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Crop leaf diseases can reflect the current health status of the crop, and the rapid and automatic detection of field diseases has become one of the difficulties in the process of industrialization of agriculture. In the widespread application of various machine learning techniques, recognition time consumption and accuracy remain the main challenges in moving agriculture toward industrialization. This article proposes a novel network architecture called YOLO V5-CAcT to identify crop diseases. The fast and efficient lightweight YOLO V5 is chosen as the base network. Repeated Augmentation, FocalLoss, and SmoothBCE strategies improve the model robustness and combat the positive and negative sample ratio imbalance problem. Early Stopping is used to improve the convergence of the model. We use two technical routes of model pruning, knowledge distillation and memory activation parameter compression ActNN for model training and identification under different hardware conditions. Finally, we use simplified operators with INT8 quantization for further optimization and deployment in the deep learning inference platform NCNN to form an industrial-grade solution. In addition, some samples from the Plant Village and AI Challenger datasets were applied to build our dataset. The average recognition accuracy of 94.24% was achieved in images of 59 crop disease categories for 10 crop species, with an average inference time of 1.563 ms per sample and model size of only 2 MB, reducing the model size by 88% and the inference time by 72% compared with the original model, with significant performance advantages. Therefore, this study can provide a solid theoretical basis for solving the common problems in current agricultural disease image detection. At the same time, the advantages in terms of accuracy and computational cost can meet the needs of agricultural industrialization.

We establish the existence of nontrivial nonnegative solution for the following 0-Dirichlet problem with mean curvature operator in the Minkowski space -div∇u1-|∇u|2=λf(x,u)inΩ,u=0on∂Ω, where Ω is a general bounded domain of RN . By bifurcation and topological methods, we determine the interval of parameter λ in which the above problem has nontrivial nonnegative solution according to sublinear or linear nonlinearity at zero.

The accurate detection and identification of plant diseases is an essential step in the development of intelligent and modernized agricultural production. This study proposes a deep learning model (PPLC-Net) incorporating dilated convolution, multi-level attention mechanism, and GAP layers. The model uses novel weather data augmentation to expand the sample size to enhance the generalization and robustness of feature extraction. The feature extraction network extends the perceptual field of the convolutional domain using sawtooth dilated convolution with a variable expansion rate, which can effectively address the problem of insufficient spatial information extraction. The lightweight CBAM attention mechanism is located in the middle layer of the feature extraction network. It is used to enhance the information representation of the model. the GAP layer prevents over-fitting of the model by reducing the number and complexity of parameters computed by the network. The validation of the retained test dataset shows that the recognition accuracy and F1 score of the PPLC-Net model are 99.702% and 98.442%, and the number of parameters and FLOPs are 15.486 M and 5.338G, respectively, which can meet the requirements of accurate and fast recognition. In addition, the proposed combined CAM visualization method can fully validate the effectiveness of the proposed model.

As global carbon reduction initiatives progress and the new energy sector rapidly develops, photovoltaic (PV) power generation is playing an increasingly significant role in renewable energy. Accurate PV output forecasting, influenced by meteorological factors, is essential for efficient energy management. This paper presents an optimal hybrid forecasting strategy, integrating bidirectional temporal convolutional networks (BiTCN), dynamic convolution (DC), bidirectional long short-term memory networks (BiLSTM), and a novel mixed-state space model (Mixed-SSM). The mixed-SSM combines the state space model (SSM), multilayer perceptron (MLP), and multi-head self-attention mechanism (MHSA) to capture complementary temporal, nonlinear, and long-term features. Pearson and Spearman correlation analyses are used to select features strongly correlated with PV output, improving the prediction correlation coefficient (R2) by at least 0.87%. The K-Means++ algorithm further enhances input data features, achieving a maximum R2 of 86.9% and a positive R2 gain of 6.62%. Compared with BiTCN variants such as BiTCN-BiGRU, BiTCN-transformer, and BiTCN-LSTM, the proposed method delivers a mean absolute error (MAE) of 1.1%, root mean squared error (RMSE) of 1.2%, and an R2 of 89.1%. These results demonstrate the model’s effectiveness in forecasting PV power and supporting low-carbon, safe grid operation.

A spanning subgraph of a graph G is called a path-factor of G if its each component is a path. A path-factor is called a P ≥ k -factor of G if its each component admits at least k vertices, where k ≥ 2 . A graph G is called a P ≥ k -factor uniform graph if for any two different edges e 1 and e 2 of G , G admits a P ≥ k -factor containing e 1 and avoiding e 2 . The degree sum of G is defined by σ k ( G ) = min X ⊆ V ( G ) { ∑ x ∈ X d G ( x ) : X is an independent set of k vertices } . In this paper, we give two degree sum conditions for a graph to be a P ≥ 2 -factor uniform graph and a P ≥ 3 -factor uniform graph, respectively.

An experimental study is conducted to describe rate-dependent shear strength in a submerged granular medium to understand the mystery of submarine landslides with extremely small slide angles and long run-out distances. The experimental apparatus allows a long-span shear strain rate, γ̇, for five orders of magnitude from 10 −4 to 10 1 s −1 . It is observed that (a) submerged sand under higher shear tend to have bigger yield strength; this positive response of rate effect is significantly affected by the magnitudes of shear strain rates. (b) the residual strength of soil is clearly affected negatively by shear strain rate, decreasing as shear strain rate increases; even small variations under lower rate cause notable differences in residual strength, indicating a novel weaking rate-dependent. The yield strength and residual strength are corresponding to the shear state of soil. Hence, it is enough experimentally to explain that as long as the submarine mass flow speeds up, the slope sliding can be kept by only a small amount of force along the slide direction, which can be calculated as the gravity component even with a small slide angle.

Using bifurcation method, we investigate the existence, nonexistence and multiplicity of positive solutions for the following Dirichlet problem involving mean curvature operator in Minkowski space -div∇v1-|∇v|2=λf(|x|,v)inBR(0),v=0on∂BR(0). We managed to determine the intervals of the parameter λ in which the above problem has zero, one or two positive radial solutions corresponding to sublinear, linear, and superlinear nonlinearities f at zero respectively. We also studied the asymptotic behaviors of positive radial solutions as λ→+∞ .

Accurate diagnosis of plant diseases is crucial for crop health. This study introduces the EDA–ViT model, a Vision Transformer (ViT)-based approach that integrates adaptive entropy-based data augmentation for diagnosing custard apple (Annona squamosa) diseases. Traditional models like convolutional neural network and ViT face challenges with local feature extraction and large dataset requirements. EDA–ViT overcomes these by using a multi-scale weighted feature aggregation and a feature interaction module, enhancing both local and global feature extraction. The adaptive data augmentation method refines the training process, boosting accuracy and robustness. With a dataset of 8226 images, EDA–ViT achieved a classification accuracy of 96.58%, an F1 score of 96.10%, and a Matthews Correlation Coefficient (MCC) of 92.24%, outperforming other models. The inclusion of the Deformable Multi-head Self-Attention (DMSA) mechanism further enhanced feature capture. Ablation studies revealed that the adaptive augmentation contributed to a 0.56% accuracy improvement and a 0.34% increase in MCC. In summary, EDA–ViT presents an innovative solution for custard apple disease diagnosis, with potential applications in broader agricultural disease detection, ultimately aiding precision agriculture and crop health management.

Early assessment of cardiovascular disease risk plays an important role in preventing cardiovascular disease, vascular age (VA) is an important indicator for early screening of cardiovascular disease risk. This study presents a pulse signal-based dataset for VA prediction. The dataset comprises 226 subjects with 1364 pulse cycles, spanning both sexes (49.6% male, 50.4% female) and an age range of 20 to 69 years. Pulse signals were denoised by Savitzky-Golay filters, and 4th-order derivatives were calculated to extract the features of pulse signal. We applied the classic statistical model Klemera Doubal method (KDM) and five artificial intelligence models to predict VA. The experimental results showed that these models can predict VA with high accuracy and stability. It indicates that using pulse signals to predict VA is a simple, non-invasive, and effective method for assessing vascular health.

We establish the existence/nonexistence and multiplicity of nontrivial nonnegative solutions for the following 0-Dirichlet problem with mean curvature operator in the Minkowski space $\\left\\{ \\begin{array}{l} - div\\left( {\\frac{{\\nabla u}}{{\\sqrt {1 - {{\\left| {\\nabla u} \\right|}^2}} }}} \\right) = \\lambda f\\left( {x,u} \\right)\\,in\\,\\Omega , \\\ u = 0\\,on\\,\\partial \\Omega , \\\ \\end{array} \\right\\}$ where Ω is a general bounded domain of ℝN. By bifurcation and topological methods, we determine the interval of parameter λ in which the above problem has zero/one/two nontrivial nonnegative solutions according to sublinear/linear/superlinear nonlinearity at zero. Moreover, we also amend a minor fault in [2, Proposition 1.1].