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Mining problems and exploring rules are the key problems in the learning process, and also the difficulties in education big data. Therefore, taking learning behavior as the research objective, this study demonstrates the collaborative training method of multi view learning interaction process driven by big data, so as to realize the tendency preference diversion of learning behavior. Based on collaborative training method of learning behavior. Through adequate experiments, we get the suitable and valuable rules, constructs the directed topological relationship of tendency preference division, and mines the feasible improvement measures and intervention mechanism. Experiments and practices show that the potential topological relationship can effectively improve and enable learning, and improve the assessment results.

The temporal sequence of learning behavior is multidimensional and continuous in MOOCs. On the one hand, it supports personalized learning methods, achieves flexible time and space. On the other hand, it also makes MOOCs produce a large number of dropouts and incomplete learning behaviors. Dropout prediction and decision feedback have become an important issue of MOOCs. This study carries out sufficient method design and decision analysis on the dropout trend. Based on a large number of learning behavior instances, we construct a multi behavior type association framework, design dropout prediction model to analyze the temporal sequence of learning behavior, then discuss the corresponding intervention measures, in order to provide adaptive monitoring mechanism for long-term tracking and short-term learning method selection, and enable adaptive decision feedback. the full experiment shows that the designed model might improve the performance of the dropout prediction, which achieves the reliability and feasibility. The whole research can provide key technical solution and decision, which has important theoretical and practical value for dropout research of MOOCs.

With the full application of MOOCs online learning, STEM multidisciplinary and knowledge structures have been achieved, but it has also resulted in a massive number of dropouts, seriously affected the learning sustainability of STEM education concepts, and made it difficult to achieve learning effectiveness. Based on the massive STEM learning behavior instances generated by MOOCs, as well as the entire learning periods, this study considers some key explicit and implicit features associated with learning behavior, and achieves the fusion of convolutional neural network and recurrent neural network through data-driven approaches, incorporates long short-term memory mechanism to develop dropout prediction methods and models. Based on the experimental results, we also discuss the relevant problems of dropouts related to STEM learning behavior, explore the key dropout temporal sequences of the learning process, identify related factors that have key impacts on learning behavior, and deduce intervention measures and early warning suggestions. The entire study can provide effective methods and decisions for researching the STEM learning behavior of MOOCs and has strong research feasibility and urgency.

Early warning recommendation is crucial for tracking learning behavior and represents a significant issue in interactive learning environments. However, an interactive learning environment-based learning process may not always achieve expected goals, leading to inefficient or ineffective learning behavior and negative emotions. Additionally, many learners fail assessments due to these issues. To address this problem, this study proposes relevant test problems for interpretable early warning recommendations based on massive learning behavior instances and potential relationships. We design an applicable learning analysis model, namely a deep-neural network based on the knowledge graph of learning behavior, and verify its feasibility and reliability through extensive experiments and data analysis. Our results demonstrate that the interactive learning process must match multi-factor analysis at different temporal sequences to determine key temporal sequences or intervals. This is limited by the classification of learning contents and interpretable concepts, which provide effective reference for subsequent learning content with similar concept classes and knowledge structures. Our approach recommends effective learning behavior in appropriate temporal sequences as soon as possible or constructs feasible intervention measures to improve learners’ participation. This research deepens and expands early warning by proposing a feasible new method and obtaining key conclusions with vital practical significance.

The aim of this study was to establish a nomogram based on clinical, radiomics, and deep transfer learning (DTL) features to predict meningioma grade. Three hundred forty meningiomas from one hospital composed the training set, and 102 meningiomas from another hospital composed the test set. The enhanced T1 WI images were used for analysis. The clinical, radiomics and DTL features were selected to construct the model. Radiomics and DTL scores were calculated. The deep transfer learning radiomics (DTLR) nomogram was developed on the basis of selected clinical features, radiomics scores and DTL scores. Receiver operating characteristic (ROC) curves and decision curve analysis (DCA) curves were drawn. The clinical features of sex, shape, indistinct margin and peritumoral edema were selected and used to construct the clinical model. Thirty-two radiomics features and 28 DTL features were selected for model construction. The clinical model had an AUC of 0.788. (95% CI: 0.6996–0.8756), with an accuracy of 0.745, a sensitivity of 0.941, and a specificity of 0.549 in the test set. The DTLR nomogram had the highest AUC of 0.866 (95% CI: 0.7984–0.9340), with an accuracy of 0.804, a sensitivity of 0.745, and a specificity of 0.863 in the test set. Compared with the other models, the DTLR nomogram had the greatest net benefit according to the DCA. There was a significant difference between the DTLR nomogram and the clinical model, no significant difference between the rest models in DeLong test.The DTLR nomogram has superior predictive value in DCA and could be a valuable method in clinical decision-making. Given the results of DeLong test, only the radiomics model is sufficient and there is no need to add DTL features. As a new attempt, the DTLR nomogram needs to be improved in the future study.

Polydimethylsiloxane (PDMS) is an excellent material for investigating the mechanism of triboelectricity as it can easily be used to construct various microstructures. In this study, micro-capacitors (MCs) and variable microcapacitors (VMCs) were embedded in PDMS by filling PDMS with silver nanoparticles (NPs) and constructing an internal cellular structure. The output performance of the triboelectric nanogenerators (TENGs) based on MCs@PDMS and VMCs@PDMS films was systematically investigated, with variation of the filling content of silver NPs and the pore ratio and size. The microstructure, permittivity, dielectric loss, and capacitance of the VMCs@PDMS films were well characterized. The output current of the TENG based on the VMCs@PDMS film was respectively 4.0 and 1.6 times higher than that of the TENGs based on the pure PDMS film and MCs@PDMS film, and the output power density of the former reached 6 W·m –2 . This study sheds light on the physical nature of conductive nanoparticle fillings and cellular structures in dielectric triboelectric polymers.

Elastography ultrasound (EUS) imaging is a vital ultrasound imaging modality. The current use of EUS faces many challenges, such as vulnerability to subjective manipulation, echo signal attenuation, and unknown risks of elastic pressure in certain delicate tissues. The hardware requirement of EUS also hinders the trend of miniaturization of ultrasound equipment. Here we show a cost-efficient solution by designing a deep neural network to synthesize virtual EUS (V-EUS) from conventional B-mode images. A total of 4580 breast tumor cases were collected from 15 medical centers, including a main cohort with 2501 cases for model establishment, an external dataset with 1730 cases and a portable dataset with 349 cases for testing. In the task of differentiating benign and malignant breast tumors, there is no significant difference between V-EUS and real EUS on high-end ultrasound, while the diagnostic performance of pocket-sized ultrasound can be improved by about 5% after V-EUS is equipped. The current use of elastography ultrasound faces challenges, including vulnerability to subjective manipulation, echo signal attenuation, unknown risks of elastic pressure and high imaging hardware cost. Here, the author shows a virtual elastography to empower low-end ultrasound devices with state-of-art elastography function.

The study of learning behaviors with multi features is of great significance for interactive cooperation. The data prediction and decision are to realize the comprehensive analysis and value mining. In this study, hierarchical learning behavior based on feature cluster is proposed. Based on the massive data in interactive learning environment, the descriptive model and learning algorithm suitable for feature clustering are designed, and sufficient experiments obtain the optimal performance indexes. The data analysis results are reliable. On this basis, the hierarchical learning behaviors based on feature clusters are visualized, the rules of different learning behaviors are summarized, then we propose the practical scheme of interactive cooperation. The hierarchical learning behaviors can be realized by feature clusters, which can effectively improve the modes of interactive cooperation, and help to improve the learning effectiveness.

In recent years, recommender systems—which provide personalized recommendations by analyzing users’ historical behavior to infer their preferences—have become essential tools across various domains, including e-commerce, streaming media, and social platforms. Recommender systems play a crucial role in enhancing user experience by mining vast amounts of data to identify what is most relevant to users. Among these, deep learning-based recommender systems have demonstrated exceptional recommendation performance. However, these “black-box” systems lack reasonable explanations for their recommendation results, which reduces their impact and credibility. To address this situation, an effective strategy is to provide a personalized textual explanation along with the recommendation. This approach has received increasing attention from researchers because it can enhance users’ trust in recommender systems through intuitive explanations. In this context, our paper introduces a novel explainable recommendation model named GCLTE. This model integrates Graph Contrastive Learning with transformers within an Encoder–Decoder framework to perform rating prediction and reason generation simultaneously. In addition, we cleverly combine the neural network layer with the transformer using a straightforward information enhancement operation. Finally, our extensive experiments on three real-world datasets demonstrate the effectiveness of GCLTE in both recommendation and explanation. The experimental results show that our model outperforms the top existing models.

To explore the relationship between glymphatic dysfunction and cognitive impairment in unilateral temporal lobe epilepsy (TLE). This study retrospectively included 38 patients with unilateral TLE and 26 age- and gender-matched healthy controls (HCs). The diffusion tensor image analysis along the perivascular space (DTI-ALPS) index, choroid plexus volume (CPV), and cognitive assessment were obtained for each participant. Neuropsychological test batteries included Montreal Cognitive Assessment (MoCA), Minimum Mental State Examination, Arithmetic Test (AT), Digit Symbol Substitution Test (DSST), Digit Span Test (DST), Boston Naming Test, Block design, Phonological Fluency Test (PFT), and Semantic Verbal Fluency (SVF). Compared to HCs, TLE patients had lower scores of MoCA, AT, DSST, DST, Block design, PFT and SVF (all  < 0.05) and lower values of mean DTI-ALPS index (1.491 ± 0.142 vs. 1.642 ± 0.123,  < 0.001). Significantly lower DTI-ALPS index values were observed in the ipsilateral hemisphere than in the contralateral hemisphere (1.466 ± 0.129 vs. 1.517 ± 0.175,  = 0.013) for patients with unilateral TLE. Correlation analyses found that SVF performance was significantly or borderline significantly associated with glymphatic function (  < 0.05 for all DTI-ALPS index and  = 0.057 for CPV) in TLE patients. Linear regression analyses showed that increased CPV and decreased DTI-ALPS index were independent risk factors for semantic fluency impairment (all  < 0.05). Furthermore, mediation analyses found the mediator role of the mean DTI-ALPS index in the relationship between choroid plexus enlargement and semantic fluency impairment (indirect effect:  = -0.182,  = -0.486 to -0.037). These findings reveal the important role of the DTI-ALPS index and CPV in SVF performance in unilateral TLE. Decreased DTI-ALPS index and increased CPV are the independent risk factors for semantic fluency impairment. The DTI-ALPS index may fully mediate the relationship between CP enlargement and SVF performance. These insights provide a radiological foundation for further investigations into the mechanism of the glymphatic system in TLE pathophysiology.