Study on mechanical properties of polymer cement slurry-coal interface transition zone

Advanced deep hole grouting reinforcement technology is proposed. The effects of VAE emulsion on cement-based materials were studied. The effects of VAE emulsion on the bonding strength between cement based materials and coal and the compressive strength of cement based materials were studied by axial tensile test and cube compression test. The microstructure characteristics of VAE modified cementitious materials and coal interface transition zone were analyzed by scanning electron microscopy. The results showed that the water cement ratio of composite grout showed a trend of first decrease and then increase with the increase of aggregate cement ratio. VAE emulsion increased the interfacial bond strength between slurry and coal and the compressive strength of slurry consolidated coal block. The porosity of cement slurry decreased with the addition of VAE emulsion, and the compactness of the effective layer structure was enhanced. The VAE emulsion diffused into the ordinary cement paste to form a polymer film “tie” structure, which improved the composition structure of the cement based material and coal interface, thus enhancing the bond strength between the cement based material and the coal body

Deep learning techniques for imaging diagnosis and treatment of aortic aneurysm

ObjectiveThis study aims to review the application of deep learning techniques in the imaging diagnosis and treatment of aortic aneurysm (AA), focusing on screening, diagnosis, lesion segmentation, surgical assistance, and prognosis prediction.MethodsA comprehensive literature review was conducted, analyzing studies that utilized deep learning models such as Convolutional Neural Networks (CNNs) in various aspects of AA management. The review covered applications in screening, segmentation, surgical planning, and prognosis prediction, with a focus on how these models improve diagnosis and treatment outcomes.ResultsDeep learning models demonstrated significant advancements in AA management. For screening and diagnosis, models like ResNet achieved high accuracy in identifying AA in non-contrast CT scans. In segmentation, techniques like U-Net provided precise measurements of aneurysm size and volume, crucial for surgical planning. Deep learning also assisted in surgical procedures by accurately predicting stent placement and postoperative complications. Furthermore, models were able to predict AA progression and patient prognosis with high accuracy.ConclusionDeep learning technologies show remarkable potential in enhancing the diagnosis, treatment, and management of AA. These advancements could lead to more accurate and personalized patient care, improving outcomes in AA management