为了庆祝数学的美丽和重要性, 以及它在每个人生活中不可或缺的作用, 国际数学联 合会 (IMU) 倡议联合国教科文组织 (UNESCO) 宣布 3 月 14 日为国际数学日 (International Day of Mathematics, IDM). 2019 年 11 月 26 日, 联合国教科文组织第 40 届大会批准了这 一宣告.

With the rapid development of artificial intelligence technology and its advantages in decision-making, smart decision-making is receiving increasing attention from both academics and practitioners. This paper uses a bibliometric approach to analyze 783 works of literature related to smart decision-making from 1965-2022 to understand the research content and development process in the field. Through co-citation analysis, this paper identifies critical publications and research clusters in the field. Combined with keyword analysis, this paper provides a systematic review of the current state of research on smart decision-making. On this basis, the conceptual framework of the field is presented. According to the existing literature and bibliometric analysis, this study provides a list of research questions from three dimensions that deserve further consideration, providing insights into the trend of using AI for decision-making in the future.

Artificial intelligence has transformed marketing and consumer lives. However, despite its multiple beneficial effects, unprecedented artificial intelligence uses pose enormous challenges to consumer privacy protection. This research aims to provide a comprehensive picture of privacy research and enhance privacy protection. We first give more specific definitions of consumer privacy according to data analytic processes. Second, we investigate how AI-related factors, privacy risk, privacy protection, and other factors influence consumer privacy decision-making, and draw a conceptual model based on the extended Antecedents-Privacy Concerns-Outcomes (APCO) model. Third, we examine the potential risks of each data analytic step and the causes of privacy risks from consumer, company, and technology perspectives, and propose privacy protection schemes correspondingly. Lastly, we also provide agenda for future research.

Drug-protein interaction (DPI) prediction in drug discovery and new drug design plays a key role, but the traditional in vitro experiments would incur significant temporal and financial costs, cannot smoothly advance drug-protein interaction research, so many computer prediction models have emerged, and the current commonly used is based on deep learning method. In this paper, a deep learning model Computer-based Drug-Protein Interaction CBSG_DPI is proposed to predict drug-protein interactions. This model uses the protein features extracted by the Computed Tomography CT and Bert method and the drug features extracted by the SMILES2Vec method and input into the graph convolutional neural network (GCN) to complete the prediction of drug-protein interactions. The obtained results show that the proposed model can not only predict drug-protein interactions more accurately but also train hundreds of times faster than the traditional deep learning model by abandoning the traditional grid search algorithm to find the best parameters.

Given the rise of artificial intelligence, big data analytics has emerged as an important tool for processing and assimilating the enormous volume of data available on social media. It is of great theoretical and practical significance to explore the public opinion diffusion process and characteristics, and users' emotions of mega sports events based on big data statistics in the social media environment. This paper takes the Jakarta Asian Games, Russian World Cup and PyeongChang Winter Olympics held in 2018 as cases, uses text mining and social network analysis methods to analyze the dissemination process of social media users' data, presents the semantic words disseminated in sports events through high-frequency word cloud diagrams, and summarizes the general rules of public opinion dissemination. The results show that the more users' participation, the greater diffusion volume, and the diffusion process shows fast increasing, short duration, scattered topics, diversified contents, and strong guidance and weak continuity of attention. The high-frequency words, except for the names of the events, such as "cheer", "win the game" and "must win", have obvious concentration of emotional words.

This article investigates the structural transformation of Ukraine's financial market within the context of innovative metaspace technologies. It presents models outlining the structural changes in financial market sectors amid the rapid development of metaspace technologies, emphasizing the systematic prognostic nonlinear dependence of deposit, credit, and currency flows on time intervals within the FinTech services' payment platform. The article introduces system methods for modeling nonlinear processes and combinatorial procedures for organizing the environment of innovative metaspace technologies within the financial market. It systematically categorizes approaches for measuring the effectiveness signs of financial market structure development in the innovative metaspace. The article argues for the need to structure and evaluate the financial market sectors of Ukraine through non-combinatorial tasks, emphasizing the convenience of describing variables influencing market structure and visually depicting the metaspace environment as a simulated FinTech ecosystem. Standardizing performance indicator measurement algorithms for precise intervals of financial resource movement is advocated. The article establishes that combinatorial tasks for structuring financial market development objects in the metaspace environment should align with configurations influenced by variable factors, such as currency, deposit, and credit flows. An analysis of transactions involving currency exchange, deposits, and loans through FinTech services' payment platform in Ukraine's financial market is presented. Additionally, the article conducts a formalized assessment of changes in currency, deposit, and credit flow transactions influenced by the digital environment of FinTech services in Ukraine's financial market sectors.

This paper proposes a new discrete-time Geo/G/1 queueing model under the control of bi-level randomized (p, N₁, N₂)-policy. That is, the server is closed down immediately when the system is empty. If N₁ (≥1) customers are accumulated in the queue, the server is activated for service with probability p (0≤ p≤1) or still left off with probability (1-p). When the number of customers in the system becomes N₂ (≥ N₁), the server begins serving the waiting customers until the system becomes empty again. For the model, firstly, we obtain the transient solution of the queue size distribution and the explicit recursive formulas of the stationary queue length distribution by employing the total probability decomposition technique. Then, the expressions of its probability generating function of the steady-state queue size and the expected steady-state queue size are presented. Additionally, numerical examples are conducted to discuss the effect of the system parameters on some performance indices. Furthermore, the steady-state distribution of queue length at epochs n^-, n and outside observer's observation epoch are explored, respectively. Finally, we establish a cost function to investigate the cost optimization problem under the constraint of the average waiting time. And the presented model provides a less expected cost as compared to the traditional N-policy.

Connected and autonomous vehicles (CAVs) are expected to coexist alongside human-driven vehicles on roads for the foreseeable future. This study explores the stability and safety of mixed traffic streams, including traditional trucks and cars alongside CAVs. The study utilizes the intelligent driver model and cooperative adaptive cruise control model to characterize human-driven vehicles (including cars and trucks) and CAVs, respectively. It investigates how different ratios of trucks and penetration rates of CAVs impact the linear stability of mixed traffic flows and delineate their stability domains. Additionally, a simulation experiment is conducted using SUMO software to assess the safety implications of traffic congestion at on-ramp bottlenecks, specifically analyzing the safety dynamics of mixed traffic streams. The findings indicate that CAVs enhance both the stability and safety of mixed traffic flows. The presence of trucks is associated with reduced stability values at similar CAVs penetration rates. In scenarios without trucks, CAVs can elevate traffic safety by 58.28%-71.28%, whereas in the presence of trucks, although the enhancement diminishes, safety levels can still improve by 48.67%-65.11%.

This paper investigates the rank reversal issue in the selection of new energy vehicle types, focusing on consumers aged 20 to 30. It employs both the AHP and the PCbHA methods to rank four types of the new energy vehicles — pure electric vehicles, plug-in hybrid electric vehicles, range-extended electric vehicles, and fuel cell vehicles, based on ten influential factors: purchase cost, maintenance cost, fuel and electricity cost, safety, passability, endurance, appearance, brand power, power, and space. To verify the effectiveness of the PCbHA method in addressing the rank reversal problem, one alternative option is removed, and the ranking is recalculated with subsequent analysis of the results. The study finds that rank reversals often stem from the closeness of alternative weights. Through sensitivity analysis, this research reveals the impact of endurance attribute weight on decision outcomes, indicating that when the endurance weight reaches 0.35, the ranking of pure electric vehicles and range-extended electric vehicles reverses.

Increasing labor income share is the core essence of optimizing the income distribution pattern and achieving common prosperity. This paper examines the impact of local fiscal pressure on the labor income share of enterprises using difference-in-difference method with a quasi-natural experiment of the nation-wide abolition of agriculture tax in 2005. We find that local fiscal pressure significantly reduces the share of labor income of enterprises. Moreover, we find that the negative relationship is more pronounced in small and medium-sized enterprises, regions with lower financial development, regions with lower fiscal self-sufficiency, and firms in labor-intensive industries. Further mechanism analysis shows that fiscal pressure leads local governments to raise tax and non-tax enforcement, increase debt raising, and thus intensify firms' financing constraints. Financing constraints firms cut human capital investment to smooth out fixed asset investment, which in turn leads to a decrease in firms' labor income share. This study not only enriches the research on local fiscal pressure and labor income share of enterprises, but also provides empirical evidence and policy implications on how to improve the primary distribution structure and achieve common prosperity.

China is currently undergoing a dynamic process of evolution in the government-business relationship, with a transition from old to new forms. A new type of government-business relationship that emphasizes "cordiality" and "cleanliness" has become the fundamental approach for governments at all levels to improve this relationship. Examining whether such a relationship can better promote entrepreneurial spirit and drive regional innovation and entrepreneurship is of great significance. This study employs national industrial and commercial enterprise registration data and the China cordial and clean government-business relationship index to construct an annual observation sample of Chinese cities at or above the prefecture level from 2016 to 2019, empirically testing the impact of cordial and clean government-business relationships on regional entrepreneurship levels and its mechanism. The findings are as follows: First, a higher degree of cordial and clean government-business relationships leads to greater improvement in regional entrepreneurship levels. On average, a 10-point increase in the cordial and clean government-business relationship index corresponds to a 2.35% increase in the number of entrepreneurial enterprises in the city, with significant economic implications. Second, cordial and clean government-business relationships play a "cost-saving effect" and "income incentive effect" in empowering regional entrepreneurship and optimizing its structure. Third, the positive impact of cordial and clean government-business relationships on regional entrepreneurship levels is stronger in non-eastern regions, small-scale, and lower-level cities, indicating a "supporting the weak" effect. Fourth, the promoting effect of cordial and clean government-business relationships on regional entrepreneurship levels is stronger when their institutionalization in the region is higher and official media propaganda is stronger. Fifth, this study finds that cordial and clean government-business relationships can significantly improve the entrepreneurial probability of micro individuals, particularly in opportunity-based entrepreneurship, with a stronger empowering effect. In conclusion, the research findings not only enrich the literature on government-business relationships and regional entrepreneurship but also provide timely decision-making support for the current reform practice of building a new type of cordial and clean government-business relationship system in China.

By constructing a two-stage model of dynamic R&D investment of enterprises under own capital investment (OC), independent venture capital (IVC) financing, and corporate venture capital (CVC) financing, this study explores the influence of different financing modes on the dynamic R&D investment decision made by enterprises. The results show that, IVC financing always promotes R&D investment of enterprises in the second stage. It can also promote the R&D investment of enterprises in the first stage when the success probability of R&D projects is higher than a certain threshold. CVC financing can promote R&D investment of enterprises in the first stage when the success probability of R&D project is higher than a certain threshold and the strategic synergy of the R&D project for the CVC parent company is greater than the risk-free project. Compared with IVC, CVC can better promote the R&D investment of enterprises in the first stage, but if the result of the R&D project in the first stage is low quality, IVC can better promote the R&D investment of enterprise in the second stage.

This paper empirically examines the relationship between customer enterprises' ESG rating and supplier enterprises' green innovation, using all A-share listed companies from 2009 to 2022 as the research samples. The empirical results show that customer enterprises' ESG rating can positively influence supplier enterprises' green innovation; customer enterprises reduce the amount of funds absorbed from supplier enterprises, encourage supplier enterprises to increase innovation investment, and improve the managers' green cognition of supplier enterprises are the three mechanisms of promoting supplier enterprises' green innovation through customer enterprises' ESG rating; supplier enterprises' market power will negatively moderate the positive relationship between customer enterprises' ESG rating and supplier enterprises' green innovation. Heterogeneity analysis shows that the positive influence of customer enterprises' ESG rating on supplier enterprises' green innovation is more significant both when customer enterprises are under greater legitimacy pressure or have more substantive ESG practices, and when supplier enterprises lack credibility or face stricter environmental regulations. Further research shows that customer enterprises' ESG rating is more effective in promoting supplier enterprises' green innovation when the uncertainty of ESG rating results is low, and that supplier enterprises' green innovation contributes not only to supplier enterprises' own ESG rating but also to supplier enterprises' total factor productivity. This paper highlights the spillover effects of ESG rating pressure from the perspective of supplier enterprises, and provides empirical evidence and management enlightenments for effectively promoting enterprise green innovation.

The inequality between economic trade benefits and carbon emission costs among cities not only reveals the inherent interaction of cities' carbon decoupling processes among but also reflects the heterogeneity of their industrial division and economic structure. Existing carbon-decoupling index models insufficiently consider the impact of intercity carbon inequality, thereby leading to the ambiguity in the development trend of industrial carbon emissions with the urban carbon decoupling progress. Therefore, this study combines input-output analysis framework and complex network methods to construct a novel carbon decoupling index model considering carbon inequality relationships. Subsequently, this study empirically analyzes the grouped development patterns of carbon emissions in various industries under China's urban carbon decoupling process in 2012, 2015, and 2017, with a focus on 309 cities. The results indicate that the distribution of carbon inequality between Chinese cities exhibits a clear demand-side concentration feature. In recent years, the carbon decoupling benefits obtained by major Chinese cities through carbon inequality have continuously increased, becoming a crucial factor in achieving low-carbon development in their production activities. With the advancement of carbon decoupling, the interdependence of city-level industrial carbon emissions demonstrates a "U"-shaped development trend, initially decoupling and then recoupling. This study contributes to enhancing the understanding of the impact of cross-regional economic environmental connections on urban carbon decoupling development. It can help city managers at different development stages formulate differentiated policy combinations to achieve stable economic development and multi-industry collaborative emissions reduction.

The pollution control strategy of enterprise is not only affected by perceived pressures, but also closely related to enterprise's own resources that can be allocated. Therefore, from the perspective of configuration, this paper analyzes what kind of pressure perception and social capital combination can promote enterprises to adopt high-level environmental pollution control strategies from the perspective of configuration. The results showed that: 1) The effective pollution control mode of enterprises can be mainly divided into three categories: Pollution control path led by government supervision and social relations, the pollution control path driven by social relations led by capital, and the pollution control path driven by social relations led by R&D. 2) "Government pressure" and "R&D platform participation" are important factors that promote enterprises to adopt high-level pollution control. The group of high-politically embedded enterprises, small-sized enterprises, and enterprises in the industrial undertaking zone has a configuration with "government pressure" as the core condition, while the group of low-politically embedded enterprises, large-sized enterprises, and enterprises in the industrial relocation zone has a configuration with "R&D platform participation" as the core condition; 3) Good social relations among entrepreneurs are the basic guarantee for all kinds of enterprises to achieve high-level pollution control. In Chinese manufacturing business environment, fully mobilizing the role of entrepreneurs' social relations is conducive to promoting the high-level pollution control process of various enterprises. The results of this study are helpful to clarify the driving path and mechanism of high-level pollution control behavior of manufacturing enterprises, provide a theoretical basis for enterprises to achieve the phenomenon of "different paths to the same end" of high-level pollution control, and provide scientific guidance for fighting the tough battle of pollution prevention.

The environmental benefits of transportation have always been a focus of environmental economics. However, due to data availability and endogeneity issues, there is limited research on the impact of urban rail transit on ambient air quality at a national level. This study innovatively matches data on subway segments in 39 Chinese cities with a high-resolution daily PM_2.5 dataset, and employs a multi-period difference-in-differences approach to empirically analyze the causal relationship between the opening of new subway segments and local air pollution. The study finds that the opening of new subway segments significantly improves ambient air quality, leading to a reduction of approximately 2.37µg/m³ in PM_2.5 concentration within a 500-meter radius. Moreover, this pollution reduction effect is more pronounced on weekdays in cities with high per capita GDP, heavy air pollution levels, and large-scale coal-fired power plants, or on the initial subway section of a certain line, as well as in the initial segments of certain subway lines. This impact may be attributed to the diversion of public transportation passenger flows from surface transportation, resulting in a decrease in vehicular traffic on the surface. Furthermore, the study identifies network effects of subway openings, as the opening of new segments improves air quality around existing subway segments, and the degree of improvement is higher for existing segments that have closer connections to the newly opened segments. The estimation of health benefits indicates that the opening of subway segments reduces premature deaths caused by PM_2.5, bringing about an estimated economic value of approximately 11.4 to 28.5 billion yuan in terms of improved health for the population covered by the subway. The findings of this study provide theoretical and empirical evidence to better understand the causal relationship between subway construction and air quality, and offer insights for the scientific planning and development of urban rail transit, contributing to urban environmental governance and sustainable development.

Under the dual-carbon goal, it is a foundation for achieving low-carbon transition of supply and demand about how demand promotes enterprises' green production and the cost transfer of green production inhibits consumption. we nest the consumption value into the utility function of consumers, and then develop enterprises' green production decision-making models where the boundary conditions of low-carbon production are identified and the inhibitory mechanisms of cost transfer on consumption are examined. The results show that: Consumption value drives demand structure and enterprise production towards low-carbon, which can be strengthen by the dual-carbon goal; the relationship between carbon price and investment revenue plays a key role in enterprises' production paths, and there is a threshold value for investment revenue; if the revenue exceeds the threshold, carbon price stimulates enterprises to invest in emission reduction, but it has a negative impact on output, and its impact degree will be weakened with the implementation of carbon-abating goals, forming an inverted U-shaped trend; the cost transfer leads to demand structure to be low-carbon upgrade.

Grey market is formed in the arbitrage opportunity brought by the price difference of the same type of merchandise in different regions, which widely exists in the marketing cycle of brand products. At the same time, brand manufacturers strategically choose the release time of products in different channels to alleviate channel conflicts. For example, compared with the synchronization strategy (release of products in both high- and low-price market, simultaneously), the postponement strategy (release of products in high- and low-priced markets, successively) is one of the most common marketing strategies. Based on it, this paper considers a production and marketing system consisting of two manufacturers, one third-party speculator and customers. Aiming at the channel selection strategy of the powerful manufacturer in the grey market environment, the channel model decision models of the powerful manufacturer with synchronous strategy and postponement strategy are constructed respectively. By comparing and analyzing the influence of the grey market and the channel models on the decision-making of the powerful manufacturer. The results show that: 1) When the third-party participates in speculation in the grey market, it is the best channel strategy for the powerful manufacturer to adopt postponement strategy; 2) the pricing strategy of the powerful manufacturer depends on the channel model selection strategy; 3) the involvement of the third-party speculators in the grey market is closely related to the transfer costs, consumers' valuation of products and the degree of substitution of products in different markets. In addition, we also analyzes the influence of the different channel strategies of the powerful manufacturer on the low-price market product pricing of the general manufacturer.

This paper considers an agricultural supply chain consisting of a downstream company and a farmer in the presence of the latter having the adulteration behavior of agricultural raw-material products, in which the downstream company promises to purchase all the effective agricultural raw-material products of the farmer with a procurement price, and the latter determines the amount of brand and counterfeit agricultural raw-material products. Then, we develop a stylized model under the settings with non-blockchain-based (traditional technology-based) traceability and blockchain-based traceability. Our results show that under the blockchain-based traceability, the downstream company can restrain the farmer' enthusiasm to produce the counterfeit raw-material products via the procurement price. In addition, under some mild conditions, the blockchain-based traceability has significantly impact on inducing the farmer to produce less counterfeit raw-material products, and makes the farmer produce more brand raw-material products. However, it is interesting to show that blind pursuit of curbing adulteration by the farmer does not necessarily bring more profits to all the parties. Further, we find that under some cases, choosing the blockchain-based traceability can reach a "win-win" situation for the two parties. Moreover, the blockchain-based traceability does not always cause the company to offer a higher retail price of the final agricultural products, and consequently, consumer surplus is not always improved.

Identifying key nodes in emergency logistics networks contributes to the deployment of maintenance measures, ensuring the stability of emergency logistics networks. We introduce a cooperative game theory approach to identify key nodes in the emergency logistics networks. By defining the feasible coalition and coalition utility in the emergency logistics network, a single-valued solution in the emergency logistics network is proposed based on the Shapley value, which is used as the measurement method of node centrality in the emergency logistics network. Firstly, it is proved that the centrality of nodes calculated by this single-valued solution is mutually independent among different independent emergency logistics networks, which reduces the computational complexity. Secondly, the feasible component efficiency and road fairness are defined, and it is proved that this solution is the unique single-valued solution satisfying the two axioms mentioned above. Finally, the rationality and robustness are illustrated by numerical examples. Taking the single-valued solution as the measurement method of node centrality in an emergency logistics network can not only fully consider the topological characteristics of the network, but also take into account the non-network characteristics of nodes, which provides strong theoretical support for the optimization and stability maintenance of emergency logistics networks.

Since innovations rely on the combination and recombination of knowledge elements that are caused by organizational collaborations, innovations are dual embedded in the interdepend networks constructed by knowledge networks and collaboration networks. Accordingly, it has significant implications for maintaining the continuous operation of interdepend networks and preventing the neck problem of innovation by unveiling the destruction of combinatorial relationships of knowledge elements within knowledge networks and the cascade effects of organizational cooperative ties in collaboration networks caused by the failure of knowledge elements based on technical sanction. On the basis of constructing the three-dimensional properties structure ofof the knowledge element, we divide the failure strategies of the knowledge element into six categories. By using the data of the new generation of information technologies industry of China from 2000-2021, we comprehensively reveal the impact of knowledge elements failure on interdepend networks in accordance with the change of resilience of structure and function. Furthermore, drawing on the mechanism of knowledge replacement, we develop the stability strategies of network resilience and explore the ways to guarantee the operation of interdepend networks via the strategies practice simulation. We find that: 1) In the knowledge networks, different knowledge elements were combined following the existing innovation logic. Although knowledge elements were connected as scattered clusters, their contents emerged with the tendency of diversity. Knowledge elements that are characterized by relatively medium expertise but high relativeness can create serious neck problems of innovation. 2) In the collaboration networks, organizations had embedded their multiple knowledge elements into the knowledge networks, the cooperation of organizations was related to several knowledge elements and could not directly break by the failure of one knowledge element. 3) The strategies of the initial calculated-based and recalculated-based obtained similar effects. 4) Three essential ways can maintain the stability of interdepend networks. Including comprehensively combining the knowledge elements with medium-degree, adequately capturing the knowledge combinatorial opportunities that were provided by knowledge elements with medium in-degree, and continuously developing knowledge elements with fundamentality and high relativeness.

Trust relationship is the main tool to determine the importance of subject in group comprehensive evaluation. However, in the existing research, trust relationship mostly depends on emotional factors and is regarded as static and fixed. This can easily lead to the "trust risk" problem. How to avoid this problem and improve the reliability of trust relationship has become one of the important research contents in group evaluation. To solve this problem, this paper first constructs a trust relationship network based on Page Rank. A dynamic trust relationship measurement method is proposed, which considers affective trust and cognitive trust comprehensively, and realizes the dynamic calculation of the interaction process of trust relationship with groups. Further, a group consensus reaching method based on dynamic trust relationship is proposed to improve the efficiency of consensus reaching. On this basis, a group evaluation method considering the dynamic trust relationship of evaluation subjects is constructed. Finally, the validity and reliability of the method are demonstrated by an example of the selection of major investment projects.

Cross-efficiency analysis is an important extension of data envelopment analysis, which evaluates decision-making units by integrating both self-evaluation efficiency and peer-evaluation efficiency. Existing Cross-efficiency analysis methods mostly focus on obtaining efficiency results, without considering the relationship between self-evaluation and peer-evaluation efficiencies or the level of acceptance of peer-evaluation efficiency by decision-making units. This paper focuses on the cross-evaluation process and introduces social network analysis to construct a trust network between self-evaluation and peer-evaluation efficiencies, treating the cross-evaluation process as a group decision-making process within the evaluation system. Based on this, a cross-evaluation method based on consensus and trust relationships is proposed. First, the interval cross efficiency matrix is calculated using both benevolent and aggressive strategy. Then, the bidirectional trust network between decision-making units is constructed using social network analysis, which directly associates self-evaluation and peer-evaluation efficiencies. Second, based on the trust network, two indicators, namely recognition consistency and representativeness level, are proposed to assign corresponding weights to decision-making units. Third, a weighted cross-evaluation maximization model is established, and a consensus analysis is performed on the peer-evaluation efficiency values of each decision-making unit to obtain the final cross efficiency value that reflects the consistency and acceptance of peer-evaluation efficiency by decision-making units. Finally, a case study is used to demonstrate the feasibility and practicality of the proposed method.

In recent years, the emergence of customized buses has provided convenient services for commuters. However, changes in road conditions, attributed to factors such as traffic congestion, bad weather, and car accidents, have increased passenger travel time and significantly impacted the punctuality of bus operations, diminishing the appeal of customized bus services. This study addresses a new commuting customized bus network design problem. It takes into account uncertain road conditions, passenger separation, and the presence of heterogeneous vehicle models. We first formulate it into a multi-objective robust optimization model based on the minimum-maximum regret criterion. This model optimizes the selection of heterogeneous vehicle models, fleet routes, travel times, and passenger assignments with the aim of minimizing both passenger travel costs and bus system operating costs. Then, to effectively solve the model, we propose a multi-objective hybrid adaptive large neighborhood search algorithm. This algorithm incorporates problem-specific characteristics, introduces a passenger separation destruction operator based on these characteristics, and integrates a crossover operator to enhance the algorithm's optimization capability. Results of extensive numerical experimental results indicate that: i) The consideration of passenger separation and heterogeneous vehicles leads to improved vehicle resource utilization efficiency. Considering passenger separation results in an average time savings of 2.44% and a reduction of 11.46% in operating costs. Similarly, the inclusion of heterogeneous vehicle models yields an average time savings of 1.74% and a decrease in operating cost by 24.95%. ii) Compared with the traditional large neighborhood search algorithm and NSGA-II, our proposed algorithm consistently produces higher-quality Pareto solutions. iii) when compared to solutions obtained under deterministic road conditions, the solutions obtained in the presence of uncertainty in road conditions achieve an average reduction of 1.42% in passenger travel cost and a decrease of 18.24% in operating cost. This confirms the necessity of conducting robust commuting customized bus network design research under uncertain road conditions.

Automated Guided Vehicles (AGVs) are a key factor in determining the operational efficiency of automated container terminals. AGVs often face travel time uncertainties due to congestion, conflicts, weather, and other factors, resulting in quay and yard crane delays that reduce operational efficiency. In this paper, we introduce a distributionally robust method based on Wasserstein distance using historical travel data. We construct a data-driven model to manage the risk of waiting at quay cranes and yard cranes and to determine the task allocation scheme for AGVs with minimal operating costs. To solve the model, we first use conditional value-at-risk to approximate the distributionally robust chance constraints. Its closed-form solution is obtained by duality theory and analytic transformation, which are then transformed into a practical form. Secondly, exact branch-and-cut algorithms, along with heuristic algorithms and corresponding acceleration strategies, are designed for problems of varying sizes in automated container terminals. Experimental results demonstrate that the distributionally robust optimization method effectively captures the uncertainties in AGV travel times. Compared to the sample average approximation, the model and solution proposed in this paper can reduce the risk of waiting at quay cranes and yard cranes by 60%, significantly enhancing the robustness of the allocation scheme.

Shaping command system is an important measure to improve quality and efficiency of organizing test and training activities. The scientific design, reasonable construction and effective use of the command information system are the key to support the construction of the test and training command system. based on the organization and command capacity needs of different types and different styles of test and training tasks, taking the combat command system of the US carrier strike group as the research object, centering on the construction problem of the marine test and training system, the description method based on Unified Architecture Frame is adopted to design the architecture of the marine test and training command information system. Elaborate on system design content, ideas and technical approach from multiple perspectives such as capability classification, business activities, resource structure and service function. At the same time, combined with the tactical application characteristics of typical combat scenarios, based on the principle of digital and physical methods combination and equivalent function simulation, research the power grouping method of the marine test and training activities and mission support mode of the command information system. The relevant achievements can provide reference and inspiration for the research on the construction and application problem of military information systems in the field of test and training.

In this study, the impact of renewable energy technological innovation (RETI) on city-level energy transition and its mechanisms are investigated in 279 Chinese cities from 2005 to 2019. The results show that, on the whole, RETI has significantly contributed to city-level energy transition, but the real contribution was made after 2011, and the contribution to the energy transition was significantly stronger in cities located in the Southwest region than in other cities. In addition, RETI has a significant contribution to the energy transition in cities with a relative abundance of renewable energy and a relative scarcity of fossil energy, but the opposite is true for cities with a relative scarcity of renewable energy and a relative abundance of fossil energy. These findings hold after circumventing sample selection bias, endogeneity, and special sample interventions. Further studies found that reducing energy costs, increasing the economic penetration of renewable energy, and reducing pollution emissions are the key channels through which RETI drives city-level energy transition. This study is the first to reveal the mechanism of RETI on energy transition at the city level, which helps to promote the implementation of national and provincial energy development strategies to the city level, and provides strong support to promote city-level energy transition with high quality.

Nowadays, although global economic rebalancing has made great progress, seeking inclusive growth is still an important issue to be solved. We propose a global value chain-based accounting framework to quantify the global trend of inclusive growth, and further use structural decomposition analysis (SDA) to explore the driving factors accounting for the change of inclusive growth. Finally, considering the deviation between domestic value-added and national income, we also conduct a comparative study on the inclusiveness of global economic growth under the territorial and ownership calibers. We find that the global income gap (especially south-north gap) under the territorial and ownership calibers shows a downward trend, indicating that the inclusive level of global economic growth is improving. However, it should be alert that the global economic inclusive growth measured by national income shows sign of deterioration at the end of the analysis period. Further analysis shows that the input structure, final demand and population jointly contribute to the reduction of the global income gap. Among them, the derivation of value-added ratio between North and South has widened the global income gap, which is offset by the increasingly strengthened intermediate and final trade linkages between North and South. In addition, the expansion of final demand scale is also an important factor to narrow the global income gap. Our paper sheds light on the global economic inclusive growth and its driving factors, and also on how to boost inclusive growth.

This article establishes a multi-period DID model using the phased implementation of state-owned capital authorization management reform, and confirms that the reform of state-owned capital authorization management is beneficial for alleviating the tax burden on state-owned enterprises. In mechanism testing, the reform of authorized operations takes the implementation of market-oriented incentives and strengthening information transparency as a two-way path, which affects the tax burden of state-owned enterprises by increasing their profit motivation and increasing the difficulty of tax deviation. The heterogeneity analysis focusing on equity structure found that the above relationship is more significant in central enterprises, state-owned enterprises with high equity concentration, low equity balance and high regional economic development level. The research conclusion is conducive to expanding the policy effectiveness evaluation dimensions of authorized operation reform, providing direct empirical evidence for reducing government intervention and alleviating policy burden in authorized operation reform, and making incremental contributions to deepening theoretical exploration of authorized operation reform and supplementing factors affecting corporate tax burden.

This paper presents in organized form a number of results that have appeared in the literature in the last two decades, concerning the design of control laws for multi-input multi-output nonlinear systems, with emphasis on the problem of stabilizing an equilibrium, and addresses, at a broad level generality, systems that are invertible from an input-output viewpoint.

In this article, the authors investigate and derive adaptive strategies for the pursuit-evasion problem where both players lack knowledge of the opponent’s cost function parameters, which has rarely been investigated in the existing literature. To address this challenge, the authors consider a basic information structure that assumes that the evader will use an adaptive learning algorithm to estimate the unknown parameters and update its adaptive strategy piecewise, whereas the pursuer will adopt a strategy based on the opponent’s choices at each time instant. By employing methods of diminishing excitation and random switching, the authors establish certain excitation conditions for signals of the closed-loop game system to ensure the strong consistency of the parameter estimates. Moreover, the authors demonstrate that the adaptive game system can asymptotically reach the Nash equilibrium, which is the same equilibrium achieved in pursuit-evasion games when all game parameters are known.

For $k$-valued (control) networks, two types of (control) invariant subspaces are proposed, namely, the state-invariant and dual-invariant subspaces, which are subspaces of the state space and dual space, respectively. Algorithms are presented to check whether a dual subspace is dual-(control) invariant, and to construct state feedback controls}. The bearing space of $k$-valued (control) networks is introduced. Using the structure of the bearing space, the universal invariant subspace is presented, which is independent of the dynamics of particular networks. Finally, the relation between the state-invariant subspaces and the dual-invariant subspaces of a network is investigated. A duality property shows that if a dual subspace is invariant, then its perpendicular state subspace is also invariant, and vice versa.

In recent years, there have been more frequent disasters occurred in China, which pose significant threats to the lives and property of the people. To cope with the increasing complexity and severity of disasters, decision-makers need to store and dispatch emergency supplies rationally based on the real situation. Current studies on regional dispatch considering multiple warehouses and demand points are insufficient, and the problems such as ‘who/how/how much to dispatch’ have not been well-answered. To solve these problems, this paper proposes three regional dispatching strategies (including strict administrative hierarchy supply dispatch, cross-administrative hierarchy supply dispatch and free and nearest supply dispatch strategies) based on a comprehensive summary of relevant case studies, then builds a multi-agent simulation model based on deprivation cost. A simulation experiment is conducted in Mengcheng County, Bozhou City, Anhui Province, and the result shows that when the regional demand is large in a short time, the free proximity strategy can minimize the total social logistics cost. On the contrary, when the regional demand is small, the differences of the total social cost among three strategies are small. In conclusion, our research suggests that, when facing severe disasters and catastrophes, governments should cooperate and coordinate on the dispatching of relief supplies. However, when facing normal disasters without the risk of life, the demand can be satisfied with the strict administrative strategy.

Identifying key segments within road networks is crucial for selecting repair objectives and optimizing repair sequences during the post-disaster recovery phase. Traditional methods for identifying key segments have not fully explored the interactions between multiple segments, particularly the significance of studying road network vulnerability under simultaneous disruptions of multiple links. To tackle this issue, this study introduces a machine learning model called transportation graph attention networks for criticality analysis (TGAT) to identify key road segments when facing multiple disruptions. This model is trained on data samples that include scenarios of multiple segment failures, utilizing the graph attention network to evaluate the influence weights between segments and calculating the criticality of each segment based on these weights. The model, trained using mean squared error as the loss function, is capable of identifying segments that play a crucial role in the performance of the road network. Taking the Kunshan City road network as an example, this paper compares the effectiveness of the TGAT method with three other methods:Degree centrality, weighted betweenness centrality, and eigenvector centrality, in optimizing repair sequences during the post-recovery phase. Experimental results indicate that the TGAT method is more effective in identifying key segments within the road network compared to the other three methods, and the repair sequence optimized using TGAT further enhances the repair performance of the road network.

Currently, one of the main challenges in practical modeling lies in the fact that training and testing data come from different distributions. Stable learning addresses this issue by decorrelating all covariates through sample reweighting, thereby achieving stable predictive performance. While machine learning methods such as stable learning show good results in experiments, there are still theoretical gaps, such as the lack of metrics for model stability under testing data and explanations for why stable learning maintains stable predictions across multiple environments. This paper proposes a new metric of stability, compares stable learning methods with ordinary least squares and explores the reasons why stable learning maintains stability across multiple environments. Finally, the paper validates the theory through simulated experiments. This research contributes to refining the theory of stability in stable learning, enhancing the understanding of stability in stable learning, and guiding the selection of practical modeling methods.

Enhancing human capital and attaining high-quality economic progress are fundamental imperatives for the comprehensive construction of a modern socialist nation. Against this backdrop, our study employs data from Chinese A-share listed companies spanning 2011 to 2019 to explore the influence of local economic growth targets on corporate human capital structure and its underlying mechanisms. Our findings reveal that heightened local economic growth targets tend to hinder the optimization and advancement of corporate human capital structures, consequently diminishing enterprises' total factor productivity. Further analysis indicates that these effects are more pronounced in regions exhibiting greater governmental intervention capacity and willingness, companies facing severe financing constraints, and industries characterized by non-high-tech and labor-intensive sectors. Mechanistically, elevated economic growth targets prompt local government officials to skew fiscal expenditure structures, curtail public service outlays, steer enterprises towards increased fixed asset investments at the expense of innovation expenditures, thereby impeding the optimization and enhancement of human capital structures. This research not only contributes to the body of literature on economic growth targets and corporate human capital but also furnishes empirical insights to facilitate the optimization of official assessment systems and the realization of high-quality economic development objectives.