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Corporate indebtedness is a powerful tool in determining a company's financial health with impacts on its image and reputation. The main objective of this research is to study the determining factors in corporate indebtedness in Portugal. It also has the secondary objectives of creating clusters of companies' behaviour in relation to the use of credit and verifying their differences in relation to the characteristics of the companies. It uses a quantitative methodology based on a questionnaire survey of 1957 Portuguese companies. The results of the factor analysis show the formation of six determining factors in corporate indebtedness, namely the negotiating relationship with banks, financing, cycle and indebtedness, company operating performance, guarantees used to obtain bank financing and financing risk analysis as well as secondary forms of bank financing. The application of cluster analysis to the six factors formed led to the classification of companies into three clusters: the resilient financial cluster, the operational excellence cluster and the strategic financial cluster. There are several statistically significant differences in the corporate financing factors in relation to the clusters to which they belong. The evidence of the factors and clusters explaining company financing provides insights for improving credit access practices and for implementing public policies that facilitate access to credit and promote economic development. [ABSTRACT FROM AUTHOR]

Abstract Blockchain has penetrated in various fields, such as finance, healthcare, supply chain, and intelligent transportation, but the value exchange between different blockchains limits their expansion. Cross-chain technology, such as notary mechanism, enables asset exchanges between different blockchain networks. However, existing research still confronts problems such as single inherent value evaluation, collusion risk, credit evaluation and unreasonable resource allocation, making it difficult to ensure the security of cross-chain asset transactions. So this paper proposes a cross-chain asset trading scheme based on edge cloud storage to improve the reliability of notaries and the security of cross-chain value exchange. Firstly, introduce the entropy weight method to determine indicators and adopt multi indicator evaluation to reduce the risk of collusion between notaries and users; Secondly, design a multi-indicator credit evaluation method to improve the accuracy of the evaluation; Finally, design a new and old notary node share allocation method to improve the rationality of resource allocation.The experiment shows that the scheme designed in this paper can reduce the risk of collusion, more accurately screen out high credit nodes to act as notaries, and make resource allocation more reasonable.

Blockchain has penetrated in various fields, such as finance, healthcare, supply chain, and intelligent transportation, but the value exchange between different blockchains limits their expansion. Cross-chain technology, such as notary mechanism, enables asset exchanges between different blockchain networks. However, existing research still confronts problems such as single inherent value evaluation, collusion risk, credit evaluation and unreasonable resource allocation, making it difficult to ensure the security of cross-chain asset transactions. So this paper proposes a cross-chain asset trading scheme based on edge cloud storage to improve the reliability of notaries and the security of cross-chain value exchange. Firstly, introduce the entropy weight method to determine indicators and adopt multi indicator evaluation to reduce the risk of collusion between notaries and users; Secondly, design a multi-indicator credit evaluation method to improve the accuracy of the evaluation; Finally, design a new and old notary node share allocation method to improve the rationality of resource allocation.The experiment shows that the scheme designed in this paper can reduce the risk of collusion, more accurately screen out high credit nodes to act as notaries, and make resource allocation more reasonable. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Shanghai University of Engineering Science / Shanghai Gongcheng Jishu Daxue Xuebao is the property of Journal of Shanghai University of Engineering Science Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To standardize the market conduct of electricity sales companies and elevate the power market management level， it is essential to conduct a credit evaluation of these companies. Therefore， based on Box-plot and orthogonalization methods， a credit evaluation model based on an enhanced discrete Hopfield neural network （DHNN） is proposed. Firstly， factors influencing the credit levels of electricity sales companies are analyzed， and a credit evaluation index system， which includes 11 indicators such as basic information， foundational management， contract management， and transaction management， is established. The weights for these indicators are determined using the Delphi method. Secondly， outliers in the indicators of electricity sales companies are addressed to derive optimal credit scores， enabling an objective assessment of their credit levels. Finally， the feasibility of the proposed model is verified through case studies. The results indicate that the model can objectively and accurately evaluate the credit levels of electricity sales companies.

Copyright of Zhejiang Electric Power is the property of Zhejiang Electric Power Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Corporate indebtedness is a powerful tool in determining a company’s financial health with impacts on its image and reputation. The main objective of this research is to study the determining factors in corporate indebtedness in Portugal. It also has the secondary objectives of creating clusters of companies’ behaviour in relation to the use of credit and verifying their differences in relation to the characteristics of the companies. It uses a quantitative methodology based on a questionnaire survey of 1957 Portuguese companies. The results of the factor analysis show the formation of six determining factors in corporate indebtedness, namely the negotiating relationship with banks, financing, cycle and indebtedness, company operating performance, guarantees used to obtain bank financing and financing risk analysis as well as secondary forms of bank financing. The application of cluster analysis to the six factors formed led to the classification of companies into three clusters: the resilient financial cluster, the operational excellence cluster and the strategic financial cluster. There are several statistically significant differences in the corporate financing factors in relation to the clusters to which they belong. The evidence of the factors and clusters explaining company financing provides insights for improving credit access practices and for implementing public policies that facilitate access to credit and promote economic development.

The purpose of introducing blockchain into electronic archives sharing and utilization is to break the information barrier between electronic archives sharing departments by relying on technologies such as smart contract and asymmetric encryption. Aiming at the problem of dynamic permission management in common access control methods, a new access control method based on smart contract under blockchain is proposed, which improves the intelligence level under blockchain technology. Firstly, the Internet attribute access control model based on smart contract is established. For the dynamic access of heterogeneous devices, the management contract, permission judgment contract and access control contract are designed; Secondly, the access object credit evaluation algorithm based on particle swarm optimization radial basis function (PSO-RBF) neural network is used to dynamically generate the access node credit threshold combined with the access policy, so as to realize the intelligent access right management method. Finally, combined with the abovemodels and algorithms, the workflow of electronic archives sharing and utilization model of multi blockchain is constructed. The experimental results show that the timeconsuming of the process increases linearly with the number of continuous access to electronic archives blocks, and the secure access control of sharing and utilization is feasible, secure and effective. [ABSTRACT FROM AUTHOR]

The aim is to improve small and medium-sized enterprises (SMEs)' core competitiveness and financing attainability using deep learning (DL) under economic globalization. Accordingly, this work constructs a supply chain symbiosis system based on DL, economics, and Stackelberg game theory following a status quo analysis of the financing status of SMEs. Afterward, a structural framework of supply chain financing (SCF) is designed. Further, it verifies the effectiveness of the proposed back propagation neural network (BPNN) credit evaluation model through specific enterprise data. The results show that the proposed internet of things (IoT)-based SCF SMEs-oriented BPNN credit evaluation model reaches a prediction accuracy of 91.4%. It effectively eliminates information asymmetry between banks and various capitals. As a result, banks can guarantee operation funds for the supply chain SMEs and help them minimize project risks by lowering financing leverage and through information transparency. [ABSTRACT FROM AUTHOR]

The development and application of blockchain technology can solve the problem of inconsistent quality levels in the physical delivery of current wheat futures transactions. This study takes wheat futures as the research object, and builds a wheat futures trading platform based on blockchain technology by analyzing the quality problems of wheat futures trading and the applicability of blockchain technology. Then, the conceptual model and quality control scheme of wheat futures trading quality evaluation process based on blockchain are proposed from the three stages of data acquisition, physical delivery and quality feedback. Thus, the quality evaluation system of wheat futures based on blockchain is constructed to provide reference for ensuring the quality of wheat futures trading and promoting the healthy development of wheat futures trading market.

With the development of computer technology,using machine learning algorithms to build automated evaluation models has become an important tool to for the financial institutions to conduct credit evaluation.However,currently,the credit evaluation model is still facing challenges:credit data is class-imbalanced and high-dimensional,meanwhile,the behavior of customers can be influenced by the changeable external environment,namely,the concept drift will occur.As a result,this paper proposes a dynamic credit evaluation model,which can achieve the flexible model update by using ensemble learning algorithm to continuously add base classifiers trained on new incremental data,and dynamically adjusting the weight of each base classifier to adapt to concept drift.When concept drift occurs,according to the detection results of concept drift,the model is able to use different forms of equalization and feature selection on credit data.In particular,for feature selection,this paper proposes an incremental feature selection algorithm combining the choice of representative samples that makes the feature selection efficient and accurate,enabling the model to simultaneously process the high-dimensional imbalanced data and adapt the concept drift of incremental credit data.Finally,this paper manages to demonstrate that the proposed dynamic model is more efficient and accurate than other prevailing algorithms on real incremental high-dimensional credit datasets.

High-tech small- and medium-sized enterprises (SMEs) play an important role in the high-quality economic development in a country. Nevertheless, due to the difficulties banks or other financial institutions have in accurately assessing their credit levels, financing difficulties have become the biggest bottleneck restricting the progress of high-tech SMEs, and therefore, this paper aims to construct a credit evaluation indicator system of high-tech SMEs. Based on prior studies and the characteristics of high-tech SMEs, this paper constructs an indicator system from financial and nonfinancial dimensions, including 22 measurement indicators reflecting the operation status, development potential, quality, and competitiveness of an enterprise. Principal component analysis (PCA) and a Delphi-analytic hierarchy process (AHP) method are employed for the evaluation. This indicator system innovates from the social capital perspective, and by setting more novel nonfinancial indicators, the system achieves a more comprehensive evaluation of credit level. This paper also performs an empirical application using the data from 125 enterprises in the Beijing–Tianjin–Hebei region of China, and further performs an empirical study on the external environment's impact on the credit level. The empirical results all show consistency with existing studies, verifying the workability and validity of the indicator system we constructed. [ABSTRACT FROM AUTHOR]

This study aims to establish which sources of financing were used and the relevance of different banking products for Portuguese companies during the pandemic. We also intend to understand the determinants of companies' financing options and what lies behind their decisions concerning the appropriate level of debt. A quantitative methodology was used, based on a questionnaire given to Portuguese companies to analyse different financing issues. The sample was composed of 1957 companies with a business volume of more than EUR 500,000 per year. The results show that Portuguese companies focused on managing liquidity and corporate risk. We found evidence that companies kept financing themselves by banking products such as in the pre-pandemic period, although 29.6% resorted to the LAE-COVID economy support line. Companies decide on the appropriate amount of debt based on the nature of the business, the phase of the life cycle in which the company is, the cash flows' volatility, accounting results, credit rating, and fiscal benefits. Academicians and companies should master the concept of company financing and adopt strategies to consider the level of debt and refine the banking products to be used. Although the literature on business financial management usually claims that all crises are the same, the COVID-19 pandemic not only caused a recession but also forced people and companies to adapt to a new environment. Portuguese companies have shown resilience and focus on their adoption of good financing practices. [ABSTRACT FROM AUTHOR]

Aiming at the credit risk control of small and medium-sized enterprises in the supply chain finance model, this paper proposes a credit risk prediction model for high dimensional and unbalanced data. Firstly, based on the Pearson-XGBoost two-stage feature selection, this paper established the supply chain financial credit evaluation index system. Secondly, with the help of the improved NM-SMOTE algorithm, it made the dataset balanced. Finally, the method used the Focal Loss function to improve the XGBoost algorithm, and optimized it by the improved particle swarm algorithm, thus this paper established the final credit evaluation model. The experimental results show that the INS-IPSO-FLXGBoost model proposed in this paper has a better prediction effect for small and medium-sized enterprises, and can identify risky enterprises more effectively. [ABSTRACT FROM AUTHOR]

In recent years, the state vigorously support the development of science and technology enterprises, with the transformation of economy and the rapid development of science and technology, science and technology enterprises in our country's economic development play a more and more important role, but the science and technology enterprises are in growth period, exist in the process of the development of a lot of risk, so there are some problems in financing. For banks, credit evaluation of enterprises is very necessary, which can effectively reduce the credit risk of banks. Based on the establishment of the credit evaluation index system of listed companies in the science and technology sector, this paper screens out 12 indicators, uses support vector machine to classify the selected sample enterprises, and trains 10 sub-support vector machines. In order to improve the accuracy, the fuzzy integral based support vector machine ensemble method is used to classify enterprises. The results show that the classification accuracy of both training samples and test samples is improved, and the integrated classification accuracy is higher than that of a single support vector machine, which also shows the feasibility and effectiveness of the model. [ABSTRACT FROM AUTHOR]

To objectively evaluate the credit level of Metaverse listed companies, this paper constructs a credit evaluation index system for Metaverse listed companies from five aspects: profitability, solvency, growth ability, operational ability, and the ability to introduce technological innovation, and select 12 listed companies in 2021. Based on the relevant financial data of Metaverse listed companies, based on the multi-criteria decision-making problem Fermat's fuzzy TOPSIS method and the hesitant fuzzy language PROMETHEE multi-attribute decision-making method, the credit levels of 12 Metaverse listed companies were calculated respectively, and then the fuzzy Borda method was used to compare the two. The evaluation results of a single method are combined to obtain a combined evaluation result. Compared with the single method, the combined evaluation results have higher confidence. The final results show that GoerTek, Longli Technology, Changxin Technology, and Xinguodu 4 listed companies in the Metaverse are ranked high and have good credit levels. From the perspective of banks, they can be given priority when choosing to issue loans. [ABSTRACT FROM AUTHOR]

With the rapid development of Internet finance industry, traditional credit risk evaluation is facing challenges in the face of massive data.Due to the unbalanced sample categories and high feature redundancy in credit evaluation, it has become the key factor affecting the classification accuracy of current evaluation.In order to solve the above problems, a method based on gray wolf optimization algorithm is proposed to process the samples under sampling and feature selection synchronously.In this me-thod, the performance of the classifier is taken as the heuristic information of the gray wolf optimization algorithm, and then the intelligent search is carried out to obtain the combination of the optimal sample and the feature set, and the tabu table strategy is introduced into the original gray wolf algorithm to avoid the algorithm falling into the local optimum.Experimental results show that the proposed method has a great improvement compared with other methods, and its performance on different data sets proves that it can effectively solve the problem of sample imbalance, reduce the dimension of feature space, and improve the accuracy of classification.Compared with the original data, the accuracy of credit risk evaluation is improved by about 3%, which proves the applicability and superiority of this method in the field of credit evaluation.

This paper presents a set of nonparametric methods for selecting credit evaluation indicators under the condition of unknown index distribution, and applies the data of four power companies as samples for application analysis. The results show that through the first round of credit feature screening based on nonparametric Bayes discrimination and the second round of nonparametric clustering, this paper finally constructs a credit feature screening model based on non parametric Bayesian discriminant and clustering analysis, and carries out application analysis. Finally, 18 credit characteristics which can be used in power market credit evaluation are screened out, and the evaluation accuracy has been significantly improved from 73.64% to 77.02%.

In view of the real-world problem of falsity and errors credit data, and the performance degradation of the credit evaluation model caused by these problems, we proposed an outlier detection algorithm, which considered two characteristics of class-imbalance and cost-sensitive in credit data. We use an anomaly detection model called EIF to optimize the credit evaluation models. EIF uses the EasyEnsemble algorithm to construct balanced data sets, and train an Isolation Forest model for anomaly detection by the balanced datasets with different disturbances. On the one hand, the balanced dataset ensures that the class-imbalance problem is solved by undersampling, on the other hand, each sub-model learns from the overall minority class samples in order to solve the cost-sensitive problem. Experiments were performed on UCI German dataset, and the test set with fake data was constructed by correlation. Compared with other anomaly detection algorithms in common credit evaluation models, the EIF-optimized model has a higher F1 score and a lower cost-sensitive error rate. In conclusion, the EIF model is effective in enhancing the performance of the credit evaluation model for forged credit datasets. [ABSTRACT FROM AUTHOR]

Abstract Since the reformation of the electricity market of China is increasing and the modernization of the national governance system is being promoted, the electricity market management system needs to be gradually improved. Credit evaluation is an essential part of the electricity market management system. Therefore, a complete credit evaluation system plays a fundamental role in ensuring that the electricity market can operate safely, stably, efficiently, and reliably. The traditional credit evaluation methods focus on the financial situation and credit records of the generating companies. With the gradual development of the electricity market transactions, the market behavior needs to be included in the credit evaluation indicator system to standardize the behavior of the generating companies and reduce the risks of the market transactions. This study establishes a complete credit evaluation system from three perspectives: market behavior, financial situation, and social responsibility. In addition, to determine the weight of indicators in the credit evaluation, the analytic hierarchy process (AHP) algorithm, fuzzy theory, and the non‐dominated sorted genetic algorithm II (NSGA‐II) algorithm are combined. The variance of the evaluation results and the deviation degree of the indicator weight are considered the objective functions of the NSGA‐II algorithm to optimize the indicator weight. Finally, comparing as an example the results of the credit evaluation before and after the optimization of an electricity market shows that the credit evaluation method can effectively distinguish the generating companies that adopt different market behaviors and has a high practical significance.

Copyright of Journal of Engineering Management / Gongcheng Guanli Xuebao is the property of Journal of Engineering Management Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the Industrial Internet of Things (IIoT), peer-to-peer (P2P) distributed energy (DE) transactions exist in various scenarios. This paper attempts to improve the intelligence, real-timeliness, and security of the direct transaction between DE generation companies (DEGCs) and users, and reduce the default frequency of distributed power (DP) transactions. For these purposes, a P2P DE transaction model for the IIoT was proposed based on blockchain. Firstly, a blockchain-based distributed energy peer-to-peer transaction framework is constructed, which is more suitable for generalized energy transactions based on typical transaction scenarios of the IIoT. Using credit value evaluation and smart contracts to ensure the transparency, openness, and non-tampering of credit scores. On this basis, the energy currency reward mechanism is used to promote the trustworthiness of transaction nodes and maintain transaction security. Finally, the P2P direct transaction based on credit value was designed to improve the transaction efficiency and security. Through case analysis, the DE transaction model for the IIoT, which is based on the credit value of the blockchain, supports fast and frequent energy transactions, as it overcomes the confirmation delays of energy blockchain transactions. The proposed model improves the efficiency of DE transactions in the IIoT, effectively suppresses default frequency, and maintains the order of DE market in the IIoT.

High-tech small- and medium-sized enterprises (SMEs) play an important role in the high-quality economic development in a country. Nevertheless, due to the difficulties banks or other financial institutions have in accurately assessing their credit levels, financing difficulties have become the biggest bottleneck restricting the progress of high-tech SMEs, and therefore, this paper aims to construct a credit evaluation indicator system of high-tech SMEs. Based on prior studies and the characteristics of high-tech SMEs, this paper constructs an indicator system from financial and nonfinancial dimensions, including 22 measurement indicators reflecting the operation status, development potential, quality, and competitiveness of an enterprise. Principal component analysis (PCA) and a Delphi-analytic hierarchy process (AHP) method are employed for the evaluation. This indicator system innovates from the social capital perspective, and by setting more novel nonfinancial indicators, the system achieves a more comprehensive evaluation of credit level. This paper also performs an empirical application using the data from 125 enterprises in the Beijing–Tianjin–Hebei region of China, and further performs an empirical study on the external environment’s impact on the credit level. The empirical results all show consistency with existing studies, verifying the workability and validity of the indicator system we constructed.

In order to ensure the scientific evaluation of the credit level of the engineering quality inspection agencies, an evaluation index system that comprehensively and systematically reflects the credit level of the engineering quality inspection agency was constructed. A preliminary indicator system was established through grounded theory. And the significance and independence of the indicator system were examined through the methods of information volume, morbidity index, correlation coefficient and variation analysis. Finally, the indicator system was evaluated based on independent information fluctuation weighting method. The rationality was tested, the results show that established index system is reasonable and effective. [ABSTRACT FROM AUTHOR]

Copyright of Revista Venezolana de Gerencia (RVG) is the property of Revista de Filosofia-Universidad del Zulia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

5G-enabled Industrial Internet of Things (IIoT) deployment will bring more severe security and privacy challenges, which puts forward higher requirements for access control. Blockchain-based access control method has become a promising security technology, but it still faces high latency in consensus process and weak adaptability to dynamic changes in network environment. This article proposes a novel access control framework for 5G-enabled IIoT based on consortium blockchain. We design three types of chaincodes for the framework named policy management chaincode (PMC), access control chaincode (ACC), and credit evaluation chaincode (CEC). The PMC and ACC are deployed on the same data channel to implement the management of access control policies and the authorization of access. The CEC deployed on another channel is used to add behavior records collected from IIoT devices and calculate the credit value of IIoT domain. Specifically, we design a two-step credit-based Raft consensus mechanism, which can select the orderer nodes dynamically to achieve fast and reliable consensus based on historical behavior records stored in the ledger. Furthermore, we implement the proposed framework on a real-world testbed and compare it with the framework based on practical Byzantine fault tolerance consensus. The experiment results show that our proposed framework can maintain lower consensus cost time with 100 ms level and achieves four to five times throughput with lower hardware resource consumption and communication consumption. Besides, our design also improves the security and robustness of the access control process. [ABSTRACT FROM AUTHOR]

At present, in the process of supply chain application of blockchain, there are performance problems dominated by Byzantine nodes. Aiming at the problems of the alliance chain based on Practical Byzantine Fault Tolerance (PBFT), a scheme combining Support Vector Machine and PBFT, namely S-PBFT, is proposed. First, SVM is used to evaluate the credit of consensus nodes. Priority is given to nodes with high creditworthiness to undertake the work of the primary node, so as to solve the consensus efficiency problem caused by view switching. Secondly, use smart contracts to ensure the effective and controllable execution of the whole process, so as to improve the authenticity and reliability of the operation results. Finally, testing with Hyperledger Fabric verified that S-PBFT can improve the throughput and latency of the blockchain-based supply chain to a certain extent. [ABSTRACT FROM AUTHOR]

Missing data is a common problem in credit evaluation practice and can obstruct the development and application of an evaluation model. Block-wise missing data is a particularly troublesome issue. Based on multi-task feature selection approach, this paper proposes a method called MMPFS to build a model for credit evaluation that primarily includes two steps: (1) dividing the dataset into several nonoverlapping subsets based on missing patterns, and (2) integrating the multi-task feature selection approach using logistic regression to perform joint feature learning on all subsets. The proposed method has the following advantages: (1) missing data do not need to be managed in advance, (2) available data can be fully used for model learning, (3) information loss or bias caused by general missing data processing methods can be avoided, and (4) overfitting risk caused by redundant features can be reduced. The implementation framework and algorithm principle of the proposed method are described, and three credit datasets from UCI are investigated to compare the proposed method with other commonly used missing data treatments. The results show that MMPFS can produce a better credit evaluation model than data preprocessing methods, such as sample deletion and data imputation. [ABSTRACT FROM AUTHOR]

Since the reformation of the electricity market of China is increasing and the modernization of the national governance system is being promoted, the electricity market management system needs to be gradually improved. Credit evaluation is an essential part of the electricity market management system. Therefore, a complete credit evaluation system plays a fundamental role in ensuring that the electricity market can operate safely, stably, efficiently, and reliably. The traditional credit evaluation methods focus on the financial situation and credit records of the generating companies. With the gradual development of the electricity market transactions, the market behavior needs to be included in the credit evaluation indicator system to standardize the behavior of the generating companies and reduce the risks of the market transactions. This study establishes a complete credit evaluation system from three perspectives: market behavior, financial situation, and social responsibility. In addition, to determine the weight of indicators in the credit evaluation, the analytic hierarchy process (AHP) algorithm, fuzzy theory, and the non‐dominated sorted genetic algorithm II (NSGA‐II) algorithm are combined. The variance of the evaluation results and the deviation degree of the indicator weight are considered the objective functions of the NSGA‐II algorithm to optimize the indicator weight. Finally, comparing as an example the results of the credit evaluation before and after the optimization of an electricity market shows that the credit evaluation method can effectively distinguish the generating companies that adopt different market behaviors and has a high practical significance. [ABSTRACT FROM AUTHOR]

In view of the problem that it is difficult to quantitatively assess the interactivity between attributes in the identification process of 2-order additive fuzzy measure, this work uses the intuitionistic fuzzy sets (IFSs) to describe and deal with the interactivity between attributes. Firstly, the interactivity between attributes is defined by the supermodular game theory. On this basis, the experts employ the intuitionistic fuzzy number (IFN) to assess the interactivity between attributes, Secondly, the opinions of all experts are aggregated by using the intuitionistic fuzzy weighted average operator (IFWA). Finally, based on the aggregated results, the intuitionistic fuzzy interaction degree between attributes is defined and calculated by the score function of IFN. Thus, a 2-order additive fuzzy measure identification method based on IFSs is further proposed. Based on the proposed method, using the Choquet fuzzy integral as nonlinear integration operator, a multi-attribute decision making (MADM) process is presented. Taking the credit evaluation of the big data listed companies in China as an application example, the feasibility and effectiveness of the proposed method is verified by the analysis results of application example. [ABSTRACT FROM AUTHOR]

In current credit evaluation methods, the credit condition of the network supplier and the credit degree of each index cannot be described well, and the credit evaluation data only source of the transaction platform have much limitation. This research proposes the method of calculating the importance and the value of the credit evaluation indexes, and proposes to put credit evaluation into big data environment. This research uses the transaction process of B2C as the case, and constructs multiple attribute weighted Petri net credit index subnet (CWPSN) for realizing the credit evaluation of the network supplier, and for presenting the correlations among the evaluation results of the credit evaluation indexes, and for presenting the importance of the indexes and the credit degree of each index, and describes the cost optimization process with credit cost optimization investment process Petri net (CCOIPPN). By the case to verify the credit evaluation method based on Petri net and the cost optimization method based on Petri net. The researches have provided methods for clearly and concretely describing the process of credit evaluation and cost optimization of network supplier, and have guidance significance for similar other researches.

The traditional food supervision system has problems such as lack of industry chain and data fragmentation, which has caused the phenomenon of field regulation to be uncomprehensive in the existing regulatory system and the disposal of response lag. Combining the characteristics of blockchain such as distribution, transparent, and collegiality with the actual needs of regional autonomy, we propose the hierarchical multi-domain blockchain (HMDBC) network structure and the secondary-check mechanism, which can support timely correction and replacement of the malicious supervision nodes by regional nodes co-governance, auxiliary verification of supervision nodes, and arbitration of superior regions. In order to optimize the supervision nodes election, we proposed fuzzy comprehensive evaluation model of credibility, which can be used to objectively and fairly evaluate the comprehensive reputation of each node in that region by considering various influencing factors of node performance indicators. Furthermore, we designed the data block structure model, which can support the supervisor node replacement. Our system can perform automatic food quality detection and warning of substandard food in the entire industrial chain, with the use of smart contracts combined with the food industry standards. Finally, comparing the complexity of the traditional blockchain system with PBFT consortium, our system is more secure, with lower broadcast complexity.