigation countermeasures based on the game perspective, to overcome the limitations of current relevant research findings and maintain the secure application of the GNSS and maximize its defensive benefit during countering. An evaluation system based on the perspective of the game between spoofing strategies and defense measures is created to build the benefit matrix, which can solve the limitation in building a benefit matrix (the vital element in a game) that conforms to the confrontation‐oriented GNSS spoofing properties. Based on the built benefit matrix, a method integrating the grey expert‐based system with the hybrid evolutionary method is developed to realize quantifying the matrix and further make the optimal decision stably, which can solve the uncertainties of qualitative indices during quantifying and no global optimal solution for benefit equations deduced from the quantified matrix. Finally, based on various judgment principles, simulation and testing results verify the effectiveness of the obtained results and the superiority of the proposed method by comparing the results solved by the method in this paper with the results obtained by other decision‐making processing methods. [ABSTRACT FROM AUTHOR]

In the context of ongoing global pandemics and political and economic instability, supporting public health organisations to improve their performance and develop their resilience is becoming a central issue in the organisational resilience research agenda. This paper offers a briefing on the viability and sustainability (v&s) approach and provides an example of application of the self‐transformation methodology (STM) at the breast care unit in an English Health Trust. The way of conducting the systemic application, the chosen methodology and tools and the way of interacting with the clients all exhibit the aspects of holistic flexibility. The paper demonstrates how the v&s approach and the STM enabled the collective identification of more effective policies and strategies for improving individual and organisational effectiveness for this health service unit, when interacting with a complex and changing environment. It also discusses the potential for adapting and replicating the approach across the healthcare sector. [ABSTRACT FROM AUTHOR]

Charging behaviours of electric vehicles (EVs) exhibit substantial randomness, making accurate prediction or modelling challenging. Furthermore, as the number of EVs continues to increase, charging stations are diversifying their offerings to accommodate distinct charging characteristics, addressing a wide spectrum of EV charging needs. Previous research mostly focused on the randomness of EVs while neglecting the heterogeneity in charging infrastructure. Therefore, this paper introduces a decentralized collaborative optimal method for EV charging stations, taking into account the varying facility types and the power limitations. First, a decentralized collaborative framework is proposed. The energy boundary model and the average laxity of EVs contribute to transforming the optimization problem into a Markov Decision Process (MDP) with uncertain transitions. Then, multi‐agent deep deterministic policy gradient multi‐individuals (MADDPG‐MI) algorithm is developed to train several heterogeneous agents presenting different types of charging facilities. Each agent makes decisions for multiple homogenous charging piles. Numerous simulation studies validate that the proposed method can effectively reduce charging costs and manages in scenarios involving either homogeneous or multiple heterogeneous charging facilities. Moreover, the MADDPG‐MI algorithm demonstrates performance consistency among multiple decision‐making units while consuming lower training resources offering enhanced scalability. [ABSTRACT FROM AUTHOR]

Aim: The study aimed to provide a comprehensive concept analysis of nursing privileges by elucidating its meaning and implications within the healthcare context. Design: A concept analysis paper. Methods: A comprehensive literature review was conducted from nursing and healthcare databases, professional nursing organizations, and regulatory bodies. Documents reviewed include research studies, policy documents and professional guidelines. The study employed Walker and Avant's eight‐step method of concept analysis. This involved identifying the uses of the concept, its underlying attributes and referents, and constructing model, borderline, related and contrary cases. The antecedents, consequences and empirical referents of nursing privileges were also determined. Results: The analysis uncovered vital attributes defining nursing privileges, encompassing professional authority, autonomy, access to resources, information, influence, decision‐making power, respect and recognition. Additionally, antecedents and consequences of nursing privilege were identified, spanning development and resource access, as well as professional satisfaction and enhanced patient care. Patient or Public Contribution: No patient or public contribution. [ABSTRACT FROM AUTHOR]

Aim: Discussing the nurses' voice behaviour could support the managers in making the right decisions and solve problems. Design: This was a discursive paper. Methods: The discursive was based on reviewing the literature. Results: Nurses play a critical role in offering useful constructive advice, which leads to management figuring out and solving problems immediately for the purpose of bettering the working environment. Therefore, we assert that trust in leadership and the leader–leader exchange system also plays a critical role in enforcing voice behaviour. Trust is a crucial aspect of voice behaviour, and integrated trust in leadership and leader–leader exchange as a possible practical suggestion for the fostering of voice behaviour are proposed. Nurse managers must maintain a sense of reciprocal moral obligation in order to nurture value‐driven voice behaviour. It is important that open dialogue, active listening and trust in leadership exist. Nurse managers must consider ways to foster mutual trust, and support and enable nurses to use voice behaviour in everyday practice. [ABSTRACT FROM AUTHOR]

Background: Acute hospital wards can be difficult places for many people living with dementia. Promoting comfort and wellbeing can be challenging in this environment. There is little evidence‐based support for professionals working on acute care wards on how to respond to distress and maximise comfort and wellbeing among patients living with dementia. Objectives: Our overall aim was to codesign an evidence‐based easy‐to‐use heuristic decision‐support framework, which was acceptable and practical but acknowledges the complex and acute nature of caring for patients with dementia in the hospital. This paper presents the development process and resulting framework. Methods: A codesign study was informed by data from (1) a literature review of the care and management of people living with dementia in acute hospitals; (2) a cohort study of comfort and discomfort in people with dementia in acute hospitals; and (3) interviews with family carers and health care professionals. We synthesised evidence from these data sources and presented to key stakeholders through codesign meetings and workshops to produce our decision‐support framework. Results: The framework consists of a series of flowcharts and operates using a three‐stage process of: (1) assess comfort/discomfort; (2) consider causes of discomfort; and (3) address patient needs to manage the discomfort. Conclusion: Working with key stakeholders, synthesising diverse quantitative and qualitative evidence to build a clinical framework is a feasible approach to help address the needs of patients living with dementia in an acute hospital setting. The result is a framework which is now ready for evaluation and implementation. Patient and Public Contribution: We worked closely with people living with dementia and family carers throughout this study, including the development of the study protocol with input on study development and design, through to inclusion in stakeholder workshops and codesign of the decision support framework. [ABSTRACT FROM AUTHOR]

This paper combines theoretical sustainable business models and conflict indices in making practical sustainable (i.e., participatory decisions involving economic, social, and environmental features within weak or strong sustainability paradigms) and rational (i.e., informed and consistent decisions within substantive or instrumental rationality) decisions with respect to what, who, where, when, and how to act. The case study, focused on an offshore gas platform, identified when (i.e., the end as opposed to the beginning of extraction activities) and where (i.e., the economic, social, and environmental contexts of the Adriatic Sea in Abruzzo region, Italy). A face‐to‐face questionnaire, submitted to stakeholders, produced the relative weights required by the tested sustainable business models (Lüdeke‐Freund et al., 2018), and it reached a conclusion about how (i.e., in favor of the majority as opposed to average decisions). An application of a linear conflict index (Fasth et al., 2018) highlighted a lack of stakeholders' representativeness and knowledge and solved these issues with a 50% increase of stakeholders involved and an additional discussion with originally invited stakeholders on specific topics, respectively. In summary, the methodology suggested in this paper produced a (strong) sustainable and (substantive) rational decision about what and who based on relative weights expressed by representative and informed stakeholders being engaged at the smallest cost and with the largest support. Thus, the contribution of this paper is twofold: Theoretically, choices among alternative sustainable businesses depend on the adopted sustainability paradigm, and practically, choices among alternative sustainable businesses should be identified according to the adopted sustainability paradigm. [ABSTRACT FROM AUTHOR]

This paper argues that there is a requirement to connect project management (PM) with innovation and sustainability to address new societal needs. Societal demands have facilitated further complexity and uncertainty within the PM discipline and have led to both theoretical and practical evolutions. Key evolutions are identified, which suggests that PM is becoming more holistic and interdisciplinary in nature so it can successfully deal with this complexity and uncertainty. Furthermore, holistic thinking naturally connects with systemic thinking, which justifies the selection of system dynamics (SD) to support PM decision‐making. An extensive critical review of SD modelling with respect to identified evolutions within the PM discipline is undertaken. The contribution of this paper connects innovation and sustainability to the PM discipline by the initial development of a causal loop diagram (CLD). [ABSTRACT FROM AUTHOR]

Introduction: The United States has been unsuccessful in containing the rapid spread of COVID‐19. The complex epidemiology of the disease and the fragmented response to it has resulted in thousands of ways in which spread has occurred, creating a situation where each community needs to create its own local, context‐specific learning model while remaining compliant to county or state mandates. Methods: In this paper, we demonstrate how cross sector collaborations can use the Cynefin Framework, a tool for decision‐making in complex systems, to guide community response to the COVID‐19 pandemic. Results: We explore circumstances under which communities can inhabit each of the four domains of systems complexity represented in the Cynefin framework: simple, complicated, chaotic, and complex, and describe the decision‐making process in each domain that balances health, economic, and social well‐being. Conclusion: This paper serves as a call to action for the creation of community learning systems to improve community resilience and capacity to make better‐informed decisions to address complex public health problems during the pandemic and beyond. [ABSTRACT FROM AUTHOR]

Procreative obligations are often discussed by evaluating only the consequences of reproductive actions or omissions; less attention is paid to the moral role of intentions and attitudes. In this paper, I assess whether intentions and attitudes can contribute to defining our moral obligations with regard to assisted reproductive technologies already available, such as preimplantation genetic diagnosis (PGD), and those that may be available in future, such as reproductive genome editing and ectogenesis, in a way compatible with person‐affecting constraints. I propose the parent–child relationship argument, which is based on the moral distinction between creating and parenting a child. Hence, I first argue that intentions and attitudes can play a role in defining our moral obligations in reproductive decisions involving PGD. Second, I maintain that if we accept this and recognize reproductive genome editing and ectogenesis as person‐affecting procedures, we should be committed to arguing that prospective parents may have moral reasons to prefer reproduction via such techniques than via sexual intercourse. In both cases, I observe an extension of our procreative responsibility beyond what is proposed by the consequentialist person‐affecting morality. [ABSTRACT FROM AUTHOR]

In a cloud environment, resources should be acquired quickly and automatically released at runtime. Traditional trajectory data partitions, indexing and query processing techniques are extended, so that they can take advantage of the cloud of large clusters of highly parallel processing capabilities. There are ways with trajectory data in the cloud database query processing. The advanced sensing techniques available today caused the existence of many types of trajectory datasets whose study can form a strong basis for decision making regarding a data context. Trajectory data generally includes trajectory classification, trajectory clustering, and trajectory associations and so on. All these tools need trajectory similarity measures to get comparisons in trajectory data. Some of the popular existing methods of trajectory similarity include Euclidean distance, semantic distance and there near variants. Each variant of these techniques exhibits notable differences in measurement of similarity and computational difficulty. This paper targeted to deal with this situation with the goal of lower computational efforts in trajectory similarity computations. A two‐phase trajectory similarity measure is defined. The first phase does point level analysis of trajectory points representing all trajectories and the following phase use the analysis to find the similarities. Using a hybrid similarity framework, new means for trajectory clustering and trajectory query processing are developed. From trajectory analysis followed by trajectory clustering, evaluation of cluster quality and processing of trajectory query are undertaken in this paper in different ways from the existing methods. All the proposed developments are tested with trajectory datasets with multiple attributes and the results are more favour to proposed framework than the existing ones. [ABSTRACT FROM AUTHOR]

The quality of management decisions is one of the main issues facing higher education institutions today. Strategic decisions taken by the higher education institutions affect policies, schemes, and actions that the institutions are considering. Machine learning is an emergent artificial intelligence field that utilizes different algorithms, analyses data, and delivers a better understanding of the data contained in a specific context. Hence, in this paper, data‐driven decision‐making model has been proposed based on artificial intelligence in colleges and universities. Student data, graduation rate and curriculum design have been analysed for administrative decision‐making in college or university based on the machine learning method. With the availability of huge quantities and high‐quality input training data, machine‐learning progressions can attain precise outcomes and enable informed decision‐making. The experimental findings show that the suggested model improves the outcome ratio of 90.72%, the performance ratio of 97.62%, prediction ratio of 96.35%, decision‐making level of 95.51%, accuracy ratio of 95.61%, an efficiency ratio of 98.14%, graduation rate of 85.86%, data security rate (95.61%) and error rate 33.21% compared to other methods. [ABSTRACT FROM AUTHOR]

This paper reviews state‐of‐the‐art numerical tools for the operation and maintenance (O&M) of offshore wind farms, focusing on decision support models for maintenance scheduling and the consideration of human and environmental uncertainty. In this review, various factors that can influence the successful conduct of maintenance operations will be examined and special attention will be paid to the most significant ones. Data‐driven technologies for improved offshore asset management are also examined and the most used data‐driven methods for modeling and optimizing turbine operation and maintenance are presented. A focus will be placed on the choice of maintenance strategy, which is the basis for the planning of operations and thus the optimization problem discussed. As offshore maintenance is a complex operation whose efficiency and safety depend on human and environmental factors, special attention will be paid to the planning strategy that minimizes the risks involved while maximizing efficiency by considering these factors. The choice of planning technique for turbine maintenance and better consideration of uncertainties are crucial areas of improvement as they can lead to better overall efficiency, higher profit margins, better safety, and improved sustainability of offshore wind farms. The paper covers the application of digital technologies for offshore wind O&M planning and the associated challenges. The paper also highlights the various environmental and human factors to be considered for the operation and maintenance of wind turbines. [ABSTRACT FROM AUTHOR]

Some decision‐making problems are required to be analysed using insufficient and vague information under an extended space of fuzziness, considering flexibility in the decision‐making process and the interdependency and interaction between the criteria. To tackle these challenges, a spherical fuzzy set (SFS), which is an extension of the ordinary picture fuzzy set (PFS), can be used to fulfil the requirement of an extended space of fuzziness by quantifying uncertainty. Similarly, the Frank operations can provide flexibility to the decision‐making process due to the involvement of a parameter, whereas the Choquet integral considers all possible interactive characteristics between different criteria. Therefore, this paper integrates these three important concepts to make more sound and realistic decisions by extending the existing arithmetic operation for spherical fuzzy numbers (SFNs) using Frank t‐norm and t‐conorm. In addition, the paper introduces some novel Choquet‐Frank aggregation operators, such as spherical fuzzy Choquet‐Frank averaging (SFCFA) and spherical fuzzy Choquet‐Frank geometric (SFCFG). To demonstrate the effectiveness of the proposed operators, a multi‐criteria decision making (MCDM) problem related to the selection of an enterprise resource planning (ERP) system is analysed for the available five options χ1,χ2,χ3,χ4, and χ5. Sensitivity analysis is performed to investigate the impact of taking variation in parameter values (θ) on aggregated values. Results confirmed that the best alternatives for SFCFA and SFCFG operators are χ3 and χ5, respectively, while χ2 is the worst alternative for both these operators. To show the effectiveness of the proposed operators, a comparative analysis has been performed to compare the results obtained from other existing aggregation operators. Results concluded that the findings resemble most of the existing operators which confirms the feasibility of the proposed operators. [ABSTRACT FROM AUTHOR]

To improve the application range of decision‐making systems for connected automated vehicles, this paper proposes a cooperative decision‐making approach for multiple driving scenarios based on the combination of multi‐agent reinforcement learning with centralized planning. Specifically, the authors derived driving tasks from driving scenarios and computed the policy functions for different driving scenarios as linear combinations of policy functions for a set of specific driving tasks. Then, the authors classified vehicle coalitions according to the relationships between vehicles and used centralized planning methods to determine the optimal combination of actions for each coalition. Finally, the authors conducted tests in two driving scenarios considering different traffic densities to evaluate the performance of the developed approach. Simulation results demonstrate that the proposed approach exhibits good robustness in multiple driving scenarios while enabling cooperative decision making for connected automated vehicles, thereby ensuring safe and rational decision making. [ABSTRACT FROM AUTHOR]

Background and Aims: Lower‐ and middle‐income countries have decentralized decision‐making at the community level, as well as community governance structures, to encourage community participation in governance processes, particularly in primary healthcare (PHC). In Tanzania, decentralization resulted in the establishment of Health Facility Governing Committees (HFGCs) to encourage community participation in the governance of primary health facilities to improve the quality and responsiveness of health service delivery. Nonetheless, despite the presence of HFGCs, PHC delivery remains ineffective and of poor quality. It is unclear who makes governance decisions at PHC facilities to ensure that services delivered are of expected quality and respond the community's needs, tastes, and preferences. This paper aims to assess the perspectives of members of the HFGC on who make governance decision in the context of fiscal decentralization. Design and Methods: A cross‐section design was used to collect both quantitative and qualitative data. A four‐multistage sampling technique was adopted to selects regions, council, health facilities, and HFGC members. Respondents who participated in structured questionnaire responses were chosen using proportional sampling, whereas those who participated in in‐depth interviews and Focus Group Discussions were chosen using purposive selection. The data was analyzed descriptively and thematically. Results: The study revealed that HFGCs members perceive that governance decisions in primary health facilities are primarily made by the health facility management, and later are presented in HFGCs. As such, HFGCs are used a passively used to justify participation in decision that was already made by the management, which contradict with the principal of decentralization that emphasizes community participation on fiscal decisions. Conclusion: Decentralization of PHC facilities does not guarantee the participation of community members in fiscal decision of their respective primary health facilities through HFGCs. HFGC is passively used governance structure to substitute community participation in primary health facilities' fiscal decisions. Enforcement mechanisms are required to facilitate effective community participation. [ABSTRACT FROM AUTHOR]