[Evolution analysis of diagnosis and treatment plans of corona virus disease 2019 based on text mining].

In order to understand the evolution of the diagnosis and treatment plans of corona virus disease 2019 (COVID-19), and provide convenience for medical staff in actual diagnosis and treatment, this paper uses the 9 diagnosis and treatment plans of COVID-19 issued by the National Health Commission during the period from January 26, 2020 to August 19, 2020 as research data to perform comparative analysis and visual analysis. Based on text mining, this paper obtained the text similarity and summarized its evolution law by expressing and measuring the similarity of the overall diagnosis and treatment plans of COVID-19 and the same modules, which provides reference for clinical diagnosis and treatment practice and other diagnosis and treatment plan formulation.

A weighted distance-based dynamic ensemble regression framework for gastric cancer survival time prediction.

Accurate prediction of gastric cancer patient survival time is essential for clinical decision-making. However, unified static models lack specificity and flexibility in predictions owing to the varying survival outcomes among gastric cancer patients. We address these problems by using an ensemble learning approach and adaptively assigning greater weights to similar patients to make more targeted predictions when predicting an individual's survival time. We treat these problems as regression problems and introduce a weighted dynamic ensemble regression framework. To better identify similar patients, we devise a method to measure patient similarity, considering the diverse impacts of features. Subsequently, we use this measure to design both a weighted K-means clustering method and a fuzzy K-means sampling technique to group patients and train corresponding base regressors. To achieve more targeted predictions, we calculate the weight of each base regressor based on the similarity between the patient to be predicted and the patient clusters, culminating in the integration of the results. The model is validated on a dataset of 7791 patients, outperforming other models in terms of three evaluation metrics, namely, the root mean square error, mean absolute error, and the coefficient of determination. The weighted dynamic ensemble regression strategy can improve the baseline model by 1.75%, 2.12%, and 13.45% in terms of the three respective metrics while also mitigating the imbalanced survival time distribution issue. This enhanced performance has been statistically validated, even when tested on six public datasets with different sizes. By considering feature variations, patients with distinct survival profiles can be effectively differentiated, and the model predictive performance can be enhanced. The results generated by our proposed model can be invaluable in guiding decisions related to treatment plans and resource allocation. Furthermore, the model has the potential for broader applications in prognosis for other types of cancers or similar regression problems in various domains.

Mechanism and anti-corrosion measures of carbon dioxide corrosion in CCUS: A review.

Carbon capture, utilization, and storage (CCUS) technology is widely recognized as a key solution for mitigating global climate change. Consequently, it has received significant attention from countries worldwide. However, carbon dioxide corrosion poses a significant challenge to CCUS and represents a bottleneck to the large-scale development and application of this technology. To mitigate this issue, this review starts with a discussion of corrosion problems in CCUS. Later, the fundamentals of the carbon dioxide corrosion mechanism are introduced. Then, the influences of various factors that affect the corrosion are highlighted, such as water content, pH, flow rate, etc. Afterward, we summarize the commonly used methods for corrosion protection, with a particular focus on inhibitor, given their eco-friendly and effective nature. Lastly, challenges and prospects are discussed to motivate future studies on developing novel, high-performance green inhibitor and studying the corresponding protection mechanisms, hoping to make some contributions to carbon emission reduction.