Search

Gallium Nitride (GaN) high-electron mobility transistors (HEMTs) are highly promising for high-frequency and high-power applications due to their superior properties, such as a wide energy bandgap and high carrier density. The performance of GaN HEMTs is significantly influenced by the interfacial states of the AlGaN barrier, and gate annealing has emerged as a key process for reducing leakage currents and enhancing DC/RF characteristics. This research investigates the impact of gate annealing on AlGaN/GaN HEMTs, focusing on two main aspects: leakage current reduction and improvements in DC and RF efficiency. Through comprehensive electrical analysis, including DC and RF measurements, the effects of gate annealing were experimentally evaluated. The results show a significant reduction in gate leakage current and noticeable improvements in DC/RF performance for the devices that underwent gate annealing. The study confirms that the annealing process can effectively enhance device performance by modifying the material properties at the gate interface. [ABSTRACT FROM AUTHOR]

Human decision-making processes are complex. It is thus challenging to mine human strategies from real games in social networks. To model human strategies in social dilemmas, we conducted a series of human subject experiments in which the temporal two-player non-cooperative games among 1092 players were intensively investigated. Our goal is to model the individuals’ moves in the next round based on the information observed in each round. Therefore, the developed model is a strategy model based on short-term memory. Due to the diversity of user strategies, we first cluster players’ behaviors to aggregate them with similar strategies for the following modeling. Through behavior clustering, our observations show that the performance of the tested binary strategy models can be highly promoted in the largest behavior groups. Our results also suggest that no matter whether in the classical mode or the dissipative mode, the influence of individual accumulated payoffs on individual behavior is more significant than the gaming result of the last round. This result challenges a previous consensus that individual moves largely depend on the gaming result of the last round. Therefore, our model provides a novel perspective for understanding the evolution of human altruistic behavior.

In the process of steel plate production, whether cold straightening is required is significant to reduce costs and improve product qualification rates. It is not effective by adopting classic machine learning judgment algorithms. Concerning the effectiveness of ensemble learning methods on improving traditional machine learning methods, a steel plate cold straightening auxiliary decision-making algorithm based on multiple machine learning competition strategies is proposed in this paper. The algorithm firstly adopts the rough set method to simplify the attributes of the conditional factors for affecting whether the steel plate cold straightening is required, and reduce the attribute dimensions of the steel plate cold straightening auxiliary decision-making data set. Secondly, the competition of training multiple different learners on the data set produces the optimal base classifier. Finally, the final classifier is generated by training weights on the optimal base classifier and combining it with a centralized strategy. While the hit rate of good products of the final classifier is 97.9%, the hit rate of defective products is 90.9%. As such, the accuracy rate is better than the single kind of simple machine learning algorithms, which effectively improves the product quality of steel plates in practical production applications.

Introduction: T wave oversensing (TWOS) is a major drawback of implantable cardioverter defibrillator (ICD) and data on predictors of TWOS in ICD is limited. We aimed to calculate a novel index of T wave safety margin (TWSM) and assess its potential for evaluating TWOS during the procedure of ICD implantation.Methods and Results: Thirty-two consecutive patients with ICD implantation were enrolled. During each procedure of ICD implantation, different ICD generators were connected to implanted sensing lead through active-fixation leads and bridging cables. R and T wave amplitudes were measured on ICD printouts according to the gain. The ICDs were programed to the most sensitive settings to reveal possible TWOS. A novel index TWSM was calculated according to the corresponding sensing algorithm of ICD. There was discrepancy of R wave amplitudes measured by different ICDs (P < 0.01). In Fortify and Teligen ICDs, T wave amplitudes showed no difference (P > 0.05) and TWSMs were sufficiently high (post sensing: 13.0 ± 7.6 and 28.3 ± 16.5, respectively, post pacing: 5.0 ± 2.2 and 4.6 ± 0.9, respectively). In nine patients with 10 TWOS episodes detected during the procedure of ICD implantation, generators with the highest TWSM were chosen. Only one TWOS episode during pacing was recorded during the 25 ± 7 mo follow-up period.Conclusions: We first propose the index of TWSM during ICD implantation as a potentially efficient predictor for TWOS. Evaluation of TWSM might help to reduce TWOS episodes in patients with high risk of TWOS. Prospective studies are warranted to validate this index and its potential to reduce TWOS episodes.