Accurately subseasonal daily sea surface temperature prediction (SSTP) is significant for forecasting and mitigating extreme climate events related to sea surface temperature (SST). However, this scale's forecasting lies at the transitional zone between short-term forecasting and long-term climate prediction, requiring simultaneous consideration of small-scale variations crucial for the former and large-scale variations fundamental to the latter. Thus, achieving precise subseasonal daily SSTPs is challenging. In this study, we introduce a novel Multi-Scale Periodic Transformer (MSPT) to predict subseasonal daily SST, which can account for temporal variations at various scales. Initially, MSPT integrates Fast Fourier Transform and multi-layer perceptron to extract all potential periodic scales and adaptively identify critical ones. Each periodic scale features an independent branch composed of patch embedding and Transformer encoder, dedicated to specifically learning temporal variations at that scale. Only the outputs of critical branches are weighted and aggregated to obtain effective multi-periodic scale characteristics. This approach effectively decouples complex temporal patterns, enabling the model to capture reliable dependencies that are beneficial for improving subseasonal forecasting. Furthermore, by introducing additional multivariate attention, our improved Transformer Encoder can capture the inherent multivariate correlations of SST dynamics, perfecting the representation of temporal variations at specific periodic scales. Extensive subseasonal forecasting experiments conducted at four locations in the South China Sea demonstrate that MSPT achieves state-of-theart performance in 10-30d subseasonal daily SSTPs, validating the effectiveness of multi-scale periodic information in improving subseasonal forecasting.

Warehouse dwell time ( WDT ) of a truck is a critical metric for evaluating plant-logistics efficiency, including the time of the truck's queuing outside and loading inside the warehouse. But WDT prediction is challenging as it is affected by diverse factors like loading distinct types and weights of the cargoes, and varying amounts of loading tasks in different time slots. Besides, each trucks’ WDT is transitively influenced by its preceding trucks’ loading time in the queue. In this paper, we propose a multi-block dwell time prediction framework consisting of LSTM model and self-attention mechanism, called SDP . In view of that low performance of SDP brought by sparse loading data of some warehouses, we further design a multi-source adaptation based block-to-block transfer learning module. We present a warehouse similarity measurement based on loading tasks allocated and loading ability of the warehouses, according to which we enhance overall prediction performance by learning from high-performance WDT prediction models of similar warehouses. Experimental results on a large-scale logistics data set demonstrate that our proposal can reduce Mean Absolute Percentage Error (MAPE) by an average of 10.0%, Mean Absolute Error(MAE) by an average of 16.5%, and Root Mean Square Error(RMSE) by an average of 17.0% as compared to the baselines.

The scheduling of Earth observation satellites presents a formidable multi-objective optimization challenge, characterized by inherent trade-offs among task completion rate, execution timeliness, and the temporal uniformity of revisits. To address this, we introduce the Multi-Satellite Observation Task Scheduling (MSOTS) framework, a novel end-to-end approach based on Multi-Agent Reinforcement Learning (MARL). This framework formulates the scheduling process as a Markov game, employing the Multi-Agent Proximal Policy Optimization (MAPPO) algorithm within a Centralized Training, Decentralized Execution (CTDE) paradigm to effectively navigate these competing objectives. Furthermore, to ensure a balanced evaluation, we propose a Composite Multi-Objective Performance Score grounded in a weighted harmonic mean. Comprehensive empirical evaluations conducted on large-scale, simulated orbital scenarios demonstrate that MSOTS significantly outperforms both traditional heuristics and existing deep reinforcement learning methods in comprehensive performance and robust efficiency. This research provides a highly effective and intelligent approach to modern satellite task scheduling.

Sea surface temperature (SST) plays a central role in regulating ocean and atmosphere interactions and influencing extreme climate events such as marine heatwaves. However, the inherent complexity and nonlinearity of SST dynamics present major challenges for achieving accurate and interpretable forecasting. To address this problem, we propose a novel interpretable framework named Multi-Temporal Scale Fusion Transformers (MTSFT), which provides a solution for prediction accuracy and explanatory power. MTSFT incorporates Enhanced Multi-Temporal Scale Periodic Features to decouple overlapping temporal patterns at daily, seasonal, and interannual scales, improving the model's ability to capture key temporal structures. Based on an improved Temporal Fusion Transformers (TFT), the framework integrates static covariates, historical environmental inputs, and known future indicators into a unified architecture. In addition, MTSFT supports multi-level interpretability by identifying dominant drivers, detecting SST anomalies, and characterizing periodic patterns across various time scales. Experimental results across typical coastal regions of China show that MTSFT consistently achieves reliable prediction performance and offers meaningful scientific insights to support marine risk assessment and climate-informed decision-making.

Ocean fronts are central to upper-ocean dynamics and ecosystem processes, yet recognizing their evolutionary trends from satellite data remains challenging. We present a 3D U-Net-based multi-task framework that jointly performs ocean-front detection (OFD) and ocean-front evolutionary trend recognition (OFETR) from sea surface temperature gradient heatmaps. Instead of cascading OFD and OFETR in separate stages that pass OFD outputs downstream and can amplify upstream errors, the proposed model shares 3D spatiotemporal features and is trained end-to-end. We construct the Zhejiang–Fujian Coastal Front Mask (ZFCFM) and Evolutionary Trend (ZFCFET) datasets from ESA SST CCI L4 products for 2002–2021 and use them to evaluate the framework against 2D CNN baselines and traditional methods. Multi-task learning improves OFETR compared with single-task training while keeping OFD performance comparable, and the unified design reduces parameter count and daily computational cost. The model outputs daily point-level trend labels aligned with the dataset’s temporal resolution, indicating that end-to-end multi-task learning can mitigate error propagation and provide temporally resolved estimates.