Understanding visitors' metaverse and in-person tour intentions during the COVID-19 pandemic: A coping perspective

The COVID-19 pandemic has posed a challenge to the development of global tourism. Metaverse tour, as an emerging travel pattern that combines metaverse characteristics, can bring positive implications for the digital transformation of tourism. However, there are few studies that integrate time and space to analyze the public’s intentions of metaverse tour and in-person tour, calling for a comprehensive research framework. Based on coping theory, a mixed-methods approach was proposed to incorporate several constructs to investigate the factors that influence public participation in metaverse tour. We further analyze the difference of public’s willingness to travel in-metaverse versus in-person at different times. This study enriches our understanding of the popularity of metaverse tour and the reasons for it, and provides insights into the further development of tourism to facilitate changes in the metaverse-based tourism pattern

PointHuman: Reconstructing Clothed Human from Point Cloud of Parametric Model

It is very difficult to accomplish the 3D reconstruction of the clothed human body from a single RGB image, because the 2D image lacks the representation information of the 3D human body, especially for the clothed human body. In order to solve this problem, we introduced a priority scheme of different body parts spatial information and proposed PointHuman network. PointHuman combines the spatial feature of the parametric model of the human body with the implicit functions without expressive restrictions. In PointHuman reconstruction framework, we use Point Transformer to extract the semantic spatial feature of the parametric model of the human body to regularize the implicit function of the neural network, which extends the generalization ability of the neural network to complex human poses and various styles of clothing. Moreover, considering the ambiguity of depth information, we estimate the depth of the parameterized model after point cloudization, and obtain an offset depth value. The offset depth value improves the consistency between the parameterized model and the neural implicit function, and accuracy of human reconstruction models. Finally, we optimize the restoration of the parametric model from a single image, and propose a depth perception method. This method further improves the estimation accuracy of the parametric model and finally improves the effectiveness of human reconstruction. Our method achieves competitive performance on the THuman dataset

Identification of robotic systems with hysteresis using Nonlinear AutoRegressive eXogenous input models

Identification of robotic systems with hysteresis is the main focus of this article. Nonlinear AutoRegressive eXogenous input models are proposed to describe the systems with hysteresis, with no limitation on the nonlinear characteristics. The article introduces an efficient approach to select model terms. This selection process is achieved using an orthogonal forward regression based on the leave-one-out cross-validation. A sampling rate reduction procedure is proposed to be incorporated into the term selection process. Two simulation examples corresponding to two typical hysteresis phenomena and one experimental example are finally presented to illustrate the applicability and effectiveness of the proposed approach

Identifying Reaction-Aware Driving Styles of Stochastic Model Predictive Controlled Vehicles by Inverse Reinforcement Learning

The driving style of an Autonomous Vehicle (AV) refers to how it behaves and interacts with other AVs. In a multi-vehicle autonomous driving system, an AV capable of identifying the driving styles of its nearby AVs can reliably evaluate the risk of collisions and make more reasonable driving decisions. However, there has not been a consistent definition of driving styles for AVs in the literature, although it is considered that the driving style is encoded in the AV's trajectories and can be identified using Maximum Entropy Inverse Reinforcement Learning (ME-IRL) methods as a cost function. Nevertheless, an important indicator of the driving style, i.e., how an AV reacts to its nearby AVs, is not fully incorporated in the feature design of previous ME-IRL methods. In this paper, we describe the driving style as a cost function of a series of weighted features. We design additional novel features to capture the AV's reaction-aware characteristics. Then, we identify the driving styles from the demonstration trajectories generated by the Stochastic Model Predictive Control (SMPC) using a modified ME-IRL method with our newly proposed features. The proposed method is validated using MATLAB simulation and an off-the-shelf experiment

Design and assessment of a reconfigurable behavioral assistive robot: a pilot study

IntroductionFor patients with functional motor disorders of the lower limbs due to brain damage or accidental injury, restoring the ability to stand and walk plays an important role in clinical rehabilitation. Lower limb exoskeleton robots generally require patients to convert themselves to a standing position for use, while being a wearable device with limited movement distance.MethodsThis paper proposes a reconfigurable behavioral assistive robot that integrates the functions of an exoskeleton robot and an assistive standing wheelchair through a novel mechanism. The new mechanism is based on a four-bar linkage, and through simple and stable conformal transformations, the robot can switch between exoskeleton state, sit-to-stand support state, and wheelchair state. This enables the robot to achieve the functions of assisted walking, assisted standing up, supported standing and wheelchair mobility, respectively, thereby meeting the daily activity needs of sit-to-stand transitions and gait training. The configuration transformation module controls seamless switching between different configurations through an industrial computer. Experimental protocols have been developed for wearable testing of robotic prototypes not only for healthy subjects but also for simulated hemiplegic patients.ResultsThe experimental results indicate that the gait tracking effect during robot-assisted walking is satisfactory, and there are no sudden speed changes during the assisted standing up process, providing smooth support to the wearer. Meanwhile, the activation of the main force-generating muscles of the legs and the plantar pressure decreases significantly in healthy subjects and simulated hemiplegic patients wearing the robot for assisted walking and assisted standing-up compared to the situation when the robot is not worn.DiscussionThese experimental findings demonstrate that the reconfigurable behavioral assistive robot prototype of this study is effective, reducing the muscular burden on the wearer during walking and standing up, and provide effective support for the subject's body. The experimental results objectively and comprehensively showcase the effectiveness and potential of the reconfigurable behavioral assistive robot in the realms of behavioral assistance and rehabilitation training

Positioning technique of coded aperture radiation imaging

With the widespread application of nuclear technology and radiation protection, the demand for radioactive sources imaging is increasing. As a high-precision imaging and positioning device for radioactive sources, the coded aperture imaging positioning system can accurately determine the location of radioactive sources and reconstruct their rough shape. This study explores the comparison of the reconstruction effects of various reconstruction algorithms in coded aperture imaging positioning on the position and shape reconstruction of radioactive sources with continuous energy spectra, to determine the advantages and disadvantages of different reconstruction algorithms and their applicable scenarios. Geant4 software was used to simulate the encoded aperture imaging positioning system, and the relevant data were obtained. Thereafter, the δ decoding, fine sampling balance decoding, and convolutional neural network (CNN) algorithms, along with the maximum likelihood maximum expected value method (MLEM) were used to program and reconstruct the location of the radioactive source. The results demonstrate that the four reconstruction algorithms can locate the radioactive source clearly; the δ decoding and fine sampling balance decoding algorithms have artifacts to reconstruct the image; and the CNN algorithm has a poor effect on the reconstruction of line and surface sources, which can be addressed by an extended training set; the contrast to noise ratio (CNR) value of the MLEM algorithm is high, and the reconstruction effect is good, however, some details of the line and surface sources reconstruction will be lost

Case report: Late in-stent thrombosis in a patient with vertebrobasilar dolichoectasia after stent-assisted coil embolization due to the discontinuation of antiplatelet therapy

Vertebrobasilar dolichoectasia (VBD) is a rare type of cerebrovascular disorder with high rates of morbidity and mortality. Due to the distinct pathological characteristics that fragmented internal elastic lamina and multiple dissections, VBD is difficult to treat and cured. Stent-assisted coil embolization is one of the main treatment modalities for such lesions. However, the duration of healing remained questionable, and there were no effective measures for evaluating endothelial coverage. Before complete endothelial coverage, the discontinuation of antiplatelet therapy may lead to fatal in-stent thrombosis; however, continued antiplatelet therapy could also result in bleeding complications. Thus, we present an autopsy case of late in-stent thrombosis due to the discontinuation of antiplatelet therapy and systematically review the literature to provide a reference for endovascular treatment and antiplatelet regimen of VBD