A good prediction of landslide displacement is an essential component for implementing an early warning system. In the Three Gorges Reservoir Area (TGRA), many landslides deform distinctly and in ...steps from April to September each year under the influence of seasonal rainfall and periodic fluctuation in reservoir water level. The sliding becomes more uniform again from October to April. This landslide deformation pattern leads to accumulated displacement versus time showing a step-wise curve. Most of the existing predictive models express static relationships only. However, the evolution of a landslide is a complex nonlinear dynamic process. This paper proposes a dynamic model to predict landslide displacement, based on time series analysis and long short-term memory (LSTM) neural network. The accumulated displacement was decomposed into a trend term and a periodic term in the time series analysis. A cubic polynomial function was selected to predict the trend displacement. By analyzing the relationships between landslide deformation, rainfall, and reservoir water level, a LSTM model was used to predict the periodic displacement. The LSTM approach was found to properly model the dynamic characteristics of landslides than static models, and make full use of the historical information. The performance of the model was validated with the observations of two step-wise landslides in the TGRA, the Baishuihe landslide and Bazimen landslide. The application of the model to those two landslides demonstrates that the LSTM model provides a good representation of the measured displacements and gives a more reliable prediction of landslide displacement than the static support vector machine (SVM) model. It is concluded that the proposed model can be used to effectively predict the displacement of step-wise landslides in the TGRA.
While modern industry has contributed to the prosperity of an increasingly urbanized society, it has also led to serious pollution problems, with discharged wastewater and exhaust gases causing ...significant environmental harm. Titanium dioxide (TiO2), which is an excellent photocatalyst, has received extensive attention because it is inexpensive and able to photocatalytically degrade pollutants in an environmentally friendly manner. TiO2 has many advantages, including high chemical stability, low toxicity, low operating costs, and environmental friendliness. TiO2 is an N-order semiconductor material with a bandgap of 3.2 eV. Only when the wavelength of ultraviolet light is less than or equal to 387.5 nm, the valence band electrons can obtain the energy of the photon and pass through the conduction band to form photoelectrons, meanwhile the valence band forms a photogenerated hole. And light in other wavelength regions does not excite this photogenerated electrons. The most common methods used to improve the photocatalytic efficiency of TiO2 involve increasing its photoresponse range and reducing photogenerated-carrier coupling. The morphology, size, and structure of a heterojunction can be altered through element doping, leading to improved photocatalytic efficiency. Mainstream methods for preparing TiO2 are reviewed in this paper, with several excellent preparation schemes for improving the photocatalytic efficiency of TiO2 introduced. TiO2 is mainly prepared using sol-gel, solvothermal, hydrothermal, anodic oxidation, microwave-assisted, CVD and PVD methods, and TiO2 nanoparticles with excellent photocatalytic properties can also be prepared. Ti-containing materials are widely used to purify harmful gases, as well as contaminants from building materials, coatings, and daily necessities. Therefore, the preparation and applications of titanium materials have become globally popular research topics.
The spherical Au nanoparticles/3D flower-like graphene was prepared by a facile and low-cost electrochemical method and used for sensitive determination of nitrite.
Display omitted
In this paper, the ...spherical Au nanoparticles and 3D flower-like structure graphene were successively deposited on glassy carbon electrode (GCE) (Au/f-GE/GCE) via a facile and two-step electrodeposition method for the detection of nitrite ions (NaNO2). The morphology and composition elements were confirmed by scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDX) and X-ray diffraction measurements (XRD). Cyclic voltammetry (CV) and differential pulse voltammetry (DPV) were used to evaluate the electrochemical behaviors of NaNO2 on the as-prepared electrode. Compared to f-GE/GCE and Au/GCE, Au/f-GE/GCE showed a sharp and obvious oxidation peak at 0.78V. The oxidation peak current of NaNO2 was linearly proportional to its concentration in the range from 0.125 to 20375.98μM, with a detection limit of 0.01μM (at S/N=3). Furthermore, the experiment results also showed that the as-prepared electrode exhibited excellent reproducibility and long-term stability, as well as good recovery when applied to the determination of NaNO2 in pickled pork samples.
An electrochemical sensor was prepared using Au nanoparticles and reduced graphene successfully decorated on the glassy carbon electrode (Au/RGO/GCE) through an electrochemical method which was ...applied to detect Sunset Yellow (SY). The as-prepared electrode was characterized by scanning electron microscopy (SEM), transmission electron microscopy (TEM), atomic force microscopy (AFM) and electrochemical measurements. The results of cyclic voltammetry (CV) proved that Au/RGO/GCE had the highest catalytic activity for the oxidation of SY as compared with GCE, Au/GCE, and RGO/GCE. Differential pulse voltammetry (DPV) showed that the linear calibration curves for SY on Au/RGO/GCE in the range of 0.002 μM–109.14 μM, and the detection limit was estimated to be 2 nM (S/N = 3). These results suggested that the obtained Au/RGO/GCE was applied to detect SY with high sensitivity, low detection limit and good stability, which provided a promising future for the development of portable sensor in food additives.
Display omitted
•An Au/RGO composite was fabricated by electrochemical deposition method.•The oxidation current of SY on the composition is up to 10 μA.•The detection range of SY is 0.002–109.14 μM with a detection limit of 2 nM.
The reliable evaluation of rock brittleness is essential in engineering geology for various applications, such as water conservancy, hydropower, transportation, energy exploration, and underground ...engineering development. This study proposes a new method for evaluating rock brittleness using a digital drilling approach. A cutting model was established to describe the relationship between the energy characteristics and mechanical parameters of the rock during the drilling process, accounting for the effects of friction and drilling fluid. Furthermore, a drilling-based index, the brittleness evaluation index (BEI), was developed to evaluate rock brittleness based on energy dissipation. We conducted drilling tests to investigate energy evolution, including cutting energy, friction energy, and liquid energy. The results indicated that cutting energy has a linear correlation with the strength parameters of rock, with a significant correlation coefficient. With increasing drilling depth, cutting energy and liquid energy exhibit a similar increasing trend, which initially remains constant and then increases linearly after a critical depth is reached. Friction energy rapidly increases with drilling depth at the beginning, and the growth rate decreases after reaching the critical point. Compared with the laboratory-determined brittleness index, our proposed method provides a reliable evaluation of rock brittleness. In summary, our study offers a practical approach to evaluating rock brittleness that could benefit various engineering applications.
•A cutting energy models was established to describe the energy characteristics of rock cutting.•The relationship between the mechanical properties and the cutting energy index was investigated.•A new evaluation method of rock brittleness was proposed based on the energy dissipation in borehole.
It is crucial to predict landslide displacement accurately for establishing a reliable early warning system. Such a requirement is more urgent for landslides in the reservoir area. The main reason is ...that an inaccurate prediction can lead to riverine disasters and secondary surge disasters. Machine learning (ML) methods have been developed and commonly applied in landslide displacement prediction because of their powerful nonlinear processing ability. Recently, deep ML methods have become popular, as they can deal with more complicated problems than conventional ML methods. However, it is usually not easy to obtain a well-trained deep ML model, as many hyperparameters need to be trained. In this paper, a deep ML method-the gated recurrent unit (GRU)-with the advantages of a powerful prediction ability and fewer hyperparameters, was applied to forecast landslide displacement in the dam reservoir. The accumulated displacement was firstly decomposed into a trend term, a periodic term, and a stochastic term by complementary ensemble empirical mode decomposition (CEEMD). A univariate GRU model and a multivariable GRU model were employed to forecast trend and stochastic displacements, respectively. A multivariable GRU model was applied to predict periodic displacement, and another two popular ML methods-long short-term memory neural networks (LSTM) and random forest (RF)-were used for comparison. Precipitation, reservoir level, and previous displacement were considered to be candidate-triggering factors for inputs of the models. The Baijiabao landslide, located in the Three Gorges Reservoir Area (TGRA), was taken as a case study to test the prediction ability of the model. The results demonstrated that the GRU algorithm provided the most encouraging results. Such a satisfactory prediction accuracy of the GRU algorithm depends on its ability to fully use the historical information while having fewer hyperparameters to train. It is concluded that the proposed model can be a valuable tool for predicting the displacements of landslides in the TGRA and other dam reservoirs.
BackgroundAnxiety symptoms are common in mental diseases and a variety of physical disorders, especially in disorders related to stress. More and more basic studies have indicated that gut microbiota ...can regulate brain function through the gut-brain axis, and dysbiosis of intestinal microbiota was related to anxiety. However, there is no specific evidence to support treatment of anxiety by regulating intestinal microbiota.AimsTo find evidence supporting improvement of anxiety symptoms by regulation of intestinal microbiota.MethodsThis systematic review of randomised controlled trials was searched based on the following databases: PubMed, EMBASE, the Cochrane Library, OVID, Web of Knowledge, China National Knowledge Infrastructure (CNKI), Wanfang Data, VIP databases and SinoMed. The retrieval time dated back to 25 July 2018. Then we screened research literatures based on established inclusion and exclusion criteria. Quality evaluation for each included study was done using the Cochrane risk of bias and the Jadad scale.ResultsA total of 3334 articles were retrieved and 21 studies were included which contained 1503 subjects. In the 21 studies, 14 chose probiotics as interventions to regulate intestinal microbiota and six chose non-probiotic ways such as adjusting daily diets. Probiotic supplements in seven studies contained only one kind of probiotic, two studies used a product that contained two kinds of probiotics and the supplements used in the other five studies included at least three kinds of probiotics. In the studies that used treatment as usual plus interventions regulating intestinal flora (IRIF) as interventions (five studies), only non-probiotic ways were effective (two studies), which means 40% of studies were effective; in the studies that used IRIF alone (16 studies, 11 studies used probiotic ways and 5 studies used non-probiotic ways), 56% of studies could improve anxiety symptoms, and 80% of studies that conducted the non-probiotic interventions were effective, while 45% of studies that used probiotic supplementations had positive effects on anxiety symptoms. Overall, 11 studies showed a positive effect on anxiety symptoms by regulating intestinal microbiota, which indicated 52% of the 21 studies were effective, and there were five studies that used probiotic supplements as interventions and six used non-probiotic interventions. In addition, it should be noted that six of seven studies showed that regulation of intestinal microbiota could treat anxiety symptoms, the rate of efficacy was 86%.ConclusionsWe find that more than half of the studies included showed it was positive to treat anxiety symptoms by regulation of intestinal microbiota. There are two kinds of interventions (probiotic and non-probiotic interventions) to regulate intestinal microbiota, and it should be highlighted that the non-probiotic interventions were more effective than the probiotic interventions. More studies are needed to clarify this conclusion since we still cannot run meta-analysis so far.
•A novel 2-methylimidazolium-functionalized silica stationary phase was prepared.•The column exhibited good selectivity in separating polar compounds.•The column showed excellent mixed-mode ...chromatographic performance.
In this paper, a novel 2-methylimidazolium-functionalized silica stationary phase was prepared and further used for hydrophilic interaction and anion-exchange mixed-mode chromatography. The stationary phase was characterized by elemental analysis and Fourier transform infrared spectrometry. The chromatographic properties of this stationary phase were investigated by hydrophilic chromatography for the separation of nucleosides, nucleobases, water soluble vitamins, sulfonamides and saccharides, and ion chromatography for the separation of inorganic anions. The effect of acetonitrile content, salt concentration and pH values of the mobile phase on the retention of the stationary phases was also investigated. Compared with 1-methylimidazolium-functionalized silica stationary phase, this new stationary phase demonstrated similar or better separation selectivity. This new column demonstrated good performance and separation selectivity even better than a commercial hydrophilic column. Besides, 2-methylimidazolium-functionalized silica is possible to be modified again and used as a precursor to derivate some new stationary phases from the 3-position nitrogen.
Landslide displacement system is generally characterized by non-stationary and nonlinear characteristics. Traditionally, many artificial neural network (ANN) models have been proposed to forecast ...landslide displacement. However, the underlying non-stationary characteristics in the landslide displacement are not captured, and the input–output variables of the ANN models are not selected nonlinearly. To overcome these drawbacks, this paper proposes the chaos theory-based discrete wavelet transform (DWT)–extreme learning machine (ELM) model to predict landslide displacement. The DWT method is adopted to decompose the landslide displacement into several low- and high-frequency components to address the non-stationary characteristics. And chaos theory is used to determine the input–output variables of the ELM model. The cumulative displacement time series of the Baishuihe and Baijiabao landslides in the Three Gorges Reservoir Area, China, are used as data sets. The results show that the chaotic DWT-ELM model accurately predicts landslide displacement. The chaotic DWT–support vector machine (SVM), chaotic DWT–back-propagation neural network (BPNN) and single chaotic ELM models are used for comparisons. The comparison results show that the chaotic DWT-ELM model achieves higher prediction accuracy than do the chaotic DWT-SVM, chaotic DWT-BPNN and the single chaotic ELM models.
•Microstructures during fretting corrosion in high temperature water were studied.•Formation of TTS was attributed to dynamic recrystallization of deformed structure.•Outmost Fe-rich spinel oxide was ...due to the solid-state growth and precipitation.•Fretting resulted in the mixing tribolayer of crushed Fe-rich and Cr-rich oxides.•Internal oxidation occurred in TTS due to short-circuits of its ultra-fine grains.
Microstructural characteristics of Alloy 690TT subjected to fretting corrosion in high temperature water were investigated. The results indicated that there were oxide, tribologically transformed structure (TTS) and deformation layers in worn subsurface. Oxide layer had a multilayered structure: the outer part consisted of Fe-rich spinel oxides and tribolayer which was composed of Fe3O4 and Cr2O3; the inner part contained local amorphous Cr-rich oxides. TTS layer consisted of equiaxed nanograins which formed due to dynamic recrystallization of deformed structure. Internal oxidation occurred within TTS layer because the nanograins provided the short-circuits for oxygen diffusion, resulting in the ellipsoid and stripe-like Cr2O3.
PDF
