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AbstractNon-traditional sport event has received much attention in recent years. The purpose of this study was to investigate the role of sport event image and attitudes toward event in predicting satisfaction with event and behavioral intentions. A sample of 182 obstacle course runners in Spain was surveyed to test the research model using structural equation modeling. The results showed that event characteristics, which is one of the dimensions of sport event image, and participants’ attitudes toward event positively influenced satisfaction, which in turn, together with attitudes, positively influenced behavioral intentions for repeat visits and recommendations. However, the destination characteristics dimension of sport event image had no impact on attitudes and satisfaction. Finally, satisfaction had a mediating effect on the relationships between event characteristics and behavioral intentions, and attitudes and behavioral intentions. The theoretical and practical implications of these results are discussed. [ABSTRACT FROM AUTHOR]

Electronic components are the main components of PCBs (printed circuit boards), so the detection and classification of ECs (electronic components) is an important aspect of recycling used PCBs. However, due to the variety and quantity of ECs, traditional target detection methods for EC classification still have problems such as slow detection speed and low performance, and the accuracy of the detection needs to be improved. To overcome these limitations, this study proposes an enhanced YOLO (you only look once) network (EC-YOLOv7) for detecting EC targets. The network uses ACmix (a mixed model that enjoys the benefits of both self-attention and convolution) as a substitute for the 3 × 3 convolutional modules in the E-ELAN (Extended ELAN) architecture and implements branch links and 1 × 1 convolutional arrays between the ACmix modules to improve the speed of feature retrieval and network inference. Furthermore, the ResNet-ACmix module is engineered to prevent the leakage of function data and to minimise calculation time. Subsequently, the SPPCSPS (spatial pyramid pooling connected spatial pyramid convolution) block has been improved by replacing the serial channels with concurrent channels, which improves the fusion speed of the image features. To effectively capture spatial information and improve detection accuracy, the DyHead (the dynamic head) is utilised to enhance the model's size, mission, and sense of space, which effectively captures spatial information and improves the detection accuracy. A new bounding-box loss regression method, the WIoU-Soft-NMS method, is finally suggested to facilitate prediction regression and improve the localisation accuracy. The experimental results demonstrate that the enhanced YOLOv7 net surpasses the initial YOLOv7 model and other common EC detection methods. The proposed EC-YOLOv7 network reaches a mean accuracy (mAP@0.5) of 94.4% on the PCB dataset and exhibits higher FPS compared to the original YOLOv7 model. In conclusion, it can significantly enhance high-density EC target recognition. [ABSTRACT FROM AUTHOR]

Background: Dysregulation of microRNAs (miRNAs) is actively involved in the pathogenesis and tumorigenicity of colorectal cancer (CRC). miR-296 was found to play either oncogenic or tumor suppressive role in human cancers. However, the status of miR-296 and its function in CRC remain unknown.Methods: The expression of miR-296 was confirmed by qRT-PCR in CRC tissues and cells, and its level was altered by corresponding miRNA vectors. Wound healing and Transwall assays were performed to detect the migration and invasion of CRC cells. The levels of proteins were measured using immunoblotting, immunohistochemistry and immunofluorescence.Results: Underexpression of miR-296 was disclosed in CRC tissues and cells. Its decreased level was evidently correlated with adverse clinical parameters and poor prognosis of CRC patients. In vitro experiments indicated that miR-296 inhibited CRC cell migration and invasion. Mechanically, miR-296 inhibited the epithelial-mesenchymal transition (EMT) of CRC cells. A negative correlation between miR-296 and S100A4 expression was observed in CRC tissues. Luciferase reporter assays indicated that miR-296 inversely regulated the luciferase activity of 3'-UTR of S100A4. Herein, S100A4 was found to be a downstream molecule of miR-296 in CRC. Furthermore, S100A4 mediated the anti-metastatic effects of miR-296 on EMT, migration and invasion of CRC cells.Conclusions: miR-296 functions as an anti-metastatic factor mainly by suppressing S100A4 in CRC. It potentially acts as a prognostic predictor and a drug-target for CRC patients. [ABSTRACT FROM AUTHOR]