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Selective two-electron (2e − ) oxygen reduction reaction (ORR) offers great opportunities for hydrogen peroxide (H 2 O 2 ) electrosynthesis and its widespread employment depends on identifying cost-effective catalysts with high activity and selectivity. Main-group metal and nitrogen coordinated carbons (M-N-Cs) are promising but remain largely underexplored due to the low metal-atom density and the lack of understanding in the structure-property correlation. Here, we report using a nanoarchitectured Sb 2 S 3 template to synthesize high-density (10.32 wt%) antimony (Sb) single atoms on nitrogen- and sulfur-codoped carbon nanofibers (Sb-NSCF), which exhibits both high selectivity (97.2%) and mass activity (114.9 A g −1 at 0.65 V) toward the 2e − ORR in alkaline electrolyte. Further, when evaluated with a practical flow cell, Sb-NSCF shows a high production rate of 7.46 mol g catalyst −1 h −1 with negligible loss in activity and selectivity in a 75-h continuous electrolysis. Density functional theory calculations demonstrate that the coordination configuration and the S dopants synergistically contribute to the enhanced 2e − ORR activity and selectivity of the Sb-N 4 moieties. Selective two-electron oxygen reduction reaction is critical electrochemical process for H 2 O 2 electrosynthesis. Here, the authors develop a Sb 2 S 3 -templated strategy to fabricate high-density atomic dispersion of Sb on N,S-codoped hollow carbon nanofiber substrate, which facilitate with the improved selectivity, catalytic mass activity and production rate of H 2 O 2 .

It is difficult to identify the historical period in which some ancient murals were created because of damage due to artificial and/or natural factors; similarities in content, style, and color among murals; low image resolution; and other reasons. This study proposed a transfer learning-fused Inception-v3 model for dynasty-based classification. First, the model adopted Inception-v3 with frozen fully connected and softmax layers for pretraining over ImageNet. Second, the model fused Inception-v3 with transfer learning for parameter readjustment over small datasets. Third, the corresponding bottleneck files of the mural images were generated, and the deep-level features of the images were extracted. Fourth, the cross-entropy loss function was employed to calculate the loss value at each step of the training, and an algorithm for the adaptive learning rate on the stochastic gradient descent was applied to unify the learning rate. Finally, the updated softmax classifier was utilized for the dynasty-based classification of the images. On the constructed small datasets, the accuracy rate, recall rate, and F1 value of the proposed model were 88.4%, 88.36%, and 88.32%, respectively, which exhibited noticeable increases compared with those of typical deep learning models and modified convolutional neural networks. Comparisons of the classification outcomes for the mural dataset with those for other painting datasets and natural image datasets showed that the proposed model achieved stable classification outcomes with a powerful generalization capacity. The training time of the proposed model was only 0.7 s, and overfitting seldom occurred.

The production effect (PE) is a phenomenon where reading words aloud, rather than silently, during study leads to improved recognition memory. Human recognition memory can be divided into recollection (recognition based on complex contextual information) and familiarity (recognition based on a sense of familiarity). This study explored how reading aloud affects recollection and familiarity using electroencephalography (EEG) in a mixed-list design. Participants encoded each list item, either aloud or silently during the study phase and made remember/know/new judgments in the test phase, while EEG data were recorded. The behavioral results replicated the classic PE pattern and indicated that the PE was present in both recollection and familiarity. At the Event-Related Potential (ERP) level, the recollection-based LPC (late positive complex) old/new effect at test was largest in the aloud condition; however, the familiarity-based FN400 old/new effect was equivalent when comparing the aloud condition and the silent condition. Moreover, this study was the first to employ multivariate pattern analysis (MVPA) to decode the time course between two distinct memory strategies (aloud vs. silent). The results revealed significant decoding between 760 and 840 ms, which is consistent with the LPC old/new effect. The paper discusses both traditional theories and the Feature Space Theory based on our results, highlighting inconsistencies with assumptions regarding unconscious retrieval in the Feature Space Theory. In summary, the current results support the role of distinctiveness (enhanced memory for auditory or action information, consistent with recollection) in the PE, rather than the role of strength (enhanced memory trace, consistent with familiarity). This study suggests that enhanced distinctiveness/recollection may be a shared mechanism underlying certain advantageous memory strategies.

[This corrects the article DOI: 10.3389/fnhum.2025.1507782.].

Attentional capture and disengagement are distinct process involved in attentional orienting. Most current studies have examined either the process of attentional capture or disengagement by manipulating stimuli associated with either positive (gains) or negative outcomes (losses). However, few studies have investigated whether attentional capture and disengagement are modulated by reward and loss outcomes. In the current study, we want to examine whether positive or negative outcomes could modulate distinguishing process of attentional capture and disengagement. Here, we manipulated different colored singleton stimuli associated with reward or loss outcomes; these stimuli were either presented at the center of screen or at the peripheral location. The participants’ task was to search the target and identify the orientation of line segment in target as quickly as possible. The results showed that people had difficulty disengaging from a central reward-distractor, in comparison to loss- and neutral-distractor when target was presented at peripheral location. Similarly, peripheral reward-distractor captured more attention than loss- and neutral-distractor when target was presented at the center of screen after central fixation disappeared. Through our discoveries, we can conclude that positive rewards can increase attentional capture and delay attentional disengagement in healthy people.

A stable enhanced superresolution generative adversarial network (SESRGAN) algorithm was proposed in this study to address the low-resolution and blurred texture details in ancient murals. This algorithm makes improvements on the basis of GANs, which use dense residual blocks to extract image features. After two upsampling steps, the feature information of the image is input into the high-resolution (HR) image space to realize an improvement in resolution, and the reconstructed HR image is finally generated. The discriminator network uses VGG as its basic framework to judge the authenticity of the input image. This study further optimized the details of the network model. In addition, three loss optimization models, i.e., the perceptual loss, content loss, and adversarial loss models, were integrated into the proposed algorithm. The Wasserstein GAN-gradient penalty (WGAN-GP) theory was used to optimize the adversarial loss of the model when calculating the perceptual loss and when using the preactivation feature information for calculation purposes. In addition, public data sets were used to pretrain the generative network model to achieve a high-quality initialization. The simulation experiment results showed that the proposed algorithm outperforms other related superresolution algorithms in terms of both objective and subjective evaluation indicators. A subjective perception evaluation was also conducted, and the reconstructed images produced by our algorithm were more in line with the general public’s visual perception than those produced by the other compared algorithms.

Ancient murals are of high artistic value and boast rich content. The accurate classification of murals is a challenging task for researchers and can be arduous even for experienced researchers. The image classification algorithms currently available are not effective in the classification of mural images with strong background noise. A new multichannel separable network model (MCSN) is proposed in this study to solve this issue. Using the GoogLeNet network model as the basic framework, we adopt a small convolution kernel for the extraction of the shallow-layer background features of murals and then decompose larger, two-dimensional convolution kernels into smaller convolution kernels, for example, 7 × 7 and 3 × 3 kernels into 7 × 1 and 1 × 7 kernels and 3 × 1 and 1 × 3 kernels, respectively, to extract important deep-layer feature information. A soft thresholding activation scaling strategy is adopted to enhance the stability of the network during training, and finally, the murals are classified through the softmax layer. A minibatch SGD algorithm is employed to update the parameters. The accuracy, recall and F1-score reached 88.16%, 90.01%, and 90.38%, respectively. Compared with mainstream classification algorithms, the model demonstrates improvement in terms of classification accuracy, generalizability, and stability to a certain extent, supporting its suitability in efficiently classifying murals.

In view of the polysemy of mural images and the style difference among mural images painted in different dynasties as well as the high energy costs of the traditional manual dynasty classification method, which resorts to mural texts and historical documents, this study proposed an adaptive enhancement capsule network (AECN) for automatic dynasty identification of mural images. Based on the original capsule network, we introduced a preconvolution structure to extract the high-level features of the mural images from Mogao Grottoes, such as color and texture. Then, we added an even activation operation to the layers of the network to enhance the fitting performance of the model. Finally, we performed adaptive modifications on the capsule network to increase the gradient smoothness of the model, based on which to optimize the model and thus to increase its classification precision. With the self-constructed DH1926 data set as the study subject, the proposed model achieved an accuracy of 84.44%, an average precision of 82.36%, an average recall rate of 83.75% and a comprehensive assessment score F1 of 83.96%. Compared with modified convolution neural networks and the original capsule network, the model proposed in study increased all the considered indices by more than 3%. It has a satisfactory fitting performance, which can extract the rich features of mural images at multiple levels and well express their semantic information. Furthermore, it has a higher accuracy and better robustness in the classification of the Mogao Grottoes murals, and therefore is of certain application values and research significance.

An increased reaction time often occurs after error responses (post-error slowing, PES). However, the role of top-down regulation in post-error processing remains debated. Impairing cognitive control function through acute stress would help to investigate the role and stage of top-down adaptive regulation in post-error processing. Here, we recruited 50 healthy male participants randomly exposed to either a stress condition (Trier Social Stress Task, TSST) or a control condition (control version of the TSST). A color-word Stroop task with different response stimulus intervals (RSI) was used to explore the effects of acute stress on different stages of post-error processing. The results showed that cortisol, heart rate, stress perception reports, and negative emotions were higher in the stress group (n = 24) than in the control group (n = 26), indicating successful stress induction. The accuracy of post-error response in the control group increased with the extension of RSI, and the reaction time decreased. However, the accuracy of 1200-ms RSI was close to that of 700-ms RSI in the stress group but significantly lower than that in the control group. The results suggested that acute stress caused the impairment of top-down adaptive regulation after error. Furthermore, our study manifested adaptive adjustment only in the late stages of post-error processing, indicating the phasic and adaptive features of post-error adjustment.