Ladder-like energy-relaying exciplex enables 100% internal quantum efficiency of white TADF-based diodes in a single emissive layer.

Development of white organic light-emitting diodes based on purely thermally activated delayed fluorescence with a single-emissive-layer configuration has been a formidable challenge. Here, we report the rational design of a donor-acceptor energy-relaying exciplex and its utility in fabricating single-emissive-layer, thermally activated delayed fluorescence-based white organic light-emitting diodes that exhibit 100% internal quantum efficiency, 108.2 lm W-1 power efficiency, and 32.7% external quantum efficiency. This strategy enables thin-film fabrication of an 8 cm × 8 cm thermally activated delayed fluorescence white organic light-emitting diodes (10 inch2) prototype with 82.7 lm W-1 power efficiency and 25.0% external quantum efficiency. Introduction of a phosphine oxide-based acceptor with a steric group to the exciplex limits donor-acceptor triplet coupling, providing dual levels of high-lying and low-lying triplet energy. Transient spectroscopic characterizations confirm that a ladder-like energy relaying occurs from the high-lying triplet level of the exciplex to a blue emitter, then to the low-lying triplet level of the phosphine oxide acceptor, and ultimately to the yellow emitter. Our results demonstrate the broad applicability of energy relaying in multicomponent systems for exciton harvesting, providing opportunities for the development of third-generation white organic light-emitting diode light sources

Data augmentation and intelligent fault diagnosis of planetary gearbox using ILoFGAN under extremely limited samples

Though the existing generative adversarial networks (GAN) have the potential for data augmentation and intelligent fault diagnosis of planetary gearbox, it remains difficult to deal with extremely limited training samples and effectively fuse the representative and diverse information. To tackle the above challenges, an improved local fusion generative adversarial network (ILoFGAN) is proposed. Time-domain waveforms are firstly transformed into the time-frequency diagrams to highlight the fault characteristics. Subsequently, a local fusion module is used to fully utilize extremely limited samples and fuse the local features. Finally, a new generator embedded with multi-head attention modules is constructed to effectively improve the accuracy and flexibility of the feature fusion process. The proposed method is applied to the analysis of planetary gearbox vibration signals. The results show that the proposed method can generate a large number of samples with higher similarity and better diversity compared with the existing mainstream GANs using 6 training samples in each type. The generated samples are used to augment the limited dataset, prominently improving the accuracy of the fault diagnosis task

Predicting human microRNA precursors based on an optimized feature subset generated by GA–SVM

AbstractMicroRNAs (miRNAs) are non-coding RNAs that play important roles in post-transcriptional regulation. Identification of miRNAs is crucial to understanding their biological mechanism. Recently, machine-learning approaches have been employed to predict miRNA precursors (pre-miRNAs). However, features used are divergent and consequently induce different performance. Thus, feature selection is critical for pre-miRNA prediction. We generated an optimized feature subset including 13 features using a hybrid of genetic algorithm and support vector machine (GA–SVM). Based on SVM, the classification performance of the optimized feature subset is much higher than that of the two feature sets used in microPred and miPred by five-fold cross-validation. Finally, we constructed the classifier miR-SF to predict the most recently identified human pre-miRNAs in miRBase (version 16). Compared with microPred and miPred, miR-SF achieved much higher classification performance. Accuracies were 93.97%, 86.21% and 64.66% for miR-SF, microPred and miPred, respectively. Thus, miR-SF is effective for identifying pre-miRNAs

Effect Assessment of Airflow Resistance by Local Airway Stenosis with 3D Printing Airway Model

Clinically noticing airway constriction can randomly cause small airway quickly closed and the surrouding airway occlusion happens subsequently. A phenomenon may happened called "avalanche phenomenon" inside airway [1]. But few study on how local airway stenosis affects the respiratory flow. Because the real local airway stenosis and its flow are still unable to be directly observed and measured. In this paper, narrow numerical model of the main and branched airway are established based on CT data of normal human airways. Then the trachea and bronchial branched airway constriction models are printed out on the 3D printer by PLA material. Finally, to measure airflow impedance of different airway models and analyze the impact of structural changes in the airway (shrink and narrow) airway impedance, we adopt independent research and development Forced Oscillation Technique(FOT). The test results preliminary show that the trachea stenosis has big effect on the airway viscous resistance (Rrs) and the elastic resistance (Xrs). The bronchial stenosis obviously increases the airway elastic resistance. This article provides a new method for the study on how local constriction affects the airflow inside airway in the future

Evaluation of the Durability and the Property of an Asphalt Concrete with Nano Hydrophobic Silane Silica in Spring-Thawing Season

In the spring-thawing season, the high frequency of freeze-soak-scour cycles in the short term is the main cause of pavement damage in the frozen region. One of the methods to improve the durability of asphalt concrete in spring-thawing season is to add suitable modifiers and additives which improve adhesion between asphalt binder and aggregate. This study evaluates the effect of nano hydrophobic silane silica (NHSS) on the performance damage of asphalt concrete (AC) in spring-thawing season. The effectiveness of nano hydrophobic silane silica was evaluated by conducting mixture tests after different freeze-soak-scour cycles, and the damage model of NHSS modified asphalt concrete was established based on the logistic damage model. The results showed that adding NHSS is an effective technique for mitigating freeze-soak-scour cycle damage of asphalt concrete in spring-thawing season. Moreover, the influence of scour, soak, and freeze—three separate factors on NHSS-modified AC in spring-thawing season—was discussed based the gray rational degree theory. The results illustrated that the freeze factor had a more significant impact on the damage process of NHSS modified asphalt concrete compared with the soak and scour factor