The Compiling Methods for Pinyin Textbooks of Teaching of Chinese as a Foreign Language: A Case Study on the Textbook for Interesting Chinese Pinyin

On the basis of reviewing and summarizing the current situation of Chinese Pinyin textbooks and considering the gains and experiences in the compiling process of The Textbook for Interesting Chinese Pinyin, this paper is to propose that Chinese Pinyin textbooks should take the difficulties for foreign learners fully into account and attach importance to the interest and practicality of teaching materials. We suggest to draw up a syllabus for Chinese Pinyin so that the teaching materials can effectively put teaching of pronunciation and speech flow together.Key words: Pronunciation; Chinese Pinyin textbooks; Compiling; Principles and method

Learning to Fuse Multiple Brain Functional Networks for Automated Autism Identification

Functional connectivity network (FCN) has become a popular tool to identify potential biomarkers for brain dysfunction, such as autism spectrum disorder (ASD). Due to its importance, researchers have proposed many methods to estimate FCNs from resting-state functional MRI (rs-fMRI) data. However, the existing FCN estimation methods usually only capture a single relationship between brain regions of interest (ROIs), e.g., linear correlation, nonlinear correlation, or higher-order correlation, thus failing to model the complex interaction among ROIs in the brain. Additionally, such traditional methods estimate FCNs in an unsupervised way, and the estimation process is independent of the downstream tasks, which makes it difficult to guarantee the optimal performance for ASD identification. To address these issues, in this paper, we propose a multi-FCN fusion framework for rs-fMRI-based ASD classification. Specifically, for each subject, we first estimate multiple FCNs using different methods to encode rich interactions among ROIs from different perspectives. Then, we use the label information (ASD vs. healthy control (HC)) to learn a set of fusion weights for measuring the importance/discrimination of those estimated FCNs. Finally, we apply the adaptively weighted fused FCN on the ABIDE dataset to identify subjects with ASD from HCs. The proposed FCN fusion framework is straightforward to implement and can significantly improve diagnostic accuracy compared to traditional and state-of-the-art methods

ECA-TFUnet: A U-shaped CNN-Transformer network with efficient channel attention for organ segmentation in anatomical sectional images of canines

Automated organ segmentation in anatomical sectional images of canines is crucial for clinical applications and the study of sectional anatomy. The manual delineation of organ boundaries by experts is a time-consuming and laborious task. However, semi-automatic segmentation methods have shown low segmentation accuracy. Deep learning-based CNN models lack the ability to establish long-range dependencies, leading to limited segmentation performance. Although Transformer-based models excel at establishing long-range dependencies, they face a limitation in capturing local detail information. To address these challenges, we propose a novel ECA-TFUnet model for organ segmentation in anatomical sectional images of canines. ECA-TFUnet model is a U-shaped CNN-Transformer network with Efficient Channel Attention, which fully combines the strengths of the Unet network and Transformer block. Specifically, The U-Net network is excellent at capturing detailed local information. The Transformer block is equipped in the first skip connection layer of the Unet network to effectively learn the global dependencies of different regions, which improves the representation ability of the model. Additionally, the Efficient Channel Attention Block is introduced to the Unet network to focus on more important channel information, further improving the robustness of the model. Furthermore, the mixed loss strategy is incorporated to alleviate the problem of class imbalance. Experimental results showed that the ECA-TFUnet model yielded 92.63% IoU, outperforming 11 state-of-the-art methods. To comprehensively evaluate the model performance, we also conducted experiments on a public dataset, which achieved 87.93% IoU, still superior to 11 state-of-the-art methods. Finally, we explored the use of a transfer learning strategy to provide good initialization parameters for the ECA-TFUnet model. We demonstrated that the ECA-TFUnet model exhibits superior segmentation performance on anatomical sectional images of canines, which has the potential for application in medical clinical diagnosis

Synthesis of Hollow ZnSnO 3

Hollow ZnSnO3 nanospheres were synthesized by a hydrothermal method using ZnO nanospheres as the hard template and raw material simultaneously. The combined characterizations of X-ray diffraction (XRD), scanning electron microscope (SEM) and high-resolution transmission electron microscopy (HRTEM) confirmed the successful preparation of hollow ZnSnO3 nanospheres. The gas-sensing results indicated that the sensor made from hollow ZnSnO3 nanospheres exhibited high sensitivity, good selectivity, and stability to ethanol at a low operating temperature of 200°C. The sensitivity was about 32 and the response and recovery time were about 4 s and 30 s for 100 ppm ethanol, respectively. The enhancement in gas-sensing properties was attributed to the hollow nanostructures and high specific surface areas of ZnSnO3

A Network-Based Approach to Investigate the Pattern of Syndrome in Depression

In Traditional Chinese Medicine theory, syndrome is essential to diagnose diseases and treat patients, and symptom is the foundation of syndrome differentiation. Thus the combination and interaction between symptoms represent the pattern of syndrome at phenotypic level, which can be modeled and analyzed using complex network. At first, we collected inquiry information of 364 depression patients from 2007 to 2009. Next, we learned classification models for 7 syndromes in depression using naïve Bayes, Bayes network, support vector machine (SVM), and C4.5. Among them, SVM achieves the highest accuracies larger than 0.9 except for Yin deficiency. Besides, Bayes network outperforms naïve Bayes for all 7 syndromes. Then key symptoms for each syndrome were selected using Fisher's score. Based on these key symptoms, symptom networks for 7 syndromes as well as a global network for depression were constructed through weighted mutual information. Finally, we employed permutation test to discover dynamic symptom interactions, in order to investigate the difference between syndromes from the perspective of symptom network. As a result, significant dynamic interactions were quite different for 7 syndromes. Therefore, symptom networks could facilitate our understanding of the pattern of syndrome and further the improvement of syndrome differentiation in depression

Deep learning-based image segmentation model using an MRI-based convolutional neural network for physiological evaluation of the heart

Background and Objective: Cardiovascular disease is a high-fatality health issue. Accurate measurement of cardiovascular function depends on precise segmentation of physiological structure and accurate evaluation of functional parameters. Structural segmentation of heart images and calculation of the volume of different ventricular activity cycles form the basis for quantitative analysis of physiological function and can provide the necessary support for clinical physiological diagnosis, as well as the analysis of various cardiac diseases. Therefore, it is important to develop an efficient heart segmentation algorithm.Methods: A total of 275 nuclear magnetic resonance imaging (MRI) heart scans were collected, analyzed, and preprocessed from Huaqiao University Affiliated Strait Hospital, and the data were used in our improved deep learning model, which was designed based on the U-net network. The training set included 80% of the images, and the remaining 20% was the test set. Based on five time phases from end-diastole (ED) to end-systole (ES), the segmentation findings showed that it is possible to achieve improved segmentation accuracy and computational complexity by segmenting the left ventricle (LV), right ventricle (RV), and myocardium (myo).Results: We improved the Dice index of the LV to 0.965 and 0.921, and the Hausdorff index decreased to 5.4 and 6.9 in the ED and ES phases, respectively; RV Dice increased to 0.938 and 0.860, and the Hausdorff index decreased to 11.7 and 12.6 in the ED and ES, respectively; myo Dice increased to 0.889 and 0.901, and the Hausdorff index decreased to 8.3 and 9.2 in the ED and ES, respectively.Conclusion: The model obtained in the final experiment provided more accurate segmentation of the left and right ventricles, as well as the myocardium, from cardiac MRI. The data from this model facilitate the prediction of cardiovascular disease in real-time, thereby providing potential clinical utility

Zeolite-like metal organic frameworks (ZMOFS): modular approach to the synthesis of organic-inorganic hybrid porous materials having a zeolite like topology

The subject invention pertains to metal organic frameworks (MOF) having zeolite-net-like topology, their methods of use, and their modes of synthesis. The ZMOFs are produced by combining predesigned tetrahedral building, generated in situ using heterochelation, with polyfunctional ligands that have the commensurate angle and the required donor groups for the chelation. Each molecular building block is contrasted of a single metal ion and ligands with both heterochelation functionality and bridging functionality. Advantageously, zeolite-net-like MOFs of the subject invention are porous and contain large functional cavities, which is useful for encapsulating large molecules

Design of joint network-channel coding based on Turbo code and its performance analysis

According to idea of joint network-channel coding, a scheme of joint network-channel coding based on Turbo code applied to multiple access relay channel was proposed, and implementation of relay node encoding and base station decoding was introduced in details. The simulation of the scheme was compared with the point to point scheme and XOR operation network-channel coding scheme. The simulation results show that the scheme can sufficiently use additional redundancy from the relay channel, and significantly improve performance of wireless communication systems