Search Results (39)

As a core enabler of the emerging low-altitude economy, UAV networks face significant security risks during operation, including malicious node infiltration and data tampering. Existing trust management schemes suffer from deficiencies such as strong reliance on infrastructure, insufficient capability for multi-dimensional trust evaluation, and vulnerability to collusion attacks. To address these issues, this paper proposes a group decision-making (GDM)-enabled dynamic trust management method, termed GDM-DTM, for low-altitude UAV networks. GDM-DTM comprises four core parts: Subjective Consistency Evaluation, Objective Consistency Evaluation, Global Consistency Evaluation, and Self-Proof Consistency Evaluation. Furthermore, the method integrates a Dynamic Trust Adjustment Mechanism with multi-attribute trust computation, enabling efficient trust evaluation independent of ground infrastructure and thereby facilitating effective malicious UAV detection. The experimental results demonstrate that under identical conditions with a malicious node ratio of 30%, GDM-DTM achieves an accuracy of 85.04% and an F-score of 91.66%. Compared to the current state-of-the-art methods, this represents an improvement of 6.04 percentage points in accuracy and 3.71 percentage points in F-score. Full article

This research aims to construct a group decision-making (GDM) method that considers decision makers’ (DMs’) willingness to adjust in a distributed hesitant fuzzy linguistic (DHFL) environment. First, to address the practical scenario where DMs may express preferences using multiple linguistic values with explicit preference strengths, this paper extends the distributed hesitant fuzzy linguistic preference relation (DHFLPR) and supplements missing probabilities. Second, we integrate multiplicative consistency and consensus within a DHFL environment to construct two preference optimization models, whose objective functions are to minimize the overall adjustment based on DMs’ willingness to adjust, thus making the decision more consistent with actual environments. Finally, the viability and effectiveness of the new method are validated by numerical examples. The results show that our new method allows individual preferences to quickly meet the consistency requirement while maximally preserving their original preferences. Additionally, the DHFLPRs maintain the fuzziness and hesitancy in the new preferences, and effectively address the issue of unequal importance among distinct linguistic preference values. Full article

Feedback in group decision-making (GDM) is an effective procedure for eliminating preference inconsistencies among experts. As the core of GDM, feedback controls the progress and cost of the process. However, the current feedback model seldom considers interference effects caused by the interaction among experts. In addition, the stubbornness of experts to change preferences through interaction is different. This study proposes a bidirectional feedback model that considers interference effects. The model integrating quantum probability theory (QPT) into a feedback mechanism has greater flexibility and is more conducive to revealing modern cognitive psychology. First, experts were classified into concordant and stubborn discordant groups according to their personality parameters. Bidirectional feedback was proposed for a stubborn discordant group to improve the efficiency of feedback process and reduce the consensus-reaching cost. QPT was then used to describe the probability of experts modifying their preferences during the game process. Combining the interference value determined by the quantum probability with the feedback mechanism, a bidirectional feedback model driven by a minimum feedback control parameter is proposed to ensure that a certain consensus level can be achieved with minimal adjustment. The proposed feedback mechanism considers interference effects produced by experts in the interaction and can capture the feelings of conflict and compromise. Full article

Information that comes from the environment reaches the brain-and-body system via sensory inputs that can operate outside of conscious awareness and influence decision processes in different ways. Specifically, decision-making processes can be influenced by various forms of implicit bias derived from individual-related factors (e.g., individual differences in decision-making style) and/or stimulus-related information, such as visual input. However, the relationship between these subjective and objective factors of decision making has not been investigated previously in professionals with varying seniority. This study explored the relationship between decision-making style and cognitive bias resistance in professionals compared with a group of newcomers in organisations. A visual “picture–picture” semantic priming task was proposed to the participants. The task was based on primes and probes’ category membership (animals vs. objects), and after an animal prime stimulus presentation, the probe can be either five objects (incongruent condition) or five objects and an animal (congruent condition). Behavioural (i.e., accuracy—ACC, and reaction times—RTs) and self-report data (through the General Decision-Making Scale administration) were collected. RTs represent an indirect measure of the workload and cognitive effort required by the task, as they represent the time it takes the nervous system to receive and integrate incoming sensory information, inducing the body to react. For both groups, the same level of ACC in both conditions and higher RTs in the incongruent condition were found. Interestingly, for the group of professionals, the GDMS-dependent decision-making style negatively correlates with ACC and positively correlates with RTs in the congruent condition. These findings suggest that, under the incongruent decision condition, the resistance to cognitive bias requires the same level of cognitive effort, regardless of seniority. However, with advancing seniority, in the group of professionals, it has been demonstrated that a dependent decision-making style is associated with lower resistance to cognitive bias, especially in conditions that require simpler decisions. Whether this result depends on age or work experience needs to be disentangled from future studies. Full article

The medical diagnosis of many critical diseases is difficult as it usually requires the combined effort of several doctors. At this time, the process of medical diagnosis is actually a group decision-making (GDM) problem. In group medical diagnosis, considering doctors’ weight information and fusing the interaction relation of symptoms remain open issues. To address this problem, a group decision-making method for intuitionistic fuzzy soft environments is proposed for medical diagnosis because the intuitionistic fuzzy soft set (IFSS) integrates the advantages of the soft set and intuitionistic fuzzy set (IFS). Intuitionistic fuzzy soft weighted Muirhead mean operators are constructed by combining Einstein operations with the Muirhead mean (MM) operator, and some properties and results are revealed. A group medical diagnosis model with unknown doctor weight information and incomplete intuitionistic fuzzy soft information is proposed. Similarity measures of the intuitionistic fuzzy soft matrix (IFSM) given by the doctors are used to estimate the incomplete information. To take into account the advantages of objective weight and subjective weight, the combined weights of doctors are calculated based on the IFSMs’ similarity measure and doctors’ grades. The developed operators are then used to combine the evaluation information and handle the correlation of input arguments in the group medical diagnosis process. Finally, a numerical problem is selected to illustrate the superiority of the proposed approach compared to related methods. The combined weights are determined to overcome the shortcomings of the single-weight method to some extent. Meanwhile, the proposed method is more comprehensive, and can provide more flexible and reasonable choices for group medical diagnosis problems. Full article

Behavioral and biological addictions can impair decision-making processes, mainly by means of a dysfunction in brain regions associated with reward and frontal areas that may lead to disadvantageous choices. Understanding these differences helps establish appropriate terminology and enhances our ability to recognize, prevent, and treat these disorders effectively. Thus, while behavioral and biological addictions share some common elements, their underlying mechanisms and impact on decision-making vary significantly. Moreover, decision-making can be measured through questionnaires (stable or “cold” measures) or dynamic tasks (hot decisions) such as the Iowa Gambling Task (IGT), which can reflect different dimensions of this process. The aim of this study was to compare decision-making from different perspectives—stable and dynamic measures—in patients with gambling addiction (GA) (n = 42) and patients with biological addictions (BA) (n = 43). Decision-making was assessed using GDMS (Decisional Styles) and the LCT (Loss Aversion), as cold decision-making measures, as well as a hot or situational task called the IGT (Iowa Gambling Task). The results revealed that GA patients exhibited lower rational style scores compared to BA patients. Additionally, GA patients showed greater loss aversion according to the LCT questionnaire. On the other hand, when analyzing the IGT results, no differences were observed between groups in the overall IG index, learning curves, or the loss aversion parameter. However, GA patients showed higher sensitivity to feedback and less consistency in their decisions. These findings highlight the differences between different types of addictions and highlight the importance of considering the type of measure used to evaluate decision-making. Full article

In this paper we offer a comprehensive review of Sociophysics, focusing on relevant models as well as selected applications in social trading, behavioral finance and business. We discuss three key aspects of social diffusion dynamics, namely Opinion Dynamics (OD), Group Decision-Making (GDM) and Knowledge Dynamics (KD). In the OD case, we highlight special classes of social agents, such as informed agents, contrarians and extremists. As regards GDM, we present state-of-the-art models on various kinds of decision-making processes. In the KD case, we discuss processes of knowledge diffusion and creation via the presence of self-innovating agents. The primary question we wish to address is: to what extent does Sociophysics correspond to social reality? For that purpose, for each social diffusion model category, we present notable Sociophysics applications for real-world socioeconomic phenomena and, additionally, we provide a much-needed critique of the existing Sociophysics literature, so as to raise awareness of certain issues that currently undermine the effective application of Sociophysics, mainly in terms of modelling assumptions and mathematical formulation, on the investigation of key social processes. Full article

Urban viaducts play a crucial role in transportation infrastructure and are closely linked to urban resilience. Accurate evaluation of their structural technical condition forms the basis for the scientific maintenance of urban viaducts. Currently, there is a lack of technical condition evaluation specifications for viaducts in China, and the existing bridge specifications that are similar do not fully align with the facility composition characteristics and maintenance management needs of viaducts. Therefore, this paper presents a technical condition assessment model for viaducts, based on existing bridge specifications. Considering the frequent damage to ancillary facilities of viaducts, the utilization of maintenance resources, and the impact on traffic operations, the model proposed in this paper adopts the Analytic Hierarchy Process (AHP) to introduce a new indicator layer for ancillary facilities. Subsequently, the weight values and deduction values of each layer of the model, as well as the findings of damage recorded in the new components, were determined using the Group Decision-Making (GDM) method and the Delphi method. This process forms a constant-weight evaluation model for assessing the technical condition of viaducts. Finally, to account for the impacts of significant damage to low-weight components on the structural condition, the variable-weight method was adopted to establish a comprehensive evaluation model with variable weights, which was then validated using practical viaduct examples. The results indicate that the variable-weight model provides a more accurate representation of the technical condition of viaducts, especially when components are severely damaged. Furthermore, this study examines the suitable conditions for implementing the constant-weight evaluation model and the variable-weight evaluation model, demonstrating that the variable-weight model is recommended when there is a significant disparity in the scores among the viaduct components, whereas the constant-weight model is applicable in other scenarios. Full article

The purpose of this prospective cohort study is to identify the predictive factors for vaginal delivery among women (n = 146) who underwent cervical ripening using a dinoprostone insert (PG) alone (13.7%), cervical ripening balloon (CRB) alone (52.7%), oral misoprostol (M) alone (4.1%), or repeated methods (R, 29.5%) for gestational diabetes mellitus (GDM) at term, and to analyze maternal and neonatal morbidity outcomes according to the method for cervical ripening. After cervical ripening, vaginal delivery occurred in 84.2% (n = 123) and was similar among groups (90.0% after PG, 83.1% after CRB, 83.3% after M, and 83.7% after R; p = 0.89). After a multivariable logistic regression analysis adjusted for potential confounders, the internal cervical os being open before cervical ripening was a predictor of vaginal delivery (adjusted odds ratio (OR) of 4.38, 95% confidence index (CI) of 1.62–13.3, p = 0.03), and previous cesarean delivery was a predictor of cesarean delivery (aOR of 7.67, 95% CI of 2.49–24.00, p < 0.01). Birthweight was also significantly associated with cesarean delivery (aOR of 1.15, 95% CI of 1.03–1.31, p = 0.02). The rates of maternal and neonatal morbidity outcomes were 10.9% (n = 16) and 19.9% (n = 29), respectively, and did not differ according to the mode of delivery and to the method used for cervical ripening. Identifying these specific high-risk women (previous cesarean delivery and internal cervical os being closed before cervical ripening) for cesarean delivery among women who underwent cervical ripening for GDM at term is important and practical for all physicians to make a decision in partnership with women. Full article

This paper presents a group decision-making (GDM) method based on q-rung orthopair fuzzy preference relations (q-ROFPRs). Firstly, the multiplicative consistent q-ROFPRs (MCq-ROFPRs) and the normalized q-rung orthopair fuzzy priority weight vectors (q-ROFPWVs) are introduced. Then, to obtain q-ROFPWVs, a goal programming model under q-ROFPRs is established to minimize their deviation from the MCq-ROFPRs and minimize the weight uncertainty. Further, a group goal programming model of ideal MCq-ROFPRs is constructed to obtain the expert weights using the compatibility measure between the ideal MCq-ROFPRs and the individual q-ROFPRs. Finally, a GDM method with unknown expert weights is solved by combining the group goal programming model and the simple q-rung orthopair fuzzy weighted geometric (Sq-ROFWG) operator. The effectiveness and practicality of the proposed GDM method are verified by solving the crucial factors in crowdsourcing task recommendation. The results show that the developed GDM method effectively considers the important measures of experts and identifies the crucial factors that are more reliable than two other methods. Full article

Sintering is a commonly used agglomeration process to prepare iron ore fines for blast furnace. The quality of sinter significantly impacts the blast furnace ironmaking process. In the vast majority of sintering plants, the judgment of sintering quality still relies on the intuitive observation of the cross section at sintering machine tail by operators, which is susceptible to the external environment and the experience of operators. In this paper, we propose a new sintering state recognition method using deep learning based feature selection and ensemble learning. First, features from the infrared thermal images of sinter cross section at the tail of the sinterer are extracted based on ResNeXt. Then, to eliminate the irrelevant, redundant and noisy features, an efficient feature selection method based on binary state transition algorithm (BSTA) is proposed to find the truly useful features. Subsequently, an ensemble learning (EL) method based on group decision making (GDM) is proposed to recognize the sintering states. Novel combination strategies considering the varying performance of the base learners are designed to further improve recognition accuracy. Industrial experiments conducted at a steel plant verify the effectiveness and superiority of the proposed method. Full article

A flood vulnerability index was constructed by coupling Geographic Information System (GIS) mapping capabilities with an Analytic Hierarchy Process (AHP) Group Decision-Making (GDM) resulting from a paired comparison matrix of expert groups to assign weights to each of the standardised criteria. A survey was sent to 25 flood experts from government organisations, universities, research institutes, NGOs, and the private sector (56% academics and 44% non-academics). Respondents made pairwise comparisons for several criteria (population, socio-economic, buildings, and exposed elements) and sub-criteria. The group priorities were obtained by combining the Consistency Ratio (CR) and Euclidean Distance (ED) measures to assess the weight of each expert and obtain a final weight for each criterion and sub-criteria. In Portugal, 23 flood-prone areas were considered, and this work contributes with a tool to assess the flood vulnerability and consequently the flood risk. The flood vulnerability index was calculated, and the relevance of the proposed framework is demonstrated for flood-prone areas, in mainland Portugal. The results showed that in all five hydrographic regions, flood-prone areas with very high vulnerability were found, corresponding to areas with a high probability of flooding. The most vulnerable areas are Ponte de Lima in the North, Coimbra, and Pombal in the Centre; Loures in the Tagus and West Region; Setúbal and Alcácer do Sal in the Alentejo Region and Monchique in the Algarve Region. This methodology has the potential to be successfully applied to other flood-prone areas, combining the opinions of stakeholders validated by a mathematical model, which allows the vulnerability of the site to be assessed. Full article

With the development of Internet technologies, the shipping industry has also entered the Industry 4.0 era, which is the era of using information technology to promote industrial change. Group decision making (GDM), as one of the key methods in decision science, can be used to obtain optimal solutions by aggregating the opinions of experts on several alternatives, and it has been applied to many fields to optimize the decision-making process. This paper provides an overview and analysis of the specific applications of GDM methods in Shipping Industry 4.0, and discusses future developments and research directions. First, the existing relevant literature is analyzed using bibliometrics. Then, the general procedure of GDM is investigated: opinion/preference representation, consensus measure, feedback mechanism, and the selection of alternatives. Next, the specific applications of GDM methods in Shipping Industry 4.0 are summarized. Lastly, possible future directions are discussed to advance this area of research. Full article

Mobile Learning (M-Learning), driven by technological digital advancement, is one of the essential formats of online learning, providing flexibility to learners. Cloud-based mobile learning (CBML) provides value additions by providing an economic alternative to E-learning. Revolutionary changes in smartphone design and features have enhanced the user experience, thus encouraging mobile learning. During the COVID-19 pandemic, E-Learning and M-Learning allowed continuing education to occur. These methods continue to offer more opportunities to learners than constrained face-to-face classroom learning. There are many main critical success factors (CSFs) and subfactors that play an influential role in sustainable M-Learning success. The current study focuses on the assessment and ranking of various main factors and subfactors of CBML. Analytic hierarchy process-group decision-making (AHP-GDM)- and fuzzy analytic hierarchy process (FAHP)-based methodologies were used to evaluate and model the main factors and subfactors of CBML in crisp and fuzzy environments. Higher education institutes must strive to address these main factors and subfactors if they are to fulfill their vision and mission in the teaching–learning system while adopting sustainable M-Learning. Full article

Ensemble learning systems (ELS) have been widely utilized for human activity recognition (HAR) with multiple homogeneous or heterogeneous sensors. However, traditional ensemble approaches for HAR cannot always work well due to insufficient accuracy and diversity of base classifiers, the absence of ensemble pruning, as well as the inefficiency of the fusion strategy. To overcome these problems, this paper proposes a novel selective ensemble approach with group decision-making (GDM) for decision-level fusion in HAR. As a result, the fusion process in the ELS is transformed into an abstract process that includes individual experts (base classifiers) making decisions with the GDM fusion strategy. Firstly, a set of diverse local base classifiers are constructed through the corresponding mechanism of the base classifier and the sensor. Secondly, the pruning methods and the number of selected base classifiers for the fusion phase are determined by considering the diversity among base classifiers and the accuracy of candidate classifiers. Two ensemble pruning methods are utilized: mixed diversity measure and complementarity measure. Thirdly, component decision information from the selected base classifiers is combined by using the GDM fusion strategy and the recognition results of the HAR approach can be obtained. Experimental results on two public activity recognition datasets (The OPPORTUNITY dataset; Daily and Sports Activity Dataset (DSAD)) suggest that the proposed GDM-based approach outperforms the well-known fusion techniques and other state-of-the-art approaches in the literature. Full article