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Background Cross-sectional studies have shown that the level of bilirubin in vivo is related to the progression of atherosclerosis, but the data from large-scale studies in China and even in the world are very limited. Objective To investigate the effect of serum total bilirubin (TBIL) and indirect bilirubin (IBIL) on the progression of carotid atherosclerosis in healthy people. Methods Retrospective cohort study was adopted to select 11 394 healthy people who had continuous physical examination and carotid intima-media thickness (cIMT) ≤1 mm displayed by carotid artery ultrasound examination as the research subjects in Hebei General Hospital from January 2010 to December 2019. Demographic characteristics, past medical history, physical examination, laboratory examination results of previous physical examination, carotid artery ultrasound and other information of subjects were collected. The subjects were divided into the progressive carotid atherosclerosis group and the non-progressive group according to whether cIMT increased during follow-up. The Cox proportional regression model was used to explore the effect of bilirubin on the progression of carotid atherosclerosis, and Cox regression analysis was performed by gender, age, body mass index (BMI) , hypertension, diabetes, smoking and alcohol consumption. Results 1 586 cases of carotid atherosclerosis progression were detected in 10 years, with a cumulative incidence rate of 13.9%. The results of Cox regression analysis showed that for every 1 standard deviation increase in TBIL and IBIL levels, the risk of carotid atherosclerosis progression decreased by 6.6% and 17.4%, respectively〔HR (95%CI) =0.934 (0.881, 0.990) ; HR (95%CI) =0.826 (0.777, 0.877) 〕 after adjustment for age, sex, BMI, smoking, alcohol consumption, hypertension, diabetes, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, total cholesterol, triglyceride, alanine aminotransferase and estimated glomerular filtration rate. Subgroup analysis showed that there was no statistical difference in the protective effects of TBIL and IBIL on different subgroups. Conclusion TBIL and IBIL levels are independent protective factors for the progression of carotid atherosclerosis in healthy subjects. Elevated bilirubin levels reduce the risk of carotid atherosclerosis progression, with IBIL being more potent in preventing carotid atherosclerosis progression.

Lithium-rich manganese-based cathode materials have gained increasing research attention because of their superior specific capacity and high operating voltage. However, they suffer from voltage decay and unsatisfied stability during cycling. Meanwhile, the lithium-rich cathode materials could barely be stably operated at low temperatures, limiting their practical application. Herein, low-temperature tolerant Li[Li0.144Ni0.136Co0.136Mn0.544]O2 cathode material is obtained via facile SnO2 decoration. With the SnO2 modification, LiF-enriched cathode-electrolyte interphase (CEI) is formed on the surface of Li[Li0.144Ni0.136Co0.136Mn0.544]O2 at both ambient and low temperatures, thereby facilitating the Li+ diffusion on the surface. Furthermore, the modified Li[Li0.144Ni0.136Co0.136Mn0.544]O2 demonstrates suppressed voltage decay, high structure stability, and low activation energy for Li+ diffusion in the CEI layer and charge transfer. Consequently, the SnO2-decorated Li[Li0.144Ni0.136Co0.136Mn0.544]O2 exhibits a high capacity of 221.0 mA h g−1 over 200 cycles, with a high capacity retention of 97.1% at 100 mA g−1 at room temperature. Remarkably, SnO2-decorated Li[Li0.144Ni0.136Co0.136Mn0.544]O2 can be successfully operated at low temperatures with a high discharge capacity and outstanding cycle stability. At 0 °C, a high discharge capacity of 111.5 mA h g−1 with a capacity retention of 79.9% is achieved over 500 cycles at 100 mA g−1. Even at −20 °C, a high capacity of 109.9 mA h g−1 and a high capacity retention of 84.2% after 400 cycles are obtained at 25 mA g−1. This work provides a facile method to enhance the performance of lithium-rich cathode materials and would promote the development of batteries based on lithium-rich cathodes for broader applications. [ABSTRACT FROM AUTHOR]

In this work, the preparation of titanium sponge by magnesium thermal method is regarded as the liquid-phase sintering process of titanium, and powder-metallurgy sintering technology is utilized to simulate the aggregation–growth and densification behavior of titanium particles in a high-temperature liquid medium (the molten Mg-MgCl2 system). It was found that compared with MgCl2, Mg has better high-temperature wettability and reduction effect, which promotes titanium particles to form a sponge titanium skeleton at lower temperature. The aggregation degree of titanium particles and the densification degree of a sponge titanium skeleton can be improved by increasing the temperature and the relative content of Mg in the melting medium. The kinetics study shows that with the increase in temperature, the porosity of the titanium particle aggregates and the sponge titanium skeleton decreases, and their density growth rate increases. With the extension of time, the aggregation degree of titanium particles and the densification degree of sponge titanium gradually increase. This work provides a theoretical reference for controlling the density of titanium sponge in industry. [ABSTRACT FROM AUTHOR]

A gearbox compound fault intelligent diagnosis method based on the period group sparse model is proposed for the problem that the fault features are coupled with each other and the fault components are superimposed on each other and difficult to be separated in the gearbox compound fault signal. Firstly, a binary sequence is constructed to embed the fault pulse period as a priori knowledge into the group sparse model to decouple and separate the composite fault signal while maintaining the amplitude and sparsity of the extracted features. Secondly, the wavelet packet energy features of the decoupled signals are extracted to improve the data quality while enhancing the characterization ability of the dictionary in the classification model. Finally, the wavelet packet energy features are imported into the sparse dictionary classification model, and the fault diagnosis is completed by outputting the fault categories using the self-driven characteristics of the data. The results show that the fault identification accuracy using the proposed method is 97%. In addition, the experimental validation under different states and working conditions with different rotational speeds allows the superiority and effectiveness of the algorithm in this paper to be tested and has the feasibility of a practical application in engineering. [ABSTRACT FROM AUTHOR]

Fabric defect detection plays a critical role for measuring quality control in the textile manufacturing industry. Deep learning-based saliency models can quickly spot the most interesting regions that attract human attention from the complex background, which have been successfully applied in fabric defect detection. However, most of the previous methods mainly adopted multi-level feature aggregation yet ignored the complementary relationship among different features, and thus resulted in poor representation capability for the tiny and slender defects. To remedy these issues, we propose a novel saliency-based fabric defect detection network, which can exploit the complementary information between different layers to enhance the representation features ability and discrimination of defects. Specifically, a multiscale feature aggregation unit (MFAU) is proposed to effectively characterize the multi-scale contextual features. Besides, a feature fusion refinement module (FFR) composed of an attention fusion unit (AFU) and an auxiliary refinement unit (ARU) is designed to exploit complementary important information and further refine the input features for enhancing the discriminative ability of defect features. Finally, a multi-level deep supervision (MDS) is adopted to guide the model to generate more accurate saliency maps. Under different evaluation metrics, our proposed method outperforms most state-of-the-art methods on our developed fabric datasets. [ABSTRACT FROM AUTHOR]