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In this study, we propose an innovative approach that enhances the performance of the backpropagation (BP) neural network in predicting the low‐cycle fatigue life of Ti‐6Al‐4V alloy by improving the dung beetle optimization (DBO) algorithm with the maximin Latin hypercube design (MLHD) strategy. To address the challenges posed by complex geometric components under different temperature conditions, this research employs finite element simulation to expand the limited experimental dataset and utilizes these data to further guide and optimize the MLHD_DBO_BP model. Test results indicate that the proposed MLHD_DBO_BP model significantly outperforms the traditional finite element method (FEM) and other neural network models in terms of fatigue life prediction performance. This research demonstrates the effectiveness of machine learning models that combine experimental and simulation data in predicting the low‐cycle fatigue life of Ti‐6Al‐4V alloy. [ABSTRACT FROM AUTHOR]

Knowledge tracing (KT), which aims to trace human knowledge learning process by using machines, has widely applied in online learning systems. It dynamically models student's knowledge states in relation to different learning factors through their learning interactions. Recently, KT has attracted many researches attention due to its good performance to using deep learning. Although most of KT models have shown outstanding results, they have limitations: either ignore the human cognitive law and learning behavior, or lack the ability to go deeper modeling to trace knowledge state. In this paper, we propose a deeper knowledge tracking model integrating cognitive theory and learning behavior (CLDKT). It united the advantages of memory network and recurrent neural network of the existing deep learning KT models for modeling student learning. To better implement CLDKT, we add the residual network (ResNet) to realize the deep modeling of learning behaviors. Extensive experiments on three open benchmark datasets to evaluate our model. Experimental results demonstrate that (I) CLDKT outperforms the state-of-the-art KT models on students' performance prediction. (II) CLDKT can deeper modeling to trace knowledge state owing to the ResNet import. (III) CLDKT has better interpretability and predictability, which proves the effectiveness of the knowledge tracing model integrating cognitive law and learning behavior. [ABSTRACT FROM AUTHOR]

With the implementation of the green development strategy and the "double carbon goal", as an important energy for sustainable development, wind power has been widely researched and vigorously developed across the world. Wind speed prediction has a major impact on the grid dispatching of wind power connection. Most current studies only focus on the deterministic prediction of wind speed. However, the traditional deterministic forecast only provides the single wind speed prediction results and cannot meet the diverse demands of dispatchers. To bridge the gap, a wind speed point-interval forecasting method is proposed that utilizes empirical wavelet transform, an improved wild horse optimization algorithm, a multi-predictor, and improved kernel density estimation. This method decomposes the wind speed sequence into stationary subsequences through empirical wavelet transform, and then optimizes three basic learners with completely different learning mechanisms to form an ensemble model using the modified wild horse optimization algorithm. Finally, the uncertainty is analysed using an improved kernel density estimation. The datasets of three sites from America's national renewable energy laboratory are used for comparison experiments with other models, and the predictions are discussed from different angles. The simulation results demonstrate that the model can produce high-precision deterministic results and high-quality probabilistic results. The reference information the model provides can be extremely valuable for scheduling operators. [ABSTRACT FROM AUTHOR]

Open in new tab Download slide OBJECTIVES This study aimed to assess the effectiveness of three-dimensional printing (3DP) in patients with complex hypertrophic cardiomyopathy requiring combined transaortic and transapical septal myectomy. METHODS We created 3DP models for 7 patients undergoing this surgery approach between June and October 2022 using silicone-like resin and conducted mock operations. The models were compared with echocardiography to identify abnormal muscle bundles and heart structures. These patients were then compared with a 1:2 matched group without 3DP, considering age, sex and additional operations. RESULTS The models mostly presenting with midventricular obstruction showed high consistency with original computed tomography data (r  = 0.978, P  <   0.001). 3DP identified more abnormal muscle bundles than echocardiography, primarily between the interventricular septum and apex. Excised specimens in mock operations mirrored those in actual myectomies. While cardiopulmonary bypass time was not significantly different, a near-20-min decrease was observed in the 3DP group (135.5 ± 31.1 vs 154.4 ± 36.6 min, P  =   0.054). CONCLUSIONS While no significant differences in surgical outcomes were observed, 3DP appeared to enhance the visualization and understanding of spatial structures (average Likert scale score 4.0), potentially contributing to surgical proficiency (overall rating score 3.9). The use of 3DP may offer additional value in the preparation and execution of operations for complex hypertrophic cardiomyopathy cases. [ABSTRACT FROM AUTHOR]