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Abstract The sustainable development strategy shifts water treatment from pollution removal to resource recovery. Here, an electrochemical resource-recovery anaerobic membrane bioreactor (eRAnMBR) that employed a magnesium plate and conductive membrane as dual anodes is presented and shows excellent performance in carbon, nitrogen, and phosphorus recovery, as well as 95% membrane anti-fouling. The Mg2+ released alters the physicochemical properties of sludge, unblocking the cake layer, and recovers ammonium and phosphate, yielding 60.64% purity and 0.08 g d−1 struvite deposited onto cathode to be separated from sludge. The enhanced direct interspecies electron transfer, along with hydrogen evolution and alkalinity increase due to the electrochemical reactions, significantly increase methane yield and purity (93.97%) of the eRAnMBR. This increased internal energy can cover the additional electricity and electrode consumption. This integrated eRAnMBR reactor boasts the benefits of short process, low maintenance, and low carbon footprint, introducing a concept for the next generation of wastewater treatment.

Abstract Distinguishing between long-term and short-term effects allows for the identification of different response mechanisms. This study investigated the long- and short-run asymmetric impacts of climate variation on tuberculosis (TB) and constructed forecasting models using the autoregressive distributed lag (ARDL) and nonlinear ARDL (NARDL). TB showed a downward trend, peaking in March-May per year. A 1 h increment or decrement in aggregate sunshine hours resulted in an increase of 32 TB cases. A 1 m/s increment and decrement in average wind velocity contributed to a decrement of 3600 and 5021 TB cases, respectively (Wald long-run asymmetry test [WLR] = 13.275, P

Abstract This study aims to explore the application value of the Bayesian Time Structure Sequence (BSTS) model in estimating the acute hemorrhagic conjunctivitis (AHC) epidemics. The reported AHC cases spanning from January 2011 to October 2022 in China were collated. Utilizing R software, the BSTS and Autoregressive Integrated Moving Average (ARIMA) models were constructed using the data from January 2011 to December 2021. The prediction effect of both models was compared using the data from January to October 2022, and finally the AHC incidence from November 2022 to December 2023 was predicted. The results indicated that forecast errors under the BSTS model were lower than those under the ARIMA model. The actual AHC incidence in July 2022 from the ARIMA model deviated from the 95% confidence interval (CI) of the predicted value. However, the observed AHC incidence from the BSTS model fell within the 95% CI of the predicted value. Notably, the BSTS model predicted 26,474 new AHC cases in China from November 2022 to December 2023, exhibiting better prediction performance compared to the ARIMA model. This indicates that the BSTS model possesses a high application value for forecasting the epidemic trends of AHC, making it a valuable tool for disease surveillance and prevention strategies.

Objective: Hemorrhagic fever with renal syndrome (HFRS) continues to pose a significant threat to global health. This study aimed to investigate both the long- and short-term asymmetric impacts of variations in meteorological variables on HFRS. Methods: The reported monthly HFRS incidence data from Shaanxi between 2004 and 2019, along with corresponding meteorological data, were collected to conduct an ecological trend analysis. Subsequently, the autoregressive distributed lag (ARDL) and nonlinear ARDL (NARDL) models were used to examine the long- and short-term asymmetric effects of climate variables on HFRS incidence. Results: Overall, a reduction in HFRS incidence was observed in Shaanxi from 2004 to 2019, with an average annual percentage change of −0.498 % (95 %CI -13.247 % to 12.602 %). HFRS incidence peaked in December and reached its lowest point in March each year. A 1 mm increase in aggregate precipitation (AP) was associated with a 4.3 % rise in HFRS incidence, while a 1 mm decrease contributed to a 3.7 % increase, indicating a long-term asymmetric impact (Wald long-term asymmetry test [WLT] = 9.072, P = 0.003). In the short term, a 1 % decrease in mean relative humidity (MRH) led to a 5.7 % decline in HFRS incidence (Wald short-term asymmetry test [WSR] = 5.978, P = 0.015). Additionally, changes in meteorological variables showed varied effects: ΔMWV(+) at a 1-month lag had a significant positive short-term effect on HFRS; ΔMRH(+) at a 3-month lag, ΔAP(+) at a 2-month lag, ΔAP(−) at a 1-month lag, ΔASH(+) at a 1-month lag, and ΔASH(−) at a 3-month lag all exhibited strong negative short-term impacts on HFRS incidence. Conclusions: Weather variability plays a significant role in influencing HFRS incidence, with both long- and short-term asymmetric and/or symmetric effects. Utilizing the NARDL model through a One Health lens offers promising opportunities for enhancing HFRS control measures.

Background To construct and evaluate a predictive model for in-hospital mortality among critically ill patients with acute kidney injury (AKI) undergoing continuous renal replacement therapy (CRRT), based on nine machine learning (ML) algorithm.Methods The study retrospectively included patients with AKI who underwent CRRT during their initial hospitalization in the United States using the medical information mart for intensive care (MIMIC) database IV (version 2.0), as well as in the intensive care unit (ICU) of Huzhou Central Hospital. Patients from the MIMIC database were used as the training cohort to construct the models (from 2008 to 2019, n = 1068). Patients from Huzhou Central Hospital were utilized as the external validation cohort to evaluate the models (from June 2019 to December 2022, n = 327). In the training cohort, least absolute shrinkage and selection operator (LASSO) regression with cross-validation was employed to select features for constructing the model and subsequently established nine ML predictive models. The performance of these nine models on the external validation cohort dataset was comprehensively evaluated based on the area under the receiver operating characteristic curve (AUROC) and the optimal model was selected. A static nomogram and a web-based dynamic nomogram were presented, with a comprehensive evaluation from the perspectives of discrimination (AUROC), calibration (calibration curve) and clinical practicability (DCA curves).Results Finally, 1395 eligible patients were enrolled, including 1068 patients in the training cohort and 327 patients in the external validation cohort. In the training cohort, LASSO regression with cross-validation was employed to select features and nine models were individually constructed. Compared to the other eight models, the Lasso regularized logistic regression (Lasso-LR) model exhibited the highest AUROC (0.756) and the optimal calibration curve. The DCA curve suggested a certain clinical utility in predicting in-hospital mortality among critically ill patients with AKI undergoing CRRT. Consequently, the Lasso-LR model was the optimal model and it was visualized as a common nomogram (static nomogram) and a web-based dynamic nomogram (https://chsyh2006.shinyapps.io/dynnomapp/). Discrimination, calibration and DCA curves were employed to assess the performance of the nomogram. The AUROC for the training and external validation cohorts in the nomogram model was 0.771 (95%CI: 0.743, 0.799) and 0.756 (95%CI: 0.702, 0.809), respectively. The calibration slope and Brier score for the training cohort were 1.000 and 0.195, while for the external validation cohort, they were 0.849 and 0.197, respectively. The DCA indicated that the model had a certain clinical application value.Conclusions Our study selected the optimal model and visualized it as a static and dynamic nomogram integrating clinical predictors, so that clinicians can personalized predict the in-hospital outcome of critically ill patients with AKI undergoing CRRT upon ICU admission.

Simultaneous inhibition of two or more pathways is playing a crucial role in the treatment of hepatocellular carcinoma with complex and diverse pathogenesis. However, there have been no reports of dual-targeting inhibitors for protein kinase membrane-associated tyrosine/threonine 1 (PKMYT1) and histone deacetylase 2 (HDAC2), which are critical targets for hepatocellular carcinoma treatment. Here, an integrated strategy of virtual screening was utilized to identify dual-targeting inhibitors for PKMYT1 and HDAC2. Notably, PKHD-5 has been identified as a potent inhibitor that selectively targets both PKMYT1 and HDAC2 with nanomolar affinity. Molecular dynamics have confirmed the strong binding stability of PKHD-5 with PKMYT1 and HDAC2. Importantly, it displayed a notably lower IC50 against the HepG2/MDR cell line, underscoring its potential to surmount drug resistance, while exhibiting minimal toxicity towards the normal liver cell line L02. Additionally, PKHD-5 has demonstrated significant antitumor proliferation effects without significant toxicity. In summary, the results suggest that PKHD-5 is a promising candidate for further preclinical studies of hepatocellular carcinoma therapy.

Abstract Hemorrhagic fever with renal syndrome (HFRS) poses a major threat in Shandong. This study aimed to investigate the long- and short-term asymmetric effects of meteorological factors on HFRS and establish an early forecasting system using autoregressive distributed lag (ARDL) and nonlinear ARDL (NARDL) models. Between 2004 and 2019, HFRS exhibited a declining trend (average annual percentage change = − 9.568%, 95% CI − 16.165 to − 2.451%) with a bimodal seasonality. A long-term asymmetric influence of aggregate precipitation (AP) (Wald long-run asymmetry [WLR] = − 2.697, P = 0.008) and aggregate sunshine hours (ASH) (WLR = 2.561, P = 0.011) on HFRS was observed. Additionally, a short-term asymmetric impact of AP (Wald short-run symmetry [WSR] = − 2.419, P = 0.017), ASH (WSR = 2.075, P = 0.04), mean wind velocity (MWV) (WSR = − 4.594, P

Abstract Background Gonorrhea has long been a serious public health problem in mainland China that requires attention, modeling to describe and predict its prevalence patterns can help the government to develop more scientific interventions. Methods Time series (TS) data of the gonorrhea incidence in China from January 2004 to August 2022 were collected, with the incidence data from September 2021 to August 2022 as the validation. The seasonal autoregressive integrated moving average (SARIMA) model, long short-term memory network (LSTM) model, and hybrid SARIMA-LSTM model were used to simulate the data respectively, the model performance were evaluated by calculating the mean absolute percentage error (MAPE), root mean square error (RMSE), and mean absolute error (MAE) of the training and validation sets of the models. Results The Seasonal components after data decomposition showed an approximate bimodal distribution with a period of 12 months. The three models identified were SARIMA(1,1,1) (2,1,2)12, LSTM with 150 hidden units, and SARIMA-LSTM with 150 hidden units, the SARIMA-LSTM model fitted best in the training and validation sets, for the smallest MAPE, RMSE, and MPE. Conclusions The overall incidence trend of gonorrhea in mainland China has been on the decline since 2004, with some periods exhibiting an upward trend. The incidence of gonorrhea displays a seasonal distribution, typically peaking in July and December each year. The SARIMA model, LSTM model, and SARIMA-LSTM model can all fit the monthly incidence time series data of gonorrhea in mainland China. However, in terms of predictive performance, the SARIMA-LSTM model outperforms the SARIMA and LSTM models, with the LSTM model surpassing the SARIMA model. This suggests that the SARIMA-LSTM model can serve as a preferred tool for time series analysis, providing evidence for the government to predict trends in gonorrhea incidence. The model's predictions indicate that the incidence of gonorrhea in mainland China will remain at a high level in 2024, necessitating that policymakers implement public health measures in advance to prevent the spread of the disease.

Abstract Background The long-term impact of COVID-19-associated public health interventions on zoonotic and vector-borne infectious diseases (ZVBs) remains uncertain. This study sought to examine the changes in ZVBs in China during the COVID-19 pandemic and predict their future trends. Methods Monthly incidents of seven ZVBs (Hemorrhagic fever with renal syndrome [HFRS], Rabies, Dengue fever [DF], Human brucellosis [HB], Leptospirosis, Malaria, and Schistosomiasis) were gathered from January 2004 to July 2023. An autoregressive fractionally integrated moving average (ARFIMA) by incorporating the COVID-19-associated public health intervention variables was developed to evaluate the long-term effectiveness of interventions and forecast ZVBs epidemics from August 2023 to December 2025. Results Over the study period, there were 1,599,647 ZVBs incidents. HFRS and rabies exhibited declining trends, HB showed an upward trajectory, while the others remained relatively stable. The ARFIMA, incorporating a pulse pattern, estimated the average monthly number of changes of − 83 (95% confidence interval [CI] − 353–189) cases, − 3 (95% CI − 33–29) cases, − 468 (95% CI − 1531–597) cases, 2191 (95% CI 1056–3326) cases, 7 (95% CI − 24–38) cases, − 84 (95% CI – 222–55) cases, and − 214 (95% CI − 1036–608) cases for HFRS, rabies, DF, HB, leptospirosis, malaria, and schistosomiasis, respectively, although these changes were not statistically significant besides HB. ARFIMA predicted a decrease in HB cases between August 2023 and December 2025, while indicating a relative plateau for the others. Conclusions China's dynamic zero COVID-19 strategy may have exerted a lasting influence on HFRS, rabies, DF, malaria, and schistosomiasis, beyond immediate consequences, but not affect HB and leptospirosis. ARFIMA emerges as a potent tool for intervention analysis, providing valuable insights into the sustained effectiveness of interventions. Consequently, the application of ARFIMA contributes to informed decision-making, the design of effective interventions, and advancements across various fields.

Media profiling offers valuable insights to enhance the objectivity and reliability of news coverage by providing comprehensive analysis, but the diversity in languages posed significant challenges to our identification of factuality and political bias of non-English sources. The limitation of existing media analysis research is its concentration on a singular high-resource language, and it hardly extends to languages beyond English. To address this, we introduce xMP, a dataset for zero-shot cross-lingual media profiling tasks. xMP’s cross-lingual test set encompasses 34 non-English languages and 18 language families, extending media profiling beyond English resources and allowing us to assess cross-lingual media profiling model performance. Additionally, we propose a method, named R-KAT, to enhance the model’s zero-shot cross-lingual transfer learning capability by building virtual multilingual embedding. Our experiments illustrate that our method improves the transferability of models in cross-lingual media profiling tasks. Additionally, we further discuss the performance of our method for different target languages. Our dataset and code are publicly available.

Abstract Background Interrupted time series (ITS) analysis is a growing method for assessing intervention impacts on diseases. However, it remains unstudied how the COVID-19 outbreak impacts gonorrhea. This study aimed to evaluate the effect of COVID-19 on gonorrhea and predict gonorrhea epidemics using the ITS-autoregressive integrated moving average (ARIMA) model. Methods The number of gonorrhea cases reported in China from January 2005 to September 2022 was collected. Statistical descriptions were applied to indicate the overall epidemiological characteristics of the data, and then the ITS-ARIMA was established. Additionally, we compared the forecasting abilities of ITS-ARIMA with Bayesian structural time series (BSTS), and discussed the model selection process, transfer function, check model fitting, and interpretation of results. Result During 2005–2022, the total cases of gonorrhea were 2,165,048, with an annual average incidence rate of 8.99 per 100,000 people. The highest incidence rate was 14.2 per 100,000 people in 2005 and the lowest was 6.9 per 100,000 people in 2012. The optimal model was ARIMA (0,1, (1,3)) (0,1,1)12 (Akaike’s information criterion = 3293.93). When predicting the gonorrhea incidence, the mean absolute percentage error under the ARIMA (16.45%) was smaller than that under the BSTS (22.48%). The study found a 62.4% reduction in gonorrhea during the first-level response, a 46.47% reduction during the second-level response, and an increase of 3.6% during the third-level response. The final model estimated a step change of − 2171 (95% confidence interval [CI] − 3698 to − 644) cases and an impulse change of − 1359 (95% CI − 2381 to − 338) cases. Using the ITS-ARIMA to evaluate the effect of COVID-19 on gonorrhea, the gonorrhea incidence showed a temporary decline before rebounding to pre-COVID-19 levels in China. Conclusion ITS analysis is a valuable tool for gauging intervention effectiveness, providing flexibility in modelling various impacts. The ITS-ARIMA model can adeptly explain potential trends, autocorrelation, and seasonality. Gonorrhea, marked by periodicity and seasonality, exhibited a downward trend under the influence of COVID-19 intervention. The ITS-ARIMA outperformed the BSTS, offering superior predictive capabilities for the gonorrhea incidence trend in China.

Abstract Objective Hepatitis C presents a profound global health challenge. The impact of COVID-19 on hepatitis C, however, remain uncertain. This study aimed to ascertain the influence of COVID-19 on the hepatitis C epidemic trend in Henan Province. Methods We collated the number of monthly diagnosed cases in Henan Province from January 2013 to September 2022. Upon detailing the overarching epidemiological characteristics, the interrupted time series (ITS) analysis using autoregressive integrated moving average (ARIMA) models was employed to estimate the hepatitis C diagnosis rate pre and post the COVID-19 emergence. In addition, we also discussed the model selection process, test model fitting, and result interpretation. Results Between January 2013 and September 2022, a total of 267,968 hepatitis C cases were diagnosed. The yearly average diagnosis rate stood at 2.42/100,000 persons. While 2013 witnessed the peak diagnosis rate at 2.97/100,000 persons, 2020 reported the least at 1.7/100,000 persons. The monthly mean hepatitis C diagnosed numbers culminated in 2291 cases. The optimal ARIMA model chosen was ARIMA (0,1,1) (0,1,1)12 with AIC = 1459.58, AICc = 1460.19, and BIC = 1472.8; having coefficients MA1=-0.62 (t=-8.06, P

Highlights Porous MXene (p-MC) films with 3D-interconnected ion transmission paths are constructed by NH3-induced in situ etching strategy. The flexible p-MC film electrodes exhibit a superior capacitance of 688.9 F g−1 with a good rate capability. The as-assembled p-MC solid-state flexible supercapacitors reveal superior electrochemical performance (99.7 F g−1 at 1 A g−1) and excellent flexibility.

Currently, the media industry is in the rapid development stage of media integration, which has brought about great changes in content production mode, presentation form, communication mechanism, operation and maintenance management, etc. At the same time, it is also faced with problems such as difficult information traceability, declining industry credibility, low data circulation quality and efficiency, difficult data security and user privacy protection, etc. Utilizing blockchain’s characteristics can solve these problems that the media industry is currently facing. This paper designs a convergence media ecology model based on blockchain (CMEM-BC), focusing on the basic elements of the model, node operation and maintenance system, node management mechanism, value circulation mechanism, and storage mechanism, trying to establish a decentralized, traceable, and immutable convergence media ecosystem. On this basis, this paper summarizes the ecological framework and ecological model of CMEM-BC. Finally, the paper describes the verification of the effectiveness of CMEM-BC in key links through simulation experiments, verifying that CMEM-BC has high originality and is more suitable for the application of convergence media ecology through model analysis and comparison.

Online news platforms have become users’ primary information sources. However, they focus on attracting users to click on the news and ignore whether the news triggers a sense of engagement, which could potentially reduce users’ participation in public events. Therefore, this study constructs four indicators by assessing user engagement to build an intelligent system to help platforms optimize their publishing strategies. First, this study defines user engagement evaluation as a classification task that divides user engagement into four indicators and proposes an extended LDA model based on user click–comment behavior (UCCB), using which the attractiveness of words in news headlines and content can be effectively represented. Second, this study proposes a deep user engagement evaluation (DUEE) model that integrates news attractiveness and multiple features in an attention-based deep neural network for user engagement evaluation. The DUEE model considers various elements that collectively determine the ability of the news to attract clicks and engagement. Third, the proposed model is compared with the baseline and state-of-the-art techniques, showing that it outperforms all existing methods. This study provides new research contributions and ideas for improving user engagement in online news evaluation.

The Bailongjiang River Basin is a high-risk area for debris flow in China. On 17 August 2020, a debris flow occurred in the Shuimo catchment, Wen County, which blocked the Baishui River, forming a barrier lake and causing significant casualties and property damage. In this study, remote sensing, InSAR, field surveys, and unmanned aerial vehicle (UAV) techniques were used to analyze the causal characteristics, material source characteristics, dynamic processes, and disaster characteristics after the debris flow. The results showed that the Shuimo catchment belongs to low-frequency debris flows, with a recurrence cycle of more than 100 years and concealed features. High vegetation coverage (72%) and a long main channel (11.49 km) increase the rainfall-triggering conditions for debris flow occurrence, making it more hidden and less noticed. The Shuimo catchment has a large drainage area of 31.26 km2, 15 tributaries, significant elevation differences of 2017 m, and favorable hydraulic conditions for debris flow. The main sources of debris flow material supply are channel erosion and slope erosion, which account for 84.4% of the total material. The collapse of landslides blocking both sides of the main channel resulted in an amplification of the debris flow scale, leading to the blockage of the Baishui River. The scale of the accumulation fan is 28 × 104 m3, and the barrier lake area is 37.4 × 104 m2. The formation mechanism can be summarized as follows: rainfall triggering → shallow landslides → slope debris flow → channel erosion → landslide damming → dam failure and increased discharge → deposition and river blockage. The results of this study provide references for remote sensing emergency investigation and analysis of similar low-frequency and concealed debris flows, as well as a scientific basis for local disaster prevention and reduction.

The new-generation manned spacecraft employs a new recovery system of parachutes and airbags. In order to realize the effective cushion of the return capsule airbag, the conversion of the single-point hanging to the double points for the spacecraft is necessary. In the conversion process, the long hanging rope and the height of the return capsule will cause a large impact and disturbance to the cabin, but the traditional dynamic modeling method is not suitable for those with complex boundary conditions. In order to solve these problems, a finite element model is proposed to analyze the suspension conversion process and simplify the parachute model based on the aerodynamic load dynamic matching control method to analyze the dynamic response characteristics, such as overload, speed, and force of the cabin, quantifying the conditions of the rope in the process. Results are generally consistent with experimental data. This study provides guidance for future optimization work and will greatly support the safe-return technology of the new-generation manned spacecraft.

Abstract Background Soil-transmitted helminths (STHs) were previously endemic in Shandong Province, China. This study aimed to analyze the STHs prevalence trend and the natural, social, and human cognitive and behavioural factors influencing the discrepancies between high and low infection levels from 2016 to 2020 in Shandong Province in eastern China. Methods STHs surveillance data of Shandong Province from 2016 to 2020 were obtained from China Information Management System for Prevention and Control of Parasitic Diseases. STHs infections were detected by modified Kato–Katz method. Comprehensive information on the natural and social factors, STHs-related knowledge and behaviours were collected through questionnaire surveys. Retrospective spatial scan analysis was performed using SaTScan v10.1 to evaluate any identified spatial clusters of STHs infection for statistical significance and Bayes discriminant analysis was used to discriminate the high or low infection groups of the villages. Results In total, 72,160 participants were involved in our survey from 2016 to 2020. The overall STHs prevalence rate was 1.13%, with the eastern region of Shandong Province having the highest rate (2.02%). The predominant species was T. trichiura, with the prevalence rate of 0.99% and the ≥ 70-year age group possessed the highest rate of 2.21%. The STHs prevalence rate showed an annual linear downward trend from 2016 to 2020 ( $$\chi_{{{\text{trend}}}}^{2}$$ χ trend 2 = 127.600, P

Abstract Background The purpose of this study is to investigate the association of rotating night shift work, CLOCK, MTNR1A, MTNR1B genes polymorphisms and their interactions with type 2 diabetes among steelworkers. Methods A case–control study was conducted in the Tangsteel company in Tangshan, China. The sample sizes of the case group and control group were 251 and 451, respectively. The logistic regression, log-linear model and generalized multifactor dimensionality (GMDR) method were used to investigate the interaction between circadian clock gene, melatonin receptor genes and rotating night shift work on type 2 diabetes among steelworkers. Relative excess risk due to interaction (RERI) and attributable proportions (AP) were used to evaluate additive interactions. Results Rotating night shift work, current shift status, duration of night shifts, and average frequency of night shifts were associated with an increased risk of type 2 diabetes after adjustment for confounders. Rs1387153 variants in MTNR1B was found to be associated with an increased risk of type 2 diabetes, which was not found between MTNR1A gene rs2119882 locus, CLOCK gene rs1801260 locus and the risk of type 2 diabetes. The association between rotating night shift work and risk of type 2 diabetes appeared to be modified by MTNR1B gene rs1387153 locus (RERI = 0.98, (95% CI, 0.40–1.55); AP = 0.60, (95% CI, 0.07–1.12)). The interaction between MTNR1A gene rs2119882 locus and CLOCK gene rs1801260 locus was associated with the risk of type 2 diabetes (RERI = 1.07, (95% CI, 0.23–1.91); AP = 0.77, (95% CI, 0.36–1.17)). The complex interaction of the MTNR1A-MTNR1B-CLOCK-rotating night shift work model based on the GMDR methods may increase the risk of type 2 diabetes (P = 0.011). Conclusions Rotating night shift work and rs1387153 variants in MTNR1B were associated with an increased risk of type 2 diabetes among steelworkers. The complex interaction of MTNR1A-MTNR1B-CLOCK-rotating night shift work may increase the risk of type 2 diabetes.

The Xing–Meng Orogenic Belt hosts an amount of low-sulfidation epithermal gold deposits. However, the tectonic-magmatic setting remains problematic, which hinders understanding the factors that control the gold endowment in the region. Wulaga deposit is the largest low-sulfidation epithermal gold deposit in the northeastern Xing–Meng Orogenic Belt. Gold mineralization occurs in the crypto-explosive breccia zone of subvolcanic granodiorite porphyry. Zircon U–Pb ages of three granodiorite apophyses and previous pyrite Rb–Sr dating (113.8 ± 4.4 Ma) indicate granodiorite porphyry and gold mineralization was coeval. The ore-related granodiorite porphyry is moderate SiO2, high-K calc-alkaline, and metaluminous, suggesting an I-type granite. Moreover, Wulaga granodiorite porphyry displays low initial 87Sr/86Sr ratios and positive εNd(t) values with TDM2(Nd) of 799.7–897.4 Ma and εHf(t) values with TDM2(Hf) of 652–785 Ma, indicating that it was derived from partial melting of the Neoproterozoic juvenile mafic lower crust. Ti-in-zircon thermometry, medium–high Sr/Y ratio, high Ba/La, and fO2 value indicate that Wulaga granodiorite porphyry formed at relatively low temperatures (∼700 °C), rich water, and high fugacity within the stability field of garnet in the juvenile lower crust. Combined with ore-related tectonic-magmatic activities in the Wulaga, Dong'an, and Sandaowanzi gold deposits, the Early Cretaceous low-sulfidation epithermal gold deposits in the Xing–Meng Orogenic Belt were formed from magmatic-hydrothermal events triggered by mutual interaction between post-orogenic lithospheric extension related to the closure of the Mongol-Okhotsk Ocean and arc-back extension associated with rollback of the Paleo-Pacific Ocean plate. These data indicate the potential existence of the Early Cretaceous epithermal gold deposits that are related to the contemporaneous igneous activity.

Abstract Background The best choice between first‐line aspiration and stent retriever for acute basilar artery occlusion remains controversial. This study aims to perform a systematic review and meta‐analysis comparing the stent retriever and direct aspiration about reported recanalization rates and periprocedural complications. Method PubMed, Embase, Web of Science, Cochrane, and Clinical Trials were searched for the studies evaluating the efficacy and safety of first‐line aspiration versus stent retriever for acute basilar artery occlusion. A standard software program (Stata Corporation) was used for end‐point analyses. Statistical significance was defined as a p‐value less than .05. Results A total of 11 studies were involved in the current study, including 1014 patients. Regarding postoperative recanalization, the pooled analysis identified a significant difference in successful recanalization (odds ratio [OR] = 1.642; 95% confidence interval (95% CI): 1.099–2.453; p = .015) and complete recanalization (OR = 3.525; 95% CI: 1.306–2.872; p = .001) between the two groups in favor of the first‐line aspiration. Concerning the complications, the first‐line aspiration could achieve a lower rate of total complication (OR = .359; 95% CI: .229–.563; p

Most existing object tracking research focuses on pedestrians and autonomous driving while ignoring sports scenes. When general object tracking models are used for ball tracking, there are often problems, such as detection omissions due to small object sizes and trajectory loss due to occlusion. To address these challenges, we propose a ball detection and tracking model called HMMATrack based on multiscale feature enhancement and multilevel collaborative matching to improve ball-tracking results from the entire process of sampling, feature extraction, detection, and tracking. It includes a Heuristic Compound Sampling Strategy to deal with tiny sizes and imbalanced data samples; an MNet-based detection module to improve the ball detection accuracy; and a multilevel cooperative matching and automatic trajectory correction tracking algorithm that can quickly and accurately correct the ball’s trajectory. We also hand-annotated SportsTrack, a ball-tracking dataset containing soccer, basketball, and volleyball scenes. Extensive experiments are conducted on the SportsTrack, demonstrating that our proposed HMMATrack model outperforms other representative state-of-the-art models in ball detection and tracking.

Pellet ore is an important raw material for blast furnace ironmaking, and its reduction and expansion performance directly affects the smooth operation and smelting indicators of the blast furnace. This paper quantitatively studied the effects of magnesium minerals such as dolomite and serpentinite on the pellet-forming performance, the microstructure after roasting, compressive strength, and the reduction expansion performance of Baiyun Ebo iron concentrate. The optimal ratio of dolomite and serpentinite to add was determined when preparing pellets using Baiyun Ebo iron concentrate powder. The results showed that the drop strength and compressive strength of the green pellet after adding serpentine were relatively higher than those after adding dolomite, indicating that controlling the MgO content in the green pellet at 2.5% using serpentine was beneficial for improving the drop strength and compressive strength. Under the condition of adding dolomite, when the MgO content was 2.5%, the compressive strength of the roasted pellet was the highest, which was 2192.6 N, and the volume expansion rate was 12.32%. Under the condition of adding serpentine, when the MgO content was 2.5%, the compressive strength of the roasted pellet was 2622.2 N, and the volume expansion rate was 9.71%. Compared with dolomite as a magnesium additive, when the reduction expansion rate of Baiyun Ebo iron concentrate was controlled within 20%, serpentine only needed to have a MgO content of about 1.5% in the pellets, while dolomite needed to have a MgO content of about 2.5%. Therefore, under the condition that the MgO contents of dolomite and serpentine were equivalent, the amount of serpentine used was lower.

In this paper, the multi-layer multi-pass welding process was used to weld 115mm 08Cr9W3Co3VNbCuBN (G115) steel pipe, the gas tungsten arc welding (GTAW) was used in root pass welding, subsequent welding by dynamic hot wire gas tungsten arc welding (TIP TIG) welding method, while the post-weld heat treatment (PWHT) of 770°C × 10h was carried out afterward. The microstructure and mechanical properties of different zones of the welded joint were analyzed by OM, SEM, XRD, microhardness and impact tests. The results showed that the ferrite and martensite were observed in the weld metal (WM), the fine-grained heat-affected zone (FGHAZ) and the coarse-grained heat-affected zone (CGHAZ) consisting of prior austenitic grain boundaries (PAGBs) and martensite, and more precipitates including coarse M23C6 carbides with fine MX-type carbonitrides were observed inside the grain boundaries and grains. The hardness distribution patterns along different locations (Cover, Fill, Root) on the welded joint cross-section were the same, from base metal (BM) to WM hardness gradually increased, and WM hardness was the highest. WM impact toughness was worse than HAZ, impact fracture mode was a mixed tough-brittle fracture but biased towards brittle fracture.

In the session-based recommendation algorithm, a better class of methods is to model the complex interaction relationship within the session as a graph structure, and then use the graph neural network to capture the deep features of the item from it. However, most models do not deeply mine the effective information contained in the sequence temporal relationship, nor do they pay attention to the auxiliary contribution of the global distance in different sessions to the item representation. This paper proposes a session-based recommendation model that integrates the temporal sequence of session interactions and the global distance-awareness of items with graph neural networks (TSDA-GNN). First, according to all session sequences, a global graph, a session graph, and a feature graph are constructed. In the global graph, the average “spacing” between items is introduced to represent the association between two items in the global graph. In the session graph, the interaction timing of the item is combined as the weight information in the relationship matrix, and the connection matrix of the session graph is constructed, which can more accurately capture the association relationship between items. Finally, by fusing the feature information of the item, using a graph neural network can deeply mine user preferences hidden in the feature information of the item. The model can not only model the information transmission relationship between items in different sessions, but also further deeply mine the hidden information of global domain and item features. Multiple experiments on two benchmark datasets show that the model proposed in this paper achieves the best results in various indicators.

While applying stochastic alternating direction method of multiplier (ADMM) methods has become enormously potential in distributed applications, improving the algorithmic flexibility can bring huge benefits. In this paper, we propose a novel stochastic optimization method based on distributed ADMM method, called Flex-SADMM. Specifically, we incorporate the variance reduced first-order information and the approximated second-order information for solving the subproblem of ADMM, which targets at the stable convergence and improving the accuracy of the search direction. Moreover, different from most ADMM based methods that require each computation node to perform the update in each iteration, we only require each computation node updates within a bounded iteration interval, this has significantly improved the flexibility. We further provide the theoretical results to guarantee the convergence of Flex-SADMM in the nonconvex optimization problems. These results show that our proposed method can successfully overcome the above challenges while the computational complexity is maintained low. In the empirical study, we have verified the effectiveness and the improved flexibility of our proposed method.

Abstract High entropy alloy is a multi-component alloy material with equal or near atomic number, which has excellent wear resistance and corrosion resistance. In this paper, AlxFeCoCrNiMn (x = 0,0.5,1.0,1.5) high entropy alloy cladding layer was prepared by laser cladding on Q345 steel plate. The phase structure, microstructure, hardness and wear resistance of the cladding layer were studied. The results show that the cladding layer of AlxFeCoCrNiMn alloy has good metallurgical bonding ability with Q345 steel. The cladding area is mainly columnar and equiaxed. The cladding layer of AlxFeCoCrNiMn alloy is mainly composed of BCC + FCC solid solution phase, which is caused by the addition of Al element under the high entropy effect. Al element promotes the formation of BCC phase structure. With the increase of Al element, the hardness of cladding layer increases. The hardness of A1.0 cladding layer is 670HV, which is close to three times of Q345 steel. When Al (x = 1.0), the hardness of the cladding layer is the highest, and it also shows better wear resistance with lower friction and wear loss.

Abstract In this study, we proposed a new data-driven hybrid technique by integrating an ensemble empirical mode decomposition (EEMD), an autoregressive integrated moving average (ARIMA), with a nonlinear autoregressive artificial neural network (NARANN), called the EEMD-ARIMA-NARANN model, to perform time series modeling and forecasting based on the COVID-19 prevalence and mortality data from 28 February 2020 to 27 June 2020 in South Africa and Nigeria. By comparing the accuracy level of forecasting measurements with the basic ARIMA and NARANN models, it was shown that this novel data-driven hybrid model did a better job of capturing the dynamic changing trends of the target data than the others used in this work. Our proposed mixture technique can be deemed as a helpful policy-supportive tool to plan and provide medical supplies effectively. The overall confirmed cases and deaths were estimated to reach around 176,570 [95% uncertainty level (UL) 173,607 to 178,476] and 3454 (95% UL 3384 to 3487), respectively, in South Africa, along with 32,136 (95% UL 31,568 to 32,641) and 788 (95% UL 775 to 804) in Nigeria on 12 July 2020 using this data-driven EEMD-ARIMA-NARANN hybrid technique. The contributions of this study include three aspects. First, the proposed hybrid model can better capture the dynamic dependency characteristics compared with the individual models. Second, this new data-driven hybrid model is constructed in a more reasonable way relative to the traditional mixture model. Third, this proposed model may be generalized to estimate the epidemic patterns of COVID-19 in other regions.

Schistosomiasis is a tropical parasitic disease that seriously endangers humans and animals. In this study, two Oncomelania snails, Oncomelania hupensis (O. hupensis) and Oncomelania weishan (O. weishan), were infected with Schistosoma japonicum (S. japonicum) cercariae during the early period, and ICR mice were subsequently infected with two kinds of miracidia that developed in male and female adult worms. In this study, isobaric tags for relative and absolute quantification (iTRAQ) were used to identify four channels: 113, 115, 117, and 119. A total of 2364 adult schistosome proteins were identified, and 1901 proteins were quantitative. Our results revealed 68 differentially expressed proteins (DEPs) in female adult worms, including 24 upregulated proteins and 44 downregulated proteins, and 55 DEPs in male adult worms, including 25 upregulated proteins and 30 downregulated proteins. LC-MS/MS and bioinformatics analysis indicated that these DEPs are mainly concentrated in cellular composition, molecular function, biological function and catabolism pathways. In summary, this proteomics analysis of adult schistosomes that hatched in two intermediate hosts helps to improve our understanding of the growth and developmental mechanisms of S. japonicum.

As a new factor of production, data element has profoundly changed our mode of production, lifestyle and social governance style. The sharing of a data element in the convergence media ecology can greatly improve the circulation of a data element and enhance the value of a data element; however, it may face problems such as insufficient sharing willingness, incomplete sharing circulation mechanism and inadequate implementation of the incentive mechanism. To solve these problems, this paper introduced the evolutionary game theory in the convergence media ecology and established the data-sharing model according to the characteristics of nodes. We analyzed the ecological node evolution path, evolutionary stable strategy and the corresponding state conditions in the model. Furthermore, we carried out the sampling experiment simulation, which verified the effectiveness of the research content in this paper. At the end of the article, we summarize and give some key factors to increase the willingness to participate in sharing in convergence media ecology. This paper enriched the research field of data element sharing in convergence media and explored the willingness and tendency of the participants. The research results can provide targeted suggestions for promoting the sharing of data elements in convergence media ecology.

The widespread popularity of digital technology has enabled the rapid dissemination of news. However, it has also led to the emergence of “fake news” and the development of a media ecosystem with serious prejudices. If early warnings about the source of fake news are received, this provides better outcomes in preventing its spread. Therefore, the issue of understanding and evaluating the credibility of media has received increasing attention. This work proposes a model of evaluating news media credibility called MiBeMC, which mimics the structure of human verification behavior in networks. Specifically, we first construct an intramodule information feature extractor to simulate the semantic analysis behavior of human information reading. Then, we design a similarity module to mimic the process of obtaining additional information. We also construct an aggregation module. This simulates human verification of correlated content. Finally, we apply regularized adversarial training strategy to train the MiBeMC model. The ablation study results demonstrate the effectiveness of MiBeMC. For the CLEF-task4 development and test dataset, the performance of the MiBeMC over state-of-the-art baseline methods is evaluated and found to be superior.

Abstract Background Phytohormone abscisic acid (ABA) is involved in the regulation of a wide range of biological processes. In Arabidopsis, it has been well-known that SnRK2s are the central components of the ABA signaling pathway that control the balance between plant growth and stress response, but the functions of ZmSnRK2 in maize are rarely reported. Therefore, the study of ZmSnRK2 is of great importance to understand the ABA signaling pathways in maize. Results In this study, 14 ZmSnRK2 genes were identified in the latest version of maize genome database. Phylogenetic analysis revealed that ZmSnRK2s are divided into three subclasses based on their diversity of C-terminal domains. The exon-intron structures, phylogenetic, synteny and collinearity analysis indicated that SnRK2s, especially the subclass III of SnRK2, are evolutionally conserved in maize, rice and Arabidopsis. Subcellular localization showed that ZmSnRK2 proteins are localized in the nucleus and cytoplasm. The RNA-Seq datasets and qRT-PCR analysis showed that ZmSnRK2 genes exhibit spatial and temporal expression patterns during the growth and development of different maize tissues, and the transcript levels of some ZmSnRK2 genes in kernel are significantly induced by ABA and sucrose treatment. In addition, we found that ZmSnRK2.10, which belongs to subclass III, is highly expressed in kernel and activated by ABA. Overexpression of ZmSnRK2.10 partially rescued the ABA-insensitive phenotype of snrk2.2/2.3 double and snrk2.2/2.3/2.6 triple mutants and led to delaying plant flowering in Arabidopsis. Conclusion The SnRK2 gene family exhibits a high evolutionary conservation and has expanded with whole-genome duplication events in plants. The ZmSnRK2s expanded in maize with whole-genome and segmental duplication, not tandem duplication. The expression pattern analysis of ZmSnRK2s in maize offers important information to study their functions. Study of the functions of ZmSnRK.10 in Arabidopsis suggests that the ABA-dependent members of SnRK2s are evolutionarily conserved in plants. Our study elucidated the structure and evolution of SnRK2 genes in plants and provided a basis for the functional study of ZmSnRK2s protein in maize.

Starch, the major component of cereal grains, affects crop yield and quality and is widely used in food and industrial applications. The biosynthesis of maize starch is a complex process involving a series of functional enzymes. However, the sophisticated regulatory mechanisms of starch biosynthetic genes have not been fully elaborated. The basic/helix-loop-helix (bHLH) transcription factors are widely distributed in eukaryotes and participate in many physiological processes. In this study, 202 bHLH encoding genes were identified in the maize genome by Blast method. ZmICE1 gene, which belongs to the ICE subfamily of the bHLH family, was obtained and expressed mainly in maize filling endosperm and co-expressed with 14 starch biosynthesis genes. Based on the comparative analyses across different plant species, we revealed that the gene structures and protein domains of the ICE subfamily were conserved between monocots and dicots, suggesting their functional conservation feature. Yeast activation and subcellular localization assays suggested that ZmICE1 had transcriptional activation activity and localized in the nucleus. Yeast one-hybrid assays confirmed that ZmICE1 could directly bind to the promoters of ZmSSIIa and ZmGBSSI. Transient gene expression analysis in maize endosperm revealed that ZmICE1 positively regulated the expression of ZmSSIIa, but inhibited the expression of ZmGBSSI. Our results indicated that ZmICE1 could function as a regulator of maize starch biosynthesis.

Maize (Zea mays L.) is one of the most important food crops in the world, and starch is the main component of its endosperm. Transcriptional regulation plays a vital role in starch biosynthesis. However, it is not well understood in maize. We report the identification of the transcription factor ZmNAC126 and its role in regulation of starch synthesis in maize. Transcriptional expression of ZmNAC126 was higher in maize endosperm and kernels than in roots or stems. ZmNAC126 shared a similar expression pattern with starch synthesis genes during seed development, and its expression pattern was also consistent with the accumulation of starch. ZmNAC126 is a typical transcription factor with a transactivation domain between positions 201 and 227 of the amino acid sequence, is located in the nucleus, and binds to CACG repeats in vitro. Yeast one-hybrid assay revealed that ZmNAC126 bound the promoters of ZmGBSSI, ZmSSIIa, ZmSSIV, ZmISA1, and ZmISA2. Transient overexpression of ZmNAC126 in maize endosperm increased the activities of promoters pZmSh2, pZmBt2, pZmGBSSI, pZmSSIIIa, and pZmBT1 but inhibited the activities of pZmISA1 and pZmISA2. ZmNAC126 thus acts in starch synthesis by transcriptionally regulating targeted starch synthesis-related genes in maize kernels.

Abstract Background Endosperm-trait related genes are associated with grain yield or quality in maize. There are vast numbers of these genes whose functions and regulations are still unknown. The biolistic system, which is often used for transient gene expression, is expensive and involves complex protocol. Besides, it cannot be used for simultaneous analysis of multiple genes. Moreover, the biolistic system has little physiological relevance when compared to cell-specific based system. Plant protoplasts are efficient cell-based systems which allow quick and simultaneous transient analysis of multiple genes. Typically, PEG-calcium mediated transfection of protoplast is simple and cost-effective. Notably, starch granules in cereal endosperm may diminish protoplast yield and integrity, if the isolation and transfection conditions are not accurately measured. Prior to this study, no PEG-calcium mediated endosperm protoplast system has been reported for cereal crop, perhaps, because endosperm cells accumulate starch grains. Results Here, we showed the uniqueness of maize endosperm-protoplast system (EPS) in conducting endosperm cell-based experiments. By using response surface designs, we established optimized conditions for the isolation and PEG-calcium mediated transfection of maize endosperm protoplasts. The optimized conditions of 1% cellulase, 0.75% macerozyme and 0.4 M mannitol enzymolysis solution for 6 h showed that more than 80% protoplasts remained viable after re-suspension in 1 ml MMG. The EPS was used to express GFP protein, analyze the subcellular location of ZmBT1, characterize the interaction of O2 and PBF1 by bimolecular fluorescent complementation (BiFC), and simultaneously analyze the regulation of ZmBt1 expression by ZmMYB14. Conclusions The described optimized conditions proved efficient for reasonable yield of viable protoplasts from maize endosperm, and utility of the protoplast in rapid analysis of endosperm-trait related genes. The development of the optimized protoplast isolation and transfection conditions, allow the exploitation of the functional advantages of protoplast system over biolistic system in conducting endosperm-based studies (particularly, in transient analysis of genes and gene regulation networks, associated with the accumulation of endosperm storage products). Such analyses will be invaluable in characterizing endosperm-trait related genes whose functions have not been identified. Thus, the EPS will benefit the research of cereal grain yield and quality improvement.

In this paper, we consider the instability of standing waves for an inhomogeneous Gross-Pitaevskii equation \[ i\psi_t +\Delta \psi -a^2|x|^2\psi +|x|^{-b}|\psi|^{p}\psi=0. \] This equation arises in the description of nonlinear waves such as propagation of a laser beam in the optical fiber. We firstly proved that there exists $\omega_*>0$ such that for all $\omega>\omega_*$, the standing wave $\psi(t,x)=e^{i\omega t}u_\omega(x)$ is unstable. Then, we deduce that if $\partial_\lambda^2S_\omega(u_\omega^\lambda)|_{\lambda=1}\leq 0$, the ground state standing wave $e^{i\omega t}u_\omega(x)$ is strongly unstable by blow-up, where $u_\omega^\lambda(x)=\lambda^{\frac{N}{2}}u_\omega( \lambda x)$ and $S_\omega$ is the action. This result is a complement to the partial result of Ardila and Dinh (Z. Angew. Math. Phys. 2020), where the strong instability of standing waves has been studied under a different assumption.

Abstract The microstructure and properties of shielded metal arc welding (SMAW) joint of P91 heat-resistant steel annealed at different holding time were observed and analyzed. The results show that the martensite and residual austenite structures in the weld zone and coarse grain heat affected zone (CGHAZ) were transformed with the extension of holding time. After 760°C+4h annealing, a large number of white rod-shaped precipitates appeared in the weld zone. The main components of precipitates were Fe, Cr, Mn, V, Si, and a small amount of oxides appeared. After 760°C+5h annealing treatment, relatively fine white granular precipitation appeared in the weld zone, and a large number of oxides and some precipitates have been dissolved. After 760°C+6h annealing treatment, the precipitated phases were composed of Fe-Cr, Fe3Si, Ni-Cr-Fe, Fe2MnAl. Compared with the annealing treatment for 5h, the microstructure of the precipitated phase is fine and uniform, dispersion distribution of second phase particles, and the material mechanics is relatively stable.

Breast cancer (BC) is one of the most common malignant tumors found in females. Previous studies have demonstrated that curcumin, which is a type of polyphenol compound extracted from Curcuma longa underground rhizome, is able to inhibit the survival of cancer cells. However, the functional role and mechanism of curcumin in BC are still unclear. The Cell Counting Kit-8 assay was performed to examine the effects of curcumin on cell viability in the BC cell lines MDA-MB-453 and MCF-7. The levels of lipid reactive oxygen species (ROS), malondialdehyde (MDA) production, and intracellular Fe2+ were determined to assess the effects of curcumin on cell ferroptosis. Western blot analysis was also carried out to detect the protein levels. Finally, the antitumorigenic effect of curcumin on BC was identified in a xenograft tumor model. In the present study, the results indicated that curcumin could dose-dependently suppress the viability of both MDA-MB-453 and MCF-7 cells. Further studies revealed that curcumin facilitated solute carrier family 1 member 5 (SLC1A5)-mediated ferroptosis in both MDA-MB-453 and MCF-7 cells by enhancing lipid ROS levels, lipid peroxidation end-product MDA accumulation, and intracellular Fe2+ levels. In vivo experiments demonstrated that curcumin could significantly hamper tumor growth. Collectively, the results demonstrated that curcumin exhibited antitumorigenic activity in BC by promoting SLC1A5-mediated ferroptosis, which suggests its use as a potential therapeutic agent for the treatment of BC.

Background Coronary heart disease (CHD) is a common cardiovascular disease with high morbidity and mortality in China. The CHD risk prediction model has a great value in early prevention and diagnosis. Methods In this study, CHD risk prediction models among rural residents in Xinxiang County were constructed using Random Forest (RF), Support Vector Machine (SVM), and the least absolute shrinkage and selection operator (LASSO) regression algorithms with identified 16 influencing factors. Results Results demonstrated that the CHD model using the RF classifier performed best both on the training set and test set, with the highest area under the curve (AUC = 1 and 0.9711), accuracy (one and 0.9389), sensitivity (one and 0.8725), specificity (one and 0.9771), precision (one and 0.9563), F1-score (one and 0.9125), and Matthews correlation coefficient (MCC = one and 0.8678), followed by the SVM (AUC = 0.9860 and 0.9589) and the LASSO classifier (AUC = 0.9733 and 0.9587). Besides, the RF model also had an increase in the net reclassification index (NRI) and integrated discrimination improvement (IDI) values, and achieved a greater net benefit in the decision curve analysis (DCA) compared with the SVM and LASSO models. Conclusion The CHD risk prediction model constructed by the RF algorithm in this study is conducive to the early diagnosis of CHD in rural residents of Xinxiang County, Henan Province.

In order to improve the added value of iron oxide scale and reduce the manufacturing cost of porous stainless steel, steel rolling iron oxide scale as an iron-containing raw material was used to prepare porous 316 stainless steel by high-temperature sintering under vacuum conditions, while carbon was used as a reducing agent and pore-forming agent, and the necessary metal powders were added. In our work, the specific reduction system was confirmed, including the sintering temperature, sintering time, vacuum degree and carbon amount, through thermodynamic calculation combined with experiments. Thermodynamic analysis results showed that the transformation process of the chromium element in the raw materials at 10−4 atm and 300~1600 °C was FeCr2O4 + Cr3O4→Cr2O3 + Cr3O4 + Cr23C6→Cr23C6 + Cr7C3 + FCC→FCC + Cr23C6→FCC→FCC + BCC→Cr(liq). The FCC phase with qualified carbon content could be obtained at 10−4 atm and 1200 °C, while 90.88 g iron oxide scale, 17.17 g carbon, 17.00 g metal chromium, 12.00 g metal nickel and 2.5 g metal molybdenum were necessary to produce 100 g porous 316 stainless steel. The porous 316 stainless steel with a carbon content of 0.025% could be obtained at 10−4 atm and 1200 °C for 180 min, while the chromium element underwent the transformation of metal, Cr→FeCr2O4→Cr23C6→Austenite. The porosity of the porous 316 stainless steel was 42.07%. The maximum size of impurity particles was 5 μm when the holding time reached 180 min. Magnetic separation was an effective method to reduce impurities in the porous stainless steel.

Extreme ultraviolet lithography objective lenses require surface figure accuracy of approximately sub-nanometer root mean square (RMS). As the key equipment for sub-nanometer accuracy figuring, the dynamic and static performance of ion beam figuring (IBF) machine tools are critical. However, the related research is not sufficient and comprehensive. To this end, a general model of dynamic and static performance requirements on three-axis IBF machine tools was established. The requirements on dynamic and static performance under different figuring process for different surface shape were comprehensively analyzed. Analysis results revealed that the three-axis IBF machine tools require typical motion accuracy better than 100 μm and certain dynamic performance for achieving sub-nanometer accuracy. According to the theoretical and simulation results, a process optimization based on analysis of dynamic and static performance requirements of IBF machine tools for sub-nanometer figuring is proposed. To verify the proposed method, a Φ90 mm mirror with 2.594 nm RMS was figured to 0.251 nm RMS by optimizing the processing parameters to ensure that the IBF machine tool with measured performance (positioning error of 52.74 μm, 53.04 μm, 37.71 μm, and maximum acceleration of 1.0 m/s2, 1.3 m/s2, and 1.5 m/s2 for axes x, y, and z, respectively) meets the performance requirements. The proposed method can promote the application of three-axis IBF machine tools in sub-nanometer accuracy figuring.

Recently, with the rapid development of mobile sensing technology, capturing scene information by mobile sensing devices in the form of images or videos has become a prevalent recording method. However, the moiré pattern phenomenon may occur when the scene contains digital screens or regular strips, which greatly degrade the visual performance and image quality. In this paper, considering the complexity and diversity of moiré patterns, we propose a novel end-to-end image demoiré method, which can learn moiré pattern elimination in both the frequency and spatial domains. To be specific, in the frequency domain, considering the signal energy of moiré pattern is widely distributed in the frequency, we introduce a wavelet transform to decompose the multi-scale image features, which can help the model identify the moiré features more precisely to suppress them effectively. On the other hand, we also design a spatial domain demoiré block (SDDB). The SDDB module can extract moiré features from the mixed features, then subtract them to obtain clean image features. The combination of the frequency domain and the spatial domain enhances the model’s ability in terms of moiré feature recognition and elimination. Finally, extensive experiments demonstrate the superior performance of our proposed method to other state-of-the-art methods. The Grad-CAM results in our ablation study fully indicate the effectiveness of the two proposed blocks in our method.

Dandelion seeds can stably diffuse owing to the dominant drag rather than the lift-based mechanism of the streamlined leaves of the plant, where this is known to favor their long-distance dispersal with the steady vortex attached. However, the generation mechanism of the vortex and the aerodynamic force exerted on the seeds through multiple filaments remain unknown. Clarifying these subjects may help realize the optimal performance of porous structures under different flight conditions. This study conducts numerical simulations to illuminate the influence of gaps and the Reynolds number (Re) on the wake structures and consequent drag force of dandelion seeds. We fabricate the seeds into circular disks composed of evenly distributed square cylinders placed in a vertical flow field with Re of 100 and 400, with the porosity of the pappus (ε) ranging from 0.887 to 0.964. We explain the geometric properties of the attached, steady vortex rings and clarify their generation mechanism, i.e., the base bleed and convection effects competed with vorticity generation, based on which the gaps are confirmed to delay chaotic vortices from occurring compared with the solid case. The weakened leeward pressure is critical for the increase in the drag coefficient to reach the peak level. The enhanced drag coefficient is several times higher than that in the solid case, endowing the seeds with a high loading capacity, and the porosity corresponding to its peak is beneficial for the structural design. These conclusions provide positive insights into the design of ventilated aircrafts with optimal long-distance dispersal performance.

Abstract In this paper, we consider the sharp thresholds of blow-up and global existence for the nonlinear Schrödinger–Choquard equation i ψ t + Δ ψ = λ 1 | ψ | p 1 ψ + λ 2 ( I α ∗ | ψ | p 2 ) | ψ | p 2 − 2 ψ . $$ i\psi _{t}+\Delta \psi =\lambda _{1} \vert \psi \vert ^{p_{1}}\psi +\lambda _{2}\bigl(I _{\alpha } \ast \vert \psi \vert ^{p_{2}}\bigr) \vert \psi \vert ^{p_{2}-2}\psi . $$ We derive some finite time blow-up results. Due to the failure of this equation to be scale invariant, we obtain some sharp thresholds of blow-up and global existence by constructing some new estimates. In particular, we prove the global existence for this equation with critical mass in the L 2 $L^{2}$ -critical case. Our obtained results extend and improve some recent results.

Abstract Background Utilization of heterosis in maize could be critical in maize breeding for boosting grain yield. However, the genetic architecture of heterosis is not fully understood. To dissect the genetic basis of yield-related traits and heterosis in maize, 301 recombinant inbred lines derived from 08 to 641 × YE478 and 298 hybrids from the immortalized F2 (IF2) population were used to map quantitative trait loci (QTLs) for nine yield-related traits and mid-parent heterosis. Results We observed 156 QTLs, 28 pairs of loci with epistatic interaction, and 10 significant QTL × environment interactions in the inbred and hybrid mapping populations. The high heterosis in F1 and IF2 populations for kernel weight per ear (KWPE), ear weight per ear (EWPE), and kernel number per row (KNPR) matched the high percentages of QTLs (over 50%) for those traits exhibiting overdominance, whereas a notable predominance of loci with dominance effects (more than 70%) was observed for traits that show low heterosis such as cob weight per ear (CWPE), rate of kernel production (RKP), ear length (EL), ear diameter (ED), cob diameter, and row number (RN). The environmentally stable QTL qRKP3–2 was identified across two mapping populations, while qKWPE9, affecting the trait mean and the mid-parent heterosis (MPH) level, explained over 18% of phenotypic variations. Nine QTLs, qEWPE9–1, qEWPE10–1, qCWPE6, qEL8, qED2–2, qRN10–1, qKWPE9, qKWPE10–1, and qRKP4–3, accounted for over 10% of phenotypic variation. In addition, QTL mapping identified 95 QTLs that were gathered together and integrated into 33 QTL clusters on 10 chromosomes. Conclusions The results revealed that (1) the inheritance of yield-related traits and MPH in the heterotic pattern improved Reid (PA) × Tem-tropic I (PB) is trait-dependent; (2) a large proportion of loci showed dominance effects, whereas overdominance also contributed to MPH for KNPR, EWPE, and KWPE; (3) marker-assisted selection for markers at genomic regions 1.09–1.11, 2.04, 3.08–3.09, and 10.04–10.05 contributed to hybrid performance per se and heterosis and were repeatedly reported in previous studies using different heterotic patterns is recommended.

Objective To examine the associations of rotating night shift work with hyperhomocysteinaemia (HHcy) odds by different exposure metrics.Design Cross-sectional study.Setting Occupational physical examination centre for steel production workers, Tangshan, China.Participants A total of 6846 steelworkers, aged 22–60 years, from the baseline survey of a Chinese occupational cohort.Primary and secondary outcome measures Different exposure metrics of night shift work, including current shift status, duration of night shifts (years), cumulative number of night shifts (nights), cumulative length of night shifts (hours), average frequency of night shifts (nights/month), average length of night shifts (hours/night) and percentage of hours on night shifts, were used to examine the effects of past and current night shift work on HHcy odds. The total homocysteine concentration in the plasma above 15 µmol/L was defined as HHcy.Results Compared with those who never worked night shifts, current night shift workers had elevated odds of HHcy (OR 1.23, 95% CI 1.06 to 1.44). Considering a person’s lifetime work schedule and compared with individuals who never worked night shifts, duration of night shifts >28 years (OR 1.35, 95% CI 1.12 to 1.61), average frequency of night shifts >7 nights/month (OR 1.25, 95% CI 1.07 to 1.47) and percentage of hours on night shifts >30% (OR 1.23, 95% CI 1.05 to 1.43) were associated with higher HHcy odds. The duration of night shifts >20 years and the average frequency of night shifts >7 nights/month could significantly increase the odds of HHcy regardless of whether the average length of night shifts was greater than 8 hours/night. After stratification by sex, no significant association was found in female workers between different exposure metrics of night shift work and HHcy.Conclusions Long duration and high frequency of night shift work are associated with higher HHcy odds among male steelworkers.

In recent years, graph neural networks (GNNS) have been demonstrated to be a powerful way to learn graph data. The existing recommender systems based on the implicit factor models mainly use the interactive information between users and items for training and learning. A user–item graph, a user–attribute graph, and an item–attribute graph are constructed according to the interactions between users and items. The latent factors of users and items can be learned in these graph structure data. There are many methods for learning the latent factors of users and items. Still, they do not fully consider the influence of node attribute information on the representation of the latent factors of users and items. We propose a rating prediction recommendation model, short for LNNSR, utilizing the level of information granularity allocated on each attribute by developing a granular neural network. The different granularity distribution proportion weights of each attribute can be learned in the granular neural network. The learned granularity allocation proportion weights are integrated into the latent factor representation of users and items. Thus, we can capture user-embedding representations and item-embedding representations more accurately, and it can also provide a reasonable explanation for the recommendation results. Finally, we concatenate the user latent factor-embedding and the item latent factor-embedding and then feed it into a multi-layer perceptron for rating prediction. Extensive experiments on two real-world datasets demonstrate the effectiveness of the proposed framework.

Abstract With the re-emergence of brucellosis in mainland China since the mid-1990s, an increasing threat to public health tends to become even more violent, advanced warning plays a pivotal role in the control of brucellosis. However, a model integrating the autoregressive integrated moving average (ARIMA) with Error-Trend-Seasonal (ETS) methods remains unexplored in the epidemiological prediction. The hybrid ARIMA-ETS model based on discrete wavelet transform was hence constructed to assess the epidemics of human brucellosis from January 2004 to February 2018 in mainland China. The preferred hybrid model including the best-performing ARIMA method for approximation-forecasting and the best-fitting ETS approach for detail-forecasting is evidently superior to the standard ARIMA and ETS techniques in both three in-sample simulating and out-of-sample forecasting horizons in terms of the minimum performance indices of the root mean square error, mean absolute error, mean error rate and mean absolute percentage error. Whereafter, an ahead prediction from March to December in 2018 displays a dropping trend compared to the preceding years. But being still present, in various trends, in the present or future. This hybrid model can be highlighted in predicting the temporal trends of human brucellosis, which may act as the potential for far-reaching implications for prevention and control of this disease.

Tumor relapse after radiotherapy is a significant challenge to oncologists, even after recent the advances in technologies. Here, we showed that cancer-associated fibroblasts (CAFs), a major component of cancer stromal cells, promoted irradiated cancer cell recovery and tumor relapse after radiotherapy. We provided evidence that CAFs-produced IGF1/2, CXCL12 and β-hydroxybutyrate were capable of inducing autophagy in cancer cells post-radiation and promoting cancer cell recovery from radiation-induced damage in vitro and in vivo in mice. These CAF-derived molecules increased the level of reactive oxygen species (ROS) post-radiation, which enhanced PP2A activity, repressing mTOR activation and increasing autophagy in cancer cells. Consistently, the IGF2 neutralizing antibody and the autophagy inhibitor 3-MA reduce the CAF-promoted tumor relapse in mice after radiotherapy. Taken together, our findings demonstrated that CAFs promoted irradiated cancer cell recovery and tumor regrowth post-radiation, suggesting that targeting the autophagy pathway in tumor cells may be a promising therapeutic strategy for radiotherapy sensitization.