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With the rapid development of Internet technology, cybersecurity threats such as security loopholes, data leaks, network fraud, and ransomware have become increasingly prominent, and organized and purposeful cyberattacks have increased, posing more challenges to cybersecurity protection. Therefore, reliable network risk assessment methods and effective network security protection schemes are urgently needed.

Based on the dynamic behavior patterns of attackers and defenders, a Bayesian network attack graph is constructed, and a multitarget risk dynamic assessment model is proposed based on network availability, network utilization impact and vulnerability attack possibility. Then, the self-organizing multiobjective evolutionary algorithm based on grey wolf optimization is proposed. And the authors use this algorithm to solve the multiobjective risk assessment model, and a variety of different attack strategies are obtained.

The experimental results demonstrate that the method yields 29 distinct attack strategies, and then attacker's preferences can be obtained according to these attack strategies. Furthermore, the method efficiently addresses the security assessment problem involving multiple decision variables, thereby providing constructive guidance for the construction of security network, security reinforcement and active defense.

A method for network risk assessment methods is given. And this study proposed a multiobjective risk dynamic assessment model based on network availability, network utilization impact and the possibility of vulnerability attacks. The example demonstrates the effectiveness of the method in addressing network security risks.

In the new era of highly developed Internet information, the prediction of the development trend of network public opinion has a very important reference significance for monitoring and control of public opinion by relevant government departments.

Aiming at the complex and nonlinear characteristics of the network public opinion, considering the accuracy and stability of the applicable model, a network public opinion prediction model based on the bald eagle algorithm optimized radial basis function neural network (BES-RBF) is proposed. Empirical research is conducted with Baidu indexes such as “COVID-19”, “Winter Olympic Games”, “The 100th Anniversary of the Founding of the Party” and “Aerospace” as samples of network public opinion.

The experimental results show that the model proposed in this paper can better describe the development trend of different network public opinion information, has good stability in predictive performance and can provide a good decision-making reference for government public opinion control departments.

A method for optimizing the central value, weight, width and other parameters of the radial basis function neural network with the bald eagle algorithm is given, and it is applied to network public opinion trend prediction. The example verifies that the prediction algorithm has higher accuracy and better stability.

The purpose of this paper is to propose a system with the highest discriminatory power by selecting an indicator system based on the K–S test according to the unique circumstances of small enterprises.

The proposed method relies on calculating the K–S test statistical magnitude of D iteratively to reach a system with the maximum discriminatory power.

The empirical results, demonstrated using 3,045 small businesses from a Chinese bank, show that credit rating system should focus on the indicator system’s discriminatory power rather than a single indicator’s discriminatory power, because the interaction between indicators affects the discriminatory power of the system.

The proposed method creates a credit rating system with the highest discriminatory power, rather than its indicators, which is a more reasonable and novel approach to credit rating.

The approach is unique because the final system will have high discriminatory power and has excellent potential for decision support. The authors believe that this contribution is theoretically and practically relevant because credit rating for small business is especially difficult and complicated.

The purpose of this paper is to propose the grey target decision method based on three-parameter interval grey number for dealing with multi-attribute decision-making problems under uncertain environment.

First, the kernel and ranking method of three-parameter interval grey number are defined, which is the basis of determining the positive and negative bull’s-eye. Next, a new distance measure of three-parameter interval grey number is defined in view of the importance of the “center of gravity” point. Furthermore, a new comprehensive bull’s-eye distance is proposed based on the kernel which integrates the distance between different attributes to the positive and negative bull’s-eye. Then attribute weights are obtained by comprehensive bull’s-eye distance minimum and grey entropy maximization.

The paper provides a grey target decision method based on three-parameter interval grey number and example analysis shows that the method proposed in this paper is more reasonable and effective.

If we have a better understanding of the distribution characteristics of three-parameter interval grey number, it is possible to have a more reasonable measure of the distance of three-parameter interval grey number.

The paper provides a grey target decision method, which can help decision maker deal with multi-attribute decision-making problems under uncertain environment.

This paper proposed the kernel and ranking method of three-parameter interval grey number, and defined a new distance measure of three-parameter interval grey number and proposed a new comprehensive bull’s-eye distance, Furthermore, this paper structured a grey target decision method based on three-parameter interval grey number.

The purpose of this paper is to illustrate how blockchain technology can improve supply chain adaptability, alignment and agility which collectively enhance competitive advantage which in turn influences firm performance.

The conceptual framework of the present study is developed by conducting an extensive literature review on blockchain technology, supply chain adaptability, alignment, agility and competitive advantage. The sample data were collected from 397 supply chain practitioners in India to validate the conceptual model. Confirmatory factor analysis was conducted to ascertain the validity of the measures used and a structural model was analyzed for testing the proposed conceptual framework.

The results of the present study show that blockchain technology can improve supply chain adaptability, alignment, agility which lead to competitive advantage, which leads to better firm performance. Besides, trust generated through blockchain use also increases firm performance.

Currently, the respondents do not have practical experience of using blockchain technology. They have responded based on their knowledge about supply chain and blockchain which they acquired from published sources. Different supply chains require different strategic choices and different information needs. But the present study assumes that all supply chain needs are identical. The present study assumes that government regulations regarding blockchain technology are favorable; however, currently, there is no legal framework to address blockchain technology. The findings of the current study indicate that companies not only should create more awareness regarding blockchain but also should actively work with IT companies that are engaged in developing blockchain-based supply chain solution. Managers, as well as IT companies and academicians, should join hands to study and develop a framework for regulating blockchain technology and suggest these to the policy actors.

The present study shows that supply chain practitioners are confident that blockchain technology will help improve supply chain parameters. These findings can help IT companies and their marketers for developing and promoting blockchain-based IT applications. In addition, the important implication for supply chain practitioners is that blockchain helps in creating a competitive advantage and increases firm performance.

The effect of IT on important supply chain variables has been studied in the past; however, there is not a single study which sheds light on how disruptive technologies such as blockchain will affect supply chain adaptability, alignment, agility and firm performance.

The purpose of this paper is to present a newly developed fuzzy-set based model for estimating, allocating, depleting, and managing contingency fund over the life cycle of construction projects.

Fuzzy set theory is utilized in the design and development of proposed contingency modelling framework to incorporate uncertainties associated with the development phases of construction projects. A set of developed indices, measures, and ratios are introduced to quantify and characterize these uncertainties. The developed framework is designed to incorporate expert opinion and provide user-system interaction.

The results obtained from the application of the developed framework on actual project case not only illustrate its accuracy, but also demonstrate its capabilities for contingency management over life cycle of construction projects. Unlike other methods, the framework provides project managers with structured method for contingency depletion utilizing a set of depletion curves and selection factors.

The novelty of the developed framework lies not only in its new developments for contingency estimating but also its modelling for contingency allocation and depletion. It is expected to be of direct value to industry professionals and academics interested in contingency management over the entire life cycle of construction projects. The proposed framework provides management functions and features beyond those generated through Monte Carlo simulation and even those developed using fuzzy set theory.