The purpose of Fuzzy Comprehensive CS-SVR Model (FCCS-SVR) is to evaluate and monitor the health status of a radar equipment and then keep its safe operation. Due to reasons such as few samples, slow changes and the nonlinear structure of data of fault monitoring signal, the health status evaluation of a radar system is quite difficult. By establishing the evaluation index system of a radar, the combination of AHP method and Entropy weight method is studied in this paper. In order to evaluate the value of health status, several optimization algorithms including PSO, GA, BA and CS are used for optimizing the parameters of SVR model. Meanwhile, in order to avoid the problem that the system is at the edge of the state, a radar health assessment method based on the combination of Fuzzy Comprehensive Evaluation and Cuckoo Search-Support Vector Regression (CS-SVR), which is named as Fuzzy Comprehensive CS-SVR (FCCS-SVR), is further proposed. The result of case analysis reflects that the state evaluation of the radar system is realized. The system performance analysis shows that the use of FCCS-SVR evaluation method provides a high recognition rate and can accurately assess the health status of the radar system.

A ship power equipments’ fault monitoring signal usually provides few samples and the data’s feature is non-linear in practical situation. This paper adopts the method of the least squares support vector machine (LSSVM) to deal with the problem of fault pattern identification in the case of small sample data. Meanwhile, in order to avoid involving a local extremum and poor convergence precision which are induced by optimizing the kernel function parameter and penalty factor of LSSVM, an improved Cuckoo Search (CS) algorithm is proposed for the purpose of parameter optimization. Based on the dynamic adaptive strategy, the newly proposed algorithm improves the recognition probability and the searching step length, which can effectively solve the problems of slow searching speed and low calculation accuracy of the CS algorithm. A benchmark example demonstrates that the CS-LSSVM algorithm can accurately and effectively identify the fault pattern types of ship power equipments.

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.

This study on soil salinity inversion in coastal tidal flats based on Sentinel-2 remote sensing imagery is significant for improving saline–alkali soils and advancing tidal flat agriculture. This study proposes an improved approach for soil salinity inversion in coastal tidal flats using Sentinel-2 imagery and a new enhanced chaotic mapping adaptive whale optimization neural network (CIWOABP) algorithm. Novel spectral indices were developed to enhance correlations with salinity, significantly outperforming traditional indexes. The CIWOABP model achieved superior validation accuracy (R² = 0.815) and reduced root mean square error (RMSE) and mean absolute error (MAE) compared to other machine learning models. The results enable the precise mapping of salinity levels, aiding salt-tolerant crop cultivation and sustainable agricultural management. This method offers a reliable framework for rapid salinity monitoring and precision farming in coastal regions.

Current domain generalization (DG) methods predominantly rely on single-level confusion strategies to enhance data diversity and extract domain-invariant features solely through marginal distribution alignment, limiting their ability to capture both global and local discriminative information. To address these limitations, this paper proposes multi-level confusion augmentation and joint DG(MCA-JDG) for fault diagnosis of rotating machinery under unseen working conditions. Specifically, we first construct a composite domain by randomly sampling from multiple real working conditions, and apply global Dir-Mixup and local Dir-FMixup to all original and composite domains to generate diverse augmented domains and enrich multi-scale feature representations. Furthermore, we introduce a joint distribution alignment scheme that simultaneously aligns marginal and conditional distributions, enhancing the domain invariance of learned features across scales. To mitigate negative transfer caused by low-quality samples during initial training phases, we design ConfMix, a confidence-based sample selection strategy that adaptively filters unreliable samples. Extensive experiments on three public rotating machinery fault diagnosis datasets demonstrate that MCA-JDG significantly outperforms state-of-the-art methods in diagnostic accuracy and generalization capability, achieving accuracies of 99.83%, 96.51%, and 92.43% on the JUST, Ottawa, and SDUST datasets, respectively.

In practical industrial scenarios, achieving an end-to-end intelligent fault diagnosis system remains a challenge due to the discrepancies in feature-space distributions caused by varying working conditions and limited computational resources. Existing domain generalization methods often overlook the trade-off between model performance and deployment efficiency. Although lightweight convolutional networks provide high inference efficiency, they exhibit limitations in feature representation and generalization performance. Therefore, this paper proposes a lightweight network architecture based on dual-mixup data augmentation (LNDMDA) to enhance the adaptability of fault diagnosis models in cross-domain environments. Firstly, a dual-mixup data augmentation mechanism combining cross-domain confusion and intra-domain confusion is constructed to significantly enhance the generalization capability of the model in complex domain transfer scenarios. Secondly, a lightweight feature extraction module combining partial convolution (PConv) and depthwise separable convolution (DSC) is designed to preserve critical diagnostic features of the signal while reducing computational complexity. In addition, this paper introduces a lightweight subspace division attention mechanism, which divides the feature map into multiple subspaces and calculates attention weights for each subspace separately, thereby efficiently capturing multi-scale fault features. Finally, comparative experiments on three public datasets demonstrate that LNDMDA consistently outperforms existing lightweight models and domain generalization methods across multiple transfer tasks, highlighting its advantages of compact model size, low computational cost, and excellent generalization performance.