Transfer function characterization for HFCTs used in partial discharge detection

High frequency current transformers (HFCTs) are widely employed to detect partial discharge (PD) induced currents in high voltage equipment. This paper describes measurements of the wideband transfer functions of HFCTs so that their influence on the detected pulse shape in advanced PD measurement applications can be characterized. The time-domain method based on the pulse response is a useful way to represent HFCT transfer functions as it allows numerical determination of the forward and reverse transfer functions of the sensor. However, while the method is accurate at high frequencies it can have limited resolution at low frequencies. In this paper, a composite time-domain method is presented to allow accurate characterization of the HFCT transfer functions at both low and high frequencies. The composite method was tested on two different HFCTs and the results indicate that the method can characterize their transfer functions ranging from several kHz to tens of MHz. Results are found to be in good agreement with frequency-domain measurements up to 50 MHz. Measurement procedures for using the method are summarized to facilitate further applications

Characteristics of the salivary microbiota in cheilitis granulomatosa

Cheilitis granulomatosa (CG) is a disturbing and persistent idiopathic lip swelling. The cause and treatment has not been wholly elucidated. Some reports infer that CG is mainly associated with dental infection but no firm or reliable microbiological evidence has been provided for a causative organism. This study aimed to evaluate whether microorganisms contribute to the etiology of CG in order to inform appropriate treatment options in clinic. Unstimulated saliva was collected from 15 CG patients who were diagnosed clinically and pathologically and 15 healthy controls (HC). DNA was extracted from the precipitate of the centrifuged saliva for 16s rRNA high-throughput sequencing using the Miseq PE300 platform. The distribution of the microbiome between the two groups was compared. CG patients had a greater microbial flora that was more diverse than the HC. Prevotella, Alloprevotella, Porphyromonas, Actinomyces, Rothia, Fusobacterium, Haemophilus, and Aggregatibacter had a significantly higher abundance in CG patients. In contrast, Streptococcus and Campylobacter were the most abundant genera in HC with a mean relative abundance of 63% and 2%, respectively. The microbiological network indicated that most of the bacteria that were enriched at greater levels in CG patients were likely to be Prevotella, Actinomyces, and Rothia. These have been shown to co-exist with other bacteria. The composition and structure of bacterial communities in CG patients were different from HC. Most of the genera observed in CG patients were associated with periodontitis and pulp infection. These findings might be helpful in understanding the etiology of CG. Further study will be needed to confirm these findings and explore the underlying pathological mechanism

Chemical, Thermal, Time, and Enzymatic Stability of Silk Materials with Silk I Structure

The crystalline structure of silk fibroin Silk I is generally considered to be a metastable structure; however, there is no definite conclusion under what circumstances this crystalline structure is stable or the crystal form will change. In this study, silk fibroin solution was prepared from B. Mori silkworm cocoons, and a combined method of freeze-crystallization and freeze-drying at different temperatures was used to obtain stable Silk I crystalline material and uncrystallized silk material, respectively. Different concentrations of methanol and ethanol were used to soak the two materials with different time periods to investigate the effect of immersion treatments on the crystalline structure of silk fibroin materials. X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), Raman scattering spectroscopy (Raman), Scanning electron microscope (SEM), and Thermogravimetric analysis (TGA) were used to characterize the structure of silk fibroin before and after the treatments. The results showed that, after immersion treatments, uncrystallized silk fibroin material with random coil structure was transformed into Silk II crystal structure, while the silk material with dominated Silk I crystal structure showed good long-term stability without obvious transition to Silk II crystal structure. α-chymotrypsin biodegradation study showed that the crystalline structure of silk fibroin Silk I materials is enzymatically degradable with a much lower rate compared to uncrystallized silk materials. The crystalline structure of Silk I materials demonstrate a good long-term stability, endurance to alcohol sterilization without structural changes, and can be applied to many emerging fields, such as biomedical materials, sustainable materials, and biosensors

Degradation Data-Driven Remaining Useful Life Estimation in the Absence of Prior Degradation Knowledge

Recent developments in prognostic and health management have been targeted at utilizing the observed degradation signals to estimate residual life distributions. Current degradation models mainly focus on a population of “identical” devices or an individual device with population information, not a single component in the absence of prior degradation knowledge. However, the fast development of science and technology provides us with many kinds of new systems, and we just have the real-time monitoring information to analyze the reliability for them. The fusion algorithm presented herein addresses this challenge by combining the excellent modeling ability of Bayesian updating method for the multilevel data and the prominent estimation ability of ECM algorithm for incomplete data. Residual life distributions and posterior distributions are first calculated through the Bayesian updating method based on random initial a priori distributions. Then the a priori distributions are revised and improved for future predictions by the ECM algorithm. Once a new signal is observed, we can reuse the fusion algorithm to improve the accuracy of residual life distributions. The applicability of this fusion algorithm is validated by a set of simulation experiments

A novel wavelet selection scheme for partial discharge signal detection under low SNR condition

Wavelet-based techniques have been widely used to extract partial discharge (PD) signals from noisy signals. Generally, the procedure consists of 3 steps: wavelet selection, decomposition scale determination, and noise estimation. Wavelet selection is the first and most important step for its successful application in PD denoising. However, despite many variants of techniques deployed, the success rate is not generally good especially when the signal to noise ratio is unity or less. This paper discusses a novel technique that addresses this issue. The technique is inspired by the concept of Shannon entropy and the associated information cost functions (ICF) in information theory. It is adaptive to the detected PD signals. The paper demonstrates that the proposed technique is effective when applied to PD signals obtained through laboratory experiments and on-site measurements. When this technique is applied to cable diagnostics, it should have the potential to extend the range of PD detection from cables

Identification and characterization of mRNAs and lncRNAs in the uterus of polytocous and monotocous Small Tail Han sheep (Ovis aries)

Background Long non-coding RNAs (lncRNAs) regulate endometrial secretion and uterine volume. However, there is little research on the role of lncRNAs in the uterus of Small Tail Han sheep (FecB++). Herein, RNA-seq was used to comparatively analyze gene expression profiles of uterine tissue between polytocous and monotocous sheep (FecB++) in follicular and luteal phases. Methods To identify lncRNA and mRNA expressed in the uterus, the expression of lncRNA and mRNA in the uterus of Small Tail Han sheep (FecB++) from the polytocous group (n = 6) and the monotocous group (n = 6) using RNA-sequencing and real-time polymerase chain reaction (RT-PCR). Identification of differentially expressed lncRNAs and mRNAs were performed between the two groups and two phases . Gene ontology (GO) and pathway enrichment analyses were performed to analyze the biological functions and pathways for the differentially expressed mRNAs. LncRNA-mRNA co-expression network was constructed to further analyses the function of related genes. Results In the follicular phase, 473 lncRNAs and 166 mRNAs were differentially expressed in polytocous and monotocous sheep; in the luteal phase, 967 lncRNAs and 505 mRNAs were differentially expressed in polytocous and monotocous sheep. GO and KEGG enrichment analysis showed that the differentially expressed lncRNAs and their target genes are mainly involved in ovarian steroidogenesis, retinol metabolism, the oxytocin signaling pathway, steroid hormone biosynthesis, and the Foxo signaling pathway. Key lncRNAs may regulate reproduction by regulating genes involved in these signaling pathways and biological processes. Specifically, UGT1A1, LHB, TGFB1, TAB1, and RHOA, which are targeted by MSTRG.134747, MSTRG.82376, MSTRG.134749, MSTRG.134751, and MSTRG.134746, may play key regulatory roles. These results offer insight into molecular mechanisms underlying sheep prolificacy

Analysis of risk factors and short-term prognostic factors of arrhythmia in patients infected with mild/moderate SARS-CoV-2 Omicron variant

BackgroundComplications, including arrhythmia, following severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) infection continue to be of concern. Omicron is the mainstream SARS-CoV-2 mutant circulating in mainland China. At present, there are few epidemiological studies concerning the relationship between arrhythmia and Omicron variant infection in mainland China.ObjectivesTo investigate the risk factors of arrhythmia in patients infected with the SARS-CoV-2 Omicron variant and the factors influencing prognosis.MethodsData from 192 Omicron infected patients with symptoms of arrhythmia (AH group) and 100 Omicron infected patients without arrhythmia (Control group) were collected. Patients in the AH group were divided into the good and poor prognosis groups, according to the follow-up results 4–6 weeks after infection. The general and clinical data between the AH and Control groups, and between the good and poor prognosis groups were compared. The variables with differences between the groups were included in the multivariate logistic regression analysis, and the quantitative variables were analyzed by receiver operating characteristic curve to obtain their cut-off values.ResultsCompared with the control group, the body mass index (BMI), proportion of patients with a history of arrhythmia, proportion of antibiotics taken, heart rate, moderate disease severity, white blood cell (WBC) count, and the aspartate aminotransferase, creatine kinase (CK), CK isoenzyme (CK-MB), myoglobin (Mb), high-sensitive troponin I (hs-cTnI), lymphocyte ratio and high sensitivity C-reactive protein (hs-CRP) levels in the AH group were significantly higher (p < 0.05). In addition, obesity (BMI ≥24 kg/m2), fast heart rate (≥100 times/min), moderate disease severity, and WBC, CK-MB and hs-cTnI levels were independent risk factors of arrhythmia for patients with Omicron infection (p < 0.05), and hs-CRP was a protective factor (p < 0.05). Compared with the good prognosis group, the age, proportion of patients with a history of arrhythmia, heart rate, proportion of moderate disease severity, and hs-CRP, CK, Mb and hs-cTnI levels were significantly higher in the poor prognosis group, while the proportion of vaccination was lower in the poor prognosis group (p < 0.05). Advanced age (≥65 years old), proportion of history of arrhythmia, moderate disease severity, vaccination, and hs-CRP, Mb and cTnI levels were independent factors for poor prognosis of patients with arrhythmia (p < 0.05).ConclusionThe factors that affect arrhythmia and the prognosis of patients infected with Omicron include obesity, high heart rate, severity of the disease, age. history of arrhythmia, WBC, hs-CRP, and myocardial injury indexes, which could be used to evaluate and prevent arrhythmia complications in patients in the future