In this paper, disturbance attenuation is considered for linear systems with partially modeled disturbance. The disturbance signal is composed of known signals and uncertain parameters that leads to some difficulties for solving the disturbance rejection problem. To overcome this issue, the original system is reformulated as a linear parameter-varying (LPV) system by absorbing the unknown parameters in disturbance. Then an adaptive state-disturbance-feedback controller relying on a dictionary of state-feedback gains and disturbance-feedback gains is designed to estimate the uncertain parameters in the LPV system. Moreover, the presence of multiple variables in the sufficient condition given to reject the external disturbance of the LPV system also brings challenges. To tackle this problem, the quadratic separation technology is applied into the sufficient condition, and the original unsolvable condition can be successfully transferred into a solvable one. Furthermore, by adding the known part of the disturbance signal into the feedback loop, more information of the whole system can be utilized. Meanwhile, the asymptotical stability of the closed-loop system can be achieved and the $H_\infty$ performance index of the closed-loop system is verified to be smaller. Numerical simulations are given to illustrate the merits of the proposed approach.

The authors consider a two-period joint inventory and pricing decision problem for a retailer facing strategic customers with behavioral preferences such as reference dependence, loss aversion and risk preferences. The authors develop and analyze a model that accounts for customers' these behavioral preferences as well as value depreciation on the product, and makes predictions on the retailer's optimal decisions. Moreover, the authors demonstrate how the presence of these behavioral preferences and primary parameters will leverage the retailer's optimal decisions. It is revealed that strategic customers' loss aversion behavior could benefit the retailer from pushing up the regular price, the stocking quantity and hence the expected profit. However, customer's value depreciation on the product will drive down these aspects. To alleviate the negative effect of the strategic customers' behavioral preferences, the authors suggest the retailer applying inventory commitment strategy and price guarantee policy, which could increase the retailer's profit beyond the rational expectation equilibrium level in some situations.

How to evaluate the system reliability through the test data of components is one of the key challenges in the field of reliability. In this study, the authors focus on calculating the Bayesian lower credible limit. Although the approximation methods are widely used in reliability evaluation, how to apply them to the Bayesian context remains to be solved. Some previous studies have attempted to address this issue. However, their approaches might result in instability, and they have imposed significant constraints on component and system structures. A high-order saddlepoint approximation method for high accuracy is proposed, as well as a feasible procedure for determining the saddlepoint method's asymptotic variable. The proposed framework allows us to analyze the components following various posterior distributions without limiting the system structure. Numerical experiments on various systems are presented to demonstrate the effectiveness and accuracy of the proposed method. In comparison, it consistently outperforms other commonly used approximation approaches.

Pre-disaster planning and post-disaster repair are critical strategies for enhancing the resilience of urban road transportation systems. This study comprehensively considers the multidimensional uncertainties in damaged road locations, damage types, and repair durations, as well as the heterogeneity of repair tasks. A two-stage stochastic programming model is developed for the emergency facility location and repair scheduling of urban road transportation systems, aiming to maximize the combined value of emergency facility coverage and overall system resilience. Based on the structural characteristics of the proposed model, a concentration set-based Sample Average Approximation (SAA) algorithm is designed. Experimental results demonstrate that the proposed resilience-optimal recovery strategy outperforms traditional approaches such as random recovery, betweenness-priority recovery, flow-priority recovery, and length-priority recovery. Moreover, the concentration set-based SAA algorithm enables efficient solution of the problem. The findings of this study provide decision-making support and algorithmic guidance for the development of facility location and emergency repair scheduling plans aimed at enhancing the resilience of urban road transportation systems.

In this paper, an unknown system dynamic estimator based sliding mode control scheme is proposed for anti-sway control of overhead cranes with unmatched disturbances and unmodeled dynamics. Through introducing a first-order low-pass filter, the unknown system dynamic estimator is designed to compensate for the unknown system dynamics including unmatched disturbances, such that the disturbance rejection ability can be enhanced. Then, a two-phase power reaching law based sliding mode controller is constructed to obtain the relatively accurate convergence time of the sliding mode variable and guarantee the fast convergence speed. The experimental results show that the proposed method can effectively achieve the satisfactory anti-sway performance and positioning accuracy of the overhead crane.

Determining the effective and efficient lower bounds of the discrepancy criterion in uniform designs is a critical aspect of experimental designs. In this work, we investigate the issue of lower bounds of the newly proposed absolute discrepancy. The lower bound of absolute discrepancy on symmetric multi-level designs is presented and some new sharp lower bounds of this criterion on two- and three-level designs are also displayed. These lower bounds can serve as evaluation metrics for design uniformity and act as benchmarks in the construction of uniform designs. The construction of uniform designs based on absolute discrepancy is also discussed.

This paper uses the OP method and event study method to study the impact of blockchain technology application on enterprise total factor productivity and stock price, and analyzes whether the application of enterprise blockchain technology has effectively promoted the improvement of enterprise total factor productivity, or just created more “foam” for the company’s stock price? The main conclusion of this paper is that the application of blockchain technology mainly promotes the improvement of total factor productivity by reducing financing constraints, and has a greater impact on the improvement of total factor productivity for large enterprises and state-owned enterprises; In addition, after the application of blockchain technology in enterprise announcements, the company’s stock price level has significantly increased, meaning that the company can obtain higher stock premiums from blockchain technology based announcements.

In practical research, the data may come from different distributions, and there are some limitations when using a single distribution model to fit the data. In order to overcome this problem, the mixed model can better adapt to complex data types. Exponential distribution and Rayleigh distribution are important life distributions in reliability analysis, and there are few related mixed models in the context of censored data. In this paper, a mixed model of two-parameter exponential distribution and two-parameter Rayleigh distribution is proposed, and the EM algorithm is used to estimate the parameters of the mixed model with right censored data. Finally, the model is applied to the actual data, and the goodness of fit test is carried out to verify that the proposed model is suitable, which further shows that the model can better adapt to the complex data characteristics and has certain practical significance.

To interpolate the specified Frenet frame, curvature and torsion, a method is proposed for the construction and shape optimization of spatial quintic F³ interpolating curves. F³ continuity of spatial curves is a special k-th order Frenet frame continuity and ensures the satisfaction of G² continuity and torsion interpolation. Firstly, a quintic Bézier curve interpolating the given F³ data is constructed, whose control points are expressed with two parameters denoting the lengths of the curve’s end tangents. Then, the optimal parameter values are determined by minimizing a quadratic energy function. Finally, by defining the objective function as the integral of a weighted sum of squared curvature and torsion, another better optimization method is proposed. Compared to the previous G² interpolation scheme, the new methods can generate curve shapes with more satisfactory curvature and torsion profiles, although using a stricter continuity constraint.

Firstly, the port-Hamiltonian differential-algebraic systems are transformed into the port-Hamiltonian ordinary differential systems with parameter $\varepsilon$. Then, based on the parameteric ordinary differential systems, two structure-preserving model reduction methods are proposed. The first method is parametric moments matching: Constructing the parametric moments based on the frequency parameter $s$ and the embedding parameter $\varepsilon$ of the parametric systems, and then obtaining the reduced-order models of the parametric systems through parametric moments matching. The reduced-order systems match the parametric moments of the original systems. Finally, by taking the embedded parameter $\varepsilon = 0$, the structure preserving reduced-order models of the original port-Hamiltonian differential-algebraic systems are obtained. The second method is low-rank balanced truncation: Using Laguerre functions to construct the low-rank decomposition factors of the controllability and observability Gramians of the parametric ordinary differential systems. The approximate balanced systems are obtained through projection, and finally, the reduced-order models are constructed by truncating the states corresponding to smaller Hankel singular values. This procedure offers adaptability and enables the construction of reduced-order models meeting specified accuracy conditions while maintaining lower computational complexity. Both algorithms use Gram-Schmidt process to construct new projection matrices, thereby preserving the differential structure of the original system. Finally, the effectiveness of the algorithms is demonstrated through a numerical example.

The frequent occurrence of societal events in today's society has a profound impact on people's daily lives and societal development. Prediction of future events helps analysts understand social dynamics, make rapid and accurate decisions as well. This paper proposes a temporal graph model and transforms the target entity prediction into a reasoning task in the temporal event graph. The model first constructs a temporal event graph based on the historical events. In order to explore the influence between different types of events, a dual attention mechanism combining nodes and edges is designed for information aggregation. After encoding the time information through gated recurrent unit, the embedding vectors are input to the fully connected layer to predict the target entity. In addition, given the repeated occurrence of societal events along the historical timeline, the model adopts a copy mechanism to modify the prediction function. Experimental results on multiple datasets demonstrate that the model outperforms other baseline models.

China has introduced a large-scale equipment upgrade policy that can renovate livestock manure collection and processing facilities. However, the impact of this policy on manure management has not yet been explored in existing research. Additionally, there is a gap in the analysis of refined market strategies regarding the collaboration between third-party companies (TPCs) and small to medium-sized livestock farmers (SMS-LFs). To address these issues, this paper constructs an evolutionary game-theoretic model that examines the equipment upgrade strategy of SMS-LFs and the classified pricing strategy of TPCs. The study incorporates prospect theory and mental accounting theory (PT-MA) to explore how farmers decide whether to invest in equipment upgrades, considering their risk preferences. By combining the expected utility function with the value perception function and adhering to the principle of those who invest receive the subsidies, the paper analyzes which party would benefit more from implementing the upgrades in the context of effective policy execution. The study conducts simulation analyses of strategies and summarizes the systemic archetypes for upgrading manure collection and processing facilities. The findings are as follows: 1) Providing large-scale equipment upgrade subsidies to TPCs, allowing them to enhance the manure collection and processing facilities for SMS-LFs, is the most effective strategy for advancing the policy. 2) TPCs should actively implement a classified pricing strategy. 3) The large-scale renewal and upgrading of livestock manure collection and treatment systems exemplify a limits to growth archetype. The solution is removing constraints from balancing loops through a policy mechanism allowing TPCs to obtain equipment renewal subsidies. This subsidy mechanism encourages TPCs to invest in upgrading manure collection and treatment facilities for SMS-LFs. Subsequently, these companies can implement a classified charging strategy to secure higher-quality manure-based raw materials. This creates an incentive mechanism that motivates SMS-LFs to increase their investments in manure treatment. Ultimately, this virtuous cycle enhances the proportion of subsidies received by SMS-LFs through improved environmental performance.

In this article, we use text analysis to extract implicit information such as specialization of economic governance from government work reports, providing a new explanation for the sources of deviation in local economic growth goals. The results are that the specialization of economic governance can bring economic growth exceeding expectations, which is reflected in the fact that the actual economic growth rate exceeds the expected goals announced in the government work report. This is related to the effective allocation of resource elements, and is also motivated by factors such as “promotion championships”. With the transformation of local government performance evaluation system, the impact of economic governance specialization on the deviation of economic growth goals has decreased. However, in cities with different regions or administrative levels, professional officials are effective in promoting economic growth. Furthermore, if there are too many prospects for the future, weak execution ability, and lower innovation as well as higher compliance with previous policies in the local government’s economic governance, that may reduce the impact of specialization in economic governance on the deviation of economic growth targets, which is not conducive to achieving economic growth exceeding expectations. This study has reference significance for better leveraging the role of the government in resource allocation as well as in economic growth.

Driven by different promotion pressures, different decisions made by government officials may change the development path of cities and directly affect the ability to cope with crises, thus playing an all-encompassing and sustained role in urban economic resilience (UER). Considering that the COVID-19 pandemic that occurred at the end of 2019 is a large external shock, which may cause a large disturbance to economic resilience, this article tests the impact of official promotion pressure (OPP) on UER using data from 265 cities in China from 2004 to 2019. This paper also explores the role of the “National Civilized City” (NCC) selection mechanism in the process. The findings indicate a positive correlation and spatial spillover effect between OPP and UER. Moreover, the impact of both civilization status and civilization intensity on OPP is negative, which means that obtaining the title weakens OPP, and the positive effect on UER is weakened. And this effect becomes increasingly obvious with the increase in the duration of the title of NCC. Furthermore, the heterogeneity analysis yields rich findings, which provide new perspectives for the policy recommendations in this paper.

Interval-valued functional principal component analysis (IFPCA) is a comprehensive evaluation method that can effectively handle continuous high-frequency data. However, most existing IFPCA methods assume that samples within intervals follow a uniform distribution, which may overlook the actual distribution of samples within intervals. This assumption may result in the omission of key features in samples, thereby affecting the accuracy of analyses. To address this issue, this study considers the internal distributional information of intervals using means and standard deviations to reflect the centralized location and discrete changes of intervals under the general distribution. The current time-varying distance function does not fully utilize this distributional information, necessitating an extension to accommodate the general distribution. Building on this, an IFPCA based on the time-varying distance function under the general distribution is proposed. This new IFPCA better utilizes the known internal information within intervals, uncovering intrinsic features of data. Simulation studies demonstrate the effectiveness of the IFPCA under the general distribution. An empirical application further confirms that the new IFPCA is superior to existing IFPCA methods.

Recent research has indicated that urban renewal can positively impact residents’ happiness. However, the reciprocal influence of residents’ happiness on urban renewal requires further exploration. Employing an inter-provincial panel dataset spanning from 2006 to 2020 and considering spatial dynamics, this study employs a spatial simultaneous equation model to analyze the mutual interaction and spatial spillover effects between residents’ happiness and urban renewal. The findings reveal a bidirectional promotion mechanism between residents’ happiness and urban renewal. Specifically, urban renewal contributes to heightened residents’ happiness, while residents’ happiness also fosters urban renewal. Moreover, a notable spatial interaction spillover effect is observed between residents’ happiness and urban renewal. The linkage between residents’ happiness and urban renewal in the focal region is intricately intertwined with the same factors in surrounding areas.

In the contemporary globalized business environment, organizations face intense competition and significant pressure to navigate uncertainties. Strategic decision-making, particularly in allocating scarce resources to innovation endeavors, is a crucial yet complex task for organizational leaders. This study addresses the gap in the existing literature by proposing a decision-making framework grounded in multi-criteria decision making (MCDM), specifically utilizing the analytic hierarchy process (AHP), to enhance strategic decision-making capabilities. The framework aims to improve resource allocation and organizational performance by integrating cognitive and affective factors influencing decision-makers. The analysis presented in this study has successfully computed the final rankings of the strategic alternatives, scanning ability, interpretation ability, and action ability within the organization. By integrating the weights assigned to each criterion and alternative, it was determined that scanning ability holds the highest value at 50.75%, followed by interpretation at 26.65%, and action at 22.58%. Additionally, the factors influencing these alternatives were ranked, with sentiment being the most significant at 0.3607, followed by emotion at 0.2123, attention at 0.2011, ideation at 0.1271, and memory at0.0986. This outcome highlights the significance of scanning ability and sentiment in strategic decision-making. This research contributes to the field by providing a model influencing strategic decision-making, offering valuable insights for managers and policymakers aiming to optimize resource allocation and drive sustainable growth.

In the modern economy, startups are not only significant drivers of innovation and technological progress but also key players in addressing employment issues and promoting economic diversification. However, startups often face substantial operational risks and uncertainties in their early stages, especially regarding financing. To uncover the impact of different resource allocations and strategic choices on financing success, this study proposes a predictive method based on the latent Dirichlet allocation (LDA) topic model and deep neural networks through an in-depth analysis of startup financing cases. We systematically collected description text data from 2,000 startups and extracted text features from these descriptions using the LDA topic model. These features, combined with several traditional numerical indicators such as industry, product type, technology type, number of employees, and company size, were used to train a deep neural network to predict startup financing outcomes. The experimental results show that the prediction performance based on the LDA topic model is significantly better than that of traditional models relying solely on numerical data. This highlights the importance of text features in predicting the success of startup financing.

Network intrusion detection plays a critical role in safeguarding network security; however, traditional detection methods often struggle with complex attacks and large-scale data. To address these challenges, we propose a novel network intrusion detection model named GCM-CSDNN, which integrates the group cloud model (GCM) with a depthwise separable convolutional neural network (CSDNN). The model introduces group cloud transformation to reduce data dimensionality and employs 3D channel fusion technology to enhance feature extraction capabilities, thereby improving both accuracy and computational efficiency. We conducted extensive experiments on multiple benchmark datasets — including UNSW-NB15, KDD99, WSN-DS, and WADI — which cover diverse network environments and attack types. Experimental results demonstrate that GCM-CSDNN significantly outperforms traditional machine learning models and deep learning models in terms of accuracy and F1-score, achieving 98.79% and98.81% respectively, and surpassing the next-best model, SSG-DCNN. Moreover, GCM-CSDNN exhibits excellent performance on high-dimensional and large-scale datasets, significantly reducing training and testing times while demonstrating strong robustness and generalization capabilities. These findings indicate that GCM-CSDNN can efficiently and accurately detect network intrusions, making it suitable for real-time network security environments requiring the processing of large volumes of data.

Recidivism among ex-offenders is a complicated socioeconomic issue that now significantly affects social security and stability. This article’s theoretical foundations are primarily life story theory and identity label theory. It also builds a conceptual model of the effects of reoffending on social stability and social security using structure equation modelling (SEM) and trajectory analysis techniques, based on data from 355 questionnaires in 10 Chinese provinces. There was an empirical test of the model. The study’s findings indicate that: 1) There is a strong negative association between social stability and social security and recidivism; 2) Income status, education level, legal awareness, prior prison experience, social recognition, and other factors are closely associated with the likelihood of reoffending; 3) Reoffending risk may significantly affect public safety through intervention crimes, such as those that immediately compromise public safety or morality.

Green production is an effective approach to achieve sustainable development. In this paper, the government determines the optimal subsidy policy under a finite budget, and then a manufacturer and a retailer play a Stackelberg game for selling green products. First, the case of subsidy for the manufacturer is discussed. It is shown that the government subsidy for per product generated by green production and the cost coefficient of the green production technology are positively correlated. Second, the case of subsidy for the retailer is discussed. By comparing the two cases, it proves that subsidy for the manufacturer generates a higher green level. Nevertheless, in some situations, subsidy for the retailer is optimal for the sales volume. Some numerical illustrations are designed to analyze the sensitivity of each subsidy policy with respect to the cost coefficient of the green production technology and the cost coefficient of the blockchain technology, and to examine the dominant region of each subsidy policy.

Carbon neutrality is of great significance to the sustainable development of human society, and carbon neutrality technology and ecological carbon sequestration are two important factors affecting carbon neutrality capacity. In this paper, we develop an economic growth model that takes into account both factors, while also considering the deadline for carbon neutrality. By the theory of optimal control, we obtain closed-form formulas for optimal consumption, investment, capital stock, and carbon neutrality capacity. Based on theoretical and numerical analysis, several policy recommendations are proposed. Specifically, countries need to set carbon-neutral targets that match their own endowments and target capital stocks. Countries or regions within the same country should choose different technical levels of carbon-neutral investment according to their different stages. Unlike usual expectations, the path of carbon neutralization capacity may decrease with the elasticity of output to investment. As the deadline approaches, investment strategies may be abnormal.

Solving the problem of outstanding accounts of private enterprises is of great significance for activating market entities, increasing labor income share, and promoting high-quality economic development. This paper selects data from A-share private listed companies from 2011 to 2021 and uses difference-in-differences method to estimate the impact, channels, and heterogeneity of local government debt liquidation special supervision on the labor income share of private enterprises. The results have found that special supervision of local government debt liquidation can significantly increase the share of labor income in private enterprises, and this conclusion still holds after a series of robustness tests. Alleviating financial pressure, improving labor employment levels, and optimizing human capital structure are the channels through which local government special supervision on debt liquidation increases the share of labor income in private enterprises. Research combining production, operation, financing, and governance shows that the higher the intensity of labor, the greater the pressure of operation, the smaller the business scale, the higher the financing constraints, and the higher the concentration of equity, the more significant the positive impact of local government debt liquidation special supervision on increasing the labor income share of private enterprises. Further analysis reveals that the special supervision of local government debt liquidation has significantly promoted the fairness of internal income distribution and labor productivity of private enterprises, and increased the high-quality development level. The research findings enrich the economic effectiveness of government debt liquidation special supervision work and have important policy implications for how to improve the labor income share of private enterprises at present.

Actively participating in the international macro cycle and enhancing the influence of foreign trade is pivotal for China to shape its new development paradigm and seize the initiative in growth. Using the natural matrix structure of monthly bilateral goods trade data from 23 major economies, this paper incorporates a cutting-edge matrix autoregression model to capture the intricate contemporaneous and intertemporal dependencies present within the trade matrix. Based on this, we extend the spillover index measurement method and combine it with the spillover network analysis method to construct international import and export trade spillover networks. Further, from a China-centric perspective, we quantitatively investigate the changes in China’s import-export trade influence under the international cycle. Results show that from a global standpoint, bilateral trade networks undergo significant structural shifts, with overall spillover intensity first increasing and then gradually weakening, embodying a transition from “globalization” to “de-globalization” traits in the international macro cycle. From China’s perspective, import spillover remains stable, while export spillover has gradually weakened since the global financial crisis and remained low during the US-China trade war and the COVID-19 pandemic. Analysis of influencing factors suggests that international total trade spillovers are significantly affected by the US Federal Reserve’s interest rate, and the geopolitical risk index of the US Granger-causes China’s export spillover index. Evidently, the dual circulation strategy, emphasizing domestic macro circulation while promoting mutual advancement with international circulation, is valuable for guarding against potential “de-globalization” risks in the international cycle and ensuring the stability of China’s economic trade. This research offers insights for understanding the international macro cycle in the new development paradigm, adjustments to the dual circulation strategy, and related policy formulation.

As an important manifestation of intelligent production, this article focuses on the signal effect of industrial robot introductions and explores how the introduction of industrial robots can promote export scales through signaling mechanism. Our research has shown that the introduction of industrial robots can promote export scales through the channel beyond productivity and product quality, suggesting a signaling effect of industrial robot adoption on export markets. Moreover, this promotion effect is not significant in domestic markets with lower levels of information asymmetry, indicating that the introduction of industrial robots also serves as a quality signal for exporting firms. We attribute the signaling effect of introducing industrial robots to two aspects: mitigating information asymmetry and improving the image of product quality. In addition, the signal effect of industrial robot introduction enables exporting firms to achieve export growth along the intensive margin, promoting both ordinary trade and intermediates trade at the product level. This study provides empirical evidence on the impact of industrial robot applications on export sales from the perspective of demand-side signaling, attributes to existing literature on the export-promoting effects of industrial robot adoption and provide practical references for implementation of industrial robot application strategies in the process of intelligent transformation in China’s manufacturing industry.

In this paper, we have set up a synergy degree model of pollution and carbon emission reductions to measure the synergy degrees of pollution and carbon emission reductions for China’s 30 provinces during2014–2021, and geographically and temporally weighted LASSO regression model to identify their key driving factors. The results obtained show that the synergy degrees of pollution and carbon emission reductions in China’s 30provinces show an upward trend with a range between 0.11 and 0.71, which also shows significant spatiotemporal characteristics with the spatial trend of “northeast-southwest”, the spatial pattern of “hot in the south and cold in the north”, and the temporal evolution of “increasing hot spots and decreasing cold spots”. Temperature, humidity, water resource utilization, energy intensity, energy structure, common wealth, environmental protection investment, and artificial intelligence technology are identified as the key drivers of the synergy of pollution and carbon emission reductions in China. Our findings not only help deeply understand pollution and carbon emission reductions, but also help improve the provincial targeted policies for pollution and carbon emission reductions in China.

Improving the green and low-carbon development mechanism is a concrete embodiment to implement the concept of “lucid waters and lush mountains are invaluable assets”, and upgrading China’s current carbon trading system is the major enabler for enhancing this mechanism. The present study links “peak carbon emissions” with carbon pricing mechanisms, and derives the economic and welfare effects of three carbon pricing policies under an identical cap on total emissions. Furthermore, the study increases the relevance and applicability of the research conclusions by treating carbon prices as endogenous variable. The CGE simulation results demonstrate that a hybrid policy which is comprised of carbon taxes and carbon trading market outperforms single-policy scenarios in terms of economic output and social impact, for example, its negative impact on GDP is less than 0.034percentage points by period 10 (base year 2020), which is the lowest in all scenarios, thus contributing to a win-win situation for the environment and economy in China. Mechanism analysis shows that the hybrid policy not only eases the pressure on key emission-reduction industries but also reduces the simulated carbon price from 113.73 CNY/ton to 57.81 CNY/ton in period 10, achieving the dual effects of “pressure-easing and production-increasing”. Moreover, the hybrid policy could increase the share of renewable energy consumption to 32.26% in later periods, thereby to some extent facilitating the decarbonization and zero-carbonization of China’s power system. On the other hand, welfare analysis reveals that under a single carbon tax scenario, the social welfare in period 15 would decrease by 0.65 percentage points compared to the baseline scenario, with the least negative impact. Therefore, we think that it is necessary to clarify the attribute positioning of the carbon tax in the following carbon pricing policy design so as to maximize the incentive effects of the carbon market.

Based on the fund flow data of inter-sectoral financial transactions from 1992 to 2020, this paper measured the risk ripple effect of share default from a macro perspective and analyzed its evolutionary characteristics. Finally, combined with major events, this paper built an inter- sectoral fund association chart to analyze the impact of major events on the risk ripple effect of share default. We yield the following results. First, the total risk ripple effect of share default shows the characteristics of constant fluctuation from 1992 to 2020, and its fluctuation is strongly correlated with major events. There is heterogeneity in the ripple effect of total share default risk in each institutional sector, which is mainly related to the functions of institutional sector. Second, the direct risk ripple effect of share default reflects the evolution characteristics of the institutional sector structure of fund source in China’s stock market. The indirect risk ripple effect of share default decreases gradually with the increase of contagion frequency, and the indirect risk ripple effect of various institutional sectors is heterogeneous. Thirdly, from the perspective of association and link structure evolution of institutional sector, different major events have a heterogeneous impact on the fund association relationship between China’s financial institutions and other institutional sectors. Clarifying the risk ripple effect of share default among sectors and analyzing its evolution characteristics is valuable for the smooth circulation of national economy.

In order to identify the mechanism by which regulatory pressure from local emergency management departments and service empowerment from work safety service institutions drive the spread of standardized work safety behavior among industrial park enterprises, based on the theories of spreading dynamics and evolutionary game theory, this study constructs an SEIR evolutionary game model to explore the strategic choices between local emergency management departments and work safety service institutions. It analyzes the process of the spread of standardized work safety behavior among industrial park enterprises under different behavioral decisions of the two entities. Furthermore, it conducts multi-scenario simulation and analysis to investigate the process and patterns of system evolution towards a benign and stable state. The results indicate that the diffusion threshold of compliant work safety behavior in industrial park enterprises can predict the evolutionary trend of such behavior within the system. The interactive behavior of “strict regulation” by local emergency management departments and“high-quality service” by service institutions is more conducive to the spread of compliant work safety behavior in industrial park enterprises. Under this strategy combination, strengthening the regulatory measures of local emergency management departments can maximally promote the spread of compliant work safety behavior in industrial park enterprises. Augmenting the regulatory capacity and efficiency of local emergency management departments, formulating attractive and deterrent reward and punishment policies to guide high-quality service provision by work safety service institutions, and stimulating proactive compliant work safety behavior by industrial park enterprises are all conducive to the formation of integrated, coordinated, and mutually constrained mechanisms for work safety governance in the park.

Emergency supplies security is an important part of national emergency management system and offers a critical support for preventing and resolving major risks. But less attention is paid to the suffering of victims, which results in a lack of humanization in the emergency supplies procurement and stockpiling, and even sparks social panic. In view of this, the implementation effect of emergency supplies prepositioning and procurement is defined as the reduced suffering of beneficiaries, and a model of emergency supplies stockpiling and purchasing considering the suffering of the victims under government commissioning is proposed. We compare the optimal decisions of the government and the supplier when the suffering of those affected by disasters is taken into consideration and when it is not. Moreover, several conditions for emergency supply chain coordination are proposed, and the option price’s range that both improves the supplier’s profit and reduces the suffering of the beneficiaries is obtained, by comparing the supplier’s profit and the total social benefit under the model of emergency supplies stockpiling and procurement under government entrustment with those under the government single stockpiling model. Then a model of emergency supplies stockpiling and procurement is constructed considering spot market procurement in the model extension. The results show that the introduction of spot market procurement into the model reduces the risk of the government’s emergency supplies inventory. Finally, numerical simulations show that:The supplier’s expected profit is most sensitive to the fluctuation of the probability of sudden disaster occurrence; When fluctuating downward from the base point, the government’s expected profit is most sensitive to fluctuations in the option exercise price, while when fluctuating upward from the base point, the government’s expected profit is most sensitive to fluctuations in the probability of a sudden disaster. The efficiency of emergency stockpiling and procurement will be improved by spot market procurement only when the option price determined by government-enterprise negotiation is in the appropriate range. The proposed model is closer to our country’s “people-oriented”concept of emergency response. The related conclusions provide theoretical support for making more accurate emergency supplies procurement and prepositioning strategies as well as coordination strategies.

The coordinated development of thermal power and renewable energy is the key to continuously promote the low-carbon transformation of electric power. In order to take into account the low-carbon nature of the power system and the sustainability of collaborative operation mode, this paper constructs a wind-solar-thermal power operation optimization model under the coupling of electricity-carbon market on the basis of considering the current endowment distribution of power generation resources and the market trading environment. This paper discusses the impact of market coupling implementation on operation results and the effect of market coupling, and carries out scenario analysis of the changes of electricity and carbon market situation. The results show that the optimization model not only promotes energy saving and emission reduction, but also increases the proportion of renewable energy, while the adjustment of parameters such as carbon price and ancillary service cost can further guide the redistribution of power generation benefits. This helps to enhance the enthusiasm of all subjects to participate in collaborative operation and achieve a balance between low-carbon and sustainable. It shows that the proposed optimization strategy is more suitable for the safe and stable transition stage under the low-carbon transformation of electric power.

This work focuses on the pure electric vehicle travel scenario with travelers of two classes: On-route charging travelers and non-on-route charging travelers. Considering the interests of both travel users and charging station operators, an optimal charging and subsidy policy model for the electric vehicle transportation system is established, which takes account of impact of three factors: Travel time, charging service level, and charging distribution balance. Revenue neutrality based congestion toll and subsidy policy for roads and charging stations is investigated to manage the travel and charging behavior of electric vehicle users, and we theoretically prove the existence of congestion toll and subsidy schemes. By introducing gap functions, the original problem is transformed into an equivalent unconstrained optimization problem, and a gradient based solving algorithm is proposed. Finally, the effectiveness of the model, algorithm, and tax neutral management schemes is verified through numerical examples. The results show that compared to the user equilibrium scenario, on-route charging users of the same OD in the system optimal scenario is distributed among long and short path more evenly, the fact of which indirectly alleviates the uneven distribution of on-route charging users at various charging stations; in the system optimal scenario, as the degree of constraint on the charging distribution equilibrium increases, on the one hand, it leads to higher external costs corresponding to the tolls charged to congested charging stations, on the other hand, it leads to an increase in the total charging time, a decrease in the total queuing time, and an increase in the total travel time of the system; The proposed tax neutral management scheme compensates for congestion tolls and subsidies on roads and charging stations, achieving zero total tax revenue and avoiding large fiscal transfers.

Compared with traditional off-line shopping, online shopping has the dilemma of information asymmetry. As an important means to solve the problem of information asymmetry in online shopping, online comments can significantly affect customer purchasing decisions and thus firms’ decisions. With respect to a supply chain competition system consisting of two manufacturers and two retailers, considering the influence of online comments on customer choice behaviors, this paper builds a game model to explore how retailers adjust product pricing and how manufacturers adjust wholesale price in response to their rivals’ decisions. First, a customer utility function considering the impact of online comments is developed; next, we construct competitive pricing models of retailers and manufacturers based on Nash game; then, we derive the models to determine the equilibrium pricing decisions for retailers and manufacturers; finally, the effects of online comments on retailers’ pricing decisions, manufacturers’ wholesale price decisions, and profits of all players are analyzed. The results show that both better online word-of-mouth and customers’ greater focus on online comments do not always induce retailers and manufacturers to increase product prices. However, when online comments provide more information about product fit, price competition between the firms weakens, that is, both retailers and manufacturers raise their respective prices. From the perspective of profit, opening up online comments in a competitive supply chain could reduce profits for both retailers and manufacturers.

In the era of the platform economy, the competitive landscape among enterprises is undergoing a shift from traditional product or customer-driven competition to one characterized by platform ecological competition. However, existing literature has yet to provide a comprehensive understanding of this evolutionary mechanism. This study employs an evolutionary game approach to construct a model of ecological cooperation comprising e-commerce platforms, logistics firms, and businesses. Through an analysis of evolutionary paths, we simultaneously consider three key mechanisms: Resource sharing, mutual benefit, and collaborative innovation. Our investigation aims to elucidate the influence of these mechanisms on the establishment and maintenance of ecological cooperation. The finding shows that resource sharing, mutual benefit, and collaborative innovation among multiple agents are essential prerequisites for fostering an ecological cooperation network in the age of platform economy. Failure to satisfy any of these conditions can lead to the collapse of such cooperation network. Furthermore, we identify several determinants, i.e. the sensitivity coefficient of services, the degree of mutual trust, and the discount associated with collaborative innovation, which positively impact the formation of ecological cooperation. Conversely, another factors such as the costs associated with ecological cooperation, the risks associated with collaborative innovation, and speculative returns exert inhibitory effects on ecological cooperation. Additionally, the efficacy of resource sharing levels on ecological cooperation is contingent upon the absorption capacity and willingness of stakeholders to engage in resource sharing. Similarly, the impact of collaborative innovation research and development investment on ecological cooperation hinges on the level of innovation risk. This study not only presents a theoretical framework for understanding the strategic decision-making process among multiple agents engaged in ecological cooperation within the context of the platform economy but also offers practical insights for enterprises seeking to establish or integrate into ecological cooperation alliances.

To prevent the excessively high ratio of product subsidies, some regions set restriction on it. Because of this, it is common for agricultural machinery enterprises to cheat subsidies by dishonest behaviors. Therefore, it is necessary to analyze its underlying mechanisms and affecting factors from the perspective of market returns. Will the agricultural machinery enterprises be rewarded for their honest behaviors in the market competition? This paper analyzes the best market returns that the two agricultural machinery enterprises will get in market competition when individually choosing the strategy of honesty or dishonesty by constructing a simultaneous game pricing model for duopoly competition, and investigates the evolution process and influencing factors of their choice of strategies based on evolutionary game model. The results show that: High subsidy intensity and low subsidy ratio restriction may result in the loss of market returns to the honest enterprise, and the extent of the losses is not only related to subsidies, but also influenced by the preference cost of farmers and government regulation. Only when its intensity for both enterprises exceeding the threshold will the regulatory has a reward and punishment effect of “rewarding honesty and punishing violations”, and has a positive effect on promoting them to choose the strategy of honesty, otherwise they will choose the strategy of dishonesty. But too strict supervision will not significantly improve the promotion effect, it will further aggravate the punishment cost shifting behavior of enterprises, resulting in the increase of purchase cost for farmers. In addition, any enterprise gains higher returns through violations with the increase of product price and sales. Therefore, taking this market feature as the basis for identifying the risk of violation into the regulatory content will help government to improve the efficiency of supervision. The above results provide the decision-making reference for government in the optimization of supervision mechanism to regulate enterprises behavior.

There is a significant spatial propagation characteristic of agricultural product price fluctuations, and the national purchase and storage strategy plays an important role in alleviating the spatial diffusion of abnormal price fluctuations of important agricultural products. Based on the perspective of coupled network cascade failure, this paper simulates the cascading failure process of the double-layer network under different attack scenarios and the impact of the implementation of the storage strategy on the price fluctuation and diffusion of agricultural products. The results show that: 1) Compared with random attacks, the collapse threshold of the double-layer network in the case of deliberate attack is lower and faster, and the collapse speed of the agricultural product price fluctuation diffusion network is always faster than that of the agricultural product storage network. 2)The double-layer network is more vulnerable when the price rises and the storage is released, while the double-layer network is more robust when the price falls and the storage is collected, and the heterogeneity of the impact of the attack strategy on the two-layer network in two different scenarios is mainly reflected in the middle and late stages of cascading failure. 3) The protection of key nodes with high topology degree and high inventory capacity of hybrid protection can effectively improve the vulnerability of the double-layer network, alleviate the “failure” of the agricultural product market, and have a significant positive impact on ensuring supply and price stability. This study will provide a theoretical reference and decision-making basis for further optimizing the agricultural product procurement and storage network and its implementation policies, and scientifically regulating the abnormal fluctuation of agricultural product prices.

As a vital component of urban transportation systems, the bike-sharing system operates on a time-based billing mode and offers “point-to-point, door-to-door” rental services, enabling users to conveniently pick up and drop off bicycles at their desired locations. At present, bike-sharing platforms encounter operational deficiencies, including inaccurate demand prediction, suboptimal bicycle allocation, and delayed collection of faulty bicycles, resulting in a significant mismatch between supply and demand. To address these challenges, this study investigates a spatio-temporal demand prediction method incorporating multi-task learning and a dynamic shared-bikes repositioning and collection approach. Firstly, a multi-gate mixture-of-experts with a bidirectional long short-term memory network is employed to jointly predict the pick-up and drop-off demands by considering the correlation between the pick-up and drop-off demands corresponding to stations. To alleviate the dependency on long time sequences, an attention mechanism is introduced to enhance the attention given to the crucial information. Furthermore, a collaborative optimization model is proposed to address the dynamic repositioning and faulty bicycle collection in the bike-sharing system, which accounts for charging decisions and mileage constraints associated with vehicles. To meet the time-sensitive requirement of large-scale dynamic repositioning management, a simulated annealing-based adaptive large neighborhood search is customized to solve the model. Finally, a comprehensive case study utilizing bike-sharing data from the New York City Citi Bike is conducted to validate the effectiveness of the proposed approach across various performance metrics: Predictive accuracy, computational efficiency, and operating costs.

In the actual online advertising inventory delivery problem, the number of users visiting the website cannot be accurately predicted, resulting in uncertainty in the supply of exposure to the target group; at the same time, uncertain covariate information will also have an impact on the user’s searching and browsing behaviors, which in turn affects the accuracy of the online advertising inventory allocation. To address these issues, this paper proposes an online advertising inventory allocation model that simultaneously considers uncertain covariate information and the supply of exposures to target demographics. Building upon existing research, the model constructs a distribution uncertainty set based on historical data, comprising both the probabilities of uncertain covariate scenarios and the moment information of the corresponding exposure supply. Furthermore, a joint stochastic chance-constrained model is developed based on this uncertainty set to ensure robustness under worst-case scenarios. The proposed model is solved using an algorithm that utilizes existing optimization software for rapid iteration. Finally, simulation analysis is conducted to validate the effectiveness of the model and algorithm.

Tourism related enterprises may bear operational risks due to significant fluctuations in tourism demand, especially after the outbreak of COVID-19, which is more pronounced in many regions. In order to more accurately describe the variability of tourism demand, this paper develops a multiscale interval decomposition ensemble framework for predicting it. Firstly, we propose a method for constructing tourist volume interval-valued time series (ITS), which derives the center and radius of ITS data based on the upper and lower limit time series. Secondly, using the bivariate empirical mode decomposition method to decompose the center and radius ITS, several decomposition component ITSs are obtained. Then, the kernel extreme learning machine optimized by particle swarm optimization (PSOKELM) is used to model and predict each decomposed component ITS. Finally, the predicted results of all decomposed component ITSs are simply added to generate center and radius forecasts, which are then converted into predicted the upper and lower limits of tourist volume ITS. Using the data of domestic and international tourist arrivals to Hawaii, an empirical study is conducted to validate the proposed method. The results show that compared with benchmark models, the proposed method has higher predictive accuracy and greater robustness, demonstrating its effectiveness in predicting the variability of tourism demand.

In this paper, a learning-driven fruit fly optimization algorithm (LDFOA) is proposed to solve the permutation flow shop scheduling problem. Firstly, in order to improve the diversity of the population, the mixed strategy was used to initialize the position of the fruit fly in the solution space. Secondly, in the smell stage, four perturbation operators are constructed to further expand the search range of fruit fly individuals. In the visual stage, the feature information of elite fruit flies is collected to establish a probability model, and the individual realizes the evolution of fruit flies through continuous learning from the probability model. In addition, the idea of iterative greedy algorithm is introduced to perform local search for the best individual, so that the fruit fly is directed to more promising regions. Finally, it used Rec and Taillard test sets to test the performance of the algorithm and compared with the current algorithm with the best effect to solve the permutation flow shop scheduling problem. The results show that LDFOA algorithm has stronger optimization ability.