Smart manufacturing, which is increasingly popular worldwide, is aided by time-series forecasting. As the volume of historical data increases, powerful forecasting techniques that reveal unknown relationships between past and future values are required to provide accurate forecasts of production and sales. Thus, in this article, a composite gate recurrent unit (GRU)-Prophet model with an attention mechanism was constructed to predict sales volume. In this composite model, Prophet model and GRU model with attention mechanism were used to capture linear and nonlinear features, respectively. The composite model was experimentally determined to be more applicable and to provide more accurate predictions than did recurrent neural network, long short-term memory, gate recurrent unit, Prophet, and autoregressive integrated moving average models. This article's composite model is suited to rapid changes in market demand and helps enterprises be more competitive in the field of smart manufacturing.

Remote sensing technology has become an indispensable core means for Earth observation. As two of the most commonly used remote sensing modalities, the fusion of optical and synthetic aperture radar (SAR) (OPT-SAR fusion) can effectively overcome the limitations of a single data source, achieve information complementarity and synergistic enhancement, thereby significantly improving the interpretation capability of multi-source remote sensing data. This paper first discusses the necessity of OPT-SAR fusion, systematically reviews the historical development of fusion technologies, and summarizes open-source resources for various tasks, aiming to provide a reference for related research. Finally, building upon recent advances in OPT-SAR fusion research and cutting-edge developments in deep learning, this paper proposes that future fusion technologies should develop in the following directions: interpretable fusion models driven by both data and knowledge, general fusion perception driven by multimodal large models, and lightweight architectures with efficient deployment strategies.

In recent years, supervised learning has seen notable progress in research on aircraft target detection using synthetic aperture radar (SAR) images, but its performance is heavily restricted by the amount of labeled data. Self-supervised learning (SSL) can address this problem by pre-training on unlabeled data to extract generalizable features. However, existing SSL methods are predominantly developed based on approaches designed for natural image processing, making them unsuitable for fully understanding SAR images. Therefore, this study proposes a self-supervised contrastive learning method (SARDet-CL) that integrates feature enhancement and imaging mechanism constraints, tailored to the characteristics and requirements of aircraft target detection tasks. Specifically, SARDet-CL enhances the model’s ability to represent spatiotemporal features through spatial feature masking and multi-temporal consistency learning, and robustly extracts target features using a dynamic threshold quantization strategy. In addition, structural information is inferred based on the SAR imaging mechanism and incorporated as an auxiliary self-supervisory signal to guide the model toward a more accurate understanding of SAR images. Experimental results based on various SAR datasets show that the proposed method outperforms other pre-training techniques in down-stream SAR target detection tasks.