A multi-model based on radiogenomics and deep learning techniques associated with histological grade and survival in clear cell renal cell carcinoma.

Objectives: This study aims to evaluate the efficacy of multi-model incorporated by radiomics, deep learning, and transcriptomics features for predicting pathological grade and survival in patients with clear cell renal cell carcinoma (ccRCC).

Methods: In this study, data were collected from 177 ccRCC patients, including radiomics features, deep learning (DL) features, and RNA sequencing data. Diagnostic models were then created using these data through least absolute shrinkage and selection operator (LASSO) analysis.

Measurement and Modeling of Spontaneous Capillary Imbibition in Coal.

Coal is a typical dual-porosity medium. The implementation process of water invasion technology in coal is actually a process of spontaneous imbibition of external water. To obtain a model of spontaneous capillary imbibition in coal, the spontaneous imbibition of water in coal samples with different production loads is conducted experimentally.