Banxia Houpu decoction (BXHPD) has been used to treat depression in clinical practice for centuries. However, the pharmacological mechanisms of BXHPD still remain unclear. Network Pharmacology (NP) approach was used to explore the potential molecular mechanisms of BXHPD in treating depression. Potential active compounds of BXHPD were obtained from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform Database. STRING database was used to build a interaction network between the active compounds and target genes associated with depression. The topological features of nodes were visualized and calculated. Significant pathways and biological functions were identified using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses. A total of 44 active compounds were obtained from BXHPD, and 121 potential target genes were considered to be therapeutically relevant. Pathway analysis indicated that MAPK signaling pathway, ErbB signaling pathway, HIF-1 signaling pathway and PI3K-Akt pathway were significant pathways in depression. They were mainly involved in promoting nerve growth and nutrition and alleviating neuroinflammatory conditions. The result provided some potential ways for modern medicine in the treatment of depression.

Scholars have paid considerable attention to the factors that affect the safety states of construction workers. However, only a few studies have focused on the safety assessment and security alerts of individual workers. In this study, the term ‘frequency statistics’ refers to the factors considered by domestic and foreign experts and scholars. The statistical results were combined with the interpretation of these factors to determine 22 factors that negatively influence the safety status of construction workers, which were used as the research object. The initial weight of the research results was integrated into the BackPropagation neural network, using the improved analytic hierarchy process to establish an early warning model for the unsafe status of construction workers. The mean squared error meets the requirements of the model and the prediction accuracy meets the requirements of the sample. The model can effectively provide an early warning and correct the initial weighting of the results. The early warning model was then applied to a project that involved the construction of a primary school in Suzhou. The follow-up results show that the safety status of the workers significantly improved. These results show that the early warning model was successfully used in the safety assessment to provide security alerts to individual workers. The data obtained by comprehensively considering both workers and experts are universal, unlike those obtained by considering only one of these two groups. Among the indicators, safety awareness, protection measures, and team cohesion most strongly negatively affected the safety statuses of the construction workers. The results of the early warning model combined with the sensitivity analysis are targeted and applicable in the practice of safety monitoring.

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Building a resilient and stable supply chain has become an important strategy for many countries. Studies have shown that the application of additive manufacturing (AM) technology in construction can help offset the negative impact of “black swan events” on supply chains. This study examines the construction industry based on AM technology and analyzes the impact of changes in the industry chain on the supply chains. The specific factors that affect the resilience of AM construction supply chains were identified through literature research and expert interviews, including 7 dimensions and 21 secondary indicators. An intuitionistic fuzzy analytic hierarchy process (IFAHP) evaluation model was established. Finally, an example of an AM construction manufacturer, YC Enterprise, was introduced to quantify the various factors and determine the weights. The results show that the essence of building a supply chain with AM is creating a closed-loop supply chain. The impact of AM construction manufacturers on supply chain resilience (SCR) is the most critical, followed by that of regulatory authorities and general contractors. The AM construction SCR assessment index system and evaluation method constructed in this paper have important significance in filling the gap in the quantitative evaluation of the impact of AM on supply chains.

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