Background: Previous research has shown that video gaming experience is associated with changes in cognitive and perceptual functions as well as neural structure and function. However, how the different types of video games differentially influence cognitive function and neuroplasticity remains unclear. Methods: In this 30-week longitudinal study, participants were randomly assigned to an action video game group or a strategy card game group. Behavioral assessments and resting-state electroencephalography (EEG) recordings were administered at six time points to evaluate changes in attention, working memory, executive function, and their neural correlates. Results: Both groups showed significant improvements in multiple cognitive tasks, but the underlying neural mechanisms differed. The action video game group showed greater increases in low-frequency EEG relative power (delta and theta bands) and more pronounced decreases in alpha-band functional connectivity at the 10-week follow-up after the end of training. Conclusions: These findings suggest that different types of video games improve cognition through distinct neuroplasticity pathways, with action games effective in optimizing neural efficiency and producing sustained effects. This study provides new insights into the cognitive and neural mechanisms of game-based enhancements and offers implications for the design of targeted digital cognitive interventions.

During the start stage of ship welding, obtaining the three-dimensional coordinates of welding target points is affected by confined installation space, surface reflection, and deployment constraints. This paper proposes a low-complexity point-wise three-dimensional localization method based on two-dimensional visual planar guidance and one-dimensional point-laser distance constraints. A direct computation model of the laser incident point in the robot base coordinate system is established from the tool center point pose, the extrinsic parameters of the point-laser module, and real-time ranging data, enabling target-point coordinate estimation without dense three-dimensional reconstruction. A dual-stage stabilization strategy is introduced by combining ranging-level filtering, spatial coordinate smoothing, and outlier suppression. Image error-based visual closed-loop alignment is further used as a pre-measurement step to ensure that the point laser acts on the target region. Experimental results show that, after workplane-level extrinsic correction, independent validation points achieve a mean three-dimensional Euclidean error of 1.54 mm with a standard deviation of 0.28 mm. The average planar error in closed-loop alignment experiments is 1.124 mm. Passive binocular depth measurement on the current platform still yields an RMSE of 6.16 mm after linear correction. A simulated fillet-weld task verifies the feasibility of the complete perception-to-execution workflow. The proposed method provides a low-complexity coordinate acquisition route for discrete welding target points before arc ignition.

Background: Schizophrenia (SCZ) and bipolar disorder (BD) exhibit substantial overlap in clinical symptoms, making differential diagnosis a persistent challenge and leading to a high risk of misdiagnosis. Recently, functional near-infrared spectroscopy (fNIRS) during the verbal fluency task (VFT) has provided more objective neurofunctional markers for distinguishing between the two disorders than traditional behavioral scales. However, most existing studies have predominantly relied on conventional, hand-crafted feature analyses. A significant limitation of this approach is the scarcity of deep learning models tailored for high-dimensional fNIRS time-series data, coupled with a lack of methods for their interpretation. Methods: In this study, a time-aware multiple instance learning model (TimeMIL) was applied to fNIRS data (healthy controls [HC]: 52 participants, SCZ: 50 participants, BD: 67 participants) collected during VFT. This model captures global dependencies in multivariate, high-dimensional time series of fNIRS, which represents each fNIRS recording as a bag and automatically locates informative temporal segments via a learnable wavelet encoder and self-attention. Furthermore, an interpretability framework integrating GradientSHAP and Integrated Gradients was proposed to quantify segment-and region-level attributions. Model performance was evaluated using 5×5-fold cross-validation and an independent test set. Results: TimeMIL achieved 0.928 ± 0.016 accuracy and a macro-averaged AUC of 0.984 ± 0.007 for three-class classification, significantly outperforming other representative models, including one-dimensional convolutional neural networks (1D-CNNs), Transformers, and temporal convolutional networks (TCNs). Attribution analyses highlighted robust differences in the orbitofrontal cortex (OFC) across HC, SCZ, and BD, suggesting its potential as a candidate discriminative feature. The frontal pole cortex (FPC) and ventrolateral prefrontal cortex (VLPFC) exhibited SCZ-specific attribution patterns. Conclusion: This study represents the first application of TimeMIL to fNIRS-based psychiatric disease classification, accompanied by a dedicated interpretability framework that illuminates prefrontal dysfunction. By integrating high accuracy, robustness, and interpretability, the proposed interpretable TimeMIL framework facilitates development of objective, neuroimaging-based tools for early screening and differential diagnosis of major psychiatric disorders, pending further validation in clinical settings.