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 from265 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.

Considering the high volatility and complexity of electricity spot price time series, a combined forecasting model based on wavelet transform and LGBM (light gradient boosting machine, LGBM) is proposed. By introducing rolling time window and wavelet transform, the dynamic multi-scale decomposition of electricity spot price series can be realized, and the frequency characteristics can be extracted to reduce its modal complexity and effectively avoid data leakage. In this study, the proposed model is constructed by utilizing the complex nonlinear feature extraction ability of the LGBM algorithm. The spot market data of Shanxi electric power is used to verify the validity of the proposed model. The results show that the proposed model is superior to the mainstream forecasting methods such as long-term and short-term memory model, support vector machine, elastic network regression model and extreme gradient lifting model in many key performance indexes, such as root mean square error, average absolute error and determination coefficient, among which the $ R^2 $ reaches 0.9792, showing high forecasting accuracy. At the same time, the proposed model shows robustness and adaptability under different market conditions, which shows the proposed model can be seen as a reliable forecasting tool for power market participants and helps to optimize trading strategies and reduce market risks.

Privatization reform is regarded as an effective strategy to reduce waiting times in the public healthcare system. This paper focuses on two modes of privatization reform: One is the competition mode, which allows private hospitals to enter the market and compete with public hospitals; the other is the collaboration mode, where public hospitals and private hospitals cooperate to achieve common goals. This paper employs a queueing model to describe the patient consultation process, analyzes the service rates and prices of public and private hospitals under different privatization reforms, and studies the impact of these reforms on the number of patients covered by medical services, patient waiting times, patient welfare and social welfare. The study finds that the competitive mode can significantly reduce patient waiting time, thereby expanding the number of patients covered by medical services and enhancing patient utility and social welfare. In contrast, while the cooperative mode can also reduce patient waiting time, it exhibits uncertainty in increasing the number of patients, patient utility, and social welfare, and can effectively promote the expansion of the number of patients covered by medical services, patient utility, and social welfare only when the service capacity of public-private partnership hospitals is relatively large or the degree of privatization is high. Finally, when private hospitals choose between the cooperative or competitive mode, it mainly depends on the subsidy rate provided by the government to public hospitals and the level of privatization pursued by public-private partnership hospitals for their own interests. Specifically, when the subsidy rate or the level of privatization is high, private hospitals are more inclined to choose the cooperative mode; conversely, they are more inclined to choose the competitive mode.

Semi-supervised graph-based dimensionality reduction is a kind of meth-od that utilizes data structure graph to deal with semi-supervised dimensionality reduction problem. However, most of these algorithms only take account of data information while ignore class label information; And they don't take account of the differences among samples in the training process, which reduces the robustness of the algorithms in the case of noise or outliers. In this paper, by combining sparse representation with self-paced learning, a self-paced learner is proposed to obtain the linear dimensionality reduction mapping based on sparse discriminant graph. In detail, the proposed method firstly constructs a sparse discriminant graph by integrating the propagation of class labels with sparse representation of data. Then, by considering the distance between each low-dimensional data point and the corresponding class anchor, and the ability of low-dimensional data to maintain the discriminative sparse structure of the original high-dimensional data, this paper proposes a self-paced learning problem for dimensionality reduction. On the one hand, the proposed method constructs a sparse discriminant graph that can extract the discriminative information of data more effectively; On the other hand, the proposed method is based on self-paced learning mechanism, which makes it can automatically calculate the importance values of training data, suppress the negative impact of unreliable data or labels, and improve the robustness of the model to noise or outliers. The results of five experimental data sets demonstrate the effectiveness of the proposed algorithm.

This paper focuses on intrinsic finite elements, exploring their applications in numerical partial differential equations and their potential connections to discrete differential geometry and topological data analysis. Driven by the numerical discretization that preserves the mathematical and physical structures of continuous problems, the paper briefly reviews the development of Finite Element Exterior Calculus (FEEC). Through the canonical discretization of the classical de Rham complex and the BGG complex, an extended finite element periodic table for form-valued differential forms is proposed, covering Whitney forms, distributional finite elements, Regge finite elements, and Hessian and div div complexes, providing a unified tool for the numerical solution of tensor problems. The paper further analyzes the potential of intrinsic finite elements in interdisciplinary applications, including Riemann-Cartan geometry, generalized continua, and gravitational wave computations.

This paper considers the real-time estimation problem of vehicle mass, which has a significant impact on driving comfort and safety. A bilinear parameter identification algorithm is proposed for a type of nonlinear identification problems, which encompass vehicle mass estimation. The feature of this nonlinear model is that two parameters to be estimated are multiplied together, which brings great difficulties to identification compared to linear models. The main idea proposed in the algorithm design is to transform the original nonlinear model into two mutually dependent linear models, which are identified by the recursive algorithms. By constructing a combined Lyapunov function, it is theoretically proved that the algorithm converges under the input excitation condition, and the convergence rate $O(1/t)$ is achieved based on some extra mild conditions. Finally, the algorithm is verified through practical experiments, with the estimated vehicle mass error of $1.06%$ on average, which shows the feasibility of the algorithm.

This paper studies a distributed policy gradient in collaborative multi-agent reinforcement learning (MARL), where agents communicating over a network aim to find an optimal policy that maximizes the average of all the agents' local returns. To address the challenges of high variance and bias in stochastic policy gradients for MARL, this paper proposes a distributed policy gradient method with variance reduction, combined with gradient tracking to correct the bias resulting from the difference between local and global gradients. The authors also utilize importance sampling to solve the distribution shift problem in the sampling process. The authors then show that the proposed algorithm finds an $\epsilon$-approximate stationary point, where the convergence depends on the number of iterations, the mini-batch size, the epoch size, the problem parameters, and the network topology. The authors further establish the sample and communication complexity to obtain an $\epsilon$-approximate stationary point. Finally, numerical experiments are performed to validate the effectiveness of the proposed algorithm.

This paper focuses on solving the distributed optimization problem with binary-valued intermittent measurements of local objective functions. In this paper, a binary-valued measurement represents whether the measured value is smaller than a fixed threshold. Meanwhile, the ``intermittent’’ scenario arises when there is a non-zero probability of not detecting each local function value during the measuring process. Using this kind of coarse measurement, the authors propose a discrete-time stochastic extremum seeking-based algorithm for distributed optimization over a directed graph. As is well-known, many existing distributed optimization algorithms require a doubly-stochastic weight matrix to ensure the average consensus of agents. However, in practical engineering, achieving double-stochasticity, especially for directed graphs, is not always feasible or desirable. To overcome this limitation, the authors design a row-stochastic matrix and a column-stochastic matrix as weight matrices in the proposed algorithm instead of relying on doubly-stochasticity. Under some mild conditions, the authors rigorously prove that agents can reach the average consensus and ultimately find the optimal solution. Finally, the authors provide a numerical example to illustrate the effectiveness of the algorithm.

In the digital economy era, data has emerged as a new factor of production. However, pervasive data quality issues pose significant challenges to releasing the value of data elements and may potentially become "grey rhinos" for digital economy development. Currently, the field of data science is advancing rapidly, highlighting the pressing need for further consolidation and summarization of research related to data quality. This is essential to effectively support the practice of data quality management and the establishment of reliable data circulation. This paper takes a systematic approach to explore the trajectory of data quality research. By employing a synthesis of diverse methodologies, we conduct a comprehensive review of relevant literature from domestic and international sources during the past 30 years. Our review reveals a logical progression in data quality research, characterized by the interconnected stages of "connotation-theory-method-application". Building upon this, we develop a framework for data quality research. Subsequently, we provide a retrospective summary encompassing the data quality connotation and dimensions, theoretical foundation development, assessment and optimization methods, and influencing factors and value effects. Finally, we explore trends in the development of data quality research and offers insights into future directions.

Using detailed data of interest-bearing debts from more than 3{,}000 local financing platforms and the 2021 annual reports from 60 major banks in China, this paper evaluates the systemic risk of the banking system potentially caused by local government debts in terms of total debt volume, economic industry and economic region, respectively. The results of stress testing show that: 1) Financing platform loans in the leasing and business service industry have the largest risk exposure among all industries and are closely connected to the real estate industry. An increase in default rate in the leasing and business service industry alone can trigger systemic risk in the banking system. 2) In terms of the regional comparison, the implicit debt of local governments exhibits higher default rates in the west and higher risk exposures in the east. The systemic risk of the banking system presents a noticeable "high in the east and low in the west" pattern under the same default rate. Additionally, the safe interval of default rates for implicit debts is narrower in the east compared to the mid-west. 3) At present, either defaults in local implicit debt (financing platform loan) or liquidity crisis triggered by explicit debt (government bond) is unlikely to cause the systemic risk in the banking system. However, combining with the risk contagion from banking networks, they can jointly cause significant losses to the banking system.

With the development of digitalization, networking and intelligence in social and economic activities, the connections between firms are becoming close, and the impact of related risks on the financial distress of firms is increasing. Existing research usually uses social network analysis methods to quantify topological structures and related risk impacts, but those methods are not applicable to heterogeneous networks containing different types of entities and relations. In particular, the related risk paths are long and the quantification of higher-order related risks propagated through indirect paths faces challenges. To end this, we design a framework for predicting financial distress of firms by incorporating higher-order related risk features. In the framework, we propose an unsupervised heterogeneous graph representation learning model to construct higher-order related risk features and develop an explainable method to mine higher-order related risk paths. Experimental evaluations demonstrate the superior predictive power of the unsupervised heterogeneous graph representation learning model over benchmark methods for financial distress prediction. In addition, the experimental results show that there are two types of higher-order related risk paths that help predict the financial distress of firms.

Under the orientation of high-quality development, the incentive problem for green transformation of Chinese enterprises urgently needs to be solved. From the perspective of regional trade agreements (RTAs), we construct a theoretical model to analyze the impact mechanism of "Open Environmental Regulations" on the green technology progress of enterprises in developing countries, and conduct an empirical study taking Chinese enterprises as an example. First, we develop a model of enterprise green innovation decision-making in an open economy and find that RTAs' environmental provisions influence the direction of enterprise technological progress through two channels: The technological incentive effect and product structure effect. Then, using the text of RTAs, country-specific bilateral trade data, and import and export data from Chinese customs, we construct the "environmental provisions embeddedness" of enterprises and employ a panel fixed-effects model to empirically test the effect of "Open Environmental Regulations" on green technological progress in Chinese enterprises. Our study finds that embedded environmental provisions significantly promote substantive green innovation in enterprises but have no significant impact on strategic green innovation. Mechanism analysis shows that RTAs' environmental provisions drive enterprises to increase R&D investment and green transformation of product structures, thereby promoting green technological progress. The green innovation effect of environmental provisions is more evident in non-heavy polluting industries, enterprises with low financing constraints, enterprises with high social attention, and enterprises with a high degree of overseas market diversification. Further research finds that some market-based environmental regulation tools amplify the green innovation effect of environmental provisions, indicating the necessity of coordinating internal and external environmental policies in the process of institutional openness. These conclusions provide new theoretical perspectives and empirical evidence for China to promote high-quality economic development and high-level environmental protection through high-level openness. Our study finds that embedded environmental provisions significantly promote substantive green innovation in enterprises but have no significant impact on strategic green innovation. Mechanism analysis shows that RTAs' environmental provisions drive enterprises to increase R&D investment and green transformation of product structures, thereby promoting green technological progress. The green innovation effect of environmental provisions is more evident in non-heavy polluting industries, enterprises with low financing constraints, enterprises with high social attention, and enterprises with a high degree of overseas market diversification. Further research finds that some market-based environmental regulation tools amplify the green innovation effect of environmental provisions, indicating the necessity of coordinating internal and external environmental policies in the process of institutional openness. These conclusions provide new theoretical perspectives and empirical evidence for China to promote high-quality economic development and high-level environmental protection through high-level openness.

Climate policy is the key to addressing climate change and realizing carbon neutrality. The assessment of climate policy is filled with uncertainties affecting economy, climate and energy through parameter uncertainty and model uncertainty. We develop a robust assessment framework based on the expected output from IAM for climate policies performance by incorporating the integrate assessment models (IAMs) and distributionlly robust optimization to address the impacts of model uncertainty and joint parameters uncertainties simultaneously on the assessment of climate policy. Besides, Wasserstein ambiguity set is utilized to demonstrate information of uncertain parameters. The results show that: 1) The stricter mitigation efforts, the less impacts of uncertainty on climate performance but the larger economic costs. 2) Uncertainty requires stronger mitigation efforts for the realization of carbon neutrality. 3) The worst-case distribution from the expected output of IAM has less effects on the expected net present value and expected global warming but brings great effects on the risk of future abrupt climate damage. 4) The temperature increase performance of climate policies is relatively robust across IAMs. Our work is the first study that develops a framework for assessing climate policies on the realization of carbon neutrality under uncertainty by integrating distributionally robust optimization and general climate-economic model, which provides a conservative and robust assessment for climate policy.