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String stability is essential for platoons. In this article, sufficient conditions for the string exponential stability of a class of longitudinal following platoon systems with time delays are investigated. Based on the constant spacing policy, a sliding mode control strategy is proposed for the look-ahead platoon with bounded unknown parameters and time delays to ensure the string exponential stability of the platoon. Numerical simulation findings including position, speed, acceleration errors, and inter-space between adjacent vehicles can verify the efficiency and feasibility of the proposed control systems. In simulations, the platoon with certain parameters and no time delays should be compared with the case with bounded unknown parameters and time delays.

In the removal of nitric oxide (NO) by sodium chlorite (NaClO2), the NaClO2 concentration is usually increased, and an alkaline absorbent is added to improve the NO removal efficiency. However, this increases the cost of denitrification. This study is the first to use hydrodynamic cavitation (HC) combined with NaClO2 for wet denitrification. Under optimal experimental conditions, when 3.0 L of NaClO2 with a concentration of 1.00 mmol/L was used to treat NO (concentration: 1000 ppmv and flow rate: 1.0 L/min), 100% of nitrogen oxides (NOx) could be removed in 8.22 min. Furthermore, the NO removal efficiency remained at 100% over the next 6.92 min. Furthermore, the formation of ClO2 by NaClO2 is affected by pH. The initial NOx removal efficiency was 84.8–54.8% for initial pH = 4.00–7.00. The initial NOx removal efficiency increases as the initial pH decreases. When the initial pH was 3.50, the initial NOx removal efficiency reached 100% under the synergistic effect of HC. Therefore, this method enhances the oxidation capacity of NaClO2 through HC, realizes high-efficiency denitrification with low NaClO2 concentration (1.00 mmol/L), and has better practicability for the treatment of NOx from ships.

Hole doping and biaxial tensile strain on T-graphene can increase its superconducting Tc to a value higher than those of the pristine T-graphene and many other 2D carbon-based superconductors.

A physics-based deep learning (DL) method termed Phynet is proposed for modeling the nonlinear pulse propagation in optical fibers totally independent of the ground truth. The presented Phynet is a combination of a handcrafted neural network and the nonlinear Schrödinger physics model. In particular, Phynet is optimized through physics loss generated by the interaction between the network and the physical model rather than the supervised loss. The inverse pulse propagation problem is leveraged to exemplify the performance of Phynet when in comparison to the typical DL method under the same structure and datasets. The results demonstrate that Phynet is able to precisely restore the initial pulse profiles with varied initial widths and powers, while revealing a similar prediction accuracy compared with the typical DL method. The proposed Phynet method can be expected to break the severe bottleneck of the traditional DL method in terms of relying on abundant labeled data during the training phase, which thus brings new insight for modeling and predicting the nonlinear dynamics of the fibers.

Brillouin optical time-domain analyzer (BOTDA) is used to monitor the temperature and strain along a fiber. So far, neural network and machine learning methods have been successfully applied for temperature extraction. But for different frequency scanning steps, different networks should be designed and trained. Here, a BOTDA assisted by kernel extreme learning machine (K-ELM) with high generalization is proposed and experimentally demonstrated. By utilizing K-ELM, the raw Brillouin gain spectra measured from BOTDA system are classified into different temperature classes. The performance of K-ELM is investigated both in simulation and experiment under different cases of signal-to-noise ratios, pump pulse widths, and frequency scanning steps. Compared with curve fitting methods, the K-ELM algorithm has better measurement accuracy of 0.3 °C and it realizes great improvement of the processing speed over 120 times. The ultrafast processing speed, high accuracy and generality make K-ELM become a highly competitive candidate for the high-speed BOTDA sensing system.

A modulation format identification (MFI) method based on the nonlinear power transformation via logistics regression is adopted for coherent optical receives systems. The amplitude variance, fourth power transformation and fast Fourier transform of input signals are utilized for special features extraction in our work. Five typical optical modulation formats (i.e.,16/32/64QAM and Q/8PSK) with the transmission rate of 28 GBaud are numerically simulated to demonstrate the feasibility. The simulation results show that our method has great performance even under low optical signal noise ratio (OSNR). Compared with the MFI algorithm based on Stokes space and asynchronous delay tapped sampling, our MFI algorithm requires less time to achieve similar performance of optical receive systems. Especially, this method exhibits tolerances to the laser linewidth and nonlinearity.

The proliferation and multi-directional differentiation potential of mesenchymal stem cells (MSCs) enabled its wide use in the development of new therapies for bone and cartilage repair. Although preliminary work has been done to verify the gene expression profile of MSCs osteogenic and chondrogenic differentiation, it is still unclear what key factors initiate the differentiation of MSCs, resulting in its limited application in bone and cartilage tissue engineering. The epigenetic mechanism mediated by histone demethylases (lysine [K]-specific histone demethylases, KDMs) is the key link in regulating MSCs lineage differentiation. The lysine-specific histone demethylase (LSD) family containing Tower domain and the histone demethylase family containing Jumonji C (JmjC) domain regulate the expression of various osteogenic-related genes, including Runt-related transcription factor 2 (

The initial state of a nonlinear optical fiber system plays a vital role in the ultrafast pulse evolution dynamic. In this work, a data-driven compressed convolutional neural network, named inverse network, is proposed to predict initial pulse distribution through a series of discrete power profiles at different propagation distances. The inverse network is trained and tested based on two typical nonlinear dynamics: (1) the pulse evolution in a fiber optical parametric amplifier system and (2) soliton pair evolution in high-nonlinear fibers. Great prediction accuracy is reached when the epoch grows to 5000 in both cases, with the normalized root mean square errors below 0.01 on the entire testing set. Meanwhile, the lightweight network is highly effective. In this work, it takes approximately 30 seconds for 5,000 epochs training with a dataset size of 900. The inverse network is further tested and analyzed on the dataset with different signal-to-noise ratios and input sizes. The results show fair stability at the deviation on the testing set. The proposed inverse network demonstrates a promising approach to optimizing the initial pulse of fiber optics systems.

The saturated gain performance of the two-pump fiber optical parametric amplifier (2-P FOPA) in the presence of random dispersion fluctuation as well as fourth-order dispersion coefficient are investigated. The gain spectra of the 2-P FOPA is simulated by solving the coupled equations numerically, considering the dispersion fluctuations along the fiber as a stochastic process with a given standard deviation and correlation length. The gain performance of 2-P FOPA is analysed with the variation of correlation length, standard deviation and fourth-order dispersion coefficient, respectively. It shows that, the gain saturation curves of 2-P FOPA exhibit obvious differences in the presence of dispersion fluctuations and fourth-order dispersion coefficients. The peak gain reduces with the increase of the standard deviation and the reduction is higher in case of shorter correlation lengths, also the variations of 2-P FOPA saturation powers are affected by the standard deviation and correlation length significantly. In addition, the performance of the 2-P FOPA varies greatly while considering the fourth-order dispersion coefficient, where the peak gain remains the same, but the 3 dB bandwidth is wider with considering the fourth-order dispersion coefficient. The numerical analysis is meaningful for the research of 2-P FOPA saturated gain performance, which having attracted great research interests in the optical fiber communication systems.

Vaccine is a biological agent for preventing and curing disease, which inducts both innate and adaptive immune mechanism to be effective. Facing potentially unknown pathogens, the current vaccine technologies have problems such as (1) prolonged development time, (2) limited production capacity, and (3) inability to guarantee biosafety. To address these issues, DNA nanostructures as carrier platforms, featured with strong immunogenicity, excellent biosecurity, and promising programmability, have attracted much attention in the development of vaccines nowadays. These DNA nanostructures, including DNA tetrahedra, DNA hydrogel, DNA nanotube, DNA dendrimer, and DNA nanoflower, could not only directly induce macrophages to secrete immune factors by modifying sizes and structures but also indirectly stimulate TLR9 immune response as carriers of CpG ODNs. In addition, DNA sequences can be combined with different antigen molecules to form an antigen presentation system to participate in the body’s adaptive immune response. This review summarizes the role of various DNA nanomaterials in the field of immunity and aims to provide new ideas for enhancing the body’s immune response against diseases and treating various immune system diseases.

Plane wave imaging (PWI) is attracting more attention in industrial nondestructive testing and evaluation (NDT&E). To further improve imaging quality and reduce reconstruction time in ultrasonic imaging with a limited active aperture, an optimized PWI algorithm was proposed for rapid ultrasonic inspection, with the comparison of the total focusing method (TFM). The effective area of plane waves and the space weighting factor were defined in order to balance the amplitude of the imaging area. Experiments were carried out to contrast the image quality, with great agreement to the simulation results. Compared with TFM imaging, the space-optimized PWI algorithm demonstrated a wider dynamic detection range and a higher defects amplitude, where the maximum defect amplitude attenuation declined by 6.7 dB and average attenuation on 12 defects decreased by 3.1 dB. In addition, the effects of plane wave numbers on attenuation and reconstruction time were focused on, achieving more than 10 times reduction of reconstruction times over TFM.

The state-of-the-art approaches of stereo matching rely on trained CNNs. However, there are not proposed effective methods in the current end-to-end deep learning stereo matching network to solve the problem that a pair of rectified stereo images are matched in the occlusions and background areas, which leading to erroneous reconstruction of 3D point clouds in these areas. In this paper, a softmax module to the back-end of the stereo matching network is added to calculate the confidence of the disparity map. Moreover, the occlusion part and the background part of the disparity map is removed according to the characteristic that the correct area and the occlusion area have a morphological difference on the waveform of the confidence response. Finally, a train running parts dataset is generated to prove our method. This work realizes the rapid three-dimensional reconstruction and measurement of the bottom part of the motor car under the single image acquisition, and providing a feasible solution for removing the occlusion and background parts in stereo matching.

Gangue, a typical waste associated with the coal mining process in northern China, was characterized before and after thermal treatment by XRF, XRD, TGA-DSC, and SEM. The pozzolanic reactivity of calcined gangue with different calcined temperature was tested by standard mortar strength and R3 calorimetry test. The performance of blended cement was evaluated by mortar strength and flow. The XRD and TGA-DSC analyses showed that the main minerals that present in the raw gangue were kaolinite, and a small amount of carbon-based compounds and metakaolin were almost the only phase in calcined gangue. After thermal treatment and depending on the temperature, it showed very high reactivity especially combined with limestone. The strength property of limestone calcined gangue cement with 45% replacement of clinker was higher than reference cement (CEM I 42.5 N) on 7 and 28 days. Furthermore, it is found that the calcined gangue has less influence on the workability of LC3 according to particle morphology compared with a typical calcined clay, based on the results of SEM observation.

Hydraulic driving processes are widely used in industry. The complexity and strong nonlinearity of these processes often make their dynamic behavior difficult to estimate. In this paper, a nonlinear dynamic analysis method is proposed to overcome this difficulty. First, a nonlinear model of complex hydraulic driving processes is derived. A solution method is then developed in order to obtain an analytical solution. Using this analytical solution, the conditions for stable or unstable motion are derived and the nonlinear dynamic behavior is estimated. The analytical results are then verified using both simulations and experiments conducted on actual hydraulic processes.

Nanohydroxyapatite (nHAp) has gained considerable concerns due to its vast potential in biomedical applications such as drug delivery, tissue engineering and bone repair. However, the preparation of HAp nanostructures in a controllable manner under environment-friendly reaction conditions remains a challenge. In recent years, the use of biological macromolecules or proteins as templates in the production of nanomaterials has gained more attention due to the relatively mild physical conditions needed for biomimetic synthesis. In this study, a novel nHAp was fabricated by employing bovine serum albumin (BSA) as template under mild condition. After that, the as-obtained nanostructured materials which have well-defined structures and morphologies were characterized by various methods. Furthermore, the rod-like shaped hydroxyapatite demonstrated improved stability properties, as well as cell viability and biocompatibility, compared to BSA free synthesized c-HAp. We expect that this pleasantly novel research will render new insights into the fabrication strategies of nanomaterials and be of practical importance for the expanding biological application.

CO2 displacement has been proposed to enhance shale gas recovery and unlock a big potential market for CO2 beneficial utilization. Theoretically, gas adsorption is inversely related to the temperature, so gas can be desorbed by elevating the temperature. This paper investigates the economic performance of enhancing shale gas recovery by injecting CO2 at high temperatures through displacement as well as desorption by rising temperatures. Influences of operation temperature and injection pressure were studied for three potential shale plays in China. Study results show that both factors exerted obvious impacts, and CO2 procurement was the largest cost component. It is found that the net revenue was not always proportional to the operation temperature, but more controlled by the injection–production ratio. This is because of the different temperature impacts to the various patterns of adsorbed CH4 and CO2 contents. Consequently, in some cases, more CO2 is needed to displace CH4 when operation temperature is raised, resulting a higher cost. The modeling results demonstrate that based on the adsorption characters of reservoirs, the productivity and profitability of CO2 enhanced gas recovery can be further improved by choosing appropriate operation temperatures.

In this paper, an active thermal imaging technique using a laser as an excitation source is used to detect aluminum specimens containing artificial defects. The presence of defects in the local excitation of the laser can produce better edge effects, using different laser excitation methods to improve edge effects. In the experiment, the laser scanning method is designed according to the characteristics of artificial defects, and the experimental data is analyzed. PPT and Sobel filtering are used to further data processing to obtain clearer defect edges and detect the detailed features of the defects. The study found that the detection effect is the best when the laser power and pulse width are large.

The time–frequency analysis by smooth Pseudo-Wigner-Ville distribution (SPWVD) is utilized for the double-line laser ultrasonic signal processing, and the effective detection of the metal surface defect is achieved. The double-line source laser is adopted for achieving more defects information. The simulation model by using finite element method is established in a steel plate with three typical metal surface defects (i.e. crack, air hole and surface scratch) in detail. Besides, in order to improve the time resolution and frequency resolution of the signal, the SPWVD method is mainly used. In addition, the deep learning defect classification model based on VGG convolutional neural network (CNN) is set up, also, the data enhancement method is adopted to extend training data and improve the defects detection properties. The results show that, for different types of metal surface defects with sub-millimeter size, the classification accuracy of crack, air holes and scratch surface are 94.6%, 94% and 94.6%, respectively. The SPWVD and CNN algorithm for processing the laser ultrasonic signal and defects classification supplies a useful way to get the defect information, which is helpful for the ultrasonic signal processing and material evaluation.

To verify the performance of longitudinal waves induced by laser phased arrays (LPA) for detection and quantitative evaluation in internal defects, the finite element method (FEM) is utilized to establish the models of LPA scanning processing. The interaction of longitudinal wave and internal defect is analyzed. Besides, the two components of the reflected longitudinal waves (the longitudinal wave component [Formula: see text] and the shear wave component [Formula: see text] are focused on the imaging of defects with the synthetic aperture focusing technique (SAFT) and total focusing method (TFM) algorithms. It shows that the imaging of internal sub-millimeter defect is obtained using the LPA. The defect size and location are simultaneously calculated, with the relative error being 6.7% and 2.9%, respectively. The proposed longitudinal wave-based LPA is a promising method for the imaging and evaluation of internal micro defects.

In this paper, an intelligent evaluation method is proposed to quantitatively characterize surface-breaking cracks based on laser ultrasonic technique and the quantized particle swarm optimized support vector regression algorithm. Based on the physical model analysis, interactions between laser-generated surface acoustic waves (SAWs) and different cracks is numerically investigated. By selecting crucial features of the transmissions and reflections after interacting with cracks, the crack depth is evaluated with the optimized algorithm. To verify the proposed method, experimental datasets containing twelve different depths were used to size the surface-breaking cracks with incomplete prior knowledge. Evaluation results showed the high accuracy of the proposed evaluations, demonstrating the feasibility of this intelligent method for various applications in industry.

Background: With incidence of all types of cancers on the rise, cancer is now the leading cause of death worldwide. Althoughprevious studies have stressed the significant role of long non-coding RNAs (LncRNAs) in the prognostic efficacy of cancer, onset and development, the relationship is still unclear for the survival analyses of most studies were multivariate. Method: A meta-analysis was implemented aiming to explore whether LncRNA, Metastasis-associated lung adenocarcinoma transcript 1 (MALAT-1), can work as a promising biomarker for prognosis in human cancers. Eligible articles were collected from multiple online databases (PubMed, Embase, CNKI, Web of Science and Scholar Google) and meta-analysis was conducted to investigate the association between the expression levels of MALAT-1 and overall survival (OS), disease-free survival (DFS) or recurrence-free survival (RFS) and clinicopathological parameters which may have an impact on the pooled results. A total of 1389 patients from 13 studies, the survival analyses of which were all univariate, were included in our meta-analysis Result: The results of our meta-analysis indicated that overexpression of MALAT-1 was significantly correlated with worse OS in cancer patients (HR = 1.941, 95% CI: 1.168-2.715, P

Background: With incidence of all types of cancers on the rise, cancer is now the leading cause of death worldwide.1Previous studies2, 3 have stressed the significant role of long non-coding RNAs (lncRNAs) in the diagnostic efficacy of cancer, onset and development. Method: A meta-analysis was implemented aiming to explore whether lncRNA, Metastasis-associated lung adenocarcinoma transcript 1 (MALAT-1), can work as a promising biomarker for prognosis in human cancers. Eligible articles were collected from multiple online databases (Pubmed, EMBASE, CNKI, Web of Science and Google Scholar) and meta-analysis was conducted to investigate the association between the expression levels of MALAT-1 and overall survival (OS), disease-free survival (DFS) or recurrence-free survival (RFS) and clinicopathological parameters which may have an impact on the pooled results. A total of 1389 patients from 13 studies were included in our meta-analysis, whose survival analysis was all univariate. Result: The results of our meta-analysis indicated that overexpression of MALAT-1 was significantly correlated with worse OS in cancer patients (HR = 1.941, 95% CI: 1.168-2.715, P

In this study, granular activated carbon (GAC) coated with cetyltrimethyl ammonium chloride (GAC-CTAC) was synthesized to remove perchlorate from water. Bench scale kinetics and adsorption isotherm tests were performed to evaluate the effects of factors affecting the adsorption efficiency, including the CTAC content, solution pH and ionic strength ( I ). When the content of CTAC on GAC increased from 0.031 to 0.16 mmol/g, the rate constant in the pseudo-second order reaction was decreased from 46.35 to 23.24 h·g/mmol, and the K value in the Freundlich equation was increased from 0.087 to 0.20 mmol/g correspondingly. Typically, GAC-CTAC exhibited a higher adsorption capacity than GAC, which highlighted the key role of CTAC. The optimum perchlorate removal by GAC or GAC-CTAC was observed at pH 2–3, equivalent to pH pzc determined by zeta potential measurement. The adsorption capacity of perchlorate onto GAC-CTAC at pH 2.5 (0.36 mmol/g) was 3.8 times as high as that onto GAC-CTAC at pH 5.6 (0.094 mmol/g). Ionic strength appeared to significantly inhibit the perchlorate adsorption on GAC-CTAC. Finally, our results demonstrated that the primary mechanisms for the adsorption of perchlorate on GAC-CTAC were associated with electrostatic interaction, surface complexation and ion exchange.

A novel catalyst for the ozonation process was prepared by loading manganese on the granular activated carbon (GAC). Nitrobenzene was used as a model refractory organic micropollutant in this study. The catalytic activity of GAC and the Mn-loaded GAC were studied respectively. The removal efficiency of nitrobenzene by Mn-loaded GAC catalyzed ozonation could reach 34.2–49.9%, with the oxidation efficiency being about 1.5–2.0 times higher than that achieved in GAC catalyzed ozonation and 2.0–3.0 times higher than that achieved by ozonation alone. The effect of pH and the t -butanol on the GAC/ozone process was discussed. The optimum condition for preparing the catalyst was studied.

Laboratory produced iron hydroxide was tested for its activity in catalytic ozonation of trace nitrobenzene (NB) in water. The catalysis mechanism was deduced from the effect of tert-butanol on the reaction and the difference of catalytic activity between several typical transition metal oxides. Effects of catalyst dose, pH, bicarbonate ion, and times of catalyst reuse on NB removal were also examined. The iron hydroxide showed high catalytic activity on ozonation of NB with removal rate of NB dissolved in distilled water increased by 44.8% at reaction time of 20min. The catalysis follows a hydroxyl radical pathway and is affected by density of hydroxyl groups on the oxide. An optimal catalyst dosage of 100mg/L existed in this experiment. Solution pH near pH of zero charged surface of the oxide favored NB removal and bicarbonate concentration of 2.38mmol/L significantly reduced it. The oxide was used for five times with no significant change of its activity and no dissolution was observed.