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In recent years, a large number of countries have connected and distributed photovoltaics in remote rural areas, aiming to promote the use of clean energy in rural areas. The solar energy that is not used in time needs to be discarded, resulting in a large amount of wasted energy. Rural areas are closely related to agricultural production, and solar energy can be used for agricultural nitrogen fixation to supplement the nitrogen needed by crops and effectively use the upcoming waste of solar energy. A photovoltaic-driven plasma reactor for nitrogen fixation in agriculture was designed in this study. The air inlet and outlet holes are arranged above and below the reactor to facilitate air entry and directly interact with the gliding arc generated at the bottom of the electrode to achieve atmospheric nitrogen fixation in agriculture. The characteristics of gliding arc development in the process of nitrogen fixation in agriculture were studied experimentally. There are two discharge modes of the gliding arc discharge: one is steady arc gliding mode (A-G Mode), and the other is breakdown gliding mode (B-G Mode). By collecting discharge signals, different discharge modes of gliding arc discharge were analyzed, and the effect of the air flow rate on the discharge period and discharge mode ratio distribution is discussed. The effects of the air flow rate on the yield, specific energy input, and energy consumption in plasma agriculture were studied. The experimental results show that with an increase in the air flow rate, the B-G mode takes up a larger proportion and the gliding arc discharge period is shortened. However, the higher the proportion of the B-G mode, the more unfavorable the production of nitrogen oxides. Although the nitrogen oxides generated by the system are not particularly excellent compared with the Haber-Bosch ammonia process (H-B process), the access to distributed photovoltaic roofs in rural and remote areas can effectively use available resources like water, air, and solar, and avoid energy waste in areas where wind and solar are abandoned. [ABSTRACT FROM AUTHOR]

Mother-to-child transmission of Human Immunodeficiency Virus (HIV) has been greatly reduced with the advance of intervention technology. However, adverse pregnancy outcomes (APOs) are still common, and little is known about the driving forces of APOs among pregnant women living with HIV in China. Between January 2004 and December 2020, a total of 638 pregnancies among pregnant women living with HIV were enrolled in this study, 84 (13.2%) pregnancies with 87 APOs were reported. Preterm birth (3.8%), ectopic pregnancy (3.4%), spontaneous abortion (2.0%), and embryo arrest (1.7%) were the most common APOs in pregnant women living with HIV. Exposure to antiretroviral drugs (ARVs) during the first trimester (RR = 4.077, 95% CI: 0.521, 1.484, P<0.001) and the first CD4+ T lymphocyte count (CD4 count)≤ 350/μl (RR = 2.227, 95% CI: 0.063, 0.991, P = 0.026) were risk factors of APOs. The age≤ 30 years (RR = −2.513, 95% CI: −1.067, −0.132, P = 0.012) was associated with the decreasing of APOs. Encouraging people to initiate combination antiretroviral therapy and reach a high CD4 count level before pregnancy would be helpful to prevent APOs. Pregnant women exposed to ARVs in the first trimester needed more attention for APOs. [ABSTRACT FROM AUTHOR]

Background: As postmenopausal osteoporotic fractures can cause higher rates of disability and mortality in women; it is essential to analyze the factors associated with primary and recurrent fractures in postmenopausal osteoporosis (PMOP) patients.Methods: Retrospective analysis of 2478 PMOP patients aged ≥ 50 years who attended the Shanghai General Hospital from January 2007 to December 2016, including 1239 patients with no fractures and 1239 patients with histories of fractures (1008 in the primary fracture group and 231 in the re-fracture group). All patients' basic clinical data, serum biochemical and bone metabolic markers, bone mineral density (BMD), and other indicators were recorded uniformly. Comparing the differences between the clinical characteristics of patients with primary and recurrent fractures, as well as the differences in the clinical characteristics of patients with primary and recurrent fractures in combination with different diseases, further analyses the risk factors for primary and recurrent fractures in PMOP patients. SPSS.26 was used for statistical analysis.Results: Compared to the unfractured group, the fractured group was older and had lower height and bone mineral density (all P < 0.01), with the re-fractured group having lower BMD at each key site than the primary fracture group (all P < 0.01). Analysis of the combined disease subgroups showed that serum BGP levels were lower in the primary and re-fracture patients with diabetes than in the non-diabetic subgroup (P < 0.05), and serum CTX levels were lower in the re-fracture group with diabetes than in the primary fracture group with diabetes (P < 0.05). Patients with recurrent fractures with cardio-vascular diseases had lower BMD than the subgroup without cardio-vascular diseases (P < 0.05) and also had lower BMD than the group with primary fractures with cardio-vascular diseases (P < 0.05). Multiple logistic regression analysis showed that advanced age, overweight, low lumbar spine and total hip BMD were risk factors for primary and recurrent fractures; and comorbid chronic liver and kidney diseases were risk factors for primary fractures.Conclusion: PMOP patients with advanced age, overweight, low bone mineral density, and comorbid chronic liver and kidney diseases are at greater risk of fractures and require early intervention to reduce fractures occurrence. Moreover, those who are elderly, overweight, and have low bone density should also be aware of the risk of re-fractures. [ABSTRACT FROM AUTHOR]

In the current stage, software defect prediction is suffering the imbalanced data problem. Traditional methods are insensitive to defect-prone modules and tend to predict defect-prone modules as defect-free modules. To deal with this problem, sampling techniques are adopted to rebalance the defect-prone and defect-free data to train the predictive model in order to improve the performance. However, it is not clear on the combined effect of the sampling techniques and the machine learning classifiers on the performance of software defect prediction. The intent of the paper is to study the performance impact on defect prediction incurred by different combinations of sampling techniques and machine learning classifiers. Specifically, we investigate three types of sampling techniques as resampling, spread subsampling and SMOTE (Synthetic Minority Over-sampling Technique), and five types of machine learning classifiers as C4.5, naive Bayes, logistic regression, support vector machine and deep learning to study their combined effect on defect prediction. By using the Friedman test and Nemenyi test, we find that there isn't an optimal method among all the 12 combinations in defect prediction. However, support vector machine and deep learning have produced the best performance stably among all the investigated projects. With ANOVA analysis, we find that the sampling techniques have great impact on the outcomes of defect prediction because they produce different data distributions for model training. Nevertheless, the sampling proportion has significant impacts on TPR (True Positive Ratio) and FPR (False Positive Ratio) while it can merely influence the AUC (Area under Curve) and Balance of logistic regression. We explain the experimental results in the paper. [ABSTRACT FROM AUTHOR]