Background: Virtual reality (VR) serious games, due to their high level of freedom and realism, influence the rehabilitation training of inhibitory control abilities in children with attention-deficit/hyperactivity disorder (ADHD). Although reward feedback has a motivating effect on improving inhibitory control, the effectiveness and differences between various forms of rewards lack empirical research. Objective: This study aimed to investigate the effectiveness of different forms of reward feedback on the inhibitory control abilities of children with attention deficits in a VR serious game environment. Methods: This study focuses on children who meet the diagnostic criteria for ADHD tendencies, using a 2 (material rewards: coin reward and token reward) × 2 (psychological rewards: verbal encouragement and badge reward) factorial between-subject design (N=84), with a control group (n=15) for pre- and posttest experiments. The experimental group received VR feedback reinforcement training, while the control group underwent conventional VR training without feedback. The training period lasted 0.5 months, with each intervention session lasting 25 minutes, occurring twice daily with an interval of at least 5 hours for 28 sessions. Before and after training, the Swanson, Nolan, and Pelham, Version IV Scale (SNAP-IV) Scale, stop signal task, inhibition conflict task, and Simon task were administered to assess the hyperactivity index and the 3 components of inhibitory control ability. The pretest included the SNAP-IV Scale and 3 task tests to obtain baseline data; the posttest involved repeating the above tests after completing all training. Data were entered and analyzed using SPSS (IBM) software. Independent sample t tests were performed on the experimental and control groups’ pre- and posttest task results to determine whether significant differences existed between group means. Paired sample t tests were also conducted on the SNAP-IV Scale’s pre- and posttest results to assess the intervention effect’s significance. Results: Reward feedback was more effective than no reward feedback in improving behaviors related to attention deficits in children. Material rewards showed significant effects in the Stop-Signal Task (F1=13.04, P=.001), Inhibition Conflict Task (F1=7.34, P=.008), and SNAP-IV test (F1=69.23, P<.001); mental rewards showed significant effects in the Stop-Signal Task (F1=38.54, P<.001) and SNAP-IV test (F1=70.78, P<.001); the interaction between the 2 showed significant effects in the Stop-Signal Task (F1=4.47, P=.04) and SNAP-IV test (F1=23.85, P<.001). Conclusions: Combining material and psychological rewards within a VR platform can effectively improve attention-deficit behaviors in children with ADHD, enhancing their inhibitory control abilities. Among these, coin rewards are more effective than token rewards, and verbal encouragement outperforms badge rewards. The combined feedback of coin rewards and verbal encouragement yields the most significant improvement in inhibitory control abilities.

At present, the rate control algorithm for multiview high-efficiency video coding (MV-HEVC) does not have the capability of efficient coding tree unit(CTU) layer bit allocation, and the video quality varies greatly for sequences with sudden scene changes or large motions. To overcome this limitation, this paper proposes a rate control algorithm for MV-HEVC based on scene detection. Firstly, we established ρ domain rate control model based on multi-objective optimization. Then, it uses image similarity to make reasonable bit allocation among viewpoints. If the video scene is switched, the image similarity is recalculated, and then the correlation between the weights of the interview point rates and the correlation between the viewpoints are analyzed. Finally, the frame layer rate control considers the layer B-frame and other factors in allocating the code rate, and the basic unit layer rate control adopts different quantization methods according to the content complexity of the CTU. Experimental results show that the proposed rate control algorithm can maintain good coding efficiency and decrease the average video quality variation by 25.29%.

Named entity recognition is a critical task in the electronic medical record management system for rehabilitation robots. Handwritten documents often contain spelling errors and illegible handwriting, and healthcare professionals frequently use different terminologies. These issues adversely affect the robot’s judgment and precise operations. Additionally, the same entity can have different meanings in various contexts, leading to category inconsistencies, which further increase the system’s complexity. To address these challenges, a novel medical entity recognition algorithm for Chinese electronic medical records is developed to enhance the processing and understanding capabilities of rehabilitation robots for patient data. This algorithm is based on a fusion classification strategy. Specifically, a preprocessing strategy is proposed according to clinical medical knowledge, which includes redefining entities, removing outliers, and eliminating invalid characters. Subsequently, a medical entity recognition model is developed to identify Chinese electronic medical records, thereby enhancing the data analysis capabilities of rehabilitation robots. To extract semantic information, the ALBERT network is utilized, and BILSTM and MHA networks are combined to capture the dependency relationships between words, overcoming the problem of different meanings for the same entity in different contexts. The CRF network is employed to determine the boundaries of different entities. The research results indicate that the proposed model significantly enhances the recognition accuracy of electronic medical texts by rehabilitation robots, particularly in accurately identifying entities and handling terminology diversity and contextual differences. This model effectively addresses the key challenges faced by rehabilitation robots in processing Chinese electronic medical texts, and holds important theoretical and practical value.

Background: The COVID-19 pandemic significantly altered global influenza transmission patterns. Whether the associations among meteorological conditions, visibility, and influenza subtype (A/H1N1, A/H3N2, and B/Victoria) positivity changed after the pandemic remains unclear. We compared these associations before and after the COVID-19 pandemic in northern China. Objective: This study aimed to characterize changes in the associations of meteorological variables and visibility with influenza subtype positivity before and after the COVID-19 pandemic and to identify subtype-specific nonlinear patterns and thresholds. Methods: Using 13 years of influenza surveillance and environmental data, we applied Extreme Gradient Boosting enhanced with Shapley additive explanations (SHAP) to characterize the relative importance and direction of associations between environmental variables and influenza subtype positivity. Generalized additive models (GAMs) were subsequently used to characterize nonlinear association patterns and identify thresholds at which the direction of model-estimated associations changed. Results: Absolute humidity (AH) remained the most influential environmental factor for A/H3N2 and A/H1N1, accounting for 32.2% and 38.5% of relative feature importance, respectively. Visibility increased in importance after the pandemic, rising from being the fourth to the second most influential environmental indicator. GAM analysis identified thresholds corresponding to points where the direction of the associations changed. For A/H3N2, AH below 4 g/m ³ was associated with positive SHAP values, whereas higher AH was predominantly associated with negative SHAP values. For A/H1N1, the direction of association changed at an AH threshold of 4.5 g/m ³ (95% CI 4.0-4.8) in the postpandemic period. Additionally, relative humidity (RH) demonstrated a complex nonlinear association with A/H3N2 positivity, with transition points at 45.1% (95% CI 43.5%-46.1%) and 69.7% (95% CI 69.2%-70.2%) in the prepandemic period and at 48.7% (95% CI 46.7%-50.4%) and 70.6% (95% CI 66.3%-72.6%) in the postpandemic period. RH values within these ranges were associated with negative SHAP values, whereas values outside these ranges were associated with positive SHAP values. In contrast, visibility showed opposing association patterns for the A/H3N2 and B/Victoria subtypes after the pandemic. Visibility below 17.1 km (95% CI 16.3-17.4) was associated with negative SHAP values for A/H3N2, whereas visibility below 18.4 km (95% CI 18.0-18.9) was associated with positive SHAP values for B/Victoria. Conclusions: AH remained a key environmental determinant of influenza A subtype positivity, while visibility became more important after the pandemic and showed opposing association patterns for A/H3N2 and B/Victoria. The identified nonlinear thresholds highlight subtype-specific environmental associations and may inform influenza surveillance and risk assessment under changing environmental conditions.

Various unwanted and unavoidable noises corrupt Heart Sound Signals (HSSs). It is strongly required to suppress respiratory sound and ambient noise due to significant reduction of clarity and interpretation of HSSs. In this paper, we propose a joint heart sound denoising using Dual-Tree Complex Wavelet Transform (DTCWT) and Adaptive Sparsity-assisted Signal Smoothing (ASASS) algorithm. In this research, the signal is first decomposed by DTCWT to obtain the multi-scale feature representation of the signal. Subsequently, ASASS suppresses pseudo-Gibbs artifacts around signal boundaries of DTCWT while implementing adaptive thresholding strategies to maximize the Signal-to-Noise Ratio (SNR). Experimental validation on the PhysioNet/CinC 2016 database and Open Access Heart Sound Dataset (OAHS Dataset) demonstrates that the proposed method significantly outperforms existing techniques. Under conditions involving Gaussian white noise (GWN) SNR of 0 dB, the proposed method achieves an SNR of 9.01 dB and a Root Mean Square Error (RMSE) of 0.032, outperforming standalone DTCWT and multiple existing models.