Fault feature extraction method based on EWT-SMF and MF-DFA for valve fault of reciprocating compressor

According to the nonlinearity and nonstationarity characteristics of reciprocating compressor vibration signal, a fault feature extraction method of reciprocating compressor based on the empirical wavelet transform (EWT) and state-adaptive morphological filtering (SMF) is proposed. Firstly, an adaptive empirical wavelet transform was used to divide the Fourier spectrum by constructing a scale-space curve, and an appropriate orthogonal wavelet filter bank was constructed to extract the AM-FM component with a tightly-supported Fourier spectrum. Then according to the impact characteristic of the reciprocating compressor vibration signal, the morphological structural elements were constructed with the characteristics of the signal to perform state-adaptive morphological filtering on the partitioned modal functions. Finally, the MF-DFA method of the modal function was quantitatively analyzed and the fault identification was performed. By analyzing the experimental data, it can be shown that the method can effectively identify the fault type of reciprocating compressor valve

OmniSeg3D: Omniversal 3D Segmentation via Hierarchical Contrastive Learning

Towards holistic understanding of 3D scenes, a general 3D segmentation method is needed that can segment diverse objects without restrictions on object quantity or categories, while also reflecting the inherent hierarchical structure. To achieve this, we propose OmniSeg3D, an omniversal segmentation method aims for segmenting anything in 3D all at once. The key insight is to lift multi-view inconsistent 2D segmentations into a consistent 3D feature field through a hierarchical contrastive learning framework, which is accomplished by two steps. Firstly, we design a novel hierarchical representation based on category-agnostic 2D segmentations to model the multi-level relationship among pixels. Secondly, image features rendered from the 3D feature field are clustered at different levels, which can be further drawn closer or pushed apart according to the hierarchical relationship between different levels. In tackling the challenges posed by inconsistent 2D segmentations, this framework yields a global consistent 3D feature field, which further enables hierarchical segmentation, multi-object selection, and global discretization. Extensive experiments demonstrate the effectiveness of our method on high-quality 3D segmentation and accurate hierarchical structure understanding. A graphical user interface further facilitates flexible interaction for omniversal 3D segmentation

Feature extraction method based on VMD and MFDFA for fault diagnosis of reciprocating compressor valve

Aiming at the nonlinearity, nonstationarity and multi-component coupling characteristics of reciprocating compressor vibration signals, an integrated feature extraction method based on the variational mode decomposition (VMD) and multi-fractal detrended fluctuation analysis (MFDFA) is proposed for a fault diagnosis for a reciprocating compressor valve. Firstly, to eliminate the noise interference, a novel VMD method with superior anti-interference performance was utilized to obtain several components of the quasi-orthogonal band-limited intrinsic mode function (BLIMF) from a strong non-stationarity vibration signal, and a consistent number K of BLIMFs was selected based on a novel criterion for all fault states. Secondly, the MFDFA method, which can describe the multi-fractal structure feature of non-stationary time series, was applied to analyze each BLIMF component, and the parameters of MFDFA were employed as the eigenvectors to reflect the structure characteristics and local scale behavior of the vibration signal. Then, the principal component analysis (PCA) was introduced to refine the eigenvectors for a higher recognition efficiency and accuracy. Finally, the vibration signals of four types of reciprocating compressor valve faults were analyzed by this method, and the faults were identified correctly by pattern classifiers of BTSVM and CNN. Further results comparison with other feature extraction methods verifies the superiority of the proposed method

Pressure-induced superconductivity in quasi-one-dimensional semimetal Ta2PdSe6\mathrm{Ta}_2 \mathrm{PdSe}_6

Here we report the discovery of pressure-induced superconductivity in quasi-one-dimensional $\mathrm{Ta}_2 \mathrm{PdSe}_6$, through a combination of electrical transport, synchrotron x-ray diffraction, and theoretical calculations. Our transport measurements show that the superconductivity appears at a critical pressure $P_{\mathrm{c}} \sim 18.3$ GPa and is robust upon further compression up to $62.6$ GPa. The estimated upper critical field $\mu_0 H_{\mathrm{c} 2}(0)$ in the pressurized $\mathrm{Ta}_2 \mathrm{PdSe}_6$ is much lower than the Pauli limiting field, in contrast to the case in its isostructural analogs $M_2 \mathrm{Pd}_{\mathrm{x}} X_5$ $(M=\mathrm{Nb}$, Ta; $X=\mathrm{S}, \mathrm{Se})$. Concomitant with the occurrence of superconductivity, anomalies in pressuredependent transport properties are observed, including sign reversal of Hall coefficient, abnormally enhanced resistance, and dramatically suppressed magnetoresistance. Meanwhile, room-temperature synchrotron x-ray diffraction experiments reveal the stability of the pristine monoclinic structure (space group $C 2 / m$ ) upon compression. Combined with the density functional theory calculations, we argue that a pressure-induced Lifshitz transition could be the electronic origin of the emergent superconductivity in $\mathrm{Ta}_2 \mathrm{PdSe}_6$.Comment: 7 pages, 7 figure

A Role for a Dioxygenase in Auxin Metabolism and Reproductive Development in Rice

SummaryIndole-3-acetic acid (IAA), the natural auxin in plants, regulates many aspects of plant growth and development. Extensive analyses have elucidated the components of auxin biosynthesis, transport, and signaling, but the physiological roles and molecular mechanisms of auxin degradation remain elusive. Here, we demonstrate that the dioxygenase for auxin oxidation (DAO) gene, encoding a putative 2-oxoglutarate-dependent-Fe (II) dioxygenase, is essential for anther dehiscence, pollen fertility, and seed initiation in rice. Rice mutant lines lacking a functional DAO display increased levels of free IAA in anthers and ovaries. Furthermore, exogenous application of IAA or overexpression of the auxin biosynthesis gene OsYUCCA1 phenocopies the dao mutants. We show that recombinant DAO converts the active IAA into biologically inactive 2-oxoindole-3-acetic acid (OxIAA) in vitro. Collectively, these data support a key role of DAO in auxin catabolism and maintenance of auxin homeostasis central to plant reproductive development

UHRF1 is required for basal stem cell proliferation in response to airway injury

Cellular senescence is a cell fate characterized by an irreversible cell cycle arrest, but the molecular mechanism underlying this senescence hallmark remains poorly understood. Through an unbiased search for novel senescence regulators in airway basal cells, we discovered that the epigenetic regulator ubiquitin-like with PHD and ring finger domain-containing protein 1 (UHRF1) is critical for regulating cell cycle progression. Upon injury, basal cells in the mouse airway rapidly induce the expression of UHRF1 in order to stimulate stem cell proliferation and tissue repair. Targeted depletion of Uhrf1 specifically in airway basal cells causes a profound defect in cell cycle progression. Consistently, cultured primary human basal cells lacking UHRF1 do not exhibit cell death or differentiation phenotypes but undergo a spontaneous program of senescence. Mechanistically, UHRF1 loss induces G1 cell cycle arrest by abrogating DNA replication factory formation as evidenced by loss of proliferating cell nuclear antigen (PCNA) puncta and an inability to enter the first cell cycle. This proliferation defect is partially mediated by the p15 pathway. Overall, our study provides the first evidence of an indispensable role of UHRF1 in somatic stem cells proliferation during the process of airway regeneration

The mediating effect of resilience and COVID-19 anxiety on the relationship between social support and insomnia among healthcare workers: a cross-sectional study

BackgroundInsomnia in healthcare workers has become a topic of concern in the health system. The high infectivity and longevity of the COVID-19 pandemic have resulted in great pressure and a high incidence of insomnia among healthcare workers. Insomnia among healthcare workers has a negative impact on high-quality healthcare services in addition to their health. Thus, it's necessary to explore insomnia's underlying mechanisms.ObjectThe present research's aims were threefold: explored the association between social support, resilience, COVID-19 anxiety, and insomnia among healthcare workers during the pandemic, elucidated the underlying mechanism of insomnia, and offered recommendations for improving the health of these workers.Materials and methodsA cross-sectional design was adopted. From May 20 to 30, 2022, 1038 healthcare workers were selected to fill out the Oslo 3-item Social Support Scale, the eight-item Athens Insomnia Scale, the Coronavirus Anxiety Scale, and the Brief Resilience Scale. Descriptive statistics and correlations were analyzed by SPSS 25.0. Mediation analysis was conducted by Mplus 8.3 using 5000 bootstrap samples.ResultsOf the participating 1038 healthcare workers, the prevalence of insomnia was 41.62% (432/1038). Significant associations were found involving insomnia, resilience, COVID-19 anxiety, and social support. Insomnia was directly affected by social support. Moreover, three indirect pathways explain how social support affected insomnia: resilience's mediating role, COVID-19 anxiety's mediating role, and the chain-mediation role of resilience and COVID-19 anxiety.ConclusionThe results validated our hypotheses and supported the opinion of Spielman et al. ‘s three-factor model of insomnia. Social support of healthcare workers has an indirect impact on insomnia in addition to its direct one via independent and chain-mediation effects of resilience and COVID-19 anxiety

Predicting depressive symptom by cardiometabolic indicators in mid-aged and older adults in China: a population-based cross-sectional study

ObjectiveDepressive symptom is a serious mental illness often accompanied by physical and emotional problems. The prevalence of depressive symptom in older adults has become an increasingly important public health priority. Our study used cardiometabolic indicators to predict depressive symptom in middle-aged and older adults in China.MethodsThe data came from the China Health and Retirement Longitudinal Study 2011 (CHARLS2011), which was a cross-sectional study. The analytic sample included 8,942 participants aged 45 years or above. The study evaluated the relationship between cardiometabolic indicators and depression by measuring 13 indicators, including body mass index (BMI), waist circumference, waist-height ratio (WHtR), conicity index, visceral adiposity index (VAI), Chinese visceral adiposity index (CVAI), lipid accumulation product (LAP), a body shape index (ABSI), body roundness index (BRI), triglyceride glucose index (TyG-index) and its correlation index (TyG-BMI, TyG-waist circumference, TyG-WHtR). Binary logistic regression analysis was used to examine the association between thirteen cardiometabolic indicators and depressive symptom. In addition, the receiver operating characteristic (ROC) curve analysis and area under curve (AUC) were used to evaluate the predictive anthropometric index and to determine the optimum cut-off value.ResultsThe study included 8,942 participants, of whom 4,146 (46.37%) and 4,796 (53.63%) were male and female. The prevalence of depressive symptom in mid-aged and older adults in China was 41.12% in males and 55.05% in females. The results revealed that BMI [AUC = 0.440, 95%CI: 0.422–0.457], waist circumference [AUC = 0.443, 95%CI: 0.425–0.460], WHtR [AUC = 0.459, 95%CI: 0.441–0.476], LAP [AUC = 0.455, 95%CI: 0.437–0.472], BRI [AUC = 0.459, 95%CI: 0.441–0.476], CVAI [AUC = 0.449, 95%CI: 0.432–0.467], TyG-BMI [AUC = 0.447, 95%CI: 0.429–0.465], and TyG-waist circumference [AUC =0.452, 95%CI: 0.434–0.470] were weak predictors of depressive symptom (p < 0.05) in males. In females, BMI [AUC = 0.470, 95%CI: 0.453–0.486], LAP [AUC = 0.484, 95%CI: 0.467–0.500], TyG-BMI [AUC = 0.470, 95%CI: 0.454–0.487], and TyG-waist circumference [AUC =0.481, 95%CI: 0.465–0.498] were weak predictors of depressive symptom (p < 0.05). On the other side, VAI, ABSI, conicity index and TyG index could not predict depressive symptom in middle-aged and older adults.ConclusionMost cardiometabolic indicators have important value in predicting depressive symptom. Our results can provide measures for the early identification of depressive symptom in middle-aged and older adults in China to reduce the prevalence of depressive symptom and improve health