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The 3D-BER enriched denitrifying microorganisms and improved nitrogen removal efficiency.

In this study, SnO2-Sb2O3/GAC particle electrodes were prepared using the dip-calcination method. The particle electrodes were characterized by scanning electron microscopy (SEM), X-ray diffraction (XRD), Brunauer Emmett Teller (BET), thermogravimetric test and linear sweep voltammetry (LSV), which proved that the metal oxide was successfully loaded on the granular activated carbon and exhibited high electrocatalytic activity and thermal stability. The effects of initial pH, electrolytic voltage, electrolyte concentration, initial phenol concentration and particle electrode dosage on the performance of the three-dimensional (3D) electrocatalytic oxidation in phenol degradation were investigated. The results showed that under the optimal conditions, the removal rates of phenol and chemical oxygen demand (COD) were 99.65% and 67.16%, respectively. Finally, it was found that the novel particle electrodes had the ability of stable operation, maintaining high-efficiency operation no less than 15 times, which further highlights their robustness and durability.

Silibinin is a flavonoid extracted from the medicinal plant Silybum marianum (milk thistle), traditionally used to treat liver disease. Recent studies have shown that the antioxidative stress and anti-inflammatory effects of milk thistle are used in the treatment of neurological diseases. Silibinin has antioxidative stress and antiapoptotic effects and reduces cognitive impairment in models of Alzheimer’s disease (AD). However, the underlying mechanism of silibinin related to improvement of cognition remains poorly understood. In this study, we used the model of lateral ventricle injection of formaldehyde to examine the related mechanism of silibinin in improving cognitive impairment disorders. Oral administration of silibinin for three weeks significantly attenuated the cognitive deficits of formaldehyde-induced mice in a Y -maze test and Morris water maze test. Y -maze results show that silibinin increases the rate of spontaneous response alternation in FA-induced mice. Silibinin increases the target quadrant spending time and decreases escape latency in the Morris water maze test. We examined the effect of silibinin on the NRF2 signaling pathway, and silibinin promoted the nuclear transfer of NRF2 and increased the expression of HO-1 but did not significantly increase the protein expression of NRF2 in the hippocampus. Well, silibinin reduces the content of DHE and decreases the levels of apoptosis of mature neuron cells. We investigated the effect of silibinin on the content of formaldehyde degrading enzymes; biochemical analyses revealed that silibinin increased GSH and ALDH2 in formaldehyde-induced mice. In addition, as one of the pathological changes of AD, TAU protein is also hyperphosphorylated in FA model mice. Silibinin inhibits the expression of GSK-3β in model mice, thereby reducing the phosphorylation of TAU proteins ser396 and ser404 mediated by GSK3β. Based on our findings, we verified that the mechanism of silibinin improving cognitive impairment may be antioxidative stress, and silibinin is one of the potentially promising drugs to prevent formaldehyde-induced cognitive impairment.

Fault diagnosis is important to stable operation of power systems, and the machine-learning-based fault-diagnosis models were widely studied because of their strong generalization ability. However, the model structures are generally designed according to the topology of power systems. Once there are changes in topology, the system fault characteristics might change, and the models structure and parameters are often required to be adjusted for applying to the new systems. To avoid frequent adjustment work of fault-diagnosis models when system topology changes, we propose a novel fault-diagnosis model for power systems. First, a new data preprocessing using gradient calculation and similarity assessment is presented, and the gradient similarities among the multichannel electrical signals are converted to the visualized similarity images, which are fed to the neural network for further processing. Second, the spatial pyramid pooling (SPP) and hashing classifier (HC) are used in the convolution neural network. With the aid of the SPP and HC techniques, the structure of the fault-diagnosis model can maintain unchanged even though there are topological changes in the power systems. To validate the effectiveness of the proposed model, several state-of-the-art fault-diagnosis models are used for comparison. The results show that the proposed model with unchanged structure is well performed in accuracy and noise immunity, and friendly to the parameters setting.

Wine-processed Angelica Sinensis is a widely used Chinese medicinal decoction piece in China. However, there are hardly any robust markers indicating the processing procedure of wine-processed Angelica Sinensis, including the amount of rice wine and processing degree. A strategy integrating untargeted metabolomics and chemometric analysis for deducing robust markers was provided and applied to the discrimination of processing procedure. First, 86 compounds were tentatively identified in wine-processed Angelica Sinensis by ultra-high-performance liquid chromatography coupled with quadrupole-time of flight mass spectrometry. Second, 93 potential chemical markers were selected using multivariate analysis, among which nine robust chemical markers were selected by verification with commercial samples. Finally, the effects of processing temperature, time, and amount of rice wine on the three selected chemical markers were investigated through a rapid analytical method. It was demonstrated that both m/z 258.1097 and m/z 238.1189 were positively correlated with the amount of rice wine and processing degree. In summary, this study introduced two candidate processing markers as robust markers for discriminating the processing procedures of wine-processed Angelica sinensis. It also proposed a strategy to provide the reference for the research of other decoction pieces. This article is protected by copyright. All rights reserved.

In this study, a heterotrophic/biofilm-electrode autotrophic denitrification reactor (HAD-BER) was constructed and nano-ɑ-Fe

In this paper, the issue of open-switch faults of voltage source inverter (VSI) is studied, an adaptive fast voltage-based real-time open-switch fault location method is proposed. Firstly, for each switching period, the voltage deviation is calculated by the expected voltage and estimated actual voltage, they are obtained via the mixed logical dynamic model (MLDM) and measured currents, respectively. Then, the voltage deviation of each phase is employed as a fault variable. By this way, avoiding the use of additional hardware, and embedding easily in system. Furthermore, considering the impact of sampling error, parameter error, dead time, delay time, and transition time, the adaptive threshold is designed. Finally, simulations and experiments are executed to verify the effectiveness of the proposed method.

Scenario forecasting methods have been widely studied in recent years to cope with the wind power uncertainty problem. The main difficulty of this problem is to accurately and comprehensively reflect the time-series characteristics and spatial-temporal correlation of wind power generation. In this paper, the marginal distribution model and the dependence structure are combined to describe these complex characteristics. On this basis, a scenario generation method for multiple wind farms is proposed. For the marginal distribution model, the autoregressive integrated moving average-generalized autoregressive conditional heteroskedasticity-t (ARIMA-GARCH-t) model is proposed to capture the time-series characteristics of wind power generation. For the dependence structure, a time-varying regular vine mixed Copula (TRVMC) model is established to capture the spatial-temporal correlation of multiple wind farms. Based on the data from 8 wind farms in Northwest China, sufficient scenarios are generated. The effectiveness of the scenarios is evaluated in 3 aspects. The results show that the generated scenarios have similar fluctuation characteristics, autocorrelation, and crosscorrelation with the actual wind power sequences.

To improve the performance and solve the restrictions of UV/chlorine process (e.g., the narrow pH application range and high disinfection by-products (DBPs) formation), a Fe

Glioblastoma (GBM) is the deadliest primary brain tumor that is highly resistant to current treatments. Polo-like kinase 1 (PLK1) and signal transducer and activator of transcription 3 (STAT3) are highly expressed in gliomas, especially GBM. Previous studies have shown reciprocal activation between PLK1 and STAT3 and that they regulate the same pools of MYC downstream. We have demonstrated that PLK1 and STAT3 levels are elevated in gliomas compared with those in normal brain tissues, and high expression of both PLK1 and STAT3 is associated with poor prognosis in TCGA. Moreover, there was direct or indirect reciprocal regulation between PLK1 and STAT3. Furthermore, we found that PLK1 and STAT3 can regulate the same pools of MYC downstream. Compared to monotherapy, combined treatment of glioma cells with PLK1 and STAT3 inhibitors, BI2536 and Stattic, respectively, showed lower expression of MYC, synergistic induction of cell invasion and apoptosis in vitro, and tumor inhibition in xenografts. PLK1 and STAT3 were able to directly regulate the expression of MYC and induce apoptosis of glioma cells through the regulation of MYC. These findings may help develop a therapeutic strategy for dual inhibition of PLK1 and STAT3 against the tumorigenesis of glioma.

In this paper, the problem of open-switch faults of a voltage source inverter system is investigated. A new fault diagnosis method based on an observer and an adaptive threshold is proposed. This method only needs to utilize the signals existing in a system without the need for extra hardware circuits. First, an observer is designed on a model of a three-phase voltage source inverter system to estimate currents. Then, an adaptive threshold is designed on fault residuals, dynamics of the currents and variations of the system operating state. Moreover, the dead time and switching delay time are also included in the threshold to consider practical applications. Furthermore, to achieve on-line real-time diagnosis and to improve the reliability of the proposed method, the averages of the current residuals in a current fundamental period are defined as diagnostic variables. Finally, the reliability and effectiveness of the proposed method are verified by simulation and experiment results.

In view of the weak generalization of traditional event recognition methods, the limitation of dependence on field knowledge of expert, the longer train time of deep neural network, and the problem of gradient dispersion, the neural network joint model, Conv-RDBiGRU, integrated residual structure was proposed. Firstly, text corpus is preprocessed by word segmentation and stop words processing and uses word embedding to form the matrix of word vectors. Then, local semantic features are extracted through convolution operation, and deep context semantic features are extracted through RDBiGRU. Finally, the learned features are activated by softmax function and the recognition results are output. The novelty of work is that we integrate residual structure into recurrent neural network and combine these methods and field of application. The simulation results show that this method improves precision and recall of Chinese emergency event recognition, and the F-value is better than other methods.

Large-scale wind farm (WF) constitutes dozens or even hundreds of wind turbines (WTs), making it complex and even impractical to develop each individual WT in detail when building WF model. Thus, the equivalent model of WF, with a reasonable reduction of the detailed model, is essential to be developed. In this paper, we propose a multi-view transfer clustering and stack sparse auto encoder (SSAE) based WF equivalent method, which can be used in the low voltage ride through (LVRT) analysis of WF. First, to obtain distinguishable deep-level and multi-view representation of wind turbine (WT), stack sparse auto encoder (SSAE) is used to extract features from the time series of several WT physical quantities, and these features are used as the clustering indicator (CI). Then, a multi-view transfer FCM (MVT-FCM) clustering algorithm, which combines transfer learning with multi-view FCM (MV-FCM), is put forward for WTs clustering. Two transfer rules are designed in this algorithm, and the clustering center and membership degree in the source domain are transferred to guide the clustering process of target domain samples. Finally, the calculation method of equivalent parameters is presented. To verify the effectiveness of the proposed method, a modified actual system in East Inner Mongolia of China is utilized for case study, and the performance of the proposed model is compared with several state-of-the-art models. Simulation results show that the equivalent errors of the proposed model decrease at least 3% when comparing with other models. Also, the error fluctuations are within 6% under different simulation conditions, which illustrates the well-performed robustness of the proposed model.

Large-scale wind farms (WFs) generally consist of hundreds of wind turbines (WTs), and the WFs simulation model construction would be complex and even impossible if we develop each individual WT in detail. Therefore, the WFs equivalent simulation model with required accuracy is essential to be developed to explore the WFs operation characteristics. This article proposes an equivalent method for large-scale WFs using incremental clustering and key parameters optimization. Firstly, to acquire more comprehensive and distinguishable representations of WTs operation characteristics, the time series of WT active power, reactive power, voltage and current are selected as the multi-view clustering indicator (CI). Then, considering the computer memory pressures encountered by traditional clustering algorithms in dealing with large-scale WFs, a novel clustering algorithm namely multi-view incremental transfer fuzzy C-means (MVIT-FCM) is proposed, and this algorithm can process the WTs clustering problems without requiring to consider the scale of the WFs. Finally, to further increase the equivalent accuracy of the WFs equivalent simulation model, key parameters in the equivalent model are found using Sobol' criterion and then optimized using the designed Q-learning based non-dominated sorting genetic algorithm II (NSGA-II). To verify the effectiveness of the proposed method, the modified WFs system in China is utilized for case study, and the performance of the proposed model is compared with several state-of-the-art models. Simulation results show that the equivalent accuracy of the proposed model is higher when comparing with other models. Also, the proposed model has the advantage of processing the WFs equivalent problems with any scales.

The ecological environment is important for the natural disaster prevention of human society. The monitoring of ecological environment quality has far-reaching practical significance for the functional construction of ecosystem services and policy coordination. Based on Landsat 8 operational land image (OLI)/thermal infrared sensor (TIRS) remote sensing image data, this study selected the normalized vegetation (NDVI), tasseled cap transformation humidity (WI), bare soil (SI), construction index (NDSI), and land surface temperature (LST) indexes from the aspects of greenness, humidity, dryness, and heat. Using spatial principal component analysis (SPCA) and the remote sensing ecological index (RSEI) analyzed the spatial differentiation characteristics and influencing factors of the original remote sensing ecological index (RSEI0). The results showed that: (1) the overall RSEI average value of the Qinling-Daba Mountains in 2017 was 0.61, and the ecological environment quality was at a “Good” level. Greenness contributed the most to the comprehensive index of the area, and vegetation distribution had a significant impact on the ecological environment quality of the study area. Heat is a secondary impact, and it has an inhibitory effect on habitat quality; (2) the overall distribution of regional ecological environment quality was quite different, with the ecological environment quality level showing a decreasing trend from low to high altitude; RSEI0 spatial heterogeneity at the optimal scale of 2 km was the largest, and the nugget effect was 88% which indicated a high degree of spatial variability, mainly affected by structural factors; (3) Slope, relief amplitude, elevation, the proportion of high-vegetation area, proportion of construction land area, and average population density significantly impact the spatial differentiation of RSEI0. The explanatory powers of slope and relief amplitude were 56.1% and 65.3%, respectively, which were the main factors affecting the spatial differentiation of the ecological environment quality in high undulation. The results can provide important scientific support for ecological environment construction and ecological restoration in the study area.

The exploration of information for aircraft wake vortex enables us to obtain new knowledge of wake turbulence separation standards. Traditional manual methods cannot work satisfactorily for the identification of great number of wake vortex data with high accuracy. Fortunately, the LiDAR intensity data can be explained by integrating LiDAR products with the strategies of computer vision. To overcome the limitation of traditional manual methods, this paper is aimed at developing an automatic method to identify a given set of wake vortices from various aircrafts. The main innovation works are outlined as follows. (1) From the wake vortex data that consists of various aircrafts measured by Wind3D 6000 LiDAR, a grayscale dataset of wake flow is constructed to boost the deep learning model for identifying aircraft wake vortex. (2) Following this, we propose a new method for the identification of aircraft wake vortex by modifying the VGG16 network, providing binary classifications of uncertain behavior patterns for wake vortices. To evaluate the proposed identification model, performance evaluation was conducted on our dataset, where experimental results revealed the values of 0.984, 0.951, 0.959, and 0.955 in terms of accuracy, precision, recall, and F1-score, respectively.

To improve the removal efficiency of low-concentration organic pollutants and enhance the oxidation efficiency of electro-Fenton (EF) process, a floating sandwich-type EF system (N/(A)/D-EF) without aeration was constructed for the first time. This EF system electro-synthesized H

Deformation and coalescence behaviors of water droplets in viscous fluid under a direct current electric field are investigated with experiments and numerical simulations. An electro-hydrodynamic model is developed based on fluid dynamics equations and electrostatic equations. A coupled level set and volume of fluid method is proposed to capture the dynamic interface. The numerical results are validated to be in good accordance with classic results in the literature. The effects of electric capillary number Ca and viscosity ratio λ on droplet deformation and coalescence are systematically analyzed. Two different droplet response modes are obtained, namely overdamping response and underdamping response. The conversion of response modes is discussed with the (Ca, λ) phase diagram. Moreover, the distributions of electric field and flow field before and after the droplets contact are compared and analyzed. Numerical results show the time evolution of liquid bridge is dominated by the interfacial tension and viscosity of continuous phase. The electric stress is extremely weak at the liquid bridge and has a slight influence on evolution of liquid bridge. The correlation formula between dimensionless liquid bridge radius and conical angle during the growth of liquid bridge is regressed. These results are helpful for the optimization of electro-coalescence process.

This paper focuses on the application range and optimal parameters for separating water-in-oil (W/O) emulsions using ultrasonic standing waves (USWs). Firstly, the effect of droplet bandings on the demulsification process is theoretically analyzed. The formation of droplet bandings coincides with droplet sedimentation when horizontal irradiation is applied, which improves the separation efficiency. On this basis, the effects of acoustic parameters and oil-water properties on demulsification are analyzed in detail. It is shown that ultrasonic treatment is an efficient demulsification method, and that maximum demulsification efficiency can be achieved by optimizing the acoustic intensity and treatment time. Excessive acoustic intensity causes acoustic cavitation in low-frequency USWs and acoustic streaming in high-frequency USWs, resulting in a low separation efficiency. Acoustic cavitation also causes secondary emulsification, dispersing tiny oil droplets into separated water. In addition, ultrasonic separation is significantly affected by oil-water properties. The demulsification efficiency of W/O emulsions decreases with increasing oil viscosity and decreasing oil-water interfacial tension. Ultrasonic treatment has significant advantages for separation of emulsions with low water content and interfacial tension. This study presents an in-depth analysis of ultrasonic separation.

Coalescence of conducting droplets dispersed in an immiscible medium can be facilitated by an electric field. However, droplets recoil promptly after contact in sufficiently high electric fields if the cone angle between droplets exceeds a critical value. To elucidate the critical condition for droplet coalescence, the behavior of two suspended droplets after contact with a direct current electric field is studied. It is shown that the critical angle is determined not only by the droplet geometry but also conductivity, surfactant concentration, and size. As the droplet conductivity increases, more identical ions accumulate on the adjacent interfaces of two droplets due to the faster ionic migration, which results in Coulombic repulsion between droplets and a reduced critical angle. For surfactant-laden droplets, film drainage induces a surfactant concentration gradient on the leading edges of droplets, and then Marangoni stress is formed to reduce the critical angle. In the case of large droplets, the bri...

To enhance the detection precision of samples with scattering characteristics by near infrared spectroscopy (NIRS), this study developed a heteromorphic sample pool and established the related 2D light intensity acquisition system, which can simultaneously acquire multi-path exit light adsorption and scattering information of the samples under test. The Intralipid-20% phantom solutions in 34 samples with different concentrations were detected, while one-dimensional (1D) exit light intensity distributions and two-dimensional (2D) exit light intensity distributions on the surface of the samples were analyzed and modeled using partial least squares. In contrast with the prediction results based on the modeling method of 1D exit light intensity distribution, the modeling method of 2D exit light intensity distribution exhibits more favorable results; specifically, correlation coefficient enhanced by 2.48%, while root mean square error reduced by 6.89%. The experimental results demonstrate that using heteromorphic sample pool can effectively achieve NIRS-based detection precision and speed of chemical components in the solutions with scattering characteristics, which can provide important references for high-throughput and high-precision detection of turbid media in analytical chemistry.

Glioma is a primary cerebral neoplasm that originates from glial tissue and spreads to the central nervous system. Long noncoding RNAs are known to play a role in glioma cells by regulating cell proliferation, migration and invasion. The aim of the present study was to investigate the mechanism by which long intergenic non‑protein coding RNA (LINC) 01138 affects glycolysis and proliferation in glioma cells via the microRNA (miR)‑375/specificity protein 1 (SP1) axis. LINC01138 expression was assessed in glioma tissues and cells using reverse transcription‑quantitative PCR and the association between LINC01138 and patient clinicopathological features was analyzed. Glucose uptake, lactic acid secretion, cell proliferation, and glycolysis‑related enzyme levels were detected following LINC01138 silencing using CCK‑8, EDU assay and western blot analysis. miR‑375 and SP1 expression levels were also assessed, and the distribution of LINC01138 in the nucleus and cytoplasm was investigated using subcellular fractionation localization. Furthermore, the binding relationships between LINC01138 and miR‑375, and between miR‑375 and SP1 were assessed via dual‑luciferase experiment, RIP and RNA pull‑down assays. Finally, xenograft transplantation models were used to verify the in vitro results. LINC01138 was highly expressed in glioma, which was independent of patient sex or age but was significantly related to tumor diameter, the World Health Organization tumor grade and lymph node metastasis. Silencing LINC01138 significantly reduced glioma glycolysis and cell proliferation. Moreover, LINC01138 acted as a competing endogenous RNA to sponge miR‑375 and promote SP1 expression. miR‑375 inhibition significantly reversed the effect of LINC01138 silencing. In addition, silencing LINC01138 significantly reduced tumor growth in vivo. The present study demonstrated that silencing LINC01138 inhibited aerobic glycolysis and thus reduced glioma cell proliferation, potentially by modulating the miR‑375/SP1 axis.

Objective To verify a behavioral device for the detection of learning, memory, and affective disorders in post-weaning socially isolated rats. Methods We tested the behavioral changes in post-weaning socially isolated rats using a multi-function closed maze, a self-developed behavioral device, against the classical mood disorder detection method, the IntelliCage system and Morris water maze. Results In the multifunctional closed maze experiment, the spatial learning and memory ability of post-weaning socially isolated rats decreased, which was consistent with the results of the water maze and IntelliCage system. Furthermore, the behavioral changes in the post-weaning socially isolated rats in the multi-function closed maze test were the same as those of the forced swimming and open field tests, indicating that the rats had depression- and anxiety-like behaviors. Conclusion A multi-function closed maze can detect emotional changes, spatial learning ability, and memory ability.

With edge devices playing an increasingly important role in our daily lives, edge computing and human-computer interaction, especially facial expression recognition, become research central issues in academia and industry. However, surprisingly, utilizing edge computing and neural networks for facial expression recognition has been neglected for many years, very few research can be found. To be focusing on such topics. In this paper, we improve Visual Geometry Group 19 with the idea of residual learning. To be specific, for each block of Visual Geometry Group 19, we add its input to its output. The result of the addition will be the input of the next block. Then, we minimize the size of our model by pruning and post-training quantization to achieve a higher efficiency and maintain the model's accuracy at the same time when deploying it on edge devices. The experiment result shows that our model has a 98.99% accuracy on the CK+ dataset. Besides, when deploying on edge devices, its inference time is less than many other popular neural networks that are designed for deploying on edge-devices.