The paper focuses on the metaverse (MVS) in e-commerce supply chains and empirically reveals the specific mechanisms through which MVS enhances e-commerce supply chain resilience (ESCR). Drawing on the literature, this paper identifies two core MVS functions, virtual–physical interaction (VPI) and avatar-based consumer service (ACS), as key mediators. Grounded in actor-network theory (ANT), this paper constructs a research model with two hypothesized pathways: “MVS → VPI → ESCR” and “MVS → ACS → ESCR”. The model then examines the direct and indirect effects of these two pathways. Based on 380 valid responses from employees in China’s e-commerce industry, this paper conducted empirical tests with structural equation modeling. The results reveal that VPI exhibits a significant mediating effect in the process of MVS influencing ESCR ( p < 0 . 001 ), and ACS also plays a positive mediating role ( p < 0 . 001 ). Theoretically, this paper illustrates the formation of ESCR and explains how MVS enhances ESCR from an ANT perspective. The synergy between ANT and MVS offers a deeper understanding of MVS in supply chains. Managerially, when developing metaverse-enabled supply chain platforms, e-commerce enterprises should highlight the integration of consumers’ participation and multi-node collaboration. Based on the two aspects, e-commerce enterprises could significantly enhance ESCR to withstand disruptions. Overall, this study addresses the research gap between MVS and ESCR and offers a theoretical foundation for future research.

The integrated development between the logistics and manufacturing sectors is pivotal for promoting the efficiency and resilience of industrial and supply chains. However, extant academic research predominantly concentrates on qualitative analyses and the measurement of coupling coordination, with scant attention devoted to the quantitative selection of contextually appropriate integrated development strategies tailored to diverse real-world scenarios. Addressing the gap, this paper proposes a novel two-stage fuzzy decision-making framework integrating Quality Function Deployment (QFD), DEMATEL (Decision-Making Trial and Evaluation Laboratory), and MULTIMOORA (Multi-Objective Optimization by Ratio Analysis plus the Full Multiplicative Form) within an interval-valued triangular fuzzy number (IVTFN) environment. The proposed IVTFN-based approach effectively captures linguistic uncertainties while preserving decision-making information integrity, enabling a three-phase analysis to enhance integration: (1) identifying key integration requirements, (2) formulating targeted functional measures, and (3) selecting improvement solutions. Empirical validation in China’s Pinglu Canal region highlights infrastructure quality enhancement and information resource integration as pivotal strategies, demonstrating the framework’s practical applicability. Rigorous sensitivity and comparative analyses further confirm the robustness and superiority of the proposed method. This research contributes a structured roadmap for policymakers and industry practitioners to foster high-quality logistics-manufacturing integration, with methodological innovations extendable to other complex systems.

• This study simultaneously addresses the issues of SOA and TP of SCN. • The proposed mathematical model considers multiple decision objectives. • A real CNC machine tools assembly company provides data for a demonstration case. • Scenario comparisons and sensitivity analyses show the model’s effectiveness. • The implementation process is data-driven, requiring no expert intervention. Effective supplier order allocation (SOA) and transportation planning (TP) are critical for the smooth operation of supply chains, especially in dynamic markets with evolving customer demands. While previous research has made significant strides in addressing these areas individually, studies that integrate both aspects while considering a comprehensive set of objectives are limited. This study introduces an innovative multi-objective model that concurrently addresses total operational costs, supplier defect rates, sustainability performance, and supply chain network disruption risks. The model applies an augmented max–min approach of fuzzy multiple objective linear programming (AMM-FMOLP), which enhances overall utility while ensuring balanced performance across all objectives. The inclusion of all five objectives in the model is essential for a holistic evaluation of supply chain performance. The model’s effectiveness and practicality are validated through a case study involving a computer numerical control (CNC) machine tool assembly company, under various scenarios. Additionally, sensitivity analysis reveals how adjustments in supply chain structure can further improve performance. Moreover, the execution process of this study does not require expert intervention, making it a unique data-driven decision model particularly suitable for intelligent supply chain management and decision-support systems. This approach enhances the flexibility and resilience of supply chains, enabling them to effectively respond to unforeseen events and minimize their impact on business operations, especially in volatile global markets.

The diagram kind of a graph is used to show accumulated data. Graphs can be utilized for a variety of purposes because this data can be either quantitative or qualitative. Graphs can be used to model different relationships and processes in physical, biological, and social media marketing systems, and in finding directions on a map. A graph with properties attached to its nodes and edges that emphasize its applicability to real-world systems is sometimes called a network. The idea of fuzzy sets has developed in numerous ways and across many areas since its establishment in 1965. Applications of this theory can be found in numerous fields for instance in recognition of patterns, management science, AI, computer science, medicine, also in control engineering. The progress of mathematics has reached a very high level and continues now. While classical graph theory is widely applied in several domains, there are instances where its outcomes can be subject to uncertainty. In order to address this challenge, the utilization of the fuzzy theory of graphs is adopted, as it offers more accurate outcomes. There is a lack of a parameterization tool in fuzzy graph theory, as a consequence Molodtsov introduced soft set theory, which is a rather recent way to talk about ambiguity and vagueness. It is becoming more and more popular among scholars and is a novel approach to uncertainty and ambiguity simulation. The concept of soft graphs offers a parameterized perspective on graphs. In this article, we defined some familiar graph families in a fuzzy soft (FS) environment and by calculating their degrees, derived important results for two versions of Sombor numbers. In the end, we discussed an application of calculated results and by comparison, checked the efficiency of Sombor numbers in a FS framework.

The availability of numerous types of aircraft and their technical capabilities are offering a wide range of alternatives. As customers have different expectations, aircraft type selection is a business strategy for the airline companies. The choice on carriers should be made in accordance with disparate dimensions such as customers' expectations, profit of the company, capacity limitations and market conditions. This study formulates aircraft type selection as a Multi Criteria Decision Making (MCDM) problem and proposes a novel model that incorporates fuzzy trigonometric norms to solve. Being differentiated from the existing models in the literature, a two-stage model is identified. In the 1st stage, a fuzzy trigonometric-based Ordinal Priority Approach (OPA) determines the criteria weights. In the 2nd stage, RAFSI (Ranking of Alternatives through Functional Mapping of Criteria Subintervals into Single Intervals) is integrated to determine the optimal aircraft type. The model simulated for a case of Turkish Airline company. Sensitivity tests justify robustness of the model. Results show that among the four options, medium-scale high-qualified but not luxury aircraft is the best option.

The concept of the Metaverse, an immersive simulated world with parallels to reality, has gained significant prominence in recent times. Initially popularized through gaming, the Metaverse is now poised to infiltrate various aspects of human life. Intelligent transportation systems represent a promising yet challenging domain for Metaverse integration. Alternative implementations can create challenges in different dimensions. A comprehensive evaluation that takes challenges and opportunities for the different dimensions into account is required in decision making process of choosing the best implementation method. This study presents the development of a novel evaluation model, the Fermatean Fuzzy Operational Competitiveness Rating (OCRA) model, which incorporates the Fermatean Fuzzy Distance Measure (FF-DM) and Relative Closeness Coefficient (FF-RCC) techniques. The model is tested in a case to rank three alternative approaches, considering criteria of four key dimensions: managerial, safety, user, and urban mobility. In the first stage, the FF-DM and FF-RCC-based tool is employed to determine the criteria weights. In the second stage, an enhanced version of the Fermatean Fuzzy OCRA model, utilizing FF-DM and FF-RCC, is employed to rank the alternatives. The findings indicate that policymakers' decisions in traffic management hold the potential to shape the trajectory of the Metaverse movement, representing an unparalleled opportunity with implications that extend beyond our current comprehension.

Urban transportation plays a crucial role in cities that host sports events. Mexico City, as one of the World Cup 2026 hosts, is one of the world's largest and most populous cities, with a population of over 22 million people. This city faces several challenges related to urban transportation. The purpose of this study is to develop a novel group decision-making framework for evaluating six sustainable alternatives for the management of urban transportation crises. Our proposed group decision framework develops the Base Criterion Method (BCM) and Combined Compromise Solution (CoCoSo) under the fuzzy ZE-numbers for the first time in the literature to obtain reliable decisions. Based on the opinions of decision-makers and expert votes on those opinions, the proposed approach offers a unique feature in decision sciences in that the reliability of decisions can be increased in two stages. Also, sensitive analyses were executed for each of the urban transportation alternatives based on the different states of the criteria groups. According to the findings, the optimum plan should be an investment in the Metro and electric Minibusses development to manage the urban transportation crisis in Mexico City. Also, findings show that applying the fuzzy ZE-numbers leads to more accurate and reliable results.

Choosing the right projects in renewable energy investments is very significant. Due to this issue, necessary improvements to the performance indicators of these projects should be made. However, every improvement made also leads to an increase in costs. There is a need for a priority analysis to find the most important factors affecting the selection of the right renewable energy projects. Accordingly, this study aims to evaluate critical determinants of renewable energy project selection and provide effective investment strategies for this situation with a new fuzzy decision-making model. First, the indicators of renewable energy project selection are analyzed by perception and expression-based quantum Spherical fuzzy M-SWARA. The weights of these determinants are also calculated by DEMATEL to check the reliability of the results. Secondly, the priorities of renewable energy project selection are ranked by considering perception and expression-based quantum Spherical fuzzy ELECTRE. This calculation is also made by TOPSIS methodology to measure the reliability of the findings. The main contribution of this manuscript is that perception and expression-based evaluation can be carried out in the proposed model. In this process, the facial expressions and emotions of the decision makers are considered so that the hesitancy of these people while answering these questions can be included in the evaluation process. Another important novelty is that a new decision-making model (M-SWARA) is also proposed. This new technique provides an opportunity to consider causality relationship between the criteria to reach the most significant ones. The weighting results are the same for both M-SWARA and DEMATEL approaches. This situation gives information that the findings are coherent and valid. Market analysis has the greatest value in both perception-based (0.272) and expression-based (0.259) evaluations. Owing to this analysis, it is possible to clearly understand the supply-demand balance in the market. The ranking results indicate that technical adequacy is the most significant priority alternative for the selection of the appropriate renewable energy alternatives.

Alzheimer's disease (AD) is a memory-related disease that occurs in the human brain where neurons become degenerative. It is an evolved form of dementia that deteriorates over time. Machine learning, an extended version of deep learning, has appeared as an optimistic strategy for AD detection. Regardless, the existing AD detection approaches have yet to acquire the expected accuracy, mainly due to unreasonable data for training and testing. In this paper, we present the Federated Deep Convolutional Neural Network Alzheimer Detection Schemes (FDCNN-AS), specifically designed for varying age groups. FDCNN-AS is an efficient framework that contains architecture, algorithm flow, and implementation. It manages AD data from various laboratories and processes it in additional clinics. Our method mixes training data models from different types of data such as positron emission tomography, summed tomography, magnetic resonance imaging, blood tests, and questionnaires about synaptic degeneration. Further, we look at some restrictions that have yet to be addressed in AD detection. These include seeing AD at different ages, extrapolating the severity of brain damage, comparing treatment and recovery rates, and finding benign and malignant ranges in AD data that has been collected. To ensure secure and privacy-preserving learning, we execute FDCNN-AS within a federated learning environment that concerns considerable laboratories and clinics. Within this setup, we operate the generic deep convolutional neural network. The experimental results indicate that FDCNN-AS performs optimally, reaching a remarkable 99% accuracy in detecting dementia Alzheimer's in the human brain.

Location factor of recommender systems has been extensively studied in the past decade. However, there is no research thoroughly analyzing location’s role in news recommendation. In this paper, a comprehensive exploration on role of location in news recommendation is presented. First of all, based on analysis of real news datasets, we find that news recommendation differs from spatial item recommendation. Location affects news consumption behaviors of users with two-fold aspects including geographic feature and semantic feature. Regarding geographic feature, location influences news recommendation according to region rather than latitude-longitude level. Furthermore, interesting news topics are also impacted by semantic feature of location. Semantic feature may play a more positive role than geographic feature. The novel findings consistently manifest that, as non-spatial items, news differ from spatial items in that location influences users' selection in terms of different pattern and degree. In summary, geographic and semantic features influence reading preference through mapping locations into special topics. Changing of location topics leads to varying of reading preference. The news datasets in this paper belong to check in data. NewsREEL dataset is from a company, and it is provided by German researcher. The location data in Twitter dataset is also check in data. NetEase news dataset are collected from NetEase news websites, and the type of location data is city or region.

Sustainable food supplier selection (SFSS) can be handled as an uncertain decision-making issue. The Pythagorean fuzzy set (PFS), a type of non-standard fuzzy set, offers an expanded description space for articulating fuzzy and uncertain data. Accordingly, this paper proposes a Pythagorean fuzzy synthetic decision method-based selection framework for solving the SFSS problem within a subjective context. Then, the weighted distance measures for the PFS are introduced to derive the importance degrees of the experts, which can provide a more objective decision result. Then, an information fusion method with a PFS-weighted power average (WPA) operator is introduced to form a group decision matrix competent to accommodate the deviation effect. Next, an extended PF-measurement of alternatives and ranking according to compromise solution (MARCOS) method integrating PF-criteria importance through inter-criteria correlation (CRITIC) is presented to calculate the priority of each supplier, which can capture the inter-correlations between criteria. Finally, a numerical example of SFSS is implemented to show the application of the proposed synthetic decision approach. Subsequently, the sensitivity analysis of distance parameters and comparison analysis among different SFSS approaches were conducted to test the rationality and advantages of the proposed framework for resolving the SFSS problem. The results show that the reported method can provide a practical way to resolve the SFSS problems with uncertain data.

PM2.5 concentration predictions can provide air pollution control, management, and early warning. However, the PM2.5 data with high-dimensionality, complexity, and dynamics pose a great challenge to achieve optimal prediction results. Convolutional Neural Networks (CNNs) has unique advantages in processing complex data and contributes to state-of-the-art performances. However, designing the architecture and selecting the learning rate for CNN are time-consuming and requires prior knowledge. Evolutionary algorithms, with the advantages of global convergence, ergodicity, robustness and adaptability, are the most commonly used methods to design the optimal framework for CNNs. Therefore, to improve the predictive performance of CNNs, this paper proposes an improved CNN method (EBRO-ICNN) which employs the enhanced battle royale optimization (EBRO) algorithm and proportional-derivative (PD) control. Firstly, the EBRO algorithm with multistrategy collaborative optimization is introduced, and validated by CEC2017 benchmark functions, which demonstrates EBRO strong global exploration capability, fast convergence speed, and low time complexity. Next, PD control is applied to adjust the learning rate of CNN (ICNN) dynamically, which improves the efficiency and stability of the network training process. At the same time, the ICNN model is optimized using the EBRO algorithm, which can reduce the human interference, generate the optimal framework automatically and enhance the prediction accuracy effectively. Finally, a private air quality dataset and two public datasets are utilized to evaluate the performance of the EBRO-ICNN model, considering 3 error evaluation metrics and 7 prediction comparison models. The experimental results demonstrate that the EBRO-ICNN model exhibits great accuracy and stability.

Developing a comprehensive data-driven strategy for evaluating the organisational culture in companies to foster digital innovation involves a multi-criteria decision-making (MCDM) problem. This needs to consider various organisational culture characteristics that influence digital innovation success, assign significance weights to each characteristic, and recognise that distinct organisational cultures may excel in different aspects necessitates the proper handling of data variations. Hence, to provide organisations seeking to align cultural practises with digital innovation objectives with valuable insights, this study aims to develop an MCDM model for evaluating and benchmarking organisational culture in companies to foster digital innovation. The benchmarking decision matrix is formulated based on the intersection of evaluation characteristics and a list of organisational culture aspects in companies. The MCDM model is developed in two phases. Firstly, a new weighting model, q-rung picture fuzzy-weighted zero-inconsistency (q-RPFWZIC), is formulated for assessing the evaluation characteristics under the q-rung picture fuzzy sets environment. Secondly, the simple additive weighting (SAW) model is formulated for benchmarking the organisational culture in companies using the extracted weights of the evaluation characteristics. The results indicate that characteristic C6 (corporate entrepreneurship) has the highest weight, with a value of 0.161, while characteristic C3 (employee participation, agility and organizational structures) and C7 (digital awareness and necessity of innovations) has the lowest weight of 0.088. Company A2 secures the top rank with a score of 0.911, satisfying eight evaluation characteristics, whereas company A7 holds the last rank order, satisfying only one evaluation characteristic, obtaining a score of 0.101. In model evaluation, several scenarios were considered in a sensitivity analysis test based on a 100% increment in weight values for each characteristic to validate the reliability of the model results.

Energy forecasting plays an important role in effective power grid management. The widespread adoption of emerging technologies and the increased reliance on renewable sources of energy have created a need for a robust and accurate system for energy forecasting. This demand is becoming increasingly relevant due to the ongoing 2022 energy crisis. Modern power systems are very complex with many complicated correlations between various forecasting factors and parameters. Furthermore, renewable energy is often dependent on weather conditions, which complicates the process of forecasting. This work presents a novel artificial intelligence (AI) driven energy forecasting tuned deep learning framework. By formatting predictors as a time series, two variations of recurrent neural networks (RNN)s have been implemented: long-short-term memory (LSTM) and gated recurrent unit (GRU) neural networks. However, both approaches present several hyperparameters that require adequate tuning to attain desirable performance. Therefore, this work also proposes an improved version of a well know swarm intelligence algorithm, the sine cosine algorithm (SCA), tasked with tackling hyperparameter tuning for both approaches. To demonstrate the improvements made, three datasets have been constructed for evaluation from publicly available real-world data that contain relevant solar, wind, and power-grid load parameters alongside weather data. The proposed metaheuristic algorithm has been subjected to a comparative analysis with several contemporary metaheuristic algorithms to showcase the improvements made. The introduced metaheuristic demonstrated the best performance with a mean square error (MSE) rate for solar generation of only 0.0132 with LSTM methods and 0.0134 with GRU. Similar performance was observed for wind power generation forecasting with a MSE of 0.00292 with LSTM and 0.00287. When tackling power grid load forecasting a median MSE of 0.0162 was attained with LSTM and 0.01504 with GRU. Therefore there is great potential for tackling these tasks using the proposed approach. The best-performing models have been analyzed using SHapley Additive exPlanations (SHAP) to determine the factors that have the highest influence on energy generation and demand.

The careful consideration and choice of Sustainable Architectural Glass Material (SAGM) is a crucial step towards reducing energy consumption and enhancing the overall sustainability of buildings. However, the current techniques employed for selecting SAGM do not take into account the aspect of sustainability in the ultimate adoption of material solutions, as perceived by the stakeholders. This study aims to develop a comprehensive evaluation criteria system and introduce a group decision support framework that utilizes Building Information Modeling technology to facilitate the selection of SAGM in large-scale projects, in accordance with the demands of various stakeholders. The proposed framework is utilized to address a selection problem related to SAGM faced by a large construction corporation in Wuhan. The findings indicate that the criteria of daylighting and thermal insulation performance hold significant importance, with insulating glass being a subtype of SAGM exhibiting superior performance. Ultimately, the proposed model is subjected to sensitivity and comparative analyses to validate its advantages over several existing methodologies. The proposed model in this study offers practitioners an optimized methodology for the selection of SAGM, while also providing a framework for the selection of other sustainable building materials.

Using self-powered sensors, traffic data may be collected continuously, efficiently, and sustainably once connected autonomous vehicles (CAVs) are a part of metaverse technology. Metaverse self-powered sensors can capture uninterrupted data that allow for activities such as the management of the traffic network, the optimization of transportation facilities, and the management of urban and intercity journeys to be performed. In addition, metaverse technology creates a new field of study. Evaluating the systems involved in current transportation activities together with the metaverse can increase the efficiency and sustainability of transportation. The main purpose of this study is to prioritize four alternatives of CAVs in metaverse with self-powered sensors using a novel decision making model. The proposed hybrid decision making framework includes two stages. In the first stage the fuzzy full consistency method (fuzzy FUCOM) is applied to find the weighting coefficients of criteria. In the second stage, a fuzzy non-linear model based on fuzzy Aczel-Alsina functions (fuzzy Aczel-Alsina weighted assessment - ALWAS method) is defined to rank the alternatives. Four alternatives are defined and evaluated using twelve different criteria under four headings, namely, technical advancement, environmental, implementation, and financial aspects. A case study has been created for the experts to evaluate the alternatives most effectively. The results of the study indicate that using self-powered sensors for integrating real-time traffic management in the metaverse is the most advantageous alternative.

The task of an information retrieval system (IRS) is to retrieve relevant information from large datasets efficiently. However, selecting the best algorithm for an IRS is a complex decision support system (DSS) problem. The cubic m-polar fuzzy set (CmPFS) model is proposed to address the multi-polarity issue during algorithm selection and to express multi-polar information under m-fuzzy intervals. In this study, we introduce the Schweizer-Sklar (SS) aggregation operator based on CmPFS and develop various new aggregation operators using CmPFS and SS t-norm and t-conorm. New operations such as addition, usual multiplication, and power of two or more Cubic m-polar fuzzy Numbers (CmPFNs) based on SS t-norm and t-conorm are also defined. We also define score and accuracy functions to determine the priorities of alternatives. Four operators are proposed as follows: Cubic m-polar fuzzy Schweizer-Sklar weighted average (CmPFSSWA), Cubic m-polar fuzzy Schweizer-Sklar ordered weighted average (CmPOWA), Cubic m-polar fuzzy Schweizer-Sklar weighted geometric (CmPFSSWG), and Cubic m-polar fuzzy Schweizer-Sklar ordered weighted geometric (CmPFSSOWG). The desired qualities of these operators are explored and used to solve the DSS problem based on CmPFS. Finally, a practical application of an IRS is presented to demonstrate the effectiveness and reliability of the proposed operators.

This study proposes a novel multi-criteria decision-making (MCDM) model based on a rough extension of the Ordinal Priority Approach (OPA) to determine the order of importance of users' perspectives on Electric Vehicle (EV) purchases. Unlike conventional methods that rely on predefined ranks for criteria weighting coefficients, the proposed rough OPA method employs an aggregated rough linguistic matrix, enabling a more precise and unbiased calculation of interval values. Moreover, the model addresses inherent uncertainties by incorporating nonlinear aggregation functions, accommodating decision makers' risk attitudes for flexible decision-making. To validate the model's efficacy, a large-scale post-EV test drive survey is conducted, enabling the determination of relative criterion importance. Sensitivity analysis confirms the robustness of the model, demonstrating that marginal changes in parameters do not alter the ranking order. The results unveil the significance of the reliability criterion and reveal that vehicle-related characteristics outweigh economic and environmental attributes in the decision-making process. Overall, this innovative MCDM model contributes to a more accurate and objective analysis, enhancing the understanding of users' preferences and supporting informed decision-making in EV purchases.