Benefit from a high store visiting cost in an omnichannel with BOPS.

Omnichannel sales surge in the coronavirus pandemic. This paper establishes an analytical model to study when a firm can benefit from implementing the omnichannel strategy of buy-online and pick-up in-store (BOPS), where the market characteristics are captured by the two-dimensional heterogeneity of product valuation and online waiting cost. The increase in the store visiting cost will reduce BOPS consumers' willingness to pay, but it will also strengthen the encroachment of BOPS on traditional dual-channel. The results show that the firm can benefit from the BOPS strategy when the store visiting cost is relatively high. This well explains the rapid development of the omnichannel with BOPS because of a high store visiting cost during COVID-19. Furthermore, sharply contrasting to the traditional dual-channel sales in which a higher store visiting cost always hurts the firm, the profit under BOPS can be nonmonotonic in the store visiting cost and the firm can benefit from a higher store visiting cost. Specifically, the combination of cross-selling effect, BOPS encroachment effect and BOPS expansion reduction effect associated with the store visiting cost can result in a U-shaped or inverse U-shaped BOPS profit. In addition, introducing BOPS motivates the firm to either increase or decrease the optimal price, conditional on the store visiting cost. For consumers, online and offline consumers can also indirectly benefit from the BOPS strategy, though they may not enjoy the BOPS service.

Semantic segmentation of COVID-19 lesions with a multiscale dilated convolutional network.

Automatic segmentation of infected lesions from computed tomography (CT) of COVID-19 patients is crucial for accurate diagnosis and follow-up assessment. The remaining challenges are the obvious scale difference between different types of COVID-19 lesions and the similarity between the lesions and normal tissues. This work aims to segment lesions of different scales and lesion boundaries correctly by utilizing multiscale and multilevel features. A novel multiscale dilated convolutional network (MSDC-Net) is proposed against the scale difference of lesions and the low contrast between lesions and normal tissues in CT images. In our MSDC-Net, we propose a multiscale feature capture block (MSFCB) to effectively capture multiscale features for better segmentation of lesions at different scales. Furthermore, a multilevel feature aggregate (MLFA) module is proposed to reduce the information loss in the downsampling process. Experiments on the publicly available COVID-19 CT Segmentation dataset demonstrate that the proposed MSDC-Net is superior to other existing methods in segmenting lesion boundaries and large, medium, and small lesions, and achieves the best results in Dice similarity coefficient, sensitivity and mean intersection-over-union (mIoU) scores of 82.4%, 81.1% and 78.2%, respectively. Compared with other methods, the proposed model has an average improvement of 10.6% and 11.8% on Dice and mIoU. Compared with the existing methods, our network achieves more accurate segmentation of lesions at various scales and lesion boundaries, which will facilitate further clinical analysis. In the future, we consider integrating the automatic detection and segmentation of COVID-19, and conduct research on the automatic diagnosis system of COVID-19.