Abstract: Thermoplastic compositions include: a polymer base resin including at least one liquid crystal polymer (LCP) resin, at least one polyphenylene ether (PPE) resin, or a combination thereof; and from at least 0.1 wt % to 10 wt % of a low-OAN carbon black. The low-OAN carbon black has an oil absorption number (OAN) of 60 cubic centimeters per 100 grams (cc/100 g) or less. The compositions have good dielectric properties.

Abstract: A poly(phenylene ether)-based composition exhibiting improved resistance to decomposition after repeated exposure to high voltages is prepared by melt-blending specific amounts of a poly(phenylene ether), an organophosphate ester flame retardant, and a surface energy reducing agent. The composition includes no more than 30 weight percent of mineral filler. Also described is an injection molded article prepared from the composition.

Abstract: Disclosed is a composition comprising from about 15 wt. % to about 80 wt. % of a thermoplastic resin, wherein the thermoplastic resin comprises a polyphenylene sulfide or a polyether ketone or a combination thereof; and from about 10 wt. % to 80 wt. % of a ceramic filler, wherein the ceramic filler comprises (a) an oxide of titanium, barium, calcium, magnesium, or copper, or strontium or a combination thereof, or (b) a titanate of calcium, magnesium, titanium, or copper or a combination thereof, and wherein the ceramic filler has a particle size of from about 0.1 ?m to about 10 ?m, wherein the composition exhibits a dielectric constant greater than 4, wherein the composition exhibits a dissipation factor less than 0.005 at 1.9 GHz, and wherein the combined weight percent value of all components does not exceed 100 wt. %, and all weight percent values are based on the total weight of the composition.

Abstract: An image processing method and apparatus and a storage medium, wherein the method particularly includes firstly acquiring an image-to-be-trained sample and a label segmentation image corresponding to the image-to-be-trained sample; inputting the image-to-be-trained sample into an image segmentation model to be trained, obtaining a first image feature of a last one output layer in the image segmentation model and a second image feature of a second last output layer when the image-to-be-trained sample is being extracted by using the image segmentation model, outputting the corresponding segmented-image samples; based on the label segmentation image and the segmented-image samples, calculating the model loss function, optimizing the model parameter, and generating the image segmentation model that has been optimized; and inputting an acquired image to be processed into the image segmentation model that has been optimized, and generating segmented images corresponding to the image to be processed.

Abstract: Disclosed herein are compositions comprising: a. from about 30 wt. % to about 60 wt. % of a polyphenylene sulfide (PPS); from greater than 20 wt. % to about 35 wt. % of a polyphenylene oxide (PPO); from about 0.01 wt. % to about 12 wt. % of a compatibilizer package, wherein the compatibilizer package comprises an ethylene-glycidyl methacrylate co-polymer, a styrene rubber copolymer, and an alicyclic saturated hydrocarbon resin present in a ratio of about 0.5:1:2 to about 3:1:4; and from about 20 wt. % to about 50 wt. % of an inorganic filler, wherein the combined weight percent value of all components does not exceed 100 wt. %, and all weight percent values are based on the total weight of the composition, and wherein the composition exhibits a plating adhesion of at least 5B when tested in accordance with an ASTM D3359 cross hatch adhesion test.

Abstract: A method for computer vision processing. The method includes projecting input visual data into a plurality of intermediate feature maps by performing a plurality of 1×1 convolution operations; generating an attention weighted map by performing attention and aggregation operations on the plurality of intermediate feature maps; generating a convolved feature map by performing shift and summation operations on the plurality of intermediate feature maps; and adding the attention weighted map and the convolved feature map based on at least one scalar.

Abstract: A thermoplastic composition includes: (a) about 30 wt % to about 60 wt % of a resin component including polyphenylene ether (PPE) and polystyrene (PS); (b) from about 35 wt % to about 65 wt % of a filler component; (c) from about 1 wt % to about 5 wt % of a flow promoter including at least a partially hydrogenated hydrocarbon resin; and (d) an elastomer component including from 2.5 wt % to 5 wt % rubber content in the composition excluding the filler component. The combined weight percent value of all components does not exceed 100 wt %, and all weight percent values are based on the total weight of the composition except as indicated in (d).

Abstract: A method for detecting image information includes: acquiring at least one sample of image pair to be processed; calculating a reconstruction loss function of the second feature extraction model based on the first image samples and the first reconstructed image feature information; calculating an adversarial loss function of the third feature extraction model based on the second reconstructed image feature information and the first image samples; optimizing the first model parameters in the first feature extraction model based on the reconstruction and the adversarial loss function to generate the optimized first feature extraction model; inputting the acquired image pair to be processed into the optimized first feature extraction model to generate the difference information. The method reduces the first feature extraction model's dependence on the labeled data and improves the model's recognition efficiency and accuracy by using the samples without the labeled difference information.

Abstract: Disclosed herein are compositions comprising: a. from about 30 wt. % to about 70 wt. % of a polyphenylene sulfide; b. from greater than 0 wt. % to about 10 wt. % of a resin comprising polyphenylene oxide; c. from about 5 wt. % to about 15 wt. % of a laser activable metal compound; and d. from about 10 wt. % to about 70 wt. % of an inorganic filler, wherein the combined weight percent value of all components does not exceed 100 wt. %, and all weight percent values are based on the total weight of the composition.

Abstract: Thermoplastic compositions include: (a) from about 10 wt. % to 60 wt. % of a first polyphenylene oxide PPO; (b) from about 0.5 wt. % to about 10 wt. % of a second PPO with low degree of polymerization and a viscosity between 0.1 dl/g and 0.3 dl/g as measured in chloroform at 25° C. using a viscometer; (c) from about 0.5 wt. % to about 10 wt. % of a general purpose polystyrene GPPS, (d) from about 0.5 wt % to about 10 wt % of an impact modifier; and (e) from about 30 wt. % to about 70 wt. % of an inorganic ceramic filler. A ratio of the first PPO and the second PPO to polystyrene is from 1:1 to 6:1. The combined weight percent value of all components does not exceed 100 wt. %, and all weight percent values are based on the total weight of the composition.

Abstract: A thermoplastic composition includes: (a) about 30 wt % to about 60 wt % of a resin component including polyphenylene ether (PPE) and polystyrene (PS); (b) from about 35 wt % to about 65 wt % of a filler component; (c) from about 1 wt % to about 5 wt % of a flow promoter including at least a partially hydrogenated hydrocarbon resin; and (d) an elastomer component including from 2.5 wt % to 5 wt % rubber content in the composition excluding the filler component. The combined weight percent value of all components does not exceed 100 wt %, and all weight percent values are based on the total weight of the composition except as indicated in (d).

Abstract: A thermoplastic composition includes a polyarylene ether component, a flame retardant additive, an impact modifier, and a laser direct structuring additive. The laser platable thermoplastic composition is capable of being plated after being activated using a laser, exhibits a plating index of greater than 0.8 when tested using X-ray fluorescence, and exhibits a heat deflection temperature of greater than 150° C. at 0.45 MPa/3.2 mm when tested in accordance with ASTM D648. In further aspects, the thermoplastic composition may further comprise a laser direct structuring additive synergist comprising a polysiloxane, a polysilane, or a silane.

Abstract: A polymer composition includes: from about 20 wt. % to about 80 wt. % of a polymer base resin; from about 10 wt. % to about 60 wt. % of a thermally conductive filler; and from about 5 wt. % to about 60 wt. % of a dielectric ceramic filler having a Dk of at least 20 when measured at 1.1 GHz or greater. The polymer composition exhibits a dielectric constant greater than 3.0 at 1.1 GHz when tested using a split post dielectric resonator and network analyzer on a sample size of 120 mm by 120 mm and 6 mm thickness according to ASTM D150. The polymer composition exhibits a dissipation factor of less than 0.002 at 1.1 GHz when tested using a split post dielectric resonator and network analyzer on a sample size of 120 mm by 120 mm and 6 mm thickness according to ASTM D150.

Abstract: A steelmaking-and-continuous-casting dispatching method and apparatus based on a distributed robust chance-constraint model. The method includes: according to parameters, an objective function and a constraint condition in steelmaking-and-continuous-casting dispatching, establishing the distributed robust chance-constraint model; by using a dual-approximation method or a linear-programming-approximation method, solving the distributed robust chance-constraint model, to obtain processing starting durations of cast batches in conticasters and processing starting durations of furnace batches in machines other than the conticasters; and by using a solved result of the distributed robust chance-constraint model as an evaluation criterion, by using a tabu-search algorithm, determining a furnace-batch sequence and a distribution theme in the steelmaking-and-continuous-casting dispatching.

Abstract: A steelmaking-and-continuous-casting dispatching method and apparatus based on a distributed robust chance-constraint model. The method includes: according to parameters, an objective function and a constraint condition in steelmaking-and-continuous-casting dispatching, establishing the distributed robust chance-constraint model; by using a dual-approximation method or a linear-programming-approximation method, solving the distributed robust chance-constraint model, to obtain processing starting durations of cast batches in conticasters and processing starting durations of furnace batches in machines other than the conticasters; and by using a solved result of the distributed robust chance-constraint model as an evaluation criterion, by using a tabu-search algorithm, determining a furnace-batch sequence and a distribution theme in the steelmaking-and-continuous-casting dispatching.

Abstract: A method for detecting image information includes: acquiring at least one sample of image pair to be processed; calculating a reconstruction loss function of the second feature extraction model based on the first image samples and the first reconstructed image feature information; calculating an adversarial loss function of the third feature extraction model based on the second reconstructed image feature information and the first image samples; optimizing the first model parameters in the first feature extraction model based on the reconstruction and the adversarial loss function to generate the optimized first feature extraction model; inputting the acquired image pair to be processed into the optimized first feature extraction model to generate the difference information. The method reduces the first feature extraction model's dependence on the labeled data and improves the model's recognition efficiency and accuracy by using the samples without the labeled difference information.

Abstract: An image processing method and apparatus and a storage medium, wherein the method particularly includes firstly acquiring an image-to-be-trained sample and a label segmentation image corresponding to the image-to-be-trained sample; inputting the image-to-be-trained sample into an image segmentation model to be trained, obtaining a first image feature of a last one output layer in the image segmentation model and a second image feature of a second last output layer when the image-to-be-trained sample is being extracted by using the image segmentation model, outputting the corresponding segmented-image samples; based on the label segmentation image and the segmented-image samples, calculating the model loss function, optimizing the model parameter, and generating the image segmentation model that has been optimized; and inputting an acquired image to be processed into the image segmentation model that has been optimized, and generating segmented images corresponding to the image to be processed.

Abstract: A laser platable thermoplastic composition includes from about 38 wt % to about 90 wt % of a thermoplastic polymer, from about 0.01 wt % to about 15 wt % of a laser activatable metal compound, and from about 0.01 wt % to about 60 wt % of a magnetic filler. The magnetic filler includes a magnetic alloy. Methods for making laser platable thermoplastic compositions and articles formed therefrom—such as an induction heater for a domestic or commercial appliance—are also described.

Abstract: A method, apparatus and storage medium for forecasting air pollutant concentration, including: constructing a training set, a validation set and a test set based on a data set; the data set is obtained by collecting pollutant concentration data and meteorological data in a predetermined length of time in a target area; constructing an adjacent matrix A of a graph structure based on the spatial distribution of monitoring stations in the target area; establishing a neural network model F(x;?|A), where x is the input data of it, including pollutant concentration data and meteorological data within predetermined time period, training the neural network model using the data of the training set, adjusting the parameters ? of the neural network model using the data of the validation set and the data of the test set, and obtaining the modified neural network model; using the modified neural network model for air pollutant concentration forecasting.