To reduce losses from agriculture as well as enhance food security, we propose a three-stage deep ensemble for early citrus disease diagnosis from actual-field images of oranges (n = 2,240) as well as lemons (n = 208). To prevent leakage, augmentation is strictly enforced following splitting (70:30 stratified) following curation, normalisation, resizing by 224 × 224. Our contribution comes from combining state-of-the-art deep features by adding explicit texture priors. Namely, we add Local Binary Patterns (LBP) as well as Grey-Level Co-occurrence Matrix (GLCM) descriptors for micro-textures of lesions (e.g., stippling, scab rims around them, chlorosis encircled by veins) as well as second statistics (e.g., contrast, homogeneity, entropy) that CNNs/ViTs tend to discount by virtue of their small size coupled with variable-field data. These hand-crafted signals are z-score normalised as well as PCA-compressed for overfit protection as well as removal of collinearity then combined by deep embeddings. InceptionV3 (90% lemon) as well as DenseNet121 (93% orange) are the best of the five pretraining CNNs (ResNet50, DenseNet121, VGG16, InceptionV3, EfficientNetB0) that we test at Stage-1. The best CNNs are enlisted with a Vision Transformer (ViT) at Stage-2 for capture of long-range contextual capture improving upon Stage-1 by 98% (lemon) as well as 97% (orange). t-SNE confirms class separation while Stage-3 employs a multiclass SVM over the combined description that achieves 99% (lemon) while holding at 97% (orange) at another curation. The pipeline outperforms single-backbone variants, minimises variance while remaining lightweight enough for deployment, thus showing that LBP + GLCM texture priors compressed by PCA but combined by CNN/ViT features substantially enhance robustness plus generalisation for in-orchard citrus disease testing.

Very fast transient over voltage (VFTO) is a unique electromagnetic transient phenomenon occurring in Gas Insulated Substation (GIS), which determines the insulation level of the GIS (above 330 kV). Unfortunately, up to the present, there is no standard VFTO waveform to test the high voltage equipment to ensure the safety of power grid. For this reason, this paper focuses on obtaining the standard VFTO waveform through finding a method to fit the curve of VFTO. Hence, a UHV disconnector operation experiment of GIS was carried out on the UHV GIS test platform. The representative waveform of VFTO generated by a single SF6 gas discharge was extracted from the measured data. Furthermore, The Fast Fourier Transformer is applied to analyze the frequency features of VFTO. As one of the switchgear operation voltage, VFTO can be concluded to be a superposition of the power‐frequency waveform with the non‐periodic impulse waveform. Thus, it is found that The Squares Method is convenient for fitting the expression of VFTO according to its transient characteristics. Then the standard VFTO waveform can be obtained from the fitting expression, which coincides well with the measured waveform. The simulation results using ATP‐EMTP software are compared with the calculation results and the measured results.

Gas insulated substations (GISs) are broadly used for transmission and distribution in electric power networks. Very fast transient overvoltage (VFTO) caused by SF₆ discharge during switching operations in a GIS may threaten the insulation of electrical equipment. In this paper, a novel VFTO suppression method with great prospects in engineering, called the spiral tube damping busbar, is proposed. The suppressing mechanism of the new method is analyzed. The structure and the design characteristics of the damping busbar are introduced as well. Moreover, a calculation method for the self-inductance of the damping busbar at high frequency is presented. According to the structural characteristics of the damping busbar, the inductance effect on suppressing VFTO is analyzed. A further improvement in damping VFTO is investigated by designing a spiral litz coil connected in series with the busbar, which increases the damping effect. The simulation results show that the improved damping busbar has a significant suppressing effect on the amplitude and the frequency of VFTO.

Diabetic Retinopathy (DR) is a critical source of blindness that can be prevented globally, and accurate analysis of retinal fundus images enables early detection. Fundus images are often affected by multiple noise sources, which impair image quality and hinder the observation of delicate retinal structures, including microaneurysms and small blood vessels. Deep learning driven denoising models are computationally intensive and prone to overfitting on small medical datasets. In order to overcome these shortcomings, the present paper suggests a Quantum Denoising Autoencoder (QDAE), a hybrid quantum–classical architecture, which uses convolutional feature coding with parameterized quantum circuits (PQCs) in latent space. The suggested QDAE applies quantum superposition and entanglement to improve the latent representations, thereby improving denoising and retinal detail preservation. Experiments on the Diabetic Retinopathy 224 × 224 (2019) dataset show that QDAE performs considerably better than classical denoising architectures, including CAE, ResNet, and DnCNN with PSNR of 38.8 dB, SSIM of 0.96, and AMI of 0.88. The approach preserves delicate retinal patterns and intensity consistency, while incurring a slight computational overhead associated with shallow quantum circuits. The results presented above demonstrate that QDAE is a potential quantum-aided architecture for denoising retinal images and a feasible preprocessing procedure in early diabetic retinopathy.

Glaucoma remains a critical cause of permanent global visual disability, and is produced by advancing destruction of the visual nerve head (ONH). Early detection is critical important in preventing vision loss. We propose a new fusion transformer pipeline, which integrates optic disc/cup and feature-based segmentation to aid in the effective screening of glaucoma, in this paper. The proposed approach integrates U-Net with an attention mechanism to cut the Optic Disc (OD) and Optic Cup (OC), enabling after processing spectral shape descriptors to evaluate Vertical Cup-to-Disc Ratio (CDR). Fundus image descriptors are extracted together with the Swin Transformer encoder to detect glaucoma at the image scale. They employ a probabilistic fusion method to merge structural biomarker (CDR) and deep learning features to finally obtain the final glaucoma classification. The framework was studied in detail on three popular publicly available datasets: LAG, ACRIMA, and DRISTHI-GS. According to the experimental results, SwinCup-DiscNet consistently outperforms the traditional CNN-based models and methods that are based only on segmentation, as it surpasses these approaches on all datasets. The framework proves to be robust, reliable, and clinically interpretable, using execution metrics like DSC IoU, accuracy measures, and F1-score, as well as Cup-to-Disc Ratio Mean Absolute Error (CDR MAE). Findings show that SwinCup-DiscNet is a highly effective clinical tool used in real-world clinical settings to detect glaucoma early.