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So far， it is still a difficult task to develop electrocatalytic hydrogen evolution systems with comparable or even better efficiency than that of Pt systems. This study proves that the nanoporous gold electrode deposited on a flexible substrate coated with a titanium adhesion layer （Pt/Au-NHs/Ti/K） has excellent electrochemical water decomposition under laser irradiation. Localized surface plasmon resonance under light irradiation increases the electrocatalytic hydrogen evolution activity of the material， which shows that its performance is comparable to that of the commercial Pt electrode. Mechanism studies show that the plasma-excited Au nanoholes structure is an effective source of hot electrons， and the underlying Ti-based adhesive layer can effectively promote the separation of hot electrons and holes. In this system， the thermal effect is negligible， and the sudden change of the current density upon laser irradiation is demonstrated by the rapid generation and injection of charge carriers. The result shows that the Pt/Au-NHs/Ti/K electrode still has good stability under 10 mA·cm-2 constant current density for 28 h.

Computational thinking (CT) is an imperative competency in the 21st century. Mindtools can assist students in understanding concepts and decomposing tasks during CT development through programming. However, students may encounter challenges in complex CT problem-solving tasks due to being confused when using mindtools without proper guidance. Research evidence shows the potential of reflection in complex CT problem-solving by regulating cognitive activities. Accordingly, this study designed a reflection-guided visualized mindtool strategy to address CT development challenges. A quasi-experiment and lag sequential analysis were conducted by recruiting 97 junior high school students to examine the effects of the proposed strategy on CT development and to explore students' behavior patterns. Results revealed that the proposed approach improved students' CT performance, CT disposition, meta-cognitive awareness, and learning motivation. Students learning with the proposed strategy exhibited more key behaviors of facilitating CT problem-solving (e.g., generalizing the knowledge, re-designing the algorithm scheme, and evaluating the feasibility of their proposed schemes) than students in the control group, revealing the essential process of CT development and enlightening teachers on guiding students to produce such learning processes when cultivating CT.

TCR stimulation triggers Ca2+ signals that are critical for T cell function and immunity. Several pore-forming α and auxiliary β subunits of voltage-gated Ca2+ channels (VGCC) were reported in T cells, but their mechanism of activation remains elusive and their contribution to Ca2+ signaling in T cells is controversial. We here identify CaVβ1, encoded by Cacnb1, as a regulator of T cell function. Cacnb1 deletion enhances apoptosis and impairs the clonal expansion of T cells after lymphocytic choriomeningitis virus (LCMV) infection. By contrast, Cacnb1 is dispensable for T cell proliferation, cytokine production and Ca2+ signaling. Using patch clamp electrophysiology and Ca2+ recordings, we are unable to detect voltage-gated Ca2+ currents or Ca2+ influx in human and mouse T cells upon depolarization with or without prior TCR stimulation. mRNAs of several VGCC α1 subunits are detectable in human (CaV3.3, CaV3.2) and mouse (CaV2.1) T cells, but they lack transcription of many 5' exons, likely resulting in N-terminally truncated and non-functional proteins. Our findings demonstrate that although CaVβ1 regulates T cell function, these effects are independent of VGCC channel activity.

NPMI is used to describe the local co-occurrence properties of words, and the collected text data is constructed into a homogeneous graph composed of words. A word-granularity graph convolutional neural network (VGCN) is used as a graph encoder to achieve parallel training of graph convolutions. The output of the VGCN is used as a kind of embedding vector for BERT, and the VGCN is fused with other embedding using a transformer for feature fusion. The reprojection layer converts the model output to semantic representation vectors, and the post-projection layer converts the semantic representation vectors to vectors for loss computation. NT-Xent is chosen as the loss function of the model, while the real-time semantic understanding effect of the model is evaluated using the Spearman correlation coefficient in statistics. The CBOW model provided by Word2vec is learned from the training set and constantly updated for each parameter to calculate semantic similarity between words. The LDA model is used to extract topic keywords from each document and calculate the probability, which finally completes the dynamic assessment of the credibility of text content based on semantic understanding. The ROUGE-L index and BLEU-4 index of the VGCN-CL model in this paper are 66.47% and 63.86%, respectively, which are 23.20% higher than the baseline model ROUGE and 25.14% higher than BLEU-4. When the content assessment results are true, the accuracy and other metrics of this paper’s model are 90.36%, 89.87%, and 90.09%, respectively, which are the best performance among all models.

Action recognition based on 3D technology is showing a trend of universal application in the field of kinesiology day by day. This paper utilizes 3D action recognition technology to develop an auxiliary teaching system suitable for college basketball teaching and training. The top-down method is used for human pose estimation to provide human skeleton characterization and shield obstacles such as background and illumination for action recognition technology. A new edge connection is added to the original graph convolution algorithm to form the human action recognition algorithm in this paper, and a spatial attention module is introduced to improve the accuracy of model action recognition. The system is equipped with two significant algorithms for human posture estimation and action recognition, and the final design is finished as an auxiliary system for college basketball teaching and training. The system can match the similarity between students’ movements and standard movements with a minimum of more than 80%, and the basketball scores of students are generally improved to more than 90 points after using the system.

Free throw shooting is an important training program for athletes, as well as the most effective way to score in the game. The study uses the 3DRS model to figure out the initial motion, fixes the vectors by figuring out the weighted bidirectional motion and smoothing the motion vectors, adds adaptive motion compensation, and suggests a way to look at training videos for free throw shooting that is based on changing the motion vector field. We test different video sequences using this paper’s algorithm and other algorithms, and compare the effects of free throw prediction before and after the motion vector field transform algorithm. We also select basketball players to conduct control experiments to investigate the effectiveness of this paper’s motion vector field transform algorithm. Our research shows that the average PSNR and SSIM values for different sequences are higher than those of other algorithms by 6.16% to 7.00% and 4.50% to 5.00%, respectively. Using the motion vector field transform also makes free throw prediction 4.3% more accurate overall. Another thing is that the athletes who were taught using the motion vector field transformation algorithm got 19.23% and 16.07% better at hitting free throws and other technical measures. This shows that the algorithm in this paper can be used in real life to teach basketball free throw shooting moves.

Background: The contextual and interactive learning research was motivated by the need to enhance the healthcare training effect of caregivers of patients with suspected COVID-19 infection during quarantine. However, there is a lack of effective approaches to integrating mobile contextual learning into interactive learning. Objectives: Therefore, this study developed a mobile augmented reality-integration contextual interactive healthcare training system, and then investigated the effects of the proposed system on anxiety, learning effects, perceived support and self-efficacies. Methods: To validate the effectiveness of the system, we conducted a quasi-experiment with a sample of 91 caregivers of patients with suspected COVID-19 infection. Results and conclusions: Results showed that the experimental group with the developed system was superior to the control group with e-pamphlet instruction in mobile devices, in terms of learning effect, perceived support, self-efficacies and reduction of anxiety during quarantine. Therefore, the mobile contextual interactive healthcare training system could be useful to improve caregivers' self-efficacies as well as their ability to care for patients in the context of COVID-19 infection prevention. Implications: This is one of the few studies of the effective approach for technology-enhanced healthcare education in the COVID-19 pandemic. Some corresponding suggestions are proposed: implementing mobile contextual interactive training activities to enhance caregivers' knowledge, skills and self-efficacies; involving social interactions to relieve caregivers' anxiety; providing professional communications and support while facing difficulties in taking care of patients with suspected COVID-19 infection.

Usually, radon detector must be calibrated every year, commonly used solid radon source calibration method and radon chamber calibration method. The former has the problems of radon source device aging, complex operation and radioactive substances vulnerable to transport control. The latter has problems such as calibration time principal and inability to achieve on-site calibration. Based on the above facts, this paper proposes an experimental method for the calibration of radon measuring instrument using uranium waste rock, which is used to realize the field calibration of seismic radon measuring instrument. The results show that the radon concentration control method has high accuracy and stability, and the relative error between the output gas radon concentration and the preset target is 1%. The comparison between the calibration experiment of uranium mine waste rock and the calibration experiment of standard radon chamber shows that the calibration values of the calibration coefficient of the two are in good agreement, the relative error is only 1.14%, and the normalized error En is only 0.135 6, which is within the expected range. It can provide theoretical support for the subsequent development of portable radon detector calibration device.