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Pumped storage technology plays a crucial role in achieving balance in power systems and enhancing the stability of energy systems. Scientific planning can help optimize the operation of power systems, promote the development of renewable energy, and conserve energy. This paper addresses the capacity planning problem of pumped storage stations in hybrid operation systems considering wind power uncertainty. A comprehensive decision-making method is proposed, based on the Delphi method, interval intuitionistic fuzzy theory, grey relational theory, entropy weight method, and prospect theory. This method comprehensively evaluates decision alternatives by considering various factors such as the uncertainty of qualitative indicators, the interrelationships between indicators, and the impacts of different risk environments. The test system in this paper is conducted on a hybrid wind-thermal-pump storage output model based on the IEEE 30-bus system. The optimal capacity planning scheme determined by the proposed method is 190 MW when decision-makers are risk-neutral or risk-averse, and 200 MW when they are risk-appetite. Subsequently, the case study verifies the impact of different load levels and proportions of wind power access on the optimal decision-making scheme, and compares it with decision-making method such as principal component analysis, technique for order preference by similarity to an ideal solution, and a combined method of Pythagorean fuzzy theory and cumulative prospect theory, validating feasibility and effectiveness. [ABSTRACT FROM AUTHOR]

The dynamic and complex environment affects the operational efficiency of automated guided vehicles (AGVs) at automated container terminals, in which artificial intelligent approaches are applied to address optimization problems in dynamic systems. This paper proposes a digital twin-based decision support approach to improve the efficiency of AGV scheduling service. Accordingly, the factors that affect AGV scheduling performance, including conflicts, failures, and battery constraints, are discussed first. Then, the framework and main steps of the proposed approach are described. The physical operation space is mapped into the virtual space, and both spaces keep synchronized to support the verification of solutions. A mathematical programming model and Q-learning algorithm are used to generate the AGV scheduling plan considering battery charging. Numerical experiments are conducted to demonstrate the effectiveness of the proposed approach. Comparisons of the digital twin-based approach, genetic algorithm (GA), and particle swarm optimization (PSO) are also made with different scale experiments. It appears that the proposed digital twin-based approach is superior to GA and PSO when solving small- and large-scale cases. A sensitivity analysis is performed concerning the battery utilization rate, task, and AGV number. Experimental results show that an optimal configuration of AGV and task can improve the battery utilization rate and reduce the completion time. • A digital twin-based decision support approach is proposed to improve scheduling. • A mathematical programming model is developed to minimize the total completion time. • Digital twin and Q-learning are used to monitor operation and obtain solutions. • Sensitivity analysis of battery utilization is made to provide equipment maintenance. • The proposed approach is superior based on the comparison of solution performances. [ABSTRACT FROM AUTHOR]

In the context of structural integrity management (SIM), effective utilization of real-time structural health monitoring (SHM) information is crucial for minimizing the expected value of life-cycle costs. To enhance the benefits of real-time SHM information for SIM, this paper introduces a framework aimed at optimizing the utilization of real-time SHM information from a life-cycle perspective, grounded in Bayesian posterior decision analysis. In addressing this issue, the paper utilizes a robust probabilistic model that is intimately connected with real-time SHM information. This model plays a crucial role in quantifying how the availability of real-time SHM data impacts the benefits of SIM. Following this quantification in the proposed analytical framework, the paper conducts a comprehensive comparative analysis of various utilization strategies, highlighting their respective efficiencies and effectiveness in the context of SIM. Through a case study centered on a weld exposed to fatigue loading, the paper not only showcases the proposed framework but also demonstrates its efficacy in identifying the optimal strategy for real-time SHM information utilization. • Bayesian preposterior decision analysis is used to optimize the information utilization. • A probabilistic model of real-time SHM information is proposed. • Identification of optimal utilization strategy of real-time SHM information are discussed. [ABSTRACT FROM AUTHOR]

• Study analyzes 1374 papers on sensitivity analysis in Scopus. • RStudio, CiteSpace, and VOSviewer are used. • China has most articles published; India leads in international cooperation. • Top three cited journals: J. Clean. Prod., Expert Syst. Appl., and Comput. Ind. Eng. • Most cited authors are Pamučar D, Kahraman C, and Zavadskas EK. In the present era, the implementation of scientifically grounded Multi-Criteria Decision Making (MCDM) has emerged as a pivotal solution to diverse decision-making challenges across various domains. Although a substantial body of exploratory, conceptual, and experimental studies exists, only 9.457% studies have incorporated sensitivity analyses to assess the robustness of MCDM methods. An exhaustive scientific exploration of sensitivity analysis within the scope of MCDM is thus lacking. This research aims to address this gap through Bibliometric Analysis while examining 1374 articles published between January 2000 and March 2023 from the Scopus database. Using RStudio (Biblioshiny), CiteSpace, and VOSviewer software, the study constructs a visual representation of the most prolific countries, institutions, and authors. Impressively, China takes the lead in article publications, while India excels in international collaboration. "An extended TODIM multi-criteria group decision-making method for green supplier selection in an interval type-2 fuzzy environment," featured in the Journal of Environmental Management, emerges as the most cited paper with a total citation of 455. The study also identifies the top three most cited journals, namely "Journal of Cleaner Production," "Expert Systems With Applications," and "Computers and Industrial Engineering." "North China Electric Power University" is the leading institute with the highest research outputs. "Pamučar D" is the most cited author, with 2594 citations and 39 articles, followed by "Kahraman C" and "Zavadskas EK. This study sheds light on trends in scientific developments and collaborations, providing a model for the application of sensitivity analysis in MCDM research and highlighting global trends. An understanding of the current state of sensitivity analysis research can assist researchers working in the entire domain of MCDM. Additionally, the visualization provides prescriptive data for future work and applications related to sensitivity analysis. [ABSTRACT FROM AUTHOR]

Concerns about child supervision are present in a large proportion of the circumstances responded to by North American child protection systems. This paper examines different perspectives on low-supervision, from parental-deficit to critical-ecological formulations, to address two questions that are central to assessing reports: why low-supervision is deemed to be harmful, and why low-supervision events occur The implications of this knowledge for child protection system responses is explored, and the conceptualization and application of supervisory neglect as a maltreatment category is critically questioned in order to tease apart what this label may represent in practice. The paper considers how thinking could be reframed to make supervisory neglect a more discrete classification which informs subsequent action, including centering the environmental hazard in supervisory neglect formulations and giving primacy to the impact on the child rather than to parent behavior. The role of normative thinking in decision-making and the risk of perpetuating social inequalities and oppressive power is also considered. More research is required to examine responses and outcomes under current systems of practice, and how child protection workers and systems reflexively analyze reports. In addition, more societal dialogue is needed to prevent widespread risk-averse thinking about how children should be supervised, which can reinforce restrictive child protection policy. [ABSTRACT FROM AUTHOR]

Timely recognition of dying is important for high quality end-of-life care however, little is known about how clinicians recognise dying. Late recognition is common and can lead to futile treatment that can prolong or increase suffering and prevent a change in the focus of care. To explore how clinicians caring for dying people recognise that they are in the last days or hours of life, as well as the factors that influence the recognition of dying. A systematically constructed integrative review of the literature. Medline, Scopus, Cumulative Index to Nursing and Allied Health Literature, PsycInfo and Allied and Complementary Medicine were searched in July 2022. Papers were included if they were original research, discussed how clinicians recognise dying, available in English language and published in 2012 or later. A constant comparison approach was applied to the analysis and synthesis of the literature. 24 papers met the inclusion criteria. There were 3 main categories identified: 'Clues and signals' refers to prompts and signs that lead a clinician to believe a person is dying, incorporating the sub-categories 'knowing the patient over time', and 'intuition and experience'. 'Recognition by others' is where clinicians come to recognise someone is dying through others. This can be through a change in the context of care such as a tool or care plan or by communication with the team. 'Culture, system and practice' refers to the cultural beliefs of a setting that influences awareness of dying and denial of death as a possibility and avoidance of naming death and dying directly. System and practice of the setting also impact on recognition of dying. This involves work pace and intensity, shift systems and timing of senior reviews of patients. Uncertainty and its impact on recognition of dying are evident throughout the findings of this review. The seeking of certainty and the absence of the possibility of dying contributes to late recognition of dying. Recognition of dying is a complex process that occurs over time, involving a combination of intuition and gathering of information, that is influenced by contextual factors. A culture where dying is not openly acknowledged or even named explicitly contributes to late recognition of dying. A shared language and consistent terminology for explicitly naming dying are needed. Uncertainty is intrinsic to the recognition of dying and therefore a shift to recognising the possibility of dying rather than seeking certainty is needed. (PROSPERO) CRD42022360900. Registered September 2022. [ABSTRACT FROM AUTHOR]

Partial data is a common challenge in many applied problems, where we can measure only the part of the solution. This paper considers the poroelasticity problem in fractured media, where only partial measurements of fracture pressures are available. The poroelasticity models have applications in many engineering and scientific fields, such as geomechanics, hydrology, and reservoir simulation. This paper aims to develop a Bayesian decision making approach using partial data and consider its application to the poroelasticity problem in fractured media. The methodology of the proposed approach combines Proper Orthogonal Decomposition, Discrete Empirical Interpolation Method, Bayesian Inversion Approach, and Bayesian Decision Theory. It allows us to compute posterior probabilities and decide according to the optimal decision rule based on the partial data. Moreover, we apply partially explicit temporal discretization to solve forward problems efficiently. For testing the proposed approach, we consider a two-dimensional model problem. We simulate the cases when we need to make a single decision about the reservoir or decisions about the elements of the set of reservoirs. The results show that the proposed approach can efficiently compute the posterior probabilities and make a decision in the presence of only partial data for both cases. [ABSTRACT FROM AUTHOR]

Considering the lack of adequate cycling infrastructure networks in many cities, decision makers must face the challenge of designing connected bicycle facility networks to ensure safe and comfortable access to urban opportunities for cyclists and the usability of infrastructure. This paper addresses the maximal covering bicycle network design problem (MCBNDP). MCBNDP involves making investment decisions to build a cycling network that is aimed at maximizing the coverage of cyclists, while maintaining a minimum total network cost at its minimum. The derived network is subject to a budget limit and accounts for the entire connectivity and directness as fundamental bicycle network design criteria. Cyclists who are located at a given origin are considered covered by the network if a connected path of dedicated cycling infrastructure links them to their desired destination, within a maximum travel distance. We propose a mixed-integer linear programming (MILP) formulation, including a two-phase solution approach to solving the MCBNDP. In addition, using a commercial solver, our MILP formulation allows exact solutions to be obtained for large-scale instances with reasonable computing times for these types of problems. This MILP formulation is employed to solve a real instance that is applied to a wide territory of analysis in Medellin city (Colombia). The findings of this paper will contribute to existing literature and support urban policymakers to better spatially allocate the resources and, consequently, maximize the impact of their investments on connected cycling infrastructure networks. Our findings indicate that access to opportunities for cyclists can be easily favored by making small improvements to the existing infrastructure to guarantee safe, direct, and comfortable cycling infrastructure. Because we specify a maximum travel distance rather than a shortest path limitation, our problem ensures that cyclists may have several possibilities for their routes, which may go along the shortest path or any other alternative, however, without exceeding the maximum travel distance. In this direction, our findings confirm that prioritizing coverage, while accounting for full network connectivity, will benefit more cyclists because of the flexible configuration of the new network, which also may ensure its usability. [ABSTRACT FROM AUTHOR]

In recent years,the efficiency of news communication has been greatly improved with the advance of Internet technology, and whether investors should pay attention to multiple levels of news when making trading decisions is worth pondering. However, Chinese scholars have paid little attention to the news hierarchy, and do not bring different levels of news into the prediction system. Therefore, this paper studies the stock price trend prediction from this perspective. This paper selects the medical industry as the representative, and adds its sub industry news and industry news to form a multi-level news system on the basis of individual stock news. In order to make better use of and extract the information contained in different levels of news, the multi-kernel learning model is incorporated on the basis of using SVM. The results suggest that the use of multi-level news can improve the accuracy of stock price trend prediction. Compared with the SVM model solely considering individual stock news, the prediction accuracy of multi-kernel learning model based on multi-level news is improved by 10%, which is of great significance for investment decision-making. [ABSTRACT FROM AUTHOR]

• We examine the effectiveness of imputation methods for missing network data. • We evaluate imputation methods under different missing data conditions and measures. • Simple reconstruction approaches often perform as well as model-based approaches. • The optimal imputation method varies by measure of interest and network type. • Researchers can use our results as a guide to making imputation decisions. Missing data is a common, difficult problem for network studies. Unfortunately, there are few clear guidelines about what a researcher should do when faced with incomplete information. We take up this problem in the third paper of a three-paper series on missing network data. Here, we compare the performance of different imputation methods across a wide range of circumstances characterized in terms of measures, networks and missing data types. We consider a number of imputation methods, going from simple imputation to more complex model-based approaches. Overall, we find that listwise deletion is almost always the worst option, while choosing the best strategy can be difficult, as it depends on the type of missing data, the type of network and the measure of interest. We end the paper by offering a set of practical outputs that researchers can use to identify the best imputation choice for their particular research setting. [ABSTRACT FROM AUTHOR]

The rapid advance of technology has led to exponential growth in the generation of data in various fields, revolutionising how information is captured, processed and analysed. The continuous flow of data has proven to be a valuable resource for agile decision-making. The complexity of being able to make decisions from different sources has led to the emergence of new techniques that aim to obtain as much information as possible to relate data efficiently, taking into account multiple perspectives. A new approach to clustering streaming data from different perspectives is introduced in this paper. It is based on evolutionary algorithms (CHC), fuzzy rules and consensus function. The proposed approach is called FuzzyMultiCHCClust-DS. This approach addresses the clustering of streaming data by optimising the clustering process taking into account traditional clustering perspectives such as k-means, Clara, Pam, Fanny and MiniBatchKMeans. It has been tested using different sets of linguistic labels, achieving a high degree of compactness and separation of the cl ustered data. [ABSTRACT FROM AUTHOR]

Risk mitigation is usually addressed in simulated environments for safety critical control. The migration of the final controller requires further adjustments due to the simulation assumptions and constraints. This paper presents the design of an experience inference algorithm for safety critical control of unknown multi-agent linear systems. The approach is inspired in the close relationship between three main areas of the brain cortex that enables transfer learning and decision making: the hippocampus, the neocortex, and the striatum. The hippocampus is modelled as a stable linear model that communicates to the striatum how the real-world system is expected to behave. The hippocampus model is controlled by an adaptive dynamic programming (ADP) algorithm to achieve an optimal desired performance. The neocortex and the striatum are designed simultaneously by an actor control policy algorithm that ensures experience inference to the real-world system. Experimental and simulations studies are carried out to verify the proposed approach. • The brain cortex has complementary mechanisms to achieve complex decision making. • The hippocampus is modelled as an ADP algorithm for experience transference. • The neocortex/striatum is modelled as a complementary adaptive actor policy. • Unbiased policies are obtained by forcing the agents to behave as in the simulation. [ABSTRACT FROM AUTHOR]

With the expansion of application scenarios, the walking-assisted lower-limb exoskeleton robot urgently needs to improve its walking ability in complex terrains. However, due to the lack of environmental understanding ability and decision-making ability, the exoskeleton can not meet the need of people with lower-limb disability in complex terrains. To improve the walking abillity, this paper proposes a method of predicting the landing area in complex terrains for walking-assisted lower-limb exoskeleton robot. Multiple sensors were used to obtain the terrain information, step length, and stride length of human walking, and a multi-type input convolutional network was built to establish the mapping relationship to learn the decision-making mechanism of human standing in a complex environment. The result shows that the established network effectively predicts the landing area. Under the condition of full training, the predicted position deviation of the landing area is controlled within 6%, and the prediction rationality of the landing area can reach 90%. [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]

Cardiac Rehabilitation (CR) has become an established intervention to support patient recovery after a cardiac event, with evidence supporting its effectiveness and cost-effectiveness in improving patient health and reducing future burden on healthcare systems. However, this evidence has focussed on the national value case for CR rather than at the point at which it is commissioned. This analysis uses the UK as a case-study to explore variation in current CR engagement and disassemble the value case from a commissioner perspective. Using data collected by the National Audit of CR (NACR), and an existing model of cost-effectiveness, we present details on the current level of CR uptake by commissioning region (Specialist Clinical Networks) in light of the current UK target of achieving 85% uptake. We then interrogate the value case for achieving the target at a commissioner level, highlighting the expected profile of health benefits and healthcare system costs over the long-term. Importantly we consider where this may differ from the national value case. Each commissioning region has a unique level of CR uptake and sociodemographic profile. Concurrently, the value case for commissioning CR relies on the upfront cost of the service being offset by long-term healthcare savings, and health improvements. The shift in the UK and internationally to more localised commissioning necessitates evidence of cost-effectiveness that better reflects the realities of those decision makers. This paper provides vital additional data to facilitate such commissioners to understand the value case in increasing CR uptake in line with national policy. • Many nations are aiming to increase rates of CR uptake. • Commissioning of services is complex and different from national decision making. • The challenges in reaching targets are different for each region. • It is important to consider equity when commissioning CR. [ABSTRACT FROM AUTHOR]

When cognitive load exceeds cognitive capacity, individuals may make poorer decisions, especially when substantial deliberation is required. Deck and Jahedi's (2015) influential work on cognitive load found that individuals whose arithmetic performance is most impacted by high cognitive load become more risk averse, less patient and more subject to the anchoring effect. Since results of cognitive load manipulation studies are mixed, replication of influential studies is essential to strengthen our understanding of the effects of cognitive load. In this paper, we attempt to closely replicate Experiment 1 in Deck and Jahedi (2015). Though we observe similar effects of cognitive load on arithmetic performance, we fail to replicate their overall results on risky choice and impatience. While we are unable to clearly identify the reasons for this non-replication, the evidence points to subtle differences in the allocation of attention and effort across subject pools. [ABSTRACT FROM AUTHOR]

Joint decision-making can be seen as the synchronization of actions and emotions, usually via nonverbal interaction between people while they show empathy. The aim of the current paper was (1) to develop an adaptive computational model for the type of synchrony that can occur in joint decision-making for two persons modeled as agents, and (2) to visualize the two persons by avatars as virtual agents during their decision-making. How to model joint decision-making computationally while taking into account adaptivity is rarely addressed, although such models based on psychological literature have a lot of future applications like online coaching and therapeutics. We used an adaptive network-oriented modelling approach to build an adaptive joint decision-making model in an agent-based manner and simulated multiple scenarios of such joint decision-making processes using a dedicated software environment that was implemented in MATLAB. Programming in the Unity 3D engine was done to virtualize this process as nonverbal interaction between virtual agents, their internal and external states, and the scenario. Although our adaptive joint decision model has general application areas, we have selected a therapeutic session as example scenario to visualize and interpret the example simulations. [ABSTRACT FROM AUTHOR]

Decision-making processes related to transportation systems are often very complex, belonging to the class of "wicked problems" in social studies. The literature and the media abound in examples of transportation planning "failures" including decisions made, delayed or withdrawn without any traceable motivations, public opposition, public opposition, extra costs/implementation times and outright cancellation, wrong traffic/revenue forecasts. Planning failures stem from several causes, including technical errors in forecasting predictable effects, lack of consensus, new governance cycles and inability to recognize the intrinsic uncertainty affecting some key variables. The underlying assumption of this paper is that the quality of the decision-making process critically depends on how the process is structured and managed. The paper proposes a conceptual model to represent and organize transport planning as a cognitive decision-making process with strong involvement of different stakeholders in various stages, accounting for different levels of uncertainty and including short- and long-term decisions options. The result is a planning process that allows for changes in objectives, strategies and decisions during different stages of the process itself, as is usually the case when different governance and socio-economic cycles succeed over time. However, the process maintains a form of procedural consistency and is intended to counteract the "Penelope syndrome", whereby decisions made under previous governance cycles are undone regardless of their merits and costs. The real-life implementation of the proposed model requires strong "political commitment" to an open and accountable decision-making process, a technical structure able to act as "process owner" coordinating technical and engagement activities, and a general societal attitude towards organized participation to public decision making. The proposed model was applied to the Regional Transportation Plan of Veneto in Italy where the above conditions applied. A number of lessons were learnt from that exercise: i) stakeholders' engagement revealed an important potential to manage uncertainty and increase consensus; ii) flexibility and robustness with respect to medium–to–high uncertainty levels already recognized at the time of the plan (immediately before the COVID-19 crisis) were an appreciated feature of the plan; iii) separation between mature decisions and those needing further analysis was a success strategy, reducing political "decision costs" (from naysaying) by delaying decisions still recognizing underlying needs; iv) reduced decision time (a largely bi-partisan vote in less than one year). • Transport-related decision-making process may lead to unsatisfactory results (failures). • The quality of the decision-making process is a key factor for a successful planning and design. • A novel cognitive participative decision-making model under uncertainty levels is proposed. • The application of the model to the Regional Transportation Plan of Veneto (Italy) is also discussed. • A number of lessons learned were derived. [ABSTRACT FROM AUTHOR]

Extensive research exists on the potential impacts of foresight; however, a comprehensive understanding of the factors that lead to foresight impact, particularly in influencing senior management decisions, is relatively sparse. This study addresses this by reporting on a Delphi and expert panel involving eight senior Canadian government foresight program leaders. These leaders were asked to help identify and then rate a list of factors that they felt resulted in their foresight projects impacting senior management decisions. Results suggested that factors such as foresight methodology, while leading to good foresight, do not necessarily result in senior decision-maker impact. Instead, criteria defined in this paper as the "consultants' toolkit," such as understanding the senior decision maker's pain points and foresight managers having a strong understanding of the organization's inner workings, play a crucial role. The expert panel discussion suggested that the importance of senior management decision-making factors depends on three mediating variables: The temporal orientation of the Department, the foresight orientation of the department's senior management, and the nature of the relationship between the foresight manager and the senior decision maker. • Paper identifies factors that lead to senior management accepting and implementing the results and recommendations of foresight projects. • A two round Delphi and expert panel was held as part of this study with eight senior Canadian government foresight program leaders • Managers identified factors that relate more to a consultant tool kit than foresight methodology that results in decision impact foresight. • A contingency model is presented with three elements which the impact factors importance • The paper highlights the importance of research partnerships that include both academics and practitioners [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]

• Property acts as a mode of abstraction in environmental offsetting decision-making. • More-than-humans are often only viewed in a property lens by decision-makers. • Property moves through a logic of exchangeability that differs from animal mobility. • Realizing the different ways animals move can lead to more just decisions. A web of species interdependencies, movements, and relationships exist within areas targeted for development. However, these areas hosting a multitude of more-than-human beings' liveliness that are often only viewed by decision-makers in terms of the property regimes that encompass them. This paper examines this phenomenon in a case study of the conflict between gopher tortoise (Gopherus polyphemus) conservation and the proposed Osceola Parkway Extension in Central Florida. Gopher tortoise conservation in Florida offers a window into how conservation, mitigation strategies like offsetting, the exchangeability of property interact. The paper highlights what happens when decision-makers assume a property lens to understand and mitigate for conflicts between development and more-than-human relations to the environment. Understanding the two distinct, out of sync movements – that of gopher tortoises and that of conservation properties in offsetting – demonstrates the ways a multitude of more-than-human relationships are obscured and abstracted into transactional pieces through a property lens that moves through a logic of exchangeability. This suggests more-than-human lives, mobilities, and relationships need to become more fully part of the discussions and decision-making processes surrounding development-environmental conflicts. Only then can we begin to work towards more just multispecies decisions. [ABSTRACT FROM AUTHOR]

Pumping wells (PWs) lie at the core of agricultural irrigation infrastructure, and their spatial configuration is of great significance for the scientific management of regional agriculture and rational use of water resources. To solve the problems of unreasonable and unscientific spatial allocation of regional agricultural irrigation PWs, this paper proposes a new strategy for updating agricultural irrigation PWs based on a spatial decision model, using Songzhuang Town, Tongzhou District, Beijing as a case study. The model utilizes constraints based on local factors such as actual land use, and proposes a novel feasible solution strategy that combines random local search and abrupt jumps to increase the probability that feasible solutions will be found, addressing the performance issues of simulated annealing algorithms, influence of initial values, and parameter sensitivity. Finally, we used the proposed method to perform updated decision analysis on the spatial configuration of PWs in the study area. The results showed that most of the crops in the study area lack irrigation, and the currently available PWs cannot meet the needs for irrigation. The new PWs added based on this decision method can be more evenly distributed within the predefined optimization unit. The proposed number of PWs to be added is 166, the density of PWs is 0.04/ha, and the crop area covered by the PWs is 3350.43 ha. In contrast to the previous PW layout, the optimized PW configuration can meet the irrigation needs of residents, indicating that the new method proposed in this paper has good potential for extension and application. [Display omitted] • A new decision-making method for renewal of agricultural irrigation PWs was proposed. • The maximum unit crop area can be covered when the buffer zone radius was set to 166 m. • The optimal spacing of agricultural irrigation wells was 332 m. • The scheme alleviated the problem of insufficient water for agricultural irrigation. • The method has popularization potential for regional agricultural water management. [ABSTRACT FROM AUTHOR]

Recently, smart data has attracted great attention in the smart city community since it can provide valuable information to support intelligent services such as planning, monitoring, and decision making. However, it imposes a big challenge to explore smart data from big data gathered from smart city with various advanced fusion and analysis approaches. This paper proposes an incremental tensor-based fuzzy c-means approach (IT-FCM) for obtaining smart data from continuously generated big data. Specifically, a weighted version of the tensor-based fuzzy c-means approach (T-FCM) is firstly proposed to cluster the dataset that combines the previous cluster centroids and the new generated data. Aiming to improve the clustering efficiency, the old data objects are represented by the centroids to avoid repeat clustering. Furthermore, this paper presents an edge-cloud-aided clustering scheme to fuse big data from different sources and perspectives and further to implement co-clustering on the fused datasets for exploring smart data. Finally, the proposed IT-FCM approach is evaluated by comparing with T-FCM regarding clustering accuracy and efficiency on two different datasets in the experiments. The results state that IT-FCM outperforms T-FCM in clustering streaming big data in terms of accuracy and efficiency for obtaining smart data. • An edge-cloud-aided computing system for co-clustering is presented. • An edge-cloud-aided computing system for co-clustering is presented. • Experiments are conducted to evaluate the presented approach. [ABSTRACT FROM AUTHOR]

Over the last twenty years, patient opinions of the received healthcare services have emerged as a crucial quality indicator and a valuable instrument for evaluating the service quality of health organizations. However, many surveyors still utilized uncertain information for conducting this evaluation process. Whereas, in the past few years, the issues of inconsistency, indeterminacy, and uncertainty in data have been growing and have become a matter of concern in dealing with decision-making issues. This paper proposes an improved RAFSI method based on a single-valued trapezoidal neutrosophic number (SVTraNN) as a multi-attribute framework for dealing with a healthcare service quality evaluation. The proposed framework consists of two features: a feature for determining numerical criteria weights involving the procedures of the Ordinal Priority Approach (OPA) and a feature for selecting the best polyclinic through contributing our new SVTraNN Harmonic and arithmetic mean to yield criterion functions in the RAFSI method. This paper then compares the results obtained by the proposed method with other SVTraNN decision-making methods. A comparative analysis and rank reversal testing of the improved method demonstrated that the multi-attribute framework proffers the option of capability and stability in overcoming inconsistency, indeterminacy, and uncertainty information efficiently during the healthcare service quality evaluation. [ABSTRACT FROM AUTHOR]

The aim of stock assessment is to provide managers with an understanding of the biomass and age-/size-composition of a stock, stock status (ideally relative to target and reference points), and to support management decision making, often through projections and application of harvest control rules. Stock assessments are increasingly being used to form the basis for the operating models used to evaluate the ability of management strategies to achieve the goals of fisheries management. Stock assessments for rock lobsters were among the first to adopt size-structured population models as the basis for stock assessment, and population model-based stock assessments have been used extensively to evaluate management strategies for rock lobster stocks in the USA, Australia, New Zealand and South Africa. This paper identifies how closely population model-based stock assessments for rock lobsters satisfy the guidelines identified for best practices for conducting stock assessments during meetings of, for example, the Center for the Advancement of Population Assessment Methodology and papers identifying best practice guidelines for stock assessment. The paper highlights some key directions for stock assessment science for rock lobsters, including considerations related to environmental variation and climate change. [ABSTRACT FROM AUTHOR]

• Discretionary or ex gratia payouts are widespread in parametric insurance programs. • Payouts attempt to secure social license to operate despite contractual basis risk. • Discretionary payouts obscure and entrench insurers' market power. • Attempts at conciliation often stoke further demands from aggrieved collectivities. • Geospatial monitoring alone cannot resolve questions of post-disaster distribution. The past decade has seen the dramatic proliferation of geospatial tools to remotely monitor, measure, and transfer weather and climate risk, particularly in the Global South. These "parametric" tools are calculative market devices used to commodify risk and facilitate its distantiated exchange via insurance markets. Yet the environmental estimates they generate are frequently wrong, sometimes grossly so. This paper investigates what happens after calculative market devices fail, analyzing so-called ex gratia ("from grace") practices in which insurers make payouts to aggrieved clients even when none are due under contractual terms. Though these payments are commonly deployed to dispel discontent, these "public secrets" of the industry have gone unnoticed in insurance scholarship. This paper documents numerous ex gratia payouts in parametric programs across Africa, disputing the myth of autonomous insurance markets that function according to formal, transparent rules administered by dispassionate calculative devices. Instead, these "discretionary" monetary transfers reintroduce human decision making in informal, black-boxed ways, cloaking the inadequacy of the parametric contracts they shore up. Far from incidental, I argue that these practices have been constitutive of the parametric insurance market's persistence and expansion. Though ex gratia payments sometimes give rise to negotiations over indemnification with aggrieved collectivities, these unfold on insurers' terms, informalizing power and delegitimizing complaint. The growing emphasis on parametric insurance as a tool for climate adaptation makes it all the more important to excavate the mechanics and politics of ex gratia, and to relinquish expectations that geospatial algorithms alone can resolve questions of post-disaster redistribution. [ABSTRACT FROM AUTHOR]

• Permanent supportive housing (PSH) is one setting which aims to provide stability and community to those who have experienced homelessness. • PSH has not been widely studied with young adults. • This paper uses mixed method to explore how young adult residents experience psychological sense of community (PSOC) before and after moving into PSH. • We find that building community takes time and requires opportunities for voices to be heard, along with ongoing personal growth (often related to substance use recovery). • Future PSH settings for young people should consider how to support residents in their individual journeys, and in building community. Amidst chronic stressors and ongoing instability, many young people who have experienced homelessness and housing instability find communities and settings which offer support and care. Permanent Supportive Housing (PSH) is one setting which aims to provide stability and community to those who have experienced homelessness, but it has not been widely studied with young adults (ages 18–25 upon moving into PSH). This paper employs a longitudinal convergent mixed methods approach – with quantitative and qualitative survey data over 1.5 years – to explore how young adult residents (N = 27 at T1; N = 20 at T2, N = 13 at T3) experience sense of community before and after moving into PSH. Using McMillan and Chavis' (1986) four-part Psychological Sense of Community (PSOC) framework to frame data collection and analysis, we find that residents' sense of community is shaped by an ongoing negotiation of personal and collective needs. Building community takes time and requires opportunities for voices to be heard (and shape change), along with ongoing personal growth – especially related to substance use recovery. Our findings offer insights for future PSH settings for young people – as well as other residential and community settings which aim to support young people. We suggest the need for future research which explores the tension of policy decision making, about substance use for example, in low-barrier support services like PSH. [ABSTRACT FROM AUTHOR]

• The centroid distance of PFNs is proposed and its properties are discussed. • Propose a score function based on the centroid distance and discuss its properties. • Applications of Pythagorean fuzzy clustering, ranking and MCDM are developed. • Some advantages of the proposed methods are showed through comparative analysis. By using Pythagorean fuzzy set (PFS), people can quantitatively describe and process information with fuzziness, and then make reasonable and effective judgments or decisions. In this process, distance measure often serves as a useful and important tool. The purpose of this paper is to explore some information processing methods and techniques in Pythagorean fuzzy environment. Firstly, the research progress of PFS is reviewed, and the main research directions and some hot issues in relevant research fields are discussed. Secondly, by analyzing the information of hesitation degree of Pythagorean fuzzy number (PFN), definitions of hesitation factor and centroid distance measure based on the representation of centroid coordinates of the hesitation regions are proposed, and properties of hesitation factor and centroid distance measure are discussed. Finally, the novel clustering algorithm, ranking method and multi criteria decision making (MCDM) method are developed in Pythagorean fuzzy environment, which are based on the new centroid distance measure. The effectiveness of the proposed algorithm and methods are verified through numerical examples and comparative analysis. The research results indicate that the centroid distance measure proposed in this paper can effectively characterize the distance between PFNs, thus making the clustering algorithm of PFNs, the ranking method of PFNs, and the Pythagorean fuzzy MCDM method have obvious superiorities and advantages. [ABSTRACT FROM AUTHOR]

Combined heat and power is an inevitable trend in the development of waste-to-power industry. Many waste incineration power generation projects that have already put into operation in China are waiting for transition and upgrading to combined heat and power projects. From a growth option perspective, this paper considers the irreversible process and uncertainty in economic benefits, introduces carbon emission trading mechanism and develops the growth option theory to design the expansion decision making model of waste-to-energy combined heat and power projects, so as to obtain the project value and find the optimal expansion timing. In this paper, a case study is carried out on the expansion decision making of a waste-to-energy combined heat and power project in Shandong Province in China. And two scenarios with specific and flexible expansion times are developed from European and American options separately, as European options require investors to expand at a certain time but American options not. The results indicate that 1) real option approach successfully improves the project value through considering uncertainty, and American option can achieve the optimal expansion time, 2) under the carbon emission trading mechanism, the carbon price affects the expansion index, which has a positive effect on the project value, 3) the linear nature of the income and cost expansion index can help investors make judgments on expansion investments. The effectiveness of the case study implies the practicality of the expansion option method for similar projects to reach dual carbon targets in cost and benefit analysis. • Real option approach is applied in energy transition in WTECHP projects. • European and American options are proposed for certain and flexible decision times. • Expansion time and triggers of WTECHP are incorporated into life cycle management. • CCER is introduced as an environmental element to evaluate expansion decision making. [ABSTRACT FROM AUTHOR]

Environmental issues drive the manufacturer to strive for green technology innovation. The manufacturer cannot strictly control all the activities in the production process, which may cause yield uncertainty. However, the existing studies that consider uncertainty factors usually assume that yield is deterministic. This paper constructs a green supply chain considering yield uncertainty and corporate social responsibility by a manufacturer and a retailer under different market power structures. We examine that yield uncertainty negatively affects the optimal decision-making strategies under four different power structures, especially in the centralized model. The retailer who adopts corporate social responsibility can alleviate the negative effects of yield uncertainty to a certain extent. Meanwhile, it is interesting that the manufacturer is not maximizing profits in the market dominated by himself and is subject to interference from yield uncertainty and corporate social responsibility. When the yield uncertainty is relatively small, the manufacturer's profit is the largest in the retailer-dominated market structure. However, when the yield uncertainty is significant, the revenue for the manufacturer is the largest in the manufacturer-dominated market. Regardless of how the yield uncertainty changes, the retailer's profit is always the largest in the retailer-led model. To alleviate the manufacturer's green costs, the retailer will share part of their revenue with the manufacturer under the Nash model. The Nash-revenue-sharing contract always benefits the manufacturer but hurts the profits of the retailer with a higher sharing coefficient. Finally, this paper provides prominent implications for managers and governments. [ABSTRACT FROM AUTHOR]

An in-depth understanding of uncertainty is the first step to making effective decisions under uncertainty. Machine/deep learning (ML/DL) has been hugely leveraged to solve complex problems involved with processing high-dimensional data. However, reasoning and quantifying different uncertainties to achieve effective decision-making have been much less explored in ML/DL than in other Artificial Intelligence (AI) domains. In particular, belief/evidence theories have been studied in Knowledge representation and reasoning (KRR) since the 1960s to reason and measure uncertainties to enhance decision-making effectiveness. Based on our in-depth literature review, only a few studies have leveraged mature uncertainty research in belief/evidence theories in ML/DL to tackle complex problems under different types of uncertainty. Our present survey paper discusses major belief theories and their core ideas dealing with uncertainty causes and types and quantifying them, along with the discussions of their applicability in ML/DL. Particularly, we discuss three main approaches leveraging belief theories in Deep Neural Networks (DNNs), including Evidential DNNs, Fuzzy DNNs, and Rough DNNs, in terms of their uncertainty causes, types, and quantification methods along with their applicability in diverse problem domains. Through an in-depth understanding of the extensive survey on this topic, we discuss insights, lessons learned, limitations of the current state-of-the-art bridging belief theories and ML/DL, and future research directions. This paper conducts an extensive survey by bridging belief theories and deep learning in reasoning and quantifying uncertainty to help researchers initiate uncertainty and decision-making research. • Identifying the causes and types of uncertainty in various belief models. • Uncertainty reasoning and quantification of belief models for decision-making. • Applying uncertainty quantification of belief models into deep learning. • Comparing uncertainty consideration in belief models and deep learning. • Insights, lessons learned, limitations, and future research directions. [ABSTRACT FROM AUTHOR]

The aim of the paper is to examine the issue of automated decision-making, i.e. decisions made by artificial intelligence. The use of automated decision-making is on the rise not only within private sectors, but also within the public sector. In today's world, people increasingly expect speedy decision-making, in particular in areas of public administration where to the so-called mass decisions are made pertaining to, i.a., taxation, social benefits, fees, permits, licences. Despite the fact that artificial intelligence has long been used in many areas of public administration in Poland, the automated, algorithmic decision-making process has not been regulated at the national level. The existent regulations pertain only to the serving and signing of documents. In Poland, there is a severe lack of comprehensive rules regulating automated-decision making, and at the same time, safeguarding administrative due process. The paper is based on analysis of normative acts and practice, supported by the study of secondary sources. [ABSTRACT FROM AUTHOR]

It is common practice in the MCDA to use several multi-criteria decision methods and then compares obtained rankings with one or two different rank correlation coefficients. The problem is that different rank correlation coefficient gives different values for the same pair of rankings, and the number of studies which tries to investigate it is small. Studying the similarity of rankings is a very important challenge in multi-criteria decision support, and the coefficients themselves seem to be the most practical ways of evaluating rankings. This paper compares chosen rank correlation coefficients to show how much different they are. Spearman's, Weighted Spearman's, Kendall Tau and Rank similarity correlation coefficient are compared statistically. The paper confirms that the coefficients are closely related, and their dependence is graphically represented, which initiates research towards allows for their better selection in the future. In conclusions, directions of further development are indicated. [ABSTRACT FROM AUTHOR]

The objective of this paper is to point out selected issues about the Total Performance Scorecard and its usefulness for Polish universities in performance measurement as well as in support in making decisions about the goals of employees and in helping them in personal develop. The carried out research, which small part is presented in this paper, indicates that current way of universities management is not really helping employees in their development as well as it does not take into account the needs of employees (specially while personal goals are set) and what's worst it can have an impact on their considerations of resignation of work. The research shows that the Total Performance Scorecard seems to be adequate for the universities. It is because this concept assumes that continuous improvement, development and learning, not only of the organization itself, but its employees as well, are very important elements of effective management of an economic unit and universities required continuous develop from their employees. [ABSTRACT FROM AUTHOR]

Due to the rapid development of artificial intelligence (AI) technologies, the medical system knows a permanent improvement in the doctor-patient relationship that has undergone a long and unproductive transition throughout the ages. Nowadays, the smart systems focused on how to obtain an accurate and detailed medication history for each patient. The purpose of this paper was to develop a method that can produce a disease profile based on the decision trees algorithm, taking into account medical reports made along years for different patients from all over the world. It is well known that in the medical decision making there are many situations where decision must be made rapidly, rigorously and efficiently. In the paper we present the basic characteristics of decision trees and the successful alternatives to the traditional induction approach with the emphasis on existing and possible future applications in biomedicine domain. [ABSTRACT FROM AUTHOR]

Multi-criteria decision-making methods aim to solve complex problems under many factors. These methods each day are more frequently applied and improved. Many researchers try to find a reliable solution for a specific problem or group of problems. More and more often, new methods are created that certain target requirements. That is why solving the specific problem can be executed on multiple methods. In this paper, we provide three new solutions for the problem presented in the referenced paper. For this purpose, the Characteristic Object's Method (COMET), the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method and the Stable Preference Ordering Towards Ideal Solution (SPOTIS) method were used. The results were then compared using selected similarity coefficients to emphasize the correlation of the resulting rankings. The research showed that choosing a method will have an impact on results and should be considered with caution. [ABSTRACT FROM AUTHOR]

Occupation-based intervention (OBI) in hand therapy has shown superior benefits in patient-reported performance and physical measures; however, only a few studies have used OBI. We developed a decision-aid to promote the use of an injured hand in the real world (Aid for Decision-making in Occupation Choice for hand; ADOC-H) To investigate the clinical utility of the ADOC-H (paper version) in patients with distal radius fractures. A prospective case series and a clinical survey for occupational therapists. This study comprised a prospective patient case series of 8 patients with distal radius fractures, treated using Volar locking plates, and a clinical survey of 4 experienced occupational therapists. No patient or therapist complaints or drop-outs were reported. Active range of motion (wrist), Grip strength, and Disabilities of the Arm, Shoulder, and Hand scores improved for all patients. The ADOC-H induced 158 activities using the injured hand, with activities of daily living (69.8%) selected earlier in the treatment period, and instrumental activities of daily living (63.3%) selected later. The feedback and case studies suggested that the ADOC-H was useful for patients who were afraid of using the hand and, interestingly, patients who were able to use their hand without pain or other problems. The clinical survey showed that most therapists found the ADOC-H effective in facilitating real-life use of an injured hand. The ADOC-H paper version is an useful tool that can be applied to facilitate patients with distal radius fractures to use their injured hands in real-life settings. • We tested the Aid for Decision-making in Occupation Choice–Hand (ADOC-H). • This case series showed that the ADOC-H is applicable to various real-life settings. • ADOC-H promotes distal radius fracture patients' daily use of the injured hand. [ABSTRACT FROM AUTHOR]

There are more complex heterogeneous preference expressions and methods (HPEMs) in heterogeneous multi-attribute group decision making (MAGDM) with several attribute sets, which makes the effective fusion of HPEMs become an important challenge in practical applications. The paper aims to propose a stochastic-integration-based decision support framework for the unified fusion of various HPEMs and provide some feasible solution methods. The construction process of decision support framework is embodied in recombining and connecting the HPEMs into "information flows" according to the traditional multi-attribute decision making procedures. For the information flows, three types of solution methods, such as the global solution method, the simplified solution method, and the consistency-measure-based solution method, are proposed to transform them into a final superiority-probability-based pairwise comparison matrix (SPM). Based on the final SPM, a ranking method is developed that uses probabilities to present the superiority relationship between two alternatives. In addition, a comparison discussion is given to highlight the advantages and disadvantages of the research. And two application examples are used to illustrate the validity of the proposed framework and solution methods. The decision support framework proposed in this paper is more open and inclusive, providing a feasible way for the unified fusion of various HPEMs. The proposed solution methods not only realize the sufficient solution, but also extend the decision results to probabilistic form, making the decision results more consistent with heterogeneous environment. The research in the paper can further enrich the methodology of heterogeneous MAGDM system and provide methods and technic support for complex group decision-making problems. [ABSTRACT FROM AUTHOR]

• Investigate a novel TrFBWM to yield the TrF criteria weights. • Develop the TrFZSG to rank the alternatives. • Propose a TrF-BWM-ZSG by fusinging the BWM and ZSG. This paper investigates a hybrid emergency decision-making (EDM) method called TrF-BWM-ZSG by combining the BWM (best-worst method) and ZSG (zero-sum game) in trapezoidal fuzzy (TrF) environment. Firstly, this paper defines the entropy and similarity for trapezoidal fuzzy numbers (TrFNs). Based on the entropy and similarity measures, a bi-objective programming model is constructed to distribute the weights for experts. Then, based on the multiplicative consistent trapezoidal fuzzy reference comparisons (TrFRCs), a novel TrFBWM is investigated to yield the TrF criteria weights. This novel TrFBWM highlights two innovations: (i) A crisp goal programming weight-determining model is constructed by using the multiplicative consistent TrFRCs; (ii) A reasonable consistency ratio is defined, in which the consistency index is determined according to the consistent transitivity of TrFRCs. Afterwards, the TrFZSG is developed to achieve the alternatives' ranking order by extending the classical ZSG into TrF environment. Lastly, the availability of the TrF-BWM-ZSG is corroborated through a real EDM case of Henan rainstorm disaster. The advantages of the TrF-BWM-ZSG are manifested by several valuable comparisons. Some desirable results derived by the TrF-BWM-ZSG are received: (i) The optimal alternative selected by the proposed TrF-BWM-ZSG suits the actual rescue situation; (ii) The proposed TrF-BWM-ZSG owns high generality since TrFN can reduce to crisp number, interval and triangular fuzzy number; (iii) The developed novel TrFBWM is helpful to preserve more inherent information of TrFNs. [ABSTRACT FROM AUTHOR]

[Display omitted] • This review identified key concepts describing decision-making for amputation. • Ninety-four papers were identified for inclusion in this review. • International Classification of functioning, Disability and Health framework used. • Personal and lifestyle factors are important considerations for amputation. • A biopsychosocial approach to decide if amputation would improve quality of life. Living with a diabetes-related foot ulcer has significant lifestyle impacts. Whilst often considered a last resort, amputation can overcome the burden of ulcer management, for an improved quality of life. However, limited research has been conducted to understand how the decision to amputate is made for people with a chronic ulcer when amputation is not required as a medical emergency. Therefore, the aim was to identify and map key concepts in the literature which describe the decision-making for diabetes-related amputations. This review followed Arksey and O'Malley's PRISMA scoping review framework. Five electronic databases and grey literature were searched for papers which described clinical reasoning and/or decision-making processes for diabetes-related amputation. Data were extracted and mapped to corresponding domains of the World Health Organisation's International Classification of functioning, Disability and Health (ICF) framework. Ninety-four papers were included. Personal factors including emotional wellbeing, quality of life, and treatment goals are key considerations for an elective amputation. It is important to consider an individual's lifestyle and personal circumstances, as well as the pathology when deciding between amputation or conservative management. This highlights the importance of a holistic and shared decision-making process for amputation which includes assessment of a person's lifestyle and function. [ABSTRACT FROM AUTHOR]