Air temperature prediction (ATP) plays a crucial role in meteorological applications, such as agricultural planning, disaster forecasting, and energy management. However, the existing methods often struggle with the challenges posed by nonstationary and nonlinear time series data. In this paper, we introduce a novel incremental-input neural network (IINN) model that is designed to improve the accuracy and stability of multivariate ATP processes. By leveraging an incremental-input mechanism, the IINN addresses key challenges such as gradient vanishing and explosion while enhancing the robustness and nonlinear modelling capacity of the model for use with high-dimensional datasets. Comprehensive evaluations conducted on the Seoul metropolitan summer temperature dataset demonstrate that the IINN achieves state-of-the-art performance across two forecasting horizons. Specifically, compared with the best-performing baseline model, the IINN produces a 6.1% MSE improvement for the minimum temperature (Tmin) and a 5.8% improvement for the maximum temperature (Tmax). Thus, this work provides a significant step forward in the field of air temperature forecasting, offering a lightweight, efficient, and interpretable solution for modelling complex, nonstationary time series. The proposed approach offers a new and practical paradigm for modelling multivariate temperature time series and shows strong potential for broader applications in environmental forecasting scenarios. © 2026 Elsevier Ltd.

Mamba is a rising model designed to distill complex patterns from historical data, providing predictive capabilities for time series forecasting tasks. Mamba's similarity to linear-based models has been criticized due to its limited ability to capture nonlinear dependencies. In this work, we propose a novel model named Embedding Channel Attention Maclaurin Einstein Mamba (CME-Mamba1.) based on Mamba framework, with both Embedding Channel Attention and Maclaurin mechanisms incorporated. To further address gradient vanishing issues, we integrate Einstein FFT algorithms, ensuring robust performance against abnormal behaviors of Mamba-based architectures. Extensive experiments conducted on 11 real-world datasets with different numbers of variates, domain focus and granularity, reveal that CME-Mamba achieves state-of-the-art performance in both MSE and MAE, while maintaining reasonable memory efficiency and low time cost. The robustness and credibility of all results are substantiated by a comprehensive convergence and stability analysis. Statistically, consolidated by the Friedman Nonparametric Test and the Wilcoxon Signed-Rank Test, CME-Mamba ranks the first place with significance over counterparts. In addition, in terms of time and memory analysis, CME-Mamba is among the top three models for time and memory efficiency. Despite this, our results further demonstrate that the main contributor is the Embedding Channel Attention Block, which greatly enhances nonlinear dependencies over datasets. The Einstein FFT Block effectively suppresses gradient vanishing occurrences and contributes considerably to performance improvements, driving CME-Mamba both stable and promising. Moreover, the Maclaurin Block based on negative feedback is asymptotically stable without additional gradient vanishing issues and pioneered in achieving synergies with other blocks and greatly enhances nonlinear dependencies. With enhanced nonlinear dependencies generated from the synergy effect of all the three blocks, CME-Mamba grows excellent to uncover complex paradigms and predict future states in various domains, especially improving the performance for periodic and high-variate situations, such as traffic flow management (≈+8%), electricity predictions(≈+6%). © 2025 Elsevier B.V.

Traditional knowledge graph (KG) approach often rely on static textbook content and overlook the dynamic, collaborative interactions in collaborative problem-solving (CPS). This study introduced a three-step integrated KG approach designed to support CPS in STEM education and examined the effective KG features that influence CPS learning outcomes. KGs were generated by combining learning materials and student dialogue data. Two types of features, graph structural features and knowledge activation features, were identified to classify knowledge nodes and analyze how students activated knowledge during CPS. Clustering analysis revealed three types of knowledge nodes: Peripheral Nodes, Core Nodes, and Degree Hubs. Furthermore, key features such as depth, branch, and activated paths showed positive correlations with group discussion performance and CPS skills but had limited influence on test scores. These findings highlight the potential of integrated KGs to support both individual and group learning in STEM education. © 2025 IEEE.

Time series modeling serves as the cornerstone of real-world applications, such as weather forecasting and transportation management. Recently, Mamba has become a promising model that combines near-linear computational complexity with high prediction accuracy in time series modeling, while facing challenges such as insufficient modeling of nonlinear dependencies in attention and restricted receptive fields caused by convolutions. To overcome these limitations, this paper introduces an innovative framework, Attention Mamba, featuring a novel Adaptive Pooling block that accelerates attention computation and incorporates global information, effectively overcoming the constraints of limited receptive fields. Furthermore, Attention Mamba integrates a bidirectional Mamba block, efficiently capturing long-short features and transforming inputs into the Value representations for attention mechanisms. Extensive experiments conducted on diverse datasets underscore the effectiveness of Attention Mamba in extracting nonlinear dependencies and enhancing receptive fields, establishing superior performance among leading counterparts. Our codes will be available on GitHub. © 2025 IEEE.