Due to information asymmetry and strategic innovation, firms often encounter challenges related to insufficient driving forces and low-quality innovation outcomes. Analysts always act as information intermediaries who help foster the advancement of corporate innovation activities and the conversion of innovation output. This study examines the impact of analyst coverage and forecasting bias on corporate innovation, employing data from China A-shared listed firms spanning the period 2007 to 2019. We measure corporate innovation from two perspectives: Input and output. Specifically, we use the ratio of research and development (R&D) expenditure to sales as a proxy for the innovation input and the number of patent citations excluding self-citations to measure innovation output. We find that analyst coverage promotes corporate innovation, which is consistent with the "bright" side of analyst coverage. However, the positive effect of analyst coverage hinges on effectively transmitting and disclosing accurate information to investors in the capital market. Based on this, analysts' forecasting bias includes forecasting dispersion and optimism bias. We find evidence that an increase in analysts' forecast dispersion leads to a decrease in corporate innovation quality. Moreover, this paper presents a novel approach by employing the regression discontinuity method to examine the effect of analyst optimistic bias on firm innovation. The empirical findings reveal that overly optimistic forecasts by analysts exacerbate innovation quality. These analyses enrich the research on analyst coverage and corporate innovation, providing an empirical basis for improving the capital market with the help of analysts.

The land price in big cities draws much attention and discussion for its skyrocketing appreciation. Most researches are from the macro perspective due to data restriction. This paper aims to investigate the critical factors in the price formation process of a land auction, using the listing auction micro bidding-level data in Beijing from 2013 to 2018. We construct a model for the relationship between quitting price and land, bidder's characteristics, housing market conditions and competitive intensity (including private and public signals), then we use OLS for identification. We find that competitive intensity increases the quitting price by causing competition and interaction between bidders. More importantly, we find evidence of cheating behavior in the land market. Results show that bidders have higher quitting prices when they are in a joint venture, and when a central SOE developer or a top10 developer exist in the joint venture. We also find different behavior of developers in the short run and long run. Our research contributes to the literature of land auctions by analyzing the price formation process and developers' behavior. We also provide supporting evidence for the government to make adjustments of the auction system and identify the cheating developers.

During the COVID-19 pandemic, the international financial markets experienced severe turbulence. Under the background of "Made in China 2025", substantial entity enterprises have a large demand for non-ferrous metals. With the enhancement of financial attributes of non-ferrous metals, it is vital to prevent financial systemic risk contagion in the non-ferrous metal markets. In this article, the ensemble empirical mode decomposition method is used to decompose the prices of eight important non-ferrous metals futures, and then the dynamic DY risk spillover index model is established from the perspectives of long-term and short-term. The risk spillover between non-ferrous metals during the COVID-19 is quantitatively analyzed from different frequency domains. The study finds that in the long run, the risk spillover relationship between non-ferrous metals remained basically stable, and the change of it after the epidemic is slight. In the short run, the risk spillover relationship has different degrees of structural changes after the outbreak of the COVID-19 pandemic. The ensemble empirical mode decomposition method can distinguish the risk spillovers in different cycles, and help to formulate policies for preventing systemic risks in the non-ferrous metal markets according to the different length of terms.

A method for internal participation in rescue decision-making of emergency volunteer teams considering psychological behavior is proposed to address the time sequence of rescue tasks. Firstly, the problem of multi-tasking and multi-operation within the emergency volunteer team is described. Secondly, considering that task leaders are influenced by behavioral and psychological factors in the evaluation, the required time for the job is used as a reference point, and the expected time that volunteers can complete the job is used as an attribute value. The task leader's prospect satisfaction value for each volunteer is calculated based on prospect theory, and the perceived utility values of disappointment theory and regret theory are calculated to measure the task leader's satisfaction with each volunteer. Furthermore, a multilayer coded genetic algorithm is used to construct an optimization model for emergency volunteer decision-making with the objective of maximizing the satisfaction value. Finally, the feasibility and effectiveness of this method are illustrated by an example analysis. The result shows that the efficiency of rescue tasks can be improved through decision optimization within the volunteer team.

This study investigates the impact of online-to-offline (O2O) platforms, such as Ele.me and Meituan, on offline sales in low-frequency-high-consumption industries, specifically a mid-to-high-end liquor distribution chain. Using data from 77 offline stores in Beijing collected during 2019–2022, the study employs a VAR model to analyze the relationship between offline sales and the use of O2O platforms. The results reveal a long-term equilibrium between the two, with most indicators showing a positive impact of O2O platforms on offline sales. The research provides valuable insights for low-frequency-high-consumption enterprises in making multi-channel decisions and quantifies the impact of O2O platforms on offline sales.

To solve the problem that the traditional cloud model can't directly process the textual review information in the recommendation algorithm, this paper combines the merits of the cloud model in transforming qualitative and quantitative knowledge with the multi-granularity advantages of probabilistic linguistic term sets in representing uncertain information, and proposes a recommendation algorithm based on cloud model in probabilistic language environment. Initially, this paper quantifies the attributes in the review text based on the probabilistic linguistic term set. Subsequently, the maximum deviation method is used to determine the weight of each attribute in the evaluation information of the product to be recommended, and the comprehensive evaluation number and attribute weight are converted into the digital characteristic value of the cloud model by using the backward cloud generator. Finally, the products are recommended and sorted based on the digital characteristic value of the cloud model. The algorithm is applied to the recommendation of 10 hotels, and the results show that the method is effective and practical, enriching the application of cloud models in the recommendation field.

Cervical cancer is the fourth most common malignancy to strike a woman globally. If discovered early enough, it can be effectively treated. Although there is a chance of error owing to human error, the Pap smear is a good tool for first screening for cervical cancer. It also takes a lot of time and effort to complete. The aim of this study was to reduce the possibility of error by automating the process of classifying cervical cancer using Pap smear images. For the purpose of this study, dual convolution neural networks with LSTM were employed to classify images due to deep learning approaches inspire distinct features and powerful classifiers for many computer vision applications. The proposed deep learning model based on convolution neural networks (CNN) with the long short-term memory (LSTM) network is to learn features which give better recognition accuracy. The overall model is known as Smear-net. In which 'smear' indicates 'pap-smear cancer cells' and 'net' refers to neural network. The parameters such as, Accuracy, Precision, Recall, Accuracy, Sensitivity, and Specificity are used to validate the models. The proposed method provides the improved accuracy of 99.57 percentage for classification of the pap-smear cells. The proposed approaches demonstrate the effectiveness of our contributions by testing and comparing with the state-of-the-art techniques.

Mountain cities are complex asymmetric dynamic network architectures, and the flight of UAVs in this environment is subject to various constraints, while efficiency is a crucial factor in the trajectory planning of police UAVs, which need to maintain high efficiency and safe flight paths between their starting and ending points, but the traditional trajectory planning method cannot meet the requirements of rapid maneuvering of police UAVs. To achieve this, a 3D terrain map is built, an objective function is established for the flight cost in the UAV trajectory planning process, and a planning algorithm called particle swarm optimization bat algorithm (PSOBA) is proposed. PSOBA combines the characteristics of the bat algorithm (BA) and the particle swarm optimization algorithm (PSO) to improve population diversity and resolve the delayed convergence issue in the last phases of BA. Simulation results show that PSOBA is more effective than BA, with a search time for the best solution that is approximately 20.43% shorter and a convergence value of the objective function that is approximately 38% smaller. PSOBA is also able to plan a quicker, shorter, and safer flight path compared to other trail planning algorithms that enhance the bat algorithm. These findings suggest that PSOBA is a powerful algorithm with potential application value in UAV trajectory planning control in the mobile intelligence era.Contribute to the service of public social security.

This paper studies an online distributed optimization problem over multi-agent systems. In this problem, the goal of agents is to cooperatively minimize the sum of locally dynamic cost functions. Different from most existing works on distributed optimization, here we consider the case where the cost function is strongly pseudoconvex and real gradients of objective functions are not available. To handle this problem, an online zeroth-order stochastic optimization algorithm involving the single-point gradient estimator is proposed. Under the algorithm, each agent only has access to the information associated with its own cost function and the estimate of the gradient, and exchange local state information with its immediate neighbors via a time-varying digraph. The performance of the algorithm is measured by the expectation of dynamic regret. Under mild assumptions on graphs, we prove that if the cumulative deviation of minimizer sequence grows within a certain rate, then the expectation of dynamic regret grows sublinearly. Finally, a simulation example is given to illustrate the validity of our results.

This paper studies the regional differences, dynamic evolution and influencing factors of regional carbon emission intensity (CEI) in 262 cities and 5 regional urban agglomerations (UAs) in China. The Dagum Gini coefficient is used to analyze the intra-regional and inter-regional differences in carbon emissions, and the temporal evolution of the absolute differences of CEI among regions is analyzed by means of kernel density estimation (KDE). The paper provides an in-depth study on the spatial difference and temporal evolution of CEI in Chinese cities and major strategic regions. Through Moran index and LISA's test, the spatial correlation of carbon emission in prefecture-level cities is tested, and its spatial agglomeration characteristics are described. It is found that China's CEI is decreasing year by year, presenting a spatial pattern of plow in the south but high in the northq. Based on the calculation of carbon emission intensity at the urban level, this paper conducts LDMI factor decomposition research on carbon emission intensity at the national and key regions, and analyzes the impact of the impact factors on carbon emission intensity. The research results provide a path for China's green development at the city level and urban agglomeration level, and a theoretical support for different regions and cities to introduce emission and carbon reduction policies.

This paper examines the data of A-share listed companies in China from 2002 to 2017, drawing on the theory of equal opportunity and market rules in M&A transactions. This paper investigates the correlation between changes in tender offer policy and M&A tendencies and performance. The findings suggest that following the policy shift and the adoption of market rules, companies that secure an exemption from the mandatory tender offer obligation not only exhibit stronger M&A tendencies but also improved long-term M&A performance. This indicates that market rules are more suitable for China and contribute to enhancing the efficiency of the M&A market. The paper also presents evidence of a moderating effect, demonstrating that exemptions from the mandatory tender offer obligation positively influence the relationship between policy change and M&A performance. Lastly, this paper finds that state-owned and large-scale firms tend to exhibit a higher degree of M&A tendencies.

Motivated by the Bagging Partial Least Squares (Bagging PLS) and Principal Component Analysis (PCA) algorithms, a novel approach known as Principal Model Analysis (PMA) method is introduced in this paper. In the proposed PMA algorithm, the PCA and the Bagging PLS are combined. In this method, multiple PLS models are trained on sub-training sets, derived from the training set using the random sampling with replacement approach. The regression coefficients of all the sub-PLS models are fused in a joint regression coefficient matrix. The final projection direction is then estimated by performing the PCA on the joint regression coefficient matrix. Subsequently, the proposed PMA method is compared with other traditional dimension reduction methods, such as PLS, Bagging PLS, Linear discriminant analysis (LDA) and PLS-LDA. Experimental results on six public datasets demonstrate that our proposed method consistently outperforms other approaches in terms of classification performance and exhibits greater stability. Additionally, it is employed in the application of financial statement fraud identification. PMA and other five algorithms are utilized to financial statement fraud which concerned by the academic community, and the results indicate that the classification of PMA surpassed that of the other methods.

In the background of the green transformation of the economy and society, the ESG performance of enterprises has been paid more and more attention in the investment decision-making. However, previous studies have inadequately explored how the ESG performance affects corporate financing costs. Based on the information asymmetry theory, this paper analyzes the impact mechanism of ESG performance on corporate financing costs. Then, taking 1044 A-share listed companies in 2016–2020 as a sample, through the sorting and analysis of ESG report disclosure and rating data, the company's ESG performance indicators are obtained, and an empirical model is built to test the relationship between ESG performance and corporate financing costs. This paper constructs a panel regression model using ESG rating data and corporate financial data and finds that in the overall sample, the higher the ESG performance, the lower the equity financing cost; The higher the ESG performance, the lower the debt financing cost. In addition, it also discussed the moderating effect of enterprise scale and media attention on the impact of ESG performance on enterprise financing costs. The empirical results show that the influence of company size on ESG performance on financing costs has a moderating effect and a positive moderating effect.

This study considers a nonlinear grey Bernoulli forecasting model with conformable fractional-order accumulation, abbreviated as CFNGBM$(1, 1, \lambda)$, to study the gross regional product in the Cheng-Yu area. The new model contains three nonlinear parameters, the power exponent $\gamma$, the conformable fractional-order $\alpha$ and the background value $\lambda$, which increase the adjustability and flexibility of the CFNGBM$(1, 1, \lambda)$ model. Nonlinear parameters are determined by the moth flame optimization algorithm, which minimizes the mean absolute prediction percentage error. The CFNGBM$(1, 1, \lambda)$ model is applied to the gross regional product of 16 cities in the Cheng-Yu area, which are Chongqing, Chengdu, Mianyang, Leshan, Zigong, Deyang, Meishan, Luzhou, Suining, Neijiang, Nanchong, Guang'an, Yibin, Ya'an, Dazhou and Ziyang. With data from 2013 to 2021, several grey models are established and results show that the new model has higher accuracy in most cases.

This paper considers sales mode selection issues for a two-echelon supply chain of perishable products involving a supplier and a retailer. Consumers' purchase desire is assumed to be positive correlation with the freshness of the product. First, the traditional sales mode is examined, in which the supplier and the retailer play a Stackelberg game. We propose a three-layer decision model for this situation, and obtain dynamic pricing strategies and selling cycle length. It is shown that the retailer has little motivation to order many perishable products so as to avoid a long selling cycle length. Second, the commission-charge mode is analyzed, in which the retailer declares its decision first. In this mode, we demonstrate that the perishable product will be on sale during the whole shelf life under a certain condition. The correlation between the sales price of each stage and the remaining shelf-life length is analyzed. Third, the superiority analysis for the two sales modes is conducted. We show the relation between the selling cycle lengths of the two modes. By our analysis, it is shown that both the supplier and the retailer gain more profits when the commission-charge mode is adopted and the commission rate locates in a certain open interval. Finally, a numerical illustration is presented to visualize the discussed models, and some supplements are made for the acquired conclusions by the illustration.

The probabilistic hesitant fuzzy multi-attribute group decision-making method introduces probability and hesitation into decision-making problems at the same time, which can improve the reliability and accuracy of decision-making results, and has become a research hotspots in recent years. However, there are still many problems, such as overly complex calculations and difficulty in obtaining probability data. Based on these, the paper proposes a multi-attribute group decision-making model based on probability hesitant fuzzy soft sets. Firstly, the definition of probabilistic hesitant fuzzy soft set is given. Then, based on soft set theory and probabilistic hesitant fuzzy set, the similarity measure of probabilistic hesitant fuzzy soft set is proposed, and the two measures are further combined. Finally, it is applied to the construction of multi-attribute group decision-making model, and the effectiveness and rationality of the model are verified by an example. The example shows that the new similarity calculation formula and algorithm model in this paper have higher accuracy, and the calculation process is more simple, it provides a feasible method for multi-attribute group decision making problems.

The worldwide spread of COVID-19 has caused a grave threat to human life, health, and socio-economic development. It is of great significance to study the transmission mechanism of COVID-19 and evaluate the effect of epidemic prevention policies. This paper employs a spatial dynamic panel data (SDPD) model to analyze the temporal and spatial spread of COVID-19, incorporating the time-varying features of epidemic transmission and the impact of geographic interconnections. Empirical studies on the COVID-19 outbreak in Shanghai during early 2022 show that the intra-regional transmission of COVID-19 dominated the cross-regional one. Additionally, strict policies are found to effectively reduce the transmission risk of COVID-19 and curb the spillover effect of the epidemic in Shanghai on other regions. Based on these results, we provide three policy suggestions. Furthermore, this research methodology can be extended to investigate other infectious diseases, thereby providing a scientific framework and theoretical basis for evaluating the spread risk of pandemics and formulating appropriate strategies.

The research topic related to the analysis of banking activity is relevant since the banking sector is complex and dynamic and requires constant monitoring and analysis to make informed decisions. Using a mathematical model of system dynamics to analyse banking activities can help banks make better decisions, manage risk, ensure the stability and efficiency of operations, and adapt to changing conditions. The purpose of the study is to determine the influence of various factors, in particular economic activity, on the final profit of the bank. Among the methods used, the analytical method, the functional method, the system analysis method, the deduction method, the synthesis method, and the comparison method were applied. In the course of this study, an extensive analytical review of banking activities was carried out using a system dynamics model, taking into account the requirements of the International Financial Reporting Standards (IFRS 9). The study included various scenario analyses to explore the impact of changes in economic conditions on the bank's loan portfolio, risk level, and profitability. Both economic growth scenarios and recession scenarios were considered in order to more fully assess their impact on the financial condition of the bank. A forecast and analysis of the risks associated with lending, investments, and other bank operations was carried out. The practical significance lies in the application of the results obtained to address issues related to banking in order to increase the efficiency of this process and achieve a new level of development.

In an era where power systems face increased cyber threats, social media data, especially public sentiment during outages, emerges as a crucial component for devising defense strategies. We present a methodology that integrates sentiment analysis of social media data with advanced reinforcement learning techniques to tackle uncertain load redistribution cyberattacks. This approach first employs VADER and Support Vector Machine (SVM) sentiment analysis on collected social media data, revealing insightful information about power outages and public sentiment. Proximal Policy Optimization (PPO), a state-of-the-art reinforcement learning method, is then applied in the second stage to leverage these insights, manage outage uncertainty, and optimize defense strategies. The efficacy of this methodology is demonstrated on a modified IEEE 6-bus system. The results underscore our approach's effectiveness in utilizing social media data for a nuanced, targeted response to cyberattacks. This pioneering methodology offers a promising direction for enhancing power grid resilience against cyberattacks and natural disasters, highlighting the value of social media sentiment analysis in power systems security.

Aiming at the problem of ignoring the importance of starting point features of trajecory segmentation in existing trajectory compression algorithms, a study was conducted on the preprocessing process of trajectory time series. Firstly, an algorithm improvement was proposed based on the segmentation algorithm GRASP-UTS (Greedy Randomized Adaptive Search Procedure for Unsupervised Trajectory Segmentation). On the basis of considering trajectory coverage, this algorithm designs an adaptive parameter adjustment to segment long-term trajectory data reasonably and the identification of an optimal starting point for segmentation. Then the compression efficiency of typical offline and online algorithms, such as the Douglas-Peucker algorithm, the Sliding Window algorithm and its enhancements, was compared before and after segmentation. The experimental findings highlight that the Adaptive Parameters GRASP-UTS segmentation approach leads to higher fitting precision in trajectory time series compression and improved algorithm efficiency post-segmentation. Additionally, the compression performance of the Improved Sliding Window algorithm post-segmentation showcases its suitability for trajectories of varying scales, providing reasonable compression accuracy.

The present research employs artificial intelligence to come up with an automatic solution for the modulation's classification of various radio signal varieties. As a result, the work we performed involved selecting the database required for supervised deep learning, evaluating the performance of current techniques on unprocessed communication signals, and suggesting a deep learning network-based method that would enable the classification of modulation types with the best possible ratio between computation time and accuracy. We started by examining the automatic classification models that are currently in usage. In light of the difficulty of forecasting in low Signal Noise Ratio (SNR) situations, we suggested an ensemble learning strategy based on adjusted ResNet and Transformer Neural Network, which is effective at extracting multi-scale features from the raw I/Q sequence data. Finally, we produced an architecture that is simple to use and apply to communication signals. The architecture of this solution is strong and optimal, enabling it to determine the type of modulation with up to 95% accuracy automatically.

Deep synthesis technology is an emerging artificial intelligence technology. There have been a large number of audio and video contents based on deep synthesis technology spreading in the Internet. In this paper, we take the deep synthetic videos on YouTube platform as the research object, and investigate the factors influencing the propagation effect of deep synthetic videos by establishing an ordered probit model. It is found that the effect of deep synthesized video transmission of YouTube platform is mainly influenced by factors such as the video type, video duration, influence of publishers and forms of fraud. In addition, the comparative analysis of ordinary video and in-depth synthesized video reveals that both the video transmission effect are significantly affected by the video type, video duration and the influence of publishers.

The 2017 Nobel Prize in Economics was awarded to Thaler, whose concept of nudges has been widely used. Although existing research has proposed numerous nudging strategies, no research comprehensively examines the role of nudging, resulting limited in attention across various fields. To fill this gap, this study aims to present how to analyze the role of nudges in the system using a systems thinking approach. The analysis indicates that nudges have a multiplier effect in the system and can achieve significant results at a small cost. We use three cases to analyze the effective impact of nudges on decision making: changing the default setting in organ donation, providing the check list to avoid mistakes in surgery, and the approach to prevent the coronavirus epidemic applied in China.

Addressing the vulnerability of contact-based keyboard password systems to disclosure, this paper proposes and validates the feasibility of a non-contact secure password system based on brain-computer interface (BCI) technology that detects steady-state visual evoked potential (SSVEP) signals. The system first lets a testee look at a digital stimulus source flashing at a specific frequency, and uses a wearable dry electrode sensor to collect the SSVEP signal. Secondly, a canonical correlation analysis method is applied to analyze the frequency of the stimulus source that the testee is looking at, and feeds back a code result through headphones. Finally, after all password codes are input, the system makes a judgment and provides visual feedback to the testee. Experiments were conducted to test the accuracy of the system, where twelve stimulus target frequencies between 10-16Hz were selected within the easily recognizable flicker frequency range of human brain, and each of them was tested for 12 times. The results demonstrate that this SSVEP-BCI-based system is feasible, achieving an average accuracy rate of 97.2%, and exhibits promising applications in various domains such as financial transactions and identity recognition.

The cornerstone of most management research revolves around improving productivity and team performance. A crucial factor influencing team performance is the shared mental model that team members hold regarding task and team member related issues. This concept has garnered increasing attention from scholars, resulting in an abundance of literature. However, there remains a noticeable scarcity of a comprehensive literature review that combines both quantitative and qualitative analyses of shared mental model studies. In order to grasp a comprehensive understanding of the current status and future trends in the area, this study employs a bibliometric approach to review the literature on shared mental models published in the Web of Science Core Collection database from 1992 to 2023. Co-citation analysis is employed to thoroughly scrutinize the structure of this research area, computationally highlighting research hotspots, revealing potential future research directions and applications, as well as pinpointing pivotal turning points and landmarks in the field. Through scientific bibliometric analysis of knowledge structures and emerging trends, this review makes a substantial contribution to the contemporary literature on shared mental models, providing valuable insights for researchers and practitioners in the field.

The new energy vehicle (NEV) subsidy policy introduced in China in 2013 has significantly boosted the adoption and sales of NEVs, with sales increasing more than 40-fold. However, the mechanisms by which subsidy policies influence the diffusion of NEVs in China remain unclear, posing challenges for governments to design future strategies. Thus, the primary objective of this paper is to empirically examine the impact of subsidy policy on the diffusion of new energy vehicles and to forecast future development trends using the grey Bass model, a predictive model suited for new product adoption forecasting. Our findings suggest that while the sales of NEVs in China will continue to rise, the growth rate will slow. Key milestones include the first inflection points for new energy vehicles and battery electric vehicles, anticipated in 2025 and 2024 respectively, with peak sales expected in 2028 and 2027. These insights are crucial for manufacturers, enabling them to adjust their production strategies timely and enhance their resilience in the market.

In the context of information overload, companies often struggle to effectively identify valuable ideas on their open innovation platforms. In this article, we propose an idea adoption strategy based on machine learning. We used data from a well-known open innovation platform, Salesforce, and extracted characteristic variables using the Information Adoption Model. Four classification models were then constructed based on AdaBoost, Random Forest, SVM and Logistic Regression models. Due to significant differences in the number of positive and negative samples in the OIP, we used the SMOTE method to address the problem of data imbalance. The results of the study showed that the ensemble learning models were more accurate in identifying valuable ideas than the individual machine learning models. When comparing the two ensemble learning models, AdaBoost outperformed Random Forest in predicting both positive and negative class samples. The SMOTE-AdaBoost model achieved a recall of 0.93, a precision of 0.92 and an impressive AUC of 0.98 in identifying adopted ideas, which could well identify valuable ideas and has implications for improving the efficiency and quality of idea adoption in OIP. The shortcoming of this work is that it only investigated a single platform. In the future, we will consider extending this method to different platforms and multiple classification problems.

Electric Vehicles (EVs), as a low-carbon means of transportation, have promptly become popular worldwide in the past decade. Since the lifespan of batteries is limited, massive of Electric Vehicle Batteries (EVBs) are being retired, resulting in a rapid increase in the demand for the recycling of retired EVBs in recent years. However, due to high recycling costs and immature recycling technologies, EV manufacturers are facing significant challenges in recycling retired EVBs. China, as the country with the largest number of EV users in the world, is exploring effective incentive policies for the recycling of retired EVBs. In this context, we developed a system dynamics model to analyze the impact of incentive polices such as, recycling subsidies, technological progress, and carbon trading on the retired EVBs recycling. Results show that: 1) recycling subsidies can improve the recycling ratios quickly in the short term, and dynamic subsidies are more efficient than static subsidies; 2) the policy of technological advancement can reduce the recovery and cascade utilization cost, thus having a positive impact on battery recycling, but the policy effect has a time-delay; 3) the carbon trading policy is unable to promote efficient recycling due to the current low carbon prices; 4) dynamic subsidy and technological advancement policies complement each other, therefore, the combination of these two policies is the best way to promote the recycling of retired EVBs and reduce carbon emissions. It is hoped that this study will contribute to the ongoing debate on policies for the industrialization of retired EVBs recycling.

attribute decision making (MADM) problems in which the attribute values take the form of hesitant q-rung orthopair fuzzy sets (H-qROFSs). Firstly, the definition of H-qROFSs and some operational laws of H-qROFSs are proposed. Then we develop a family of hesitant q-rung orthopair fuzzy maclaurin symmetric mean aggregation operators, such as the hesitant q-rung orthopair fuzzy maclaurin symmetric mean (Hq-ROFMSM) operator, the hesitant q-rung orthopair fuzzy weighted maclaurin symmetric mean (Hq-ROFWMSM) operator, the hesitant q-rung orthopair fuzzy dual maclaurin symmetric mean (Hq-ROFDMSM) operator, the hesitant q-rung orthopair fuzzy weighted dual maclaurin symmetric mean (Hq-ROFWDMSM) operator. And the properties and special cases of these proposed operators are studied. Furthermore, an approach based on the Hq-ROFWMSM operator is proposed for multiple attribute decision making problems under hesitant q-rung orthopair fuzzy environment. Finally, a numerical example and comparative analysis is given to illustrate the application of the proposed approach.

In recent years, decision-making under uncertainty has attracted substantial attention in both academia and industry, with a growing number of organizations prioritizing decision support for talent evaluation. Vague set theory has been recognized as a powerful tool to address the ambiguity of problem parameters and manage uncertainty. This paper introduces a novel talent evaluation method that harnesses the potential of vague sets. We construct a vague set Ordered Weighted Averaging (OWA) operator for offering a robust solution to intricate decision-making problems, especially in talent evaluation. The application of the OWA operator augments the decision-making process by providing a mechanism to handle the aggregation of information in a more flexible and comprehensive manner. Experimental results show the effectiveness of the proposed method, presenting an alternative for decision-makers, aiding them in selecting their preferred choices amidst uncertainty.

Tourism has become the primary leisure activity for modern people, and a scenic spot's appeal to tourists is closely linked to the quality of supporting catering services within the area. Improving satisfaction with catering services in these scenic spots has become a key factor in promoting the development of urban tourist destinations. Using Zhangzhou, a tourist city, as an example, this paper conducts an empirical study on the satisfaction of catering services in Zhangzhou's scenic spots through IPA analysis. Firstly, based on the scale of relevant influencing factors affecting catering service satisfaction in Zhangzhou's scenic spots, a field questionnaire survey has been conducted. Three main levels, five core elements, and 31 sub-indicators have been summarized to construct the evaluation system for catering service satisfaction in scenic spots.Through the analysis of IPA data and the examination of various indicators related to catering services in Zhangzhou's scenic spots, we analyze the influencing factors affecting tourists' satisfaction with catering services. Meanwhile, we also introduce the 4Ps demand theory of catering service. Finally, based on the analysis and evaluation of IPA quadrant and the 4Ps demand theory, we initially construct the 4Ps improvement path. This will provide theoretical and practical references for enhancing the catering service satisfaction of tourists in scenic spots.