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Frequency drop due to loss of massive generation is a threat to power system frequency stability. Under-frequency load shedding (UFLS) is the principal measure to prevent successive frequency declination and blackouts. Based on traditional stage-by-stage UFLS scheme, a new continuous UFLS scheme is proposed in this paper to shed loads proportional to frequency deviation. The characteristic of the proposed scheme is analyzed with a closed-form solution of frequency dynamics. Frequency threshold and time delay are added to make the proposed scheme practical. A line-by-line scheme based on precise load control is introduced to implement the continuous scheme for systems without enough continuously controllable loads. The load shedding scale factor of the proposed scheme is tuned with an analytical method to achieve adaptability to different operating conditions. The adaptability of the proposed scheme is validated with 39-bus New England model and simplified Shandong Power Grid of China. [ABSTRACT FROM AUTHOR]

Objective: To examine the prevalence of snoring during pregnancy and its effects on key pregnancy outcomes. Methods: Pregnant women were consecutively recruited in their first trimester. Habitual snoring was screened by using a questionnaire in the 1st and 3rd trimester, respectively. According to the time of snoring, participants were divided into pregnancy onset snorers, chronic snorers and non-snorers. Logistic regressions were performed to examine the associations between snoring and pregnancy outcomes. Results: Of 3 079 pregnant women, 16.6% were habitual snorers, with 11.7% were pregnancy onset snorers and 4.9% were chronic snorers. After adjusting for potential confounders, chronic snorers were independently associated with gestational diabetes mellitus (GDM) (RR 1.66, 95%CI 1.09–2.53). Both pregnancy onset and chronic snorers were independently associated with placental adhesion (RR 1.96, 95%CI 1.17–3.27, and RR 2.33, 95%CI 1.22–4.46, respectively). Pregnancy onset snorers were at higher risk of caesarean delivery (RR 1.37, 95%CI 1.09–1.73) and having macrosomia (RR 1.54, 95%CI 1.05–2.27) and large for gestational age (LGA) (RR 1.71, 95%CI 1.31–2.24) infants. In addition, being overweight or obese before pregnancy plays an important role in mediating snoring and adverse pregnancy outcomes. Conclusions: Maternal snoring may increase the risk of adverse pregnancy outcomes, and being overweight or obese before pregnancy with snoring is remarkable for researchers. Further studies are still needed to confirm our results. [ABSTRACT FROM AUTHOR]

The characterization of heavy metal pollution is urgently needed in modern environmental studies. However, traditional geochemical methods for detecting soil heavy metals are rather time-consuming and expensive. In recent years, non-destructive and rapid magnetic techniques seem promising in monitoring soil pollution but it is questionable how this relates to heavy metal concentrations. Therefore, in order to understand the correlation of heavy metal pollution with environmental magnetism, magnetic susceptibility (χ LF ) and the concentrations of As, Cd, Cr, Cu, Ni, Pb and Zn were measured in the topsoil (0–15 cm) collected from Kaifeng City, China. In this study, the spatial distribution of heavy metals and χ LF , as well as the correlation between pollution load index ( PLI ) and χ LF were carried out. Results show that the contamination factor ( CF ) values of different heavy metals follow the order: Cd (10.48) > Zn (2.28) > Pb (1.68) > Cu (1.51) > Ni (0.81) > Cr (0.80) > As (0.65). The average pollution load index ( PLI ) of the metals is 2.53, representing a moderate pollution level as a whole of the city soil. In general, similar spatial distribution patterns of heavy metals and χ LF were found in this research, which decrease progressively from the southeast/east to the northwest/west in the study area. High concentrations of heavy metals and high levels of χ LF appears around the southeast, the north of the older city (within the ancient city wall), and along the Longxi–Haizhou Railway. Moreover, contents of As, Cd, Cr, Cu, Ni, Pb and Zn in soils and PLI are significantly positively correlated with their χ LF . The results further attest that the measurement of χ LF is a simple, rapid and quantitative method for the assessment of heavy metal contamination of soils. When χ LF ≤ 71 × 10 − 8 m 3 kg − 1 , the soil is considered to be non-polluted; 71 × 10 − 8 < χ LF ≤ 162 × 10 − 8 m 3 kg − 1 represents slightly polluted soils; 162 × 10 − 8 < χ LF ≤ 253 × 10 − 8 m 3 kg − 1 moderate pollution, and χ LF ≥ 253 × 10 − 8 m 3 kg − 1 corresponds to heavily polluted soils. However, the standard is exclusively valid for the study area and cannot be simply “transferred” to other polluted areas. [ABSTRACT FROM AUTHOR]

Background: Osteoporosis is a gradually recognized health problem with risks related to disease history and living habits. This study aims to establish the optimal prediction model by comparing the performance of four prediction models that incorporated disease history and living habits in predicting the risk of Osteoporosis in Chongqing adults., Methods: We conduct a cross-sectional survey with convenience sampling in this study. We use a questionnaire From January 2019 to December 2019 to collect data on disease history and adults' living habits who got dual-energy X-ray absorptiometry. We established the prediction models of osteoporosis in three steps. Firstly, we performed feature selection to identify risk factors related to osteoporosis. Secondly, the qualified participants were randomly divided into a training set and a test set in the ratio of 7:3. Then the prediction models of osteoporosis were established based on Artificial Neural Network (ANN), Deep Belief Network (DBN), Support Vector Machine (SVM) and combinatorial heuristic method (Genetic Algorithm - Decision Tree (GA-DT)). Finally, we compared the prediction models' performance through accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC) to select the optimal prediction model., Results: The univariate logistic model found that taking calcium tablet (odds ratio [OR] = 0.431), SBP (OR = 1.010), fracture (OR = 1.796), coronary heart disease (OR = 4.299), drinking alcohol (OR = 1.835), physical exercise (OR = 0.747) and other factors were related to the risk of osteoporosis. The AUCs of the training set and test set of the prediction models based on ANN, DBN, SVM and GA-DT were 0.901, 0.762; 0.622, 0.618; 0.698, 0.627; 0.744, 0.724, respectively. After evaluating four prediction models' performance, we selected a three-layer back propagation neural network (BPNN) with 18, 4, and 1 neuron in the input layer, hidden and output layers respectively, as the optimal prediction model. When the probability was greater than 0.330, osteoporosis would occur., Conclusions: Compared with DBN, SVM and GA-DT, the established ANN model had the best prediction ability and can be used to predict the risk of osteoporosis in physical examination of the Chongqing population. The model needs to be further improved through large sample research.

An external quality assessment (EQA) program for the molecular detection of avian influenza A (H7N9) virus was implemented by the National Center for Clinical Laboratories (NCCL) of China in June 2013. Virus-like particles (VLPs) that contained full-length RNA sequences of the hemagglutinin (HA), neuraminidase (NA), matrix protein (MP), and nucleoprotein (NP) genes from the H7N9 virus (armored RNAs) were constructed. The EQA panel, comprising 6 samples with different concentrations of armored RNAs positive for H7N9 viruses and four H7N9-negative samples (including one sample positive for only the MP gene of the H7N9 virus), was distributed to 79 laboratories in China that carry out the molecular detection of H7N9 viruses. The overall performances of the data sets were classified according to the results for the H7 and N9 genes. Consequently, we received 80 data sets (one participating group provided two sets of results) which were generated using commercial (n = 60) or in-house (n = 17) reverse transcription-quantitative PCR (qRT-PCR) kits and a commercial assay that employed isothermal amplification method (n = 3). The results revealed that the majority (82.5%) of the data sets correctly identified the H7N9 virus, while 17.5% of the data sets needed improvements in their diagnostic capabilities. These "improvable" data sets were derived mostly from false-negative results for the N9 gene at relatively low concentrations. The false-negative rate was 5.6%, and the false-positive rate was 0.6%. In addition, we observed varied diagnostic capabilities between the different commercially available kits and the in-house-developed assays, with the assay manufactured by BioPerfectus Technologies (Jiangsu, China) performing better than the others. Overall, the majority of laboratories have reliable diagnostic capacities for the detection of H7N9 virus.