Beihang University

About: Beihang University is a education organization based out in Beijing, China. It is known for research contribution in the topics: Control theory & Microstructure. The organization has 67002 authors who have published 73507 publications receiving 975691 citations. The organization is also known as: Beijing University of Aeronautics and Astronautics.

Development and Validation of a Deep Learning System to Detect Glaucomatous Optic Neuropathy Using Fundus Photographs.

Abstract: Importance A deep learning system (DLS) that could automatically detect glaucomatous optic neuropathy (GON) with high sensitivity and specificity could expedite screening for GON. Objective To establish a DLS for detection of GON using retinal fundus images and glaucoma diagnosis with convoluted neural networks (GD-CNN) that has the ability to be generalized across populations. Design, Setting, and Participants In this cross-sectional study, a DLS for the classification of GON was developed for automated classification of GON using retinal fundus images obtained from the Chinese Glaucoma Study Alliance, the Handan Eye Study, and online databases. The researchers selected 241 032 images were selected as the training data set. The images were entered into the databases on June 9, 2009, obtained on July 11, 2018, and analyses were performed on December 15, 2018. The generalization of the DLS was tested in several validation data sets, which allowed assessment of the DLS in a clinical setting without exclusions, testing against variable image quality based on fundus photographs obtained from websites, evaluation in a population-based study that reflects a natural distribution of patients with glaucoma within the cohort and an additive data set that has a diverse ethnic distribution. An online learning system was established to transfer the trained and validated DLS to generalize the results with fundus images from new sources. To better understand the DLS decision-making process, a prediction visualization test was performed that identified regions of the fundus images utilized by the DLS for diagnosis. Exposures Use of a deep learning system. Main Outcomes and Measures Area under the receiver operating characteristics curve (AUC), sensitivity and specificity for DLS with reference to professional graders. Results From a total of 274 413 fundus images initially obtained from CGSA, 269 601 images passed initial image quality review and were graded for GON. A total of 241 032 images (definite GON 29 865 [12.4%], probable GON 11 046 [4.6%], unlikely GON 200 121 [83%]) from 68 013 patients were selected using random sampling to train the GD-CNN model. Validation and evaluation of the GD-CNN model was assessed using the remaining 28 569 images from CGSA. The AUC of the GD-CNN model in primary local validation data sets was 0.996 (95% CI, 0.995-0.998), with sensitivity of 96.2% and specificity of 97.7%. The most common reason for both false-negative and false-positive grading by GD-CNN (51 of 119 [46.3%] and 191 of 588 [32.3%]) and manual grading (50 of 113 [44.2%] and 183 of 538 [34.0%]) was pathologic or high myopia. Conclusions and Relevance Application of GD-CNN to fundus images from different settings and varying image quality demonstrated a high sensitivity, specificity, and generalizability for detecting GON. These findings suggest that automated DLS could enhance current screening programs in a cost-effective and time-efficient manner.

High-throughput theoretical optimization of the hydrogen evolution reaction on MXenes by transition metal modification

Abstract: Electrocatalysis has the potential to become a more sustainable approach to generate hydrogen as a clean energy source and chemical feedstock. Finding a stable, eco-friendly, low cost and highly efficient catalyst is one of the prerequisites to realize large-scale industrial electrocatalytic hydrogen production. Two-dimensional metal carbide and nitride (MXene) materials have shown characteristics of promising hydrogen evolution reaction (HER) catalysts, but challenges in terms of both hydrogen adsorption strength and reaction rate still need to be addressed. In addition, previous theoretical studies of MXenes for the HER focus mainly on the thermodynamics (e.g. hydrogen adsorption energy) rather than the kinetics of the reaction (e.g. energy barrier and reaction rate). In this work, we utilize high-throughput computational methods to study both the HER thermodynamics and kinetics of M2XO2 type MXenes and how their HER activity can be enhanced by the modification of different transition metal (TM) adatoms. Compared to the relatively weak HER activity observed for the majority of pristine MXenes, the addition of TM adatoms on the MXene surface is predicted to enhance their HER activity significantly. The presence of TM not only optimizes the Gibbs free energy of hydrogen adsorption (ΔGH) but also reduces the H2 production activation barrier. Intriguingly, we observed a HER mechanism preference switch from Volmer–Heyrovsky observed on pristine MXenes to Volmer–Tafel after modification with TM adatoms. On the basis of in-depth and systematic exploration of the electronic structure and interaction between hydrogen and MXenes, the origin of the mechanism preference switch is linked to the TM-induced electron redistribution on the surface of the MXene.

Joint Power Allocation and Beamforming for Non-Orthogonal Multiple Access (NOMA) in 5G Millimeter-Wave Communications

Abstract: In this paper we explore non-orthogonal multiple access (NOMA) in millimeter-wave (mmWave) communications (mmWave-NOMA). In particular, we consider a typical problem, i.e., maximization of the sum rate of a 2-user mmWave-NOMA system. In this problem, we need to find the beamforming vector to steer towards the two users simultaneously subject to an analog beamforming structure, while allocating appropriate power to them. As the problem is non-convex and may not be converted to a convex problem with simple manipulations, we propose a suboptimal solution to this problem. The basic idea is to decompose the original joint beamforming and power allocation problem into two sub-problems which are relatively easy to solve: one is a power and beam gain allocation problem, and the other is a beamforming problem under a constant-modulus constraint. Extension of the proposed solution from 2-user mmWave-NOMA to more-user mmWave-NOMA is also discussed. Extensive performance evaluations are conducted to verify the rational of the proposed solution, and the results also show that the proposed sub-optimal solution achieve close-to-bound sum-rate performance, which is significantly better than that of time-division multiple access (TDMA).

Synergistically Optimizing Electrical and Thermal Transport Properties of BiCuSeO via a Dual-Doping Approach

Abstract: The layered oxyselenide BiCuSeO system is known as one of the high-performance thermoelectric materials with intrinsically low thermal conductivity. By employing atomic, nano- to mesoscale structural optimizations, low thermal conductivity coupled with enhanced electrical transport properties can be readily achieved. Upon partial substitution of Bi3+ by Ca2+ and Pb2+, the thermal conductivity can be reduced to as low as 0.5 W m−1 K−1 at 873 K through dual-atomic point-defect scattering, while a high power factor of ≈1 × 10−3 W cm−1 K−2 is realized over a broad temperature range from 300 to 873 K. The synergistically optimized power factor and intrinsically low thermal conductivity result in a high ZT value of ≈1.5 at 873 K for Bi0.88Ca0.06Pb0.06CuSeO, a promising candidate for high-temperature thermoelectric applications. It is envisioned that the all-scale structural optimization is critical for optimizing the thermoelectricity of quaternary compounds.