Aspect-oriented modeling can uncover potential design faults, yet most existing work fails to achieve both separation and composition in a natural and succinct way. This study presents an aspect-oriented modeling and ...Aspect-oriented modeling can uncover potential design faults, yet most existing work fails to achieve both separation and composition in a natural and succinct way. This study presents an aspect-oriented modeling and analysis approach with hierarchical Coloured Petri Nets(HCPN). HCPN has sub-models and well-defined semantics combining a set of submodels. These two characteristics of HCPN are nicely integrated into aspect oriented modeling. Submodels are used to model aspects while the combination mechanism contributes to aspects weaving. Furthermore, the woven aspect oriented HCPN model can be simulated and analyzed by the CPN Tools. A systematic web application case study is conducted. The results show the system original properties are satisfied after weaving aspects and all design flaws are revealed. As such, the approach can support web application design and analysis in an aspect-oriented fashion concisely and effectively.展开更多
It is important to understand the process of cancer cell metastasis and some cancer characteristics that increase disease risk.Because the occurrence of the disease is caused by many factors,and the pathogenesis proce...It is important to understand the process of cancer cell metastasis and some cancer characteristics that increase disease risk.Because the occurrence of the disease is caused by many factors,and the pathogenesis process is also complicated.It is necessary to use interpretable and visual modeling methods to characterize this complex process.Machine learning techniques have demonstrated extraordinary capabilities in identifying models and extracting patterns from data to improve medical prognostic decisions.However,in most cases,it is unexplainable.Using formal methods to model can ensure the correctness and understandability of prediction decisions in a certain extent,and can well visualize the analysis process.Coloured Petri Nets(CPN)is a powerful formal model.This paper presents a modeling approach with CPN and machine learning in breast cancer,which can visualize the process of cancer cell metastasis and the impact of cell characteristics on the risk of disease.By evaluating the performance of several common machine learning algorithms,we finally choose the logistic regression algorithm to analyze the data,and integrate the obtained prediction model into the CPN model.Our method allows us to understand the relations among the cancer cell metastasis and clearly see the quantitative prediction results.展开更多
基金supported by the NSF of China under grants No. 61173048 and No. 61300041Specialized Research Fund for the Doctoral Program of Higher Education under grant No. 20130074110015+2 种基金the Fundamental Research Funds for the Central Universities under Grant No.WH1314038the Humanities and Social Science Research Planning Fund of the Education Ministry of China under grant No.15YJCZH201the Research Innovation Program of Shanghai Municipal Education Commission under grant No. 14YZ134
文摘Aspect-oriented modeling can uncover potential design faults, yet most existing work fails to achieve both separation and composition in a natural and succinct way. This study presents an aspect-oriented modeling and analysis approach with hierarchical Coloured Petri Nets(HCPN). HCPN has sub-models and well-defined semantics combining a set of submodels. These two characteristics of HCPN are nicely integrated into aspect oriented modeling. Submodels are used to model aspects while the combination mechanism contributes to aspects weaving. Furthermore, the woven aspect oriented HCPN model can be simulated and analyzed by the CPN Tools. A systematic web application case study is conducted. The results show the system original properties are satisfied after weaving aspects and all design flaws are revealed. As such, the approach can support web application design and analysis in an aspect-oriented fashion concisely and effectively.
基金This work was supported in part by the Natural Science Foundation of Shaanxi Province(No.2021JM-205)the Fundamental Research Funds for the Central Universities.
文摘It is important to understand the process of cancer cell metastasis and some cancer characteristics that increase disease risk.Because the occurrence of the disease is caused by many factors,and the pathogenesis process is also complicated.It is necessary to use interpretable and visual modeling methods to characterize this complex process.Machine learning techniques have demonstrated extraordinary capabilities in identifying models and extracting patterns from data to improve medical prognostic decisions.However,in most cases,it is unexplainable.Using formal methods to model can ensure the correctness and understandability of prediction decisions in a certain extent,and can well visualize the analysis process.Coloured Petri Nets(CPN)is a powerful formal model.This paper presents a modeling approach with CPN and machine learning in breast cancer,which can visualize the process of cancer cell metastasis and the impact of cell characteristics on the risk of disease.By evaluating the performance of several common machine learning algorithms,we finally choose the logistic regression algorithm to analyze the data,and integrate the obtained prediction model into the CPN model.Our method allows us to understand the relations among the cancer cell metastasis and clearly see the quantitative prediction results.
