Search

Abstract Background The curriculum system is a central component in achieving the goals and specifications of talent training schemes. However, problems and difficulties exist in curriculum provision due to a lack of curriculum system design logic. This study aimed to investigate the correlation between the university curriculum system and graduate quality and to reveal the design logic of the curriculum system. Methods A total of 699 stomatology graduates from a university in Zhejiang were selected as research subjects from 2015 to 2022. The students’ curriculum system and graduate quality data were collected and classified. The graduate quality information contained (1) the National Board Dental Examination (NBDE) pass rate and score, (2) the postgraduate entrance rate and destination, and (3) the employment rate and work institution. Regression analysis was also conducted to assess the correlation between the curriculum system and graduate quality. Results Regression analysis revealed significant associations between general education, specialization, and stomatology X courses and NBDE score, postgraduate entrance rate and destination, and work institution. All courses except public elective courses had significant impacts on NBDE score. General basic, medical English, and employment guidance courses significantly influenced the postgraduate entrance rate and destination. Restricted elective and public elective courses had significant effects on employment rate and work institution. Conclusions Increasing the quality of specialized and stomatology X courses in the curriculum system is beneficial for deepening graduates’ expertise and enhancing their education. Moreover, English courses are suggested to be offered in the early stage to lay a better language foundation.

Abstract As one of the three major outdoor components of the railroad signal system, the track circuit plays an important role in ensuring the safety and efficiency of train operation. Therefore, when a fault occurs, the cause of the fault needs to be found quickly and accurately and dealt with in a timely manner to avoid affecting the efficiency of train operation and the occurrence of safety accidents. This article proposes a fault diagnosis method based on multi-scale attention network, which uses Gramian Angular Field (GAF) to transform one-dimensional time series into two-dimensional images, making full use of the advantages of convolutional networks in processing image data. A new feature fusion training structure is designed to effectively train the model, fully extract features at different scales, and fusing spatial feature information through spatial attention mechanisms. Finally, experiments are conducted using real track circuit fault datasets, and the accuracy of fault diagnosis reaches 99.36%, and our model demonstrates better performance compared to classical and state-of-the-art models. And the ablation experiments verified that each module in the designed model plays a key role.

As a commonly used mode of transportation in people’s daily lives, the normal operation of railway transportation is crucial. The track circuit, as a key component of the railway transportation system, is prone to malfunctions due to environmental factors. However, the current method of inspecting track circuit faults still relies on the experience of on-site personnel. In order to improve the efficiency and accuracy of fault diagnosis, we propose to establish an intelligent fault diagnosis system. Considering that the fault data are a one-dimensional time series, this paper presents a fault diagnosis method based on the UNet-LSTM network (ULN). The LSTM network is established on the basis of fault data and used for ZPW-2000A track circuit fault diagnosis. However, the use of a single LSTM network has a high error rate in the common fault diagnosis of track circuits. Therefore, this paper proposes a feature extraction method based on the UNet network. This method is used to extract the features of the original data and then input them into the LSTM network for fault diagnosis. Through experiments with on-site fault data, it has been verified that this method can accurately classify seven common track circuit faults. Finally, the superiority of the method is verified by comparing it with other commonly used fault classification methods.

IntroductionGlobal warming has led to frequent heat waves, causing global organisms to face severe survival challenges. However, the way in which heat waves threaten the fitness and survival of animals remains largely unclear. Oxidative damage and immunity are widely considered the link between heat waves and threats to animals.MethodsTo evaluate the oxidative damage caused by heat waves and to reveal the physiological resistance to heat waves by the antioxidant defense of animals from different latitudes, we exposed both high-latitude (Zhejiang) and low-latitude (Hainan) populations of Chinese pond turtle (Mauremys reevesii) to simulate heat waves and a moderate thermal environment for 1 week, respectively. Next, we compared the oxidative damage by malondialdehyde (MDA) and antioxidant capacity by superoxide dismutase (SOD), catalase (CAT), glutathione peroxidase (GPx), and total antioxidant capacity (T-AOC) in the liver tissues and evaluated the innate immunity by serum complement protein levels (C3, C4) and lysozyme activity in plasma of turtles.Results and discussionWe found that heat waves significantly increased the content of MDA and the activity of CAT, whereas it decreased the activity of SOD, T-AOC, and GSH/GSSG in turtles from low latitudes. Furthermore, heat waves increased CAT activity but decreased GSH/GSSG in turtles from high latitudes. Although the turtles from high latitudes had higher levels of innate immunity, the heat waves did not affect the innate immunity of C3, C4, or lysozyme in either population. These results indicate that the low-latitude population suffered higher oxidative damage with lower antioxidant capacities. Therefore, we predict that Chinese pond turtles from low latitudes may be more vulnerable to heat waves caused by climate warming. This study reveals the physiological and biochemical resistance to heat waves in Chinese pond turtles from different latitudes and highlights the importance of integrative determination of fitness-related responses in evaluating the vulnerability of ectotherms from different latitudes to climate warming.

In image classification of tomato leaf diseases based on deep learning, models often focus on features such as edges, stems, backgrounds, and shadows of the experimental samples, while ignoring the features of the disease area, resulting in weak generalization ability. In this study, a self-attention mechanism called GD-Attention is proposed, which considers global pixel value distribution information and guide the deep learning model to give more concern on the leaf disease area. Based on data augmentation, the proposed method inputs both the image and its pixel value distribution information to the model. The GD-Attention mechanism guides the model to extract features related to pixel value distribution information, thereby increasing attention towards the disease area. The model is trained and tested on the Plant Village (PV) dataset, and by analyzing the generated attention heatmaps, it is observed that the disease area obtains greater weight. The results achieve an accuracy of 99.97% and 27 MB parameters only. Compared to classical and state-of-the-art models, our model showcases competitive performance. As a next step, we are committed to further research and application, aiming to address real-world, complex scenarios.