Bridging Consumers’ Self-Brand Distance through Virtual-Reality: Perspective from Presence Experiences

Virtual-reality (VR) technology seems to be an efficient tool for consumer-brand relationship management since it could affect individuals’ psychological distance by enhancing their presence experiences. However, the effects of VR on individuals’ psychological distance are inconsistent. Based on the customer experience framework and construal level theory, these inconsistent effects could be attributed to the two aspects, namely, internal components of presence experience (i.e., immersive presence and realistic presence) and different impacts of vividness modes (i.e., modeling mode and panoramic mode). To address the above research gap, this study plans to investigate the relationships among consumers’ self-brand distance, presence experiences, vividness modes, and interactivity. An experiment will be conducted to collect empirical data in the VR-simulated shopping environment. The analysis of covariance could be used to examine the hypotheses. This research could offer implications to the literature and practice related to VR shopping and consumer-brand relationship management

A Research on Tourism E-Business of Shanxi

E-business has become a new growth point of current economic development in the world. The natural adaptability between tourism and E-business gives birth to tourism E-business. The tourism resources in Shanxi are unique, yet its tourism development level and the abundant resources are not harmonious. To transform its resource advantage into economic advantage, developing tourism E-business is very urgent and necessary. In this article, through quantitative analysis and qualitative analysis methods, we find out the problems existing in the development of tourism E-business in Shanxi, and put forward corresponding development measures, especially the mobile E-tourism based on 3G technology, which is a new pattern and will become the development direction of Shanxi’s tourism E-business in the future. The research aims at providing practical references for the tourism management departments and enterprises in Shanxi to enhance its competitiveness and promote its sustainable development

How Online Extended Reality (XR) Promotes Consumer Offline Engagement

Using extended-reality (XR) simulation to replicate physical surroundings has become increasingly prevalent in engaging online consumers with offline businesses. However, the efficacy of this XR technology remains ambiguous. To justify the huge investments in XR-related technologies, we investigate the impacts of extended surroundings on consumers’ offline engagement with associated businesses. Specifically, we utilize a natural experimental design on a leading housing platform that applies XR simulation to present the surrounding environment of housing estates. By combining propensity score matching and difference-in-differences, our findings indicate that extended surroundings increase consumer offline engagement outcomes, particularly word-of-mouth volume, and valence. Furthermore, we examine the heterogeneous effects moderated by three business characteristics. To our knowledge, this is the first to examine the impacts of XR simulation of extended surroundings. Therefore, this research offers significant implications for the literature and practice related to XR and omnichannel marketing

S2SNet: A Pretrained Neural Network for Superconductivity Discovery

Superconductivity allows electrical current to flow without any energy loss, and thus making solids superconducting is a grand goal of physics, material science, and electrical engineering. More than 16 Nobel Laureates have been awarded for their contribution to superconductivity research. Superconductors are valuable for sustainable development goals (SDGs), such as climate change mitigation, affordable and clean energy, industry, innovation and infrastructure, and so on. However, a unified physics theory explaining all superconductivity mechanism is still unknown. It is believed that superconductivity is microscopically due to not only molecular compositions but also the geometric crystal structure. Hence a new dataset, S2S, containing both crystal structures and superconducting critical temperature, is built upon SuperCon and Material Project. Based on this new dataset, we propose a novel model, S2SNet, which utilizes the attention mechanism for superconductivity prediction. To overcome the shortage of data, S2SNet is pre-trained on the whole Material Project dataset with Masked-Language Modeling (MLM). S2SNet makes a new state-of-the-art, with out-of-sample accuracy of 92% and Area Under Curve (AUC) of 0.92. To the best of our knowledge, S2SNet is the first work to predict superconductivity with only information of crystal structures. This work is beneficial to superconductivity discovery and further SDGs. Code and datasets are available in https://github.com/zjuKeLiu/S2SNetComment: Accepted to IJCAI 202

Linguistic experience acquisition for novel stimuli selectively activates the neural network of the visual word form area

The human ventral visual cortex is functionally organized into different domains that sensitively respond to different categories, such as words and objects. There is heated debate over what principle constrains the locations of those domains. Taking the visual word form area (VWFA) as an example, we tested whether the word preference in this area originates from the bottom-up processes related to word shape (the shape hypothesis) or top-down connectivity of higher-order language regions (the connectivity hypothesis). We trained subjects to associate identical, meaningless, non-word-like figures with high-level features of either words or objects. We found that the word-feature learning for the figures elicited the neural activation change in the VWFA, and learning performance effectively predicted the activation strength of this area after learning. Word-learning effects were also observed in other language areas (i.e., the left posterior superior temporal gyrus, postcentral gyrus, and supplementary motor area), with increased functional connectivity between the VWFA and the language regions. In contrast, object-feature learning was not associated with obvious activation changes in the language regions. These results indicate that high-level language features of stimuli can modulate the activation of the VWFA, providing supportive evidence for the connectivity hypothesis of words processing in the ventral occipitotemporal cortex

KXNet: A Model-Driven Deep Neural Network for Blind Super-Resolution

Although current deep learning-based methods have gained promising performance in the blind single image super-resolution (SISR) task, most of them mainly focus on heuristically constructing diverse network architectures and put less emphasis on the explicit embedding of the physical generation mechanism between blur kernels and high-resolution (HR) images. To alleviate this issue, we propose a model-driven deep neural network, called KXNet, for blind SISR. Specifically, to solve the classical SISR model, we propose a simple-yet-effective iterative algorithm. Then by unfolding the involved iterative steps into the corresponding network module, we naturally construct the KXNet. The main specificity of the proposed KXNet is that the entire learning process is fully and explicitly integrated with the inherent physical mechanism underlying this SISR task. Thus, the learned blur kernel has clear physical patterns and the mutually iterative process between blur kernel and HR image can soundly guide the KXNet to be evolved in the right direction. Extensive experiments on synthetic and real data finely demonstrate the superior accuracy and generality of our method beyond the current representative state-of-the-art blind SISR methods. Code is available at: https://github.com/jiahong-fu/KXNet.Comment: Accepted by ECCV202

Future Changes in Mean and Extreme Monsoon Precipitation in the Middle and Lower Yangtze River Basin, China, in the CMIP5 Models

In this study, the potential future changes of mean and extreme precipitation in the middle and lower Yangtze River basin (MLYRB), eastern China, are assessed using the models of phase 5 of the Coupled Model Intercomparison Project (CMIP5). Historical model simulations are first compared with observations in order to evaluate model performance. In general, the models simulate the precipitation mean and frequency better than the precipitation intensity and extremes, but still have difficulty capturing precipitation patterns over complex terrains. They tend to overestimate precipitation mean, frequency, and intensity while underestimating the extremes. After correcting for model biases, the spatial variation of mean precipitation projected by the multimodel ensemble mean (MME) is improved, so the MME after the bias correction is used to project changes for the years 2021–50 and 2071–2100 relative to 1971–2000 under two emission scenarios: RCP4.5 and RCP8.5. Results show that with global warming, precipitation will become less frequent but more intense over the MLYRB. Relative changes in extremes generally exceed those in mean precipitation. Moreover, increased precipitation extremes are also expected even in places where mean precipitation is projected to decrease in 2021–50. The overall increase in extreme precipitation could potentially lead to more frequent floods in this already flood-prone region