Accurately forecasting gasoline volatility is significant for risk management, economic analysis, and option pricing formulas for future contracts. This study proposes a novel interval-valued hierarchical decomposition and ensemble (IHDE) approach to investigate gasoline price volatility. Our interval-based IHDE method can decompose the complex price process into different components to capture the distinct features of each component, which is helpful for forecasting and analyzing complex price processes. By using interval-valued data, the dynamics of gasoline prices in terms of levels and variations can be fully utilized in this study. Fully utilizing the informational gain of interval-valued data improves forecasting performance. In forecasting weekly gasoline volatility, we document that the proposed IHDE approach outperforms the GARCH, EGARCH, CARR, and ACI models, indicating the importance of capturing features of different frequency components and utilizing the informational gain of interval-valued data for gasoline volatility forecasts.

The purpose of the study is to develop an approach to optimising the investment decision-making process using the dynamic programming method and its further integration into the enterprise information system using new technologies. The following methods were used in the study: System-structural analysis, dynamic programming method, graphical, and tabular methods. As a result of the conducted study, the role of information in the investment process was determined. An approach to the formation of the structure of an enterprise information system designed to optimise the investment decision-making process was proposed. The components of the structural elements of this system are considered — information collection, processing, and interpretation. A roadmap for information support for the development and implementation of an investment project is proposed, the blocks of which correspond to a certain direction of information processing, depending on the stage of implementation of the investment project — preparation, implementation, and final stage. The existing solutions are considered, and ready-made software products designed to optimise the investment process are characterised, primarily from the standpoint of risk assessment. An approach to optimising the investment decision-making process based on the dynamic programming method is proposed. An example of using this method to select one of the proposed alternatives according to the profit maximisation criterion is given. The results of the analysis can be used by the management board of enterprises to optimise the investment decision-making process.

This study investigates the role of carbon emissions trading (CET) in advancing regional high-quality and green development (QGD) from quality and efficiency perspectives. Firstly, theoretical analysis via an evolutionary game model formulates hypotheses. Secondly, a multi-period DID model, based on panel data from 30 Chinese provinces (2007-2020), evaluates the impact of CET pilot policies on provincial QGD. Robustness and heterogeneity analyses assess variations by region, industrial structure, and economic development. Additionally, the moderating effects of government regulation and market conditions on the CET-QGD relationship are examined. Key findings include: 1) CET significantly improves both the quality and efficiency of QGD, with a stronger impact on efficiency. 2) Geographical heterogeneity reveals CET impacts peak in eastern provinces, moderate in central regions, and diminish in the west, amplified by industrialization and economic development. 3) Indirect governmental regulations, such as environmental spending and resource taxes, strengthen CET's effectiveness, whereas direct interventions like pollution control investments may hinder green transformation, adversely impacting QGD. 4) Increased carbon trading volumes improve QGD quality, while elevated prices enhance quality but reduce efficiency by favoring short-term emissions cuts over long-term green transitions. Therefore, this study recommends addressing regional disparities, balancing regulation with market mechanisms, and ensuring a competitive, transparent national CET market.

Developing rural revitalization industries is crucial for consolidating and expanding the achievements of poverty alleviation and establishing a solid material foundation for comprehensive rural revitalization. The "New Community Factory" in the Qinba Mountain area of Shaanxi Province, as a typical model for industrial revitalization, contributes to the high-quality development of rural areas. This article is based on the ocean model theory and conducts a comprehensive assessment and prevention of industrial risks in the "New Community Factory" model. The results indicate that the industrial risks faced by the "New Community Factory" throughout its development process fall into the category of "medium risk". Among them, the policy risk and environmental risk were highest during the period from 2014 to 2016, the economic risk was highest during the period from 2017 to 2019, and the scale risk and development risk were highest during the period from 2020 to 2022. To address prominent risks such as environmental risk and economic risk, it is urgent for the government to implement special financial policies, strengthen talent cultivation and guidance, support independent brand innovation, and improve the internal and external environment to promote the gathering and development of rural revitalization industries. This article not only enriches and expands the research scope of the ocean model but also has theoretical and practical significance for improving the risk assessment system of rural revitalization industries.

As an important path to promote the transmission of international knowledge and technology, the impact of two-way FDI coordinated development on one country's technological innovation efficiency deserves in-depth exploration. This study uses the fixed effect panel stochastic frontier analysis model and the coupled coordination model to measure the technological innovation efficiency and the two-way FDI coordinated development level of China's 31 provincial administrative regions from 2011 to 2021, and uses the fixed effect panel least squares regression model, the fixed effect panel quantile regression model, the Moran's index model, the fixed effect spatial Durbin panel model and the fixed effect panel β convergence model to test the impact of two-way FDI coordinated development on technological innovation efficiency. The results show that two-way FDI coordinated development can directly and significantly promote the improvement of technological innovation efficiency in the province, and with the improvement of technological innovation efficiency, the promoting effect of two-way FDI coordinated development has also become stronger. In addition, two-way FDI coordinated development can also indirectly generate significant positive spatial spillover effects on the technological innovation efficiency of neighboring provinces, narrow the technological innovation efficiency gap between regions, and thereby promote the efficient and balanced development of regional technological innovation activities. The research conclusions reveal the positive impact of two-way FDI coordinated development on the efficient and balanced development of regional technological innovation activities, which has enlightening significance for building a strong country in science and technology.

In this paper, an evaluation index system for the coordinated development of regional economy was constructed by considering the aspects of regional development coordination, economic development coordination, economic and social coordination, and resource and environment coordination, which is used to perform comprehensive evaluation of the coordinated development of regional economy. On this basis, the impact of the upgrading of industrial structure, scientific and technological innovation, and government intervention on the coordinated development of regional economy were also investigated. According to the findings of our study, the coordinated development of regional economy in the eastern region was obviously better than that in the central region, while the western region had the lowest coordinated development of regional economy; the upgrading of regional industrial structure promoted the coordinated development of regional economy, while the promoting effect of the scientific and technological innovation on the coordinated development of regional economy were partly achieved by its promotion of the upgrading of industrial structure; the effect of government intervention couldn't be well reflected in the coordination index of regional economy.

This paper investigates the influence of various knowledge roles on knowledge diffusion empirically. Exponential random graph models (ERGM) are constructed, which provides a novel perspective for examining the factors that influence knowledge diffusion. Our empirical findings reveal that the endogenous structural effects of the network have a significant impact on the formation of diffusion relationships in citation networks and that there is a correlation between the number of the three knowledge roles - contributors, seekers and brokers - and the likelihood of citation relationship formation in citation networks.

Quantum computation and artificial intelligence for military applications bring new capabilities, leading toquantum warfare. In this paper, we present a Holistic Warfare Model, introduced as a high-dimensional entangled warfare category including asymmetric conflicts. Its underlying metaphysics is entangled states fusion: This is the macroscopic entanglement concept inspired by high-dimensional quantum computation with the entangled wave-functions. From this entangled view, wars and battles are seen essentially as a holistic phenomenon: The distinct operation behaviors within different battlefields tend to the worldwide war. The mathematical Holistic Warfare framework developed in this paper expresses this fundamental view of arbitrary many interaction conflicts, each of them defined by its own battle-manifold and evolution simultaneously on the planet; we call this entangled category mathcal{WAR}. At last, we introduce the research directions in terms of the future wars, such multilateral conflict, cognitive warfare, biological warfare and unmanned operation so on.

Different from the traditional financial sector, science and technology finance can effectively support scientific and technological activities, which is of great significance to enhance our country's independent innovation capacity and achieve high-quality economic development. This paper uses the entropy method to comprehensively evaluate the development level of science and technology finance from four dimensions: resources, funds, financing and output. Based on the panel data of 31 provinces from 2007 to 2021, a spatial econometric model is constructed to empirically test the impact of science and technology finance on technological innovation. It is found that sci-tech finance not only has a significant promoting effect on local technological innovation, but also has an obvious spatial spillover effect. If the spatial spillover effect is not considered, the impact of sci-tech finance on technological innovation will be underestimated. Further research shows that in the eastern region, the direct effect and spatial spillover effect of sci-tech finance on technological innovation are more significant, and sci-tech finance improves the level of regional technological innovation by easing the financing constraints of enterprises and optimizing the industrial structure. The research of this paper provides data support for evaluating the impact of science and technology finance, and also provides policy reference for exploring the path of technological innovation promotion.

The application of industrial robots has significantly advanced the process of intelligentization within the manufacturing sector, fundamentally transforming firm labor structures and providing critical opportunities for high-quality economic development. Utilizing data on robot applications in Chinese manufacturing firms from 2012 to 2022, we analyze the impact of industrial robot usage on the human capital structure of firms. Our findings indicate: 1) The application of industrial robots exerts substitution effects and creation effects, optimizing the human capital structure of firms. 2) Channel analysis indicates that industrial robot application optimizes the human capital structure through three pathways: enhancing safety production, improving innovation levels, and upgrading labor quality. 3) The heterogeneity test indicates that the use of industrial robots significantly enhances the upgrading of human capital structures in labor-intensive firms, firms experiencing rapid technological advancements, and regions with a more developed factor market. Against the backdrop of the national policy of "delayed retirement" aimed at mitigating the impacts of an aging population and rising labor costs, this study offers policy insights and empirical evidence to support firm quality enhancement and high-quality economic development.

Artificial intelligence innovation is an engine for the formation of new quality productivity, which not only generates substantial social wealth but also exerts profound influence on employment opportunities and household consumption patterns. The key to promoting high-quality economic development lies in addressing the realistic dilemma of insufficient household consumption through enhancing demand-side capacity and optimizing the product supply system with the aid of artificial intelligence innovation. Based on the artificial intelligence innovation task model, this paper establishes a theoretical framework for the impact of artificial intelligence innovation on household consumption, providing a comprehensive understanding of the channels through which artificial intelligence innovation influences household consumption. Additionally, numerical simulations are conducted to validate the proposed theoretical model. Furthermore, empirical tests are conducted on the theoretical model using input-output data from various provinces between 2012 and 2020, as well as patent data from the State Intellectual Property Office. The findings indicate that: 1) Artificial intelligence innovation stimulates household consumption, with the promotion effect observed across various categories of consumption. Among them, the impact on enjoyment consumption is particularly significant, serving as a driving force for enhancing the quality and expansion of household consumption. Heterogeneity analysis reveals that artificial intelligence innovation has a stronger promotion effect on household consumption in the eastern region, urban areas, and industries characterized by high-skilled factor intensity. 2) The analysis of supply paths reveals that artificial intelligence innovation enhances household consumption through enhanced productivity and the realization of product innovation. Moreover, product innovation serves as the primary driver for promoting this effect. 3) The demand path analysis reveals that the "job creation effect" induced by artificial intelligence innovation exerts a lesser impact on the skill premium compared to the inhibitory "catfish effect", thereby enhancing household consumption capacity and fostering consumption growth.

Empowering green innovation with digital elements is a key path for enterprises to cultivate competitive advantages and achieve green upgrading. The accompanying question is whether digital transformation can effectively empower comprehensive iterative optimization of green innovation? Previous studies have only discussed the positive role of digital transformation in green innovation, but have overlooked the status and structural changes of innovation activities in different fields within green innovation during digital transformation, failing to clarify the current stage matching problem and internal mechanism between digitization and greenization. Based on this, this article classifies the green patent information retrieval of A-share listed companies from 2007 to 2022 according to the "International Patent Classification Green List", and summarizes the green innovation of management and design, production energy conservation, and end of pipe treatment according to application fields. Based on the resource allocation theory and technology consistency theory analysis, the asymmetric effect of digital transformation on green innovation is empirically examined. Research has found that digital transformation has significantly promoted green innovation in business management and design, but its empowering effect on green innovation in production energy conservation and end of pipe treatment is insufficient; compared to the application of digital business scenarios, the layout and development of digital underlying technologies have a more significant asymmetric effect on green innovation; from the perspective of R&D resource allocation, digital transformation promotes green innovation in business management and design, which is not only the result of adding new R&D resources, but also comes at the cost of squeezing out R&D resources for green innovation in other fields. Meanwhile, the tripartite governance factors of green innovation (market, government, and society) play an important role in the coordinated evolution of digitization and greening. The research conclusion of this article reflects the current situation of cultivating green competitive advantages through digital elements, that is, digital transformation mainly achieves the improvement of resource utilization efficiency in business processes through green upgrading in the field of information technology, and has a significant "local empowerment" effect. This discovery not only provides practical reference for the government to formulate more targeted policies and measures for the coordination of industrialization and informatization, but also provides practical basis for the optimization of enterprise innovation resource allocation and the formulation of innovation strategies.

Green innovation is a key factor for firms to promote the sustainable development. Its relationship with firm performance has received extensive attention. What is largely missing from the existing studies, however, is the in-depth analysis and comparison on the impacts of diverse green innovation on different firm performance. Therefore, based on the data of Chinese listed firms from 2008 to 2022, this paper analyzes and compares the impacts of green management innovation and green technology innovation on firm short-term and long-term performance. The results show that, within the sample interval, green innovation can improve firm performance. However, green management innovation only has a significant positive effect on short-term performance, while green technology innovation only has a significant positive effect on long-term performance. This paper further finds that stakeholder engagement significantly strengthens both of these two boosting effects. Regional marketization only significantly strengthens the boosting effect of green technology innovation on long-term performance, and industrial competition has no significant moderating effect in the relationship between green innovation and firm performance.

Digital twin map physical entities through simulation modeling, significantly enhancing system reliability and reducing maintenance costs by utilizing data fusion, behavior simulation, optimization decision-making, and visualization through virtual-physical interaction. In intelligent operation and maintenance practices, common challenges include poor data quality, scarcity of abnormal samples, and unclear degradation processes. Digital twin technology offers a novel paradigm to address these issues. This paper systematically reviews simulation and modeling techniques within digital twin applications, summarizing recent research advancements in key areas such as anomaly detection, remaining useful life prediction, fault diagnosis, and operation and maintenance decision-making. Focusing on the demands for intelligent equipment operation and maintenance, we summarize the research findings and technical pathways related to digital twin-driven intelligent maintenance. Based on prior theoretical research and practical applications, we propose a four-level hierarchy for digital twin-driven intelligent operation and maintenance. Furthermore, we illustrate the application of digital twin-driven intelligent maintenance in real-world scenarios with a case study on naval equipment. Finally, considering current research and engineering practices, this paper proposes future research directions to provide insights and guidance for digital twin-driven intelligent maintenance across the equipment life cycle.

Under the background of the accelerated reconstruction of the global industrial chain and supply chain, the United States tries to implement the friend-shoring and near-shoring strategy to reduce the dependence of its industrial chain and supply chain on China. Economies such as Southeast Asia and Mexico have become the main destination of China's industrial transfer, which is bound to have a negative impact on the impact scope, profitability and risk resistance of China's industrial sector in the global value chain. This paper uses the trade data of intermediate goods from the multi-regional input-output (MRIO) database to construct the global production network model, and extract the real network (null model) and artificial network (counterfactual model) that reflect the backbone of the global value chain from different perspectives, respectively. On this basis, it analyzes the potential impact of the United States' trade policy towards China on the restructuring of the global production network and the relocation risk of China's industrial chain. The results show that the friend-shoring strategy of the United States relying on Altasia and the near-shoring strategy relying on the United States-Mexico-Canada Agreement, and Canada will lead to the partial decoupling of the industrial chain and supply chain in the global scope, and moreover, the friend-shoring strategy has intensified the trend of economic anti-globalization and the risk of relocation of China's industrial chain. Finally, this paper puts forward policy suggestions to improve the resilience and security level of China's industrial chain and supply chain under the background of the United States' de-risking China-reliant supply chains.

Corporate transnational operation is an important link that cannot be ignored in building a new development pattern of dual circulation, but whether it will exacerbate internal shareholder opportunistic behavior has not been answered by existing research. The article takes Chinese A-share listed companies from 2007 to 2021 as samples and empirically explores the impact of corporate transnational operation on the stock selling behavior and stock selling motivation of internal shareholders based on the "Stock Selling Triangle Model" proposed in this article. Research has found that transnational operations will significantly exacerbate the stock selling behavior by internal shareholders of enterprises. By analyzing the motivation, we find that internal shareholders' stock selling behavior in multinational enterprises has obvious opportunistic characteristics and arbitrage tendency, which means that multinational corporations have a high risk of illegal stock selling behavior. This conclusion still holds after a series of robustness tests, such as multiple time point difference-in-difference model, Bartik instrumental variable method. The mechanism analysis found that transnational operation aggravated the market risk, information asymmetry and foam phenomenon faced by enterprises, which constituted the pressure, opportunity and excuse for stock selling behavior of internal shareholders. The heterogeneity tests find that corporate multinational operation mainly aggravated the stock selling behavior of directors, but does not exacerbate the stock selling behavior of supervisors and executives. The economic consequences tests find that the arbitrage stock selling of international enterprises will aggravate the risk of stock price collapse. However, continuous and stable export scale, strong policy supervision and good institutional investor governance environment, internal control environment and audit governance environment can effectively curb the internal shareholder arbitrage and stock selling under the background of enterprise internationalization strategy. This paper enriches the literatures in the field of corporate internationalization strategy and internal shareholder arbitrage stock selling. The research conclusions have practical significance for guiding stakeholders in the capital market to pay attention to the potential illegal stock selling risks of internationally operated enterprises.

Servitization has emerged as a vital strategic option for manufacturing enterprises in mitigating market challenges. Extensive research has been conducted in various domains of business operations to investigate the key aspects of implementing this strategy. However, there is currently a dearth of observations from the supply chain perspective. Hence, this study aims to explore the influence of trade credit from suppliers as a focal point in the execution of servitization strategy. By employing signaling theory and empirical data from publicly listed manufacturing companies, it examines the potential impact of signals emanating from enterprise servitization on trade credit. The empirical analysis reveals a U-shaped relationship between servitization and trade credit. Moreover, the firm's financial flexibility negatively moderates this U-shaped relationship, whereas the relevance of services does not demonstrate a significant influence. Furthermore, subgroup analysis indicates that state-owned and light industry enterprises exhibit a relatively attenuated U-shaped relationship between servitization and trade credit compared to non-state-owned enterprises and equipment manufacturing firms. This article provides empirical evidence validating the effect of servitization on trade credit in the manufacturing industry, thereby offering crucial theoretical and managerial insights to scholars and practitioners in the field of operations management.

The rise of social media has altered the information landscape and participant behavior in capital markets. Against this backdrop, this study utilizes Sina Weibo data and explores the impact of listed companies' social media account usage on market reactions to analyst reports. Using regression analysis and instrumental variable methods, we find that if a listed company updates its Sina Weibo within a week before the release of an analyst report, market reactions to that report will significantly decrease. This suggests that a company's Weibo can partially substitute analyst reports in conveying information to the market. Furthermore, the more frequent the Weibo posts, the lengthier the posts, and the higher the volume of comments and reposts, the lower the proportion of institutional shareholdings in the listed company and the fewer analysts following it. The more pronounced the diminishing effect of Weibo usage on market reactions induced by analyst reports becomes. This study enriches our understanding of the interplay between information intermediaries in capital markets and the role social media plays in information dissemination.

This paper innovatively investigates the relationship between abnormal tone of China's listed companies, decided by managers' strategic choices, and bond credit spreads, from the perspective of earnings communication conferences, which are highly interactive in real time and difficult to prepare fully beforehand. The results show that the managers' abnormal tone at earnings communication conferences is significantly and negatively related to bond credit spreads. The mechanism analysis finds that abnormal tone is significantly and positively related to firms' future performance, and negatively associated with firms' bankruptcy risk, indicating that the abnormal tone, with incremental information, can be used as a reliable signal. This shows that the abnormal tone is consistent with the incremental information view, with incremental information other than financial quantitative information, which makes the pricing of the credit spread of the bonds more accurate. In addition, the negative relationship between abnormal tone and bond credit spreads is more pronounced in firms with less information transparency, lower institutional investor ownership, private firms, and bonds with higher bond coupon. Finally, the abnormal tone is significantly and positively associated with credit ratings and issue sizes of bond, significantly and negatively associated with issue spreads. The above results suggest that the abnormal tone of Chinese listed company's earnings presentations influences the cost of direct bond financing for companies, making higher-quality companies have lower financing costs and resulting in a multidimensional positive cross-market spillover effect on the bond market. These findings have important implications for improving bond market pricing efficiency, resolving bond market risk, and promoting high-quality information disclosure in the capital market.

In the carbon neutrality background, forestry carbon sink projects have high investment cost and long return cycle, and the carbon emitting enterprise faces difficulties such as capital constraints. Based on realistic forestry carbon sink financing mechanisms, we develop three financing modes, i.e., bank carbon sink expected return pledge, industrial investment fund and BOT. In this paper, we compare and analyze the equilibrium results of the carbon emitting enterprise under different financing modes from the perspective of profit and carbon sink output. The study shows that when carbon emission is high, the enterprise prefers the industrial investment fund financing mode based on profit maximization, regardless of the change of own capital; When own capital is high but carbon emission is low, the enterprise prefers bank pledge financing mode; When both own capital and carbon emission are low, BOT financing mode is the best financing mode for the enterprise. Based on the perspective of sustainable development, the government should promote enterprises to adopt the industrial investment fund financing mode to achieve a win-win situation for both social economy and environment.

According to the three different forms of extended producer responsibility (EPR) system in practice, aiming at the manufacturing-remanufacturing competition system composed of an original equipment manufacturer (OEM) and an independent remanufacturer (IR), three kinds of mixed regulations with EPR characteristics combined with the carbon tax are designed by constructing a dynamic game model. This paper discusses the effect of introducing the connotation of the EPR system on improving the efficiency of carbon tax policy. The results show that the existence of the carbon reduction technology spillover effect is significant for the performance of mixed regulations. Compared with the carbon tax policy, implementing the three mixed regulations can increase the consumer surplus and environmental performance and have more robust incentive effects on emission reduction and remanufacturing. However, the scope of the application is quite different. The mixed regulation based on levy and subsidy and reward and penalty can better balance the incentive effect of emission reduction, corporate profits, and environmental performance and bring higher social welfare to some extent. The conclusion of this study has a specific reference value for the combination design of EPR and carbon tax.

The increase of data scale and the acceleration of data update frequency bring challenges for efficiency evaluation. Free Disposal Hull (FDH) is a classical efficiency evaluation method under non-convex technology. Compared with data envelope analysis under convex technology, the efficiency solving process of FDH is more complicated, and it is difficult to ensure the timeliness of the evaluation results under the situation of rapid data updating. To address the above problems, firstly, the fast enumeration algorithm (FEA) based on the dominance and reference relationship between decision-making units is proposed on the basis of the existing enumeration algorithms, which is used to calculate the FDH efficiency of large-scale samples. Furthermore, based on the transitivity of the dominance and the reference relationship, the dynamic fast enumeration algorithm (DFEA) is proposed to update the efficiency results. Finally, the effectiveness of the algorithm is verified through numerical simulations and the application of the evaluation of doctors in the Haodaifu platform. The experimental results show that, compared with the enumeration algorithm, the time for FEA to complete the evaluation of the FDH efficiency of large-scale samples is significantly reduced, and the DFEA is capable of updating the FDH efficiency of large-scale samples in real time.

The accelerated degradation testing (ADT) under constant stress is an effective means for reliability assessment. It extrapolates product reliability under normal stress conditions by analyzing degradation data at elevated stress levels. Common approaches for handling degradation data include the one-step and two-step methods. The one-step method entails model parameter estimation and subsequent inference by maximizing the log-likelihood function based on degradation data. On the other hand, the two-step method first estimates pseudo-lifetimes for each sample, which is then converted into accelerated life testing (ALT) analysis. With the advancement in computational capabilities, the one-step method has become computationally more tractable, and previous research has compared both one-step and two-step methods in non-accelerated contexts. However, literature comparing these two methods in the context of ADT remains scarce. This study aims to systematically compare these two methods for constant stress ADT systems, providing more accurate and efficient guidance for selecting reliability assessment methods suitable for ADT. In this paper, we introduce criteria for distinguishing between overestimation and underestimation of true lifetimes using pseudo-lifetimes and compare the performance of the one-step and two-step methods in estimating the mean time to failure (MTTF) through numerical simulations under linear degradation conditions. Furthermore, we apply these methods to a set of classic datasets, and the results align with the simulation findings. In summary, the simulation results indicate that, for various sample sizes and numbers of observations, the one-step method demonstrates higher accuracy in product MTTF assessment compared to the two-step method based on pseudo-lifetimes with different distributions. This advantage is particularly pronounced in small sample scenarios.

Making full use of the current uncertain information in high-frequency trading data can help improve the modeling and prediction performance of asset volatility in the complex and volatile financial market environment. This article incorporates it into the realized multiplicative error model to develop the realized real-time MEM model for joint modeling of volatility and realized volatility. Unlike existing models, the new model treats the random error term obtained from current realized measure scaled by its volatility as the real-time intraday factor of high-frequency information, thereby characterizing the conditional volatility of asset returns as a mixed function driven by both historical realized measures and real-time intraday factor. Under the framework of the new model, we discuss some important properties such as the conditional distribution theorem and related properties, the weak and strict stationary conditions, the quasi-maximum likelihood estimation method, and the out-of-sample multi-step-ahead volatility prediction theorem. In addition, the proposed model is further extended to incorporate the leverage effect and volatility feedback effect of high-frequency current information. Taking four international stock datasets as the research object, the empirical results show that: 1) The current uncertain information of high-frequency data makes the conditional distribution of the realized measure have time-varying kurtosis characteristic, which enhances the ability to model volatility of financial returns. 2) Compared to benchmark models, the realized real-time MEM models provide higher out-of-sample forecasts in terms of volatility, conditional distribution of realized measure, and volatility at risk (VolaR).

In recent years, the global capital market is in a sharp shock situation. Gold's safe-haven and value-protection functions are more prominent, and its price prediction is highly regarded by investors. We combine MEMD's multi-frequency scale synchronous decomposition function and WGAN-GP's efficient extraction capability for complex patterns, and propose a multi-frequency scale integrated prediction model MEMD-WGAN-GP based on an influencing factors system using LASSO. Firstly, we select 30 indicators from macro policy, gold, stock and crude oil market, and construct an influencing factors system with LASSO. Secondly, we decompose explanatory variables and gold price synchronously using MEMD, to obtain IMFs under different frequency scales, and build WGAN-GP prediction model for each IMF. Then, we optimize the combination of IMFs and integrate the predicted values of optimized IMFs to obtain the overall predicted gold price. Finally, the predictive performance of MEMD-WGAN-GP is evaluated under different market conditions, and the results show that our model has the best trend prediction ability, the smallest regression prediction error and the lowest prediction lag.

The fast-growing social media not only provides merchants with a platform for product marketing but also serves as a channel for product sales. The introduction of social e-commerce channels alongside traditional e-commerce channels broadens the consumer market but intensifies channel competition. Therefore, manufacturers should fully consider user characteristics to make effective channel strategy selections. Based on three different supply chain structures: not introducing social channels, introducing self-operated social channels, and introducing third-party social channels, we build a model to study the impact of social channels on manufacturers. We show that when manufacturers introduce self-operated social channels, as the price competition between channels intensifies, if the potential demand for social channels is lower, it will reduce the wholesale price; conversely, it will increase the price. Moreover, when the potential demand for social channels is larger or when it is smaller, the degree of price competition is weaker, the proportion of fans is lower, and the difference between fans and general users is higher, social channels are always introduced. Furthermore, under the condition that manufacturers introduce social channels, as the difference between fans and general users widens, if the potential demand for social channels is lower, the possibility of serving as social retailers increases; conversely, the possibility of cooperating with third-party social retailers increases.

Three types of parking facilities, i.e., on-street, off-street, and shared parking facilities, often coexist in an urban region, and their service characteristics are significantly different. The traffic system within the region typically exhibits uncertainty both in its evolutionary processes and in the observation of its state indicators. This study aims to minimize the total travel cost of participants in the regional traffic system, including area transit costs, parking search costs, management costs, and walking costs. To achieve this, a dual-driven model based on rolling optimization and data fusion estimation is proposed to design dynamic supply strategies for multi-type parking services in the region. The effectiveness of the strategy is verified through Monte Carlo numerical simulations. As a result, it is found that: First, the proposed dynamic optimization problem can be equivalently transformed into a quadratic programming problem with inequality constraints, and if a solution exists, it is guaranteed to be the unique global optimum; second, when considering uncertainties in both process and observation, a significant discrepancy may arise between the system observation outcomes and the system's target trajectory; finally, the introduction of Kalman filter can effectively reduce the gap between the posterior state estimation and the target trajectory, thereby enhancing traffic efficiency in the region and reducing the total travel cost.

In the port hinterland drayage operation, the uncertainty of turnaround time in ports brings significant challenges. This paper proposes an optimization method for external truck scheduling based on the prediction of turnaround time in ports. By analyzing the historical gate data of ports, extracting relevant features, and applying random forecast methods, a prediction model for the turnaround time of trucks is trained. In the optimization model for external truck scheduling, the turnaround time is fed back into the optimization model through repeated calls to the random forecast prediction model, thus formulating a more reliable container truck scheduling scheme. With the gate data of a container terminal in South China, the prediction model is trained, and the results show that it has high accuracy in fitting the prediction of port container truck turnaround time, and the goodness of fit is more than 0.9. In the numerical experiment of the optimization model, the median error is all distributed in 1%, indicating that the combination of machine learning-based prediction of turnaround time in ports and scheduling optimization can reduce the disturbance of uncertainty in turnaround time on the scheduling plan, and improve the reliability and effectiveness of the scheduling provided by drayage companies.

This paper concerns the decentralized event-based $H_{\infty}$ filter design problem for networked dynamic system (NDS). A more practical situation is studied, in which the communication between subsystems is affected by uncertainties and only local sampled measurement output is available for each filter in the developed filter scheme. Firstly, an event-triggered mechanism is introduced for each subsystem to process the sampled output in order to reduce the communication load. Secondly, on the basis of the well-posedness, the augmented filtering error system composed of the original NDS and the filter is modeled as a time-delay system of high dimension. After that, by employing the Lyapunov functional approach and space construction method, novel computationally attractive sufficient conditions are derived to check the well-posedness, asymptotic stability and $H_{\infty}$ performance of the augmented filtering error system. Thirdly, a co-design method of the filter and event-trigger matrices is obtained by using Finsler lemma and slack matrix approach. Finally, a numerical example is provided to demonstrate the effectiveness of the derived design approach.

This paper focuses on the McKean-Vlasov system's stochastic optimal control problem with Markov regime-switching. To this end, the authors establish a new Itô's formula using the linear derivative on the Wasserstein space. This formula enables us to derive the Hamilton-Jacobi-Bellman equation and verification theorems for McKean-Vlasov optimal controls with regime-switching using dynamic programming. As concrete applications, the authors first study the McKean-Vlasov stochastic linear quadratic optimal control problem of the Markov regime-switching system, where all the coefficients can depend on the jump that switches among a finite number of states. Then, the authors represent the optimal control by four highly coupled Riccati equations. Besides, the authors revisit a continuous-time Markowitz mean-variance portfolio selection model (incomplete market) for a market consisting of one bank account and multiple stocks, in which the bank interest rate, the appreciation and volatility rates of the stocks are Markov-modulated. The mean-variance efficient portfolios can be derived explicitly in closed forms based on solutions of four Riccati equations.

This paper investigates the output tracking control problem of heterogeneous linear multi-agent systems with a novel dynamic event-triggered control strategy. In contrast to existing observer methods, the learning algorithm is first developed and applied to the observer such that the each observer corresponding to each follower can provide an optimal estimation of the leader's state by optimizing a specified cost function. Then, a controller consisting of the observer's state and the agent's state is designed and learned by a data-based off-policy learning algorithm to achieve the optimal output tracking control. Under the learned gain matrix, to reduce the communication burden for each agent, a model-free dynamic event-triggered control strategy for each agent is developed to realize the optimal event-triggered output tracking control without depending on any prior knowledge. Rigorous analysis shows that the proposed algorithms not only ensure the model-free output tracking control while saving the limited bandwidth, but also exclude Zeno behavior. Finally, a numerical example is provided to verify the theoretical analysis.

In this paper, the linear water wave equation is used to describe the fluctuation of an ideal water body in a two-dimensional bounded rectangular region, and the Craig-Sulem transform is used to transform the water wave equation into a linear development equation with velocity potential function and wave height as state variables. In this paper, we assume that the wave height of water wave is the measured output of the system, and on this basis, we analyze the recognizability of the water depth and velocity potential function, and design a synchronous identification algorithm to estimate the water depth and potential function from the wave level. In this paper, a numerical identification algorithm based on adjoint method is designed, which can effectively estimate both water depth and potential function. Firstly, the traditional quadratic target functional is improved to target functional with system model constraints by introducing Lagrange multipliers. Secondly, the adjoint equation of the water wave equation is derived by the Lagrange functional differential formula, and the gradient of the target functional is obtained by solving the equation. Finally, the gradient descent method is used to estimate the water depth and velocity potential functions iteratively, and the effectiveness and accuracy of the proposed algorithm are verified by numerical simulations.

To enhance supply chain efficiency, it has become a standard production mode for geographically diverse factories to collaborate in completing production tasks. Constructing scheduling models that reflecting real-world conditions and designing simple yet effective optimization algorithms are key to achieving efficient collaboration. Distributed flexible job shop scheduling problem has emerged as a promising tool for modeling and optimizing such problems. However, existing researches rarely account for sequence-dependent setup time for machines, instead simplifying the time as constant. This approximation can lead to inferior schedules, thereby impeding system efficiency. This paper establishes a mixed-integer programming model to describe the distributed flexible job shop scheduling problem with sequence-dependent setup time. A Q-learning based iterated greedy algorithm is proposed to solve it, wherein the Q-learning mechanism is utilized to dynamically select the appropriate perturbation magnitude, effectively overcoming the decline in search performance caused by unreasonable perturbation in conventional iterative greedy algorithms. By introducing the correlation between machines' setup time and operation sequences into benchmark instances for distributed flexible job shop scheduling with machine eligibility constraints, 207 instances are constructed. The proposed algorithm is compared with three iterative greedy algorithms with fixed perturbation magnitudes, simulated annealing, scatter search, backtrack search-based hyper-heuristic and random permutation descent-based hyper-heuristic. Experimental results demonstrate that the proposed Q-learning based iterative greedy algorithm achieves higher search quality and faster convergence speed.

While the franchise-based express delivery industry has developed rapidly, there are also issues of unstable cooperation caused by conflicting interests and low service quality, which makes it difficult to satisfy the increasing demand for high-quality and high-service from consumers. To this end, this paper uses the method of evolutionary game to dynamically analyze the evolutionary stability of the courier company's regulatory strategy, the production behavior of terminal franchisees and the government's regulatory rewards and punishments strategy, and reveals the impact of different decision parameters on evolutionary stability, demonstrating the conditions for evolutionary stability. The numerical analysis results indicate that when the risk cost and profit-sharing ratio are in different threshold intervals, the game system between express delivery companies and franchisees presents four different evolutionary stability results. In addition, when formulating reward and punishment policies, the government should ensure that the sum of rewards and punishments for all parties is greater than their speculative gains, in order to ensure the standardized operation and cooperative stability of express service enterprises. The results of this paper are of great significance to the establishment of a suitable default punishment system, risk identification and early warning mechanisms, and enhancing the government's regulatory functions, while creating a favorable market operating environment.

The Proportional-Integral-Derivative (PID) control has enjoyed significant success and widespread adoption in aviation, automotive, robotics, and various other domains. However, to align with the current trend of networked control systems and optimize communication resource utilization, the authors introduce an extended PID (EPID) control framework that leverages an event-triggered mechanism. This controller is designed for single-input single-output (SISO) high-order affine nonlinear systems, overcoming the limitation of traditional PID control, which typically guarantees stability only for second-order systems. Leveraging the open unbounded parameter manifold and event-triggered conditions of the controller parameters, the authors prove through the Lyapunov method that our proposed controller achieves uniformly ultimately bounded stability and guarantees the absence of the Zeno phenomenon in the event-triggered EPID (ET-EPID) system. The efficacy of the ET-EPID control system approach is exhibited through simulation of a third-order system as well as practical experiment conducted on a second-order direct current motor.

The allocation of heterogeneous battlefield resources is crucial in Command and Control (C2). Balancing multiple competing objectives under complex constraints so as to provide decision-makers with diverse feasible candidate decision schemes remains an urgent challenge. Based on these requirements, a constrained multi-objective multi-stage weapon-target assignment (CMOMWTA) model is established in this paper. To solve this problem, three constraint-feature-guided multi-objective evolutionary algorithms (CFG-MOEAs) are proposed under three typical multi-objective evolutionary frameworks (i.e., NSGA-II, NSGA-III, and MOEA/D) to obtain various high-quality candidate decision schemes. Firstly, a constraint-feature-guided reproduction strategy incorporating crossover, mutation, and repair is developed to handle complex constraints. It extracts common row and column features from different linear constraints to generate the feasible offspring population. Then, a variable-length integer encoding method is adopted to concisely denote the decision schemes. Moreover, a hybrid initialization method incorporating both heuristic methods and random sampling is designed to better guide the population. Systemic experiments are conducted on three CFG-MOEAs to verify their effectiveness. The superior algorithm CFG-NSGA-II among three CFG-MOEAs is compared with two state-of-the-art CMOMWTA algorithms, and extensive experimental results demonstrate the effectiveness and superiority of CFG-NSGA-II.

In this paper, a cross-sensor generative self-supervised learning network is proposed for fault detection of multi-sensor. By modeling the sensor signals in multiple dimensions to achieve correlation information mining between channels to deal with the pretext task, the shared features between multi-sensor data can be captured, and the gap between channel data features will be reduced. Meanwhile, in order to model fault features in the downstream task, the salience module is developed to optimize cross-sensor data features based on a small amount of labeled data to make warning feature information prominent for improving the separator accuracy. Finally, experimental results on the public datasets FEMTO-ST dataset and the private datasets SMT shock absorber dataset (SMT-SA dataset) show that the proposed method performs favorably against other STATE-of-the-art methods.

New urbanization construction is an essential driver for expanding domestic demand and a critical measure to facilitate internal circulation. This paper evaluates the impact of new urbanization pilot projects on the consumption levels of the rural migrant using data from the China Migrants Dynamic Survey. The study reveals that, compared to non-pilot areas, the consumption levels of rural migrants in pilot areas have significantly improved, and a series of robustness checks support this conclusion. Mechanism analysis indicates that the pilot projects have increased income, enhanced access to public services, and strengthened a sense of identity, all of which contribute to the increased consumption levels of rural migrants. The pilot projects have a more substantial effect in regions with low dialect diversity and more effectively raise the consumption levels of employed and intra-provincial migrants. Additionally, the pilot projects have boosted both daily and housing consumption of migrants, with a more pronounced effect in counties and county-level cities. The findings provide theoretical explanation and empirical evidence for establishing a long-term mechanism to expand domestic demand.

How digital platforms enable enterprises to achieve disruptive innovation is a key concern for managers in the context of the platform economy. Building on the literature on platform ecosystem and dynamic capability, we explore the effect of digital platform capabilities on disruptive innovation. Leveraging data from 209 Chinese high-tech enterprises that have either developed their digital platforms or integrated with existing ones, we find that digital platform capabilities have a significantly positive impact on disruptive innovation. We further show that structural flexibility and organizational unlearning partially mediate the relationship between digital platform capabilities and disruptive innovation. Specifically, digital platforms empower shaping structural flexibility, dismantling rigid organizational routines, and identifying emerging niche markets targeted for disruptive innovation on the one hand. On the other hand, they facilitate organizational unlearning, breaking away from existing knowledge path dependencies, and acquiring complementary knowledge required for disruptive innovation. Additionally, structural flexibility has a significant positive impact on organizational unlearning, and both factors serve as chain mediators between digital platform capabilities and disruptive innovation. This study deepens our understanding of the formation mechanisms behind disruptive innovation in high-tech enterprises within the platform economy framework. It addresses the practical question of which capabilities high-tech enterprises need to cultivate for disruptive innovation from a micro perspective. These insights provide valuable theoretical guidance for enterprises seeking to leverage digital platforms for achieving disruptive innovation in the context of ongoing digital transformation.