中国科学院数学与系统科学研究院期刊网

30 June 2026, Volume 46 Issue 7
    

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  • LI Xuan, WANG Yixuan, HAN Zhongjie
    Journal of Systems Science and Mathematical Sciences. 2026, 46(7): 2109-2119. https://doi.org/10.12341/jssms241062
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    This paper mainly discusses the long-time behavior of solutions to a class of degenerate heat-beam coupled partial differential dynamical systems. The model consists of two parts: One part is described by a degenerate heat equation, and the other part is described by the Timoshenko beam equation. These two parts are coupled together at a common boundary through certain transmission conditions. Based on the frequency domain analysis method, this paper provides a refined estimate of the polynomial stability of solutions to this type of coupled system under smooth initial conditions and determines an explicit relationship between the decay rate of the system and the degree of heat degeneracy.
  • LIU Shuchang, SONG Gongfei
    Journal of Systems Science and Mathematical Sciences. 2026, 46(7): 2120-2132. https://doi.org/10.12341/jssms240421
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    This paper addresses the fixed-time consensus and formation tracking control problems for multi-agent systems (MAS) under specific environmental noise and mechanical thermal noise. A more efficient fixed-time controller is proposed, and its stability is proved using Lyapunov theory. The controller is shown to ensure the stability and effectiveness of the MAS in achieving fixed-time consensus under a noisy environment, with simulations conducted on undirected graph topologies. Additionally, the study considers the time-varying fixed-time formation tracking (FTF) control problem for nonlinear MAS subject to random disturbances. By utilizing neighboring agent information and nonsmooth analysis techniques, a discontinuous control protocol is designed, and the expected upper bound for the random settling time of the system is derived. Numerical simulations are conducted to verify the feasibility and effectiveness of the proposed approach, demonstrating its applicability in practical scenarios with specific noise conditions.
  • XIE Yaru, TIAN Congyue
    Journal of Systems Science and Mathematical Sciences. 2026, 46(7): 2133-2145. https://doi.org/10.12341/jssms241026
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    In response to the noise problem, the article adopts a controller design method to attenuate the sound wave equation and achieve the effect of controlling noise. Time delay is considered in the controller design, and a differential controller is designed using the idea of system equivalence. The specific steps are as follows: Firstly, we need to identify the exponentially decaying system and use it as the target system. Secondly, based on the form of integral feedback control, we establish equivalent transformations between systems to ensure that the studied system is equivalent to the target system. The appropriate parameter function is determined during the calculation process to obtain the expression of the controller. Thirdly, we consider the solvability of parameter functions and the boundedness of transformations. Finally, numerical simulations are conducted and the conclusion is drawn that under the action of the controller, the original system exhibited exponential decay Therefore, this method can effectively control noise.
  • YANG Shunjiao, ZOU Yunlei
    Journal of Systems Science and Mathematical Sciences. 2026, 46(7): 2146-2154. https://doi.org/10.12341/jssms240461
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    Inspired by the Routh-Hurwitz stability criterion for planar linear systems, we investigate in this paper the stability of a class of planar nonlinear systems with unknown parameters. Drawing upon the framework of homogeneous system theory, we study the stability of a nonlinear system, which is inherently a homogeneous system, under various conditions of unknown parameters. The analysis employs both Lyapunov's method and the particular solution method to derive insights into the system's stability characteristics. As a result, several sufficient conditions, and necessary and sufficient conditions for stability are derived. The particular solution method adopted in this paper can be effectively applied to analyze the instability of the system, thereby enriching the methods available for the stability analysis of nonlinear systems. Furthermore, the results obtained in this paper have practical applications in the controller design of certain nonlinear systems. To demonstrate this, specific examples are provided to illustrate how the results can be utilized in practice.
  • XU Shuai, SUN Ziwen
    Journal of Systems Science and Mathematical Sciences. 2026, 46(7): 2155-2171. https://doi.org/10.12341/jssms240651
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    To address the stability challenges faced by industrial cyber-physical systems (ICPS) under non-periodic denial-of-service (DoS) attacks, this research investigates an adaptive event-triggered sliding mode control strategy based on observers. A mathematical model is developed for DoS attacks constrained by attack frequency and duration, accompanied by the construction of an observer to estimate the system state. A sliding mode controller is designed to mitigate the effects of DoS attacks, and an adaptive event-triggering mechanism is employed to conserve communication resources. The stability criteria and conditions are solved using techniques involving piecewise Lyapunov functions and $H_\infty $ theory, which also guide the coordinated tuning of observer and controller gains. With a batch processing reactor system as the target of control, simulations carried out on the Matlab platform confirm that the proposed control strategy effectively ensures system stability amidst non-periodic DoS attacks, simultaneously reducing communication resource consumption.
  • HE Jianxiang, GUAN Kexin
    Journal of Systems Science and Mathematical Sciences. 2026, 46(7): 2172-2188. https://doi.org/10.12341/jssms250134
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    This study explores in depth the impact mechanism of state-owned institutional investors' shareholding on corporate investment efficiency, especially in the context of mixed ownership reform. Combining theoretical analysis and empirical testing, it reveals the key role of state-owned institutional investors in improving corporate investment efficiency. Research has shown that an increase in the shareholding ratio of state-owned institutional investors has a significant promoting effect on improving the investment efficiency of enterprises. In addition, factors such as corporate governance level, enterprise size, industry competition level, and cash flow volatility also have a direct impact on the investment efficiency of enterprises. Further research has found that digital transformation and green innovation of enterprises play an important intermediary role between the shareholding of state-owned institutional investors and the efficiency of enterprise investment. State owned institutional investors accelerate the process of digital transformation and green innovation by providing financial support, strategic guidance, and resource allocation. Digital transformation can improve information processing capabilities and optimize resource allocation efficiency, while green innovation can indirectly enhance the investment efficiency of enterprises by improving resource utilization efficiency and enhancing market competitiveness, enabling state-owned institutional investors to hold shares can indirectly improve the investment efficiency of enterprises. This article not only enriches the relevant research on the economic consequences of state-owned institutional investors, but also provides a new theoretical perspective for understanding the mechanism of improving investment efficiency of enterprises. At the same time, it provides important theoretical basis and policy inspiration for deepening the reform of state-owned enterprises, optimizing the layout of state-owned capital, and enhancing the overall competitiveness of enterprises in the new era.
  • CHANG Ximing, KANG Zifan, FENG Ziyan, SUN Huijun
    Journal of Systems Science and Mathematical Sciences. 2026, 46(7): 2189-2207. https://doi.org/10.12341/jssms240817
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    The rapid expansion of shared mobility services has introduced innovative solutions for urban transportation. Ride-hailing platforms, facilitated through user-friendly smartphone applications, seamlessly connect individual preferences with immediate vehicle availability. In carpooling services, passengers can share a ride in the same vehicle, setting their respective destinations as waypoints to increase vehicle utilization. This study proposes a carpooling and dispatching model for ride-hailing services based on a self-attention reinforcement learning network. Initially, a carpooling travel topology network is constructed, considering factors like passenger pick-up and drop-off times and locations. An on-demand algorithm is designed to identify ride-hailing orders suitable for carpooling. Subsequently, a self-attention reinforcement learning network is employed for order dispatching optimization. Through the implementation of policy gradient techniques for learning and training, the integration of masking methods ensures the efficacy of order dispatching. Leveraging the strengths of “offline training & online decision-making”, the proposed strategy tackles the challenges of enhancing the real-time responsiveness of large-scale ride-hailing dispatching services. Finally, the real-world case study is conducted based on ride-hailing orders in Beijing, China. Results underscore the efficiency of the order dispatching algorithm in achieving near-optimal route selections while reaching a real-time demand response. Although carpooling slightly increases passenger waiting times, it significantly boosts ride-hailing operational efficiency, alleviates traffic congestion, and mitigates environmental pollutants.
  • CHEN Wei, CHEN Yuankun, MA Yongkai, BAI Chunguang
    Journal of Systems Science and Mathematical Sciences. 2026, 46(7): 2208-2222. https://doi.org/10.12341/jssms240768
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    Under the carbon cap-and-trade mechanism, in order to solve the problem of renewable energy cooperative investment strategy, this paper constructs a two-level power supply chain composed of power generators and electricity sellers, considers the intermittently of renewable energy, and studies the problem of renewable energy investment decision-making between power generators and electricity sellers under three different cooperation modes: No cooperation, semi-cooperation and full cooperation. Through the analysis of the equilibrium solution, the following main conclusions are drawn: 1) When the carbon emission per unit of traditional energy power is small, the total renewable energy investment of the supply chain in the non-cooperation model is the largest, while when the carbon emission per unit of traditional energy power is large, the total renewable energy investment in the supply chain in the semi cooperation model is the largest; 2) The supply chain profit under the complete cooperation model is higher, the supply chain profit under the semi cooperation model is lower, and the supply chain profit under the non-cooperation model is the lowest; 3) The increase in carbon emissions per unit of power from traditional energy sources will increase the amount of renewable energy investment and electricity prices, and reduce the electricity demand and total supply chain profits when renewable energy is intermittent.
  • PENG Xuanhua, HUANG Bo, HE Jialu
    Journal of Systems Science and Mathematical Sciences. 2026, 46(7): 2223-2238. https://doi.org/10.12341/jssms240887
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    This study investigates risk contagion mechanisms and driving factors in the smart grid industry chain under the major event shocks using a DCC-GJR-GARCH-MES model. By integrating complex network theory, event breakpoint analysis, and MST algorithms, we examine network evolution across upstream, midstream, and downstream sectors during crises like the COVID-19 pandemic, OPEC production adjustments, Russia-Ukraine conflict, and stock market crashes. Machine learning combined with SHAP values identifies risk drivers and enables early warnings. Key findings reveal: 1) Upstream sectors show greater stability than mid/downstream counterparts during shocks, with enhanced local clustering in risk networks. Systemically important enterprises act as risk transmitters while peripheral firms exhibit weaker linkages; 2) Public health emergencies exert a greater impact on tail risk contagion networks compared to other events, with drivers showing significant time-varying characteristics across different shock types and industry segments; 3) Firm size, interconnectedness, and profitability consistently emerge as critical drivers, with XGBoost models demonstrating strong systemic risk prediction capabilities. These insights reveal sector-specific vulnerabilities and response mechanisms under external shocks, offering strategic guidance for optimizing risk management in smart grid supply chains.
  • LI Wenpan, WANG Jinmei
    Journal of Systems Science and Mathematical Sciences. 2026, 46(7): 2239-2257. https://doi.org/10.12341/jssms240743
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    In the context of information asymmetry, how to stimulate third-party logistics service providers to provide the optimal logistics service level is the key issue which restricts the development of e-commerce enterprises. Based on above, according to the multi-agent problem on the logistics outsourcing for e-commerce enterprises and the double moral hazard problem of hidden effort for e-commerce enterprises and third-party logistics service providers, combining the specific features of logistics outsourcing activities for e-commerce enterprises, the principal-agent theory is adopted to establish an incentive contract model considering consumer behavior (i.e. consumer preference, consumer evaluation and consumer refund). Results show: 1) Guided by consumer behavior, e-commerce enterprises and third-party logistics service providers can effectively mitigate moral hazards stemming from their own limitations in capabilities and costs. 2) With the increase in the benefit-sharing coefficient and consumers' preference for logistics services, the effort of third-party logistics service providers will be improved, leading to a reduction in the cost ratio associated with discrepancies in consumer evaluations. 3) E-commerce enterprises do not intensify their cooperation and supervision efforts solely because of revenue sharing. However, the sensitivity of consumers to logistics service levels drives e-commerce enterprises to enhance their cooperation and supervision efforts.
  • MIN Feng, XIAO Min, LIU Heng, HUANG Chengdai, CAO Jinde
    Journal of Systems Science and Mathematical Sciences. 2026, 46(7): 2258-2272. https://doi.org/10.12341/jssms240893
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    Currently, most studies on predator-prey models with slow-fast effect focus on temporal dynamics, while relatively fewer investigations address the influence of slow-fast effect on spatial pattern evolution in predator-prey systems. This paper establishes a slow-fast modified Leslie-Gower predator-prey model incorporating time-delay and reaction-diffusion. We investigate the conditions for Turing instability and Hopf bifurcation, derive analytical expressions for bifurcation thresholds using time-delay as the bifurcation parameter, and explore how slow-fast effect impacts spatial patterns and stability region. The study reveals that reaction-diffusion drives Turing instability, with different diffusion coefficients inducing distinct spatial patterns. Time-delay can trigger Hopf bifurcation near bifurcation threshold. Both slow-fast effect and time-delay significantly alter pattern morphology, generating rich spatial configurations. Furthermore, slow-fast effect nonlinearly modulate stability region of system. These findings demonstrate that slow-fast effect plays a crucial role in spatial pattern evolution and stability regulation in predator-prey systems.
  • GAO Rong, HUA Kexin
    Journal of Systems Science and Mathematical Sciences. 2026, 46(7): 2273-2289. https://doi.org/10.12341/jssms240764
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    The rapid development of the e-commerce economy and the upgrading of eco-consumption demand have made e-commerce platforms one of the main driving forces for the sale of green products, which also brings about a diversification of logistics service strategies, along with some new challenges such as delivery delays. Due to uncertain market environment caused by intense competition, there is insufficient sample data to infer demand for new green products. Therefore, considering the consumers' perception of the time difference between the actual and promised delivery time, uncertainty theory is applied to study the scenario selection problem under four combination scenarios consisting of different e-commerce sales modes (resale mode or agency mode) and logistics service strategies (logistics outsourcing or logistics sharing) in the green supply chain. It is assumed that supply chain members seek to maximize profits under uncertain demand with a certain confidence level, from which four Stackelberg decentralized game models are constructed. Furthermore, an effective delivery time reward-penalty mechanism for non-self-supporting logistics strategies is designed. The results show that the green investment cost coefficient, the maximum delivery cost and the promised delivery time have a negative impact on supply chain behavioral performance. Moreover, the logistics sharing strategy outperforms the logistics outsourcing strategy, and the win-win-win equilibrium can be achieved under the logistics outsourcing strategy within a certain range of commission rate and decision-maker confidence level in different e-commerce sales modes. More importantly, the delivery time reward-penalty mechanism has a positive impact on supply chain members and society as a whole, and the relationship between the optimal profitability of the actual incentivized party and the promised delivery time depends on the incentive strength when consumers and the product owner implement equal reward-penalty incentives for delivery time.
  • GONG Yande, JIANG Xinze
    Journal of Systems Science and Mathematical Sciences. 2026, 46(7): 2290-2307. https://doi.org/10.12341/jssms250008
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    With the rapid development of the platform economy, e-commerce platforms' store brands have been presented with opportunities for swift growth. Digital traceability technology, as an effective measure to enhance consumer trust in store brands, has become a key to strengthening brand credibility. In view of this, this paper constructs four e-commerce supply chain decision models with and without the introduction of digital traceability technology, considering both the competition between national brand products and store brand products, and the two sale modes of reselling and agency selling. It deeply explores the impact of different sale modes on the decision-making of e-commerce platforms to adopt digital traceability technology. The study shows that high product substitutability and high consumer trust can both lead to higher profit levels for manufacturers and e-commerce platforms, and the introduction of digital traceability technology can create greater profit margins for both manufacturers and e-commerce platforms. By comparing the profits of both parties under different sale modes for national brand products, it is found that for manufacturers, when the investment cost of digital traceability technology is low and the commission rate is low, the profits under the reselling mode are higher than under the agency selling mode. Conversely, if the investment cost is high, regardless of the commission rate, the agency selling mode is the best choice. For e-commerce platforms, when the commission rate is high, the profits under the agency selling mode are higher than under the reselling mode; If the commission rate is low, then the profits under the reselling mode are higher. Finally, this paper further explores the corporate decision-making when sharing the cost of digital traceability technology. It is always the best strategy for manufacturers to choose the reselling mode to sell their national brand products, and when the commission rate for national brand products and the investment cost of digital traceability technology are both low, the profits of e-commerce platforms under the agency selling mode for national brand products will be higher.
  • LI Jizi, FANG Ying, LI Liudi, WANG Yong
    Journal of Systems Science and Mathematical Sciences. 2026, 46(7): 2308-2323. https://doi.org/10.12341/jssms240726
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    Cross border IP (intellectual property) authorization for enterprises has become one of the important means to enhance core competitiveness and realize technological monetization. Parallel imports, commonly referred to as “grey markets”, involve the act of exporting genuine products from one country to another without authorization from the intellectual property owner. This article explores whether companies adopt blockchain technology to curb parallel import behavior in gray markets when authorizing cross-border IP by constructing a Stackelberg game model under different market structures. Research has found that in a gray market, when consumers have lower trust in authorized manufacturers' products and are more sensitive to time costs, both parties are willing to adopt blockchain, which can better curb the gray market. When there is no gray market, adopting blockchain can curb the risk of false reporting by authorized manufacturers, but it can increase their sales and price, while also increasing the authorization fee for IP authorized manufacturers.
  • TANG Yong, WANG Wusong
    Journal of Systems Science and Mathematical Sciences. 2026, 46(7): 2324-2351. https://doi.org/10.12341/jssms240680
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    Data is becoming an increasingly important factor of production in the digital economy. However, the controversy surrounding data property rights is hindering the process of data factor marketization, making it difficult to fully release the value of data factors. The article explores a scheme for allocating public data property rights in scenarios where shared property rights are involved. It does so by analyzing the mechanism of public data property rights allocation and establishing a shared property rights allocation framework based on federated blockchain and game theory. Using adverse drug reaction monitoring data as an example, this article demonstrates how federated blockchain characteristics can ensure fairness and auditability in the property right allocation process. The article achieves quantitative analysis of the property right share of multiple subjects in the property right allocation process through the evolutionary game and cooperative game. The research results indicate that the framework and scheme for allocating property rights not only maximize the rights and interests of multiple parties during the definition process, but also analyze the allocation of property rights among multiple parties during the allocation process using empirical quantitative relationships.
  • SUN Jiayi, HAN Yujie, YAO Fengmin
    Journal of Systems Science and Mathematical Sciences. 2026, 46(7): 2352-2376. https://doi.org/10.12341/jssms250005
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    Based on Nash non-cooperative game theory and variational inequality theory, this paper constructs a supply chain network equilibrium model under the combination of historical emission method and industry benchmark method, aiming at the production decision of different carbon emission manufacturers under different carbon quota allocation strategies. By comparing the effects of single carbon policy and complex carbon policy on supply chain network equilibrium, the implementation conditions of the optimal carbon quota allocation policy are proposed and its effect is verified. Secondly, it expands the supply chain network equilibrium model in which the government is a participant, and reveals the double leverage effect of the linkage between carbon quota allocation ratio and carbon price when the government is an endogenous player, which provides a quantitative decision-making tool for the design of “precise carbon control” policy. Finally, the model is simulated by projection shrinkage algorithm. The conclusion is as follows: Under the historical emission method, compared with expanding production scale, the two manufacturers with different carbon emissions are more inclined to achieve the optimal profit through carbon quota trading, while retailers are faced with the risk of profit compression. Under the industry benchmark method, when the unit carbon quota is greater than the unit carbon emission, the profit advantage of the emission reduction manufacturer over the ordinary manufacturer will gradually decrease with the increase of the carbon trading price; when the unit carbon quota is less than the unit carbon emission, the profit of the two types of manufacturers will decrease with the increase of the carbon trading price, and the profit of the retailer is generally stable in both cases. Under the composite carbon quota policy, when the unit carbon quota is greater than the unit carbon emission, the industry benchmark method accounts for a large proportion, and the profits and social welfare of the two types of manufacturers are the best, but the profit growth rate of the ordinary manufacturers is higher than that of the emission reduction manufacturers. When the unit carbon quota is smaller than the unit carbon emission, the larger proportion of historical emission method is more advantageous to the profits and social welfare of the two types of manufacturers. The profit of retailers gradually decreases with the increase of the proportion of historical emission method. After the government is included in the decision-making body, the increase in carbon price significantly increased the income of emission reduction manufacturers through cap-and-surplus trading, while ordinary manufacturers could only pass on the cost due to technology locking, resulting in a negative imbalance in social welfare. This study provides theoretical support and decision-making basis for the government's carbon quota allocation policy and supply chain network response strategy.
  • ZHANG Faming, JIANG Yimeng, LUO Qian, LI Si
    Journal of Systems Science and Mathematical Sciences. 2026, 46(7): 2377-2398. https://doi.org/10.12341/jssms240904
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    To improve the accuracy and reliability of multidimensional group evaluation information integration, this paper proposes a new method based on an improved power geometric Bonferroni mean operator. First, considering the differences in expert evaluations, the credibility of evaluation information is measured using cosine similarity and improved similarity at both the indicator and expert levels. Second, an HJ-CD model combining the advantages of Hamming and Jaccard similarities is constructed to quantify the proximity of expert evaluation information. Third, using the $P_2$GBM and GBM operators, the evaluation information and its credibility are integrated, and unreasonable information is corrected through forward correction, followed by layer-by-layer aggregation. Finally, the evaluation information is converted into real numbers using interval weighted midpoints to obtain the final evaluation result. The method is applied to the integrity evaluation of a coalbed methane compressor system, and comparative analysis demonstrates its feasibility and reasonableness.
  • LI Yakun, HU Haiju
    Journal of Systems Science and Mathematical Sciences. 2026, 46(7): 2399-2419. https://doi.org/10.12341/jssms240888
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    Based on the Chinese herbal medicine supply chain where the medicinal farmers have the adulteration behavior, this paper studies the governance strategy selection of Chinese herb enterprises against the adulteration behavior of medicine farmers under the quality sampling inspection mechanism. The authors construct the supply chain Stackelberg game models for four scenarios, namely, without governance strategy, return penalty governance strategy, liability cost-sharing governance strategy and combined governance strategy. The operational differences in the Chinese herbal medicine supply chain under different governance strategies are comparatively analyzed. Governance strategy choices for Chinese herb enterprises are revealed. The findings of this paper are as follows: Under the return penalty governance strategy and combined governance strategy, when the unit penalty is higher than a certain level, it can effectively eliminate the adulteration of medicinal farmers. In contrast, the liability cost-sharing governance strategy is unable to eliminate adulteration. The increase in the return penalty parameter benefits the governance of adulteration and the profits of Chinese herb enterprises, but not the profits of medicinal farmers. The increase in the liability cost-sharing parameter benefits the profits of both parties, but not the governance of adulteration. The medicinal farmers prefer the Chinese herb enterprises to choose the liability cost-sharing governance strategy, and they least prefer the Chinese herb enterprises to choose the return penalty governance strategy. From the perspective of governing adulteration, the Chinese herb enterprises should choose the return penalty governance strategy. From the perspective of gaining profits, the Chinese herb enterprises should choose the combined governance strategy and set a higher unit penalty.
  • ZHANG Zhixia, XU Ruliang, SHAO Bilin
    Journal of Systems Science and Mathematical Sciences. 2026, 46(7): 2420-2437. https://doi.org/10.12341/jssms240374
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    Examining the dynamics of how group opinions evolve by considering the influence of the public environment and individual characteristics is crucial for accurately understanding the trends in public opinion propagation. It also offers theoretical guidance for managing the public opinion ecosystem. This paper analyzes the influence of group environment and individual characteristic factors in the formation of social individuals' opinions, and then constructs a heterogeneous opinion dynamic model, based on HK model, which account for environmental noise and individual opinion tolerance. Finally, it conducts with a simulation for analyzing the law of opinion evolution and the interpretability of the model to opinion evolution is verified by application in real-world network. The results demonstrate that individuals are prone to reach consensus with group views under the influence of high environmental noise, while low viewpoint tolerance thresholds make it more difficult to reach consensus and lead to fluctuating characteristics of opinion evolution. This research underscores the significant role of environmental factors and individual traits in opinion evolution, offering a fresh perspective for understanding and governing public opinion.
  • ZHANG Kexian, LI Hongmei
    Journal of Systems Science and Mathematical Sciences. 2026, 46(7): 2438-2458. https://doi.org/10.12341/jssms240989
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    The water rights trading mechanism is a market-oriented institutional arrangement for promoting the construction of water ecological civilization and realizing national water-saving actions. Exploring how it affects the green transformation decisions of high water consuming enterprises and promotes green technology innovation is of great significance for practicing high-quality development. This article establishes a quasi-natural experiment and explores the impact and mechanism of the water rights trading policy on green innovation based on the samples of high water-consuming enterprises. The results show that the water rights trading mechanism can drive the “incremental improvement” of green technology innovation in high water-consuming enterprises, and this conclusion is still reliable and effective after robustness testing. Heterogeneity analysis shows that water rights trading mechanisms are more conducive to stimulating green technology innovation in state-owned, large, western regions, regions with low water resource endowment and highly competitive enterprises. The mechanism indicates that the water rights trading mechanism mainly enhances green technology innovation through channels such as increasing executives' green attention and R&D investment in high water-consuming enterprises; Institutional investors' shareholding has promoted enterprises' green innovation, but the equity financing cost has a hindering effect. The results provide useful insights for promoting water rights trading mechanisms and enterprises' green innovation.