<p style='text-indent:20px;'>More and more garment enterprises begin to pay attention to the importance of recycling, take the corresponding recycling strategy to recycle garment products and remanufacture, forming a closed-loop supply chain (CLSC). In reality, recycling is a complex system, the recycling strategy of clothing brands will not only affect the reverse channel of closed-loop supply chain, but also affect the consumer demand of forward channel, and then affect the profit of supply chain. In order to solve this problem, we propose a CLSC composed of a manufacturer, a retailer and a collector, establish three different Stackelberg leadership models, and derive the optimal recycling strategy. Our results show that consumers' sensitivity to the recycling price will affect the optimal decision of supply chain members. The increase of the recycling market is not always beneficial to the profits of supply chain members. By comparing the profits of the three models, it is found that the retailer leadership model is the most effective scenario of CLCS. The results of this paper provide a reference for garment enterprises to formulate recycling strategies.</p>

The process of garment production has always been a black box. The production time of different clothing is different and has great changes, thus managers cannot make a production plan accurately. With the world entering the era of industry 4.0 and the accumulation of big data, machine learning can provide services for the garment manufacturing industry. The production cycle time is the key to control the production process. In order to predict the production cycle time more accurately and master the production process in the garment manufacturing process, a neural network model of production cycle time prediction is established in this paper. Using a trained neural network to predict the production cycle time, the overall error of 6 groups is within 5%, and that of 3 groups is between 5% and 10%. Therefore, this neural network can be used to predict the future production cycle time and predict the overall production time of clothing.

Since early 2019, the Lunar Penetrating Radar (LPR) onboard Chang'e‐4 (CE‐4)’s Yutu‐2 rover has been gathering data relating to the subsurface structure of the Von Kármán crater within the South Pole‐Aitken Basin (SPA) on the lunar farside. Low‐frequency radar data have the potential of carrying geological information of about 300 m worth of strata below the traversed path. Forty‐two days’ data have revealed a bifurcated structure within the layered structure beneath the CE‐4 surveying area for the first time, affecting the overlying reflectors between 90 and 310 m. This study suggests that, based on the morphological characteristics, thickness, depth (timing sequence) and direction of the newly found structure, its origin might be linked to the deposition of ejecta from the Schrödinger impact. The local stratigraphy is interpreted as consisting of distinct geological layers, corresponding to the superposition of ejecta from different impact craters, paleo‐regolith, and basaltic lava flows.

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