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Author Correction: Non-targeted LC–MS metabolomics reveals serum metabolites for high-altitude adaptation in Tibetan donkeys
Cu<sup>δ+</sup> Site-Enhanced Adsorption and Crown Ether-Reconfigured Interfacial D<sub>2</sub>O Promote Electrocatalytic Dehalogenative Deuteration
Optimization design of cross border intelligent marketing management model based on multi layer perceptron-grey wolf optimization convolutional neural network
Abstract The cross-border intelligent marketing algorithm based on traditional linear models is relatively single in information feature extraction, making it difficult to effectively handle complex scenarios containing a large amount of implicit information in users and markets, resulting in poor personalized marketing effectiveness. To address this issue, this article proposes a cross-border intelligent marketing model that integrates rating information and user labels using a multi-layer perceptron grey wolf optimization and convolutional neural network (MLP-GWO-CNN). This model extracts implicit high-order information through nonlinear methods and can handle complex and sparse marketing data. Firstly, a dual path deep network structure was designed, in which one path was modeled using a multi-layer perceptron (MLP) to extract user interest features based on historical interaction ratings; Another path utilizes Convolutional Neural Networks (CNN) to extract semantic features from user label information and construct item feature representations. In response to the sensitivity of MLP algorithm to initial values and its tendency to fall into local optima, this paper uses GWO algorithm to optimize MLP. Next, the latent feature vectors generated by MLP and CNN are fused in the output layer to generate the final predictive marketing strategy last. Experiments were conducted using a real cross-border e-commerce dataset, and the results showed that compared with traditional recommendation algorithms, the MLP-GWO-CNN model proposed in this paper performs better in utilizing user tag information, effectively improving the accuracy and personalization of marketing recommendations. The accuracy of the model is over 89%, and the recall rate is over 90%.
Systemic inflammation index is a predictive and prognostic factor in patients with liposarcoma or leiomyosarcoma treated with trabectedin
Fractional Spinon Quasiparticles in Open-Shell Triangulene Spin-1/2 Chains
Enhanced nighttime nail detection using improved YOLOv5 for precision road safety
Periodic detection and disinfection maintenance of dental unit waterlines in dental simulation head model laboratories
Abstract Dental simulation head model laboratories are crucial for clinical simulation training for stomatological students, yet the maintenance of their dental unit waterlines (DUWLs) has been overlooked. This study investigated water contamination in DUWLs within these laboratories and proposed solutions. Water samples were collected from 12 dental chairs in three laboratories at three time points: the beginning, middle, and end of the semester. At the start of the semester, severe contamination was observed, with colony counts of 11,586 $$\:\pm\:$$ 1715 CFU/ml for high-speed handpieces and 5375 $$\:\pm\:$$ 874 CFU/ml for three ways syringes. As the semester progressed, colony counts gradually decreased but remained above clinical thresholds. Both 20 mg/L organochlorine disinfectant and 20 mg/L chlorine dioxide were effective in reducing bacterial contamination below standard ranges three days post-disinfection. Microbial diversity analysis revealed Proteobacteria and Bacteroidota as the dominant bacterial phyla, with Ascomycota as the dominant fungal phylum. Potentially pathogenic bacteria such as Pseudomonas, Burkholderia-Caballeronia-Paraburkholderia, Ralstonia, Mycobacterium, Legionella, Paenibacillus, Streptomyces, Acinetobacter, and Prevotella, as well as fungi like Fusarium and Penicillium, were detected. Therefore, urgent attention is needed to address DUWL contamination in dental laboratories, and it is recommended to disinfect DUWLs using either 20 mg/L organochlorine disinfectant or 20 mg/L chlorine dioxide every three days.