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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]

The issue of pensions has become increasingly topical. This paper presents the ordered weighted averaging real average pension (OWARAP) operator. The OWARAP operator is based on the ordered weighted averaging (OWA) operator and calculates the future average retirement benefit taking into account price changes. Moreover, this work extends the OWARAP operator by using order-inducing variables, generalized means, and probabilities. This paper ends by analyzing the applicability of the OWARAP operator and its extensions in forecasting the real average Social Security benefits for retired workers in each state of the United States (U.S.). The results demonstrate the usefulness of the proposed approach in retirement decision making. [ABSTRACT FROM AUTHOR]

This paper is a conceptual exploration and diffractive reading of refugee/(im)migrant education through multiple lenses, including data-driven decision making, critical refugee studies, new materialism and critical feminist and posthumanist studies, and trans theorizations such as Black trans feminism. After a brief introduction to "the field" of refugee/(im)migrant education, the paper turns to diffractive readings of refugee/(im)migrant education as means of exploring what is the matter, as in the material and discursive substance, in refugee/(im)migrant education, and why and how (including when, where, and by whom) does that matter come to matter? The paper concludes with discoveries, or findings, from this diffractive, transdisciplinary exploration and considerations for educators, policymakers, researchers, activists, and other actors (co)constituting and "becoming with" refugee/(im)migrant education. [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]

This paper explores the integrated optimization of complex coupled industrial manufacturing systems and production strategies based on user customization needs. Two optimization metrics are considered: one is whether the production process of engineering manufacturing is simplified, and the other is whether it is based on the customization requirements of the customer. These two metrics are interrelated, and cases may even be conflicting. Considering the interdependence between engineering manufacturing and user requirements, this paper develops an integrated customized modular engineering manufacturing process to minimize production and maintenance costs and improve efficiency while meeting user customization requirements. This paper takes expert evaluation as an important decision indicator and optimizes the production process strategy on this basis. Finally, a case study is given to illustrate the applicability of the proposed process model. [ABSTRACT FROM AUTHOR]

In today's world, a significant amount of global energy is used in buildings. Unfortunately, a lot of this energy is wasted, because electrical appliances are not used properly or efficiently. One way to reduce this waste is by detecting, learning, and predicting when people are present in buildings. To do this, buildings need to become "smart" and "cognitive" and use modern technologies to sense when and how people are occupying the buildings. By leveraging this information, buildings can make smart decisions based on recently developed methods. In this paper, we provide a comprehensive overview of recent advancements in Internet of Things (IoT) technologies that have been designed and used for the monitoring of indoor environmental conditions within buildings. Using these technologies is crucial to gathering data about the indoor environment and determining the number and presence of occupants. Furthermore, this paper critically examines both the strengths and limitations of each technology in predicting occupant behavior. In addition, it explores different methods for processing these data and making future occupancy predictions. Moreover, we highlight some challenges, such as determining the optimal number and location of sensors and radars, and provide a detailed explanation and insights into these challenges. Furthermore, the paper explores possible future directions, including the security of occupants' data and the promotion of energy-efficient practices such as localizing occupants and monitoring their activities within a building. With respect to other survey works on similar topics, our work aims to both cover recent sensory approaches and review methods used in the literature for estimating occupancy. [ABSTRACT FROM AUTHOR]

This paper undertakes a revisit of the sunk cost fallacy, which refers to the tendency of people to persist investing resources into something, even if it is destined to have no good outcome. We emphasize that the utilities associated with different alternatives are not static for decision makers, which is exactly opposite to the traditional perspective. This paper argues that the utility of an option may change due to the choice of another option, suggesting that decisions considered irrational by the traditional analytical method, i.e., sunk cost fallacy, may be rational. We propose a novel analytical method for decision making with sunk cost when considering the utility change and validate the effectiveness of this method through mathematical modeling and computational experiments. This paper mathematically describes such decision-making problems, analyzing the impact of changes in the utilities across different alternatives on decision making with a real-world example. Furthermore, we develop a two-stage stochastic optimization model for such decision-making problems and employ the sample average approximation (SAA) method to solve them. The results from computational experiments indicate that some decisions traditionally considered irrational are, in fact, rational when the utility of an option changes as a result of choosing another option. This paper, therefore, highlights the significance of incorporating utility changes into the decision-making process and stands as a valuable addition to the literature, offering a refreshed and effective decision-making method for improved decision making. [ABSTRACT FROM AUTHOR]

The optimization of aircraft trajectories involves balancing operating costs and climate impact, which are often conflicting objectives. To achieve compromised optimal solutions, higher-level information such as preferences of decision-makers must be taken into account. This paper introduces the SolFinder 1.0 module, a decision-making tool designed to identify eco-efficient aircraft trajectories, which allow for the reduction of the flight's climate impact with limited cost penalties compared to cost-optimal solutions. SolFinder 1.0 offers flexible decision-making options that allow users to select trade-offs between different objective functions, including fuel use, flight time, NOx emissions, contrail distance, and climate impact. The module is included in the AirTraf 3.0 submodel, which optimizes trajectories under atmospheric conditions simulated by the ECHAM/MESSy Atmospheric Chemistry model. This paper focuses on the ability of the module to identify eco-efficient trajectories while solving a bi-objective optimization problem that minimizes climate impact and operating costs. SolFinder 1.0 enables users to explore trajectory properties at varying locations of the Pareto fronts without prior knowledge of the problem results and to identify solutions that limit the cost of reducing the climate impact of a single flight. [ABSTRACT FROM AUTHOR]

Digital financial inclusion and common prosperity are pivotal elements in promoting the sustainable socioeconomic development of China. This study introduces a novel Multi-Criteria Decision Analysis (MCDA) method to evaluate the Common Prosperity Index (CPI). Using this index, alongside the Digital Financial Inclusion Index (DFII) released by Peking University, it examines the evolution of the coupling coordination relationship between digital financial inclusion and common prosperity within the Yangtze River Delta (YRD) urban agglomeration from 2011 to 2021. By integrating gravity models and social network analysis, in this paper, we thoroughly investigate the spatiotemporal evolution characteristics of the spatial network of this coupling coordination relationship. The results indicate that both the DFII and CPI generally exhibit an upward trend, but the decline in the coupling degree reflects a weakened interaction strength between them. Specifically, Anhui significantly lags behind Jiangsu, Zhejiang, and Shanghai in the development of digital financial inclusion and common prosperity, indicating regional development imbalances. Furthermore, the strength of spatial connections in city coupling coordination has significantly increased, with Nanjing's siphon effect on cities in Anhui becoming markedly stronger, and the number of core cities in the network increasing, which demonstrates a geographical proximity feature in network development. Additionally, the overall network characteristics are transitioning towards higher density and "small-world" properties, suggesting a trend toward network stabilization. The disparity in centrality among cities has decreased, with an overall enhancement in centrality, where the spatial spillover effects from core areas such as Hangzhou-Ningbo, Nanjing-Changzhou, and Shanghai-Suzhou-Wuxi significantly promote the development of peripheral cities. Based on these findings, this paper proposes policy recommendations for the sustainable development of digital financial inclusion and common prosperity in the YRD region. [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]

Background: After curative surgery for early-stage breast cancer, patients face a decision on whether to undergo surgery alone or to receive one or more adjuvant treatments, which may lower the risk of recurrence. Variations in survival outcomes are often marginal but there are differences in the side effects and other features of the options that patients may value differently. Hence, the patient's values and preferences are critical in determining what option to choose. It is well-researched that the use of shared decision making and patient decision aids can support this choice in a discussion between patient and clinician. However, it is still to be investigated what impact the timing and format of the patient decision aid have on shared decision making outcomes. In this trial, we aim to investigate the impact of a digital pre-consult compared to a paper-based in-consult patient decision aid on patients' involvement in shared decision making, decisional conflict and preparedness to make a decision. Methods: The study is a randomised controlled trial with 204 patients at two Danish oncology outpatient clinics. Eligible patients are newly diagnosed with early-stage breast cancer and offered adjuvant treatments after curative surgery to lower the risk of recurrence. Participants will be randomised to receive either an in-consult paper-based patient decision aid or a pre-consult digital patient decision aid. Data collection includes patient and clinician-reported outcomes as well as observer-reported shared decision making based on audio recordings of the consultation. The primary outcome is the extent to which patients are engaged in a shared decision making process reported by the patient. Secondary aims include the length of consultation, preparation for decision making, preferred role in shared decision making and decisional conflict. Discussion: This study is the first known randomised, controlled trial comparing a digital, pre-consult patient decision aid to an identical paper-based, in-consult patient decision aid. It will contribute evidence on the impact of patient decision aids in terms of investigating if pre-consult digital patient decisions aids compared to in-consult paper-based decision aids support the cancer patients in being better prepared for decision making. Trial registration: ClinicalTrials.gov (NCT05573022). [ABSTRACT FROM AUTHOR]

Background: The concept of a financial scarcity mindset has raised much attention as an explanation for poor decision-making and dysfunctional behavior. It has been suggested that financial scarcity could also impair dietary behavior, through a decline in self-control. Underlying cognitive mechanisms of tunneling (directing attention to financial issues and neglecting other demands), cognitive load (a tax on mental bandwidth interfering with executive functioning) and time orientation (a shift towards a present time horizon, versus a future time horizon) may explain the association between financial scarcity and self-control related dietary behavior. The current scoping review gathers recent evidence on how these mechanisms affect dietary behavior of people experiencing financial scarcity. It builds on a theoretical framework based on insights from behavioral economics and health psychology. Methods: A literature search was executed in six online databases, which resulted in 9.975 papers. Search terms were tunneling, cognitive load and time orientation, financial scarcity, and dietary behavior. Screening was performed with ASReview, an AI-ranking tool. In total, 14 papers were included in the scoping review. We used PRISMA-ScR guidelines for reporting. Results: Limited evidence indicates that a scarcity mindset could increase tunneling, through attentional narrowing on costs of food, which then directly impacts dietary behavior. A scarcity mindset involves experiencing financial stress, which can be understood as cognitive load. Cognitive load decreases attentional capacity, which could impair self-control in dietary choices. Financial scarcity is related to a present time orientation, which affects dietary choices by shifting priorities and decreasing motivation for healthy dietary behavior. Conclusions: A scarcity mindset affects dietary behavior in different ways. Tunneling and a shift in time orientation are indicative of an attentional redirection, which can be seen as more adaptive to the situation. These may be processes indirectly affecting self-control capacity. Cognitive load could decrease self-control capacity needed for healthy dietary behavior because it consumes mental bandwidth. How a changing time orientation when experiencing financial scarcity relates to motivation for self-control in dietary behavior is a promising theme for further inquiry. [ABSTRACT FROM AUTHOR]

Background: The concept of a financial scarcity mindset has raised much attention as an explanation for poor decision-making and dysfunctional behavior. It has been suggested that financial scarcity could also impair dietary behavior, through a decline in self-control. Underlying cognitive mechanisms of tunneling (directing attention to financial issues and neglecting other demands), cognitive load (a tax on mental bandwidth interfering with executive functioning) and time orientation (a shift towards a present time horizon, versus a future time horizon) may explain the association between financial scarcity and self-control related dietary behavior. The current scoping review gathers recent evidence on how these mechanisms affect dietary behavior of people experiencing financial scarcity. It builds on a theoretical framework based on insights from behavioral economics and health psychology. Methods: A literature search was executed in six online databases, which resulted in 9.975 papers. Search terms were tunneling, cognitive load and time orientation, financial scarcity, and dietary behavior. Screening was performed with ASReview, an AI-ranking tool. In total, 14 papers were included in the scoping review. We used PRISMA-ScR guidelines for reporting. Results: Limited evidence indicates that a scarcity mindset could increase tunneling, through attentional narrowing on costs of food, which then directly impacts dietary behavior. A scarcity mindset involves experiencing financial stress, which can be understood as cognitive load. Cognitive load decreases attentional capacity, which could impair self-control in dietary choices. Financial scarcity is related to a present time orientation, which affects dietary choices by shifting priorities and decreasing motivation for healthy dietary behavior. Conclusions: A scarcity mindset affects dietary behavior in different ways. Tunneling and a shift in time orientation are indicative of an attentional redirection, which can be seen as more adaptive to the situation. These may be processes indirectly affecting self-control capacity. Cognitive load could decrease self-control capacity needed for healthy dietary behavior because it consumes mental bandwidth. How a changing time orientation when experiencing financial scarcity relates to motivation for self-control in dietary behavior is a promising theme for further inquiry. [ABSTRACT FROM AUTHOR]

This paper reflects the results of research analyzing models of multi-attribute decision-making based on fuzzy preference relations. Questions of constructing the corresponding multi-attribute models to deal with quantitative information concomitantly with qualitative information based on experts' knowledge are considered. Human preferences may be represented within the fuzzy preference relations and by applying diverse other preference formats. Considering this, so-called transformation functions reduce any preference format to fuzzy preference relations. This paper's results can be applied independently or as part of a general approach to solving a wide class of problems with fuzzy coefficients, as well as within the framework of a general scheme of multi-criteria decision-making under conditions of uncertainty. The considered techniques for fuzzy preference modeling are directed at assessing, comparing, choosing, prioritizing, and/or ordering alternatives. These techniques have served to develop a computing system for multi-attribute decision-making. It has been implemented in the C# programming language, utilizing the ".NET" framework. The computing system allows one to represent decision-makers' preferences in one of five preference formats. These formats and quantitative estimates are reduced to nonreciprocal fuzzy preference relations, providing homogeneous preference information for decision procedures. This paper's results have a general character and were applied to analyze power engineering problems. [ABSTRACT FROM AUTHOR]

Although child-centred care is increasingly referred to within the nursing literature, a clear definition of child-centred care and clarity around the concept is yet to be achieved. The objectives of this review were to examine the following: (1) What constitutes the concept of child-centred care in healthcare? (2) How has the concept of child-centred care developed? (3) What is the applicability of child-centred care and what are its limitations? (4) How does the concept of child-centred care benefit and inform children's healthcare? In total, 2984 papers were imported for screening, and, following the removal of duplicates and screening, 21 papers were included in the scoping review. The findings suggest that child-centred care is an emerging, ambiguous poorly defined concept; no clear consensus exists about what constitutes child-centred care. Although it seems antithetical to argue against child-centred care, little robust evidence was identified that demonstrates the impact and benefit of child-centred care. If child-centred care is to be a sustainable, convincing model to guide practice and compete with other models of care, it needs to establish robust evidence of its effectiveness, the impact on children and their families, as well as the wider impacts on the healthcare system. [ABSTRACT FROM AUTHOR]

Sequential decision-making under multiple objective functions includes the problem of exhaustively searching for a Pareto-optimal policy and the problem of selecting a policy from the resulting set of Pareto-optimal policies based on the decision maker's preferences. This paper focuses on the latter problem. In order to select a policy that reflects the decision maker's preferences, it is necessary to order these policies, which is problematic because the decision-maker's preferences are generally tacit knowledge. Furthermore, it is difficult to order them quantitatively. For this reason, conventional methods have mainly been used to elicit preferences through dialogue with decision-makers and through one-to-one comparisons. In contrast, this paper proposes a method based on inverse reinforcement learning to estimate the weight of each objective from the decision-making sequence. The estimated weights can be used to quantitatively evaluate the Pareto-optimal policies from the viewpoints of the decision-makers preferences. We applied the proposed method to the multi-objective reinforcement learning benchmark problem and verified its effectiveness as an elicitation method of weights for each objective function. [ABSTRACT FROM AUTHOR]

In a democratic regime, voting is crucial to making collective decisions. Unfortunately, although this activity has great significance and value, little effort has been made to improve the way we vote. Paper ballots are still the most used method, although this method is relatively simple, brings many inconveniences, and represents a contradiction to the modern world and its advances. This paper mostly focuses on a review study of blockchain-based voting systems. It aims at identifying the strategies and the guidelines as well as provides a comprehensive end-to-end electronic voting system based on blockchain, with the help of cryptographic techniques such as zero-knowledge proofs to improve privacy. The novelty of this paper is that we tackle the limitations of electronic voting systems found in the literature, including cost, identity management, and scalability problems. Our purpose is to provide key elements for organizations on how to design their proper electronic voting system based on blockchain technology. [ABSTRACT FROM AUTHOR]

Optimizing jamming strategies is crucial for enhancing the performance of cognitive jamming systems in dynamic electromagnetic environments. The emergence of frequency-agile radars, capable of changing the carrier frequency within or between pulses, poses significant challenges for the jammer to make intelligent decisions and adapt to the dynamic environment. This paper focuses on researching intelligent jamming decision-making algorithms for Intra-Pulse Frequency Agile Radar using deep reinforcement learning. Intra-Pulse Frequency Agile Radar achieves frequency agility at the sub-pulse level, creating a significant frequency agility space. This presents challenges for traditional jamming decision-making methods to rapidly learn its changing patterns through interactions. By employing Gated Recurrent Units (GRU) to capture long-term dependencies in sequence data, together with the attention mechanism, this paper proposes a GA-Dueling DQN (GRU-Attention-based Dueling Deep Q Network) method for jamming frequency selection. Simulation results indicate that the proposed method outperforms traditional Q-learning, DQN, and Dueling DQN methods in terms of jamming effectiveness. It exhibits the fastest convergence speed and reduced reliance on prior knowledge, highlighting its significant advantages in jamming the subpulse-level frequency-agile radar. [ABSTRACT FROM AUTHOR]

In order to solve the problem of emergency decision-making with incomplete information and deal with the accident information in different time series at the scenes of major accidents, this paper proposes a method of sequential decision-making by utilizing the relevant knowledge of D-S evidence theory and game theory. Firstly, we took an oil tank fire accident as an example and sorted out historical cases and expert experiences to establish a logical relationship between key accident scenes and accident scene symptoms in the accident. Meanwhile, we applied the logistic regression analysis method to obtain the basic probability distribution of each key accident scene in the oil tank fire, and on this basis, we constructed an evidence set of the fire. Secondly, based on the D-S evidence theory, we effectively quantified the knowledge uncertainty and evidence uncertainty, with the incomplete and insufficient information taken as an evidence system of the development of key accident scenes to construct a situation prediction model of these accident scenes. Thirdly, based on the game theory, we viewed emergency decision-makers and major accidents as two sides of the game to compare and analyze accident states at different time points and solve the contradiction between loss costs of decision-making and information collection costs. Therefore, this paper has provided a solution for the optimization of accident schemes at different time stages, thus realizing the sequential decision-making at the scenes of major accidents. Furthermore, we combined the situation prediction model with sequential decision-making, with the basic steps described below: (1) We drew up an initial action plan in the case of an extreme lack of information; then, we (2) started to address the accident and constructed a framework of accident identification, (3) collected and dealt with the continuously added evidence information with the evolution of the accident, (4) calculated the confidence levels of key accident scenarios after evaluating different evidence and then predicted the accident state in the next stage, and (5) calculated the profit–loss ratio between the current decision-making scheme and the decision-making scheme of the next stage. Finally, we (6) repeated steps (3) to (5) until the accident completely vanished. We verified the feasibility of the proposed method with the explosion accident of the Zhangzhou P.X. project in Fujian on 6 April used as an example. Based on the D-S evidence theory, this method employs approximate reasoning on the incomplete and insufficient information obtained at the scenes of major accidents, thus realizing the situation prediction of key scenes of these accidents. Additionally, this method uses the game theory to solve the contradiction between decision-making loss costs and information collection costs, thus optimizing the decision-making schemes at different time stages of major accidents. [ABSTRACT FROM AUTHOR]

University campuses, characterized by their urban amenities, socio-cultural atmosphere, and diverse user population, play a significant role as public places in small cities. The physical layout of campus locations influences our views toward schooling. The concept of an inclusive campus climate refers to how people and groups perceive and interact with the environment within the campus community. The campus atmosphere encompasses the variety of people, their unique experiences, and the interpersonal contact among them. The membership functions of type-1 (T1) fuzzy sets (FSs) are represented in two dimensions, whereas the membership functions of type-2 (T2) FSs are represented in three dimensions. The introduction of this new third dimension allows for the direct modeling of uncertainties by providing more degrees of freedom. In addition, the membership value of T2 FSs is more adept at managing ambiguities. In this paper, an interval type-2 fuzzy analytic network process (IT2 FANP) is used to evaluate the weight of criteria decided by experts in the evaluation of campus climate factors with trapezoidal fuzzy numbers. The aim of this paper is to evaluate the campus climate factors using an IT2 FANP methodology. For this problem, the three main criteria and nine subcriteria were defined by experts. Therefore, the ranking of main criteria is "Physical Environment" > "Conceptual Environment" > "Administrative Environment", and the ranking of subcriteria is found as "Social Environment" > "Exterior" > "Interior" > "Cultural Environment" > "Psychological Environment" > "Inclusive Goal and Perception" > "Economic Factors" > "Accreditation" > "Legislation on Inclusive Design" by IT2 FANP methodology. [ABSTRACT FROM AUTHOR]

The application of blockchain has become a trend in the development of supply chain finance. Aiming to bridge the gap in the existing literature, this paper investigates a supply chain finance system based on blockchain technology which contains a manufacturer, a retailer and a financial institution and incorporates blockchain costs into the model. Firstly, this paper establishes a supply chain finance model based on blockchain technology and it presents a comparison with the process employed under the traditional model. Secondly, this paper establishes the revenue mathematical model of supply chain finance based on blockchain technology. Thirdly, the optimal decisions of each participant under centralized and decentralized decision-making are proved and obtained, respectively, and the influencing factors of the optimal decisions are analyzed. Finally, the conclusions are verified via simulations. This study finds that, when blockchain is used, the benefits of each participant in the chain are increased. In addition, centralized decision-making, which is more optimal in the traditional model, is also enhanced under blockchain. This paper demonstrates the superiority of blockchain-enabled supply chain finance in terms of model and revenue. This provides some suggestions for companies in the supply chain with regard to solving the problem of financing difficulties. [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]

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]

Background: The climate crisis is a significant risk to the health and wellbeing of children, young people, and future generations. While there are calls for children and young people's engagement in climate decision making, current power structures limit their participation. This paper aimed to understand children's perspectives about the impact of the climate crisis on their futures, their ability to influence climate decisions, and strategies and mechanisms to facilitate their greater engagement in decisions made about the climate crisis. Methods: Online in-depth interviews were conducted with n = 28 children (aged 12–16 years) across Australia. Photo elicitation techniques were used to prompt discussion about how the climate crisis impacted their futures, their ability to influence climate decisions, and strategies and mechanisms to engage them in climate decision making. A reflexive approach to thematic analysis was used to construct three themes from data. Images were analysed for ascribed meanings. Results: First, participants stated that they and future generations will inherit the climate crisis from older generations, specifically decision makers. Second, they described a need to address a range of age-related barriers that limit children and young people's engagement in climate decision making, including perceptions about their capabilities. Finally, they discussed strategies and mechanisms to embed children and young people's perspectives within climate decision making, including at civic and political levels. Conclusions: Children and young people have the right to be involved in decisions made about the climate crisis which significantly impact their futures, including their health and wellbeing. They argue for structural changes to embed their views in climate decision making, and describe a range of engagement strategies and mechanisms to structure their perspectives and knowledge with decision making processes. Furthermore, genuine involvement of children and young people in climate discussions must avoid youthwashing and tokenistic participation. The public health community can help address barriers to youth participation in climate action and should actively engage and collaborate with children and young people to facilitate their political and democratic influence over the climate crisis. This involves making room and creating an accessible seat at the decision making table to ensure their perspectives are embedded in climate decisions. [ABSTRACT FROM AUTHOR]

Background: Globally, a growing number of calls to formalize and strengthen evidence-support systems have been released, all of which emphasize the importance of evidence-informed decision making. To achieve this, it is critical that evidence producers and decision-makers interact, and that decision-makers' evidence needs can be efficiently translated into questions to which evidence producers can respond. This paper aims to create a taxonomy of demand-driven questions for use by evidence producers, intermediaries (i.e., people working in between researchers and decision-makers) and decision-makers. Methods: We conducted a global cross-sectional survey of units providing some type of evidence support at the explicit request of decision-makers. Unit representatives were invited to answer an online questionnaire where they were asked to provide a list of the questions that they have addressed through their evidence-support mechanism. Descriptive analyses were used to analyze the survey responses, while the questions collected from each unit were iteratively analyzed to create a mutually exclusive and collectively exhaustive list of types of questions that can be answered with some form of evidence. Results: Twenty-nine individuals completed the questionnaire, and more than 250 submitted questions were analysed to create a taxonomy of 41 different types of demand-driven questions. These 41 questions were organized by the goal to be achieved, and the goals were grouped in the four decision-making stages (i) clarifying a societal problem, its causes and potential impacts; (ii) finding and selecting options to address a problem; (iii) implementing or scaling-up an option; and (iv) monitoring implementation and evaluating impacts. Conclusion: The mutually exclusive and collectively exhaustive list of demand-driven questions will help decision-makers (to ask and prioritize questions), evidence producers (to organize and present their work), and evidence-intermediaries (to connect evidence needs with evidence supply). [ABSTRACT FROM AUTHOR]

This paper considers a risk-averse Markov decision process (MDP) with non-risk constraints as a dynamic optimization framework to ensure robustness against unfavorable outcomes in high-stakes sequential decision-making situations such as disaster response. In this regard, strong duality is proved while making no assumptions on the problem's convexity. This is necessary for some real-world issues, e.g., in the case of deprivation costs in the context of disaster relief, where convexity cannot be ensured. Our theoretical results imply that the problem can be exactly solved in a dual domain where it becomes convex. Based on our duality results, an augmented Lagrangian-based constraint handling mechanism is also developed for risk-averse reinforcement learning algorithms. The mechanism is proved to be theoretically convergent. Finally, we have also empirically established the convergence of the mechanism using a multi-stage disaster response relief allocation problem while using a fixed negative reward scheme as a benchmark. [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]

Selecting emergency suppliers from a wide range of candidates based on their performance under each criterion can be regarded as a multi-criterion decision making (MCDM) problem. Existing MCDM models to solve the emergency supplier selection problem ignored situations where large-scale suppliers exist, the influence of criteria weights on the robustness of ranking results, and the influence of psychology of regret aversion and disappointment aversion on decision results. To make up for these deficiencies, this paper proposes an MCDM model to solve emergency supplier selection problem with large-scale alternatives. Firstly, to avoid the influence of criteria weights on ranking of alternatives, the Robustness, Correlation, and Standard Deviation (ROCOSD) method is introduced to determine objective weights of criteria based on three objectives. Secondly, the τ-balanced clustering method is applied to cluster large-scale alternatives into balanced clusters. Next, considering the psychology of regret aversion and disappointment aversion of decision makers, a two-stage method is proposed to rank alternatives, which identifies the optimal alternative within each cluster and forms a new cluster consisting of these optimal alternatives in the first stage, and selects the optimal alternative from the new-formed cluster in the second stage. A numerical case is given to validate the proposed model. [ABSTRACT FROM AUTHOR]

The present study aims to explore the forms paternalistic communication can take in doctor–patient interactions and how they should be considered from a normative perspective. In contemporary philosophical debate, the problem with paternalism is often perceived as either undermining autonomy (the autonomy problem) or the paternalist viewing their judgment as superior (the superiority problem). In either case, paternalism is problematized mainly in a general, theoretical sense. In contrast, this paper investigates specific doctor–patient encounters, revealing distinct types of paternalistic communication. For this study, I reviewed videorecorded encounters from a Norwegian hospital to detect paternalism—specifically, doctors overriding patients' expressed preferences, presumably to benefit or protect the patients. I identified variations in paternalistic communication styles—termed paternalist modes—which I categorized into four types: the fighter, the advocate, the sympathizer, and the fisher. Drawing on these findings, I aim to nuance the debate on paternalism. Specifically, I argue that each paternalist mode carries its own normative implications and that the autonomy and the superiority problems manifest differently across the modes. Furthermore, by illustrating paternalism in communication through real‐life cases, I aim to reach a more comprehensive understanding of what we mean by paternalistic doctors. [ABSTRACT FROM AUTHOR]

This article delves into the critical realm of modern computer cluster management. It focuses on the effect that the increasing heterogeneity of the clusters has on the workload managers. The proposed schedulers consider node properties instead of job properties to make decisions, which is something not currently done by mainstream scheduling algorithms. In order to increase the knowledge in this topic, this paper proposes two novel algorithms whose main task is to choose the best compute nodes to schedule the incoming jobs. To this effect, they exclusively take into account the properties of the nodes, instead of the common trend of considering the properties of the jobs. The experimental results show that these algorithms outperform well-known heuristic algorithms found in the literature. [ABSTRACT FROM AUTHOR]

Introduction: This paper outlines the design, implementation, and usability study results of the patient empowerment process for chronic disease management, using Patient Reported Outcome Measurements and Shared Decision-Making Processes. Background: The ADLIFE project aims to develop innovative, digital health solutions to support personalized, integrated care for patients with severe long-term conditions such as Chronic Obstructive Pulmonary Disease, and/or Chronic Heart Failure. Successful long-term management of patients with chronic conditions requires active patient self-management and a proactive involvement of patients in their healthcare and treatment. This calls for a patient-provider partnership within an integrated system of collaborative care, supporting self-management, shared-decision making, collection of patient reported outcome measures, education, and follow-up. Methods: ADLIFE follows an outcome-based and patient-centered approach where PROMs represent an especially valuable tool to evaluate the outcomes of the care delivered. We have selected 11 standardized PROMs for evaluating the most recent patients' clinical context, enabling the decision-making process, and personalized care planning. The ADLIFE project implements the "SHARE approach' for enabling shared decision-making via two digital platforms for healthcare professionals and patients. We have successfully integrated PROMs and shared decision-making processes into our digital toolbox, based on an international interoperability standard, namely HL7 FHIR. A usability study was conducted with 3 clinical sites with 20 users in total to gather feedback and to subsequently prioritize updates to the ADLIFE toolbox. Results: User satisfaction is measured in the QUIS7 questionnaire on a 9-point scale in the following aspects: overall reaction, screen, terminology and tool feedback, learning, multimedia, training material and system capabilities. With all the average scores above 6 in all categories, most respondents have a positive reaction to the ADLIFE PEP platform and find it easy to use. We have identified shortcomings and have prioritized updates to the platform before clinical pilot studies are initiated. Conclusions: Having finalized design, implementation, and pre-deployment usability studies, and updated the tool based on further feedback, our patient empowerment mechanisms enabled via PROMs and shared decision-making processes are ready to be piloted in clinal settings. Clinical studies will be conducted based at six healthcare settings across Spain, UK, Germany, Denmark, and Israel. [ABSTRACT FROM AUTHOR]

Objective: The aim of the study was to find tools to assess patient characteristics that would help in choosing between orthotopic neobladder and ileal conduit in patients undergoing radical cystectomy. An additional goal was to search for aids that improve preoperative counseling to support patients in the decision-making process. Methods: A systematic review of MEDLINE, Web of Science, and Scopus databases was conducted, according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) statement, in April 2024. Inclusion criteria were specified in PICO format. Two reviewers independently screened titles/abstracts and full papers. Upon study selection, the results and conclusions from the studies were abstracted and quantitatively summarized in the results section of this article. Results: Seven articles, involving a total 834 patients, were included. One article described frailty, two reviewed cognitive status, one article described functional dexterity, one described personality, two articles reviewed patients' values and goals, and one article reviewed role of patient–physician dialogue in the context of choosing UD after RC. The reviewed articles identified tools and approaches that could be valuable in evaluating the suitability for continent urinary diversion (CUD) or incontinent urinary diversion (ICUD). Conclusions: This is the first systematic review that summarizes the new available methods of patient assessment which improve preoperative counseling and choosing the most suitable UD after RC. Efficient tools for this purpose are still missing, and further studies that will aid in creating a simple aid for patient selection are necessary. [ABSTRACT FROM AUTHOR]

Various factors influence wildfire probability, including land use/land cover (LULC), fuel types, and their moisture content, meteorological conditions, and terrain characteristics. The Adriatic Sea coastal area in Croatia has a long record of devastating wildfires that have caused severe ecological and economic damages as well as the loss of human lives. Assessing the conditions favorable for wildfires and the possible damages are crucial in fire risk management. Adriatic settlements and ecosystems are highly vulnerable, especially during summer, when the pressure from tourist migration is the highest. However, available fire risk models designed to fit the macro-scale level of assessment cannot provide information detailed enough to meet the decision-making conditions at the local level. This paper describes a model designed to assess wildfire risks at the meso-scale, focusing on environmental and anthropogenic descriptors derived from moderate- to high-resolution remote sensing data (Sentinel-2), Copernicus Land Monitoring Service datasets, and other open sources. Risk indices were integrated using the multi-criteria decision analysis method, the analytic hierarchy process (AHP), in a GIS environment. The model was tested in three coastal catchments, each having recently experienced severe fire events. The approach successfully identified zones at risk and the level of risk, depending on the various environmental and anthropogenic conditions. [ABSTRACT FROM AUTHOR]

This paper concerns the extension of the Heritage Digital Twin Ontology introduced in previous research to describe the reactivity of digital twins used for cultural heritage documentation by including the semantic description of sensors and activators and all of the process of interacting with the real world. After analysing previous work on the use of digital twins in cultural heritage, a summary description of the Heritage Digital Twin Ontology is provided, and the existing applications of digital twins to cultural heritage are overviewed, with references to reviews summarising the large production of scientific contributions on the topic. Then, a novel ontology named the Reactive Digital Twin Ontology is described, in which sensors, activators, and the decision processes are also semantically described, turning the previous synchronic approach to cultural heritage documentation into a diachronic one. Some case studies exemplify this theory. [ABSTRACT FROM AUTHOR]

Customer churn prediction is vital for organizations to mitigate costs and foster growth. Ensemble learning models are commonly used for churn prediction. Diversity and prediction performance are two essential principles for constructing ensemble classifiers. Therefore, developing accurate ensemble learning models consisting of diverse base classifiers is a considerable challenge in this area. In this study, we propose two multi-objective evolutionary ensemble learning models based on clustering (MOEECs), which are include a novel diversity measure. Also, to overcome the data imbalance problem, another objective function is presented in the second model to evaluate ensemble performance. The proposed models in this paper are evaluated with a dataset collected from a mobile operator database. Our first model, MOEEC-1, achieves an accuracy of 97.30% and an AUC of 93.76%, outperforming classical classifiers and other ensemble models. Similarly, MOEEC-2 attains an accuracy of 96.35% and an AUC of 94.89%, showcasing its effectiveness in churn prediction. Furthermore, comparison with previous churn models reveals that MOEEC-1 and MOEEC-2 exhibit superior performance in accuracy, precision, and F-score. Overall, our proposed MOEECs demonstrate significant advancements in churn prediction accuracy and outperform existing models in terms of key performance metrics. These findings underscore the efficacy of our approach in addressing the challenges of customer churn prediction and its potential for practical application in organizational decision-making. [ABSTRACT FROM AUTHOR]

In the field of visualization, understanding users' analytical reasoning is important for evaluating the effectiveness of visualization applications. Several studies have been conducted to capture and analyze user interactions to comprehend this reasoning process. However, few have successfully linked these interactions to users' reasoning processes. This paper introduces an approach that addresses the limitation by correlating semantic user interactions with analysis decisions using an interactive wire transaction analysis system and a visual state transition matrix, both designed as visual analytics applications. The system enables interactive analysis for evaluating financial fraud in wire transactions. It also allows mapping captured user interactions and analytical decisions back onto the visualization to reveal their decision differences. The visual state transition matrix further aids in understanding users' analytical flows, revealing their decision-making processes. Classification machine learning algorithms are applied to evaluate the effectiveness of our approach in understanding users' analytical reasoning process by connecting the captured semantic user interactions to their decisions (i.e., suspicious, not suspicious, and inconclusive) on wire transactions. With the algorithms, an average of 72% accuracy is determined to classify the semantic user interactions. For classifying individual decisions, the average accuracy is 70%. Notably, the accuracy for classifying 'inconclusive' decisions is 83%. Overall, the proposed approach improves the understanding of users' analytical decisions and provides a robust method for evaluating user interactions in visualization tools. [ABSTRACT FROM AUTHOR]

In this paper, we present four categories of covering-based intuitionistic hesitant fuzzy rough set (CIHFRS) models using intuitionistic hesitant fuzzy β -neighborhoods (IHF β -neighborhoods) and intuitionistic hesitant fuzzy complementary β -neighborhoods (IHFC β -neighborhoods. Through theoretical analysis of covering-based IHFRS models, we propose the intuitionistic hesitant fuzzy TOPSIS (IHF-TOPSIS) technique for order of preference by similarity to an ideal solution, addressing multicriteria decision-making (MCDM) challenges concerning the assessment of IHF data. A compelling example aptly showcases the suggested approach. Furthermore, we address MCDM problems regarding the assessment of IHF information based on CIHFRS models. Through comparison and analysis, it is evident that addressing MCDM problems by assessing IHF data using CIHFRS models proves more effective than utilizing intuitionistic fuzzy data with CIFRS models or hesitant fuzzy information with CHFRS models. IHFS emerges as a unique and superior tool for addressing real-world challenges. Additionally, covering-based rough sets (CRSs) have been successfully applied to decision problems due to their robust capability in handling unclear data. In this study, by combining CRSs with IHFS, four classes of CIFRS versions are established using IHF β -neighborhoods and IHFC β -neighborhoods. A corresponding approximation axiomatic system is developed for each. The roughness and precision degrees of CBIHFRS models are specifically talked about. The relationship among these four types of IHFRS versions and existing related versions is presented based on theoretical investigations. A method for MCDM problems through IHF information, namely, IHF-TOPSIS, is introduced to further demonstrate its effectiveness and applicability. By conducting a comparative study, the effectiveness of the suggested approach is evaluated. [ABSTRACT FROM AUTHOR]

In the majority of Abhidharmic Buddhist schools, smṛti serves the crucial function of ensuring the recollection of past events and is thus conceptualized as memory in scholarly discourse nowadays. Nevertheless, upon closer examination of various doctrinal perspectives, the interpretation of smṛti diverges significantly among different schools. Notably, it is deemed to be omnipresent in every moment of perception within the Sarvāstivādin tradition, while in Yogācāra, it is regarded as a specific response to an experienced object. This paper seeks to delve into the concept of smṛti within the latter tradition, with a specific focus on the Cheng Weishi Lun (成唯識論, henceforth: CWSL). Drawing upon the foundational principles of Yogācāra doctrine, which posit the ālayavijñāna as an underlying substratum responsible for retaining past information, I contend that smṛti embodies a cognitive faculty that intensifies the apprehension of learned objects when they are present. Furthermore, I argue that smṛti serves as the inception point for a series of cognitive processes acquiring knowledge that helps decision-making. Additionally, I will demonstrate that the function of smṛti in the CWSL diverges from both traditional Buddhist conceptions of memory and contemporary understandings thereof. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has placed enormous pressure on healthcare systems. In the first line of the "war" against the virus, hospitals tried to maintain their general operations, while supplementing their services with COVID-19 patient care. To tackle the new difficulties, hospitals had to develop strategic response plans, and were in need of finding the most effective management structure for their institution. The focus of this paper is the aforementioned management structures. After overviewing the international literature, we identified three different approaches and we named them the Horizontal, Vertical, and Intermedier Approach. The separation is based on the following features: decision making, information distribution, command forwarding, intra-hospital communication channels, hierarchy, and control. After summarizing the international experiences, we introduce a Hungarian centrum hospital's approach, as the institution was assigned to fulfil COVID-19 centrum hospital duties in the middle of the first wave. The North-Buda Szent János Central hospital was in need of restructuring their management structure, and the leadership decided to transform it into a hierarchical, vertical structure, operating with centralized decision making and personal control. This control–command system idea came from the hospital's Internist Head Coordinator Physician, who is the first author of our article, and had serious military medicine experiences (in Afghanistan). [ABSTRACT FROM AUTHOR]

Integrating large language model (LLM) agents within game theory demonstrates their ability to replicate human-like behaviors through strategic decision making. In this paper, we introduce an augmented LLM agent, called the private agent, which engages in private deliberation and employs deception in repeated games. Utilizing the partially observable stochastic game (POSG) framework and incorporating in-context learning (ICL) and chain-of-thought (CoT) prompting, we investigated the private agent's proficiency in both competitive and cooperative scenarios. Our empirical analysis demonstrated that the private agent consistently achieved higher long-term payoffs than its baseline counterpart and performed similarly or better in various game settings. However, we also found inherent deficiencies of LLMs in certain algorithmic capabilities crucial for high-quality decision making in games. These findings highlight the potential for enhancing LLM agents' performance in multi-player games using information-theoretic approaches of deception and communication with complex environments. [ABSTRACT FROM AUTHOR]

Given the rapid advancement of artificial intelligence, understanding the foundations of intelligent behaviour is increasingly important. Active inference, regarded as a general theory of behaviour, offers a principled approach to probing the basis of sophistication in planning and decision-making. This paper examines two decision-making schemes in active inference based on "planning" and "learning from experience". Furthermore, we also introduce a mixed model that navigates the data complexity trade-off between these strategies, leveraging the strengths of both to facilitate balanced decision-making. We evaluate our proposed model in a challenging grid-world scenario that requires adaptability from the agent. Additionally, our model provides the opportunity to analyse the evolution of various parameters, offering valuable insights and contributing to an explainable framework for intelligent decision-making. [ABSTRACT FROM AUTHOR]

Accurate precipitation prediction is very important for meteorological disaster prevention, water resources management, and agricultural decision making. To improve the accuracy of precipitation prediction, a hybrid model based on variational mode decomposition (VMD), crested porcupine optimization algorithm (CPO), and long short-term memory model (LSTM) is proposed in this paper. The model first uses VMD to decompose the precipitation time series into intrinsic mode functions of different frequencies to capture the multi-scale characteristics of precipitation data. Then, the CPO algorithm is used to optimize LSTM adaptive parameters to improve the global search ability and robustness of the model. Finally, the decomposed precipitation component is input into the LSTM network to learn the spatiotemporal dependence relationship and improve the ability of long-term prediction. The experimental results show that compared with the traditional LSTM model, CPO-LSTM model, and VMD-LSTM model, the hybrid model achieves better performance in many evaluation indices and effectively improves the accuracy of precipitation prediction. The application of the model can provide an effective tool for the fields of meteorology and water resources management, as well as provide new ideas for related research. [ABSTRACT FROM AUTHOR]

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]

Dataset biases are notoriously detrimental to model robustness and generalization. The identify-emphasize paradigm appears to be effective in dealing with unknown biases. However, we discover that it is still plagued by two challenges: A, the quality of the identified bias-conflicting samples is far from satisfactory; B, the emphasizing strategies only produce suboptimal performance. In this paper, for challenge A, we propose an effective bias-conflicting scoring method (ECS) to boost the identification accuracy, along with two practical strategies — peer-picking and epoch-ensemble. For challenge B, we point out that the gradient contribution statistics can be a reliable indicator to inspect whether the optimization is dominated by bias-aligned samples. Then, we propose gradient alignment (GA), which employs gradient statistics to balance the contributions of the mined bias-aligned and bias-conflicting samples dynamically throughout the learning process, forcing models to leverage intrinsic features to make fair decisions. Furthermore, we incorporate self-supervised (SS) pretext tasks into training, which enable models to exploit richer features rather than the simple shortcuts, resulting in more robust models. Experiments are conducted on multiple datasets in various settings, demonstrating that the proposed solution can mitigate the impact of unknown biases and achieve state-of-the-art performance. [ABSTRACT FROM AUTHOR]

The paper presents the results of research on the influence of risk factors on the implementation of railway investments in Poland (build stage) and deals with a detailed diagnosis of relation between factors. The application of DEcision MAking Trial and Evaluation Laboratory (DEMATEL) method for the analyses allowed to develop a cause-and-effect model of key factors and analyse the importance of the factors. Eleven factors identified in eariel studies as the most important risk factors were examined. It was found that the factors: errors in the preparation of tender documents (10.38%), errors in project documentation (10.02%), improperly estimated time of completion of the investment by the Employer (9.82%), internal regulations of PKP Polskie Koleje Państwowe S.A. (Polish State Railways) not coordinated with the provisions of contracts (9.51%) have the highest degree of importance. Factors: too many external institutions involved in the investment process and internal regulations of PKP Polskie Koleje Państwowe S.A. (Polish State Railways) not coordinated with the provisions of contracts, have the greatest net impact on the other factors. The relations between the factors and factors importance are valuable knowledge for engineers, enabling the project to be implemented according to the planned schedule and investment cost. [ABSTRACT FROM AUTHOR]

Background: This systematic review contributes to the understanding of the characteristics of built food environments that may be associated with choices of alternative protein foods (APF). Using the built food environment typology proposed by Downs et al., we investigated various environmental structures (e.g., supermarkets, other retailers, farmers' markets, restaurants, schools, and online vendors) and the characteristics that may facilitate or hinder consumers' choices. For example, facilitators and barriers may refer to the physical characteristics of environmental structures, food presentation practices, the organizational strategies or policies operating in the setting, or the actions that retailers or consumers engage in while selling, serving, choosing, trying, or purchasing APF in these environmental structures. Methods: A systematic review (PROSPERO database preregistration; no. CRD42023388700) was conducted by searching 13 databases for peer-reviewed journals focusing on the fields of economics and business, agriculture, medical sciences, and social sciences. Data searches, coding, and quality evaluations were conducted by at least 2 researchers. A total of 31 papers (36 original studies) were included. The risk of bias was evaluated with the Joanna Briggs Institute quality evaluation tool, with 24 publications presenting low risk of bias. Results: The findings indicate that perceived and actual availability facilitate consumers' APF choices across a built food environment. Several barriers/facilitators were associated with APF choices in specific types of built food environments: the way food is presented in produce sections (supermarkets), consumer habits in terms of green and specialty shopping (grocery stores), and mismatches among retailer actions in regard to making APF available in one type of food environment structure (e-commerce) and consumers' preferences for APF being available in other food environment structures (supermarkets, grocery stores). The effect of a barrier/facilitator may depend on the APF type; for example, social norms regarding masculinity were a barrier affecting plant-based APF choices in restaurants, but these norms were not a barrier affecting the choice of insect-based APF in restaurants. Conclusions: Addressing barriers/facilitators identified in this review will help in developing environment-matching interventions that aim to make alternative proteins mainstream. Trial registration: PROSPERO database registration: #CRD42023388700. [ABSTRACT FROM AUTHOR]

This paper aims to discuss the microvascular reconstruction of the orbit and adjacent regions and to propose a checklist to aid the reconstructive surgeon in this challenging undertaking. The discussion is based on a literature review that includes 32 retrospective case series, 5 case reports published in the last 34 years in PubMed, and 3 textbook chapters. Additionally, it relies on the senior author's expertise, described in a case series, and two case reports published elsewhere. Classifications and treatment algorithms on microvascular orbit reconstruction generally disregard patient-related factors. A more holistic approach can be advantageous: patient-related factors, such as age, comorbidities, prognosis, previous interventions, radiotherapy, and the wish for maximal dental rehabilitation and a prosthetic eye, have the same importance as defect-related considerations and can inform the choice of a reconstructive option. In this manuscript, we examine defect- and patient-related factors and new technologies, provide a checklist, and examine future directions. The checklist is intended as a tool to aid in the decision-making process when reconstructing the orbital region with microvascular flaps. [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]

Simple Summary: The microenvironment of hepatocellular carcinoma is a really complex milieu, containing a wide variety of cells with specific tasks, belonging to both the mesenchimal and immunitary systems, the extracellular matrix, growth factors, proinflammatory cytokines, and translocated bacterial products. This intricate environment represents a source of potential targets in order to establish a wide and multitask therapeutic strategy for hepatocellular carcinoma. Hepatocellular carcinoma is a malignant tumor that originates from hepatocytes in an inflammatory substrate due to different degrees of liver fibrosis up to cirrhosis. In recent years, there has been growing interest in the role played by the complex interrelationship between hepatocellular carcinoma and its microenvironment, capable of influencing tumourigenesis, neoplastic growth, and its progression or even inhibition. The microenvironment is made up of an intricate network of mesenchymal cells, immune system cells, extracellular matrix, and growth factors, as well as proinflammatory cytokines and translocated bacterial products coming from the intestinal microenvironment via the enterohepatic circulation. The aim of this paper is to review the role of the HCC microenvironment and describe the possible implications in the choice of the most appropriate therapeutic scheme in the prediction of tumor response or resistance to currently applied treatments and in the possible development of future therapeutic perspectives, in order to circumvent resistance and break down the tumor's defensive fort. [ABSTRACT FROM AUTHOR]