Electrical conductivity is one of several outstanding features of graphene–polymer nanocomposites, but calculations of this property require the intricate features of the underlying conduction processes to be accounted for. To this end, a novel Monte Carlo method was developed. We first established a randomly distributed graphene nanoplatelet (GNP) network. Then, based on the tunneling effect, the contact conductance between the GNPs was calculated. Coated surfaces (CSs) were next set up to calculate the current flow from the GNPs to the polymer. Using the equipotential approximation, the potentials of the GNPs and CSs met Kirchhoff’s current law, and, based on Laplace equation, the potential of the CSs was obtained from the potential of the GNP by the walk-on-spheres (WoS) method. As such, the potentials of all GNPs were obtained, and the electrical conductivity of the GNP polymer composites was calculated. The barrier heights, polymer conductivity, diameter and thickness of the GNP determining the electrical conductivity of composites were studied in this model. The calculated conductivity and percolation threshold were shown to agree with experimental data.

In order to monitor the growth and development of tomatoes, and improve the efficiency of flower and fruit thinning and tomato picking, we propose a model TF-YOLOv5s for the detection of tomato flowers and fruits in natural environments. Based on the YOLOv5s model, a C3Faster module is introduced to reduce the number of parameters and calculations while maintaining detection accuracy. The regular convolution is replaced by depth-wise separable convolution (DWConv) to avoid parameter redundancy. To improve the convergence and accuracy of the model, we replace Complete Intersection over Union (CIoU) loss with Efficient Intersection over Union (EIOU) loss. The Squeeze and Excitation (SE) attention mechanism is added to improve the model’s sensitivity to the features of the tomato flowers and fruits. Compared with the baseline model, the number of parameters is reduced by 54.5%, the weight file is reduced by 52.8%, the Floating-point Operation Per second (FLOPs) is reduced by 48.7%, and the detection accuracy of tomato flowers and fruits is increased by 1.4%. Furthermore, the improved algorithm is deployed on two edge computing devices to verify its effectiveness. Experimental results show that the algorithm in this paper can achieve high detection with less computational resources. This algorithm has the potential value of application in practical tomato production.

Underwater object detection technology is widely used in fields such as ocean exploration. However, due to the complex underwater environment, issues like light attenuation and scattering lead to low detection accuracy, which fails to meet the requirements. To address these issues, we propose an improved YOLOv8n-based model called YOLOv8-UC. This model incorporates a modified Dilation-wise Residual (DWR) C2f module to enhance the ability to extract features from the network’s high-level expandable receptive fields. It also integrates the Large Separable Kernel Attention (LSKA) module with the SPPF of YOLOv8 to enhance multi-scale feature extraction capabilities, reducing the loss of details. To solve the problem of redundant parameters and computational load in the detection head, the original detection head is replaced with a shared parameter structure, and RepConv is introduced. Additionally, the Inner-SIoU loss function is improved by using auxiliary boundaries at different scales to accelerate bounding box regression and improve detection accuracy. Experimental results show that the designed YOLOv8-UC achieves an mAP@0.5 of 79.3%, with a 6.9% increase in detection accuracy (P) and a 5.9% increase in precision (mAP@0.5) compared to YOLOv8n, demonstrating the effectiveness and application prospects of this method.