As decision-making problems become increasingly complex, ensuring both efficiency and decision quality in group decision-making (GDM) has become a significant challenge. When a large number of alternatives are involved, traditional single-stage processes may impose substantial cognitive and time burdens on decision-makers (DMs). To address this issue, this study proposes a two-stage group decision-making (TS-GDM) framework, consisting of a screening stage for preliminary filtering and a selection stage for comprehensive evaluation. The proposed framework incorporates an opinion aggregation method in the screening stage, an opinion dynamics model in the selection stage, and an expert extraction method based on simple random sampling. Then, a case study is conducted to demonstrate the model’s ability to capture both consensus and fragmentation. Simulation results further confirm the robustness of the proposed approach and highlight the critical role of the screening stage. In addition, parameter analysis reveals that both the structure of the social trust network and the similarity of opinions among DMs significantly influence the propagation and evolution of group opinions. Overall, the findings of this study provide valuable insights into two-stage group decision-making processes and offer practical guidance for designing efficient frameworks that achieve reliable and high-quality decisions.

Decision-making processes are significantly influenced by internal social network interactions and external information inputs. While previous research has highlighted the role of social networks in opinion evolution, the dynamics of information dissemination and its interaction with these networks are less understood. To bridge this gap, we introduce the Social-Information-Opinion Dynamic Supernetwork (SIO-DS) model, which integrates critical factors such as the impact of external information and opinion propagation, alongside the influence of internal social network structures and individual willingness to adjust opinions. This model takes into account the varied levels of confidence and individualized dynamic influence among decision makers, recognizing both their asymmetry and diversity. It performs opinion dynamics using bounded confidence models and parameters that govern information dissemination. We found that scale-free networks, which feature influential leaders, are more effective at reaching consensus compared to small-world networks, which are hindered by limited inter-group connections. The speed of information dissemination is critical; moderate speeds help in maintaining a stable consensus by balancing social influence, while very fast or slow speeds risk exacerbating polarization based on how social influence is managed. The SIO-DS model has broad implications for enhancing decision-making in corporate management by optimizing network structures, in public policy by managing public opinion, and in crisis management by developing effective communication strategies. Ultimately, this model not only deepens our understanding of opinion dynamics but also provides practical tools for improving decision-making quality and efficiency in various contexts.

The development of intensive care medicine is inseparable from the diversified monitoring data. Intensive care medicine has been closely integrated with data since its birth. Critical care research requires an integrative approach that embraces the complexity of critical illness and the computational technology and algorithms that can make it possible. Considering the need of standardization of application of big data in intensive care, Intensive Care Medicine Branch of China Health Information and Health Care Big Data Society, Standard Committee has convened expert group, secretary group and the external audit expert group to formulate Chinese Experts’ Consensus on the Application of Intensive Care Big Data (2022). This consensus makes 29 recommendations on the following five parts: Concept of intensive care big data, Important scientific issues, Standards and principles of database, Methodology in solving big data problems, Clinical application and safety consideration of intensive care big data. The consensus group believes this consensus is the starting step of application big data in the field of intensive care. More explorations and big data based retrospective research should be carried out in order to enhance safety and reliability of big data based models of critical care field.

This paper aims to propose a new multi-attribute decision making (MADM) method in complicated and fuzzy decision-making environment. To express both decision makers (DMs') quantitative and qualitative evaluation information comprehensively and consider their high hesitancy in giving their assessment values in MADM process, we combine q-rung dual hesitant fuzzy sets (q-RDHFSs) with uncertain linguistic variables and develop a new tool, called the q-rung dual hesitant uncertain linguistic sets (q-RDHULSs). First, the definition, operations and comparison method of q-RDHULSs are proposed. Second, given the interrelationship among multiple q-rung dual hesitant uncertain linguistic variables (q-RDHULVs) we introduce some aggregation operators (AOs) to fuse q-rung dual hesitant uncertain linguistic (q-RDHUL) information based on the Muirhead mean, i.e. the q-RDHUL Muirhead mean operator, the q-RDHUL weighted Muirhead mean operator, the q-RDHUL dual Muirhead mean operator, and the q-RDHUL weighted dual Muirhead mean operator. To cope with MADM problems with q-RDHUL information, we propose a new method based on the proposed AOs. Afterwards, we apply the proposed method to an enterprise informatization level evaluation problem to verify its effectiveness. In addition, we also explain why our proposed method is more powerful and flexible than others.

互联网时代,人们交往的范围和空间被不断拓展,目前社交网络、虚拟3D世界、视频会议等虚拟场景在生活和工作中已得到广泛应用.作为共享的沉浸式虚拟网络形态,"元宇宙"有助于加速现实世界的数字化转型,不同行业所涉及的元宇宙场景大都依托于区块链、虚拟交互、人工智能等新兴技术.在数字出版行业,用户的交互感知与虚拟体验需求也在逐渐增加,如何构建元宇宙场景下的数字出版技术生态体系是推动产业升级的重要问题.本文基于元宇宙的使能技术,提出了数字出版相关业务场景的深度融合技术架构,并从数据、软件、硬件以及网络四个层面剖析了数字出版业发展涉及的元宇宙相关使能技术.此外,基于数字出版行业在图书出版、用户视角、监管视角下元宇宙场景的探讨,提出了元宇宙背景下数字出版业务的新模态,预期能够为数字出版行业的可持续发展提供技术与运营启示.

Intensive care unit (ICU)-acquired infection is a common cause of poor prognosis of sepsis in the ICU. However, sepsis-associated ICU-acquired infections have not been fully characterized. The study aims to assess the risk factors and develop a model that predicts the risk of ICU-acquired infections in patients with sepsis. Methods We retrieved data from the Medical Information Mart for Intensive Care (MIMIC) IV database. Patients were randomly divided into training and validation cohorts at a 7:3 ratio. A multivariable logistic regression model was used to identify independent risk factors that could predict ICU-acquired infection. We also assessed its discrimination and calibration abilities and compared them with classical score systems. Results Of 16,808 included septic patients, 2,871 (17.1%) developed ICU-acquired infection. These patients with ICU-acquired infection had a 17.7% ICU mortality and 31.8% in-hospital mortality and showed a continued rise in mortality from 28 to 100 days after ICU admission. The classical Systemic Inflammatory Response Syndrome Score (SIRS), Sequential Organ Failure Assessment (SOFA), Oxford Acute Severity of Illness Score (OASIS), Simplified Acute Physiology Score II (SAPS II), Logistic Organ Dysfunction Score (LODS), Charlson Comorbidity Index (CCI), and Acute Physiology Score III (APS III) scores were associated with ICU-acquired infection, and cerebrovascular insufficiency, Gram-negative bacteria, surgical ICU, tracheostomy, central venous catheter, urinary catheter, mechanical ventilation, red blood cell (RBC) transfusion, LODS score and anticoagulant therapy were independent predictors of developing ICU-acquired infection in septic patients. The nomogram on the basis of these independent predictors showed good calibration and discrimination in both the derivation (AUROC = 0.737; 95% CI, 0.725–0.749) and validation (AUROC = 0.751; 95% CI, 0.734–0.769) populations and was superior to that of SIRS, SOFA, OASIS, SAPS II, LODS, CCI, and APS III models. Conclusions ICU-acquired infections increase the likelihood of septic mortality. The individualized prognostic model on the basis of the nomogram could accurately predict ICU-acquired infection and optimize management or tailored therapy.

Decision makers (DMs) are often hesitant about the evaluations for subjective or objective reasons, and established preference relations always have various limitations on the way that DMs express themselves. Aiming at investigating the consensus reaching process where DMs needs more decision freedom, this study proposes a novel decision support model for AHP with q-rung dual hesitant fuzzy preference relations (q-RDHFPRs). To do this, we give the definition of q-RDHFPRs and explore the corresponding operational rules. On account of this, we propose a family of algorithms to check and improve consistency and consensus of q-RDHFPRs, which can automatically obtain scientific and effective consensus results. Moreover, a priority method for q-RDHFPRs is proposed to rank the alternatives. The procedure of the q-RDHF-AHP is given in detail, and the example of risk evaluation of Hospital-acquired infections is employed to demonstrate our results. Comparative analyses show that the proposed q-RDHF-AHP method is more powerful for coping with the hesitant and uncertain situations and greatly expands the information description scope of the method.

Abstract In recent years, the development of electric vehicles has received extensive attention. However, how to choose a suitable charging pile manufacturer for electric vehicles is a matter of concern. The selection of charging pile manufacturers involves many factors, and it is hard for decision makers (DMs) to provide accurate assessments due to the uncertainty of subjective or objective factors. As a combination of q-rung orthopair fuzzy set (q-ROFS) and dual hesitant fuzzy set (DHFS), q-rung dual hesitant fuzzy set (q-RDHFS) provides more possibilities for information expression and gives DMs greater decision-making freedom. Because of the advantages of q-RDHFS in expressing uncertain information, we propose a novel decision method to capture DMs’ hesitant information with q-rung dual hesitant fuzzy elements (q-RDHFEs) to obtain the optimal scheme. Firstly, Frank t-norm and t-conorm (FTT) is well known for its flexibility in coping with compatibility compared to traditional algebraic operation. Considering the advantages of FTT, we extend FTT to q-RDHFS and provide the definition of Frank operational rules of q-RDHFS. Subsequently, according to generalized power average (GPA) and generalized power geometric (GPG) operators, some corresponding operators based on the novel operational laws are proposed. Then, with the proposed operators, a novel multi-attribute decision-making (MADM) method under q-RDHFS environment is introduced and applied to the selection of charging pile manufacturers. Finally, compared with the existing methods, the method proposed in this paper can better handle extreme evaluation information and is more flexible in operation.

The interval-valued q-rung dual hesitant linguistic (IVq-RDHL) sets are widely used to express the evaluation information of decision makers (DMs) in the process of multi-attribute decision-making (MADM). However, the existing MADM method based on IVq-RDHL sets has obvious shortcomings, i.e., the operational rules of IVq-RDHL values have some weaknesses and the existing IVq-RDHL aggregation operators are incapable of dealing with some special decision-making situations. In this paper, by analyzing these drawbacks, we then propose the operations for IVq-RDHL values based on a linguistic scale function. After it, we present novel aggregation operators for IVq-RDHL values based on the power Hamy mean and introduce the IVq-RDHL power Hamy mean operator and IVq-RDHL power weighted Hamy mean operator. Properties of these new aggregation operators are also studied. Based on these foundations, we further put forward a MADM method, which is more reasonable and rational than the existing one. Our proposed method not only provides a series of more reasonable operational laws but also offers a more powerful manner to fuse attribute values. Finally, we apply the new MADM method to solve the practical problem of patient admission evaluation. The performance and advantages of our method are illustrated in the comparative analysis with other methods.

Fuzzy theories are widely used in multi-attribute group decision-making (MAGDM) problems to describe uncertain and hesitant information. The recently proposed probabilistic linguistic q-rung orthopair fuzzy set (PLq-ROFS) is capable in dealing with quantitative and qualitative information simultaneously. However, in many actual situations, decision-makers (DMs) prefer to utilize interval values to express their minds and evaluations, this paper employs interval values to represent the probabilistic distribution of the membership degrees (MDs) and non-membership degrees (NMDs), and proposes the interval-valued PLq-ROFS (IVPLq-ROFS). Based on which, a novel two-stage TOPSIS approach is introduced. The IVPLq-ROFS weighted extended power average (IVPLq-ROFWEPA) operator is proposed to obtain the comprehensive matrix and weights of each attribute, while a further advanced TOPSIS model is constructed to get the final rank of alternatives. An example of a new-type smart city development evaluation problem is given to illustrate the efficacy of the proposed approach. Results show that our approach is more flexible, more adaptable, more accurate, more freedom, and has much lower calculation complexity in the calculation process of the MAGDM problem. The contributions of the proposed method are mainly manifested in giving the concept of IVPLq-ROFSs, and proposing a novel two-stage TOPSIS model for dealing with MAGDM problems under IVPLq-ROFSs.

The linguistic Pythagorean fuzzy sets (LPFSs), which employ linguistic terms to express membership and non-membership degrees, can effectively deal with decision makers’ complicated evaluation values in the process of multiple attribute group decision-making (MAGDM). To improve the ability of LPFSs in depicting fuzzy information, this paper generalized LPFSs to cubic LPFSs (CLPFSs) and studied CLPFSs-based MAGDM method. First, the definition, operational rules, comparison method and distance measure of CLPFSs are investigated. The CLPFSs fully adsorb the advantages of LPFSs and cubic fuzzy sets and hence they are suitable and flexible to depict attribute values in fuzzy and complicated decision-making environments. Second, based on the extension of power Hamy mean operator in CLPFSs, the cubic linguistic Pythagorean fuzzy power average operator, the cubic linguistic Pythagorean fuzzy power Hamy mean operator as well as their weighted forms were introduced. These aggregation operators can effectively and comprehensively aggregate attribute values in MAGDM problems. Besides, some important properties of these operators were studied. Finally, we presented a new MAGDM method based on CLPFSs and their aggregation operators. Illustrative examples and comparative analysis are provided to show the effectiveness and advantages of our proposed decision-making method.

[目的]研究相关性分析、关联规则挖掘和时间序列预测方法在手术室运营预测与优化的应用.[方法]基于50000余例手术记录数据,分别提出手术指标相关性分析、手术室资源关联规则挖掘和手术量时间序列预测方法,并据此探讨大型医院手术室运营预测与优化策略.[结果]75％的手术操作时长与其他手术指标呈强线性相关性.FP-Growth算法在最小置信度0.85下能获得可靠的手术室资源使用规律.利用周手术量时间序列提高至少37.5％的预测精确度.[局限]所用的手术室运营数据没有与其他医疗信息系统的数据关联,限制了该方法应用在医院其他部门的运营优化.同时,该方法在不同的医院运营环境中还需要进一步检验.[结论]所提方法为实现数据驱动的大型医院手术室运营预测与优化目标提供方法论指导.

The recently proposed interval-valued q-rung dual hesitant fuzzy sets (IVq-RDHFSs) allow the possible membership degrees and non-membership degrees to be denoted by some series of interval values, which can effectively deal with decision makers' (DMs') hesitancy in multiple attribute decision-making (MADM) process. The purpose of this paper is to propose a new decision-making method under IVq-RDHFSs. First, by pointing out the drawback of existing score function of interval-valued q-rung dual hesitant fuzzy element (IVq-RDHFE), a new score function is proposed. Second, to effectively aggregate IVq-RDHFEs this paper proposes the interval-valued q-rung dual hesitant fuzzy power Hamy mean operator and the interval-valued q-rung dual hesitant fuzzy power weighted Hamy mean operator. Compared with existing aggregation operators of IVq-RDHFEs, the newly proposed operators can not only deal with the complicated interrelationship among attributes, but also felicitously handle DMs' unreasonable evaluation information. Hence, our operators are suitable to deal with practical MADM problems. Third, a new MADM method is presented based on the proposed operators, and the main steps are clearly demonstrated. Lastly, we conduct case study to show the effectiveness of our proposed method.

The aim of this paper is to propose a new multi-attribute decision-making (MADM) method to rank all feasible alternatives in complex decision-making scenarios and determine the optimal one. To this end, we first propose the notion of interval-valued q-rung dual hesitant linguistic sets (IVq-RDHLSs) by combining interval-valued q-rung dual hesitant fuzzy (IVq-RDHF) sets with linguistic terms set. The proposed IVq-RDHLSs utilize IVq-RDHF membership and non-membership degrees to assess linguistic terms, so that they can fully express decision-makers’ evaluation information. Additionally, some related concepts such as the operational rules, score and accuracy functions, and ranking method of IVq-RDHLSs are presented. Considering the good performance of the classical Maclaurin symmetric mean (MSM) in integrating fuzzy information, we further generalize MSM into IVq-RDHLSs to propose the interval-valued q-rung dual hesitant linguistic MSM operator, the interval-valued q-rung dual hesitant linguistic dual MSM operator, as well as their weighted forms. Afterwards, we study the applications of IVq-RDHLSs and their aggregation operators in decision-making and propose a new MADM method. Some real decision-making problems in daily life are employed to prove the rightness of the proposed method. We also attempt to demonstrate the advantages and superiorities of our proposed method through comparing with some other methods in this paper.

The interval-valued q-rung dual hesitant fuzzy sets (IVq-RDHFSs) effectively model decision makers' (DMs') evaluation information as well as their high hesitancy in complicated multi-attribute decision-making (MADM) situations. Note that the IVq-RDHFSs only depict DMs' evaluation values quantificationally but overlook their qualitative decision information. To improve the performance of IVq-RDHFSs in dealing with fuzzy information, we incorporate the concept of uncertain linguistic variables (ULVs) into them and propose a new tool, called interval-valued q-rung dual hesitant uncertain linguistic sets (IVq-RDHULSs). Then we investigate MADM approach with interval-valued q-rung dual hesitant uncertain linguistic (IVq-RDHUL) information. Afterwards, the concept of IVq-RDHULSs as well as their operations and ranking method are proposed. Further, we propose a set of IVq-RDHUL aggregation operators (AOs) on the basis of the powerful Muirhead mean, i.e., the IVq-RDHUL Muirhead mean operator, the IVq-RDHUL weighted Muirhead mean operator, the IVq-RDHUL dual Muirhead mean operator, and the IVq-RDHUL weighted dual Muirhead mean operator. The significant properties of the proposed AOs are also discussed in detail. Lastly, we try to introduce a new method to MADM issues in IVq-RDHUL context based on the newly developed AOs.

The recently proposed q-rung orthopair fuzzy sets (q-ROFSs) have been proved to be an effective tool to describe decision makers' evaluation information and this paper attempts to propose a new multi-attribute group decision-making (MAGDM) method with q-rung orthopair fuzzy information. First of all, we propose a new score function of q-rung orthopair fuzzy numbers (q-ROFNs) by taking the hesitancy degree into account. When considering to fuse q-ROFNs, this paper tries to propose some novel aggregation operators. The power geometric (PG) operator has the ability of reducing or eliminating the bad influence of decision makers' unreasonable assessments on final decision results. Hence, we extend PG to q-ROFSs and propose the q-ROF power geometric operator and its weighted form. The most prominent advantage of dual Muirhead mean (DMM) is that it can capture the interrelationships among any numbers of input arguments. To take full advantages of PG and DMM, we further combine PG with DMM within q-rung orthopair fuzzy environment and propose the q-rung orthopair fuzzy power dual Muirhead mean, and q-rung orthopair fuzzy weighted power dual Muirhead mean operators. The proposed operators can reduce the negative effects of unreasonable evaluations on the decision results, and simultaneously take the interrelationship among any numbers of input arguments into account. In addition, we propose a new MAGDM method based on the proposed aggregation operators. Finally, we provide numerical examples to demonstrate the validity and merits of the proposed method.

BACKGROUND AND OBJECTIVE:Type 2 diabetes mellitus (T2DM) complications seriously affect the quality of life and could not be cured completely. Actions should be taken for prevention and self-management. Analysis of warning factors is beneficial for patients, on which some previous studies focused. They generally used the professional medical test factors or complete factors to predict and prevent, but it was inconvenient and impractical for patients to self-manage. With this in mind, this study built a Bayesian network (BN) model, from the perspective of diabetic patients' self-management and prevention, to predict six complications of T2DM using the selected warning factors which patients could have access from medical examination. Furthermore, the model was analyzed to explore the relationships between physiological variables and T2DM complications, as well as the complications themselves. The model aims to help patients with T2DM self-manage and prevent themselves from complications.METHODS:The dataset was collected from a well-known data center called the National Health Clinical Center between 1st January 2009 and 31st December 2009. After preprocess and impute the data, a BN model merging expert knowledge was built with Bootstrap and Tabu search algorithm. Markov Blanket (MB) was used to select the warning factors and predict T2DM complications. Moreover, a Bayesian network without prior information (BN-wopi) model learned using 10-fold cross-validation both in structure and in parameters was added to compare with other classifiers learned using 10-fold cross-validation fairly. The warning factors were selected according the structure learned in each fold and were used to predict. Finally, the performance of two BN models using warning features were compared with Naïve Bayes model, Random Forest model, and C5.0 Decision Tree model, which used all features to predict. Besides, the validation parameters of the proposed model were also compared with those in existing studies using some other variables in clinical data or biomedical data to predict T2DM complications.RESULTS:Experimental results indicated that the BN models using warning factors performed statistically better than their counterparts using all other variables in predicting T2DM complications. In addition, the proposed BN model were effective and significant in predicting diabetic nephropathy (DN) (AUC: 0.831), diabetic foot (DF) (AUC: 0.905), diabetic macrovascular complications (DMV) (AUC: 0.753) and diabetic ketoacidosis (DK) (AUC: 0.877) with the selected warning factors compared with other experiments.CONCLUSIONS:The warning factors of DN, DF, DMV, and DK selected by MB in this research might be able to help predict certain T2DM complications effectively, and the proposed BN model might be used as a general tool for prevention, monitoring, and self-management.

The q-rung orthopair fuzzy sets (q-ROFSs) have been proved to be an efficient tool in expressing decision makers’ (DMs) evaluation values in multiple attribute group decision-making (MAGDM) procedure. To more effectively represent DMs’ evaluation information in complicated MAGDM process, this paper proposes a new tool, called cubic q-rung orthopair fuzzy sets (Cq-ROFSs), based on the combination of q-ROFSs with interval-valued q-ROFSs. Then, we investigate MAGDM problems in which DMs’ preference information is given in terms of cubic q-rung orthopair fuzzy numbers. First, the definition, operations and comparison method of Cq-ROFSs are introduced. Second, to effectively aggregate cubic q-rung orthopair fuzzy information we propose the cubic q-rung orthopair fuzzy power average operator, the cubic q-rung orthopair fuzzy power Muirhead mean operator as well as their weighted forms. We illustrate the powerfulness and flexibility of the proposed operators in fusing cubic q-rung orthopair fuzzy decision-making information. Third, on the basis of the proposed operators we give the main steps of a novel cubic q-rung orthopair fuzzy MAGDM method. We utilize the method to solve real MAGDM problems to prove its effectiveness and validity. Finally, we explain why DMs should choose our proposed method rather than some others through comparison analysis.

OBJECTIVE:The aim of the study was to evaluate the diagnostic value of soluble fragment of cytokeratin 19 (CYFRA21-1) tests in detecting non-small cell lung cancer (NSCLC), including squamous cell carcinoma, lung adenocarcinoma, and large cell carcinoma. METHODS:The relevant studies were identified from PubMed, Embase and the Cochrane Library before November 2018. Summary estimates for sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio of CYFRA21-1 tests for the diagnosis of NSCLC were calculated using the random effects model. A summary receiver operating characteristic (SROC) curve was used to assess the overall effectiveness of the test. Meta-DiSc 1.4 and Stata11.0 were applied to the statistical analysis. Publication bias was detected using Egger's test. RESULTS:A total of 22 studies consisting of 7910 NSCLC patients (squamous cell carcinoma/lung adenocarcinoma/large cell carcinoma) and 2630 benign lesions patients that met the inclusion criteria were included. The meta-analysis showed that CYFRA21-1 tests had a relatively high accuracy for squamous cell carcinoma detection and a lower accuracy for lung adenocarcinoma detection. The overall sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio of CYFRA21-1 tests for squamous cell carcinoma detection were 0.72 (95% confidence interval (CI) 0.70, 0.74), 0.94 (95% CI 0.92, 0.95), 9.73 (95% CI 7.06, 13.40), 0.37 (95% CI 0.29, 0.47), and 27.30 (95% CI 17.68, 42.16), respectively. The area under the SROC curve was 0.9171 (Q* = 0.8500). No publication bias was tested in the squamous cell carcinoma (P = 0.567) and lung adenocarcinoma (P = 0.378) groups. CONCLUSIONS:CYFRA21-1 tests might be appropriate for detecting squamous cell carcinoma.