In this paper, an affine frequency division multiplexing aided grant-free random access (AFDM-GF-RA) system is designed for low Earth orbit (LEO) satellite Internet of Things (SIoT) networks, where users employ the GF-RA to access the satellite concurrently by configuring the AFDM modulation. In order to evaluate the system’s performance, average bit error probability (ABEP) bounds are first derived by Cholesky decomposition, which are verified by simulation results. Furthermore, a block sparse prior expectation propagation detection (BSPEP) is developed for joint active users detection (AUD) and data detection (DD). User activity probability is considered as a prior for joint detection. Concurrently, the accuracy of AUD is further improved by combining the block sparsity characteristics of the user transmission signals. Simulation results show that the proposed detector outperforms the classical oracle least squares (OLS) benchmark and approaches the theoretical ABEP bound at high signal-to-noise ratio (SNR).

Recently, the electroencephalogram (EEG) has been widely adopted as a quantitative indicator for monitoring emotional modulation during virtual reality (VR) experiences. Although VR emotion-induction materials are continuously being developed, few methods have been proposed for constructing VR scenarios through psychophysiological calibration. To achieve precise emotional regulation, we propose an integrated psychophysiological oriented EEG-based VR scenario modeling approach for emotion induction. This methodology constructs 3D VR scenarios by extending 2D elements through core processes: (1) deconstruction of emotion-annotated 2D images to extract visual-audio emotional patterns, (2) development of immersive environments with dynamic camera trajectories, and (3) integration of audio stimuli and real-world physical configurations to form multi-sensory emotional stimuli. During emotioninduction experiments, EEG signals reflecting emotional states were captured and analyzed for each scenario. We implemented multidimensional calibration of emotion-induction materials by: (a) calculating emotion indicators from EEG signals, (b) combining these with traditional assessments using the Self-Assessment Manikin (SAM) scale, and (c) calibrating scenarios for distinct emotion categories. This yields psychophysiologically calibrated VR scenarios for emotion induction, delivering standardised affective stimuli with synchronised EEG datasets to advance affective computing research.

In order to accurately predict the flight transit time at airports of different sizes, this paper proposes a two-stage flight transit time dynamic prediction model based on ensemble learning and Transformer to jointly and dynamically predict the flight transit time of multiple airports. First, considering the continuity of flight operation, the flight information is associated, and the key features are extracted from the historical information of the flight to construct a multi-airport flight transit data set; secondly, the parameter transfer and connection of the flight transit time are carried out; thirdly, the Bayesian optimization algorithm is introduced to optimize the hyperparameters of the LightGBM and XGBoost models, and the optimal hyperparameters are used to make a preliminary prediction of the flight transit time in the first stage; finally, combined with the real-time delay data and the actual operation of the previous flight, the Transformer model is used to dynamically adjust the initial flight prediction time in the second stage to obtain the final prediction result. The experimental results show that the two-stage prediction results reduce the MAE by 2.42 and 2.49 minutes compared with the one-stage MAE, which proves the effectiveness of this method, and it is better than the random forest, support vector machine and neural network models in terms of prediction error.

Virtual reality (VR) mindfulness meditation has emerged as a prominent emotion regulation strategy in recent years. Current research often seeks to enhance meditation effectiveness through biofeedback and overlooks the trajectory of emotional changes and the changing needs during regulation. In this study, we propose an electroencephalography (EEG)–based positive feedback mechanism for VR mindfulness meditation aimed at optimizing the effects of emotion regulation. This mechanism consists of three modules: 1) EEG-based emotional state computation; 2) process-based relaxation assessment; and 3) adaptive positive decision feedback. Collectively, these components form a computation-assessment-feedback closed-loop system that objectively quantifies emotions while enabling real-time decision adjustments based on emotional trends, thereby enhancing user engagement and emotion regulation efficacy through personalized feedback. The contribution of the proposed feedback mechanism was evaluated through a randomized controlled trial (N= 36). The results indicated that both physiological measures and self-reported relaxation significantly increased when compared to interventions without feedback. These findings validate that the EEG-based positive feedback mechanism effectively enhances emotion regulation while providing additional insights into improving both the engagement and effectiveness within digital mental health interventions.

As generative AI (GenAI) rapidly evolves, human-AI collaboration emerges as a prevalent new working style. However, within this collaborative pipeline, multiple stakeholders are involved besides the user and the system itself, raising controversy around ownership over co-creations. In this paper, we explored everyday users’ sense of ownership toward human-AI co-creation, aiming to provide insights for practitioners on future GenAI design to enhance user experience. We identify three primary factors associated with people’s perception of psychological ownership towards human-AI co-creation and systematically analyze individuals’ approaches to assessing these factors. The findings serve to inform strategies for facilitating an appropriate sense of ownership for productive and safe usage of GenAI tools.

Using mobile phones to shoot video is considerably common in our daily life. However, novice users have difficulty in controlling the camera properly due to lack of professional knowledge and skill. In this paper, in order to assist novice users in learning and imitating professional camera movement from watching high quality sample videos, we propose ARCAM, an Augmented Reality (AR) based video shooting guidance method for novice users. Using AR, we visualized the concept of camera movement and embedded it into natural scene to provide real-time guidance. User can follow the guidance while shooting video by matching a calibration frame to the guidance, to achieve the desired camera movement. We conducted a user study comparing the effectiveness of ARCAM to a traditional static arrow guidance. Results showed that ARCAM was more effective in helping users understand the camera work in the sample videos and move the camera with more accuracy. Our work provides insights on designing mobile video shooting application and suggests that AR has great potential in assisting novice video shooters.