Objective To analyze the influence of professional identity and academic efficacy on the professional attitude of higher vocational nursing students. Methods A total of 656 higher vocational nursing students were investigated with general information questionnaire, academic self-efficacy scale, professional identity scale, and professional attitude scale of nursing students. Results The score of professional attitude of higher vocational nursing students was (106.34 ± 9.86), which is found to be in the upper middle level. The professional attitude of higher vocational nursing students is positively correlated with academic self-efficacy (P < 0.05), and with learning ability self-efficacy (P < 0.01). Professional cognition, professional will, and professional values have a positive predictive effect on the professional attitude of higher vocational nursing students (P < 0.05). Conclusions Academic self-efficacy and professional identity are important predictors of professional attitude. Learning behavior self-efficacy, professional cognition, professional will, and professional values have a significant impact on the establishment of positive professional attitude.

Complementary sets of sequences (CSSs) are widely used in many applications, such as active sensing and wireless communication. The construction of CSS has attracted considerable attention over the past few decades. In this paper, efficient and comprehensive computational algorithms for CSS design are proposed. We seek to minimize complementary integral sidelobe level (CISL) under multiple constraints, including unimodular, peak-to-average power ratio, discrete phase, and spectrum compatible constraint. The task of CSS design can be formulated as solving a nonconvex constraint optimization problem. As this problem is difficult to tackle directly, we resort to the general majorization-minimization (MM) method. By utilizing the inherent algebraic structure of the objective function, we construct the majorizing function via two consecutive applications of the MM method and obtain a closed-form solution by a couple of FFT operations at each iteration. The relationship between MM-based algorithms and derivative-based algorithms is revealed. Our algorithms are more flexible and widely applicable, and the numerical experiment results demonstrate the effectiveness and superiority over the existing state-of-art algorithms.

The trend of global Sea Surface Temperature (SST) has attracted widespread attention in several ocean-related fields such as global warming, marine environmental protection and marine biodiversity. Sea surface temperature is influenced by climate change; with the accumulation of data from ocean remote sensing observations year by year, many scholars have started to use deep learning methods for SST prediction. In this paper, we use a dynamic region partitioning approach to process ocean big data and design a framework applied to a global SST short-term prediction system. On the architecture of a Long Short-Term Memory (LSTM) network, two deep learning multi-region SST prediction models are proposed, which extract temporal and spatial information of SST by encoding, using feature transformation and decoding to predict future multi-step states. The models are tested using OISST data and the model performance is evaluated by different metrics. The proposed MR-EDLSTM model and MR-EDConvLSTM model obtained the best results for short-term prediction, with RMSE ranging from 0.2712 °C to 0.6487 °C and prediction accuracies ranging from 97.60% to 98.81% for ten consecutive days of prediction. The results show that the proposed MR-EDLSTM model has better prediction performance in coastal areas, while the MR-EDConvLSTM model performs better in predicting the sea area near the equator. In addition, the proposed deep learning model has a smaller RMSE compared to the forecasting system based on the ocean model, indicating that the deep learning method has certain advantages in predicting global SST.

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In recent years, active matter has attracted tremendous research interest. Active matter displays a rich phenomenology, such as super-diffusion, huge fluctuation and collective motion. The shape of active agents plays a critical role in the self-assembly of active matter. Understanding the oligomers' dynamics of active agents is a first step to study the self-assembly of massive agents. Here, we design a self-properlling particle with the ‘+’ shape using the Hexbug robot and investigate the dynamics of oligomers composed of these particles. To track the position of particles, the top of the particles is marked by black cards with white dots in the center. We find that these particles can agglomerate together to form stable oligomers (consisting of two, three, or four particles). We study the dynamics by analyzing the trajectorys, mean-square displacement, angular velocity, angular velocity distribution and the curvature distribution. We find the dynamics can be divided into two types. One is the combination of eccentric rotation with small circular radius and irregular translation, which occurs in the system with the zero resultant force and nonzero torque. The other is the eccentric rotation with a large circular radius, which appears in the system where both the resultant force and torque are not zero. In addition, we find translational dynamics of oligomers displays a super diffusion in a short time scale influenced by the confirguration of oligomers. Further, the larger torque and the smaller moment of inertia result in the bigger angle speed of oligomers. Moreover, we investigate the curvature distribution of the trimer and find that the faster angle speed the trimer has, the bigger curvature it has.