提出一种输出本质安全型准阻抗源Boost电压变换器,介绍了其电路的基本组成,分析了准阻抗源网络中电感器和电容器的元件选型。该电路由准阻抗源电路和Boost斩波电路组成,然后连接电感电容(inductance and capacitance,LC)滤波电路,控制...提出一种输出本质安全型准阻抗源Boost电压变换器,介绍了其电路的基本组成,分析了准阻抗源网络中电感器和电容器的元件选型。该电路由准阻抗源电路和Boost斩波电路组成,然后连接电感电容(inductance and capacitance,LC)滤波电路,控制信号为脉宽调制波。使用MATLAB/Simulink完成电路仿真,在仿真基础上搭建实物实验电路,实验结果验证了仿真及电路的正确性和可行性。展开更多
Traditional 3Ni weathering steel cannot completely meet the requirements for offshore engineering development,resulting in the design of novel 3Ni steel with the addition of microalloy elements such as Mn or Nb for st...Traditional 3Ni weathering steel cannot completely meet the requirements for offshore engineering development,resulting in the design of novel 3Ni steel with the addition of microalloy elements such as Mn or Nb for strength enhancement becoming a trend.The stress-assisted corrosion behavior of a novel designed high-strength 3Ni steel was investigated in the current study using the corrosion big data method.The information on the corrosion process was recorded using the galvanic corrosion current monitoring method.The gradi-ent boosting decision tree(GBDT)machine learning method was used to mine the corrosion mechanism,and the importance of the struc-ture factor was investigated.Field exposure tests were conducted to verify the calculated results using the GBDT method.Results indic-ated that the GBDT method can be effectively used to study the influence of structural factors on the corrosion process of 3Ni steel.Dif-ferent mechanisms for the addition of Mn and Cu to the stress-assisted corrosion of 3Ni steel suggested that Mn and Cu have no obvious effect on the corrosion rate of non-stressed 3Ni steel during the early stage of corrosion.When the corrosion reached a stable state,the in-crease in Mn element content increased the corrosion rate of 3Ni steel,while Cu reduced this rate.In the presence of stress,the increase in Mn element content and Cu addition can inhibit the corrosion process.The corrosion law of outdoor-exposed 3Ni steel is consistent with the law based on corrosion big data technology,verifying the reliability of the big data evaluation method and data prediction model selection.展开更多
Talking about space and Africa,Zambia stands out because of an incredible story.As early as 1964,a Zambian teacher named Edward Nkoloso famously vowed to help his country to beat America to become the first country to...Talking about space and Africa,Zambia stands out because of an incredible story.As early as 1964,a Zambian teacher named Edward Nkoloso famously vowed to help his country to beat America to become the first country to put a man on the moon,and then go to Mars.Despite his passion to launch a rocket that would send 12 astronauts,a space girl and cats to the moon,Nkoloso’s dream never came to fruition.Through his Zambian Space Academy,Nkoloso’s team of Zambian astronauts,whom he called“Afronauts,”underwent strenuous training,but it all came to nought because the United Nations wouldn’t give him the$700 million he needed to fund the space programme.展开更多
Efficient water quality monitoring and ensuring the safety of drinking water by government agencies in areas where the resource is constantly depleted due to anthropogenic or natural factors cannot be overemphasized. ...Efficient water quality monitoring and ensuring the safety of drinking water by government agencies in areas where the resource is constantly depleted due to anthropogenic or natural factors cannot be overemphasized. The above statement holds for West Texas, Midland, and Odessa Precisely. Two machine learning regression algorithms (Random Forest and XGBoost) were employed to develop models for the prediction of total dissolved solids (TDS) and sodium absorption ratio (SAR) for efficient water quality monitoring of two vital aquifers: Edward-Trinity (plateau), and Ogallala aquifers. These two aquifers have contributed immensely to providing water for different uses ranging from domestic, agricultural, industrial, etc. The data was obtained from the Texas Water Development Board (TWDB). The XGBoost and Random Forest models used in this study gave an accurate prediction of observed data (TDS and SAR) for both the Edward-Trinity (plateau) and Ogallala aquifers with the R<sup>2</sup> values consistently greater than 0.83. The Random Forest model gave a better prediction of TDS and SAR concentration with an average R, MAE, RMSE and MSE of 0.977, 0.015, 0.029 and 0.00, respectively. For the XGBoost, an average R, MAE, RMSE, and MSE of 0.953, 0.016, 0.037 and 0.00, respectively, were achieved. The overall performance of the models produced was impressive. From this study, we can clearly understand that Random Forest and XGBoost are appropriate for water quality prediction and monitoring in an area of high hydrocarbon activities like Midland and Odessa and West Texas at large.展开更多
文摘提出一种输出本质安全型准阻抗源Boost电压变换器,介绍了其电路的基本组成,分析了准阻抗源网络中电感器和电容器的元件选型。该电路由准阻抗源电路和Boost斩波电路组成,然后连接电感电容(inductance and capacitance,LC)滤波电路,控制信号为脉宽调制波。使用MATLAB/Simulink完成电路仿真,在仿真基础上搭建实物实验电路,实验结果验证了仿真及电路的正确性和可行性。
基金supported by the National Nat-ural Science Foundation of China(No.52203376)the National Key Research and Development Program of China(No.2023YFB3813200).
文摘Traditional 3Ni weathering steel cannot completely meet the requirements for offshore engineering development,resulting in the design of novel 3Ni steel with the addition of microalloy elements such as Mn or Nb for strength enhancement becoming a trend.The stress-assisted corrosion behavior of a novel designed high-strength 3Ni steel was investigated in the current study using the corrosion big data method.The information on the corrosion process was recorded using the galvanic corrosion current monitoring method.The gradi-ent boosting decision tree(GBDT)machine learning method was used to mine the corrosion mechanism,and the importance of the struc-ture factor was investigated.Field exposure tests were conducted to verify the calculated results using the GBDT method.Results indic-ated that the GBDT method can be effectively used to study the influence of structural factors on the corrosion process of 3Ni steel.Dif-ferent mechanisms for the addition of Mn and Cu to the stress-assisted corrosion of 3Ni steel suggested that Mn and Cu have no obvious effect on the corrosion rate of non-stressed 3Ni steel during the early stage of corrosion.When the corrosion reached a stable state,the in-crease in Mn element content increased the corrosion rate of 3Ni steel,while Cu reduced this rate.In the presence of stress,the increase in Mn element content and Cu addition can inhibit the corrosion process.The corrosion law of outdoor-exposed 3Ni steel is consistent with the law based on corrosion big data technology,verifying the reliability of the big data evaluation method and data prediction model selection.
文摘Talking about space and Africa,Zambia stands out because of an incredible story.As early as 1964,a Zambian teacher named Edward Nkoloso famously vowed to help his country to beat America to become the first country to put a man on the moon,and then go to Mars.Despite his passion to launch a rocket that would send 12 astronauts,a space girl and cats to the moon,Nkoloso’s dream never came to fruition.Through his Zambian Space Academy,Nkoloso’s team of Zambian astronauts,whom he called“Afronauts,”underwent strenuous training,but it all came to nought because the United Nations wouldn’t give him the$700 million he needed to fund the space programme.
文摘Efficient water quality monitoring and ensuring the safety of drinking water by government agencies in areas where the resource is constantly depleted due to anthropogenic or natural factors cannot be overemphasized. The above statement holds for West Texas, Midland, and Odessa Precisely. Two machine learning regression algorithms (Random Forest and XGBoost) were employed to develop models for the prediction of total dissolved solids (TDS) and sodium absorption ratio (SAR) for efficient water quality monitoring of two vital aquifers: Edward-Trinity (plateau), and Ogallala aquifers. These two aquifers have contributed immensely to providing water for different uses ranging from domestic, agricultural, industrial, etc. The data was obtained from the Texas Water Development Board (TWDB). The XGBoost and Random Forest models used in this study gave an accurate prediction of observed data (TDS and SAR) for both the Edward-Trinity (plateau) and Ogallala aquifers with the R<sup>2</sup> values consistently greater than 0.83. The Random Forest model gave a better prediction of TDS and SAR concentration with an average R, MAE, RMSE and MSE of 0.977, 0.015, 0.029 and 0.00, respectively. For the XGBoost, an average R, MAE, RMSE, and MSE of 0.953, 0.016, 0.037 and 0.00, respectively, were achieved. The overall performance of the models produced was impressive. From this study, we can clearly understand that Random Forest and XGBoost are appropriate for water quality prediction and monitoring in an area of high hydrocarbon activities like Midland and Odessa and West Texas at large.
