Abstract: This disclosure is a condensate evaporation device. An air conditioning apparatus includes a compressor, an evaporator and a condenser connected with one another. A water tray is arranged to receive the condensate. A water distribution module includes a water separator base and a water separator piece. The water separator piece and the water separator base are combined to define a water channel. The condensate flows through the water channel to evenly flow out. A multi-folded water absorbing body is arranged on one side of the water separator base to absorb the condensate flowed out from the water separator base. The water tank is arranged on a bottom side of the multi-folded water absorbing body. A fan is arranged on one side of the multi-folded water absorbing body. Accordingly, the condensate may be evaporated efficiently.

Abstract: A device includes a semiconductor fin, and a gate stack on sidewalls and a top surface of the semiconductor fin. The gate stack includes a high-k dielectric layer, a work-function layer overlapping a bottom portion of the high-k dielectric layer, and a blocking layer overlapping a second bottom portion of the work-function layer. A low-resistance metal layer overlaps and contacts the work-function layer and the blocking layer. The low-resistance metal layer has a resistivity value lower than second resistivity values of both of the work-function layer and the blocking layer. A gate spacer contacts a sidewall of the gate stack.

Abstract: A training device and a training method for reducing hypertonic are disclosed. The training device includes a base, a driving circuit, two pedals, a control circuit, and a switch circuit. The driving circuit is fixed on the base. Each of the two pedals is coupled to the base. The driving circuit drives each of the two pedals to swing repeatedly between a first position and a second position relative to the base. When the control circuit executes a training program, the control circuit actuates the driving circuit.

Abstract: A dynamic frame reconstruction apparatus and method for medical image processing is disclosed which reduces the computationally expensive reconstruction of images but which retains the accuracy of the image reconstruction. A convolutional neural network is used to cluster the dynamic data into groups of frames, each group sharing similar radiotracer distribution. In one embodiment, groups of frames that have similar reconstruction parameters are determined, and scatter and random estimations are computed once and shared among each of the frames in the same frame group.

Abstract: A lighting module, an electronic device, and a display panel are provided. The lighting module includes a carrier, a first metal circuit layer, a first transparent conductive layer, a first insulating layer, a second transparent conductive layer, a second metal circuit layer, a bonding structure layer, and a plurality of lighting units. The bonding structure layer is configured to allow the second metal circuit layer to be well bonded to the first insulating layer, so that a resistance value of the lighting module is decreased, and a pressure drop is reduced.

Abstract: A deep learning (DL) convolution neural network (CNN) reduces noise in positron emission tomography (PET) images, and is trained using a range of noise levels for the low-quality images having high noise in the training dataset to produce uniform high-quality images having low noise, independently of the noise level of the input image. The DL-CNN network can be implemented by slicing a three-dimensional (3D) PET image into 2D slices along transaxial, coronal, and sagittal planes, using three separate 2D CNN networks for each respective plane, and averaging the outputs from these three separate 2D CNN networks. Feature-oriented training can be implemented by segmenting each training image into lesion and background regions, and, in the loss function, applying greater weights to voxels in the lesion region. Other medical images (e.g. MRI and CT) can be used to enhance resolution of the PET images and provide partial volume corrections.

Abstract: Systems and methods of facilitating determination of risk of coronary artery disease (CAD) based at least in part on one or more measurements derived from non-invasive medical image analysis. The methods can include accessing a non-invasive generated medical image, identifying one or more arteries, identifying, regions of plaque within an artery, analyzing the regions of plaque to identify low density non-calcified plaque, non-calcified plaque, or calcified plaque based at least in part on density, determining a distance from identified regions of low density non-calcified plaque to one or more of a lumen wall or vessel wall, determining embeddedness of the regions of low density non-calcified plaque by one or more of non-calcified plaque or calcified plaque, determining a shape of the more regions of low density non-calcified plaque, and generating a display of the analysis to facilitate determination of one or more of a risk of CAD of the subject.

Abstract: Systems and methods of facilitating determination of risk of coronary artery disease (CAD) based at least in part on one or more measurements derived from non-invasive medical image analysis. The methods can include accessing a non-invasive generated medical image, identifying one or more arteries, identifying, regions of plaque within an artery, analyzing the regions of plaque to identify low density non-calcified plaque, non-calcified plaque, or calcified plaque based at least in part on density, determining a distance from identified regions of low density non-calcified plaque to one or more of a lumen wall or vessel wall, determining embeddedness of the regions of low density non-calcified plaque by one or more of non-calcified plaque or calcified plaque, determining a shape of the more regions of low density non-calcified plaque, and generating a display of the analysis to facilitate determination of one or more of a risk of CAD of the subject.

Abstract: Systems and methods of facilitating determination of risk of coronary artery disease (CAD) based at least in part on one or more measurements derived from non-invasive medical image analysis. The methods can include accessing a non-invasive generated medical image, identifying one or more arteries, identifying, regions of plaque within an artery, analyzing the regions of plaque to identify low density non-calcified plaque, non-calcified plaque, or calcified plaque based at least in part on density, determining a distance from identified regions of low density non-calcified plaque to one or more of a lumen wall or vessel wall, determining embeddedness of the regions of low density non-calcified plaque by one or more of non-calcified plaque or calcified plaque, determining a shape of the more regions of low density non-calcified plaque, and generating a display of the analysis to facilitate determination of one or more of a risk of CAD of the subject.

Abstract: Systems and methods of facilitating determination of risk of coronary artery disease (CAD) based at least in part on one or more measurements derived from non-invasive medical image analysis. The methods can include accessing a non-invasive generated medical image, identifying one or more arteries, identifying, regions of plaque within an artery, analyzing the regions of plaque to identify low density non-calcified plaque, non-calcified plaque, or calcified plaque based at least in part on density, determining a distance from identified regions of low density non-calcified plaque to one or more of a lumen wall or vessel wall, determining embeddedness of the regions of low density non-calcified plaque by one or more of non-calcified plaque or calcified plaque, determining a shape of the more regions of low density non-calcified plaque, and generating a display of the analysis to facilitate determination of one or more of a risk of CAD of the subject.

Abstract: Systems and methods of facilitating determination of risk of coronary artery disease (CAD) based at least in part on one or more measurements derived from non-invasive medical image analysis. The methods can include accessing a non-invasive generated medical image, identifying one or more arteries, identifying, regions of plaque within an artery, analyzing the regions of plaque to identify low density non-calcified plaque, non-calcified plaque, or calcified plaque based at least in part on density, determining a distance from identified regions of low density non-calcified plaque to one or more of a lumen wall or vessel wall, determining embeddedness of the regions of low density non-calcified plaque by one or more of non-calcified plaque or calcified plaque, determining a shape of the more regions of low density non-calcified plaque, and generating a display of the analysis to facilitate determination of one or more of a risk of CAD of the subject.

Abstract: Systems and methods of facilitating determination of risk of coronary artery disease (CAD) based at least in part on one or more measurements derived from non-invasive medical image analysis. The methods can include accessing a non-invasive generated medical image, identifying one or more arteries, identifying, regions of plaque within an artery, analyzing the regions of plaque to identify low density non-calcified plaque, non-calcified plaque, or calcified plaque based at least in part on density, determining a distance from identified regions of low density non-calcified plaque to one or more of a lumen wall or vessel wall, determining embeddedness of the regions of low density non-calcified plaque by one or more of non-calcified plaque or calcified plaque, determining a shape of the more regions of low density non-calcified plaque, and generating a display of the analysis to facilitate determination of one or more of a risk of CAD of the subject.

Abstract: A method, apparatus, and computer instructions stored on a computer-readable medium perform latent image feature extraction by performing the functions of receiving image data acquired during an imaging of a patient, wherein the image data includes motion by the patient during the imaging; segmenting the image data to include M image data segments corresponding to at least N motion phases having shorter durations than a duration of the motion by the patient during the imaging, wherein M is a positive integer greater than or equal to a positive integer N; producing, from the M image data segments, at least N latent feature vectors corresponding to the motion by the patient during the imaging; and performing a gated reconstruction of the N motion phases by reconstructing the image data based on the at least N latent feature vectors.

Abstract: A deep learning (DL) convolution neural network (CNN) reduces noise in positron emission tomography (PET) images, and is trained using a range of noise levels for the low-quality images having high noise in the training dataset to produce uniform high-quality images having low noise, independently of the noise level of the input image. The DL-CNN network can be implemented by slicing a three-dimensional (3D) PET image into 2D slices along transaxial, coronal, and sagittal planes, using three separate 2D CNN networks for each respective plane, and averaging the outputs from these three separate 2D CNN networks. Feature-oriented training can be implemented by segmenting each training image into lesion and background regions, and, in the loss function, applying greater weights to voxels in the lesion region. Other medical images (e.g. MRI and CT) can be used to enhance resolution of the PET images and provide partial volume corrections.

Abstract: A method for decolorizing a dyed textile comprising a synthetic fiber and a disperse dye, the method includes contacting the dyed textile with a super critical fluid thereby extracting at least a portion of the disperse dye from the textile into the super critical fluid and forming an at least partially decolorized textile.

Abstract: A deep learning (DL) convolution neural network (CNN) reduces noise in positron emission tomography (PET) images, and is trained using a range of noise levels for the low-quality images having high noise in the training dataset to produceuniform high-quality images having low noise, independently of the noise level of the input image. The DL-CNN network can be implemented by slicing a three-dimensional (3D) PET image into 2D slices along transaxial, coronal, and sagittal planes, using three separate 2D CNN networks for each respective plane, and averaging the outputs from these three separate 2D CNN networks. Feature-oriented training can be implemented by segmenting each training image into lesion and background regions, and, in the loss function, applying greater weights to voxels in the lesion region. Other medical images (e.g. MRI and CT) can be used to enhance resolution of the PET images and provide partial volume corrections.

Abstract: An adjustable head angle bicycle assembly includes a steerer tube; a head tube that includes a first annular surface protruding radially inward from an inner wall at the upper end, and a second annular surface protruding radially inward from the inner wall at the lower end; an upper cup that includes a third annular surface engaged with the first annular surface of the head tube; and a lower cup that includes a fourth annular surface engaged with the second annular surface of the head tube.

Abstract: Systems and methods of facilitating determination of risk of coronary artery disease (CAD) based at least in part on one or more measurements derived from non-invasive medical image analysis. The methods can include accessing a non-invasive generated medical image, identifying one or more arteries, identifying, regions of plaque within an artery, analyzing the regions of plaque to identify low density non-calcified plaque, non-calcified plaque, or calcified plaque based at least in part on density, determining a distance from identified regions of low density non-calcified plaque to one or more of a lumen wall or vessel wall, determining embeddedness of the regions of low density non-calcified plaque by one or more of non-calcified plaque or calcified plaque, determining a shape of the more regions of low density non-calcified plaque, and generating a display of the analysis to facilitate determination of one or more of a risk of CAD of the subject.

Abstract: A display apparatus includes a driving substrate and a first light emitting diode element. The driving substrate has a plurality of driving structures. Each of the driving structures includes a first pad, a second pad, a third pad and a fourth pad. The plurality of driving structures include a first driving structure. The first light emitting diode element is electrically connected to a first pad and a second pad of the first driving structure, and the first light emitting diode element crosses a line connecting a third pad and a fourth pad of the first driving structure. A manufacturing method of the display apparatus is also provided.

Abstract: A transparent plastic integrally molded goblet has a cup-shaped part, a columnar first leg and a disc-shaped first base integrally molded from transparent plastics. The bottom edge of the cup-shaped part extends downwards to form the first leg. The bottom end of the first leg integrally forms the first base. The cup-shaped part has a circumferential wall which encloses the circumference of a first space. The top edge of the circumferential wall forms an annular first ring frame, and the central part of bottom edge of the first base downwards forms a first convex pin. The integral molding of transparent plastics can enhance the appearance integrity of the goblet, the manufacture of the goblet is easy and the product yield is increased.