First-order methods play a central role in large-scale optimization due to their low per-iteration cost and scalability. Early research primarily focused on unconstrained problems or optimization problems with simple constraints. Motivated by the rapid growth of constrained machine learning and engineering applications, recent years have witnessed increasing interest in the design and theoretical analysis of first-order methods for functional constrained optimization problems, where constraints are defined through complex functions. This survey provides a systematic review of first-order algorithms for solving optimization problems with functional constraints. We cover a broad range of problem settings, including deterministic linearly constrained convex problems, deterministic nonlinearly constrained convex problems, problems with nonconvex objectives and convex constraints, fully nonconvex constrained problems, as well as stochastic optimization problems with both convex and nonconvex structures. For each class of problems, we summarize representative algorithms and their associated complexity guarantees, focusing on the number of iterations required to obtain either an $\epsilon$-optimal solution or an $\epsilon$-KKT point. By unifying existing results across different problem structures and algorithmic frameworks, this survey highlights current theoretical limits, identifies key assumptions such as constraint qualifications, and outlines promising directions for future research.

This paper presents a primal-dual prediction-correction (PD-PC) method for solving linearly constrained time-varying convex optimization problems, which frequently arise in control, signal processing, and online learning applications. The proposed method establishes a novel integration of primal-dual gradient dynamics with a discrete-time prediction-correction structure, specifically designed for problems with time-dependent linear constraints. A tunable memory parameter is introduced in the prediction phase to perform linear extrapolation using past iterates, enabling a flexible trade-off between the amount of historical information stored and the computational cost of correction. In the correction phase, primal and dual variables are updated via gradient descent-ascent iterations, thus maintaining the computational efficiency of a first-order method without requiring Hessian or high-order derivative computations. Theoretical analysis shows that the method achieves $\mathcal{O}(h^2)$ asymptotic tracking accuracy for both primal and dual variables, matching the state-of-the-art performance among first-order methods even in unconstrained settings. Numerical experiments on problems with both time-invariant and time-varying constraints validate the theoretical findings and demonstrate the method's effectiveness.

The cooperative output regulation problem for unknown linear multi-agent systems has been studied by both policy-iteration method and value-iteration method via distributed internal model approach. However, the original results were limited to single-input single-output linear multi-agent systems under the assumption that the communication digraph is acyclic. Recently, the authors have extended the existing result to multi-input multi-output linear multi-agent systems over a general static and connected digraph by a more efficient value-iteration method. Since the policy-iteration method is simpler and has a much faster convergence rate than the value-iteration method, in this paper, the authors further apply the policy-iteration method to the cooperative output regulation problem of unknown multi-input multi-output multi-agent systems over a general static and connected digraph. Compared with the existing policy-iteration method, the proposed policy-iteration approach not only drastically reduces the computational cost, but also significantly weakens the solvability conditions. Moreover, by introducing a virtual exosystem, the proposed policy-iteration approach eliminates the need for employing a distributed observer. As a result, the data collection can start at any time, and the computing cost for each agent is also reduced.

Using ESG rating data of Sino-Securities Index Information Service from 2009 to 2020, this paper examines the impact of listed companies' ESG rating on the level of stock price, financial and operational risk in the next year. The study finds that better ESG rating has a significant inhibitory effect on all three risk levels of enterprises in the next year. Specifically, for the risk of stock price crash, ESG rating higher than the benchmark level, as a strong market signal, has a more significant reduction in the risk level of stock price crash. The trading volume of individual stocks, which reflects the attention of investors, has an intermediary effect on ESG to reduce the risk of enterprise stock price crash. ESG of large-scale enterprises that occupy an important position in the market and attract more attention from investors has a stronger inhibitory effect on the risk of stock price crash; In addition, the negative relationship between ESG and the risk of stock price crash is more significant after the implementation of the “Environmental Protection Law”. For financial risk, ESG has a marginal diminishing effect on reducing corporate financial risk, and the improvement of ESG rating from low to medium can improve the level of corporate financial risk. At the same time, enterprises' voluntary disclosure of non-financial information could strengthen the inhibitory effect of ESG on financial risks. For operational risk, ESG rating has a marginal diminishing effect on reducing operational risk; At the same time, the nature of equity has a moderating effect on the reduction of operating risks by ESG rating. Compared with private enterprises, ESG has a stronger inhibition effect on the operation risk of state-owned enterprises. Finally, the sub-sample heterogeneity test results based on the length of enterprise life in this paper show that the inhibitory effect of ESG rating on risk is stronger for enterprises with a long establishment age, but weaker for enterprises with a short establishment age.

Based on investor behavior data from the Eastmoney Guba, this paper employs the effective transfer entropy method to investigate whether the mediating role of stock forums in the stock market better represents investor attention or sentiment. The findings reveal that: 1) Investor attention in stock forums conveys more valuable information flows to the stock market, providing a more accurate representation of investor behavior; 2) The net comment volume, as a key indicator of investor attention, exhibits strong information flow correlations with the stock market, significantly influencing stock volatility, with its effect extending up to one month into the future; 3) Across posts with different sentiments (positive, neutral, and negative), investor attention consistently transmits information flows that predict stock volatility, while investor sentiment lacks significant incremental value in transmitting information flows to the market. This study further underscores the mediating role of stock forums in capturing investor attention, particularly highlighting the unique predictive value of net comment volume, offering investors a novel perspective on analyzing online stock forum data. Future research should focus on integrating more comprehensive datasets and conducting in-depth explorations to enhance the accuracy of market forecasts and the scientific rigor of investment decision-making.

Focusing on the supply side of vehicle charging service, this study proposes two differential game models for charging pile operation decision-making, with the government, operator, and third-party platform (hereinafter referred to as the platform) as the participants. The two differential game models are decentralized (i.e., operators and platforms aim to maximize their own interests) and centralized (i.e., operators and platforms aim to maximize the overall interests of both). The results show that under the equilibrium state with fixed revenue distribution ratios between operator and platform, compared with the decentralized decision-making mode, the centralized decision-making mode can improve the efforts of operator and platform, service quality and social benefit. When the platform is relatively weak and has a lower share of revenue, adopting the centralized decision-making mode can achieve a Pareto improvement in the revenues of both operator and platform; conversely, when the platform is relatively strong and has a higher share of revenue, adopting the decentralized decision-making mode can increase the revenues of both operator and platform. This indicates that as platform develops from weak to strong, the decision-making mode of the charging service market may shift from centralized to decentralized. At this time, the proportion of government policy support will increase, and social benefit and service quality may decrease.

In this paper, the stability and robustness of a recurrent neural network (RNN) controller with saturation function and ReLU function as activation function are analyzed for first-order linear uncertain systems. The necessary conditions for the closed-loop system to converge to the non-zero target and the suffcient conditions for exponential stability are provided. The quantitative relationships between the recurrent neural network controller’s parameters and the robustness of the initial state value, the target value and the unknown parameter of the plants are analyzed. The analysis results show that the RNN controllers with ReLU function as the activation function have stronger robustness.

The registration-based system represents a pivotal reform in China’s capital market development. The inquiry system reform aims to transfer pricing authority more substantially to market participants and enhance IPO pricing effciency. Consequently, systematic research evaluating IPO pricing effciency and the effects of inquiry system reforms under the registration-based framework have attracted considerable scholarly attention. This paper employs a bilateral stochastic frontier model to measure IPO pricing effciency across 2365 listed companies in China’s A-share market from 2016 to 2023, and empirically verifies the systematic impact of inquiry system reforms on IPO pricing under the registration-based system. Research findings indicate that during 2016–2023, underpricing effects dominated overpricing effects in A-share market initial offerings, with overall pricing 7.06% below reasonable levels, exhibiting distinct characteristics across different boards, years, ownership structures, and break-even status. The inquiry system reform generally elevated initial offering prices, primarily driven by the distinctive characteristics of the STAR Market, while other boards demonstrated declining trends. During the registration-based system expansion phase, significant competitive dynamics emerged between the STAR Market and ChiNext Board, while reform effects on the main board remained limited. Furthermore, the study identifies two critical transmission pathways explaining these impacts: The number of inquiry institutions and the effectiveness of price quotations. Based on these conclusions, this paper proposes targeted recommendations for regulatory authorities to guide future inquiry system reforms.

Sparsity is a crucial assumption in high-dimensional modeling, as only a small subset of variables typically exert significant influence on the response in high-dimensional regression analysis. Based on varying coeffcient models, this paper proposes a varying sparse coeffcient mixed-effects quantile regression (VSCMEQ) model for longitudinal data, which incorporates variable selection. In this model, the coeffcient functions are estimated using B-splines, and penalties are imposed on both random and fixed effects to investigate the influence of relevant important factors, including varying effects and constant effects. Finally, the proposed method is applied to the Primary Biliary Cirrhosis (PBC) dataset to analyze disease progression, identifying the influence of significant factors on disease progression (biomarkers) at different quantiles.

This paper considers the supply interruption of a supply chain composed by two suppliers and one retailer, where one supplier is an integrated supply and marketing supplier and the other supplier is a pure supplier, and the retailer makes replenishment from the latter supplier and makes and emergency order from the former when the supply is interrupted. For this system, we mainly investigate the retail price decision and the emergency replenishment from the supply and retailing integrated supplier in the case of a supply interruption with a random end time from the second supplier. Based on maximizing the benefits of each member in the supply chain, we establish an optimization model, and its solution is obtained via a theoretical analysis which gives the optimal decision for each member of the supply chain. Some numerical experiments are made which give the impact analysis of main parameters on the optimal decision of each member of the supply chain and their benefits for the supply interruption period.

Trend filtering is a widely used method for extracting long-term trends and eliminating short-term noise from time series data. In order to accurately capture the global change pattern and local fluctuation of the potential trend, this paper proposes the generalized trend filtering model with composite $\ell_0$ constraint (L0CTF) based on the primitive function representing sparsity, and the optimality theory is analyzed. However, solving the L0CTF model is a challenging task because of the combinatorial property and indivisibility of the composite $\ell_0$ function. Therefore, based on the properties of composite $\ell_0$ function, this paper reformulates the L0CTF model as a mixed integer programming problem with special ordered sets of type 1 and analyzes its equivalence with L0CTF in the sense of global optimal solution. Finally, experimental results on simulated and real data sets show that the proposed method is superior to some mainstream trend filtering methods in extracting potential trends.

In engineering practice, exact failure times of individual components are generally not available. In contrast, only the number of component failures and the system’s cumulative operating time are known, which leads to the aggregate lifetime data. Inference of lifetime distributions based on the aggregate lifetime data is of great challenge. This paper proposes a moment-based point estimation method, and uses the bias-corrected Bootstrap method to construct confidence intervals for quantities of interest. The maximum likelihood method needs the likelihood function of the aggregate lifetime data, however, it is only applicable to a few distributions that have the closure property with respect to the operation of convolution. Differently, the proposed method does not utilize the likelihood function, thus it applies to more distributions. Finally, both the simulation study and the real data analysis are performed for demonstration and illustration.

This paper aims to investigate some recent progress on inverse source problems of timeharmonic wave equations and establish the stability in general cases. For scattering models of deterministic and stochastic wave equations, we show the methodology to obtain stability and summarize existing theoretical and numerical results.

In order to generate structured representations of conflict events, and identify the logical relations of events, the key events and event evolution patterns, this paper proposes a conflict event ontology models and an automated construction framework of event-centric conflict knowledge graph. Graph neural network incorporating attention mechanism and dependent syntactic analysis are leveraged to solve long-range dependencies, and RoBERTa-based fine-tuning is employed to extract the implicit event relations. The results show that both algorithms of event and event relation detection in this paper outperform the comparing algorithms. Additionally, the MPCCN algorithm identifies key events and critical paths that influence the development of conflict events. The event-centric conflict knowledge graph not only can be used to identify the key events and evolution paths of conflict events, but also provides multi-level analysis of conflict events, and improves the comprehensiveness and scientificity of decision-making.

The efficient and accurate estimation of sensitivity distribution parameters and quantiles are crucial for the design and evaluation of the reliability of pyrotechnic products. The approach developed in this paper employs a Bayesian framework to establish a semiparametric generalized linear model for sensitivity data, using the Hamiltonian Monte Carlo algorithm for posterior inference. Within this framework, the deviance information criterion and the logarithm of the pseudo-marginal likelihood are used in a data-driven manner to select the optimal model. Extensive simulation comparisons demonstrate that the proposed method can accurately estimate sensitivity distribution parameters in the case of small sample sizes. Finally, the new method is applied to two real datasets, validating its effectiveness. The new method provides an alternative and complementary modeling tool for the analysis of sensitivity data.

Duadic codes are an important class of cyclic codes. It is interesting to construct duadic codes whose minimum distance has the square-root lower bound. In this paper, we propose two construction methods of odd-like duadic codes whose minimum distance has the square-root lower bound. Two classes of odd-like duadic codes with the square-root lower bound on the minimum distance are obtained.

The authors extend the marginal coordinate test for predictor contribution (Cook, 2004) to the case with multivariate responses. Instead of explicitly specifying the link functions between the responses and the predictors, an asymptotic test is proposed under the normality assumption of the predictors as well as an asymmetry assumption about the unknown regression mean function. When these assumptions are violated, the asymptotic test with elliptical trimming and clustering is still valid with desirable numerical performances.

This paper examines whether the parametric regression model is correctly specified for both source and target data and whether the regression pattern in the source domain aligns with that of the target domain. This evaluation is a critical prerequisite for applying model-based transfer learning methods under covariate shift assumptions. Traditional regression model checks and two-sample regression tests are insufficient to address this issue. To overcome these limitations, the authors propose a novel adaptive-to-regression test statistic that is asymptotically distribution-free. Under the null hypothesis, the test follows a chi-square weak limit, preserving the significance level and enabling critical value determination without resampling techniques. Additionally, the authors systematically analyze the test’s power performance, highlighting its sensitivity to different sub-local alternatives that deviate from the null hypothesis. Numerical studies, including simulations, assess finite-sample performance, and a real-world data example is provided for illustration.

Hydraulic actuators are widely used in industrial control systems, where precise displacement control is critical to system performance. Traditional physical modeling methods struggle to accurately capture the nonlinear and time-series characteristics of hydraulic actuators, limiting their application in complex environments. This paper proposes a displacement modeling method for hydraulic actuators based on a long short-term memory (LSTM) neural network. By collecting time-series data of voltage input and displacement output, an LSTM network is employed to characterize the dynamic behavior of hydraulic actuators. The LSTM network effectively captures long-term dependencies in the data, adapting to the nonlinear time-series properties of hydraulic systems. During model training, the mean squared error is used as the optimization objective, and the effectiveness of the model is validated through experiments. The experimental results demonstrate that, compared to traditional methods, the LSTM network achieves lower prediction errors on the validation set, exhibiting stronger modeling capabilities and higher accuracy.

The national independent innovation demonstration zone (NIIDZ), as an important engine leading innovative development, takes institutional and policy reforms as a starting point to radiate and drive the coordinated development of surrounding regions. The gradual improvement of the high-speed rail (HSR) network has opened up a new pattern for the “ual circulation” and expanded the scope of the NIIDZ's innovation spillover effects. Based on data of HSR city pairs from 2008 to 2019 in China, this paper examines the impacts and mechanisms of the improvement in innovation levels of ordinary cities after the opening of HSR connected to NIIDZs by applying a staggered DID model. The empirical results are as follows. Firstly, the opening of HSR connected to NIDDZs significantly improves the innovation levels of ordinary cities. Secondly, the innovation spillover effects are more pronounced for cities in the eastern region, cities with a better innovation environment, and large-scale cities. Thirdly, the innovation spillover effects are realized by utilizing innovation endowment, government-guided innovation and demonstration driving effects. This paper provides empirical evidence and policy insights for innovation-driven development in the context of HSR network. It optimizes the spatial allocation of innovation resources and accelerates the development of new quality productive forces, achieving high-quality economic development.

Multi-source data are commonly encountered nowadays. The analysis of multi-source data is important for unleashing the data potential and realizing data value. However, many multi-source data still exist in the form of “ata silos”. Interconnection between data remains extremely challenging. Meanwhile, the data security issue is a significant concern, making it crucial to achieve secure development of multi-source data while protecting data privacy. To address these challenges, we propose a privacy-protected paradigm for multi-source data analysis. This method is based on the federated learning framework, enabling different data sources to collaborate on data analysis tasks without exposing their raw data. Meanwhile, to further prevent malicious attacks on data, we incorporate differential privacy into federated learning by adding noise to the transmitted data to protect individual-level information. Finally, we demonstrates the practical application of the proposed paradigm using the example of predicting violation risks of enterprises. By combining data from various departments, the prediction accuracy can be well enhanced.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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