Impact of Interactivity on Guanxi Network Building in the Wechat Moments: A Social Capital Perspective

Mobile social platform such as Wechat Moments has gained great popularity in China in the past few years. However, there are still a lack of studies that focus on Guanxi network building in the virtual social community. Drawing upon interactivity and social capital theory, this study develops a research model to examine the influence of perceived interactivity on users\u27 social capital and Guanxi network in the Wechat Moments. An empirical survey was conducted in China and 287 valid data were collected from Wechat users. Structural equation modelling analysis was used to test the research model. The empirical results suggest that interactivity has a strong influence on social interaction and shared understanding, which in turn promote users\u27 Guanxi network in the Wechat Moments. A post-hoc analysis further suggests that the influence of interactivity on Guanxi network is contingent upon network size. Theoretical and practical implications are illustrated in the final section

Impact of Gamification on Consumers’ Online Impulse Purchase: The Mediating Effect of Affect Reaction and Social Interaction

Drawing upon the stimulus-organism-response (S-O-R)framework, this study developeda theoretical model to examine the impact mechanismof two gamification features on individuals’ impulse purchase in the context of Double Eleven. An empirical survey was conducted and 716 valid questionnaires were collected from consumers using Taobao and Tmall platforms in China.Structural equation modelling method was used to examine the research model. The empirical results suggestedthat rewards giving and badges upgradinggamification features werepositivelyassociated with perceived enjoyment and social interaction reactions, which in turn hadstrong influenceson consumers’ impulse purchase. This study providesnew insights in understandingonline impulsive buyingbehaviorsby incorporatingthe mechanism of gamificationin the new research context of Double Eleven

Examining Individuals’ Utilization of SPOC: Extending the Task-Technology Fit Model with Online and Offline Perspective

Small Private Online Course (SPOC) platform enables individuals to carry out their learning tasks both online and offline. In order to understand individuals’ utilization of SPOC, this study develops a research model to examine the joint influences of three types of perceived fit manifested in perceived technology-task fit (TTF), perceived individual-technology fit (ITF) and perceived online-offline fit (OOF). A survey was conducted in a famous university of China and 371 data were collected from students who selected courses on the SPOC platform. Structural equation modelling method was used to examine the research model. The empirical results suggest that ITF is the most significant antecedent of individual performance expectancy, followed by OOF and TTF. Moreover, individual performance expectancy has a positive influence on user satisfaction and individuals’ continuance intention in the SPOC platform. A post-hoc analysis further indicates that student’s GPA positively moderates the relationship between online participation behavior and course performance. This study extends the traditional perceived fit framework by introducing perceived online-offline fit, and uncovers the antecedents and outcomes of individuals’ utilization in the emerging research context of SPOC

Examining Individuals’ Ads Click Intention in the Wechat Moments: A Lens of Elaboration Likelihood Model

Drawing upon elaboration likelihood model (ELM), we compared the dual routesin determining users’ ads click intentions and examined the mediation mechanism of cognitive vs. affective trust on the influence processesin the Wechat Moments. A scenario-based survey was conducted in a university of China, and 183 data was collected. Structural equation modelling analysis was used to test the research model. The empirical results suggestedthat content personalization and social recommendation weresignificant antecedents of ads click intention, and their effects weremediated by cognitive trust and affective trust. Moreover, amulti-group analysis indicatedthat the two influence processes weremoderated by prior product experience. Theoretical and practical implications areillustrated in the final section

GPT-4V(ision) as A Social Media Analysis Engine

Recent research has offered insights into the extraordinary capabilities of Large Multimodal Models (LMMs) in various general vision and language tasks. There is growing interest in how LMMs perform in more specialized domains. Social media content, inherently multimodal, blends text, images, videos, and sometimes audio. Understanding social multimedia content remains a challenging problem for contemporary machine learning frameworks. In this paper, we explore GPT-4V(ision)'s capabilities for social multimedia analysis. We select five representative tasks, including sentiment analysis, hate speech detection, fake news identification, demographic inference, and political ideology detection, to evaluate GPT-4V. Our investigation begins with a preliminary quantitative analysis for each task using existing benchmark datasets, followed by a careful review of the results and a selection of qualitative samples that illustrate GPT-4V's potential in understanding multimodal social media content. GPT-4V demonstrates remarkable efficacy in these tasks, showcasing strengths such as joint understanding of image-text pairs, contextual and cultural awareness, and extensive commonsense knowledge. Despite the overall impressive capacity of GPT-4V in the social media domain, there remain notable challenges. GPT-4V struggles with tasks involving multilingual social multimedia comprehension and has difficulties in generalizing to the latest trends in social media. Additionally, it exhibits a tendency to generate erroneous information in the context of evolving celebrity and politician knowledge, reflecting the known hallucination problem. The insights gleaned from our findings underscore a promising future for LMMs in enhancing our comprehension of social media content and its users through the analysis of multimodal information

Identification of Aircraft Wake Vortex Based on SVM

The aircraft wake vortex has important influence on the operation of the airspace utilization ratio. Particularly, the identification of aircraft wake vortex using the pulsed Doppler lidar characteristics provides a new knowledge of wake turbulence separation standards. This paper develops an efficient pattern recognition-based method for identifying the aircraft wake vortex measured with the pulsed Doppler lidar. The proposed method is outlined in two stages. (i) First, a classification model based on support vector machine (SVM) is introduced to extract the radial velocity features in the wind fields by combining the environmental parameters. (ii) Then, grid search and cross-validation based on soft margin SVM with kernel tricks are employed to identify the aircraft wake vortex, using the test dataset. The dataset includes wake vortices of various aircrafts collected at the Chengdu Shuangliu International Airport from Aug 16, 2018, to Oct 10, 2018. The experimental results on dataset show that the proposed method can identify the aircraft wake vortex with only a small loss, which ensures the satisfactory robustness in detection performance

Joint Relay Selection and Power Allocation for the Physical Layer Security of Two-Way Cooperative Relaying Networks

In this paper, we investigate the physical layer security of cooperative two-way relay transmission systems using the amplify-and-forward (AF) protocol in the presence of an eavesdropper. A joint relay selection (RS) and power allocation (PA) scheme is proposed to protect the source-destination transmission against the eavesdropper. However, due to the high computational complexity, it is difficult to obtain the optimal solution for the system secrecy rate. Fortunately, an approximate optimal solution by using the particle swarm optimization (PSO) algorithm is derived. In the simulations, we use random relay selection with optimal power allocation (RRS-OPA) and equal power allocation with optimal relay selection (EPA-ORS) as benchmark schemes to verify the effectiveness of the proposed method. The simulation results show that the proposed method outperforms both RRS-OPA and EPA-ORS and significantly improves the system performance with low complexity

Effect of Laser Quenching-Shock Peening Strengthening on the Microstructure and Mechanical Properties of Cr12MoV Steel

The automobile covering parts mold is a key piece of equipment in the automobile industry, and its drawbead is the core element that affects the life of the mold and the quality of the parts made. Due to the complex structure of the mold cavity for covering parts, there exist differences between material flow characteristics, load conditions, stress strain, failure forms and so on in the surface of different parts of its drawbead and the different directions of the same part of the drawbead, thus putting forward new requirements for material strengthening. For the differentiated lose efficacy forms of the dangerous end faces of the tension bars, this study carried out research into the effect of laser quenching–shock peening strengthening (LQ-LSP) on the organization, plastic deformation resistance and wear resistance of Cr12MoV steel. It was shown that the microhardness (722.30 HV) and residual stress (−383.84 MPa) of the specimens were further enhanced after laser quenching–shock peening composite strengthening. The residual austenite content of the specimen was reduced to 0.8%, and the eutectic carbide distribution morphology was improved. After three rounds of laser composite peening, the specimens had the smallest displacement of the nanoindentation load–depth curve, which exhibited the greatest nanohardness (20.0 Pa) and modulus of elasticity (565.25 Pa), while reducing the coefficient of friction (0.61) and surface roughness (0.152 Ra). The smooth and flat surface of the specimen with shallow and narrow plow grooves improved the resistance of Cr12MoV steel to plastic deformation and wear

High fidelity simulation of ultrafine PM filtration by multiscale fibrous media characterized by a combination of X-ray CT and FIB-SEM

Air filtration mechanisms in the composite filter media used in practical applications are important and challenging to understand because the component fibers could have various size scales and morphologies. In this work, a three-dimensional digital model of nanofiber-based filter media was reconstructed for the first time based on the X-ray tomography data for the cellulose substrate and the Focused Ion Beam-Scanning Electron Microscope (FIB-SEM) image analysis for the several micrometers thick (3.82–7.90 μm) electrospun polyvinylidene fluoride (PVDF) nanofiber membrane. Besides the high-resolution model where the details of the fibrous structures were fully resolved, another low-resolution model with approximated unresolved structures was also established. Filtration simulations utilizing these models were conducted considering the drag force, Brownian diffusion and aerodynamic slip. The simulated filtration efficiencies agreed well with the experiments for particles of 70–400 nm, including the most penetrating particle size (MPPS, 100–200 nm). Moreover, the structure-resolved models had higher accuracy but higher computational costs, while the unresolved simulations saved much running time but over-predicted the filtration efficiency, especially for smaller particles (<100 nm). Our study presents a comprehensive strategy for investigating the composite filter media with multiscale complex structures using a combination of advanced characterization technologies and modular simulation models