Global warming is a major environmental issue facing humanity, and the resulting climate change has severely affected the environment and daily lives of people. China attaches great importance to and actively responds to climate change issues. In order to achieve the “dual carbon” goal, it is necessary to clearly define the emission reduction path and scientifically predict future carbon emissions, which is the basis for setting emission reduction targets. To ensure the accuracy of data, this study applies the emission coefficient method to calculate the carbon emissions from the energy consumption in 30 provinces, regions, and cities in China from 1997 to 2021. Considering the spatial correlation between different regions in China, we propose a new machine learning prediction model that incorporates spatial weighting, namely, an LSTM-CNN combination model with spatial weighting. The spatial weighting explains the spatial correlation and the combined model is used to analyze the carbon emissions in the 30 provinces, regions, and cities of China from 2022 to 2035 under different scenarios. The results show that the LSTM-CNN combination model with four convolutional layers performs the best. Compared with other models, this model has the best predictive performance, with an MAE of 8.0169, an RMSE of 11.1505, and an $R^2$ of 0.9661 on the test set. Based on different scenario predictions, it is found that most cities can achieve carbon peaking before 2030. Some cities need to adjust their development rates based on their specific circumstances in order to achieve carbon peaking as early as possible. This study provides a research direction for deep learning time series forecasting and proposes a new predictive method for carbon emission forecasting.

Several challenges hinder accurate and physically consistent dynamic parameter estimation in power systems, particularly under scenarios involving limited measurements, strong system nonlinearity, and high variability introduced by renewable integration. Although data-driven methods such as Physics-Informed Neural Networks (PINNs) provide a promising direction, they often suffer from poor generalization and training instability when faced with complex dynamic regimes. To address these challenges, we propose a Residual Physics-Informed Neural Network (Res-PINN) framework, which integrates a residual neural architecture with the swing equation to enhance estimation robustness and precision. By replacing the traditional multilayer perceptron (MLP) in PINN with residual connections and injecting normalized time into each network layer, the proposed model improves temporal awareness and enables stable training of deep networks. A physics-constrained loss formulation is employed to estimate inertia and damping parameters without relying on large-scale labeled datasets. Extensive experiments on a 4-bus, 2-generator power system demonstrate that Res-PINN achieves high parameter estimation accuracy across various dynamic conditions and outperforms traditional PINN and Unscented Kalman Filter (UKF) methods. It also exhibits strong robustness to noise and low sensitivity to hyperparameter variations. These results show the potential of Res-PINN to bridge the gap between physics-guided learning and practical power system modeling and parameter identification.

Queue length is a crucial measurement of traffic signal control at urban intersections. Conventional queue length estimation methods mostly still rely on fixed detectors. The development of connected vehicles (CV) provides massive amounts of vehicle trajectory data, and the queue length estimation based on CV data has received considerable attention in recent years. However, most existing CV-based methods require the prior knowledge of the penetration rate of CV and vehicle arrivals, but the estimation of these prior distributions has not been well studied. To address this issue, this paper proposes a cycle-based queue length estimation method under partially connected vehicle (CV) environment, with the prior vehicle arrivals being unknown. The empirical Bayes method is adopted to estimate the arrival rate by leveraging the observed queued CV information such as the number and positions. The hyperparameter estimation problem of the prior distribution is solved by the maximum likelihood estimation (MLE) method. To validate the proposed queue length estimation method, a simulation environment with partially connected vehicles is established using VISSIM and Python for data generating. The results in terms of normalized mean absolute errors (NMAE) and normalized root mean square errors (NRMSE) show that the proposed method could produce accurate and reliable estimated queue length under various CV penetration rates.

LegendreNet is a novel graph neural network (GNNs) model that addresses stability issues present in traditional GNN models such as ChebNet, while also more effectively capturing higher-order dependencies within graphical data. Compared to traditional GNNs models such as GCN, LegendreNet is better equipped to handle large-scale graphical data, demonstrating superior performance on such datasets. Furthermore, Legendre polynomials, which are a set of completely orthogonal polynomials, are capable of approximating any function to arbitrary precision within a bounded interval. As such, when applied to graph neural networks, Legendre polynomials provide a more precise and stable means of fitting spectral filters to graphical data. This enables LegendreNet to more accurately capture graphical features when dealing with complex graphical data, and to exhibit greater robustness in adversarial attack scenarios. Compared to traditional GNNs methods, LegendreNet offers improved modeling and generalization capabilities, making it a more effective solution across various graphical data applications. Our experiments have demonstrated that our model outperforms state-of-the-art methods on large-scale graphical datasets. The code for LegendreNet is available at https://github.com/12chen20/LegendreNet.

The accurate identification of natural disasters is crucial in ensuring effective post-disaster relief efforts. However, the existing models for disaster classification often incur high costs. To address this, we propose leveraging the most advanced pre-trained large language models, which offer superior generative and multimodal understanding capabilities. Using a question-answering approach, we extract textual descriptions and category prediction probabilities for disaster scenarios, which are then used as input to our proposed Mamba Multimodal Disaster Recognition Network (Mamba-MDRNet). This model integrates a large pre-trained model with the Mamba mechanism, enabling the selection of the most reliable modality information as a robust basis for scene classification. Extensive experiments demonstrate consistent performance improvements across various visual models with heterogeneous architectures. Notably, integrating EfficientNet within Mamba-MDRNet yielded 97.82% accuracy for natural scene classification, surpassing the performance of the CNN (91.75%), ViT (94.50%), and ResNet18 (97.25%). These results highlight the potential of multimodal models combining large models and the Mamba mechanism for disaster type prediction.

This article presents parametric bootstrap (PB) approaches for hypothesis testing and interval estimation for the regression coefficients and the variance components of panel data regression models with complete panels. The PB pivot variables are proposed based on sufficient statistics of the parameters. On the other hand, we also derive generalized inferences and improved generalized inferences for variance components in this article. Some simulation results are presented to compare the performance of the PB approaches with the generalized inferences. Our studies show that the PB approaches perform satisfactorily for various sample sizes and parameter configurations, and the performance of PB approaches is mostly the same as that of generalized inferences with respect to the expected lengths and powers. The PB inferences have almost exact coverage probabilities and Type I error rates. Furthermore, the PB procedure can be simply carried out by a few simulation steps, and the derivation is easier to understand and to be extended to the incomplete panels. Finally, the proposed approaches are illustrated by using a real data example.