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Aimed at the limitation that existing hyperspectral classification methods were mainly oriented to small-scale images, this paper proposed a new large-scale hyperspectral remote sensing image classification method, LS3EU-Net++ (Lightweight Encoder and Integrated Spatial Spectral Squeeze and Excitation U-Net++). The method optimized the U-Net++ architecture by introducing a lightweight encoder and combining the Spatial Spectral Squeeze and Excitation (S3E) Attention Module, which maintained the powerful feature extraction capability while significantly reducing the training cost. In addition, the model employed a composite loss function combining focal loss and Jaccard loss, which could focus more on difficult samples, thus improving pixel-level accuracy and classification results. To solve the sample imbalance problem in hyperspectral images, this paper also proposed a data enhancement strategy based on “copy–paste”, which effectively increased the diversity of the training dataset. Experiments on large-scale satellite hyperspectral remote sensing images from the Zhuhai-1 satellite demonstrated that LS3EU-Net++ exhibited superiority over the U-Net++ benchmark. Specifically, the overall accuracy (OA) was improved by 5.35%, and the mean Intersection over Union (mIoU) by 12.4%. These findings suggested that the proposed method provided a robust solution for large-scale hyperspectral image classification, effectively balancing accuracy and computational efficiency.

Porcine epidemic diarrhea virus (PEDV) has emerged in American countries, and it has reemerged in Asia and Europe, causing significant economic losses to the pig industry worldwide. In the present study, the 17GXCZ-1ORF3d strain, which has a naturally large deletion at the 172–554 bp position of the ORF3 gene, together with the 17GXCZ-1ORF3c strain, was serially propagated in Vero cells for up to 120 passages. The adaptability of the two strains gradually increased through serial passages in vitro. Genetic variation analysis of the variants of the two strains from different generations revealed that the naturally truncated ORF3 gene in the 17GXCZ-1ORF3d variants was stably inherited. Furthermore, the survival, viral shedding and histopathological lesions following inoculation of piglets demonstrated that the virulence of 17GXCZ-1ORF3d-P120 was significantly attenuated. These results indicate that the naturally truncated ORF3 gene may accelerate the attenuation of virulence and is involved in PEDV virulence together with mutations in other structural genes. Importantly, immunization of sows with G2b 17GXCZ-1ORF3d-P120 increased PEDV-specific IgG and IgA antibody levels in piglets and conferred partial passive protection against heterologous G2a PEDV strains. Our findings suggest that an attenuated strain with a truncated ORF3 gene may be a promising candidate for protection against PEDV.

The chronological classification of Jingdezhen blue and white porcelain in past dynasties has high academic and socioeconomic research value. In this study, RGB, non-imaging spectral, and imaging spectral datasets of Jingdezhen blue and white porcelain were constructed with small samples, and the spectral range was between 350-950 nm. The transfer learning classification and identification model of blue and white porcelain was established based on the VGG16 and ResNet50 models. On this basis, Dempster-Shafer (DS) evidence theory was used to construct a chronological classification and identification model of blue and white porcelain imaging spectral data that combined spatial and spectral information. The experimental comparison shows that in the case of small samples, the method incorporating the ResNet50 network model and stepwise discriminant analysis combined with the Long Short-Term Memory (LSTM) algorithm had the best classification effect, and the classification accuracy reached 90.47%. This study provides a new method and idea for the nondestructive identification and classification of cultural relics.

State-of-energy (SOE) estimation helps to enhance the safety of battery operation and predict vehicle range. However, the voltage plateau of the LiFePO4 (LFP) battery presents a significant challenge for SOE estimation. Therefore, this paper introduces a significantly varying mechanical force feature to tackle the flat voltage curve in the mid-SOE region. A fusion model that integrates a bidirectional long short-term memory (BiLSTM) network, particle swarm optimization (PSO), and Kalman filter (KF) algorithm is proposed for SOE estimation. The BiLSTM is applied to fully capture the temporal dependencies from inputs to output over both local and long cycles. Subsequently, PSO is employed to optimize the parameters of KF, which is utilized to smooth the results of the BiLSTM network, thereby achieving highly accurate SOE estimation. Experimental results across different operating conditions and temperatures reveal that the introduction of mechanical force significantly improves SOE estimation accuracy. Compared to models using only traditional electrical and thermal features, the model with the introduction of mechanical force achieves average improvements of 67.06%, 66.38%, and 66.46% for the root mean square error (RMSE), maximum absolute error (MAXE), and mean absolute error (MAE), respectively. Moreover, the generalizability and robustness of the proposed method are further confirmed by the comparison of different models and preload forces.

As a clean and renewable energy source with huge reserves, hot dry rock geothermal resources have received wide attention. The geothermal field plays a crucial role in studying the heat source mechanism of hot dry rock, defining target areas, and evaluating resources. In this study, the three-dimensional structural inversion of the Gonghe Basin is carried out using magnetotelluric sounding, and the Curie isothermal surface is obtained by analyzing regional aeromagnetic data. By coupling low-resistance and high-conductivity zones with temperature distribution and integrating the Curie isothermal surface with high-temperature anomalies of some melts, we constructed an initial temperature field model based on comprehensive geophysical data. The temperature field model of the Gonghe Basin is established by using the adaptive finite-element temperature conduction control equation and the constraints of the temperature data from geothermal wells. The temperature field model provides a basis for the future exploration of hot dry rock resources in the Gonghe area.

Accurate State of Health (SOH) estimation of lithium-ion batteries (LIBs) is critical for ensuring the safety of electric vehicles and improving the reliability of battery management systems (BMS). However, the use of individual health features (HFs) and the selection of hyperparameters can increase the data processing burden on the BMS and reduce the accuracy of data-driven models. To address the above issue, this paper proposes a novel SOH estimation method for lithium-ion batteries based on the PSO–GWO–LSSVM prediction model with multi-dimensional health feature extraction. To comprehensively capture the battery aging mechanisms, four categories of health features—time, energy, similarity, and second-order features—are extracted from the LIBs charging segments. The correlation between HFs and SOH is comprehensively evaluated through Pearson and Spearman correlation analyses, followed by Gaussian filtering and outlier detection to enhance feature quality. With strong generalization and robustness, least squares support vector machine (LSSVM) is widely applied to nonlinear computations and function approximation. To improve LSSVM model accuracy and efficiency, this paper develops a novel prediction model that uses particle swarm optimization (PSO) combined with grey wolf optimization (GWO) algorithms to optimize the LSSVM model. The generalization performance of the proposed method is validated through comparative experiments using a battery dataset provided by the Center for Advanced Life Cycle Engineering (CALCE) Research Center at the University of Maryland. Experimental results show that the coefficient of determination (R2) consistently exceeds 0.985, with the average absolute error in SOH prediction for four batteries remaining around 0.5%. The comparative experiments demonstrate that the proposed method has a certain degree of accuracy, robustness, and generalization capability.