In traditional management decision-making， the “problem guidance” research paradigm is often adopted， which simplifies and assumes the actual management problems of the original ecology， abstracts them into theoretical problems through scenario stripping， and conducts theoretical research after mathematical processing. The research conclusions are tested for authenticity and then applied in practice to further improve the theory in application. However， this research paradigm faces many limitations when applied to complex systems and their management activities with reduction theory is irreversible （non additive integrity， complex integrity） properties， mainly because the "problem guidance" research paradigm cannot decipher the complex integrity in the management activities and processes of complex systems. To address this issue， considering that scenarios are the macro form， evolution， and evolutionary path formed by management activities， environment， and management activity-environment composite systems at the overall level， and contain all the detailed information of management activities and processes， scenarios are adopted to depict the complex integrity of complex system management and proposes a “scenario guidance” research paradigm. Based on the theoretical consistency between big data and scenarios in complex integrity， a conceptual framework and overall approach for modeling complex system management scenario driven by big data have been designed， forming a method for reverse modeling of complex system management scenarios based on scenario big data collected or recorded through observation or experimental methods.

Culture， as a crucial variable influencing organizational， group， and individual behavior， has consistently been a core topic in management and organizational behavior studies. Since the reform and opening up， China has undergone a great transformation from a planned economy to a socialist market economy. Understanding how culture has changed during this process has become an urgent question. The marketization process in China is not only an economic transformation but also a profound social and cultural shift. Therefore， studying the impact of marketization on the inheritance and evolution of Chinese culture holds significant theoretical and practical importance. Based on the dialectical materialist view of culture， the theory of social change， culture and human development， and the theory of cultural identity， it is hypothesized that the marketization process significantly affects the inheritance and change of cultural values. To test the hypothesis， survey data from 5，916 respondents across 31 provinces in China’s mainland are calculated. Using multi-level analysis methods， the impact of marketization on cultural values and the underlying mechanisms is explored. Hofstede’ s cultural model provides a multidimensional framework that allows us to map China’ s culture from various perspectives and analyze the specific effects of marketization on different cultural values dimensions. The research approach comprises two main aspects： first， utilizing Hofstede’ s cultural dimensions to calculate the cultural indices of each province and the values scores of participants； second， applying multi-level analysis methods to investigate the impact of marketization levels on these cultural values dimensions and the underlying mechanisms. It particularly focuses on the cross-level mediating role of individual perceptions of industrial and commercial civilization between marketization levels and individual values in this paper. It is shown that Hofstede’ s concept of the cultural dimensions of countries （regions） is generally effective in the Chinese context， which verifies the argument that culture presents certain trends of change with the rapid economic and social transformation and the further deepening of the marketization process： from restraint to indulgence. At the same time， it is found evidence of the persistence and even strengthening of unique Chinese cultural traditions： high respect for authority， high collectivism， a masculine society， and low uncertainty avoidance. Moreover， it is confirmed that individual perceptions of industrial and commercial civilization play a cross-level mediating role in the relationship between marketization levels and individual values. These findings provide evidence for understanding the unique characteristics and evolutionary trends of Chinese culture in the context of marketization. They offer theoretical support for a more comprehensive and in-depth understanding of China， its economic and social governance model， and the management and organizational behavior of Chinese enterprises.

With the continuous advancement of information technology， social media has permeated extensively into people’s daily lives and production activities， emerging as a primary conduit for information dissemination and social interaction. Compared to traditional offline social networks， notable changes have occurred in the evolution of user relationship networks within social media， a domain that has garnered considerable academic attention in recent years. However， current research topics concerning social media user relationship networks exhibit a fragmented landscape， lacking a systematic review and holistic interpretation grounded in a specific theoretical framework. To address this research gap， it aims to systematically review and synthesize the relevant content concerning social media users’ relationship networks. 326 relevant publications spanning from 2001 to 2024 are analyzed， sourced from the Core Journals of China National Knowledge Infrastructure （CNKI） and Web of Science. A framework diagram is constructed for social media users’ relationship networks based on the element-process framework from general system theory. The analysis is conducted from two primary aspects： research dimensions from the perspective of elements and influencing mechanisms of social media user relationship networks from the perspective of process. The findings reveal that from the element perspective， research on social media user relationship networks unfolds around three aspects： characterization， detection， and prediction of user relationship network features. The focus of these studies is on user relationships or networks， with research perspectives grounded in either static or dynamic contexts. From the process perspective， the influencing mechanisms within social media user relationship networks are explored in terms of influencing factors and effects across multiple levels， and these factors and effects are anchored in various specific scenarios. Building upon this analysis and incorporating contemporary characteristics， three future research trends are identified： online-offline integration， contextual diversity， and negativity research. The contributions of this study lie in providing a systematic framework and insights for domestic scholars to conduct deeper theoretical and empirical research in the field of social media user relationship networks. Additionally， it offers scientific evidence and decision-making references for social media enterprises， platform operators， and policymakers.

In the field of manufacturing， quality management plays a critical role in enhancing companies' core competitiveness. Traditional approaches to quality management have rapidly evolved， transitioning from an emphasis on quality inspection to quality monitoring， and ultimately to full life-cycle quality management. However， with the rapid development of the global Internet in the 21st century， "smart+" technologies， characterized by intelligence and automation， have ushered in a new era of quality management techniques. Consequently， data has emerged as the cornerstone of modern quality management across every stage of the product life cycle. Data manifests in diverse forms， including text， numeric， images， video， and other formats. This diversity not only promotes the continuous innovation and data collection methods， but also imposes higher demands on the theoretical depth of data analysis and the applicability of analytical models. Complex data characteristics， such as data imbalance and multi-source heterogeneity， pose significant challenges to the quality management technologies. In this context， it focuses on the process of manufacturing， with a focus on quality inspection， monitoring， and operations and maintenance （O&M）. It summarizes the cutting-edge technologies that address complex data in quality management and provides insights into prospective research directions in this field.

The development of artificial intelligence and machine learning technology has greatly improved the efficiency of financial information processing， especially the ability to mine hidden data variables that have important impacts on the stock market. It has been found that investors’ attention has a great impact on the stock market. Therefore， it starts from the implicit variable of investors’ attention in this paper， and reversal online portfolio strategies are studied. Firstly， the code and abbreviation of the stock are used as search terms， and the sum of the two in Baidu Index is obtained through web crawler technology. Based on search index and smoothing method， a measure of investor attention is constructed. Then， combined with the reversal effect of the stock market， the reversal online portfolio strategy is designed which takes into account the investors’ attention. Finally， a backtest is conducted using six Chinese stock datasets with differences in data sources and stock sizes. The results indicate that the strategy designed in this paper performs well in terms of returns and risks， improving existing online portfolio strategies based on mean reversal effect. The BD-AC strategy outperforms benchmark and traditional strategies on most datasets. Furthermore， sensitivity analysis is also conducted on two parameters of the strategy， including the time window and the lag window， which demonstrates good robustness within a certain range of parameters. It contributes to the field of online portfolio research in this paper by constructing a measure of investor attention based on Baidu index and introducing it into the Anticor model to improve the strategy.

This study introduces an innovative strategy for detecting financial fraud by examining the abnormal distribution characteristics within corporate information disclosures. Financial fraud not only results in substantial losses for investors but also undermines the stability of capital markets. By utilizing Benford’s Law and Zipf’s Law， the study develops a set of indicators to identify financial fraud through the detection of anomalies in the distribution patterns of numerical and textual disclosures， and it demonstrates the effectiveness of these indicators within the context of the Chinese market. Unlike traditional methods， which typically rely on financial data or the analysis of tone and thematic content， this approach offers superior interpretability， is independent of time-series or cross-sectional data， and is applicable to a broad array of financial fraud scenarios.The research first investigates whether large-scale numerical and textual disclosure data adhere to the expected distributional laws. The findings reveal that over 80% of companies’ annual financial data conform to Benford’s Law， while textual data in MD&A disclosures largely follows Zipf’s Law. Building on these insights， the study introduces two anomaly detection metrics： the KS statistic for numerical disclosures and the e-value for textual disclosures. Subsequent empirical analysis confirms significant differences in the KS and \epsilon-values between fraudulent and non-fraudulent firms. Specifically， a greater deviation from the expected natural distribution， as indicated by higher KS and \epsilon-values， correlates with a higher probability of financial fraud. Through the application of machine learning and deep learning models， the study finds that incorporating these abnormal distribution features enhances fraud detection accuracy， precision， recall， and F1 scores by up to 17% to 26%. These results offer valuable incremental insights for financial fraud detection in the Chinese market， contributing to enhanced detection capabilities for both regulators and investors.The key conclusion of this study is that abnormal distribution characteristics in both numerical and textual disclosures serve as effective tools for financial fraud detection， significantly improving detection accuracy. However， the research has certain limitations， such as sample matching challenges and data filtering constraints. Future studies should expand the scope to include a wider range of company types and smaller sample sizes. Additionally， future research could explore technological advancements to further optimize the identification of abnormal distribution features， thereby improving the applicability and predictive power of financial fraud detection strategies.

Due to the time-sensitive nature of distribution orders， each order is required to be completed within the shortest possible time， and considering the characteristics of large number of distribution orders during peak periods， geographically dispersed， and dynamic and random distribution of riders， it is extremely challenging to seek an order allocation and route optimization scheme with low delivery cost and high customer satisfaction. In order to solve the problems of dynamic generation of takeout orders and the continuous change of riders， a drone assisted riders delivery mode is proposed. The bimodal Gaussian function is used to simulate the generation of new orders in the distribution process， and a two-stage optimization model is constructed with the minimum distribution cost and the overall maximum customer satisfaction as the objective functions. In the first stage， an initial optimization model for rider delivery is established for static customers， and in the second stage， a dynamic adjustment model for drone assisted rider delivery is established for dynamically generated new orders. A two-stage heuristic algorithm is designed for this model to solve the problem， the first stage uses an AP （Affinity propagation） clustering algorithm improved based on K-means and KNN （K-Nearest Neighbor） classification algorithms for dynamic order allocation， and the second stage utilizes a taboo search algorithm improved by combining the insertion algorithm for route optimization. The effectiveness and feasibility of the model and algorithm are verified through case simulation. The results show that：（1） Compared with the traditional rider delivery mode， the drone assisted riders delivery mode can effectively reduce the number of riders and reduce the delivery cost. And with the larger scale of new orders， the advantages of the drone assisted rider delivery mode become more obvious. （2） Through real-time adjustment of delivery routes and optimization of order allocation， the empty load rate and transportation costs can be reduced， and operational efficiency can be improved. （3） The addition of drones can effectively optimize the distribution path and schedule of orders， avoiding delays caused by traffic congestion or unreasonable routes. It helps to reduce operating costs and further improve the overall efficiency and service quality of the logistics industry.

In the distributed management environment， multiple projects may be located in different regions， so the global resource transfer times cannot be neglected when making multi-project scheduling plans. Besides， activity durations may be affected by various uncertain factors （e.g.， breakdown of production equipment） in the actual scheduling， resulting in deterioration or even infeasibility of the pre-determined scheduling plans. A distributed multi-project scheduling problem considering resource transfer times and uncertain activity durations is studied. Robust project scheduling is employed to tackle uncertainty. It is named as the robust distributed resource-constrained multi-project scheduling problem with transfer times （robust DRCMPSP-TT）. In the robust DRCMPSP-TT， consider the parallel execution of multiple projects in a distributed decision-making environment. The duration of the activities is uncertain， and the execution of the activities requires both local and global resources. Obtain the multi-project baseline scheduling and global resource transfer plans through problem-solving.

To formulate this problem， a two-stage model containing local scheduling and global coordination decision-making is established based on the multi-agent system. In the local scheduling optimization model， each PA focuses on the robustness of the single-project baseline scheduling plan， while in the global coordination decision-making model， the CA is concerned with the robustness of the multi-project scheduling plan. Moreover， a two-stage algorithm integrating time buffer addition and resource flow network construction is designed to solve the problem. In the first stage， the local scheduling problem is solved by the deeply optimized branch-and-bound algorithm to generate local baseline schedules with insertion time buffers. In the second stage， based on the local baseline schedules， a heuristic resource allocation algorithm is developed to optimize additional resource arcs to coordinate global resource allocation and transfer effectively.

The experimental research is executed based on 24 instances generated by RanGen1 software. According to the number of projects and activities in each multi-project， these instances are divided into 4 problem subsets， denoted 2_10， 2_30， 5_10， and 5_30. Each problem subset contains 6 multi-project instances with different problem parameters. Experimental results show that variations in the problem size and the degree of duration uncertainty have an impact on the robustness of the baseline schedule. Compared with the existing branch-and-bound algorithms and various heuristic algorithms， the proposed two-stage algorithm can effectively improve the robustness of the baseline schedule and is more suitable for solving the studied problem. The experimental results further verified that the timely project completion probability increases with the increase of the deadline and approach 1 at different speeds under different duration variability levels. In addition， by analyzing the relationship between project deadlines and the timely project completion probability， quantitative reference opinions are provided for managers to determine reasonable deadlines.

It makes up for the vacancy of the DRCMPSP and opens the door to further study the DRCMPSP with resource transfer and uncertain activity durations in this study. It mainly adopts the proactive scheduling strategy to solve robust DRCMPSP-TT， while combining proactive scheduling with reactive scheduling to solve this problem can be a topic for future research. In addition， the global resource availability is often uncertain due to various unexpected and uncontrollable conditions such as equipment failure， and DRCMPSP-TT under uncertain global resource availability can be further investigated.

Product recalls are a growing concern for public safety， leading governments and businesses to prioritize timely and accurate recall decisions. While recalling defective vehicles is crucial for consumer safety and property rights， it can also have significant financial and reputational consequences for automobile manufacturers. To mitigate these risks， companies are shifting focus towards proactive defect detection and prevention measures. The rise of online defect complaints， facilitated by advances in internet and social media， presents an opportunity to address this issue. However， existing research on product recalls has primarily focused on their causes and consequences， neglecting recall prediction. Similarly， research on defect complaints has centered on categorization， factors influencing complaints， and organizational strategies， often overlooking important auxiliary information within complaint narratives. Thus， there is a need to explore effective approaches for utilizing online defect complaint data to predict automobile recalls.

A novel four-layer metadata-based supervised segmented topic model （abbreviated MsSTM） is proposed， which integrates auxiliary information from defect complaints and addresses the challenges associated with analyzing short texts. This model facilitates the extraction of latent defect topics and estimation of recall probabilities using a large-scale dataset comprising online defect complaints. Experimental findings demonstrate that MsSTM successfully extracts topics about core components， body accessories and after-sales service from defect complaints. Furthermore， the intensity of these topics exhibits correlations with complaint quantity and manufacturer characteristics. The domestic automakers should pay more attention to the after-sales service， while the joint ventures should care more about the odor， navigation and suspension system. Finally， the predictive stability of MsSTM for automobile recalls surpasses that of various comparative models， achieving an impressive ROC-AUC value of 82.74%， reflecting a notable 10.42% enhancement compared to the best ROC-AUC value among the comparative models. The findings carry important implications for relevant automobile manufacturers and government agencies in devising recall strategies， promoting timely enhancements in automotive safety， mitigating the occurrence of traffic accidents， and safeguarding consumer safety and property rights.

After sudden disasters， the affected people’s demand for emergency supplies sees a sharp increase in number and variety. If governments can’t deliver the needed supplies to the affected areas immediately， the victims are subject to suffer from pain owing to the lack of critical emergency supplies. For example， at the beginning of COVID-19 outbreak， people are forced to purchase medical supplies like masks and protective suits. One of the most important reasons is that the amount of key medical supplies is insufficient. There exist some deficiencies in the current emergency supplies security system in our country. Therefore， to continuously optimize the emergency supplies security system is the key of raising government efficiency of emergency management， which is of great significance.Emergency supplies as a kind of important emergency resources， is the material foundation for the implementation of emergency rescue. A five-level emergency supplies reserve system has been built in our country. Although the ability of emergency rescue is improved significantly， the problems such as inadequate reserve of emergency supplies， lack of disaster-characterized emergency supplies， and low level of emergency supplies reserve socialization still exist. Emergency supplies reserve socialization is a useful supplement to national emergency supplies reserve system that makes social materials in the market as a source of emergency rescue. Agreement reserve under government consigning is an important form of emergency supplies reserve socialization， under which the governments trust some companies to pre-store emergency supplies. It attempts to study the mode of emergency supplies agreement reserve under government consigning. The governments as the leader， need to actively carry out emergency rescue and deliver the needed supplies to the affected areas in a timely manner. On the other hand， they must adopt measures to reduce casualties and relieve pain of the victims， and control costs as the same time. To run the mode of emergency supplies agreement reserve， the governments need to set a quantity or price mechanism under which agreement enterprises are willing to store emergency supplies.From literature review， it is found that an increasing number of researchers pay attention to prepositioning strategies of emergency supplies. However， less attention has been paid to human suffering in the prepositioning of emergency supplies， which leads to an imbalance between emergency supply and demand， even a serious shortage. This exacerbates the amount of suffering caused by the victims. In view of this， it attempts to analyze how human suffering affects the decisions of the governments and the enterprises. To facilitate analysis， an option contract is introduced into a relief supply chain which consists of a government agency and a supplier. Furthermore， the averted total amount of human suffering is described， and a prepositioning model of emergency supplies considering human suffering via an option contract is built. The optimal decision strategies of the government and the supplier in different situations are attained. Several conditions under which this relief supply chain is coordinated are uncovered. Moreover， combining a case study， the proposed model is compared with the prepositioning model without considering human suffering and the government-single prepositioning model. The conditions are determined to secure the supplier’s willingness to engage in the emergency prepositioning arrangement and to improve social benefits.In our study， since the alleviated human suffering is formulated as social benefit， the proposed model is closer to our country’s “people-oriented” concept of emergency management. The related conclusions not only provide theoretical support for making more accurate emergency supplies prepositioning strategies as well as coordination mechanism， but also help to establish a stable trust-agent relationship between the government and the supplier.

Crowdsourcing is a mode of transportation in which social vehicles accept to deviate from their own routes to deliver goods to others for a small amount of compensation. With the popularity of the sharing economy， it has become an important means to reduce logistics and transportation costs. However， there is a significant difference in the transport capacity of vehicles in the crowdsourcing model， and customer needs are split to meet when participating in logistics delivery. At the same time， when enterprises employ both their own vehicles and social crowdsourced vehicles for logistics delivery， the key to reducing transportation costs lies in coordinating the routes of these two types of vehicles. In this paper， considering that customer demand can be split， a collaborative transportation routing model with split demands is established. Then， a genetic algorithm for relational model-assisted evaluation is designed. The algorithm framework consists of two layers： the upper layer adopts the genetic algorithm of 0-1 coding to assign customers to different types of vehicles， and the lower layer adopts the genetic algorithm of real number coding for the distribution results of the upper layer to solve the subproblems of routing problems for hired vehicles and crowdsourcing vehicles respectively. In order to speed up the efficiency of the lower genetic algorithm， support vector machine model is used to learn the relationship between the good and bad of the solution pairs for assisting the search of the lower genetic algorithm. Finally， some test instances based on benchmark instances are designed， and numerical experiments are conducted. The results demonstrate that the proposed algorithm performs well. The management enlightenment obtained from the research is as follows： （1） Enterprises can adopt collaborative transportation mode to reduce transportation costs. （2） In the process of reducing enterprise operating costs， crowd-sourced vehicles with larger available capacity should be given priority.

In reality， a contractor often implements several projects concurrently while during this course， how to maintain a dynamic balance between cash outflows and cash inflows is a key issue for the contractor to deal with. It should be noted that over the course of the project， the distribution of cash flows is closely related to the arrangement of the project schedule. As a result， through reasonable project scheduling， the contractor can effectively coordinate cash outflows and inflows and thus best achieve a positive cash flow balance. Based on the realistic background aforementioned， this paper studies the decentralized resource-constrained multi-project scheduling problem with the objective of cash flow dynamic balance. In the problem， the schedule of each project is arranged under its local resource constraints whereas the enterprise headquarter exerts its impact on project scheduling through the allocation of global resources among different projects.First， based on the discussion of realistic and theoretical backgrounds of the research problem， a multi-project scheduling optimization model is constructed under the objective of minimizing the maximum cash flow gap of the contractor. The model consists of two submodels， namely local project scheduling submodel and global resource allocation submodel. The former optimally arranges schedule for each single project under the constraint of its local resources and based on the results obtained， the latter optimally allocates and coordinates global resources among multiple projects so as to realize the best dynamic balance between the contractor’s cash outflows and inflows. Through the analysis of the constructed model， three basic properties of the studied problem are proposed， thus providing supports for the effective solution of the problem.Second， due to the characteristic of the problem， which is NP-hard and includes two interrelated subproblems， a simulated annealing - tabu search algorithm is developed. In the algorithm， the local project scheduling subproblem is solved by a simulated annealing - tabu search algorithm， where a tabu list is employed to prevent the algorithm from visiting identical feasible solutions repeatedly at high temperature stage， while the global resource allocation subproblem is tackled using a sequential game based algorithm， which deals with global resource conflicts effectively by postponing the relevant activities. To enhance the searching efficiency of the developed algorithm， an improvement measure is designed for the algorithm for the local project scheduling subproblem based on the properties of the studied problem.Finally， in order to evaluate the performance of the algorithm and the effect of its improvement measure， a large-scale computational experiment is conducted on a data set generated randomly. In the experiment， taking the multi-start iteration improvement algorithm as the comparison algorithm， the two versions of the developed algorithm， i.e.， the simulated annealing - tabu search algorithm equipped with and without the improvement measure， are tested and the results are also compared with those obtained by the simulated annealing， tabu search， and multistart iteration improvement algorithms. Based on the computational results， the advantage of the simulated annealing - tabu search algorithm including the improvement measure over other algorithms is verified and through the sensitivity analysis of the impact of key parameters on the objective function， the trends of the maximum cash flow gap of individual projects and multiple projects varying with the parameters are discussed.The conclusions of the research in this paper are as follows： The simulated annealing - tabu search algorithm with the improvement measure is the most promising algorithm for the studied problem. The advantage of this algorithm over other algorithms grows with the increase in the activity number of project， local resource strength， and project deadline or the decrease in the local resource factor. The maximal cash flow gap in individual projects descends as the local resource strength， advance payment proportion， milestone activity number， and project deadline go up， while ascends as the local resource factor and discount rate climb. When the global resource factor drops or the global resource strength and project deadline grow， the increment of the maximal cash flow gap in multi-project， which is caused by handling global resource conflicts， decreases.

With regards to cases of real network security management， the choice of security defence strategy is a common problem. However， the existing methods to choose security defence strategy is limited by the network administrators’ cognition and network topology. Therefore， an optimal way is proposed to select internet security defence strategy based on prospect theory. At first， several problems in optimizing defence strategy selection for sub-networks and calculates comprehensive prospect utility values for both the offensive and the defensive are described. Then， an optimal model is constructed for the choice of internet security strategy based on the prospect theory. This model considers the cost of defence and topology of network. Later， the optimal defence strategies are computed with different defence costs， network topology and the limited rationality of the defenders. Meanwhile， the influence law of defender rationality degree parameters on defense strategy optimization are researched. The optimal defence strategies model expands the application of offensive and defensive games， not only offers a new way to optimize the option of network security defence strategy against limited rationality， but also expands the application field of security game. Finally， a network security problem in logistic system is demonstrated as an example to prove the validity of the proposed method.

In the current international pattern characterized by significant changes， adaptations and reorganization， energy issues are no longer simply matters of supply or development， but a comprehensive consideration involving national security and international strategy. However， the recent years' local disparities in energy supply and demand， coupled with electricity shortages in China's energy market， have laid bare the deficiencies in China's coal management. At present， these shortcomings primarily manifest in three key areas： firstly， as a large thermal power generation country， China's electric coal supply risk prevention awareness and response ability is insufficient； secondly， the electric coal stockpile reserve mechanism is not perfect； thirdly， there is a deficiency in suitable risk assessment models. Therefore， to improve China's risk control level in electric coal risk control， especially in electric coal inventory， in addition to strengthening the risk prevention awareness， appropriate risk assessment models should also be constructed and the mechanism for electric coal inventory reserve should be improved. Therefore， the thermal power plant is taken as the research object in this paper， and the risk assessment indexes are established considers the risk factors affecting the management of electric coal inventory from three levels of macro-environment （policy， economy， natural environment and market）， meso-environment （information transmission and enterprise cooperation） and micro-environment （business operation and coal yard condition）. On this basis， by analyzing the advantages and disadvantages of the SVM assessment model， the regularization parameter \lambda is introduced to prevent the overfitting problem， and the tuna swarm optimization algorithm（TSO）is used to optimize the model regularization parameter\lambda， the kernel function parameter \sigma and other related parameters， and thus the risk combination evaluation model of the tuna swarm optimization algorithm and the least-squares support vector machine （TSO-LS-SVM） is proposed. Finally， the proposed model is applied to 200 electric coal enterprises in China to analyze the examples in the context of reality， and the specific research results are as follows. （1）The indicators are selected from four aspects considering the digitalization background： information effectiveness， information sharing degree， information symmetry and vulnerability of information transmission mechanism， and a combination of quantitative and qualitative methods is used to construct the inventory risk assessment index system of electric coal enterprises. （2） The applicability of the proposed TSO-LS-SVM model in the assessment of electric coal inventory risk is verified through case analysis. The superiority of the proposed method is verified by comparing TSO， whale optimization algorithm （WOA） and particle swarm optimization （PSO）， for it has faster convergence speed， higher accuracy， smaller mean square error， and the best performance in the assessment of coal inventory risk. （3） Sensitivity analysis is conducted on risk factors， and it is found that the top five risk indicators are coal loss， policy opportunities， facility construction， employee quality and information transmission， providing scientific guidance and basis for the proposal of electric coal risk control strategy. Based on the conclusions above， certain management insights are provided for enhancing the risk management and control level of electric coal enterprises， improving the electric coal stockpile reserve mechanism， and ensuring the stable supply of energy.

Wind energy， a significant source of clean energy， has undergone rapid global development in recent years. However， wind power ramp events， caused by sudden changes in wind speed， adversely affect the power grid， potentially causing damage to wind turbines and leading to frequency instability. Existing research indicates that improving the prediction accuracy for these events is crucial for enhancing preventative capabilities. The low proportion of wind power ramp events within power data， coupled with a scarcity of corresponding samples， presents a significant challenge to improving prediction accuracy. Prediction methods for wind power ramp events are primarily categorized as either indirect or direct. Indirect prediction entails initially forecasting wind power， followed by the identification of ramp events. The drawback of this approach is that its predictive accuracy decreases significantly as the prediction horizon increases， making it unsuitable for long-term forecasting. Conversely， direct prediction first identifies historical wind power ramp events and then generates predictions. While the primary prediction targets are typically Boolean values， amplitude， and rate， existing models often fail to predict key information such as the start time and location of these events. A novel dual-meta pool prediction model is proposed capable of directly predicting the wind power ramp event vector. For data processing， the Hilbert curve is employed to map one-dimensional time series data onto a two-dimensional matrix， thereby effectively preserving positional information. A dataset expansion method based on random number generation is adopted to address the paucity of wind power ramp event data. Furthermore， a meta-data classification method based on time interval labels is proposed to reduce the dimensionality of the output data and the complexity of the prediction model. A two-stage classification model based on convolutional neural networks is proposed to establish the mapping between data and labels. For each tuple of meta-data， a corresponding predictive model for the meta-method is designed， and the mapping relationship between the meta-method pool and the meta-data pool is established by using the convolutional neural network with time interval labels. In contrast to traditional methods that employ a single prediction model for all data， the proposed method effectively diminishes the complexity of the neural network， enhances the relevance of data processing， and yields superior prediction accuracy. For the empirical analysis， output power data from three wind farms （SALTCRK1， DUNDWF3 and MEWF1） are randomly selected from Australian Energy Market Operator （AEMO） website （www.aemo.com.au）.The dataset covers the period from September 15， 2021， to June 17， 2023， with a 5-minute resolution and rated installed capacities of 54MW， 121MW， and 180MW， respectively. The empirical results for the proposed model show recall rates of 43.90%， 46.77%， and 43.12%， and mean absolute errors of 11.555， 23.861， and 24.558， respectively. Although the proposed dual-meta pool prediction model introduces an innovative approach， considerable scope remains for enhancing data processing techniques and predictive methodologies， necessitating further refinement through continued research efforts.

Effectively improving the drug quality and safety level and promoting the digital transformation of pharmaceutical enterprises is one of the hot issues that the government and the whole society are concerned about. The coupling of prospect theory and Moran process are introduced to construct the dynamic evolutionary game process of the drug quality management model of pharmaceutical enterprises. Based on the Markov probability transition matrix， the probability of mutual intrusion between the blockchain management model and the traditional management model is calculated， and the conditions for the two strategies to achieve evolutionary stability under the dominance of expected returns and external factors are obtained. The results are verified by numerical simulation using Matlab 2022b. It is found that， first of all， when the government subsidy amount is high， the blockchain management model tends to become a dominant strategy， and as the government subsidy amount decreases， the traditional management model gradually invades the blockchain management model； secondly， the greater the detection probability and penalty amount of government departments for drug quality and safety incidents， the more conducive it is to promote pharmaceutical enterprises to choose blockchain management models； thirdly， under the dominance of expected returns， if the loss of benefits brought by the traditional management model is higher than the cost of using blockchain technology， the blockchain management model will dominate the market； finally， when the game revenue meets certain conditions， the blockchain management model will become a dominant strategy only when the number of pharmaceutical enterprises in the market is below a certain threshold. Combined with model solving and simulation analysis， countermeasures and suggestions are provided for pharmaceutical enterprises' drug quality management decisions.

In order to promote the comprehensive development of economy， over-exploitation of various resources have increased， resulting in various emergencies such as natural disasters and accidents. It is of great significance to study how to improve the effectiveness of decision-making under the imperfect and complex scenarios. The main problems are uncertainties and risk issues in the procedures， which are difficult to be effectively addressed. For reflecting the uncertainty of decision information and limited rationality of decision-makers in an effective way， a new emergency response decision-making based on multi-granularity probabilistic linguistic and double reference points is presented. Firstly， multi-granularity probabilistic linguistic variables are used to express the decision-makers’ evaluation of each key risk factor， and the weights of key risk factors are determined based on an improved method to address multi-granularity probabilistic linguistic information. Secondly， the cumulative prospect theory， which can embody the characteristic of loss-aversion of decision-makers， is applied to the multi-granularity probabilistic linguistic environment， then， the perceived values of each feasible alternative’s estimated effect are calculated by the certain double reference points. Thirdly， the earn perceived weight and loss perceived weight of each feasible alternative’s estimated probability is determined， on this basis， the ultimate perceived value of each alternative is obtained by combining the weights of key risk factors， and the best alternatives can be determined. Finally， the response to forest fire illustrates the effectiveness of the proposed method， and verifies the superiority by comparative analysis. The proposed integrated model based on multi-granularity probabilistic linguistic and double reference points not only represent the imprecise and vague information more effectively， but also consider the decision makers’ bounded rationality under risk comprehensively， and then provide some clues for the effective processing of information theories in emergency decision-making process.

Manufacturing is a vital driving force for high-quality economic development， and the manufacturing chain is the main traditional operation mode of the manufacturing industry. The platform business model driven by digital technology has significant advantages in terms of network effects and value capture. However， the single traditional manufacturing chain model is difficult to meet the growing market demand and the complex and ever-changing business environment. Relying solely on the emerging platform model is also hard to maintain sustainable development and growth in the fierce competition of the real economy. Both the single manufacturing chain and platform structures have certain model deficiencies and development barriers. The integration of the two provides a new development opportunity for the platform transformation of the manufacturing industry. From the existing research， there is a lack of definition and analysis of the concept system of the new business model of the integration of platforms and supply chains， as well as a lack of systematic analysis of the relevant characteristics and operational decisions under the “manufacturing chain + platform” dual-mode system.

Based on business practices and research results， the concept and theoretical framework of the “manufacturing chain + platform” dual-mode are proposed. Firstly， based on the relevant concepts and theories of supply chains and platforms， and combining the practices of enterprises such as Xiaomi， Huawei， and Apple， the concept definition of the “manufacturing chain + platform” dual-mode is proposed to describe the business model of the integration of manufacturing chains and platforms. Then， according to the different development sequences， two different classifications of the “manufacturing chain + platform” dual-mode are provided. Further， the system characteristics of the “manufacturing chain + platform” dual-mode are sorted out from the three levels of network， product， and value. Secondly， an in-depth analysis of the “manufacturing chain + platform” dual-mode structure is conducted from the three dimensions of static structure， dynamic structure， and interaction structure based on the system structure framework and key components of the dual-mode， and the roles of each member and their interrelationships are introduced in detail， and the structural characteristics of the “manufacturing chain + platform” dual-mode are analyzed. Thirdly， the academic researches related to the “manufacturing chain + platform” dual-mode are summarized from three dimensions： the platform system structure under the interaction of software and hardware， the formation mechanism of the integration of manufacturing chains and platforms from the perspective of dynamic evolution， and the competitive and cooperative relationship between members of manufacturing chains and platforms from the ecological perspective. Finally， based on the existing research results， future research prospects are discussed. Future research can be conducted from the perspectives of mining and verifying the system structure characteristics of the dual-mode， dividing the development stages of the dual-mode and its dynamic evolution， and governing the multi-agent competitive and cooperative relationships among members of the dual-mode.

In conclusion， the “manufacturing chain + platform” dual-mode is defined and analyzed， aiming to better understand and grasp the connotation and characteristics of this emerging business model. It theoretically expands and improves the theoretical system of supply chains and platforms， and provides theoretical guidance and practical support for enterprises in the new business environment.

In planning platform-type new product preannouncement information， the market expectations and strategy choices of consumers， developers， and competitors on the platform for new product preannouncement will be directly affected by the release sequence of the previews. A game model construction method is adopted in this paper to construct game models of three types of preannouncement strategies （synchronous， developer-first， and consumer-first） under a duopoly competition. The new product pricing， the number of participants， platform profit， and social welfare Nash equilibrium under different strategies are analyzed. The internal mechanism of the effects of the market expectation multiplier effect， information asymmetry， user preference， and cross-network effect on the selection of preannouncement strategy is studied. The findings show that： 1） when the platform preference of consumers and developers is large or the cross-network effect is small， the synchronous pre-announcement strategy is the platform enterprise's profit-oriented strategy； 2） The profits of platform firms are higher under the asynchronous preannouncement strategy， and it is more beneficial for platform firms to release the information firstly to the users （consumers and developers） with smaller platform preference or cross-network effect when its platform preference intensity or cross-network effect of both users is a medium value. 3） The expected multiplier effect of new product preannouncement is not always beneficial to the growth of platform firms’ profits and total social welfare， the moderate expected multiplier effect is the basic condition for the above three strategies to be adopted， and platform firms will not conduct any new product announcement activities when the expected multiplier effect is sufficiently large.

In reality， both third parties and distributors have incentives to engage in gray market speculation， driven by the arbitrage opportunities created by price differentials between different markets. At the same time， the coexistence of multiple channels in the gray market environment makes consumers' purchasing behavior more complex， they can easily get the price information from different channels by using search engines. Thus， consumers are more cautious when purchasing gray-market products and are more sensitive to the price， and they tend to take the price of licensed products in the high-priced market as an important reference point， which results in the reference price effect.The impact of reference price effect on pricing decisions and profits of supply chain members is investigated under the distributor gray market and the third-party gray market， respectively. The optimal gray market structure with different member preferences is also analyzed. Research shows that in the distributor gray market， the reference price effect is a “double-edged sword” for the manufacturer. In the early stage of market development， the reference price effect may help him quickly occupy the market and increase market share. However， after the market enters a mature stage， the reference price effect will lead to gray market expansion， damage his profit， and have a negative impact on his brand reputation. In the third-party gray market， the manufacturer faces more difficulties in dealing with the development of the gray market， due to the independence of the third-party speculator from the system. The impact of reference price effect on the operation of the gray market is also more ambiguous. Comparing two types of gray market structures， it is found that without reference price effect， the distributor gray market is more unfavorable for the manufacturer's market expansion through all channels， thus having a significant negative impact on his profit. A counterintuitive finding is that the distributor's participation in the gray market does not necessarily result in higher profit than the third-party gray market. The third-party gray market may become a Pareto improvement of the distributor gray market when specific conditions are met. Finally， by introducing a numerical example， it is found that the reference price effect will make the comparison of members' profits more complex under different gray market structures. The reference price effect will lead to a decrease in social welfare under any gray market structure. Regardless of whether there is a reference price effect， the social welfare under the third-party gray market is always higher than that under the distributor gray market.The findings of this paper provide decision-making references for manufacturers to reasonably respond to the development of gray market under different gray market structures and reference price effects. At the same time， it also provides theoretical references for exploring the preferences of system members for different gray market structures and seeking the optimal structure.

Upgrading energy structure can effectively reduce the carbon emissions of the energy system， which have significantly promoted the integration of renewable energies into the electricity system. With the increasing use of renewable energy in the electricity market generation， the electricity market faces severer supply fluctuation and imbalance challenges. Demand-response programs， such as the price-based program and the incentive-based program， are widely used to induce consumers to change their electricity consumption patterns. However， the existing demand-response program mainly focused on the load management， and has barely considered the impact of renewable energy supply fluctuations. Motivated by these practical challenges， a Stackelberg game model between the electricity system operators and end users is constructed to investigate when the renewable energy integrates into the electricity supply system， how the system operator’s emergency energy control strategy influences the system operator’s demand-response mechanism design decision. The results show that the effect of emergency energy control strategy on the design decision differs for different demand-response programs. For example， under the incentive-based demand-response program， the system operator who does not use the emergency energy will adjust its incentive strategy according to the fixed electricity price in the market； Otherwise， the mechanism design decision of the system operator who implements the price-based program or is willing to use the emergency energy under the incentive-based program， is independent to the fixed electricity price. In addition， the results show that demand-response programs are not always the optimal choice， and there is also no dominant emergency energy control strategy for the two types of demand-response programs. Finally， the two types of demand-response mechanisms are compared with respect to their efficiency in improving the responsiveness of the consumers. The results of this paper can help the system operator to clearly understand the effect of renewable energy supply fluctuations on the electricity market and select a reasonable demand-response mechanism， providing decision support for design of the electricity market trading mechanism.

With the development of information technology， many consumers will post online reviews after purchasing and experiencing products on e-commerce platforms. A new experience product is any product whose quality cannot be easily assessed ex ante by the firm and by consumers. It is intuitive that the information generated through reviews should help consumers that have not yet experienced the product to better assess the product. Thus， they can buy it at a better time. However， such information is also available to the firm， which can learn from consumer experiences about its own quality. Such information can allow the firm to better price the product. However， both the firm and the consumers learn about the product’s quality for different purposes. Some strategic consumers may also delay purchases through social learning online reviews to free ride on better information about the product’s quality and in anticipation of the firm’s price decrease. On the other hand， the firm may want to adjust their pricing strategies to prevent these consumers from waiting until later when the price drops.In this paper， review information is endogenously generated from consumer reviews， and the informativeness of reviews is determined by the number of online reviews in the first period， which may affect a firm's dynamic pricing strategy. In particular， it is not clear whether and how a firm could exploit the endogenously generated quality information and how it should adjust its pricing strategy. In fact， the main problem of our paper is the quality information updating process formed by strategic consumers who have different prior beliefs through the social learning of online reviews with different review informativeness. Also， whether the reviews provide an incentive for the firm to increase or decrease its price in the second period and， anticipating the firm's strategy， whether more or fewer consumers strategically delay their purchase compared to the no-review case.By constructing a two-period sales model consisting of a monopoly firm， an e-commerce platform， and strategic consumers， consumers have prior beliefs before purchasing the product in the first period. After purchasing and experiencing the product， the consumer posts online reviews on the e-commerce platform based on his perceived quality level. In the second period， the latter consumers update their prior beliefs through social learning and then make purchase decisions， and firms can observe the distribution of reviews to adjust their pricing strategies for new experience products dynamically.When consumers have the lower prior quality of product and the online reviews have good reference effect， the lower-informativeness reviews will make the firm tend to sell more products in the first period； When consumers have the higher prior quality of product and the online reviews have bad reference effect， the low-informativeness reviews incline firms to sell more products in the second period； If consumers have the lower prior quality of product and the online reviews have good reference effect， when online reviews have low informativeness， firm should lowerP_{\mathrm{2}}^{\mathrm{*}}. When user-generated reviews have high informativeness， firm should increaseP_{\mathrm{2}}^{\mathrm{*}}. If consumers have high prior quality of product and the user-generated reviews have bad reference effect， when user-generated reviews have low informativeness， the firm should increaseP_{\mathrm{2}}^{\mathrm{*}}.When online reviews have high informativeness， firm should lowerP_{\mathrm{2}}^{\mathrm{*}}.Contrary to intuition， when the prior quality is slightly lower （or slightly higher） than the average perceived quality， there is a 'distortion effect' on the impact of the review information on the dynamic pricing strategy of the firm， consumers will follow the purchase of others blindly. Contrary to the traditional view， user-generated reviews may reduce consumer surplus. When the prior quality is low and the average perceived quality is higher than the prior quality， the lower -informativeness reviews can lead to a lose-lose situation for both the firm and consumers. Firm profits and consumer surplus decrease at the same time， and the total social welfare will also decrease.

Under the joint promotion of the government and consumers， the green agricultural supply chain under mass customization based on community group purchasing is gradually emerging. Both the production and sale of agricultural products have shown a trend of multi-level development. However， at the same time， as the scale of the supply chain continues to grow， the output， quality and price competition between supply chain enterprises and multi-level agricultural products has become increasingly fierce. In the face of a large number of agricultural producers and sellers and thousands of personalized consumers， supply chain enterprises need to develop more detailed production planning and sales strategies based on the heterogeneous preferences of consumers， so as to more accurately provide differentiated agricultural products that match the needs of consumers， and to truly realize the docking between the farm and the table. This issue has received keen attention from many scholars and related enterprises.

Therefore， based on the green agricultural supply chain network composed of multiple producers， retailers and consumer communities， a network equilibrium model for green agricultural supply chain under mass customization considering consumer heterogeneity preferences is constructed， in which producers simultaneously produce agricultural products of different quality grades and sell them to different consumer communities through retailers. On this basis， the equilibrium conditions of producers， retailers and consumer communities are proposed， and production planning and sales strategies based on consumers’greenness preferences are explored for different consumer communities. Finally， the impact of factors such as agricultural production， logistics costs and types of consumer communities on the decisions and profits of supply chain enterprises is further analyzed. Through theoretical derivation and numerical simulation， the equilibrium of the green agricultural supply chain under mass customization is comprehensively discussed， so as to provide strategic suggestions for related industries and supply chain enterprises.

The results show that （i） selling medium and high quality products at the same time and properly cultivating the consumers’ preferences are both conducive to promote the popularization of green agricultural products while it is difficult for the supply chain to achieve high returns if relying solely on high-end consumer communities； （ii） it can improve the overall revenue of the supply chain if all parties cooperate to expand the green agricultural market rather than compete； （iii） when agricultural production is insufficient， selling agricultural products to ordinary communities with high preferences and high-end communities with low preferences could help to improve the resilience of supply chain； （iv） customized production and sales of agricultural products to only one type of consumer community is beneficial to enterprises， while customized production and sales to both types of communities is beneficial to consumers. The conclusions of the study can provide reference for decision-making of relevant enterprises and research in related fields.

As China’s economy evolves and consumer awareness of social responsibility grows， manufacturing firms face more complex markets and increased competition. Many are enhancing their competitiveness while also addressing corporate social responsibility （CSR） within supply chains. However， some still view CSR donations as a financial burden， and may not have fully integrated CSR into their strategic framework. Notably， during crises that demand relief donations， certain competitive manufacturing firms choose to contribute despite recognizing potential drawbacks to their own interests. This raises an important question： what motivates these firms to make such donations？ To address this issue， eight supply chain game models are developed that consider horizontal interactions and manufacturers’ donation-based CSR behavior， and the effects of quality differences and horizontal interactions on manufacturers’ CSR donation decisions， channel members’ performances， consumer surplus， and social welfare under Cournot and Bertrand competition in a competitive supply chain consisting of two retailers and two manufacturers with quality differences are investigated. （i） In both Cournot and Bertrand competition， it is observed that manufacturers are consistently incentivized to engage in CSR donations when their competitors do not. However， when competitors also adopt CSR strategies， the findings reveal that there is a unique Nash equilibrium strategy for manufacturers under Cournot competition， while two Nash equilibrium strategies under Bertrand competition； （ii） When market competition is relatively low， lower product quality disparities could put two manufacturers fall into a Prisoner’s Dilemma in both Cournot and Bertrand competition. This finding is the first to offer a partial explanation of why competitive corporations engage in donation-based CSR—possibly as a result of the Prisoner’s Dilemma—from the standpoint of chain-to-chain competition； （iii） Manufacturers are more motivated to engage in CSR Donation under Cournot competition than that under Bertrand competition； （iv） The high-quality manufacturer is more effective at implementing CSR Donation in Bertrand competition， whereas the low-quality manufacturer is more effective at doing so in Cournot competition； （v） Manufacturer-retailer collaboration in CSR Donation might have a negative impact on the consumer surplus and social welfare under certain conditions. The findings can offer valuable insights for corporate managers， enabling more effective implementation of CSR donations across varying competitive market environments.

With the development of consumption upgrading， consumers' consumption of fashion goods has changed from price orientation （cost orientation） to service / quality orientation. In the cross-border import e-commerce logistics operation， the integrated overseas warehouse logistics provider can provide value-added services， which often affects the consumer demand of customers. The product of the promised customer order cycle time execution rate， anti-counterfeiting traceability rate and customized service satisfaction rate is defined as the logistics value-added service level. To better meet the needs of customers， more and more cross-border e-commerce outsourcing logistics to integrated overseas warehouse logistics providers. Taking the cross-border e-commerce supply chain composed of a cross-border import e-commerce and an integrated overseas warehouse logistics provider as the research object， consumer demand is regarded as a function of fashion goods pricing， value-added logistics service level and random factors， and the optimization decision of logistics value-added service level and fashion goods pricing of cross-border e-commerce supply chain is studied.It is found that the level of logistics value-added service in cooperative game is much higher than that in non cooperative game； the pricing of fashion products is lower than that of non cooperative game， which makes the scale of cross-border e-commerce transactions higher than that of non cooperative game. Meanwhile， the increase of logistics value-added service coefficient， the decrease of cost coefficient of value-added service level and the decrease of price coefficient of fashion goods will have a beneficial impact on logistics value-added service level and system profit， and the performance of cooperative game is more prominent than non cooperative game.A very important finding is that when the logistics value-added service coefficientb>\mathrm{2.3}holds， or the price coefficienta\le \mathrm{2.3}holds， the cross-border e-commerce supply chain under the cooperative game can realize the win-win cooperation between the two parties with no coordination contract.In other cases， to coordinate the supply chain， the sensitivity analysis of cooperative game supply chain profit sharing contract shows that logistics providers can improve the profits of both parties by improving service level coefficient， reducing service level cost coefficient， and reducing price coefficient. For cross-border e-commerce retailer， the biggest benefit is from the decline of price coefficient.The management enlightenment of this study is that cross-border e-commerce retailers should focus on promoting and selling fashion products with good quality， fashionable design， and novel styles to lead consumer demand. Logistics providers focus on improving the level of value-added services and reducing the cost-of-service level. With the integrated cooperation of the two parties， reducing consumers' sensitivity to the price coefficient and improving consumers' sensitivity to the level of value-added services can automatically achieve a win-win cooperation.

Blockchain technology can effectively solve the vaccine safety problem. When vaccine supply chain members work with a blockchain platform， there are two main charging ways for the blockchain platform， namely the charging by fixed fee and the charging by service fee. However， current research has not considered the impact of different charging methods on vaccine supply chain operational decisions and coordination strategies. Based on this， the Stackelberg game models of the traditional vaccine supply chain and the blockchain-based vaccine supply chain under different charging scenarios are constructed， the influence of key factors on the optimal decision are analyzed， the conditions for cooperation between the vaccine supply chain and the blockchain platform and the selection of charging methods of the blockchain platform are discussed， and the coordination strategies of blockchain-based vaccine supply chain under different charging scenarios are studies.The results show that reducing the time taken by vaccinators to test vaccines and controlling the number of problematic vaccines can effectively improve the profit of vaccine supply chain. When both the proportion of identified as problematic vaccines and the marginal service cost of the blockchain platform are low， the introduction of the blockchain platform is more beneficial to vaccine supply chain. When the marginal service cost of blockchain platform is high， the charging method by service fee should be selected. Under the charging scenario of fixed fee， when the marginal service cost is equal to a certain value， the vaccine supply chain does not need coordination； when the marginal service cost is below the value， the revenue-sharing and fixed subsidy combined contract can realize the coordination of vaccine supply chain. Whether the vaccine supply chain can achieve coordination under the charging scenario of service fee is related not only to the marginal service cost of the blockchain platform and revenue sharing proportion， but also to the total service fee charged by the blockchain platform to the vaccine manufacturer and the vaccination unit.

In the evolving landscape of digital commerce， online channel expansion strategies are increasingly changing. While some companies have added conventional B2C e-commerce platforms such as JD.com， others have introduced O2O platforms like Doordash that offer instant delivery services. Despite the growth in such strategies， there is a scarcity of research comparing the effects of different types of online channel expansions on offline sales. Drawing upon the omni-channel continuum framework， B2C e-commerce platforms are delineated as “vertical strategies” and O2O instant delivery platforms are delineated as “complete strategies.” This paper investigates their relative impacts on physical store sales and examines the moderating role of store density on the effectiveness of omni-channel strategies. Associative Network Theory is used to formulate our hypotheses. Unique daily sales data from 1，696 offline stores and 9 online platforms of a high-end wine brand in China are analyzed， spanning from April 2019 to June 2022. Through linear and panel regression models， it is found that both complete and vertical strategies significantly enhance offline sales. However， complete （vs. vertical） strategies have a stronger synergistic effect on offline sales. Moreover， the results indicate that higher store density amplifies the positive effects of vertical strategies on offline sales. Several robustness checks validate our results. This research shed light on the strategic importance of selecting different types of channels in enhancing offline sales.

“Courtesy demands reciprocity” is deeply rooted in Chinese social culture， and the proverb “Courtesy on one side only lasts not long” is a driving force behind the prevalence of gift-giving in various public social settings. Typically， the act of giving a gift serves as an expression of respect or concern for the recipient， or it may involve seeking something in return. In the case of utilitarian goods with a purpose or assistance in mind， the gift-giver must consider not only the quality of the items but also the protection of the recipient’s privacy. Enterprises， as providers of goods， must also grapple with these considerations. The traceability function of blockchain technology can partially mitigate the sale of counterfeit products， alleviating consumers' concerns about product quality. However， it is crucial to note that the application of blockchain technology is not entirely without risk. When consumers' private information becomes part of the blockchain due to a transaction， the enterprise may not explicitly guarantee that it won't be used for other purposes. This potential misuse of personal information increases the risk of exposing other sensitive data about consumers and can negatively impact the reputation of the enterprises involved. Considering the specific social and cultural context of China， it is indeed worthwhile to explore the impact of blockchain technology's empowerment on decision-making within the gift product supply chain.The application of blockchain technology can indeed curb the sale of counterfeit products to a certain extent， but it also gives rise to privacy concerns among various stakeholders， including gift-givers （consumers） and recipients. To delve deeper into this issue， it is approached from different blockchain application scenarios and production/marketing modes. Hotelling model is employed to construct a competition model for the gift product supply chain. This model allows us to analyze the evolving impact of product audience preferences on the gift product market. Furthermore， the conditions under which the integration of blockchain technology are investigated into the gift product supply chain is viable.The results reveal that consumer cost aversion， cost regret， and recipient dissatisfaction exert a significant influence on the decision-making processes of supply chain members. Specifically， the recipient's focus on product features directly impacts the service level and market share of the supply chain. Conversely， consumer undifferentiated preferences not only weaken the recipient's influence but also reduce the gift supply chain's willingness to combat counterfeit products by implementing blockchain technology. Furthermore， when recipients place greater emphasis on safeguarding their privacy information， an increase in recipient dissatisfaction leads to an expansion of off-chain market share and an enhancement of on-chain privacy protection. However， it also results in a reduction of on-chain market share and off-chain reputation maintenance levels. As the gap between cost aversion and cost regret narrows， the impact of recipient dissatisfaction on supply chain members' decision-making and market share diminishes. Additionally， in cases where both on-chain information leakage and off-chain sale of counterfeit products are present， it is found that， compared to decentralized decision-making， product audience preferences wield a more substantial influence on the service level and market share of the supply chain when centralized decision-making is adopted. This underscores the importance for integrated production and marketing enterprises to prioritize audience preferences when seeking to enhance market competitiveness. Moreover， for gift product supply chains that integrate production and marketing， it is imperative to consider the preferences of the product audience when contemplating the introduction of blockchain technology.

An in-depth analysis of the new development concept’s impact on the definition of regional subject characteristics， as well as the scope of carbon emission rights and responsibilities， expands the connotations and boundaries of the principles of equity， efficiency， sustainability， and feasibility. A three-tier， 30-dimensional initial carbon quota allocation system is constructed， and the maximum deviation method is used to comprehensively measure regional differences and estimate the initial carbon quotas of China’s 30 provincial regions over the next 15 years， with performance evaluations conducted from the integrated dimensions of equity， efficiency， and emission reduction costs. The research results indicate that the new development concept has expanded the connotations and boundaries of existing distribution principles in areas such as regional coordinated development， factor flow allocation， social co-construction and sharing， and green low-carbon development. Changes in the definition of regional subject characteristics and the scope of carbon emission rights and responsibilities will significantly affect the distribution results of initial carbon quotas， thereby affecting the fairness， efficiency， and emission reduction costs of the distribution plan. The new plan can better reflect existing distribution principles and has better distribution performance. Regional characteristics such as ecological endowment and green sustainable development capabilities have a significant impact on the surplus and deficit of each region’s initial carbon quotas， which helps to promote regional green sustainable development and coordinated carbon reduction.

In the context of subsidy decline， the implementation of the “dual credit” policy will further realize the low-carbon development of the auto market through market mechanism regulation. Under the combined effect of the decline of government subsidies and the “dual credit” policy， it focuses on the changes in the automobile market equilibrium that considers carbon emissions. Specifically， under the two policies， consider how to optimize production， pricing， and emission reduction research and development strategies for fuel vehicles in the automotive market that simultaneously produces new energy vehicles and fuel vehicles， and discuss the decline in government subsidies and the “dual credit” policy for the automotive market. The impact of optimal production， pricing， and emission reduction research and development strategies， and finally an in-depth analysis of the impact of subsidy decline and the impact of the optimization of the auto market under the “dual credit” policy on carbon emissions. It is found that： 1） The impact of the integral transaction price under the “dual credit” policy on the optimal emission reduction R&D investment of fuel vehicles is non-linear. When the decline of government subsidies is low or high， the government should set higher and lower transaction price of points as an incentive for automakers to increase R&D investment in emission reduction of fuel-fueled vehicles； 2） The national target value of fuel consumption per 100 kilometers has an impact on the optimal emission reduction R&D investment and car demand of gasoline vehicles. The impact is non-linear. When the decline in government subsidies is low or high， the government should set a lower and higher target value of fuel consumption per 100 kilometers to increase the demand of new energy vehicles and reduce the demand of gasoline vehicles； 3） The implementation of the “dual credit” policy has made the automobile market increase investment in fuel vehicle emissions reduction research and development when government subsidies have declined. At the same time， the continued decline of government subsidies may lead to a continuous decline in carbon emissions in the automobile market.