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.