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Current research on image classification has combined convolutional neural networks (CNNs) and transformers to introduce inductive biases to the model, enhancing its ability to handle long-range dependencies. However, these integrated models have limitations. Standard CNNs have a static nature, restricting their convolution from dynamically adjusting to input images, thus limiting feature expression capabilities. In addition, the static nature of CNNs impedes the seamless integration between features dynamically generated by self-attention mechanisms and static features generated by convolution when combined with transformers. Furthermore, during image processing, each model stage contains abundant information that cannot be fully utilized by single-scale convolution, ultimately impacting the network’s classification performance. To tackle these challenges, we propose WoodGLNet, a real-time multi-scale pyramid network that aggregates global and local information in an input-dependent manner and facilitates feature interaction through three scales of convolution. WoodGLNet utilizes efficient multi-scale global spatial decay attention modules and input-dependent multi-scale dynamic convolutions at different stages, enhancing the network’s inductive biases and expanding the effective receptive field. In CIFAR100 and CIFAR10 image classification tasks, WoodGLNet-T achieves Top-1 accuracies of 76.34% and 92.35%, respectively, outperforming EfficientNet-B3 by 1.03 and 0.86 percentage points. WoodGLNet-S and WoodGLNet-B attain Top-1 accuracies of 77.56%, 93.66%, and 80.12%, 94.27%, respectively. The experimental subjects of this study were sourced from the Shandong Province Construction Structural Material Specimen Museum, tasked with wood testing and requiring high real-time performance. To assess WoodGLNet’s real-time detection capabilities, 20 types of precious wood from the museum were identified in real time using the WoodGLNet network. The results indicated that WoodGLNet achieved a classification accuracy of up to 99.60%, with a recognition time of 0.013 s per single image. These findings demonstrate the network’s exceptional real-time classification and generalization abilities. [ABSTRACT FROM AUTHOR]

This document is a letter to the editor in response to an article titled "Geriatric distal femoral fractures: post-operative complications and nine-year mortality—a retrospective analysis of two tertiary trauma centres." The authors of the letter express their gratitude to the original authors for their research but also raise some concerns and issues that need further exploration. They question the impact of postoperative nursing care and convalescent sites on patient mortality, the accuracy of statistical analysis regarding gender as a predictor of mortality, and the lack of a clear postoperative weight-bearing protocol. The authors hope that their interpretations and observations will contribute to the readers' understanding of the topic. [Extracted from the article]

This letter to the editor responds to a study on the role of environmental and seasonal factors in surgical site infection after spinal surgery. The authors express gratitude for the study but also raise some points for further exploration. They note that the study did not provide diagnostic criteria for surgical site infection and suggest using the standards proposed by the Centers for Disease Control and Prevention. The authors also highlight the need to consider surgical complications and the gap between recommended and observed wound care practices. Overall, the letter aims to contribute to a better understanding of the study. [Extracted from the article]