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Agricultural drought mainly stems from reduced soil moisture and precipitation, and it causes adverse impacts on the growth of crops and vegetation, thereby affecting agricultural production and food security. In order to develop drought mitigation measures, reliable agricultural drought forecasting is essential. In this study, we developed an agricultural drought forecasting model based on canonical vine copulas in three dimensions (3C-vine model) in which antecedent meteorological drought and agricultural drought persistence were utilized as predictors. Furthermore, a meta-Gaussian (MG) model was selected as a reference to evaluate the forecast skill. The agricultural drought in China in August of 2018 was selected as a typical case study, and the spatial patterns of 1- to 3-month lead forecasts of agricultural drought utilizing the 3C-vine model resembled the corresponding observations, indicating the good predictive ability of the model. The performance metrics – the Nash–Sutcliffe efficiency (NSE), the coefficient of determination (R2), and the root-mean-square error (RMSE) – showed that the 3C-vine model outperformed the MG model with respect to forecasting agricultural drought in August for diverse lead times. Moreover, the 3C-vine model exhibited excellent forecast skill with respect to capturing the extreme agricultural drought over different selected typical regions. This study may help to guide drought early warning, drought mitigation, and water resource scheduling.

The impact of droughts on vegetation is essentially manifested as the transition of water shortage from the meteorological to ecological stages. Therefore, understanding the mechanism of drought propagation from meteorological to ecological drought is crucial for ecological conservation. This study proposes a method for calculating the probability of meteorological drought to trigger ecological drought at different magnitudes in northwestern China. In this approach, meteorological and ecological drought events during 1982–2020 are identified using the three-dimensional identification method; the propagated drought events are extracted according to a certain spatiotemporal overlap rule, and propagation probability is calculated by coupling the machine learning model and C-vine copula. The results indicate that (1) 46 drought events are successfully paired with 130 meteorological and 184 ecological drought events during 1982–2020, and ecological drought exhibits a longer duration but smaller affected area and severity than meteorological drought; (2) a quadratic discriminant analysis (QDA) classifier performs the best among the 11 commonly used machine learning models which are combined with four-dimensional C-vine copula to construct the drought propagation probability model; and (3) the hybrid method considers more drought characteristics and a more detailed propagation process which addresses the limited applicability of the traditional method to regions with large spatial extent.

Extreme climatic events such as drought and high temperatures are frequently occurring under the global climate change scenarios causing severe threats to crop yield. Several studies have used individual events, e.g., meteorological drought (dry event), high temperatures (hot event), or compound dry–hot events as predictors of agricultural drought severity. However, only a few studies considered the situation when at least one dry event or hot event appeared. Moreover, the ability to depict drought phenomena, in terms of duration and severity, with the same drought index for different time scales was often inconsistent. This study adopted the meta-Gaussian model (MG) to predict agricultural drought severity for the crops and vegetation growing seasons (from April to September) from the year 1948 to 2014 in China by using two predictors, namely, standardized dry or hot events index (SDHEI) and joint soil moisture deficit index (JSDI). The SDHEI was built based on monthly precipitation and daily temperature, and the JSDI was calculated by combining 1-, 3-, 6-, 9-, and 12-month standardized soil moisture index (SSI) based on monthly root zone soil moisture. The prediction performance of the MG was assessed by employing the Brier skill score (BSS), Nash–Sutcliffe efficiency coefficient (NSE), and root mean square error (RMSE). The severe agricultural drought in the months of April–September of 2010 in China was used as a case study. A visual comparison of this event indicated that the lead JSDI predictions for 1–3 months showed a resembled behavioral pattern with corresponding historical observations for most of the areas of China; however, it had some disagreements in the spatial extent and severity of the agricultural drought in some of the regions. The MG had a great performance with BSS > 0.4, NSE > 0.6, and RMSE

Studying the spatial pattern and determinants of migrant workers’ income is vital for grasping their mobility tendencies to better administer to this urban floating population and propel urbanization. Using 2017 data from the China Migrants Dynamic Survey, this paper adopts trend surface, standard deviation ellipse, and spatial correlation methods to describe the spatial pattern of migrant workers' income in China. It then employs econometric models to examine the spatial effects and determinants of migrant workers' income. The spatial pattern demonstrates an obvious cluster phenomenon for the migrant workers' income in prefectural cities. Based on this social context, our empirical results further show that migrant workers' income has a spatial dependence effect. Migrant workers' income is positively affected by the migrant workers' individual characteristics (demographics, outflow characteristics, social integration) and negatively affected by the external factors (public social services, economic development environment) of the cities into which they flow. There is salient spatial variation in the determinants of migrant workers' income in prefectural cities, which are divided into multifactorial influence zones, work experience and house price influence zones, and education and family influence zones by cluster analysis, and into eastern and midwestern influence zones according to China's three economic areas.

The impact of droughts on vegetation is essentially manifested as the transition of water shortage from the meteorological to ecological stages. Therefore, understanding the mechanism of drought propagation from meteorological to ecological drought is crucial for ecological conservation. This study proposes a method for calculating the probability of meteorological drought to trigger ecological drought at different magnitudes in Northwestern China. In this approach, meteorological and ecological drought events during 1982–2020 are identified using the three-dimensional identification method; the propagated drought events are extracted according to a certain spatio-temporal overlap rule; and propagation probability is calculated by coupling machine learning model and C-vine copula. The results indicate that: (1) 46 drought events are successfully paired by 130 meteorological and 184 ecological drought events during 1982–2020; ecological drought exhibits a longer duration, but smaller affected area and severity than meteorological drought; (2) Quadratic Discriminant Analysis (QDA) classifier performs the best among the 11 commonly used machine learning models which is combined with four-dimensional C-vine copula to construct drought propagation probability model; (3) the hybrid method considers more drought characteristics and more detailed propagation process which addresses the limited applicability of the traditional method to regions with large spatial extent.

Direct finite element analysis of continuous spot welding was implemented with reverse geometric models of the actual stamping parts that comprise the back-side panel. This reverse panel showed apparent gaps after assembly and yielded to the electrodes and fixtures during welding, causing significant deformations. We acquired the total residual deformation in continuous simulation through a 3-D comparison between the original panel and the final deformed panel. This total deformation indicates that the deformations in the two processes offset with each other. We also performed a spot welding experiment and measured the actual residual deformation to validate the total deformation in simulation. They show good agreement in terms of deformation trend and magnitude. Finally, we designed seven comparative simulations to investigate the influence of the clamping pressure, electrode force, and spot welding sequence on the residual deformation. The results show that the sole increase of clamping pressure causes a deformation reduction in plate P4, while that of electrode force causes a deformation increase in P3. When the welding sequences of processes 1 and 2 are both reversed, the plate with the lowest deformation changes from P4 to P3. The final deformations in parts with multiple processes and multiple spot welds result from nonlinear interaction among many factors. The influence of a single factor change on the final deformation will be balanced by the remaining factors. This is why the fluctuations in total deformation are not significant between the standard simulation and each of the seven comparative simulations. The value of this article lies in the parameters optimization of spot welding based on reverse engineering and direct finite element analysis.

Agricultural drought is caused by reduced soil moisture and precipitation and affects the growth of crops and vegetation, and in turn agricultural production and food security. For developing measures for drought mitigation, reliable agricultural drought forecasting is essential. In this study, we developed an agricultural drought forecasting model based on canonical vine copulas under three-dimensions (3C-vine model), in which the antecedent meteorological drought and agricultural drought persistence were utilized as predictors. Besides, the meta-Gaussian (MG) model was selected as a reference model to evaluate the forecast skill. The agricultural drought in August of 2018 was selected as a case study, and the spatial patterns of 1–3-month lead forecasts of agricultural drought utilizing the 3C-vine model resembled the corresponding observations, indicating the predictive ability of the model. The performance metrics (NSE, R2, and RMSE) showed that the 3C-vine model outperformed the MG model for August under diverse lead times. Also, the 3C-vine model exhibited excellent forecast skills in capturing the extreme agricultural drought over different selected typical regions. This study may help with drought early warning, drought mitigation, and water resources scheduling.

Understanding the relationship among different types of drought is crucial for drought mitigation and early warnings. Much attention has been recently focused on the propagation from meteorological drought (MD) to hydrological drought (HD); however, the influences of human activities on drought propagation have rarely been explored. The novelty of the study was to propose an effective framework to quantify the impacts of human activities on MD-HD propagation. We adopted the framework to comprehensively evaluate the anthropic impacts on hydrological drought variations and time, thresholds, and probabilities of MD-HD propagation in the Weihe River Basin (WRB) during different periods. The results showed that human activities did significantly disturb HD variations and MD-HD propagation characteristics. Specifically, human activities increased the frequency and extremes of HD and weakened its correlation with MD. The MD-HD propagation characteristics showed spatiotemporal differences across three subbasins because of the different levels of human activities. The thresholds of MD triggering different levels of HD generally became larger with change rates from 1% to 143% and 3% to more than 189% during two periods, respectively. Meanwhile, we also found that the thresholds became distinctly smaller, which could only be observed in spring and winter. Moreover, the relationship between natural and human-induced probabilities of HD occurrence showed three patterns with the increase of MD severity. The quantitative results of this study can provide guide information on adaptation strategies to promote drought preparedness in the WRB. The proposed framework can be also applied in other regions to improve the understanding of hydrological drought mechanisms.

The Gravity Recovery and Climate Experiment (GRACE) mission operated between March 2002 and October 2017, providing monthly global terrestrial water storage anomalies (TWSA), which are essential in investigating global climate change and the hydrological cycle. Its continuation GRACE-Follow On (GRACE-FO) was launched in May 2018, leading to an 11-months data gap between them. Therefore, it is of high significance to fill the data gap. However, existing studies have not yet achieved high prediction accuracy in filling the data gap over Northwest China (NWC). Based on the CSR RL06 spherical harmonics (SH) solution from April 2002 to June 2017, this study combined the Variational Mode Decomposition (VMD) with the Long Short-Term Memory (LSTM) model to fill the data gap over NWC. Two conventional LSTM models were set as control groups: LSTM-1, for which GLDAS-based TWS, temperature, and precipitation were used as input, and LSTM-2, for which TWSA data of the previous 12 months were used as input. In general, the two conventional LSTM models showed poor performance. In contrast, the integrated VMD-LSTM model achieved significantly higher prediction accuracy (NSE = 0.974, CC = 0.989, and RMSE = 0.808 cm) and exhibited higher reliability compared to the single LSTM models. The proposed method offers a means toward bridging the gap in GRACE-based TWSA data over NWC. It also provides a reference for similar regions around the world.

Lipid accumulation and progressive necroinflammation play pivotal roles in the development of diabetic nephropathy. C1q tumour necrosis factor-related protein-3 (CTRP3) is an adipokine with pleiotropic functions in cell proliferation, glucose and lipid metabolism, and inflammation. However, the mechanism and involvement of CTRP3 in lipid metabolism and the necroinflammation of renal tubular cells remain unclear. Here, we report that CTRP3 expression decreased in a time- and concentration-dependent manner in high glucose-stimulated HK-2 cells. We noted that the overexpression of CTRP3 or recombinant CTRP3 (rCTRP3) treatment prevented high glucose-induced lipid accumulation by inhibiting the expression of sterol regulatory element-binding protein-1 and increasing the expression of peroxisome proliferator-activated receptor-α and ATP-binding cassette A1. Moreover, the nucleotide-binding oligomerisation domain-like receptor protein 3-mediated inflammatory response and mixed lineage kinase domain-like protein-dependent necroinflammation were inhibited by CTRP3 overexpression or rCTRP3 treatment in HK-2 cells cultured in high glucose. Furthermore, lipotoxicity-induced by palmitic acid was found to be involved in necroinflammation in HK-2 cells, and CTRP3 displayed the same protective effect. CTRP3 also activated the adenosine monophosphate-activated protein kinase (AMPK) pathway, whereas adenine 9-β-D-arabinofuranoside, an AMPK inhibitor, replicated the protective effects of CTRP3. Besides, using kidney biopsies from patients with diabetes, we found that decreased CTRP3 expression was accompanied by increased lipid deposition, as well as the structural and functional injury of renal tubular cells. Our findings demonstrate that CTRP3 affects lipid metabolism and necroinflammation in renal tubular cells via the AMPK signalling pathway. Thus, CTRP3 may be a potential therapeutic target in diabetic renal injury.

Direct finite element analysis that takes stamping dimensional deviations into account was achieved based on coupled electro-thermo-mechanical method and reverse engineering in the current research. The two continuous resistance spot welding processes of the reverse established back side panel were simulated. Its residual stresses and deformation were compared with those of the ideal back side panel. Large distortion presents in the reverse back side panel while only slight deformation exists in the ideal one. The former is validated to be closer to the deformation of the actual welded back side panel. The peak residual stresses after processes 1 and 2 remain almost unchanged in the reverse back side panel while those in the ideal one show an increasing trend. The final residual stresses of the reverse and the ideal back side panel are both high but the former is lower than the later. The stamping dimensional deviations are responsible for these differences. They lead to obvious attenuation of geometric features and large assembly clearances in the reverse back side panel. These assembly clearances allow large deformations of the stamping parts during the welding process. Meanwhile, these clearances make it relatively freer for the heated sheet metal to expand thermally in the thickness direction, resulting in less or even no residual stress in this direction. These clearances also promote the release of the thickness-direction component of the tensile residual stress in the extension direction of sheet by allowing deformation in the thickness direction.

Renal fibrosis is characterized by glomerulosclerosis and tubulointerstitial fibrosis in diabetic nephropathy (DN). We aimed to evaluate the effects of PP2 on renal fibrosis of DN. GSE33744 and GSE86300 were downloaded from the GEO database. Firstly, 839 DEGs were identified between nondiabetic and diabetic mice renal glomerular samples. COX-2 was selected to assess the effects of PP2 on renal glomerulosclerosis. In db/db mice, PP2 decreased the expression of COX-2, phosphorylated p65, and fibrotic proteins, accompanied with attenuated renal glomerulosclerosis. In cultured glomerular mesangial cells, high glucose- (HG-) induced p65 phosphorylation and COX-2 expression were attenuated by PP2 or NF-κB inhibitor PDTC. PP2, PDTC, or COX-2 inhibitor NS-398 ameliorated abnormal proliferation and expression of fibrotic proteins induced by HG. Secondly, 238 DEGs were identified between nondiabetic and diabetic mice renal cortex samples. UCP2 was selected to assess the effects of PP2 on renal tubulointerstitial fibrosis. In db/db mice, PP2 decreased the expression of PPARγ and UCP2, accompanied with attenuated renal tubulointerstitial fibrosis and EMT. In cultured proximal tubular cells, HG-induced PPARγ and UCP2 expression was inhibited by PP2 or PPARγ antagonist GW9662. PP2, GW9662, or UCP2 shRNA ameliorated HG-induced EMT. These results indicated that PP2 ameliorated renal fibrosis in diabetic mice.

Translation Quality Estimation is critical to reducing post-editing efforts in machine translation and to cross-lingual corpus cleaning. As a research problem, quality estimation (QE) aims to directly estimate the quality of translation in a given pair of source and target sentences, and highlight the words that need corrections, without referencing to golden translations. In this paper, we propose Verdi, a novel framework for word-level and sentence-level post-editing effort estimation for bilingual corpora. Verdi adopts two word predictors to enable diverse features to be extracted from a pair of sentences for subsequent quality estimation, including a transformer-based neural machine translation (NMT) model and a pre-trained cross-lingual language model (XLM). We exploit the symmetric nature of bilingual corpora and apply model-level dual learning in the NMT predictor, which handles a primal task and a dual task simultaneously with weight sharing, leading to stronger context prediction ability than single-direction NMT models. By taking advantage of the dual learning scheme, we further design a novel feature to directly encode the translated target information without relying on the source context. Extensive experiments conducted on WMT20 QE tasks demonstrate that our method beats the winner of the competition and outperforms other baseline methods by a great margin. We further use the sentence-level scores provided by Verdi to clean a parallel corpus and observe benefits on both model performance and training efficiency., Accepted by The Web Conference 2021

Li-ion batteries attract great attention due to the rapidly increasing and urgent demand for high energy storage devices. MAX phase compounds, layered ternary transition metal carbides and/or nitrides show promise as candidate materials of electrodes for Li-ion batteries. However, the highest specific capacity reported up to now is relatively low (180 mA h g