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This paper constructs a Bayesian network structure learning model using both the Bayesian network and the Notears algorithm. Combined with acyclic graph and conditional probability table, the structure and parameters of BN are described respectively. Focusing on the basic methods of Bayesian network structure, the hybrid algorithm based on Notears is chosen as the basic method of structure learning by analyzing the application of the evaluation search method, constraint method and hybrid search algorithm. The results show that the ideal education management method should focus on the integration with real life, so much so that it has a low rate of detachment from reality as well as a low rate of theory, accounting for 0.35 and 0.4, respectively. The traditional way of management of ideology and political education in colleges and universities is more theoretical and detached from the actual situation of education and management, with theoretical management accounting for 0.75, and management is inefficient, with a percentage of 0.6. The research in this paper promotes, to a certain extent, the innovation of the management of the ideological education in colleges and universities, for the management of ideological education in colleges and universities.

为绿色高效制备生物柴油，利用L-天冬氨酸离子液体（\[Asp\]HSO4）联合猪胰脂肪酶催化油酸制备油酸甲酯，采用单因素实验探究了反应时间、醇酸物质的量比、\[Asp\]HSO4用量、猪胰脂肪酶用量和反应温度对转化率的影响，在此基础上，采用正交实验进行优化，得到\[Asp\]HSO4联合猪胰脂肪酶催化油酸酯化反应的最优工艺条件为\[Asp\]HSO4用量6%、醇酸物质的量比5.5∶ 1、猪胰脂肪酶用量4%、反应时间21 h、反应温度45 ℃，在此条件下转化率可达91.91%。\[Asp\]HSO4可降低甲醇和温度对猪胰脂肪酶催化活性的影响，对猪胰脂肪酶催化油酸酯化具有协同效应。 In order to prepare biodiesel greenly and efficiently，L-Aspartic acid ionic liquid (\[Asp\]HSO4) combined with porcine pancreatic lipase was used to catalyze the esterification of oleic acid to prepare methyl oleate. The effects of reaction time, molar ratio of methanol to oleic acid, amount of \[Asp\]HSO4, amount of porcine pancreatic lipase, and the reaction temperature on the conversion rate were investigated by single factor experiment.Then the esterification process conditions were optimized by orthogonal experiment as follows: amount of \[Asp\]HSO4 6%, molar ratio of methanol to oleic acid 5.5∶ 1, amount of porcine pancreatic lipase 4%, reaction time 21 h, and reaction temperature 45 ℃. The conversion rate could reach 91.91% under the optimal conditions.\[Asp\]HSO4can reduce the effects of methanol and temperature on the catalytic activity of porcine pancreatic lipase,and has synergistic effect on the esterification of oleic acid catalyzed by porcine pancreatic lipase.

Objectives: Accurate segmentation of focal cortical dysplasia (FCD) lesions from MR images plays an important role in surgical planning and decision but is still challenging for radiologists and clinicians. In this study, we introduce a novel transformer-based model, designed for the end-to-end segmentation of FCD lesions from multi-channel MR images. Methods: The core innovation of our proposed model is the integration of a convolutional neural network-based encoder-decoder structure with a multiscale transformer to augment the feature representation of lesions in the global field of view. Transformer pathways, composed of memory- and computation-efficient dual-self-attention modules, leverage feature maps from varying depths of the encoder to discern long-range interdependencies among feature positions and channels, thereby emphasizing areas and channels relevant to lesions. The proposed model was trained and evaluated on a public-open dataset including MR images of 85 patients using both subject-level and voxel-level metrics. Results: Experimental results indicate that our model offers superior performance both quantitatively and qualitatively. It successfully identified lesions in 82.4% of patients, with a low false-positive lesion cluster rate of 0.176 ± 0.381 per patient. Furthermore, the model achieved an average Dice coefficient of 0.410 ± 0.288, outperforming five established methods. Conclusion: Integration of the transformer could enhance the feature presentation and segmentation performance of FCD lesions. The proposed model has the potential to serve as a valuable assistive tool for physicians, enabling rapid and accurate identification of FCD lesions. The source code and pre-trained model weights are available at https://github.com/zhangxd0530/MS-DSA-NET. Critical relevance statement: This multiscale transformer-based model performs segmentation of focal cortical dysplasia lesions, aiming to help radiologists and clinicians make accurate and efficient preoperative evaluations of focal cortical dysplasia patients from MR images. Key Points: The first transformer-based model was built to explore focal cortical dysplasia lesion segmentation. Integration of global and local features enhances the segmentation performance of lesions. A valuable benchmark for model development and comparative analyses was provided. [ABSTRACT FROM AUTHOR]

This article presents a generative model for the inverse design of dual-band filters based on a type of modified complementary split-ring resonator (CSRR). It consists of a series of convolutional neural networks that incorporate the conditional deep convolutional generative adversarial network (GAN) technique. The filters are designed by etching the modified CSRRs on the surface of substrate-integrated waveguides. This design allows us to achieve two passbands with a compact size. In this GAN-based generative model, the CSRRs are represented as two-dimensional matrices. Each matrix corresponds to a training sample of the designed filter, and its S-parameters are extracted through an HFSS simulation. Both the matrices and the S-parameters are fed into the model as the training datasets. Different CSRRs with various sizes are employed for a wider applicable frequency band. Normalized matrices and normalized S-parameters are utilized to simplify the complex generative model resulting from the variations in CSRR sizes. The effectiveness of the generative model is validated through four design examples of dual-band filters, with their center frequencies located within 5 to 18 GHz. The inference time for each design is approximately 18.5 min. The measurement results of the fabricated filters are in good agreement with the simulation ones. [ABSTRACT FROM AUTHOR]

A self‐equalized linear‐phase microstrip bandpass filter (BPF) utilizing negative group delay (NGD) parallel‐coupled three‐line (PCTL) units is presented in this letter. The NGD PCTL unit possesses an NGD valley in its frequency response of group delay (GD). By a combination of two or more NGD valleys, the sharp positive GD peaks at passband edges of a conventional coupled‐line BPF are counterbalanced, resulting in a linear phase response over the entire passband. A prototype of linear‐phase BPF in S‐band is designed by replacing two coupled‐line units in a three‐pole conventional BPF with the proposed NGD PCTL units. Measured passband GD fluctuation of the BPF utilizing NGD PCTL units is 0.5 ns while that of the BPF using conventional coupled‐line units is 1.7 ns, a considerable improvement of 70.5% is achieved with no increase in overall time delay. The out‐of‐band suppression is well retained, and the circuit size is 0.18 λg2. [ABSTRACT FROM AUTHOR]

Background: Structural magnetic resonance imaging (sMRI) is vital for early Alzheimer's disease (AD) diagnosis, though confirming specific biomarkers remains challenging. Our proposed Multi-Scale Self-Attention Network (MUSAN) enhances classification of cognitively normal (CN) and AD individuals, distinguishing stable (sMCI) from progressive mild cognitive impairment (pMCI). Objective: This study leverages AD structural atrophy properties to achieve precise AD classification, combining different scales of brain region features. The ultimate goal is an interpretable algorithm for this method. Methods: The MUSAN takes whole-brain sMRI as input, enabling automatic extraction of brain region features and modeling of correlations between different scales of brain regions, and achieves personalized disease interpretation of brain regions. Furthermore, we also employed an occlusion sensitivity algorithm to localize and visualize brain regions sensitive to disease. Results: Our method is applied to ADNI-1, ADNI-2, and ADNI-3, and achieves high performance on the classification of CN from AD with accuracy (0.93), specificity (0.82), sensitivity (0.96), and area under curve (AUC) (0.95), as well as notable performance on the distinguish of sMCI from pMCI with accuracy (0.85), specificity (0.84), sensitivity (0.74), and AUC (0.86). Our sensitivity masking algorithm identified key regions in distinguishing CN from AD: hippocampus, amygdala, and vermis. Moreover, cingulum, pallidum, and inferior frontal gyrus are crucial for sMCI and pMCI discrimination. These discoveries align with existing literature, confirming the dependability of our model in AD research. Conclusion: Our method provides an effective AD diagnostic and conversion prediction method. The occlusion sensitivity algorithm enhances deep learning interpretability, bolstering AD research reliability. [ABSTRACT FROM AUTHOR]