ObjectiveThe large-scale epidemic of Coronavirus Disease 2019 has triggered unprecedented physical and psychological stress on health professionals. This study aimed to investigate the prevalence and risk factors of burnout syndrome, and the relationship between burnout and depressive symptoms among frontline medical staff during the COVID-19 epidemic in China.MethodsA total of 606 frontline medical staff were recruited from 133 cities in China using a cross-sectional survey. The Maslach Burnout Inventory was used to assess the level of burnout. Depressive symptoms were assessed (...) by the Patient Health Questionnaire Depression.ResultsDuring the COVID-19 pandemic, 36.5% of the medical staff experienced burnout. Personal and work-related factors were independently associated with burnout, including age, family income, having physical diseases, daily working hours, and profession of nurse. The correlation coefficients between the scores of each burnout subscale and the scores of depressive symptoms were 0.57 for emotional exhaustion, 0.37 for cynicism, and −0.41 for professional efficacy.ConclusionsOur findings suggest that the prevalence rate of burnout is extremely high among medical staff during the COVID-19 pandemic, which is associated with other psychological disorders, such as depression. Psychological intervention for medical staff is urgently needed. Young and less experienced medical staff, especially nurses, should receive more attention when providing psychological assistance. (shrink)

Person reidentification aims to recognize a specific pedestrian from uncrossed surveillance camera views. Most re-id methods perform the retrieval task by comparing the similarity of pedestrian features extracted from deep learning models. Therefore, learning a discriminative feature is critical for person reidentification. Many works supervise the model learning with one or more loss functions to obtain the discriminability of features. Softmax loss is one of the widely used loss functions in re-id. However, traditional softmax loss inherently focuses on the feature (...) separability and fails to consider the compactness of within-class features. To further improve the accuracy of re-id, many efforts are conducted to shrink within-class discrepancy as well as between-class similarity. In this paper, we propose a circle-based ratio loss for person re-identification. Concretely, we normalize the learned features and classification weights to map these vectors in the hypersphere. Then we take the ratio of the maximal intraclass distance and the minimal interclass distance as an objective loss, so the between-class separability and within-class compactness can be optimized simultaneously during the training stage. Finally, with the joint training of an improved softmax loss and the ratio loss, the deep model could mine discriminative pedestrian information and learn robust features for the re-id task. Comprehensive experiments on three re-id benchmark datasets are carried out to illustrate the effectiveness of the proposed method. Specially, 83.12% mAP on Market-1501, 71.66% mAP on DukeMTMC-reID, and 66.26%/63.24% mAP on CUHK03 labeled/detected are achieved, respectively. (shrink)

With the popularity of computer-based testing, it is easier to collect item response times in psychological and educational assessments. RTs can provide an important source of information for respondents and tests. To make full use of RTs, the researchers have invested substantial effort in developing statistical models of RTs. Most of the proposed models posit a unidimensional latent speed to account for RTs in tests. In psychological and educational tests, many tests are multidimensional, either deliberately or inadvertently. There may be (...) general effects in between-item multidimensional tests. However, currently there exists no RT model that considers the general effects to analyze between-item multidimensional test RT data. Also, there is no joint hierarchical model that integrates RT and response accuracy for evaluating the general effects of between-item multidimensional tests. Therefore, a bi-factor joint hierarchical model using between-item multidimensional test is proposed in this study. The simulation indicated that the Hamiltonian Monte Carlo algorithm works well in parameter recovery. Meanwhile, the information criteria showed that the bi-factor hierarchical model is the best fit model. This means that it is necessary to take into consideration the general effects and the multidimensionality of the RT in between-item multidimensional tests. (shrink)

本书作者直握生命的本质,着眼于道德对生命的提升和超越,论证道德教化对生命的提升与激励,抓住了道德教化的灵魂。.

Due to the individual differences controlling brain-computer interfaces, the applicability and accuracy of BCIs based on motor imagery are limited. To improve the performance of BCIs, this article examined the effect of transcranial electrical stimulation on brain activity during MI. This article designed an experimental paradigm that combines tES and MI and examined the effects of tES based on the measurements of electroencephalogram features in MI processing, including the power spectral density and dynamic event-related desynchronization. Finally, we investigated the effect (...) of tES on the accuracy of MI classification using linear discriminant analysis. The results showed that the ERD of the μ and β rhythms in the left-hand MI task was enhanced after electrical stimulation with a significant effect in the tDCS group. The average classification accuracy of the transcranial alternating current stimulation group and transcranial direct current stimulation group were improved significantly compared to the pre-and pseudo stimulation groups. These findings indicated that tES can improve the performance and applicability of BCI and that tDCS was a potential approach in regulating brain activity and enhancing valid features during noninvasive MI-BCI processing. (shrink)

Research showed that action real-time strategy gaming experience is related to cognitive and neural plasticity, including visual selective attention and working memory, executive control, and information processing. This study explored the relationship between ARSG experience and information transmission in the auditory channel. Using an auditory, two-choice, go/no-go task and lateralized readiness potential as the index to partial information transmission, this study examined information transmission patterns in ARSG experts and amateurs. Results showed that experts had a higher accuracy rate than amateurs. (...) More importantly, experts had a smaller stimulus-locked LRP component than amateurs on no-go trials, while the response-locked LRP component on go trials did not differ between groups. Thus, whereas amateurs used an asynchronous information transmission pattern, experts used a reduced asynchronous information transmission pattern or a synchronous pattern where most of processing occurred prior to response execution – an information transmission pattern that supports rapid, error-free performance. Thus, experts and amateurs may use different information transmission patterns in auditory processing. In addition, the information transmission pattern used by experts is typically observed only after long-term auditory training according to past research. This study supports the relationship between ARSG experience and the development of information processing patterns. (shrink)