Abstract: The present disclosure provides a system for measuring dynamic physical properties of a rack is configured to simulate various states of a rock, which facilitates the research of the dynamically changing physical properties of the rock. the system including: a kettle (202); a sample clamping device (10) including a lower end rod (103) and a fixed upper end rod (102), and a sample cavity is disposed between the upper end rod (103) and the lower end rod (102) and is located in the kettle (202); an overburden pressure device (400) configured to drive the lower end rod (103) to move up and down; a sample sheath (109) configured to sleeve a sample (101) located in the sample cavity, the sample sheath (109) being contractible inwards under pressure; a confining pressure maintaining device (402) configured to control a pressure in the kettle; and a heater (201) configured to control a temperature in the kettle.

Abstract: The invention discloses a coordination type zirconium phosphotungstate catalyst and its application in catalytic hydrogenation of furfural, belonging to the field of heterogeneous catalysis. The zirconium phosphotungstate catalyst prepared by the invention not only has good catalytic effect on the conversion of furfural to furfuryl alcohol, but also has mild reaction conditions. The yield of solid line furfuryl alcohol can be 98% if it can be reacted for 1 h at 120 ° C., and the amount of catalyst is less, which greatly reduces the energy consumption in the prior art. In addition, the zirconium phosphotungstate prepared by the invention is easy to separate, has good stability for catalyzing the hydrogenation of furfural to furfuryl alcohol, and is a new, efficient and green catalyst.

Abstract: Embodiments of the disclosure provide systems and methods for domain adaptation between a plurality of source domains and a target domain. The artificial intelligence method includes receiving labeled data from the plurality of source domains and unlabeled data from the target domain. The method further includes separately training, by a processor, a plurality of source classifiers each corresponding to a source domain using the labeled data received from the respective source domains. The method also includes selecting a subset of the labeled data received from each source domain based on a similarity between the selected labeled data and the unlabeled data of the target domain. The method additionally includes refining, by the processor, each source classifier using the selected subset of the labeled data, and predicting labels of the unlabeled data using the refined source classifiers.

Abstract: Embodiments of the disclosure provide systems and methods for domain adaptation between a plurality of source domains and a target domain. The artificial intelligence method includes receiving labeled data from the plurality of source domains and unlabeled data from the target domain. The method further includes separately training, by a processor, a plurality of source classifiers each corresponding to a source domain using the labeled data received from the respective source domains. The method also includes selecting a subset of the labeled data received from each source domain based on a similarity between the selected labeled data and the unlabeled data of the target domain. The method additionally includes refining, by the processor, each source classifier using the selected subset of the labeled data, and predicting labels of the unlabeled data using the refined source classifiers.

Abstract: The present application relates to an ultrasound fusion imaging method and an ultrasound fusion image navigation system. The ultrasound fusion imaging method includes a selection step, a registration step and a fusion step. The selection step is for selecting at least one ultrasound image from at least one previously stored piece of ultrasound video data according to an input instruction. The ultrasound video data includes an ultrasound image obtained by acquiring a target object in at least one plane and position indicating information corresponding to the ultrasound image. The registration step is for registering the selected at least one ultrasound image with a modality image. The registration process uses the location of the position indicating information of the at least one ultrasound image. The fusion step is for fusing the registered ultrasound image with the modality image.

Abstract: The present application relates to an ultrasound fusion imaging method and an ultrasound fusion image navigation system. The ultrasound fusion imaging method includes a selection step, a registration step and a fusion step. The selection step is for selecting at least one ultrasound image from at least one previously stored piece of ultrasound video data according to an input instruction. The ultrasound video data includes an ultrasound image obtained by acquiring a target object in at least one plane and position indicating information corresponding to the ultrasound image. The registration step is for registering the selected at least one ultrasound image with a modality image. The registration process uses the location of the position indicating information of the at least one ultrasound image. The fusion step is for fusing the registered ultrasound image with the modality image.