Yimin Wu

Detecting student behaviour objects in classroom environments is crucial for assessing educational progress, optimizing teaching strategies and improving student learning outcomes. With the ongoing advancement of educational informatization, analysing classroom behaviour has become an important tool for enhancing teaching quality and personalized learning. However, current student behaviour object detection models based on CNN and Transformer architectures face challenges such as large parameter sizes and high inference delays when deployed on edge devices in classrooms, limiting their practical application. To address these issues, this study proposes a lightweight student behaviour detection framework based on the Mamba architecture, aimed at balancing computational efficiency and detection accuracy. First, the framework based on the state‐space model (SSM) efficiently captures global dependencies, using local convolutions to enhance detection accuracy and scene understanding while maintaining real‐time performance. Second, the C2CGA module increases attention diversity through feature splitting, self‐attention, cascading and projected concatenation, deepening the network while reducing computational overhead. Finally, the A2CMoCA module aggregates multi‐scale features, improving the learning of small objects and occluded behaviours. Experiments on a self‐built classroom behaviour dataset (containing eight typical teaching behaviours) show that the proposed method achieves 91.5% detection accuracy while maintaining a lightweight design. Compared to the baseline model, its computational efficiency (5.9G FLOPs) is reduced by 56.6%, the parameter size is compressed to 3.65 M (a 39% reduction) and the inference speed is 3.2 ms, meeting the real‐time monitoring requirements in classroom teaching scenarios.

In the context of the global transition toward carbon neutrality and the circular economy, remanufacturing has emerged as a vital strategy for enhancing resource efficiency and reducing environmental impact. However, the remanufacturing sector faces significant uncertainties—including fluctuations in market demand, variability in the quality of returned products, and dynamic policy changes. These factors collectively challenge production decision-making and system sustainability. Following the preferred peporting items for systematic reviews and meta-analyses (PRISMA) guidelines, this study conducted a systematic review and bibliometric analysis of 98 core articles published between 2015 and 2024, with a focused examination of three interdisciplinary themes: (1) decision-making and optimization under uncertainty, (2) supply chain coordination and policy mechanisms, and (3) digital transformation and the application of emerging technologies. A novel micro–meso–macro analytical framework is proposed to integrate fragmented findings. The results highlight a paradigm shift from static models to dynamic, real-time decision-making systems, facilitated by digital twins (DTs), blockchain, and intelligent algorithms. Furthermore, the study identifies the synergistic effects of carbon-financial instruments and policy incentives in aligning economic and environmental objectives. This research develops a systematic framework to understand and address uncertainties in remanufacturing, offering policymakers and industry practitioners actionable insights to enhance the resilience, sustainability, and global applicability of remanufacturing systems.

The carbon emission trading market is crucial for reducing emissions, conserving energy, and enhancing the climate and environment. Studying carbon price forecasting can encourage China’s involvement in international carbon financial instruments trading and promote the development of China’s pilot carbon markets. This study employs ensemble empirical mode decomposition (EEDM) to convert the initial carbon price, which is a non-stationary signal, into several intrinsic mode functions and residual terms. Subsequently, hybrid machine learning methods are used to estimate the carbon price. For empirical analysis, the three main carbon markets with large trading volumes, Guangdong, Hubei, and Shenzhen, are selected from the eight pilot carbon markets. To achieve short-term carbon price prediction, a hybrid model combining a genetic algorithm (GA) and back propagation (BP) neural network is used. This model effectively addresses the issue of neural networks falling into local optimization. The results indicate that the hybrid algorithm is significantly superior to other algorithms for short-term prediction. Additionally, another hybrid model, combining the least squares support vector machine (LSSVM) and particle swarm optimization (PSO) algorithms, is employed to reduce forecast error while minimizing search parameters, which is not possible with traditional neural network models.

The time-varying spillover effect of China’s crude oil futures market and new energy market has an important impact on promoting the green development of China’s economy. This study uses the dynamic connectedness method based on DCC-GARCH model to analyze the time-varying spillover effects between Shanghai crude oil futures and various industries in new energy markets. The results show that there was a stable volatility correlation and high degree of connectedness between Shanghai crude oil futures and the new energy stock market. The new energy vehicle and energy storage industries were driving the market, while Shanghai crude oil futures and both wind power and photovoltaic industries were driven by the market.With the analysis results, the study provides scientific policy recommendations for the development of China’s crude oil futures market and new energy market, which are expected to contribute to the sustainable development of the energy market.

The spillover effect of the energy markets and the CET plays an important role in guiding the realization of two-carbon target; using the network spillover methodology of Diebold, Yilmaz, Jozef Barunk, and TomasKrehlk, we examine both the static and dynamic connectedness of CO₂ emissions trading (CET) market, steam coal market, new energy, and traditional energy market in China from early Dec 2013 to the end of July 2021. At last, we verified the stability of the model and obtained the following findings: (1) the total spillover effect index is 13.91 between those markets, and it is mainly focused on short term. Moreover, the dynamic spillover effect is time-varying, and it is greatly influenced by the domestic and international environment; (2) the connectedness of the CET market with other energy markets is neutral, the development of new energy market is strong, it is the main transmitter to other markets, especially to the traditional energy market except for the steam coal market, and the coal market is an effect transmitter. These results provide a theoretical reference for investors and policy makers who are concerned with the return connectedness among the CET market, new energy market, and steam coal market in China.