Abstract: The present disclosure relates to a printed circuit board including, a first insulating layer, a first metal layer disposed on the first insulating layer, a bridge disposed on the first metal layer and including a bridge insulating layer and a bridge circuit layer, a second insulating layer disposed on the first insulating layer and covering at least a portion of the bridge, a second metal layer disposed on the second insulating layer, and a connecting via penetrating the bridge and the second insulating layer to connect the first metal layer to the second insulating layer. The connecting via is spaced apart from the bridge circuit layer.

Abstract: An organic light-emitting device and a flat panel display device, the organic-light emitting device including an anode; a cathode; and an organic layer therebetween including an emission layer, a hole transport region between the anode and the emission layer, the hole transport region including at least one of a hole injection layer, a hole transport layer, and an electron blocking layer, an electron transport region between the emission layer and the cathode, the electron transport region including at least one of a hole blocking layer, an electron transport layer, and an electron injection layer, and a buffer layer between the emission layer and the electron transport region, wherein the buffer layer includes a biscarbazole-based derivative and triphenylene-based derivative, and a triplet energy (ET1) of the biscarbazole-based derivative or the triphenylene-based derivative and a triplet energy (ET2) of a dopant of the emission layer satisfy the following relationship: ET1>ET2.

Abstract: A method of reinforcement learning of a neural network device for generating a verification vector for verifying a circuit design comprising a circuit block includes inputting a test vector to the circuit block, generating one or more rewards based on a coverage corresponding to the test vector, the coverage being determined based on a state transition of the circuit block based on the test vector, and applying the one or more rewards to a reinforcement learning.

Abstract: An orthodontic wire bending device includes a providing part, a bending unit and a cutting part. The providing part is configured to provide a wire. The bending unit is disposed at a front side of the providing part, and includes a fixing part and a bending part. The fixing part is configured to fix the wire. The bending part is configured to bend the wire fixed by the fixing part. The cutting part is configured to cut the wire bent by the bending part. The bending part includes a bending module, and the bending module is rotated along a circumferential direction or moves along a direction to make contact with at least one side of the wire for bending the wire.

Abstract: Methods and electronic devices are provided for processing an input. A first object is displayed on a display of the electronic device. A first input related to the first object is received from a user of the electronic device. The first object is moved according to the first input. A speed at which the first object is moving is determined. The speed is compared with a preset threshold. An instruction corresponding to the first input is determined based on a comparison of the speed with the preset threshold. The instruction is executed.

Abstract: Methods and electronic devices are provided for processing an input. A first object is displayed on a display of the electronic device. A first input related to the first object is received from a user of the electronic device. The first object is moved according to the first input. A speed at which the first object is moving is determined. The speed is compared with a preset threshold. An instruction corresponding to the first input is determined based on a comparison of the speed with the preset threshold. The instruction is executed.