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Abstract Background The social culture, gender stereotypes, and social status of nursing in China impact the professional commitment of male nursing students. Increasing the professional commitment of male nursing students is critical for alleviating the shortage of nurses and addressing the gender gap. The outbreak of the COVID-19 pandemic has caused widespread public concern about the nursing profession and may have changed people’s perception of the profession, which in turn may have a positive impact on the professional commitment of male nursing students in China. Objective This research aimed to assess how the COVID-19 pandemic affected the professional commitment of male nursing students in China and to analyze the impact of the COVID-19 pandemic on potential factors that might affect their professional commitment. Methods A descriptive comparative study was conducted. This study involved total 368 male nursing students from five 4-year universities in Henan Province, China. 190 male nursing students were included before the COVID-19 pandemic (October to November 2019) and 178 were included after the COVID-19 pandemic (April to May 2023). The data, such as the sociodemographic characteristics and professional commitment of the two samples, were collected using a paper-and-pencil questionnaire. The research instruments used included the Professional Commitment Scale, the Multidimensional Scale of Perceived Social Support and the General Self-efficacy Scale. The data analysis employed χ2-tests, t-tests and hierarchical regression models. Results The proportion of Chinese male nursing students who are interested in nursing and do not intend to leave the major has increased after the pandemic (65.17% vs. 29.47%, 61.80% vs. 39.47%, respectively). Male nursing students exhibited greater professional commitment (3.30 ± 0.63) after the pandemic compared to before the pandemic (2.77 ± 0.43). The various factors, such as grade, intend to leave the major, interest in nursing, social support, and general self-efficacy, were linked to professional commitment of male nursing students in China. Conclusions Male nursing students in China exhibited greater professional commitment after the pandemic. This phenomenon may be attributed to the public’s increased recognition of the significance of nursing work during the pandemic, and the social status and image of nursing profession have been improved. Given the changes brought about by the COVID-19, strategies should be considered by nursing decision makers and schools to strengthen the professional commitment of male nursing students and increase the number of male nurses in the post-pandemic era.

Wireless capsule endoscopy (WCE) has become an important tool for gastrointestinal examination due to its non-invasive nature and minimal patient discomfort. However, the quality of WCE images is often limited by built-in lighting and the complex gastrointestinal environment, particularly in the region filled with small intestinal villi. Additionally, the morphology of these villi usually serves as a crucial indicator for related diseases. To address this, we propose a novel method to enhance the clarity of small intestinal villi in WCE images. Our method uses a guided filter to separate the low- and high-frequency components of WCE images. Illumination gain factors are calculated from the low-frequency components, while gradient gain factors are derived from Laplacian convolutions on different regions. These factors enhance the high-frequency components, combined with the original image. This approach improves edge detail while suppressing noise and avoiding edge overshoot, providing clearer images for diagnosis. Experimental results show that our proposed method achieved a 45.47% increase in PSNR compared to classical enhancement algorithms, a 12.63% improvement in IRMLE relative to the original images, and a 31.84% reduction in NIQE with respect to the original images.

SLAM is a critical technology for enabling autonomous navigation and positioning in unmanned vehicles. Traditional visual simultaneous localization and mapping algorithms are built upon the assumption of a static scene, overlooking the impact of dynamic targets within real-world environments. Interference from dynamic targets can significantly degrade the system’s localization accuracy or even lead to tracking failure. To address these issues, we propose a dynamic visual SLAM system named BY-SLAM, which is based on BEBLID and semantic information extraction. Initially, the BEBLID descriptor is introduced to describe Oriented FAST feature points, enhancing both feature point matching accuracy and speed. Subsequently, FasterNet replaces the backbone network of YOLOv8s to expedite semantic information extraction. By using the results of DBSCAN clustering object detection, a more refined semantic mask is obtained. Finally, by leveraging the semantic mask and epipolar constraints, dynamic feature points are discerned and eliminated, allowing for the utilization of only static feature points for pose estimation and the construction of a dense 3D map that excludes dynamic targets. Experimental evaluations are conducted on both the TUM RGB-D dataset and real-world scenarios and demonstrate the effectiveness of the proposed algorithm at filtering out dynamic targets within the scenes. On average, the localization accuracy for the TUM RGB-D dataset improves by 95.53% compared to ORB-SLAM3. Comparative analyses against classical dynamic SLAM systems further corroborate the improvement in localization accuracy, map readability, and robustness achieved by BY-SLAM.

Endangered species detection plays an important role in biodiversity conservation and is significant in maintaining ecological balance. Existing deep learning-based object detection methods are overly dependent on a large number of supervised samples, and building such endangered species datasets is usually costly. Aiming at the problems faced by endangered species detection, such as low accuracy and easy loss of location information, an efficient endangered species detection method with fewer samples is proposed to extend the few-shot object detection technique to the field of endangered species detection, which requires only a small number of training samples to obtain excellent detection results. First, SE-Res2Net is proposed to optimize the feature extraction capability. Secondly, an RPN network with multiple attention mechanism is proposed. Finally, for the classification confusion problem, a weighted prototype-based comparison branch is introduced to construct weighted category prototype vectors, which effectively improves the performance of the original classifier. Under the setting of 30 samples in the endangered species dataset, the average detection accuracy value of the method, mAP50, reaches 76.54%, which is 7.98% higher than that of the pre-improved FSCE method. This paper also compares the algorithm on the PASCOL VOC dataset, which is optimal and has good generalization ability compared to the other five algorithms.

Chouioia cunea (Yang) (Hymenoptera: Eulophidae) is an indigenous pupal endoparasitoid that effectively attacks the exotic fall webworm Hyphantria cunea (Drury) (Lepidoptera: Arctiidae) in China. In this novel association, the parasitoid's venom has played an important role in subduing the immune defense of the host although little is known about the composition and functions of the parasitoid's venom. We therefore first identified the parasitoid's major venom proteins using electrospray ionization--mass spectrometry (ESI-MS). Approximately 207 different proteins were identified from C. cunea's venom; among them 26 types widely existed in other endoparasitoids' venom, including calreticulin and arginine kinase, which inhibited the host immune system. [ABSTRACT FROM AUTHOR]

Developing bimetallic electrocatalysts with synergistic effect is a promising strategy to facilitate the electrocatalytic reduction of CO2 (ECR-CO2). Electrodeposition strategy due to its convenience and time-saving property has been widely used in ECR-CO2, however, the disadvantage of electrodeposition method is the treatment of electroplating liquid waste. Aiming for recovering metal ions from electroplating liquid waste and developing high-performance catalysts for ECR-CO2, a bimetallic Bi3Cu1 is synthesized by green-cleaning method. The Bi3Cu1 catalyst exhibits a promising formate (HCOO-) faradaic efficiency (FE) of 95.1% at -0.75 V vs. RHE in an H-type cell. Moreover, excellent durability is also obtained, maintaining a high FEHCOO- of 89.4% after 20 h electrolysis. In addition, the experimental results show that the KOH is a better choice than the KHCO3 electrolyte for ECR-CO2. Bi3Cu1 shows enhanced current density and FE of formate, owing to the faster mass transport in the gas-fed continuous-flow cell and the high ionic conductivity of KOH electrolyte. [ABSTRACT FROM AUTHOR]