Research on Predicting Remain Useful Life of Rolling Bearing Based on Parallel Deep Residual Network

The prediction of bearing remaining useful life (RUL) plays a pivotal role in ensuring the safe operation of machinery and reducing maintenance loss. Traditional prediction methods only consider the features of one domain or integrate the features of multiple domains into a one-dimensional sequence as the model input, which leads to some inaccuracy in prediction. In order to improve the prediction accuracy, a bearing RUL prediction method based on the parallel deep residual convolution neural network (P-ResNet), which is considered both time-domain features and time–frequency features, is proposed in this paper. Synchronous wavelet transform (SWT) is adopted to extract time–frequency features from original vibration signals. Both the time domain features and time–frequency domain features after dimension reduction by PCA are used as input to P-ResNet, which contains two series of parallel convolution operations to learn the time–frequency features and time-domain features, respectively, to ensure the comprehensiveness of information-bearing degradation. The residual layers were added to enhance the learning ability of time–frequency features. Kalman filter algorithm was used to smooth the prediction results. The IEEE PHM 2012 Data Challenge datasets were used as data sources for model training and prediction. Compared with the traditional convolutional neural network (CNN), the P-ResNet model maintains the synchronization of global and local information and has a stronger learning ability. The experiment data validate the effectiveness of the proposed method, and the comparison between the prosed methods and the others proves the superiority of the proposed method

Role of SIRT2 in regulating the dexamethasone-activated autophagy pathway in skeletal muscle atrophy

The proteolytic autophagy system is involved in a major regulatory pathway in dexamethasone (Dex)-induced muscle atrophy. Sirtuin 2 (SIRT2) is known to participate in modulating autophagy signaling, exerting effects in skeletal muscle atrophy. We aimed to determine the effects of SIRT2 on autophagy in Dex-induced myoatrophy. Mice were randomly divided into the normal, Dex, and sirtinol groups. C2C12 cells were differentiated into myotubes and transfected with short hairpin (sh)-Sirt2-green fluorescent protein (GFP) or Sirt2-GFP lentivirus. To evaluate the mass and function of skeletal muscles, we measured the myofiber cross-sectional area, myotube size, gastrocnemius muscle wet weight/body weight ratio (%), and time-to-exhaustion. The SIRT2, myosin heavy chain (MyHC), LC3, and Beclin-1 expression levels were detected by western blotting and quantitative reverse transcription-polymerase chain reaction. Inhibition of SIRT2 markedly attenuated the muscle mass and endurance capacity. The same phenotype was observed in Sirt2-shRNA-treated myotubes, as evidenced by their decreased size. Conversely, SIRT2 overexpression alleviated Dex-induced myoatrophy in vitro. Moreover, SIRT2 negatively regulated the expression of the LC3b and Beclin-1 in skeletal muscles. These findings suggested that SIRT2 activation protects myotubes against Dex-induced atrophy through the inhibition of the autophagy system; this phenomenon may potentially serve as a target for treating glucocorticoid-induced myopathy.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Template synthesis of nitrogen-doped carbon nanocages–encapsulated carbon nanobubbles as catalyst for activation of peroxymonosulfate

Nitrogen-doped carbon nanocages–encapsulated carbon nanobubbles (CBs@NCCs) were feasibly fabricated by the in situ thermal conversion of Co–Fe Prussian blue analogues (Co–Fe PBAs) coated with polydopamine (PDA) shells. Interestingly, PBA cores can act as a self-sacrificing template and decompose during high-temperature treatment. PDA shells play a crucial role in stabilizing the steric architecture, supplementing nitrogen-doping of CBs@NCCs under high-temperature treatments. When compared with carbon nanobubbles (CBs) without the protection of carbon nanocages, CBs@NCCs possess higher specific surface area and pore volume. The contributions of a unique configuration and proper nitrogen modification are significant for improving the peroxymonosulfate (PMS) activation performance of CBs@NCCs, which is expected to be a promising alternative to other conventional carbocatalysts and metal oxides. Moreover, the applicability of the as-synthesized carbocatalysts was systematically investigated by adjusting several operating parameters, and some ubiquitous anions and natural organic matters (NOMs) were also taken into account in methylene blue (MB) degradation. The radical evolution and PMS activation mechanism are investigated by radical quenching and electron paramagnetic resonance (EPR) tests, which revealed that sulfate radicals (SO4˙−) and singlet oxygen (1O2) are simultaneously responsible for the overall MB removal in a CBs@NCCs-800/PMS system. This study may provide a broader perspective for upgrading the catalytic efficiency of various green heterogeneous carbocatalysts

Fabrication of Thorny Au Nanostructures on Polyaniline Surfaces for Sensitive Surface-Enhanced Raman Spectroscopy

Here we demonstrate, for the first time, the fabrication of Au nanostructures on polyaniline (PANI) membrane surfaces for surface enhanced Raman spectroscopy (SERS) applications, through a direct chemical reduction by PANI. Introduction of acids into the HAuCl₄ solution leads to homogeneous Au structures on the PANI surfaces, which show only sub-ppm detection levels toward the target analyte, 4-mercaptobenzoic acid (4-MBA), because of limited surface area and lack of surface roughness. Thorny Au nanostructures can be obtained through controlled reaction conditions and the addition of a capping agent poly (vinyl pyrrolidone) (PVP) in the HAuCl₄ solution and the temperature kept at 80 °C in an oven. Those thorny Au nanostructures, with higher surface areas and unique geometric feature, show a SERS detection sensitivity of 1 × 10^–9 M (sub-ppb level) toward two different analyte molecules, 4-MBA and Rhodamine B, demonstrating their generality for SERS applications. These highly sensitive SERS-active substrates offer novel robust structures for trace detection of chemical and biological analytes

Superior Strong and Tough Nacre-Inspired Materials by Interlayer Entanglement

Natural materials teach that mechanical dissipative interactions relieve the conflict between strength and toughness and enable fabrication of strong yet tough artificial materials. Replicating natural nacre structure has yielded rich biomimetic materials; however, stronger interlayer dissipation still waits to be exploited to extend the performance limits of artificial nacre materials. Here, we introduce strong entanglement as a new artificial interlayer dissipative mechanism and fabricate entangled nacre materials with superior strength and toughness, across molecular to nanoscale nacre structures. The entangled graphene nacre fibers achieved high strength of 1.2 GPa and toughness of 47 MJ/m3, and films reached 1.5 GPa and 25 MJ/m3. Experiments and simulations reveal that strong entanglement can effectively dissipate interlayer energy to relieve the conflict between strength and toughness, acting as natural folded proteins. The strong interlayer entanglement opens up a new path for designing stronger and tougher artificial materials to mimic but surpass natural materials