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A novel loop heat pipe used for data center with a liquid line wick is designed, and its one-dimensional steady-state mathematical model is developed based on the energy and thermodynamic equilibrium of each component and the simulation results were validated by comparing with the experimental data in this work. The compensation chamber of the loop heat pipe was removed, and a section of capillary wick was added in the end of liquid line in order to reduce heat leakage and vapor backflow and increase working medium circulation power. The mathematical model of the novel loop heat pipe can be used to predict the operating temperature of each characteristic point with small relative errors of

Based on the traditional structure of in plane switching liquid crystal display, a cylindrical liquid crystal micro-lens array driven by interdigital electrodes is proposed, and its performance is studied. Firstly, the director distribution and relative phase difference distribution of liquid crystal molecules were calculated, which shows that this structure can be used to fabricate cylindrical liquid crystal micro-lens. Then, by comparing lenses of difference sizes, it is found that narrow electrodes should be selected for lens production, and the centre of the electrode gap should be selected as the central axis of a single liquid crystal micro-lens. Finally, a certain electrode structure is selected to explore its focal length, focusing quality and zoom time. The results show that the focal length of the liquid crystal micro-lens decreases gradually with the increase of the driving voltage. When the driving voltage is between 3 Vrms and 5.5 Vrms, the liquid crystal micro-lens has good focusing quality, and its corresponding focal length changes from 1.63 mm to 20.28 mm. Based on this structure, when the liquid crystal lens is driven by the above two voltages alternately, the time required for liquid crystal molecules to reach stability is 2.35 s and 3.87 s respectively. [ABSTRACT FROM AUTHOR]

Short-term traffic flow prediction (STFP) is one of the key technologies in Intelligence Transportation System (ITS). With the development of artificial intelligence technology, deep learning has been employed to STFP and certain achievements have been obtained. However, further improvement of STFP accuracy is constrained by the presence of noise, modal aliasing in the modal components and insufficient feature extraction of neural networks. Therefore, to more accurately capture the changing trend of traffic flow in different time periods, and increase the reliability and interpretability of the prediction models, improved variational mode decomposition (IVMD) and self-attention mechanism (SAM) based hybrid convolutional neural network (CNN) and long short term memory network (LSTM) are proposed for STFP in this study, benefited from the modal decomposition of IVMD and redistribution of the neural weights in CNN-LSTM networks by SAM, the proposed IVMD-CNN-LSTM-SAM effectively suppresses the influence of randomness and volatility of traffic flow, and accurately predict the short-term lane occupancy in the designated area. Furthermore, this study analyses the mechanism of SAM and IVMD in STFP, and proves the superiority of IVMD and SAM in signal decomposition with physical significance and weight allocation respectively. Therefore, the interpretability of the proposed neural network model is significantly improved. Eleven competitive neural networks forecasting methods are used as benchmarks and numerical studies based on actual traffic flow validate the accuracy and stability of the proposed IVMD-CNN-LSTM-SAM. Mean absolute percentage error and goodness of fit of the proposed model are 0.81% and 0.9968, respectively. [ABSTRACT FROM AUTHOR]

The thermistor was fabricated by filling the nematic liquid crystal (NLC) material IM95100-000 into a liquid crystal device with an interdigital electrode structure. In the temperature range of 25 °C–120 °C, the temperature response of the thermistor under different voltages was measured with a step length of 5 °C to study its performance. The experimental results show that the resistance value of the thermistor decreases exponentially with increasing temperature in the range of 25 °C–120 °C. The temperature resistance characteristics at different voltages can satisfy the Steinhart-Hart formula well and the nonlinear fitting coefficient can reach 0.999; under the voltage of 1 V, the total resistance percentage changes by 93.1%, the material constant B25-120 value can reach 3299.07 K, and the temperature coefficient of resistance (TCR) can reach 0.98%/K. As a temperature sensor, it is twice as sensitive as that without the interpolated electrode structure, and the sensitivity can be further improved as the temperature decreases. Through the stability and recovery measurements, it is found that the resistance has no obvious deviation, which verifies the possibility of long-term application of this thermistor. [ABSTRACT FROM AUTHOR]

Thymocyte antigen-1 (THY-1)is a potential target for rheumatoid arthritis (RA) treatment, and THY-1 positive fibroblast-like synoviocytes (FLS) are enriched in the synovium of RA patients and participate in angiogenesis to accelerate RA progression. In this study, we screened an antibody targeting THY-1 (THY-1 Ab) and explored its mechanism in alleviating RA progression. THY-1 Ab was screened from ScFv phage antibody library by phage display technology (PDT). THY-1 Ab–treated collagen induced arthritis (CIA) mice had lower degree of arthritis scores. We explore the mechanism of THY-1 Ab in alleviating RA progression. THY-1 Ab can remarkably inhibit the secretion of pro-inflammatory factors and promote the secretion of anti-inflammatory factors. Further experiments showed that THY1 Ab downregulated the expression of JUNB by the hsa_circ_0094342/miRNA-155–5P/SPI1 axis, inhibited RA angiogenesis and osteoclast differentiation, and relieved RA progression. These findings support that THY-1 Ab is a promising therapeutic antibody for RA treatment. [ABSTRACT FROM AUTHOR]

Acute lung injury (ALI) is a life-threatening disease with high incidence and mortality rates. Urolithin A (UA) is a pomegranate intestinal flora metabolite with anti-inflammatory, antioxidant, and anti-aging properties. Ferroptosis is a critical factor in lipopolysaccharide (LPS)-induced acute lung injury (ALI). However, the link between UA and ferroptosis is unknown. The purpose of this research was to look into the role of UA in regulating LPS-induced ferroptosis in ALI. The current study used LPS to injure two models, one BEAS-2B cell injury model and one ALI mouse model. UA effectively alleviated LPS-induced ALI compared to the LPS group by lowering in vivo lung wet/dry weight ratio, reactive oxygen species, and malondialdehyde production, as well as superoxide dismutase, catalase, and glutathione depletion. Furthermore, by increasing GPX4 and SLC7A11 expression and decreasing Fe 2+ levels, lung histopathological damage, inflammatory cytokine secretion, and ferroptosis levels can be significantly reduced. The Keap1-Nrf2/HO-1 pathway was upregulated by UA, which inhibited LPS-induced ALI and ferroptosis. ML385 inhibited UA's protective effect against LPS-induced ALI. These findings suggested that UA could be a novel potential therapeutic target for ALI., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Lou, Wang, He, Yang, Meng, Wang, Jin, Cai and Cai.)