AbstractObjective: Early childhood caries has become a globally crucial oral health problem over the decades. Most studies have discussed the association between low birth weight and early childhood caries; however, studies focusing on high birth weight have been relatively limited. This study aimed to assess the impact of high birth weight on the incidence and severity of dental caries in 4–5-year-old children. Subjects and Methods: Study subjects included 491 children from a birth cohort study at 4–5 years of age. Data on dental caries, prenatal and perinatal factors, and socio-demographic determinants were recorded. Logistic regression models adjusted for potential confounders were performed to analyze the data. Two-sided P-value < 0.05 was considered statistically significant. Results: Of the 491 children, the prevalence of dental caries was 48.7%. High birth weight (≥ 4,000 g) was significantly associated with increased incidence of dental caries (OR, 2.000; CI 95% 1.062–3.765), and the relatively enhanced risk OR was further increased in subjects experiencing caries (dmft ≥ 3) (OR, 2.437; CI 95% 1.306–4.549) compared with the normal birth weight (2,500–3,999 g). Conclusions: High birth weight is a risk factor for early childhood caries. Particular attention should be paid to children with birth weight more than or equal to 4,000 grams.
The Climate Hazard Group InfraRed Precipitation with Stations (CHIRPS) dataset was examined for its variability and performance in explaining precipitation variations, forecasting, and drought monitoring in Southeast Asia (SEA) for the period of 1981–2020. By using time-series analysis, the Standardized Precipitation Index (SPI), and the Autoregressive Integrated Moving Average (ARIMA) model this study established a data-driven approach for estimating the future trends of precipitation. The ARIMA model is based on the Box Jenkins approach, which removes seasonality and keeps the data stationary while forecasting future patterns. Depending on the series, ARIMA model annual estimates can be read as a blend of recent observations and long-term historical trend. Methods for determining 95 percent confidence intervals for several SEA countries and simulating future annual and seasonal precipitation were developed. The results illustrates that Bangladesh and Sri Lanka were chosen as the countries with the greatest inaccuracies. On an annual basis, Afghanistan has the lowest Mean Absolute Error (MAE) values at 33.285 mm, while Pakistan has the highest at 35.149 mm. It was predicted that these two countries would receive more precipitation in the future as compared to previous years.
Smoothed Particle Hydrodynamics (SPH) has outstanding advantages in dealing with nonlinear problems. However, it is difficult to find an efficient and accurate non-reflecting boundary for SPH. In this paper, the scaled boundary finite element (SBFE) virtual particle boundary is proposed to model the non-reflecting characteristics of the boundary in SPH. It is implemented by 2–4 layers of SBFE virtual particles whose pressure and velocity are calculated by the Lagrange interpolation from the nearby SBFE nodes. The SBFE virtual particle boundary can effectively and accurately simulate the transmission process of pressure waves on the boundary, and eliminate the influence of the reflecting waves on pressure and velocity fields.
Numerous microRNAs participate in regulating the pathological process of autophagy. We have found miR-296-5p is one of the most significantly down-regulated microRNAs in a high concentration of sodium fluoride. However, it is not clear whether miR-296-5p augments autophagy in dental fluorosis. Our purpose is to explore the function of miR-296-5p in regulating autophagy of excessive fluoride development. Thus, the cell line of ameloblasts LS8 was exposed to a 1.5 mM dose of NaF and miR-296-5p-mimics, Real-time qPCR, CCK-8 assays, Fluorescence imaging and Western blot analysis were performed. Autophagy was observed. As our results indicated, miR-296-5p overexpression in mouse LS8 cells significantly accelerated autophagy. The autophagy inhibition effect of miR-296-5p underexpression was consistent with the effect of the AMPK inhibitor. And we found that the expression of LC3II was decreased via down-regulation of AMPK. The change of ULK1 by miR-296-5p may be accomplished through AMPK. Thus, miR-296-5p may improve the secretion of autophagic mediators by activating AMPK/ULK1 expression in fluorosis, suggesting that miR-296-5p, AMPK/ULK1 may be potential therapeutic targets under the higher fluoride stimulation.
Discovering causal relationships from observational data is a challenging topic in artificial intelligence. Recent works formulate causal discovery as a continuous optimization problem with a differentiable acyclic constraint. Although these methods have achieved considerable performance improvement, they have two drawbacks: 1) they require a relatively large number of training samples; and 2) their performance will substantially deteriorate when facing heterogeneous noise. To address these problems, we first propose a low-order conditional independence (CI) constraint for the continuous optimization problem, and then design a soft version of the constraint by transforming it to a regularization term in the loss function of the continuous optimization problem. We show the convergence of continuous optimization with our constraint under some mild conditions, and the consistency of causal structure learning with the CI regularization. Extensive experiments on both synthetic and real-world datasets show that with our CI constraint or regularization, existing continuous optimization methods can achieve considerable performance improvement of causal discovery, especially when sample size is small.
Induction motors (IMs) are widely used in many manufacturing processes and industrial applications. The harsh work environment, long-time enduring, and overloads mean that it is subjected to broken rotor bar (BRB) faults. The vibration signal of IMs with BRB faults consists of the reliable modulation information used for fault diagnosis. Cyclostationary analysis has been found to be effective in identifying and extracting fault feature. The estimators of cyclic modulation spectrum (CMS) and fast spectral correlation (FSC) based on the short-time fourier transform (STFT) have higher cyclic frequency resolution, which has proven efficient in demodulating second order cyclostationary (CS2) signals. However, these two estimators have limitations of processing the maximum cyclic frequency αmax that is smaller than Fs/2 (Fs is the sampling frequency) according to Nyquist’s Theorem. In addition, they have lower carrier frequency resolution due to the fixed window size used in STFT. In order to resolve the initial shortcomings of the CMS and FSC methods, in this paper, we extended the analysis of CMS algorithm based on the continuous wavelet transform (CWT), which enlarged the maximum cyclic frequency range to Fs/2 and provides higher carrier frequency resolution because the CWT has the advantage of multi-resolution analysis. The reliability and applicability of the proposed method for fault components localization were validated by CS2 simulation signals. Compared to CMS and FSC methods, the proposed approach shows better performance by analyzing vibration signals between healthy motor and faulty motor with one BRB fault under 0%, 20%, 40%, and 80% load conditions.
Nacelle cowl design is dedicated to generating a three-dimensional surface with optimal drag performance. Most previous approaches focused primarily on the design method driven by streamwise profiles, which introduced no additional aerodynamically significant variables into the geometric constraints. Therefore, to explore more design spaces for drag reduction of the nacelle cowl, this paper proposes a design refinement method by adding a circular curve that passes through the locations of maximum thickness of given streamwise profiles at various angles toward the nacelle axis, which are regarded as the design variables. A proof-of-concept study was modeled using a combination of computer aided design, Latin hypercube sampling, computational fluid dynamics (CFD) simulation using Reynolds-averaged Navier–Stokes equations closed by a k-ω shear stress transport model, and a kriging surrogate model, in order to search for the optimal solution in design space. The result of optimization shows that the aerodynamic performance of the nacelle cowl was optimized significantly, with a 28.50% drag coefficient reduction with only three angular variables taken to create the design space, illustrating the feasibility of the proposed approach.
In this paper, a fault diagnosis method for wind power system (WPS) converters realized by K-Nearest Neighbor algorithm (KNN) is proposed. Firstly, the mathematical model of the WPS of the Doubly-Fed Induction Generator (DFIG) with dual PWM converter is established. Then based on this model, the KNN algorithm is used for fault diagnosis. The eigenvalues used to train the fault diagnosis model are the three-phase current and capacitor voltage values under the six fault states. Finally, through the simulation verification, the results indicate that this fault diagnosis method can realize the reliable fault diagnosis of the wind power system converter described in this paper.
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