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Abstract: Several authors have highlighted the role of intuition in expertise. In particular, a large amount of data has been collected about intuition in expert nursing, and intuition plays an important role in the influential theory of nursing expertise developed by Benner [1984. From Novice to Expert: Excellence and Power in Clinical Nursing Practice. Addison–Wesley, Menlo Park, CA]. We discuss this theory, and highlight both data that support it and data that challenge it. Based on this assessment, we propose a new theory of nursing expertise and intuition, which emphasizes how perception and conscious problem solving are intimately related. In the discussion, we propose that this theory opens new avenues of enquiry for research into nursing expertise. [Copyright &y& Elsevier]

The main aim of this paper is to present a new multi-attribute decision-making (MADM) approach for solving the problems under the uncertain and complex environment. The key challenges during any MADM problem are how to quantify the objective uncertainty information in the data and how to aggregate such collective information. To answer this, in this paper, we utilize the concept of the bipolar fuzzy information to mark the information in terms of the positive and negative support. To aggregate this different information, we propose some power aggregation operators based on the Aczel-Alsina operators (AAO). The AAOs are the generalized t-norm based operations with an additional parameter to analyze the influence of the expert preferences. Based on these AAO and bipolar fuzzy information, we stated bipolar fuzzy AA power weighted averaging and geometric operators and investigate their features. Later, based on these operators, we establish a MADM algorithm to solve the decision-making problems. The applicability of the stated algorithm is demonstrated through a case study related to quantum computing. The comparative studies and advantages of the study are also analyzed with the various prevailing theories. [ABSTRACT FROM AUTHOR]

Media ethics is an area of inquiry that has been of great concern among journalists and public relations professionals as well as scholars across the world. Empirical research on media ethics of comparative nature has increased, providing a descriptive account on the current situations across different countries. Yet, theoretical efforts to explain global media ethics are so far inexistent. The scope of this paper is to provide a conceptual model for explaining media ethics decision-making processes that takes into account personal, professional and environment values as main influencing forces. The proposed model suggests that besides considering personal factors, such as education, background, experience, gender, etc., and country specific factors, such as political, economic, socio-cultural conditions, one should first and foremost understand the degree to which personal, professional, and environment values influence one’s judgment. This model is the first of its kind in providing an explanation of differences in global media ethics by considering the interconnectedness of different values at micro, meso, and macro levels. [ABSTRACT FROM AUTHOR]

This research paper focuses on the development of advanced mathematical models for disease diagnosis and prediction, and the subsequent creation of automated systems based on these models. These systems leverage a range of mathematical models and incorporate cutting-edge information technology achievements to provide medical professionals with valuable decision-making support. By amalgamating mathematical rigor and technological innovation, this research endeavors to enhance the accuracy and efficiency of medical diagnoses, thereby improving patient care and healthcare outcomes. This study delves into the persistent need for contemporary information systems, where information plays a crucial role in decision-making. It aims to provide an objective approach to addressing pressing medical challenges, particularly in disease diagnosis and prediction, enhancing the effectiveness of these critical tasks. Automated medical information systems, built on advanced mathematical models, significantly empower physicians. Machine diagnostics, relying on deterministic logic, the phase interval method, and information-probabilistic logic, bolster diagnostic capabilities. Functional entropy enables individuals to handle vague information, aiding decision-making. Assessing imprecision and uncertainty computationally diminishes subjectivity, while employing fuzzy set theory enhances diagnostic modeling. Mathematical models assess diagnostic indicators, and linguistic variables quantify resemblance. The diagnostic model for primary biliary cirrhosis and active hepatitis utilizes a diagnostic table and gradient projection. This comprehensive study advances medical diagnostics through mathematical models and automated systems, addressing critical healthcare challenges. [ABSTRACT FROM AUTHOR]

• There has been a limited scope of investigation of the contextual factors affecting driver decision-making during narrow passage interactions. • The study found that the likelihood of giving way may alter due to characteristics of the interaction partners, including their vehicle type. • It was also found that factors, external to the direct interaction partners, may affect the likelihood of giving way, such as the presence of vehicles beyond the direct interaction partners. • This paper validates previous communication findings in a UK context. Narrow passage interactions have received increased attention from academics seeking to create behavioural models of the interaction and those looking to define how autonomous vehicles (AVs) should interact with their human counterparts in a composite road system. Despite this increased attention, many factors remain unexplored in the narrow passage literature, with the literature also encompassing few driving culture contexts. To this end, this study employs an explorative survey to identify additional factors that affect driver decision-making during narrow passage interactions, as well as driver perceptions of different communications in a UK context. The study's 243 participants were presented with a range of different narrow passage scenarios and asked to indicate how likely they were to give way/yield to a vehicle approaching the narrow passage from the opposite direction. In addition, they also completed the Multidimensional Driving Style Inventory to identify their driving styles and asked to identify which signals they look for from their interaction partner during narrow passage interactions, as well as the meaning of those signals. The results of the study show that situational characteristics such as the vehicle type being interacted with, being in a rush and being followed by vehicles alter the likelihood of drivers giving way at narrow passages, whilst a person's driving style can also indicate how likely someone is to give way to another vehicle. These results highlight the factors that are considered by drivers, increasing our understanding of the factors that need to be incorporated in driver behaviour models and in AV development. [ABSTRACT FROM AUTHOR]

During the COVID-19 crisis, leaders of academic libraries had to make various decisions to retain library relevance and maintain essential services. The COVID-19 crisis raised the question of libraries' value to their universities more than ever. Libraries were facing financial difficulties but also operational challenges due to the services built around physical libraries. This paper uses a mixed methods approach to investigate academic library leaders' decision-making during the first year of the COVID-19 crisis. It combines quantitative and qualitative data from previous studies with primary data collected by the author to determine what kind of decisions university library leaders faced during the crisis and why. These studies show that leaders were concerned about the following main challenges: lack of access to physical services and collections, the safety of staff and patrons, new ways of working, and the library's role during the crisis. The results show that library leaders made decisions in smaller groups or some cases, individually due to the lack of time or information. While there have been numerous studies on libraries' response to the COVID-19 crisis in the past three years, this paper focuses mainly on academic library leaders and their decision-making to solve the crisis for their libraries. [ABSTRACT FROM AUTHOR]

Alpine skiing beginners are easily injured if they fail make the right decisions when faced with unexpected situations. Excellent decision-making requires extensive training, while alpine skiing training is limited by time and space. To address these issues, this paper combines VR and machine learning techniques to develop a high fidelity VR skiing decision-making training system based on HCPS framework. The aim is to train beginner skiers in decision-making by creating automated and repeatable virtual scenarios. Firstly, physical world (ski resort environment, and user motions) was mapped into the VR world using the HCPS framework. Afterwards, the user's decision motions were made based on the scenarios provided, while being identified through an improved SVM (94.5% accuracy). The motion recognition results were fed into the expert system to assess the feasibility of the decision-making. Finally, the feasibility of the system was assessed with a combination of subjective and objective approaches. The results showed that the VR system significantly improved the participants' skiing decision-making ability and speed compared to traditional 2D video training. The high fidelity environment reduced participants' motion sickness symptoms and resulted in a better sensory experience. In addition, participants were more focused in the high fidelity environment. Overall, these results demonstrated the construct validity of the virtual ski decision-making training system. This study changes the traditional training method to create an immersive training environment, and provides an effective avenue for the development of sports training. [Display omitted] • Designs processes and algorithmic models for virtual ski decision systems are presented. • Interaction between user and system via IMU and EEG signals. • The feasibility of the system is assessed through a combination of subjective and objective methods. • Voting classifier achieves higher action recognition rate. • VR training system improves decision-making skills and speed. [ABSTRACT FROM AUTHOR]

• An AI-based approach to deal with uncertainties and complexities in urban VPP study. • Utilise the reinforcement learning's robustness and efficiency for optimal VPP policy in urban environments. • The reinforcement learning AI's capacity for optimal demand response and energy trading is validated. • Statistical analysis is provided to verify the proposed approach's performance and suitability. Virtual Power Plants (VPPs) are becoming popular for managing energy supply in urban environments with Distributed Energy Resources (DERs). However, decision-making for VPPs in such complex environments is challenging due to multiple uncertainties and complexities. This paper proposes an approach that optimizes decision-making for VPPs using Reinforcement Learning (RL) in urban environments with diverse supply-demand profiles and DERs. The approach addresses challenges related to integrating renewable energy sources and achieving energy efficiency. An RL-based VPP system is trained and tested under different scenarios, and a case study is conducted in a real-world urban environment. The proposed approach achieves multi-objective optimization by performing actions such as load-shifting, demand offsetting, and providing ancillary services in response to demand, renewable generators, and market signals. The study validates the effectiveness and robustness of the proposed approach under complex environmental conditions. Results demonstrate that the approach provides optimized decisions in various urban environments with different available resources and supply-demand profiles. This paper contributes to understanding the use of RL in optimizing VPP decision-making and provides valuable insights for policymakers and practitioners in sustainable and resilient cities. [ABSTRACT FROM AUTHOR]

Science plays an increasingly important role in responding to the issues arising from natural threats and anthropogenic activities in the Antarctic region. As one of the most prominent scientific non-governmental organizations (NGOs) in Antarctic governance, the Scientific Committee on Antarctic Research (SCAR) participates in the Antarctic Treaty System (ATS) as an observer, informing the process of decision-making. To research on how SCAR informs decision-making, the study conducts empirical analysis of SCAR papers and examines their submission, adoption, and how they inform the decision-making process of the ATS. Based on the empirical analysis of SCAR papers, the study finds that with deeper involvement of SCAR in Antarctic governance, the number of papers submitted by SCAR shows an upward tendency, and the types of papers display the diversity. The actors submitting joint papers together with SCAR become diversified, and the Antarctic Treaty Consultant Parties (ATCPs) are the most important among these actors. The number of adopted papers also increases, especially the number adopted as resolutions, which promotes the revision of relevant rules. SCAR's scientific functions in Antarctic governance are driven by the advancement of the ATS, the internal reform of SCAR, and supports from decision-makers. Given the limits of SCAR's scientific functions based on the theory of science–policy interface, the cooperation among SCAR, increasing numbers of ATCPs, and other actors may better respond to the emerging challenges in the Antarctic region. [ABSTRACT FROM AUTHOR]

• The contribution of biodiversity researchers to policy-making is linked to how they see decision-making. • The rationalist model of decision often prevails in researchers' assumptions regarding the social uptake of their results. • To supplement this rationalist model, 3 dimensions of decision-making are presented, and their combination leads to 27 different contexts. • Researchers' contributions vary according to the type of decision-making they may contribute to. • Information is often used in an argumentative way, which has important consequences on how researchers treat social values. Science and knowledge are called to participate in the institutional, political and behavioral changes that are necessary to address biodiversity loss. Knowledge however does not possess direct performative powers, and the relationships between knowledge production and use are complex, as has been documented for multiple issues and by different fields of research. This paper proposes to unpack three dimensions of "decision-making". By taking the field of ecosystem service assessment and valuation as its main example, the paper discusses and illustrates how this unpacking can help researchers situate themselves in the diversity of decision-making contexts that can exist, and how this can influence the contributions they can make to try and solve biodiversity issues they are involved in. After describing some key characteristics of the "rationalist" vision of decision, it suggests that looking at the temporal continuity of decision-making processes (e.g., a one-time moment versus a continuous and diffuse process), the degree of conflict in a process, and the range of actors involved and their modes of interaction, widely expands the variety of ways that ecosystem service assessment and valuation can be involved in decision-making. The paper uses three case studies to illustrate this variety, and discusses some implications for researchers, both in terms of acknowledging their situatedness and of future research prospects. [ABSTRACT FROM AUTHOR]

Nowadays, the agriculture constitutes one of the leading sources of foods and it contributes to the national income and livelihood of most countries. However, in the last two decades, the agriculture is facing several challenges such as the increasing demand for food, the extreme weather conditions, the rising of climate change, etc. Hence, in order to overcome these challenges, the agriculture needs to be smarter by integrating new technologies with the aim to enhance the productivity and reduce the agriculture cost and waste. Recently, the emergence of Internet of Things (IoT) technology has led to a new revolution in the agriculture called as smart agriculture. Such technology consists of a set of sensors that allow to remotely monitoring the field conditions and sending real-time data about their status to the farmer for a decision-making. However, the smart agriculture suffers from the huge amount of collected data that complicates the decision-making process of the farmer and consumes the limited available energy of the sensors. In this paper, we propose a real-time AGRiculture mOnitoring, called AGRO, mechanism for efficiently sensing the soil moisture of a field and enhancing the irrigation system. AGRO divides the monitored field into small zones, called grids, where each grid assigned a grid leader (GL). Then, AGRO consists of three phases: smart sensing, energy saving and decision-making. The first phase aims to efficiently sense the soil moisture condition and update the farmer about the progress of the field status while reducing the amount of data transmitted to the GL. The second phase studies the variation of the field status during successive period times and allows to adapt the sensing frequency of the sensor in order save its energy and extend its lifetime. The last phase aims to study the data collected by the sensors at the same grid in order to allow the farmer to take a right decision based on a predefined decision table. We conducted a set of simulations in order to show the efficiency of our mechanism in terms of data reduction, energy conservation and accurate decision-making compared to other existing techniques. [ABSTRACT FROM AUTHOR]

Microplastic pollution is increasingly recognised as a global environmental challenge which stems from the rapid growth of the use of petrochemical-derived plastic. As researchers and practitioners face a myriad of environmental challenges, oceanic microplastic pollution has so far dominated interest. However, airborne microplastics present an increasing environmental and public health concern. There is currently a need for research addressing this emerging challenge, and at the same time, the lack of knowledge and consensus regarding airborne microplastics presents an obstacle to action. The purpose of this study is to utilise a participatory Structured Decision-Making (SDM) approach to understand the perspectives of a range of stakeholders involved in the microplastics landscape, and subsequently refine common research priorities and knowledge gaps to advance the field. Through two participatory workshops, we first defined shared objectives of stakeholders and then negotiated best courses of action to achieve these objectives based on discussion between stakeholders and facilitators. The qualitative approach taken has enabled the full, complex and multidisciplinary aspects of the research into airborne microplastic pollution to be considered. Our findings highlight some important potential consequences of airborne microplastic pollution, including impacts on human health, and the need for more interdisciplinary research, and collaborative, integrated approaches in this area. As a result of the first workshop, five fundamental objectives on the theme of airborne microplastics were identified. As a direct consequence of this, participants identified 84 actions split across eight themes, which are outlined later in this paper. • A participatory Structured Decision-Making approach was utilized to understand perspectives of a wide range of stakeholders. • Important drivers and consequences of airborne microplastic pollution were identified. • Interdisciplinary research, and collaborative, integrated approaches were deduced as the most important actions between all stakeholders. [ABSTRACT FROM AUTHOR]

Emergency decision-making is vital for nations or communities because it increases emergency management's effectiveness and legitimacy, which in turn greatly minimizes environmental damage, fatalities, and economic loss. The evaluation of emergency judgements must take into account significant inaccuracy, fuzzyness, and ambiguity. While high risk and uncertainty are frequently characteristics of emergency decision-making (EDM) circumstances. The current EDM methodologies do not take into account the psychological behavior of the decision makers in addition to the various emergency situations and the various responses. In this paper, we presented a new method based on a Complex Probabilistic Hesitant Fuzzy Soft Set (CPHFSS) as represented by membership in 2-D with hesitant probability. The idea that is being put forth captures the ambiguity and takes into account emergency situations while also taking into account the psychological state of the decision-makers involved in the EDM procedure. The introduction of a new score function for the CPHFSS serves as the second objective of this paper, which is to address a number of comparability issues. We examine a novel family of hybrid operators for CPHFSS that utilize numerous independent variables as a solution to these unforeseen issues. The group decision-making strategy and EDAS technique are then suggested employing these operators. Later, an emergency decision-making situation involving a major fire in China is used to demonstrate the validity of the algorithms. • Concept of complex probabilistic hesitant fuzzy soft set is presented. • Introduce a new score function for the CPHFSS. • Group decision-making strategy and EDAS technique presented. • Emergency decision-making problems discussed in the study. • Case study related to industrial disaster emergency decision-making discuss. [ABSTRACT FROM AUTHOR]

Abstract Catering supplier selection is multi-criteria decision-making (MCDM) problem faced by airline industry. The evaluation process primarily involves the assessment of several complex factors for selecting a catering supplier that will best meet the airline requirements. There are a few applications of the catering supplier selection model. The approach of this paper is towards the solution for catering supplier selection. This study integrates the analytical hierarchy process (AHP) with a type of performance rating involving the determination a degree of utility by additive ratio assessment (ARAS) and multi-choice goal programming (MCGP). A real-life case study in selecting a catering supplier is also presented. Highlights • How to choose the best catering supplier to increase customer's satisfaction is crucial for the airline. • An integrated method combining AHP, ARAS and MCGP is proposed to solve the MCDM problem of catering supplier selection. • This new model considers both qualitative and quantitative decision criteria to select the best catering supplier. • This paper is to propose an efficient and simple method to assist airlines in choosing the best catering supplier. [ABSTRACT FROM AUTHOR]

Abstract Valuation encompasses far more than the practice of estimating the financial value of environmental services and changes. It extends to almost any socio-political process in which local actors seek to shape the development of their places and environments by describing what matters about these places. This paper draws on diverse engagements with participants involved in high-profile marine development decisions in New Zealand to examine value-articulating institutions (VAIs), i.e., the formal and informal ways that values of affected parties are elicited, received and evaluated by decision-makers. Our study shows that formal VAIs can be alienating for many participants, demanding huge investments of time and personal sacrifices, demoralising members of communities when their local knowledge and expertise is delegitimised. Such processes can leave communities shaken, conflict-riven and unhappy, and undermine the legitimacy of democratic institutions. All VAIs are imperfect – using informal along with formal VAIs can provide alternative spaces for people to present and describe their values. The paper describes in situ experiences that provide an informal alternative to more formal VAIs. Highlights • Valuation for decision-making can create a destructive path through communities. • All value articulating institutions are imperfect. • Practice should be transparent, democratic and open to new ways to receive values. • In situ experiences can be an effective way to receive intangible values. [ABSTRACT FROM AUTHOR]

Abstract Target searching in an unknown environment is a traditional research issue in the multiagent area. In some real cases, the agents do not only discover the targets; instead, they have subsequent tasks that must be completed before a deadline. In this paper, these cases are abstracted as the agents searching for target locations in an unknown environment and then occupying these target locations within a limited time. The agents can obtain rewards by occupying the target locations, and the goal of this problem is to maximize net income, defined as total reward minus the moving cost of the agents. This problem can be transformed into the traditional problems, and then be solved by previous related algorithms. However, this approach is not optimal. In this paper, we present a method that combines previous algorithms and a decision-making algorithm. The experiments demonstrate that the method containing our decision-making algorithm can lead to higher net income than simply using previous algorithms. Highlights • The multiagent target searching and occupancy problem is analyzed. • Previous algorithms are combined with a new decision-making algorithm. • The method containing our decision-making algorithm can lead to better results. [ABSTRACT FROM AUTHOR]

Abstract The offshore wind farm has developed fast as a type of abundant, clean, renewable energy sources. The offshore wind farm located close to the shore especially in busy waterways may have significantly impact on the maritime safety. This paper proposes a fuzzy-MADM (multiple attribute decision-making) method for site selection of the offshore wind farm by comprehensively considering the feasibility of installation (i.e. cost and production) and maritime safety. The kernel of this approach is to establish a three-layer decision-making framework after identifying the influencing factors from previous works, to derive the decision matrix by integrating the influencing factor and to obtain the weights of the attributes by using the Analytic Hierarchy Process. The proposed approach is applied to a real site selection of offshore wind farm in the Eastern China Sea. The result is reasonable because it agrees with the discussion of a workshop and further analysis. Moreover, it illustrates that the maritime safety is a predominant factor for selection of offshore wind farm. Consequently, this paper provides a practical and quantitative tool for site selection of offshore wind farm. Highlights • Fuzzy-MADM is applied for offshore wind farm in busy waterways. • The feasibility of installation and maritime safety are comprehensively considered. • A three-layer decision making framework is established for site selection. [ABSTRACT FROM AUTHOR]

• 31 out of 1465 studies were selected for different CF decision-making stages; • The paper assessed five key CF decision-making stages for both building and infrastructure; • A generalised CF management decision-making framework was developed. There is an urgent need for all industries to go beyond the existing set of policies against greenhouse gas (GHG) emissions to combat climate change. As the major producer (19%) of GHG, the construction industry needs to optimise its carbon footprint (CF) decision-making processes to provide better management solutions against global warming. Identification and evaluation of the key factors that affect CF decision-making are critical yet limited. Based on our knowledge, no existing study compares the different characteristics of construction projects' CF management approaches through the whole life cycle phases (including assessment, optimisation, and comparison) to enable CF-related knowledge transfer between the building and infrastructure sectors. To meet these knowledge gaps, this paper reviewed 1,465 studies related to infrastructure and building CF projects and selected 31 studies to evaluate academic researchers' contributions toward the different CF decision-making stages for the building and infrastructure projects' whole lifecycle. The paper systematically reviewed and assessed the existing information in five key CF decision-making stages: project drafting stage, methodology choosing stage, data collection stage, CF analysis stage, and CF visualisation stage. A generalised CF decision-making framework is also developed to inform future CF management. The limitations of the existing studies and the benefits of the developed framework were summarised. This study identifies the key information required from the perspective of CF management decision-makers and contributes a universal CF decision-making framework for state-of-the-art CF management practice. [ABSTRACT FROM AUTHOR]

Industry 4.0 arrives with a multitude of technological advances which impact the different components of a product's value chain. Most existing research investigate the application of Industry 4.0 technologies in various parts of the manufacturing process. However, there is still a lack of comprehensive research on the impact of Industry 4.0 technologies on decision-making and how it must be adapted in order to truly benefit from the advantages brought by these technologies. This paper is thus intended to give more enlightenment on the subject, with evidence brought from the textile and clothing industry. Following this objective, a systematic literature review of research articles associated with decision-making in the Industry 4.0 era is performed with a focus on the textile and clothing industry. Articles were retrieved from the Scopus database, and the selection procedure resulted in 61 research papers. Moreover, a content analysis was performed using tables and graphs featuring quantitative results which are grounded on the proposed taxonomy. This study permits to identify some decision-making trends such as data-driven, real-time, decentralized, integrated, and sustainable decision-making. Following these decision-making trends and technologies brought by Industry 4.0, we discuss the keys for a competitive textile and clothing industry of the future. Finally, challenges facing the industry are presented following two aspects including digitalization and Industry 4.0 technologies implementation as well as environmental and social responsibility. [ABSTRACT FROM AUTHOR]

Also known as the privacy paradox, recent research on online behavior has revealed discrepancies between user attitude and their actual behavior. More specifically: While users claim to be very concerned about their privacy, they nevertheless undertake very little to protect their personal data. This systematic literature review explores the different theories on the phenomenon known as the privacy paradox. Drawing on a sample of 32 full papers that explore 35 theories in total, we determined that a user’s decision-making process as it pertains to the willingness to divulge privacy information is generally driven by two considerations: (1) risk-benefit evaluation and (2) risk assessment deemed be none or negligible. By classifying in accordance with these two considerations, we have compiled a comprehensive model using all the variables mentioned in the discussed papers. The overall findings of the systematic literature review will investigate the nature of decision-making (rational vs. irrational) and the context in which the privacy paradox takes place, with a special focus on mobile computing. Furthermore, possible solutions and research limitation issues will be discussed. [ABSTRACT FROM AUTHOR]

An ecosystem services perspective can provide a useful means of understanding, in human well-being terms, the type, scale and value of environmental impacts deriving from the deployment of renewable energy technologies. This paper provides the first thematic review of the ecosystem service impacts commonly associated with developing geothermal areas for power projects. In this study, the typical ecosystem service impacts of geothermal power projects are classified using the Common International Classification of Ecosystem Services (CICES) typology. Next, in order to develop a guide for future practitioners, an analysis is conducted of the most suitable valuation methods for the respective ecosystem service impacts. A pluralist approach is advised to aide decision-making, involving the use of monetary and non-monetary information. A number of non-market valuation studies may be required to estimate the total economic value of affected geothermal ecosystems, likely including the contingent valuation and travel cost methods. The more intangible ecosystem services associated with geothermal areas, such as artistic inspiration and landscape aesthetics, are best valued using non-monetary approaches, including deliberative methods. Finally, in recognition of the importance of having a strong physical basis underpinning non-market valuation techniques, this paper critically assesses the merits of the most appropriate data sources for future environmental economists working in a geothermal context. A literature review reveals that neither Environmental Impact Assessments (EIA) nor Life Cycle Analysis (LCA) studies in a geothermal context have embedded an ecosystem service perspective into their processes. EIA are closest to fulfilling the needs of environmental economists, encompassing the majority of ecosystem service impacts, yet further methodological progress is recommended to ensure that all project stakeholders are given voice and arbitrage in the data-gathering process. [ABSTRACT FROM AUTHOR]

• Decision-making impairment in suicidal behavior showed reinforcement learning deficit. • The impairment was described under risk but not under ambiguity conditions. • It was independent of clinical status and seemed to be a cognitive stable trait. Impaired decision-making (DM) is well-known in suicidal behavior (SB). We aimed to review the evidence on DM and its mediating factors in SB and perform a meta-analysis on DM assessed using the Iowa Gambling Task (IGT). We conducted a search on databases of papers published on DM and SB up to 2020: 46 studies were included in the systematic review, and 18 in the meta-analysis. For meta-analysis, we compared DM performance between suicide attempters (SAs) and patients (PCs) or healthy controls (HCs). The systematic review showed that SAs have greater difficulties in all DM domains. The meta-analysis found worse IGT performance among SAs in comparison with PCs and HCs. A meta-regression did not find differences for age, gender, psychiatric disorder, and clinical status. Our findings indicate that SAs exhibited deficits in DM under conditions of risk though not ambiguity. Worse DM was independent of age, gender, psychiatric disorder, and suggested that DM impairment could be considered a cognitive trait of suicidal vulnerability, a risk factor and an attribute of SAs. [ABSTRACT FROM AUTHOR]

Proton exchange membrane fuel cell (PEMFC), as an attractive alternative power source, has seen its increasing deployment in both automotive and small stationary applications. To improve the durability of the PEMFC system, recent research has engaged in developing prognostics and health management methods. Although the prognostics methods have been extensively studied to improve the prediction accuracy, some critical issues have not been fully addressed. For example, few studies have looked into the prognostics methods by different criteria and under dynamic operation conditions, and none of them have investigated the data availability and quality for PEMFC prognostics. Due to the lack of more comprehensive and general prognostics methods as well as the limitations in data, studies in the post-prognostics decision-making phase have hardly ever been initiated. This paper tends to provide a full review of the existing prognostics research by analysing the prognostics scales, horizon, threshold, and the use of methods. The data used in the previous studies has also been investigated. Moreover, four principal directions of post-prognostics decision-making have been proposed and discussed. According to the findings, research challenges and development perspectives in the aspects of data, prognostics and decision-making are proposed. • The methodology for prognostics research of PEMFC is reviewed. • Further development of prognostics should rely on improving performance metrics. • The methodology of post-prognostics decision-making is proposed. • Challenges and perspectives on data, prognostics and decision-making are discussed. [ABSTRACT FROM AUTHOR]

Agent-based models are particularly suitable to reflect the dynamics of humans, nature, and their interactions, making them a crucial approach for understanding social-ecological systems. The formalisations of human decision-making are central to resulting model behaviours. Despite awareness of the complexity of human behaviour in social-ecological systems research, scholars tend to represent human decision-makers as simplified, perfectly informed rational optimisers, without explicitly considering the fit with decision context. Key reasons are a lacking uptake of social theories and insights. To advance, we need a practice of reflecting, sharing, and inquiring on the justification of the decision model fit with its context. This paper stimulates this practice by 1) supporting the justification of decision model (DM) fit by describing the DM landscape and providing guiding questions; and 2) by supporting researchers in considering alternative DMs through a survey-based impression of modeller practices, and through highlighting DM frontiers as inspiration for future research. • More behavioural realism in models requires a better decision model fit. • We need a practice of reflecting, sharing, and inquiring on decision model fit. • The landscape of decision models has many dimensions. • Justifying decision model fit is crucial for advancing theory development and model validity. • Consider alternative decision models. [ABSTRACT FROM AUTHOR]

This paper proposes risk-informed decision-making and control methods for autonomous vehicles (AVs) under severe driving conditions, where many vehicle interactions occur on slippery roads. We assume that the AV should approach a specific safe zone in case of vehicle malfunctioning. In normal situations, the driving behavior of the AV is based on the deterministic finite-state machine (FSM) that makes an appropriate real-time decision depending on the driving condition, which is efficient when compared with some conventional decision-making algorithms; alternatively, in emergency situations, the AV first has to be prevented the rear-end conflicts while the specific safe zone is simultaneously determined by evaluating the level of risk via two safety level indicators, i.e., Time-To-Collision (TTC) and Deceleration Rate to Avoid the Crash (DRAC). The safe path that guides the AV to avoid car crashes is generated based on the trajectory optimization theory (i.e., Pontryagin maximum principle), and the AV follows it based on the linear time-varying model predictive control (LTV-MPC), which ensures the AV's lateral stability. We verify the effectiveness of the proposed decision-making and control strategies in various test scenarios, and the results show that the AV behaves appropriately according to the behaviors of surrounding vehicles and road condition. • Hierarchical integrated decision-making and control strategies for autonomous vehicles in emergency situations. • Optimal path planning generation based on the trajectory optimization theory under a mixed traffic road. • Evaluating the level of risk via the safety level indicators of Time-to-Collision and Deceleration Rate to Avoid the Crash. [ABSTRACT FROM AUTHOR]

Duration of cargo operation (loading or discharging) is of paramount importance for ship owners and port managers since idle time at terminal significantly boost to the cost of providing shipping service. Hence, it is important to choose the appropriate shiploader type for handling bulk materials. This paper aims to present a comprehensive multi-criteria decision making (MCDM) method extended by interval type-2 fuzzy sets (IT2FSs) for selecting appropriate shiploader type in maritime transportation. The proposed approach incorporates Analytical Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) under IT2FSs environment to overcome more of the uncertainty of experts’ judgement and expression in decision-making. The proposed approach adopts a sensitivity analysis to investigate the impact of key performance indicator (KPI) under numerous conditions. Besides its robust theoretical background, this paper prompts practical benefits to decision-makers (port owners or port managers) who make investment decision for appropriate shiploader type in dry bulk cargo shipment. [ABSTRACT FROM AUTHOR]

Global warming, as many other threats, requires more activity from the government, and people inquire about this, rightfully so. However, the public and private sectors nowadays have a joint responsibility with society. Global warming has come out of the scientific community and shows that climate change is an important social, economic, and political issue. Currently the carbon dioxide (CO 2 ) emissions by transportation sector are the main polluter of the planet. For this reason, in this paper we propose a reflection on transport, which is an important sector of the economy, responsible for the generation of many jobs and wealth but, at the same time, it has negative impacts on people, companies, and the environment. Recent investigations demonstrate that mobility problems affects the life quality of the people, business productivity and gross domestic product (GDP) performance. In the face of the changes that we are experiencing, it is important to find alternatives that assistance governments in the decision-making to solve the problem of traffic in large and medium-size cities and bring well-being to the population. The goal is to provide accurate tool to help take the best decisions in situations that most of the time has a lack of economic resources and transparency. Another motivation is to increase the productivity of companies and achieve sustainable development by reducing mobility problems and CO 2 emissions. As outcomes of the paper, an in-depth study is presented on the subject that indicates a gap in the literature with fuzzy logic applied to sustainability. We indicate a sustainable option for the transport sector using algorithms for decision-making, and broaden the discussion on climate change and solutions to reduce the impacts caused by humanity. When the models were applied through a conceptual study, it was possible to provide an instrument generated on the use of algorithms that can aid decision-making by getting qualitative data from a discussion with stakeholders on a topic. Finally, the document identifies the importance of the participation of stakeholder, reinforces legitimacy and transparency in decision-making processes and above all present a tool that can help governments to solve this problem. In short, this work proposes an original and valuable tool to be applied in the decision-making process for the sustainable transport sector based on the stakeholder engagement. [ABSTRACT FROM AUTHOR]

In this paper, the operation scheduling of future distribution network is discussed under assumption of multi-microgrids. The economic operation scheduling of distribution network operator in the presence of autonomous microgrids is formulated as a game problem. Due to participation of multiple players with different objective functions, the multi follower bi-level programming is intended in the energy management scheme. From the distribution network operator's perspective, the decision-making strategy seeks to minimize the operation costs of distribution network operator. Distribution network operator interconnecting with upstream grid and interaction with microgrids, schedules distributed energy resources and loads in economical matter to operate in optimal operation point. On the other hand, microgrid owners fed electrical loads interacting with the other microgrids and distribution network operator implementing demand response program during the scheduling horizon to acquire maximum profit. Therefore, the key focus of this approach is decision-making of distribution network operator which will be affected, not only by the reactions of microgrids, but also by the exchanging power between the microgrids. The paper discusses the theoretical background, architecture and algorithms of the proposed multi follower bi-level programming, and demonstrates the performance of distribution network operator and microgrids by means of simulation on the radial distribution test system. [ABSTRACT FROM AUTHOR]

‘Experiential knowledge’ is increasingly recognised as an important influence on reproductive decision-making. ‘Experiential knowledge of disability’ in particular is a significant resource within prenatal testing/screening contexts, enabling prospective parents to imagine and appraise future lives affected by disability. However, the concept of ‘experiential knowledge’ has been widely critiqued for its idiosyncrasy, its impermanence and consequently its perceived inferiority to (medical) knowledge. This paper explores some of these key critiques of experiential knowledge through an analysis of its constitution and uses in the context of reproductive decision-making. Seventeen UK-resident women with Spinal Muscular Atrophy (SMA), or with SMA in their family, took part in two in-depth interviews: one in 2007–9 and the other in 2013–4. By comparing and contrasting these women's accounts at two time points, this paper demonstrates the stark contrast between ‘lived experience’ of SMA (the visceral everyday realities of life with the condition) and the various way(s) this experience was transformed into, and presented as, ‘knowledge’ through the processes of making, and accounting, for reproductive decisions. The analysis highlights that multiple, distinct and sometimes competing experiential frameworks are used to conceptualise SMA across time and context. However, rather than evidence of its fallibility, this finding highlights that ‘knowledge’ is an inappropriate vessel with which to capture and transfer ‘experiential knowledge’. Rather, we need to consider how to value such insight in ways that harnesses its inherent strength without leaving it vulnerable to the epistemological critiques attracted by labelling it ‘knowledge’. [ABSTRACT FROM AUTHOR]

Planning and decision-making are two of the cognitive functions involved in the solution of problems. These functions, among others, have been studied from the point of view of a new field known as cognitive informatics focused on the development of cognitive architectures, autonomous agents, and human robots that are capable of showing human-like behavior. We present an exhaustive study of current biological and computational models proposed in the fields of neuroscience, psychology, and cognitive informatics. Also, we present a deep review of the brain areas involved in planning, decision-making, and affection. However, the majority of the proposed computational models are seeking to mimic human external behavior. This paper aims to contribute to the cognitive informatics field with an innovative cognitive computational model of planning and decision-making. The two main differences of our model with respect to the current models in the literature are: (i) our model considers affective and motivational information as a basic and essential trigger in planning and decision-making processes; (ii) our model attempts to mimic both the internal human brain as well as the external human behavior. We developed a computational model capable of offering a direct mapping from human brain areas to computational modules of our model. Thus, in this paper we present our model from a conceptual, formal, and computational approach in order to show how our proposal must be implemented. Finally, a set of tests were conducted in order to validate our proposal. These tests show an interesting comparison between the behavior of our prototype and the behavior exhibited by some people involved in a case study. [ABSTRACT FROM AUTHOR]

This paper presents a SOC estimation model in real-time applications such as smart grid. Authors list the existing SOC estimation methods and propose a SOC model to overcome the limitations of the precedent methods presented in the literature. Furthermore, this paper proposes a scalable, self-adaptive, and generic SOC model to meet the smart grid requirement. Multi-agent system approach is proposed to manage the smart grid and a co-simulation is proposed enabling the physical variables modeling while the discrete decision-making is given by developing agents under Jade framework. The results analyses have shown a SOC model which depends on the battery use conditions that can match with all batteries technologies and therefore can be suitable for smart grid applications. [ABSTRACT FROM AUTHOR]

Within aerospace and defence sectors, organisations are adding value to their core corporate offerings through services. These services tend to emphasise the potential to maintain future revenue streams and improved profitability and hence require the establishment of cost effective strategies that can manage uncertainties within value led services e.g. maintenance activities. In large organisations, decision-making is often supported by information processing and decision aiding systems; it is not always apparent whose decision affects the outcome the most. Often, accountability moves away from the designated organisation personnel in unforeseen ways, and depending on the decisions of individual decision makers, the structure of the organisation, or unregulated operating procedures may change. This can have far more effect on the overall system reliability – leading to inadequate troubleshooting, repeated down-time, reduced availability and increased burden on Through-life Engineering Services. This paper focuses on outlining current industrial attitudes regarding the No Fault Found (NFF) phenomena and identifies the drivers that influence the NFF decision-making process. It articulates the contents of tacit knowledge and addresses a knowledge gap by developing NFF management policies. The paper further classifies the NFF phenomenon into five key processes that must be controlled by using the developed policies. In addition to the theoretical developments, a Petri net model is also outlined and discussed based on the captured information regarding NFF decision-making in organisations. Since NFF decision-making is influenced by several factors, Petri nets are sought as a powerful tool to realise a meta-model capability to understand the complexity of situations. Its potential managerial implications can help describe decision problems under conditions of uncertainty. Finally, the conclusions indicate that engineering processes, which allow decision-making at various maintenance echelons, can often obfuscate problems that then require a systems approach to illustrate the impact of the issue. [ABSTRACT FROM AUTHOR]

The diagnosis of obsessive-compulsive disorder (OCD) is based on the presence of specific symptoms and their consequence in the lives of those that exhibit them. It is likely that these symptoms emerge from a neurocognitive vulnerability in the mental life of the individual which has a basis in neurophysiology. The prominence of doubt/uncertainty/lack of confidence (These terms are used interchangeably in this paper.), in the clinical presentation of many patients suffering from OCD leads to our consideration of the cognitive basis for this phenomenon. In this paper, we propose that OCD emerges from a perturbation in the decision-making process. Specifically, we hypothesize that there is diminished confidence, conviction, or certainty with regard to assimilating the information necessary to reach a decision. Recent advances in the neuroscience of decision-making provide an opportunity to further our understanding of the vulnerability underlying OCD. [ABSTRACT FROM AUTHOR]

Using the Outer Hebrides Inshore Fisheries Group (OHIFG) in Scotland as a case study, and stakeholder participation theory as a basis for analysis, this paper explores the conditions that are important for making stakeholder participation work in fisheries management, under the ‘modern governance context’. It argues that stakeholder participation can function well under less than what the research literature identifies as ideal circumstances, namely despite on-going stakeholder conflict and a deficient devolution of management responsibility. Even with the absence of a formal conflict resolution mechanism and without a clear delineation by government as to who has the right to formally assume a management role, OHIFG stakeholders in Scotland have been heavily involved in developing a management plan and have made significant progress in implementing it. This paper argues that in the absence of formal mechanisms for conflict resolution and management, informal mechanisms may do the job. Secondly, it is argued that conflicts can in fact have a positive function for stakeholder participation as they bring stakeholders together, clarifying and communicating amongst themselves their interests and values. [ABSTRACT FROM AUTHOR]

The software architectural decisions are crucial and critical to the success of a software project life cycle. The set of relevant design decisions affects the quality of the software architecture. In this paper, a systematic decision-making framework is proposed by considering management and organizational factors and design goals/parameters that affect software architecture (SA) and integrating it with the technique for order preference by similarity to ideal solution (TOPSIS) to evaluate and select the quality software architecture. An illustrative case study is also mentioned to show the applicability of the proposed framework. The framework suggested in the paper should enable an architect and other key stakeholders of the software architecture to efficiently identify, evaluate and select the software architecture. [ABSTRACT FROM AUTHOR]

This paper presents a systemic Adaptation Assessment Framework (AAF) conceived to guide agricultural adaptation mainstreaming into broader policy domains, particularly, those related to rural development. Starting from a review of the main phases and linkages within the adaptation process, the AAF is built around key Land Surface Modelling, Multi-Criteria Evaluation and Decision-Making informative capabilities. The resulting quantitative, comparative and integrative concepts and findings are explored to answer fundamental questions: "what to adapt", "how to adapt", and "who adapts". From this, a refreshing outlook of the targets, mechanisms and attitudes that shape agricultural adaptation is proposed as part of transformational changes, multifunctional landscapes, resilience building, and sustainable agriculture. These visions are seen here as mainstreaming facilitators. Finally, the AAF is tested on two regional-type sub-alpine study cases, revealing different mainstreaming possibilities in a context of climate change. • A novel adaptation assessment framework based on three levels – system, territorial and action – is introduced. • The products from this framework are explored to improve the formulation and implementation of agricultural adaptation. • Transformations, multifunctionality, resilience and sustainability could facilitate agricultural adaptation mainstreaming into rural policy. • The framework is applied to a sub-alpine case-study by linking guidelines and indicators. [ABSTRACT FROM AUTHOR]

In operational engineering maintenance situations, limitations on time, resource or the information available often inhibit rigorous analysis on complex decision problems. Decision-makers who are compelled to act in such circumstances, may be informed by some level of analysis if available, or else may have to rely on their unsupported judgement. This paper presents three engineering risk decision-making case studies across a 20 year span from the rail, aerospace, and military aviation contexts, highlighting the fallibilities of using unsupported judgements in an unstructured manner. To help situate this type of decision situation, we provide a descriptive model of the decision space which extends an existing description from the discipline of decision analysis. Furthermore, to help make and describe the distinction between unsupported and supported thinking, we provide another descriptive model, this time drawing parallels with the distinction made between Type 1 and Type 2 reasoning. This model is an extension of the default-interventionist model from cognitive psychology. The paper concludes that there is a pressing need to provide some form of support to engineering decision-makers facing operational decisions under severe time pressure. While the ultimate aim must be to improve the quality of decision-making, improved transparency is an important additional benefit. Increased emphasis on decision justification and self-awareness are suggested as potential ways of improving this situation. A further contribution of this paper is to identify and strengthen linkages between safety science and two other relevant disciplines, decision analysis and psychology. Such linkages make it easier to communicate across traditional disciplinary boundaries and may provide opportunities for interdisciplinary learning or suggest future directions for collaborative research. • Three engineering case studies highlight operational constraints on decisions. • Attention is focused on unsupported operational decision-making. • Extended decision space model provides a linkage with decision analysis. • Extended default-interventionist model provides a linkage with psychology. • Organisational risk systems are unable to cater adequately for unsupported judgement. [ABSTRACT FROM AUTHOR]

This paper reviews and classifies the traffic optimisation schemes of current mainline railway into two groups. One is to improve the efficiency of traffic management by providing resolutions for traffic conflicts, while the other is to improve trains’ driving behaviour by providing driver assistance or introducing train automation. Based on a comparison of these two groups, this paper proposes to combine the functions of traffic management and train automation into an integrated optimisation model. This model includes the following contributions. First, in the function of traffic management, this paper explores the flexibility in generating different train running trajectories to prevent potential traffic conflicts. The trajectory can improve traffic flow by avoiding unplanned train stops. This is regarded as a supplementary conflict resolution to train reordering or rerouting or retiming. Second, this paper defines a series of train control commands to determine different intensities of the train’s tractive force and braking force. These commands are seen as the key to train automation. Moreover, a decision-making procedure is introduced to select the most attractive train running trajectory or train control command according to different optimisation objectives. Lastly, this paper proves the importance of bidirectional communication between traffic management and train automation based on a case study. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a method for resolving decision problems concerning multiple criteria in relation to a finite set of decision alternatives. This approach makes use of paraconsistent logic, Pavelka style fuzzy logic and many-valued similarity. To demonstrate the robustness of the method, two data sets, one on the performance of five mobile phone operators in Ghana and the other, a numerical example have been analysed and the rankings compared correspondingly with those of three existing dominant Multi-Attribute Decision Making (MADM) approaches, namely Elimination and Choice Translating Reality II (ELECTRE II); Preference Ranking Organisation MeTHod for Enrichment Evaluation (PROMETHEE I and II) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Apart from providing a ranking that is similar to these three famous outranking methods, the novel approach has the edge over them due to its ability to relatively handle large size decision problems - decision problems with numerous criteria and alternatives - without much difficulty. [ABSTRACT FROM AUTHOR]

Every person, from an early age, has to make decisions to resolve situations that arise in life. In general, different people make different decisions in the same situation, since decision-making takes into account different factors such as age, emotional state, experience, among others. We can make decisions about situations that we classify as: more important than others, routine, unexpected, or trivial. However, making the correct decision(s) in a timely manner for these situations is one of the most complex and delicate challenges that human beings face. This is due to the arduous mental process required to be carried out. Providing such behavior to a virtual entity is possible through the use of Cognitive Architectures (CAs). CAs are an approach for modeling human intelligence and behavior. This paper presents an functional bioinspired computational decision-making model to satisfy the physiological needs of hunger and thirst. Our proposal considers as black boxes other cognitive functions that are part of a general CA (named Cuäyöllötl or brain in Nahuatl). In the proposed case study, it is proved that the decision-making process plays an essential role in determining the objective and selecting the object that satisfies the established need. [ABSTRACT FROM AUTHOR]

Nowadays, one important challenge in cyber-physical production systems is updating dynamic production schedules through an automated decision-making performed while the production is running. The condition of the manufacturing equipment may in fact lead to schedule unfeasibility or inefficiency, thus requiring responsiveness to preserve productivity and reduce the operational costs. In order to address current limitations of traditional scheduling methods, this work proposes a new framework that exploits the aggregation of several digital twins, representing different physical assets and their autonomous decision-making, together with a global digital twin, in order to perform production scheduling optimization when it is needed. The decision-making process is supported on a fuzzy inference system using the state or conditions of different assets and the production rate of the whole system. The condition of the assets is predicted by the condition-based monitoring modules in the local digital twins of the workstations, whereas the production rate is evaluated and assured by the global digital twin of the shop floor. This paper presents a framework for decentralized and integrated decision-making for re-scheduling of a cyber-physical production system, and the validation and proof-of-concept of the proposed method in an Industry 4.0 pilot line of assembly process. The experimental results demonstrate that the proposed framework is capable to detect changes in the manufacturing process and to make appropriate decisions for re-scheduling the process. [ABSTRACT FROM AUTHOR]

During the process of collision avoidance, especially in a multi-ship encounter situation, the dynamic interactions among individual ships impose a significant impact on collision avoidance decision-making. It is imperative, therefore, that collision avoidance decisions are formulated with a comprehensive consideration of not only the current direct collision conflict but also the potential conflicts due to planned collision avoidance actions. To address this requirement, this paper proposes a dynamic conflict cluster detection method for collision avoidance decision-making in multi-ship encounters. The involved ships are clustered into stable temporal-dependent ship conflict groups taking into account both conflict connectivity and the potential spatiotemporal interactions originating from planned collision avoidance actions. The conflict cluster detection model is implemented within a framework to achieve hierarchical coordinated collision avoidance decision-making. By a simulation experiment of an 11-ship encounter, the proposed method successfully discerns the ships with conflicts and provides feasible collision avoidance decisions. Compared to the non-cluster collision avoidance methods, the proposed method generates the results with acceptable deviating distance and number of collision avoidance actions at minimum computation load. It has been demonstrated that the proposed method is both effective and efficient for officers on board and operators at Vessel Traffic Services centers in real-life navigation. • Propose a conflict cluster detection method considering direct conflicts and potential inference. • Introduce conflict cluster parameters as constraints for conflict resolution. • Incorporate coordination strategy into cluster-based decision-making framework. • Compare to the non-cluster methods to demonstrate the efficiency and practicity. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a novel family of operators for single-hidden layer feed-forward neural networks that operate on multivariate fuzzy-valued functions. These operators are based on the fractional mean value of the approximating function. The construction of these operators involves the use of sigmoidal functions, providing multiple choices for their implementation. We demonstrate the modeling capabilities of our operators employing a novel combined neural network system that incorporates the basic fuzzy inference mechanism and fuzzy neural networks. This system represents the connection strength among the output neurons using multivariate fuzzy-valued functions, enhancing decision-making processes. Additionally, we prove the convergence properties of these operators with respect to the Pompeiu-Hausdorff metric. By controlling the asymptotic decay of the sigmoidal function, we achieve accurate Jackson-type estimations using the modulus of continuity of fuzzy-valued functions. [ABSTRACT FROM AUTHOR]

This paper presents a decision-making algorithm for ship collision avoidance. First, an algorithm for the planar distribution of collision risk is described. The level of collision risk was calculated as a function of the plane coordinates based on the predicted encounter state. It enables the visualization of all the potential collision risks as a collision risk map and the calculation of the integrative collision risk against several ships, considering COLREGs. Second, an optimal route-planning algorithm that can consider the maneuvering characteristics of the ship is proposed. Because a ship cannot change its course and speed suddenly, the nodes and their connections can be set to limit the course and speed change amounts to appropriate ranges. The optimal route was obtained as the minimum-cost route along node connections. Finally, a decision-making algorithm for ship collision avoidance was developed by combining the proposed collision risk mapping and optimal route-planning algorithm. It was applied to representative encounter situations and evaluated by an evaluation method. Further, the proposed algorithm was compared with state-of-art algorithms. Thus, safe and efficient decision-making for collision avoidance can be achieved even in congested situations by appropriately changing both a course and a speed depending on the encounter situation. • An algorithm to calculate a planar distribution of collision risk considering COLREGs is proposed. • An algorithm for optimal routing for collision avoidance by both course and speed changes is proposed. • A decision-making method for collision avoidance is developed by combining the proposed two algorithms. • The developed method can provide a safer and more efficient collision avoidance plan even in congested situations. [ABSTRACT FROM AUTHOR]

The management of uncertainty in decision-making problems remains a very challenging and timely research issue despite many proposals. An interesting topic in this area in recent years has been Granular Computing. In this paper, we present a coherent framework for various models of information granules, using hierarchies of information for dealing with knowledge acquired and processed at different levels of abstraction. Granular Computing, a paradigm for handling higher types of uncertainty in decision analysis, is considered a new conceptual and algorithmic asset of decision-making studies and, in particular, data-driven decision-making. Most relevant studies have focused on categories of information granules, namely intervals, fuzzy sets, rough sets, and shadowed sets. To better understand this promising area, this study provides a comprehensive overview of Granular Computing for decision-making through literature analysis, enhancing a variety of extensions, applications, and challenges. • We propose a comprehensive review for granular computing in decision making. • We provide a bibliometric review for granular computing in decision making. • We review main applications in granular computing in decision making. • We provide some future directions for granular computing in decision making. [ABSTRACT FROM AUTHOR]

• Novel holistic transformer vector-representation network for modeling scenes/agents. • Simultaneous output multi-modal trajectory prediction and behavior/lane decision. • Organically integrated system of attention mechanisms based on multiple cues. • Attention-weighted regularized trajectory prediction using behavior decision tasks. Trajectory prediction and behavioral decision-making are two important tasks for autonomous vehicles that require a good understanding of the environmental context. Notably, behavioral decisions are better made by referring to the outputs of trajectory predictions. However, most current solutions perform these tasks separately. Therefore, this paper proposes a new joint holistic transformer network that combines multiple cues to predict trajectories and make behavioral decisions simultaneously. To better explore the intrinsic relationships among cues, the network uses existing knowledge and adopts three kinds of attention mechanisms: the sparse multi-head type for reducing noise impact, feature selection sparse type for optimally using partial prior knowledge, and multi-head with sigmoid activation type for optimally using posteriori knowledge. Compared with other trajectory prediction models, the proposed model has a better comprehensive performance and good interpretability. Perceptual noise robustness experiments demonstrate that the proposed model has good noise robustness. Thus, simultaneous trajectory prediction and behavioral decision-making combining multiple cues are accomplished, which reduces computational costs and enhances semantic relationships between scenes and agents. [ABSTRACT FROM AUTHOR]

New technologies are often considered direct competitors to humans in the realm of decision-making. This paper explores a novel approach to augmenting human decision-making through technology. Specifically, drawing on the brain's unique ability to learn from sensory experiences, we introduce sensory substitution , the encoding of information in an alternative sensory modality, as a method to improve decision-making. In a within-subject design (N = 48), we show that translating numerical information into sensory experiences (i.e., tactile stimulation administered to a person's body) results in higher decision accuracy in a multiple-cue learning task. Response time analyses, participants' self-reports, and cognitive modeling all suggest that the benefits afforded by sensory substitution are the result of a shift from explicit rule abstraction to configural learning. That is, rather than deliberately inferring decision rules, participants develop intuitive, perceptual strategies to accurately predict outcomes. Together, our findings suggest that sensory substitution could enhance decision-making by training "gut instincts" rather than deliberate decision-making skills. • The manuscript presents the first empirical work demonstrating the use of sensory substitution in a decision-making context. • Translating abstract numerical data into tactile stimulation leads to higher performance in a multiple-cue learning task. • The benefits afforded by sensory substitution are due to a shift from explicit rule abstraction to configural learning. [ABSTRACT FROM AUTHOR]

Caregivers of children with medical complexity (CMC) face decisions about tracheostomy. The objectives of this paper are to identify facilitators and barriers to tracheostomy decision-making (TDM) process for CMC. Using phenomenology as its methodologic orientation, this qualitative study conducted in North Carolina between 2013 and 2015 consists of semistructured interviews with 56 caregivers of 41 CMC who received tracheostomies, and 5 focus groups of 33 health care providers (HCP) at a tertiary care children's hospital involved in TDM for CMC. Participants were asked to share their experiences and perspectives on the TDM process. Qualitative data were transcribed, coded, and organized into themes as is consistent with thematic content analysis. Five themes were identified. 1) Caregivers perceived decision about tracheostomy for their children was theirs to make. 2) Strategies that increased caregivers' active participation in the TDM process facilitated the TDM process. 3) Caregiver emotional stress and lack of understanding about tracheostomy were barriers. 4) Good HCP communication during the TDM process was valued; poor communication was a barrier. 5) Collaboration among HCP-facilitated TDM, especially when nurses were involved, whereas fragmentation in care was a barrier. Caregivers take a primary role in the TDM process. Many caregiver and HCP-level facilitators and barriers for TDM exist. Augmenting the facilitators and reducing the barriers identified in this study could improve the TDM process for CMC. [ABSTRACT FROM AUTHOR]