Study on Microwave-Assisted Ignition Using a Novel Aero-Engine Combustor.

Microwave plasma can improve the performance of ignition and combustion, as well as reduce pollutant emissions. By designing a novel microwave feeding device, the combustor can be used as a cavity resonator to generate microwave plasma and improve the performance of ignition and combustion. In order to feed the energy of microwave into the combustor as much as possible, and effectively adapt to the change in resonance frequency of combustor during ignition and combustion, the combustor was designed and manufactured by optimizing the size of slot antenna and setting the tuning screws, according to the simulation results of HFSS software (version: 2019 R 3).

Improving the brain image resolution of generalized q-sampling MRI revealed by a three-dimensional CNN-based method.

Background: Understanding neural connections facilitates the neuroscience and cognitive behavioral research. There are many nerve fiber intersections in the brain that need to be observed, and the size is between 30 and 50 nanometers. Improving image resolution has become an important issue for mapping the neural connections non-invasively.

Disrupted white matter network of brain structural connectomes in bipolar disorder patients revealed by q-ball imaging.

Background: Structural and functional brain changes have been found to be associated with altered emotion and cognition in patients with bipolar disorder (BD). Widespread microstructural white matter abnormalities have been observed using traditional structural imaging in BD. q-Ball imaging (QBI) and graph theoretical analysis (GTA) improve the specificity and sensitivity and high accuracy of fiber tracking.

Surface roughness prediction of aircraft after coating removal based on optical image and deep learning.

To quickly evaluate the surface quality of aircraft after coating removal, a surface roughness prediction method based on optical image and deep learning model is proposed. In this paper, the "optical image-surface roughness" data set is constructed, and SSEResNet for regression prediction of surface roughness is designed by using feature fusion method. SSEResNet can effectively extract the detailed features of optical images, and Adam method is used for training optimization.

Magnetic Resonance-Based Synthetic Computed Tomography Using Generative Adversarial Networks for Intracranial Tumor Radiotherapy Treatment Planning.

The purpose of this work is to develop a reliable deep-learning-based method that is capable of synthesizing needed CT from MRI for radiotherapy treatment planning. Simultaneously, we try to enhance the resolution of synthetic CT. We adopted pix2pix with a 3D framework, which is a conditional generative adversarial network, to map the MRI data domain into the CT data domain of our dataset.

A CNN-Based Autoencoder and Machine Learning Model for Identifying Betel-Quid Chewers Using Functional MRI Features.

Betel quid (BQ) is one of the most commonly used psychoactive substances in some parts of Asia and the Pacific. Although some studies have shown brain function alterations in BQ chewers, it is virtually impossible for radiologists' to visually distinguish MRI maps of BQ chewers from others. In this study, we aimed to construct autoencoder and machine-learning models to discover brain alterations in BQ chewers based on the features of resting-state functional magnetic resonance imaging.

Investigating the white matter correlates of reading performance: Evidence from Chinese children with reading difficulties.

Purpose: Reading comprehension is closely associated with word recognition, particularly at the early stage of reading development. This association is reflected in children with reading difficulties (RD) who demonstrate poor reading comprehension along with delayed word recognition or reduced recognition accuracy. Although the neural mechanisms underlying reading comprehension and word recognition are well studied, few has investigated the white matter (WM) structures that the two processes potentially share.

Effects of crystallization and non-lattice oxygen atoms on Cu(x)O-based resistive switching memory.

In this work, the effects of crystallization and non-lattice oxygen atoms on the Cu(x)O-based memory device are investigated. The 150 degrees C-deposited Cu(x)O film possesses a larger amount of non-lattice oxygen atoms than those deposited at the higher temperatures, leading to the formation of AIOy interface layer during the sputtering deposition of Al top electrode. Resistive switching occurring within the interface layer is easily controlled, so the set and reset voltages are decreased.

Formation and rupture of Ag conductive bridge in ZrO2-based resistive switching memory.

ZrO2-based resistive switching memory has attracted much attention according to its possible application in the next-generation nonvolatile memory. However, the resistive switching mechanism of the ZrO2-based memory device is still controversial. In this study, the mechanism of the ZrO2-based memory device is demonstrated that the resistive switching occurs because of the migration of Ag+ ions.

Effect of non-lattice oxygen on ZrO2-based resistive switching memory.

ZrO2-based resistive switching memory has attracted much attention according to its possible application in the next-generation nonvolatile memory. The Al/ZrO2/Pt resistive switching memory with bipolar resistive switching behavior is revealed in this work. The thickness of the ZrO2 film is only 20 nm.