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  • ZHOU Xinmin, LIU Wenjie, YAN Zhishen, HU Huaiyu
    Journal of System Science and Mathematical Science Chinese Series. https://doi.org/10.12341/jssms23204
    Accepted: 2024-01-31
    Accurate prediction of the demand for shared bikes at each station can improve the efficiency of shared bike management and distribution, and effectively prevent the risk of public order caused by the imbalance of supply and demand. By comprehensively considering the influence of meteorological characteristics, time characteristics and historical data on the demand, a model based on similar days and PSO-Elman neural network was proposed. Firstly, the influence of time characteristics on bicycle demand was studied and the time characteristics were screened. Then, Pearson correlation coefficient was used to verify and select the key meteorological characteristics affecting the demand. Then the grey relational degree algorithm is used to calculate the similarity between the historical data and the day to be predicted and select the similar day. Finally, combined with similar daily data and historical data, the PSO-Elman neural network prediction model was constructed to simulate and forecast the demand of bicycles in peak hours. The results show that compared with Elman and the bicycle demand prediction model which does not consider the meteorological characteristics and time characteristics comprehensively, the prediction results of the proposed model have higher accuracy.
  • LI Delong, LAI Ziqi, WANG Tianhua, CHAI Ruirui
    Journal of System Science and Mathematical Science Chinese Series. https://doi.org/10.12341/jssms23705
    Accepted: 2024-01-24
    The setting of the lowest sampling rate of the subway white list security inspection channel is a difficult problem to balance safety and efficiency of the white list channel. This paper summarizes and condenses three kinds of white list credit supervision modes of subway security inspection: government supervision, professional institution supervision and meta-regulation supervision and constructs the lowest sampling rate model of white list channel considering credit preference under the two scenarios of "credit" constraint and "credit + convenience" constraint. The main findings are as follows: (i) When the influence of the convenience income of passenger transport is not considered, the credit supervision mode with the largest credit gain-loss distance is dominant, and the meta-regulation supervision mode is generally better than the professional institution supervision mode; (ii) The higher the mutual recognition of the results of passenger credit supervision among regulatory agencies, the lower the lowest sampling rate of the white list channel; (iii) The credit gain-loss distance is positively related to the degree of credit preference of white list passengers, and the two types of credit preference are superposed; (iv) Credit constraints and convenience constraints are complementary. At the same time, when the mutual recognition rate of government departments and professional institutions for credit supervision results is low, or the credit preference of white list passengers for both types of credit is low, the sensitivity of passenger convenience to the lowest sampling rate of white list channels will be significantly enhanced; (v) When the proportion of white list passengers is too high or the number of white list channels is too small, the convenience constraint effect of white list passengers will be significantly reduced, and even the credit constraint effect will be eroded. Finally, the lowest sampling rate of white list channels under the "credit + convenience" constraint will be higher than the value under the "credit" constraint.
  • Chen Wei, LUO Wen, LIANG Kairong, BAI Chunguang
    Journal of System Science and Mathematical Science Chinese Series. https://doi.org/10.12341/jssms23770
    Accepted: 2024-01-23
    This paper introduces a noncooperative-cooperative biform game model under simultaneous model, electricity generator-leader model, and electricity retailer-leader model. It delves into the decision-making problem of new energy investment in the power system. The results show that: (1) The new energy investment under different power structures depends on the cost coefficient of traditional energy investment. (2) Both generators and retailers prefer to be leaders in the supply chains, aiming to maximize their individual profits. However, the overall profit of the supply chain is optimized in the simultaneous model. (3) An increase in the cost coefficient of new energy investment will reduce the investment in new energy and the profitability of generators and retailers, but the new energy investment cost-sharing ratio of the electricity generator will increase. (4) An increase in the preference coefficient of new energy will increase the investment in new energy and the new energy investment cost-sharing ratio of the electricity retailer will increase, thus increasing the profitability of both the generator and the retailer.
  • Wang Jun, Yang Jiaqi
    Journal of System Science and Mathematical Science Chinese Series. https://doi.org/10.12341/jssms23559
    Accepted: 2024-01-19
    In this paper, an fault-tolerant control strategy combining fixed-time state feedback control and output feedback control is designed to realize the problem of heterogeneous fault multi-agent system fault-tolerant output consensus in the presence of external finite energy interference. Firstly, an adaptive sliding mode observer is proposed to obtain the fault reconstruction value of the actuator under the condition of overcoming the external limited energy interference. Secondly, for the followers who cannot obtain the leader state, a distributed fixed-time observer is designed to realize that each follower can estimate the leader state within a fixed time. Then, the fault reconstruction value is introduced into the distributed fault-tolerant output consensus control protocol. In addition, Lyapunov stability theory is used to solve the fault-tolerant output consensus control gain. Finally, a leader and four followers conduct simulation experiments on the fixed-time fault-tolerant output consensus control of heterogeneous multi-agent systems, which verifies the effectiveness and feasibility of the proposed method.
  • YAN Zhihua, TANG Xijin, FAN Shuo, JIA Jun
    Journal of System Science and Mathematical Science Chinese Series. https://doi.org/10.12341/jssmsKSS23886
    Accepted: 2024-01-17
    In order to generate structured representations of strategic military events, and identify the logical relations of events, the key events and event evolution patterns, this paper proposes a military event ontology models and an automated construction framework of event-centric knowledge graph. Graph neural network incorporating attention mechanism and dependent syntactic analysis are leveraged to solve long-range dependencies, and RoBERTa-based fine-tuning is employed to extract the implicit event relations. The results show that both algorithms of event and event relation detection in this paper outperform the comparing algorithms. The military event-centric knowledge graph can be used to identify the key events and evolution paths of strategic military events, but also provides multi-level analysis of strategic military events, and improve the comprehensiveness and scientificity of decision-making.
  • WEI Zikai, TANG Xijin
    Journal of System Science and Mathematical Science Chinese Series. https://doi.org/10.12341/jssmsKSS23871
    Accepted: 2024-01-17
    With the accelerated advancement of digital government construction, online administrative inquiry plays an indispensable role in social governance of China. In order to explore the key factors affecting the effectiveness of online administrative inquiry, this study focuses on the relevant data from the Luzhou online administrative inquiry platform ``Please Speak Up''. This study adopts a text data mining method combining various machine learning and deep learning models to identify characteristic variables in online administrative inquiry texts, construct two public satisfaction classification models. And multiple explainable methods are used to explain the model results from both structural and semantic features. The research finds that variables such as administrative inquiry sentiment, length of administrative inquiry text, type of appeal, response sentiment, type of response agency, length of response time all have varying degrees of influence on public satisfaction. In addition, the explainable framework constructed by this study can also effectively identify key content in online administrative inquiry, such as time, location, and organization names.
  • YUAN Liuyang, WANG Dawei, JIA Shihui, CHI Xiaoni
    Journal of System Science and Mathematical Science Chinese Series. https://doi.org/10.12341/jssms23030
    Accepted: 2024-01-16
    In this paper, based on the combination of the robust principal component analysis(WSNM-RPCA) model with weighted $S_{p}$norm minimization and the generalized robust principal component analysis(GRPCA) model, a new generalized robust principal component analysis(GWSLRPCA) model is reconstructed by using the $l_{2,1}$norm, which improves the accuracy of the recovery of the important rank components of the matrix, and uses the alternating direction multiplier method of random ordering to solve the new model. The numerical experiment results show that the new model GWSLRPCA can not only separate the effective low rank information of the picture and other noise parts from the picture polluted by mixed noise, but also has better image restoration effect.In terms of objective evaluation criteria, GWSLRPCA data are also better than Mean-Filter, WSNM-RPCA and GRPCA models.
  • WANG Shuying, WANG Tong, HUANG He
    Journal of System Science and Mathematical Science Chinese Series. https://doi.org/10.12341/jssms23610
    Accepted: 2024-01-15
    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 conduct a joint modeling study among the three component. 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.
  • HE Yaxing, TANG Yinghui
    Journal of System Science and Mathematical Science Chinese Series. https://doi.org/10.12341/jssms23149
    Accepted: 2024-01-12
    This paper examines an ~$M/({{G}_{1}},{{G}_{2}})/1$ repairable queuing system with Bernoulli interrupting vacations, starting failures and providing two phases of services under $Min(N,V)$-policy control, where the server goes on vacation once the system becomes empty, and if the number of customers arriving at the system during the vacation reaches ~$N$, the server interrupts the vacation with probability ~$p(0\le p\le 1)$, or continue the vacation with probability ~$1-p$, and the service station may fail during its busy and idle periods. The transient and stationary distributions of the queue size at any time are discussed by using the renewal process theory, the total probability decomposition technique and Laplace transform. We obtain Laplace transform expressions of transient queue size distribution with respect to ~$t$ , and the explicit expressions for the probability generating function of the stationary queue size distribution. In addition, some other important performance measures are also discussed. Finally, the system cost optimization problem is discussed from an economic perspective. And employing the renewal reward theory, the explicit expression for the long-run expected cost per unit time is given. Then, when the service time and the repair time obey the ~$PH$ distributions, the one-dimensional optimal control policy ~${N}^{ *}$ and the two-dimensional optimal control policy ~$({N}^{*},{T}^{*})$ with (without) expected waiting time constraint to minimize the expected system cost are investigated through numerical examples, and the effect of the parameter ~$p$ on the optimal control policies is discussed.
  • REN Hong-Mei, Tian Shou-Fu, Zhong Ming, Liu Ji-Chuan
    Journal of System Science and Mathematical Science Chinese Series. https://doi.org/10.12341/jssms2024-JSSC-0473
    Accepted: 2024-01-08
    In this work, we utilize the Fourier neural operator (FNO) for the first time to investigate the derivative nonlinear Schrödinger (DNLS) equation and fractional derivative nonlinear Schrödinger (fDNLS) equation. For the DNLS equation, we successfully establish the mappings between the initial conditions of the equation and their respective solutions. The transition process of the soliton to the M-type wave is studied, and the periodic solution is also obtained. Simultaneously, the FNO learning method is employed to investigate the transformation process of the periodical rogue wave. Moreover, we focus on learning the mapping between the fractional order exponential space and the soliton in the fDNLS equation. By comparing the data-driven solution with the exact solution, the powerful approximation capability of the FNO network is highlighted. Finally, we discuss the effects of the full-connected layer $P$ and the activation function on the characterization ability of the network.
  • GUO Xiaole SUN Xiangkai
    Journal of System Science and Mathematical Science Chinese Series. https://doi.org/10.12341/jssms23720
    Accepted: 2024-01-08
    Nonconvex and nonsmooth semi-infinite optimization with uncertainty is an important subclass of uncertain optimization fields due to its widely applications in machine learning, signal processing and other fields. This paper is devoted to consider a class of nonconvex and nonsmooth semi-infinite optimization problems with uncertain data appearing in both the objective functions and constraints. A Mixed type robust dual problem for this uncertain optimization problem in terms of robust optimization methodology. The robust weak, strong and converse duality relations between them are obtained in terms of assumptions of generalized convexity and a new robust-type subdifferential constraint qualification.
  • LIU Shujun, CHEN Jindong, MA Yanhong
    Journal of System Science and Mathematical Science Chinese Series. https://doi.org/10.12341/jssmsKSS23891
    Accepted: 2024-01-05
    Addressing the challenges of passenger difficulty in hailing rides and insufficient vehicle supply during peak period, this paper based on the actual scenario of ride-hailing platforms implementing both surge pricing and surge subsidy strategies during peak period, considering the heterogeneity of driver supply prices. By incorporating the reference point dependence theory of driver decision-making and aiming to maximize platform expected profits, a Stackelberg game model is established to analyze the interaction between the platform and drivers. Kuhn-Tucker theorem is applied to determine the optimal service price and subsidy level for the platform during peak period. The results reveal that the expected profit of platforms during the peak period showed an \inverted U-shape" trajectory as subsidies increased. When the subsidy level is within a critical range, the expected profits of platform initially increase and then decrease with increasing service prices. The maximum expected profits of platform are achieved when the critical value (peak point) is reached. By adopting appropriate pricing and subsidy strategies, ride-hailing platforms during peak period can effectively reduce the risk of driver attrition, adjust supply-demand relationships, and enhance the benefits for both the platform and drivers.
  • MA Jingyu, LI Quanlin
    Journal of System Science and Mathematical Science Chinese Series. https://doi.org/10.12341/jssms23673
    Accepted: 2024-01-04
    Active mining behaviour of miners is fundamental to maintaining the secure and stable operation of blockchain systems, as well as realising the sustainable development of blockchain ecosystem. Therefore, effectively incentivizing the mining behaviour of the miners is of significant theoretical and practical importance. In this paper, we focus on investigating a PoW (Proof of Work) blockchain queueing system. By establishing a continuous-time Markov reward process of the GI/M/1 type, we compute the expression for the miners’ long-run average profit and provide a sufficient condition for their profitability. In addition, to evaluate the miners’ short-run benefits and risks, we utilize RG-factorization based on the Markov reward process to obtain expectations and variances of the short-run accumulated profits. We hope that the methodology and results derived in this paper can shed light on the study of mining incentives in blockchain systems, while also provide novel ideas and mathematical analysis approaches for economics and management issues related to blockchain technology.
  • GONG Yanbing, XU Boxuan, LIU Gaofeng
    Journal of System Science and Mathematical Science Chinese Series. https://doi.org/10.12341/jssms23357
    Accepted: 2024-01-03
    Cloud model similarity measurement plays an important role in uncertain classification, clustering and decision-making. Therefore, the quality of similarity measurement methods directly affects the application effect of cloud model. Based on the analysis of the shortcomings of existing cloud model similarity measurement methods, the fuzzy lattice closeness and shape similarity of cloud model are constructed, and a new cloud model comprehensive similarity measurement method based on normal OWA operator is proposed. From the point of view of the hyper entropy expectation curve cluster of the cloud model, this method comprehensively considers the location and shape characteristics of the cloud model. The comparison between the simulation example and the existing similarity method shows that the method is scientific and reasonable. Secondly, aiming at the multi-attribute decision-making problem with completely unknown weight information and uncertain attribute index value, a linguistic multi-attribute decision-making method based on comprehensive similarity of cloud model is proposed. Finally, the feasibility and effectiveness of this method are illustrated by an example of new product development scheme selection in pharmaceutical enterprises.
  • ZHOU Yufeng, ZHANG Qinzi, CHENG Jiahao, WU Changzhi, LI Zhi
    Journal of System Science and Mathematical Science Chinese Series. https://doi.org/10.12341/jssms23506
    Accepted: 2024-01-03
    The traditional decentralized inventory management mode of blood banks and hospitals is more and more difficult to cope with the increasingly normal long periodic blood shortage in China. Therefore, this paper proposed a joint inventory optimization problem for platelets to reduce blood shortage. An age-based platelet joint inventory optimization model with transshipment was built considering the life cycle, system cost, shortage, and outdating. For comparisons, the traditional decentralized inventory management model and the joint inventory optimization model without transshipment were also built. All the three models were described as biobjective mixed integer nonlinear programming. According to the characteristics of the proposed models, an improved NSWOA was designed. Numerical experimental results demonstrate the effectiveness of the models and algorithm. The improved NSWOA is superior to NSPSO and NSGWO. The results also show that the joint inventory management mode with transshipment can reduce shortage and outdating, and improve inventory performance under blood shortage situations.
  • SUN linhui, SUN Yue, WU Anbo, WANG Xinping
    Journal of System Science and Mathematical Science Chinese Series. https://doi.org/10.12341/jssms23499
    Accepted: 2024-01-03
    The issue of climate change is becoming increasingly severe, and today’s businesses need to shift from previous economic-centric thinking to an environmentally friendly economic dual-focused mindset, actively participating in carbon emission reduction. To analyze the long-term emission reduction decisions of manufacturers and retailers in the supply chain under carbon quota trading, we introduce the concept of low-carbon goodwill to describe the impact of reference low-carbon practices on market demand. Considering members’ altruistic preferences, we construct four Stackelberg differential game models: centralized decision-making, decentralized decision-making, manufacturer altruism, and retailer altruism. These models analyze the optimal equilibrium strategies for members in different scenarios and compare them. The results show that strict carbon trading policies can motivate companies to reduce carbon emissions, but the adverse effects of reference low-carbon levels should not be ignored. Altruistic behavior effectively weakens these adverse effects, leading to Pareto improvements in the carbon reduction levels of manufacturers and the low-carbon promotion levels of retailers, promoting companies’ green and low-carbon transformations. Compared to scenarios where manufacturers exhibit altruistic behavior, retailers’ altruism brings greater returns to the low-carbon supply chain.
  • WANG Haiyan, SHEN Chongjie
    Journal of System Science and Mathematical Science Chinese Series. https://doi.org/10.12341/jssms23678
    Accepted: 2024-01-01
    The rise of the digital economy and e-commerce has made it possible for suppliers to invade the retail market through online channels. This paper respectively explores the conditions for supplier channel intrusion and the optimal decision-making of supply chain members in online single-channel and dual-channel supply chains under the scenario of whether to open online reviews. Research shows: (1) In a single-channel supply chain, when online reviews are positive and have a high level of effectiveness, opening online reviews can help increase demand and achieve a "triple win" for suppliers, retailers and consumers. (2) The improvement of direct sales satisfaction is not always beneficial to supplier intrusion. When online reviews are not enabled or the online reviews are negative after activation, the increase in direct sales satisfaction has a positive impact on supplier intrusion; when online reviews When reviews are positive and their effectiveness level is high, increased direct sales satisfaction will inhibit supplier intrusion. (3) In a dual-channel supply chain, opening online reviews will not benefit all channel members, and the retailer's profits will be It is always damaged, but when the effectiveness level of online reviews meets certain conditions, opening online reviews will improve supplier profits and consumer welfare levels.
  • LIU Suhang, WANG Huiyuan, LI Xinmin
    Journal of System Science and Mathematical Science Chinese Series. https://doi.org/10.12341/jssms23585
    Accepted: 2023-12-28
    Meta-analysis is a statistical method that systematically integrates, analyzes and synthesizes the results of multiple independent studies to reach more accurate and comprehensive conclusions. To solve model uncertainty in the prediction for meta-analysis, an optimal model averaging prediction method is proposed based on Mallows criterion, and the optimality of Mallows model averaging (MMA) estimator under square loss is discussed. Finally, simulation studies were conducted to evaluate and compare the performance of MMA, Jackknife model average (JMA), S-AIC and S-BIC model average estimation under information criteria, and all methods are applied to analyze the data set of BCG vaccine for illustration. The results show that the MMA estimation is superior to other model average estimations in prediction regardless of whether the variance and sample size is large or small.
  • GAO Yang, LI Yuan
    Journal of System Science and Mathematical Science Chinese Series. https://doi.org/10.12341/jssms23616
    Accepted: 2023-12-26
    An adaptive dynamic programming (ADP) algorithm based on strategy iteration (PI) is proposed for optimal pulse control of discrete-time nonlinear systems. Firstly, the system is transformed into a discrete-time nonlinear pulse control system by introducing the constraint of pulse interval set, and the optimal performance index function under pulse control is obtained according to the Hamilton-Jacobi-Bellman equation. Secondly, an ADP algorithm based on PI is proposed to solve the optimal control problem of the pulse system, and the convergence analysis of the pulse system is given. Compared with the value iteration (VI) algorithm, PI converges faster while ensuring system stability. Then a strategy evaluation algorithm is proposed, which relaxes the initial conditions of PI algorithm and solves the difficult problem of initial value selection. Finally, a simulation example is given to verify the effectiveness of the proposed algorithm.
  • WANG Xing, PENG Qian
    Journal of System Science and Mathematical Science Chinese Series. https://doi.org/10.12341/jssms23592
    Accepted: 2023-12-24
    In this paper, to address the challenges of semantic granularity and limited flexibility in effective portfolio investment caused by the inadequate perception of stock price fluctuations in portfolio return prediction models, this paper proposes a comprehensive system prediction model for stock returns by integrating the Sentiment Situation Evaluation Score Model (SESTM) and Graphical Lasso (GLASSO). Firstly, we introduce quantile regression to model stock price volatility, defining volatility width sequence and volatility mean sequence to identify vocabulary related to positive return fluctuations. Next, the SESTM model is employed to extract perception vocabulary related to stock price volatility from news announcements and generate news sentiment scores based on closely related themes and matching dictionaries associated with policies, valuations, and market sentiments. Finally, by combining the GLASSO method, we construct a network structure of interdependence among stock prices and develop individual stock portfolio strategies based on this network. Empirical experiments are conducted using stocks from the biotechnology vaccine sector during the epidemic period to compare network interdependence and sentiment situation evaluation models. The results show that firstly, investment strategies constructed based on perception vocabulary are more suitable for short-term predictions; secondly, incorporating information the reflected partial correlations in the interdependent network,the average daily logarithmic returns of the investment portfolio reached 1.6%, which is a 14.3% improvement by 14.3% compared to not considering partial correlations,and it is twice the average daily logarithmic returns of a random combination 0.7%.Moreover, the highest return increased from 3.117 for a random combination to 3.605, showing a significant improvement of 15.6%.These results indicate that the combination model of SESTM+GLASSO provides an efficient and superior system prediction model through a comprehensive approach that can analyze the network interdependence among stock prices and accurately predict stock returns for formulating corresponding investment strategies. It has positive implications for advancing statistical research in dynamic price perception and deepening generation-based cross-modal tasks within large language models.
  • JIANG Wejin, ZHOU Wei, WANG Haijuan, HAN Yuqing, CHEN Yilin, WU Yuting
    Journal of System Science and Mathematical Science Chinese Series. https://doi.org/10.12341/jssms23534
    Accepted: 2023-12-24
    The main factor in ensuring the smooth execution of tasks in mobile swarm-aware networks is the choice of participants, but most of the existing work relies on third-party service platforms to complete the sensing tasks, thus ignoring the importance of participant privacy. In order to achieve the best quality of the completion of the perceptual task, this paper studies the participant selection strategy SWPS in the perceptual link, mainly focusing on the distance between the participant and the perceptual task, the inference of missing data, the protection of the privacy of the participant’s location and the task budget, and the recruitment cost is controlled within a certain range while maximizing the task completion quality under the premise of protecting user privacy. Firstly, the position of the participant is encrypted by the order-preserving encryption algorithm and Merkle tree to ensure the personal privacy of the participant. Secondly, through the method of sparse perception, a small number of participants are recruited to achieve cost control, and the calculation of participant reputation value and the distance between the participant and the perceived task area is introduced into the trust value to determine the choice of participants. Finally, the data collected by the task participants is obtained, and the missing data is inferred by using the method of compressed sensing for the sparsity data. Through simulation experiments and related work comparison, SWPS has good performance in participant selection and privacy protection, which can ensure data quality and data accuracy, and protect participant privacy and security.
  • ZHU Zhiguo, SUN Yi, WANG Xiening, WAN Xiaoji
    Journal of System Science and Mathematical Science Chinese Series. https://doi.org/10.12341/jssms23594
    Accepted: 2023-12-24
    With the rise of social media such as "Mafengwo", more and more tourists have become increasingly inclined toward the more flexible and freer selforganizing "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 recommendation and travel satisfaction of tourism groups.
  • FU Zhu, ZHOU Ming, ZHANG Chenjun, LIU Peng
    Journal of System Science and Mathematical Science Chinese Series. https://doi.org/10.12341/jssmsKSS23900
    Accepted: 2023-12-22
    To reveal the direct and indirect impact of the business environment on the innovation efficiency of small and medium-sized enterprises (smes) in China, this paper takes 115 smes listed on the GEM from 2015 to 2019 as data samples, uses the entropy method to measure the indicators of the business environment of enterprises, and uses the SBM method to measure the overall innovation efficiency and stage innovation efficiency of smes. The Tobit regression model is constructed to analyze the impact of business environment on enterprise innovation efficiency and its sub-stage efficiency, two intermediary variables of digitization degree and innovate willingness are introduced and the step-up regression method and Bootstrap method are applied to investigate the transmission mechanism of business environment on enterprise innovation efficiency and its sub-stage, so as to explain the internal logic of business environment affecting enterprise innovation efficiency. The results show that: 1) The business environment has significant positive effect on the innovation efficiency of overall technological innovation and its knowledge transformation and achievement transformation stage of smes. 2) The indirect effects of business environment on the innovation efficiency of overall and its two sub-stage of sems are significantly positive through the mediating variables of digitization degree or innovation willingness. 3) The heterogeneity analysis by region, whether they are specialized and innovative and industry shows that, the business environment has a significant positive effect on the innovation efficiency and overall and its sub-stage of smes belonging to the eastern region and specialized new enterprises, and the business environment has a significant positive effect on the overall innovation efficiency of some manufacturing smes, and a significant negative effect on some manufacturing and service industries smes.
  • TANG Changyi, WANG Lei
    Journal of System Science and Mathematical Science Chinese Series. https://doi.org/10.12341/jssms23837
    Accepted: 2023-12-22
    In auditing practice, there are some underestimated issues, such as auditors and clients engaging in herd behavior, the impact of herd behavior on audit risk, and how to avoid audit risk. We have established an evolutionary game theory framework to address the audit detection risk problem. Subsequently, theoretical proofs and numerical analyses have been provided regarding a specific case. Faced with insufficient information and constantly changing auditing standards, stakeholders tend to exhibit more common and reasonable behavior, which is imitation or herd behavior. Conversely, imitation behavior further magnifies market volatility and audit risk. In other words, the Evolutionary Equilibrium manifests as a limit cycle, amplifying the fluctuation range of audit failure compared to the mixed equilibrium point in traditional games. Therefore, periodic adjustments to auditing standards can effectively mitigate audit risks.
  • XIONG Jingjing, JI Zhijian
    Journal of System Science and Mathematical Science Chinese Series. https://doi.org/10.12341/jssms23623
    Accepted: 2023-12-18
    This paper studies the stabilization problem of heterogeneous multi-agent systems composed of first-order and second-order dynamic agents in a signed directed graph. By utilizing the knowledge of Laplacian matrix and graph theory, corresponding protocols are designed for second-order and first-order dynamic agents, respectively. Based on the layering theory proposed in this paper, independent strongly connected components (SBiSCC) of structural equilibrium are utilized to design control parameters. The necessary and sufficient conditions for achieving stabilization of first-order and second-order heterogeneous systems in communication topology are given. Finally, the article provides several simulation verification theoretical results.
  • CHEN Meiling, YU Hanjun
    Journal of System Science and Mathematical Science Chinese Series. https://doi.org/10.12341/jssms23695
    Accepted: 2023-12-18
    To solve the problem of non-closed linear operations in distributional data, this paper proposes time series models for distributional data in Bayes space. Under the framework of symbolic data analysis, distributional data are known as numerical modal data whose realizations can be histograms, empirical distributions or empirical estimates of parametric distributions. Since the elements of the data table are probability density functions, cumulative distribution functions or quantile functions which carry information with constraints, standard methods are not appropriate for their statistical processing. In this paper, the specific features of density functions are accounted for in Bayes space whose linear operations are closed, and the space of density functions form a complete inner product space with good algebraic properties. To build up a concise methodology for distributional time series, numeric characteristics of distributional time series, the difference operator and the lag operator are first defined by linear operations and inner products of probability density functions in Bayes space. Furthermore, the methods for model specification and parameter estimation of the distributional AR model, MA model, ARMA model and the distributional ARIMA model are deduced with a complete modelling scheme. Finally, two series of simulation experiments and a real data analysis demonstrate the usefulness and effectiveness of the proposed methods for distributional time series.
  • SUN Jingyun, MA Xiaowen
    Journal of System Science and Mathematical Science Chinese Series. https://doi.org/10.12341/jssms23647
    Accepted: 2023-12-14
    Taking the main components of the SSE 50 Index as the research object, this paper first constructs a comprehensive evaluation index of stock risk based on fractal and S-utility theory, which as the basis of selecting stocks for portfolio construction. Then, under the assumption that the return on financial assets obeys Asymmetric Laplacian distribution, the CVaR value is adopted to measure the risk of portfolio, and then an M-CVaR optimal asset allocation model is constructed, which is transformed into a quadratic programming problem to solve. In the empirical analysis stage, the sliding window method is used to dynamically adjust the optimal allocation ratio of the best stock set with monthly, quarterly, semi-annual and one-year cycles respectively. The results show that some stock investment sets screened by fractal and S-type expected utility theory can obtain better investment returns than all stocks participating in the portfolio, and the asset allocation scheme with one-year adjustment cycle can obtain higher cumulative return and Sharp ratio than other adjustment cycles.
  • ZHENG Wenzhen, TANG Xijin
    Journal of System Science and Mathematical Science Chinese Series. https://doi.org/10.12341/jssmsKSS23883
    Accepted: 2023-12-14
    The hot lists on social media platforms serve as a convergence and showcase for hot topic information, which provides significant insights into our understanding of current popular discussions. However, due to the issues of vocabulary sparsity and short text length in hot list texts, traditional LDA and neural network-based topic mining models face poor performance in topic aggregation. To address this, this paper proposes a topic modeling framework enhanced by a large language model—STAB. This framework combines the generative enhancement capabilities of large language models for text data with the excellent performance of document embeddings in topic modeling, enabling the extraction of meaningful topics from short text datasets. Experimental results on multiple datasets show that our framework outperforms existing topic modeling methods in terms of general objective evaluation metrics and applications in downstream tasks.
  • HUANG Xiaohui, YAN Zhihua, TANG Xijin
    Journal of System Science and Mathematical Science Chinese Series. https://doi.org/10.12341/jssmsKSS23868
    Accepted: 2023-12-13
    Recognizing the primary factors that influence information diffusion on social media platforms holds significant importance in the containment of harmful information spread. Previous research has primarily utilized regression analysis to identify variables that have a significant impact on retweets. However, these approaches have been limited in terms of interpretability. Using statistical modeling and causal inference, this study analyzes the variables that affect retweets from user and text features. Subsequently, the dose-response function is generated to elucidate the causal relationship of the text sentiment to retweets. Additionally, considering the potential collection bias in observed social media datasets, this study uses topical clustering for data filtration. In the experimental analysis of Twitter dataset related to the Vaccine discussion and presidential election, we have identified the variables that impact the retweets, and investigated the causal impact of text sentiment to retweets.
  • LI Sheng-li, SHU Ting, WEI Cui-ping, SANG Yu-zheng
    Journal of System Science and Mathematical Science Chinese Series. https://doi.org/10.12341/jssmsKSS23869
    Accepted: 2023-12-13
    In order to solve the problem of large group classification decisionmaking in social network environment, firstly, we construct a personalized similarity threshold learning model, integrate the similarity with social network, and get the modified social network; then, we use sub-network segmentation algorithm to group decision-makers, and compute the group preferences of subgroups through DeGroot model; next, we integrate the degree of consistency of the preference order, cohesion of subgroups and the number of their members in the process of aggregation of group preferences, and compute the optimal weight assignment of three indicators through parameter learning, and then compute the weights of subgroups. Secondly, in the process of group preference aggregation, we combine the degree of preference order consistency, subgroup cohesion and its number of members, and calculate the optimal weight assignment of the three indexes through parameter learning to compute the subgroup weights. Finally, the validity and feasibility of the method are verified by an example.
  • FAN Yu, REN Minhui, ZHANG Jian
    Journal of System Science and Mathematical Science Chinese Series. https://doi.org/10.12341/jssmsKSS23867
    Accepted: 2023-12-13
    Consumer satisfaction is essential for the production and operation of enterprises. In this regard, a framework SAFCS is proposed using word vectors derived from online reviews. Important nouns were selected from online reviews using contrastive attention based on BERT word vectors. The UMAP-PCA method was used for dimension reduction, and consumer satisfaction dimensions corresponding to the domain were obtained after clustering. Attribute-opinion phrases from online reviews were acquired via dependency parsing, and a pre-trained language model was utilized to achieve sentiment classification of the attribute-opinion phrases. Empirical analysis was conducted using reviews from four clothing brands: AT, GRN, LN, and TB. The results indicate that consumers have obvious characteristics in their attention to various dimensions, and at the same time, compared to negative reviews, consumers tend to conduct comprehensive evaluations when posting positive reviews. Finally, the study results provided guidance on the preferred production and operational approaches for the brands.
  • MENG Xiangjun, CHEN Jindong, ZHANG Jian
    Journal of System Science and Mathematical Science Chinese Series. https://doi.org/10.12341/jssmsKSS23873
    Accepted: 2023-12-13
    This study focuses on the prediction of credit risk for small and medium-sized enterprises (SMEs) by leveraging a comprehensive indicator system that incorporates unstructured textual information such asannual report texts and news reports. The Recursive Feature Elimination (RFE) method is utilized to select original indicators and indicators such as annual report text complexity, annual report sentiment tendency and news sentiment polarity for SMEs are incorporated. By utilizing Bayesian optimization-based XGBoost (BO-XGBoost) and other methodologies,the predictive performance of various machine learning models is compared across different sets of feature attributes. Furthermore, the SHAP (SHapley Additive exPlanations) interpretability method is employed to provide visual and comprehensive explanations of the model at both the local and global levels. The research demonstrates that the inclusion of unstructured textual feature indicators significantly enhances the predictive performance of the models, thereby highlighting the valuable predictive role of these features in assessing credit risk for SMEs. BO-XGBoost outperforms the baseline prediction performance, and the unstructured textual features rank highly in terms of importance. The SHAP waterfall plot, scatter plot, and dependence plot are used to explain the reasons for misjudgment cases, the polarity and degree of features impact on model’s output, the evolutionary trends between unstructured textual features and credit risk. The empirical conclusions are further theoretically supported by principal-agent theory and other theories.
  • WU Jiu Jing, GUO Wen Wen
    Journal of System Science and Mathematical Science Chinese Series. https://doi.org/10.12341/jssms23638
    Accepted: 2023-12-12
    Due to "the curse of dimensionality", both parametric and non-parametric high-dimensional tests are exposed to the issue of low power. Currently, there are two approaches to enhance the power of high-dimensional tests: 1) add an indicative function to the test statistic, and use the marginal information to promote the power of high-dimensional tests, called as power enhancement. 2) apply the sample splitting technique for dimensionality reduction of variables to improve the power of high-dimensional tests, named dimensionality reduction method. Based on these two ideas, this paper proposes the hypothesis tests via power enhancement and dimensionality reduction for high-dimensional means, regression coefficients and independence respectively. Numerical results demonstrate that the power enhancement method can obtain high power under both sparse and dense hypotheses. But the test level depends on the selection of the original test statistic and the threshold parameter. The dimensionality reduction method can control the significance level pretty well without considering the threshold parameter selection. Under the sparse hypothesis, the dimensionality reduction method possesses high power, but it performs lower than the power enhancement under the dense hypothesis.
  • CHEN Zhijuan, JI Heping, MA Changfeng, ZHANG Shunming
    Journal of System Science and Mathematical Science Chinese Series. https://doi.org/10.12341/jssms22794
    Accepted: 2023-12-11
    This paper uses the textual vectorization method to digitize the text of earnings conference call of Chinese Listed Firms, and then analyzes whether managers choose the tone strategically and how investors respond to it. It is the first time that the management tone is refined into market, industry and corporate component. We find that managers strategically arrange their tones for their own interests at earnings conference call; and the net positive market tone moves the stock price up in the long window of the event. Furthermore, investors can gain from analyzing the corporate tone of firms with high investor attention during normal market and industry situations. This paper shows that the text messages disclosure on earnings conference call can provide a valuable information, also provides a new perspective for management tone analysis. In addition, under the highly dependence of semantics on context within Chinese cultural background, this paper provides new empirical evidence for such hot issues as investors’ information acquisition and comprehension.
  • Xu Jia, Yao Yong, QIN Xiao-lin
    Journal of System Science and Mathematical Science Chinese Series. https://doi.org/10.12341/jssms23582CM
    Accepted: 2023-12-07
    It is well known that if a polynomial $f\in \mathbb{R}[x]$ is strictly positive on the unit box $I^n=[0,1]^n$, then $f$ can be written as a Bernstein expansion with strictly positive coefficients. However, the above conclusion no longer holds if $f$ has zero points on $I^{n}$. In this paper, we consider with the case of $f$ with corner zero points (vertices of $I^n$). As a result, we provide a necessary and sufficient condition for the Bernstein expansion of $f$ with non-negative coefficients when the zeros are only at corner of $I^n$. Our method rely on constructing the $d$-multiple form whose terms are homogeneous, the problem is transformed into the verification of coefficients of a given $d$-multiple form.
  • WANG Bei, TANG Xijin
    Journal of System Science and Mathematical Science Chinese Series. https://doi.org/10.12341/jssms23697
    Accepted: 2023-12-07
    Visiting collaborative institutions and participating in academic conferences serve as crucial methods for scholars to share knowledge. Analyzing and extracting insights from scholars’ academic activity information can effectively unveil the research directions, collaboration dynamics, and other informative details. This study focuses on the personnel academic activity announcements on research institution websites. By means of the natural language processing techniques, we establish a framework for extracting information. Specifically, we apply pretrained word vectors to address the textual similarity of conference names and propose a text similarity calculation method that combines syntactic structure and semantic similarity to achieve entity unification. Building upon this framework, this paper investigates the temporal and geographical aspects of academic activities. We employ methods such as social network analysis to probe into an institution’s research domains, key scholars, and more. Additionally, expenditure cost is estimated. The study realizes fine-grained extraction, mining, and visualization of academic activity information, providing valuable support for decision-making departments.
  • LI Yi, WANG Tong-xin, SHI Zhong-yu
    Journal of System Science and Mathematical Science Chinese Series. https://doi.org/10.12341/jssms23350
    Accepted: 2023-12-06
    In the field of spatial investigation, it is rare for a well-distributed sample to have units close to each other at the same time. The cube method using geographic coordinates as the equilibrium variable has the shortcoming of not capturing the spatial dependence of the total unit. In this paper, the idea of systematic sampling is innovatively introduced on the basis that cube method can capture the overall spatial trend effectively. The sampling design method proposed in this paper makes full use of the geographic coordinate information while keeping the relative position of the unit space, and gathers similar unit points to obtain the ordered population, so that the original population has a "spatial trend". First, the sampling unit ordering problem is transformed into a single source shortest path problem while maintaining the spatial correlation. Then, Dijkstra algorithm is used to obtain the ordered population, and the initial inclusion probability is updated in the flight phase of the fast cube method for ordered space sampling. Finally, through the simulation study and empirical analysis, it is shown that sorting the spatial population according to the principle of similar units being close to each other can reduce the sampling error and obtain a more balanced sample, so as to verify the feasibility of the proposed sampling idea.
  • YE Wuyi, ZHANG Shan, JIAO Shoukun
    Journal of System Science and Mathematical Science Chinese Series. https://doi.org/10.12341/jssms23369
    Accepted: 2023-12-05
    In order to investigate the impact of significant economic or political events on the dependence of financial markets, we construct the factorial hidden Markov Copula model (FHM-Copula) that allows the coefficients of dependence to follow a regime-switching process in high-dimensional state space. The FHM-Copula model is able to capture external shocks of varying magnitude, direction, duration, and short or long-term from significant events to the dependence. In the empirical study, we analyze the dynamic dependence between the stock markets of China and other BRICS countries by adopting the FHM-Copula approach. Our findings indicate that the FHM-Copula model can effectively identify the external shocks caused by significant events such as the subprime crisis, the European debt crisis, the Chinese stock market crash, China's taking over the BRICS presidency and the COVID-19 epidemic on the dependence between the stock markets of China and other BRICS countries. Our works not only provide a theoretical analysis framework based on the information shock perspective for the study of dynamic dependence among financial variables, but also provide a reference for investors and government regulators in investment decision and risk management.
  • HU Sensen, LU Jingyi
    Journal of System Science and Mathematical Science Chinese Series. https://doi.org/10.12341/jssms23532
    Accepted: 2023-12-04
    The price mechanism failure caused by fake quality information disclosure restricts the development of the agricultural product market. The transparent and traceable feature of information in the blockchain provides a new solution to the problem of fake quality information disclosure in the agricultural product supply chain. However, the high cost of blockchain, consumer preferences in the market, and block traceability accuracy all affect the strategy of adopting blockchain. This paper uses the signaling game and sets price as the signal to explore blockchain adoption strategies and quality information disclosure strategies in the agricultural product supply chain. This paper found that:(1)The agricultural supermarket will adopt blockchain technology only when the information increase is high; when farmers’ planting cost is low, the agricultural supermarket will be more willing to adopt blockchain.(2)When blockchain technology is not adopted and the planting cost is low, low-quality farmers have the incentive to deliberately set high prices to confuse the market.(3)The adoption of blockchain technology by the agricultural supermarket may harm farmers’ profits. Only when the consumers’ preference is more information-sensitive, all participants in the agricultural product supply chain can benefit from blockchain.
  • DUAN Xingde, WU Zhenhuan, ZHANG Wenzhuan
    Journal of System Science and Mathematical Science Chinese Series. https://doi.org/10.12341/jssms23521
    Accepted: 2023-12-04
    Under the Bayesian framework, this paper develops a Tweedie compound Poisson partial linear mixed model on the basis of Bayesian P-spline approximation to nonparametric function for longitudinal semicontinuous data. It is quite difficult to directly implement Bayesian computation because the probability density function for Tweedie compound poisson distribution is not analytically tractable. Therefore, inspired by the data-augmentation strategy, we introduce a latent variable to obtain the joint probability density function of a semi-continuous random variable and the latent variable, and conduct the Bayesian statistical inference based on this joint probability density function. Furthermore, a hybrid algorithm combining the block Gibbs sampler and the Metropolis-Hastings algorithm is proposed for producing the joint Bayesian estimates of unknown parameters, random effects and nonparametric function, as well as the predicted value of latent variables. Finally, several simulation studies and a real example are presented to illustrate the proposed methodologies.