Gesture recognition with millimeter-wave radar has broad application prospects in human-computer interaction. However, traditional recognition methods generate overly redundant features and construct large-scale networks, rendering them unsuitable for embedded devices with limited memory. To address this challenge, we propose a sparse-driven dynamic gesture recognition network in millimeter-wave radar, named TCNFormer (Time Convolutional Network and transFormer). Firstly, we employ a two-dimensional fast Fourier transform (2D-FFT) to obtain the range-Doppler maps (RDM). These maps are then processed through incoherent integration of multiple frames to produce Doppler-time maps (DTM). We subsequently use the orthogonal matching pursuit algorithm to achieve a sparse representation of the Doppler-time trajectories and integrate the RDM to extract the range features of gestures, obtaining the multi-dimensional sparse sequences encompassing the range-Doppler-time feature. We then design a TCNFormer network tailored to these multi-dimensional sparse sequences. This network leverages a shallow TCN to learn local features, a Transformer network to capture global features and an adaptive weighting method to fuse these local and global features effectively. Experimental results demonstrate that our network fully exploits the sparse multi-dimensional sequences, achieving a recognition accuracy of 99.17% on a self-built dataset. The parameter size of the network is only 0.13M, significantly outperforming existing state-of-the-art models in relevant metrics, thereby proving its suitability for embedded applications in human-computer interaction.

The El Niño–Southern Oscillation (ENSO) is inherently asymmetric, a primary characteristic where its warm phase (El Niño) and cold phase (La Niña) differ in amplitude, spatial pattern, and temporal evolution. This review synthesizes over two decades of research to provide a comprehensive overview of ENSO asymmetry. It systematically examines the observed manifestations, evaluates the competing physical mechanisms, and analyzes the ongoing challenges in climate modeling. The key findings in the literature indicate that this asymmetry is driven by complex interactions of nonlinear processes, where atmospheric mechanisms such as state-dependent westerly wind bursts and threshold responses of deep convection are now considered dominant driving factors, which are subsequently amplified and modulated by oceanic feedback. The main challenge in this field is that most of the current state-of-the-art climate models underestimate ENSO asymmetry, which is related to mean-state bias and brings uncertainty to future predictions. Furthermore, a key finding from recent projection studies is that while the asymmetry in ENSO’s sea surface temperature is expected to weaken in a warmer climate, the asymmetry of its global rainfall impacts may paradoxically be amplified. Future research should focus on balanced improvements in ocean and atmospheric model components, development of new diagnostic tools to clarify the roles of different feedbacks, or establishment of a framework that clearly links asymmetry to the full spectrum of ENSO diversity. By consolidating the current state of knowledge and highlighting key unresolved questions, this work provides an essential roadmap to improve the prediction and projection of Earth’s most far-reaching mode of climate variability.

As a critical land cover type, tidal flats play an essential role in carbon sequestration, ecological conservation, and disaster risk reduction. However, tidal flat areas exhibit significant spatiotemporal variability due to the dynamic influences of tides, waves, and precipitation. Moreover, the complex characteristics of tidal flat farmlands in high-resolution remote sensing imagery, coupled with the limited capabilities of conventional extraction methods, preclude large-scale, high-precision farmland delineation. To address this challenge, this study develops a comprehensive tidal flat farmland dataset using high-resolution remote sensing imagery and proposes a novel remote sensing foundation model based on a Sandglass module encoder-decoder architecture. The key contributions of this work encompass four major innovations:1. Development of a novel lightweight Multi-Scale Edge-Aware Module (MSEAM) that integrates learnable gradient operators with parallel multi-scale convolutions, effectively suppressing background noise while adaptively enhancing critical edge regions; 2. Construction of an efficient Sandglass encoder-decoder structure employing a lightweight Sandglass network as the feature extraction backbone, combined SCSE modules, achieving enhanced feature representation capabilities while reducing parameter count and computational complexity; 3. Introduction of an Adaptive Feature Fusion Module (AFM) that facilitates effective integration of high-level and low-level semantic information through a dynamic weight allocation mechanism, emphasizing useful channel features; 4. Integrating the enhanced Sandglass encoder-decoder model with the large-scale vision-language model CLIP. Experimental validation demonstrates that the proposed model achieves an accuracy of 96.75% and mIoU of 90.42% on the high-resolution tidal flat farmland dataset. So the model can realize the extraction of fine geographic patches of farmland in the coastal tidal flat area, and the extracted plot data can be used as the base map data for the observation and research of crop phenology, which is of great significance for the precise delineation of crop planting areas.

The El Niño–Southern Oscillation (ENSO) is the most consequential mode of interannual climate variability on the planet, yet its prediction has become complex due to the inability of classical paradigms to explain the observed co-evolution of the tropical mean state and interannual variability on decadal timescales. This article synthesizes the extensive research on ENSO rectification, exploring a paradigm that resolves this causality problem by recasting ENSO as an active architect of its own mean state. Tracing the intellectual development of this theory, starting from fundamental concepts such as the “dynamical thermostat” and “heat pump” hypotheses, modern analysis has identified the core physical mechanism as nonlinear dynamical heating (NDH), which is rooted in nonlinear heat advection during asymmetric ENSO cycles. The convergence of evidence from forced ocean models and observational diagnostics confirms a rectified signal characterized by an off-equatorial spatial pattern, providing a primary mechanism for tropical Pacific decadal variability (TPDV). By establishing a coherent framework linking high-frequency asymmetry with low-frequency variations, this review lays the foundation for future research and emphasizes the critical role of the rectification effect in improving decadal climate prediction.