Aim:To investigate the mediation role of social support in the relationship between a sense of coherence and the perception of professional interests among Chinese registered nurses.Background: Job burnout is become exceedingly common among registered nurses.Perceived professional benefits and a sense of coherence can help regulate nurses' negative emotions, reduce job burnout and turnover intention and increase nurses' subjective well-being. However, little is known about the mediating effect of social support on sense of coherence and perceived professional benefits. Methods: FromMay to August 2017, data from 765 Chinese registered nurses were collected from a 4-part questionnaire: general information, the sense of coherence scale, social support rating scale and perceived professional benefits questionnaire. The independent factors of perceived professional benefits were tested by multiple regression analysis. Structural equation model was used to study the moderating effect of social support.

Background: Low job satisfaction is the most common cause of nurses' turnover and influ-

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Background: Nurses displayed low levels of subjective well-being and high turnover intention. How to enhance the subjective well-being and decrease the turnover rate of nurses is of great importance. However, little is known about whether work engagement mediates between character strengths and subjective well-being. The study aims to explore character strengths, work engagement and subjective well-being in nurses, and to determine whether work engagement plays a mediating role between the relationship. Material and Methods: From December 2017 to December 2018, 450 Chinese registered nurses completed the character strengths scale, work engagement scale, and subjective well-being scale. The relationship between study variables was tested by Pearson correlation. The mediating effect of work engagement was tested by the bootstrap method. Results: The results indicated the following: (1) the 4 elements of character strengths and work engagement were significantly and positively correlated with subjective well-being; (2) character strengths could significantly predict both work engagement and subjective well-being; (3) work engagement played a mediating role in this relationship. Conclusions: Character strengths affect subjective well-being in Chinese registered nurses, and work engagement plays a mediating variable among the relations. Therefore, nurses are encouraged to foster their character strengths and improve their level of work engagement for their subjective well-being. Following the results, the study recommends that nursing managers be aware of the importance of using character strengths in nursing work, taking actions to excavate nurses' character strengths and encouraging nurses to use character strengths in clinical work to promote engagement and well-being. In the meantime, interventions to improve the level of subjective well-being based on nurses' character strengths should be considered. Med Pr. 2022;73(4):295-304

Satellite‐based rainfall products have great potential for estimating rainfall erosivity, as they can provide continuous spatiotemporal distribution of precipitation estimates over large areas, especially for monitoring in areas with complex terrain and extreme climates. This paper uses the daily rainfall derived from the satellite‐based CHIRPS product on the GEE platform and Zhang's daily rainfall erosivity model to calculate the rainfall erosivity on the Loess Plateau during the period of 1981–2020. The accuracy of rainfall erosivity for the CHIRPS product is evaluated by comparing the results to estimates from national meteorological stations, and then the calculation formula of rainfall erosivity is optimized to improve the accuracy of rainfall erosivity based on CHIRPS products. The results show that the annual average rainfall of the CHIRPS product at the locations of national meteorological stations in 1981–2020 is 473.7 mm, which is 6.8% higher than that observed by the meteorological stations. The multi‐year average value of daily rainfall and the yearly average rainfall both for days with rainfall larger than or equal to 12 mm are 15.9% and 18.2% greater than those of the meteorological stations, respectively, and eventually resulting in an overestimation of rainfall erosivity on the Loess Plateau by 44.0%. The annual mean rainfall erosivity interpolated based on meteorological stations and calculated from the gridded CHIRPS product in 1981–2020 is 1344.2 MJ·mm/(hm2·h) and 2013.7 MJ·mm/(hm2·h), respectively. This result suggests that rainfall erosivity estimated by gridded CHIRPS product is overestimated by 49.8%, of which 46.1% is due to the overestimation of erosive rainfall in gridded CHIRPS and 3.7% is caused by site density and interpolation. Satellite‐based CHIRPS product is similar to the observations of meteorological stations in total rainfall and trends, but differs in rainfall frequency and intensity, which is an important reason for the difference in rainfall erosivity between satellite‐based rainfall products and meteorological stations. The CHIRPS‐based rainfall erosivity calculated using the optimized parameters reduces the overestimation from 49.8% to 3.2%, greatly reducing the satellite‐based rainfall erosivity estimation error.

<abstract> <p>Motor imagery (MI) is a traditional paradigm of brain-computer interface (BCI) and can assist users in creating direct connections between their brains and external equipment. The common spatial patterns algorithm is the most popular spatial filtering technique for collecting EEG signal features in MI-based BCI systems. Due to the defect that it only considers the spatial information of EEG signals and is susceptible to noise interference and other issues, its performance is diminished. In this study, we developed a Riemannian transform feature extraction method based on filter bank fusion with a combination of multiple time windows. First, we proposed the multi-time window data segmentation and recombination method by combining it with a filter group to create new data samples. This approach could capture individual differences due to the variation in time-frequency patterns across different participants, thereby improving the model's generalization performance. Second, Riemannian geometry was used for feature extraction from non-Euclidean structured EEG data. Then, considering the non-Gaussian distribution of EEG signals, the neighborhood component analysis (NCA) algorithm was chosen for feature selection. Finally, to meet real-time requirements and a low complexity, we employed a Support Vector Machine (SVM) as the classification algorithm. The proposed model achieved improved accuracy and robustness. In this study, we proposed an algorithm with superior performance on the BCI Competition IV dataset 2a, achieving an accuracy of 89%, a kappa value of 0.73 and an AUC of 0.9, demonstrating advanced capabilities. Furthermore, we analyzed data collected in our laboratory, and the proposed method achieved an accuracy of 77.4%, surpassing other comparative models. This method not only significantly improved the classification accuracy of motor imagery EEG signals but also bore significant implications for applications in the fields of brain-computer interfaces and neural engineering.</p> </abstract>

Visual diagnostic tests must have a high degree of consistency in their measurements (high reliability) to ensure accurate assessment of perceptual abilities. The current study assessed test–retest reliability and practice effects in the Leuven Perceptual Organisation Screening Test (L-POST) in 144 healthy volunteers, with time intervals between 0 and 756 days. We used Pearson's and intraclass correlation analysis, Bland–Altman analysis and multilevel modelling. Results from our analyses converged and supported an adequate reliability of the L-POST. Multilevel modelling demonstrated an absence of practice effect, suggesting that the L-POST is suitable for repeat administration. This study suggests that the L-POST has adequate reliability and is suitable for repeat administration even at short intervals. This study provides the basis for a more systematic evaluation for neuropsychological assessments, which can lead to the development of more reliable assessment batteries.