Predicting sales and cross-border e-commerce supply chain management using artificial neural networks and the Capuchin search algorithm.

E-commerce provides a large selection of goods for sale and purchase, which promotes regular transactions and commodity flows. Efficient distribution of goods and precise estimation of customer wants are essential for cost reduction. In order to improve supply chain efficiency in the context of cross-border e-commerce, this article combines machine learning approaches with the Internet of Things. The suggested approach consists of two main stages. Order prediction is done in the first step to determine how many orders each merchant is expected to get in the future. In the second phase, allocation operations are conducted and resources required for each retailer are supplied depending on their needs and inventory, taking into account each store's inventory as well as the anticipated sales level. This suggested approach makes use of a weighted mixture of neural networks to anticipate sales orders. The Capuchin Search Algorithm (CapSA) is used in this weighted combination to concurrently enhance the learning and ensemble performance of models. This indicates that an effort is made to reduce the local error of the learning model at the model level via model weight adjustments and neural network configuration. To guarantee more accurate output from the ensemble model, the best weight for each individual component is found at the ensemble model level using the CapSA method. This method yields the ensemble model's final output in the form of weighted averages by choosing suitable weight values. With a Root Mean Squared Error of 2.27, the suggested technique has successfully predicted sales based on the acquired findings, showing a minimum decrease of 2.4 in comparison to the comparing methodologies. Additionally, the suggested method's strong performance is shown by the fact that it was able to minimize the Mean Absolute Percentage Error by 14.67 when compared to other comparison approaches.

Therapeutic application of circular RNA aptamers in a mouse model of psoriasis.

Efforts to advance RNA aptamers as a new therapeutic modality have been limited by their susceptibility to degradation and immunogenicity. In a previous study, we demonstrated synthesized short double-stranded region-containing circular RNAs (ds-cRNAs) with minimal immunogenicity targeted to dsRNA-activated protein kinase R (PKR). Here we test the therapeutic potential of ds-cRNAs in a mouse model of imiquimod-induced psoriasis. We find that genetic supplementation of ds-cRNAs leads to inhibition of PKR, resulting in alleviation of downstream interferon-α and dsRNA signals and attenuation of psoriasis phenotypes. Delivery of ds-cRNAs by lipid nanoparticles to the spleen attenuates PKR activity in examined splenocytes, resulting in reduced epidermal thickness. These findings suggest that ds-cRNAs represent a promising approach to mitigate excessive PKR activation for therapeutic purposes.