Alzheimer's disease (AD) is a neurodegenerative condition that causes cognitive decline over time. Because existing diagnostic approaches for AD are limited, improving upon previously established diagnostic models based on genetic biomarkers is necessary. Firstly, four AD gene expression datasets were collected from the Gene Expression Omnibus (GEO) database. Two datasets were used to establish diagnostic models, and the other two datasets were used to verify the model effect. We merged GSE5281 with GSE44771 as the training dataset and found 120 DEGs. Then, we used random forest (RF) to screen 6 key genes (KLF15, MAFF, ITPKB, SST, DDIT4, and NRXN3) as being critical for separating AD and normal samples. The weights of these key genes were measured, and a diagnostic model was created using an artificial neural network (ANN). The area under the curve (AUC) of the model is 0.953, while the accuracy is 0.914. In the final step, two validation datasets were utilized to assess AUC performance. In GSE109887, our model had an AUC of 0.854, and in GSE132903, it had an AUC of 0.810. To summarize, we successfully identified key gene biomarkers and developed a new AD diagnostic model.

In this paper, numeric simulations are performed for three dimension models built according to actual surface cleaner in airport runway rubber mark cleaning vehicle using ANSYS FLUENT software on the basis of previous research finished by the authors. After analyzing the simulated flow fields under different standoff distances between nozzle outlet and runway surface and different discharge pressures at nozzle outlet, the relationships of normal stress and shear stress at striking point to outlet pressure and standoff distance are obtained. Finally, the most appropriate discharge pressure at nozzle outlet for the studied surface cleaner model is found, and this will provide theoretical basis for future rubber mark cleaning process in airports and equipment model selection in subsequent design of airport runway rubber mark cleaning vehicles.