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Sleep quality leads to many healthy problems, which has caused global concern. Most studies have focused on the sleep quality in some large cities, ignoring the elderly in rural areas who may have more serious sleep problems. Therefore, this study aimed to understand the sleep status of the elderly in rural areas of Anhui Province and the influence of environmental factors on it. The data of carbon dioxide (CO2) concentrations for this study were obtained from the Dalhousie University atmospheric composition analysis group. The data of normalized differential vegetation index (NDVI) in 2014 was produced and processed by remote sensing inversion on the basis of medium resolution satellite images. The 2686 individuals were selected from rural areas of Anhui Province by multi-stage stratified cluster sampling. The Pittsburgh Sleep Quality Index (PSQI) was used to assess sleep quality. The independent, interactive, and mediating effects of CO2 and green space on sleep quality were evaluated by generalized linear model, interaction model, and mediating effect model, respectively. In this study, we found that the prevalence of sleep disturbance in the elderly was 58.40%. In the univariate model, we found that the risk of sleep disturbance increased by 1.6% for each 1 μg/m3 increase in concentrations of CO2, while decreased by 1.5% for every 0.1 increase in NDVI. In the interaction model, the negative correlation between green space and sleep quality decreased with concentrations of CO2 increased. In addition, green space was a mediating factor between CO2 and sleep quality in the mediating effect model.

Air pollution and its negative effects on health of people have been a global concern. Many studies had found a strong association between air pollutants and risk of death, but few had focused on the effects of six pollutants and rural areas. Our study aimed to investigate the effects of six air pollutants (CO, NO2, O3, PM2.5, PM10, and SO2) on non-accidental and respiratory deaths in rural areas of Anhui Province by adjusting for confounding factors, and to further clarify which populations were susceptible to death associated with air pollution. In the first phase of the analysis, the generalized additive models were combined with the distributed lag non-linear models to evaluate the individual effects of air pollution on death in each area. In the second stage, random-effects models were used to aggregate the associations between air pollutants and mortality risk in nine areas. Overall, six pollutants had the strongest effects on the risk of death on the lag 07 days. The associations between PM2.5 and NO2 and daily non-accidental deaths were strongest, with maximum RR (lag 07): 1.63 (1.37-1.88) and 1.67 (1.37-1.96). The maximum pooled effects of association between six air pollutants and RD were PM2.5, with RR (lag 07): 1.89 (1.45-2.34). PM2.5 and PM10 had significant differences between the elderly and the non-elderly with respectively, RRR: 1.22 (1.04-1.41) and 1.26 (1.11-1.42). In general, we found that six air pollutants were the important risk factors for deaths (deaths from respiratory disease and non-accidental) in rural areas of Anhui Province. PM10 and PM2.5 had a considerable impact on the elderly.

Previous efforts on data-based reconstruction focused on complex networks with pairwise or two-body interactions. There is a growing interest in networks with higher-order or many-body interactions, raising the need to reconstruct such networks based on observational data. We develop a general framework combining statistical inference and expectation maximization to fully reconstruct 2-simplicial complexes with two- and three-body interactions based on binary time-series data from two types of discrete-state dynamics. We further articulate a two-step scheme to improve the reconstruction accuracy while significantly reducing the computational load. Through synthetic and real-world 2-simplicial complexes, we validate the framework by showing that all the connections can be faithfully identified and the full topology of the 2-simplicial complexes can be inferred. The effects of noisy data or stochastic disturbance are studied, demonstrating the robustness of the proposed framework.

It is unclear whether weight change after middle adulthood influences the risk of thyroid cancer. The aim of this study was to investigate associations between the risk of papillary thyroid cancer (PTC) and body mass index (BMI) and weight change after middle adulthood (age 35).A matched case-control study based on three hospitals included 516 pairs of cases newly diagnosed with PTC and controls. Current height and weight after defecation in the morning were measured by trained nurses. During measurement, all subjects were requested to wear lightweight clothing and no shoes. Weight at age 35 was self-reported. BMI and weight change were modeled as continuous and categorical variables. Conditional and unconditional logistic regression models were used to estimate the odds ratio (OR) and 95% confidence interval (95%CI) for the association between BMI and weight change after middle adulthood and PTC.After adjustment for covariates, measured BMI at the time of current diagnosis was positively associated with PTC (OR 1.16, 95%CI 1.10-1.21). According to WHO BMI guidelines for Asia-Pacific populations, the OR (95%CI) for PTC risk in obesity was 2.99 (1.92-4.67) compared to normal weight (p-trend0.001). Moreover, PTC was positively associated with BMI at age 35; the OR (95%CI) for PTC risk per unit increase in BMI was 1.06 (1.02-1.11). Compared to stable weight (changed0.5 kg/year), weight gain ≥1.0 kg/year after middle adulthood was positively associated with PTC (OR 2.57, 95%CI 1.39-4.76, p-trend0.001). Compared to maintaining non-overweight status, the PTC risk was significantly increased in those individuals who gained weight and became overweight after middle adulthood (OR 3.82, 95%CI 2.50-5.85).This study showed that high BMI and obesity were positively associated with increased risk of PTC, and weight gain after middle adulthood also could elevate the PTC risk.

Goiter is one of common endocrine diseases, and its etiology has not been fully elucidated. The changes in trace elements' levels have an important impact on the thyroid. We designed a case-control study, which involved 383 goiter cases and 383 matched controls. We measured these elements in the urine of participants by inductively coupled plasma optical emission spectrometry (ICP-OES), graphite furnace atomic absorption spectrometry (GFAAS) and As3+-Ce4+ catalytic spectrophotometry. Least Absolute Shrinkage and Selection Operator (LASSO) regression was used to select the elements into multi-element models, conditional logistic regression models were applied to analyze the association between elements and goiter risk. Bayesian kernel machine regression (BKMR) model was used to depict elements' mixtures and evaluate their joint effects. Finally, 7 elements were included in the multi-element model. We found that the concentrations of lithium (Li), strontium (Sr) and barium (Ba) had a negative effect with goiter risk, and lead (Pb) and iodine (I) showed an extreme positive effect. Additionally, compared with the lowest levels, patients with highest quartiles of I and Pb were 6.49 and 1.94 times more likely to have goiter, respectively. On the contrary, in its second and third quartiles, arsenic (As) showed a negative effect (both OR

From the view of nanostructures, hierarchical lamellar heterostructures are one of the ideal candidates for high-performance electrode materials. Herein, hierarchical lamellar Co3O4–CoMoO4 heterostructures (Co3O4–CoMoO4 HLHs) are synthesized via a facile solvothermal approach with subsequent annealing treatment. The obtained HLHs consist of lamellar parallel stacked CoMoO4 nanosheets, where Co3O4 nanoflakes uniformly grow into an array on the surface of CoMoO4 nanosheets. The formation mechanism of Co3O4–CoMoO4 HLHs can be attributed to the sequential precipitation of CoMoO4 and Co3O4 precursors during the solvothermal process. The early nucleated parallel stacked 2D nanosheets serve as templates for the growth of Co hydroxide nanoflake arrays. Owing to the structural advantage, the as-obtained Co3O4–CoMoO4 HLHs endow the constructed electrode with enhanced electrochemical activity, high specific capacity and long-term cycling stability.

Background: Thyroid cancer (TC) has inflicted huge threats to the health of mankind. Chlorophenols (CPs) were persistent organic pollutant and can lead to adverse effects in human health, especially in thyroid. However, epidemiological studies have revealed a rare and inconsistent relationship between internal exposure to CPs and TC risk. This study was designed to examine the association between urinary CPs and risk of TC in Chinese population.Methods: From June 2017 to September 2019, a total of 297 histologically confirmed TC cases were recruited. Age and gender matched controls were enrolled at the same time. Gas chromatography-mass spectrometry (GC-MS) was used to determine the levels of three CPs in urine. Conditional logistic regression models were adopted to assess the potential association. Restricted cubic spline function was used to explore the non-liner association.Results: After adjusting for confounding factors, multivariate analysis showed that, compared with the first quartile, the fourth quartile concentrations of 2,4-dichlorophenol (2,4-DCP), 2,4,6-trichlorophenol (2,4,6-TCP) and pentachlorophenol(PCP) were associated with TC risk [odds ratio (OR)2,4-DCP =2.28, 95%confidence interval (CI): 1.24-4.18; OR2,4,6-TCP =3.09, 95% CI: 1.66-5.77; ORPCP =3.30, 95% CI: 1.71-6.36, respectively]. When CPs were included in the multivariate model and restricted cubic spline function as continuous variables, presenting significant dose-response relationships. Meanwhile, whether in the TC group with tumor diameter > 1 cm or metastatic TC, the changes of 2,4,6 TCP and PCP concentrations were positively correlated with the risk of TC. Conclusions: Our study suggests that higher concentrations of urinary CPs are associated with increased TC risks. Moreover, 2,4,6-TCP and PCP have certain effects on the invasiveness of thyroid cancer. Targeted public health policies should be formulated to reduce the CPs pollution. These findings need further in-depth studies to confirm and relevant mechanism also needed to be clarified.

Additional file 2. Additional Information Part I

Additional file 1. Additional Data S I, II

Additional file 3. Additional Information Part II & III

This paper was aimed to explore the potential pharmacodynamics effect of Euonymus alatus in the treatment of nephritis based on integrated chemomics and network biology. The chemical constituent database of E. alatus was constructed by consulting litera-ture and using online database. The chemical constituents were identified by UPLC-Q-TOF/HRMS~E and UNIFI software. On this basis, a series of comparisons, molecular docking studies and in-depth analysis of the chemical constituents and nephritis disease targets were carried out with use of network biology method, and the potential pharmacodynamic effect of E. alatus for the treatment of nephritis was investigated by reviewing the existing. In this study, 62 chemical constituents were collected in the database of chemical consti-tuents of E. alatus, and 24 chemical constituents were identified by mass spectrum. Subsequently, based on the network biology me-thod, 22 important chemical constituents and 5 key targets were obtained by reverse screening. Molecular docking study showed that a total of 11 chemical constituents such as quercetin, kaempferol, and catechinmay be the potential material basis for E. alatus in the treatment of nephritis. Starting with chemomics and using the technology of network biology, we established a network interaction model between drug components and disease targets in this study. Through the interaction between targets in complex networks, we can find the key targets easily and quickly. By docking the key targets with small drug molecules, we can screen out the potential pharmacodynamic components, providing a reference for the follow-up study of active ingredients.

Hypertension (HTN) was a major preventable cause of cardiovascular disease (CVD), contributing to a huge disease burden. Ambient temperature, air pollution and green space were important influencing factors of HTN, and few studies have assessed the effects and interactions of ambient temperature, air pollution and green space on HTN in rural areas. In this study, we selected 8400 individuals randomly in rural areas of Anhui Province by a multi-stage stratified cluster sampling. A total of 8383 individuals were included in the final analysis. We collected particulate pollutants and meteorological data from the local air quality monitoring stations and National Center for Meteorological Science from January 1 to December 31, 2020, respectively. The normalized differential vegetation index (NDVI) of Anhui Province in 2020 was produced and processed by remote sensing inversion on the basis of medium resolution satellite images. The average annual mean exposure concentrations of air pollution, meteorological factors, and NDVI were calculated for each individual based on the geocoded residential address. HTN was defined according the Chinese Guidelines for Prevention and Treatment of HTN. The effects and interactions of ambient temperature, air pollution and green space on HTN were evaluated by generalized linear model and interaction model, respectively. In this study, the prevalence of HTN was 24.14%. The adjusted odd ratio of HTN for each 1 μg/m3 increasing in PM2.5 and PM10, 1 °C of ambient temperature, and 0.1 of NDVI were:1.276 (1.013, 1.043), 1.012 (1.006, 1.018), 0.862 (0.862, 0.981) and 0.669 (0.611, 0.733), respectively. The results showed that air pollutants were positively correlated with HTN, while ambient temperature and green space were negatively correlated with HTN. Meanwhile, the negative associations of green space on HTN could decrease with the increasing concentrations of air pollution, but increase with the rising of ambient temperature.

In the field of public health, treatment of multidrug-resistant (MDR) bacterial infection is a great challenge. Herein, we provide a solution to this problem with the use of graphene oxide-silver (GO-Ag) nanocomposites as antibacterial agent. Following established protocols, silver nanoparticles were grown on graphene oxide sheets. Then, a series of in vitro studies were conducted to validate the antibacterial efficiency of the GO-Ag nanocomposites against clinical MDR Escherichia coli (E. coli) strains. GO-Ag nanocomposites showed the highest antibacterial efficiency among tested antimicrobials (graphene oxide, silver nanoparticles, GO-Ag), and synergetic antibacterial effect was observed in GO-Ag nanocomposites treated group. Treatment with 14.0 µg ml−1 GO-Ag treatment could greatly inhibit bacteria growth; remaining bacteria viabilities were 4.4% and 4.1% for MDR-1 and MDR-2 E. coli bacteria, respectively. In addition, with assistance of photothermal effect, effective sterilization could be achieved using GO-Ag nanocomposites as low as 7.0 µg ml−1. Fluorescence imaging and morphology characterization uncovered that bacteria integrity was disrupted after GO-Ag nanocomposites treatment. Cytotoxicity results of GO-Ag using human-derived cell lines (HEK 293T, Hep G2) suggested more than 80% viability remained at 7.0 µg ml−1. All the results proved that GO-Ag nanocomposites are efficient antibacterial agent against multidrug-resistant E. coli.

In the field of public health, treatment of multidrug-resistant (MDR) bacterial infection is a great challenge. Herein, we provide a solution to this problem with the use of graphene oxide-silver (GO-Ag) nanocomposites as antibacterial agent. Following established protocols, silver nanoparticles were grown on graphene oxide sheets. Then, a series of in vitro studies were conducted to validate the antibacterial efficiency of the GO-Ag nanocomposites against clinical MDR Escherichia coli (E. coli) strains. GO-Ag nanocomposites showed the highest antibacterial efficiency among tested antimicrobials (graphene oxide, silver nanoparticles, GO-Ag), and synergetic antibacterial effect was observed in GO-Ag nanocomposites treated group. Treatment with 14.0 µg ml−1 GO-Ag treatment could greatly inhibit bacteria growth; remaining bacteria viabilities were 4.4% and 4.1% for MDR-1 and MDR-2 E. coli bacteria, respectively. In addition, with assistance of photothermal effect, effective sterilization could be achieved using GO-Ag nanocomposites as low as 7.0 µg ml−1. Fluorescence imaging and morphology characterization uncovered that bacteria integrity was disrupted after GO-Ag nanocomposites treatment. Cytotoxicity results of GO-Ag using human-derived cell lines (HEK 293T, Hep G2) suggested more than 80% viability remained at 7.0 µg ml−1. All the results proved that GO-Ag nanocomposites are efficient antibacterial agent against multidrug-resistant E. coli.

In the field of public health, treatment of multidrug-resistant (MDR) bacterial infection is a great challenge. Herein, we provide a solution to this problem with the use of graphene oxide-silver (GO-Ag) nanocomposites as antibacterial agent. Following established protocols, silver nanoparticles were grown on graphene oxide sheets. Then, a series of in vitro studies were conducted to validate the antibacterial efficiency of the GO-Ag nanocomposites against clinical MDR Escherichia coli (E. coli) strains. GO-Ag nanocomposites showed the highest antibacterial efficiency among tested antimicrobials (graphene oxide, silver nanoparticles, GO-Ag), and synergetic antibacterial effect was observed in GO-Ag nanocomposites treated group. 14.0 µg ml−1 GO-Ag treatment could greatly inhibit bacteria growth, remained bacteria viabilities were 4.4% and 4.1% for MDR-1 and MDR-2 E. coli bacteria, respectively. In addition, with assistance of photothermal effect, effective sterilization could be achieved using GO-Ag nanocomposites as low as 7.0 µg ml−1. Fluorescence imaging and morphology characterization uncovered that bacteria integrity was disrupted after GO-Ag nanocomposites treatment. Cytotoxicity results of GO-Ag using human-derived cell lines (HEK 293T, Hep G2) suggested more than 80% viability remained at 7.0 µg ml−1. All the results proved that GO-Ag nanocomposites are efficient antibacterial agent against multidrug-resistant E. coli.

In the field of public health, treatment of multidrug-resistant (MDR) bacterial infection is a great challenge. Herein, we provide a solution to this problem with the use of graphene oxide-silver (GO-Ag) nanocomposites as antibacterial agent. Following established protocols, silver nanoparticles were grown on graphene oxide sheets. Then, a series of in vitro studies were conducted to validate the antibacterial efficiency of the GO-Ag nanocomposites against clinical MDR Escherichia coli (E. coli) strains. GO-Ag nanocomposites showed the highest antibacterial efficiency among tested antimicrobials (graphene oxide, silver nanoparticles, GO-Ag), and synergetic antibacterial effect was observed in GO-Ag nanocomposites treated group. 14.0 µg ml−1 GO-Ag treatment could greatly inhibit bacteria growth, remained bacteria viabilities were 4.4% and 4.1% for MDR-1 and MDR-2 E. coli bacteria, respectively. In addition, with assistance of photothermal effect, effective sterilization could be achieved using GO-Ag nanocomposites as low as 7.0 µg ml−1. Fluorescence imaging and morphology characterization uncovered that bacteria integrity was disrupted after GO-Ag nanocomposites treatment. Cytotoxicity results of GO-Ag using human-derived cell lines (HEK 293T, Hep G2) suggested more than 80% viability remained at 7.0 µg ml−1. All the results proved that GO-Ag nanocomposites are efficient antibacterial agent against multidrug-resistant E. coli.

In the field of public health, treatment of multidrug-resistant (MDR) bacterial infection is a great challenge. Herein, we provide a solution to this problem with the use of graphene oxide-silver (GO-Ag) nanocomposites as antibacterial agent. Following established protocols, silver nanoparticles were grown on graphene oxide sheets. Then, a series of in vitro studies were conducted to validate the antibacterial efficiency of the GO-Ag nanocomposites against clinical MDR Escherichia coli (E. coli) strains. GO-Ag nanocomposites showed the highest antibacterial efficiency among tested antimicrobials (graphene oxide, silver nanoparticles, GO-Ag), and synergetic antibacterial effect was observed in GO-Ag nanocomposites treated group. 14.0 µg ml−1 GO-Ag treatment could greatly inhibit bacteria growth, remained bacteria viabilities were 4.4% and 4.1% for MDR-1 and MDR-2 E. coli bacteria, respectively. In addition, with assistance of photothermal effect, effective sterilization could be achieved using GO-Ag nanocomposites as low as 7.0 µg ml−1. Fluorescence imaging and morphology characterization uncovered that bacteria integrity was disrupted after GO-Ag nanocomposites treatment. Cytotoxicity results of GO-Ag using human-derived cell lines (HEK 293T, Hep G2) suggested more than 80% viability remained at 7.0 µg ml−1. All the results proved that GO-Ag nanocomposites are efficient antibacterial agent against multidrug-resistant E. coli.

In the field of public health, treatment of multidrug-resistant (MDR) bacterial infection is a great challenge. Herein, we provide a solution to this problem with the use of graphene oxide-silver (GO-Ag) nanocomposites as antibacterial agent. Following established protocols, silver nanoparticles were grown on graphene oxide sheets. Then, a series of in vitro studies were conducted to validate the antibacterial efficiency of the GO-Ag nanocomposites against clinical MDR Escherichia coli (E. coli) strains. GO-Ag nanocomposites showed the highest antibacterial efficiency among tested antimicrobials (graphene oxide, silver nanoparticles, GO-Ag), and synergetic antibacterial effect was observed in GO-Ag nanocomposites treated group. Treatment with 14.0 µg ml−1 GO-Ag treatment could greatly inhibit bacteria growth; remaining bacteria viabilities were 4.4% and 4.1% for MDR-1 and MDR-2 E. coli bacteria, respectively. In addition, with assistance of photothermal effect, effective sterilization could be achieved using GO-Ag nanocomposites as low as 7.0 µg ml−1. Fluorescence imaging and morphology characterization uncovered that bacteria integrity was disrupted after GO-Ag nanocomposites treatment. Cytotoxicity results of GO-Ag using human-derived cell lines (HEK 293T, Hep G2) suggested more than 80% viability remained at 7.0 µg ml−1. All the results proved that GO-Ag nanocomposites are efficient antibacterial agent against multidrug-resistant E. coli.

In the field of public health, treatment of multidrug-resistant (MDR) bacterial infection is a great challenge. Herein, we provide a solution to this problem with the use of graphene oxide-silver (GO-Ag) nanocomposites as antibacterial agent. Following established protocols, silver nanoparticles were grown on graphene oxide sheets. Then, a series of in vitro studies were conducted to validate the antibacterial efficiency of the GO-Ag nanocomposites against clinical MDR Escherichia coli (E. coli) strains. GO-Ag nanocomposites showed the highest antibacterial efficiency among tested antimicrobials (graphene oxide, silver nanoparticles, GO-Ag), and synergetic antibacterial effect was observed in GO-Ag nanocomposites treated group. 14.0 µg ml−1 GO-Ag treatment could greatly inhibit bacteria growth, remained bacteria viabilities were 4.4% and 4.1% for MDR-1 and MDR-2 E. coli bacteria, respectively. In addition, with assistance of photothermal effect, effective sterilization could be achieved using GO-Ag nanocomposites as low as 7.0 µg ml−1. Fluorescence imaging and morphology characterization uncovered that bacteria integrity was disrupted after GO-Ag nanocomposites treatment. Cytotoxicity results of GO-Ag using human-derived cell lines (HEK 293T, Hep G2) suggested more than 80% viability remained at 7.0 µg ml−1. All the results proved that GO-Ag nanocomposites are efficient antibacterial agent against multidrug-resistant E. coli.

In the field of public health, treatment of multidrug-resistant (MDR) bacterial infection is a great challenge. Herein, we provide a solution to this problem with the use of graphene oxide-silver (GO-Ag) nanocomposites as antibacterial agent. Following established protocols, silver nanoparticles were grown on graphene oxide sheets. Then, a series of in vitro studies were conducted to validate the antibacterial efficiency of the GO-Ag nanocomposites against clinical MDR Escherichia coli (E. coli) strains. GO-Ag nanocomposites showed the highest antibacterial efficiency among tested antimicrobials (graphene oxide, silver nanoparticles, GO-Ag), and synergetic antibacterial effect was observed in GO-Ag nanocomposites treated group. Treatment with 14.0 µg ml−1 GO-Ag treatment could greatly inhibit bacteria growth; remaining bacteria viabilities were 4.4% and 4.1% for MDR-1 and MDR-2 E. coli bacteria, respectively. In addition, with assistance of photothermal effect, effective sterilization could be achieved using GO-Ag nanocomposites as low as 7.0 µg ml−1. Fluorescence imaging and morphology characterization uncovered that bacteria integrity was disrupted after GO-Ag nanocomposites treatment. Cytotoxicity results of GO-Ag using human-derived cell lines (HEK 293T, Hep G2) suggested more than 80% viability remained at 7.0 µg ml−1. All the results proved that GO-Ag nanocomposites are efficient antibacterial agent against multidrug-resistant E. coli.

The adsorption behavior and hydrolysis mechanism of COS and CS2 on a graphene surface were studied by using density functional theory. It could be concluded that the most stable adsorption configuration for COS was in the form of straight line at the B site, and the adsorption energy was − 0.830 eV. However, the most stable adsorption structure for CS2 with an adsorption energy of − 0.867 eV was the three atoms in CS2 interacting with the C atoms on the graphene surface. By contrasting the two values, the conclusion that the CS2 would adsorb on the graphene surface firstly when the COS and CS2 exist simultaneously was easy to draw. Meanwhile, for the H2O, the maximal adsorption energy was − 0.244 eV and the corresponding configurations were in the form of a downward and upward “V”. So the COS and CS2 will adsorb on the graphene surface first when they react with H2O. Then, the gaseous H2O and adsorptive COS/CS2 create a low-energy system. The energy barrier values of COS and CS2 were 122.648 and 314.108 kcal/mol, respectively, during the hydrolysis process on the graphene surface. And the reaction energies were − 6.335 and 167.561 kcal/mol, respectively. Namely, when the two molecules resulted in the same final products, the COS initiated the reaction more easily.

The influences of different preparation conditions for surface characteristics on removing organic sulfur were studied. From BET, XRD, FTIR, DRIFTS, TG/DTA, CO 2 -TPD results, it can be seen that these preparation conditions had great influences on the pore structure, specific surface area, crystal structure and surface functional groups. The micropore volume, amorphous structure and alkalinity site strength played major roles in desulfurization process. H 2 S was oxidized by oxygen containing functional groups, such as COO, C O. H 2 O molecule could be converted into some groups, such as CH and C OH groups, and promoted the hydrolysis reaction. The strong alkalinity site was the key factor for chemical adsorption and hydrolysis. H 2 O molecule, CH, C OH groups promoted the hydrolysis reaction and COO, C O groups promoted the oxidation of H 2 S on the surface of WSB. Meanwhile, the main desulfurization process over WSB after carbonization was adsorption and it changed to hydrolysis reaction after activation on the surface of WSB. Furthermore, the reaction mechanism was investigated by DRIFTS measurement according to the change of surface functional groups.

Walnut shell-based activated carbon (WSBAC) was prepared used to simultaneously remove H2S, COS and CS2 from yellow phosphorus tail gas and closed carbide furnace tail gas. The influences of different preparation conditions (carbonization temperature, activating agent species, activating agent content, activation temperature) on the simultaneous removal efficiency of H2S, COS and CS2 were investigated. The results showed that the WSBAC carbonizated at 600 °C, then activated with 0.5 times KOH at 700 °C had the highest sulfur capacity (52.67 mgS/gWSBAC). The structure and surface properties of the catalysts were characterized by Brunauer–Emmett–Teller (BET) method, and the results showed that the high BET surface area (484.26 m2/g), high micropore volume (0.261 cm3/g) and more pore sizes of 0.3–1.8 nm were conducive to the simultaneous removal of H2S, COS and CS2. X-ray photoelectron spectroscopy results showed that the most of products on the exhausted WSBAC were S/SO4 2− species which accumulated on the WSBAC’s surface and had a negative effect on the adsorption activity.

The mean wind speed, wind speed distribution, wind shear exponent and wind power density would be affected by terrain, atmospheric stability and equipments, are important parameters in the process of wind resource assessment. However, Conventional methods have always ignored the effects of atmospheric stability, which might make results deviate from the actual situation. The main purpose of this work is to present effects of atmospheric stability on wind resource parameters. Taking 4 years’ measured data as case study, six parameters in wind resource assessing are calculated under different atmospheric stabilities; influential rules of above assessing parameters under varying atmospheric stability are analyzed respectively in order to obtain a systemic relationship between atmospheric stability and wind resource characteristic parameters which is tested by 5th year’s data. Based on these studies, a hub height wind speed extrapolation model is proposed. This model could use different wind shear exponent under various atmospheric stabilities during wind speed extrapolating, rather than an average wind shear for the whole wind farm. In such way, much accurate extrapolating hub wind speed in wind resource assessing is employed, and errors in wind resource assessment might decline. Results validate the simplicity, accuracy and practicality of the proposed method.

The measurement of Born cross section of $e^+e^-\to ZH$ process is one of the major goals of the future Circular Electron Positron Collider, which may reach a precision of 0.5%. Such unprecedented precision must be guaranteed by both theoretical and experimental sides, such as the calculations of high order corrections, the knowledge of the $��^{\mathrm{B}}(e^+e^-\to ZH)$ line shape. The uncertainty of radiative correction factor due to lack of knowledge of the $��^{\mathrm{B}}(e^+e^-\to ZH)$ line shape is evaluated in this work. Therefore, the dedicated data-taking schemes are proposed in order to precisely calculate the ISR correction factor., 9 pages, 5 figures

The load-carrying capacity and the stability of wind power generation are different from those of conventional generation. The current evaluation method almost considers the capacity credit of each region as a fixed value, which underestimates the contribution of wind power generation to the power system adequacy. To solve this problem, the wind power continuous output model is proposed in this paper based on the nonparametric joint probability density distribution of prediction error. This model can well describe the wind power output but is complicated. Improved wind power two-state model is developed. Under the condition that expected energy not supplied (EENS) is unchangeable, various wind power operating capacity credit, defined by equivalent firm capacity (EFC), equivalent conventional generation capacity (ECGC) and equivalent load-carrying capability (ELCC), is evaluated by both models with measurements and predictions. It can be observed that the results calculated by the proposed models are similar, and they are more reasonable and effective than the existing models.

Supplemental material, IMR882221 Supplemetal Material for Tolvaptan in reversing worsening acute heart failure: A systematic review and meta-analysis by Lili Wang, Qianhui Zhang, Meixia Liu, Shuxia Chen, Shuang Han, Jing Li and Rongpin Du in Journal of International Medical Research

As a mainstream algorithm for inferring probabilistic graphical models, belief propagation (BP) algorithm is one of the most important methods to solve the joint probability distribution in the stochastic block model. However, existing methods either lead to low accuracy in dealing with the core-periphery structure problem, or the theoretical derivation is difficult to understand due to a large number of approximation, or both exist. Of course, the reason for low accuracy comes from too many approximations. The main reason for many approximations and complex theoretical derivation is that the joint probability distribution in the inference process of the stochastic block model is not directly solved by the BP algorithm, that is, the graph (network) being processed is not consistent with the graph considered in the probabilistic graph model. Therefore, in this paper, a mean-field approximation is developed to modify the joint probability distribution to make the BP algorithm match perfectly, which makes the theoretical derivation easy to understand. Finally, the effectiveness of the proposed method is validated by the experimental results.

Ball-milled Ti/TiC composite particles (TiC nanoparticles assembled on Ti microparticles) were designed, prepared, and mixed with Al-Si-Mg powder to fabricate an Al-Si-Mg-Ti alloy with TiC nanoparticles (Al-Si-Mg-Ti/TiC) by selective laser melting (SLM). Microstructure features, solidification behavior, and mechanical properties were investigated, and the relationship among them was established. The SLM-manufactured Al-Si-Mg-Ti/TiC material exhibited fine equiaxed-shaped α(Al) grains with nanoscale Si4Ti5 phases and Mg segregation along the grain boundaries. This structure benefited from heterogeneous nucleation as well as the grain growth restriction capabilities of TiC nanoparticles on α(Al), fast diffusion of Ti in the superheated Al liquid, and high chemical activity of Ti to Si during solidification. Furthermore, Ti enrichment in some local areas of the high-temperature pool and the consequently intense Marangoni convection improved the wettability between TiC nanoparticles and liquid Al without the interfacial reaction. Consequently, the SLM-manufactured Al-Si-Mg-Ti/TiC showed a high ultimate tensile strength of up to 562 ± 7 MPa and an elongation of up to 8.8 % ± 1.3 % before fracture. These increased mechanical properties are attributed to the combined effect of grain refinement and Orowan and load-bearing strengthening mechanisms.

Heterogeneous mean-field theory is commonly used methodology to study dynamical processes on complex networks, such as epidemic spreading and phase transitions in spin models. In this paper, we propose an improved heterogeneous mean-field theory for studying the Ising model on complex networks. Our method shows a more accurate prediction in the critical temperature of the Ising model than the previous heterogeneous mean-field theory. The theoretical results are validated by extensive Monte Carlo simulations in various types of networks.

To screen the optimum method for deproteination of Dioscorea nipponica polysaccharides.The ratio of protein removing and polysaccharides remaining were used as indicator,four deproteination methods were evaluated.PapainSevage method was the best one, in which the deproteination rate was 91. 24%,and 80. 12% of polysaccharide was remained. The optimum conditions for deproteinization were as follows, hydrolysing the substrates with 2. 0% papain( p H 7. 0) at 55 ℃ for 122 min, and one times with Sevage method.The papain-Sevage is the best deproteination method for purifying Dioscorea nipponica polysaccharides.

The framework of statistical inference has been successfully used to detect the meso-scale structures in complex networks, such as community structure, core-periphery (CP) structure. The main principle is that the stochastic block model (SBM) is used to fit the observed network and the learnt parameters indicate the group assignment, in which the parameters of model are often calculated via an expectation-maximization (EM) algorithm and a belief propagation (BP) algorithm is implemented to calculate the decomposition itself. In the derivation process of the BP algorithm, some approximations were made by omitting the effects of node's neighbors, the approximations do not hold if networks are dense or some nodes holding large degrees. As a result, for example, the BP algorithm cannot well detect CP structure in networks and even yields wrong detection because the nodal degrees in core group are very large. In doing so, we propose an improved BP algorithm to solve the problem in the original BP algorithm without increasing any computational complexity. By comparing the improved BP algorithm with the original BP algorithm on community detection and CP detection, we find that the two algorithms yield the same performance on the community detection when the network is sparse, for the community structure in dense networks or CP structure in networks, our improved BP algorithm is much better and more stable. The improved BP algorithm may help us correctly partition different types of meso-scale structures in networks., Comment: 7 pages, 5 figures

Neogenkwanine I (1), a new daphnane-type diterpene with 4,7-ether group, along with four known ones (2–5), were isolated from Daphne genkwa. The structure including absolute configurations of 1 was established on the basis of NMR, 13C-NMR and ECD calculations and CD exciton chirality analysis. 13C-NMR and ECD calculations of daphnane-type diterpenes were reported here for the first time. All of the diterpenes were screened for their cytotoxic activities against MCF-7 and Hep3B cell lines. The cytotoxicity structure- activity relationship of compounds was illustrated with the absence of ortho-ester group of daphnane-type diterpenes.

It has been recognized that many complex dynamical systems in the real world require a description in terms of multiplex networks, where a set of common, mutually connected nodes belong to distinct network layers and play a different role in each layer. In spite of recent progress towards data based inference of single-layer networks, to reconstruct complex systems with a multiplex structure remains largely open. We articulate a mean-field based maximum likelihood estimation framework to solve this outstanding and challenging problem. We demonstrate the power of the reconstruction framework and characterize its performance using binary time series from a class of prototypical duplex network systems that host two distinct types of spreading dynamics. In addition to validating the framework using synthetic and real-world multiplex networks, we carry out a detailed analysis to elucidate the impacts of structural and dynamical parameters as well as noise on the reconstruction accuracy and robustness., Comment: One main context and a supplementary information

Taking Chengdu plain as the research object, using 2014 TM image and the data of social economic development, from the perspective of urban and suburban, studied the differences of the coupling relationships between landscape pattern and economic development in the studying area, the results are as follows: the coupling degree show that the coupling degree Chengdu plain is 0.411, the coupling coordination degree is 0.332, which is high strength and low coordination. The coupling degree is 0.498 and the coupling coordination degree is 0.436 in the urban, which is high strength and normal coordination. The coupling degree is 0.342 and the coupling coordination degree is 0.325 in the suburban, which is low strength and low coordination. The coupling differences in urban and rural between the economic development and landscape pattern is because the result by natural conditions and human activities, the human economic activity is the leading cause of urban and rural landscape pattern differences.

C9H10CeO11, triclinic, P1̄ (no. 2), a = 6.9100(14) Å, b = 9.1700(18) Å, c = 10.720(2) Å, α = 69.53(3)°, β = 83.56(3)°, γ = 87.01(3)°, V = 632.3(2) Å3, Z = 2, R gt(F) = 0.0265, wR ref(F 2) = 0.0487, T = 298(2) K.

An invasive-attack-resistant physical unclonable function (PUF) with strong reliability is presented. The mismatch of capacitor ratios in real fabrication is sampled by a switched-capacitor (SC) circuit and further amplified by a latch-styled sense amplifier. Transmission lines connected to the sampling capacitors are used to cover and protect the whole chip from outside invasive attacks. The proposed SC PUF is fabricated using HJ standard 0.18 $\mu{\rm m}$ CMOS process. Simulation results show that the invasive-attack-resistant sensitivity is less than 2.56 fF. The failed outside invasive attack efforts verify the anti-invasive-attack property of the PUF unit. Besides, measured results show that the proposed SC PUF circuit with 3 V/1.8 V-VDD achieves an error rate 1x-5.5x lower than other PUF implementations. An error correction block is also presented to achieve a nearly zero error rate. The measured average error rate, bias, average intra-die HD and average inter-die HD of the 3 V/1.8 V-VDD SC PUF with error correction block is 0%, 46.72%, 0%, and 49.84% respectively.

CdS quantum dots/ Boron and Nitrogen co-doped TiO2 photocatalyst emulsions were synthesized by a complexation-controlled hydrolysis method at room temperature. Boron compounds and Nitrogen compounds were added to TiO2 precursor solution, and CdS quantum dots were attached to TiO2 particles surfaces by using chemical reaction. The hybird composite photocatalysts were characterized by XRD, UV-Vis. Their photocatalytic properties were evaluated through the degradation of acid red 3R dye. The results indicate that when TiO2 emulsion add to Boron ions, Nitrogen ions, and CdS quantum dots, the photocatalytic performance of the emulsion is the best.

PbS quantum dots/ Boron and Nitrogen co-doped TiO2 photocatalyst emulsions were synthesized by a complexation-controlled hydrolysis method at room temperature. Boron compounds and Nitrogen compounds were added to TiO2 precursor solution, and PbS quantum dots were attached to TiO2 particles surfaces by using chemical reaction. The hybird composite photocatalysts were characterized by nanoparticle size analyzer. Their photocatalytic properties were evaluated through the degradation of acid red 3R dye. The results indicate that when TiO2 emulsion added to Boron ions, Nitrogen ions, and PbS quantum dots, the reflux time is 15 min, the photocatalytic performance of the emulsion is the best.