High-speed permanent magnet (HSPM) machines are pivotal in aerospace, industrial automation, and electric vehicles owing to their exceptional power density and compact structure. However, their design optimization faces significant challenges arising from the inherent complexity of multiphysics interactions, the computational cost of high-fidelity simulations, and the limitations of conventional optimization methods in handling conflicting objectives. To overcome these issues, this article proposes a novel collaborative framework that synergizes finite element analysis (FEA), interpretable machine learning Light Gradient Boosting Machine (LightGBM), and deep reinforcement learning (DRL) for efficient electromagnetic-thermal co-optimization of HSPM machines. First, construct a FEA dataset covering key structural variables and multiphysics field responses. A high-fidelity surrogate model (SM) based on the LightGBM was developed. The SHapley Additive exPlanations (SHAP)-partial dependence plots (PDP) interpretable analysis of this model revealed critical nonlinear mechanisms. This surrogate model is subsequently embedded within the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm, forming a data-driven DRL optimization model that dynamically balances electromagnetic performance and thermal constraints. The proposed framework, termed MPOSM-TD3, is experimentally validated to simultaneously reduce the maximum winding temperature by 15.3$^\circ \text{C}$and improve operational efficiency by 0.94%.

Session-based travel packages recommendation aims to predict users’ next click based on their current and historical sessions recorded by Online Travel Agencies (OTAs). Recently, an increasing number of studies attempted to apply Graph Neural Networks (GNNs) to the session-based recommendation and obtained promising results. However, most of them do not take full advantage of the explicit latent structure from attributes of items, making learned representations of items less effective and difficult to interpret. Moreover, they only combine historical sessions (long-term preferences) with a current session (short-term preference) to learn a unified representation of users, ignoring the effects of historical sessions for the current session. To this end, this article proposes a novel session-based model named STR-VGAE, which fills subtasks of the travel packages recommendation and variational graph auto-encoders simultaneously. STR-VGAE mainly consists of three components: travel packages encoder , users behaviors encoder , and interaction modeling . Specifically, the travel packages encoder module is used to learn a unified travel package representation from co-occurrence attribute graphs by using multi-view variational graph auto-encoders and a multi-view attention network. The users behaviors encoder module is used to encode user’ historical and current sessions with a personalized GNN, which considers the effects of historical sessions on the current session, and coalesce these two kinds of session representations to learn the high-quality users’ representations by exploiting a gated fusion approach. The interaction modeling module is used to calculate recommendation scores over all candidate travel packages. Extensive experiments on a real-life tourism e-commerce dataset from China show that STR-VGAE yields significant performance advantages over several competitive methods, meanwhile provides an interpretation for the generated recommendation list.

Predicting grain losses and waste rate (LWR) is critical for agricultural planning and grain policy development. Capturing the stage interaction and generating robust features are the main challenges in grain LWR prediction. In this article, we propose MTGA, a Multitask Gated recurrent unit (GRU) Autoencoder, approach to 1) obtain the robust feature representation for the prediction task and 2) explore the time-ordered interactions among different stages of the grain chain. Specifically, we design multiple GRU encoder-decoder pairs to co-reconstruct the stage features in a common space for robust feature learning. Then, an attention mechanism is proposed better to fuse the reconstructed features from the GRU encoder-decoder pairs. Furthermore, we utilize the multitask for reconstructed loss and grain LWR prediction. We introduce the reconstructed loss task as an auxiliary task to help us to represent the robust features. Besides, we introduce the LWR prediction as main task to learn the parameters for prediction task. We collected the data with questionnaires, interviews, or data from grain management institutes for experiments. The evaluation results show that grain LWR prediction by our approach achieves the best results compared to several state-of-the-art prediction models. Moreover, our method gains overall performance decline of 12.5-18.3% on mean absolute error and root mean square error metrics.

As an e-commerce feature, the personalized recommendation is invariably highly-valued by both consumers and merchants. The e-tourism has become one of the hottest industries with the adoption of recommendation systems. Several lines of evidence have confirmed the travel-product recommendation is quite different from traditional recommendations. Travel products are usually browsed and purchased relatively infrequently compared with other traditional products (e.g., books and food), which gives rise to the extreme sparsity of travel data. Meanwhile, the choice of a suitable travel product is affected by an army of factors such as departure, destination, and financial and time budgets. To address these challenging problems, in this article, we propose a Probabilistic Matrix Factorization with Multi-Auxiliary Information (PMF-MAI) model in the context of the travel-product recommendation. In particular, PMF-MAI is able to fuse the probabilistic matrix factorization on the user-item interaction matrix with the linear regression on a suite of features constructed by the multiple auxiliary information. In order to fit the sparse data, PMF-MAI is built by a whole-data based learning approach that utilizes unobserved data to increase the coupling between probabilistic matrix factorization and linear regression. Extensive experiments are conducted on a real-world dataset provided by a large tourism e-commerce company. PMF-MAI shows an overwhelming superiority over all competitive baselines on the recommendation performance. Also, the importance of features is examined to reveal the crucial auxiliary information having a great impact on the adoption of travel products.