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Instrumental and emotional information influence paradoxically on people’s decision-making, and similar influences are more evident in e-commerce scenarios where physical information is limited. This study aims to construct a systematic explanatory framework for the influence of multidimensional recommendation information diversity (RID) on users' click and purchase decisions based on the social support theory (SST).

This study analyses 453,176 data from 67,079 users of a Chinese e-commerce platform, applying lasso algorithmic techniques and cross-fit partialling-out (XPO) regression for empirical analysis.

The study finds that instrumental support information diversity (ISID) and emotional support information diversity (ESID) play divergent roles, and that the effects of both on user decision-making are inconsistent with mode-flip and marginal change. Differences in users' information craving and information overload processing mechanisms for instrumental and emotional information, leading to an inverted U-shaped effect of ISID on consumption decisions, while ESID has a U-shaped effect. Additionally, supplier certification eliminates the marginal change in ESID, and products with a high information standardisation degree eliminate the marginal change in ISID.

Research results reveal the opposing roles of the two types of RID and the application boundaries of their roles, providing empirical evidence for academic research.

In this paper, numerical modelling and dynamical response analysis were performed for the HL-2M vacuum vessel (VV) and shielding plates (SPs) during a plasma disruption by using an updated ANSYS parametric design language (APDL) code developed by the authors. The purpose of this paper is to investigate the influence of the SPs on the dynamical response of VV owing to a transient electromagnetic (EM) force and to optimize the design of SPs in view of the minimization of the structural dynamic response.

The Lagrangian approach, i.e. the moving coordinate scheme developed by the authors, was updated to tackle the EM-mechanical coupling effect in the dynamic response analysis of the VV-SPs system due to the transient EM force during plasma disruptions. To optimize the structural design of HL-2M SPs, the influence of the key parameters of SPs, i.e., the side length, thickness and material properties, was clarified on the basis of the numerical results and an optimized design of SPs was obtained.

The updated APDL code of the Lagrangian approach is efficient for the transient dynamical response analysis of the VV-SPs system owing to the EM force. The SP of a smaller side length, larger thickness tungsten material better mitigates the dynamical response of the VV-SPs system.

The Lagrangian approach was updated for the EM–mechanical coupling dynamical response analysis of the VV-SPs system, and the influence of the SP parameters on the dynamical response of the VV-SPs system of HL-2M Tokamak was clarified.

The purpose of this study is to test the concept of a relatively low cost but biocompatible customized surgical guide printing method using a new composite material for the FDM process to support accurate virtual model reconstruction in CT.

Current additive manufacturing printed surgical guides have problems of scanning artifacts or low computed tomography (CT) values for virtual model reconstruction in CT-assisted surgical operations. These tools always face difficulties in precise positioning due to the effect of human soft tissues and manually made unstable landmarks. To solve this problem, this paper proposes a modified material, polyetheretherketone powder mixed with barium sulfate powder, for printing customized surgical guides with relatively low cost to support a synchronized scanning strategy, for the accurate reconstruction of human tissues and in vitro models.

A set of benchmarking experiments and clinical simulation cases were conducted. The results showed that the proposed solution can be used to print surgical guides to form stable and clear CT graphs for three-dimensional digital model reconstruction. Human tissues and in vitro models can be accurately reconstructed using clear CT graphs without any scanning artifacts or difficulties in image segmentation for virtual model reconstruction, thus facilitating accurate operation guidance and positioning.

This method has wide application potential for printing modular or customized surgical guides with low cost and reusability, especially for surgical operations using CT-assisted navigation systems in underdeveloped regions where medical device costs are a critical issue.

Object detection models have gained considerable popularity as they aid in lot of applications, like monitoring, video surveillance, etc. Object detection through the video tracking faces lot of challenges, as most of the videos obtained as the real time stream are affected due to the environmental factors.

This research develops a system for crowd tracking and crowd behaviour recognition using hybrid tracking model. The input for the proposed crowd tracking system is high density crowd videos containing hundreds of people. The first step is to detect human through visual recognition algorithms. Here, a priori knowledge of location point is given as input to visual recognition algorithm. The visual recognition algorithm identifies the human through the constraints defined within Minimum Bounding Rectangle (MBR). Then, the spatial tracking model based tracks the path of the human object movement in the video frame, and the tracking is carried out by extraction of color histogram and texture features. Also, the temporal tracking model is applied based on NARX neural network model, which is effectively utilized to detect the location of moving objects. Once the path of the person is tracked, the behaviour of every human object is identified using the Optimal Support Vector Machine which is newly developed by combing SVM and optimization algorithm, namely MBSO. The proposed MBSO algorithm is developed through the integration of the existing techniques, like BSA and MBO.

The dataset for the object tracking is utilized from Tracking in high crowd density dataset. The proposed OSVM classifier has attained improved performance with the values of 0.95 for accuracy.

This paper presents a hybrid high density video tracking model, and the behaviour recognition model. The proposed hybrid tracking model tracks the path of the object in the video through the temporal tracking and spatial tracking. The features train the proposed OSVM classifier based on the weights selected by the proposed MBSO algorithm. The proposed MBSO algorithm can be regarded as the modified version of the BSO algorithm.

Since the beginning of 2020, COVID-19 has dictated to the world what can be done or not. Log downs of countries, cities and businesses dominated the daily life of everybody. Especially the hospitality and tourism industry went through constant changes in rules and regulations. Tourists were not sure if they were allowed to travel or have to cancel their vacations. Hotels needed to be aware of COVID-19 registration and hygiene. In China, the Zero-COVID policies were wicked, strict and specific and should support daily businesses in the country. A specially used instrument was the variety of QR codes in place to prevent virus outbreaks and monitored tourism movements. For example, the city health QR code of each city/region in China, or the travel QR code to ensure safe travel. This chapter firstly looking into the ideas and policies behind the QR code system in China and secondly discovers through examples its efficiency. The final step is to propose a universal strategy on how QR codes and their policies can support safe and healthy hospitality and tourism recovery during the current pandemic crisis and protect tourists and the hospitality and tourism industry in future pandemic situations.

With the continuous improvement of social and economic development as well as the rising level of demand for spiritual life, the design of cultural and creative products has ushered in new opportunities and challenges. Therefore the research related to cultural and creative products design is an inevitable choice for industrial innovation and market competition. The article aims to analyze the frontier hotspots and trend evolution of theoretical research on cultural and creative design in China by presenting different research fields, personnel and institutions embodied by cultural and creative products, thus providing a forward-looking development reference for China's special cultural and creative product design practice.

In this paper, the visualized citation analysis tool — CiteSpace V is used to analyze and map the relevant literature of Chinese cultural and creative design in CNKI (Chinese National Knowledge Infrastructure) as the data sources, through bibliometric methods, in order to study the application development of Chinese cultural and creative design and provide reference for the application research of Chinese cultural and creative industries.

With the improvement of China's comprehensive strength, cultural self-confidence and strategy are being paid more and more attention by scholars; with the future research of China's cultural and creative design theory, cross-integration becomes the future trend; with the gradual maturity of China's cultural and creative design methodology, how to choose the right method for pioneering research is a difficulty for the future development of cultural and creative design; with the continuous development of China's economy, the method construction of innovative cultural and creative industry system becomes the focus of future research; with the continuous progress of science and technology, the integration of emotion and product industry is the mainstream of future development on cultural and creative design.

Through an objective empirical analysis of the development of Chinese cultural and creative products, it will broaden the research horizons of relevant scholars, understand the development direction of China's cultural and creative industries, enrich the design practice application of Chinese cultural and creative products, enhance the understanding of international counterparts on Chinese cultural and creative design research and promote the exchange among international counterparts.

To improve the speed and accuracy of turbine blade film cooling design process, the most advanced deep learning models were introduced into this study to investigate the most suitable define for prediction work. This paper aims to create a generative surrogate model that can be applied on multi-objective optimization problems.

The latest backbone in the field of computer vision (Swin-Transformer, 2021) was introduced and improved as the surrogate function for prediction of the multi-physics field distribution (film cooling effectiveness, pressure, density and velocity). The basic samples were generated by Latin hypercube sampling method and the numerical method adopt for the calculation was validated experimentally at first. The training and testing samples were calculated at experimental conditions. At last, the surrogate model predicted results were verified by experiment in a linear cascade.

The results indicated that comparing with the Multi-Scale Pix2Pix Model, the Swin-Transformer U-Net model presented higher accuracy and computing speed on the prediction of contour results. The computation time for each step of the Swin-Transformer U-Net model is one-third of the original model, especially in the case of multi-physics field prediction. The correlation index reached more than 99.2% and the first-order error was lower than 0.3% for multi-physics field. The predictions of the data-driven surrogate model are consistent with the predictions of the computational fluid dynamics results, and both are very close to the experimental results. The application of the Swin-Transformer model on enlarging the different structure samples will reduce the cost of numerical calculations as well as experiments.

The number of U-Net layers and sample scales has a proper relationship according to equation (8). Too many layers of U-Net will lead to unnecessary nonlinear variation, whereas too few layers will lead to insufficient feature extraction. In the case of Swin-Transformer U-Net model, incorrect number of U-Net layer will reduce the prediction accuracy. The multi-scale Pix2Pix model owns higher accuracy in predicting a single physical field, but the calculation speed is too slow. The Swin-Transformer model is fast in prediction and training (nearly three times faster than multi Pix2Pix model), but the predicted contours have more noise. The neural network predicted results and numerical calculations are consistent with the experimental distribution.

This paper creates a generative surrogate model that can be applied on multi-objective optimization problems. The generative adversarial networks using new backbone is chosen to adjust the output from single contour to multi-physics fields, which will generate more results simultaneously than traditional surrogate models and reduce the time-cost. And it is more applicable to multi-objective spatial optimization algorithms. The Swin-Transformer surrogate model is three times faster to computation speed than the Multi Pix2Pix model. In the prediction results of multi-physics fields, the prediction results of the Swin-Transformer model are more accurate.

This study investigates the ubiquitous business model and e-commerce strategy of WeChat as a platform. The potential of WeChat as a tool for sustainable entrepreneurial development has received limited attention from researchers. The purpose of this study is to uncover the hidden issues associated with WeChat's role in e-commerce development. It also examines the opportunities and limitations of using WeChat for e-commerce. The study employs a qualitative research approach, including an extensive literature review and ethnographic study. The results demonstrate that leveraging mobile apps like WeChat can greatly enhance entrepreneurial tendencies and foster sustainable entrepreneurship, thus contributing to poverty eradication in emerging nations.

The purpose of this paper is to explore how the intensity use of WeChat is associated with perceived social capital and psychological well-being factors among Chinese international students in Germany.

Based on an online survey data collected from 212 Chinese international college students in Germany, correlation analysis and structural equation modeling were sequentially implemented to deal with the research questions.

The correlation analysis results indicate that the intensity of WeChat use is positively correlated with bridging, bonding, and maintained social capital. Additionally, a path model demonstrates that bonding social capital is positively related to life satisfaction and negatively related to the sense of loneliness. Furthermore, both bonding and maintained social capital mediate the relationship between WeChat use and these two well-being outcomes.

Theoretically, the research is an initial study contributing to the existing social media literature on evaluating the effects of WeChat use on social capital and well-being. Practically, these obtained results can be beneficial to the understanding the dynamics of how social media may potentially impact students’ social connectedness and life quality.

Although WeChat has become the most prevalent social networking site in Mainland China, the social and psychological implications of the emerging technology are not completely understood. The paper offers evidence that WeChat has functioned as an efficient platform for sojourners to develop diverse types of social capital and promote well-being in an intercultural setting.