38 research outputs found
Erratum to: Fully coupled forward-backward stochastic differential equations on Markov chains
Comparison between posterior dynamic stabilization and posterior lumbar interbody fusion in the treatment of degenerative disc disease: a prospective cohort study
Diversity and distribution of autotrophic microbial community along environmental gradients in grassland soils on the Tibetan Plateau
Removal of 17β-Estradiol by Activated Charcoal Supported Titanate Nanotubes (TNTs@AC) through Initial Adsorption and Subsequent Photo-Degradation: Intermediates, DFT calculation, and Mechanisms
A low-cost composite of activated charcoal supported titanate nanotubes (TNTs@AC) was developed via the facile hydrothermal method to remove the 17β-estradiol (E2, a model of pharmaceutical and personal care products) in water matrix by initial adsorption and subsequent photo-degradation. Characterizations indicated that the modification occurred, i.e., the titanate nanotubes would be grafted onto the activated charcoal (AC) surface, and the micro-carbon could modify the tubular structure of TNTs. E2 was rapidly adsorbed onto TNTs@AC, and the uptake reached 1.87 mg/g from the dual-mode model fitting. Subsequently, the adsorbed E2 could be degraded 99.8% within 2 h under ultraviolet (UV) light irradiation. TNTs@AC was attributed with a unique hybrid structure, providing the hydrophobic effect, π−π interaction, and capillary condensation for E2 adsorption, and facilitating the electron transfer and then enhancing photocatalytic ability for E2-degradation. In addition, the removal mechanism of E2 was elucidated through the density functional theory calculation. Our study is expected to provide a promising material for environmental application
Risk factors for postoperative complication after spinal fusion and instrumentation in degenerative lumbar scoliosis patients
Degradation Prediction of GaN HEMTs under Hot-Electron Stress Based on ML-TCAD Approach
In this paper, a novel approach that combines technology computer-aided design (TCAD) simulation and machine learning (ML) techniques is demonstrated to assist the analysis of the performance degradation of GaN HEMTs under hot-electron stress. TCAD is used to simulate the statistical effect of hot-electron-induced, electrically active defects on device performance, while the artificial neural network (ANN) algorithm is tested for reproducing the simulation results. The results show that the ML-TCAD approach can not only rapidly obtain the performance degradation of GaN HEMTs, but can accurately predict the progressive failure under the work conditions with a mean squared error (MSE) of 0.2, informing the possibility of quantitative failure data analysis and rapid defect extraction via the ML-TCAD approach