Self-efficacy mediates the effect of professional identity on learning engagement for nursing students in higher vocational colleges: A cross-sectional study.

BACKGROUND: Learning engagement is a crucial predictor of academic achievement. It is essential to understand the factors influencing learning engagement among nursing students, especially from the learner's perspective, which is notably scarce but vital for designing effective educational interventions. OBJECTIVES: This study aims to investigate the mediating effect of self-efficacy on the relationship between professional identity and learning engagement for nursing students in higher vocational colleges. DESIGN: A cross-sectional electronic survey was conducted. SETTING: The study was conducted in four higher vocational colleges located in Guangdong Province, China. PARTICIPANTS: A total of 944 first- and second-year nursing students participated in the study between October and November 2022. METHODS: Data were collected with questionnaires on general information, professional identity, self-efficacy, and learning engagement and analyzed with SPSS 26.0 and PROCESS v4.1 (Model 4), exploring relationships among professional identity, self-efficacy, and learning engagement through Pearson correlations, multivariate regression, and mediation analysis with 5000 bootstrap samples. RESULTS: The participants exhibited moderate levels of professional identity (85.37 ± 13.52), self-efficacy (25.58 ± 5.74), and learning engagement (71.26 ± 16.17), which were all significantly correlated with each other (P < 0.01). In the model of the mediating effect, professional identity directly (ß = 0.811, t = 27.484, P < 0.001) and indirectly [ß = 0.112,95%CI (0.074-0.154)] significantly predicts college students' learning engagement; professional identity has a significant positive predictive effect on self-efficacy (ß = 0.182, t = 14.459, P < 0.001) and self-efficacy significantly predicts learning engagement (ß = 0.614, t = 8.292, P < 0.001). Furthermore, the direct effect of professional identity on learning engagement (0.699) and its mediating effect (0.112) account for 86.19 % and 13.81 % of the total effect (0.811), respectively. CONCLUSION: Participants exhibited moderate levels of professional identity, self-efficacy, and learning engagement. Professional identity and self-efficacy are interconnected and positively correlated, influencing learning engagement among nursing students, which highlights the need to foster these qualities to enhance education and future practice.

Characterizing the evolution life cycle of the Sunkoshi landslide in Nepal with multi-source SAR data.

A catastrophic landslide disaster happened on 2 August 2014 on the right bank of Sunkoshi River in Nepal, resulting in enormous casualties and severe damages of the Araniko highway. We collected multi-source synthetic aperture radar (SAR) data to investigate the evolution life cycle of the Sunkoshi landslide. Firstly, Distributed Scatterers SAR Interferometry (DS-InSAR) technology is applied to analyze 20 ALOS PALSAR images to retrieve pre-disaster time-series deformation. The results show that the upper part, especially the top of the landslide, has long been active before collapse, with the largest annual LOS deformation rate more than - 30 mm/year. Time series deformations measured illustrate that rainfall might be a key driving factor. Next, two pairs of TerraSAR-X/TanDEM-X bistatic data are processed to identify the landslide affected area by intensity change detection, and to generate pre- and post-disaster DSMs. Surface height change map showed maximum values of - 150.47 m at the source region and 55.65 m in the deposit region, leading to a debris volume of 5.4785 ± 0.6687 million m3. Finally, 11 ALOS-2 PALSAR-2 and 82 Sentinel-1 SAR images are analyzed to derive post-disaster annual deformation rate and long time series displacements of the Sunkoshi landslide. The results illustrated that the upper part of the landslide were still in active deformation with the largest LOS displacement velocity exceeding - 100 mm/year.

Mapping and characterizing displacements of active loess slopes along the upstream Yellow River with multi-temporal InSAR datasets.

Landslides and debris flows in the Loess Plateau pose great threats to human lives and man-made infrastructure, such as buildings and expressways. Thus, the detection and monitoring of the stability of slopes are crucial in geohazard prevention and management. In this study, the time series synthetic aperture radar interferometry (InSAR) analysis method that combines persistent scatters (PSs) and distributed scatters (DSs) is employed to detect and map active slopes along the upstream Yellow River from the Longyang Gorge dam to the Lijia Gorge dam using one ALOS PALSAR data stack from 2006 to 2011 and two Sentinel-1 data stacks from 2015 to 2017. More than 100 active slopes in a total coverage of 222.5 km2 were identified. Through a time series displacement analysis of active slopes, we found that changes in the water content of loess slopes induced by rainfall or reservoir impoundment might be a major factor that can activate unstable slopes or accelerate the movement of active slopes.

Parallel Connected Generative Adversarial Network with Quadratic Operation for SAR Image Generation and Application for Classification.

Thanks to the availability of large-scale data, deep Convolutional Neural Networks (CNNs) have witnessed success in various applications of computer vision. However, the performance of CNNs on Synthetic Aperture Radar (SAR) image classification is unsatisfactory due to the lack of well-labeled SAR data, as well as the differences in imaging mechanisms between SAR images and optical images. Therefore, this paper addresses the problem of SAR image classification by employing the Generative Adversarial Network (GAN) to produce more labeled SAR data. We propose special GANs for generating SAR images to be used in the training process. First, we incorporate the quadratic operation into the GAN, extending the convolution to make the discriminator better represent the SAR data; second, the statistical characteristics of SAR images are integrated into the GAN to make its value function more reasonable; finally, two types of parallel connected GANs are designed, one of which we call PWGAN, combining the Deep Convolutional GAN (DCGAN) and Wasserstein GAN with Gradient Penalty (WGAN-GP) together in the structure, and the other, which we call CNN-PGAN, applying a pre-trained CNN as a discriminator to the parallel GAN. Both PWGAN and CNN-PGAN consist of a number of discriminators and generators according to the number of target categories. Experimental results on the TerraSAR-X single polarization dataset demonstrate the effectiveness of the proposed method.

Health Diagnosis of Major Transportation Infrastructures in Shanghai Metropolis Using High-Resolution Persistent Scatterer Interferometry.

Since the Persistent Scatterer Synthetic Aperture Radar (SAR) Interferometry (PSI) technology allows the detection of ground subsidence with millimeter accuracy, it is becoming one of the most powerful and economical means for health diagnosis of major transportation infrastructures. However, structures of different types may suffer from various levels of localized subsidence due to the different structural characteristics and subsidence mechanisms. Moreover, in the complex urban scenery, some segments of these infrastructures may be sheltered by surrounding buildings in SAR images, obscuring the desirable signals. Therefore, the subsidence characteristics on different types of structures should be discussed separately and the accuracy of persistent scatterers (PSs) should be optimized. In this study, the PSI-based subsidence mapping over the entire transportation network of Shanghai (more than 10,000 km) is illustrated, achieving the city-wide monitoring specifically along the elevated roads, ground highways and underground subways. The precise geolocation and structural characteristics of infrastructures were combined to effectively guide more accurate identification and separation of PSs along the structures. The experimental results from two neighboring TerraSAR-X stacks from 2013 to 2016 were integrated by joint estimating the measurements in the overlapping area, performing large-scale subsidence mapping and were validated by leveling data, showing highly consistent in terms of subsidence velocities and time-series displacements. Spatial-temporal subsidence patterns on each type of infrastructures are strongly dependent on the operational durations and structural characteristics, as well as the variation of the foundation soil layers.

Ship Detection in SAR Image Based on the Alpha-stable Distribution.

This paper describes an improved Constant False Alarm Rate (CFAR) ship detection algorithm in spaceborne synthetic aperture radar (SAR) image based on Alphastable distribution model. Typically, the CFAR algorithm uses the Gaussian distribution model to describe statistical characteristics of a SAR image background clutter. However, the Gaussian distribution is only valid for multilook SAR images when several radar looks are averaged. As sea clutter in SAR images shows spiky or heavy-tailed characteristics, the Gaussian distribution often fails to describe background sea clutter. In this study, we replace the Gaussian distribution with the Alpha-stable distribution, which is widely used in impulsive or spiky signal processing, to describe the background sea clutter in SAR images. In our proposed algorithm, an initial step for detecting possible ship targets is employed. Then, similar to the typical two-parameter CFAR algorithm, a local process is applied to the pixel identified as possible target. A RADARSAT-1 image is used to validate this Alpha-stable distribution based algorithm. Meanwhile, known ship location data during the time of RADARSAT-1 SAR image acquisition is used to validate ship detection results. Validation results show improvements of the new CFAR algorithm based on the Alpha-stable distribution over the CFAR algorithm based on the Gaussian distribution.

[Application of image correction in 3D reconstruction of mandible from CT slices].

Precision registration of serial sections is an important step for 3-D image reconstruction. It directly affects the accuracy of the reconstructed result and parameter computation. This problem has been studied and demonstrated by many investigators, but the whole process has not yet reached good performance. In this paper, we discussed the registration of serial sections image of mandible and put forward a method of the soft registration-based transformation on the basis of the hard registration in consideration of the speciality of 3-D image reconstruction for the serial sections of mandible. Employing control points and using Affine Transformation and Extended Hough Transformation, we solved the problem of displacement on 3-D image reconstruction for serial secons and paved the way for reconstructing the mandible microstructure with reality. The results of experiments indicate that the 3D image reconstructed after registration has only a little distortion.