Multi-scale variations and impact factors of carbon emission intensity in China.

China's carbon emissions have developed swiftly in recent decades, which will not only affect the nation's own sustainable development, but have a potentially negative impact on global climate stability. Given that socioeconomic development is susceptible to regional heterogeneity and geographic scales, a systematic exploration of spatiotemporal variations of carbon emission intensity (CEI) and their drivers across different levels is conducive to enacting more reasonable and efficient measures for emission reduction. However, there is still a lack of comprehensive analysis of these issues. In this paper, we attempted to quantify and compare the spatiotemporal evolution and spatial spillover effects of impact factors on CEI from nighttime light imagery and socioeconomic data at two China's administrative levels by utilizing the variation coefficient, spatial autocorrelation model and spatial econometric methods. The results showed that the spatiotemporal variations of CEI were greater at the prefecture level compared to the provincial level during 2000-2017. There were significant positive spatial autocorrelation of CEI at two administrative levels, and self-reinforcing agglomeration was more substantial at the prefectural level than that provincial level. While the local spatial clustering of CEI of each administrative level altered with scale dependence, the binary spatial structure (High-High and Low-Low) of CEI remained relatively steady in China. Various driver factors not only had direct effects on local CEI, but had spatial spillover effects on neighboring areas. Our findings illustrate that China's CEI is sensitive to the space-time hierarchy of multi-mechanisms, and suggest that "proceed in the light of local conditions" strategies can assist the Chinese government for CEI mitigation.

Individualized model for predicting COVID-19 deterioration in patients with cancer: A multicenter retrospective study.

The 2019 novel coronavirus has spread rapidly around the world. Cancer patients seem to be more susceptible to infection and disease deterioration, but the factors affecting the deterioration remain unclear. We aimed to develop an individualized model for prediction of coronavirus disease (COVID-19) deterioration in cancer patients. The clinical data of 276 cancer patients diagnosed with COVID-19 in 33 designated hospitals of Hubei, China from December 21, 2019 to March 18, 2020, were collected and randomly divided into a training and a validation cohort by a ratio of 2:1. Cox stepwise regression analysis was carried out to select prognostic factors. The prediction model was developed in the training cohort. The predictive accuracy of the model was quantified by C-index and time-dependent area under the receiver operating characteristic curve (t-AUC). Internal validation was assessed by the validation cohort. Risk stratification based on the model was carried out. Decision curve analysis (DCA) were used to evaluate the clinical usefulness of the model. We found age, cancer type, computed tomography baseline image features (ground glass opacity and consolidation), laboratory findings (lymphocyte count, serum levels of C-reactive protein, aspartate aminotransferase, direct bilirubin, urea, and d-dimer) were significantly associated with symptomatic deterioration. The C-index of the model was 0.755 in the training cohort and 0.779 in the validation cohort. The t-AUC values were above 0.7 within 8 weeks both in the training and validation cohorts. Patients were divided into two risk groups based on the nomogram: low-risk (total points ≤ 9.98) and high-risk (total points > 9.98) group. The Kaplan-Meier deterioration-free survival of COVID-19 curves presented significant discrimination between the two risk groups in both training and validation cohorts. The model indicated good clinical applicability by DCA curves. This study presents an individualized nomogram model to individually predict the possibility of symptomatic deterioration of COVID-19 in patients with cancer.

Two-step hydrothermal synthesis of peanut-shaped molybdenum diselenide/bismuth vanadate (MoSe2/BiVO4) with enhanced visible-light photocatalytic activity for the degradation of glyphosate.

The novel peanut-like shaped MoSe2/BiVO4 composites were fabricated via a facile two-step hydrothermal method and their photocatalytic activity was evaluated by the degradation of glyphosate under the visible light irradiation. The efficient transfer of photogenerated carriers and the charge separation were confirmed by photoluminescence spectra (PL), electrochemical impedance spectroscopy (EIS) and photocurrent. The coupling of MoSe2 and BiVO4 remarkably enhanced the photocatalytic activity. The 0.15MoSe2/BiVO4 composite showed the highest photocatalytic performance for the degradation of glyphosate among all the as-prepared photocatalysts and presented the high stability and good reusability in the photocatalytic reactions.

Nickel sulfide/graphitic carbon nitride/strontium titanate (NiS/g-C3N4/SrTiO3) composites with significantly enhanced photocatalytic hydrogen production activity.

NiS/g-C3N4/SrTiO3 (NS/CN/STO) composites were prepared using a facile hydrothermal method. The synergistic effect of g-C3N4/SrTiO3 (CN/STO) heterojunction and NiS cocatalyst enhanced the photocatalytic hydrogen evolution activity of NS/CN/STO. A hydrogen production rate of 1722.7 µmol h-1 g-1 was obtained when the 2%NiS/20%g-C3N4/SrTiO3 (2NS/20CN/STO) was used for the photocatalytic hydrogen evolution in the presence of methanol used as a sacrificial agent under UV-vis light irradiation; the photocatalytic hydrogen production rate of 2NS/20CN/STO is 32.8, 8.9 and 4.2 times the value of that obtained with pure g-C3N4, SrTiO3 and 20%g-C3N4/SrTiO3 (20CN/STO), respectively. Moreover, in photoelectrochemical investigations when compared with 20CN/STO, SrTiO3 and g-C3N4, 2NS/20CN/STO exhibited significant photocurrent enhancement. The heterojunction and cocatalyst in NS/CN/STO improved the charge separation efficiency and the lifetime of the charge carriers, leading to the enhanced generation of electrons for photocatalytic hydrogen production.