Two new regularization algorithms for solving the first-kind Volterra integral equation, which describes the pressure-rate deconvolution problem in well test data interpretation, are developed in this paper. The main ...Two new regularization algorithms for solving the first-kind Volterra integral equation, which describes the pressure-rate deconvolution problem in well test data interpretation, are developed in this paper. The main features of the problem are the strong nonuniform scale of the solution and large errors (up to 15%) in the input data. In both algorithms, the solution is represented as decomposition on special basic functions, which satisfy given a priori information on solution, and this idea allow us significantly to improve the quality of approximate solution and simplify solving the minimization problem. The theoretical details of the algorithms, as well as the results of numerical experiments for proving robustness of the algorithms, are presented.展开更多
Objective: Challenges remain in current practices of colorectal cancer(CRC) screening, such as low compliance,low specificities and expensive cost. This study aimed to identify high-risk groups for CRC from the genera...Objective: Challenges remain in current practices of colorectal cancer(CRC) screening, such as low compliance,low specificities and expensive cost. This study aimed to identify high-risk groups for CRC from the general population using regular health examination data.Methods: The study population consist of more than 7,000 CRC cases and more than 140,000 controls. Using regular health examination data, a model detecting CRC cases was derived by the classification and regression trees(CART) algorithm. Receiver operating characteristic(ROC) curve was applied to evaluate the performance of models. The robustness and generalization of the CART model were validated by independent datasets. In addition, the effectiveness of CART-based screening was compared with stool-based screening.Results: After data quality control, 4,647 CRC cases and 133,898 controls free of colorectal neoplasms were used for downstream analysis. The final CART model based on four biomarkers(age, albumin, hematocrit and percent lymphocytes) was constructed. In the test set, the area under ROC curve(AUC) of the CART model was 0.88 [95%confidence interval(95% CI), 0.87-0.90] for detecting CRC. At the cutoff yielding 99.0% specificity, this model’s sensitivity was 62.2%(95% CI, 58.1%-66.2%), thereby achieving a 63-fold enrichment of CRC cases. We validated the robustness of the method across subsets of test set with diverse CRC incidences, aging rates, genders ratio, distributions of tumor stages and locations, and data sources. Importantly, CART-based screening had the higher positive predictive value(1.6%) than fecal immunochemical test(0.3%).Conclusions: As an alternative approach for the early detection of CRC, this study provides a low-cost method using regular health examination data to identify high-risk individuals for CRC for further examinations. The approach can promote early detection of CRC especially in developing countries such as China, where annual health examination is popular but regular CRC-specific screening is rare.展开更多
This investigation deals with the intelligent system for parallel fault-tolerant diagnostic tests construction. A modified parallel algorithm for fault-tolerant diagnostic tests construction is proposed. The algorithm...This investigation deals with the intelligent system for parallel fault-tolerant diagnostic tests construction. A modified parallel algorithm for fault-tolerant diagnostic tests construction is proposed. The algorithm is allowed to optimize processing time on tests construction. A matrix model of data and knowledge representation, as well as various kinds of regularities in data and knowledge are presented. Applied intelligent system for diagnostic of mental health of population which is developed with the use of intelligent system for parallel fault-tolerant DTs construction is suggested.展开更多
Unified modeling language （UML） is a powerful graphical modeling language with intuitional meaning. It provides various diagrams to depict system characteristics and complex environment from different viewpoints and...Unified modeling language （UML） is a powerful graphical modeling language with intuitional meaning. It provides various diagrams to depict system characteristics and complex environment from different viewpoints and different application layers. UML-based software development and modeling environments have been widely accepted in industry, including areas in which safety is an important issue such as spaceflight, defense, automobile, etc. To ensure and improve software quality becomes a main concern in the field. As one of the key techniques for software quality, software testing can effectively detect system faults. UML based software testing based is an important research direction in software engineering. The key to software testing is the generation of test cases. This dissertation studies an approach to generating test cases from UML statecharts.展开更多
文摘Two new regularization algorithms for solving the first-kind Volterra integral equation, which describes the pressure-rate deconvolution problem in well test data interpretation, are developed in this paper. The main features of the problem are the strong nonuniform scale of the solution and large errors (up to 15%) in the input data. In both algorithms, the solution is represented as decomposition on special basic functions, which satisfy given a priori information on solution, and this idea allow us significantly to improve the quality of approximate solution and simplify solving the minimization problem. The theoretical details of the algorithms, as well as the results of numerical experiments for proving robustness of the algorithms, are presented.
基金supported by funding from Beijing Municipal Science & Technology Commission, Clinical Application and Development of Capital Characteristic (No. Z161100000516003)National Natural Science Foundation of China (No. 31871266)
文摘Objective: Challenges remain in current practices of colorectal cancer(CRC) screening, such as low compliance,low specificities and expensive cost. This study aimed to identify high-risk groups for CRC from the general population using regular health examination data.Methods: The study population consist of more than 7,000 CRC cases and more than 140,000 controls. Using regular health examination data, a model detecting CRC cases was derived by the classification and regression trees(CART) algorithm. Receiver operating characteristic(ROC) curve was applied to evaluate the performance of models. The robustness and generalization of the CART model were validated by independent datasets. In addition, the effectiveness of CART-based screening was compared with stool-based screening.Results: After data quality control, 4,647 CRC cases and 133,898 controls free of colorectal neoplasms were used for downstream analysis. The final CART model based on four biomarkers(age, albumin, hematocrit and percent lymphocytes) was constructed. In the test set, the area under ROC curve(AUC) of the CART model was 0.88 [95%confidence interval(95% CI), 0.87-0.90] for detecting CRC. At the cutoff yielding 99.0% specificity, this model’s sensitivity was 62.2%(95% CI, 58.1%-66.2%), thereby achieving a 63-fold enrichment of CRC cases. We validated the robustness of the method across subsets of test set with diverse CRC incidences, aging rates, genders ratio, distributions of tumor stages and locations, and data sources. Importantly, CART-based screening had the higher positive predictive value(1.6%) than fecal immunochemical test(0.3%).Conclusions: As an alternative approach for the early detection of CRC, this study provides a low-cost method using regular health examination data to identify high-risk individuals for CRC for further examinations. The approach can promote early detection of CRC especially in developing countries such as China, where annual health examination is popular but regular CRC-specific screening is rare.
文摘This investigation deals with the intelligent system for parallel fault-tolerant diagnostic tests construction. A modified parallel algorithm for fault-tolerant diagnostic tests construction is proposed. The algorithm is allowed to optimize processing time on tests construction. A matrix model of data and knowledge representation, as well as various kinds of regularities in data and knowledge are presented. Applied intelligent system for diagnostic of mental health of population which is developed with the use of intelligent system for parallel fault-tolerant DTs construction is suggested.
文摘Unified modeling language （UML） is a powerful graphical modeling language with intuitional meaning. It provides various diagrams to depict system characteristics and complex environment from different viewpoints and different application layers. UML-based software development and modeling environments have been widely accepted in industry, including areas in which safety is an important issue such as spaceflight, defense, automobile, etc. To ensure and improve software quality becomes a main concern in the field. As one of the key techniques for software quality, software testing can effectively detect system faults. UML based software testing based is an important research direction in software engineering. The key to software testing is the generation of test cases. This dissertation studies an approach to generating test cases from UML statecharts.
