Aims:The aims are to describe the key components of family integrated care intervention for preterm infants in the neonatal intensive care unit (NICU) and assess the impact on breastfeeding outcomes for those infants.Design: A scoping review.
Methods:We conducted a systematic study search based on the databases, including PubMed, Scopus, Cochrane, Web of Science, MEDLINE, CINAHL, CNKI and Wanfang Database in December 2022. The search time ranged from database establishment to 31 December 2022. Papers by manual searching were also listed on the references. We adopted Joanna Briggs Institute Reviewer's Manual methodology and followed the PRISMA guidelines for Scoping Reviews (PRISMA-ScR) to conduct the review.Two independent reviewers filtered the papers, extracted data and synthesized the findings. A table was used to extract data and synthesize results.Results: After systematic searching, 11 articles that implemented family integrated care (FIcare) were finally included in this scoping review. By analysing the implementation of this nursing model, we identified seven main components: NICU staff training, parent education, parent participation in infants' care, parent involvement in medical plans, peer support, NICU environmental support and mobile app for parents. Based on the extracted breastfeeding data, this scoping review concludes that family integrated care shows a positive effect on increasing breastfeeding rates at discharge. Through this scoping review, we find that family integrated care is feasible and it can support breastfeeding of preterm infants. Further studies will be needed to provide more evidence that family integrated care could facilitate breastfeeding of preterm infants. Impact: This scoping review provides evidence for the positive role of family integrated care on breastfeeding outcomes. The analysis may contribute to the implementation of family integrated care. No patient or public contribution: No further public or patient contribution was made in view of the review-based nature of the research.
Abstract. With the rapid development of earth observation technology, the remote sensing data gradually becomes the significant data resources for spatiotemporal data analysis. However, to realize the efficient management and quick real-time service of the multi-source, multi-scale and multi-spectral is the key problem for the data management units. This paper brings up a novel technology system and resolution for online and real-time service of the massive remote sensing images by innovatively developing a data management model based on an extensible discrete global grid, inventing the grid-based search and dispatch method for remote sensing big data, firstly building a parameter-driven dynamic map service mode for multi -source, multi-scale and multi-spectral remote sensing big data. This method has greatly improved the efficiency for data management units, such as the Chinese government, corporations etc. and has produced a lot of social and economic benefits.
Abstract. With the popularization of geographic information data applications, new requirements are put forward for the rapid update of vector data. The overall update of vector data is expensive and time-consuming. Therefore, we need to use various technical means to intelligently sense the changes of geographical entities and realize the active monitoring of the changes of vector data. In this paper, the building layers that are closely related to human beings and gradually become active geographic entities with the urbanization process are selected to monitor the location of the vector data to be updated. This paper first trains the model using single building layer tiles and image tiles. Then, based on the trained model, the location where the building layer tiles are inconsistent with the image tiles is found in the area to be detected. According to different situations, we set different thresholds to find the position to be updated in the vector data. After manual discrimination, the overall accuracy of the method proposed in this paper is 89%. This paper provides new insights into the update discovery of vector data. In addition, by further improving the boundary accuracy of extracted buildings, the extracted building results can be directly applied to the fusion update of vector data.
Lightweight block ciphers are normally used in low-power resource-constrained environments, while providing reliable and sufficient security. Therefore, it is important to study the security and reliability of lightweight block ciphers. SKINNY is a new lightweight tweakable block cipher. In this paper, we present an efficient attack scheme for SKINNY-64 based on algebraic fault analysis. The optimal fault injection location is given by analyzing the diffusion of a single-bit fault at different locations during the encryption process. At the same time, by combining the algebraic fault analysis method based on S-box decomposition, the master key can be recovered in an average time of 9 s using one fault. To the best of our knowledge, our proposed attack scheme requires fewer faults, is faster to solve, and has a higher success rate than other existing attack methods.
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