Gland cancer is a high-incidence disease that endangers human health,and its early detection and treatment require efficient,accurate,and objective intelligent diagnosis methods.In recent years,the advent of machine l...Gland cancer is a high-incidence disease that endangers human health,and its early detection and treatment require efficient,accurate,and objective intelligent diagnosis methods.In recent years,the advent of machine learning techniques has yielded satisfactory results in intelligent gland cancer diagnosis based on clinical images,significantly improving the accuracy and efficiency of medical image interpretation while reducing the workload of doctors.The focus of this study is to review,classify,and analyze intelligent diagnosis methods for imaging gland cancer based on machine learning and deep learning.This paper briefly introduces some basic imaging principles of multimodal medical images,such as the commonly used computed tomography(CT),magnetic resonance imaging(MRI),ultrasound(US),positron emission tomography(PET),and pathology.In addition,the intelligent diagnosis methods for imaging gland cancer were further classified into supervised learning and weakly supervised learning.Supervised learning consists of traditional machine learning methods,such as K-nearest neighbor algorithm(KNN),support vector machine(SVM),and multilayer perceptron,and deep learning methods evolving from convolutional neural network(CNN).By contrast,weakly supervised learning can be further categorized into active learning,semisupervised learning,and transfer learning.State-of-the-art methods are illustrated with implementation details,including image segmentation,feature extraction,and optimization of classifiers.Their performances are evaluated through indicators,such as accuracy,precision,and sensitivity.In conclusion,the challenges and development trends of intelligent diagnosis methods for imaging gland cancer were addressed and discussed.展开更多
A comprehensive energy-saving sail (CES) has been proposed in order to promote energy saving and emission reduction from shipping. Wind energy is harvested for propulsion and electrical generator at the same time by a...A comprehensive energy-saving sail (CES) has been proposed in order to promote energy saving and emission reduction from shipping. Wind energy is harvested for propulsion and electrical generator at the same time by a unique structure of CES. A CFD (Computational Fluid Dynamics) code is verified by a case of arc wind sail, and it is used to simulate the pressure and velocity around the CES. The results show that the outlet velocity of air tunnel V<sub>o</sub> and wind velocity V<sub>i</sub> serve as an equation V<sub>o</sub> ≈ 1.31V<sub>i</sub>, which means the CES can effectively improve the conversion efficiency. In addition, it is found that V<sub>o</sub> increases with the tunnel diameter to some extend over which it will keep almost constant.展开更多
The Appellate Body report in January 2012 had supported the decision of Panel in the"China-measures related to the exportation of various raw materials"case(WT/DS394,395,398)and affirmed that China's res...The Appellate Body report in January 2012 had supported the decision of Panel in the"China-measures related to the exportation of various raw materials"case(WT/DS394,395,398)and affirmed that China's restrictions(such as tariffs and quota measures)on the exportation of raw materials violated rules put forth by the WTO,which were required to be modified.In this case China's right to invoke Article 20 of GATT1994("general exception")to justify its exemption from the guidelines in Article 11.3 of the WTO Accession Protocol was denied by the Panel and the Appellate Body.This was due to the fact that the phrasing in Article 11.3 of Protocol failed to mention"GATT."This was the consequence of the two interpretation approaches the Dispute Settlement Body(DSB)adopted-a narrow textual interpretation and a subjective presumption of"legislative silence."The inappropriate use of the two methods of interpretation lead to an imbalance between the right and obligation of China under the additional obligations that were imposed upon China by the WTO,which create a negative impact on China's rare earth case and the protection of domestic natural resources.展开更多
基金Supported by National Natural Science Foundation of China(62102036).
文摘Gland cancer is a high-incidence disease that endangers human health,and its early detection and treatment require efficient,accurate,and objective intelligent diagnosis methods.In recent years,the advent of machine learning techniques has yielded satisfactory results in intelligent gland cancer diagnosis based on clinical images,significantly improving the accuracy and efficiency of medical image interpretation while reducing the workload of doctors.The focus of this study is to review,classify,and analyze intelligent diagnosis methods for imaging gland cancer based on machine learning and deep learning.This paper briefly introduces some basic imaging principles of multimodal medical images,such as the commonly used computed tomography(CT),magnetic resonance imaging(MRI),ultrasound(US),positron emission tomography(PET),and pathology.In addition,the intelligent diagnosis methods for imaging gland cancer were further classified into supervised learning and weakly supervised learning.Supervised learning consists of traditional machine learning methods,such as K-nearest neighbor algorithm(KNN),support vector machine(SVM),and multilayer perceptron,and deep learning methods evolving from convolutional neural network(CNN).By contrast,weakly supervised learning can be further categorized into active learning,semisupervised learning,and transfer learning.State-of-the-art methods are illustrated with implementation details,including image segmentation,feature extraction,and optimization of classifiers.Their performances are evaluated through indicators,such as accuracy,precision,and sensitivity.In conclusion,the challenges and development trends of intelligent diagnosis methods for imaging gland cancer were addressed and discussed.
文摘A comprehensive energy-saving sail (CES) has been proposed in order to promote energy saving and emission reduction from shipping. Wind energy is harvested for propulsion and electrical generator at the same time by a unique structure of CES. A CFD (Computational Fluid Dynamics) code is verified by a case of arc wind sail, and it is used to simulate the pressure and velocity around the CES. The results show that the outlet velocity of air tunnel V<sub>o</sub> and wind velocity V<sub>i</sub> serve as an equation V<sub>o</sub> ≈ 1.31V<sub>i</sub>, which means the CES can effectively improve the conversion efficiency. In addition, it is found that V<sub>o</sub> increases with the tunnel diameter to some extend over which it will keep almost constant.
文摘The Appellate Body report in January 2012 had supported the decision of Panel in the"China-measures related to the exportation of various raw materials"case(WT/DS394,395,398)and affirmed that China's restrictions(such as tariffs and quota measures)on the exportation of raw materials violated rules put forth by the WTO,which were required to be modified.In this case China's right to invoke Article 20 of GATT1994("general exception")to justify its exemption from the guidelines in Article 11.3 of the WTO Accession Protocol was denied by the Panel and the Appellate Body.This was due to the fact that the phrasing in Article 11.3 of Protocol failed to mention"GATT."This was the consequence of the two interpretation approaches the Dispute Settlement Body(DSB)adopted-a narrow textual interpretation and a subjective presumption of"legislative silence."The inappropriate use of the two methods of interpretation lead to an imbalance between the right and obligation of China under the additional obligations that were imposed upon China by the WTO,which create a negative impact on China's rare earth case and the protection of domestic natural resources.
基金supported by the National Basic Research Program of China (2020YFA0210700 and 2016YFA0301004)the National Natural Science Foundation of China (21871009, 21527803, 22125102, and 22105218)Shanghai Sailing Program (21YF1454400)。
