中国科学院数学与系统科学研究院期刊网

25 February 2025, Volume 45 Issue 2
    

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  • LI Junhong, WANG Hongpin, YANG Xiaoguang
    Journal of Systems Science and Mathematical Sciences. 2025, 45(2): 311-343. https://doi.org/10.12341/jssms23843
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    Salary incentives are an important means for enterprises to stimulate employees' work passion, creative potential and improve corporate performance. On the one hand, the internal pay gap has a positive motivating effect and promotes the effort of employees. On the other hand, it can also lead to a sense of unfairness and cause some employees to feel “flat”. This paper constructs a mathematical model including core executives, non-core executives, and ordinary employees to analyze the impact of internal salary gaps on corporate performance, and conducts empirical research using data from privately-owned listed companies in Shanghai and Shenzhen from 2008 to 2020. Both theoretical and empirical results show that the relationship between pay gap within management, executive-employee pay gap, the degree of compensation incentives of non-core executives and corporate performance all show an inverted U shape. Further empirical research shows that non-core executive-employee pay gap has the strongest effect on corporate performance, while core executive-employee pay gap has the smallest effect on corporate performance. This research shows that non-core executive-employee pay gap is the most important compensation relationship within the company and core executive-employee pay gap is of least importance. In addition, in the salary incentive design of private enterprises, the “constraint” of operating profit is greater than the “constraint” of operating income, which reflects that private enterprises pay attention to seizing the key points.
  • WANG Fang, WU Chengxuan, YU Lean
    Journal of Systems Science and Mathematical Sciences. 2025, 45(2): 344-356. https://doi.org/10.12341/jssms240001
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    To quantify the impact of the stability of power supply on the development of the digital economy, a system dynamics model is constructed, including variables such as power supply installed capacity and the scale of the digital economy in the three industries. The model explores the mechanism of how the stability of power supply affects the development of the digital economy in scenarios such as power outages and electricity rationing. The results show that the scale of China's digital economy will maintain a high growth trend during the “14th Five-Year Plan” period and is expected to exceed 70 trillion yuan by 2025. The scale of the digital economy will decrease with the reduction of power supply. If the average daily electricity generation time or the number of working days per year decreases by 1%, 5%, and 10%, the digital economy scale will decrease by an average of 9.28%, 14.24%, and 20.43% respectively. By promoting technological innovation to improve the value-added coefficients of various industries, the impact of power outages or generator failures on the development of the digital economy can be reduced. Finally, policy recommendations are proposed to enhance power supply stability in China.
  • GAI Shuwen, LIU Qihang, LI Sibo
    Journal of Systems Science and Mathematical Sciences. 2025, 45(2): 357-375. https://doi.org/10.12341/jssms240083
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    With the rise of shale oil industry and the acceleration of low-carbon transformation process of China's energy structure, the role of clean energy in China's energy market has become increasingly significant. The timely discovery of the risk linkage characteristics and evolution rules of China's major energy markets has important practical value for the sustainable development of China's energy system. Based on this, this paper uses the co-integration test method with structural change points and the research framework of DCC-GARCH-SJC-Copula to conduct a detailed analysis of China's six major segmented energy markets from different perspectives. The research results show that the risk characteristics of structural mutations, dynamic adjustments, and asymmetric tail-dependent structures are common in China's energy system. The occurrence of major macro events such as OPEC production restriction, natural gas pricing reform, and renewable energy subsidy policy adjustments have significantly affected the structural changes in the long-term linkage relationship of China's energy system. According to the calculation results, various energy markets show different risk characteristics before and after the structural change point. Among them, the wind-solar market has the strongest risk linkage correlation, while the natural gas-coal market is the weakest. The crude oil and natural gas markets have “decoupled” significantly in recent years. Compared with the non-renewable energy market, the renewable energy market shows more significant risk characteristics. In addition, it is worth noting that under the influence of government regulation, pricing mechanism and other factors, the risk of simultaneous “slumping” of energy market prices in the context of extreme events is significantly greater than the risk of simultaneous “surging”. Based on this, this paper puts forward a series of policy suggestions with a view to providing important reference for the pricing reform of China's energy system.
  • WANG Weiqing, LI Yuqing, WANG Liukai, LI Mengting, FU Zeyi
    Journal of Systems Science and Mathematical Sciences. 2025, 45(2): 376-397. https://doi.org/10.12341/jssms23910
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    Considering the impact of mixed information for high-dimensional portfolios, this paper introduces the idea of mixed information extraction under the Vine Copula framework, substitutes the mixed data sampling model MIDAS into high-dimensional D-Vine Copula, and proposes a CVaR portfolio selection model based on high-dimensional D-Vine Copula-MIDAS, so as to simultaneously address the challenges of “dimension disaster” and “insufficient mixed information extraction” under the framework of Copula. Firstly, estimate the multivariate conditional joint distribution of assets based on the high-dimensional D-Vine Copula-MIDAS model; Secondly, simulate the dynamic features of assets returns based on the estimated joint distribution. Finally, the optimal investment weight of the assets is obtained by minimizing CVaR, thereby establishing a minimum CVaR portfolio selection model. This paper selects 7 stocks on the Chinese new energy market for empirical studies, and the results show that the CVaR portfolio selection model based on high-dimensional D-Vine Copula-MIDAS can fully reveal and simulate the dynamic features of financial assets returns and obtain lower investment risks.
  • HAN Xiaoya, YANG Xinyuan, ZHANG Huichen
    Journal of Systems Science and Mathematical Sciences. 2025, 45(2): 398-412. https://doi.org/10.12341/jssms23390
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    Considering the influence of consumer environmental awareness, the Stac-kelberg game model between enterprises and e-commerce platforms is constructed under the government's carbon cap-and-trade policy. Enterprises choose resale or agency sale to enter the platform. E-commerce platforms make efforts to promote green consumption by developing green marketing, and the green market promotes enterprises to reduce emissions. This paper studies dynamic pricing and green level decision-making of enterprises under resale and agent sale, and analyzes the economic and environmental benefits of supply chain under different sales models. The analysis results show that under certain conditions, social influence and consumers' environmental awareness play a positive role in the benefits of corporate; However, excessive carbon trading prices will inhibit the carbon reduction rate and environmental benefits of the supply chain; The level of commission rate affects the choice of marketing mode of enterprises. When the commission rate is low, the agent sales mode is the best. When the commission rate is high, the cost sharing contract can realize the win-win situation of enterprises in economy and environment.
  • WANG Ren, WANG Jiarui, CHEN Ming
    Journal of Systems Science and Mathematical Sciences. 2025, 45(2): 413-432. https://doi.org/10.12341/jssms240039
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    The frequent occurrence of emergencies and the current complex international environment have highlighted the important role of supplier resilience evaluation in improving the security and stability of industrial supply chains. In view of this, this paper takes the supplier resilience evaluation problem under the environment of interval-valued intuitionistic fuzzy information as the research object, and proposes the consistent criterion weight and expert weight determination method considering the risk preference of decision makers. Firstly, a non-consistency index is constructed based on the maximum deviation between the actual assessment value and the assessment value after assigning the criterion or expert weights, and then the Min-Max criterion weight optimization model and expert weight optimization model are constructed by minimizing the index; at the same time, a risk preference coefficient is introduced into the model to deal with the differences in risk preferences caused by industry heterogeneity; and the results of the experts' classification are introduced into the expert weight optimization model to depict the correlation between experts of the same type; the three-term conjugate gradient algorithms are designed to solve the weight optimization model; finally, the reasonableness and validity of the method are verified by the example of the evaluation of the toughness of the supplier, and the method is complementary to the enterprise management and the weight determination method of the group decision-making theory.
  • LIU Wei, WANG Yingming
    Journal of Systems Science and Mathematical Sciences. 2025, 45(2): 433-455. https://doi.org/10.12341/jssms23709
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    In group decision-making under the social network environment, the weight of experts in the group and the trust relationship between experts are key factors that affect consensus reaching. However, in many studies, the trust relationship remains unchanged and the expert weight is only determined by the trust relationship. Therefore, this paper innovatively proposes a group consensus decision-making method that considers the social influence and trust evolution of experts, effectively promoting the reaching of group consensus. Firstly, the incomplete social trust matrix is transformed into a complete social trust matrix using trust propagation and aggregation methods. Then, the social influence of experts is obtained based on their additive preference relationship and social trust matrix, and the weights of each expert are obtained. Subsequently, a trust evolution model is established based on whether the optimal solution of each expert has been adopted and the difference between the ranking vectors of each expert's solution and the group's solution. Based on the trust evolution model, a consensus reaching process considering trust evolution is proposed. By using simulation methods, the weight coefficients of various indicators in social influence are calculated, and the feasibility of the proposed consensus reaching method is verified to demonstrate the rationality and effectiveness of the proposed model. Finally, a numerical example is presented to illustrate the detailed solution process of the method proposed in this paper, further demonstrating the feasibility and effectiveness of the model.
  • CHEN Youyu, ZHAO Jiayi, DENG Qianjie, LIU Chunxia
    Journal of Systems Science and Mathematical Sciences. 2025, 45(2): 456-469. https://doi.org/10.12341/jssms23694
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    Enterprise credit risk events have profoundly affected the healthy operation of companies, industries and markets. In order to more comprehensively reflect the characteristics of enterprise credit risk, we introduce text disclosure indicators consisting of management tone and tone manipulation based on the traditional credit risk indicator system, and select machine learning methods to identify enterprise credit risk. The conclusions of the study are as follows: 1) The inclusion of both management tone and tone manipulation in a firm's credit risk indicator system can improve the accuracy of credit risk identification, and the accuracy of credit risk identification is highest when both are included simultaneously; 2) Textual information has the functions of information increment and information manipulation, and the introduction of intonation manipulation can more objectively reflect the real situation in China; 3) By optimizing the existing credit risk identification models, it is found that the combined model based on AdaBoost algorithm, random forest and support vector machines has the strongest predictive ability, which can significantly improve the credit risk identification ability, and can provide methodological guidelines for the application of machine learning methods under small samples. This study can provide empirical evidence and decision support for the design of corporate credit risk identification index system and the optimization of credit risk identification methods.
  • ZHU Zhiguo, SUN Yi, WANG Xiening, WAN Xiaoji
    Journal of Systems Science and Mathematical Sciences. 2025, 45(2): 470-484. https://doi.org/10.12341/jssms23594
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    With the rise of social media such as “Mafengwo”, more and more tourists have become increasingly inclined toward the more flexible and freer self-organizing “tour group” rather than the traditional and standard “tour packages”. Different from traditional individual personalized recommendation, it has become a hot issue with important practical value that how to better aggregate the heterogeneous tour preferences of members for accurate tourist group recommendation. To this end, the method of Local Outlier Factor (LOF) is firstly adopted for data preprocessing to identify the outlier users with large differences in tour interests, and then the tourist groups can be preliminarily clustered. Next, the model ANC-TGR (attention-based neural collaborative tourist group recommendation) is proposed. In this model, the tour preference representation of a tourist group can be accurately aggregated through a well-designed two-layer attention network of “item-level” and “user-level”, and the representation vector is further input into a neural collaborative filtering recommendation framework for accurately recommending the Top-$N$ attractions for the tourist group. In the datasets of Mafengwo (with groups) and Foursquare (without groups), the experimental results confirm that the proposed model ANC-TGR, which further optimizes the preference representation of the fusion tourism group, compared with the optimal benchmark model, increased by 10.45%, 10.48%, and 10.07%, 10.87% on the metrics of HR@10 and NDCG@10, respectively. This paper provides technical support to improve the accuracy of attraction recommendations and travel satisfaction of tourism groups.
  • LIU Xinlei, XU Xiuli
    Journal of Systems Science and Mathematical Sciences. 2025, 45(2): 485-501. https://doi.org/10.12341/jssms23422
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    This paper considers the equilibrium strategy of the M/M/1 retrial queue with two types of parallel customers and an $N$-policy. In this queuing system, two kinds of customers enter the system in parallel and respectively follow the negative exponential distribution of different parameters. An arriving customer will be served immediately if the server stays idle; Otherwise, the customer enters the retrial space and waits for retry. There are some necessary conditions for the server to start service: The number of two types of customers in the retrial space reaches a given threshold $N$. Moreover, the service rate dynamically changes according to the number of waiting customers in retrial space. A benefit function is introduced according to revenue-cost theory, and equilibrium analysis is conducted for two types of parallel customers in a fully observable case. The average social benefits of the system are also analyzed. Finally, the numerical examples are used to visualize the changes in customer behavior strategies and the average social benefits as the different system parameters.
  • ZHANG Boyu, MA Zhanyou, REN Jie, JIANG Zishu
    Journal of Systems Science and Mathematical Sciences. 2025, 45(2): 502-512. https://doi.org/10.12341/jssms23463
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    To cope with the common traffic congestion problem in peer-to-peer (P2P) sharing networks, this paper used the queuing theory to model and analyze it. We introduced the strategy of vacation interruption. When the number of requested nodes in the system exceeded the threshold, vacation was interrupted to reduce system congestion. Furthermore, we considered the problem that P2P networks were vulnerable to malicious node attacks and established a queuing model of M/M/$c$ with start-up period, shutdown period, synchronous multiple working vacations, vacation interruption, and negative customer. By using the Quasi-Birth-Death process and the matrix geometric solution, we obtained the steady-state probability distribution and other system performance indicators. Finally, through numerical experiments, we analyzed the influence of system parameters on performance indicators and the personal and social benefits of the system. This finding provided a theoretical basis for improving the performance of P2P networks and solving traffic congestion problems.
  • SUN Wei, ZHANG Kaiting, LI Shiyong
    Journal of Systems Science and Mathematical Sciences. 2025, 45(2): 513-529. https://doi.org/10.12341/jssms240025
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    For the mismatch between supply and demand of medical resources in the hierarchical medical system, two modes are proposed: The cooperation mode composed of a tertiary hospital and a government-run and managed community hospital, and the competition mode composed of a tertiary hospital and a market-run and managed community hospital. Through the construction of a three-stage Stackelberg queueing game model to analyze the dynamic decision-making process among delay-sensitive patients, a community hospital and a tertiary hospital, which will give the patient referral rate, the community hospital's service capacity planning, the tertiary hospital's transfer patient rate or pricing strategy. It is found that under the cooperative mode, if the tertiary hospital emphasizes the waiting time of patients and actively refers patients with minor illnesses, and its benefit will be increased. Under the competitive mode, the patient referral rate, service capacity of the community hospital and the profit of the tertiary hospital are higher, which means that the appropriate competition can promote each part to seek for a more optimal strategy. Numerical results show that when the number of patients in the tertiary hospital is large, the total patient waiting time is smaller in the competitive mode, and when the number of patients in the community hospital is large, the cooperative mode is more favorable.
  • WANG Zhenzhen, HUANG Zhehao, DONG Hao
    Journal of Systems Science and Mathematical Sciences. 2025, 45(2): 530-553. https://doi.org/10.12341/jssms23929
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    The financial attribute of crude oil plays an important role in the measurement of crude oil market risk and its spillover effect with financial market risk. In this paper, the VMD-LZ method is used to decompose and reconstruct the financial attribute components of oil market price and risk. Furthermore, we employ the MVMQ-CAViaR method to measure static risk spillovers in the crude oil market and financial market under different income trends and whether the financial attributes of crude oil are considered. Finally, this paper uses DY-spillover index to measure the dynamic spillover index between crude oil market and financial market or different industries, and analyzes its heterogeneity. Empirical results show that considering the financial attributes of crude oil, we can better grasp the evolution characteristics of crude oil market risk in high and low frequency. Further research on risk spillover effects between the crude oil market and the financial market shows that the financial market risk reduces the volatility of the crude oil market price, while the crude oil market risk plays a role in promoting the financial market risk. What's more, return trend, risk trend and oil financial attributes have a significant impact on the risk spillover effect between the crude oil market and financial market.
  • QIU Lixia, FANG Donghui, WANG Junying
    Journal of Systems Science and Mathematical Sciences. 2025, 45(2): 554-562. https://doi.org/10.12341/jssms23895
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    In this paper, we introduce a weaker constraint qualification than that in reference (Suzuki, 2021) and establish a Karush-Kuhn-Tucker type optimality condition for quasiconvex programming in terms of Greenberg-Pierskalla subdifferential when the objective function and the constraint functions are extended real-valued upper semi-continuous quasiconvex functions.
  • ZHANG Yifan, REN Haojie
    Journal of Systems Science and Mathematical Sciences. 2025, 45(2): 563-586. https://doi.org/10.12341/jssms23657
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    The anomaly detection has been a widely concerned topic of great application value and research importance for a long time. There are many machine learning algorithms dealing with the anomaly detection problem without clear statistical guarantee on the degree of false discovery. We propose a general framework for anomaly detection based on conformal inference that enables online false discovery rate control and does not rely on any model or distribution assumptions. The proposed procedure can incorporate different machine learning algorithms and online multiple hypothesis testing algorithms, thus providing a flexible and versatile approach for anomaly detection. We verify the effectiveness of the proposed procedure on simulated data and apply it to Server Machine Dataset to detect anomalies.
  • LI Xiaoying, SHAN Xian, ZHANG Zheshuo, YOU Jinyu, XIE Yu
    Journal of Systems Science and Mathematical Sciences. 2025, 45(2): 587-602. https://doi.org/10.12341/jssms23745
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    The noise tolerance problem of support vector regression (SVR) models has been one of the popular research directions in the current regression field. In this paper, a robust loss function (i.e., G-Loss) has been constructed and an online SVR algorithm based on kernel regression (OGSVR) has been proposed to deal with data containing noisy labels. The G-Loss solves the sparsity problem of the model by setting the penalty weights in a sub-region and introducing $\varepsilon$-insensitive bands at the same time. To solve the dynamic data stream regression problem, this paper uses stochastic gradient descent algorithm to solve the online SVR model. Several experiments on synthetic datasets, UCI benchmark datasets and real datasets show that the OGSVR algorithm obtains good performance compared with the online $\varepsilon$-SVR, online LS-SVR and online Canal-SVR algorithms on datasets with different noise levels, and in most cases, it can obtain high prediction accuracy in a short running time.
  • HE Bangqiang, WANG Long
    Journal of Systems Science and Mathematical Sciences. 2025, 45(2): 603-612. https://doi.org/10.12341/jssms23437
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    The article investigates the problem of parameter estimation and variable selection in high-dimensional semiparametric variable coefficient measurement error models with missing response variables. Firstly, based on the inverse probability weighting method, a random auxiliary vector for parameter partial correction and a nonparametric partial estimation function for correction are constructed. Under appropriate conditions, it is proven that the nonparametric estimation for correction follows asymptotic normality. Then, an empirical logarithmic likelihood ratio statistic for the correction parameter part is constructed and it is suggested that penalized empirical likelihood (PEL) be used to select variables. Under appropriate conditions, it is proven that the proposed penalized empirical estimate has oracle characteristics and obeys the asymptotic chi-square distribution under the null hypothesis. Monte Carlo simulation research suggests that the proposed estimation performs well in finite samples. Finally, a real data analysis is provided.
  • WANG Shuying, WANG Tong, HUANG He
    Journal of Systems Science and Mathematical Sciences. 2025, 45(2): 613-629. https://doi.org/10.12341/jssms23610
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    Interval censored data and panel count data are two types of incomplete data that often appear in survival analysis. In this paper, we consider the joint modeling analysis of the two types of data, also the dependent observation process is included. Then we introduce two frailties to characterize the correlation among the failure time, counting process and observation process. This study conducts a joint modeling study among the three components. The two-step estimation procedure is proposed to realize the parameter estimation of the established model. Then, numerical simulation is carried out, and the simulation results show that the proposed method performs well. Finally, the proposed method is applied to the real data of cardiac allograft vasculopathy research.
  • WANG Qiming, WANG Qinghan, ZHOU Liang
    Journal of Systems Science and Mathematical Sciences. 2025, 45(2): 630-638. https://doi.org/10.12341/jssms240033
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    Interval-value data contains more information than point-value data, which has become a hot topic in the application of complex big data. Most of the existing interval-value regression models are built on the basis of representative element framework or traditional interval-value subtraction, but the representative element framework model does not involve the whole interval in the operation, and the traditional interval-value subtraction has unreasonable operation results. In order to solve the above problems, this paper proposes a $p$-dimensional interval-value regression model under the framework of generalized Hukuhara (gH) difference. On the basis of ensuring reasonable interval operation results, a regression residual evaluation method is constructed based on the support function. The least squares estimation is derived, and the characteristics of the regression parameters under 1-dimensional condition are discussed. Finally, Monte Carlo simulation is used to verify the effectiveness and accuracy of the new method.
  • WANG Han, CHEN Wangxue
    Journal of Systems Science and Mathematical Sciences. 2025, 45(2): 639-650. https://doi.org/10.12341/jssms23681
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    Bilal distribution is the most exploited distribution for life-time data analysis. In this paper, we study the modified maximum likelihood estimator (MLE) of the parameter $\theta$ for Bilal distribution under simple random sampling (SRS) and ranked set sampling (RSS) and obtain finite sample properties and large sample properties of the modified MLE. Numerical results show that modified MLE under RSS can be real competitors against the ones based on SRS, when the sample size is used. Numerical results also show that the mean square error of classical MLE is very close to the variance of modified MLE under the same sampling, but modified MLE with explicit solution is more convenient to use in practice.