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To further understand the regulatory network and molecular mechanisms of gene expression after skin burns, we performed bioinformatics analysis of gene expression profiles of skin burn samples and identified key genes associated with skin burns. The GSE8056 and GSE139028 datasets were downloaded from the Gene Expression Omnibus database for analysis and validation. The limma package was used to screen for differentially expressed genes (DEGs). Gene ontology and pathway enrichment analyses (KEGG) were then performed. Subsequently, LASSO regression analysis was performed on DEGs and a regulatory network map of skin burn-related genes was constructed. Finally, the infiltration of immune cells was calculated and coexpression network maps of immune-related key genes and skin regeneration genes were constructed. Analysis of the GSE8056 dataset showed that 432 genes were upregulated and 351 genes were downregulated. The DEGs were mainly focused on immune response and skin regeneration. Meanwhile, these two groups of pivotal genes were significantly associated with abnormal infiltration of nine immune cells. GSE139028 validation revealed that three hub genes associated with skin burn immunity were differentially expressed, except for S100A8, while only the DPT gene was differentially expressed among the seven hub genes associated with skin regeneration. In short, the effect of skin burn on patients is to regulate the expression of immune-related genes UPP1, MMP1, MMP3, and skin regeneration-related gene DPT, which may be the key target for the treatment of skin burn. [ABSTRACT FROM AUTHOR]

Given the lack of sufficient historical data for aircraft landing gear retractor systems, a model-based fault diagnosis approach is needed to overcome this data deficiency. Meanwhile, inherent uncertainties are inevitable in engineering practice, and it is a great challenge to construct a model that accurately reflects the complexity of the actual system under uncertain conditions. Due to the urgent need for reliable model-based diagnostic methods and the need to cope with inherent uncertainties, this paper proposes an improved fault diagnostic method aimed at increasing the diagnostic efficiency of the landing gear retractor system, a critical component in aircraft take-off and landing operations. Due to a lack of historical data, the model-based fault diagnosis method can solve the problem of lack of data. The proposed uncertainty method addresses the challenge of multiple sources of uncertainty by using subsystems to reduce complexity. Fault diagnosis is achieved by comparing residuals with thresholds derived from a diagnostic bond graph (DBG) model. To address the problem of limited fault data, we modeled and simulated the landing gear retractor system using AMESim®. In addition, the linear fractional transform (LFT) approach has been used to resolve parametric uncertainties, but is unable to resolve system structural uncertainties. Therefore, we also analyzed the comparative fault diagnosis results derived from the linear fractional transformation-DBG (LFT-DBG) and the subsystem-DBG approaches. The experimental results support the effectiveness of the subsystem approach in improving fault diagnosis accuracy and reliability, highlighting its potential as a viable diagnostic strategy in aerospace engineering applications. [ABSTRACT FROM AUTHOR]

In order to improve the test capability of automated test systems to deal with complex systems, the concept of a signal-oriented test system model was proposed to guide the design of test systems and the implementation of test work and to analyze the test results. First, a method for establishing signal-oriented test system model is proposed, which is based on IEEE 1641 standard, and combined with the unit under test requirements and test objectives. Second, corresponding verification methods are proposed in the mathematical domain and signal domain. Finally, a verification method based on gene expression programming algorithm is designed to verify the complex signal-oriented test system model. In addition, the battery charge regulator module of the NS2 satellite verified the effectiveness of the signal-oriented test system model. Compared with the traditional identification method, the identification accuracy of the simple system (with mathematical model) has been significantly improved; for complex systems (no mathematical model), the traditional method is difficult to work, and the gene expression programming algorithm still has a good effect. The actual verification shows that the signal-oriented test system model, which combines the advantages of the mathematical model and the physical model, can well represent the testing requirements of the complex system.