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The SnO2–MoO3–C composite anode for Li-ion batteries (LIBs) was produced via simple hydrothermal synthesis and dry ball milling. In this hybrid, nanosized-SnO2/MoO3 nanoparticles were encapsulated in plate-like graphite. MoO3 additive can protect Sn from aggregating and accelerate the reversible conversion reaction (Sn/Li2O hybrid transformation into SnO2). The SnO2–MoO3–C displays high initial coulombic efficiency of 70% (average for five cells), high reversible specific capacity of 1338.3 mAhg−1 at 0.2 A g-1 after 300 cycles, superior rate capacity of 715.08 mAhg−1 at 5 A g-1 and long-term cyclic stability, with capacity of 571.9 mAhg−1 at 2.0 A g-1 after 1000 cycles. Owing to its superior performance and simple synthesis, the SnO2–MoO3–C composite is a promising anode material for LIBs.

To improve the computing accuracy and simulation efficiency of fatigue reliability estimation for turbine rotor, a multi-agent collaborative modelling (MACM) approach is proposed by absorbing the strengths of improved differential evolution (IDE) algorithm and neural network model into decomposed-collaborative strategy. The fatigue reliability estimation framework is presented in respect of MACM approach. Furthermore, the fatigue reliability estimation of a typical turbine rotor is considered as engineering case to evaluate the presented approach with respect to material variabilities, load fluctuations and model randomness. The estimation results reveal that the probabilistic fatigue life of a turbine rotor under reliability 99.87% is 6 050 cycles; fatigue strength coefficient σf′ and strain range Δɛt play a leading role on the fatigue life since their effect probabilities of 45% and 36%, respectively. The comparisons of methods (direct Monte Carlo simulation (MCS), quadratic polynomial (QP), neural network (NN), neural network agent (NNA)) show that the presented MACM approach holds high efficiency and accuracy for fatigue reliability estimation of turbine rotor.

Compressor blade seriously affects the reliability and stability of aircraft engine. Probabilistic flutter evaluation of compressor blade can effectively quantify failure risk and improve aeroelastic stability. To improve the computational efficiency of probabilistic flutter evaluation, a surrogate modeling approach (called as PSO-LSSVR) is presented by absorbing the strength of particle swarm optimization (PSO) algorithm and least-squares support vector regression (LSSVR). We first mathematically modeled the PSO-LSSVR and then introduce the corresponding probabilistic flutter framework. With respect to high-nonlinearity and strong coupling of limit-state function, the probabilistic flutter evaluation of NASA Rotor 37 is performed to evaluate the proposed approach. Compared with Monte Carlo method, quadratic polynomial (QP), regular support vector regression (SVR and LSSVR), the presented PSO-LSSVR possesses the highest computing efficiency while keeping acceptable computing precision.

Besides its role as the blueprint of life, DNA can also alert the cell to the presence of microbial pathogens as well as damaged or malignant cells. A major sensor of DNA that triggers the innate immune response is cyclic guanosine monophosphate (GMP)-adenosine monophosphate (AMP) (cGAMP) synthase (cGAS), which produces the second messenger cGAMP. cGAMP activates stimulator of interferon genes (STING), which activates a signaling cascade leading to the production of type I interferons and other immune mediators. Recent research has demonstrated an expanding role of the cGAS-cGAMP-STING pathway in many physiological and pathological processes, including host defense against microbial infections, anti-tumor immunity, cellular senescence, autophagy, and autoimmune and inflammatory diseases. Biochemical and structural studies have elucidated the mechanism of signal transduction in the cGAS pathway at the atomic resolution. This review focuses on the structural and mechanistic insights into the roles of cGAS and STING in immunity and diseases revealed by these recent studies.

Objective: Bushen Chengyun granule (BCG) is an empirical treatment for female infertility (FI) caused by low endometrial receptivity (LER) involving a poorly understood mechanism. In this study, network pharmacology was used to explore the potential therapeutic mechanism of BCG on FI caused by LER. Methods: The corresponding herb targets were obtained by conducting a search in the Traditional Chinese Medicine Systems Pharmacology Database and PubMed-reported literature. Disease targets were obtained from the following databases: Comparative Toxicogenomics Database, Human Phenotype Ontology, and Therapeutic Target Database. Treatments for LER using BCG have used target matching (BCG – LER target). Then, the predicted targets were uploaded to the Search Tool for the Retrieval of Interacting Genes/Proteins database for gene ontology enrichment and Kyoto Encyclopedia of Genes and Genomes pathway analyses. Furthermore, triptorelin acetate for injection + menotrophin + chorionic gonadotropin for injection were used to establish a mouse model of blastocyst implantation disorder and to evaluate the in vivo effect of BCG on blastocyst implantation. Results: Overall, 156 bioactive chemical components and 1092 targets of BCG were identified. The results indicated that 482 biological processes (FDR

The performance and reliability of aircraft engine are seriously affected by multiple failures induced by multi-physical loads. Multi-failure probabilistic design is an effective measure to estimate the multi-failure response traits and quantify the multi-failure risk for the improvement of component reliability. In this paper, we propose a neural network regression-distributed collaborative strategy (NNR-DCS) based on a developed two-step error control technique, to improve the efficiency and accuracy of multi-failure probabilistic analysis. We firstly mathematically model NNR-DCS and then introduce the corresponding multi-failure probabilistic framework. With respect to various failure modes such as deformation failure, stress failure and strain failure, the multi-failure probabilistic analysis of a turbine bladed disk is conducted to evaluate the proposed method. From this simulation, we gain the probabilistic distribution features, reliability degree and sensitivity degree of each failure mode and overall failure modes on turbine bladed disk, which provides a useful reference for improving the reliability and performance of aircraft engine. The comparison of methods (Monte Carlo method, RSM, DCRSM, DCFRM, NNR and NNR-DCS) shows that the proposed NNR-DCS holds high efficiency and accuracy for multi-failure probabilistic analysis. The efforts of this study offer an effective way for multi-failure evaluation from a probabilistic perspective and shed light on the multi-objective reliability-based design optimization of complex structures besides turbine bladed disk.

Low cycle fatigue (LCF) seriously affects the performance and reliability of turbomachinery like aeroengine. Probabilistic LCF assessment can effectively quantify risk and improve components reliability. To improve the accuracy and efficiency of probabilistic LCF life assessment, a distributed collaborative (DC)-wavelet neural network regression (WNNR) (called as DC-WNNR) surrogate model is developed by proposing Bayesian regularization-Quasi Newton (BR-QN) error control technique. The mathematical model of DC-WNNR is structured and the corresponding probabilistic framework of LCF life assessment is introduced. The probabilistic LCF life assessment for turbine discs is regarded as one case to evaluate the proposed method with respect to various uncertainties such as material property, load fluctuation and model variability. The analysis results reveal that the reliability-based LCF life of a turbine disc under reliability 99.87% is 6 058 cycles; fatigue strength coefficient σf′ and strain range Δɛt play a leading role on the fatigue life failure since their effect probabilities of 45% and 36%, respectively. The comparison of methods (Monte Carlo method, WNNR, DC response surface method and DC-WNNR) shows that the DC-WNNR holds high efficiency and accuracy for the probabilistic LCF assessment. The present efforts offer an effective way for predicting and evaluating structural LCF failure from a probabilistic perspective.