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Student cognitive models are playing an essential role in intelligent online tutoring for programming courses. These models capture students’ learning interactions and store them in the form of a set of binary responses, thereby failing to utilize rich educational information in the learning process. Moreover, the recent development of these models has been focused on improving the prediction performance and tended to adopt deep neural networks in building the end-to-end prediction frameworks. Although this approach can provide an improved prediction performance, it may also cause difficulties in interpreting the student’s learning status, which is crucial for providing personalized educational feedback. To address this problem, this paper provides an interpretable cognitive model named HELP-DKT, which can infer how students learn programming based on deep knowledge tracing. HELP-DKT has two major advantages. First, it implements a feature-rich input layer, where the raw codes of students are encoded to vector representations, and the error classifications as concept indicators are incorporated. Second, it can infer meaningful estimation of student abilities while reliably predicting future performance. The experiments confirm that HELP-DKT can achieve good prediction performance and present reasonable interpretability of student skills improvement. In practice, HELP-DKT can personalize the learning experience of novice learners.

Colorectal cancer (CRC) has become one of the top ten malignant tumors with a high incidence rate and mortality. Due to the lack of a good CRC screening program, most of the CRC patients are being transferred at the time of treatment. The conventional treatment cannot effectively improve the prognosis of CRC patients, and the target drugs can significantly prolong the overall survival of patients in the advanced stage. However, the use of single drug may lead to acquired drug resistance and various serious complications. Therefore, combined targeted drug therapy is the main alternative treatment with poor effect of single targeted drug therapy, which has important research significance for the treatment of CRC. Therefore, this study intends to culture CRC cell lines in vitro at the cell level and intervene with the GLP-1 receptor agonist liraglutide. The effects of liraglutide on the PI3K/Akt/mTOR signal pathway and CRC cell proliferation, cycle, migration, invasion, and apoptosis are explored by detecting cell proliferation, cycle, migration, invasion, and apoptosis and the expression of related mRNA and protein. The results showed that liraglutide, a GLP-1 receptor agonist, could block the CRC cell cycle, reduce cell proliferation, migration, and invasion and promote apoptosis by inhibiting the PI3K/Akt/mTOR signal pathway.

Sound-based predictive maintenance (PdM) is a critical enabler for ensuring operational continuity and productivity in industrial systems. Due to the diversity of equipment types and the complexity of working environments, numerous feature engineering methods and anomaly diagnosis models have been developed based on sound signals. However, existing reviews focus more on the structures and results of the detection model, while neglecting the impact of the differences in feature engineering on subsequent detection models. Therefore, this paper aims to provide a comprehensive review of the state-of-the-art feature extraction methods based on sound signals. The judgment standards in the sound detection models are analyzed from empirical thresholding to machine learning and deep learning. The advantages and limitations of sound detection algorithms in varied equipment are elucidated through detailed examples and descriptions, providing a comprehensive understanding of performance and applicability. This review also provides a guide to the selection of feature extraction and detection methods for the predictive maintenance of equipment based on sound signals.

The application of neural network model in intelligent diagnosis usually encounters challenges such as continuous adjustment of network parameters and significant cost in training the network facing numerous complex physiological data. To address this challenge, this paper introduces a fuzzy SZGWO-ELM neural network model for medical disease aid diagnosis with fuzzy membership function and ELM network to refine the improved Gray Wolf optimization algorithm. Firstly, the Z-type membership function is introduced as the inertia weight to get a balance for the grey wolf in seeking the optimal solution globally and locally and ensuring fast convergence. Secondly, the S-type membership function is utilized as the adaptive weight to flexibly adjust the grey wolf search step size to facilitate a quick approximation of the optimal solution. Finally, the improved Gray Wolf optimization algorithm is used to optimize the parameters of the ELM neural network model, termed as SZGWO-ELM. This method can eliminate the need for extensive network parameter adjustments and quickly locate the optimal solution to the problem using a lightweight neural network. The performance of the SZGWO is assessed by using metrics like convergence, mean, and standard deviation. Multiple experiments reveal that this method shows superior performance. Furthermore, five publicly accessible medical disease datasets from UCI were conducted to evaluate the performance of SZGWO-ELM network model comparing with different classify model, and the results in terms of precision, sensitivity, specificity and accuracy can achieve 99.52%, 94.14%, 99.26% and 96.08%, respectively, which illustrate that the proposed SZGWO-ELM neural network significantly enhance the model’s accuracy, providing better support for doctors in disease diagnosis.

In coal and rock recognition technology, the acquisition of sound signals is affected by background noise. It is challenging to extract cutting features and accurately identify cutting patterns effectively. Therefore, this paper proposes an approach for combined noise reduction of the cutting sound signal based on the improved adaptive noise complete ensemble empirical mode decomposition (ICEEMDAN) and a singular value decomposition (SVD). First, the method used the ICEEMDAN method to decompose the noisy signal into several intrinsic mode functions (IMF). It calculated the correlation coefficient between the IMF component and the noisy signal and then selected the noisy IMF components based on the threshold formula. Meanwhile, this method constructed a Hankel matrix of the noisy IMF component signals. It used SVD technology to obtain the singular values. According to the singular value standard energy spectrum curve, the paper determined the order of the effective singular value and removed the noise component in the signal. Then, the denoised IMF and noiseless IMF components are superimposed and reconstructed to obtain the noise-reduced cutting sound signal. Finally, it applied simulation signal and simulated shearer cutting experiment to verify the performance of the method. The results show that the proposed method can effectively remove the influence of background noise in the signal and retain the characteristic frequencies of the original cutting sound signal. Compared with traditional noise reduction methods, the ICEEMDAN-SVD combined noise reduction method performs better in noise reduction evaluation standards of signal-noise ratio and root mean square error. It achieved a better noise reduction effect, which could help coal and rock recognition technology based on sound signals.

During operation, the acoustic signal of the drum shearer contains a wealth of information. The monitoring or diagnosis system based on acoustic signal has obvious advantages. However, the signal is challenging to extract and recognize. Therefore, this paper proposes an approach for acoustic signal processing of a shearer based on the parameter optimized variational mode decomposition (VMD) method and a clustering algorithm. First, the particle swarm optimization (PSO) algorithm searched for the best parameter combination of the VMD. According to the results, the approach determined the number of modes and penalty parameters for VMD. Then the improved VMD algorithm decomposed the acoustic signal. It selected the ideal component through the minimum envelope entropy. The PSO was designed to optimize the clustering analysis, and the minimum envelope entropy of the acoustic signal was regarded as the feature for classification. We then use a shearer simulation platform to collect the acoustic signal and use the approach proposed in this paper to process and classify the signal. The experimental results show that the approach proposed can effectively extract the features of the acoustic signal of the shearer. The recognition accuracy of the acoustic signal was high, which has practical application value.

Collaborative learning is a prevalent learning method, and modeling and predicting student performance in such paradigms is an important task. Most current methods analyze this complex task solely based on the frequency of student activities, overlooking the rich spatial and temporal features present in these activities, as well as the diverse textual content provided by various learning artifacts. To address these challenges, we choose a software engineering course as the study subject, where students are required to team up and complete a software project together. In this paper, we propose a novel Global-local Optimized grAph Transformer framework for collaborative learning, termed GOAT. Specifically, we first construct the dynamic knowledge concept-enhanced interaction graphs with nodes representing both students and relevant software engineering concepts, and edges illustrating interactions. Additionally, we incorporate spatial-aware and temporal-aware modules to capture the respective information, enabling the modeling of dynamic interactions within and across learning teams over time. A global-local optimization module is introduced to model intricate relationships within and between teams, highlighting commonalities and differences among team members. Our framework is backed by theoretical analysis and validated through extensive experiments on real-world datasets, which demonstrate its superiority over existing methods.

Recently, deep neural network-based cognitive models such as deep knowledge tracing have been introduced into the field of learning analytics and educational data mining. Despite an accurate predictive performance of such models, it is challenging to interpret their behaviors and obtain an intuitive insight into latent student learning status. To address these challenges, this paper proposes a new learner modeling framework named the EAKT, which embeds a structured cognitive model into a transformer. In this way, the EAKT not only can achieve an excellent prediction result of learning outcome but also can depict students’ knowledge state on a multi-dimensional knowledge component (KC) level. By performing the fine-grained analysis of the student learning process, the proposed framework provides better explanatory learner models for designing and implementing intelligent tutoring systems. The proposed EAKT is verified by experiments. The performance experiments show that the EAKT can better predict the future performance of student learning(more than 2.6% higher than the baseline method on two of three real-world datasets). The interpretability experiments demonstrate that the student knowledge state obtained by EAKT is closer to ground truth than other models, which means EAKT can more accurately trace changes in the students’ knowledge state.

Facial beauty analysis is an important topic in human society. It may be used as a guidance for face beautification applications such as cosmetic surgery. Deep neural networks (DNNs) have recently been adopted for facial beauty analysis and have achieved remarkable performance. However, most existing DNN‐based models regard facial beauty analysis as a normal classification task. They ignore important prior knowledge in traditional machine learning models which illustrate the significant contribution of the geometric features in facial beauty analysis. To be specific, landmarks of the whole face and facial organs are introduced to extract geometric features to make the decision. Inspired by this, we introduce a novel dual‐branch network for facial beauty analysis: one branch takes the Swin Transformer as the backbone to model the full face and global patterns, and another branch focuses on the masked facial organs with the residual network to model the local patterns of certain facial parts. Additionally, the designed multi‐scale feature fusion module can further facilitate our network to learn complementary semantic information between the two branches. In model optimisation, we propose a hybrid loss function, where especially geometric regulation is introduced by regressing the facial landmarks and it can force the extracted features to convey facial geometric features. Experiments performed on the SCUT‐FBP5500 dataset and the SCUT‐FBP dataset demonstrate that our model outperforms the state‐of‐the‐art convolutional neural networks models, which proves the effectiveness of the proposed geometric regularisation and dual‐branch structure with the hybrid network. To the best of our knowledge, this is the first study to introduce a Vision Transformer into the facial beauty analysis task.

When the shearer cuts coal or rock with different hardness, it will produce corresponding cutting state information. This paper develops a simulation cutting experiment system for the drum shearer based on similarity theory. It took the spiral cutting drum of a shearer as the research target and derived the principal similarity coefficients through the dimensional analysis method. Meanwhile, this paper designed the structure of the cutting power system and hydraulic system. Then, it chose a certain amount of coal powder as an aggregate, cement 325# as cementing material, sand, and water as auxiliary materials to prepare simulated coal samples. The paper adopted the orthogonal experiment method and used a proportion of cement, sand, and water as the influencing factors in designing a simulated coal sample preparation plan. In addition, it utilized the range analysis method to research the influence of various factors on the density and compressive strength of simulated coal samples. Finally, it conducted simulated coal sample cutting tests. The results show that the density of the simulated coal samples is between 1192.59 Kg/m3–1483.51 Kg/m3, and the compressive strength range reaches 0.16 MPa–3.94 MPa. The density of the simulated coal sample is related to the mass proportion of cement and sand. When the ratio gradually increases, the influence of sand increases. Furthermore, the compressive strength is linearly proportional to the proportion of cement. The self-designed simulation cutting experiment system could effectively carry out the relevant experiments and obtain the corresponding cutting condition signals through the sensors. There are differences in vibration signals generated by cutting different strength materials. Extracting the kurtosis value as the characteristic value can distinguish various cutting modes, which can provide a reliable experimental solution for the research of coal-rock identification.