Search results

With the continuous growth of online education, the quality issue of online educational videos has become increasingly prominent, causing students in online learning to face the dilemma of knowledge confusion. The existing mechanisms for controlling the quality of online educational videos suffer from subjectivity and low timeliness. Monitoring the quality of online educational videos involves analyzing metadata features and log data, which is an important aspect. With the development of artificial intelligence technology, deep learning techniques with strong predictive capabilities can provide new methods for predicting the quality of online educational videos, effectively overcoming the shortcomings of existing methods. The purpose of this study is to find a deep neural network that can model the dynamic and static features of the video itself, as well as the relationships between videos, to achieve dynamic monitoring of the quality of online educational videos.

The quality of a video cannot be directly measured. According to previous research, the authors use engagement to represent the level of video quality. Engagement is the normalized participation time, which represents the degree to which learners tend to participate in the video. Based on existing public data sets, this study designs an online educational video engagement prediction model based on dynamic graph neural networks (DGNNs). The model is trained based on the video’s static features and dynamic features generated after its release by constructing dynamic graph data. The model includes a spatiotemporal feature extraction layer composed of DGNNs, which can effectively extract the time and space features contained in the video's dynamic graph data. The trained model is used to predict the engagement level of learners with the video on day T after its release, thereby achieving dynamic monitoring of video quality.

Models with spatiotemporal feature extraction layers consisting of four types of DGNNs can accurately predict the engagement level of online educational videos. Of these, the model using the temporal graph convolutional neural network has the smallest prediction error. In dynamic graph construction, using cosine similarity and Euclidean distance functions with reasonable threshold settings can construct a structurally appropriate dynamic graph. In the training of this model, the amount of historical time series data used will affect the model’s predictive performance. The more historical time series data used, the smaller the prediction error of the trained model.

A limitation of this study is that not all video data in the data set was used to construct the dynamic graph due to memory constraints. In addition, the DGNNs used in the spatiotemporal feature extraction layer are relatively conventional.

In this study, the authors propose an online educational video engagement prediction model based on DGNNs, which can achieve the dynamic monitoring of video quality. The model can be applied as part of a video quality monitoring mechanism for various online educational resource platforms.

This study aims to investigate factors impacting host country consumers’ attitudes toward acquirers’ corporate brands and target brands after cross-border acquisitions (CBAs).

Surveys were conducted with US consumers using two fictitious CBA scenarios in the automobile industry.

Consumer ethnocentric tendencies (CETs) are negatively related to attitudes toward a CBA event; attitudes toward a CBA event are positively related to post-CBA attitudes toward the acquirer's corporate brand; brand-image fit is positively related to attitudes toward a CBA event, and post-CBA attitudes toward the acquirer's corporate brand and the target brand; post-CBA attitudes toward the acquirer's corporate brand and the target brand are positively related.

This study is limited in the sample, analysis approaches, context and factors examined. Future research could use more representative samples and both quantitative and qualitative methodologies; conduct more tests; examine real CBAs in different industries and countries; and investigate effects of other factors affecting attitudes toward the CBA event and post-CBA brand attitudes.

Managers should consider CETs and brand-image fit and strategically influence attitudes toward a CBA event and post-CBA brand attitudes.

It investigates the mediating effect of attitudes toward a CBA event on the relationship between CETs and post-CBA attitudes toward the acquirer's corporate brand and the effects of brand-image fit on attitudes toward a CBA event and post-CBA brand attitudes.

The inclusion of esports as an official event in the Hangzhou Asian Games is an important step towards the institutionalisation of esports. The significance of this event marks that Asia once again takes a lead in the global esportisation. This chapter investigates a series of history events in the inclusion process of esports into the comprehensive Games in Asia using process sociology and actor network theory (ANT). This study will analyse the type characteristics of esports events in Hangzhou Asian Games, whilst examining how key stakeholders' interact and balance in the network composed of international sports organisations, host of the event, emerging esports organisations and esports game companies. The chapter also examines the functions of global game industrial economic geography, local cultural politics, esports geopolitics and Olympic values in esports sportization, aiming to reveal the implications of esports inclusion in the Asian Games on the debate of whether esports meets the criteria to be classified as a ‘sport’ and its enlightenment of digital strategy to the inclusion esports in the Olympics.

This study aims to manufacture cotton fabric with thermal regulation performance by using the composite phase change material (CPCM) prepared by coating paraffin doped with expanded graphite (EG), and the thermal effect of the fabric material was evaluated and characterized.

EG/paraffin CPCM with shape stability and enhanced thermal conductivity were prepared by the impregnation method and then finished on the surface of cotton fabric with coating technology. The microstructure, crystal structure, chemical composition, latent heat property and thermal conductivity were analyzed by scanning electron microscope, x-ray diffraction, Fourier transform infrared spectroscopy, differential scanning calorimeter and thermal constant analyzer. The photo-thermal effect of the coated fabric was studied by a thermal infrared imager.

CPCM prepared with a mass ratio of EG to paraffin of 1:8 showed excellent shape stability and low paraffin leakage rate. The latent heat of the CPCM was 51.6201 J/g and the thermal conductivity coefficient was increased by 11.4 times compared with the mixed paraffin. After the CPCM was coated on the surface of the cotton fabric, the light-to-heat conversion rate of the C-EG/PA3 sample was improved by 86.32% compared with the original fabric. In addition, the coated fabric showed excellent thermal stability and heat storage performance in the thermal cycling test.

EG can improve the shape stability and thermal conductivity of paraffin but will reduce the latent heat energy.

The method developed provided a simple and practical solution to improving the thermal regulation performance of fabrics.

Combining paraffin wax with fabrics in a composite way is innovative and has certain applicability in improving the thermal properties of fabrics.

During the coronavirus disease 2019 (COVID-19) pandemic, ubiquitous social media has become a primary channel for information dissemination, social interactions and recreational activities. However, it remains unclear how social media usage influences nonpharmaceutical preventive behavior of individuals in response to the pandemic. This paper aims to explore the impacts of social media on COVID-19 preventive behaviors based on the theoretical lens of empowerment.

In this paper, survey data has been collected from 739 social media users in China to conduct structural equation modeling (SEM) analysis.

The results indicate that social media empowers individuals in terms of knowledge seeking, knowledge sharing, socializing and entertainment to promote preventive behaviors at the individual level by increasing each person's perception of collective efficacy and social cohesion. Meanwhile, social cohesion negatively impacts the relationship between collective efficacy and individual preventive behavior.

This study provides insights regarding the role of social media in crisis response and examines the role of collective beliefs in the influencing mechanism of social media. The results presented herein can be used to guide government agencies seeking to control the COVID-19 pandemic.

The purpose of this paper is to deal with the practical challenge faced by modern logistics enterprises to accurately evaluate driving performance with high computational efficiency under the disturbance of road smoothness and to identify significantly associated performance influence factors.

The authors cooperate with a logistics server (G7) and establish a driving grading system by constructing real-time inertial navigation data-enabled indicators for both driving behaviour (times of aggressive speed change and times of lane change) and road smoothness (average speed and average vibration times of the vehicle body).

The developed driving grading system demonstrates highly accurate evaluations in practical use. Data analytics on the constructed indicators prove the significances of both driving behaviour heterogeneity and the road smoothness effect on objective driving grading. The methodologies are validated with real-life tests on different types of vehicles, and are confirmed to be quite effective in practical tests with 95% accuracy according to prior benchmarks. Data analytics based on the grading system validate the hypotheses of the driving fatigue effect, daily traffic periods impact and transition effect. In addition, the authors empirically distinguish the impact strength of external factors (driving time, rainfall and humidity, wind speed, and air quality) on driving performance.

This study has good potential for providing objective driving grading as required by the modern logistics industry to improve transparent management efficiency with real-time vehicle data.

This study contributes to the existing research by comprehensively measuring both road smoothness and driving performance in the driving grading system in the modern logistics industry.

This paper aims to demonstrate the effect of varying chromium content on the wear behavior of white cast iron, to study the interaction relationship between cementite and pearlite in white cast iron, while estimating their contribution rate in abrasive wear.

To study interaction of cementite-pearlite of white cast irons with different chromium content in three-body abrasive wear, three kinds of chromium white cast iron, bulk single-phase cementite, pure pearlite samples and the white cast iron (WCI), were prepared using the melting and casting technique. The so-called pure pearlite samples have the same chemical composition, microstructure and properties as the pearlite matrix in white cast iron.

Results indicated that the interaction has a negative value. Its absolute value decreased with increasing chromium addition. Meanwhile, a high load resulted in an increased interaction value. The contribution rate of cementite to interaction, which was higher than that of pearlite, increased with increasing chromium addition. This indicated cementite was a main phase. Besides, the reductive size of abrasive has a significant effect on the contribution rate at the high load. These prominent cementite occurred fracture, when small size abrasive indented the matrix. These result in the absence of a protective effect of cementite during wear process. Eventually, the contribution rate of cementite decreased significantly.

This paper demonstrates the effect of varying chromium content on wear behavior of white cast iron, to study the interaction relationship between cementite and pearlite in white cast iron while estimating their contribution rate in abrasive wear.

In order to solve the current imbalance of academic resources within the discipline, this article builds a three-dimensional talent evaluation model based on the topic–author–citation based on the z index and proposes the ZAS index to evaluate scholars on different research topics within the discipline.

Based on the sample data of the CSSCI journals in the discipline of physical education in the past five years, the keywords were classified into 13 categories of research topics including female sports. The ZAS index of scholars on topic of female sports and so on was calculated, and quantitative indexes such as h index p index and z index were calculated. Comparative analysis of the evaluation effect was performed.

It is found that compared with the h index and p index, the z index achieves a better balance between the quantity, quality and citation distribution of scholars' results and effectively recognizes that the citation quality is higher and the number of citations of each paper is more balanced. In addition, compared to the z index, this article is based on a ZAS index model with an improved three-dimensional topic–author–citation relationship in research fields such as female sports.

It can identify some outstanding scholars who are engaged in small-scale or emerging topic research such as female sports and are excellent in different research areas. Talents create an objective and fair evaluation environment. At the same time, the ranking ability of ZAS indicators in the evaluation of talents is the strongest, and it is expected to be used in practical evaluations.