A meta-analysis of the risk factors for neurosurgical surgical site infection following craniotomy.

The purpose of the meta-analysis was to evaluate and compare the risk factors for neurosurgical surgical site infection (SSI) after craniotomy. Using dichotomous or contentious random or fixed effect models, the odds ratio (OR) and mean difference (MD) with 95% confidence intervals (CIs) were computed based on the examination of the meta-analysis results. Eighteen analyses, covering 11 068 craniotomies between 2001 and 2023, were included in the current meta-analysis. Subjects with SSIs had a significantly younger age (MD, -2.49; 95% CI, -2.95 to -2.04, p < 0.001), longer operation duration (MD, 10.21; 95% CI, 6.49-13.94, p < 0.001) and longer length of postoperative hospital stay (MD, 1.52; 95% CI, 0.45-2.60, p = 0.006) compared to subjects with no SSI with craniotomy. However, no significant difference was found between craniotomy subjects with SSIs and with no SSI in gender (OR, 0.90; 95% CI, 0.76-1.07, p = 0.23), and combination with other infection (OR, 3.93; 95% CI, 0.28-56.01, p = 0.31). The data that were looked at showed that younger age, longer operation duration and longer length of postoperative hospital stay can be considered as risk factors of SSI in subjects with craniotomy; however, gender and combination with other infections are not. Nonetheless, consideration should be given to their values because several studies only involved a small number of patients, and there are not many studies available for some comparisons.

Extraction of water bodies from high-resolution remote sensing imagery based on a deep semantic segmentation network.

The precise delineation of urban aquatic features is of paramount importance in scrutinizing water resources, monitoring floods, and devising water management strategies. Addressing the challenge of indistinct boundaries and the erroneous classification of shadowed regions as water in high-resolution remote sensing imagery, we introduce WaterDeep, which is a novel deep learning framework inspired by the DeepLabV3 + architecture and an innovative fusion mechanism for high- and low-level features. This methodology first creates a comprehensive dataset of high-resolution remote sensing images, then progresses through the Xception baseline network for low-level feature extraction, and harnesses densely connected Atrous Spatial Pyramid Pooling (ASPP) modules to assimilate multi-scale data into sophisticated high-level features. Subsequently, the network decoder amalgamates the elemental and intricate features and applies dual-line interpolation to the amalgamated dataset to extract aqueous formations from the remote images. Experimental evidence substantiates that WaterDeep outperforms its existing deep learning counterparts, achieving a stellar overall accuracy of 99.284%, FWIoU of 95.58%, precision of 97.562%, recall of 95.486%, and F1 score of 96.513%. It also excels in the precise demarcation of edges and the discernment of shadows cast by urban infrastructure. The superior efficacy of the proposed method in differentiating water bodies in complex urban environments has significant practical applications in real-world contexts.

Comprehensive evaluation for the sustainable development of fresh agricultural products logistics enterprises based on combination empowerment-TOPSIS method.

To solve the problems of environmental pollution and resource waste caused by the rapid development of cold chain logistics of fresh agricultural products and improve the competitiveness of logistics enterprises in the market, a performance evaluation method of cold chain logistics enterprises based on the combined empowerment-TOPSIS was proposed. Firstly, from the five dimensions of cold supply chain capacity, service quality, economic efficiency, informatization degree and development ability, a comprehensive evaluation system of logistics enterprises' sustainable development is constructed, which consists of 16 indicators, such as storage and preservation capacity, distribution accuracy, and equipment input rate. Then, G1 method and entropy weight method are used to calculate the subjective and objective weights of the evaluation indicators, and the combined weights are calculated with the objective of minimizing the deviation of the subjective and objective weighted attributes. Finally, the TOPSIS method is used to calculate the comprehensive evaluation indicators. The results show that the established performance evaluation model can effectively evaluate the performance of fresh agricultural products logistics enterprises and provide theoretical basis for enterprise logistics management.