Optimizing the cooling system plays a central role for capping the data center power consumption. However, the performance of traditional cooling management strategies is not satisfactory due to the complexity of thermodynamic process. Recently, several works leveraged Reinforcement Learning (RL) to improve the energy efficiency of data center cooling system. While they demonstrated that it is possible to reduce the cooling power consumption via RL, there are still some key challenges that have to be addressed before field deployment, such as safe operation guarantee and sample complexity, etc. In this paper, we propose SafeCool , an actor-critic Model-Based Reinforcement Learning (MBRL) algorithm for data center cooling management. SafeCool incorporates two system models, i.e., a transition model to predict the future system state and a risk model to estimate the negative effect of executing an action. The safety of proposed algorithm is ensured by Model Predictive Control (MPC) and risk-guided exploration. In addition, by employing the MBRL framework, SafeCool achieves higher sample efficiency and accelerated convergence. Simulations using real-world workload trace reveal that SafeCool saves up to 13.18% cooling power compared with state-of-the-art MBRL data center cooling solutions.

Predicting the remaining useful life (RUL) of lithium-ion batteries is crucial for enhancing their reliability and safety. Addressing the issue of inaccurate RUL predictions caused by the nonlinear decay resulting from capacity regeneration, this paper proposes an indirect lithium-ion battery RUL prediction method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and convolutional neural network (CNN)–bidirectional gated recurrent unit (BiGRU). The method extracts Health Indicators (HI) from the battery-charging stage and employs CEEMDAN to decompose HI into several components. These components are then input into a component prediction model for forecasting. Finally, the predicted component results are fused and input into a capacity prediction model to achieve indirect RUL prediction. Validation is conducted using the lithium-ion battery dataset provided by NASA. The results indicate that, under prediction starting points (STs) of 80 and 100, the maximum average absolute errors do not exceed 0.0096 and 0.0081, and the maximum root mean square errors do not exceed 0.0196 and 0.0115, demonstrating high precision and reliability.

Emotion plays a crucial role in human communication, as it adds depth and richness to conversations. In recent years, there has been growing interest in developing conversation systems with the ability to generate emotions. However, to create more engaging and realistic interactions, it is essential to consider the influence of personality on emotion generation. This paper proposes a novel approach that combines personality modeling with emotion generation for conversation systems. By incorporating personality traits into the emotion generation process, we aim to create more personalized and contextually appropriate emotional responses. Drawing from BigFive model and emotion computation techniques, our model takes into account individual differences in personality to generate emotions that align with each user’s unique characteristics. Experiments show that combining emotion modeling with personality in a dialogue system helps improve the performance of emotion generation models. Additionally, it is also verified that our approach outperforms other baselines on several metrics.

Accurate prediction of the state of health (SOH) in lithium batteries (LiBs) is essential for ensuring operational safety, extending battery lifespan, and enabling effective second-life applications. However, achieving precise SOH prediction under small-sample conditions remains a significant challenge due to inherent variability among battery cells and capacity recovery phenomena, which result in irregular degradation patterns and hinder effective feature extraction. To overcome these challenges, this study introduces an advanced autoencoder-based method specifically designed for SOH prediction in small-sample scenarios. This method employs a multi-encoder structure—comprising token, positional, and temporal encoders—to comprehensively capture the multi-dimensional characteristics of SOH sequences. Furthermore, a BHTP module is integrated to facilitate feature fusion and enhance the model’s stability and interpretability. By utilizing a pre-training and fine-tuning strategy, the proposed method effectively reduces computational complexity and the number of model parameters while maintaining high prediction accuracy. The validation of the NASA 18650 lithium cobalt oxide battery dataset under various discharge strategies shows that the proposed method achieves fast convergence and outperforms traditional prediction methods. Compared with other models, our method reduces the RMSE by 0.004 and the MAE by 0.003 on average. In addition, ablation experiments show that the addition of the multi-encoder structure and the BHTP module improves the RMSE and MAE by 0.008 and 0.007 on average, respectively. These results highlight the robustness and utility of the proposed method in real battery management systems, especially under data-scarce conditions.

Precipitation inversion techniques are crucial in meteorological research, aiming to accurately detect the location and intensity of precipitation events, which is vital for extreme weather response strategies. This study introduces a new infrared precipitation inversion method, Residual Deep Forest, which is based on a deep forest model with two key enhancements. Firstly, a feature optimization filter module is used to ensure high correlation among features in the cascade forest, a component of the deep forest model. This optimization reduces computational burden while maintaining efficiency. Secondly, embedding a residual structure within the cascade forest creates the residual cascade forest, improving feature expression, information processing, and computational efficiency on high-dimensional, large-scale precipitation data. Experimental results show that Residual Deep Forest surpasses traditional deep forest models and other classical machine learning techniques in precipitation inversion accuracy, especially in identifying high-intensity rainfall. The model achieves an Average Recall of 85.97%, a Probability of Detection of 75.46%, and a False Alarm Ratio of 49.13%. Ablation experiments demonstrate that the FOF module reduces training time by 3.56%, testing time by 1.24%, and memory usage by 3.97%, while the RCF module reduces these metrics by 1.30%, 0.90%, and 2.23%, respectively. The improvements in AR, POD, and FAR, along with the reduction in computational resources, highlight the model's enhanced efficiency and performance. This method leverages infrared remote sensing to enhance the scope and accuracy of precipitation monitoring, reducing reliance on radar and other ground-based instruments, and significantly improving precipitation inversion accuracy while minimizing spatial and temporal complexity.

Accurate prediction of bus arrival time is critical to improving transit service reliability and scheduling efficiency. The estimated time of arrival (ETA) is jointly determined by two processes, namely inter-station travel and station dwell, which are dominated by different traffic mechanisms and thus follow essentially different operating patterns. Most existing methods model the entire route in a unified way, making it difficult to account for both processes simultaneously. This paper therefore proposes a feature-separation prediction method. For inter-station travel time, which is driven by the continuous evolution of traffic flow, we design a Dual-Branch Spatio-Temporal Graph Convolutional Network (DSTGCN); its spectral graph convolution and spatial self-attention branches work together to capture road-network topology and the spatial propagation of traffic flow. For station dwell time, which follows a long-tailed distribution, we design a Spatio-Temporal Graph Convolutional Network with Transformer (STGCN-Trans) and introduce a robust loss function to suppress the effect of extreme outliers. Comparative and ablation experiments show that both models achieve the lowest RMSE, reducing it by 21.7% and 6.7% over the second-best baselines, respectively. They also remain stable under disturbances such as peak hours and extreme weather.