Anti-risk Ability Research Articles

Overload cascading failure detection algorithm for multi-layer supply chain network considering delay probability factor

At present, the supply chain is multi-layered and complicated, and its failure detection is based solely on the characteristic threshold, which does not lack the ability to describe the complex supply chain. An overload cascading failure detection algorithm for multi-layer supply chain network considering delay probability is proposed. Based on the multi-layer supply chain network including raw material suppliers, manufacturers, distributors and retailers, the overload cascading failure model of the multi-layer supply chain network is constructed through networking, and the overload cascading failure process of the multi-layer supply chain network is described through three aspects: initial load, node capacity and overload node load distribution. By monitoring and analyzing the status update messages of each node in the supply chain network, and considering the delay probability factor, the node and path that may lead to cascading failure are identified by predicting the delay probability of the next message through the historical message delay probability, and the overload cascading failure detection of the multi-layer supply chain network is realized. The experimental results show that the algorithm can effectively detect the failure of each overloaded enterprise node in the multi-layer supply chain network, which is helpful to enhance the early warning and response ability of the cascade failure of the supply chain network. The algorithm can realize the failure detection of supply chain network under different attack conditions and initial failure ratio of nodes, and measure the anti-risk ability of supply chain network. Under deliberate attack and when the initial attack node is a supplier network layer node, it has a greater impact on the multi-layer supply chain network.

Corporate bond default prediction using bilateral topic information of credit rating reports

The default of corporate bonds can result in large financial losses as well as irreparable harm to investors’ trust and the economy as a whole, which implies that the identification of corporate bond default must be done promptly and properly. Current studies mainly rely on accounting and/or macroeconomic data and use the credit rank (CR) to disclose the credit status of corporate bonds in the default prediction task. However, the textual data of credit rating reports (CRRs) contain richer and more comprehensive information and are neglected in related work. In this paper, we propose a novel framework that draws on the unstructured data in CRR to predict the default of corporate bonds. We extract the rating opinion sentences (categorized as positive and negative) from the collected CRR files and use latent Dirichlet allocation (LDA) models to mine topic information. The bilateral topic information of positive and negative opinions can reflect the anti-risk ability and potential risk of corporate bonds, respectively, based on which the constructed topic features are used for default prediction. Results on real-world Chinese corporate bonds dataset show that the bilateral topic information of CRR can significantly improve the predicting power of models (LR, SVM, KNN and MLP) under three performance metrics (AUC, KS and H-measure). By analyzing the ranking of topic features using SHAP value, the proposed framework can explain the factors that affect bond defaults, which can provide a basis for the decision-making of investment behavior.

The impact of digital transformation on enterprise export resilience: Evidence from China

External shocks have negatively affected trade activities around the world, how to improve the enterprise's anti-risk ability to external shocks has gained widespread attention. This study explores whether and how the digital transformation affects enterprise export resilience after the 2008 financial crisis. By expanding an export growth model and utilizing a merged dataset from the Chinese customs trade and A-share listed company database, we find that digital transformation significantly enhances export resilience, and this effect is more prominent in private enterprise as well as high-tech enterprise types. The mechanism analysis shows that digital transformation promotes enterprise export resilience through cost reduction and demand diversification channels. In addition, the effect of digital transformation is more evident on enterprises exporting to high-income countries. Furthermore, this paper finds that the smart city pilot policy has strengthened the promoting effect of digital transformation on enterprise export resilience. These findings shed insights into the relationship between the digital transformation and enterprise's export behavior in the post-crisis era.

Enhancing export resilience through smart cities: a city-level quasi-natural experiment in China

ABSTRACT Faced with currently numerous uncertainties at home and abroad, the construction of new infrastructure plays a critical role in enhancing region’s ability to cope with external shocks. Based on resilience perspective, this paper takes smart city pilot as a quasi-natural experiment and utilizes time-varying difference-in-difference (DID) model to investigate the policy effect on city export resilience in ChinA. Research shows that smart city pilot significantly improves city’s anti-risk ability after external shocks, which is manifested as stronger export resilience in pilot cities. Mechanism tests show that smart city pilot promotes export resilience through diversification, efficiency-improved, and innovation-driven channels. Moreover, the effect of smart city pilot is more evident on cities in central and western regions, as well as exporting to high-income countries. These findings shed new insights into the relationship between the smart city pilot and city export behaviour and provide references for Chinese government to promote export upgrading in the post-crisis erA.

Study on the technical management practice of rock burst prevention and control - Case study of Yankuang Energy Group Company Limited

To ensure the on-site implementation of regulations and technical measures for rock burst prevention and control, taking Yankuang Energy Group Company Limited. as an example, an on-site technical management system for preventing and controlling rock burst in coal mines is established. This on-site technical management system is based on the principles of “zero rock burst accident”, “graded management and control”, “general manager and chief engineer responsibility”, and “scientific, systematic, streamlined, and efficient management”. This system includes a technical management system and an on-site management mode, among which the former includes an organizational system, an institutional system, a technical data management system, and a comprehensive supervision and management system. Each system is interrelated, mutually promoting and supporting the other system. The on-site management mode includes five aspects and six links. These “five aspects” refer to the standardization of safe production, normalization of on-site operations, processization of system operation, quantification of antirisk ability, and demonstration of mine management. The “six links” on-site management mode includes the management system, source risk management and control, dynamic risk analysis, monitoring and control, on-site implementation and management, and emergency management. The construction of an on-site technical management system for rock burst prevention and control can ensure the timely detection and rectification of problems, remove management loopholes, and prevent the occurrence of rock burst disasters.

Improving Business Strategies Can Increase Economic Recovery in The Later Stages of The Epidemic

At the beginning of 2020, the occurrence of COVID-19 had a great impact on the operation of fitness clubs. Through the analysis, this paper explores the ways for fitness clubs to resolve risks and reduce losses during the epidemic period, and puts forward suggestions for the management transformation of fitness clubs, in order to help fitness clubs turn risks into opportunities in the post-epidemic era of epidemic prevention and control, and improve their management ability and anti-risk ability.

A Study on the Impact of Corporate Financial Accounting Management System on Corporate Innovation Under Sustainable Development Strategy

In the complex and changeable macro-economy, the sustainable development of enterprises is closely related to their ability to resist risks. To analyze the influencing factors of listed enterprises’ anti risk ability, this study constructs an indicator system of influencing variables through principal component analysis. The financial risk early warning method based on improved RBF neural network is constructed. Afterwards, the relevance of the indicator system was confirmed by the Kaiser-Meyer–Olkin (KMO) test. And in simulation experiments, it compared the classification performance of the BP neural network and the improved RBF model combined with the clustering algorithm. In the training dataset, the classification accuracy of the BP neural network was 79.6% while that of the improved RBF model was as high as 93.6%. In the 30 test datasets, the BP neural network appeared to have seven false positives. In the 30 test datasets, the BP neural network produced seven false judgments, while the proposed method only had three judgment errors. Several experiments showed that the BGD-RBF financial risk early warning model effectively maintained a high performance in the classification accuracy of enterprise financial status. It is more reliable than the traditional BP neural network method.

The Impact of Supplementary Pension on Household Risky Financial Asset Allocation

With increasingly serious aging problem in China, supplementary pension, as the second pillar of the endowment insurance system, is expected to play an important role to relieve the pension pressure. Nowadays, people in China are exposed to various investment tools, but they still prefer to invest in risk-free financial assets. The allocation of household financial assets is not diversified enough. While supplementary pension is expected to improve financial structure of households. It may enhance the anti-risk ability of households, rational allocation of household assets, the ability of value-adding, and thus reduce the pressure of society to cope with the aging population. This paper selects the 2019 China Household Finance Survey (CHFS) database, using STATA software to investigate the impact of supplementary pension on risky financial asset allocation of Chinese households by applying probit and tobit models. The results show that supplementary pension can significantly increase the willingness of households to purchase risky financial products but has no significant effect on increasing the proportion of risky assets being held.

Key security measurement method of authentication based on mobile edge computing in urban rail transit communication network

In the rail transit industry, the communication network is also widely used in various professions. Rail transit stations have the characteristics of narrow space, high degree of airtightness, limited sight lines, and uneven distribution of personnel in various periods. Although the security degree of rail transit entrances and exits is relatively reliable compared with that of stations, the anti-risk ability is fragile, and the consequences are often disastrous. To solve the problems of low-security access rate, long response time and ineffective identification of attack components of urban rail transit communication platform users, a security measurement method based on mobile edge computing (MEC) for the authentication key of urban rail transit communication network is proposed. Firstly, the wireless communication service of the urban rail transit platform is analyzed, and the data key protocol is constructed to obtain the hyper-chaos key pair of wireless communication information. Then, the double chaotic mapping model is applied to determine the key form and input parameters, and the bit key stream is obtained, which is subjected to exclusive operation with the plaintext for key decryption. Next, the block matching structure is used to analyze the associated eigenvalues that affect the security risk of urban rail transit communication networks. Meanwhile, the MEC server is used to accelerate the extraction of its feature values. Finally, in the environment of MEC, the paper constructs a risk identification model, evaluates the security strength of the authentication key, controls the threshold value, analyzes the impact factors and weights of communication network security, and completes the traceability measurement of communication network security risk. Experimental results show that the security access times of the MEC platform are senior, the maximum attack component obtained is 23500, which is consistent with the actual value, and the global maximum response time is 0.30 ms.

The Spatial and Temporal Evolution Pattern and Influencing Factors of Urban Human Settlement Resilience in Three Provinces of Northeast China

It is widely recognized that urban resilience is one of the core goals of urban development. As an important part of a city, the resilience level of urban human settlements directly affects the development trend of urban resilience. However, at present, research results on the resilience of urban human settlements are very rare, are mainly concentrated in the central region of China, and rarely take into account the economically backward northeastern region. Therefore, in order to better improve the anti-risk ability of the urban human settlement environment system in three provinces of Northeast China, fully implement the strategic goal of “Comprehensive Revitalization of Northeast China”, and achieve high-quality urban development, this paper focuses on 34 prefecture-level cities in three provinces of Northeast China and proposes an urban human settlement resilience evaluation system with 36 indicators in five dimensions, namely, the natural system, human system, housing system, supporting system, and social system. Using the entropy weight method, the Dagum Gini coefficient, and a geographical probe model, the changes in the resilience level of each city from 2005 to 2020 were measured, and the urban living environment was assessed in terms of the adaptability and resilience of the development level in each subsystem based on the temporal and spatial evolution law and its influencing factors. The following conclusions were drawn: (1) The development level of urban human settlement resilience in the three provinces in Northeast China showed an N-shaped development trend from 2005 to 2020, but the regional differences were significant, and the overall spatial pattern was “high in the south and low in the north”. (2) In terms of the overall difference, the overall difference in urban human settlement resilience in the three northeastern provinces of China was small: the inter-regional difference was the main source of the difference, and the intra-regional difference was the secondary source. The regional differences were in the order of Heilongjiang Province > Liaoning Province > Jilin Province, indicating that Jilin Province had the smallest difference and that the resilience level of urban human settlements does not show a balanced development trend. In terms of the average Gini coefficient between regions, the order of difference was Liaoning Province–Heilongjiang Province > Jilin Province–Liaoning Province > Jilin Province–Heilongjiang Province, indicating that the difference between Liaoning Province and Heilongjiang Province was the most significant. (3) The “natural system”, “human system”, “living system”, “supporting system”, and “social system” had significant spatial and temporal heterogeneity and significantly affected the resilience level of urban human settlements in the three provinces in Northeast China. Among them, the “social system” has always been the main factor affecting the resilience level of urban human settlements.

Urban resilience in China's eight urban agglomerations: evolution trends and driving factors.

Improving urban resilience (UR) and enhancing urban anti-risk ability are important foundations for promoting the high-quality development of new urbanization. This research employs the time-varying entropy method to evaluate the resilience level of 138 cities within China's eight urban agglomerations (UAs) between 2005 and 2019. Additionally, the Dagum Gini coefficient and the kernel density estimation method are utilized to examine the spatial disparities and distribution dynamics of UR across the eight UAs. The results of this investigation indicate that (1) the collective UR performance of the eight UAs has experienced an upward trend. However, a notable spatial disparity exits, which is primarily attributed to the differences among the UAs. (2) The overall UR development of the eight UAs has a certain gradient effect, and the UR within each UA has different degrees of polarization characteristics. (3) For the eight UAs as a whole, per capita savings deposits, capitalization of foreign capital, and per capita fiscal expenditure are the three most important driving factors. Within each UA, there was heterogeneity in the main influencing factors. The interplay between any two factors amplifies their individual driving effects on the spatial differentiation of UR.

Transformation Mechanism of Supply Chain Micro and Small Enterprises in the Context of the Internet

The development of intelligent manufacturing has significantly impacted the traditional manufacturing industry, so it is urgent to change the traditional business model and production mode. As the world economy is in a downturn, small and micro enterprises in the subordinate position of the supply chain are facing a considerable threat. On the one hand, the substitutability is substantial; there is no bargaining power, which makes the enterprise's anti-risk ability 0. On the other hand, it is subject to significant restrictions, and the core enterprise thunder will protect all of the enterprises in the chain. This paper focuses on the risk identification and transformation mechanism of micro and small enterprises and whether micro and small enterprises have the conditions for transformation and upgrading in the context of the Internet. Based on the Bayesian formula, the AHP method model is the theoretical basis to help enterprises make decisions.

The Advantages and Risks of M&A Across Country: Evidence from Infineon

By taking over Cypress Semiconductor, Infineon completed a stage of its development strategy. This study takes the case of Infineon take over Cypress Semiconductor as an example to analyse the advantages and risks of M&A across country for acquirer. In the great backdrop of semiconductor industry’s fast development, this measure completes the short come and expand the products’ scope of Infineon’s business, making automobile electronics the pillar part of its revenue. The strengthen of business in China also helps to improve the firm’s revenue. It is obvious that M&A across country brings the acquirer new market and new guests in other region outside its own country, and improves one’s industrial technology. Besides advantages, M&A across country brings risks on different aspects. In financial aspect, it may lay capital burden on the firm, causing difficulty in turnover. Besides, politics of the two firms’ country and laws are possible to be sensitive. Companies need to be careful not to violate them. For the corporations who want to complete a successful M&A, they should better look into the detailed information of both themselves and the target firm, including their needs and anti-risk ability. Carefully assessments toward the risks and benefits are import before making the final decision. These results shed light on guiding further exploration of M&A with multi-nations.

A hybrid approach for optimizing deep excavation safety measures based on Bayesian network and design structure matrix

Considering the dynamic risk factors and risk situation throughout the entire deep excavation operations, timely adjustment and optimization of safety measures can enhance the practicality of construction technical plans on sites. A digital and quantitative model representing the practical risk situation of the deep excavation is urgently required for realizing the prediction, optimization, and control of the actual construction state. Thus, this research aims to propose a real-world-oriented model integrating Bayesian network (BN) and design structure matrix (DSM) for decision-making in safety risk management. First, risk factors were identified, and the BN model was established to evaluate the anti-risk ability of the construction site. Then, a multi-objective safety measure optimization model under specific constraints was established. Particularly, the DSM was adopted to express the control relationship between risk factors and safety measures. Moreover, with genetic algorithms applied, the optimal safety measure set for on-site safety risk management can be obtained. For model validation, a deep excavation project of metro construction in Wuhan, China, was selected as a case study. The hybrid optimization model showed the characters of initiative and timeliness in construction risk management. By providing the timely and optimized combination of safety measures, the dynamic decision-making approach can proactively and effectively improve the risk resistance ability of construction sites.

Tencent Game Competitive Advantage Analysis

In 2019, COVID-19 swept the world, significantly blowing the Chinese and global markets. Maintaining market share has become the goal of Chinese and even global enterprises. Based on huge market research and data, a few enterprises are still maintaining growth momentum under the sluggish market environment, and the Tencent game is one of them. This article takes "Tencent Game Competitive Advantage Analysis" as the topic, selects Tencent Game as the research object, analyzes the online game industry environment using Porters five forces model, and analyzes Tencent Games competitive advantages from the aspects of research and development, marketing, operation, and anti-risk ability so that it remains in the leading position in the game market and still obtains much economic income in the face of adverse circumstances. By analyzing Tencents competitive advantages, it has great reference value and experience for the future development of other online game companies.

Research on the Influence Mechanism of ESG Performance on Institutional Investors' Shareholding: Empirical evidence from China

Since the beginning of the COVID-19 pandemic, the labor force and supply chain have been greatly impacted, the global economy has been in a downturn, and the changes in environmental and social risks have made companies that lack a solid market position or financial foundation suffer great losses. In the new era, institutional investors are in urgent need of judgment criteria other than traditional financial data (such as ESG performance) to ensure that they can select investment projects with high potential and strong anti-risk ability. This paper takes Chinese listed companies from 2010 to 2021 as samples to empirically test the influence mechanism of ESG performance on institutional investors' shareholding. The results show that: (1) ESG performance can significantly promote the shareholding of institutional investors; (2) There is a significant relationship between ESG performance and enterprise internal indicators. Good ESG performance is inversely proportional to business risk and proportional to enterprise value, which reflects the internal economic indicators valued by institutional investors to a certain extent. (3) A good ESG is manifested externally, which significantly promotes the attention of corporate media and analysts, enhances corporate reputation, has a dominant position in public opinion, and increases the shareholding of institutional investors.

A combinatorial model for natural gas industrial customer value portrait based on value assessment and clustering algorithm

Frequent geopolitical events have reduced the stability of natural gas supply and caused drastic price fluctuations, which poses a new challenge to the natural gas consumer market. To improve the anti-risk ability of the natural gas industrial market, this study constructs a new customer value portrait framework to discern the industrial customer value based on different types of behavioral features and the emerging trends of the natural gas market. Specifically, we rediscover the value composition of natural gas industrial customers and establish a set of indicators to reflect the customer value in different dimensions with mixed data types. Then, a visualizable customer value classification model has been established by combining Gower’s dissimilarity coefficient with the PAM clustering algorithm. To ensure the accuracy of the clustering results, the optimal number of clusters is determined by gap statistics and elbow point, and the average silhouette method is used to detect the clustering effect as well as used in misclassified sample identification. To verify the applicability of the model, we used a certain amount of natural gas industrial customer data from a large state-owned oil and gas enterprise for application analysis and effectively divided customer value into three groups, demand-serving, demand-potential, and demand-incentive, according to their value characteristics and behavioral features. The results indicate that the framework proposed in this study can reasonably reflect and better characterize natural gas industrial customers’ value with different types of behavioral feature data, which can provide technical support for big data smart natural gas consumer marketing.

Chemical Looping Enhanced Oil Shale-to-Liquid Fuels Process: Modeling, Parameter Optimization, and Performance Analysis

The solid heat carrier moving bed with internals is an advanced oil shale retorting technology. However, the retorting gas produced by pyrolysis is generally used as fuel gas. The content of CO, H2, and CH4 in the retorting gas is high, and direct combustion leads to resource waste and environmental pollution. In addition, heteroatomic sulfur and nitrogen, as well as unsaturated hydrocarbons, reduce the quality of shale oil. To solve these problems, this paper proposed a chemical looping enhanced oil shale-to-liquid fuels (CLeSTL) process. The chemical looping hydrogen production technology is applied to convert retorting gas to hydrogen, and the hydrogen produced is used for shale oil hydrogenation to improve the oil quality. In this paper, the new process is modeled and simulated; then technoeconomic analysis is carried out. Technical analysis shows that shale oil yield is increased from 65% to 95.7% and the yield of light fraction is increased from 20% to 64%–83%. Economic analysis shows that the CLeSTL process with ligh fraction hydrogenation has the highest investment profit rate and large profit space. In addition, when the oil price is lower than 50 USD/bbl, the investment profit is 5%, which shows strong anti-risk ability.

Research on the Investment Value of Stock Exchange ETF in China Based on Factor Analysis and Cluster Analysis

A well-managed portfolio with thoughtful investment selection depends on the market. It is challenging to manually track share market prices because they fluctuate based on a variety of reasons. This article sorts out the traditional theory of domestic research fund performance evaluation, and selects 12 indicators suitable for analyzing ETF performance in China for factor analysis. The selected 629 sample funds are listed stock ETF issued from 2015 to 2020 and extended to 2022. Clustering methods used is k-means clustering with a Euclid distance and with shape-based distance measure. Factor analysis shows that the current ETF fund research indicators can be divided into 4 categories, namely income factor, anti-risk ability factor, management ability factor and volume factor. According to these three types of indicators, samples are grouped into 7 categories, and the study shows that most of the listed stock ETF funds have good risk diversification ability, and the performance of various funds varies greatly.

China’s shipping market supervision system under theRCEP: Influence, challenges and countermeasures

The entry into force of the Regional Comprehensive Economic Partnership (RCEP), the world’s largest free trade agreement, has injected new vitality into multilateralism and free trade, against a background of global economic and political instability. Its core concept is to liberalize and facilitate trade. Regulation of the shipping market warrants attention. Under the RCEP, the international shipping industry is characterized by a digital development trend in shipping supervision, continuous upgrading of shipping management and supervision capabilities, and more open and transparent shipping market supervision. However, in the process of implementing new rules for shipping market supervision, there are still some challenges, such as logistics risks and loopholes in shipping supply chains, insufficient coordination of shipping supervision among RCEP member countries, and an imperfect legal and regulatory system. This paper therefore suggests that under the RCEP, China should strengthen the anti-risk ability of shipping supply chains, promote coordinated supervision among member countries, strengthen environmental protection, and promote the coordination of digital supervision.