Abstract: The methods and systems provided can automatically determine an Arteriolar-to-Venular diameter Ratio, AVR, in blood vessels, such as retinal blood vessels and other blood vessels in vertebrates. The AVR is an important predictor of increases in the risk for stroke, cerebral atrophy, cognitive decline, and myocardial infarct.

Abstract: A method for evaluating the reliability of a sealing structure in a multi-failure mode based on an Adaboost algorithm. The Adaboost algorithm is adopted to carry out a classification iterative training on the seal ring failure related data of a small sample until a classification error of set classifier meets a precision requirement; then the failure probability of the sealing structure is calculated under the fluctuation condition of related parameters by adopting the important sampling method, and further the reliability of the sealing structure is evaluated in the multi-failure mode. The present invention solves the problems of long time consumption and complex calculation process of reliability evaluation in multi-failure mode of the complex structure.

Abstract: The methods and systems provided can automatically determine an Arteriolar-to-Venular diameter Ratio, AVR, in blood vessels, such as retinal blood vessels and other blood vessels in vertebrates. The AVR is an important predictor of increases in the risk for stroke, cerebral atrophy, cognitive decline, and myocardial infarct.

Abstract: This invention relates to a robust optimal design method for photovoltaic cells. Firstly, the deterministic optimal model is established, which is solved by Monte Carlo method to obtain the maximum output power value of optimization objective and its corresponding design variable value, and then the design variable value obtained from deterministic optimization is deemed as the initial point of the mean value of the robust optimal design variable. Later, the robust optimal model is solved by Monte Carlo method in order to obtain the mean value of design variable, and then appropriate materials and manufacturing techniques are selected for corresponding photovoltaic components according to the design variable obtained, so as to achieve the robust optimal design of photovoltaic cells. In fact, this invention improves the output stability and reliability of photovoltaic cells.

Abstract: This invention relates to a robust optimal design method for photovoltaic cells. Firstly, the deterministic optimal model is established, which is solved by Monte Carlo method to obtain the maximum output power value of optimization objective and its corresponding design variable value, and then the design variable value obtained from deterministic optimization is deemed as the initial point of the mean value of the robust optimal design variable. Later, the robust optimal model is solved by Monte Carlo method in order to obtain the mean value of design variable, and then appropriate materials and manufacturing techniques are selected for corresponding photovoltaic components according to the design variable obtained, so as to achieve the robust optimal design of photovoltaic cells. In fact, this invention improves the output stability and reliability of photovoltaic cells.

Abstract: A method for evaluating the reliability of a sealing structure in a multi-failure mode based on an Adaboost algorithm. The Adaboost algorithm is adopted to carry out a classification iterative training on the seal ring failure related data of a small sample until a classification error of set classifier meets a precision requirement; then the failure probability of the sealing structure is calculated under the fluctuation condition of related parameters by adopting the important sampling method, and further the reliability of the sealing structure is evaluated in the multi-failure mode. The present invention solves the problems of long time consumption and complex calculation process of reliability evaluation in multi-failure mode of the complex structure.

Abstract: The methods and systems provided can automatically determine an Arteriolar-to-Venular diameter Ratio, AVR, in blood vessels, such as retinal blood vessels and other blood vessels in vertebrates. The AVR is an important predictor of increases in the risk for stroke, cerebral atrophy, cognitive decline, and myocardial infarct.

Abstract: The methods and systems provided can automatically determine an Arteriolar-to-Venular diameter Ratio, AVR, in blood vessels, such as retinal blood vessels and other blood vessels in vertebrates. The AVR is an important predictor of increases in the risk for stroke, cerebral atrophy, cognitive decline, and myocardial infarct.

Abstract: The methods and systems provided can automatically determine an Arteriolar-to-Venular diameter Ratio, AVR, in blood vessels, such as retinal blood vessels and other blood vessels in vertebrates. The AVR is an important predictor of increases in the risk for stroke, cerebral atrophy, cognitive decline, and myocardial infarct.