Abstract: A testing platform for evacuated tube high-temperature superconducting magnetic levitation (HTS maglev) under a high-speed operation state, including an evacuated tube, a supporting platform assembly, a model train and a gantry. The supporting platform assembly is arranged in the evacuated tube, and is provided with a permanent magnet track and a stator winding. A mover and a cryogenic dewar are arranged at a bottom of the model train, and multiple superconducting bulks are arranged in the cryogenic dewar. A side wall of the model train is made of a metal material. The gantry is arranged on the supporting platform assembly, and permanent magnets are arranged on upright posts of the gantry. A testing method using the testing platform is also provided.

Abstract: A dynamic simulation test platform for ultra-high-speed evacuated tube magnetic levitation (maglev) transportation includes an evacuated tube having a transition section and a vacuum section, a vacuum maintaining system, a motor supporting platform, and a model train. One end of the evacuated tube is provided with a first isolation door, and the other end is closed. A second isolation door is provided inside the evacuated tube. The vacuum maintaining system is connected to the transition section and the vacuum section. The motor supporting platform is provided in the evacuated tube and extends outside the transition section. The motor supporting platform is provided with a stator winding and a permanent-magnet track. A mover and a cryogenic dewar are provided at a bottom of the model train. The cryogenic dewar is provided with a superconducting bulk. A test method using the test platform is further provided.

Abstract: A comprehensive dental readiness platform is presented. Dental patient data including an image, proposed treatments, and a dental form are received and processed by first machine learning models to obtain clinical findings and predicted values for fields of the dental form. The clinical findings and other results are processed by a second machine learning model to obtain predictions of a future dental condition of a patient. The second machine learning model utilizes an ensemble of Transformer Neural Networks, Long-Short-Term-Memory Networks, Convolutional Neural Networks, and Tree-Based Algorithms to predict the dental readiness classification, dental readiness durability, dental readiness error, dental emergency likelihood, prognosis, and alternative treatment options.

Abstract: A dental procedure, one or more dental images, and documentation are processed to extract data and label and/or measure dental anatomy or pathologies using a first stage. The extracted data and labels are processed with a second stage to obtain predictions of deficiencies of the dental images and documentation. The predictions may include tasks to remedy the deficiencies, adjudication likelihood, instant payment amount, patient fee, and average time to payment. The first stage and second stage may each include a plurality of machine learning models. The second stage may include a plurality of machine learning models coupled to a concatenation layer. Inputs to the concatenation layer may include outputs of hidden layers of the plurality of machine learning models. The concatenation layer may take the extracted data and labels as inputs.

Abstract: A machine learning model is trained to predict pixel spacing, distance, and volumetric measurements. Training images are obtained by inpainting around an original image and scaling the inpainted image to obtain the training image having a different pixel spacing than the original image. The machine learning model may include an encoder, a transformer, a first TC layer, and a second TC layer. During training, loss may be obtained from a comparison of the output to the first TC layer to a coarse pixel spacing matrix and a comparison of the output of the second TC layer to a fine pixel spacing matrix. During utilization, the pixel spacing of an image may be obtained using the machine learning model and used to correct the image or measurements obtained from the image.

Abstract: A comprehensive dental readiness platform is presented. Dental patient data including an image, proposed treatments, and a dental form are received and processed by first machine learning models to obtain clinical findings and predicted values for fields of the dental form. The clinical findings and other results are processed by a second machine learning model to obtain predictions of a future dental condition of a patient. The second machine learning model utilizes an ensemble of Transformer Neural Networks, Long-Short-Term-Memory Networks, Convolutional Neural Networks, and Tree-Based Algorithms to predict the dental readiness classification, dental readiness durability, dental readiness error, dental emergency likelihood, prognosis, and alternative treatment options.

Abstract: A machine learning model is trained to predict pixel spacing, distance, and volumetric measurements. Training images are obtained by inpainting around an original image and scaling the inpainted image to obtain the training image having a different pixel spacing than the original image. The machine learning model may include an encoder, a transformer, a first TC layer, and a second TC layer. During training, loss may be obtained from a comparison of the output to the first TC layer to a coarse pixel spacing matrix and a comparison of the output of the second TC layer to a fine pixel spacing matrix. During utilization, the pixel spacing of an image may be obtained using the machine learning model and used to correct the image or measurements obtained from the image.

Abstract: An interface enables a user to select a block type, place an instance of that block type in a schematic and connect the instance to other instances. Each block type defines processing of dental data, such as dental images according to any of a plurality of modalities and defines logic, such as if statements, to determine an output (positive/negative) for instances of that block type. Logic may include Boolean expressions relating to results of the inf statements. The logic may operate with respect to data derived from patient data using a machine learning model trained to measure dental anatomy, measure dental pathologies, or diagnose dental conditions. A workflow may be created with instances to determine the appropriateness of a dental treatment.

Abstract: A dental form image may be processed with a segmentation network to identify point labels corresponding to reference point labels of a reference form. The image and the point labels along with a reference image and the reference point labels may be processed by a pair of encoders to obtain offsets. Text blobs may be identified from portions of the image corresponding to the reference point labels, such as with correction according to the offsets. Image portions and text blobs for each field of the dental form may be processed to extract text. Intermediate values of machine learning models used to extract text may be input to a machine learning model estimating a procedure code for the dental form. Machine learning models may be used to correctly identify a provider referenced by the dental form.