Search

Emerging millimeter‐wave wireless communication systems require high‐performance radio frequency switches to support advanced functionality such as frequency agility, circuit reconfigurability, and beamforming. Ferroelectric (FE)‐gated AlGaN/GaN high‐electron‐mobility transistors (HEMTs) (FeHEMTs) are developed and demonstrated as a technology option for this important role. AlGaN/GaN HEMT heterostructures grown by metal‐organic chemical vapor deposition are augmented with a FE gate stack comprising atomic layer deposition‐grown HfZrO2. In addition, highly doped regrown source and drain Ohmic contacts are integrated into an improved fabrication process flow. This improved process leads to contact resistances of 0.105 Ω mm and a reduction in switch on‐resistance as compared to prior reports, as well as drain current densities of 1 A mm−1 and measured ft = 54 GHz for gate lengths of 0.1 μm. The improved contact and gate length scaling makes FeHEMTs a promising candidate for mm‐wave switch applications achieving a reported switch figure of merit of fCO = 1.55 THz. [ABSTRACT FROM AUTHOR]

According to the statistical results of the reciprocating compressor maintenance in chemical enterprises, the probability of faults caused by wear or damage of vulnerable parts inside the cylinder is close to 80%. Now, fault diagnosis of vulnerable parts inside the cylinder is relied on vibration signal, acoustic emission signal, or thermal parameters. However, it is difficult to extract eigenvalues from vibration signals and acoustic emission signals, which can be disturbed by noise easily. Thermal parameters are relatively stable and less affected by noise. The measurement of thermal parameters requires drilling testing holes through the cylinder wall, which will decrease the strength of the cylinder. According to the working principle of the compressor, fault of the vulnerable parts inside the cylinder would change the pressure and temperature distribution at the suction and the discharge port of the cylinder, while these state parameters are monitored by the parameter monitoring system on most of the compressors. For these reasons, a fault prediction model based on state parameters learning is proposed in this paper aiming to fault diagnosis of vulnerable parts inside the cylinder, which could make fault prediction without adding new hardware cost, such as sensors. Optimized back propagation neural network method by genetic algorithm (GA-BP) is applied to establish the fault prediction model to describe normal working process of the compressor. When fault of vulnerable parts inside the cylinder occurs, monitored pressure and temperature would deviate from the predicted value by the fault prediction. The degree of the deviation is adopted to the fault diagnosis. Then, experiments simulating the fault condition were carried out, and it shows that the accuracy of fault diagnosis using this method could exceed 95%. Verification test shows that the proposed method could successfully predict the fault before the unplanned shutdown. [ABSTRACT FROM AUTHOR]

Aims: Cognitive dysfunction associated with chronic pain may be caused by impaired synaptic plasticity. Considering the impact of silent information regulator 1 (SIRT1) on synaptic plasticity, we explored the exact role of SIRT1 in cognitive impairment caused by chronic pain. Methods: We evaluated the memory ability of mice with the fear conditioning test (FCT) after spared nerve injury (SNI) model. Western blotting and immunofluorescence were used to analyze the expression levels of SIRT1. Hippocampal synaptic plasticity was detected with Golgi staining, transmission electron microscopy, and long‐term potentiation (LTP). In the intervention study, AAV9‐CaMKIIα‐Cre‐EGFP was injected to SIRT1flox/flox mice to knockdown the expression levels of SIRT1. Besides, SNI mice were injected with AAV2/9‐CaMKIIα‐SIRT1‐3*Flag‐GFP or SRT1720 to increase the expression levels or enzymatic activity of SIRT1. Results: Our current results indicated that cognitive function in SNI mice was impaired, SIRT1 expression in glutaminergic neurons in the hippocampal CA1 area was downregulated, and synaptic plasticity was altered. Selective knockdown of SIRT1 in hippocampus damaged synaptic plasticity and cognitive function of healthy mice. In addition, the impaired synaptic plasticity and cognitive dysfunction of SNI mice could be improved by the upregulation of SIRT1 expression or enzyme activity. Conclusions: Reduced SIRT1 expression in hippocampus of SNI mice may induce cognitive impairment associated with chronic pain by mediating the impaired synaptic plasticity. [ABSTRACT FROM AUTHOR]