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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]

Using multi-criteria decision making (MCDM) technique to rank alternatives is a well-known area of study in which aggregation operators, such as ordered weighted averaging (OWA) and induced OWA (IOWA) operators, play an important role in merging information and producing an overall ranking. The distance measures from ideal argument values in aggregation operators have gained attention in recent literature. Distance measures are traditionally used as argument variables, which leads to the depiction that the attribute cannot be aggregated directly. In this paper, a generalized form of distance-induced OWA (DIOWA) operators is proposed with distance measures used as order-inducing variables. A distinctive benefit of DIOWA operators is that they permit us to consider ideal argument values while simultaneously also taking the attribute values as argument variables. Three variants of DIOWA operators are proposed and investigated, namely a) the Hamming distance-induced OWA (HDIOWA) operator, b) the normalized Hamming distance-induced OWA (NHDIOWA) operator, and c) the weighted Hamming distance-induced OWA (WHDIOWA) operator. We highlight their important properties and provide proofs to necessary theorems, and also suggest the determination methods for calculating their associated weights. We discuss further extensions of the proposed DIOWA operators with the help of generalized and quasi-arithmetic means. We discuss the use of our proposed family of operators for two different decision making situations, and demonstrate their validity by an illustrative numerical example. Finally, we apply the proposed operators to a real-life problem of ranking Chinese provinces for their science and technology (S&T) development levels. The proposed operators are shown to be a useful addition to the aggregation toolbox for decision analysts. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to propose a novel hybrid framework for evaluating and benchmarking trustworthy artificial intelligence (AI) applications in healthcare by using multi-criteria decision-making (MCDM) techniques under a new fuzzy environment. To develop such a framework, a new decision matrix has been built, and then integrated with q-ROF2TL-FWZIC (q‐Rung Orthopair Fuzzy 2‐Tuple Linguistic Fuzzy-Weighted Zero-Inconsistency) and q-ROF2TL-CODAS (q‐Rung Orthopair Fuzzy 2‐Tuple Linguistic Combinative Distance-Based Assessment). In this integration, q-ROF2TL-FWZIC is utilized for assigning the weights of evaluation attributes of trustworthy AI, while q-ROF2TL-CODAS is employed for benchmarking trustworthy AI applications. Findings show that the q-ROF2TL-FWZIC method effectively weights the evaluation attributes. The transparency attribute receives the highest importance weight (0.173566825), whereas the human agency and oversight criterion has the lowest weight (0.105741901). The remaining attributes are distributed in between. Moreover, alternative_4 receives the highest rank order (score of 7.370410417), while alternative_13 receives the lowest rank order (score of −4.759794397). To evaluate the validity of the proposed framework, systematic ranking and sensitivity analysis assessments were employed. [ABSTRACT FROM AUTHOR]

In literature, many studies try to analyze healthcare usage and generated decision support systems. In this paper, the aim is to generate a decision support system for insurance companies in the United States according to the federal dataset. The data set contains variables from the United States Healthcare Administration formal data group and they are; group of age, group of healthcare insurance, group of nationality, Corporate Social Responsibility (CSR) usage, and Advance Premium Tax Credit (APTC) usage. The best model for each state in the United States is determined with the dataset from 2014 November to 2015 February. In this study, various statistical models are attempted to generate independent and optimal models for each state. In the model selection process, three different metrics are used. Accuracy rate, f1score and precision score are used in the model selection process and models are compared to each other according to these three metrics for each state uniquely. Consequently, the proposed model is used with a decision support scheme to support customer service workers in healthcare insurance companies. The decision support system under consideration can facilitate a substantial decrease in the duration of interactions between customer service representatives and clients. Additionally, this system can generate more logically sound and efficient customer offers. [ABSTRACT FROM AUTHOR]

Aiming at three issues in large-scale group decision-making (LSGDM): risk attitudes, dynamically changing roles, and dynamically adjust evaluation information of decision makers (DMs), this paper constructs an LSGDM model by considering risk attitudes and dynamically changing roles. Firstly, we introduce the concept of risk attitudes and the method of identification and creatively combine it with Decision Making Trial and Evaluation Laboratory to divide DMs into four categories including core DMs, driven DMs, independent DMs, and followers. Considering the dynamically changing roles of DMs, an adjustment method for evaluation information is proposed. Then a double recognition mechanism is developed to determine whether the consensus has been reached. Moreover, we construct a method to determine the weights of DMs. Based on the above research, an LSGDM model based on probabilistic double hierarchy linguistic term set is constructed and applied to a practical problem of the selection of emergency medical distribution schemes. Finally, a sensitivity analysis is performed to examine the impact of different adjustment coefficients on the results, and the validity of our method is further illustrated by comparative analysis. [ABSTRACT FROM AUTHOR]

The paper proposes a new information set of multiple interacting fuzzy linguistic set(MIFLS) to describe the interaction of uncertain linguistic information. The property of the MIFLS and its characteristic of processing consensus information are also discussed, the introduction of MIFLS provides a new perspective to improve the consistency of decision makers' evaluation information, and the application of MIFLS can improve the accuracy of decision making. Furthermore, we propose an optimization consistency information model based on the MIFLS and TOPSIS method, an example of emergency decision plan is used to illustrate the applicability and efficiency of the proposed model. The optimal consistency adjustment is not only the highly consistent evaluation information, but also the minimal difference between the original information and the adjusted information. The proposed MIFLS is fit for dealing with the interaction of decision information, which means MIFLS is one of the effective tools to processing the interaction of uncertain fuzzy set and dealing with the emergency decision making problem. More important, the MIFLS not only provides a way to quantify the correlation of multi-attribute information, but also provides a method how to improve the consistency of decision-maker information. [ABSTRACT FROM AUTHOR]

Surrogate modelling techniques have opened up new possibilities to overcome the limitations of computationally intensive numerical models in various areas of engineering and science. However, while fundamental in many engineering applications and decision-making, the incorporation of uncertainty quantification into meta-models remains a challenging open area of research. To address this issue, this paper presents a novel stochastic simulation approach combining sparse polynomial chaos expansion (PCE) and Stochastic Kriging (SK). Specifically, the proposed approach adopts adaptive sparse PCE as the trend model in SK, achieving both global and local prediction capabilities and maximizing the role of the stochastic term to conduct uncertainty quantification. To maximize the generalization and computational efficiency of the meta-model, the Least Angle Regression (LAR) algorithm is adopted to automatically select the optimal polynomial basis in the PCE. The computational effectiveness and accuracy of the proposed approach are appraised through a comprehensive set of case studies and different quality metrics. The presented numerical results and discussion demonstrate the superior performance of the proposed approach compared to the classical ordinary SK model, offering high flexibility for the characterization of both extrinsic and intrinsic uncertainty for a wide variety of problems. [ABSTRACT FROM AUTHOR]

• M2MHub tool uses MCDA and GIS to support planning of shared mobility hubs. • Criteria are selected considering hubs as places for seamless multimodal transfers. • Weights of location criteria and sub-criteria in urban areas are calculated by AHP. • The model is applied to Barcelona to create heat maps of desirable locations. • The tool could help administrations and operators to achieve sustainable mobility. In the move towards sustainable urban mobility through seamless intermodality, European cities are faced with the possibility of implementing shared mobility systems. They constitute an opportunity to create new urban hubs, considered as nodes of intermodality and places of urban intensity. In order to effectively plan the future locations of shared mobility hubs, this paper outlines the methodological framework that sustains the design of a new decision-support tool, the so-called Mcda2MobilityHub (M2MHub) , 1 1 Link to the M2MHub tool, which is available as an open plugin for QGIS: https://github.com/natrouk/Mcda2MobilityHub-desicion-support-tool%5fQGIS-plugin. using Multi-Criteria Decision Analysis (MCDA) and Geographic Information Systems (GIS). For this purpose, the analysis of existing literature and interviews with local authorities and transport operators, led to the identification of six main location criteria (mobility, compactness, diversity of uses, profile of potential users, spatial configuration and environment). The criteria weights were calculated using the Analytic Hierarchy Process (AHP) and the framework was applied to the case study of the city of Barcelona. The results obtained are presented in the form of heat maps, highlighting the flexibility of the decision-support tool to transform open data into appropriate indicators and detailed maps. This output can be used to support future policies for planning shared mobility hubs as places for convenient multimodal transfers, which also enable social activities and improve the quality of life in European cities. [ABSTRACT FROM AUTHOR]

This paper proposes a variable domain hybrid decision-based friction optimization control strategy for train multi-wheelsets. This strategy aims to achieve the stable operation of creep speed for train on both flat and ramped railway lines. Firstly, a transfer factor-based creep speed prediction model is proposed. The model integrates the axle load transfer mechanism in the prediction process of creep speed by the transfer factor. Then, an adaptive hybrid decision (AHD)-based weight mechanism is proposed. The AHD incorporates a dual adaptive mechanism of the axle load transfer and slipping fault to make weight decisions. Finally, a variable domain transfer search(VDTS)-based friction coefficient allocation method is proposed. The VDTS designs a finite gradient learning to correct the search domain and establishes the transfer search equation based on domain boundaries. The proposed strategy is verified on the hardware-in-the-loop(HIL)-based platform. The detailed comparative analyses are conducted with three other relevant strategies to evaluate the optimization effectiveness of the creep speed for the wheelsets, which encompass the comparison of creep speed differentials as well as the power losses in the train. The test results show that the proposed method can maintain consistent speeds of different wheelsets within the stable domain, while reducing the slipping power loss of wheelsets. [ABSTRACT FROM AUTHOR]

• Developed a RAD generation framework to generate traffic crash characteristics. • Used a multi-layered decision process for developing a RAD dataset. • Can serve as a universal benchmark system for model frameworks in safety research. In this paper, a framework is outlined to generate realistic artificial data (RAD) as a tool for comparing different models developed for safety analysis. The primary focus of transportation safety analysis is on identifying and quantifying the influence of factors contributing to traffic crash occurrence and its consequences. The current framework of comparing model structures using only observed data has limitations. With observed data, it is not possible to know how well the models mimic the true relationship between the dependent and independent variables. Further, real datasets do not allow researchers to evaluate the model performance for different levels of complexity of the dataset. RAD offers an innovative framework to address these limitations. Hence, we propose a RAD generation framework embedded with heterogeneous causal structures that generates crash data by considering crash occurrence as a trip level event impacted by trip level factors, demographics, roadway and vehicle attributes. Within our RAD generator we employ three specific modules: (a) disaggregate trip information generation, (b) crash data generation and (c) crash data aggregation. For disaggregate trip information generation, we employ a daily activity-travel realization for an urban region generated from an established activity-based model for the Chicago region. We use this data of more than 2 million daily trips to generate a subset of trips with crash data. For trips with crashes crash location, crash type, driver/vehicle characteristics, and crash severity. The daily RAD generation process is repeated for generating crash records at yearly or multi-year resolution. The crash databases generated can be employed to compare frequency models, severity models, crash type and various other dimensions by facility type – possibly establishing a universal benchmarking system for alternative model frameworks in safety literature. [ABSTRACT FROM AUTHOR]

The decision-making on green retrofitting of existing buildings relies on both explicit and implicit knowledge, and its efficiency and reliability need improvement. Intelligent approaches that can sufficiently utilize the text information of existing projects are required to provide more suitable strategies for green retrofitting. This paper describes a decision-making approach combining Case-Based Reasoning (CBR) and Random Forest (RF), which can identify similar cases from the database containing 109 green retrofit projects and revise outdated measures. A practical project case study shows that the revised retrofit measures can reduce Energy Use Intensity (EUI) by 37%. The proposed approach optimizes and standardizes CBR processes and provides guidance for coping with semi-structured green retrofit decision-making problems, thereby promoting sustainable development and intelligent management in the construction field. The system prototype will be developed and promoted after the case database is expanded. • A CBR-RF approach is designed to select more energy-efficient strategies for building green retrofit. • RF is proven to optimize the case revision stage of CBR to avoid outdated retrofit measures. • The information on existing green retrofit projects is standardized and cross-platform used. • This approach provides intelligent guidance for retrofit decision-making, improving efficiency and reliability. [ABSTRACT FROM AUTHOR]

Most prior work on online reviews assumes that the value of reviews for consumers lies only in providing information about the product or the consumer's experience with the product. Yet, consumers also post and interact with pseudo-reviews, defined as reviews that describe exaggerated product experiences while trying to be entertaining. To address the nature and consequences of pseudo-reviews, this study develops a framework for understanding their value and how they affect consumer decision-making. First, we define how pseudo-reviews differ from other types of reviews. Then, we empirically examine how pseudo-reviews moderate the relationship between online review valence (how positive or negative the review is) and purchase intention. Finally, using a dual-processing theoretical lens, we examine how pseudo-reviews affect consumer decision-making through systematic and affective processing. We test our model through two experiments. The first study finds that pseudo-reviews reduce the impact of review valence on purchase intention when compared to genuine reviews. The second study shows that pseudo-reviews affect uncertainty and amusement, and these, in turn, affect purchase intention. We discuss the implications of pseudo-reviews for theory and practice. • Previous studies on online reviews often ignore the amusement value of online reviews. • The paper focuses on pseudo-reviews and develops a framework to understand their influence on consumer decisions. • For positive reviews, pseudo-reviews decrease the positive effect of review valence on purchase intention. • Pseudo-reviews affect consumer decisions by increasing both uncertainty and amusement. [ABSTRACT FROM AUTHOR]

In multiple attribute decision making (MADM) problems, when belief distributions (BDs) are used to express the preference information of decision-maker, obtaining the attribute weights using the difference among the assessments is a complex but interesting challenge. This paper introduces the weighted similarity measure of belief distribution to distinguish the alternatives to the greatest extent. After proposing the concepts of similarity measure and weighted similarity measure for two belief distribution vectors, an optimization model is developed to generate the attribute weights. After that, the comparison rules and the ideal belief distributions are defined, then the regret theory is extended to rank alternatives with the consideration of the psychological behavior of the decision-maker. The main characteristic of the proposed method involves: (1) An optimization model based on weighted similarity measure is proposed to derive attribute weights in BD environment; (2) The psychological behavior is taken into account to assist the decision-maker in avoiding regret; Finally, the applicability and validity of the proposed method are demonstrated by a medical example in which doctors could make the diagnosis with minimal regret. [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]

Online reviews are a valuable tool for Multi-Criteria Decision Making (MCDM) because they provide insightful information on several aspects. However, not all reviews yield complete information on all the necessary criteria for the MCDM problem. In practice, users might not effectively analyze all of them due to a lack of knowledge or time constraints. Despite this shortcoming, these online reviews still offer useful information that would be lost if neglected. Therefore, this paper proposes a framework that offers a new approach to using online reviews in MCDM and effectively addresses missing information on a given criterion or criteria. This new approach first establishes an information processing mechanism to transform online reviews into Probabilistic Linguistic Term Sets (PLTSs) with interval uncertainty. Afterward, a weight determination model is defined to calculate the criteria weights, which are given as interval values to reflect the uncertainty of the criteria and consider information from online reviews to better capture the opinions of reviewers. Next, the Evidential Reasoning (ER) algorithm is extended to the PLTSs with interval uncertainty environment to fuse the evaluation information and generate the interval-valued expected utilities of the alternatives by considering the uncertainty related to the missing information. Finally, a case study is conducted to illustrate the implementation process of the proposed framework, and a comparative analysis is carried out to evaluate its performance. • A new information processing mechanism is proposed to transform online reviews while considering missing information. • A new interval weight determination model is defined. • The evidential reasoning algorithm is extended to fuse online reviews and generate the interval-valued expected utilities. [ABSTRACT FROM AUTHOR]

To compare shared decision making (SDM) and satisfaction with care (SWC), an indicator of care quality, between children with special healthcare needs (CSHCN) and parents and to assess the association between SDM and SWC in both groups. We recruited CSHCN ≥ 7 years and parents from 15 outpatient facilities that completed a paper questionnaire assessing SDM (highest vs. lower levels of SDM) and SWC. Differences in SDM and SWC were assessed with McNemar and paired t-tests. We used adjusted linear mixed models to investigate cross-sectional associations between SDM and SWC. Based on data from 275 CSHCN and 858 parents, 39% and 64% of CSHCN and parents reported the highest level of SDM (p < 0.0001). No difference in SWC was observed (p = 0.36). Perceived SDM was associated with SWC in both groups (both p < 0.0001). Associations between SDM and SWC reinforce the role of SDM for care quality. Large proportions of CSHCN and parents reporting suboptimal levels of SDM highlight the need for effective programs to promote SDM in the target population. Until effective programs become available, healthcare professionals can use existing opportunities to involve CSHCN and parents in consultations (e.g., provide sufficient opportunities to ask questions). [ABSTRACT FROM AUTHOR]

This study explores how shared decision-making (SDM) is integrated in undergraduate nursing and medical education. A dual-method design was applied. The integration of SDM in medicine and nursing education programs (i.e. SDM on paper) was explored through document analyses; the integration of SDM in curricula (i.e. SDM in class) through interviews with teachers and curriculum coordinators (N = 19). A majority of the education programs featured SDM, mostly non-explicit. In curricula SDM was generally implicitly featured in compulsory courses across all study years. SDM was often integrated into preexisting theories and models and taught through various methods and materials. Generally, teachers and supervisors were not trained in SDM themselves. They assessed students' competence in SDM in a summative manner. Overall, SDM was featured in undergraduate nursing and medical education, however, very implicitly. • Shared decision-making is featured in undergraduate nursing and medical education • Nonetheless, shared decision-making is variedly and mostly implicitly integrated • Teachers and supervisors often are not trained in shared decision-making • The competency of taking the patient's perspective into account should receive more attention [ABSTRACT FROM AUTHOR]

• We study the relationship between women's empowerment and adherence to recommended infant and young child feeding practices in Central Asia. • We simultaneously analyze three dimensions of empowerment: attitude toward domestic violence, social independence and decision-making power. • Woman's decision-making power—a measure of her instrumental agency—predicts greater adherence to recommended IYCF practices. • A woman's attitude toward domestic violence and social independence are not consistent predictors of adherence to recommended IYCF practices. • Associations between decision-making power and IYCF practices vary little with child gender, cohabitation with one's mother-in-law, or wealth. This paper examines the relationship between women's empowerment and infant and young child feeding practices in Central Asia using Demographic and Health Survey datasets collected during 1995–2017. We employ a measure of women's empowerment with three dimensions that is available for many recent surveys as well as a measure of decision-making power over use of one's own income present for income-earning mothers in all surveys. We identify a positive association between a woman's decision-making power—a measure of her instrumental agency—and adherence to World Health Organization–recommended feeding practices. We find little significant association between a woman's attitude toward domestic violence, or her degree of social independence, and adherence to recommended feeding practices. Our results further show that the association between women's decision-making power and feeding practices varies little with child gender, whether or not she cohabitates with her mother-in-law, or household wealth. We thus provide evidence from Central Asia that policies and programs intended to empower women can improve child feeding practices, with similar benefits across a variety of household types. [ABSTRACT FROM AUTHOR]

• Aid withdrawals/suspensions are qualitatively different from giving no aid, and frame development. • Aid suspensions follow a long chain of understudied political calculations and decisions by donors and recipients. • Further research is needed on the political calculations and options used by aid-receiving states to discipline donors. In this introduction to the special issue, "Foreign Aid Withdrawals and Suspensions: Why, When and Are They Effective," we both summarize the current state of the literature and outline a robust new agenda for studying aid suspensions and withdrawals. A common contribution of the papers in this special issue is that they emphasize that donors and aid-recipient states have more options available to them than previous literature has allowed and that it is the creative ways in which aid-recipient governments seek to discipline their donors that make the effective use of conditionality so challenging. In this introduction, we not only summarize what we know about aid suspensions and withdrawals but also begin to unpack the complex decision-making that underlies aid suspensions, providing a simplified decision tree that can guide future research. Overall, we emphasize that, far from being a niche issue, aid suspensions and withdrawals are a fundamental part of the political economy of foreign aid and that much more work is needed to understand how recipient governments make decisions about how to respond or not to respond to (threats of) aid suspensions and withdrawals and how donors factor such political calculations into their initial or subsequent decision-making. The article highlights both the challenges and the opportunities of unpacking the complex decision-making behind aid suspensions and withdrawals. [ABSTRACT FROM AUTHOR]

In contemporary healthcare systems, the selection and judicious choice of the right medical device becomes paramount consideration for accurate diagnosis, patient care, mitigate risk and cost incurred on diagnosis of disease. Moreover, medical devices are requisite indispensable tools to help healthcare professionals for effective diagnosis, selection of the right treatment amongst different options, and continuous monitoring of the patient. Thus, to select the best manufacturing company is as equally important to achieve the desired goals. This paper presents a decision making algorithm based on fuzzy theory that helps the decision maker in selecting the most suitable worldwide medical device manufacturer. Fuzzy theory is extensively used in managing databases and analyzing data to summarize information and draw meaningful conclusions. This paper utilizes the aggregation operators within the context of the complex q-rung orthopair fuzzy environment by utilizing Yager t-norm and t-conorm operations. Some aggregation operators are applied, namely, complex q-rung orthopair fuzzy Yager weighted geometric operator, complex q-rung orthopair fuzzy Yager ordered weighted geometric operator, complex q-rung orthopair fuzzy Yager weighted averaging operator and complex q-rung orthopair fuzzy Yager ordered weighted averaging operator. Additionally, the notable properties of these operators are proved. These aggregation operators are generated specifically for multi-criteria decision-making. Decision making algorithm is used to choose the most relevant medical device manufacturer (alternative) to validate its efficacy. The results demonstrate the versatility of parameters, adaptability to multi-criteria decision making, and efficiency of the proposed aggregation operators and algorithm. Test with specific criteria is also conducted to verify the legality and validity of the decision making algorithm. At last, the acquired results underwent a comparative analysis against various established methodologies, confirming the method's capacity to produce precise and accurate outcomes. • Multi criteria decision making algorithm is devised in fuzzy environment. • Aggregation operators are introduced and notable properties are investigated. • Test with specific criteria is conducted for the validity of proposed operators. • The algorithm is utilized in medical industry to choose best device manufacturer. [ABSTRACT FROM AUTHOR]

In the Big Data era, context-aware mobile recommender systems are crucial in assisting citizens and tourists in making informed decisions, providing a suitable way for users to find the relevant data. These systems should be proactive, able to detect the ideal time and location to provide recommendations for a specific item or activity. To accomplish this, push-based recommender systems can be employed, utilizing context rules to determine when a recommendation should be initiated. However, there is very limited reported experience in defining and implementing such systems and a complete generic solution that adapts flexibly to the preferences of users and protects their privacy is still missing. In this paper, we present a novel approach where appropriate types of recommendations are provided automatically, without the need for user input. Our proposal allows users to easily activate, deactivate, customize, and create rules for improved personalization. Additionally, the module that, based on the context, decides the types of recommendations required is executed on the user's mobile device, reducing wireless communication and safeguarding the user's privacy, as context data are evaluated locally. To illustrate the approach, we have developed R-Rules, a prototype for Android devices focused on the triggering of recommendation rules, which provides a friendly user interface that facilitates user personalization. We have evaluated various technological options and demonstrated the feasibility, performance, and scalability of the proposal, as well as its suitability to users' needs. • Generic architecture for mobile push-based recommendations. • Decision module executing on the user device: privacy and wireless savings. • Flexible user-defined rules to decide when to fire a specific type of recommendation. • Android prototype and extensive experimental evaluation. [ABSTRACT FROM AUTHOR]

Intelligent computing is an independent algorithm system that imitates the laws and principles of nature's biological chain to design and solve practical problems in modern society, it can help people better handle some big data problems through artificial intelligence technology and different cognitive characteristics. Especially, when dealing with group decision problems, it is often considered as human behavior based on cognitive computing. The q-rung orthopair fuzzy set (q-ROFS) is not only a generalization of Pythagorean fuzzy set (PFS), but also can deal with the decision problem of multi-source information in a wider area. In this paper, we first give the detailed explanations of the relationship between the q-ROFS and q-rung orthopair fuzzy number (q-ROFN)by an example, and itis pointed out that there are some defects in the existing two ranking methods for the Pythagorean fuzzy numbers (PFNs) and intuitionistic fuzzy numbers (IFNs) through counterexamples, and then all q-ROFNs are mapped into the unit triangle of the first quadrant through q-compression mapping, and the concept of distance factor for q-ROFNs is proposed by Euclidean distance between each q-ROFN and the maximum element (1,0).Secondly, a novel inverse score function for q-ROFNs are put forward by the distance factor and hesitancy degree, and the monotonicity of the inverse score function are discussed. Finally, a new ranking criterion of q-ROFNs is given by the new score formula, and then the rationality of the ranking criterion is proved, and through the method of solving the conditional extreme value of multivariate function (Lagrange multiplier method), there is indeed an extreme value point (corresponding to the maximum q-ROFN) on υ = μ. Besides, a q-rung orthopair fuzzy decision-making method for multi-source information is given by the new ranking criterion and aggregation operations, and the comparison of examples shows that the inverse score function and decision-making method do have many advantages in the q-ROFN environment. [ABSTRACT FROM AUTHOR]

In this paper we investigate the notion of legibility in sequential decision tasks under uncertainty. Previous works that extend legibility to scenarios beyond robot motion either focus on deterministic settings or are computationally too expensive. Our proposed approach, dubbed PoLMDP, is able to handle uncertainty while remaining computationally tractable. We establish the advantages of our approach against state-of-the-art approaches in several scenarios of varying complexity. We also showcase the use of our legible policies as demonstrations in machine teaching scenarios, establishing their superiority in teaching new behaviours against the commonly used demonstrations based on the optimal policy. Finally, we assess the legibility of our computed policies through a user study, where people are asked to infer the goal of a mobile robot following a legible policy by observing its actions. [ABSTRACT FROM AUTHOR]

Tunnel structure are often subjected to continuous deformation caused by the creep effect of soft rock, leading to posing significant long-term operational risks. This paper focuses on ensuring the safe operation of a typical soft rock tunnel in the complex and dangerous mountainous area of western China. On the basis of studying the structural mechanic characteristics of the tunnel under the influence of soft rock creep, the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) model based on Analytic Hierarchy Process (AHP) and Entropy Weight Method (EWM) to determine subjective and objective weights, and game theory (GT) to obtain comprehensive weights was established. Additionally, the differential evolution-grey wolf optimization (DE-GWO) improved extreme learning machine (ELM) model was also constructed. By comparing the safety evaluation results of the two models on typical monitoring sections of soft rock tunnel structure, it is demonstrated that the proposed knowledge-based decision-making and data-driven models effectively and accurately evaluated the long-term safety of typical monitoring sections. Ulteriorly, to address the issue of structure safety evaluation caused by the inability to comprehensively monitor the entire tunnel (missing monitoring data), the DE-GWO improved ELM model was further adopted, and the structure safety status of the entire tunnel was precisely deduced. [ABSTRACT FROM AUTHOR]

This paper addresses the design of a decision making and motion planning system for lane change maneuvers considering energy efficiency. A novel decision making algorithm is proposed to check the desirability of performing the lane change. The algorithm is based on a utility function that consists of different performance criteria, including energy consumption. The execution of the decided maneuver involves a lower-level motion planning and control system for the longitudinal and lateral directions. For the longitudinal direction, an energy-efficient Model Predictive Controller (MPC) is designed, which considers the safety boundaries as well as other constraints, such as comfort, traffic laws, and physical limitations of the system. For the lateral direction, the desired trajectory is planned based on a parameterized sigmoid function. The lateral tracking is then realized by a PID controller. Finally, to evaluate the performance of the designed algorithms, a fuel consumption map of an internal combustion engine (ICE) is approximated by a second-order multivariate polynomial. Simulation results demonstrate the capability of the proposed algorithm to safely perform the lane change maneuver in different scenarios and for two vehicle models, including a simplified vehicle dynamic model and a high-fidelity IPG CarMaker model. [Display omitted] • A new lane change decision making is proposed considering energy consumption. • An integrated energy-efficient planning and control framework is designed using MPC. • The framework considers safety, comfort, energy consumption, and actuator limitation. • The fuel map of an internal combustion engine is fitted to second-order polynomial. • The obtained polynomial is used in the optimization problem of energy-efficient MPC. • Simulation study for a simple mathematical model and a high-fidelity CarMaker model. [ABSTRACT FROM AUTHOR]

• Proposing a physical asset criticality analysis decision model based on BIA. • Risk Appetite and physical asset continuity measures are related. • Defining sub-criteria for impact and likelihood estimation in criticality analysis. The paper tackles the issue of managing physical assets in asset-intensive industries, where continuity plans are crucial for business operations during disruptions. However, resource constraints often hinder companies from implementing these plans for all their assets. Moreover, efforts to maximize resource utilization efficiency and minimize environmental impacts are limited to isolated disciplines, necessitating an integrated approach across natural, social, and technological systems. To address these challenges, this study proposes a framework based on a hybrid fuzzy BWM-TOPSIS methodology for identifying critical physical assets. The framework incorporates Business Impact Analysis (BIA) to determine essential products, a physical asset risk assessment matrix (PARAM) to assess critical assets based on likelihood and impact, and a mathematical model to estimate asset continuity parameters. The framework is applied to a petrochemical company in a case study, offering a guide for estimating the probability and impact of physical asset failure while accounting for inherent uncertainties. By adopting this framework, companies can efficiently manage their physical assets, prioritize continuity plans, and address resource constraints and uncertainties. This research contributes to practical tools and approaches that assist industries in sustaining operations and ensuring business continuity during disruptions. [ABSTRACT FROM AUTHOR]

• Evaluating different models' perspectives for real-time adversarial attack detection in VANETs. • Identifying and preprocessing DSRC adversarial data using standard and fusion preprocessing approaches. • Selection for the golden ML model using a fuzzy decision-making framework for VANETs. • Evaluating the efficiency of the developed framework in four assessment processes. This paper addresses various issues in the literature concerning adversarial attack detection in Vehicular Ad-hoc Networks (VANETs). These issues include the failure to consider both normal and adversarial attack perspectives simultaneously in Machine Learning (ML) model development, the lack of diversity preprocessing techniques for VANETs communication datasets, the inadequate selection guidelines for real-time adversarial attack detection models, and the limited emphasis on explainability in adversarial attack detection. In this study, we propose an original fuzzy decision-making framework that incorporates multiple fusion standpoints. Our framework aims to evaluate multi-ML models for real-time adversarial attack detection in VANETs, focusing on three stages. The first stage involves identifying and preprocessing Dedicated Short-Range Communication (DSRC) data using standard and fusion preprocessing approaches. Two communication scenarios, normal and jammed, are considered, resulting in two DSRC datasets. In the second stage, we develop multi-ML models based on the DSRC datasets using standard preprocessing and feature fusion preprocessing for dataset-1 and dataset-2, respectively. The third stage evaluates the multi-ML models using a fuzzy decision-making approach based on the Fuzzy Decision by Opinion Score Method (FDOSM) and an adversarial attack decision fusion matrix. The External Fusion Decision (EFD) settings of the FDOSM address individual ranking variance, provide a unique rank and select the best model. Experimental results demonstrate that the K-Nearest Neighbors Algorithm (kNN) model achieves the highest explain score of 0.2048 in dataset-1 using standard preprocessing, while the Random Forest (RF) model applied to dataset-2 using fusion preprocessing emerges as the most robust and golden model against adversarial attacks, with a score of 0.1819. This finding suggests that the fusion preprocessing approach using Principal Component Analysis (PCA) is more suitable for addressing normal and adversarial attack perspectives. Furthermore, our fuzzy framework undergoes evaluation in terms of systematic rank, sensitivity analysis, explainability analysis, and comparison analysis. Overall, this framework provides valuable insights for researchers and practitioners in VANETs, informing the execution, selection, and interpretation of multi-ML models to tackle adversarial attack detection problems effectively. The new fuzzy framework demonstrates that multi-ML models based on feature fusion preprocessing are more effective. [ABSTRACT FROM AUTHOR]

It is demonstrated that higher-order patterns beyond pairwise relations can significantly enhance the learning capability of existing graph-based models, and simplex is one of the primary form for graphically representing higher-order patterns. Predicting unknown (disappeared) simplices in real-world complex networks can provide us with deeper insights, thereby assisting us in making better decisions. Nevertheless, previous efforts to predict simplices suffer from two issues: (i) they mainly focus on 2- or 3-simplices, and there are few models available for predicting simplices of arbitrary orders, and (ii) they lack the ability to analyze and learn the features of simplices from the perspective of dynamics. In this paper, we present a Higher-order Neurodynamical Equation for Simplex Prediction of arbitrary order (HNESP), which is a framework that combines neural networks and neurodynamics. Specifically, HNESP simulates the dynamical coupling process of nodes in simplicial complexes through different relations (i.e., strong pairwise relation, weak pairwise relation, and simplex) to learn node-level representations, while explaining the learning mechanism of neural networks from neurodynamics. To enrich the higher-order information contained in simplices, we exploit the entropy and normalized multivariate mutual information of different sub-structures of simplices to acquire simplex-level representations. Furthermore, simplex-level representations and multi-layer perceptron are used to quantify the existence probability of simplices. The effectiveness of HNESP is demonstrated by extensive simulations on seven higher-order benchmarks. Experimental results show that HNESP improves the AUC values of the state-of-the-art baselines by an average of 8.32%. Our implementations will be publicly available at: https://github.com/jianruichen/HNESP. [ABSTRACT FROM AUTHOR]

Machine Learning (ML) is growing in popularity and is applied in numerous fields to solve complex problems. Opportunistic Networks are a type of Ad-hoc Network where a contemporaneous path does not always exist. So, forwarding methods that assume the availability of contemporaneous paths does not work. ML techniques are applied to resolve the fundamental problems in Opportunistic Networks, including contact probability, link prediction, making a forwarding decision, friendship strength, and dynamic topology. The paper summarises different ML techniques applied in Opportunistic Networks with their benefits, research challenges, and future opportunities. The study provides insight into the Opportunistic Networks with ML and motivates the researcher to apply ML techniques to overcome various challenges in Opportunistic Networks. [ABSTRACT FROM AUTHOR]

The impact between time series may differ at different frequencies and time periods, and certain external events may cause sharp fluctuations in new financial markets. It is essential to conduct additional analyses on the extreme relationships in the time-frequency domain. However, extreme causal relationships and time-varying characteristics under various shocks usually cannot be detected simultaneously. Therefore, we reveal the extreme impacts and dynamic changes in global economic conditions on renewable energy by extending a fresh extreme wavelet-based method. Our results reveal significant extreme asymmetric impacts of economic conditions on renewable energy. Moreover, this effect was reinforced by extreme events such as the outbreak of the global financial crisis and the Russia–Ukraine conflict. This paper provides a deep analysis of economic conditions and renewable energy from a time-frequency domain perspective, which can aid investors and policy-makers in making relevant decisions under extreme conditions. • A extreme wavelet-based method is developed to reveal the extreme impacts and dynamic changes. • The significant extreme asymmetric between global economic conditions and renewable energy is confirmed. • A simulation technique is proposed to verify the robustness of our method. [ABSTRACT FROM AUTHOR]

The world is confronted with increasing droughts exacerbated by the inefficiency of irrigation, and water-saving irrigation (WSI) holds the potential to alleviate this dilemma. Since individual farmers are important participants in WSI, it is imperative to account for their decision preferences. In addition, previous research did not consider the decision reverse in the intertemporal choice of WSI construction. Recognizing the presence of bounded rationality, particularly in terms of time preference inconsistency, a hyperbolic discounting model is constructed. The implicit subsidy, explicit subsidy, and improved explicit subsidy scenarios are simulated to analyze the decision process of individual farmers in WSI construction. The proposed methods are applied to discuss the construction of WSI within the Taocheng District, Hebei Province. The results show that: (1) The participation and stability of individual WSI construction demonstrated an increase in both the explicit subsidy and improved explicit subsidy. (2) Compared with the implicit subsidy, there was a 29.6% decrease in the number of participants who chose not to engage in WSI construction at different intertemporal choice points in the explicit subsidy scenario, and the inconsistency of choice in different intertemporal choice points decreased from 70.4% to 31.4%. (3) In the improved explicit subsidy scenario, the inconsistency of choice in different intertemporal choice points will be further reduced to 22.5%. In order to promote WSI construction for water conservation, the policy implications of the subsidy mechanism are proposed in this paper. • Introducing individual bounded rationality into the water-saving irrigation analysis. • Simulate intertemporal choice behavior under three different scenarios. • A hyperbolic discounting model was constructed to analyze the NPV of utility. • The proposed "Refund then Collect" shows advantages over the "Collect then Refund". [ABSTRACT FROM AUTHOR]

Modern grids are facing a reduction of system inertia and primary frequency regulation capability due to the high penetration of distributed energy resources. In this paper, a decision-making framework is proposed to facilitate community-based virtual power plants (cVPPs) to promptly provide ancillary services to the grid. A non-convex cVPP decision-making model is established to optimize the operational plans of a cVPP's internal energy resources and the bids it puts in a local energy market to minimize the cVPP's operation cost. Bidding and management strategies will be automatically executed by solving each participating cVPP's decision-making problem. Due to its nature of high complexity and intractability, the problem is transformed into a partially observable Markov game model and solved by a multi-agent actor transformer-based critic method. A shared transformer encoder is used in the critic network to extract more robust features from the cVPPs' observations and actions. Numerical simulation demonstrates that the proposed method can effectively support cVPPs to autonomously generate energy bidding and management strategies without acquiring other cVPPs' private information. • An autonomous decision-making framework for cVPPs is developed to support local load balancing. • A decision-making model is established to minimize the operation cost of the cVPP. • The decision-making problem is transformed into a partially observable Markov game model. • A novel multi-agent actor transformer-based critic method is proposed. [ABSTRACT FROM AUTHOR]

Automatic driving in expressways is generally considered to be the easiest for commercial landing, and vehicle behavior decision-making is the core of automatic driving technology, which directly affects the safety and comfort of vehicle driving. In this paper, an automatic lane change strategy based on prior knowledge and data-driven is proposed for expressway. Our method decouples autonomous driving decisions into two processes. Firstly, we propose an optimization model of lane change features based on prior knowledge, which not only provides a basis for the selection of vehicle features but also effectively reduces the interference of invalid features. The driving risk field is used to screen the vehicle targets that have potential influence on the lane change of the ego vehicle, and the maximal information coefficient evaluates the effectiveness of the features of the vehicle targets through the maximal information coefficient to optimize the lane change features. Then, a data-driven lane-changing model is proposed based on Attention-BiLSTM (Bi-directional Long Short-Term Memory), which transforms vehicle lane-changing into time series prediction, fully explores the relationship between lane-changing features in the context information, and effectively improves the anti-interference ability of the system against accidental errors. In addition, the attention mechanism can adaptively adjust the weight of lane-changing features and effectively capture the important information that needs attention in different lane-changing states, further improving the accuracy of the model. Finally, the validity of the model is verified by remote sensing data sets and real vehicle experiments. The test results show that the test accuracy of our method is 95.1% in data sets and 94.2% in real vehicle experiments, which fully meets the requirements of lane change decisions about automatic driving. [ABSTRACT FROM AUTHOR]

Health inequalities are a perennial concern for policymakers and in service delivery to ensure fair and equitable access and outcomes. As health inequalities are socially influenced by employment, income, and education, this impacts healthcare services among socio-economically disadvantaged groups, making it a pertinent area for investigation in seeking to promote equitable access. Researchers widely acknowledge that health equity is a multi-faceted problem requiring approaches to understand the complexity and interconnections in hospital planning as a precursor to healthcare delivery. Operations research offers the potential to develop analytical models and frameworks to aid in complex decision-making that has both a strategic and operational function in problem-solving. This paper develops a simulation-based modelling framework (SimulEQUITY) to model the complexities in addressing health inequalities at a hospital level. The model encompasses an entire hospital operation (including inpatient, outpatient, and emergency department services) using the discrete-event simulation method to simulate the behaviour and performance of real-world systems, processes, or organisations. The paper makes a sustained contribution to knowledge by challenging the existing population-level planning approaches in healthcare that often overlook individual patient needs, especially within disadvantaged groups. By holistically modelling an entire hospital, socio-economic variations in patients' pathways are developed by incorporating individual patient attributes and variables. This innovative framework facilitates the exploration of diverse scenarios, from processes to resources and environmental factors, enabling key decision-makers to evaluate what intervention strategies to adopt as well as the likely scenarios for future patterns of healthcare inequality. The paper outlines the decision-support toolkit developed and the practical application of the SimulEQUITY model through to implementation within a hospital in the UK. This moves hospital management and strategic planning to a more dynamic position where a software-based approach, incorporating complexity, is implicit in the modelling rather than simplification and generalisation arising from the use of population-based models. [ABSTRACT FROM AUTHOR]

Many Latinx and Indigenous Mexican populations in the United States Southwest live in unincorporated communities in the US-Mexico borderlands called colonias. These environmental justice communities often lack basic infrastructure, including healthcare services, prompting many to seek services across the border. However, due to geopolitical factors more vulnerable caregivers are limited to utilize healthcare services in the US. This paper reports the experiences and healthcare decision-making of caregivers living in colonias in the US-Mexico border region who care for children with respiratory health conditions. This study was carried out from September to December 2020. Focus groups and interviews were conducted with Latinx and Indigenous Mexican caregivers of children with asthma or respiratory distress. Qualitative interviews elicited caregivers' perspectives on the environmental factors affecting children's chronic health conditions and use of healthcare services. The analysis employed the concept of structure vulnerability to theorize geography as a structural determinant of health for caregivers faced with making healthcare decisions for their suffering from respiratory health conditions. A survey was administered to collect basic sociodemographic information. A total of 36 caregivers participated in the study. Structural factors including unincorporated community status and government inaction intersected with social determinants of health to prompt caregivers to cross the US-Mexico border to access healthcare services in Mexico for their children. Yet, more vulnerable caregivers (i.e., those without documentation status in the US) and their children, accessing healthcare services in the US was not an option limiting caregivers' ability to meet their children's healthcare needs. In such cases, geography acts as a structural determinant of health. This study shows the importance of geography in health. Rural unincorporated colonias located in the borderlands are precariously located and lack basic critical infrastructure including healthcare access. Within such places, historically and socially marginalized populations become invisible, are subject to the health effects of environmental hazards, and are limited depending on their positionality and thus vulnerability to healthcare services. • Municipal unincorporation contributes to health disparities among Latinx families in US-Mexico borderlands. • Latinx children in colonias are disproportionately exposed to environmental toxins. • Geography acts as a structural determinant of health for Latinx families in colonias. • A binational approach to health services should be prioritized for families in the borderlands. [ABSTRACT FROM AUTHOR]

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]

Irrigation decision-making has evolved from solely depending on farmers' decisions taken based on the visual analysis of field conditions to making decisions based on crop water need predictions generated using machine learning (ML) techniques. This paper reviews ML related articles to discuss how ML has been used to enhance irrigation decision making. We reviewed 16 studies that used ML approaches for irrigation scheduling prediction and decision-making focusing on the input features, algorithms used and their applicability in real world conditions. ML performances in terms of accuracy, water conservation compared to fixed or threshold-based methods are discussed along with modeling performances. Informed by the 16 research studies, we assessed constraints to the adoption of ML in irrigation decision making at field scale, which include limited data availability coupled with data sharing constraints, and a lack of uncertainty quantification as well as the need for physics informed ML based irrigation scheduling models. To address these limitations, we discussed approaches in future research such as integrating process-based models with ML, incorporating expert knowledge into the modeling procedure, and making data and tools Findable, Accessible, Interoperable, and Reusable (FAIR). These approaches will improve ML modeling outcomes and boost the availability of farm-related data and tools for FAIRer data-driven applications of irrigation modeling. • Machine learning algorithms used in irrigation decision making are explained. • Irrigation demand computation by machine learning models is analyzed. • Limitations of the application of machine learning in irrigation are discussed. • Ways to incorporate physics into machine learning models for irrigation are presented. [ABSTRACT FROM AUTHOR]

• This scoping review explores the international literature between 2000 and 2022, to investigate the barriers and enablers to care-leavers engagement with multi-agency support services. • Thematic analysis informed the identification and analysis of documented enablers, barriers, and outcomes to engagement with care leavers. The results were used to populate a logic model to summarise the inputs, outputs and outcomes following care leaver engagement with multi-agency support. • Key themes identified around the barriers and enablers to engagement with care-leavers included Barriers: Identity, Trust in Services, Inadequate Support; Enablers: Turning points, Continuity of Support, A Good Worker, and Person-centred Approaches; and Outcomes: Improved Confidence, Awareness of Rights and Engagement. • We suggest that further qualitative research is needed with a variety of stakeholders including care-leavers to elicit the factors that contribute to their engagement with services, and to understand what engagement looks like. Many care-leavers experience poor individual and social outcomes. Care-leavers involvement with decision making and consistent supportive relationships with professionals can facilitate a more successful transition to independent living, including better well-being and social outcomes. Not all care-leavers engage with or participate in after-care services. There has been little systematic or structured modelling of what effective enablement through multi-agency support looks like, and the enablers and barriers to care-leaver engagement have not been identified. A scoping review of the international literature was conducted. Eighteen papers were identified, and a thematic synthesis used to derive themes associated with barriers and enablers to care-leavers engagement with services, and subsequent outcomes. The findings were used to populate a logic model illustrating the relationship between the mechanisms contributing to better outcomes for care-leavers: including inputs, activities, and outputs. Barriers to engagement included: identity, independence, trust in services and inadequate support. Enablers included persistent and consistent support, time and turning points, and having an authentic trusted professional within the service. The key findings focused on the need for flexible and accessible services, a gradual introduction to the after-care concept and a proactive approach by professionals, especially immediately after leaving care. This research co-produced with care-leavers contributes to a better understanding of the nature of enablers and barriers to engagement with multi- agency support services. The initial logic model derived from the literature will inform the development and measurement of a practice model and toolkit for professionals. [ABSTRACT FROM AUTHOR]

In recent years, university students have reported increased symptoms of stress and anxiety, which can negatively impact their mental well-being and academic performance. However, many students do not seek or receive support for these challenges. With recent technological advances in intelligent agents and their expanding capabilities, there is a potential for intelligent agents to extend and complement mental health care interventions and enhance access to care. Recognizing that there are many further steps to developing user-centred evidence-based approaches for mental health challenges, as a first step to developing effective interventions using intelligent agents, it is important to identify design elements and functionalities that are perceived as engaging and useful by students. In this paper, we present the findings of an online survey with 1054 undergraduate students to explore students' perceptions of, and preferences for, using different types of intelligent agents (e.g., virtual agents, social robots, etc.) to support their mental well-being, specifically to cope with feelings of stress and anxiety in social situations typically encountered within a university context (e.g., engaging in a group discussion, delivering presentations, expressing opinions, etc.). Students were asked to complete a questionnaire to explore their experience of stress and anxiety in the university context, as well as their perceptions of, and preferences for using different intelligent agents as assistive tools to cope with such feelings. The results provide insights into different design elements as well as social and technical capabilities to consider when designing intelligent agents to help address stress and anxiety among university students. • We surveyed 1054 undergraduate students on using intelligent agents to manage anxiety. • Public speaking was reported as the most anxiety-provoking situation at university. • Students preferred animal-like robots and conversational agents to cope with anxiety. • Intelligent agents would help manage anxiety by providing personalized coaching. • We discuss design implications for intelligent agents supporting students' anxiety. [ABSTRACT FROM AUTHOR]

Single-pilot operations (SPO), i.e., one pilot on board the flight in charge of all the operations, has received extensive attention in recent years. SPO involves several participants including the pilot, the autopilot, and the ground operator, and it is of great significance to determine the roles of these participants under the SPO mechanism. However, the evaluation of SPO mechanism has received little attention. Aiming at providing a reliable approach for SPO mechanism evaluation under uncertainty, this paper proposes a novel hesitant 2-tuple linguistic Pythagorean fuzzy decision-making method based on hesitant fuzzy linguistic term sets (HFLTS), 2-tuple linguistic information, Pythagorean fuzzy sets (PFS), and VIKOR method to handle the SPO mechanism evaluation problem. Firstly, based on the analysis of SPO, the evaluation criteria system for SPO mechanism is established. Then, considering the uncertain information in the SPO mechanism evaluation process, the HFLTS is used to represent the linguistic judgments of experts under uncertainty. Next, the HFLTSs of different experts are converted into 2-tuple linguistic information and PFSs to model the overall judgments of different criteria. Finally, the extended VIKOR method is adopted to evaluate and rank different SPO mechanisms. A case study of SPO mechanism evaluation for the ground proximity warning system (GPWS) is used to demonstrate the effectiveness and feasibility of the proposed method, and the SPO mechanism that the autopilot being responsible for GPWS is evaluated by the proposed method to be optimal. Sensitivity and comparison analyses further confirm that the proposed method could provide reliable and reasonable SPO mechanism evaluation results. In conclusion, the proposed method presents a novel and effective way to evaluate and select appropriate SPO mechanisms. [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]

Diverse citizens need to be involved in net zero transitions to ensure policy interventions do not entrench inequalities for people from minoritised or disenfranchised groups and ensure public engagement with rapid social changes. This paper illustrates the importance of designing enjoyable engagement activities aimed at segmented citizen and community groups, to ensure a broad cross-section of society can participate in environmental debate and policymaking. We analysed an evaluation sample of 857 people, representing 10.3% of the 8302 people directly engaged with the ClairCity project from six European countries. Women were under-represented in our activities, and younger participants preferred non-traditional forms of engagement like online games. Across all activities, multiple regression analyses found statistically significant relationships between younger age groups and enjoyment, and a positive correlation between enjoyment and understanding of air quality. Behaviour change intention was also significantly positively correlated to understanding, with 74% of participants indicating they would make a behaviour change to improve air quality. While climate change and air pollution are serious issues, public engagement to raise awareness and improve participation in policymaking does not have to be. To fully realise citizen-led emissions reduction, policymakers need to co-create engagement activities which are enjoyable, inclusive, and attractive to diverse citizens representing regional demographics. Ultimately, the more enjoyable and relevant the engagement activities, the more understanding people gain about the issues, and are therefore more likely to make a change to individual or community behaviours to reduce air pollution and carbon emissions and improve public and environmental health. [Display omitted] • Diverse communities need representation in climate/air pollution policymaking. • One size does not fit all: different activities attracted different demographics. • Activity enjoyment was significantly positively correlated to understanding. • Understanding was significantly positively correlated to behaviour change intent. • Policymakers should plan enjoyable activities to enable social learning between groups. [ABSTRACT FROM AUTHOR]

The pervasiveness of the Internet coupled with the advantages of cloud have almost made cloud services a necessity for organizations and individuals alike. However, it is a complex decision to choose a cloud vendor because of the large number of criteria and alternatives. Furthermore, the criteria in the case of cloud vendor selection are often interactive (synergic or opposing). To address this need, an approach is presented in this paper to select the most suitable cloud vendor while considering the nuances of human decision making, such as interactive criteria, and varying behavioural characteristics of the decision-makers (DMs). To this end, the recent Choquet logit models are deployed and generalized to add to their modelling abilities. Specifically, the following choice models are developed: generalized Choquet logit, generalized attitudinal Choquet logit, generalized Choquet mixed logit, and generalized attitudinal Choquet mixed logit models. The proposed generalized logit models have the Choquet logit, and the attitudinal Choquet logit models as their special cases. Thus the contributions of this work are two-fold: first an approach is developed to select suitable cloud vendor considering the human nuances and the actual practical aspects of the problem, and secondly a new class of powerful choice models is developed which may be useful in aiding any complex human decision-making, in which the cognitive power of the DM proves to be limited. The method is designed to consider the real nuances of the human decision making such as varying attitudes and varying degree of criteria interaction. The effectiveness of the proposed models in cloud vendor selection is tested through a real world case study from the Indian state of Tamil Nadu. In the case-study, there are 9 cloud vendors in the fray and 4 experts with different attitudes and different suggestions for the best cloud vendor. An approach is developed to solve this problem and a final ranking of the cloud vendors is determined considering experts suggestions. Apart from finding the best cloud vendor, the case study also helps in studying the role of an expert's attitudinal tendency on his preferences. Thus, concomitantly, the proposed approach also guides how to choose the best alternative based on the opinions of different experts with different attitudinal tendencies, without necessitating a forced consensus. • New probabilistic discrete choice models. • Representation of the complex decision making attitude. • Consideration of interaction among the criteria. • Variants of the proposed logit models. • Real Application. [ABSTRACT FROM AUTHOR]

Atanassov's intuitionistic fuzzy sets play a vital role in handling vagueness and uncertainty successfully as they associate an additional non-membership degree to each element of the universal set. Interval-valued intuitionistic fuzzy (IVIF) sets are more generalized quantities in this direction as they use intervals (⊂ [ 0 , 1 ]) to define the membership and non-membership grades of each element of the set. However, without defining basic arithmetic operations on the IVIF values/sets, the study is incomplete. This paper broadly examines the division and subtraction operations over any arbitrary IVIF values/sets using the concept of Hamming distance between them. A deterministic linear optimization model is proposed and solved in order to obtain the complete expressions for these operations. Next, various fundamental properties and relationships are examined and proved for the proposed arithmetic operations over IVIF values/sets. Furthermore, an example of a multi-criteria decision-making problem under uncertain conditions is studied and analyzed using the developed arithmetic operations on IVIF numbers. • Derived the generalized subtraction and division operations for IVIF values/sets. • The notion of Hamming distance and dual-simplex algorithm are used. • A deterministic LPP is proposed to obtain the expressions for the operations. • The fundamental properties and relationships for the basic operations are explored. • A case study of multi-criteria decision-making problems has been discussed. [ABSTRACT FROM AUTHOR]

The classification of huge complex datasets to extract the information needed for making informed decisions has gained a lot of importance in today's scenario due to the outspurt of data. With an increase in dataset size, the means of obtaining the desired information via data clustering techniques also became complex. This paper illustrates a framework for selecting the best combination of the Kernel function and the Lattice structure for better classification based on the size of the data. The performance of the SOM majorly depends on the choice of Kernel Function, Lattice Structures, and the size of the datasets. The types of kernel functions deployed in this study are Gaussian, Exponential, and Laplacian Kernel along with the lattice Structures such as hexagonal, Rectangular, and Square which result in nine combinations respectively. These nine combinations of the Kernel Function and the Lattice Structure have been tested on datasets ranging from small to large size using the performance metrics. A frequency table has been arrived at based on the classification of the dataset size, combinations of Kernel functions, and Lattice Structures. The results of this study infer that the combination of Gaussian Kernel -Square Lattice performs better for small-size datasets wherein the Exponential and Laplacian Kernel functions along with Hexagonal Lattice perform better for mid-size datasets, while the Laplacian Kernel – Square Lattice perform better for large size datasets. [ABSTRACT FROM AUTHOR]