Abstract: The present disclosure relates to a packaging structure and a method for forming the same. The packaging structure includes a frame, a chip, a pin structure, and a second molding layer. The frame includes a first molding layer and a base island running through the first molding layer along a first direction. The frame has an upper surface and a lower surface disposed opposed along the first direction. The chip is disposed on the lower surface of the frame and electrically connected to the base island. The pin structure is disposed on the lower surface of the frame and includes a plurality of pin rings nested in sequence, each of which includes a plurality of pins disposed around an outer circumference of the chip. The pins are electrically connected to the chip. The second molding layer covers the lower surface of the frame, the chip, and the pin structure.

Abstract: Methods and apparatuses (including systems and devices), including computer-implemented methods for segmenting, correcting and/or modifying a three-dimensional (3D) model of a subject's oral cavity to determine individual components such as teeth, gingiva, tongue, palate, etc., that may be selective and/or collectively digitally manipulated. In some implementations, artificial intelligence uses libraries of labeled 2D images and 3D dental models to learn how to segment a 3D dental model of a subject's oral cavity using 2D images, height map and/or other data and projection values that relate the 2D images to the 3D model. As noted herein, the dental classes can include a variety of intra-oral and extra-oral objects and can be represented as binary values, discrete values, a continuum of height map data, etc. In some implementations, several dental classes are predicted concurrently.

Abstract: Apparatuses, systems, and methods for importing and/or transferring data, including dental data, from three-dimensional (3D) dental models to two-dimensional (2D) dental images may manipulate the 3D dental model based on a temporomandibular joint. A 2D projected image is generated from a patient's 3D dental model. If the 2D projected image matches (within a threshold) to a patient's 2D dental image (from a photo or the like), then dental data may be transferred. If the 2D projected image does not match, then the 3D dental model may be modified (based on the temporomandibular joint) and a new 2D projected image generated. In this manner, the positions of the 3D dental model may be iterated to generate multiple 2D projected images.

Abstract: The present disclosure provides a thin film transistor, a display substrate and a display device, and belongs to the field of display technology. The thin film transistor of the present disclosure includes: a base, and a gate, an active layer, a source and a drain located on the base, where the gate includes a first gate and a second gate which are sequentially provided on the base and are electrically connected to each other; the active layer is located between the first gate and the second gate, and orthographic projections of the first gate and the second gate on the base are partially overlapped with an orthographic projection of the active layer on the base, and the orthographic projections of the first gate and the second gate on the base are partially overlapped with each other.

Abstract: Embodiments include receiving image data comprising a representation of teeth, wherein the image data comprises a set of pixel locations for the teeth and depth values associated with the pixel locations in the set of pixel locations. The method further includes generating new image based on applying one or more functions to the image data, wherein the one or more functions adjust one or more color channels of the set of pixel locations based at least in part on the depth values.

Abstract: Disclosed is an intelligent monitoring method for soil moisture status based on remote sensing technology, including the following steps: dividing a monitoring region into a plurality of sub-regions by a processing end, acquiring image data of various sub-regions on the basis of remote sensing satellites, acquiring multi-source data related to soil moisture status of the monitoring region on the basis of sensor devices, and pre-processing the image data and the multi-source data; comprehensively analyzing the image data and the multi-source data, and assessing soil moisture status of the various sub-regions; and comprehensively analyzing overall soil moisture status of the monitoring region according to the soil moisture status of the various sub-regions. The soil moisture status of the monitoring region is assessed after the multi-source data is comprehensively analyzed by the monitoring method, bringing about a more comprehensive and accurate analysis.

Abstract: Apparatuses and methods for dental image analysis including determining pixel sizes of an image. A pixel size enables conversion of a two-dimension image-based measurement into a 3D measurement. The pixel sizes vary over the image due to differences in distance between the camera taking the image and the object of interest. A three-dimensional model may be registering to the two-dimension image, and the pixel sizes may be identified on a per-object (e.g., per-tooth) basis, per-region basis or at each point in the image. The pixel size scaling may be used to identify features in the image for diagnostic or other dental application purposes.

Abstract: Provided are a new compound as represented by formula (I) having a dual antagonistic effect of an angiotensin II receptor and an endothelin receptor, and the use thereof in the preparation of a drug.

Abstract: Systems and methods of simulating dental treatments are disclosed. A method may include capturing a first 2D image of a patient's face, including the patient's teeth, building a parametric 3D model of the patient's teeth and gingiva based on the first 2D image, developing a simulated outcome of a dental treatment of the patient's teeth by rendering the parametric 3D model with the patient's teeth in one or more positions and/or orientations corresponding to the treatment goals of the dental treatment plan, and rendering a second 2D image of the patient's face with gingiva and teeth according to a simulated outcome of the dental treatment plan. As noted herein, the dental treatment plan may include orthodontic and/or restorative elements. The simulated outcome may correspond to estimated outcomes and/or intended outcomes of the dental treatment plan.

Abstract: Described herein is are compositions and methods for treating an articulating joint disorders, comprising systemically administering to a subject a nucleoside reverse transcriptase inhibitor (NRTI).

Abstract: Systems and methods of simulating dental treatments are disclosed. A method may include capturing a first 2D image of a patient's face, including the patient's teeth, building a parametric 3D model of the patient's teeth and gingiva based on the first 2D image, developing a simulated outcome of a dental treatment of the patient's teeth by rendering the parametric 3D model with the patient's teeth in one or more positions and/or orientations corresponding to the treatment goals of the dental treatment plan, and rendering a second 2D image of the patient's face with gingiva and teeth according to a simulated outcome of the dental treatment plan. As noted herein, the dental treatment plan may include orthodontic and/or restorative elements. The simulated outcome may correspond to estimated outcomes and/or intended outcomes of the dental treatment plan.

Abstract: Provided is an amorphous silicon thin-film transistor including an amorphous silicon semiconductor layer, a source electrode, and a drain electrode that are successively disposed on a base substrate. Ions doped by an ion implantation process are present in a region, proximal to the source electrode and the drain electrode, of the amorphous silicon semiconductor layer. A concentration of the ions in a surface region, proximal to the source electrode and the drain electrode, of the amorphous silicon semiconductor layer is greater than or equal to 5*10{circumflex over (?)}20 atoms/cc.

Abstract: A method for dental treatment may include receiving photos of a patient's dentition, gathering treatment parameters for at treatment plan. The treatment parameters may represent attributes of the treatment plan selected by a doctor for the patient's dentition of a patient. The method may include generating, using the photos and the treatment parameters, one or more intelligent guidance rules to guide application of at least a portion of the treatment parameters to the patient's dentition, generating instructions to apply the intelligent patient guidance rules to the patient's dentition, and providing the instructions to the patient to apply the at least a portion of the treatment plan in accordance with the intelligent patient guidance rules.

Abstract: A method for treatment-based photo guidance may include receiving a treatment plan, determining the movement of a first one or more teeth based on the movement of tooth models in the treatment plan, and determining one or more photo views for capturing the movement of the first one or more teeth.

Abstract: A method for image-based orthodontic treatment may include receiving image data of a patient's dentition and an orthodontic appliance, identifying, from the image data, the orthodontic appliance, calculating a misalignment height of a misalignment of the orthodontic appliance with respect to the patient's dentition, determining whether the misalignment height satisfies a misalignment threshold, and in response to satisfying the misalignment threshold, providing a notification.

Abstract: A method of dental treatment may include receiving photos of a person's dentition, identifying a stage of a treatment plan administered to the person's dentition, gathering a three-dimensional (3D) model of the person's dentition corresponding to the stage of the treatment plan, projecting attributes of the 3D model of the person's dentition onto an image plane to get a projected representation of the person's dentition at the stage of the treatment plan, comparing the photos to the projected representation to derive an error image representing the comparison, and analyzing the error image for discrepancies, wherein the discrepancies represent one or more deviations of the person's dentition from the stage of the treatment plan.

Abstract: Methods, apparatuses, and systems are disclosed for simulating a tooth whitening procedure. The simulation may generally obtain a patient's two-dimensional or three-dimensional dental scan and color space convert color information from a first or native color space to a color space based on an International Commission on Illumination color space (CIELAB). The simulation may include modifying color information in the CIELAB color space then returning the modified color information to the native color space.

Abstract: An item recall method includes: behavior data is acquired, where the behavior data includes items and item information of each item; target behavior data containing a retrieval category word is extracted from the behavior data; retrieval words of each item and a retrieval frequency of each retrieval word are acquired in a reverse correlation manner; word segmentation is performed on the item information to obtain multiple item segmented words; a similarity between all retrieval words and the multiple item segmented words is calculated; whether the similarity is greater than a first preset threshold or not is determined, and if yes, then a retrieval word is extracted as an expansion word of the retrieval category word; and item recall is performed according to the retrieval category word and the expansion word.

Abstract: Methods and apparatuses (including systems and devices), including computer-implemented methods for segmenting, correcting and/or modifying a three-dimensional (3D) model of a subject's oral cavity to determine individual components such as teeth, gingiva, tongue, palate, etc., that may be selective and/or collectively digitally manipulated. In some implementations, artificial intelligence uses libraries of labeled 2D images and 3D dental models to learn how to segment a 3D dental model of a subject's oral cavity using 2D images, height map and/or other data and projection values that relate the 2D images to the 3D model. As noted herein, the dental classes can include a variety of intra-oral and extra-oral objects and can be represented as binary values, discrete values, a continuum of height map data, etc. In some implementations, several dental classes are predicted concurrently.