Nitrogen Removal in a Horizontal Subsurface Flow Constructed Wetland Estimated Using the First-Order Kinetic Model

We monitored the water quality and hydrological conditions of a horizontal subsurface constructed wetland (HSSF-CW) in Beijing, China, for two years. We simulated the area-based constant and the temperature coefficient with the first-order kinetic model. We examined the relationships between the nitrogen (N) removal rate, N load, seasonal variations in the N removal rate, and environmental factors—such as the area-based constant, temperature, and dissolved oxygen (DO). The effluent ammonia (NH4 + -N) and nitrate (NO3 −-N) concentrations were significantly lower than the influent concentrations (p \u3c 0.01, n = 38). The NO3 −-N load was significantly correlated with the removal rate (R 2 = 0.96, p \u3c 0.01), but the NH4 + -N load was not correlated with the removal rate (R 2 = 0.02, p \u3e 0.01). The area-based constants of NO3 −-N and NH4 + -N at 20 ◦C were 27 ± 26 (mean ± SD) and 14 ± 10 m·year−1 , respectively. The temperature coefficients for NO3 −-N and NH4 + -N were estimated at 1.004 and 0.960, respectively. The area-based constants for NO3 −-N and NH4 + -N were not correlated with temperature (p \u3e 0.01). The NO3 −-N area-based constant was correlated with the corresponding load (R 2 = 0.96, p \u3c 0.01). The NH4 + -N area rate was correlated with DO (R 2 = 0.69, p \u3c 0.01), suggesting that the factors that influenced the N removal rate in this wetland met Liebig’s law of the minimum

Health Status-Based Predictive Maintenance Decision-Making via LSTM and Markov Decision Process

Maintenance decision-making is essential to achieve safe and reliable operation with high performance for equipment. To avoid unexpected shutdown and increase machine life as well as system efficiency, it is fundamental to design an effective maintenance decision-making scheme for equipment. In this paper, we propose a novel maintenance decision-making method for equipment based on Long Short-Term Memory (LSTM) and Markov decision process, which can provide specific maintenance strategies in different degradation stages of the system. Specifically, the LSTM model is firstly applied to predict the remaining service life of equipment to distinguish its health state quantitatively. Then, based on the bearing residual life prediction curve, the degradation process model is constructed, and the corresponding parameters of the model are identified. Finally, the bearing degradation curve is obtained by the degradation process model, based on which the Markov decision process model is constructed to provide accurate maintenance strategies for different health conditions of system. To demonstrate the effectiveness of the proposed method, an experimental study with the full life cycle data set of rolling bearings is carried out. The experimental results show that the proposed method can achieve efficient maintenance decisions for bearings under different health states, which provides a feasible solution for the maintenance of bearing systems

Pollution characteristics and ecological risk assessment of heavy metals in the surface sediments from a source water reservoir

Surface sediment samples were collected from a source water reservoir in Zhejiang Province, East of China to investigate pollution characteristics and potential ecological risk of heavy metals. The BCR sequential extraction method was used to determine the four chemical fractions of heavy metals such as acid soluble, easily reducible, easily oxidizable and residual fractions. The heavy metals pollution and potential ecological risk were evaluated systematically using geoaccumulation index (Igeo) and Hakanson potential ecological risk index (H′). The results showed that the sampling sites from the estuaries of tributary flowing through downtowns and heavy industrial parks showed significantly (p < 0.05) higher average concentrations of heavy metals in the surface sediments, as compared to the other sampling sites. Chemical fractionation showed that Mn existed mainly in acid extractable fraction, Cu and Pb were mainly in reducible fraction, and As existed mainly in residual fraction in the surface sediments despite sampling sites. The sampling sites from the estuary of tributary flowing through downtown showed significantly (p < 0.05) higher proportions of acid extractable and reducible fractions than the other sampling sites, which would pose a potential toxic risk to aquatic organisms as well as a potential threat to drinking water safety. As, Pb, Ni and Cu were at relatively high potential ecological risk with high Igeo values for some sampling locations. Hakanson potential ecological risk index (H′) showed the surface sediments from the tributary estuaries with high population density and rapid industrial development showed significantly (p < 0.05) higher heavy metal pollution levels and potential ecological risk in the surface sediments, as compared to the other sampling sites

Risk Factors for Postoperative Acute Kidney Injury in Patients Undergoing Redo Cardiac Surgery Using Cardiopulmonary Bypass

Objective: This paper aimed to investigate the incidence and risk factors of postoperative acute kidney injury (AKI) in adult patients undergoing redo cardiac surgery with cardiopulmonary bypass (CPB), and explore the impact of AKI on early outcomes. Methods: A total of 116 patients undergoing redo cardiac surgery with CPB between November 2017 and May 2021 were included. Patients were divided into two groups, AKI group and non-AKI group, according to the Kidney Disease Improving Global Outcomes criteria. Perioperative variables were retrospectively collected and analyzed. Risk factors for the development of AKI were investigated by univariate and multiple logistic regression models. Clinical outcomes were also compared between the groups. Results: Postoperative AKI occurred in 63 patients (54.3%), among whom renal replacement therapy was required in 12 patients (19.0%). The mechanical ventilation time (AKI: 43.00 (19.00, 72.00) hours; non-AKI: 18.00 (15.00, 20.00) hours; p p = 0.010), hospital length of stay since operation (AKI: 12.00 (8.00, 18.00) days; non-AKI: 9.00 (7.00, 12.50) days; p = 0.024), dialysis (AKI: 12.00 (19.05%); non-AKI: 0 (0%); p = 0.001), reintubation (AKI: 7.00 (11.11%); non-AKI: 0 (0%); p = 0.035), and hospital mortality (AKI: 8.00 (12.70%); non-AKI: 0 (0%); p = 0.020) were all higher in the AKI group than in the non-AKI group. Multivariate analysis revealed that high aspartate aminotransferase (OR, 1.028, 95% CI, 1.003 to 1.053, p = 0.025), coronary angiogram within 2 weeks before surgery (OR, 3.209, 95% CI, 1.307 to 7.878, p = 0.011) and CPB time (OR, 1.012, 95% CI, 1.005 to 1.019, p = 0.001) were independent risk factors for postoperative AKI. Conclusions: High aspartate aminotransferase, coronary angiogram within 2 weeks before surgery and CPB time seem to be associated with an increased incidence of postoperative AKI in patients with redo cardiac surgery

The brackish-water bivalve Waagenoperna from the Lower Jurassic Badaowan Formation of the Junggar Basin and its palaeoenvironmental and palaeogeographic significance

The brackish-water bivalve Waagenoperna Tokuyama, 1959 is reported from the Lower Jurassic Badaowan Formation at four localities, along the southern margin and western margin of the Junggar Basin. Taphonomic features recorded in the field indicate that it occurs in autochthonous or parautochthonous assemblages. The autecology of Waagenoperna therefore yields information on the palaeoenvironment of the area. The restriction of Waagenoperna to marine and brackish-water settings suggests that the sea water once reached these areas during the Sinemurian. This paper discusses the palaeogeographic implications and suggests an ingression of the sea water from the west to the western and southern part of the Junggar Basin. Additionally, the two Waagenoperna species collected from the Haojiagou section in the Junggar Basin are taxonomically documented

Facet-Dependent Gas Adsorption Selectivity on ZnO: A DFT Study

Semiconductor-based gas sensors are of great interest in both industrial and research settings, but poor selectivity has hindered their further development. Current efforts including doping, surface modifications and facet controlling have been proved effective. However, the “methods-selectivity” correlation is ambiguous because of uncontrollable defects and surface states during the experiments. Here, as a case study, using a DFT method, we studied the adsorption features of commonly tested gases—CH2O, H2, C2H5OH, CH3COCH3, and NH3—on facets of (Formula presented.), (Formula presented.) and (Formula presented.). The adsorption energies and charge transfers were calculated, and adsorption selectivity was analyzed. The results show (Formula presented.) has obvious CH2O adsorption selectivity; (Formula presented.) has a slight selectivity to C2H5OH and NH3; and (Formula presented.) has a slight selectivity to H2, which agrees with the experimental results. The mechanism of the selective adsorption features was studied in terms of polarity, geometric matching and electronic structure matching. The results show the adsorption selectivity is attributed to a joint effort of electronic structure matching and geometric matching: the former allows for specific gas/slab interactions, the latter decides the strength of the interactions. As the sensing mechanism is probably dominated by gas–lattice interactions, this work is envisioned to be helpful in designing new sensing material with high selectivity

Optical turn-on sensor based on graphene oxide for selective detection of D-glucosamine

By incorporating the well-known fluorophore 8-aminoquinoline into graphene oxide, we have successfully prepared a turn-on fluorescent sensor capable of specific detection of D-glucosamine with a high selectivity and sensitivity. This methodology provides a new concept for the design and development of highly selective and sensitive turn-on optical sensors for selective detection of aminosaccharides and many other biomolecules.4 page(s

Exposures to ambient air pollutants increase prevalence of sleep disorder in adults: Evidence from Wuhan Chronic Disease Cohort Study (WCDCS)

Background: Sleep disorder contributes to memory dysfunction and chronic diseases. Clear evidence of environment disturbance, such as residential noise, are associated with an increased risk of sleep disorder. However, not enough studies have been conducted on association between residential air pollutants and sleep disorder. We sought to determine whether exposures to residential air pollutants associated with risk of sleep disorder among adults. Methods: Using the dataset of the Wuhan Chronic Disease Cohort Study (WCDCS), we investigated the prevalence of sleep disorder and five sleep disorder symptoms in the study. The data of air pollutants (including PM10, PM2.5, NO2, SO2 and O3) were obtained from 10 air quality monitoring stations in Wuhan. We utilized logistic regression model to evaluate the associations of five types of air pollutants with odds ratio (OR) of sleep disorder and symptoms. The potential moderating effects of socio-demographic factors in the associations were explored using the interaction effects model. Results: Of the study participants, 52.1 % had sleep disorder. Exposures to higher concentrations of air pollutants were associated with increased prevalence of sleep disorder. For example, per interquartile range (IQR) increases in concentrations of PM10, PM2.5 or SO2 corresponded to the increase of sleep disorder increased prevalence at 14.7 % (adjusted odds ratio (aOR) = 1.147, 95 %CI:1.062, 1.240), 8.9 % (aOR = 1.089, 95 %CI: 1.003, 1.182) and 15.8 % (aOR = 1.158, 95 %CI: 1.065, 1.260). For symptoms specific analyses, significant linkages of PM10, PM2.5, SO2 with difficulty in falling asleep, wake up after falling asleep and early awaken were observed. Moderating effects of age and place of residence on the linkages of PM10 with increased prevalence of sleep disorder were identified. Conclusion: Higher level of air pollution exposure could increase the prevalence of sleep disorder. Middle-aged and elderly population, as well as the rural residents are more likely to suffer from sleep disorder