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China's most important three urban agglomerations, the Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD) and Pearl River Delta (PRD), are expected to play a leading role in energy saving and emissions reduction in China. Based on the biennial Malmquist productivity index and the non-radial direct distance function, this study improved the data envelopment analysis (DEA) model to evaluate the energy-saving and emissions-reduction performance (EEP) of these three urban agglomerations. The empirical results indicated that BTH did not show improvement in performance, whereas YRD and PRD showed a significant improvement. The annual growth rate of performance was higher in the PRD than in the other two urban agglomerations. The “catch-up effect” of the three urban agglomerations vanished after 2010, and the possible reasons for this change are the large-scale economic stimulus plan adopted by the government to address the global crisis and the recent decline of economic growth. The technical progress level increased at first, then decline, and increased again. This study also found that β convergence of performance existed in BTH and YRD. However, there was no β convergence of performance in PRD.

Human coronaviruses are RNA viruses that are sensitive to ultraviolet (UV) radiation. Sunlight contains UVA (320–400 nm), UVB (260–320 nm) and UVC (200–260 nm) action spectra. UVC can inactivate coronaviruses, including severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2). The incidence and mortality of coronavirus disease 2019 (COVID-19) are considered to be correlated with vitamin D levels. Vitamin D synthesis in human skin is closely related to exposure to UVB radiation. Therefore, the incidence and mortality of COVID-19 are also considered to be correlated with Vitamin D levels. In this study, Spearman and Kendall rank correlation analysis tests were used to analyze the correlation between the average percent positive of five human coronaviruses (SARS-CoV-2, CoVHKU1, CoVNL63, CoVOC43, and CoV229E) in the U.S. and the corresponding sunlight UV radiation dose The results indicated that the monthly average percent positive of four common coronaviruses was significantly negatively correlated with the sunlight UV radiation dose. The weekly percent positive of SARS-CoV-2 during April 17, 2020 to July 10, 2020 showed a significant negative correlation with the sunlight UV radiation dose in census regions 1 and 2 of the U.S. while no statistical significance in the other regions. Additionally, sunlight UV radiation also showed some negative effects with respect to the early SARS-CoV-2 transmission., Graphical abstract Unlabelled Image, Highlights • Significant negative correlation was found between sunlight UV dose and percent positive of four common HCoV in the U.S. • Percent positive of SARS-CoV-2 in census regions 1 and 2 of U.S. showed significant negative correlation with sunlight UV. • Negative association between the sunlight UV dose and the COVID-19 early transmission in the U.S. was further indicate.

Background: Although significant seasonality had been reported among common human coronaviruses (NL63, 229E, HKU1, and OC43) and influenza viruses (IVA and IVB), the potential seasonal pattern of coronavirus disease 2019 (COVID-19) transmission and its interaction with winter influenza remains to be seen. Understanding the seasonality of and correlation between severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and influenza viruses may contribute to the prevention and control of the COVID-19 pandemic in the coming flu season. Methods: We used time series decomposition to separate the seasonal factors of four common human coronaviruses and two influenza viruses in the United States from 2014 to 2020. Test results and confirmed time series in the United States from the 14th to 45th weeks of 2020 were used to analyse the seasonality of SARS-CoV-2. We tried to eliminate the seasonality of SARS-CoV-2 by applying the average seasonal factors of the other beta-coronaviruses. After seasonal adjustment, we identified a correlation between COVID-19 and influenza. Additionally, we collected and analysed the weekly percent positive of influenza virus cases in nine census divisions in the United States between the 1st and 45th weeks of 2020, and the same period in 2016-2019, to verify this correlation. Findings: We identified a moderate correlation between betacoronaviruses and influenza viruses from 2014-2020. The seasonally adjusted Spearman correlation coefficient between coronavirus OC43 and influenza A virus (IVA) was 0.39, and 0.41 between coronavirus HKU1 and influenza B virus (IVB), while no similar relationship was identified between alphacoronaviruses and influenza viruses. The seasonally adjusted Spearman correlation coefficient between SARS-CoV-2 and influenza viruses among nine census divisions in the United States from the 14th to 45th weeks of 2020 showed a moderate negative correlation in most census divisions, with mean correlation coefficients of -0.352 between SARS-CoV-2 and IVA, -0.327 between SARS-CoV-2 and IVB. The weekly percent positive of influenza virus cases in the United States showed a significant reduction between the 14th and 45th weeks of 2020 and the same weeks in 2016-2019, which partly verified the negative correlation between COVID-19 and influenza. Interpretation: As synchronous seasonality commonly exists among human coronaviruses and influenza viruses, seasonal adjustment is necessary before the pathogen-pathogen correlation analysis can be performed. The present study revealed a negative correlation between SARS-CoV-2 and influenza viruses, which was obscured by strong synchronous seasonality. According to our study, COVID-19 may unfortunately increase in winter due to its seasonal pattern, but a simultaneous outbreak of influenza and COVID-19 may not occur this winter due to the negative interaction between these two pathogens. Funding Statement: This study was funded by the Science and Technology Innovation Committee of Shenzhen Municipality (JCYJ20190808152613121) and the Shenzhen-Hong Kong Institute of Brain Science – Shenzhen Fundamental Research Institutions (2019SHIBS0003). Declaration of Interests: We declare no competing interests.

This study investigated the impact of industrial activity and energy structure on environmental pollution from the demand and supply side with the panel data source from 2000 to 2016 of 30 provinces in China. Empirical results show that, in the long-term, increasing RD the latter, namely, capital-embodied technological progress, can reduce wastewater emissions because of the possible performance of capital quality upgrading. In the short-term, only coal consumption structure and capital-embodied technological progress have similar effects on the pollution emissions as they are in the long term, reflecting that the policymaking is different in the short-term and long-term. Furthermore, this study confirms that environmental regulations affect industrial pollution through energy efficiency and energy structure changes. The findings show that adjusting the energy structure is the primary channel to emissions reduction.

This study employs a Luenberger-Hicks-Moorsteen productivity indicator to measure the total factor energy efficiency (TFEE) of the Yangtze River Economic Belt (YREB) of China from 2005 to 2016. The relative importance analysis method is used to investigate the driving forces of the TFEE. The main results are as follows. (a) The TFEE has an average growth rate of 3.3% during the study period. The TFEE declines first, then rebounds, and declines again. The contribution of technological efficiency changes to the TFEE growth is almost zero which indicating no catch-up effect in the sample period. Technological progress contributes a negative impact to the TFEE, with an average annual decrease rate of 3.4%. The scale efficiency changes, with an average annual growth rate of 6.7%, are extremely important for the growth of TFEE. (b) The TFEE of the western region is narrowing the gap with the central and western regions, while the gap between the central region and the eastern region is increasing. (c) Research investment can improve the growth of TFEE significantly, while government expenditure and industrial structure are not. And government expenditure, economic development, and research investment are top factors to explain the variation of TFEE.

The long-term excessive use of chlorothalonil (CTN) can lead to serious environmental pollution, which presents a cause for concern. Here, we investigated the degradation of CTN by using an effective strain, TUAH-1, which was identified as Enterobacter cloacae according to the morphological approach, 16S rDNA gene sequence and phylogenetic tree analysis. Treatment with TUAH-1 demonstrated that the maximum processing capability was 74 mg CTN per gram dry cells in 48 h under optimum conditions (30 °C–35 °C, pH 7.0). After treatment with 21.3 mg dry cells, the degradation efficiency was 97.4% for 20 mg l -1 CTN in the aqueous phase. Meanwhile, treatment with crude enzyme solution containing 2.0 mg ml -1 protein led to complete degradation of 20.0 mg l -1 CTN within 10 h, which is significantly faster than that of TUAH-1. The results also showed that CTN could not be detected after treatment with 5.68 g TUAH-1 per kg soil for 48 h when the original CTN content in the soil was 10 mg kg -1 . After treatment with THAH-1 or its crude proteins, many degradation products were detected by HPLC-MS, suggesting two possible degradation pathways. The mechanism of CTN degradation by TUAH-1 was noted to be multienzymatic catalysis comprising glyceraldehyde-3-phosphate dehydrogenase and glutathione S-transferase.

Objective The discontinuation of antiepileptic drugs (AEDs) is an important treatment decision for epilepsy patients who have been seizure-free for 2 years or longer. Some patients experience seizures relapse after AED withdrawal. The prognostic value of electroencephalograms (EEGs) for seizure relapse following AED withdrawal is controversial. To our knowledge, this is the first meta-analysis to address whether EEG data can be used to guide the discontinuation of AEDs. Method We performed a meta-analysis of cohort studies that reported original EEG data from before AED withdrawal and recurrence after AED-withdrawal. The quality of each study was assessed using the Newcastle–Ottawa Scale. Results Fifteen studies including a total of 2349 participants were included in this meta-analysis. This meta-analysis of 15 studies demonstrates that an abnormal electroencephalogram was a predictor of the risk of relapse. Additionally, paroxysmal, slowing, spike and wave activities on electroencephalograms were associated with increased risk of relapse. Conclusion We reveal that abnormal EEG records, particularly paroxysmal abnormalities, before AED withdrawal predicted a high risk of relapse. Slowing and spike and wave activities also exhibited moderate predictive values. Significance Our findings suggest that, EEGs might be an important prognostic tool for antiepileptic drug reduction.

Analysis is conducted on the sources of several fitting errors of the grey Verhulst model, including approximation construction of the background value, inconsistency among the parameters of the difference and differential forms, the objective function setting of parameter estimation, and so on. However, constructing the precise background value of this model is complicated. A new parameter estimation method based on weighted least squares is proposed by transforming the model and using the approximate transformation of the objective function. Moreover, weights are determined by the converted form of target functions. In addition, the properties of the model under the new parameter form are studied. The proposed model avoids the process of precise background value construction as well as conversion from differential to difference equations. Empirical analysis shows that the method is effective.

The reconstruction of defect profiles based on ultrasonic guided waves means the acquisition of defect profiles and parameters from ultrasonic guided wave inspection signals, and it is the key for the inversion of ultrasonic guided waves. A method for the reconstruction of 2-D profiles based on kernelized extreme learning machine (ELM) is presented, and quantum genetic algorithm (QGA) is adopted to optimize the cost parameter C and kernel parameter @c of kernelized ELM. The input data sets of kernelized ELM are defect echo signals, and the output data sets are 2-D profile parameters. The mapping from defect echo signals to 2-D profiles is established. The sample database is achieved by practical experiments and numerical simulations. Then, 2-D profile reconstruction of artificial defects in ultrasonic guided wave testing is implemented with QGA-kernelized ELM. To compare the generalization performance and reconstruction results, another reconstruction model based on LS-SVM is designed simultaneously with the same kernel. Finally, experimental results indicate that proposed method possesses faster speed, lower computational complexity and better generalization performance, and it is a feasible and effective approach to reconstruct 2-D defect profiles.