The rapid rise of live streaming commerce in China has transformed the retail environment, with electronic word-of-mouth (eWOM) emerging as a pivotal factor in shaping consumer behavior. As a digital evolution of traditional word-of-mouth, eWOM gains particular significance in live streaming contexts, where real-time interactions foster immediacy and engagement. This study investigates how eWOM influences consumer purchase intentions within Chinese live streaming platforms, employing the Information Adoption Model (IAM) as theoretical framework. Using a grounded theory approach, this research applies NVivo for data coding and analysis to explore the cognitive and emotional processes triggered by eWOM during live streaming. Findings indicate that argument quality, source credibility, and information quantity significantly enhance consumer trust and perceived usefulness of information, which, in turn, drives information adoption and purchase intention. Furthermore, the study reveals that social interaction between live streaming anchors and audiences amplifies the influence of consumers' internal states on information adoption. This study enhances the Information Adoption Model (IAM) by introducing social interaction as a moderator between consumers' internal states toward live streaming eWOM and their adoption of information, highlighting the value of social interaction in live streaming. It also incorporates information quantity, showing how eWOM quantity affects trust and perceived usefulness. Furthermore, the study contributes to exploring how factors like argument quality, source credibility, and information quantity shape consumer trust and perceived usefulness, offering insights into the cognitive and emotional processes of information adoption in live streaming.

The objective of this paper is to analyze the impact of infrastructure financing on economic growth in emerging markets through the application of both quantitative and qualitative research methodologies. In this study, the research will employ both primary and secondary data to investigate the impact of different structures of infrastructure financing on the performance of the economy through interviews with the stakeholders and policy documents alongside quantitative data from the World Bank and the IMF. The quantitative analysis employs the econometric models to establish the effect of infrastructure investment on the GDP growth of the selected countries, India, China, Brazil, and Nigeria. Additional secondary qualitative data obtained from interviews with policymakers and financial specialists from Brazil, India, and South Africa offer more practical information regarding the efficiency of the discussed financing approaches. This paper is therefore able to conclude that appropriate management of infrastructure investments, particularly those that involve the PPP, are central to the development of the economy. However, certain drawbacks such as the lack of regularity of data and the disparity in the effectiveness of financing instruments by the regions are pointed out. The research provides policy implications to policymakers and investors who wish to finance infrastructure in the emerging economy to enhance economic growth in the long run.

This article investigates how green logistics influences Vietnam’s trade balance with Association of Southeast Asian Nations (ASEAN) countries. By using the gravity model, the article applies fixed effects (FEM) and random effects (REM) to analyze panel data on trade balance, GDP, population, trade openness, and the green logistics index of Vietnam with ASEAN countries from 2012 to 2018. The research findings indicate that green logistics has not significantly affected Vietnam’s export trade balance with ASEAN countries. The article suggests solutions for the Vietnamese government and export businesses to enhance Vietnam’s trade balance with ASEAN countries by integrating green logistics activities. By following these recommendations, Vietnam can ensure that international trade aligns with environmental conservation, laying the groundwork for sustainable and inclusive economic development in Vietnam.

Breast cancer was a prevalent form of cancer worldwide. Thermography, a method for diagnosing breast cancer, involves recording the thermal patterns of the breast. This article explores the use of a convolutional neural network (CNN) algorithm to extract features from a dataset of thermographic images. Initially, the CNN network was used to extract a feature vector from the images. Subsequently, machine learning techniques can be used for image classification. This study utilizes four classification methods, namely Fully connected neural network (FCnet), support vector machine (SVM), classification linear model (CLINEAR), and KNN, to classify breast cancer from thermographic images. The accuracy rates achieved by the FCnet, SVM, CLINEAR, and k-nearest neighbors (KNN) algorithms were 94.2%, 95.0%, 95.0%, and 94.1%, respectively. Furthermore, the reliability parameters for these classifiers were computed as 92.1%, 97.5%, 96.5%, and 91.2%, while their respective sensitivities were calculated as 95.5%, 94.1%, 90.4%, and 93.2%. These findings can assist experts in developing an expert system for breast cancer diagnosis.

Benzoxazine resin, a new type of phenolic resin, has many advantages, such as a strong molecular design, no small molecular release in the curing process, excellent thermal stability and mechanical properties, and a high residual carbon ratio. Thus, it is important for electronic communication industry matrix material. To meet the needs of high-frequency and high-speed communication technology for low-dielectric polymer resin, the low-dielectric modification of benzoxazine resin is of great significance to the high frequency and high-speed propagation of the signal, which attracts a wide range of materials researchers’ attention. In this paper, we review a series of studies on the low dielectric modification of benzoxazine resin in recent years, including the synthesis of new monomers, inorganic - organic hybridization, copolymerization with other resins, and low molecular weight benzoxazine resin research trends.