Supercapacitors are an important part of the hybrid energy storage system for electric vehicles (EVs.) They not only protect the battery, but also improve the starting performance, acceleration performance, and regenerative braking performance of the EVs. Accurate modeling and state of charge (SOC) estimation of supercapacitors are essential for reliability, resilience, and safety in hybrid energy storage system operations. In this paper, a fractional order dynamic equivalent circuit model of the supercapacitor is proposed. Through the parameter identification and the experimental analysis, it is proved that the fractional equivalent circuit has better precision. Furthermore, the SOC estimation method of particle filter and Kalman filter is investigated, and a method of estimating supercapacitor SOC is proposed by combining the particle filter and the fractional Kalman filter. The validation results prove that the proposed method has better accuracy and real-time performance based on the fractional order.
Using Pyracantha fortuneana,Rosa laevigata Michaux and milk powder as raw materials.A new healthcare yogurt with special flavour and abundant nutrients was produced by fermentation with yoghurt starter cultures.The DVS yogurt starter was selected to the production strain by contrast,and through orthogonal experiments,the optimum fermentation conditions were determined as follows: mixed juice from Pyracantha fortuneana and Rosa laevigata Michaux 30 mL/100 mL,milk powder 12 g/100 mL and sucrose 7 g/100 mL,with the addition of 0.20 g/100 mL stabilizer,3 mL/100 mL yoghurt starter cultures,fermentation temperature 40 ℃,fermentation time 8 h and post-ferm entation 24 h.
Adv. Mater. 2015, 27, 5241–5247 The following errors that appeared in the above-mentioned article are corrected here: Page 5241, right column, the last line: “LiF3SO3” should be “LiCF3SO3”; Page 5242, right column, line 1: “LiF3SO3” should be “LiCF3SO3”; Page 5243, right column, line 6: “LiF3SO3” should be “LiCF3SO3”. The authors apologize for any inconvenience caused.
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Background: Before the COVID-19 pandemic, tuberculosis is the leading cause of death from a single infectious agent worldwide for the past 30 years. Progress in the control of tuberculosis has been undermined by the emergence of multidrug-resistant tuberculosis. The aim of the study is to reveal the trends of research on medications for multidrug-resistant pulmonary tuberculosis (MDR-PTB) through a novel method of bibliometrics that co-occurs specific semantic Medical Subject Headings (MeSH). Methods: PubMed was used to identify the original publications related to medications for MDR-PTB. An R package for text mining of PubMed, pubMR, was adopted to extract data and construct the co-occurrence matrix-specific semantic types. Biclustering analysis of high-frequency MeSH term co-occurrence matrix was performed by gCLUTO. Scientific knowledge maps were constructed by VOSviewer to create overlay visualization and density visualization. Burst detection was performed by CiteSpace to identify the future research hotspots. Results: Two hundred and eight substances (chemical, drug, protein) and 147 diseases related to MDR-PTB were extracted to form a specific semantic co-occurrence matrix. MeSH terms with frequency greater than or equal to six were selected to construct high-frequency co-occurrence matrix (42 × 20) of specific semantic types contains 42 substances and 20 diseases. Biclustering analysis divided the medications for MDR-PTB into five clusters and reflected the characteristics of drug composition. The overlay map indicated the average age gradients of 42 high-frequency drugs. Fifteen top keywords and 37 top terms with the strongest citation bursts were detected. Conclusion: This study evaluated the literatures related to MDR-PTB drug therapy, providing a co-occurrence matrix model based on the specific semantic types and a new attempt for text knowledge mining. Compared with the macro knowledge structure or hot spot analysis, this method may have a wider scope of application and a more in-depth degree of analysis. Keywords: multidrug-resistant tuberculosis, pulmonary tuberculosis, medication trends, specific semantic types, MeSH tree, pubMR
To accurately describe the voltage performance of supercapacitor and estimate its energy, an equivalent circuit model with a controlled current source is proposed. First, the self-discharge effect of the supercapacitor is investigated based on experimental analysis at different charge and discharge stages. Furthermore, the controlled current source is utilized to reflect the self-discharge effect of the supercapacitor. The recursive least square method is introduced to identify the model parameters, and the particle swarm optimization algorithm is adopted to identify the parameters of the controlled current source at different stages. On this basis, the supercapacitor energy is estimated. Results show that the proposed model and the parameter identification method can effectively reveal the supercapacitor's voltage performance. Moreover, the proposed model provides more accurate energy estimation for the supercapacitor.
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Background Birthweight have profound impacts on health status throughout lifetime, however, the relationship between maternal ferritin level in pregnancy and birthweight of the newborn remains controversial. Objective This retrospective cohort research was to analyze the association between maternal ferritin levels during pregnancy with birthweight outcomes, primarily for low birthweight (LBW) and small for gestational age (SGA). Methods Newborns weighing lower than 2,500 grams were defined as LBW. SGA is defined as birthweight lower than the 10 th percentile of the distribution of newborns' birthweight of the same gestational age. Multivariable logistic regressions have been used to explore the association of maternal ferritin levels and birthweight related outcomes, in which the ferritin concentration was logarithm transformed in the model. We further used restricted cubic spline models to explore linear/non-linear dose–response manners of ferritin level and birthweight outcomes. Results A total of 3,566 pregnant women were included in the study. In the results of the present study, we observed that maternal ferritin levels were linearly associated with the risk of LBW ( p- trend = 0.005) and SGA ( p- trend = 0.04), with the adjusted odds ratios (ORs) of 1.78 (95% CI 1.37–2.32) for LBW and 1.87 (95% CI 1.38–2.54) for SGA with an increase in Ln-ferritin concentrations per unit. The adjusted ORs across quartiles of ferritin levels were 2.14 (95% CI 1.03–4.47) for Quartile 2, 3.13 (95% CI 1.47–6.69) for Quartile 3, and 3.63 (95% CI 1.52–8.68) for Quartile 4 for LBW. The adjusted ORs of LBW and SGA among women using supplemental iron were 0.56 (95% CI 0.38, 0.85) and 0.65 (95% CI 0.40, 1.05) compared with non-users, respectively. Conclusions Our findings found a linear dose–response relationship between ferritin levels and an increased risk of poor birthweight outcomes, suggesting that maternal ferritin level during pregnancy may provide an additional predictor for differentiating poor birthweight related outcomes. Further exploration should be conducted to ensure maternal ferritin thresholds and iron supplement doses.
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