In 2011, Zhang's research team, in cooperation with Li and Liu, reported the Pt1/FeOx atomically dispersed catalysts for the first time in the world, and based on this, proposed the...

Qianliexin capsule (QLX) is a standardized traditional Chinese herbal preparation that has long been used to treat chronic non‐bacterial prostatitis (CNP) and benign prostatic hyperplasia (BPH). This study investigated the anti‐inflammatory activity of QLX in improving lower urinary tract symptoms (LUTS) associated with CNP and BPH. Rat models of CNP and BPH were induced by oestradiol or testosterone (hormonal imbalance) or chemical inflammation (carrageenan). QLX significantly relieved LUTS in CNP and BPH rat model by reducing prostate enlargement, epithelial thickness, pain response time, urine volume and bleeding time, and by improving prostatic blood flow. The expression of the pro‐inflammatory cytokines interleukin (IL)‐1β and tumour necrosis factor (TNF)‐α, the pro‐inflammatory transcription factor nuclear factor kappa‐light‐chain‐enhancer of activated B cells (NF‐κB), and inflammasome components (NLRP3, caspase‐1 and ASC) in CNP and BPH tissues was reduced by QLX addition. QLX treatment was followed by reduced cellular malondialdehyde and increased superoxide dismutase, catalase and glutathione peroxidase activity, consistent with antioxidant activity. Increases in Beclin‐1 expression and the LC3II/I ratio following QLX treatment indicated that autophagy had been induced. QLX relieved LUTS in CNP and BPH rat models by inhibiting inflammation. The underlying mechanisms included inhibition of inflammasome activation, NF‐κB activation, oxidant stress and autophagy.

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Many smart factories use the smart grid for power system automation, and its wireless control technology requires low-time-delay and high-reliability communication. Guessing random additive noise decoding algorithm has outstanding short packet error correction performance. In the decoding process, the order of noise parameter combination affects the decoding delay. Aiming at the communication problem of the smart grid in the process of factory power supply and distribution, this study analyzes the characteristics of the original noise parameter ranking algorithm. When the steady-state flip probability is large, more search times are required to obtain the correct combination of noise parameters, which means that greater delay is required for decoding in the time-varying channel. To solve the aforementioned problems, this study optimizes the noise parameter ranking before the noise error mode arrangement and proposes a noise parameter ranking algorithm for predicting the symbol string. First, the channel perception is completed by edge computing. Then, the algorithm uses the obtained soft information to rank the channel noise parameters. Simulation results show that the proposed algorithm has better search performance than the original sorting algorithm, especially when the channel parameter b is greater than 0.5. Finally, by comparing the BM Decoding Algorithm of BCH with different noise parameter ranking algorithms of decoding, the results show that the noise parameter ranking algorithm proposed in this study has better decoding performance in the environmental channel of the smart factory, so as to improve the reliability of the smart grid in the process of factory power supply and distribution.

Smart grids are being expanded in scale with the increasing complexity of the equipment. Edge computing is gradually replacing conventional cloud computing due to its low latency, low power consumption, and high reliability. The CORDIC algorithm has the characteristics of high-speed real-time processing and is very suitable for hardware accelerators in edge computing devices. The iterative calculation method of the CORDIC algorithm yet leads to problems such as complex structure and high consumption of hardware resource. In this paper, we propose an RDP-CORDIC algorithm which pre-computes all micro-rotation directions and transforms the conventional single-stage iterative structure into a three-stage and multi-stage combined iterative structure, thereby enabling it to solve the problems of the conventional CORDIC algorithm with many iterations and high consumption. An accuracy compensation algorithm for the direction prediction constant is also proposed to solve the problem of high ROM consumption in the high precision implementation of the RDP-CORDIC algorithm. The experimental results showed that the RDP-CORDIC algorithm had faster computation speed and lower resource consumption with higher guaranteed accuracy than other CORDIC algorithms. Therefore, the RDP-CORDIC algorithm proposed in this paper may effectively increase computation performance while reducing the power and resource consumption of edge computing devices in smart grid systems.