Impacts of agri-food e-commerce on traditional wholesale industry:Evidence from China

Rapidly expanding studies investigate the effects of e-commerce on company operations in the retail market.However,the interaction between agri-food e-commerce(AEC)and the traditional agri-food wholesale industry(AWI)has not received enough attention in the existing literature.Based on the provincial panel data from 2013 to 2020 in China,this paper examines the effect of AEC on AWI,comprising three dimensions:digitalization(DIGITAL),agrifood e-commerce infrastructure and supporting services(AECI),and agri-food e-commerce economy(AECE).First,AWI and AEC are measured using an entropy-based combination of indicators.The results indicate that for China as a whole,AWI has remained practically unchanged,whereas AEC exhibits a significant rising trend.Second,the findings of the fixed-effect regression reveal that DIGITAL and AECE tend to raise AWI,whereas AECI negatively affects AWI.Third,threshold regression results indicate that AECI tends to diminish AWI with three-stage inhibitory intensity,which manifests as a first increase and then a drop in the inhibition degree.These results suggest that with the introduction of e-commerce for agricultural product circulation,digital development will have catfish effects that tend to stimulate the vitality of the conventional wholesale industry and promote technical progress.Furthermore,the traditional wholesale industry benefits financially from e-commerce even while it diverts part of the traditional wholesale circulation for agricultural products.

Impact of BRICS Trade Facilitation on China's Exports Using Cross-Border E-Commerce

With the rapid growth of the global digital economy, cross-border e-commerce, as an emerging form of trade, has gradually become a powerful engine to promote the development of global trade. BRICS is an important force in the global economy, and the progress of the BRICS countries' trade facilitation level has an important impact on the global trade environment. This paper conducts an in-depth study of the dynamic changes in BRICS trade facilitation from 2013 to 2022, and uses an extended gravity model to analyze the specific impact of this change on China's exports using cross-border e-commerce. The results show that although the BRICS countries have made some progress in trade facilitation, the overall level still needs to be improved, and there are obvious differences among member countries. However, the improvement of trade facilitation among BRICS countries has undoubtedly brought significant positive effects to China's exports using cross-border e-commerce.

Research on Current Situation and Legal Regulation of Cosmetics Live Streaming E-commerce in China

Analyze the compatibility between cosmetics and live streaming e-commerce from its own nature,marketing means and supply chain characteristics.According to the prominent problems,sort out the relationship between all parties in the cosmetics live e-commerce industry chain.Combined with the latest regulatory policies of live streaming e-commerce and cosmetics,the responsibilities of different subjects in cosmetics live streaming e-commerce are summarized,and relevant suggestions and countermeasures are put forward for the standardization and development of live streaming e-commerce.Cosmetics brand owners are the first responsible persons for product quality.Anchors,as a mixed identity between intermediary,advertising spokesperson and operator,should bear stricter joint and several liability when recommending products related to consumers’health.If anchors fail to clearly identify themselves in the recommendation process,thus causing consumers to mistake them for the operator of the cosmetics,they should assume the obligations of the operator.

Predicting Purchasing Behavior on E-Commerce Platforms: A Regression Model Approach for Understanding User Features that Lead to Purchasing

This research introduces a novel approach to improve and optimize the predictive capacity of consumer purchase behaviors on e-commerce platforms. This study presented an introduction to the fundamental concepts of the logistic regression algorithm. In addition, it analyzed user data obtained from an e-commerce platform. The original data were preprocessed, and a consumer purchase prediction model was developed for the e-commerce platform using the logistic regression method. The comparison study used the classic random forest approach, further enhanced by including the K-fold cross-validation method. Evaluation of the accuracy of the model’s classification was conducted using performance indicators that included the accuracy rate, the precision rate, the recall rate, and the F1 score. A visual examination determined the significance of the findings. The findings suggest that employing the logistic regression algorithm to forecast customer purchase behaviors on e-commerce platforms can improve the efficacy of the approach and yield more accurate predictions. This study serves as a valuable resource for improving the precision of forecasting customers’ purchase behaviors on e-commerce platforms. It has significant practical implications for optimizing the operational efficiency of e-commerce platforms.

Research on Professional Teaching Reform of the E-Commerce Major in Vocational Colleges Under the Internet Era

With the rapid development of science and technology,the face of human society has undergone great changes;with the emergence of the Internet era,all kinds of educational technology,equipment,and software in vocational colleges have been widely used to carry out education and teaching,and has achieved remarkable results.Based on this,colleges and universities’electronic commerce(e-commerce)professional teachers should try to rely on the Internet to build information teaching classrooms,introduce advanced methods to build efficient classrooms by integrating teaching resources,and optimize the top-level design,so as to activate the classroom atmosphere,mobilize students’emotions,make them immersed in the teaching of electronic commerce courses.In view of this,this paper combines the existing theory and experience,first analyzes the dilemma faced by the current teaching of e-commerce in vocational colleges,then discusses the practical significance of teaching reform based on the Internet era,and lastly puts forward the specific practice path.

Analysis of the Current Situation, Influential Factors, and Countermeasures of Rural E-commerce Development

The development of rural e-commerce is becoming an important driver for the transformation of China’s rural economy,and with the rapid development of information technology and the upgrading of the agricultural industry,rural e-commerce is showing a vigorous momentum of development.Traditionally,agricultural products are mainly sold through traditional farmers’markets,which are subjected to geography and channel limitations,resulting in inefficient circulation of agricultural products.This paper analyzes the definition,the status quo,as well as the influencing factors of rural e-commerce development.On this basis,countermeasures for the advancement of rural e-commerce development are put forward.

Spatial Structure of China's E-commerce Express Logistics Network Based on Space of Flows

The intermediate link compression characteristics of e-commerce express logistics ne tworks influence the tradition al mode of circulation of goods and economic organization,and alter the city spatial pattern.Based on the theory of space of flows,this study adopts China Smart Logistics Network relational data to build China's e-commerce express logistics network and explore its spatial structure characteristics through social network analysis(SNA),the PageRank technique,and geospatial methods.The results are as follows:the network density is 0.9270,which is close to 1;hence,indicating that e-commerce express logistics lines between Chinese cities are nearly complete and they form a typical network structure,thereby eliminating fragmented spaces.Moreover,the average minimum number of edges is 1.1375,which indicates that the network has a small world effect and thus has a high flow efficiency of logistics elements.A significant hierarchical diffusion effect was observed in dominant flows with the highest edge weights.A diamond-structured network was formed with Shanghai,Guangzhou,Chongqing,and Beijing as the four core nodes.Other node cities with a large logistics scale and importance in the network are mainly located in the 19 city agglomerations of China,revealing the fact that the development of city agglomerations is essential for promoting the separation of experience space and changing the urban spatial pattern.This study enriches the theory of urban networks,reveals the flow laws of modern logistics elements,and encourages coordinated development of urban logistics.

Transaction Frequency Based Trust for E-Commerce

Most traditional trust computing models in E-commerce do not take the transaction frequency among participating entities into consideration,which makes it easy for one party of the transaction to obtain a high trust value in a short time,and brings many disadvantages,uncertainties and even attacks.To solve this problem,a transaction frequency based trust is proposed in this study.The proposed method is composed of two parts.The first part is built on the classic Bayes analysis based trust modelswhich are ease of computing for the E-commerce system.The second part is the transaction frequency module which can mitigate the potential insecurity caused by one participating entity gaining trust in a short time.Simulations show that the proposed method can effectively mitigate the self-promoting attacks so as to maintain the function of E-commerce system.

Enhanced E-commerce Fraud Prediction Based on a Convolutional Neural Network Model

The rapidly escalating sophistication of e-commerce fraud in recent years has led to an increasing reliance on fraud detection methods based on machine learning.However,fraud detection methods based on conventional machine learning approaches suffer from several problems,including an excessively high number of network parameters,which decreases the efficiency and increases the difficulty of training the network,while simultaneously leading to network overfitting.In addition,the sparsity of positive fraud incidents relative to the overwhelming proportion of negative incidents leads to detection failures in trained networks.The present work addresses these issues by proposing a convolutional neural network(CNN)framework for detecting ecommerce fraud,where network training is conducted using historical market transaction data.The number of network parameters reduces via the local perception field and weight sharing inherent in the CNN framework.In addition,this deep learning framework enables the use of an algorithmiclevel approach to address dataset imbalance by focusing the CNN model on minority data classes.The proposed CNN model is trained and tested using a large public e-commerce service dataset from 2018,and the test results demonstrate that the model provides higher fraud prediction accuracy than existing state-of-the-art methods.

An Enhanced Intelligent Intrusion Detection System to Secure E-Commerce Communication Systems

Information and communication technologies are spreading rapidly due to their fast proliferation in many fields.The number of Internet users has led to a spike in cyber-attack incidents.E-commerce applications,such as online banking,marketing,trading,and other online businesses,play an integral role in our lives.Network Intrusion Detection System(NIDS)is essential to protect the network from unauthorized access and against other cyber-attacks.The existing NIDS systems are based on the Backward Oracle Matching(BOM)algorithm,which minimizes the false alarm rate and causes of high packet drop ratio.This paper discussed the existing NIDS systems and different used pattern-matching techniques regarding their weaknesses and limitations.To address the existing system issues,this paper proposes an enhanced version of the BOM algorithm by using multiple pattern-matching methods for the NIDS system to improve the network performance.The proposed solution is tested in simulation with existing solutions using the Snort and NSL-KDD datasets.The experimental results indicated that the proposed solution performed better than the existing solutions and achieved a 5.17%detection rate and a 0.22%lower false alarm rate than the existing solution.

Analysis of the Development Environment and Trend of Cross-Border E-commerce in China

With the conclusion of the novel coronavirus pandemic and the increasingly complex market environment,China’s cross-border e-commerce has entered a new phase of development.The external landscape is evolving rapidly,and there is a gradual improvement in laws and regulations governing cross-border e-commerce,coupled with increased government support.Despite the impact of the COVID-19 pandemic on the market economy,overall development has been steadily improving.The Internet population is expanding,the online retail market is experiencing rapid growth,the consumption structure is undergoing transformation and upgrading,and the e-commerce market is demonstrating significant potential.The advancement of technologies such as big data,artificial intelligence,blockchain,and supply chain has provided more efficient operational support for the cross-border e-commerce industry.Against the backdrop of the emergence of new forms of cross-border e-commerce in China post-pandemic,this paper utilizes the PEST model to analyze the macro environment of cross-border e-commerce in China and project its future development trends.

Investigation and Countermeasures of the Development of Chinese Pharmaceutical E-commerce in the B2C Model Based on PEST-SWOT Analysis

Objective To analyze the problems of pharmaceutical e-commerce and provide strategies for its development in the future B2C mode since drug network management has great potential in China.Methods By collecting,identifying,and conducting literature research,PEST-SWOT identification and positioning of pharmaceutical e-commerce in the B2C mode were carried out.Results and Conclusion A PEST-SWOT analysis matrix was established to analyze the status of B2C pharmaceutical e-commerce,and to summarize its advantages,disadvantages,opportunities and threats from four perspectives of politics,economy,society and technology.Suggestions on cultivating compound talents proficient in medicine and e-commerce,exploring online payment methods for medical insurance,integration of upstream and downstream of the industrial chain and data sharing are put forward to promote the healthy and long-term development of pharmaceutical e-commerce under the background of big data.

Growth, Population Parameters and Stock Status of Sarotherodon galilaeus in Samandeni Reservoir, Burkina Faso

Mango tilapia, Sarotherodon galilaeus is one of the most caught fish species in the Samandeni multi-species fishing sites of which, few data on its biology and exploitation are available. The study aimed to Assess the stock status of S. galilaeus. Sampling was conducted from March, 2021 to February 2022 based on commercial fish catches to analyze growth parameters, first sexual maturity size and harvest status of the stock. A total of 572 specimens including 297 females and 275 males were examined. The stock assessment was performed by using the Length based Bayesian method of Biomass (LBB) and that of growth by the ELEFAN method. The growth parameters showed a seasonality of growth and females appeared to grow faster than males. On the other hand, males had a greater asymptotic length than females. Results on the estimated length of fish at first maturity showed that females firstly reached the maturity compared to males. The relative biomass (B/B0) estimated for the stock was higher than the relative biomass that produces maximum sustainable yield (BMSY/B0) indicating healthy biomass. In addition, the length at first sexual maturity was less than the length at the first catch, indicating the absence of overfishing of growth. In addition, extending the study to the various stocks of the reservoir would be important for the sustainable management of the Samandeni high economic fishing area.

Structured Multi-Head Attention Stock Index Prediction Method Based Adaptive Public Opinion Sentiment Vector

The present study examines the impact of short-term public opinion sentiment on the secondary market,with a focus on the potential for such sentiment to cause dramatic stock price fluctuations and increase investment risk.The quantification of investment sentiment indicators and the persistent analysis of their impact has been a complex and significant area of research.In this paper,a structured multi-head attention stock index prediction method based adaptive public opinion sentiment vector is proposed.The proposedmethod utilizes an innovative approach to transform numerous investor comments on social platforms over time into public opinion sentiment vectors expressing complex sentiments.It then analyzes the continuous impact of these vectors on the market through the use of aggregating techniques and public opinion data via a structured multi-head attention mechanism.The experimental results demonstrate that the public opinion sentiment vector can provide more comprehensive feedback on market sentiment than traditional sentiment polarity analysis.Furthermore,the multi-head attention mechanism is shown to improve prediction accuracy through attention convergence on each type of input information separately.Themean absolute percentage error(MAPE)of the proposedmethod is 0.463%,a reduction of 0.294% compared to the benchmark attention algorithm.Additionally,the market backtesting results indicate that the return was 24.560%,an improvement of 8.202% compared to the benchmark algorithm.These results suggest that themarket trading strategy based on thismethod has the potential to improve trading profits.

Digital Marketing and B2B Cross-Border E-Commerce: A Case Study of Alibaba International Station in China

The rapid development of the“Internet+”has ushered in a new era of global digital technology innovation.Concurrent with the deepening trends of economic globalization and integration,international trade is progressively undergoing extensive digitization,with cross-border e-commerce emerging as a significant manifestation of this transformation.Within this landscape,the cross-border e-commerce B2B platform is anticipated to become a pivotal driver for China’s cross-border trade growth,representing a strategic opportunity for trade transformation.This paper provides a comprehensive examination of the concepts,characteristics,and development status of cross-border e-commerce and B2B platforms,considering both global and Chinese perspectives.Focusing on a prominent case study,Alibaba International Station,a B2B cross-border e-commerce platform,the paper delves into its business model and core competencies,offering a thorough analysis of its role in the intricacies of international e-commerce and its contribution to the field.Drawing on insights gained,the paper concludes by presenting targeted recommendations from both the platform and government perspectives.These suggestions are informed by a nuanced understanding of the development opportunities and challenges facing China’s cross-border e-commerce B2B platforms.

Design Strategies and Practice Paths for Improving Urban Quality in the Stock Era

In the middle and later stages of urbanization development,the growth of the real estate industry will stagnate,and urban renewal will become the mainstream.With the advancement of urban renewal,there are still problems in improving the quality of cities in the stock era and their design strategies.This paper analyzed the Linping Old City organic renewal project and the Xishui River ecological governance project in the stock era of urban quality improvement by sorting out the current development status,historical background,planning types,and design strategies of quality improvement in the stock era from the perspective of urban renewal,combining with project overview,main problems,design methods,and design content.Urban renewal is the leading direction for promoting urban development and construction on a global scale,and countries formulate different plans and practices based on their local characteristics.Urban renewal strategies should be diversified,and focus on livable environments,urban characteristics,etc.,while considering human factors,green innovation,etc.,in order to achieve smart community management and enhance the economic and social benefits brought by urban attractiveness.For successful cases such as the Linping Old City and Xishui River ecological governance project,corresponding urban quality improvement strategies and implementation plans should be formulated according to local conditions,with emphasis on social participation and people’s livelihood improvement.This study can help urban planning pay more attention to rational utilization and upgrading of existing urban resources,adapt to the current urban development situation,and promote sustainable urban development.

Exploring the Relationship Between Patent Forward Citation and Stock Return Rate Using Empirical Data of China Stock Market

A novel indicator called price-citation was proposed.Based on the company integrated patent database of China listed companies of common stocks(A-shares)with the stock price and the stock return rate data,more than two thousand of A-shares from 2017 to 2020 were selected.The effect of the traditional patent forward citation and the price-citation for discriminating the stock return rate was thoroughly analyzed via ANOVA.The A-shares of forward citation counts above the average showed higher stock return rate means than the A-shares having patents but receiving no forward citations.The price-citation,combining both the financial and patent attributes,defined as the multiplication of the current stock price and the currently receiving forward citation count,showed its excellence in discriminating the stock return rate.The A-shares of higher price-citation showed significantly higher stock return rate means while the A-shares of lower price-citation showed significantly lowest stock return rate means.The price-citation effect had not been changed by COVID-19 though COVID-19 affected the social and economic environment to a considerable extent in 2020.

Soil Organic Carbon Stock Variation under Different Soil Types and Land Uses in the Sub-Humid Noun Plain, Western Cameroon

This study was conducted to assess the current stock of soil organic carbon under different agricultural land uses, soil types and soil depths in the Noun plain in western Cameroon. Three sites were selected for the study, namely Mangoum, Makeka and Fossang, representative of the three dominant soil types of the noun plain (Andosols, Acrisols and Ferralsols). Three land uses were selected per site including natural vegetation, agroforest and crop field. Soil was sampled at three depths;0 - 20 cm, 20 - 40 cm, and 40 - 60 cm. Analysis of variance showed that soil type did not significantly influence carbon storage, but rather land uses and soil depth. SOCS decreased significantly with depth in all the sites, with an average stock of 66.3 ± 15.8 tC/ha at 0 - 20 cm, compared to an average stock of 33.3 ± 7.4 tC/ha at 40 - 60 cm. SOCS was significantly highest in the natural formation with 57.2 ± 19.7 tC/ha, and lowest in cultivated fields, at 37.7 ± 10.6 tC/ha. Andosols, with their high content of coarse fragments, stored less organic carbon than Ferralsols and Acrisols.

National Soil Organic Carbon Stocks Inventories under Different Mangrove Forest Types in Gabon

Gabonese’s estuary is an important coastal mangrove setting and soil plays a key role in mangrove carbon storage in mangrove forests. However, the spatial variation in soil organic carbon (SOC) storage remain unclear. To address this gap, determining the SOC spatial variation in Gabonese’s estuarine is essential for better understanding the global carbon cycle. The present study compared soil organic carbon between northern and southern sites in different mangrove forest, Rhizophora racemosa and Avicennia germinans. The results showed that the mean SOC stocks at 1 m depth were 256.28 ± 127.29 MgC ha−1. Among the different regions, SOC in northern zone was significantly (p p < 0.001). The deeper layers contained higher SOC stocks (254.62 ± 128.09 MgC ha−1) than upper layers (55.42 ± 25.37 MgC ha−1). The study highlights that low deforestation rate have led to less CO2 (705.3 Mg CO2e ha−1 - 922.62 Mg CO2e ha−1) emissions than most sediment carbon-rich mangroves in the world. These results highlight the influence of soil texture and mangrove forest types on the mangrove SOC stocks. The first national comparison of soil organic carbon stocks between mangroves and upland tropical forests indicated SOC stocks were two times more in mangroves soils (51.21 ± 45.00 MgC ha−1) than primary (20.33 ± 12.7 MgC ha−1), savanna and cropland (21.71 ± 15.10 MgC ha−1). We find that mangroves in this study emit lower dioxide-carbon equivalent emissions. This study highlights the importance of national inventories of soil organic carbon and can be used as a baseline on the role of mangroves in carbon sequestration and climate change mitigation but the variation in SOC stocks indicates the need for further national data.

Stock Price Prediction Based on the Bi-GRU-Attention Model

The stock market, as one of the hotspots in the financial field, forms a data system with a huge volume of data and complex relationships between various factors, making stock price prediction an area of keen interest for further in-depth mining and research. Mathematical statistics methods struggle to deal with nonlinear relationships in practical applications, making it difficult to explore deep information about stocks. Meanwhile, machine learning methods, particularly neural network models and composite models, which have achieved outstanding results in other fields, are being applied to the stock market with significant results. However, researchers have found that these methods do not grasp the essential information of the data as well as expected. In response to these issues, researchers are exploring better neural network models and combining them with other methods to analyze stock data. Thus, this paper proposes the ABiGRU composite model, which combines the attention mechanism and bidirectional gated recurrent unit (GRU) that can effectively extract data features for stock price prediction research. Models such as LSTM, GRU, and Bi-LSTM are selected for comparative experiments. To ensure the credibility and representativeness of the research data, daily stock price indices of BYD are chosen for closing price prediction studies across different models. The results show that the ABiGRU model has a lower prediction error and better fitting effect on three index-based stock prices, enhancing the learning efficiency of the neural network model and demonstrating good prediction stability. This suggests that the ABiGRU model is highly adaptable for stock price prediction.