LISS 2023

The concept of the Metaverse has been discussed and explored for several decades, with notable references in virtual reality (VR) and virtual social spaces. However, its applications in healthcare are still in their infancy. Researchers have explored its potential applications in smart healthcare from various angles, but few studies have shown how physical activity in virtual environments can support spatial learning. In this paper, we envision a new concept and application of the “Retroverse” characterized by embodied learning and incorporating virtual environments into a reality-based experience. Our main goal is to stimulate people’s memory through physical fitness activities in the virtual environment. By intelligently optimizing the sports equipment and campus management system at Hong Kong University of Science and Technology (Guangzhou), we connected the “Campus Brain” with a stationary bicycle. Through the “Virtual Campus Ride”,” visitors can get a 360-degree view of the campus while exercising either individually or in teams. This embodied learning experience allows users to interact with each other based on a digital representation of the real world. We believe that this interactive and physically engaging trend of smart healthcare can be further developed into simulation-based training and psychological therapy. In the future, such experiential learning or rehabilitation could be supported by AI-generated content in a computer-generated environment.

This article focuses on the issue of telecommunications customer management, and studies customer group discovery methods based on customer communication relationships and customer attributes. A density clustering algorithm is proposed that considers both communication customer attributes and communication relationships between customers. The customer clustering algorithm is based on an undirected weighted network with integrated similarity. It sorts the nodes in the network according to their strength, starting from the object with the highest node strength. The pruned nodes in the undirected weighted network that have a direct connection to the clustering starting point after removing edges that are less than the similarity threshold are clustered into one group. The starting point of this clustering is the core node, while also obtaining hub nodes and isolated nodes. The empirical analysis of customer data from a telecommunications company in Beijing shows that the customer clustering algorithm proposed in this study can effectively discover customer groups and identify core objects within the customer group.

Recently, the increasing need for energy, water, and material forced the world to seek new solutions by considering sustainability to come through the vicious circle of continuous production and consumption. Therefore, the concept of the circular economy is gaining popularity across the world with its promise of a better future for the next generations. Herein, the cities as the most problematic, highly populated, and industrialized areas are one of the main concerns of the debate. Even though the concept of the circular economy is quite fresh and in the early stages, there are already some actions in cities focused on the green transportation of urban areas. The countries such as China, the European Union, and the USA have already added the circular economy concept for urban mobility to their schedule. The term urban mobility covers all kinds of transportation infrastructure-related problems within the urban areas including congestion, planning, mode share, and optimizations. This paper will review the recent actions in cities based on the circular economy approach from the perspective of urban mobility. Even though there are lots of initiatives for sustainable transportation all around the world, green transportation applications in the frame of the circular economy are highly questionable. Therefore, the aim is to present and unroll achievements on the topic, to give an idea about in-use projects, and to question sustainability.

We study the game of co-product processing between two manufacturers under the condition of fixed-proportions. Each manufacturer processes one raw material to simultaneously produce a fixed proportion of multiple linked products. There is an alternative competition between the similar products of different manufacturers. When a similar products of the manufacturer is out of stock, some unsatisfied customers will buy the similar products of another manufacturer. They maximize their personal profit and loss by choosing the amount of processing. In this paper, firstly, a single period processing game model is established when the total demand and market share are random, and it proves that the equilibrium processing quantity and the equilibrium profit and loss value will increase with the substitution rate of each co-products. Secondly, further considering the problem of multi-period processing game, the equilibrium processing strategy is given and found that the equilibrium processing volume of manufacturers will decrease with the increase of the initial inventory of themselves and competitors. Finally, simulation examples are used to analyze the influence of competition and its strength and output ratio on the co-products system.

The establishment of a regional common brand for agricultural products plays a crucial role in achieving agricultural and rural modernization and promoting the industrialization of agricultural products on a large scale. The branding of agricultural products enables the conversion of regional industrial advantages into market value, making it an essential tool for driving, integrating, and advancing the development of rural industries. The central government and various levels of government have implemented numerous policies aimed at fostering the development of regional public brands for agricultural products. Assessing and analyzing these policies holds significant importance in enhancing the formulation of effective policies. Therefore, this study employs a combination of quantitative and qualitative methods to examine the policy tools related to regional public branding of agricultural products. A coding system for these policy tools is developed using 30 policy documents as research samples, and content analysis is conducted to categorize and analyze the textual data. Based on the coding, semantic network analysis and co-occurrence analysis are done for different types of policy instruments. It is analyzed that the current policy instruments are mainly of coercive type, supplemented by hybrid type, and less of voluntary type. Additionally, the policy instruments exhibit a structural imbalance. It is observed that the coercive and hybrid policy instruments align more effectively with the policy objectives, whereas the voluntary policy instruments do not align as well with the policy objectives.

This study examines the optimal order decision of an e-commerce platform retailer based on a wholesale price contract, considering customer returns, in response to an increase in the number of impulsive consumers under the speedy expansion of live e-commerce. By maximizing the profit models both without and with impulsive consumers, we show that the optimal decisions are the only existence for both cases. The optimal order quantity decreases as the refund price increases. Maximum expected profit for the retailer and the optimal order quantity are decreasing in the proportion of impulsive consumers. Comparative analysis shows that the optimal order quantity and the maximum expected profit with impulsive consumers are less than those without impulsive ones. The impact of customer returns on retailer decisions is greater when impulsive consumers exist.

Under the complex and turbulent economic and social environment, supply chain collaborative innovation (SCCI) has become an important way for enterprises to optimize supply chain structure and business processes, resolve major risks and realize sustainable development. This paper uses CiteSpace software to visually analyze the theoretical research of SCCI in China. The results show that: (1) The theoretical evolution of SCCI go through three stages: exploration period, development period and prosperity period. The exploratory stage focuses on the cause analysis of SCCI theory. The development period explores the application of SCCI from multiple fields. The theory of prosperous period closely follows the new technological change and industry 4.0 development trend. (2) The keyword co-occurrence analysis, shows the relationship between SCCI and industrial cluster, innovation performance, evolutionary game and other keywords, which provides guidance and suggestions for solving internal and external collaborative problems of domestic supply chain enterprises. (3) The new technological revolution and industry 4.0 era promote the theoretical research frontier to the direction of digital and intelligent SCCI, and promote the continuous deepening of the theoretical research results.

Cities that have the means for ferry and rail transportation options for their urban mobility have high levels of potential benefits in terms of economy, environment, and sustainability. However, to take full advantage of these systems’ benefits, the integration between these different modes of public transportation systems must be improved. In this study, the goal is to optimize the integration between rail and ferry transportation modes using a developed innovative advanced traveler information system. By using historical data (such as ridership, and weather) from the corresponding authorities and conducted revealed preference survey results, insight into the current state of the rail and ferry transportation systems can be obtained using machine learning and pattern recognition methods. With the constraints and the objective function obtained from the current state diagnosis, the integration between the rail and ferry transportation systems can be optimized by using different optimization methods. Hence, public buses and alternative transportation modes such as bike- sharing can also be utilized to further improve the integration between rail and ferry transportation systems and make them more sustainable.

In the context of the rapid development of rural e-commerce, the current development model of rural logistics with “resource sharing and common distribution” as the core is analysed, and a set of nationwide evaluation indicators is designed, taking into account infrastructure, technology and equipment, information technology, services, sustainability and institutional mechanisms. This paper aims to evaluate and study the development of rural logistics in China. This paper lays the foundation for evaluating and researching China’s rural e-commerce logistics service activities.

This study addresses the increasing frequency of customer returns and its impact on decision-making and profitability in a supply chain with a single supplier and retailer. It proposes a unique buyback contract that includes different prices for unsold and returned products. The focus is on decentralized decision-making models. The results indicate that the retailer’s order quantity and profit are positively correlated with the buyback prices and negatively correlated with the return rate. The supplier’s profit decreases as the buyback price for unsold products increases, but increases with a higher penalty factor. The optimal buyback price for returned products is set at the salvage value of the product to maximize profitability.

In order to solve the frequency offset caused by the inconsistence of RF transceiver frequency, the Costas loop algorithm is used to realize carrier synchronization. On the basis of analyzing the principle of the algorithm, the feasibility of Costas loop algorithm based on arctangent in the actual transceiver environment is verified by combining MATLAB with the actual transceiver environment. In FPGA based engineering implementation, table lookup method is used to reduce the time delay of feedback loop and thus reduce the clock frequency of FPGA. Simulation and engineering verification show that the loop implemented in this work can realize carrier synchronization, provide correct data for subsequent demodulation and decoding, and realize synchronous demodulation of BPSK modulated signals.

With the accelerated modernization of China’s agriculture and the rapid growth of the agricultural consumer market, supply chain management of agricultural products is of great interest. As the behavior of supply chain members such as sales effort and fairness concerns can have a great impact on the overall supply chain management decisions. Therefore, in this paper, we introduce options contracts to investigate the ordering and coordination strategies of a single-cycle two-stage agricultural supply chain considering that the agricultural retailer is a fair concern and provides sales effort. The results show that in the case where the retailer only orders options, there is a relationship between the effect of the retailer’s fair concern on order quantity and the parameters of the option contract, with the retailer’s order quantity being an increasing function of fair concern when the option price is less than a critical value, and a decreasing function in the opposite case. In addition, by discussing the supply chain coordination mechanism, it is found that the level of retailers’ fairness concern does not change the coordination of options contracts and that under certain conditions, supply chain coordination can be achieved. The results of the study provide some meaningful suggestions for agricultural supply chain management.

As the number of new energy vehicles has increased, so has the number of discarded batteries and the issue of battery recycling. Most of the existing double credit models only consider the competition between car manufacturers and do not consider battery recycling. So this paper considers a model of competition between two car manufacturers under a dual-credit policy where the battery manufacturer earns points for recycling its batteries. The impact of changes in the value of the points earned by battery manufacturers for recycling batteries on the price and profitability of each manufacturer is examined. It is found that in the case where the battery manufacturer recycles the battery with points, the optimal price of each manufacturer is lower than the optimal price of recycling the battery without points, and the total profit of each manufacturer is higher than the total profit of recycling the battery without points. And as the integral value of recycled batteries becomes larger, the profit of producing each new energy vehicle increases, and the total profit of battery manufacturers and manufacturers that only produce new energy vehicles increases.

This paper studies the problem of designing combinatorial double auctions for online freight platforms, in which the platform can take advantage of the Internet to gather the demands from small shippers, form shipper collaboration, and then chooses the right carriers to meet these demands. The problem faced by the online platform is to efficiently allocate transactions and determine the appropriate transaction prices for both shippers and carriers under incomplete information. To address the problem, we propose the Padding-based Combinatorial Double Auction mechanism where the shippers bid for the transportation services for a route while the carriers bid for the transportation service combinations. We show that the mechanism is incentive-compatible, individually rational, budget-balanced, and asymptotically efficient. Furthermore, we find by the numerical studies that shipper collaboration can be effective in increasing social welfare, improving shippers’ and carriers’ utility, and increasing trading volumes at larger market sizes.

Based on the data analysis of 50 domestic commercial banks from 2011 to 2021, this paper empirically verifies the principle and influence channel of digital finance on Systematic risk of banks. Through empirical analysis, it can be seen that the development of digital finance promotes Systematic risk of banks. Through empirical analysis, the development of digital finance has promoted the Systematic risk of banks and has heterogeneous characteristics. The development of digital finance has played a greater role in promoting the Systematic risk of non-public banks. The relationship between financial disintermediation, business competition, credit concentration and Assumption of risk and the Systematic risk of banks has multiple Mesomeric effect. Digital finance promotes Systematic risk of banks through financial disintermediation, and inhibits Systematic risk of banks through business competition, credit concentration and Assumption of risk. The promotion of financial disintermediation on Systematic risk hedged the inhibition of banking competition, Assumption of risk and loan agglomeration, and ultimately increased the Systematic risk of banks. The benign development of urbanization has intensified the promotion of digital finance on Systematic risk of banks, and the increase of domestic demand scale has weakened the promotion of digital finance on Systematic risk of banks.

Manufacturing enterprises’ production amount of finished products varies every day, requiring repeated coordination between transportation demand and truck supply. In the case of third-party logistics companies undertaking the transportation of finished products, as several companies are involved, manufacturing enterprises have difficulties in releasing dynamic information on transportation needs in real time, resulting in truck queueing or truck vacancy. This paper draws on the idea of the truck appointment system (TAS) to design the new appointment rule. We set the optimal appointment quota to ensure that the queue is short and there are trucks waiting to transport the next batch of finished products. In the numerical experiment, we solve for the optimal appointment quota.

Issuing local debt is an important way for the government to raise development funds, and its impact on industrial structure upgrading has always been controversial. This paper employs the fixed effect model and the panel data of 30 provinces in China from 2010–2017 to study local government debt’s influence on local industrial structure upgrading progress. Results show that government debt could boost industrial structure upgrading, and this mechanism works via promoting innovation activities. However, such promoting effect brings side effect: the decrease of total factor productivity (TFP). What is more, regional differences are evident: The eastern region is more sensitive to such influence. At this stage, China needs to improve the proportion of productive-debt and keep improving the governance of grassroots officials.

With the continuous development of the medical field and the rapid advancement of information technology, the application of medical data has become a crucial component of medical practice. The introduction of machine learning methods can analyse large amounts of medical data in a more detailed and rapid manner. By comprehensively evaluating patient demographic information, diagnosis, treatment, and other data in the electronic medical record, we can understand the patient’s health status and potential dangers, and provide a powerful auxiliary support for the doctor’s diagnosis and treatment. Traumatic haemorrhagic shock is a life-threatening clinical condition, and early and accurate prediction and intervention is essential for patient survival and recovery. In this study, for the injury condition of traumatic haemorrhagic shock, based on the emergency database of the General Hospital of the Chinese People’s Liberation Army, the inclusion and exclusion criteria of the research experiments were designed under the guidance of professional clinicians, from which the data of relevant patients’ medical indicators were extracted and data preprocessing was carried out. In the model construction stage, the study used five algorithms to construct a traumatic haemorrhagic shock prediction model using simple machine learning models including Logistic Regression, CART, and ensemble learning including Random Forest, XGBoost, and neural network structured multilayer perceptual machine MLP respectively, and the key indicators screened by doctors were used as the inclusion features. In the model evaluation stage, the study used a ten-fold cross-validation method to assess the prediction effect of different machine learning models by indicators such as ROC curve and AUC. The results showed that the XGBoost algorithm had the largest average AUC and the overall prediction effect was better than the other models, and the model had good prediction effect with potential clinical application. Finally, this study used the SHAP method to explain the value of different features to the prediction model from global and individual perspectives, making the model more credible.

Acute hypotension episodes (AHE) are the most common adverse events in post-traumatic emergencies, and the occurrence of acute hypotension is very dangerous for the life safety of critically ill patients. This can lead to fainting or shock, irreversible organ damage or even death. This study uses the mimic IV database to screen key indicators for acute post-traumatic hypotension using the XGBoost method, to select vital signs indicators based on timeliness, economy and convenience of clinical application to construct a simple hypotension scoring tool, and to specify the rationale and process for the construction of the scoring tool. The scoring of the condition can also assist healthcare professionals in the rapid diagnosis of the cause of the patient’s condition. The use of specific scores to indicate the extent of a patient’s condition not only facilitates patient understanding, but also allows the system to be used more frequently by doctors in the clinical setting. The results show that a risk of traumatic haemorrhagic shock scoring tool based on five vital signs - systolic blood pressure, diastolic blood pressure, heart rate, respiratory rate and blood pressure - can accurately identify patient outcomes and differentiate the severity of injuries, providing some clinical decision support.

Acute hypotension is a common emergency of dangerous diseases, which can cause fainting or shock, lead to irreversible organ damage and even death of patients, and require timely and effective intervention after the occurrence. If patients with acute hypotension can be accurately identified in time and effective intervention measures can be taken, the mortality and disability rate can be greatly reduced. According to the inclusion and exclusion criteria, 1535 patients (214 Experimental Group and 1321 Control Group) were extracted from the intensive care medical information Mart (MIMIC) - IV, and the cross-sectional data were extracted. Data cleaning, missing value interpolation and other pre-processing processes were carried out. Feature engineering was used to select key indicators, A set of interpretable key indicator sets (Heart Rate, Systolic Blood Pressure, International Normalized Ratio, Diastolic Blood Pressure, Thrombin Time Measurement, Carbon Dioxide, Hemoglobin Measurement, White Blood Cells, Lactate) were obtained. Four machine learning algorithms, XGBoost, LR, KNN, MLP, were used to integrate the models, and the voting algorithm was used to establish and verify the prediction models of acute hypotension. The area under the receiver operating characteristic curve (AUROC) was used to evaluate the performance of the model. The results showed that the performance of voting’s integrated model was significantly better than that of other models’ time series data (AUROC = 0.973). Through the research of medical field based on machine learning and the construction of clinical acute hypotension prediction model, this paper hopes to make contributions to domestic emergency treatment in theory and practice.

A company distributes goods to stores all over the country through one warehouse. The warehouse processes orders from different stores daily and decides on order allocation and shipping routes for each vehicle. In a long-haul transport, drivers must obey traffic safety regulations, such as day/night speed limits and continuous driving hours. This paper considers a long-haul vehicle routing problem in terms of time windows and order priorities. An integer programming model is built to minimize the total transportation time of all vehicles. Then, a multi-conditional clustering method based on K-means is adopted to achieve regional division. Moreover, a Tabu Search (TS) algorithm, based on the regional division with creating mixed neighborhood structure, is proposed to optimize the solutions for the model. The preliminary results of a series of experiments, which are conducted on real data, are able to verify the effectiveness and efficiency of the proposed algorithm.

Blood, as a precious and scarce resource, faces the dual challenge of waste and shortage in China’s existing blood supply chain. In this study, we investigate the blood supply chain consisting of a blood center and two hospitals, and develop an inventory optimization model that considers blood timeliness and blood type substitutability. The model aims to minimize blood expiration rate while ensuring acceptable blood timeliness, and to study inventory optimization strategies. A TA-TS-HA heuristic algorithm is used to analyze the case, and the results show that inventory control strategies significantly affect the blood expiration rate in each entity, while blood type substitutability has a significant impact on the overall blood expiration rate and shortage rate of the blood supply chain. Considering blood type substitutability within hospitals is better than not considering it. The model provides decision-making ideas for inventory optimization of the blood supply chain as a whole.

From the perspective of green and sustainable development, this paper, based on studying the supply chain composed of fresh food e-commerce and logistics service providers, considers consumers’ freshness and green preferences, and the government’s reward and punishment mechanism for promoting the investment in circular incubators. Construct the supply chain game model, introduce the coordination mechanism of the “cost-sharing + benefit-sharing” contract, and discuss how to set the government reward and punishment mechanism and the feasibility of the coordination mechanism. The study found that appropriate government rewards and punishments can increase the proportion of investment in circular incubators in the supply chain; cooperation between fresh food e-commerce and logistics service providers in sharing revenue and costs can increase the proportion of investment in circular incubators and achieve Pareto improvement of the profits of each member of the supply chain.

The cybersecurity operation & management of the information system is essentially for business service [1]. Cybersecurity should take the business as the perspective, to the business system life cycle as the span. The cybersecurity technology and management should be combined, which make security risk can be controllable. Base on the current status of cybersecurity operation & management in universities and colleges, this paper explores feasible optimization methods and measures for cybersecurity management from aspects such as information asset management, security vulnerability closed-loop management, and access control.

There has been increasing recognition of the importance of environmental sustainability in recent years and the need for immediate action. Regardless of the necessity of green finance, investments in such products are in fact beneficial for investors. Adopting the DEA-BCC approach, we test the financial effectiveness of 112 European corporations in 2021, with an aim to unveil the key determinants. The main results show Nordic and Benelux countries, and information technology, industrials, and health care sectors holding the highest green efficiency, while Southern European countries and the real estate sector share the lowest. These findings seem to lead to an important suggestion: financing subsidiaries, low-emission subsidiaries and low borrowing costs will improve the green efficiency of European companies.

Overconfidence is a common behavior bias that affects the decision maker’s equilibrium strategy in competition. To explore the impact of overconfidence on the inventory game, this paper establishes an asymmetric newsvendor game model with N participators that considers overconfidence. The study found that when the newsvendor key ratios are identical the decision makers should ignore the impact of rival’s decisions. The pull-to-center effect can be observed in asymmetrical overconfident newsvendor games. Overconfident newsvendor may earn more when competing with the rational newsvendor, while the rational newsvendor may earn less. The overconfidence can offset the impact of competitive effect on order decisions. In addition, this paper explains the reason why the total order volume of the newsvendor game system is not lower than the total order volume of the centralized newsvendor system.

In view of the negative emotions caused by the addiction of short video app users, the paper analyzes the influencing factors of users’ discontinuous usage behavior, and provides theoretical support for users and short video Apps operators to prevent network addiction. Based on social cognition theory and negative emotions, a theoretical model of short video Apps users’ discontinuous usage behavior was constructed, empirical study was carried out through questionnaire and collection data was analyzed through structural equation model. The results were as follows: usage addiction causes users’ guilty, fatigue, anger and disappointment. Guilty, fatigue and disappointment have a positive impact on users’ discontinuous usage intention, which leads to users’ discontinuous usage behavior. However, anger has no significant impact on users’ discontinuous usage intention.

This study aims to establish an interpretable machine learning model for predicting acute traumatic coagulation. We used the MIMIC IV database and extracted 2814 patients based on medical inclusion and exclusion criteria. Four machine learning models are established and results show that the ATC model based on XGBoost algorithm has the best performance, with an AUC of 0.95 and an accuracy of 96%. Then we used XGBoost model to calculate the contribution of each feature value to the model and Shap Value method to analyze the contribution of feature values to prediction from both the entire sample and a single sample.

SMEs (small and medium enterprises) are more prone to default due to the problem of information asymmetry with banks and a lack of suitable collateral. Banks face both opportunities and challenges due to the high demand for loans from SMEs, and identifying credit risk has become their primary concern. Restructured sentence for clarity and concision. This paper aims to predict the credit loan status of SMEs in Chinese banks by utilizing an open data set provided by the Digital China Innovation Competition. Decision tree and random forest models are used to construct a classification model, which is then analyzed along with its important attributes. Results indicate that both pruning decision tree and random forest models are effective in identifying credit risks for SMEs.

Although the adoption of electric vehicles can significantly lessen energy shortages and environmental pollution, it also raises issues with recycling and disposal of used batteries. A full service provider that offered three services—retailing, recycling, and reusing—appears on the market in this situation, creating a new model that has never been researched or applied previously. By using a Stackelberg game model and concentrating on how this provider would affect recycling decisions in the closed-loop supply chain, this study investigates the effects that the advent of this integrated provider will have on the electric vehicle batteries recycling market. According to the study, the integrated provider would decide to cut consumer subsidies within a given range in order to lessen the impact that rising battery dismantle costs will have on rising marginal profits. Second, the manufacturer will decide to raise the price of virgin batteries to offset the rise in the average cost of raw materials brought on by the increase in the battery raw material savings factor, which would affect the integrated provider’s overall income. Additionally, the supply chain’s overall income will rise before falling, therefore the recycling savings factor cannot be either too high or too low. The primary contribution of this research is the analysis of the provider’s optimal pricing strategy in the closed-loop supply chain, which can lead to recommendations for the long-term growth of this comprehensive service provider.

The traditional vehicle routing problem (VRP) is a typical NP-hard problem in combinatorial optimization, based on the premise that customer demands are indivisible. However, in practical logistics operations, sometimes splitting demands can lead to better cost reduction in transportation. In this paper, we establish an integer programming model for split delivery vehicle routing problem (SDVRP) with capacity constraint and design an improved Ant Colony Algorithm tailored to the characteristics of the model, with the main design idea being the innovation of a mechanism for selecting splitting points. Through computational experiments, we compare the solution results with the traditional VRP, demonstrating the superiority of demand splitting. Additionally, we compare the results with those obtained in other studies using the same instances, confirming that the algorithm proposed in this paper has certain advantages in solving the split delivery vehicle routing problem.

China’s energy structure is undergoing significant changes as a result of the quick development of new energy technologies. Building and enhancing the carbon emission policy system for China’s transportation industry requires scientific prediction of carbon emissions with the aim of carbon peaking. This paper adopts the LMDI (Logarithmic Mean Divisia Index) method to decompose the driving factors and the LEAP (Long-Range Energy Alternatives Planning) model to predict the trajectory of China’s transportation sector’s carbon emissions. The findings indicate that while an increase in GDP per capita will result in an increase in carbon emissions, an improvement in the energy structure and a decrease in the intensity of transportation will contribute to a reduction. Additionally, the boosting effect of GDP per capita on carbon emissions in 2019 has been able to counteract the inhibiting effects of energy structure and transportation intensity, where road freight, which is primarily powered by diesel, is the largest source of carbon emissions. According to the baseline scenario, the first carbon peak will occur in 2023 due to the liberalization of epidemic prevention and control and the decrease in the share of road freight, while the second peak will happen in 2032 due to the combined effects of economic growth and advancements in carbon reduction technology. As a result, carbon emissions will exhibit a fluctuating peak tendency, indicating that the industry should pay attention to these features and adopt scientific development strategies. Therefore, the primary routes for China’s transportation sector to reach a carbon peak in the future include optimizing energy structure, developing carbon-reducing technologies, lowering the proportion of road freight, encouraging multi-modal transport development, and optimizing transport structure.

This paper proposed a bi-objective programming model to address the hub location-allocation problem international logistics enterprises faced. The model considers high time requirement of express export business and includes a time window constraint that considers the sum of transport vehicle departure time, vehicle driving time, sorting time of service center, and the latest arrival time of vehicles at the port. The model aims to minimize the total transportation cost and construction costs while maximizing the service level. Firstly, to predict vehicle transportation costs, a binary regression equation was constructed using distance and volume as independent variables, based on the sklearn machine learning library. The resulting mixed integer programming model was then solved using the Gurobi solver. Using operational data from international logistics enterprises in Guangdong Province, two site selection schemes were obtained, prioritizing either total cost or service level. The results of the study indicate that the location scheme prioritizing total cost can effectively reduce costs, while the scheme prioritizing service level can ensure that the service level remains unaffected by the addition of new service centers.

Irregular events like natural disasters can cause drastic fluctuations in container ocean freight prices, making it challenging for traditional forecasting techniques to accurately forecast their complex dynamics. The ensemble empirical mode decomposition (EEMD) method and the gated recurrent unit (GRU) network are suitable for the cases. Therefore, this paper proposes an EEMD-GRU combined forecasting model. Empirical analysis is conducted using the China containerized freight index (CCFI) data from March 2020 to March 2022. The forecasts of the EEMD-GRU model were compared with those of the ARIMA, LSTM, GRU, EEMD-ARIMA, and EEMD-LSTM models. The results indicate that the proposed significantly outperforms its rivals in terms of MAPE, RMSE, and MAE. This shows good potential of EEMD-GRU to be a powerful tool for stakeholders and government authorities.

This paper studies the regional warehouse location-inventory problem under the influence of interruption risk on the three-level distribution network of central warehouse, regional warehouse and store, considering that a store can be replenished by a central warehouse or a regional warehouse. A nonlinear 0–1 integer programming model is constructed, and the model is transformed into a linear 0–1 integer programming problem by using the piecewise linear approximation method. The location decision and inventory decision are realized by using the precise algorithm. The case study shows that considering dual-source replenishment under the risk of interruption can reduce the cost of regional warehouse construction, while considering the risk of interruption can reduce the possible emergency costs in the future.

With the increasingly fierce competition of mobile phone industry, it is very important to analyze the actual usage requirements from the perspective of customers. To understand whether the user experience is improved after the upgrading of mobile phones and whether there are differences in the use perception of users for the improvement of the performance of different functional modules, this paper uses LDA model to mine the topic of Huawei brand three series of mobile phone reviews, visually presents the hot topics that users pay attention to, and compares the parameter information with the hot topics of consumer reviews. This paper has reference significance in helping mobile phone manufacturers rationally allocate budget use and master the promotion and marketing focus of new mobile phone products in the process of product iterative research and development.

Fresh food e-commerce has gradually become an indispensable consumption channel in people's daily life, especially in the context of epidemics as well as natural disasters and other emergencies, more and more demand for fresh food consumption has shifted to online. Due to the perishable characteristics of fresh products, the risk management of fresh e-commerce supply chain is particularly important and plays a decisive role in whether fresh e-commerce can develop in the long run. This paper takes the fresh food e-commerce supply chain as the research object, and through simulation, studies the dynamic trend of supply chain operation risk and the effect of risk control measures, and the results show that the supply chain benefits best when the risk control cost accounts for 10% ~ 15% of the total cost of supply chain operation, which provides a reference for the risk management of fresh food e-commerce.

Due to the uncertainty in the demand for relief supplies after a disaster, the government always faces the double risk of inventory buildup and shortages. Based on this, this paper constructs a relief supplies purchasing model based on bidirectional option contract consisting of the government and two suppliers, taking into account the probability of a disaster striking. We derive the optimal decision on the quantity of relief supplies at the beginning of the reserve period and the quantity of options sold by the two suppliers, analyze the parameter conditions to achieve supply chain coordination, and compare it with the separate government reserve model. We find that the bidirectional option model both increases the total quantity of government relief supplies reserves and reduces the risk of actual government storage of relief supplies. Finally, the numerical illustration and sensitivity analysis is performed to the bidirectional contract can be a win-win for government and suppliers, and the prepositioning quantity is positively related to the call option execution price and the put option execution price.

This paper focuses on the allocation problem of contracted power generation at different time periods on the power plant side, within the context of using a contract-based planning management mode for power generation. Based on the waterfall iteration algorithm, which addresses the allocation of contracted power generation quantity, the study considers the power output characteristics of the grid units and the forecasted grid load. It provides optimization algorithms and suggestions for different patterns of load forecasting curves, aiming to better meet the electricity demand of urban areas.

Communication links in flying ad-hoc networks (FANETs) often experience interruptions or intermittent interruptions. The interruption of links directly affects the reliability of data transmission between different nodes, and also increases the resource overhead and transmission delay of the network. Random linear network coding (RLNC) can adapt well to changes in network topology and has greater robustness to link quality. But its decoding success probability will significantly decrease with the increase of link hops. In this paper, we propose a reliable transmission mechanism of broadcasting collaborating with network coding based on RLNC. Through the coding gain of network coding and the gain of the designed broadcasting mechanism, it can effectively improve the packet transmission reliability of FANET nodes in the scenario with link interruption. The simulation results show that the proposed mechanism has higher data transmission reliability and less overhead ratio compared to the typical RLNC mechanism. Moreover, under the same overhead ratio, the data transmission reliability of the proposed mechanism is mostly superior to the retransmission redundancy mechanism Hybrid Automatic Repeat reQuest (HARQ).

According to the traffic travel research, the reason for the dependence of large cities on cars is largely due to the low willingness of the school-age teenagers’ active school travel. Therefore, the distance between rail stations and schools becomes an important factor affecting the travel of school-age passengers and the urban car traffic. In this article, through grasping POI city data, combining ArcGIS software and related POI data and field investigation, two very large compared to Beijing and Shanghai urban rail transit site and school station spacing and general studies radius and its local node traffic conditions, reveal two cities and the difference of different regional public transportation efficiency caused by the impact, In order to discuss how to integrate the relationship between public transportation and schools in the future, and then provide reference for the optimization of urban pattern.

In recent years, China’s cross-border logistics industry has developed rapidly. The increase in types of goods and the segmentation of the transportation industry market have led to significant differences in the sensitivity of various types of goods to time. In order to optimize the cargo flow organization and match the goods with the modes of transportation, a mixed integer programming model that considers the classification of goods is proposed to solve the optimal transportation plan for various types of export products in each city to optimize the current cargo flow organization ensures the rational use of transportation resources. The results show that the food processing industry and equipment manufacturing industry are more suitable for transportation by China Railway Express, while products from the metal smelting and metal product industries are more suitable for traditional shipping.

The online labor markets have facilitated the growth of customized services through digital platforms that connect employers with workers. However, the limited interactive information between workers and tasks has led to a “cold-start problem”, which limits the effectiveness of personalized task recommendation systems. To address this challenge, we purposed a personalized recommendation system for task recommendation, which combines the multi-field feature interaction and meta-learning. Our approach aims to lcapture concealed associations between multi-field features derived from both workers and tasks, thereby obtaining more meaningful worker preferences. Moreover, it captures workers personalized preferences with minimal interactions via meta learning, significantly enhancing cold-start recommendation performance. We evaluate the proposed model on a real-world dataset obtained from Freelancer.com, and the results demonstrate its superiority over three benchmark methods. By matching tasks with the most suitable workers, our system has the potential to reduce task completion times and enhance overall task quality.

Both professional and amateur manufacturers, subsidized by the governments, have joined the emergency production of personal protective equipment (PPE) to combat the COVID-19 pandemic. In this paper we examine the impacts of government financial subsidies on the pricing decisions of professional and amateur manufacturers and retailers concerning PPE supplies. Considering a two-supplier one-retailer supply chain, we build game-theoretic models and derive the players’ optimal pricing decisions. We explore the impacts of government subsidies under three different scenarios, namely subsidizing the manufacturers only, subsidizing the retailer only, and subsidizing both the manufacturers and retailer. Through sensitivity analyses of the key model parameters, we find that government subsidies can lower the retail price of PPE. We obtain the necessary conditions for when and how manufacturers and retailers should be subsidized to control the price of PPE. We also find that government subsidies can increase the consumers’ benefit by controlling their levels. Our study provides managerial insights on government subsidy policy design and the pricing decisions of substitute PPE supplies from different manufacturers during the pandemic.

The emergence of the online labor market has been gradual, but workers face challenges in finding tasks that align with their interests among the vast number of available tasks. Consequently, the study of task recommendation algorithms becomes crucial for the advancement of the online labor market. In this paper, we propose a method which utilizing contrastive learning in task recommendation of online labor market. We use constrastive learning to pre-train better embedding features to represent workers and tasks. These features are employed to initialize the embedding layer of popular recommendation system models, thereby exploring the model’s effectiveness further. We find that this method can improve the performance of existing recommendation models. Experimental results using an online labor market dataset indicate that our approach, which incorporates features learned through contrastive learning into PNN, WideDeep, or DeepFM recommendation models, leads to improvements in five evaluation metrics. These results demonstrate the benefits of our method in recommending tasks within the online labor market. The primary contribution of this paper is the utilization of pre-trained embeddings obtained through contrastive learning to initialize the embedding layer within popular recommendation system models.

Focusing on the rapid development of takeaway APPs in recent years, analyzing the impact of consumer consumption tendency social through takeaway APP attributes on consumer satisfaction and continuous use intention is not only a supplement and expansion of the field of relationship between takeaway APPs and consumers, but also focuses on the advantages and problems of takeaway APPs and other related companies. This study collected data by questionnaire method and used SPSS exploratory factor analysis and AMOS structural equation modeling method to analyze the data. The study found and determined the measurement scale of each attribute, containing 30 question items in 5 dimensions. The results of the study show that the social nature of consumer tendency has a direct positive influence on the price, convenience, and safety of the takeaway APP attributes, and an indirect positive influence on the intention of persistent use through consumer satisfaction.

For example, in megacities such as Beijing, where rail transit dominates commuting, the peak demand of urban residents for fresh agricultural products is highly uneven in time and space due to the high separation of residential centers and employment centers. As a result, the production and supply of fresh agricultural products have attracted extensive attention from researchers in recent years. Therefore, building a community-oriented intelligent fresh agricultural products distribution system, improving the level of community governance, and improving people’s well-being have become important issues faced by super-large cities. How to study the time-space mismatch mechanism between consumer demand and supply in different scenarios, so as to meet consumers’ differentiated demands for fresh products, and how to establish an efficient storage and distribution system for fresh products in communities, so as to improve the shopping experience and satisfaction of community residents are urgent problems to be solved. In view of the above scientific problems, this paper discusses a number of technical means, using space-time big data, complex networks, intelligent algorithms and other technologies to analyze consumer demand, optimize product supply and logistics distribution and other aspects to solve the problem. In particular, based on the construction of logistics information, the reliability, safety and flexibility of the storage and distribution of live products in communities are improved, so as to provide more high-quality live products supply services for community residents.

The disclosure of product quality information has an important impact on the members of the supply chain. Technological development allows manufacturing companies to obtain accurate quality information in advance, and at the same time forms information asymmetry between buyers and sellers, and sellers have more private information. Information disclosure can limitedly alleviate the information asymmetry between the two sides. This paper intends to explore the product quality information disclosure strategy in the supply chain. In the case of uncertain product quality, manufacturers can afford certain technical costs to obtain private information and decide whether to disclose information to update the buyer’s beliefs. The manufacturer sells the goods in two stages. In the first stage, the quality of the products is uncertain. According to the information disclosed, the buyer can choose the first stage to buy the goods at a discount but bear the uncertainty of the quality, and can also buy the goods in the second stage. This paper intends to establish a two-period game theory model to study the manufacturer’s equilibrium quality information disclosure strategy and revenue.

Whether the vehicle electrification will reduce carbon emission in the China’s transportation industry? the relevant study is less. Therefore, carbon emissions from road transportation in China were calculated and predicted from the perspective of fuel life cycle. A two-level econometric model of influencing factors was constructed to analyze the influence of each influencing factor on carbon emissions of road transport. Based on the PSO-LSSVM model, the emission reduction potential of road transport industry under the background of vehicle electrification under different emission reduction policy intensity scenarios was predicted, and the impact of emission reduction policy intensity on emission reduction potential was analyzed. Carbon emission intensity is an important factor affecting the carbon emission of road transportation in China. According to the result, it is suggested that the Chinese government enhance the greening of power generation, promote the electrification of vehicles, and accelerate the emission reduction progress of China’s road transport.

In order to alleviate urban traffic problems and improve the stable operation of transportation network, it is necessary to analyze the network structure of urban rail transit. Based on complex network theory, this study analyzes the robustness of rail transit network, identifies key nodes and puts forward suggestions for robustness optimization. Taking the rail transit in Beijing as an example, the topology model of the rail transit network is first established based on Space-L. Secondly, based on the complex network theory, the intrinsic structural characteristics of the network are mined. Then, the network efficiency are selected as the criteria for network robustness, and the robustness level of the network is studied by means of deliberate attack and random attack, and the key stations in the transportation network are identified. Finally, the robustness optimization measures of transportation network structure are proposed. The study is of great significance to the safe and stable operation of Beijing’s rail transit network, and also provides reference value for rail transit construction in other cities from the perspective of structural optimization.

This paper investigates the problem of pre-sale decisions in online e-commerce where consumers have time preferences. In pre-sales, consumers’ valuation of a product is not certain until they receive it, and their purchase decision is not only influenced by price, but also by the length of pre-sales. By choosing a pre-sale strategy that takes into account consumers’ time preferences, retailers can not only anticipate market demand and increase sales margins, but also win market share and serve consumers better. Based on these observations, this paper investigates how retailers can determine the optimal price decision for a general discounted pre-sale model when considering pre-sale length and consumer time preference.

Taking the carbon emission panel data of China’s 30 provinces from 2010 to 2019 as the research object, examines the spatiotemporal pattern of carbon emission intensity and examines the spatial correlation of China’s inter-provincial carbon emissions using the global spatial autocorrelation. Selecting the time and space double fixed effects of the spatial Dubin model to analyze the degree of carbon emissions affected by the local and neighboring provinces. This will theoretically enable the development of differentiated carbon emission reduction policies in China. The results show that: (1) Significant positive spatial connection is observed in China’s inter-provincial carbon emissions, with the characteristics of spatial aggregation. (2) Economic development, urbanization level, and foreign trade dependence play a significant role in promoting inter-provincial carbon emissions, while investment policies and energy intensity have inhibiting effects on carbon emissions.

This paper examines the potential applications of Metaverse technology in the aviation industry, including design, inspection, manufacturing, quality control, maintenance, as well as command and control. The Aviation Industry Metaverse offers a multi-dimensional world through data sharing, independent user identity IP, and fusion of virtuality and reality. For example, Virtual design centers can reduce R&D costs and improve efficiency, while virtual inspections ensure safety and accuracy. Virtual manufacturing workshops can improve production efficiency and product quality. To create an Aviation Metaverse, three technologies must be utilized: information access technology, core Metaverse technology, and data technology. The information access architecture enables users to experience all-round sensory immersion experiences involving sight, hearing, touch, and natural interaction through XR terminals, natural interaction, and dynamic simulation methods. The core Metaverse technology can provide technicians with unified, sustainable, and application-oriented technical support, including the “cloud & network & edge” collaborative platform architecture and digital twin technology. In addition, Aviation Metaverse needs to build an integrated data platform to achieve shareable, high-security, and high-value data services.

The challenges of increasing competition induced by globalisation motivate enterprises enthuse over participating collaborative networks, such as the Digital Business Ecosystems (DBEs). A DBE is a socio-technical network of individuals, organisations and technologies that collectively co-create values through the collaboration facilitated by shared digital platforms. Considering its enormous impact which allows companies to gain their competitiveness and improve resilience, the concept of DBE becomes attractively prominent. However, the increasing collaboration complexity in DBEs lead to uncertainty about firms’ organisational and technological capabilities prior to engaging in DBE. To address this issue, firstly, the purpose of this paper is to survey the DBEs in typical industries with a comparison of their main DBE features. Secondly, to facilitate the companies to participate in a DBE, the prerequisites and potential indicators that prepares firms prior to engagement in collaborative DBEs are identified by adopting Organisation Semiotics (OS) theory. Finally, this study also analyses the strengths and weakness of current proposals regarding the establishment of critical criteria for collaborative digital business ecosystems.

To address the problem of long waiting time during the turnaround operation of the iron ladle in the blast furnace (BF)- basic oxygen furnace (BOF) section of Tangsteel New Area. Establish queuing network model based on queuing theory knowledge to study. Construct a two-dimensional Markov process and apply the method of equilibrium equation to solve the steady-state distribution of the ladle queuing system in Tangsteel New Area, and obtain the performance index of the system. The actual manufacturing process logistics and scheduling in Tangsteel New Area is analyzed by experimental calculation. The results show that the optimization of iron ladle turnover quantity provides a theoretical reference for the optimization of logistics in Tangsteel New Area.

This study examined the universal linkage of big data-a key technical problem in big data application-in consideration of the human brain’s cognition of the external world. Based on set theory, we used data fields to construct a universal set data description model. We defined the various basic operations of the universal set by analyzing the properties of set elements, the description method, the relationship with the AI algorithm system, and the factor fields of the universal set data. Further, based on the data description model of the universal set, for data barriers—a typical bottleneck—we developed a universal data linkage coordination method based on the idea of multi objective optimization. Then, using rail transit safety chain data as a real-world example, we simulated the linkage analysis process for safety risk factor data based on universal set theory. In this way, this study proposes a feasible strategy for applying universal set theory to big data.

With the rapid development of the global digital economy, data has become an important fundamental strategic resource for countries. However, the issues of data access, sharing, and usage are becoming increasingly prominent. How to effectively integrate cross domain and cross platform data resources has become an important prerequisite for reflecting the value of data. Aiming at the current problems faced by crossed domain data integration, this paper constructs a crossed domain data integration architecture based on the concept of data fabric, including data source layer, data directory layer, Knowledge Graph layer, data integration layer and data application layer. Elaborated on the roles, capabilities, and core technologies of each level, proposed key technologies that need to be broken through, and provided a theoretical basis and reference architecture for the practical application of cross domain data integration.

With the development of information technology, the concept of Metaverse has gradually entered our vision. The Metaverse is a virtual world that can interact and integrate with the real world. The aviation Metaverse integrates a number of emerging technologies to create an immersive aviation virtual interaction platform, and uses media, digital and virtual elements to bring people immersive and scene based experiences, thus realizing the deep integration of the aviation industry and tourism industry. This paper designs the ecological architecture of the aviation Metaverse from three perspectives of application, technology and market, and designs the technical architecture of the aviation Metaverse from four perspectives of access, basic technology, data and application.

In recent years, big data has become a popular trend, widely used in various fields to provide guidance. For selling products online, the platform provides an online review function that enables businesses to use these review data to understand the popularity of the product in the market. Many businesses intuitively get product ratings through customer ratings, while also exploring the direction of product improvement by analyzing the reviews left by customers. In this study, a comprehensive evaluation model was established to obtain a more accurate customer satisfaction evaluation by combining customer scoring and comments, quantifying reviews with the help of TextBlob, and using the entropy weight method to determine the weight of scores and reviews. This study takes the three types of products of Company S as an example, and explores different products and different types of market improvement strategies of the same product by analyzing their evaluation data, aiming to provide more valuable information for the company’s sales.

Energy saving, and emission reduction are one of the most crucial tasks for the current and future high-quality progression of China’s economy and society, and as an efficient and healthy transportation mode, high-speed rail (HSR) is significant for improving China’s ecological environment. In this paper, we take the “quasi-natural experiment” of whether to open HSR as an opportunity to empirically analyze the mitigation effect of emissions, spatial heterogeneity, and mechanism of the effect of HSR’s opening based on the panel data of China from 2006 to 2020 and using double difference model. The results clarify that HSR can effectively suppress urban pollutant emissions, including urban sulfur dioxide emissions and wastewater emissions, with an emission reduction effect, and the subsequent robustness tests confirm this conclusion. Heterogeneity test’s results by region display that the effect of HSR on energy saving and emission reduction shows a decreasing trend from West, Central to East. Additionally, the operation of HSR declines the emission of pollutant sulfur dioxide mainly through the STI effect, and the impact on wastewater emission is not significant. In the current strategic context of a strong transportation country and green transportation, this study contributes to an in-depth understanding of the importance of upgrading transportation infrastructure for regional sustainable development.

Digital transformation offers a wealth of business opportunities for various enterprises across the world. This paper focuses on the saturation state test problem of (business) concept space, to help enterprises automatically identify business growth points through policy texts. Firstly, the concept space saturation is defined based on the variable-scale data analysis theory. In order to determine whether a concept space has reached the saturation state, the expected information quantity measurement of concept space is proposed. After establishing the saturation state test mechanism of thinking theme identification process, an algorithm of saturation state test of concept space (SST-CS) is also put forward. A case study on the real policy texts in urban green transportation industry demonstrates that the proposed SST-CS could identify business opportunities efficiently.

College students innovative undertaking can help improve the vitality of regional economic development. However, the success rate of college students’ entrepreneurship is not high due to the problems of inexperience and lack of capital. Some efforts are needed to ensure successful entrepreneurship for college students. Existing studies mainly focus on entrepreneurs themselves and the impact of the external environment on entrepreneurship without considering the abundant information in venture capital data. This study mines the data of venture capital projects from the perspective of entrepreneurial projects and investment events. Clustering, as a typical data mining task, can induce the commonness of similar data and distinguish the individuality of different data. Besides, clustering method is more suitable for handling unlabeled venture capital data than other supervised methods such as classification. Therefore, this study establishes a model of venture capital project segmentation based on clustering techniques. Specifically, to cope with the characteristics that the collected entrepreneurial data contain different types of attributes, the clustering algorithm for incomplete data sets with mixed numeric and categorical attributes is adopted and improved to subdivide entrepreneurial projects and investment events into five types, respectively. By analyzing the characteristics of each type, we summarize the general pattern of the same type of entrepreneurial projects and investment events to support college students’ entrepreneurial decisions or school and social assistance policy formulation.

This paper takes listed companies with venture capital backgrounds in China’s SMEs, GEM from 2014–2019 and STAR from 2019–2020 April, divides venture capital ownership backgrounds, and adopts cross-sectional multiple regressions to explore the impact of venture capital ownership backgrounds on the IPO price suppression rate of invested firms and its mechanism of action. It is found that joint investment by venture capitalists with different ownership backgrounds can reduce the IPO price suppression rate of enterprises, and venture capitalists with Sino-foreign joint venture backgrounds have a positive effect on the IPO price suppression rate of enterprises. It is confirmed that joint investment can effectively reduce the IPO price suppression rate of enterprises by forming social networks and mobilizing resources to enhance enterprise value. The above-mentioned effects are significantly stronger in the STAR sample, indicating that the pilot registration system, with its multiple rounds of inquiries and public responses, effectively conveys information to the market, allowing investors to form more rational judgments, and has a more significant effect on reducing the IPO price suppression rate. Sino-foreign joint venture capital has a stronger positive effect on the IPO price suppression rate due to its high reputation, which attracts investors more significantly. The positive impact on the IPO price suppression rate is stronger.

To solve the problem of expensive and difficult drug purchase for patients is a series of pain points. With the goal of further reducing drug prices, game theory, multi-objective optimization theory and optimization theory are used to build a medical supply chain model considering Direct-to-Patient under the condition of double objectives. The formation mechanism of drug prices and the realization of balanced economic benefits and social benefits are explored respectively, and coordination contracts are designed to coordinate and optimize the model. Research has shown that excessive social benefit preferences will reduce the total revenue of the drug supply chain; With the increase of social benefit preference, the decline rate of economic benefits under Decentralized decision-making is the slowest. Specialized supply chain operation division is conducive to the implementation of the Direct-to-Patient sales model; The “Government Subsidy + Revenue Sharing” contract can achieve dual Pareto improvements in economic benefits and social benefits for Direct-to-Patient.

With each e-commerce platform’s increasingly sophisticated grasp of shopping festival activities, many platforms continue to introduce new shopping festival “games” to innovate their marketing activities, thereby helping to increase annual sales. This paper mainly introduces the development and innovation of Tmall as the creator of the Double Eleven Shopping Festival in its marketing strategy over the years since its creation, mainly dividing its development history into four stages: 2009–2011 as its formation stage; 2012–2014 as its development stage; 2015–2018 as its maturity stage; and 2019 to the present as its innovation stage. Then, this paper use web crawler technology to crawl the relevant blog post data released by netizens in Weibo platform, then clean the data, and finally analyze the comment data for word frequency and sentiment analysis, to explore the changes in marketing strategies and consumers’ emotional attitudes toward the “Double Eleven” shopping festival, in order to provide reference for the marketing planning of e-commerce platforms during the shopping festival, so as to meet the needs of Internet users in a more targeted manner.

The rapidly advancing digital society is experiencing a significant scarcity of data science professionals, and there are numerous job opportunities emerging in the field of data science. Having a clear understanding of the employment landscape and talent distribution in this domain can greatly assist individuals seeking employment, yet the current theoretical research in data science lacks investigation in this area. Utilizing the most recent data science talent data, this study employs a combination of statistical analysis and cluster mining techniques to provide an overview of the employment landscape in data science, as well as the distinguishing characteristics of different types of professionals, taking into account industry conditions and internal segmentation. It outlines the employment trends observed within the field of data science and offers recommendations to job seekers regarding potential job opportunities, salary expectations, and benefits in this realm.

Community group buying is a new type of group buying. Similar to traditional group buying, it mainly relies on scale sales and word-of-mouth effects to create rich profits. The quality factor is particularly important in the operation process of community group buying. In this paper, we assume that there is a first-order linear function relationship between the quality effort of the group leader and the quality effort of the platform. The results show that the optimal quality effort is directly proportional to the quality effort coefficient and the profit-sharing coefficient, but inversely proportional to the cost coefficient. The optimal quality effort decision of the platform will also be affected by the Group-leader’s profit sharing. Under the goal of maximizing the total profit of the supply chain, this paper also obtains the profit-sharing coefficient between the platform and the group leader.

Taking 30 provinces, autonomous regions and municipalities in China from 2011 to 2020 as research objects, a spatial autocorrelation analysis was conducted on employment quality, and the time-space dual fixed effect model of the spatial Durbin model was selected to analyze the direction and magnitude of the influence factors on employment quality on itself and its neighboring provinces. The research showed that the level of industrialization and informatization promoted the development of employment quality in China's provinces, and the industrial structure had a significant negative effect on the development of employment quality.

The valuation of data assets is a critical task in the digital economy, enabling organizations to make informed decisions and allocate resources effectively. However, traditional economic approaches to data asset valuation have limitations in capturing the distinct characteristics and challenges of data assets. In this paper, we propose a semiotic framework for data asset valuation, drawing inspiration from organizational semiotics. Our framework acknowledges that data valuation is a process of sense-making, known as semiosis, and identifies three key semiotic aspects - syntactics, semantics, and pragmatics - that shape the value realization of data assets. By incorporating crucial factors such as subjectivity, context dependency, and purpose dependency, we offer a structured approach comprising aspects, factors, and metrics for data asset valuation. The proposed framework not only contributes to advancing the field of data asset valuation but also serves as a foundation for further studies and exploration in this area.