With the development and integration of artificial intelligence (AI) and virtual reality technology, metahuman powered by AI is gradually becoming an important force in service marketing. Considering the novel characteristics of metahuman services, this study aims to identify and validate the core dimensions of the metahuman service quality. Online review analysis, multiple regression analysis, and fuzzy-set Qualitative Comparative Analysis (fsQCA) were employed to analyze data in this study. The results show that nine dimensions of metahuman service quality, including stability, complementary, efficiency, continuous improvement, empathy, accessibility, personalization, cultural sociability, and fault tolerance, significantly contribute to customer satisfaction. Moreover, the results suggest that there are five multiple and equally effective combinations of metahuman service quality dimensions that can achieve high consumers’ satisfaction. This study contributes to the avatar literature by unveiling the complexity behind the adoption of AI-powered avatar. It also offers meaningful practical implications for marketing managers to optimize the combination of metahuman service quality.

Full-body tracking in virtual reality improves presence, allows interaction via body postures, and facilitates better social expression among users. However, full-body tracking systems today require a complex setup fixed to the environment (e.g., multiple lighthouses/cameras) and a laborious calibration process, which goes against the desire to make VR systems more portable and integrated. We present HybridTrak, which provides accurate, real-time full-body tracking by augmenting inside-out1 upper-body VR tracking systems with a single external off-the-shelf RGB web camera. HybridTrak uses a full-neural solution to convert and transform users’ 2D full-body poses from the webcam to 3D poses leveraging the inside-out upper-body tracking data. We showed HybridTrak is more accurate than RGB or depth-based tracking methods on the MPI-INF-3DHP dataset. We also tested HybridTrak in the popular VRChat app and showed that body postures presented by HybridTrak are more distinguishable and more natural than a solution using an RGBD camera.

To a great extent, the quality of teaching determines the level of trained talents. Now it has entered the era of intelligent education, coupled with the rapid development of the Internet, has produced a large number of teaching data, which also makes the monitoring and evaluation of teaching quality become particularly difficult. In view of the above problems, this paper proposes the construction of big data monitoring platform for teaching quality under intelligent education. In this paper, the OPC UA unified architecture is used for communication between devices, and the information configuration is based on the spring boot framework to achieve data collection. Then, data processing is based on GRU neural network, and spark distributed computing framework is used to improve the efficiency of data operation. Finally, the monitoring effect is realized by constructing the evaluation system.

As one of the three provinces in Northeast China, Liaoning Province is one of the major industrial provinces. With the development of industrialization, the urban environmental pollution in Liaoning Province is becoming more and more serious, which has brought a huge impact on people's daily lives. In order to strengthen the monitoring of urban pollution, this paper proposes a study on the intelligent monitoring platform for urban pollution in Liaoning Province based on big data. This paper combines big data and intelligent monitoring technology to study the intelligent monitoring platform for urban pollution in Liaoning Province based on big data. The monitoring of air pollution in a city in Liaoning Province through simulation experiments verified the reliability of this platform. This paper finds that the average response time of the Liaoning Province's urban pollution intelligent monitoring platform based on big data proposed in this paper is 2.142 seconds, the transaction passing rate is 100%, and the platform performance test can pass. Through this platform, online real-time monitoring and remote control can be carried out in real time to grasp the dynamic changes of the environment, so that environmental problems can be found and treated in a timely manner, the environmental protection department's ability to prevent and control environmental pollution can be effectively improved, and it can also provide future urban pollution monitoring research.

As sensors and interactive devices become ubiquitous and transition outdoors and into the wild, we are met with the challenge of mass deployment and actuation. We present E-seed, a biomimetic platform that consumes little power to deploy, harvests energy from nature to install, and functions autonomously in the field. Each seed can individually self-drill into a substrate by harvesting moisture fluctuations in its ambient environment. As such, E-seed acts as a shape-changing interface to autonomously embed functional devices and interfaces into the soil, with the potential of aerial deployment in hard-to-reach locations. Our system is constructed primarily from wood veneer, making it lightweight, inexpensive, and biodegradable. In this paper, we detail our fabrication process and showcase demos that leverage the E-seed platform as a self-drilling interface. We envision that possible applications include soil sensors, sampling, and environmental monitoring for agriculture and reforestation.

Living things in nature have long been utilizing the ability to "heal" their wounds on the soft bodies to survive in the outer environment. In order to impart this self-healing property to our daily life interface, we propose Self-healing UI, a soft-bodied interface that can intrinsically self-heal damages without external stimuli or glue. The key material to achieving Self-healing UI is MWCNTs-PBS, a composite material of a self-healing polymer polyborosiloxane (PBS) and a filler material multi-walled carbon nanotubes (MWCNTs), which retains mechanical and electrical self-healability. We developed a hybrid model that combines PBS, MWCNTs-PBS, and other common soft materials including fabric and silicone to build interface devices with self-healing, sensing, and actuation capability. These devices were implemented by layer-by-layer stacking fabrication without glue or any post-processing, by leveraging the materials' inherent self-healing property between two layers. We then demonstrated sensing primitives and interactive applications that extend the design space of shape-changing interfaces with their ability to transform, conform, reconfigure, heal, and fuse, which we believe can enrich the toolbox of human-computer interaction (HCI).