The present paper studied the effects of calcination temperatures (200–800 °C) on the appearance, mineral composition, and active SiO2 content in attapulgite and investigated the effects of attapulgite before and after calcination on the chemically bonded water content, the degree of reaction of cement paste, and the mechanical properties such as the flexural strength, compressive strength, and splitting-tensile strength of cement mortar. The results indicate that the calcination temperature changes the mineral composition of attapulgite, thereby affecting the hydration activity of cement-based materials. The attapulgite calcined at 500 °C (AT500) has the best enhancement on the hydration activity of cement-based materials. The calcination at 500 °C is most beneficial to the dissolution of SiO2, and the content of SiO2 reaches 20.96%. The contents of chemically bonded water in the samples incorporated with calcined attapulgite reduced and that of the samples incorporated with AT500 at 28 d is the same as that of the control group. The reaction degree of AT500 is 78.61% at 28 days. Calcined attapulgite clay can reduce the energy consumption of the cement industry and promote the sustainable development of attapulgite clay.

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ObjectiveTo compare the performance of three machine learning algorithms with the tumor, node, and metastasis (TNM) staging system in survival prediction and validate the individual adjuvant treatment recommendations plan based on the optimal model.MethodsIn this study, we trained three machine learning madel and validated 3 machine learning survival models-deep learning neural network, random forest and cox proportional hazard model- using the data of patients with stage-al3 NSCLC patients who received resection surgery from the National Cancer Institute Surveillance, Epidemiology, and End Results (SEER) database from 2012 to 2017,the performance of survival predication from all machine learning models were assessed using a concordance index (c-index) and the averaged c-index is utilized for cross-validation. The optimal model was externally validated in an independent cohort from Shaanxi Provincial People’s Hospital. Then we compare the performance of the optimal model and TNM staging system. Finally, we developed a Cloud-based recommendation system for adjuvant therapy to visualize survival curve of each treatment plan and deployed on the internet.ResultsA total of 4617 patients were included in this study. The deep learning network performed more stably and accurately in predicting stage-iii NSCLC resected patients survival than the random survival forest and Cox proportional hazard model on the internal test dataset (C-index=0.834 vs. 0.678 vs. 0.640) and better than TNM staging system (C-index=0.820 vs. 0.650) in the external validation. The individual patient who follow the reference from recommendation system had superior survival compared to those who did not. The predicted 5-year-survival curve for each adjuvant treatment plan could be accessed in the recommender system via the browser.ConclusionDeep learning model has several advantages over linear model and random forest model in prognostic predication and treatment recommendations. This novel analytical approach may provide accurate predication on individual survival and treatment recommendations for resected Stage-iii NSCLC patients.